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Human brain mapping 43(1), 37-55 (2022). doi:10.1002/hbm.25029 special issue: "Special Issue: The ENIGMA Consortium: the first 10 years / Issue Edited by: P.M. Thompson, N. Jahanshad, L. Schmaal, J.A. Turner, A. Winkler, S.I. Thomopoulos, G.F. Egan, P. Kochunov", Published by Wiley-Liss, New York, NY

Special Issue: The ENIGMA Consortium: the first 10 years., Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes., European Community's Seventh Framework Programme, Grant/Award Numbers: 278948, 602450, 603016, 602805; US National Institute of Child Health and Human Development, Grant/Award Numbers: RO1HD050735, 1009064, 496682; QIMR Berghofer Medical Research Institute and the Centre for Advanced Imaging, University of Queensland; ICTSI NIH/NCRR, Grant/Award Number: RR025761; European Community's Horizon 2020 Programme, Grant/Award Numbers: 667302, 643051; Vici Innovation Program, Grant/Award Numbers: #91619115, 016-130-669; NWO Brain & Cognition Excellence Program, Grant/Award Number: 433-09-229; Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL); Spinozapremie, Grant/Award Number: NWO-56-464-14192; Biobanking and Biomolecular Resources Research Infrastructure, Grant/Award Numbers: 184.033.111, 184.021.007; Netherlands Organization for Health Research and Development (ZonMW), Grant/Award Numbers: 480-15-001/674, 024.001.003, 911-09-032, 056-32-010, 481-08-011, 016-115-035, 31160008, 400-07-080, 400-05-717, 451-04-034, 463-06-001, 480-04-004, 904-61-193, 912-10-020, 985-10-002, 904-61-090; NIMH, Grant/Award Number: R01 MH090553; Geestkracht programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Numbers: NWO/SPI 56-464-14192, NWO-MagW 480-04-004, 433-09-220, NWO 51.02.062, NWO 51.02.061; National Center for Advancing Translational Sciences, National Institutes of Health, Grant/Award Number: UL1 TR000153; National Center for Research Resources; National Center for Research Resources at the National Institutes of Health, Grant/Award Numbers: NIH 1U24 RR025736-01, NIH 1U24 RR021992; NIH Institutes contributing to the Big Data to Knowledge; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Numbers: U54EB020403, G0500092; National Institute of Mental Health, Grant/Award Numbers: R01MH117014, R01MH042191; Fundación Instituto de Investigación Marqués de Valdecilla, Grant/Award Numbers: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III, Grant/Award Numbers: PI14/00918, PI14/00639, PI060507, PI050427, PI020499; Swedish Research Council, Grant/Award Numbers: 523-2014-3467, 2017-00949, 521-2014-3487; South-Eastern Norway Health Authority; the Research Council of Norway, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Icahn School of Medicine at Mount Sinai; Seventh Framework Programme (FP7/2007-2013), Grant/Award Number: 602450; National Institutes of Health, Grant/Award Numbers: R01 MH116147, R01 MH113619, R01 MH104284; South London and Maudsley NHS Foundation Trust; the National Institute for Health Research (NIHR)

Contains fulltext : 248376.pdf (Publisher’s version ) (Open Access) For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

This study reports movement patterns and home range estimates of an Andean fox (Lycalopex culpaeus) in Cotopaxi National Park in Ecuador, representing the first GPS-tagging of the species. The GPS functioned well during the 197-day tracking period. Home range sizes ranged between 4.9 and 8.1 km2, depending on the estimation method. Movement speeds averaged 0.17 km/h at day versus 0.87 km/h at night, and distance traveled averaged 0.23 km at day versus 0.89 km at night. These preliminary results highlight the importance of collecting unbiased, high-quality data which enables an enhanced understanding on mammal behavior and human/animal interaction.

Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.

Purpose of review Neuroimaging research on attention-deficit/hyperactivity disorder (ADHD) continues growing in extent and complexity, although it has yet to become clinically meaningful. We review recent MRI research on ADHD, to identify robust findings, current trends and challenges. Recent findings We identified 40 publications between January 2019 and September 2020 reporting or reviewing MRI research on ADHD. Four meta-analyses have presented conflicting results regarding across-study convergence of functional and resting-state functional (fMRI and R-fMRI) studies on ADHD. On the other hand, the Enhancing NeuroImaging Genetics Through Meta-Analysis international consortium has identified statistically robust albeit small differences in structural brain cortical and subcortical indices in children with ADHD versus typically developing controls. Other international consortia are harnessing open-science efforts and multimodal data (imaging, genetics, phenotypic) to shed light on the complex interplay of genetics, environment, and development in the pathophysiology of ADHD. We note growing research in 'prediction' science, which applies machine-learning analysis to identify biomarkers of disease based on big data. Summary Neuroimaging in ADHD is still far from informing clinical practice. Current large-scale, multimodal, and open-science initiatives represent promising paths toward untangling the neurobiology of ADHD.

The link between Toxoplasma gondii infection and multiple sclerosis remains controversial. In the present study, we aimed to determine the association between T. gondii seropositivity and multiple sclerosis. Using an age- and gender-matched case-control study, we studied 45 patients who had multiple sclerosis attended in two public hospitals and 225 control subjects without this disease and other neurological disorders in Durango City, Mexico. Serum samples of cases and controls were analyzed for detection of anti-Toxoplasma IgG using a commercially available enzyme-linked immunoassay. One (2.22%) of the 45 patients with multiple sclerosis, and 15 (6.67%) of the 225 control subjects without this disease were seropositive for anti-T. gondii IgG antibodies. No statistically significant difference (OR = 0.31; 95% CI: 0.04–2.47; P = 0.48) in seroprevalence of anti-T. gondii IgG antibodies between cases and controls was found. The frequency of T. gondii seropositivity did not vary among cases and controls about sex or age groups. Results of this study do not support an association between seropositivity to T. gondii and multiple sclerosis. However, additional research with larger sample sizes to confirm this lack of association should be conducted.

Serotonin and dopamine are associated with multiple psychiatric disorders. How they interact during development to affect subsequent behavior remains unknown. Knockout of the serotonin transporter or postnatal blockade with selective serotonin reuptake inhibitors (SSRIs) leads to novelty-induced exploration deficits in adulthood, potentially involving the dopamine system. Here, we show in the mouse that raphe nucleus serotonin neurons activate ventral tegmental area dopamine neurons via glutamate co-transmission and that this co-transmission is reduced in animals exposed postnatally to SSRIs. Blocking serotonin neuron glutamate co-transmission mimics this SSRI-induced hypolocomotion, while optogenetic activation of dopamine neurons reverses this hypolocomotor phenotype. Our data demonstrate that serotonin neurons modulate dopamine neuron activity via glutamate co-transmission and that this pathway is developmentally malleable, with high serotonin levels during early life reducing co-transmission, revealing the basis for the reduced novelty-induced exploration in adulthood due to postnatal SSRI exposure.

Objective Impaired multisensory integration in autism spectrum disorder (ASD) may arise from functional dysconnectivity among brain systems. Our study examines the functional connectivity integration between primary modal sensory regions and heteromodal processing cortex in ASD, and whether abnormalities in network integration relate to clinical severity. Method We studied a sample of 55 high-functioning ASD and 64 healthy control (HC) male children and adolescents (total n = 119, age range 7−18 years). Stepwise functional connectivity analysis (SFC) was applied to resting state functional magnetic resonance images (rsfMRI) to characterize the connectivity paths that link primary sensory cortices to higher-order brain cognitive functional circuits and to relate alterations in functional connectivity integration with three clinical scales: Social Communication Questionnaire, Social Responsiveness Scale, and Vineland Adaptive Behavior Scales. Results HC displayed typical functional connectivity transitions from primary sensory systems to association areas, but the ASD group showed altered patterns of multimodal sensory integration to heteromodal systems. Specifically, compared to the HC group, the ASD group showed the following: (1) hyperconnectivity in the visual cortex at initial link step distances; (2) hyperconnectivity between sensory unimodal regions and regions of the default mode network; and (3) hypoconnectivity between sensory unimodal regions and areas of the fronto-parietal and attentional networks. These patterns of hyper- and hypoconnectivity were associated with increased clinical severity in ASD. Conclusion Networkwise reorganization in high-functioning ASD individuals affects strategic regions of unimodal-to-heteromodal cortical integration predicting clinical severity. In addition, SFC analysis appears to be a promising approach for studying the neural pathophysiology of multisensory integration deficits in ASD.

Glyphonycteris is poorly known genus of bats that inhabit lowlands and forested mountains of Central and South America. This genus comprises three nominal species, with G. sylvestris being the most widely distributed species in South America. The karyological data previously available for this genus are restricted to those resulting from conventional staining techniques applied to a few specimens from Surinam. Here, we report the first record of G. sylvestris in Argentina extending its southern distribution. Moreover, our new locality represents the seventh record from the Atlantic Forests Biome and the first from Araucaria Moist Forest ecoregions. Our specimen overall exhibits the diagnostic characteristics described for the species except for some differences in the distribution of hairs on the wings and limbs. The fecal sample obtained from our specimen contained only seeds of Piper aduncum. The karyotype was 2n = 22 with a FN = 42; the analysis of fluorochrome banding patterns in the chromosomal pericentromeric regions shows DAPI (4,6-diamidino-2-phenylindole) positive blocks in pairs 5 and 8, whereas pericentromeric regions stained with CMA3 (Chromomicine A3) are positive in pairs 1, 2, 3, 4 and 11. Moreover, the interstitial region of chromosomes shows DAPI and CMA3 positive blocks in pairs 1, 2, 3, 4 and 5. These karyological data are novel to G. sylvestris, contributing to the scarce knowledge of the genus. Thus, filling blanks of information and enabling further comparative chromosome analysis. Fil: Sanchez, Mariano Sebastian. Universidad Nacional de Misiones. Facultad de Ciencias Exactas, Químicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas; Argentina Fil: Labaroni, Carolina Alicia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas; Argentina. Universidad Nacional de Misiones. Facultad de Ciencias Exactas, Químicas y Naturales; Argentina Fil: Castellanos Insuasti, Francisco Xavier. Universidad Nacional de Misiones. Facultad de Ciencias Exactas Químicas y Naturales. Departamento de Genética; Argentina Fil: Baldo, Juan Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas; Argentina

Neuroimaging research seeks to identify biomarkers to improve the diagnosis, prognosis, and treatment of attention-deficit/hyperactivity disorder (ADHD), although clinical translation of findings remains distant. Resting-state functional magnetic resonance imaging (R-fMRI) is increasingly being used to characterize functional connectivity in the brain. Despite mixed results to date and multiple methodological challenges, dominant hypotheses implicate hyperconnectivity across brain networks in patients with ADHD, which could be the target of pharmacological treatments. We describe the experience and results of the Clínica Universidad de Navarra (Spain) Metilfenidato (CUNMET) pilot study. CUNMET tested the feasibility of identifying R-fMRI markers of clinical response in children with ADHD undergoing naturalistical pharmacological treatments. We analyzed cross-sectional data from 56 patients with ADHD (18 treated with methylphenidate, 18 treated with lisdexamfetamine, and 20 treatment-naive patients). Standard preprocessing and statistical analyses with attention to control for head motion and correction for multiple comparisons were performed. The only results that survived correction were noted in contrasts of children who responded clinically to lisdexamfetamine after long-term treatment vs. treatment-naive patients. In these children, we observed stronger negative correlations (anticorrelations) across nodes in six brain networks, which is consistent with higher across-network functional segregation in patients treated with lisdexamfetamine, i.e., less inter-network interference than in treatment-naive patients. We also note the lessons learned, which could help those pursuing clinically relevant multidisciplinary research in ADHD en route to eventual personalized medicine. To advance reproducible open science, our report is accompanied with links providing access to our data and analytic scripts.

Objective Some studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here, we performed the largest ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium. Methods We analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,933 people with ADHD and 1,829 unaffected controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modeling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries. Results There was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t = 2.1, p = .04). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t = 2.7, p = .01) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohen’s d from −0.18 to 0.18) and would not survive study-wide correction for multiple testing. Conclusion Prior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait., Journal of Child Psychology and Psychiatry, 62 (10), ISSN:0021-9630, ISSN:1469-7610

Significance Functional connectivity within the default mode network in patients with major depressive disorder has been frequently reported to be abnormal but with contradicting directions in previous studies with small sample sizes. In creating the REST-meta-MDD consortium containing neuroimaging data of 1,300 depressed patients and 1,128 normal controls from 25 research groups in China, we found decreased default mode network functional connectivity in depressed patients, driven by patients with recurrent depression, and associated with current medication treatment but not with disease duration. These findings suggest that default mode network functional connectivity remains a prime target for understanding the pathophysiology of depression, with particular relevance to revealing mechanisms of effective treatments., Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

Objective To determine the seroprevalence of Leptospira immunoglobulin (Ig)G and IgM antibodies and its association with the characteristics of the study population from the northern Mexican city of Durango, Mexico. Methods Through a cross-sectional study design, inhabitants of Durango City, Mexico were surveyed between June 2018 and November 2018. Serum samples from the subjects were analysed for anti- Leptospira IgG and IgM antibodies using commercially available enzyme-linked immunosorbent assays. Sociodemographic, clinical, behavioural and housing characteristics were recorded. Data were analysed by bivariate and multivariate analyses. Results The study enrolled 413 people, of which 124 (30.0%) and 137 (33.2%) were positive for anti- Leptospira IgG antibodies and anti- Leptospira IgM antibodies, respectively. Multivariate analysis showed that Leptospira seropositivity was associated with professional occupation, alcohol consumption, ill clinical status, memory impairment and a history of surgery. Conclusions This is the first study to report the seroepidemiology of Leptospira infection in an urban general population in the north of Mexico. The seroprevalence of Leptospira infection found was higher than those previously reported in Mexican studies.

Early life is a sensitive period, in which enhanced neural plasticity allows the developing brain to adapt to its environment. This plasticity can also be a risk factor in which maladaptive development can lead to long-lasting behavioral deficits. Here, we test how early-life exposure to the selective-serotonin-reuptake-inhibitor (SSRI), fluoxetine, affects motivation, and dopaminergic signaling in adulthood. We show for the first time that mice exposed to fluoxetine in the early postnatal period exhibit a reduction in effort-related motivation. These mice also show blunted responses to amphetamine and reduced dopaminergic activation in a sucrose reward task. Interestingly, we find that the reduction in motivation can be rescued in the adult by administering bupropion, a dopamine-norepinephrine reuptake inhibitor used as an antidepressant and a smoke cessation aid but not by fluoxetine. Taken together, our studies highlight the effects of early postnatal exposure of fluoxetine on motivation and demonstrate the involvement of the dopaminergic system in this process.SIGNIFICANCE STATEMENTThe developmental period is characterized by enhanced plasticity. During this period, environmental factors have the potential to lead to enduring behavioral changes. Here, we show that exposure to the SSRI fluoxetine during a restricted period in early life leads to a reduction in adult motivation. We further show that this reduction is associated with decreased dopaminergic responsivity. Finally, we show that motivational deficits induced by early-life fluoxetine exposure can be rescued by adult administration of bupropion but not by fluoxetine.

Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N=369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.

Special Issue: The ENIGMA Consortium: the first 10 years.-- Karolinska Schizophrenia Project (KaSP)., Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3–90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns., National Institute of Mental Health, Grant/Award Numbers: MH104284, MH116147, R01MH113619, R01 MH090553, R01MH117014, R01MH042191; Karolinska Institutet; Stockholm County Council; Southern and Eastern Norway Regional Health Authority; German Centre for Cardiovascular Research; DZHK; Siemens Healthineers; University of Queensland; US National Institute of Child Health and Human Development, Grant/Award Number: RO1HD050735; Australian National Health and Medical Research Council; Eunice Kennedy Shriver National Institute of Child Health & Human Development; National Institute on Drug Abuse, Grant/Award Numbers: UL1 TR000153, 1 U24 RR025736-01, 1 U24 RR021992; Brain Injury Fund Research Grant Program; Indiana State Department of Health Spinal Cord; Parents Against Childhood Epilepsy; Epilepsy Therapy Project, Fight Against Childhood Epilepsy and Seizures; Epilepsy Foundation; American Epilepsy Society; Knut and Alice Wallenberg Foundation; European Community's Horizon 2020 Programme; Vici Innovation Program; NWO Brain & Cognition Excellence Program; Netherlands Organization for Scientific Research; Hersenstichting Nederland; Netherlands Organization for Health Research and Development; Geestkracht Programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; Universiteit Utrecht; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Center for Advancing Translational Sciences; National Institutes of Health; National Center for Research Resources; Consortium grant, Grant/Award Number: U54 EB020403; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Number: G0500092; Fundación Instituto de Investigación Marqués de Valdecilla, Grant/Award Numbers: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III, Grant/Award Numbers: PI14/00918, PI14/00639, PI060507, PI050427, PI020499; the Research Council, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Swedish Research Council; European Community's Seventh Framework Programme, Grant/Award Number: 602450; King's College London; South London and Maudsley NHS Foundation Trust; Biomedical Research Centre; National Institute for Health Research.

Previous neuroimaging studies have revealed abnormal functional connectivity of brain networks in patients with major depressive disorder (MDD), but findings have been inconsistent. A recent big‐data study found abnormal intrinsic functional connectivity within the default mode network in patients with recurrent MDD but not in first‐episode drug‐naïve patients with MDD. This study also provided evidence for reduced default mode network functional connectivity in medicated MDD patients, raising the question of whether previously observed abnormalities may be attributable to antidepressant effects. The present study (ClinicalTrials.gov identifier: NCT03294525) aimed to disentangle the effects of antidepressant treatment from the pathophysiology of MDD and test the medication normalization hypothesis. Forty‐one first‐episode drug‐naïve MDD patients were administrated antidepressant medication (escitalopram or duloxetine) for 8 weeks, with resting‐state functional connectivity compared between posttreatment and baseline. To assess the replicability of the big‐data finding, we also conducted a cross‐sectional comparison of resting‐state functional connectivity between the MDD patients and 92 matched healthy controls. Both Network‐Based Statistic analyses and large‐scale network analyses revealed intrinsic functional connectivity decreases in extensive brain networks after treatment, indicating considerable antidepressant effects. Neither Network‐Based Statistic analyses nor large‐scale network analyses detected significant functional connectivity differences between treatment‐naïve patients and healthy controls. In short, antidepressant effects are widespread across most brain networks and need to be accounted for when considering functional connectivity abnormalities in MDD., The present study aimed to disentangle the effects of antidepressant treatment from the pathophysiology of MDD and test the medication normalization hypothesis. Both Network‐Based Statistic analyses and large‐scale network analyses revealed intrinsic functional connectivity decreases in extensive brain networks after treatment, while no significant functional connectivity differences were found between treatment‐naïve patients and healthy controls. These results indicate that antidepressant effects are widespread across most brain networks and need to be accounted for when considering functional connectivity abnormalities in MDD.

ObjectiveAttention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. We aimed to directly compare all three disorders. The ENIGMA consortium is ideally positioned to investigate structural brain alterations across these disorders.MethodsStructural T1-weighted whole-brain MRI of controls (n=5,827) and patients with ADHD (n=2,271), ASD (n=1,777), and OCD (n=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. We examined subcortical volume, cortical thickness and surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults using linear mixed-effects models adjusting for age, sex and site (and ICV for subcortical and surface area measures).ResultsWe found no shared alterations among all three disorders, while shared alterations between any two disorders did not survive multiple comparisons correction. Children with ADHD compared to those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller ICV than controls and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared to adult controls and other clinical groups. No OCD-specific alterations across different age-groups and surface area alterations among all disorders in childhood and adulthood were observed.ConclusionOur findings suggest robust but subtle alterations across different age-groups among ADHD, ASD, and OCD. ADHD-specific ICV and hippocampal alterations in children and adolescents, and ASD-specific cortical thickness alterations in the frontal cortex in adults support previous work emphasizing neurodevelopmental alterations in these disorders.

BackgroundBeyond detecting brain lesions or tumors, comparatively little success has been attained in identifying brain disorders such as Alzheimer’s disease (AD), based on magnetic resonance imaging (MRI). Many machine learning algorithms to detect AD have been trained using limited training data, meaning they often generalize poorly when applied to scans from previously unseen populations. Therefore, we built a practical brain MRI-based AD diagnostic classifier using deep learning/transfer learning on dataset of unprecedented size and diversity. MethodsA retrospective MRI dataset pooled from more than 217 sites/scanners constituted the largest brain MRI sample to date (85,721 scans from 50,876 participants) between January 2017 and August 2021. Next, a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, was built as a sex classifier with high generalization capability. The sex classifier achieved 94.9% accuracy and served as a base model in transfer learning for the objective diagnosis of AD. FindingsAfter transfer learning, the model fine-tuned for AD classification achieved 91.3% accuracy in leave-sites-out cross-validation on the Alzheimer's Disease Neuroimaging Initiative (ADNI, 6,857 samples) dataset and 94.2%/93.6%/90.5% accuracy for direct tests on three unseen independent datasets (AIBL, 669 samples / MIRIAD, 644 samples / OASIS, 1,123 samples). When this AD classifier was tested on brain images from unseen mild cognitive impairment (MCI) patients, MCI patients who finally converted to AD were 3 times more likely to be predicted as AD than MCI patients who did not convert (65.2% vs 20.6%). Predicted scores from the AD classifier showed significant correlations with illness severity. InterpretationIn sum, the proposed AD classifier could offer a medical-grade marker that have potential to be integrated into AD diagnostic practice.

ObjectivesThis study aimed to determine the seroprevalence of Toxoplasma gondii (T. gondii) infection in pregnant women in Matehuala City, Mexico; and the associated risk factors.DesignA cross-sectional study.SettingMatehuala City, Mexico.Participants311 pregnant women.Primary and secondary outcome measuresSera of women were analysed for anti-T. gondii IgG and IgM antibodies by commercially available immunoassays. Bivariate and multivariate analyses were used to assess the association between T. gondii seroprevalence and the characteristics of the pregnant women.ResultsThirteen (4.2%) of the 311 pregnant women studied were positive for anti-T. gondii IgG antibodies. No anti-T. gondii IgM antibodies were found in anti-T. gondii IgG seropositive women. No association between seropositivity and history of blood transfusion, transplantation, caesarean sections, deliveries, miscarriages or number of pregnancies was found. Logistic regression analysis of sociodemographic, behavioural and housing variables showed that availability of potable water at street represented a risk factor for T. gondii infection (age-adjusted OR=2.18; 95% CI: 1.05 to 4.53; p=0.03), whereas being born in Mexico was a protective factor for infection (age-adjusted OR=0.01; 95% CI: 0.001 to 0.35; p=0.008).ConclusionsIn this first study on the seroepidemiology of T. gondii infection in pregnant women in Matehuala, we conclude that the seroprevalence of T. gondii infection is low and similar to those reported in pregnant women in other Mexican cities. However, the seroprevalence found is lower than those reported in pregnant women in other countries in the Americas and Europe. Two risk factors associated with T. gondii infection were identified. Results of the present study may help for the optimal planning of preventive measures against toxoplasmosis in pregnant women.

Delineating age-related cortical trajectories in healthy individuals is critical given the association of cortical thickness with cognition and behaviour. Previous research has shown that deriving robust estimates of age-related brain morphometric changes requires large-scale studies. In response, we conducted a large-scale analysis of cortical thickness in 17,075 individuals aged 3-90 years by pooling data through the Lifespan Working group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium. We used fractional polynomial (FP) regression to characterize age-related trajectories in cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma (LMS) method. Inter-individual variability was estimated using meta-analysis and one-way analysis of variance. Overall, cortical thickness peaked in childhood and had a steep decrease during the first 2-3 decades of life; thereafter, it showed a gradual monotonic decrease which was steeper in men than in women particularly in middle-life. Notable exceptions to this general pattern were entorhinal, temporopolar and anterior cingulate cortices. Inter-individual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results reconcile uncertainties about age-related trajectories of cortical thickness; the centile values provide estimates of normative variance in cortical thickness, and may assist in detecting abnormal deviations in cortical thickness, and associated behavioural, cognitive and clinical outcomes.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalised on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine the age-related morphometric trajectories of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum early in life; the volume of the basal ganglia showed a gradual monotonic decline thereafter while the volumes of the thalamus, amygdala and the hippocampus remained largely stable (with some degree of decline in thalamus) until the sixth decade of life followed by a steep decline thereafter. The lateral ventricles showed a trajectory of continuous enlargement throughout the lifespan. Significant age-related increase in inter-individual variability was found for the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to derive risk predictions for the early identification of diverse clinical phenotypes.

The article's abstract is no available.

Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization.HighlightsDynamic network reliability is highly dependent on many methodological decisionsThe default multilayer community detection algorithm generates erroneous resultsReliability-optimized intra-/inter-layer coupling parameters are dataset-dependentScan duration is a much stronger determinant of reliability than scan conditionMovies are the most reliable condition, requiring at least 20 min of data

Background: Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels. Results: The group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10−3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients. Conclusions: Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.

Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice. To justify the significant effort required for sharing data, funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a case study that provides direct evidence of the impact of open sharing on brain imaging data use and resulting peer-reviewed publications. We demonstrate that openly shared data can increase the scale of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings dispel the myth that scientific findings using shared data cannot be published in high-impact journals, suggest the transformative power of data sharing for accelerating science, and underscore the need for implementing data sharing universally., Data sharing is recognized as a way to promote scientific collaboration and reproducibility, but some are concerned over whether research based on shared data can achieve high impact. Here, the authors show that neuroimaging papers using shared data are no less likely to appear in top-ranked journals.

Moving from group level to individual level functional parcellation maps is a critical step for developing a rich understanding of the links between individual variation in functional network architecture and cognitive and clinical phenotypes. Still, the identification of functional units in the brain based on intrinsic functional connectivity and its dynamic variations between and within subjects remains challenging. Recently, the bootstrap analysis of stable clusters (BASC) framework was developed to quantify the stability of functional brain networks both across and within subjects. This multi-level approach utilizes bootstrap resampling for both individual and group-level clustering to delineate functional units based on their consistency across and within subjects, while providing a measure of their stability. Here, we optimized the BASC framework for functional parcellation of the basal ganglia by investigating a variety of clustering algorithms and similarity measures. Reproducibility and test-retest reliability were computed to validate this analytic framework as a tool to describe inter-individual differences in the stability of functional networks. The functional parcellation revealed by stable clusters replicated previous divisions found in the basal ganglia based on intrinsic functional connectivity. While we found moderate to high reproducibility, test-retest reliability was high at the boundaries of the functional units as well as within their cores. This is interesting because the boundaries between functional networks have been shown to explain most individual phenotypic variability. The current study provides evidence for the consistency of the parcellation of the basal ganglia, and provides the first group level parcellation built from individual-level cluster solutions. These novel results demonstrate the utility of BASC for quantifying inter-individual differences in the functional organization of brain regions, and encourage usage in future studies.

Background Cholesterol has been associated as a risk factor for cardiovascular disease. Recently, however, there is growing evidence about crucial requirement of neuron membrane cholesterol in the organization and function of the 5-HT1A serotonin receptor. For this, low cholesterol level has been reported to be associated with depression and suicidality. However there have been inconsistent reports about this finding and the exact relationship between these factors remains controversial. Therefore, we investigated the link between serum cholesterol and its fractions with depression disorder and suicide attempt in 467 adult subjects in Mexican mestizo population. Methods Plasma levels of total cholesterol, triglycerides, and high-density lipoprotein cholesterol (HDL-c) and low density lipoprotein cholesterol (LDL-c) were determined in 261 MDD patients meeting the DSM-5 criteria for major depressive disorder (MDD), 59 of whom had undergone an episode of suicide attempt, and 206 healthy controls. Results A significant decrease in total cholesterol, LDL-cholesterol, VLDL-cholesterol and triglyceride serum levels was observed in the groups of MDD patients and suicide attempt compared to those without suicidal behavior (p < 0.05). After adjusting for covariates, lower cholesterol levels were significantly associated with MDD (OR 4.229 CI 95% 2.555 – 7.000, p

BackgroundData-driven approaches can capture behavioral and biological variation currently unaccounted for by contemporary diagnostic categories, thereby enhancing the ability of neurobiological studies to characterize brain-behavior relationships.MethodsA community-ascertained sample of individuals (N=347, ages 18–59) completed a battery of behavioral measures, psychiatric assessment, and resting state functional magnetic resonance imaging (R-fMRI) in a cross-sectional design. Bootstrap-based exploratory factor analysis was applied to 49 phenotypic subscales from 10 measures. Hybrid Hierarchical Clustering was applied to resultant factor scores to identify nested groups. Adjacent groups were compared via independent samples t-tests and chi-square tests of factor scores, syndrome scores, and psychiatric prevalence. Multivariate Distance Matrix Regression examined functional connectome differences between adjacent groups.ResultsReduction yielded six factors, which explained 77.8% and 65.4% of the variance in exploratory and constrained exploratory models, respectively. Hybrid Hierarchical Clustering of these 6 factors identified 2, 4, and 8 nested groups (i.e., phenotypic communities). At the highest clustering level, the algorithm differentiated functionally adaptive and maladaptive groups. At the middle clustering level, groups were separated by problem type (maladaptive groups; internalizing vs. externalizing problems) and behavioral type (adaptive groups; sensation-seeking vs. extraverted/emotionally stable). Unique phenotypic profiles were also evident at the lowest clustering level. Group comparisons exhibited significant differences in intrinsic functional connectivity at the highest clustering level in somatomotor, thalamic, basal ganglia, and limbic networks.ConclusionsData-driven approaches for identifying homogenous subgroups, spanning typical function to dysfunction not only yielded clinically meaningful groups, but captured behavioral and neurobiological variation among healthy individuals as well.

Objective To examine whether childhood attention-deficit/hyperactivity disorder (ADHD) predicts homelessness in adulthood, and whether the persistence of childhood ADHD through adolescence influences the likelihood of homelessness. Method A 33-year prospective, controlled, follow-up was performed of clinic-referred, 6- to 12-year-old boys of white ethnicity with ADHD (probands; mean = 8), at a mean age of 41 years (follow-up [FU] = 41). Comparisons, children without ADHD from the same medical center, were matched for age and socioeconomic status (SES). Both groups were evaluated at a mean age of 18 years (FU18). Homelessness was assessed at FU41 in 134 of 207 probands (65%) and 136 of 178 (76%) comparisons. We tested the following: the relationship between childhood ADHD and homelessness; whether adolescent dysfunctions (conduct disorder, non-alcohol substance use disorder, arrests, and school dropout) accounted for this relationship, if found; and whether ADHD that persisted through FU18 elevated probands' homelessness rate. Results Probands had significantly higher rates of homelessness than comparisons (23.7% vs. 4.4%; χ 2 1 = 21.15, df = 1, p p = .01). Probands with persistent ADHD through adolescence had significantly more homelessness than remitted probands (χ 2 1 = 12.73, p p = .09). Conclusion Among boys of white ethnicity who were followed into adulthood, childhood ADHD was associated with an elevated rate of homelessness. Findings point to the need for clinical monitoring of childhood ADHD through adolescence, even when ADHD does not persist, in hopes of mitigating a cascade of malfunction that includes homelessness.

Children with the autosomal dominant single gene disorder, neurofibromatosis type 1 (NF1), display multiple structural and functional changes in the central nervous system, resulting in neuropsychological cognitive abnormalities. Here we assessed the pathological functional organization that may underlie the behavioral impairments in NF1 using resting-state functional connectivity MRI. Coherent spontaneous fluctuations in the fMRI signal across the entire brain were used to interrogate the pattern of functional organization of corticocortical and corticostriatal networks in both NF1 pediatric patients and mice with a heterozygous mutation in the Nf1 gene (Nf1+/-). Children with NF1 demonstrated abnormal organization of cortical association networks and altered posterior-anterior functional connectivity in the default network. Examining the contribution of the striatum revealed that corticostriatal functional connectivity was altered. NF1 children demonstrated reduced functional connectivity between striatum and the frontoparietal network and increased striatal functional connectivity with the limbic network. Awake passive mouse functional connectivity MRI in Nf1+/- mice similarly revealed reduced posterior-anterior connectivity along the cingulate cortex as well as disrupted corticostriatal connectivity. The striatum of Nf1+/- mice showed increased functional connectivity to somatomotor and frontal cortices and decreased functional connectivity to the auditory cortex. Collectively, these results demonstrate similar alterations across species, suggesting that NF1 pathogenesis is linked to striatal dysfunction and disrupted corticocortical connectivity in the default network.

Cortical development is characterized by distinct spatial and temporal patterns of maturational changes across various cortical shape measures. There is a growing interest in summarizing complex developmental patterns into a single index, which can be used to characterize an individual’s brain age. We conducted this study with two primary aims. First, we sought to quantify covariation patterns for a variety of cortical shape measures, including cortical thickness, gray matter volume, surface area, mean curvature, and travel depth, as well as white matter volume, and subcortical gray matter volume. We examined these measures in a sample of 869 participants aged 5-18 from the Healthy Brain Network (HBN) neurodevelopmental cohort using the Joint and Individual Variation Explained (Lock et al., 2013) method. We validated our results in an independent dataset from the Nathan Kline Institute - Rockland Sample (NKI-RS; N=210) and found remarkable consistency for some covariation patterns. Second, we assessed whether covariation patterns in the brain can be used to accurately predict a person’s chronological age. Using ridge regression, we showed that covariation patterns can predict chronological age with high accuracy, reflected by our ability to cross-validate our model in an independent sample with a correlation coefficient of 0.84 between chronologic and predicted age. These covariation patterns also predicted sex with high accuracy (AUC=0.85), and explained a substantial portion of variation in full scale intelligence quotient (R2=0.10). In summary, we found significant covariation across different cortical shape measures and subcortical gray matter volumes. In addition, each shape measure exhibited distinct covariations that could not be accounted for by other shape measures. These covariation patterns accurately predicted chronological age, sex and general cognitive ability. In a subset of NKI-RS, test-retest (

Factors genètics; Desordre d'ús de substàncies Factores genéticos; Desorden de uso de sustancias Genetic factors; Substance use disorder; ADGRL3 (LPHN3) Genetic factors are strongly implicated in the susceptibility to develop externalizing syndromes such as attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, and substance use disorder (SUD). Variants in the ADGRL3 (LPHN3) gene predispose to ADHD and predict ADHD severity, disruptive behaviors comorbidity, long-term outcome, and response to treatment. In this study, we investigated whether variants within ADGRL3 are associated with SUD, a disorder that is frequently co-morbid with ADHD. Using family-based, case-control, and longitudinal samples from disparate regions of the world (n = 2698), recruited either for clinical, genetic epidemiological or pharmacogenomic studies of ADHD, we assembled recursive-partitioning frameworks (classification tree analyses) with clinical, demographic, and ADGRL3 genetic information to predict SUD susceptibility. Our results indicate that SUD can be efficiently and robustly predicted in ADHD participants. The genetic models used remained highly efficient in predicting SUD in a large sample of individuals with severe SUD from a psychiatric institution that were not ascertained on the basis of ADHD diagnosis, thus identifying ADGRL3 as a risk gene for SUD. Recursive-partitioning analyses revealed that rs4860437 was the predominant predictive variant. This new methodological approach offers novel insights into higher order predictive interactions and offers a unique opportunity for translational application in the clinical assessment of patients at high risk for SUD R01 DA039881/DA/NIDA NIH HHS/United States. DA039881/U.S. Department of Health & Human Services NIH, National Institute on Drug Abuse (NIDA)/

Contains fulltext : 204879pub.pdf (Publisher’s version ) (Open Access) Contains fulltext : 204879pos.pdf (Author’s version postprint ) (Open Access) OBJECTIVE: Neuroimaging studies show structural alterations of various brain regions in children and adults with attention deficit hyperactivity disorder (ADHD), although nonreplications are frequent. The authors sought to identify cortical characteristics related to ADHD using large-scale studies. METHODS: Cortical thickness and surface area (based on the Desikan-Killiany atlas) were compared between case subjects with ADHD (N=2,246) and control subjects (N=1,934) for children, adolescents, and adults separately in ENIGMA-ADHD, a consortium of 36 centers. To assess familial effects on cortical measures, case subjects, unaffected siblings, and control subjects in the NeuroIMAGE study (N=506) were compared. Associations of the attention scale from the Child Behavior Checklist with cortical measures were determined in a pediatric population sample (Generation-R, N=2,707). RESULTS: In the ENIGMA-ADHD sample, lower surface area values were found in children with ADHD, mainly in frontal, cingulate, and temporal regions; the largest significant effect was for total surface area (Cohen's d=-0.21). Fusiform gyrus and temporal pole cortical thickness was also lower in children with ADHD. Neither surface area nor thickness differences were found in the adolescent or adult groups. Familial effects were seen for surface area in several regions. In an overlapping set of regions, surface area, but not thickness, was associated with attention problems in the Generation-R sample. CONCLUSIONS: Subtle differences in cortical surface area are widespread in children but not adolescents and adults with ADHD, confirming involvement of the frontal cortex and highlighting regions deserving further attention. Notably, the alterations behave like endophenotypes in families and are linked to ADHD symptoms in the population, extending evidence that ADHD behaves as a continuous trait in the population. Future longitudinal studies should clarify individual lifespan trajectories that lead to nonsignificant findings in adolescent and adult groups despite the presence of an ADHD diagnosis.

Neuroimaging studies indicate that children with attention-deficit/hyperactivity disorder (ADHD) present alterations in several functional networks of the sensation-to-cognition spectrum. These alterations include functional overconnectivity within sensory regions and underconnectivity between sensory regions and neural hubs supporting higher order cognitive functions. Today, it is unknown whether this same pattern of alterations persists in adult patients with ADHD who had never been medicated for their condition. The aim of the present study was to assess whether medication-naïve adults with ADHD presented alterations in functional networks of the sensation-to-cognition spectrum. Thirty-one medication-naïve adults with ADHD and twenty-two healthy adults underwent resting-state functional magnetic resonance imaging (rs-fMRI). Stepwise functional connectivity (SFC) was used to characterize the pattern of functional connectivity between sensory seed regions and the rest of the brain at direct, short, intermediate, and long functional connectivity distances, thus covering the continuum from the sensory input to the neural hubs supporting higher order cognitive functions. As compared to controls, adults with ADHD presented increased SFC degree within primary sensory regions and decreased SFC degree between sensory seeds and higher order integration nodes. In addition, they exhibited decreased connectivity degree between sensory seeds and regions of the default-mode network. Consistently, the higher the score in clinical severity scales the lower connectivity degree between seed regions and the default mode network. The authors thank the volunteers who generously contributed to this research. This work was supported by the Ministerio de Economía y Competitividad research grant (SAF2012‐32362) and the Instituto de Salud Carlos III (Miguel Servet Type I (CP16/00096), AES Grant (PI17/00064), and P‐FIS Grant (FI18/00255)).

The purposes of this study were to examine the impact of 3 weeks of amphetamine administration on intrinsic connectome-wide connectivity patterns in adults with attention-deficit/hyperactivity disorder (ADHD) and explore the association between stimulant-induced symptom improvement and functional connectivity alteration.Participants included 19 adults (age 20-55 years) diagnosed with ADHD using the Diagnostic and Statistical Manual of Mental Disorders, 4th ed., Text Revision (DSM-IV-TR) criteria per the Adult Clinician Diagnostic Scale taking part in amphetamine trials. For each patient, two 6-minute resting-state functional magnetic resonance imaging (R-fMRI) scans were acquired at baseline and after treatment. A fully data-driven multivariate analytic approach (i.e., multivariate distance matrix regression [MDMR]) was applied to R-fMRI data to characterize the distributed pharmacological effects in the entire functional connectome. Clinical efficacy was assessed using ADHD rating scale with adult prompts and the Adult Self-Report Scale v1.1 Symptom Checklist. We linked stimulant-induced functional connectivity changes to symptom amelioration using Spearman's correlation.Three weeks of administration of a stimulant significantly reduced ADHD symptoms. MDMR-based analyses on R-fMRI data highlighted the left dorsolateral prefrontal cortex (DLPFC, a key cognitive control region) and the medial prefrontal cortex (MPFC, the anterior core of default network) whose distributed patterns of functional connectivity across the entire brain were altered by psychostimulants. Follow-up intrinsic functional connectivity revealed that stimulants specifically decreased the positive functional connectivity between DLPFC-insula, DLPFC-anterior cingulate cortex, and MPFC-insula. Importantly, these functional connectivity changes are associated with symptom improvement.These results suggested that ADHD is associated with increased functional integration or decreased functional segregation between core regions of cognitive control, default, and salience networks. The apparent normalization of intrinsic functional interaction in these circuits (i.e., increased functional segregation) may underlie the clinical benefits produced by 3 weeks of amphetamine treatment.

Rumination is strongly and consistently correlated with depression. Although multiple studies have explored the neural correlates of rumination, findings have been inconsistent and the mechanisms underlying rumination remain elusive. Functional brain imaging studies have identified areas in the default mode network (DMN) that appear to be critically involved in ruminative processes. However, a meta-analysis to synthesize the findings of brain regions underlying rumination is currently lacking. Here, we conducted a meta-analysis consisting of experimental tasks that investigate rumination by using Signed Differential Mapping of 14 fMRI studies comprising 286 healthy participants. Furthermore, rather than treat the DMN as a unitary network, we examined the contribution of three DMN subsystems to rumination. Results confirm the suspected association between rumination and DMN activation, specifically implicating the DMN core regions and the dorsal medial prefrontal cortex subsystem. Based on these findings, we suggest a hypothesis of how DMN regions support rumination and present the implications of this model for treating major depressive disorder characterized by rumination.

Meditation is a type of mental training commonly applied in clinical settings and also practiced for general well-being. Brain functional connectivity (FC) patterns associated with meditation have revealed its brain mechanisms. However, the variety of FC methods applied has made it difficult to identify brain communication patterns associated with meditation. Here we carried out a coordinate-based meta-analysis to get preliminary evidence of meditation effects on changing brain network interactions. Fourteen seed-based, voxel-wise FC studies reported in 13 publications were reviewed; 10 studies with seeds in the default mode network (DMN) were meta-analyzed. Seed coordinates and the effect sizes in statistically significant regions were extracted, based on 170 subjects in meditation groups and 163 subjects in control groups. Seed-based d-mapping was used to analyze meditation versus control FC differences with DMN seeds. Meditation was associated with increased connectivity within DMN and between DMN and somatomotor network and with decreased connectivity between DMN and frontoparietal network (FPN) as well as ventral attention network (VAN). The pattern of decreased within-DMN FC and increased between-network FC (FPN and DAN with DMN) was more robust in highly experienced meditators compared to less experienced individuals. The identified neural network interactions may also promote meditation’s effectiveness in clinical interventions for treating physical and mental disorders.

Key Points Question Is greenness surrounding schools and kindergartens associated with symptoms of attention-deficit/hyperactivity disorder among children? Findings In this large cross-sectional study of 59 754 Chinese children, attendance at schools or kindergartens in greener areas was associated with lower odds of having attention-deficit/hyperactivity disorder symptoms. Meaning These findings may be useful for policy makers and health care professionals to develop strategies (eg, planning for green spaces around schools and kindergartens) to mitigate the attention-deficit/hyperactivity disorder burden., This cross-sectional study evaluates the association between greenness surrounding schools or kindergartens and symptoms of attention-deficit/hyperactivity disorder among Chinese children., Importance Few studies have investigated the association between greenness and childhood attention-deficit/hyperactivity disorder (ADHD). Objective To evaluate the association between greenness surrounding schools or kindergartens and symptoms of ADHD in children. Design, Setting, and Participants This population-based cross-sectional study was performed between April 2012 and January 2013 in 7 cities in northeastern China. This analysis included 59 754 children (aged 2-17 years) from 94 schools and kindergartens, who had resided in the study area for 2 years or longer. Data were analyzed from April 15, 2019, to October 10, 2019. Exposures Greenness surrounding each child’s school or kindergarten was estimated using 2 satellite image–derived vegetation indexes: the normalized difference vegetation index and the soil-adjusted vegetation index. Main Outcomes and Measures Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) scales were used to measure ADHD symptoms (9 inattention symptoms and 9 hyperactivity-impulsivity symptoms). Parents or guardians rated the frequency of each of 18 ADHD symptoms during the preceding 6 months. Children with 6 or more symptoms of either inattention or hyperactivity-impulsivity were defined as having ADHD symptoms. Generalized linear mixed models were applied to estimate the association between greenness and ADHD symptoms. Results The mean (SD) age of the 59 754 study participants was 10.3 (3.6) years, and 29 494 (49.4%) were girls. A total of 2566 participants (4.3%) had ADHD symptoms. Greenness levels differed substantially across schools and kindergartens. The normalized difference vegetation index within 500 m of a school or kindergarten ranged from −0.09 to 0.77. Greater greenness levels were associated with lower odds of ADHD symptoms. In covariate-adjusted models, a 0.1-unit increase in normalized difference vegetation index or soil-adjusted vegetation index within 500 m of a school or kindergarten was significantly associated with lower odds of ADHD symptoms (odds ratios, 0.87 [95% CI, 0.83-0.91] and 0.80 [95% CI, 0.74-0.86], respectively; P

A brain-computer-interface (BCI)-based attention training game system has shown promise for treating attention deficit/hyperactivity disorder (ADHD) children with inattentive symptoms. However, little is known about brain network organizational changes underlying behavior improvement following BCI-based training. To cover this gap, we aimed to examine the topological alterations of large-scale brain functional networks induced by the 8-week BCI-based attention intervention in ADHD boys using resting-state functional magnetic resonance imaging method. Compared to the non-intervention (ADHD-NI) group, the intervention group (ADHD-I) showed greater reduction of inattention symptoms accompanied with differential brain network reorganizations after training. Specifically, the ADHD-NI group had increased functional connectivity (FC) within the salience/ventral attention network (SVN) and increased FC between task-positive networks (including the SVN, dorsal attention (DAN), somatomotor, and executive control network) and subcortical regions; in contrast ADHD-I group did not have this pattern. In parallel, ADHD-I group had reduced degree centrality and clustering coefficient as well as increased closeness in task-positive and the default mode networks (prefrontal regions) after the training. More importantly, these reduced local functional processing mainly in the SVN were associated with less inattentive/internalizing problems after 8-week BCI-based intervention across ADHD patients. Our findings suggest that the BCI-based attention training facilitates behavioral improvement in ADHD children by reorganizing brain functional network from more regular to more random configurations, particularly renormalizing salience network processing. Future long-term longitudinal neuroimaging studies are needed to develop the BCI-based intervention approach to promote brain maturation in ADHD. NMRC (Natl Medical Research Council, S’pore) MOH (Min. of Health, S’pore) Published version

Cortical development is characterized by distinct spatial and temporal patterns of maturational changes across various cortical shape measures. There is a growing interest in summarizing complex developmental patterns into a single index, which can be used to characterize an individual’s brain age. We conducted this study with two primary aims. First, we sought to quantify covariation patterns for a variety of cortical shape measures, including cortical thickness, gray matter volume, surface area, mean curvature, and travel depth, as well as white matter volume, and subcortical gray matter volume. We examined these measures in a sample of 869 participants aged 5-18 from the Healthy Brain Network (HBN) neurodevelopmental cohort using the Joint and Individual Variation Explained (Lock et al., 2013) method. We validated our results in an independent dataset from the Nathan Kline Institute - Rockland Sample (NKI-RS; N=210) and found remarkable consistency for some covariation patterns. Second, we assessed whether covariation patterns in the brain can be used to accurately predict a person’s chronological age. Using ridge regression, we showed that covariation patterns can predict chronological age with high accuracy, reflected by our ability to cross-validate our model in an independent sample with a correlation coefficient of 0.84 between chronologic and predicted age. These covariation patterns also predicted sex with high accuracy (AUC=0.85), and explained a substantial portion of variation in full scale intelligence quotient (R(2)=0.10). In summary, we found significant covariation across different cortical shape measures and subcortical gray matter volumes. In addition, each shape measure exhibited distinct covariations that could not be accounted for by other shape measures. These covariation patterns accurately predicted chronological age, sex and general cognitive ability. In a subset of NKI-RS, test-retest (