Cortical profiles of numerous psychiatric disorders and normal development share a common pattern.

The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of psychiatric disorders. The pubertal timing of the expression of PC1-related genes implicates disrupted neurodevelopment in the pathogenesis of the spectrum of psychiatric diseases emerging during adolescence.

Predicting alcohol dependence from multi-site brain structural measures.

To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored in a mega-analysis of previously published datasets from 2,034 AD and comparison participants spanning 27 sites curated by the ENIGMA Addiction Working Group. Data were grouped into a training set used for internal validation including 1,652 participants (692 AD, 24 sites), and a test set used for external validation with 382 participants (146 AD, 3 sites). An exploratory data analysis was first conducted, followed by an evolutionary search based feature selection to site generalizable and high performing subsets of brain measurements. Exploratory data analysis revealed that inclusion of case- and control-only sites led to the inadvertent learning of site-effects. Cross validation methods that do not properly account for site can drastically overestimate results. Evolutionary-based feature selection leveraging leave-one-site-out cross-validation, to combat unintentional learning, identified cortical thickness in the left superior frontal gyrus and right lateral orbitofrontal cortex, cortical surface area in the right transverse temporal gyrus, and left putamen volume as final features. Ridge regression restricted to these features yielded a test-set area under the receiver operating characteristic curve of 0.768. These findings evaluate strategies for handling multi-site data with varied underlying class distributions and identify potential biomarkers for individuals with current AD.

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.

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

Alcohol dependence decreases functional activation of the caudate nucleus during model-based decision processes.

BACKGROUND: Impaired decision making, a key characteristic of alcohol dependence (AD), manifests in continuous alcohol consumption despite severe negative consequences. The neural basis of this impairment in individuals with AD and differences with known neural decision mechanisms among healthy subjects are not fully understood. In particular, it is unclear whether the choice behavior among individuals with AD is based on a general impairment of decision mechanisms or is mainly explained by altered value attribution, with an overly high subjective value attributed to alcohol-related stimuli. METHODS: Here, we use a functional magnetic resonance imaging (fMRI) monetary reward task to compare the neural processes of model-based decision making and value computation between AD individuals (n = 32) and healthy controls (n = 32). During fMRI, participants evaluated monetary offers with respect to dynamically changing constraints and different levels of uncertainty. RESULTS: Individuals with AD showed lower activation associated with model-based decision processes in the caudate nucleus than controls, but there were no group differences in value-related neural activity or task performance. CONCLUSIONS: Our findings highlight the role of the caudate nucleus in impaired model-based decisions of alcohol-dependent individuals.

The interaction of child abuse and rs1360780 of the FKBP5 gene is associated with amygdala resting-state functional connectivity in young adults.

Extensive research has demonstrated that rs1360780, a common single nucleotide polymorphism within the FKBP5 gene, interacts with early-life stress in predicting psychopathology. Previous results suggest that carriers of the TT genotype of rs1360780 who were exposed to child abuse show differences in structure and functional activation of emotion-processing brain areas belonging to the salience network. Extending these findings on intermediate phenotypes of psychopathology, we examined if the interaction between rs1360780 and child abuse predicts resting-state functional connectivity (rsFC) between the amygdala and other areas of the salience network. We analyzed data of young European adults from the general population (N = 774; mean age = 18.76 years) who took part in the IMAGEN study. In the absence of main effects of genotype and abuse, a significant interaction effect was observed for rsFC between the right centromedial amygdala and right posterior insula (p < .025, FWE-corrected), which was driven by stronger rsFC in TT allele carriers with a history of abuse. Our results suggest that the TT genotype of rs1360780 may render individuals with a history of abuse more vulnerable to functional changes in communication between brain areas processing emotions and bodily sensations, which could underlie or increase the risk for psychopathology.

White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group.

Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.

Cortical Surfaces Mediate the Relationship Between Polygenic Scores for Intelligence and General Intelligence.

Recent large-scale, genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with general intelligence. The cumulative influence of these loci on brain structure is unknown. We examined if cortical morphology mediates the relationship between GWAS-derived polygenic scores for intelligence (PSi) and g-factor. Using the effect sizes from one of the largest GWAS meta-analysis on general intelligence to date, PSi were calculated among 10 P value thresholds. PSi were assessed for the association with g-factor performance, cortical thickness (CT), and surface area (SA) in two large imaging-genetics samples (IMAGEN N = 1651; IntegraMooDS N = 742). PSi explained up to 5.1% of the variance of g-factor in IMAGEN (F1,1640 = 12.2-94.3; P < 0.005), and up to 3.0% in IntegraMooDS (F1,725 = 10.0-21.0; P < 0.005). The association between polygenic scores and g-factor was partially mediated by SA and CT in prefrontal, anterior cingulate, insula, and medial temporal cortices in both samples (PFWER-corrected < 0.005). The variance explained by mediation was up to 0.75% in IMAGEN and 0.77% in IntegraMooDS. Our results provide evidence that cumulative genetic load influences g-factor via cortical structure. The consistency of our results across samples suggests that cortex morphology could be a novel potential biomarker for neurocognitive dysfunction that is among the most intractable psychiatric symptoms.

Amygdala functional connectivity in major depression - disentangling markers of pathology, risk and resilience.

BACKGROUND: Limbic-cortical imbalance is an established model for the neurobiology of major depressive disorder (MDD), but imaging genetics studies have been contradicting regarding potential risk and resilience mechanisms. Here, we re-assessed previously reported limbic-cortical alterations between MDD relatives and controls in combination with a newly acquired sample of MDD patients and controls, to disentangle pathology, risk, and resilience. METHODS: We analyzed functional magnetic resonance imaging data and negative affectivity (NA) of MDD patients (n = 48), unaffected first-degree relatives of MDD patients (n = 49) and controls (n = 109) who performed a faces matching task. Brain response and task-dependent amygdala functional connectivity (FC) were compared between groups and assessed for associations with NA. RESULTS: Groups did not differ in task-related brain activation but activation in the superior frontal gyrus (SFG) was inversely correlated with NA in patients and controls. Pathology was associated with task-independent decreases of amygdala FC with regions of the default mode network (DMN) and decreased amygdala FC with the medial frontal gyrus during faces matching, potentially reflecting a task-independent DMN predominance and a limbic-cortical disintegration during faces processing in MDD. Risk was associated with task-independent decreases of amygdala-FC with fronto-parietal regions and reduced faces-associated amygdala-fusiform gyrus FC. Resilience corresponded to task-independent increases in amygdala FC with the perigenual anterior cingulate cortex (pgACC) and increased FC between amygdala, pgACC, and SFG during faces matching. CONCLUSION: Our results encourage a refinement of the limbic-cortical imbalance model of depression. The validity of proposed risk and resilience markers needs to be tested in prospective studies. Further limitations are discussed.

Weather conditions influence the number of psychiatric emergency room patients.

The specific impact of weather factors on psychiatric disorders has been investigated only in few studies with inconsistent results. We hypothesized that meteorological conditions influence the number of cases presenting in a psychiatric emergency room as a measure of mental health conditions. We analyzed the number of patients consulting the emergency room (ER) of a psychiatric hospital in Berlin, Germany, between January 1, 2008, and December 31, 2014. A total of N = 22,672 cases were treated in the ER over the study period. Meteorological data were obtained from a publicly available data base. Due to collinearity among the meteorological variables, we performed a principal component (PC) analysis. Association of PCs with the daily number of patients was analyzed with autoregressive integrated moving average model. Delayed effects were investigated using Granger causal modeling. Daily number of patients in the ER was significantly higher in spring and summer compared to fall and winter (p < 0.001). Three PCs explained 76.8% percent of the variance with PC1 loading mostly on temperature, PC2 on cloudiness and low pressure, and PC3 on windiness. PC1 and PC2 showed strong association with number of patients in the emergency room (p < 0.010) indicating higher patient numbers on warmer and on cloudy days. Further, PC1, PC2, and PC3 predicted the number of patients presenting in the emergency room for up to 7 days (p < 0.050). A secondary analysis revealed that the effect of temperature on number of patients was mostly due to lower patient numbers on cold days. Although replication of our findings is required, our results suggest that weather influences the number of psychiatric patients consulting the emergency room. In particular, our data indicate lower patient numbers during very cold temperatures.

Cortical surface-based threshold-free cluster enhancement and cortexwise mediation.

Threshold-free cluster enhancement (TFCE) is a sensitive means to incorporate spatial neighborhood information in neuroimaging studies without using arbitrary thresholds. The majority of methods have applied TFCE to voxelwise data. The need to understand the relationship among multiple variables and imaging modalities has become critical. We propose a new method of applying TFCE to vertexwise statistical images as well as cortexwise (either voxel- or vertexwise) mediation analysis. Here we present TFCE_mediation, a toolbox that can be used for cortexwise multiple regression analysis with TFCE, and additionally cortexwise mediation using TFCE. The toolbox is open source and publicly available (https://github.com/trislett/TFCE_mediation). We validated TFCE_mediation in healthy controls from two independent multimodal neuroimaging samples (N = 199 and N = 183). We found a consistent structure-function relationship between surface area and the first independent component (IC1) of the N-back task, that white matter fractional anisotropy is strongly associated with IC1 N-back, and that our voxel-based results are essentially identical to FSL randomise using TFCE (all PFWE <0.05). Using cortexwise mediation, we showed that the relationship between white matter FA and IC1 N-back is mediated by surface area in the right superior frontal cortex (PFWE  < 0.05). We also demonstrated that the same mediation model is present using vertexwise mediation (PFWE  < 0.05). In conclusion, cortexwise analysis with TFCE provides an effective analysis of multimodal neuroimaging data. Furthermore, cortexwise mediation analysis may identify or explain a mechanism that underlies an observed relationship among a predictor, intermediary, and dependent variables in which one of these variables is assessed at a whole-brain scale. Hum Brain Mapp 38:2795-2807, 2017. © 2017 Wiley Periodicals, Inc.

Association study between the neurexin-1 gene and tardive dyskinesia.

OBJECTIVE: Tardive dyskinesia (TD) is a motor side effect that may develop after long-term antipsychotic treatment. Schizophrenia has recently been associated with the Neurexin-1 (NRXN1) gene that codes for a cell adhesion molecule in synaptic communication. METHODS: This study examined five NRXN1 single-nucleotide polymorphisms (SNPs) for possible association with the occurrence and severity of TD in 178 schizophrenia patients of European ancestry. RESULTS: We did not find these SNPs to be significantly associated with TD. CONCLUSIONS: More research is needed with additional SNPs and in bigger samples before we can completely rule out the role of NRXN1 in TD.

Brain white matter development is associated with a human-specific haplotype increasing the synthesis of long chain fatty acids.

The genetic and molecular pathways driving human brain white matter (WM) development are only beginning to be discovered. Long chain polyunsaturated fatty acids (LC-PUFAs) have been implicated in myelination in animal models and humans. The biosynthesis of LC-PUFAs is regulated by the fatty acid desaturase (FADS) genes, of which a human-specific haplotype is strongly associated with ω-3 and ω-6 LC-PUFA concentrations in blood. To investigate the relationship between LC-PUFA synthesis and human brain WM development, we examined whether this FADS haplotype is associated with age-related WM differences across the life span in healthy individuals 9-86 years of age (n = 207). Diffusion tensor imaging was performed to measure fractional anisotropy (FA), a putative measure of myelination, of the cerebral WM tracts. FADS haplotype status was determined with a single nucleotide polymorphism (rs174583) that tags this haplotype. Overall, normal age-related WM differences were observed, including higher FA values in early adulthood compared with childhood, followed by lower FA values across older age ranges. However, individuals homozygous for the minor allele (associated with lower LC-PUFA concentrations) did not display these normal age-related WM differences (significant age × genotype interactions, p(corrected) < 0.05). These findings suggest that LC-PUFAs are involved in human brain WM development from childhood into adulthood. This haplotype and LC-PUFAs may play a role in myelin-related disorders of neurodevelopmental origin.

Alterations in neural Theory of Mind processing in euthymic patients with bipolar disorder and unaffected relatives.

OBJECTIVES: Behavioral deficits in the Theory of Mind (ToM) have been robustly demonstrated in bipolar disorder. These deficits may represent an intermediate phenotype of the disease. The aim of this study was: (i) to investigate alterations in neural ToM processing in euthymic patients with bipolar disorder, and (ii) to examine whether similar effects are present in unaffected relatives of patients with bipolar disorder suggesting that ToM functional activation may be, in part, due to genetic risk for the disease. METHODS: A total of 24 euthymic patients with bipolar disorder, 21 unaffected first-degree relatives, and 81 healthy controls completed a ToM task while undergoing functional magnetic resonance imaging. RESULTS: We observed reduced bilateral activation of the temporoparietal junction (TPJ) and diminished functional fronto-temporoparietal connectivity in patients compared to controls. Relatives tended towards intermediate temporoparietal activity and functional coupling with medial prefrontal areas. There was also evidence for a potentially compensatory enhanced recruitment of the right middle temporal gyrus and stronger connectivity between this region and the medial prefrontal cortex in relatives. CONCLUSIONS: These findings provide further evidence of altered neural ToM processing in euthymic patients with bipolar disorder. Further, our findings in relatives lend support to the idea that altered ToM processing may act as an intermediate phenotype of the disorder.

Genetic variation in CYP3A43 is associated with response to antipsychotic medication.

Genetic variation in cytochrome enzymes is known to affect drug metabolism and influence treatment response. Recently, the rs472660 variant in CYP3A43 has been associated with olanzapine response and clearance. In this study, we investigated the impact of rs472660 and the putatively functional marker rs680055 on antipsychotic response. We genotyped the rs472660 and rs680055 single nucleotide polymorphisms (SNPs) in N = 152 schizophrenia patients of European descent collected at two sample sites who were predominately treated with second generation antipsychotics for up to 6 months. Treatment response was assessed prospectively using Brief Psychiatric Rating Scale (BPRS) scores. Statistical analysis was performed using Chi square and analysis of covariance. The rs680055 SNP was significantly associated with treatment response. Carriers of the minor allele had significantly lower BPRS scores at study end (p = 5.9 × 10(-4)) with 8 % of the variance being explained by rs680055 genotype. Post hoc analyses revealed that this effect was present in both samples and in both genders. The rs472660 SNP was also associated with response (p = 0.027); however, this finding was not significant after multiple test correction. This is the first evidence that the rs680055 missense mutation influences antipsychotic response. Although our finding for rs472660 was only a non-significant trend after correction, our results still support the notion that this SNP may play a role in antipsychotic response. Despite the fact that the functional role of CYP3A43 in antipsychotic metabolism is not fully understood yet, our study provides an important contribution to understanding genetic factors of antipsychotic response.

Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates.

INTRODUCTION: Advances in image segmentation of magnetic resonance images (MRI) have demonstrated that multi-atlas approaches improve segmentation over regular atlas-based approaches. These approaches often rely on a large number of manually segmented atlases (e.g. 30-80) that take significant time and expertise to produce. We present an algorithm, MAGeT-Brain (Multiple Automatically Generated Templates), for the automatic segmentation of the hippocampus that minimises the number of atlases needed whilst still achieving similar agreement to multi-atlas approaches. Thus, our method acts as a reliable multi-atlas approach when using special or hard-to-define atlases that are laborious to construct. METHOD: MAGeT-Brain works by propagating atlas segmentations to a template library, formed from a subset of target images, via transformations estimated by nonlinear image registration. The resulting segmentations are then propagated to each target image and fused using a label fusion method. We conduct two separate Monte Carlo cross-validation experiments comparing MAGeT-Brain and basic multi-atlas whole hippocampal segmentation using differing atlas and template library sizes, and registration and label fusion methods. The first experiment is a 10-fold validation (per parameter setting) over 60 subjects taken from the Alzheimer's Disease Neuroimaging Database (ADNI), and the second is a five-fold validation over 81 subjects having had a first episode of psychosis. In both cases, automated segmentations are compared with manual segmentations following the Pruessner-protocol. Using the best settings found from these experiments, we segment 246 images of the ADNI1:Complete 1Yr 1.5 T dataset and compare these with segmentations from existing automated and semi-automated methods: FSL FIRST, FreeSurfer, MAPER, and SNT. Finally, we conduct a leave-one-out cross-validation of hippocampal subfield segmentation in standard 3T T1-weighted images, using five high-resolution manually segmented atlases (Winterburn et al., 2013). RESULTS: In the ADNI cross-validation, using 9 atlases MAGeT-Brain achieves a mean Dice's Similarity Coefficient (DSC) score of 0.869 with respect to manual whole hippocampus segmentations, and also exhibits significantly lower variability in DSC scores than multi-atlas segmentation. In the younger, psychosis dataset, MAGeT-Brain achieves a mean DSC score of 0.892 and produces volumes which agree with manual segmentation volumes better than those produced by the FreeSurfer and FSL FIRST methods (mean difference in volume: 80 mm(3), 1600 mm(3), and 800 mm(3), respectively). Similarly, in the ADNI1:Complete 1Yr 1.5 T dataset, MAGeT-Brain produces hippocampal segmentations well correlated (r>0.85) with SNT semi-automated reference volumes within disease categories, and shows a conservative bias and a mean difference in volume of 250 mm(3) across the entire dataset, compared with FreeSurfer and FSL FIRST which both overestimate volume differences by 2600 mm(3) and 2800 mm(3) on average, respectively. Finally, MAGeT-Brain segments the CA1, CA4/DG and subiculum subfields on standard 3T T1-weighted resolution images with DSC overlap scores of 0.56, 0.65, and 0.58, respectively, relative to manual segmentations. CONCLUSION: We demonstrate that MAGeT-Brain produces consistent whole hippocampal segmentations using only 9 atlases, or fewer, with various hippocampal definitions, disease populations, and image acquisition types. Additionally, we show that MAGeT-Brain identifies hippocampal subfields in standard 3T T1-weighted images with overlap scores comparable to competing methods.

A framework for a brain-derived nosology of psychiatric disorders.

Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.

Exploratory study on association of genetic variation in TBC1D1 with antipsychotic-induced weight gain.

BACKGROUND: Previous studies have shown that antipsychotics with high propensity for antipsychotic-induced weight gain (AIWG) influence glucose transporter type 4 (GLUT4) mediated glucose intake. Variation in the gene encoding TBC1 domain family member 1 (TBC1D1), a Rab-GTPase activating protein regulating GLUT4 trafficking, has been associated with obesity. Therefore, we investigated the impact of TBC1D1 polymorphisms on AIWG. METHODS: We analyzed rs9852 and rs35859249 in TBC1D1 in 195 schizophrenia subjects treated mostly with clozapine or olanzapine for up to 14 weeks. Association was tested using analysis of variance and analysis of covariance with change (%) from baseline weight as the dependent variable. RESULTS: Analysis of covariance showed a non-significant trend for lower weight gain in carriers of the T-allele of rs9852 than in C-allele homozygotes (p = 0.063). This effect was more pronounced in the subgroup of patients treated with clozapine or olanzapine (p = 0.024). For rs35859249, no significant association with AIWG could be detected. CONCLUSIONS: This is the first study examining the association between TBC1D1 and AIWG. The moderate association of rs9852, located in the 3'UTR near a miRNA binding site, indicates an influence of TBC1D1 on AIWG. Further investigations remain necessary to elucidate the role of this gene in AIWG.

Diurnal oscillations of MRI metrics in the brains of male participants.

Regulation of biological processes according to a 24-hr rhythm is essential for the normal functioning of an organism. Temporal variation in brain MRI data has often been attributed to circadian or diurnal oscillations; however, it is not clear if such oscillations exist. Here, we provide evidence that diurnal oscillations indeed govern multiple MRI metrics. We recorded cerebral blood flow, diffusion-tensor metrics, T1 relaxation, and cortical structural features every three hours over a 24-hr period in each of 16 adult male controls and eight adult male participants with bipolar disorder. Diurnal oscillations are detected in numerous MRI metrics at the whole-brain level, and regionally. Rhythmicity parameters in the participants with bipolar disorder are similar to the controls for most metrics, except for a larger phase variation in cerebral blood flow. The ubiquitous nature of diurnal oscillations has broad implications for neuroimaging studies and furthers our understanding of the dynamic nature of the human brain.

Effects of urban living environments on mental health in adults.

Urban-living individuals are exposed to many environmental factors that may combine and interact to influence mental health. While individual factors of an urban environment have been investigated in isolation, no attempt has been made to model how complex, real-life exposure to living in the city relates to brain and mental health, and how this is moderated by genetic factors. Using the data of 156,075 participants from the UK Biobank, we carried out sparse canonical correlation analyses to investigate the relationships between urban environments and psychiatric symptoms. We found an environmental profile of social deprivation, air pollution, street network and urban land-use density that was positively correlated with an affective symptom group (r = 0.22, Pperm < 0.001), mediated by brain volume differences consistent with reward processing, and moderated by genes enriched for stress response, including CRHR1, explaining 2.01% of the variance in brain volume differences. Protective factors such as greenness and generous destination accessibility were negatively correlated with an anxiety symptom group (r = 0.10, Pperm < 0.001), mediated by brain regions necessary for emotion regulation and moderated by EXD3, explaining 1.65% of the variance. The third urban environmental profile was correlated with an emotional instability symptom group (r = 0.03, Pperm < 0.001). Our findings suggest that different environmental profiles of urban living may influence specific psychiatric symptom groups through distinct neurobiological pathways.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain.

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.