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Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain., Competing Interests: Competing interests statement:D.H.W. served as Site PI for studies by Biogen, Merck, and Eli Lilly/Avid. He has received consulting fees from GE Healthcare and Neuronix. He is on the DSMB for a trial sponsored by Functional Neuromodulation. A.J.S. receives support from multiple NIH grants (P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, U01 AG024904, R01 LM013463, R01 AG068193, T32 AG071444, U01 AG068057, and U01 AG072177). He has also received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in-kind contribution of PET tracer precursor); Bayer Oncology (Scientific Advisory Board); Eisai (Scientific Advisory Board); Siemens Medical Solutions, Inc. (Dementia Advisory Board); and Springer-Nature Publishing (Editorial Office Support as Editor-in-Chief, Brain Imaging, and Behavior). O.C. reports having received consulting fees from AskBio (2020) and Therapanacea (2022), having received payments for writing a lay audience short paper from Expression Santé (2019), and that his laboratory has received grants (paid to the institution) from Qynapse (2017 to present). Members of his laboratory have co-supervised a Ph.D. thesis with myBrainTechnologies (2016 to 2019) and with Qynapse (2017 to present). O.C.’s spouse is an employee and holds stock options of myBrainTechnologies (2015 to present). O.C. has a patent registered at the International Bureau of the World Intellectual Property Organization (PCT/IB2016/0526993, J.-B. Schiratti, S. Allassonniere, O.C., S. Durrleman, A method for determining the temporal progression of a biological phenomenon and associated methods and devices) (2017). M.E. receives support from multiple NIH grants, the Alzheimer’s Association, and the Alzheimer’s Therapeutic Research Institute. M.V.Z. serves as a consultant and/or speaker for the following pharmaceutical companies: Eurofarma, Lundbeck, Abbott, Greencare, Myralis, and Elleven Healthcare.

We investigated whether morphological brain changes occurred in brain regions associated with body-weight homeostasis during acute antipsychotic treatment, and if so, whether they were related to changes in body mass and metabolic profile. Twenty-two antipsychotic-naive patients with first-episode schizophrenia received either risperidone long acting injection or flupenthixol decanoate over 13 weeks and were compared by structural MRI with 23 matched healthy volunteers at weeks 0, 4 and 13. Images were reconstructed using freesurfer fully-automated whole brain segmentation. The ventral diencephalon and prefrontal cortex were selected to represent the homeostatic and hedonic food intake regulatory systems respectively. Body mass was measured at weeks 0, 7 and 13 and fasting glucose and lipid profiles at weeks 0 and 13. Linear mixed effect models indicated significant group(⁎)time interactions for the ventral diencephalon volumes bilaterally. Ventral diencephalon volume reduction was strongly correlated bilaterally with body mass increase and HDL-cholesterol reductions, and unilaterally with blood glucose elevation. There were no significant changes in prefrontal cortical thickness. These findings implicate the ventral diencephalon, of which the hypothalamus is the main component, in the acute adipogenic and dyslipidaemic effects of antipsychotic medication., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Human brain volumes change throughout life, are highly heritable, and have been associated with general cognitive functioning. Cross-sectionally, this association between volume and cognition can largely be attributed to the same genes influencing both traits. We address the question whether longitudinal changes in brain volume or in surface area in young adults are under genetic control and whether these changes are also related to general cognitive functioning. We measured change in brain volume and surface area over a 5-year interval in 176 monozygotic and dizygotic twins and their non-twin siblings aged 19 to 56, using magnetic resonance imaging. Results show that changes in volumes of total brain (mean = -6.4 ml; 0.5% loss), cerebellum (1.4 ml, 1.0% increase), cerebral white matter (4.4 ml, 0.9% increase), lateral ventricles (0.6 ml; 4.8% increase) and in surface area (-19.7 cm(2),1.1% contraction) are heritable (h(2) = 43%; 52%; 29%; 31%; and 33%, respectively). An association between IQ (available for 91 participants) and brain volume change was observed, which was attributed to genes involved in both the variation in change in brain volume and in intelligence. Thus, dynamic changes in brain structure are heritable and may have cognitive significance in adulthood., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Background: Late life depression is associated with volumetric reductions of gray matter and increased prevalence of subcortical white matter lesions. Previous studies have shown a poorer treatment outcome in those with more severe structural brain abnormalities. In this study, quantitative and semi-quantitative magnetic resonance imaging (MRI) measures were studied in relation to response to a 12-week controlled antidepressant monotherapy trial., Methods: MRI (1.5 T) brain scans of 42 elderly inpatients with major depression, of which 23 were non-responder to a controlled 12-week antidepressant monotherapy trial, were acquired. In addition, clinical outcome was assessed after a one year period. Measures were volumes of global cerebral and subcortical structures., Results: After controlling for confounding, no differences were found between non-responders and responders after 12 weeks and after one year in volumes of cerebral gray and white matter, orbitofrontal cortex, hippocampus and white matter lesions., Conclusions: Structural brain measures associated with late life depression may not be related to short-term treatment response., (Copyright (c) 2007 John Wiley & Sons, Ltd.)

Background: Schizophrenia patients have difficulty inhibiting automatic saccades. Many studies have failed to resolve whether healthy first-degree relatives share the same deficit. Measures of brain activity may be more sensitive than behavioral measures. In patients, the saccadic inhibition deficit has been related to impaired frontostriatal functioning. This study attempts to establish whether this abnormality is also present in unaffected relatives of patients., Methods: Functional brain images were acquired during prosaccades and antisaccades in 16 control subjects and 16 unaffected siblings of schizophrenia patients using an event-related functional magnetic resonance imaging design. Eye movements were measured during scanning., Results: The task activated a network of regions corresponding to the oculomotor system. Siblings and control subjects did not differ during execution of prosaccades. During antisaccades, siblings did not activate the caudate nucleus. Siblings and control subjects did not differ on the percentage of antisaccade errors., Conclusions: Siblings did not appropriately activate the striatum during antisaccades, similar to what has been reported in patients. Siblings, however, did not make significantly more errors during antisaccades, indicating that they were able to compensate for the inactive caudate. Future research is needed to assess the potential of this striatal deficit as (genetic) risk factor for schizophrenia.

The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders.

This cross-sectional study assesses distinct neuroanatomical dimensions of autism spectrum disorder using novel semisupervised machine learning methods and tests whether the dimensions can serve as endophenotypes also in individuals without autism spectrum disorder. Key Points: Question: Are there multiple distinct dimensions/clusters that coexist to account for the underlying neuroanatomical, clinical, and genetic heterogeneity in autism spectrum disorder (ASD)? Findings: In this cross-sectional study that included 669 adolescents to adults, a 3-dimensional endophenotypic representation to capture the neuroanatomical heterogeneity of ASD was discovered. The 3 clinically distinct dimensions showed partially overlapping imaging and genetic signatures compared with schizophrenia and were present also in the general population. Meaning: This dimensional representation of ASD may quantify disease susceptibility at the individual level and highlight potentially high-risk individuals in the general population and serves as a prototype for precision diagnostics and treatment. Importance: Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment. Objective: To assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations. Design, Setting, and Participants: This cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022. Main Outcomes and Measures: The trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations. Results: Heterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10−6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate–adjusted P =.048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10−4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] β, 0.83 [0.02]; P = 4.22 × 10−6). Conclusions and Relevance: This cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses. [ABSTRACT FROM AUTHOR]

Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain‐age). The choice of the ML approach in estimating brain‐age in youth is important because age‐related brain changes in this age‐group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5–22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged 9–10 years and another comprising 594 individuals aged 5–21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree‐based, and kernel‐based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross‐validation folds, number of extreme outliers, and sample size. Tree‐based models and algorithms with a nonlinear kernel performed comparably well, with the latter being especially computationally efficient. Extreme Gradient Boosting (MAE of 1.49 years), Random Forest Regression (MAE of 1.58 years), and Support Vector Regression (SVR) with Radial Basis Function (RBF) Kernel (MAE of 1.64 years) emerged as the three most accurate models. Linear algorithms, with the exception of Elastic Net Regression, performed poorly. Findings of the present study could be used as a guide for optimizing methodology when quantifying brain‐age in youth. [ABSTRACT FROM AUTHOR]

Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. We investigated neuroanatomical subtypes in a multi-institutional multi-ethnic cohort, using novel semi-supervised machine learning methods designed to discover patterns associated with disease rather than normal anatomical variation. Structural MRI and clinical measures in established schizophrenia (n = 307) and healthy controls (n = 364) were analysed across three sites of PHENOM (Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging) consortium. Regional volumetric measures of grey matter, white matter, and CSF were used to identify distinct and reproducible neuroanatomical subtypes of schizophrenia. Two distinct neuroanatomical subtypes were found. Subtype 1 showed widespread lower grey matter volumes, most prominent in thalamus, nucleus accumbens, medial temporal, medial prefrontal/frontal and insular cortices. Subtype 2 showed increased volume in the basal ganglia and internal capsule, and otherwise normal brain volumes. Grey matter volume correlated negatively with illness duration in Subtype 1 (r = -0.201, P = 0.016) but not in Subtype 2 (r = -0.045, P = 0.652), potentially indicating different underlying neuropathological processes. The subtypes did not differ in age (t = -1.603, df = 305, P = 0.109), sex (chi-square = 0.013, df = 1, P = 0.910), illness duration (t = -0.167, df = 277, P = 0.868), antipsychotic dose (t = -0.439, df = 210, P = 0.521), age of illness onset (t = -1.355, df = 277, P = 0.177), positive symptoms (t = 0.249, df = 289, P = 0.803), negative symptoms (t = 0.151, df = 289, P = 0.879), or antipsychotic type (chi-square = 6.670, df = 3, P = 0.083). Subtype 1 had lower educational attainment than Subtype 2 (chi-square = 6.389, df = 2, P = 0.041). In conclusion, we discovered two distinct and highly reproducible neuroanatomical subtypes. Subtype 1 displayed widespread volume reduction correlating with illness duration, and worse premorbid functioning. Subtype 2 had normal and stable anatomy, except for larger basal ganglia and internal capsule, not explained by antipsychotic dose. These subtypes challenge the notion that brain volume loss is a general feature of schizophrenia and suggest differential aetiologies. They can facilitate strategies for clinical trial enrichment and stratification, and precision diagnostics. [ABSTRACT FROM AUTHOR]

Background. Studying offspring of schizophrenia (SZo) and bipolar disorder patients (BDo) provides important information on the putative neurodevelopmental trajectories underlying development toward severe mental illnesses. We compared intracranial volume (ICV), as a marker for neurodevelopment, and global and local brain measures between SZo or BDo and control offspring (Co) in relation to IQ and psychopathology. Methods. T1-weighted magnetic resonance imaging (MRI) brain scans were obtained from 146 participants (8-19 years; 40 SZo, 66 BDo, 40 Co). Linear mixed models were applied to compare ICV, global, and local brain measures between groups. To investigate the effect of ICV, IQ (four subtests Wechsler Intelligence Scale for Children/Wechsler Adult Intelligence Scale-III) or presence of psychopathology these variables were each added to the model. Results. SZo and BDo had significantly lower IQ and more often met criteria for a lifetime psychiatric disorder than Co. ICV was significantly smaller in SZo than in BDo (d =−0.56) and Co (d =−0.59), which was largely independent of IQ (respectively, d =−0.54 and d =−0.35). After ICV correction, the cortex was significantly thinner in SZo than in BDo (d =−0.42) and Co (d =−0.75) and lateral ventricles were larger in BDo than in Co (d = 0.55). Correction for IQ or lifetime psychiatric diagnosis did not change these findings. Conclusions. Despite sharing a lower IQ and a higher prevalence of psychiatric disorders, brain abnormalities in BDo appear less pronounced (but are not absent) than in SZo. Lower ICV in SZo implies that familial risk for schizophrenia has a stronger association with stunted early brain development than familial risk for bipolar disorder. [ABSTRACT FROM AUTHOR]

Rapid and reactive control of movement is essential in a dynamic environment and is disrupted in several neuropsychiatric disorders. Nonhumanprimate neurophysiology studies havemadesignificant contributions to our understanding of how saccadic eye movements can be rapidly inhibited, changed, and monitored. These results highlight a frontostriatal network involved in gaze control and provide a strong basis for understanding how cognitive control of action is implemented in the human brain. The goal of the present study was to bridge human and nonhuman primate studies by investigating reactive control of eye movements during fMRI using a task that has been used in neurophysiology studies: the search-step task. This task requires a speeded response to a visual target (no-step trial). Onaminority(40%)of trials, the target jumps to a new location and participants are instructed to inhibit the initially planned saccade and redirect gaze toward the new location (redirect trial). Compared with no-step trials, greater activation in a frontal oculomotor network, including front a land supplementary eye fields (SEFs),andthe striatum was observed during correctly executed redirect trials. Individual differences in stopping efficiency were related to striatal activation. Further, greater activation in SEF was in a region anterior to that activated during visually guided saccades and scaled positively with error magnitude, suggesting a prominent role in response monitoring. Combined, these data lend new evidence for a role of the striatum in reactive saccade control and further clarify the role of SEF in action inhibition and performance monitoring. [ABSTRACT FROM AUTHOR]

Heavy ecstasy use has been associated with neurocognitive deficits in various behavioral and brain imaging studies. However, this association is not conclusive owing to the unavoidable confounding factor of polysubstance use. The present study, as part of the Netherlands XTC Toxicity study, investigated specific effects of ecstasy on working memory, attention, and associative memory, using functional magnetic resonance imaging (fMRI). A large sample (n=71) was carefully composed based on variation in the amount and type of drugs that were used. The sample included 33 heavy ecstasy users (mean 322 pills lifetime). Neurocognitive brain function in three domains: working memory, attention, and associative memory, was assessed with performance measures and fMRI. Independent effects of the use of ecstasy, amphetamine, cocaine, cannabis, alcohol, tobacco, and of gender and IQ were assessed and separated by means of multiple regression analyses. Use of ecstasy had no effect on working memory and attention, but drug use was associated with reduced associative memory performance. Multiple regression analysis showed that associative memory performance was affected by amphetamine much more than by ecstasy. Both drugs affected associative memory-related brain activity, but the effects were consistently in opposite directions, suggesting that different mechanisms are at play. This could be related to the different neurotransmitter systems these drugs predominantly act upon, that is, serotonin (ecstasy) vs dopamine (amphetamine) systems.Neuropsychopharmacology (2008) 33, 247–258; doi:10.1038/sj.npp.1301415; published online 25 April 2007 [ABSTRACT FROM AUTHOR]

Heavy ecstasy use in humans has been associated with cognitive impairments and changes in cognitive brain function supposedly due to damage to the serotonin system. There is concern that even a single dose of 3,4-methylenedioxymethamphetamine may be neurotoxic, but very little is known about the consequences of a low dose of ecstasy for cognitive brain function. The objective of the study was to assess the effects of a low dose of ecstasy on human cognitive brain function using functional magnetic resonance imaging (fMRI). We prospectively studied, as part of the NeXT (Netherlands XTC toxicity) study, sustained effects of a low dose of ecstasy on brain function in 25 subjects before and after their first episode of ecstasy use (mean 2.0 ± 1.4 ecstasy pills, on average 11.1 ± 12.9 weeks since last ecstasy use), compared to 24 persistent ecstasy-naive controls, also measured twice and matched with the novice users on age, gender, IQ, and cannabis use. Cognitive brain function was measured in the domains of working memory, selective attention, and associative memory using fMRI. No significant effects were found of a low dose of ecstasy on working memory, selective attention, or associative memory neither at the behavioral level nor at the neurophysiological level. This study yielded no firm evidence for sustained effects of a low dose of ecstasy on human cognitive brain function. The present findings are relevant for the development of prevention and harm reduction strategies. Furthermore, the study is relevant to the discussion concerning potential therapeutic use of ecstasy. [ABSTRACT FROM AUTHOR]

Rationale: Excessive use of cannabis may have long-term effects on cognitive abilities. Mild impairments have been found in several cognitive domains, particularly in memory and attention. It is not clear, however, whether these effects also occur with moderate, recreational use of cannabis. Furthermore, little is known about underlying brain correlates. Objectives: The aim of this study is to assess brain function in frequent but relatively moderate cannabis users in the domains of working memory and selective attention. Methods: Functional magnetic resonance imaging was used to examine verbal working memory and visuo-auditory selective attention in ten frequent cannabis users (after 1 week of abstinence) and ten nonusing healthy controls. Groups were similar in age, gender and estimated IQ. Results: Cannabis users and controls performed equally well during the working memory task and the selective attention task. Furthermore, cannabis users did not differ from controls in terms of overall patterns of brain activity in the regions involved in these cognitive functions. However, for working memory, a more specific region-of-interest analysis showed that, in comparison to the controls, cannabis users displayed a significant alteration in brain activity in the left superior parietal cortex. Conclusion: No evidence was found for long-term deficits in working memory and selective attention in frequent cannabis users after 1 week of abstinence. Nonetheless, frequent cannabis use may affect brain function, as indicated by altered neurophysiological dynamics in the left superior parietal cortex during working memory processing. [ABSTRACT FROM AUTHOR]

Behavioral studies have shown that consistent practice of a cognitive task can increase the speed of performance and reduce variability of responses and error rate, reflecting a shift from controlled to automatic processing. This study examines how the shift from controlled to automatic processing changes brain activity. A verbal Sternberg task was used with continuously changing targets (novel task, NT) and with constant, practiced targets (practiced task, PT). NT and PT were presented in a blocked design and contrasted to a choice reaction time (RT) control task (CT) to isolate working memory (WM)-related activity. The three-dimensional (3-D) PRESTO functional magnetic resonance imaging (fMRI) sequence was used to measure hemodynamic responses. Behavioral data revealed that task processing became automated after practice, as responses were faster, less variable, and more accurate. This was accompanied specifically by a decrease in activation in regions related to WM (bilateral but predominantly left dorsolateral prefrontal cortex (DLPFC), right superior frontal cortex (SFC), and right frontopolar area) and the supplementary motor area. Results showed no evidence for a shift of foci of activity within or across regions of the brain. The findings have theoretical implications for understanding the functional anatomical substrates of automatic and controlled processing, indicating that these types of information processing have the same functional anatomical substrate, but differ in efficiency. In addition, there are practical implications for interpreting activity as a measure for task performance, such as in patient studies. Whereas reduced activity can reflect poor performance if a task is not sensitive to practice effects, it can reflect good performance if a task is sensitive to practice effects. [ABSTRACT FROM AUTHOR]

Abstract: Interest is growing in the neurotoxic potential of cannabis on human brain function. We studied non-acute effects of frequent cannabis use on hippocampus-dependent associative memory, investigated with functional Magnetic Resonance Imaging (fMRI) in 20 frequent cannabis users and 20 non-users matched for age, gender and IQ. Structural changes in the (para)hippocampal region were measured using voxel-based morphometry (VBM). Cannabis users displayed lower activation than non-users in brain regions involved in associative learning, particularly in the (para)hippocampal regions and the right dorsolateral prefrontal cortex, despite normal performance. VBM-analysis of the (para)hippocampal regions revealed no differences in brain tissue composition between cannabis users and non-users. No relation was found between (para)hippocampal tissue composition and the magnitude of brain activity in the (para)hippocampal area. Therefore, lower brain activation may not signify neurocognitive impairment, but could be the expression of a non-cognitive variable related to frequent cannabis use, for example changes in cerebral perfusion or differences in vigilance. [Copyright &y& Elsevier]