Biology subjects

Holmes, A. J.

Publications and source records attributed to Holmes, A. J..

34 records · Page 2Linked to original sources

bioRxiv2023

In-vivo whole-cortex estimation of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth

A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here we non-invasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically-plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio marker is sensitive to the GABA-agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then investigate the relationship between the E/I ratio marker and neurodevelopment. We find that the E/I ratio marker declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio marker (especially in association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 years old) and Asian (7.2 to 7.9 years old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our findings open the door to studying how disrupted E/I trajectories may lead to cognitive dysfunction in psychopathology that emerges during youth. SignificanceHealthy brain function requires a delicate balance of neural excitation (E) and inhibition (I). In animals, this balance - the E/I ratio - is known to decrease with the maturation of inhibitory circuitry during healthy development. However, in humans, the normative development of cortex-wide E/I ratio remains unclear. Here, we use a biophysical model and non-invasive brain scans to estimate a marker of E/I ratio. Spatial changes in our E/I ratio marker are consistent with a drug that decreases E/I ratio. We also find that our cortex-wide E/I ratio marker decreases during development. Furthermore, North American and Asian children with lower E/I ratio, especially in higher-order cortex, have better cognitive performance. Overall, E/I ratio is a potential index of healthy neurocognitive development.

neuroscience↗

bioRxiv2022

Atypical language network lateralization is reflected throughout the macroscale functional organization of cortex

Hemispheric specialization is a fundamental feature of human brain organization. However, it is not yet clear to what extent the lateralization of specific cognitive processes may be evident throughout the broad functional architecture of cortex. While the majority of people exhibit left-hemispheric language dominance, a substantial minority of the population shows reverse lateralization. Using twin and family data from the Human Connectome Project, we provide evidence that atypical language dominance is associated with global shifts in cortical organization. Individuals with atypical language organization exhibited corresponding hemispheric differences in the macroscale functional gradients that situate discrete large-scale networks along a continuous spectrum, extending from unimodal through association territories. Analyses revealed that both language lateralization and gradient asymmetries are, in part, driven by genetic factors. These findings pave the way for a deeper understanding of the origins and relationships linking population-level variability in hemispheric specialization and global properties of cortical organization.

neuroscience↗

bioRxiv2022

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local- global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (GITHUB_LINK).

neuroscience↗

bioRxiv2022

There is no fundamental trade-off between prediction accuracy and feature importance reliability

There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.

neuroscience↗

bioRxiv2020

The 3D architecture and molecular foundations of de novo centriole assembly via bicentrioles

Abstract/SummaryCentrioles are structurally conserved organelles, composing both centrosomes and cilia. In animal cycling cells, centrioles often form through a highly characterized process termed canonical duplication. However, a large diversity of eukaryotes form centrioles de novo through uncharacterized pathways. This unexplored diversity is key to understanding centriole assembly mechanisms and how they evolved to assist specific cellular functions. Here, combining electron microscopy and tomography, we show that during spermatogenesis of the moss Physcomitrium patens, centrioles are born as a co-axially oriented centriole pair united by a cartwheel. We observe that microtubules emanate from those bicentrioles, which localize to the spindle poles during cell division. Thereafter, each bicentriole breaks apart, and the two resulting sister centrioles mature asymmetrically, elongating specific microtubule triplets and a naked cartwheel. Subsequently, two cilia are assembled which are capable of beating asynchronously. We further show that conserved cartwheel and centriole wall components, SAS6, BLD10 and POC1 are expressed during spermatogenesis and are required for this de novo biogenesis pathway. Our work supports a scenario where centriole biogenesis is more diverse than previously thought and that conserved molecular modules underlie diversification of this essential pathway.

cell biology↗

bioRxiv2020

Meta-matching: a simple framework to translate phenotypic predictive models from big to small data

There is significant interest in using brain imaging data to predict non-brain-imaging phenotypes in individual participants. However, most prediction studies are underpowered, relying on less than a few hundred participants, leading to low reliability and inflated prediction performance. Yet, small sample sizes are unavoidable when studying clinical populations or addressing focused neuroscience questions. Here, we propose a simple framework - "meta-matching" - to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in boutique studies. The key observation is that many large-scale datasets collect a wide range inter-correlated phenotypic measures. Therefore, a unique phenotype from a boutique study likely correlates with (but is not the same as) some phenotypes in some large-scale datasets. Meta-matching exploits these correlations to boost prediction in the boutique study. We applied meta-matching to the problem of predicting non-brain-imaging phenotypes using resting-state functional connectivity (RSFC). Using the UK Biobank (N = 36,848), we demonstrated that meta-matching can boost the prediction of new phenotypes in small independent datasets by 100% to 400% in many scenarios. When considering relative prediction performance, meta-matching significantly improved phenotypic prediction even in samples with 10 participants. When considering absolute prediction performance, meta-matching significantly improved phenotypic prediction when there were least 50 participants. With a growing number of large-scale population-level datasets collecting an increasing number of phenotypic measures, our results represent a lower bound on the potential of meta-matching to elevate small-scale boutique studies.

neuroscience↗

bioRxiv2020

Hidden population modes in social brain morphology: Its parts are more than its sum

The complexity of social interactions is a defining property of the human species. Many social neuroscience experiments have sought to map perspective taking, empathy, and other canonical psychological constructs to distinguishable brain circuits. This predominant research paradigm was seldom complemented by bottom-up studies of the unknown sources of variation that add up to measures of social brain structure; perhaps due to a lack of large population datasets. We aimed at a systematic de-construction of social brain morphology into its elementary building blocks in the UK Biobank cohort (n=~10,000). Coherent patterns of structural co-variation were explored within a recent atlas of social brain locations, enabled through translating autoencoder algorithms from deep learning. The artificial neural networks learned rich subnetwork representations that became apparent from social brain variation at population scale. The learned subnetworks carried essential information about the co-dependence configurations between social brain regions, with the nucleus accumbens, medial prefrontal cortex, and temporoparietal junction embedded at the core. Some of the uncovered subnetworks contributed to predicting examined social traits in general, while other subnetworks helped predict specific facets of social functioning, such as feelings of loneliness. Our population-level evidence indicates that hidden subsystems of the social brain underpin interindividual variation in dissociable aspects of social lifestyle.

neuroscience↗

bioRxiv2020

Connectivity patterns of task-specific brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and link to molecular architecture

BackgroundDespite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology may be reflected in variability across the collective set of functional brain connections. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. MethodsWe investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior molecular imaging in healthy populations. ResultsTen-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. ConclusionsWe revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, the present work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.

neuroscience↗

bioRxiv2020

Shared and unique brain network features predict cognition, personality and mental health in childhood

The manner through which individual differences in brain network organization track population-level behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, the focus of most studies on single behavioral traits has come at the expense of capturing broader relationships across behaviors. Here, we utilized a large-scale dataset of 1858 typically developing children to estimate whole-brain functional network organization that is predictive of individual differences in cognition, impulsivity-related personality, and mental health during rest and task states. Predictive network features were distinct across the broad behavioral domains: cognition, personality and mental health. On the other hand, traits within each behavioral domain were predicted by highly similar network features. This is surprising given decades of research emphasizing that distinct brain networks support different mental processes. Although tasks are known to modulate the functional connectome, we found that predictive network features were similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood, yet are unique to different behavioral domains.

neuroscience↗

bioRxiv2020

Convergent molecular, cellular, and neural signatures of major depressive disorder

Major depressive disorder emerges from the complex interactions of biological systems that span across genes and molecules through cells, circuits, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to the onset and maintenance of depression requires a multi-scale approach, encompassing measures of brain structure and function as well as genetic and cell-specific genomic data. Here, we examined anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Genome Superstruct Project, and ENIGMA (combined N[≥]23,723). Integrative analyses incorporated measures of cortical gene expression, post-mortem patient transcriptional data, depression GWAS, and single-cell transcription. Neuroimaging correlates of depression and negative affect were consistent across the three independent datasets. Linking ex-vivo gene downregulation with in-vivo neuroimaging, we found that genomic correlates of depression-linked neuroimaging phenotypes tracked gene downregulation in post-mortem cortical tissue samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data implicated somatostatin interneurons and astrocytes as consistent cell associates of depression, through both in-vivo imaging and ex-vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multi-scale approaches, the genomic correlates of depression-linked brain function and structure were enriched for known and novel disorder relevant molecular pathways. These findings bridge across levels to connect specific genes, cell classes, and biological pathways to in-vivo imaging correlates of depression. Key FindingsO_LIMajor depressive disorder and negative affect are associated with replicable profiles of cortical anatomy and function across independent population-level neuroimaging datasets (combined N[≥]23,723). C_LIO_LISomatostatin interneurons are consistent spatial transcriptional associates of in-vivo depression-linked imaging phenotypes. C_LIO_LIIntegrative single-cell gene expression analysis associate somatostatin interneurons and astrocytes with both in-vivo depression-linked imaging and ex-vivo gene downregulation in independent MDD cortical tissue samples. C_LIO_LITranscriptional correlates of in-vivo depression imaging phenotypes selectively capture gene downregulation in post-mortem tissue samples from patients with depression, but not other psychiatric disorders. C_LIO_LIIndicating that some cell classes are preferentially sensitive to inherited disease liability, genome-wide risk for depression is enriched among interneurons, but not glia. C_LIO_LIGene associates of depression-linked anatomy and function identify specific neurotransmitter systems, molecular signaling pathways, and receptors, suggesting possible targets for pharmaceutical intervention. C_LI

neuroscience↗

bioRxiv2019

Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

BackgroundThere is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thus neglecting the potential influence of huge swaths of the brain.\n\nMethodsA multivariate data-driven approach (partial least squares; PLS) was utilized to identify latent components linking a large set of clinical, cognitive and personality measures to whole-brain resting-state functional connectivity (RSFC) patterns across 224 participants. The participants were either healthy (N=110) or diagnosed with bipolar disorder (N=40), attention-deficit/hyperactivity disorder (N=37), schizophrenia (N=29) or schizoaffective disorder (N=8). In contrast to traditional case-control analyses, the diagnostic categories were not utilized in the PLS analysis, but were helpful for interpreting the components.\n\nResultsOur analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction and impulsivity. Each component was associated with a unique whole-brain RSFC signature and shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network, and its connectivity with subcortical structures and cortical executive networks.\n\nConclusionsWe identified three distinct dimensions with dissociable (but overlapping) whole-brain RSFC signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span across diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.

neuroscience↗

bioRxiv2019

Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR. HighlightsO_LIGlobal signal regression improves RSFC-behavior associations C_LIO_LIGlobal signal regression improves RSFC-based behavioral prediction accuracies C_LIO_LIImprovements replicated across two large-scale datasets and methods C_LIO_LITask-performance measures enjoyed greater improvements than self-reported ones C_LIO_LIGSR beneficial even after ICA-FIX C_LI

neuroscience↗

bioRxiv2019

Relationships between depressive symptoms and brain responses during emotional movie viewing emerge in adolescence

Affective disorders such as major depression are common but serious illnesses characterized by altered processing of emotional information. Although the frequency and severity of depressive symptoms increase dramatically over the course of childhood and adolescence, much of our understanding of their neurobiological bases comes from work characterizing adults responses to static emotional information. As a consequence, relationships between depressive brain phenotypes and naturalistic emotional processing, as well as the manner in which these associations emerge over the lifespan, remain poorly understood. Here, we apply static and dynamic inter-subject correlation analyses to examine how brain function is associated with clinical and non-clinical depressive symptom severity in 112 children and adolescents (7-21 years old) who viewed an emotionally evocative clip from the film Despicable Me during functional MRI. Our results reveal that adolescents with greater depressive symptom severity exhibit atypical fMRI responses during movie viewing, and that this effect is stronger during less emotional moments of the movie. Furthermore, adolescents with more similar item-level depressive symptom profiles showed more similar brain responses during movie viewing. In contrast, childrens depressive symptom severity and profiles were unrelated to their brain response typicality or similarity. Together, these results indicate a developmental change in the relationships between brain function and depressive symptoms from childhood through adolescence. Our findings suggest that depressive symptoms may shape how the brain responds to complex emotional information in a dynamic manner sensitive to both developmental stage and affective context.

neuroscience↗

bioRxiv2018

The transcriptional landscape of cortical interneurons underlies in-vivo brain function and schizophrenia risk

Inhibitory interneurons orchestrate information flow across cortex and are implicated in psychiatric illness. Although classes of interneurons have unique functional properties and spatial distributions throughout the brain, the relative influence of interneuron subtypes on brain function, cortical specialization, and illness risk remains elusive. Here, we demonstrate stereotyped organizational properties of somatostatin and parvalbumin related transcripts within human and non-human primates. Interneuron spatial distributions recapitulate cortico-striato-thalamic functional networks and track regional differences in functional MRI signal amplitude. In the general population (n=9,627), parvalbumin-linked genes account for an enriched proportion of genome-wide heritable variance in in-vivo functional MRI signal amplitude. This relationship is spatially dependent, following the topographic organization of parvalbumin expression in independent post-mortem brain tissue. Finally, genetic risk for schizophrenia is enriched among interneuron-linked genes and predictive of cortical signal amplitude in parvalbumin-biased regions. These data indicate that the molecular genetic basis of resting-state brain function across cortex is shaped by the spatial distribution of interneuron-related transcripts and underlies individual differences in risk for schizophrenia.\n\nKey FindingsO_LISpatial distributions of somatostatin (SST) and parvalbumin (PVALB) are negatively correlated in mature human and non-human primate cortex, paralleling patterns observed in utero.\nC_LIO_LISST and PVALB are differentially expressed within distinct limbic and somato/motor cortico-striato-thalamic networks, respectively.\nC_LIO_LIIn-vivo resting-state signal amplitude is heritable in the general population and tracks relative SST/PVALB expression across cortex.\nC_LIO_LISingle-nucleotide polymorphisms tied to PVALB-related genes account for an enriched proportion of the heritable variance in resting-state signal amplitude.\nC_LIO_LIPVALB-mediated heritability of resting-state signal amplitude in the general population is spatially heterogeneous, mirroring the cortical expression of PVALB in independent post-mortem brain tissue.\nC_LIO_LIPolygenic risk for schizophrenia is enriched among interneuron-linked genes and predicts resting-state signal amplitude in a manner that also follows the cortical expression of PVALB.\nC_LI

neuroscience↗

bioRxiv2018

Cultivation of 146 Clinical Strains of Nocardia species

Nocardia species generally grow well on culture media in contrast to limited metabolic activities. Yet there are rare studies that have examined systematically the effect of culture media on the clinical recovery of these Gram-positive branching bacilli, particularly in relation to many recently described or revised Nocardia species. We analyzed 146 clinical Nocardia strains, of 24 species, recovered at the University of Texas M. D. Anderson Cancer Center from 2002-2018 for their growth on culture media. Among media setups to culture routine bacteria, fungi, and acid-fast bacilli (AFB), the AFB media alone recovered 35 strains as did fungal media with 26 strains and routine media with 10 strains. Seventy five strains were recovered on two and three media setups. On AFB media most Nocardia nova strains (24 of 29) grew, significantly more than all other strains did. Likewise, all four strains of Nocardia mikamii were cultivated solely on AFB media. Fungal media yielded all six strains of Nocardia wallacei that were resistant to tobramycin and/or amikacin; this growth could be explained by the presence of gentamycin in one fungal medium. Specimen source also affected the recovery: more Nocardia strains grew on every media setup from normally sterile specimens than from nonsterile specimens. These results suggest that diverse culture media support the growth of many Nocardia species. The better recoveries of these organisms from AFB media and fungal media are likely attributable to decontamination of specimens on AFB culture, incorporation of selective antimicrobial agents into the media, and prolonged incubation.

microbiology↗

bioRxiv2017

Genetic Architecture of Subcortical Brain Structures in Over 40,000 Individuals Worldwide

Subcortical brain structures are integral to motion, consciousness, emotions, and learning. We identified common genetic variation related to the volumes of nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen, and thalamus, using genome-wide association analyses in over 40,000 individuals from CHARGE, ENIGMA and the UK-Biobank. We show that variability in subcortical volumes is heritable, and identify 25 significantly associated loci (20 novel). Annotation of these loci utilizing gene expression, methylation, and neuropathological data identified 62 candidate genes implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.

genetics↗