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1. Atypical functional connectivity of temporal cortex with precuneus and visual regions may be an early-age signature of ASD

Author

Yaqiong Xiao, Teresa H. Wen, Lauren Kupis, Lisa T. Eyler, Vani Taluja, Jaden Troxel, Disha Goel, Michael V. Lombardo, Karen Pierce, and Eric Courchesne

Subjects
Autism spectrum disorder, Superior temporal cortex, Resting-state functional connectivity, Language networks, Social visual attention, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Social and language abilities are closely intertwined during early typical development. In autism spectrum disorder (ASD), however, deficits in social and language development are early-age core symptoms. We previously reported that superior temporal cortex, a well-established social and language region, shows reduced activation to social affective speech in ASD toddlers; however, the atypical cortical connectivity that accompanies this deviance remains unknown. Methods We collected clinical, eye tracking, and resting-state fMRI data from 86 ASD and non-ASD subjects (mean age 2.3 ± 0.7 years). Functional connectivity of left and right superior temporal regions with other cortical regions and correlations between this connectivity and each child’s social and language abilities were examined. Results While there was no group difference in functional connectivity, the connectivity between superior temporal cortex and frontal and parietal regions was significantly correlated with language, communication, and social abilities in non-ASD subjects, but these effects were absent in ASD subjects. Instead, ASD subjects, regardless of different social or nonsocial visual preferences, showed atypical correlations between temporal–visual region connectivity and communication ability (r(49) = 0.55, p

Published
2023
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2. The interplay of daily affect and impulsivity measured by mobile surveys in bipolar disorder

Author

Madison K. Titone, Colin Depp, Federica Klaus, Jessica Carrasco, Jared W. Young, and Lisa T. Eyler

Subjects
Bipolar disorder, Affect, Impulsivity, Ecological momentary assessment, Time-lagged analysis, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurophysiology and neuropsychology, QP351-495
Abstract

Abstract Background Impulsivity is a prominent feature of bipolar disorder associated with various negative sequelae; moreover, it may be a precursor to shifts in affect or mood, but little is known about its association with affect on a day-to-day timescale. Ecological momentary assessments (a method that captures moment-to-moment ratings of psychological states by repeatedly sampling the same individual) of impulsivity and affect using mobile surveys allow for more nuanced examination of mechanisms of mood and behavior dysregulation. However, few existing studies have validated an ecological momentary assessment of impulsivity in bipolar disorder and examined its time-lagged associations with positive and negative affect. 70 participants with bipolar disorder and 102 healthy comparisons participated in an intensive longitudinal study: they underwent 14 days of ecological momentary assessment data collection annually for 1–4 years. Multiple measures of impulsivity and affect were collected using self-report, behavioral, and ecological momentary assessment modalities; these measures were compared, and levels of impulsivity were compared between bipolar disorder and healthy comparison groups. Time-lagged analyses using daily means explored the next-day predictive relationship of impulsivity on positive/negative affect, and vice versa. Results The ecological momentary measure of impulsivity was moderately correlated with the self-report but not behavioral impulsivity measure. Bipolar disorder participants evinced higher self-report, behavioral, and daily impulsivity than healthy comparison participants. Time-lagged analyses revealed a bi-directional association between high impulsivity and high next-day negative (but not positive) affect. Post hoc analyses showed that impulsivity specifically predicted next-day anger and anxiety. Conclusions Our multimodal assessment of impulsivity allowed for an examination of the day-to-day course of impulsivity and affect, crucial steps toward understanding the mechanisms of mood symptom and episode onset in bipolar disorder.

Published
2022
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3. Compassion as a tool for allyship and anti-racism

Author

Melissa M. Karnaze, Ramya M. Rajagopalan, Lisa T. Eyler, and Cinnamon S. Bloss

Subjects
compassion, empathy, allyship, anti-racism, systemic racism, Psychology, BF1-990
Abstract

Racist systems, policies, and institutions subvert the quality of life for minoritized individuals and groups, across all indicators, from education and employment, to health, to community safety. Reforms to address systemic racism may be accelerated with greater support from allies who identify with the dominant groups that derive advantage from such systems. Although enhancing empathy and compassion for impacted individuals and groups may foster greater allyship with and support of minoritized communities, little work to date has assessed the relationships among compassion, empathy, and allyship. After reviewing current work in the area, this perspective offers insights into the utility and specific components of a compassion-based framework that can be used to combat racism, using findings from a survey study in which we investigated the relationship between validated psychometric measures of compassion and allyship with minoritized communities. Several subdomains of compassion, as measured among individuals identifying as non-Black, correlate significantly with levels of felt allyship with Black or African American communities. These findings inform recommendations for compassion-focused research, including development and testing of interventions to promote allyship, advocacy, and solidarity with minoritized groups, and support efforts to undo longstanding structural racisms that have patterned inequality in the United States.

Published
2023
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4. Associations among executive function Abilities, free Water, and white matter microstructure in early old age

Author

Daniel E. Gustavson, Derek B. Archer, Jeremy A. Elman, Olivia K. Puckett, Christine Fennema-Notestine, Matthew S. Panizzon, Niranjana Shashikumar, Timothy J. Hohman, Angela L. Jefferson, Lisa T. Eyler, Linda K. McEvoy, Michael J. Lyons, Carol E. Franz, and William S. Kremen

Subjects
Executive control, Cognitive control, Working memory, Abnormal white matter, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract

Background: Studies have investigated white matter microstructure in relation to late-life cognitive impairments, with fractional anisotropy (FA) and mean diffusivity (MD) measures thought to capture demyelination and axonal degradation. However, new post-processing methods allow isolation of free water (FW), which captures extracellular fluid contributions such as atrophy and neuroinflammation, from tissue components. FW also appears to be highly relevant to late-life cognitive impairment. Here, we evaluated whether executive functions are associated with FW, and FA and MD corrected for FW (FAFWcorr and MDFWcorr). Method: We examined 489 non-demented men in the Vietnam Era Twin Study of Aging (VETSA) at mean age 68. Two latent factors capturing ‘common executive function’ and ‘working-memory specific’ processes were estimated based on 6 tasks. Analyses focused on 11 cortical white matter tracts across three metrics: FW, FAFWcorr, and MDFWcorr. Results: Better ‘common executive function’ was associated with lower FW across 9 of the 11 tracts. There were no significant associations with intracellular metrics after false discovery rate correction. Effects also appeared driven by individuals with MCI (13.7% of the sample). Working memory-specific tasks showed some associations with FAFWcorr, including the triangularis portion of the inferior frontal gyrus. There was no evidence that cognitive reserve (i.e., general cognitive ability assessed in early adulthood) moderated these associations between executive function and FW or FA. Discussion: Executive function abilities in early old age are associated primarily with extracellular fluid (FW) as opposed to white matter (FAFWcorr or MDFWcorr). Moderation analyses suggested cognitive reserve does not play a strong role in these associations, at least in this sample of non-demented men.

Published
2023
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5. Fatness, fitness and the aging brain: A cross sectional study of the associations between a physiological estimate of brain age and physical fitness, activity, sleep, and body composition

Author

David Wing, Lisa T. Eyler, Eric J. Lenze, Julie Loebach Wetherell, Jeanne F. Nichols, Romain Meeusen, Job G. Godino, Joshua S. Shimony, Abraham Z. Snyder, Tomoyuki Nishino, Ginger E. Nicol, Guy Nagels, and Bart Roelands

Subjects
BrainAge, Visceral adipose tissue, Sleep quantity, Maximal cardiovascular fitness, Successful aging, Body composition, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Introduction: Changes in brain structure and function occur with aging. However, there is substantial heterogeneity both in terms of when these changes begin, and the rate at which they progress. Understanding the mechanisms and/or behaviors underlying this heterogeneity may allow us to act to target and slow negative changes associated with aging. Methods: Using T1 weighted MRI images, we applied a novel algorithm to determine the physiological age of the brain (brain-predicted age) and the predicted age difference between this physiologically based estimate and chronological age (BrainPAD) to 551 sedentary adults aged 65 to 84 with self-reported cognitive complaint measured at baseline as part of a larger study. We also assessed maximal aerobic capacity with a graded exercise test, physical activity and sleep with accelerometers, and body composition with dual energy x-ray absorptiometry. Associations were explored both linearly and logistically using categorical groupings. Results: Visceral Adipose Tissue (VAT), Total Sleep Time (TST) and maximal aerobic capacity all showed significant associations with BrainPAD. Greater VAT was associated with higher (i.e,. older than chronological) BrainPAD (r = 0.149 p = 0.001)Greater TST was associated with higher BrainPAD (r = 0.087 p = 0.042) and greater aerobic capacity was associated with lower BrainPAD (r = −0.088 p = 0.040). With linear regression, both VAT and TST remained significant (p = 0.036 and 0.008 respectively). Each kg of VAT predicted a 0.741 year increase in BrainPAD, and each hour of increased TST predicted a 0.735 year increase in BrainPAD. Maximal aerobic capacity did not retain statistical significance in fully adjusted linear models. Discussion: Accumulation of visceral adipose tissue and greater total sleep time, but not aerobic capacity, total daily physical activity, or sleep quantity and/or quality are associated with brains that are physiologically older than would be expected based upon chronological age alone (BrainPAD).

Published
2022
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6. Mobile survey engagement by older adults is high during multiple phases of the COVID-19 pandemic and is predicted by baseline and structural factors

Author

Federica Klaus, Elizabeth Peek, Avery Quynh, Ashley N. Sutherland, Divya Selvam, Raeanne C. Moore, Colin A. Depp, and Lisa T. Eyler

Subjects
ecological momentary assessment, survey, adherence, withdrawal, stay-at-home, mobile phone, Medicine, Public aspects of medicine, RA1-1270, Electronic computers. Computer science, QA75.5-76.95
Abstract

Digital surveys, such as mobile phone ecological momentary assessment (EMA), bear the potential to assess and target individual wellbeing in a personalized, real-time approach and allow for interaction in situations when in-person contact is not possible, such as during the coronavirus pandemic. While the use of digital technology might especially benefit research in older adults who find themselves in circumstances of reduced mobility, little is known about their barriers to adherence. We investigated baseline and structural factors that predict study withdrawal and adherence from daily smartphone EMA self-report surveys in the StayWELL Study. The StayWELL study is a longitudinal, observational study on the relationship between social restrictions during the coronavirus pandemic and mental well-being in 95 community-dwelling older aged adults (67–87 years) who were participants in a randomized clinical trial using EMA. Withdrawal was associated with less research staff changes and less likely in participants that reached the study mid-point. No baseline characteristics predicted withdrawal. Main reasons for withdrawal were communication issues, i.e. staff not being able to contact participants. We found an adherence rate of 82% and no fatigue effects. Adherence was predicted by education status, study participation duration, reaching the study midpoint and time between study start and enrollment. COVID infections or supporting people in the household was not related to adherence. To conclude, it is feasible to conduct an EMA study in older people without impacting engagement during a pandemic. Furthermore, personal characteristics and smartphone operating system (Android vs. iOS) used did not relate to engagement, allowing for a broad distribution of digital health technologies. Our study adds information on single predictive variables relevant for adherence and withdrawal from EMA smartphone surveys in older people that can inform the design of future digital EMA research to maximize engagement and reliability of study results.

Published
2022
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7. Peripheral inflammation levels associated with degree of advanced brain aging in schizophrenia

Author

Federica Klaus, Tanya T. Nguyen, Michael L. Thomas, Sharon C. Liou, Benchawanna Soontornniyomkij, Kyle Mitchell, Rebecca Daly, Ashley N. Sutherland, Dilip V. Jeste, and Lisa T. Eyler

Subjects
inflammation, brain, schizophrenia, aging, MRI, TNFα, Psychiatry, RC435-571
Abstract

Brain structural abnormalities have been demonstrated in schizophrenia (SZ); these resemble those seen in typical aging, but are seen at younger ages. Furthermore, SZ is associated with accelerated global brain aging, as measured by brain structure-based brain predicted age difference (Brain-PAD). High heterogeneity exists in the degree of brain abnormalities in SZ, and individual differences may be related to levels of peripheral inflammation and may relate to cognitive deficits and negative symptoms. The goal of our study was to investigate the relationship between brain aging, peripheral inflammation, and symptoms of SZ. We hypothesized older brain-PAD in SZ vs. healthy comparison (HC) participants, as well as positive relationships of brain-PAD with peripheral inflammation markers and symptoms in SZ. We analyzed data from two cross-sectional studies in SZ (n = 26; M/F: 21/5) and HC (n = 28; 20/8) (22–64 years). Brain-PAD was calculated using a previously validated Gaussian process regression model applied to raw T1-weighted MRI data. Plasma levels of inflammatory biomarkers (CRP, Eotaxin, Fractalkine, IP10, IL6, IL10, ICAM1, IFNγ, MCP1, MIP1β, SAA, TNFα, VEGF, VCAM1) and cognitive and negative symptoms were assessed. We observed a higher brain-PAD in SZ vs. HC, and advanced brain age relative to chronological age was related to higher peripheral levels of TNFα in the overall group and in the SZ group; other inflammatory markers were not related to brain-PAD. Within the SZ group, we observed no association between cognitive or negative symptoms and brain-PAD. These results support our hypothesis of advanced brain aging in SZ. Furthermore, our findings on the relationship of the pro-inflammatory cytokine TNFα with higher brain-PAD of SZ are relevant to explain heterogeneity of brain ages in SZ, but we did not find strong evidence for cognitive or negative symptom relationships with brain-PAD.

Published
2022
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8. The Impact of Genes and Environment on Brain Ageing in Males Aged 51 to 72 Years

Author

Nathan A. Gillespie, Sean N. Hatton, Donald J. Hagler, Anders M. Dale, Jeremy A. Elman, Linda K. McEvoy, Lisa T. Eyler, Christine Fennema-Notestine, Mark W. Logue, Ruth E. McKenzie, Olivia K. Puckett, Xin M. Tu, Nathan Whitsel, Hong Xian, Chandra A. Reynolds, Matthew S. Panizzon, Michael J. Lyons, Michael C. Neale, William S. Kremen, and Carol Franz

Subjects
predicted brain ageing, twin, gene, longitudinal predicted brain aging, MRI, development, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimer’s Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 51–72 years. Biometrical genetic analyses “twin models” revealed significant and highly correlated estimates of additive genetic heritability ranging from 59 to 75%. Multivariate longitudinal modeling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences.

Published
2022
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9. Alcohol intake and brain white matter in middle aged men: Microscopic and macroscopic differences

Author

Linda K. McEvoy, Christine Fennema-Notestine, Jeremy A. Elman, Lisa T. Eyler, Carol E. Franz, Donald J. Hagler, Jr, Sean N. Hatton, Michael J. Lyons, Matthew S. Panizzon, Anders M. Dale, and William S. Kremen

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract

Heavy alcohol consumption is associated with deleterious changes in the brain but associations of moderate alcohol intake are not well understood. We examined the association of alcohol consumption with brain white matter health in 377 middle-aged men (56–66 years old; mean 61.8 ± 2.6 years) who were participants in the Vietnam Era Twin Study of Aging (VETSA). T1-, T2-, proton density-, and diffusion-weighted magnetic resonance images were obtained. Diffusion measures were quantified from 12 major white matter tracts. Global white matter lesion (WML) burden was also quantified. Mixed effects linear models examined differences in diffusivity and WMLs by amount of alcohol intake. Analyses adjusted for numerous demographic, health, and lifestyle variables. An inverted-U association was found between alcohol intake and fractional anisotropy (FA) in several tracts, including the inferior-frontal-occipital fasciculus, uncinate fasciculus, superior longitudinal fasciculus, the forceps minor and the anterior thalamic radiations. In these tracts, FA increased with increasing alcohol intake, peaking with moderate alcohol intake (9–28 drinks in 14 days), and declining with heavier intake. Associations remained significant after exclusion of individuals with diabetes or hypertension. There was a U-shaped association in WML burden with highest burden among never drinkers and heavy drinkers (>28 drinks in 14 days). This association was no longer significant after exclusion of individuals with hypertension, since WML burden among heavy drinkers no longer differed from that of other drinkers. This suggests that hypertension related to heavy alcohol intake may contribute to WML burden observed among heavy drinkers. Together, these correlational results suggest that among middle-aged men, moderate drinking may be associated with metrics of better white matter health, particularly microstructural measures, whereas drinking beyond recommended guidelines may be associated with both microstructural and macrostructural white matter damage. Keywords: Neuroimaging, Diffusion-weighted imaging, Fractional anisotropy, Aging, White matter lesion, White matter hyperintensity, DTI, Ethanol

Published
2018
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10. White matter disease in midlife is heritable, related to hypertension, and shares some genetic influence with systolic blood pressure

Author

Christine Fennema-Notestine, PhD, Linda K. McEvoy, PhD, Randy Notestine, MS, Matthew S. Panizzon, PhD, Wai-Ying Wendy Yau, Carol E. Franz, PhD, Michael J. Lyons, PhD, Lisa T. Eyler, PhD, Michael C. Neale, PhD, Hong Xian, PhD, Ruth E. McKenzie, PhD, and William S. Kremen, PhD

Subjects
White matter, MRI, Brain, Hypertension, Blood pressure, Heritability, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract

White matter disease in the brain increases with age and cardiovascular disease, emerging in midlife, and these associations may be influenced by both genetic and environmental factors. We examined the frequency, distribution, and heritability of abnormal white matter and its association with hypertension in 395 middle-aged male twins (61.9 ± 2.6 years) from the Vietnam Era Twin Study of Aging, 67% of whom were hypertensive. A multi-channel segmentation approach estimated abnormal regions within the white matter. Using multivariable regression models, we characterized the frequency distribution of abnormal white matter in midlife and investigated associations with hypertension and Apolipoprotein E-ε4 status and the impact of duration and control of hypertension. Then, using the classical twin design, we estimated abnormal white matter heritability and the extent of shared genetic overlap with blood pressure. Abnormal white matter was predominantly located in periventricular and deep parietal and frontal regions; associated with age (t = 1.9, p = 0.05) and hypertension (t = 2.9, p = 0.004), but not Apolipoprotein ε4 status; and was greater in those with uncontrolled hypertension relative to controlled (t = 3.0, p = 0.003) and normotensive (t = 4.0, p = 0.0001) groups, suggesting that abnormal white matter may reflect currently active cerebrovascular effects. Abnormal white matter was highly heritable (a2 = 0.81) and shared some genetic influences with systolic blood pressure (rA = 0.26), although there was evidence for distinct genetic contributions and unique environmental influences. Future longitudinal research will shed light on factors impacting white matter disease presentation, progression, and potential recovery.

Published
2016
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11. BrainAge of patients with severe late-life depression referred for electroconvulsive therapy

Author

Margot J. Wagenmakers, Mardien L. Oudega, Federica Klaus, David Wing, Gwendolyn Orav, Laura K.M. Han, Julia Binnewies, Aartjan T.F. Beekman, Dick J. Veltman, Didi Rhebergen, Eric van Exel, Lisa T. Eyler, Annemieke Dols, Psychiatry, APH - Aging & Later Life, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, Anatomy and neurosciences, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neurodegeneration, Central Academic Services, and Faculty of Physical Education and Physical Therapy

Subjects
Psychiatry and Mental health, Clinical Psychology
Abstract

BACKGROUND: Severe depression is associated with accelerated brain aging. BrainAge gap, the difference between predicted and observed BrainAge was investigated in patients with late-life depression (LLD). We aimed to examine BrainAge gap in LLD and its associations with clinical characteristics indexing LLD chronicity, current severity, prior to electroconvulsive therapy (ECT) and ECT outcome. METHODS: Data was analyzed from the Mood Disorders in Elderly treated with Electroconvulsive Therapy (MODECT) study. A previously established BrainAge algorithm (BrainAge R by James Cole, (https://github.com/james-cole/brainageR)) was applied to pre-ECT T1-weighted structural MRI-scans of 42 patients who underwent ECT. RESULTS: A BrainAge gap of 1.8 years (SD = 5.5) was observed, Cohen's d = 0.3. No significant associations between BrainAge gap, number of previous episodes, current episode duration, age of onset, depression severity, psychotic symptoms or ECT outcome were observed. LIMITATIONS: Limited sample size. CONCLUSIONS: Our initial findings suggest an older BrainAge than chronological age in patients with severe LLD referred for ECT, however with high degree of variability and direction of the gap. No associations were found with clinical measures. Larger samples including are needed to better understand brain aging and to evaluate the usability of BrainAge gap as potential biomarker of prognosis an treatment-response in LLD. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02667353.

Published
2023

13. Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning

Author

Patrick H. Luckett, Ki Yun Park, John J. Lee, Eric J. Lenze, Julie Loebach Wetherell, Lisa T. Eyler, Abraham Z. Snyder, Beau M. Ances, Joshua S. Shimony, and Eric C. Leuthardt

Subjects
General Medicine
Abstract

OBJECTIVE Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data. METHODS Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme. RESULTS The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations. CONCLUSIONS Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.

Published
2023

14. Global and local cortical folding alterations are associated with neurodevelopmental subtype in bipolar disorders: a sulcal pits analysis

Author

Antoine Lefrere, Guillaume Auzias, Pauline Favre, Irène Kaltenmark, Josselin Houenou, Camille Piguet, Mircea Polosan, Lisa T Eyler, Mary L Phillips, Amelia Versace, Michèle Wessa, Colm McDonald, Dara M Cannon, Paolo Brambilla, Marcella Bellani, Christine Deruelle, and Raoul Belzeaux

Subjects
Psychiatry and Mental health, Clinical Psychology, Bipolar disorder, Neurodevelopment, Premotor, Psychosis, Early onset, Imaging
Abstract

Analyzing cortical folding may provide insight into the biological underpinnings of neurodevelopmental diseases. A neurodevelopmental subtype of bipolar disorders (BD-ND) has been characterized by the combination of early age of onset and psychotic features. We investigate potential cortical morphology differences associated with this subtype. We analyze, for the first time in bipolar disorders, the sulcal pits, the deepest points in each fold of the cerebral cortex.We extracted the sulcal pits from anatomical MRI among 512 participants gathered from 7 scanning sites. We compared the number of sulcal pits in each hemisphere as well as their regional occurrence and depth between the BD-ND subgroup (N = 184), a subgroup without neurodevelopmental features (BD, N = 77) and a group of healthy controls (HC, N = 251).In whole brain analysis, BD-ND group have a higher number of sulcal pits in comparison to the BD group. The local analysis revealed, after correction for multiple testing, a higher occurrence of sulcal pits in the left premotor cortex among the BD-ND subgroup compared to the BD and the HC groups.Our findings confirm that BD-ND is associated with a specific brain morphology revealed by the analysis of sulcal pits. These markers may help to better understand neurodevelopment in mood disorder and stratify patients according to a pathophysiological hypothesis.

Published
2023

15. Bipolar I and bipolar <scp>II</scp> subtypes in older age: Results from the Global Aging and Geriatric Experiments in Bipolar Disorder ( <scp>GAGE‐BD</scp> ) project

Author

Alexandra J. M. Beunders, Federica Klaus, Almar A. L. Kok, Sigfried N. T. M. Schouws, Ralph W. Kupka, Hilary P. Blumberg, Farren Briggs, Lisa T. Eyler, Brent P. Forester, Orestes V. Forlenza, Ariel Gildengers, Esther Jimenez, Benoit H. Mulsant, Regan E. Patrick, Soham Rej, Martha Sajatovic, Kaylee Sarna, Ashley Sutherland, Joy Yala, Eduard Vieta, Luca M. Villa, Nicole C. M. Korten, Annemieke Dols, APH - Mental Health, Psychiatry, APH - Aging & Later Life, Neurology, and Amsterdam Neuroscience - Neurodegeneration

Subjects
cognition, Aging, impairment, Bipolar Disorder, Clinical Sciences, comorbidities, elderly, functioning, Clinical Research, Humans, Cognitive Dysfunction, Aetiology, Biological Psychiatry, Aged, Psychiatry, geriatrics, Depression, Neurosciences, Serious Mental Illness, older-age bipolar disorder, Brain Disorders, Psychiatry and Mental health, Cross-Sectional Studies, Mental Health, diagnostic subtypes, 2.4 Surveillance and distribution
Abstract

ObjectivesThe distinction between bipolar I disorder (BD-I) and bipolar II disorder (BD-II) has been a topic of long-lasting debate. This study examined differences between BD-I and BD-II in a large, global sample of OABD, focusing on general functioning, cognition and somatic burden as these domains are often affected in OABD.MethodsCross-sectional analyses were conducted with data from the Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE-BD) database. The sample included 963 participants aged ≥50 years (714 BD-I, 249 BD-II). Sociodemographic and clinical factors were compared between BD subtypes including adjustment for study cohort. Multivariable analyses were conducted with generalized linear mixed models (GLMMs) and estimated associations between BD subtype and (1) general functioning (GAF), (2) cognitive performance (g-score) and (3) somatic burden, with study cohort as random intercept.ResultsAfter adjustment for study cohort, BD-II patients more often had a late onset ≥50 years (p=0.008) and more current severe depression (p=0.041). BD-I patients were more likely to have a history of psychiatric hospitalization (p

Published
2022

16. Longitudinal association of executive function and structural network controllability in the aging brain

Author

Rongxiang Tang, Jeremy A. Elman, Carol E. Franz, Anders M. Dale, Lisa T. Eyler, Christine Fennema-Notestine, Donald J. Hagler, Michael J. Lyons, Matthew S. Panizzon, Olivia K. Puckett, and William S. Kremen

Subjects
Male, Controllability, Aging, 1.1 Normal biological development and functioning, Neurosciences, Brain, Magnetic Resonance Imaging, Basic Behavioral and Social Science, Executive Function, Cognitive aging, Cognition, Clinical Research, Underpinning research, 2.3 Psychological, Structural network, Behavioral and Social Science, Neurological, Humans, Biomedical Imaging, Mental health, social and economic factors, Aetiology, Geriatrics and Gerontology, Aged
Abstract

Executive function encompasses effortful cognitive processes that are particularly susceptible to aging. Functional brain networks supporting executive function—such as the frontoparietal control network and the multiple demand system—have been extensively investigated. However, it remains unclear how structural networks facilitate and constrain the dynamics of functional networks to contribute to aging-related executive function declines. We examined whether changes in structural network modal controllability—a network’s ability to facilitate effortful brain state transitions that support cognitive functions—are associated with changes in executive function cross-sectionally and longitudinally. Diffusion-weighted imaging and neuropsychological testing were conducted at two time points (Time 1: ages 56 to 66, N = 172; Time 2: ages 61 to 70, N = 267) in community-dwelling men from the Vietnam Era Twin Study of Aging. An executive function factor score was computed from six neuropsychological tasks. Structural networks constructed from white matter connectivity were used to estimate modal controllability in control network and multiple demand system. We showed that higher modal controllability in control network and multiple demand system was associated with better executive function at Time 2, after controlling for age, young adult general cognitive ability, and physical health status. Moreover, changes in executive function over a period of 5 to 6 years (Time 1-Time 2, N = 105) were associated with changes in modal controllability of the multiple demand system and weakly in the control network over the same time period. These findings suggest that changes in the ability of structural brain networks in facilitating effortful brain state transitions may be a key neural mechanism underlying aging-related executive function declines and cognitive aging.

Published
2022

17. Symptom Severity Mixity in Older-Age Bipolar Disorder: Analyses From the Global Aging and Geriatric Experiments in Bipolar Disorder Database (GAGE-BD)

Author

Lisa T. Eyler, Farren B.S. Briggs, Annemiek Dols, Soham Rej, Osvaldo P. Almeida, Alexandra J.M. Beunders, Hilary P. Blumberg, Brent P. Forester, Regan E. Patrick, Orestes V. Forlenza, Ariel Gildengers, Esther Jimenez, Eduard Vieta, Benoit H. Mulsant, Sigfried Schouws, Nadine P.G. Paans, Sergio Strejilevich, Ashley Sutherland, Shangying Tsai, Martha Sajatovic, Neurology, Psychiatry, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, and APH - Mental Health

Subjects
Cohort Studies, Aging, Mania, Psychiatry and Mental health, Bipolar Disorder, Cross-Sectional Studies, Humans, Geriatrics and Gerontology, Aged
Abstract

Objective: Some individuals with bipolar disorder (BD) experience manic and depressive symptoms concurrently, but data are limited on symptom mixity in older age bipolar disorder (OABD). Using the Global Aging & Geriatric Experiments in Bipolar Disorder Database, we characterized mixity in OABD and associations with everyday function. Methods: The sample (n = 805), from 12 international studies, included cases with both mania and depression severity ratings at a single timepoint. Four mixity groups were created: asymptomatic (A), mixed (Mix), depressed only (Dep), and manic only (Man). Generalized linear mixed models used mixity group as the predictor variable; cohort was included as a random intercept. Everyday function was assessed with the Global Assessment of Functioning score. Results: Group proportions were Mix (69.6%; n = 560), followed by Dep (18.4%; n = 148), then A (7.8%; n = 63), then Man (4.2%; n= 34); levels of depression and mania were similar in Mix compared to Dep and Man, respectively. Everyday function was lowest in Mix, highest in A, and intermediate in Man and Dep. Within Mix, severity of depression was the main driver of worse functioning. Groups differed in years of education, with A higher than all others, but did not differ by age, gender, employment status, BD subtype, or age of onset. Conclusions: Mixed features predominate in a cross-sectional, global OABD sample and are associated with worse everyday function. Among those with mixed symptoms, functional status relates strongly to current depression severity. Future studies should include cognitive and other biological variables as well as longitudinal designs to allow for evaluation of causal effects.

Published
2022

19. Higher cortical thickness/volume in Alzheimer’s-related regions: Protective factor or risk factor?

Author

McKenna E. Williams, Jeremy A. Elman, Tyler R. Bell, Anders M. Dale, Lisa T. Eyler, Christine Fennema-Notestine, Carol E. Franz, Nathan A. Gillespie, Donald J. Hagler, Michael J. Lyons, Linda K. Mcevoy, Michael C. Neale, Matthew S. Panizzon, Chandra A. Reynolds, Mark Sanderson-Cimino, and William S. Kremen

Subjects
Aging, General Neuroscience, Neurology (clinical), Geriatrics and Gerontology, Developmental Biology
Published
2023

20. Physical Health Burden Among Older Men and Women With Bipolar Disorder

Author

Osvaldo P. Almeida, Annemiek Dols, Machteld A.J.T. Blanken, Soham Rej, Hilary P. Blumberg, Luca Villa, Brent P. Forester, Orestes V. Forlenza, Ariel Gildengers, Eduard Vieta, Esther Jimenez, Benoit Mulsant, Sigfried Schouws, Shangying Tsai, Nicole C.M. Korten, Ashley Sutherland, Farren B.S. Briggs, Leon Flicker, Lisa T. Eyler, Martha Sajatovic, APH - Mental Health, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Psychiatry, and Neurology

Subjects
Male, Psychiatry and Mental health, Aging, Bipolar Disorder, Cross-Sectional Studies, Databases, Factual, Prevalence, Humans, Female, Geriatrics and Gerontology, Aged
Abstract

Objectives: To compare the prevalence of physical morbidities among men and women with older adult bipolar disorder (OABD), and men with and without OABD. Methods: Cross-sectional analysis of the collaborative Global Aging & Geriatric Experiments in Bipolar Disorder (GAGE-BD) database and non-OABD data from the Health in Men Study. OABD defined as bipolar disorder among adults aged greater than or equal to 50 years. Outcomes of interest were diseases affecting the cardiovascular, respiratory, gastrointestinal, renal, musculoskeletal and endocrinological systems. Results: We examined 1407 participants with OABD aged 50–95 years, of whom 787 were women. More women than men showed evidence of morbidities affecting the respiratory, gastrointestinal, musculoskeletal and endocrinological systems. More men with than without OABD showed evidence of cardiovascular, renal and endocrinological diseases. Conclusion: GAGE-BD data showed that physical morbidities affect more women than men with OABD, and more men with than without OABD. The underlying reasons for these differences require clarification.

Published
2022

21. Childhood Disadvantage Moderates Late Midlife Default Mode Network Cortical Microstructure and Visual Memory Association

Author

Rongxiang Tang, Jeremy A Elman, Anders M Dale, Stephen M Dorros, Lisa T Eyler, Christine Fennema-Notestine, Daniel E Gustavson, Donald J Hagler, Michael J Lyons, Matthew S Panizzon, Olivia K Puckett, Chandra A Reynolds, Carol E Franz, and William S Kremen

Subjects
Aging, Geriatrics and Gerontology
Abstract

Background Childhood disadvantage is a prominent risk factor for cognitive and brain aging. Childhood disadvantage is associated with poorer episodic memory in late midlife and functional and structural brain abnormalities in the default mode network (DMN). Although age-related changes in DMN are associated with episodic memory declines in older adults, it remains unclear if childhood disadvantage has an enduring impact on this later-life brain–cognition relationship earlier in the aging process. Here, within the DMN, we examined whether its cortical microstructural integrity—an early marker of structural vulnerability that increases the risk for future cognitive decline and neurodegeneration—is associated with episodic memory in adults at ages 56–66, and whether childhood disadvantage moderates this association. Methods Cortical mean diffusivity (MD) obtained from diffusion magnetic resonance imaging was used to measure microstructural integrity in 350 community-dwelling men. We examined both visual and verbal episodic memory in relation to DMN MD and divided participants into disadvantaged and nondisadvantaged groups based on parental education and occupation. Results Higher DMN MD was associated with poorer visual memory but not verbal memory (β = −0.11, p = .040 vs β = −0.04, p = .535). This association was moderated by childhood disadvantage and was significant only in the disadvantaged group (β = −0.26, p = .002 vs β = −0.00, p = .957). Conclusions Lower DMN cortical microstructural integrity may reflect visual memory vulnerability in cognitively normal adults earlier in the aging process. Individuals who experienced childhood disadvantage manifested greater vulnerability to cortical microstructure-related visual memory dysfunction than their nondisadvantaged counterparts who exhibited resilience in the face of low cortical microstructural integrity.

Published
2023

22. Associations between depression and cardiometabolic health: A 27-year longitudinal study

Author

Anders M. Dale, Amy J. Jak, Kristy Cuthbert, Michael C. Neale, Lisa T. Eyler, Carol E. Franz, William S. Kremen, Richard L. Hauger, Xin Tu, Richard Vandiver, McKenna E. Williams, Chandra A. Reynolds, Ole A. Andreassen, Daniel E. Gustavson, Jeremy A. Elman, Mark W. Logue, Matthew S. Panizzon, Michael J. Lyons, Nathan A. Gillespie, Christine Fennema-Notestine, Nathan Whitsel, Ruth E. McKenzie, Rosemary Toomey, Mark Sanderson-Cimino, Graham M L Eglit, Hillary L. Ditmars, and Hong Xian

Subjects
Male, Adult, Aging, medicine.medical_specialty, Longitudinal study, Hypercholesterolemia, Cardiovascular, Article, 03 medical and health sciences, 0302 clinical medicine, Sleep Apnea Syndromes, Erectile Dysfunction, Clinical Research, Risk Factors, Internal medicine, Behavioral and Social Science, History of depression, medicine, Psychology, Humans, Longitudinal Studies, Stroke, Applied Psychology, Depression (differential diagnoses), 030304 developmental biology, Aged, Psychiatry, 0303 health sciences, business.industry, Depression, Prevention, Cardiometabolic health, Neurosciences, Sleep apnea, medicine.disease, Twin study, Brain Disorders, Substance abuse, Polygenic risk scores, Psychiatry and Mental health, Mental Health, Good Health and Well Being, Hypertension, Public Health and Health Services, Sleep Research, business, Body mass index, 030217 neurology & neurosurgery
Abstract

BackgroundClarifying the relationship between depression symptoms and cardiometabolic and related health could clarify risk factors and treatment targets. The objective of this study was to assess whether depression symptoms in midlife are associated with the subsequent onset of cardiometabolic health problems.MethodsThe study sample comprised 787 male twin veterans with polygenic risk score data who participated in the Harvard Twin Study of Substance Abuse (‘baseline’) and the longitudinal Vietnam Era Twin Study of Aging (‘follow-up’). Depression symptoms were assessed at baseline [mean age 41.42 years (s.d. = 2.34)] using the Diagnostic Interview Schedule, Version III, Revised. The onset of eight cardiometabolic conditions (atrial fibrillation, diabetes, erectile dysfunction, hypercholesterolemia, hypertension, myocardial infarction, sleep apnea, and stroke) was assessed via self-reported doctor diagnosis at follow-up [mean age 67.59 years (s.d. = 2.41)].ResultsTotal depression symptoms were longitudinally associated with incident diabetes (OR 1.29, 95% CI 1.07–1.57), erectile dysfunction (OR 1.32, 95% CI 1.10–1.59), hypercholesterolemia (OR 1.26, 95% CI 1.04–1.53), and sleep apnea (OR 1.40, 95% CI 1.13–1.74) over 27 years after controlling for age, alcohol consumption, smoking, body mass index, C-reactive protein, and polygenic risk for specific health conditions. In sensitivity analyses that excluded somatic depression symptoms, only the association with sleep apnea remained significant (OR 1.32, 95% CI 1.09–1.60).ConclusionsA history of depression symptoms by early midlife is associated with an elevated risk for subsequent development of several self-reported health conditions. When isolated, non-somatic depression symptoms are associated with incident self-reported sleep apnea. Depression symptom history may be a predictor or marker of cardiometabolic risk over decades.

Published
2023

24. Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals

Author

Sean R, McWhinney, Christoph, Abé, Martin, Alda, Francesco, Benedetti, Erlend, Bøen, Caterina, Del Mar Bonnin, Tiana, Borgers, Katharina, Brosch, Erick J, Canales-Rodríguez, Dara M, Cannon, Udo, Dannlowski, Ana M, Diaz-Zuluaga, Lorielle, Dietze, Torbjørn, Elvsåshagen, Lisa T, Eyler, Janice M, Fullerton, Jose M, Goikolea, Janik, Goltermann, Dominik, Grotegerd, Bartholomeus C M, Haarman, Tim, Hahn, Fleur M, Howells, Martin, Ingvar, Tilo T J, Kircher, Axel, Krug, Rayus T, Kuplicki, Mikael, Landén, Hannah, Lemke, Benny, Liberg, Carlos, Lopez-Jaramillo, Ulrik F, Malt, Fiona M, Martyn, Elena, Mazza, Colm, McDonald, Genevieve, McPhilemy, Sandra, Meier, Susanne, Meinert, Tina, Meller, Elisa M T, Melloni, Philip B, Mitchell, Leila, Nabulsi, Igor, Nenadic, Nils, Opel, Roel A, Ophoff, Bronwyn J, Overs, Julia-Katharina, Pfarr, Julian A, Pineda-Zapata, Edith, Pomarol-Clotet, Joaquim, Raduà, Jonathan, Repple, Maike, Richter, Kai G, Ringwald, Gloria, Roberts, Alex, Ross, Raymond, Salvador, Jonathan, Savitz, Simon, Schmitt, Peter R, Schofield, Kang, Sim, Dan J, Stein, Frederike, Stein, Henk S, Temmingh, Katharina, Thiel, Sophia I, Thomopoulos, Neeltje E M, van Haren, Holly, Van Gestel, Cristian, Vargas, Eduard, Vieta, Annabel, Vreeker, Lena, Waltemate, Lakshmi N, Yatham, Christopher R K, Ching, Ole A, Andreassen, Paul M, Thompson, Tomas, Hajek, Mcwhinney, Sean R, Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, Del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J, Cannon, Dara M, Dannlowski, Udo, Diaz-Zuluaga, Ana M, Dietze, Lorielle, Elvsåshagen, Torbjørn, Eyler, Lisa T, Fullerton, Janice M, Goikolea, Jose M, Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus C M, Hahn, Tim, Howells, Fleur M, Ingvar, Martin, Kircher, Tilo T J, Krug, Axel, Kuplicki, Rayus T, Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlo, Malt, Ulrik F, Martyn, Fiona M, Mazza, Elena, Mcdonald, Colm, Mcphilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa M T, Mitchell, Philip B, Nabulsi, Leila, Nenadic, Igor, Opel, Nil, Ophoff, Roel A, Overs, Bronwyn J, Pfarr, Julia-Katharina, Pineda-Zapata, Julian A, Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai G, Roberts, Gloria, Ross, Alex, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter R, Sim, Kang, Stein, Dan J, Stein, Frederike, Temmingh, Henk S, Thiel, Katharina, Thomopoulos, Sophia I, van Haren, Neeltje E M, Van Gestel, Holly, Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi N, Ching, Christopher R K, Andreassen, Ole, Thompson, Paul M, Hajek, Tomas, Clinical Cognitive Neuropsychiatry Research Program (CCNP), Psychiatry, Child and Adolescent Psychiatry / Psychology, and Clinical Child and Family Studies

Subjects
obesity, Bipolar Disorder, body mass index, 03 medical and health sciences, 0302 clinical medicine, SDG 3 - Good Health and Well-being, SCHIZOPHRENIA, Cluster Analysis, Humans, BRAIN, Biological Psychiatry, Body mass index, GRAY-MATTER VOLUME, METABOLIC SYNDROME, 2. Zero hunger, MAJOR DEPRESSIVE DISORDER, INSULIN-RESISTANCE, ABNORMALITIES, 1ST-EPISODE, heterogeneit, surface area, cortical thickness, Magnetic Resonance Imaging, Temporal Lobe, 3. Good health, 030227 psychiatry, Psychiatry and Mental health, bipolar disorders, WHITE, heterogeneity, 030217 neurology & neurosurgery
Abstract

Aims: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. Methods: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. Results: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. Conclusions: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.

Published
2022

25. Paradoxical cognitive trajectories in men from earlier to later adulthood

Author

Graham M L Eglit, Jeremy A. Elman, Linda K. McEvoy, Michael C. Neale, Daniel E. Gustavson, Mark Sanderson-Cimino, McKenna E. Williams, Sean N. Hatton, Mark W. Logue, Chandra A. Reynolds, William S. Kremen, Richard L. Hauger, Carol E. Franz, Lisa T. Eyler, Christine Fennema-Notestine, Amy J. Jak, Hong Xian, Anders M. Dale, Xin M. Tu, Nathan A. Gillespie, Nathan Whitsel, M. Panizzon, Olivia K. Puckett, Michael J. Lyons, Donald J. Hagler, Ruth E. McKenzie, and Rosemary Toomey

Subjects
Male, Cognitive aging, Aging, Twins, Neuropsychological Tests, Executive Function, Cognition, immune system diseases, Medicine, Longitudinal Studies, Young adult, skin and connective tissue diseases, General Neuroscience, Neuropsychology, Brain, Middle Aged, Memory, Short-Term, cardiovascular system, Twin Studies as Topic, Life course approach, Mental health, Adult, Clinical Sciences, General cognitive ability, Article, Young Adult, Apolipoproteins E, Memory, Behavioral and Social Science, Acquired Cognitive Impairment, Humans, Dementia, cardiovascular diseases, Aged, Neurology & Neurosurgery, business.industry, Working memory, Neurosciences, medicine.disease, Twin study, Brain Disorders, Short-Term, Neurology (clinical), Geriatrics and Gerontology, business, Developmental Biology, Demography
Abstract

Because longitudinal studies of aging typically lack cognitive data from earlier ages, it is unclear how general cognitive ability (GCA) changes throughout the life course. In 1173 Vietnam Era Twin Study of Aging (VETSA) participants, we assessed young adult GCA at average age 20 and current GCA at 3 VETSA assessments beginning at average age 56. The same GCA index was used throughout. Higher young adult GCA and better GCA maintenance were associated with stronger specific cognitive abilities from age 51 to 73. Given equivalent GCA at age 56, individuals who had higher age 20 GCA outperformed those whose GCA remained stable in terms of memory, executive function, and working memory abilities from age 51 to 73. Thus, paradoxically, despite poorer maintenance of GCA, high young adult GCA still conferred benefits. Advanced predicted brain age and the combination of elevated vascular burden and APOE-ε4 status were associated with poorer maintenance of GCA. These findings highlight the importance of distinguishing between peak and current GCA for greater understanding of cognitive aging.

Published
2022

26. What we learn about bipolar disorder from large-scale neuroimaging

Author

Christian K. Tamnes, Bartholomeus C M Haarman, Jair C. Soares, Ole A. Andreassen, Viola Oertel, Theodore D. Satterthwaite, G. Tronchin, Michael Stäblein, Bradley J. MacIntosh, Melissa Pauling, Christopher R.K. Ching, Daniel H. Wolf, Dick J. Veltman, Ingrid Agartz, Bernhard T. Baune, Salvador Sarró, Mon-Ju Wu, Scott C Fears, Eduard Vieta, Melissa J. Green, Neeltje E.M. van Haren, Yann Quidé, Erlend Bøen, Yash Patel, Igor Nenadic, Martin Alda, Lisa T. Eyler, Arnaud Pouchon, Danai Dima, Tomáš Paus, Irene Bollettini, Torbjørn Elvsåshagen, Rachel M. Brouwer, Lakshmi N. Yatham, Michael Bauer, Caterina del Mar Bonnín, C. McDonald, Udo Dannlowski, Bronwyn Overs, Edith Pomarol-Clotet, Cristian Vargas Upegui, Oliver Gruber, Henricus G. Ruhé, Márcio Gerhardt Soeiro-de-Souza, Edouard Duchesnay, Hilary P. Blumberg, Tilo Kircher, Miho Ota, Michael Berk, Christoph Abé, Andreas Jansen, Kang Sim, Heather C. Whalley, Derrek P. Hibar, Roel A. Ophoff, Georgios V Thomaidis, Henrik Walter, Sophia Frangou, Michèle Wessa, Dara M. Cannon, Cara M. Altimus, Allison C. Nugent, Rodrigo Machado-Vieira, Orwa Dandash, Marcella Bellani, Unn K. Haukvik, Philip B. Mitchell, Ling-Li Zeng, Christian Knöchel, Jose Manuel Goikolea, Sonja M C de Zwarte, Francesco Benedetti, Sara Poletti, Janice M. Fullerton, Carlos A. Zarate, Aart H. Schene, Dan J. Stein, Chantal Henry, Tristram A. Lett, Mikael Landén, Daniel L Pham, Paolo Brambilla, Silvia Alonso-Lana, Sophia I. Thomopoulos, Carlos López-Jaramillo, Tomas Hajek, Bernd Kramer, G. Delvecchio, Maria M. Rive, Lars T. Westlye, Erick J. Canales-Rodríguez, Victoria L. Ives-Deliperi, Dominik Grotegerd, Beny Lafer, Abraham Nunes, Carrie E. Bearden, Raymond Salvador, Joaquim Radua, Amy C Bilderbeck, Xavier Caseras, Paul M. Thompson, Jorge R. C. Almeida, Pauline Favre, Gloria Roberts, David C. Glahn, Dag Alnæs, Julian A Pineda-Zapata, Tiril P. Gurholt, Mircea Polosan, Josselin Houenou, Fabiano G. Nery, Leila Nabulsi, Mary L. Phillips, Fleur M. Howells, Ana M. Díaz-Zuluaga, Elisa M T Melloni, Ching, C. R. K., Hibar, D. P., Gurholt, T. P., Nunes, A., Thomopoulos, S. I., Abe, C., Agartz, I., Brouwer, R. M., Cannon, D. M., de Zwarte, S. M. C., Eyler, L. T., Favre, P., Hajek, T., Haukvik, U. K., Houenou, J., Landen, M., Lett, T. A., Mcdonald, C., Nabulsi, L., Patel, Y., Pauling, M. E., Paus, T., Radua, J., Soeiro-de-Souza, M. G., Tronchin, G., van Haren, N. E. M., Vieta, E., Walter, H., Zeng, L. -L., Alda, M., Almeida, J., Alnaes, D., Alonso-Lana, S., Altimus, C., Bauer, M., Baune, B. T., Bearden, C. E., Bellani, M., Benedetti, F., Berk, M., Bilderbeck, A. C., Blumberg, H. P., Boen, E., Bollettini, I., del Mar Bonnin, C., Brambilla, P., Canales-Rodriguez, E. J., Caseras, X., Dandash, O., Dannlowski, U., Delvecchio, G., Diaz-Zuluaga, A. M., Dima, D., Duchesnay, E., Elvsashagen, T., Fears, S. C., Frangou, S., Fullerton, J. M., Glahn, D. C., Goikolea, J. M., Green, M. J., Grotegerd, D., Gruber, O., Haarman, B. C. M., Henry, C., Howells, F. M., Ives-Deliperi, V., Jansen, A., Kircher, T. T. J., Knochel, C., Kramer, B., Lafer, B., Lopez-Jaramillo, C., Machado-Vieira, R., Macintosh, B. J., Melloni, E. M. T., Mitchell, P. B., Nenadic, I., Nery, F., Nugent, A. C., Oertel, V., Ophoff, R. A., Ota, M., Overs, B. J., Pham, D. L., Phillips, M. L., Pineda-Zapata, J. A., Poletti, S., Polosan, M., Pomarol-Clotet, E., Pouchon, A., Quide, Y., Rive, M. M., Roberts, G., Ruhe, H. G., Salvador, R., Sarro, S., Satterthwaite, T. D., Schene, A. H., Sim, K., Soares, J. C., Stablein, M., Stein, D. J., Tamnes, C. K., Thomaidis, G. V., Upegui, C. V., Veltman, D. J., Wessa, M., Westlye, L. T., Whalley, H. C., Wolf, D. H., Wu, M. -J., Yatham, L. N., Zarate, C. A., Thompson, P. M., and Andreassen, O. A.

Subjects
mega-analysis, Stress-related disorders Donders Center for Medical Neuroscience [Radboudumc 13], cortical surface area, Review Article, 0302 clinical medicine, Manic-depressive illness, Multicenter Studies as Topic, Spectrum disorder, Review Articles, bipolar disorder, Cerebral Cortex, Trastorn bipolar, neuroimaging, Radiological and Ultrasound Technology, 05 social sciences, ENIGMA, HUMAN BRAIN, Magnetic Resonance Imaging, psychiatry, 3. Good health, Neurology, Meta-analysis, Scale (social sciences), Anatomy, Psychology, Clinical risk factor, Clinical psychology, MRI, MAJOR PSYCHIATRIC-DISORDERS, Schizoaffective disorder, 050105 experimental psychology, 03 medical and health sciences, Magnetic resonance imaging, Neuroimaging, Meta-Analysis as Topic, SDG 3 - Good Health and Well-being, Imatges per ressonància magnètica, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, Bipolar disorder, HIPPOCAMPAL VOLUMES, mega‐analysis, GRAY-MATTER VOLUME, SPECTRUM DISORDER, volume, DIABETES-MELLITUS, cortical thickness, COGNITIVE IMPAIRMENT, medicine.disease, Mental illness, meta-analysis, meta‐analysis, RC0321, Neurology (clinical), SCHIZOAFFECTIVE DISORDER, PSYCHOTIC FEATURES, 030217 neurology & neurosurgery
Abstract

MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness., This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.

Published
2022

27. Essential data dimensions for prospective international data collection in older age bipolar disorder (OABD): Recommendations from the GAGE-BD group

Author

Paola Lavin, Soham Rej, Andrew T. Olagunju, Antonio L. Teixeira, Annemieke Dols, Martin Alda, Osvaldo P. Almeida, Kursat Altinbas, Vicent Balanzá‐Martínez, Izabela G. Barbosa, Hilary P. Blumberg, Farren Briggs, Cynthia Calkin, Kristin Cassidy, Brent P. Forester, Orestes V. Forlenza, Tomas Hajek, Barthomeus C. M. Haarman, Esther Jimenez, Beny Lafer, Benoit Mulsant, Stephen O. Oluwaniyi, Regan Patrick, Joaquim Radua, Sigfried Schouws, Harmehr Sekhon, Christian Simhandl, Jair C. Soares, Shang‐Ying Tsai, Eduard Vieta, Luca M. Villa, Martha Sajatovic, Lisa T. Eyler, Neurology, Psychiatry, APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, and Amsterdam Neuroscience - Neurodegeneration

Subjects
Psychiatry and Mental health, Biological Psychiatry
Abstract

Background: By 2030, over 50% of individuals living with bipolar disorder (BD) are expected to be aged ≥50 years. However, older age bipolar disorder (OABD) remains understudied. There are limited large-scale prospectively collected data organized in key dimensions capable of addressing several fundamental questions about BD affecting this subgroup of patients. Methods: We developed initial recommendations for the essential dimensions for OABD data collection, based on (1) a systematic review of measures used in OABD studies, (2) a Delphi consensus of international OABD experts, (3) experience with harmonizing OABD data in the Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD, n ≥ 4500 participants), and (4) critical feedback from 34 global experts in geriatric mental health. Results: We identified 15 key dimensions and variables within each that are relevant for the investigation of OABD: (1) demographics, (2) core symptoms of depression and (3) mania, (4) cognition screening and subjective cognitive function, (5) elements for BD diagnosis, (6) descriptors of course of illness, (7) treatment, (8) suicidality, (9) current medication, (10) psychiatric comorbidity, (11) psychotic symptoms, (12) general medical comorbidities, (13) functioning, (14) family history, and (15) other. We also recommend particular instruments for capturing some of the dimensions and variables. Conclusion: The essential data dimensions we present should be of use to guide future international data collection in OABD and clinical practice. In the longer term, we aim to establish a prospective consortium using this core set of dimensions and associated variables to answer research questions relevant to OABD.

Published
2023

28. Focal adhesion is associated with lithium response in bipolar disorder: evidence from a network-based multi-omics analysis

Author

Vipavee Niemsiri, Sara Brin Rosenthal, Caroline M. Nievergelt, Adam X. Maihofer, Maria C. Marchetto, Renata Santos, Tatyana Shekhtman, Ney Alliey-Rodriguez, Amit Anand, Yokesh Balaraman, Wade H. Berrettini, Holli Bertram, Katherine E. Burdick, Joseph R. Calabrese, Cynthia V. Calkin, Carla Conroy, William H. Coryell, Anna DeModena, Lisa T. Eyler, Scott Feeder, Carrie Fisher, Nicole Frazier, Mark A. Frye, Keming Gao, Julie Garnham, Elliot S. Gershon, Fernando S. Goes, Toyomi Goto, Gloria J. Harrington, Petter Jakobsen, Masoud Kamali, Marisa Kelly, Susan G. Leckband, Falk W. Lohoff, Michael J. McCarthy, Melvin G. McInnis, David Craig, Caitlin E. Millett, Francis Mondimore, Gunnar Morken, John I. Nurnberger, Claire O’ Donovan, Ketil J. Øedegaard, Kelly Ryan, Martha Schinagle, Paul D. Shilling, Claire Slaney, Emma K. Stapp, Andrea Stautland, Bruce Tarwater, Peter P. Zandi, Martin Alda, Kathleen M. Fisch, Fred H. Gage, and John R. Kelsoe

Subjects
Psychiatry, Cellular and Molecular Neuroscience, Psychiatry and Mental health, Mental Health, Human Genome, Psychology and Cognitive Sciences, Genetics, Biological Sciences, Serious Mental Illness, Molecular Biology, Medical and Health Sciences, Brain Disorders
Abstract

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric = 1.28E–09 and 4.10E–18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

Published
2023

30. Lifestyle and the aging brain: interactive effects of modifiable lifestyle behaviors and cognitive ability in men from midlife to old age

Author

Mark Sanderson-Cimino, Ruth McKenzie, Matthew S. Panizzon, Carol E. Franz, Christine Fennema-Notestine, Jeremy A. Elman, McKenna E. Williams, William S. Kremen, Hong Xian, Anders M. Dale, Chandra A. Reynolds, Sean N. Hatton, Xin M. Tu, Michael C. Neale, Rahul C. Pearce, Nathan Whitsel, Michael J. Lyons, Donald J. Hagler, Richard L. Hauger, Lisa T. Eyler, Rosemary Toomey, Teresa Warren, Nathan A. Gillespie, and Olivia K. Puckett

Subjects
Male, Gerontology, Aging, Disease, Neurodegenerative, Alzheimer's disease brain signature, Alzheimer's Disease, Cognition, Cognitive Reserve, Medicine, Aging brain, Young adult, skin and connective tissue diseases, Cognitive reserve, General Neuroscience, Age Factors, Modifiable lifestyle behavior, Middle Aged, White Matter, Neurological, Biomedical Imaging, Independent Living, Adult, White matter abnormalities, Clinical Sciences, General cognitive ability, Affect (psychology), Article, Young Adult, Alzheimer Disease, Clinical Research, Behavioral and Social Science, Acquired Cognitive Impairment, Humans, Dementia, Healthy Lifestyle, Life Style, Aged, Behavior, Neurology & Neurosurgery, business.industry, Prevention, Neurosciences, Mild cognitive impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), medicine.disease, Twin study, Brain Disorders, Brain aging, Neurology (clinical), Geriatrics and Gerontology, business, Developmental Biology
Abstract

We examined the influence of lifestyle on brain aging after nearly 30 years, and tested the hypothesis that young adult general cognitive ability (GCA) would moderate these effects. In the community-dwelling Vietnam Era Twin Study of Aging (VETSA), 431 largely non-Hispanic white men completed a test of GCA at mean age 20. We created a modifiable lifestyle behavior composite from data collected at mean age 40. During VETSA, MRI-based measures at mean age 68 included predicted brain age difference (PBAD), Alzheimer's disease (AD) brain signature, and abnormal white matter scores. There were significant main effects of young adult GCA and lifestyle on PBAD and the AD signature (ps ≤ 0.012), and a GCA-by-lifestyle interaction on both (ps ≤ 0.006). Regardless of GCA level, having more favorable lifestyle behaviors predicted less advanced brain age and less AD-like brain aging. Unfavorable lifestyles predicted advanced brain aging in those with lower age 20 GCA, but did not affect brain aging in those with higher age 20 GCA. Targeting early lifestyle modification may promote dementia risk reduction, especially among lower reserve individuals.

Published
2021

31. The Association between Brain Age and Alcohol Use

Author

Makaya Funk‐White, David Wing, Lisa T. Eyler, Alison A. Moore, Emilie T. Reas, and Linda K. McEvoy

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2022

32. Longitudinal genetic and environmental relationships between structural‐ and diffusion‐based Alzheimer’s disease neuroimaging signatures

Author

McKenna E. Williams, Nathan A. Gillespie, Tyler Reed Bell, Anders M. Dale, Jeremy A. Elman, Lisa T. Eyler, Christine Fennema‐Notestine, Carol E Franz, Donald J. Hagler, Michael J. Lyons, Linda K. McEvoy, Michael C. Neale, Matthew S. Panizzon, Chandra A. Reynolds, Mark E. Sanderson‐Cimino, and William S. Kremen

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2022

33. Clinical correlates of late-onset versus early-onset bipolar disorder in a global sample of older adults

Author

Paola, Lavin, Gabriella, Buck, Osvaldo P, Almeida, Chien-Lin, Su, Lisa T, Eyler, Annemieke, Dols, Hilary P, Blumberg, Brent P, Forester, Orestes V, Forlenza, Ariel, Gildengers, Benoit H, Mulsant, Shang-Ying, Tsai, Eduard, Vieta, Sigfried, Schouws, Farren B S, Briggs, Ashley, Sutherland, Kaylee, Sarna, Joy, Yala, Melis, Orhan, Nicole, Korten, Martha, Sajatovic, Soham, Rej, Neurology, Psychiatry, APH - Mental Health, and Amsterdam Neuroscience - Neurodegeneration

Subjects
Psychiatry and Mental health, Geriatrics and Gerontology
Abstract

Objectives: Late-onset bipolar disorder (LOBD) represents a significant subgroup of bipolar disorder (BD). However, knowledge for this group is mostly extrapolated from small studies in subjects with early/mixed age of illness onset. In this global sample of older adults with BD (OABD: ≥50 years old) we aim to characterize the sociodemographic and clinical presentation of LOBD (≥40 years at BD onset) compared to early-onset BD (EOBD: 0.05). Late-onset bipolar disorder was associated with higher endocrine comorbidities (odds ratio = 1.48, [95%CI = 1.0,12.1], p = 0.03). This difference did not remain significant when subjects with BD onset ≥50 years old were analyzed. Limitations: This study is limited by the retrospective nature of the variable age of onset and the differences in evaluation methods across studies (partially overcame by harmonization processes). Conclusion: The present analysis is in favor of the hypothesis that LOBD might represent a similar clinical phenotype as classic EOBD with respect to core BD symptomatology, functionality, and comorbid physical conditions. Large-scale global collaboration to improve our understanding of BD across the lifespan is needed.

Published
2022

34. Demographic and clinical characteristics of lithium-treated older adults with bipolar disorder

Author

Orestes V, Forlenza, Tomas, Hajek, Osvaldo P, Almeida, Alexandra J M, Beunders, Hilary P, Blumberg, Farren B S, Briggs, Vanessa J R, De-Paula, Annemiek, Dols, Lisa T, Eyler, Brent P, Forester, Ariel, Gildengers, Esther, Jimenez, Nicole C M, Korten, Beny, Lafer, Sean R, McWhinney, Benoit, Mulsant, Soham, Rej, Kaylee, Sarna, Sigfried, Schouws, Ashley, Sutherland, Shangying, Tsai, Eduard, Vieta, Joy, Yala, Martha, Sajatovic, Psychiatry, APH - Mental Health, Neurology, and Amsterdam Neuroscience - Neurodegeneration

Subjects
Male, Psychiatry and Mental health, Bipolar Disorder, Lithium Compounds, Humans, Female, Lithium, Middle Aged, Aged, Antipsychotic Agents, Demography
Abstract

OBJECTIVES: There is limited information on the characteristics of older adults with bipolar disorder (OABD) treated with lithium, along with safety concerns about its use by older adults. The aim of the present study is to describe the demographic and clinical characteristics of OABD receiving lithium therapy, using data from the Global Ageing & Geriatric Experiments in Bipolar Disorder (GAGE-BD).EXPERIMENTAL PROCEDURES: Cross-sectional analysis of the GAGE-BD dataset to determine differences and similarities between lithium users and non-users. We analysed data from 986 participants aged 50 years or older (mean age 63.5 years; 57.5% females) from 12 study sites. Two subgroups ('Lithium'; 'Non-lithium') were defined according to the current prescription of lithium. We compared several outcomes between these groups, controlling for age, gender, and study site.RESULTS: OABD treated with lithium had lower scores on depression rating scales and were less likely to be categorised as with moderate or severe depression. There was a lower proportion of lithium users than non-users among those with evidence of rapid cycling and non-bipolar psychiatric diagnoses. Assessment of global cognitive state and functionality indicated better performance among lithium users. The current use of antipsychotics was less frequent among lithium users, who also reported fewer cardiovascular comorbidities than non-users.CONCLUSION: We found several potentially relevant differences in the clinical profile of OABD treated with lithium compared with those treated with other mood stabilisers. However, the interpretation of the present results must take into account the methodological limitations inherent to the cross-sectional approach and data harmonisation.

Published
2022

36. Long‐term associations of cigarette smoking in early mid‐life with predicted brain aging from mid‐ to late life

Author

Michael C. Neale, Rahul C. Pearce, Matthew S. Panizzon, Tyler Bell, William S. Kremen, Mark Sanderson-Cimino, Daniel E. Gustavson, Sean N. Hatton, Christine Fennema-Notestine, Chandra A. Reynolds, Anders M. Dale, Hong Xian, Rosemary Toomey, Xin M. Tu, Erik J. Buchholz, Linda K. McEvoy, Jeremy A. Elman, Nathan Whitsel, Lisa T. Eyler, Ruth McKenzie, Shandell Pahlen, Mc Kenna E. Williams, Carol E. Franz, Richard L. Hauger, Donald J. Hagler, Nathan A. Gillespie, Michael J. Lyons, and Olivia K. Puckett

Subjects
Adult, Male, Aging, longitudinal, Adolescent, Population, Medicine (miscellaneous), Medical and Health Sciences, smoking, Cigarette Smoking, Substance Misuse, Young Adult, Atrophy, Cigarette smoking, Clinical Research, Tobacco, Humans, Medicine, Dementia, Prospective Studies, education, Prospective cohort study, Brain aging, Aged, education.field_of_study, Tobacco Smoke and Health, alcohol, business.industry, Prevention, Psychology and Cognitive Sciences, Neurosciences, Substance Abuse, imaging, Brain, PBAD, Middle Aged, medicine.disease, Confidence interval, Brain Disorders, Psychiatry and Mental health, Good Health and Well Being, Neurological, Female, Observational study, business, Demography
Abstract

Background and aims Smoking is associated with increased risk for brain aging/atrophy and dementia. Few studies have examined early associations with brain aging. This study aimed to measure whether adult men with a history of heavier smoking in early mid-life would have older than predicted brain age 16-28 years later. Design Prospective cohort observational study, utilizing smoking pack years data from average age 40 (early mid-life) predicting predicted brain age difference scores (PBAD) at average ages 56, 62 (later mid-life) and 68 years (early old age). Early mid-life alcohol use was also evaluated. Setting Population-based United States sample. Participants/cases Participants were male twins of predominantly European ancestry who served in the United States military between 1965 and 1975. Structural magnetic resonance imaging (MRI) began at average age 56. Subsequent study waves included most baseline participants; attrition replacement subjects were added at later waves. Measurements Self-reported smoking information was used to calculate pack years smoked at ages 40, 56, 62, and 68. MRIs were processed with the Brain-Age Regression Analysis and Computation Utility software (BARACUS) program to create PBAD scores (chronological age-predicted brain age) acquired at average ages 56 (n = 493; 2002-08), 62 (n = 408; 2009-14) and 68 (n = 499; 2016-19). Findings In structural equation modeling, age 40 pack years predicted more advanced age 56 PBAD [β = -0.144, P = 0.012, 95% confidence interval (CI) = -0.257, -0.032]. Age 40 pack years did not additionally predict PBAD at later ages. Age 40 alcohol consumption, but not a smoking × alcohol interaction, predicted more advanced PBAD at age 56 (β = -0.166, P = 0.001, 95% CI = -0.261, -0.070) with additional influences at age 62 (β = -0.115, P = 0.005, 95% CI = -0.195, -0.036). Age 40 alcohol did not predict age 68 PBAD. Within-twin-pair analyses suggested some genetic mechanism partially underlying effects of alcohol, but not smoking, on PBAD. Conclusions Heavier smoking and alcohol consumption by age 40 appears to predict advanced brain aging by age 56 in men.

Published
2021

37. Relationships between daily mood states and real-time cognitive performance in individuals with bipolar disorder and healthy comparators: A remote ambulatory assessment study

Author

Colin A. Depp, Emma M. Parrish, Jessica Bomyea, Lisa T. Eyler, Emily W Paolillo, Tess F. Filip, and Raeanne C. Moore

Subjects
Bipolar Disorder, media_common.quotation_subject, Neuropsychological Tests, Article, Cognition, medicine, Humans, Cognitive Dysfunction, Effects of sleep deprivation on cognitive performance, Bipolar disorder, media_common, Neuropsychology, medicine.disease, Cognitive test, Sadness, Affect, Clinical Psychology, Mood, Neurology, Neurology (clinical), medicine.symptom, Cognition Disorders, Psychology, Mania, Clinical psychology
Abstract

OBJECTIVE: Neuropsychological impairments are observed in individuals with Bipolar Disorder (BD), yet knowledge of how cognitive deficits unfold in real-time remains limited. Given intraindividual variability in mood observed in people with BD, and the potential for mood and cognition to be mutually influential, we employed ambulatory assessment technologies to examine potential contemporaneous (same survey) and lagged (next survey) relationships of congition and mood. METHODS: Outpatients with BD (n=46) or no psychiatric disorders (heathy volunteers [HV]; n=20) completed in-laboratory neurobehavioral assessments and 14 days of smartphone-administered mobile cognitive tests and ratings of affective variables. Linear mixed effects models were used to analyze real-time relationships between mobile cognitive test performance and mood. RESULTS: On in-laboratory tests, participants with BD showed worse cognitive performance than HVs as well as mild depression severity; mood and cognitive performance were unrelated. On mobile cognitive tests and surveys, participants with BD showed somewhat worse cognitive performance and ratings of lower energy and greater sadness relative to HV participants. Among those with BD, mania and sadness earlier in the day related to worse processing speed and better working memory performance, respectively, on the next survey. In contrast, same survey ratings of greater stress related to better working memory, and greater happiness related to better processing speed. CONCLUSIONS: Real-time assessments of mood and cognition provide incremental information beyond what can be gleaned from laboratory assessments. Understanding how these affect-related changes in processing speed emerge and play out in daily life may provide clinically useful information for treatment planning.

Published
2021

38. Correlates of poor sleep based upon wrist actigraphy data in bipolar disorder

Author

Benchawanna Soontornniyomkij, Lisa T. Eyler, Colin A. Depp, Christopher N. Kaufmann, David Wing, Ho-Kyoung Yoon, Ellen E. Lee, Celestine Christensen, Ashley N. Sutherland, Sonia Ancoli-Israel, and Faculty of Physical Education and Physical Therapy

Subjects
Sleep Wake Disorders, medicine.medical_specialty, Bipolar Disorder, Audiology, Wrist, Affect (psychology), Young Mania Rating Scale, Medical and Health Sciences, Article, 03 medical and health sciences, 0302 clinical medicine, Clinical Research, Behavioral and Social Science, medicine, Humans, Bipolar disorder, Biological Psychiatry, Sleep Wake Disorders/diagnosis, Psychiatry, Data reduction, business.industry, Psychology and Cognitive Sciences, Neurosciences, Actigraphy, medicine.disease, Sleep in non-human animals, Brain Disorders, 030227 psychiatry, Poor sleep, Psychiatry and Mental health, Mental Health, medicine.anatomical_structure, Female, Sleep onset, Sleep Research, Sleep, business, 030217 neurology & neurosurgery
Abstract

BackgroundWrist-worn actigraphy can objectively measure sleep, and has advantages over self-report, particularly for people with Bipolar Disorder (BD) for whom self-reports might be influenced by affect. Clinically useful data reduction approaches are needed to explore these complex data.MethodsWe created a composite score of sleep metrics in BD based on 51 BD and 80 healthy comparison (HC) participants. Subjects wore an actigraph for up to 14 consecutive 24-h periods, and we assessed total sleep time (TST), wake after sleep onset (WASO), percent sleep (PS), and number of awakenings (NA). We focused on participants who had at least 5 nights of actigraphy data. We computed z-scores for within-person means of sleep measures for BD subjects versus HCs, which were averaged to create a composite measure. We correlated this composite with participant characteristics, and used LASSO regression to identify sleep measures best explaining variability in identified correlates.ResultsSleep measures and the composite did not differ between BDs and HCs; however, there was considerable variability in z-scores among those with BD. In BDs, the composite score was higher in women (t(49)=2.28, p=0.027) and those who were employed (t(34)=2.34, p=0.025), and positively correlated with medication load (r=0.41, p=0.003) while negatively correlated with Young Mania Rating Scale (YMRS; r=-0.35, p=0.030). In LASSO regression, TST and NA best explained medication load while PS best explained employment and YMRS.ConclusionWhile a composite score of sleep metrics may provide useful information about sleep quality globally, our findings suggest that selection of theory-driven sleep measures may be more clinically meaningful.

Published
2021

39. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author

Yash Patel, Jean Shin, Christoph Abé, Ingrid Agartz, Clara Alloza, Dag Alnæs, Sonia Ambrogi, Linda A. Antonucci, Celso Arango, Volker Arolt, Guillaume Auzias, Rosa Ayesa-Arriola, Nerisa Banaj, Tobias Banaschewski, Cibele Bandeira, Zeynep Başgöze, Renata Basso Cupertino, Claiton H.D. Bau, Jochen Bauer, Sarah Baumeister, Fabio Bernardoni, Alessandro Bertolino, Caterina del Mar Bonnin, Daniel Brandeis, Silvia Brem, Jason Bruggemann, Robin Bülow, Juan R. Bustillo, Sara Calderoni, Rosa Calvo, Erick J. Canales-Rodríguez, Dara M. Cannon, Susanna Carmona, Vaughan J. Carr, Stanley V. Catts, Sneha Chenji, Qian Hui Chew, David Coghill, Colm G. Connolly, Annette Conzelmann, Alexander R. Craven, Benedicto Crespo-Facorro, Kathryn Cullen, Andreas Dahl, Udo Dannlowski, Christopher G. Davey, Christine Deruelle, Covadonga M. Díaz-Caneja, Katharina Dohm, Stefan Ehrlich, Jeffery Epstein, Tracy Erwin-Grabner, Lisa T. Eyler, Jennifer Fedor, Jacqueline Fitzgerald, William Foran, Judith M. Ford, Lydia Fortea, Paola Fuentes-Claramonte, Janice Fullerton, Lisa Furlong, Louise Gallagher, Bingchen Gao, Si Gao, Jose M. Goikolea, Ian Gotlib, Roberto Goya-Maldonado, Hans J. Grabe, Melissa Green, Eugenio H. Grevet, Nynke A. Groenewold, Dominik Grotegerd, Oliver Gruber, Jan Haavik, Tim Hahn, Ben J. Harrison, Walter Heindel, Frans Henskens, Dirk J. Heslenfeld, Eva Hilland, Pieter J. Hoekstra, Sarah Hohmann, Nathalie Holz, Fleur M. Howells, Jonathan C. Ipser, Neda Jahanshad, Babette Jakobi, Andreas Jansen, Joost Janssen, Rune Jonassen, Anna Kaiser, Vasiliy Kaleda, James Karantonis, Joseph A. King, Tilo Kircher, Peter Kochunov, Sheri-Michelle Koopowitz, Mikael Landén, Nils Inge Landrø, Stephen Lawrie, Irina Lebedeva, Beatriz Luna, Astri J. Lundervold, Frank P. MacMaster, Luigi A. Maglanoc, Daniel H. Mathalon, Colm McDonald, Andrew McIntosh, Susanne Meinert, Patricia T. Michie, Philip Mitchell, Ana Moreno-Alcázar, Bryan Mowry, Filippo Muratori, Leila Nabulsi, Igor Nenadić, Ruth O'Gorman Tuura, Jaap Oosterlaan, Bronwyn Overs, Christos Pantelis, Mara Parellada, Jose C. Pariente, Paul Pauli, Giulio Pergola, Francesco Maria Piarulli, Felipe Picon, Fabrizio Piras, Edith Pomarol-Clotet, Clara Pretus, Yann Quidé, Joaquim Radua, J. Antoni Ramos-Quiroga, Paul E. Rasser, Andreas Reif, Alessandra Retico, Gloria Roberts, Susan Rossell, Diego Luiz Rovaris, Katya Rubia, Matthew D. Sacchet, Josep Salavert, Raymond Salvador, Salvador Sarró, Akira Sawa, Ulrich Schall, Rodney Scott, Pierluigi Selvaggi, Tim Silk, Kang Sim, Antonin Skoch, Gianfranco Spalletta, Filip Spaniel, Dan J. Stein, Olaf Steinsträter, Aleks Stolicyn, Yoichiro Takayanagi, Leanne Tamm, Maria Tavares, Alexander Teumer, Katharina Thiel, Sophia I. Thomopoulos, David Tomecek, Alexander S. Tomyshev, Diana Tordesillas-Gutiérrez, Michela Tosetti, Anne Uhlmann, Tamsyn Van Rheenen, Javier Vazquez-Bourgón, Meike W. Vernooij, Eduard Vieta, Oscar Vilarroya, Cynthia Weickert, Thomas Weickert, Lars T. Westlye, Heather Whalley, David Willinger, Alexandra Winter, Katharina Wittfeld, Tony T. Yang, Yuliya Yoncheva, Jendé L. Zijlmans, Martine Hoogman, Barbara Franke, Daan van Rooij, Jan Buitelaar, Christopher R.K. Ching, Ole A. Andreassen, Elena Pozzi, Dick Veltman, Lianne Schmaal, Theo G.M. van Erp, Jessica Turner, F. Xavier Castellanos, Zdenka Pausova, Paul Thompson, Tomas Paus, Pediatric surgery, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, General Paediatrics, ARD - Amsterdam Reproduction and Development, Paediatrics, Radiology & Nuclear Medicine, Epidemiology, Clinical Cognitive Neuropsychiatry Research Program (CCNP), German Research Foundation, University of Münster, National Health and Medical Research Council (Australia), University of Cape Town, National Research Foundation (South Africa), Swinburne Universit, Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L. Cooper Foundation, Society of Mental Health Research, Swedish Research Council, Swedish Foundation for Strategic Research, Swedish Brain Foundation, Health Research Board (Ireland), Russian Foundation for Basic Research, University of Zurich, Pratt Foundation, Ramsay Health Care, Viertel Charitable Foundation, Schizophrenia Research Institute, European Commission, Australian Research Council, Instituto de Salud Carlos III, National Institute for Health and Care Research (US), National Institute for Health Research (UK), Ministry of Health of the Czech Republic, Bill & Melinda Gates Foundation, South African Medical Research Council, Carnegie Corporation of New York, Wellcome Trust, Medical Research Council (UK), Medical Research Scotland, Netherlands Organization for Scientific Research, Ambrogi, Sonia, Banaschewski, Tobias, Bandeira, Cibele Edom, Cupertino, Renata, Calderoni, Sara, Cannon, Dara, Carr, Vaughan, Chew, Qian Hui, Coghill, David, Cullen, Kathryn, Dahl, Andreas, Epstein, Jeffery, Foran, William, Fortea, Lydia, Fuentes-Claramonte, Paola, Fullerton, Janice M., Furlong, Lisa, Gallagher, Louise, Gao, Si, Gotlib, Ian, Haavik, Jan, Henskens, Frans, Hilland, Eva, Hoekstra, Pieter J, Howells, Fleur M, Ipser, Jonathan, Jørgensen, Jes Kristian, Karantonis, James A., Lawrie, Stephen, Research Institute of the Hospital for Sick Children and University of Toronto, University of Toronto, Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Canada, KG Jebsen Centre for Psychosis Research, University of Oslo (UiO)-Institute of Clinical Medicine-Oslo University Hospital [Oslo], Universidad Complutense de Madrid = Complutense University of Madrid [Madrid] (UCM), Mental Health Sciences Unit, University College of London [London] (UCL), Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Clinical and Behavioral Neurology - Neuroscienze e riabilitazione, IRCCS Fondazione Santa Lucia [Roma], Heidelberg University, Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), Universität Heidelberg [Heidelberg] = Heidelberg University, Clinical Neuropsychology, IBBA, APH - Mental Health, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Hospital Sant Joan de Déu [Barcelona], and Centro de Investigación Biomédica en Red Salud Mental [Madrid] (CIBER-SAM)

Subjects
Bipolar Disorder, Autism Spectrum Disorder, [SDV]Life Sciences [q-bio], Neurogenesis, pathology [Premature Birth], Neurodevelopment, Cortical surface area, pathology [Autism Spectrum Disorder], Cortical growth, methods [Magnetic Resonance Imaging], SDG 3 - Good Health and Well-being, Pregnancy, 130 000 Cognitive Neurology & Memory, Humans, ddc:610, Child, Biological Psychiatry, Cerebral Cortex, pathology [Depressive Disorder, Major], Depressive Disorder, Major, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Infant, Newborn, Magnetic Resonance Imaging, Mental illness, Attention Deficit Disorder with Hyperactivity, Premature Birth, genetics [Autism Spectrum Disorder], Female, Psychiatric disorders, 170 000 Motivational & Cognitive Control
Abstract

[Background]: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life., [Methods]: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed., [Results]: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth., [Conclusions]: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy., This work was supported by the German Research Foundation (DFG Grant Nos. HA7070/2-2, HA7070/3, HA7070/4 [to TH]) and IZKF of the medical faculty of Münster (Grants No. Dan3/012/17 [to UD] and MzH 3/020/20 [to TH]), and NHMRC projects (Grant No. 1064643 [to BJH] and 1024570 [to CGD]). The CIAM study was supported by the University of Cape Town Research Committee, South African National Research Foundation, and the South African Medical Research Council (principal investigator [PI], Fleur M. Howells) and Grant No. R01MH117601 (to NJ). This work was funded by the German Research Foundation (Grant Nos. FOR2107 JA 1890/7-1 and FOR2107 JA 1890/7-2 [to AJ]) and Swinburne University scholarship/Australian Postgraduate Award (to JK). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to JK). This work was funded by the German Research Foundation (Grant Nos. FOR2107 KI588/14-1 and FOR2107 KI588/14-2 [to TK]). The St. Göran study was supported by grants from the Swedish Research Council (Grant No. 2018-02653 [to ML]), the Swedish foundation for Strategic Research (Grant No. KF10-0039 [to ML]), the Swedish Brain foundation (Grant No. FO2020-0261 [to ML]), and the Swedish Government under the LUA/ALF agreement (Grant Nos. ALF 20170019 and ALFGBG-716801 [to ML]). This work was supported by RFBR (Grant No. 20-013-00748 [to IL, AST]) and funded by the Health Research Board (Grant No. HRA_POR/2011/100 [to CM]). The Australian Schizophrenia Research Bank (ASRB) was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation, and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. This work was supported by Deutsche Forschungsgemeinschaft (Grant Nos. DFG NE 2254/2-1, NE 2254/3-1, NE2254/4-1 [to IN]), the University Research Priority Program “Integrative Human Physiology” at the University of Zurich (to ROT), an NHMRC Senior Principal Research Fellowship (Grant No. 1105825 [to CPa]), an NHMRC L3 Investigator Grant (Grant No. 1196508 [to CPa]), and NHMRC Program Grant (Grant No. 1150083 [to CPa]). ASRB was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (Agreement No. 798181 [to GP]). ASRB was supported by the NHMRC (Enabling Grant, ID 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. The Imaging Genetics in Psychosis study was funded by Project Grants from the NHMRC (Grant Nos. APP630471 and APP1081603 [to YQ]) and the Macquarie University’s Australian Research Council (ARC) Centre of Excellence in Cognition and its Disorders (Grant No. CE110001021 [to YQ]). This work was supported by the Spanish Ministry of Science, Innovation and Universities/Economy and Competitiveness/Instituto de Salud Carlos III (Grant Nos. PI11/01766 and CPII19/00009 [to JR]), co-financed by European Regional Development Fund funds from the European Commission (“A Way of Making Europe”). ASRB was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation, and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. SR was supported by an NHMRC Senior Fellowship (Grant No. GNT1154651). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to SR). This work was supported by NIHR; MRC (to KR), NIH (Grant Nos. MH-094268, MH-105660, and MH-107730 [to ASS]). The Neuroimaging of the Children's Attention Project cohort was funded by NHMRC, Australia (Grant No. 1065895). Earlier funding for the cohort as also provided by NHMRC (Grant No. 1008522) and a grant from the Collier Foundation. The ACPU cohort was funded by NHMRC, Australia (Project Grant Nos. 384419 and 569533 [to TS]). This work was supported by research grants from the National Healthcare Group, Singapore (Grant Nos. SIG/05004, SIG/05028, and SIG /1103), and the Singapore Bioimaging Consortium (RP C009/2006 [to KS]), and the Ministry of Health, Czech Republic - conceptual development of research organization (“Institute for Clinical and Experimental Medicine - IKEM, IN 00023001” [to ASk]). This study was supported by the Italian Ministry of Health (Grant No. RC/17-18-19-20-21/A [to GS]) and Ministry of Health of the Czech Republic (Grant No. NU20-04-00393 [to FS]). The Drakenstein Child Health Study (DCHS) cohort is funded by the Bill and Melinda Gates Foundation (Grant No. OPP 1017641) and the South African Medical Research Council. This DCHS contribution was made possible in part by a grant from Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the author (DJS). STRADL study was supported and funded by the Wellcome Trust Strategic Award “Stratifying Resilience and Depression Longitudinally” (Grant No. 104036/Z/14/Z), and the Medical Research Council Mental Health Pathfinder Award “Leveraging routinely collected and linked research data to study the causes and consequences of common mental disorders” (MRC, Grant No. MC_PC_17209). Scottish Bipolar Family Study (SBFS) was supported by National Health Service Research Scotland, through the Scottish Mental Health Research Network (www.smhrn.org.uk), who provided assistance with subject recruitment and assessments. SBFS was conducted at the Brain Research Imaging Centre (http://www.bric.ed.ac.uk), which is supported by SINAPSE (Scottish Imaging Network, a Platform for Scientific Excellence, http://www.sinapse.ac.uk). Processing of the datasets used the resources provided by the Edinburgh Compute and Data Facility (http://www.ecdf.ed.ac.uk/) (ASt). TVR was supported by an NHMRC Early Career Fellowship (Grant No. GNT1088785). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to TVR). EV was supported by the Spanish Ministry of Science and Innovation (PI18/00805) integrated into the Plan Nacional de I+D+I and co-financed by the ISCIII-Subdirección General de Evaluación and the FEDER; the Instituto de Salud Carlos III; the CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental); by the CERCA Programme/Generalitat de Catalunya and the Secretaria d’Universitats i Recerca del Departament d’Economia I Coneixement (Grant No. 2017SGR1355). This study was also supported by the Departament de Salut de la Generalitat de Catalunya, Pla Estratègic de Recerca i Innovació en Salut (PERIS) 2016-2020 (Grant No. SLT006/17/00345) and the European Union Horizon 2020 research and innovation program (EU.3.1.1. Understanding health, wellbeing and disease: Grant Nos. 754907 and EU.3.1.3 [to EB]; Treating and managing disease: Grant No. 945151 [to EV]). This study was supported by the National Center for Complementary and Integrative Health (Grant Nos. R21AT009173 and R61AT009864 [to TTY]); by the National Center for Advancing Translational Sciences (CTSI), National Institutes of Health, through UCSF-CTSI (Grant No. UL1TR001872 [to TTY]); by the American Foundation for Suicide Prevention (Grant No. SRG-1-141-18 [to TTY]); by UCSF Research Evaluation and Allocation Committee (REAC) and J. Jacobson Fund (to TTY); by the NIMH (Grant No. R01MH085734 [to TTY]); and by the Brain and Behavior Research Foundation (formerly NARSAD) (to TTY). This work was supported by a personal Veni grant to MH from the Netherlands Organization for Scientific Research (NWO, Grant No. 91619115 [to MH]) and European Community’s Horizon 2020 Programme (H2020/2014 – 2020) (Grant Agreements Nos. 667302 [CoCA], 728018 [Eat2beNICE], and 847879 [PRIME] [to BF]). JBu has been supported by the EU-AIMS (European Autism Interventions) and AIMS-2-TRIALS programmes, which receive support from Innovative Medicines Initiative Joint Undertaking Grant Nos. 115300 and 777394, the resources of which are composed of financial contributions from the European Union’s FP7 and Horizon 2020 Programmes, and from the European Federation of Pharmaceutical Industries and Associations companies’ in-kind contributions, and AUTISM SPEAKS, Autistica and SFARI; and by the Horizon 2020–supported programme CANDY (Grant No. 847818 [to JBu]). This work is supported by Grant No. NIA T32AG058507 and NIH Grant No. U54EB020403 from the Big Data to Knowledge (BD2K) Program (to CRKC). ENIGMA MDD work is supported by NIH (Grant Nos. U54 EB020403 [to PT], R01 MH116147 [to PT], and R01 MH117601 [to NJ and LS]). LS was supported by an NHMRC Career Development Fellowship (Grant No. 1140764). This work was supported by the National Center for Research Resources at the NIH (Grant Nos. NIH 1 U24 RR021992 [Function Biomedical Informatics Research Network], NIH 1 U24 RR025736-01 [Biomedical Informatics Research Network Coordinating Center; http://www.birncommunity.org]). TGMvE is supported by ENIGMA’s NIH BD2K initiative (Grant No. U54 EB020403), ENIGMA Sex Differences (Grant No. R01MH116147), and ENIGMA-COINSTAC: Advanced Worldwide Transdiagnostic Analysis of Valence System Brain Circuits (Grant No. R01MH121246). This work was supported by the NIH (Grant No. R01MH121246 [to JT, Calhoun, and TGMvE]). This work was supported in part by NIH (Grant No. U54 EB020403 [to PT]).

Published
2022

42. Chemokine MCP1 is associated with cognitive flexibility in schizophrenia: A preliminary analysis

Author

Federica Klaus, Sharon C. Liou, Lisa T. Eyler, Tanya T. Nguyen, and Kyle R. Mitchell

Subjects
Oncology, medicine.medical_specialty, Chemokine, Psychosis, Inflammation, Neuropsychological Tests, Executive Function, 03 medical and health sciences, Cognition, 0302 clinical medicine, Internal medicine, medicine, Humans, Effects of sleep deprivation on cognitive performance, Chemokine CCL2, Biological Psychiatry, biology, business.industry, Cognitive flexibility, medicine.disease, 030227 psychiatry, Peripheral, Psychiatry and Mental health, Schizophrenia, biology.protein, medicine.symptom, Cognition Disorders, business, 030217 neurology & neurosurgery
Abstract

Background Peripheral levels of pro-inflammatory biomarkers have been shown to be altered in schizophrenia (SZ) and associated with cognitive impairments, but their relevance to specific cognitive domains remains unclear. Methods Plasma levels of cytokines, chemokines, and vascular biomarkers were quantified and compared between SZ and healthy comparison (HC) groups. Cognition was assessed using the Delis-Kaplan Executive Function System Trail Making (TM) and Color Word Interference (CWI) tests. Linear regression analyses examined differential relationships of inflammatory biomarkers with executive function between groups. Results Plasma levels of TNFα, ICAM1, and MCP1 were higher in individuals with SZ compared to HCs. Higher level of MCP1 was associated with increased CWI Inhibition Switching Errors in SZ but not HCs. Conclusion Like other studies, we found evidence for increased peripheral inflammation in SZ. We also showed that SZ with particularly high MCP1 levels had poor cognitive flexibility. Interventions to reduce chemokine elevations might prove beneficial for cognitive performance.

Published
2021

43. Genetic and Environmental Influences on Structural and Diffusion-Based Alzheimer’s Disease Neuroimaging Signatures Across Midlife and Early Old Age

Author

McKenna E. Williams, Nathan A. Gillespie, Tyler R. Bell, Anders M. Dale, Jeremy A. Elman, Lisa T. Eyler, Christine Fennema-Notestine, Carol E. Franz, Donald J. Hagler, Michael J. Lyons, Linda K. McEvoy, Michael C. Neale, Matthew S. Panizzon, Chandra A. Reynolds, Mark Sanderson-Cimino, and William S. Kremen

Subjects
Cognitive Neuroscience, Radiology, Nuclear Medicine and imaging, Neurology (clinical), Biological Psychiatry
Abstract

Composite scores of MRI-derived metrics in brain regions associated with Alzheimer's disease (AD), commonly termed 'AD signatures,' have been developed to distinguish early AD-related atrophy from normal age-associated changes. Diffusion-based gray matter signatures may be more sensitive to early AD-related changes compared to thickness/volume-based signatures, demonstrating their potential clinical utility. The timing of early (i.e., midlife) changes in AD signatures from different modalities, and whether diffusion- and thickness/volume-based signatures each capture unique, AD-related phenotypic or genetic information, remains unknown.Our validated thickness/volume signature, our novel mean diffusivity (MD) signature, and an MRI-derived measure of brain age were used in biometrical analyses to examine genetic and environmental influences on the measures, as well as phenotypic and genetic relationships between measures over 12 years. Participants were 736 men from three waves of the Vietnam Era Twin Study of Aging (VETSA; baseline age=56.1, SD=2.6, range=51.1-60.2). Subsequent waves were at approximately 5.7-year intervals.MD and thickness/volume signatures were highly heritable (56-72%). Baseline MD signatures predicted thickness/volume signatures over a decade later, but baseline thickness/volume signatures showed a significantly weaker relationship with future MD signatures. AD signatures and brain age were correlated, but each measure captured unique phenotypic and genetic variance.Cortical MD and thickness/volume AD signatures are heritable, and each signature captures unique variance that is also not explained by brain age. Moreover, results are in line with changes in MD emerging before changes in cortical thickness, underscoring the utility of MD as a very early predictor of AD risk.

Published
2022

44. Temporal relationships of ecological momentary mood and actigraphy-based sleep measures in bipolar disorder

Author

Molly Patapoff, Marina Ramsey, Madison Titone, Christopher N. Kaufmann, Atul Malhotra, Sonia Ancoli-Israel, David Wing, Ellen Lee, Lisa T. Eyler, and Faculty of Physical Education and Physical Therapy

Subjects
Bipolar Disorder, Ecological Momentary Assessment, Medical and Health Sciences, behavioral disciplines and activities, Article, Clinical Research, 2.3 Psychological, Sleep Initiation and Maintenance Disorders, Behavioral and Social Science, mental disorders, Humans, Variability, Aetiology, Biological Psychiatry, Psychiatry, Depression, Psychology and Cognitive Sciences, Actigraphy, Brain Disorders, Affect, Psychiatry and Mental health, Mental Health, Good Health and Well Being, social and economic factors, Sleep, Sleep Research
Abstract

Sleep disturbances are a key feature of bipolar disorder (BD), and poor sleep has been linked to mood symptoms. Recent use of ecological momentary assessment (EMA) has allowed for nuanced exploration of the sleep-mood link; though, the scale and directionality of this relationship is still unclear. Using EMA, actigraphy, and self-reported sleep measures, this study examines the concurrent and predictive relationships between sleep and mood. Participants with BD (n=56) wore actigraphy devices for up to 14 days and completed validated scales and daily EMA surveys about mood and sleep quality. Linear mixed models were used to examine overall and time-lagged relationships between sleep and mood variables. EMA mood ratings were correlated with validated rating scales for depression, mania, anxiety, and impulsivity. Poor self-reported sleep quality was associated with worse overall ratings of sadness and anger. Worse self-reported sleep quality was associated with greater sadness the following day. Higher daytime impulsivity was associated with worse sleep quality the following night. Exploratory analyses found relationships between worse and more variable mood (sadness, anger, and impulsivity) with worse and more variable sleep that evening (efficiency, WASO, and sleep onset time). The sample size was modest, fairly homogenous, and included mainly euthymic persons with BD. EMA-based assessments of mood and sleep are correlated with validated scale scores and provide novel insight into intra-individual variability. Further work on the complex two-way interactions between sleep and mood is needed to better understand how to improve outcomes in BD.

Published
2022

45. Association between body mass index and subcortical brain volumes in bipolar disorders–ENIGMA study in 2735 individuals

Author

Eduard Vieta, Jose Manuel Goikolea, Joaquim Raduà, Janice M. Fullerton, Lakshmi N. Yatham, Peter R. Schofield, Carlos López-Jaramillo, Tomas Hajek, Edith Pomarol-Clotet, Henk Temmingh, Francesco Benedetti, Ulrik Fredrik Malt, Erlend Bøen, Roel A. Ophoff, Bartholomeus C M Haarman, Cristian Vargas, Kang Sim, Katharina Thiel, Ole A. Andreassen, Tim Hahn, Lisa T. Eyler, Philip B. Mitchell, Christopher R.K. Ching, Axel Krug, Jonathan Repple, Annabel Vreeker, Dara M. Cannon, Sandra Meier, Colm McDonald, Holly Van Gestel, Hannah Lemke, Maike Richter, Caterina del Mar Bonnín, Udo Dannlowski, Tilo Kircher, Martin Alda, Mikael Landén, Janik Goltermann, Torbjørn Elvsåshagen, Genevieve McPhilemy, Jonathan Savitz, Susanne Meinert, Igor Nenadic, Simon Schmitt, Bronwyn Overs, Katharina Brosch, Dan J. Stein, Raymond Salvador, Dominik Grotegerd, Nils Opel, Martin Ingvar, Sean R. McWhinney, Erick J. Canales-Rodríguez, Elena Mazza, Gloria Roberts, Paul M. Thompson, Neeltje E.M. van Haren, Tiana Borgers, Fiona M. Martyn, Frederike Stein, Julia-Katharina Pfarr, Benny Liberg, Julian A Pineda-Zapata, Christoph Abé, Lena Waltemate, Tina Meller, Kai Ringwald, Ana M. Díaz-Zuluaga, Elisa M T Melloni, Rayus Kuplicki, Leila Nabulsi, Fleur M. Howells, Psychiatry, Child and Adolescent Psychiatry / Psychology, Mcwhinney, S. R., Abe, C., Alda, M., Benedetti, F., Boen, E., del Mar Bonnin, C., Borgers, T., Brosch, K., Canales-Rodriguez, E. J., Cannon, D. M., Dannlowski, U., Diaz-Zuluaga, A. M., Elvsashagen, T., Eyler, L. T., Fullerton, J. M., Goikolea, J. M., Goltermann, J., Grotegerd, D., Haarman, B. C. M., Hahn, T., Howells, F. M., Ingvar, M., Kircher, T. T. J., Krug, A., Kuplicki, R. T., Landen, M., Lemke, H., Liberg, B., Lopez-Jaramillo, C., Malt, U. F., Martyn, F. M., Mazza, E., Mcdonald, C., Mcphilemy, G., Meier, S., Meinert, S., Meller, T., Melloni, E. M. T., Mitchell, P. B., Nabulsi, L., Nenadic, I., Opel, N., Ophoff, R. A., Overs, B. J., Pfarr, J. -K., Pineda-Zapata, J. A., Pomarol-Clotet, E., Radua, J., Repple, J., Richter, M., Ringwald, K. G., Roberts, G., Salvador, R., Savitz, J., Schmitt, S., Schofield, P. R., Sim, K., Stein, D. J., Stein, F., Temmingh, H. S., Thiel, K., van Haren, N. E. M., Gestel, H. V., Vargas, C., Vieta, E., Vreeker, A., Waltemate, L., Yatham, L. N., Ching, C. R. K., Andreassen, O., Thompson, P. M., Hajek, T., and Clinical Cognitive Neuropsychiatry Research Program (CCNP)

Subjects
medicine.medical_specialty, Bipolar Disorder, Hippocampus, Amygdala, Article, Body Mass Index, Cellular and Molecular Neuroscience, Lateral ventricles, SDG 3 - Good Health and Well-being, Neuroimaging, Internal medicine, medicine, Humans, Risk factor, Molecular Biology, business.industry, Brain, medicine.disease, Magnetic Resonance Imaging, Obesity, Comorbidity, Psychiatry and Mental health, medicine.anatomical_structure, Cardiology, business, Body mass index, Neuroscience
Abstract

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.

Published
2021

46. Altered Neuronal Support and Inflammatory Response in Bipolar Disorder Patient-Derived Astrocytes

Author

Michael McCarthy, Maria C. Marchetto, Renata Santos, Maxim N. Shokhirev, Arianna Mei, Kelly J. Heard, Ruth Oefner, Galina Erikson, Krishna C. Vadodaria, Vipula Racha, Lisa T. Eyler, John R. Kelsoe, Ana P.D. Mendes, Fred H. Gage, The Salk Institute for Biological Studies, University of California [San Diego] (UC San Diego), University of California, Institut de psychiatrie et neurosciences de Paris (IPNP - U1266 Inserm), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), University of California (UC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), and Martinez Rico, Clara

Subjects
0301 basic medicine, Bipolar Disorder, glia, medicine.medical_treatment, [SDV.MHEP.PSM] Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Interleukin-1beta, Biochemistry, neuronal activity, Transcriptome, 0302 clinical medicine, cytokine, Premovement neuronal activity, Induced pluripotent stem cell, Neurons, iPSC, biology, Phenotype, mood disorders, psychiatry, 3. Good health, Cytokine, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], medicine.symptom, Neuroglia, medicine.medical_specialty, Clinical Sciences, Induced Pluripotent Stem Cells, Inflammation, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Neuropathology, Article, 03 medical and health sciences, Internal medicine, Genetics, medicine, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Interleukin 6, [SDV.BC] Life Sciences [q-bio]/Cellular Biology, IL-6, Interleukin-6, astrocytes, Cell Biology, Coculture Techniques, IL-6 Stem Cell Reports, 030104 developmental biology, Endocrinology, inflammation, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, biology.protein, Biochemistry and Cell Biology, 030217 neurology & neurosurgery, Developmental Biology
Abstract

Summary Bipolar disorder (BD) is characterized by cyclical mood shifts. Studies indicate that BD patients have a peripheral pro-inflammatory state and alterations in glial populations in the brain. We utilized an in vitro model to study inflammation-related phenotypes of astrocytes derived from induced pluripotent stem cells (iPSCs) generated from BD patients and healthy controls. BD astrocytes showed changes in transcriptome and induced a reduction in neuronal activity when co-cultured with neurons. IL-1β-stimulated BD astrocytes displayed a unique inflammatory gene expression signature and increased secretion of IL-6. Conditioned medium from stimulated BD astrocytes reduced neuronal activity, and this effect was partially blocked by IL-6 inactivating antibody. Our results suggest that BD astrocytes are functionally less supportive of neuronal excitability and this effect is partially mediated by IL-6. We confirmed higher IL-6 in blood in a distinct cohort of BD patients, highlighting the potential role of astrocyte-mediated inflammatory signaling in BD neuropathology., Highlights • Bipolar disorder astrocytes are functionally less supportive of neuronal activity • Bipolar disorder astrocytes response to IL-1β is transcriptionally distinct • IL-6 secretion in bipolar disorder astrocytes reduces neuronal activity • Bipolar disorder patients show higher circulating levels of IL-6 in blood, In this article, Gage and collaborators show that astrocytes differentiated from induced pluripotent stem cells generated from bipolar disorder patients are functionally less supportive of neuronal activity. Bipolar disorder astrocytes' response to pro-inflammatory cytokines is characterized by a unique transcriptional response and increased IL-6 secretion that directly and negatively impacted on neuronal activity. Increased peripheral IL-6 was confirmed in a distinct clinical cohort highlighting the potential role of astrocyte-mediated inflammatory signaling in the neuropathology of bipolar disorder.

Published
2021

47. Psychiatry Diversity Leadership in Academic Medicine: Guidelines for Success

Author

Christina Mangurian, Ruth S. Shim, Eraka Bath, Lisa T. Eyler, Jean Marie Alves-Bradford, Ayana Jordan, Nhi Ha Trinh, Carolyn I. Rodriguez, and Helena Hansen

Subjects
medicine.medical_specialty, Faculty, Medical, media_common.quotation_subject, Context (language use), Medical and Health Sciences, Racism, Physicians, Women, Sociopolitical Issues, 03 medical and health sciences, 0302 clinical medicine, Medical, Physicians, Political science, Institution, medicine, Humans, Women, Academic Psychiatry, Psychiatry, media_common, Equity (law), African Americans, Social work, Psychology and Cognitive Sciences, Faculty, United States, 030227 psychiatry, Black or African American, Leadership, Psychiatry and Mental health, Feeling, Female, Inclusion (education), 030217 neurology & neurosurgery, Diversity (business)
Abstract

Author(s): Jordan, Ayana; Shim, Ruth S; Rodriguez, Carolyn I; Bath, Eraka; Alves-Bradford, Jean-Marie; Eyler, Lisa; Trinh, Nhi-Ha; Hansen, Helena; Mangurian, Christina | Abstract: Dr. A, a Black woman researcher and clinician-educator in the Department of Psychiatry at a prestigious academic institution, was appointed chair of the Diversity, Equity, and Inclusion (DEI) committee in 2017, in the context of public complaints and media coverage concerning sexism and racism. Following recent high-profile murders of Black people by police, Dr. A is called upon by the department chair to develop "antiracist programming." As a first step, Dr. A drafts a strong message against racismandexcessive police force.Despite the statement being vetted by all senior leadership, the department chair insists thatDr.Aplace only her name on it, in case it is not "well received." Dr. A receives negative e-mails in response to the statement, with minimal support from the department. Not to be dismayed, Dr. A assembles a DEI committee to plan an "antiracist" strategy for the entire department, without financial support, protected time, or additional resources. The DEI committee progress is slowed by the lack of institutional and departmental commitment. Additionally, several well-meaning non- Black colleagues in the department organize responses to the murders-including protests, town hall events, and public statements-without concerted coordination with the DEI committee, implying that "nothing was being done." In parallel, Dr. A is tasked with organizing and dispatching the limited number of Black psychiatrists, psychologists, and social workers in the department to deliver care to the entire Black community at her institution. Feeling overwhelmed by the number of tasks she has been asked to initiate-while navigating a tense climate, with minimal financial or administrative departmental support-Dr. A requests a meeting with the department chair and executive committee. At the meeting, Dr. A, a tenured faculty memberfor the past 10 years, is addressed by another colleague's name-the only other Black professor in the department. When Dr. A points out this microaggression, a senior colleague tells her she is being "too sensitive," and is "overreacting." Despite this interaction, Dr. A highlights multiple institutional barriers, including the structural racism that minimizes the work of DEI initiatives in the department. She presents her proposal for protected time and resources to successfully execute robust antiracist programming but is toldmoneyis tight and to "scale back" and "do the best you can.".

Published
2021

48. Links between objective sleep and sleep variability measures and inflammatory markers in adults with bipolar disorder

Author

Lisa T. Eyler, Marina Ramsey, Ellen E. Lee, Benchawanna Soontornniyomkij, Atul Malhotra, Sonia Ancoli-Israel, Christopher N. Kaufmann, Colin A. Depp, and Sarah A. Graham

Subjects
Adult, Sleep Wake Disorders, medicine.medical_specialty, Bipolar Disorder, Evening, Inflammation, Article, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, medicine, Humans, Bipolar disorder, Biological Psychiatry, Aged, business.industry, Wake time, Actigraphy, Middle Aged, medicine.disease, Comorbidity, Sleep in non-human animals, Sleep time, 030227 psychiatry, Psychiatry and Mental health, Cross-Sectional Studies, medicine.symptom, Sleep, business, Biomarkers, 030217 neurology & neurosurgery
Abstract

Premature mortality and increased physical comorbidity associated with bipolar disorder (BD) may be related to accelerated biological aging. Sleep disturbances and inflammation may be key mechanisms underlying accelerated aging in adults with BD. To our knowledge, these relationships have not been examined rigorously. This cross-sectional study included 50 adults with BD and 73 age- and sex-comparable non-psychiatric comparison (NC) subjects, age 26–65 years. Participants were assessed with wrist-worn actigraphy for total sleep time (TST), percent sleep (PS), and bed/wake times for 7 consecutive nights as well as completing scales for subjective sleep quality. Within-individual variability in sleep measures included intra-individual standard deviation (iSD) and atypicality of one evening's sleep. Blood-based inflammatory biomarkers included interleukin (IL)-6, C-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-α). Linear regression analyses tested relationships of mean and iSD sleep variables with inflammatory marker levels; time-lagged analyses tested the influence of the previous evening's sleep on inflammation. BD participants had worse subjective sleep quality, as well as greater TST iSD and wake time iSD compared to the NC group. In all participants, higher TST iSD and lower mean PS were associated with higher IL-6 levels (p = 0.04, ηp2 = 0.042; p = 0.05, ηp2 = 0.039, respectively). Lower mean PS was associated with higher CRP levels (p = 0.05, ηp2 = 0.039). Atypicality of the previous night's TST predicted next day IL-6 levels (p = 0.05, ηp2 = 0.04). All of these relationships were present in both BD and NC groups and remained significant even after controlling for sleep medications. Overall, sleep measures and their variability may influence inflammatory markers in all adults. Thus, sleep may be linked to the inflammatory processes believed to underlie accelerated aging in BD.

Published
2021

49. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Author

Thomas Frodl, Eduard Vieta, Sean N. Hatton, Sophia I. Thomopoulos, Jens Sommer, Fábio L.S. Duran, Pauline Favre, Cynthia H.Y. Fu, Tony T. Yang, Knut Schnell, Tilo Kircher, Gloria Roberts, Lyubomir I. Aftanas, Norbert Hosten, Elena Pozzi, Henrik Walter, Pedro G.P. Rosa, Oliver Gruber, Colm G. Connolly, Klaus Berger, Jonathan Repple, Geraldo Busatto Filho, Tomas Hajek, Bernd Kramer, Salvador Sarró, Ulrik Fredrik Malt, Richard Dinga, Danai Dima, Mikael Landén, Laura K.M. Han, Jonathan Savitz, Peter R. Schofield, Axel Krug, Maria M. Rive, Caterina del Mar Bonnín, Edith Pomarol-Clotet, Olaf Steinsträter, Chantal Henry, Udo Dannlowski, Henricus G. Ruhé, Mauricio H. Serpa, Quinn McLellan, Benson Mwangi, Philipp G. Saemann, Christopher G. Davey, Marie-José van Tol, Mircea Polosan, Torbjørn Elvsåshagen, Nynke A. Groenewold, Marcus V. Zanetti, Claas Kähler, Jair C. Soares, Steven J.A. van der Werff, Kathryn R. Cullen, Lachlan T. Strike, Ilya M. Veer, Beata R. Godlewska, Giovana Zunta-Soares, Xavier Caseras, Janice M. Fullerton, Bronwyn Overs, Tiffany M. Chaim-Avancini, Lisa T. Eyler, Theodore D. Satterthwaite, Martin Ingvar, Ramona Leenings, Angela Carballedo, Brenda W.J.H. Penninx, Ian B. Hickie, James H. Cole, Elena Filimonova, Márcio Gerhardt Soeiro-de-Souza, Rayus Kuplicki, Leila Nabulsi, Ben J. Harrison, Aart H. Schene, Ivan V. Brak, Nic J.A. van der Wee, Hans J. Grabe, Katharina Wittfeld, Anouk Schrantee, Matthew D. Sacchet, Margaret J. Wright, Dan J. Stein, Erlend Bøen, Heather C. Whalley, Egle Simulionyte, Fleur M. Howells, Tim Hahn, Lianne Schmaal, Garrett M. Timmons, Bartholomeus C M Haarman, Kang Sim, Andrew M. McIntosh, Moji Aghajani, Jim Lagopoulos, Anne Uhlmann, Rodrigo Machado-Vieira, Jose Manuel Goikolea, Mon-Ju Wu, Christopher R.K. Ching, Dara M. Cannon, Liesbeth Reneman, Andreas Jansen, Josselin Houenou, Ian H. Gotlib, Bonnie Klimes-Dougan, Raymond Salvador, Maria J. Portella, Ole A. Andreassen, Greig I. de Zubicaray, Robert Vermeiren, Bryon A. Mueller, Nils R. Winter, Dick J. Veltman, Neda Jahanshad, Stefan Frenzel, Philip B. Mitchell, Colm McDonald, Henry Völzke, Daniel H. Wolf, Katie L. McMahon, Evgeny Osipov, Marco Hermesdorf, Tiffany C. Ho, Bernhard T. Baune, Paul M. Thompson, Glenda MacQueen, Andre F. Marquand, Maria Concepcion Garcia Otaduy, Vasileios Zannias, Christoph Abé, Ashley N. Sutherland, Sarah E. Medland, Beny Lafer, Erick J. Canales-Rodríguez, Geoffrey B. Hall, Martin Alda, Henk Temmingh, Sonya Foley, Verena Enneking, Frank P. MacMaster, Dominik Grotegerd, Joaquim Radua, Baptiste Couvy-Duchesne, André Aleman, Radiology and Nuclear Medicine, ANS - Brain Imaging, ANS - Compulsivity, Impulsivity & Attention, APH - Personalized Medicine, APH - Mental Health, Ontwikkelingspsychologie (Psychologie, FMG), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Clinical Neuropsychology, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Radiology and nuclear medicine, Pediatric surgery, Amsterdam Reproduction & Development (AR&D), Anatomy and neurosciences, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Complex Trait Genetics, and APH - Digital Health

Subjects
Adult, Male, medicine.medical_specialty, Aging, Adolescent, Stress-related disorders Donders Center for Medical Neuroscience [Radboudumc 13], BF, Health outcomes, Article, Cellular and Molecular Neuroscience, 03 medical and health sciences, Lateral ventricles, Young Adult, 0302 clinical medicine, Atrophy, Internal medicine, medicine, Humans, ddc:610, Longitudinal Studies, Molecular Biology, diagnostic imaging [Brain], Brain aging, Depression (differential diagnoses), 030304 developmental biology, Aged, 0303 health sciences, Depressive Disorder, Major, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], business.industry, Depression, 220 Statistical Imaging Neuroscience, Brain, Chronological age, Middle Aged, medicine.disease, Magnetic Resonance Imaging, 030227 psychiatry, Psychiatry and Mental health, Increased risk, RC0321, Major depressive disorder, Female, Age of onset, business, 030217 neurology & neurosurgery, Neuroscience
Abstract

BackgroundMajor depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset.MethodsWe performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain predicted age difference (brain-PAD).FindingsOn average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohen’s d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004).InterpretationThis highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates.FundingThis work was supported, in part, by NIH grants U54 EB020403 and R01 MH116147.

Published
2021

50. Brain charts for the human lifespan

Author

Armin Raznahan, Eric Courchesne, Andrea Parolin Jackowski, Kamen A. Tsvetanov, Cameron T. Ellis, R.C. Gur, Bin Bae J, Park Mtm, Pedro A. Valdes-Sosa, Simon N. Vandekar, Jacob W. Vogel, Juan Zhou, Machteld Marcelis, Kiho Im, Patricia Ellen Grant, Minhui Ouyang, Blesa Cabez M, Michael V. Lombardo, Sarah E. Morgan, James P. Boardman, Adamson C, Calhoun Vd, Delarue M, James H. Cole, Pichet Binette A, Roberto Toro, David H. Rowitch, Nynke A. Groenewold, Kevin M. Anderson, David T.W. Jones, Michael Schöll, Wang Ys, Aiden Corvin, R.E. Gur, Damien A. Fair, Gareth Ball, Herma Lina Schaare, Andrew Zalesky, Evdokia Anagnostou, Michael J. Meaney, Taki Y, Gareth J. Sullivan, Warrier, Petra E. Vértes, Chixiang Chen, Lisa T. Eyler, Wei Liao, Tomáš Paus, Jeremy A. Elman, Phillip McGuire, Hisham Ziauddeen, William S. Kremen, Etienne Vachon-Presseau, E.T. Bullmore, Christophe Tzourio, White, Hammill Cf, Mothersill D, Richard N. Henson, Jiang Qiu, Duncan E. Astle, Fabrice Crivello, Paul C. Fletcher, Chertavian C, Kim K, Jennifer Crosbie, Russell Schachar, Gabriel A. Devenyi, Manfred G. Kitzbichler, Tianye Jia, Trey Hedden, Sang Jae Lee, Ross D. Markello, Silke Kern, Ian M. Goodyer, Keith A. Johnson, Frauke Beyer, Bernard Mazoyer, A. Heinz, Sylvane Desrivières, Rosenberg, Gary Donohoe, Ong Mq, Alexander D. Edwards, Dan J. Stein, Nenad Medic, Zuo Xn, Travis T. Mallard, Peter Fonagy, Lindsay W. Victoria, Ingmar Skoog, Avram J. Holmes, Jason P. Lerch, Jed T. Elison, Jianfu Li, John H. Gilmore, Rosemary Holt, Caitlin K. Rollins, Carol E. Franz, Pedro Mario Pan, Saashi A Bedford, Yang N, Jonathan C Ipser, Richard A. I. Bethlehem, Tuulari Jj, Stolicyn A, Hua Huang, Bratislav Misic, Conor Liston, Ayub M, Lisa Ronan, Yeo Bt, Sophie Adler, Charles J. Lynch, Faith M. Gunning, Konrad Wagstyl, M. Mallar Chakravarty, John Suckling, Theodore D. Satterthwaite, Bharath Holla, Yap Seng Chong, Jinglei Lv, Jakob Seidlitz, Niall J Bourke, Xinlei Qian, Simon Baron-Cohen, Cynthia M. Ortinau, Deirel Paz Linares, Thyreau B, René S. Kahn, Aaron P. Schultz, Vanessa Cropley, Eric Westman, Mitchell Valdés-Sosa, Rik Ossenkoppele, André Zugman, Hasse Karlsson, Sylvia Villeneuve, Katja Heuer, Di Biase Ma, Margaret L. Westwater, Sofie L. Valk, David J. Sharp, Brigitte Landeau, Matthew Borzage, Kirsten A. Donald, Timothy Rittman, Richard Beare, Giovanni Abrahão Salum, Gunter Schumann, Ryuta Kawashima, Romero-Garcia R, John Blangero, Yun Hj, Russel T. Shinohara, Nicolas Crossley, Simon K. Warfield, Karen Pierce, George S. Alexopoulos, Katharine Dunlop, David C. Glahn, Francois Lalonde, Anqi Qiu, Lana Vasung, Gaël Chételat, Lídice Galán-García, Clifford R. Jack, Reisa A. Sperling, Anna Zettergren, Elizabeth Kelley, Arno Villringer, Andrea Mechelli, Benegal, Aaron Alexander-Bloch, Nicholas B. Turk-Browne, van Amelsvoort T, John D. Lewis, Heather C. Whalley, A. V. Witte, Zdenka Pausova, Joel T. Nigg, Heather J. Zar, Raymond J. Dolan, Christopher D. Smyser, Jay N. Giedd, Lena Palaniyappan, Ali Gholipour, Areces-Gonzalez A, Peter B. Jones, Jacqueline Hoare, Oskar Hansson, Linnea Karlsson, C Pantelis, Paly L, Bonnie Auyeung, Jorge Bosch-Bayard, Bethlehem, Richard [0000-0002-0714-0685], White, Simon [0000-0001-8642-7037], Astle, Duncan [0000-0002-7042-5392], Baron-Cohen, Simon [0000-0001-9217-2544], Henson, Rik [0000-0002-0712-2639], Jones, Peter [0000-0002-0387-880X], Kitzbichler, Manfred [0000-0002-4494-0753], Rittman, Timothy [0000-0003-1063-6937], Rowitch, David [0000-0002-0079-0060], Tsvetanov, Kamen A. [0000-0002-3178-6363], Westwater-Wozniak, Margaret [0000-0002-2918-0979], Ziauddeen, Hisham [0000-0003-4044-1719], Apollo - University of Cambridge Repository, British Academy, Autism Research Trust, National Institute of Mental Health (US), UK Research and Innovation, Medical Research Council (UK), National Institute for Health and Care Research (US), Wellcome Trust, University of Cambridge, Cambridge Biomedical Research Centre, University of Cambridge [UK] (CAM), University of Pennsylvania, Yale University [New Haven], Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Physiopathologie et imagerie des troubles neurologiques (PhIND), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Child and Adolescent Psychiatry Department [AP- HP Hôpital Robert Debré], AP-HP Hôpital universitaire Robert-Debré [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Département de Neuroscience - Department of Neuroscience, Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, MUMC+: MA Med Staf Spec Psychiatrie (9), Neurology, Amsterdam Neuroscience - Neurodegeneration, 3R-BRAIN, AIBL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBRE, cVEDA, ENIGMA Developmental Brain Age Working Group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, The Mayo Clinic Study of Aging, NSPN, POND, The PREVENT-AD Research Group, VETSA, [Bethlehem, R. A. I.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Auyeung, B.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Baron-Cohen, S.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Bedford, S. A.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Holt, R.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Lombardo, M. V.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Bethlehem, R. A. I.] Univ Cambridge, Dept Psychiat, Brain Mapping Unit, Cambridge, England, [Kitzbichler, M. G.] Univ Cambridge, Dept Psychiat, Brain Mapping Unit, Cambridge, England, [Seidlitz, J.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Vogel, J. W.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Gur, R. E.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Gur, R. C.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Jackowski, A. P.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Satterthwaite, T. D.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Alexander-Bloch, A. F.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Seidlitz, J.] Childrens Hosp Philadelphia, Dept Child & Adolescent Psychiat & Behav Sci, Philadelphia, PA 19104 USA, [Alexander-Bloch, A. F.] Childrens Hosp Philadelphia, Dept Child & Adolescent Psychiat & Behav Sci, Philadelphia, PA 19104 USA, [Seidlitz, J.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Chertavian, C.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Gur, R. E.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Gur, R. C.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Alexander-Bloch, A. F.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [White, S. R.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Goodyer, I. M.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Henson, R. N.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Jones, P. B.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Kitzbichler, M. G.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Medic, N.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Morgan, S. E.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Romero-Garcia, R.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Ronan, L.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Suckling, J.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Vertes, P. E.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Warrier, V.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Westwater, M. L.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Ziauddeen, H.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Bullmore, E. T.] Univ Cambridge, Dept Psychiat, Cambridge, England, [White, S. R.] Univ Cambridge, MRC Biostat Unit, Cambridge, England, [Vogel, J. W.] Univ Penn, Lifespan Informat & Neuroimaging Ctr, Philadelphia, PA 19104 USA, [Satterthwaite, T. D.] Univ Penn, Lifespan Informat & Neuroimaging Ctr, Philadelphia, PA 19104 USA, [Anderson, K. M.] Yale Univ, Dept Psychol, New Haven, CT USA, [Ellis, C. T.] Yale Univ, Dept Psychol, New Haven, CT USA, [Turk-Browne, N. B.] Yale Univ, Dept Psychol, New Haven, CT USA, [Adamson, C.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Ball, G.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Beare, R.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Jackowski, A. P.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Adamson, C.] Monash Univ, Dept Med, Melbourne, Vic, Australia, [Beare, R.] Monash Univ, Dept Med, Melbourne, Vic, Australia, [Adler, S.] UCL Great Ormond St Inst Child Hlth, London, England, [Alexopoulos, G. S.] Weill Cornell Med, Dept Psychiat, Weill Cornell Inst Geriatr Psychiat, New York, NY USA, [Anagnostou, E.] Univ Toronto, Dept Pediat, Toronto, ON, Canada, [Anagnostou, E.] Holland Bloorview Kids Rehabil Hosp, Toronto, ON, Canada, [Pierce, K.] Holland Bloorview Kids Rehabil Hosp, Toronto, ON, Canada, [Areces-Gonzalez, A.] Univ Elect Sci & Technol China, MOE Key Lab NeuroInformat, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Paz-Linares, D.] Univ Elect Sci & Technol China, MOE Key Lab NeuroInformat, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Areces-Gonzalez, A.] Univ Pinar del Rio Hermanos Saiz Montes de Oca, Pinar Del Rio, Cuba, [Astle, D. E.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Henson, R. N.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Whalley, H. C.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Auyeung, B.] Univ Edinburgh, Sch Philosophy Psychol & Language Sci, Dept Psychol, Edinburgh, Midlothian, Scotland, [Pausova, Z.] Univ Edinburgh, Sch Philosophy Psychol & Language Sci, Dept Psychol, Edinburgh, Midlothian, Scotland, [Ayub, M.] Queens Univ, Dept Psychiat, Ctr Neurosci Studies, Kingston, ON, Canada, [Ayub, M.] UCL, Mental Hlth Neurosci Res Dept, Div Psychiat, London, England, [Bae, J.] Seoul Natl Univ, Bundang Hosp, Dept Neuropsychiat, Seongnam, South Korea, [Ball, G.] Univ Melbourne, Dept Paediat, Melbourne, Vic, Australia, [Baron-Cohen, S.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge Lifetime Asperger Syndrome Serv CLASS, Cambridge, England, [Benegal, V.] Natl Inst Mental Hlth & Neurosci NIMHANS, Ctr Addict Med, Bengaluru, India, [Beyer, F.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Villringer, A.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Witte, A. V.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Blangero, J.] Univ Texas Rio Grande Valley, South Texas Diabet & Obes Inst, Dept Human Genet, Edinburg, TX USA, [Blesa Cabez, M.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Boardman, J. P.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Sullivan, G.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Borzage, M.] Univ Southern Calif, Childrens Hosp Los Angeles, Keck Sch Med, Fetal & Neonatal Inst,Div Neonatol,Dept Pediat, Los Angeles, CA 90007 USA, [Bosch-Bayard, J. F.] Montreal Neurol Inst, Ludmer Ctr Neuroinformat & Mental Hlth, McGill Ctr Integrat Neurosci, Montreal, PQ, Canada, [Bosch-Bayard, J. F.] McGill Univ, Montreal, PQ, Canada, [Chakravarty, M. M.] McGill Univ, Montreal, PQ, Canada, [Bourke, N.] Imperial Coll London, Dept Brain Sci, London, England, [Sharp, D.] Imperial Coll London, Dept Brain Sci, London, England, [Alexander-Bloch, A. F.] Imperial Coll London, Dept Brain Sci, London, England, [Bourke, N.] Dementia Res Inst, Care Res & Technol Ctr, London, England, [Calhoun, V. D.] Georgia State Univ, Triinst Ctr Translat Res Neuroimaging & Data Sci, Georgia Inst Technol, Atlanta, GA 30303 USA, [Calhoun, V. D.] Emory Univ, Atlanta, GA 30322 USA, [Chakravarty, M. M.] Douglas Mental Hlth Univ Inst, Cerebral Imaging Ctr, Comp Brain Anat CoBrA Lab, Montreal, PQ, Canada, [Chen, C.] Univ Penn, Penn Stat Imaging & Visualizat Ctr, Dept Biostat Epidemiol & Informat, Perelman Sch Med, Philadelphia, PA 19104 USA, [Shinohara, R. T.] Univ Penn, Penn Stat Imaging & Visualizat Ctr, Dept Biostat Epidemiol & Informat, Perelman Sch Med, Philadelphia, PA 19104 USA, [Chetelat, G.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Delarue, M.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Landeau, B.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Paly, L.] Normandie Univ, PhIND Physiopathol & Imaging Neurol Disorders, Inst Blood & Brain Caen Normandie, UNICAEN,INSERM,U1237, Caen, France, [Chong, Y. S.] Agcy Sci Technol & Res, Singapore Inst Clin Sci, Singapore, Singapore, [Chong, Y. S.] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Obstet & Gynaecol, Singapore, Singapore, [Cole, J. H.] UCL, Ctr Med Image Comp CMIC, London, England, [Cole, J. H.] UCL, Dementia Res Ctr DRC, London, England, [Corvin, A.] Trinity Coll Dublin, Dept Psychiat, Dublin, Ireland, [Costantino, M.] Douglas Mental Hlth Univ Inst, Cerebral Imaging Ctr, Verdun, PQ, Canada, [Costantino, M.] McGill Univ, Undergrad Program Neurosci, Montreal, PQ, Canada, [Courchesne, E.] Univ Calif San Diego, Dept Neurosci, San Diego, CA 92103 USA, [Courchesne, E.] Univ Calif San Diego, Autism Ctr Excellence, San Diego, CA 92103 USA, [Crivello, F.] Univ Bordeaux, Inst Neurodegenerat Disorders, CNRS UMR5293, CEA, Bordeaux, France, [Mazoyer, B.] Univ Bordeaux, Inst Neurodegenerat Disorders, CNRS UMR5293, CEA, Bordeaux, France, [Cropley, V. L.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Di Biase, M. A.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Lv, J.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Zalesky, A.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Hammill, C. F.] Hosp Sick Children, Toronto, ON, Canada, [Schachar, R. J.] Hosp Sick Children, Toronto, ON, Canada, [Crossley, N.] Pontificia Univ Catolica Chile, Sch Med, Dept Psychiat, Santiago, Chile, [Crossley, N.] Kings Coll London, Dept Psychosis Studies, Inst Psychiat Psychol & Neurosci, London, England, [McGuire, P.] Kings Coll London, Dept Psychosis Studies, Inst Psychiat Psychol & Neurosci, London, England, [Crossley, N.] Inst Milenio Intelligent Healthcare Engn, Santiago, Chile, [Delorme, R.] Robert Debre Univ Hosp, AP HP, Child & Adolescent Psychiat Dept, Paris, France, [Delorme, R.] Inst Pasteur, Human Genet & Cognit Funct, Paris, France, [Desrivieres, S.] Kings Coll London, Inst Psychiat Psychol & Neurosci, Social Genet & Dev Psychiat Ctr, London, England, [Devenyi, G. A.] Douglas Mental Hlth Univ Inst, McGill Dept Psychiat, Cerebral Imaging Ctr, Montreal, PQ, Canada, [Devenyi, G. A.] McGill Univ, Dept Psychiat, Montreal, PQ, Canada, [Di Biase, M. A.] Harvard Med Sch, Brigham & Womens Hosp, Dept Psychiat, Boston, MA 02115 USA, [Dolan, R.] UCL, Max Planck UCL Ctr Computat Psychiat & Ageing Res, London, England, [Dolan, R.] Wellcome Ctr Human Neuroimaging, London, England, [Wagstyl, K.] Wellcome Ctr Human Neuroimaging, London, England, [Donald, K. A.] Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Div Dev Paediat, Cape Town, South Africa, [Donald, K. A.] Univ Cape Town, Neurosci Inst, Cape Town, South Africa, [Groenewold, N. A.] Univ Cape Town, Neurosci Inst, Cape Town, South Africa, [Donohoe, G.] Natl Univ Ireland Galway, Sch Psychol, Ctr Neuroimaging Cognit & Genom NICOG, Galway, Ireland, [Dunlop, K.] Weill Cornell Med, Dept Psychiat, Weil Family Brain & Mind Res Inst, New York, NY USA, [Lynch, C.] Weill Cornell Med, Dept Psychiat, Weil Family Brain & Mind Res Inst, New York, NY USA, [Edwards, A. D.] Kings Coll London, Ctr Dev Brain, London, England, [Edwards, A. D.] Evelina London Childrens Hosp, London, England, [Edwards, A. D.] MRC Ctr Neurodev Disorders, London, England, [Elison, J. T.] Univ Minnesota, Mason Inst Dev Brain, Dept Pediat, Inst Child Dev, Minneapolis, MN USA, [Fair, D. A.] Univ Minnesota, Mason Inst Dev Brain, Dept Pediat, Inst Child Dev, Minneapolis, MN USA, [Feczko, E.] Univ Minnesota, Mason Inst Dev Brain, Dept Pediat, Inst Child Dev, Minneapolis, MN USA, [Ellis, C. T.] Haskins Labs Inc, New Haven, CT USA, [Elman, J. A.] Univ Calif San Diego, Dept Psychiat, Ctr Behav Genet Aging, La Jolla, CA 92093 USA, [Franz, C. E.] Univ Calif San Diego, Dept Psychiat, Ctr Behav Genet Aging, La Jolla, CA 92093 USA, [Kremen, W. S.] Univ Calif San Diego, Dept Psychiat, Ctr Behav Genet Aging, La Jolla, CA 92093 USA, [Eyler, L.] VA San Diego Healthcare, Desert Pacific Mental Illness Res Educ & Clin Ctr, San Diego, CA USA, [Eyler, L.] Univ Calif San Diego, Dept Psychiat, Los Angeles, CA USA, [Fletcher, P. C.] Univ Cambridge, Dept Psychiat, Cambridge Biomed Campus, Cambridge, England, [Fletcher, P. C.] Wellcome Trust MRC Inst Metab Sci, Cambridge Biomed Campus, Cambridge, England, [Fletcher, P. C.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Jones, P. B.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Suckling, J.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Ziauddeen, H.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Fonagy, P.] UCL, Dept Clin Educ & Hlth Psychol, London, England, [Fonagy, P.] Anna Freud Natl Ctr Children & Families, London, England, [Galan-Garcia, L.] Cuban Ctr Neurosci, Havana, Cuba, [Valdes-Sosa, M. J.] Cuban Ctr Neurosci, Havana, Cuba, [Gholipour, A.] Boston Childrens Hosp, Computat Radiol Lab, Boston, MA USA, [Warfield, S. K.] Boston Childrens Hosp, Computat Radiol Lab, Boston, MA USA, [Giedd, J.] Univ Calif San Diego, Dept Child & Adolescent Psychiat, San Diego, CA 92103 USA, [Giedd, J.] Univ Calif San Diego, Dept Psychiat, San Diego, CA 92103 USA, [Gilmore, J. H.] Univ N Carolina, Dept Psychiat, Chapel Hill, NC 27515 USA, [Glahn, D. C.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Im, K.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Mathias, S. R.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Rodrigue, A.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Glahn, D. C.] Harvard Med Sch, Boston, MA 02115 USA, [Im, K.] Harvard Med Sch, Boston, MA 02115 USA, [Johnson, K. A.] Harvard Med Sch, Boston, MA 02115 USA, [Mathias, S. R.] Harvard Med Sch, Boston, MA 02115 USA, [Rodrigue, A.] Harvard Med Sch, Boston, MA 02115 USA, [Schultz, A. P.] Harvard Med Sch, Boston, MA 02115 USA, [Sperling, R. A.] Harvard Med Sch, Boston, MA 02115 USA, [Grant, P. E.] Harvard Med Sch, Fetal Neonatal Neuroimaging & Dev Sci Ctr, Boston Childrens Hosp, Div Newborn Med & Neuroradiol, Boston, MA 02115 USA, [Groenewold, N. A.] Univ Cape Town, SA MRC Unit Child & Adolescent Hlth, Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Cape Town, South Africa, [Zar, H. J.] Univ Cape Town, SA MRC Unit Child & Adolescent Hlth, Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Cape Town, South Africa, [Gunning, F. M.] Weill Cornell Med, Dept Psychiat, Weill Cornell Inst Geriatr Psychiat, New York, NY USA, [Victoria, L. W.] Weill Cornell Med, Dept Psychiat, Weill Cornell Inst Geriatr Psychiat, New York, NY USA, [Hammill, C. F.] Mouse Imaging Ctr, Toronto, ON, Canada, [Hansson, O.] Lund Univ, Dept Clin Sci Malmo, Clin Memory Res Unit, Malmo, Sweden, [Hansson, O.] Skane Univ Hosp, Memory Clin, Malmo, Sweden, [Hedden, T.] Icahn Sch Med Mt Sinai, Dept Neurol, New York, NY 10029 USA, [Hedden, T.] Harvard Med Sch, Massachusetts Gen Hosp, Dept Radiol, Athinoula Martinos Ctr Biomed Imaging, Boston, MA 02115 USA, [Heinz, A.] Charite Univ Med Berlin, Charite Campus Mitte, Berlin, Germany, [Heinz, A.] Free Univ Berlin, Charite Campus Mitte, Berlin, Germany, [Heinz, A.] Humboldt Univ, Dept Psychiat & Psychotherapy, Charite Campus Mitte, Berlin, Germany, [Heuer, K.] Max Planck Inst Human Cognit & Brain Sci, Dept Neuropsychol, Leipzig, Germany, [Heuer, K.] Univ Paris, Paris, France, [Toro, R.] Univ Paris, Paris, France, [Hoare, J.] Univ Cape Town, Dept Psychiat, Cape Town, South Africa, [Holla, B.] NIMHANS, Dept Integrat Med, Bengaluru, India, [Holla, B.] NIMHANS, Dept Psychiat, Accelerator Program Discovery Brain Disorders Usi, Bengaluru, India, [Holmes, A. J.] Yale Univ, Dept Psychol, New Haven, CT USA, [Villeneuve, S.] Yale Univ, Dept Psychol, New Haven, CT USA, [Holmes, A. J.] Yale Univ, Dept Psychiat, New Haven, CT 06520 USA, [Villeneuve, S.] Yale Univ, Dept Psychiat, New Haven, CT 06520 USA, [Huang, H.] Childrens Hosp Philadelphia, Radiol Res, Philadelphia, PA 19104 USA, [Ouyang, M.] Childrens Hosp Philadelphia, Radiol Res, Philadelphia, PA 19104 USA, [Huang, H.] Univ Penn, Dept Radiol, Perelman Sch Med, Philadelphia, PA 19104 USA, [Ipser, J.] Univ Cape Town, Dept Psychiat & Mental Hlth, Clin Neurosci Inst, Cape Town, South Africa, [Jack, C. R., Jr.] Mayo Clin, Dept Radiol, Rochester, MN USA, [Jones, D. T.] Mayo Clin, Dept Radiol, Rochester, MN USA, Univ Fed Sao Paulo, Dept Psychiat, Sao Paulo, Brazil, [Jackowski, A. P.] Natl Inst Dev Psychiat, Beijing, Peoples R China, [Jia, T.] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai, Peoples R China, [Jia, T.] Fudan Univ, Minist Educ, Key Lab Computat Neurosci & Brain Inspired Intell, Shanghai, Peoples R China, [Jia, T.] Kings Coll London, Inst Psychiat Psychol & Neurosci, Ctr Populat Neurosci & Precis Med PONS, SGDP Ctr, London, England, [Johnson, K. A.] Massachusetts Gen Hosp, Dept Neurol, Harvard Aging Brain Study, Boston, MA 02114 USA, [Schultz, A. P.] Massachusetts Gen Hosp, Dept Neurol, Harvard Aging Brain Study, Boston, MA 02114 USA, [Sperling, R. A.] Massachusetts Gen Hosp, Dept Neurol, Harvard Aging Brain Study, Boston, MA 02114 USA, [Johnson, K. A.] Brigham & Womens Hosp, Dept Neurol, Ctr Alzheimer Res & Treatment, 75 Francis St, Boston, MA 02115 USA, [Sperling, R. A.] Brigham & Womens Hosp, Dept Neurol, Ctr Alzheimer Res & Treatment, 75 Francis St, Boston, MA 02115 USA, [Johnson, K. A.] Massachusetts Gen Hosp, Dept Radiol, Boston, MA USA, [Jones, D. T.] Mayo Clin, Dept Neurol, Rochester, MN USA, [3R-BRAIN] Mayo Clin, Dept Neurol, Rochester, MN USA, [Kahn, R. S.] Icahn Sch Med Mt Sinai, Dept Psychiat, New York, NY 10029 USA, [Karlsson, H.] Univ Turku, Dept Psychiat, Dept Clin Med, Turku, Finland, [Karlsson, L.] Univ Turku, Dept Psychiat, Dept Clin Med, Turku, Finland, [Tuulari, J. J.] Univ Turku, Dept Psychiat, Dept Clin Med, Turku, Finland, [Karlsson, H.] Univ Turku, FinnBrain Birth Cohort Study, Turku Brain & Mind Ctr, Turku, Finland, [Karlsson, L.] Univ Turku, FinnBrain Birth Cohort Study, Turku Brain & Mind Ctr, Turku, Finland, [Tuulari, J. J.] Univ Turku, FinnBrain Birth Cohort Study, Turku Brain & Mind Ctr, Turku, Finland, [Karlsson, H.] Turku Univ Hosp, Turku, Finland, [Karlsson, L.] Turku Univ Hosp, Turku, Finland, [Tuulari, J. J.] Turku Univ Hosp, Turku, Finland, [Karlsson, H.] Turku Univ Hosp, Ctr Populat Hlth Res, Turku, Finland, [Karlsson, L.] Turku Univ Hosp, Ctr Populat Hlth Res, Turku, Finland, [Karlsson, H.] Univ Turku, Turku, Finland, [Karlsson, L.] Univ Turku, Turku, Finland, [Kawashima, R.] Tohoku Univ, Inst Dev Aging & Canc, Aoba Ku, Sendai, Miyagi, Japan, [Taki, Y.] Tohoku Univ, Inst Dev Aging & Canc, Aoba Ku, Sendai, Miyagi, Japan, [Thyreau, B.] Tohoku Univ, Inst Dev Aging & Canc, Aoba Ku, Sendai, Miyagi, Japan, [Kelley, E. A.] Queens Univ, Ctr Neurosci Studies, Dept Psychol, Kingston, ON, Canada, [Kelley, E. A.] Queens Univ, Ctr Neurosci Studies, Dept Psychiat, Kingston, ON, Canada, [Kern, S.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Skoog, I.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Zettergren, A.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Kern, S.] Sahlgrens Univ Hosp, Psychiat Cognit & Old Age Psychiat Clin, Reg Vastra Gotaland, Gothenburg, Sweden, [Skoog, I.] Sahlgrens Univ Hosp, Psychiat Cognit & Old Age Psychiat Clin, Reg Vastra Gotaland, Gothenburg, Sweden, [Kim, K. W.] Seoul Natl Univ, Dept Brain & Cognit Sci, Coll Nat Sci, Seoul, South Korea, [Kim, K. W.] Seoul Natl Univ, Bundang Hosp, Dept Neuropsychiat, Seongnam, South Korea, [Kim, K. W.] Seoul Natl Univ, Dept Psychiat, Coll Med, Seoul, South Korea, [Kim, K. W.] SNU MRC, Inst Human Behav Med, Seoul, South Korea, [Lalonde, F.] NIMH, Sect Dev Neurogenom, Human Genet Branch, Bethesda, MD 20892 USA, [Raznahan, A.] NIMH, Sect Dev Neurogenom, Human Genet Branch, Bethesda, MD 20892 USA, [Lee, S.] Seoul Natl Univ, Coll Nat Sci, Dept Brain & Cognit Sci, Seoul, South Korea, [Lerch, J.] Univ Toronto, Dept Med Biophys, Toronto, ON, Canada, [Lerch, J.] Univ Oxford, Nuffield Dept Clin Neurosci, FMRIB, Wellcome Ctr Integrat Neuroimaging, Oxford, England, [Lewis, J. D.] McGill Univ, Montreal Neurol Inst, Montreal, PQ, Canada, [Li, J.] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Liao, W.] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Valdes-Sosa, P. A.] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Liston, C.] Weill Cornell Med, Dept Psychiat, New York, NY USA, [Liston, C.] Weill Cornell Med, Brain & Mind Res Inst, New York, NY USA, [Lombardo, M. V.] Ist Italiano Tecnol, Ctr Neurosci & Cognit Syst UniTn, Lab Autism & Neurodev Disorders, Rovereto, Italy, [Lv, J.] Univ Sydney, Sch Biomed Engn, Sydney, NSW, Australia, [Lv, J.] Univ Sydney, Brain & Mind Ctr, Sydney, NSW, Australia, [Mallard, T. T.] Univ Texas Austin, Dept Psychol, Austin, TX 78712 USA, [Marcelis, M.] Maastricht Univ, Sch Mental Hlth & Neurosci, Dept Psychiat & Neuropsychol, EURON,Med Ctr, Maastricht, Netherlands, [Marcelis, M.] Inst Mental Hlth Care Eindhoven GGzE, Eindhoven, Netherlands, [Markello, R. D.] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ, Canada, [Misic, B.] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ, Canada, [Vasung, L.] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ, Canada, [Mazoyer, B.] Douglas Mental Hlth Univ Inst, Ludmer Ctr Neuroinformat & Mental Hlth, Montreal, PQ, Canada, [Meaney, M. J.] Douglas Mental Hlth Univ Inst, Ludmer Ctr Neuroinformat & Mental Hlth, Montreal, PQ, Canada, [Meaney, M. J.] Singapore Inst Clin Sci, Singapore, Singapore, [Mechelli, A.] Bordeaux Univ Hosp, Bordeaux, France, [Morgan, S. E.] Univ Cambridge, Dept Comp Sci & Technol, Cambridge, England, [Morgan, S. E.] Alan Turing Inst, London, England, [Vertes, P. E.] Alan Turing Inst, London, England, [Mothersill, D.] Natl Coll Ireland, Sch Business, Dept Psychol, Dublin, Ireland, [Mothersill, D.] Natl Univ Ireland Galway, Sch Psychol, Galway, Ireland, [Mothersill, D.] Natl Univ Ireland Galway, Ctr Neuroimaging & Cognit Genom, Galway, Ireland, [Mothersill, D.] Trinity Coll Dublin, Dept Psychiat, Dublin, Ireland, [Nigg, J.] Oregon Hlth & Sci Univ, Dept Psychiat, Sch Med, Portland, OR 97201 USA, [Ong, M. Q. W.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Qian, X.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Zhou, J. H.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Ortinau, C.] Washington Univ, Dept Pediat, St Louis, MO 63130 USA, [Ossenkoppele, R.] Vrije Univ Amsterdam, Alzheimer Ctr Amsterdam, Amsterdam UMC, Dept Neurol,Amsterdam Neurosci, Amsterdam, Netherlands, [Ossenkoppele, R.] Lund Univ, Clin Memory Res Unit, Lund, Sweden, [Palaniyappan, L.] Univ Western Ontario, Robarts Res Inst, London, ON, Canada, [Palaniyappan, L.] Univ Western Ontario, Brain & Mind Inst, London, ON, Canada, [Pan, P. M.] Fed Univ Sao Poalo UNIFESP, Dept Psychiat, Sao Poalo, Brazil, [Pan, P. M.] Natl Inst Dev Psychiat Children & Adolescents INP, Sao Poalo, Brazil, [Zugman, A.] Natl Inst Dev Psychiat Children & Adolescents INP, Sao Poalo, Brazil, [Pantelis, C.] Univ Melbourne, Dept Psychiat, Melbourne Neuropsychiat Ctr, Carlton, Vic, Australia, [Pantelis, C.] Melbourne Hlth, Carlton, Vic, Australia, [Pantelis, C.] Univ Melbourne, Melbourne Sch Engn, Parkville, Vic, Australia, [Pantelis, C.] Florey Inst Neurosci & Mental Hlth, Parkville, Vic, Australia, [Park, M. M.] Western Univ, Schulich Sch Med & Dent, Dept Psychiat, London, ON, Canada, [Rollins, C. K.] Univ Montreal, Dept Psychiat, Fac Med, Montreal, PQ, Canada, [Rollins, C. K.] Univ Montreal, CHU St Justine, Montreal, PQ, Canada, [Romero-Garcia, R.] Univ Toronto, Dept Psychiat, Toronto, ON, Canada, [Romero-Garcia, R.] Univ Toronto, Dept Psychol, Toronto, ON, Canada, [Rosenberg, M. D.] Univ Toronto, Dept Physiol, Toronto, ON, Canada, [Rosenberg, M. D.] Univ Toronto, Dept Nutr Sci, Toronto, ON, Canada, [Paz-Linares, D.] Cuban Neurosci Ctr, Havana, Cuba, [Pichet Binette, A.] McGill Univ, Fac Med, Dept Psychiat, Montreal, PQ, Canada, [Villeneuve, S.] McGill Univ, Fac Med, Dept Psychiat, Montreal, PQ, Canada, [Pichet Binette, A.] Douglas Mental Hlth Univ Inst, Montreal, PQ, Canada, [Villeneuve, S.] Douglas Mental Hlth Univ Inst, Montreal, PQ, Canada, [Qiu, J.] Southwest Univ, Sch Psychol, Chongqing, Peoples R China, [Qiu, A.] Natl Univ Singapore, N1 Inst Hlth, Dept Biomed Engn, Singapore, Singapore, [Rittman, T.] Univ Cambridge, Dept Clin Neurosci, Cambridge, England, [Tsvetanov, K. A.] Univ Cambridge, Dept Clin Neurosci, Cambridge, England, [Rollins, C. K.] Harvard Med Sch, Dept Neurol, Boston, MA 02115 USA, [Rollins, C. K.] Boston Childrens Hosp, Dept Neurol, Boston, MA USA, [Romero-Garcia, R.] Univ Seville, Dpto Fisiol Med & Biofis, Inst Biomed Sevilla IBiS HUVR CSIC, Seville, Spain, [Rosenberg, M. D.] Univ Chicago, Dept Psychol, 5848 S Univ Ave, Chicago, IL 60637 USA, [Rosenberg, M. D.] Univ Chicago, Inst Neurosci, Chicago, IL USA, [Rowitch, D. H.] Univ Cambridge, Dept Paediat, Cambridge, England, [Rowitch, D. H.] Univ Cambridge, Wellcome MRC Cambridge Stem Cell Inst, Cambridge, England, [Salum, G. A.] Univ Fed Rio Grande Sul UFRGS, Hosp Clin Porto Alegre, Dept Psychiat, Porto Alegre, RS, Brazil, [Salum, G. A.] Natl Inst Dev Psychiat INPD, Sao Paulo, Brazil, [Schaare, H. L.] Max Planck Inst Human Cognit & Brain Sci, Otto Hahn Grp Cognit Neurogenet, Leipzig, Germany, [Schaare, H. L.] Res Ctr Juelich, Inst Neurosci & Med INM 7 Brain & Behav, Julich, Germany, [Schultz, A. P.] Massachusetts Gen Hosp, Dept Radiol, Athinoula Martinos Ctr Biomed Imaging, Charlestown, MA USA, [Schumann, G.] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Ctr Populat Neurosci & Stratified Med PONS, Shanghai, Peoples R China, [Schumann, G.] Charite Campus Mitte, Dept Psychiat & Psychotherapy, Charite Mental Hlth, PONS Ctr, Berlin, Germany, [Scholl, M.] Univ Gothenburg, Wallenberg Ctr Mol & Translat Med, Gothenburg, Sweden, [Scholl, M.] Univ Gothenburg, Dept Psychiat & Neurochem, Gothenburg, Sweden, [Scholl, M.] UCL, Queens Sq Inst Neurol, Dementia Res Ctr, London, England, [Sharp, D.] UK Dementia Res Inst, Care Res & Technol Ctr, London, England, [Shinohara, R. T.] Univ Penn, Perelman Sch Med, Dept Radiol, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Neurol, St Louis, MO 63110 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Pediat, St Louis, MO 63110 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Radiol, St Louis, MO 63110 USA, [Stein, D. J.] Univ Cape Town, Dept Psychiat, SA MRC Unit Risk & Resilience Mental Disorders, Cape Town, South Africa, [Stein, D. J.] Univ Cape Town, Neurosci Inst, Cape Town, South Africa, [Stolicyn, A.] Univ Edinburgh, Ctr Clin Brain Sci, Div Psychiat, Edinburgh, Midlothian, Scotland, [Whalley, H. C.] Univ Edinburgh, Ctr Clin Brain Sci, Div Psychiat, Edinburgh, Midlothian, Scotland, [Toro, R.] Inst Pasteur, Dept Neurosci, Paris, France, [Traut, N.] Inst Pasteur, Dept Neurosci, Paris, France, [Traut, N.] Univ Paris 05, Ctr Res & Interdisciplinar CRI, Paris, France, [Tsvetanov, K. A.] Univ Cambridge, Dept Psychol, Cambridge, England, [Turk-Browne, N. B.] Yale Univ, Wu Tsai Inst, New Haven, CT USA, [Tuulari, J. J.] Univ Turku, Dept Clin Med, Turku, Finland, [Tuulari, J. J.] Univ Turku, Turku Coll Sci Med & Technol, Turku, Finland, [Tzourio, C.] Univ Bordeaux, Bordeaux Populat Hlth Res Ctr, CHU Bordeaux, U1219,INSERM, Bordeaux, France, [Vachon-Presseau, E.] McGill Univ, Fac Dent Med & Oral Hlth Sci, Montreal, PQ, Canada, [Valdes-Sosa, P. A.] McGill Univ, Alan Edwards Ctr Res Pain AECRP, Montreal, PQ, Canada, [Valk, S. L.] Forschungszentrum Julich, Inst Neurosci & Med 7, Julich, Germany, [Valk, S. L.] Max Planck Inst Human Cognit & Brain Sci, Leipzig, Germany, [van Amelsvoort, T.] Maastricht Univ, Dept Psychiat & Neurosychol, Maastricht, Netherlands, [Vandekar, S. N.] Vanderbilt Univ, Dept Biostat, 221 Kirkland Hall, Nashville, TN 37235 USA, [Villeneuve, S.] Vanderbilt Univ, Med Ctr, Dept Biostat, Nashville, TN USA, [Villringer, A.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Witte, A. V.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Zuo, X. N.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Wang, Y. S.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Yang, N.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Yeo, B.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Zuo, X. N.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Wang, Y. S.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Yang, N.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Zuo, X. N.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Wang, Y. S.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Yang, N.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Zuo, X. N.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Wang, Y. S.] Chinese Acad Sci, Res Ctr Lifespan Dev Brain & Mind, Inst Psychol, Beijing, Peoples R China, [Yang, N.] Chinese Acad Sci, Res Ctr Lifespan Dev Brain & Mind, Inst Psychol, Beijing, Peoples R China, [Westman, E.] Karolinska Inst, Ctr Alzheimer Res, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Stockholm, Sweden, [Witte, A. V.] Univ Leipzig, CRC 1052 Obes Mech, Fac Med, Leipzig, Germany, [Zhou, J. H.] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Translat MR Res, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, N1 Inst Hlth, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Inst Digital Med, Singapore, Singapore, [Yun, H.] Natl Univ Singapore, Integrat Sci & Engn Programme ISEP, Singapore, Singapore, [Zar, H. J.] Univ Melbourne, Dept Biomed Engn, Melbourne, Vic, Australia, [Zhou, J. H.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Translat Magnet Resonance Res, Singapore, Singapore, [Ziauddeen, H.] Univ Cambridge, Wellcome Trust MRC Inst Metab Sci, Cambridge, England, [Zugman, A.] NIMH, NIH, Bethesda, MD 20892 USA, [Zugman, A.] Escola Paulista Med, Dept Psychiat, Sao Paulo, Brazil, [Zuo, X. N.] Nanning Normal Univ, Sch Educ Sci, Key Lab Brain & Educ, Nanning, Peoples R China, British Academy Postdoctoral fellowship, NIMH, UKRI Medical Research Council, NIHR Cambridge Biomedical Research Centre, NIHR Senior Investigator award, MRC research infrastructure award, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Ontario Brain Institute

Subjects
631/378/2649, OpenPain Project, KNE96, Growth, Psychiatric-disorders, DISEASE, 3R-BRAIN, Brain charts, MRI Brain, OASIS-3, Disease, CCNP, 631/378/2571, UMN BCP, Multidisciplinary, medicine.diagnostic_test, PSYCHIATRIC-DISORDERS, article, Brain, Human brain, ASSOCIATION, Magnetic Resonance Imaging, Harvard Aging Brain Study, The Mayo Clinic Study of Aging, NSPN, medicine.anatomical_structure, GROWTH, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ddc:500, BURDEN, WHITE-MATTER, FinnBrain, Harvard Aging Brain Study, Organization, Mri, MRI, medicine.medical_specialty, Concurrent validity, MODELS, Cam-CAN, Longevity, CALM Team, POND, Neuroimaging, Burden, ORGANIZATION, AIBL, The PREVENT-AD Research Group, VETSA, Cortical thickness, Association, Physical medicine and rehabilitation, FinnBrain, IMAGEN, KNE96, White-matter, medicine, Humans, ASRB, 631/378/1689, COBRE, business.industry, 631/378/2611, Brain morphometry, Neurosciences, Alzheimer’s Disease Repository Without Borders Investigators, Magnetic resonance imaging, Alzheimer’s Disease Neuroimaging Initiative, Anthropometry, Body Height, Brain growth, Birth, 59/57, Normative, IMAGEN, ENIGMA Developmental Brain Age working group, NSPN, business, CCNP, 3R-BRAIN, CORTICAL THICKNESS, Developing Human Connectome Project, ENIGMA Developmental Brain Age working group, The PREVENT-AD Research Group, VETSA, Bullmore, E.T
Abstract

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes., R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre.

Published
2022

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