Biology subjects

Lerch, J.

Publications and source records attributed to Lerch, J..

18 recordsLinked to original sources

bioRxiv2026

A hippocampal neuroimaging signature of neurovascular insulin signalling links metabolism to mood

Depression is a leading cause of global disability and is increasingly linked to systemic metabolic dysfunctions, including insulin resistance. However, the biological pathways connecting metabolic state to affective symptoms are unresolved. A growing body of evidence indicates that insulin, beyond its role in systemic glucose homeostasis, supports brain metabolism, synaptic function and cognition, yet its contribution to mood regulation remains unclear. Here, we identify a human hippocampal metabolic signature associated with glycaemic variation in individuals with depression. Using multimodal neuroimaging, we show that hippocampal concentrations of GABA and lactate correlate with HbA1c and with mood severity, while functional connectivity between the hippocampus and the default mode network tracks affective symptoms independently of adiposity. To causally probe these relationships, we generated a mouse model of hippocampus-specific insulin receptor depletion. Unexpectedly, reducing insulin signalling at the blood brain barrier enhanced neuronal metabolism and attenuated anxiety-like behaviour. Notably, hippocampal lactate and GABA levels similarly tracked anxiety-related behaviour in mice, mirroring their association with symptom severity in humans. Together, these findings identify a conserved neurometabolic signature linking hippocampal insulin signalling to affective state, and reveal that brain insulin resistance exerts context- and cell-type-specific effects on behaviour. This work establishes a mechanistic basis for brain insulin resistance in depression and highlights the hippocampus as a critical hub for metabolic modulation of mood.

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bioRxiv2026

Charting developmental trajectories of dynamic brain networks during emotional face processing

Emotional face processing is a critical component in the development of social cognition through childhood. The neural mechanisms supporting this development can be understood by tracking age-related changes in brain activity. Prior work in this area with MEG relies on static, bandlimited, or region-specific measures, which do not capture the dynamic, distributed nature of brain activity. Here we used Dynamic Network Modes (DyNeMo) to analyze MEG data from a large cohort of typically developing individuals (N=224, ages 5-40) during an emotional faces vigilance task. DyNeMo is a data-driven, generative modelling approach which captures functional networks as a set of whole-brain spatiospectral "modes" whose relative mixture (i.e., activation levels) can change dynamically in response to stimuli. We inferred six modes from the MEG data and characterized developmental trajectories in task-related activation and mode connectivity. Across modes we observed distinct developmental trajectories in both measures. With respect to mode activation, a visual mode and a frontotemporal mode (whose labels reflect their respective spatial profiles) increased nonlinearly with age; meanwhile, activation in temporal and sensorimotor modes decreased linearly with age. Meanwhile, connectivity broadly increased with age in all modes, but with different degrees of nonlinearity. These results suggest developmental dissociations between different modes (e.g., visual versus sensorimotor), as well as within individual modes (task-related mode activation versus connectivity). These results provide a comprehensive and complex picture of functional network development underlying emotional face processing. Significance StatementBrain networks supporting social cognition undergo profound changes from childhood through adolescence to adulthood. However, current understanding of how these networks develop has been limited by conventional analyses of brain imaging data, which typically provide a static picture of brain activity. Here we leveraged a cutting-edge, data-driven modeling approach (DyNeMo) to characterize age-related changes in distributed and dynamic networks supporting emotional face processing, in a large cohort of children and young adults (N=224). We inferred six functional networks whose activation levels changed rapidly in response to emotional faces. Network activation and connectivity exhibited profound and distinct non-linear changes with age, indicating that emotional face processing is supported by complex interactions among multiple dynamic networks, each with different maturational trajectories.

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bioRxiv2026

Non-invasive measurement of neurotransmitter-specific glucose metabolism in the human brain using proton-observed proton-edited 13C-MRS (POPE13C-MRS)

Non-invasive measurement of neurotransmitter-specific glucose metabolism in the human brain remains a major challenge, limiting mechanistic insight into excitatory-inhibitory imbalance across neurological and psychiatric disorders. Existing approaches either lack neurotransmitter specificity or require specialised hardware that constrains clinical applicability. Here, we introduce proton-observed proton-edited 13C magnetic resonance spectroscopy (POPE13C-MRS), an approach that enables non-invasive detection of glutamate, GABA, and lactate metabolism using standard proton MRI hardware. The method combines administration of 13C-labelled glucose with targeted proton editing to detect 1H-13C satellite resonances. Using a cross-species proof-of-concept approach, we demonstrate consistent detection of neurotransmitter-specific 13C labelling in mice and feasibility in the human brain at ultra-high field. POPE13C-MRS provides a readout of glutamatergic and GABAergic metabolism, enabling in vivo assessment of excitatory-inhibitory metabolic balance. This establishes a means for probing neurometabolic coupling with potential applications in translational and clinical neuroscience.

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bioRxiv2025

Relative contribution of gonads and sex chromosomes to sex differences in cell-type gene expression in the mouse medial septum and sex-biased disease risk

BackgroundWhile sex differences in the brain have traditionally been attributed to gonadal hormones, emerging evidence points to regulation by sex chromosomes. This study aims to differentiate the influence of gonads versus sex chromosomes on cellular gene expression in the mouse medial septum (MS), a critically understudied brain region. MethodsUsing single nucleus RNA-sequencing and the Sex Chromosome Trisomy mouse model, we (1) quantified sex differences in cellular gene expression and (2) isolated sex-biasing effects by identifying perturbed cell types, differentially expressed genes, biological pathways, and gene networks, which were integrated with GWAS data to explore links with sex-biased human phenotypes. ResultsOur analysis revealed that volumetric sex differences in the MS are mirrored by widespread transcriptomic changes across cell types. Critically, genetic effects displayed elevated relevance compared to sex hormones in driving sex-biased gene expression. These effects converge to regulate synaptic/neuronal development, transcriptional regulation, and cellular metabolism. Sex chromosome-associated DEGs were enriched for various human disorders, suggesting a cellular and mechanistic basis for their sex-biased patterns. ConclusionsOur findings challenge the classical gonad-centric views of sexual differentiation, as the MS displays sex-biased transcriptional regulation driven by sex chromosome-associated effects that are highly relevant for human health.

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bioRxiv2025

Fractionation of sex differences in human cortical anatomy

Humans show reproducible sex differences in regional cortical volume (CV), but it remains unclear how these arise from underlying sex-biases in the two biologically dissociable determinants of CV: surface area (SA) and cortical thickness (CT). Moreover, limited access to experimental methods in humans has hindered direct studies of the causal drivers of regional sex differences in the human cortex, although rodent models have argued for both chromosomal and gonadal contributions to sex-biased mammalian cortical development. Here, we first use structural neuroimaging data in two independent human cohorts (combined N=1,754; 967 females) to quantify and spatially resolve the differential contributions of SA and CT to observed sex differences in CV. These dissociable facets of sex-biased cortical organization are highly reproducible and align with distinct functional networks and histo-molecular signatures. We then leverage complementary neuroimaging data in clinical case-control cohorts (combined N=313) featuring variations in X and Y chromosome dosage (sex chromosome aneuploidies) and testicular hormone production (isolated GnRH deficiency) to establish that regions of sex-biased CV, SA and CT in humans are enriched for congruent anatomical effects of X-chromosome dosage (e.g., primary sensory and insular cortices) and gonadal hormones (e.g. dorsomedial frontal and temporo-parietal-occipital regions). Taken together, these findings substantially advance both the breadth and granularity of our understanding regarding sex-biased cortical organization in humans - disambiguating sex effects on regional CV, SA and CT and nominating their potential genetic and endocrine causes.

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bioRxiv2025

Biological subtyping of autism via cross-species fMRI

It is frequently assumed that the phenotypic heterogeneity in autism spectrum disorder reflects underlying pathobiological variation. However, direct evidence in support of this hypothesis is lacking. Here, we leverage cross-species functional neuroimaging to examine whether variability in brain functional connectivity reflects distinct biological mechanisms. We find that fMRI connectivity alterations in 20 distinct mouse models of autism (n=549 individual mice) can be clustered into two prominent hypo- and hyperconnectivity subtypes. We show that these connectivity profiles are linked to distinct signaling pathways, with hypoconnectivity being associated with synaptic dysfunction, and hyperconnectivity reflecting transcriptional and immune-related alterations. Extending these findings to humans, we identify analogous hypo- and hyperconnectivity subtypes in a large, multicenter resting state fMRI dataset of n=940 autistic and n=1036 neurotypical individuals. Remarkably, hypo- and hyperconnectivity autism subtypes are replicable across independent cohorts (accounting for 25.1% of all autism data), exhibit distinct functional network architecture, are behaviorally dissociable, and recapitulate synaptic and immune mechanisms identified in corresponding mouse subtypes. Our cross-species investigation, thus, decodes the heterogeneity of fMRI connectivity in autism into distinct pathway-specific etiologies, offering a new empirical framework for targeted subtyping of autism.

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bioRxiv2025

Decoding the neural basis of sensory phenotypes in Autism

BackgroundDifferences in sensory processing are a defining characteristic of autism, affecting up to 87% of autistic individuals. These differences cause widespread perceptual changes that can negatively impact cognition, development, and daily functioning. Recent research identified five sensory processing phenotypes with varied behavioural presentations; however, their neural basis remains unclear. This study aims to ground these sensory phenotypes in unique patterns of functional connectivity. MethodsWe analyzed data from 146 autistic participants in the Province of Ontario Neurodevelopmental Network. We classified participants into sensory phenotypes using k-means clustering of scores from the Short Sensory Profile. We then computed a connectivity matrix from 200 cortical and 32 subcortical regions and calculated graph-theoretic measures (betweenness centrality, strength, local efficiency, clustering coefficient) to assess information exchange between these regions. We then trained machine learning models to use these measures to classify between all pairs of sensory phenotypes. ResultsWe replicated that our sample of autistic participants was best categorized into five sensory phenotypes. The machine learning models distinguished 7/10 phenotype pairs using graph-theoretic measures (p < 0.005). Information exchange within and between the somatomotor network, orbitofrontal cortex, posterior parietal cortex, prefrontal cortex and subcortical areas were highly predictive of sensory phenotype. ConclusionsThis study shows that distinct sensory phenotypes in autism correspond with unique patterns of functional connectivity. Cortical, subcortical, and network-level connectivity all play a role in shaping distinct sensory processing styles in autism. These findings lay the groundwork for understanding these phenotypes and highlight opportunities to develop interventions in cases of maladaptive sensory processing.

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bioRxiv2024

In vivo quantification of creatine kinase kinetics in mouse brain using 31P-MRS at 7 Tesla

31P-MRS is a method of choice for studying neuroenergetics in vivo, but its application in the mouse brain have been limited, often restricted to ultra-high field (>7 Tesla) MRI scanners. Establishing its feasibility on more readily available preclinical 7 Tesla (T) scanners would create new opportunities to study metabolism and physiology in murine models of brain disorders. Here, we demonstrate that the apparent forward rate constant (kf) of creatine kinase (CK) can be accurately quantified using a progressive saturation-transfer approach in the mouse brain at 7T. We also find that a reduction of approximately 20% in the breathing rate of anesthetized mice can lead to a 36% increase in kf attributable to a drop in intracellular pH and mitochondrial ATP production. To achieve this, we used a test-retest analysis to assess the reliability and repeatability of 31P-MRS acquisition, analysis and experimental design protocols. We report that most 31P-containing metabolites can be reliably measured using a localized 3D-ISIS sequence, which showed highest SNR amplitude, SNR consistency and minimal T2 relaxation signal loss. Using this protocol, our study identifies, for the first time, key physiological factors influencing mouse brain energy homeostasis in vivo and provides a methodological basis that will guide future studies interested in implementing 31P-MRS on preclinical 7T scanners.

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bioRxiv2023

Diffusion MRS tracks distinct trajectories of neuronal development in the cerebellum and thalamus of rat neonates.

It is currently impossible to non-invasively assess cerebellar cell structure during early development. Here we propose a novel approach to non-invasively and longitudinally track cell-specific development using diffusion-weighted magnetic resonance spectroscopy in combination with microstructural modelling. Tracking metabolite diffusion allows us to probe cell-specific developmental trajectories in the cerebellum and thalamus of healthy rat neonates from post-natal day (P) 5 to P30. Additionally, by comparing different analytical and biophysical microstructural models we can follow the differential contribution of cell bodies and neurites during development. The thalamus serves as a control region to assess the sensitivity of our method to microstructural differences between the regions. We found significant differences between cerebellar and thalamic metabolites diffusion properties. For most metabolites, the signal attenuation is stronger in the thalamus, suggesting less restricted diffusion compared to the cerebellum. There is also a trend for lower signal attenuation and lower ADCs with increasing age, suggesting increasing restriction of metabolite diffusion. This is particularly striking for taurine in the thalamus. We use biophysical modelling to interpret these differences. We report a decreased sphere fraction (or an increased neurite fraction) with age for taurine and total creatine in the cerebellum, marking dendritic growth. Surprisingly, we also report a U-shape trend for segment length (the distance between two embranchments in a dendritic tree) in the cerebellum agreeing with age-matching morphometry of openly available 3D-Purkinje reconstructions. Results demonstrate that diffusion-weighted MRS probes early cerebellar neuronal development non-invasively.

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bioRxiv2023

FUSDelta14 mutation impairs normal brain development and causes systemic metabolic alterations

FUS (Fused in sarcoma) is a ubiquitously expressed DNA/RNA binding protein. Mutations in FUS cause aggressive juvenile forms of amyotrophic lateral sclerosis (ALS), as in the case with the FUSDelta14 mutation. While most studies have focused on the role of FUS in motor neuron degeneration, little is known about the effect of FUS mutations in the whole body, and the impact of FUS mutations in the correct development of the nervous system. We studied pleiotropic phenotypes in a physiological knock-in mouse model carrying the FUSDelta14 mutation in homozygosity. RNA sequencing was conducting in six different tissues (frontal cortex, spinal cord, tibialis anterior muscle, white and brown adipose tissue and liver) to identify the genes and pathways altered by the FUSDelta14 mutant protein in the systemic transcriptome. Additionally, brain structural magnetic resonance imaging (MRI) and histological characterisation was conducted in young mice to study the role of FUS mutation in the brain development. FUS mutant protein was upregulated and mislocalised in the cytoplasm in most cells of the tissues analysed. We identified few genes commonly altered in all tissues by this mutation, although most genes and pathways affected were generally tissue-specific. Phenotypic assessment of mice revealed systemic metabolic alterations related to the pathway changes identified. MRI brain scans revealed that homozygous FUSDelta14 brains were smaller and displayed significant morphological alterations including a thinner cortex, reduced neuronal number and increased gliosis, which correlated with early cognitive impairment and fatal seizures. We demonstrated that the disease aetiology of FUS mutations can include neurodevelopmental and systemic alterations, which should be taken into consideration in the clinic.

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bioRxiv2022

Host genetics maps to behaviour and brain structure in mice

Gene-environment interactions in the postnatal period have a long-term impact on neurodevelopment. To effectively model neurodevelopment in the mouse, we incorporated several validated behavioural tests to develop a behavioural pipeline that measures translationally relevant milestones of development in mice. The behavioral phenotype of 1060 wild type and genetically-modified mice was examined in parallel with structural brain imaging. The influence of genetics, sex, and early life stress on behaviour and neuroanatomy was determined using traditional statistical and machine learning methods. The results demonstrated that neuroanatomical diversity was primarily associated with genotype whereas behavioural phenotypic diversity was observed to be more susceptible to gene-environment interactions.

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bioRxiv2021

Why do hippocampal mossy cells matter? It depends on the frequency and context.

The discrimination of similar episodes and places, and their representation as distinct memories, depend on a process called pattern separation that relies on the circuitry of the hippocampal dentate gyrus (DG). Mossy cells (MCs) are key neurons in the circuitry, but how they influence DG network dynamics, function, and seizure risk has not been fully elucidated. We found the net impact of MCs was inhibitory at physiological frequencies connected with learning and behaviour, and their absence associated with deficits in pattern separation and spatial memory; at higher frequencies, their net impact was excitatory, and their absence protected against seizures. Thus, MCs influence DG outputs in a highly dynamic manner that varies with frequency and context. One-Sentence SummaryHippocampal mossy cells are required for learning and memory; but their absence protects against seizures.

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bioRxiv2021

Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation

High resolution in situ hybridization (ISH) images of the brain capture spatial gene expression at cellular resolution. These spatial profiles are key to understanding brain organization at the molecular level. Previously, manual qualitative scoring and informatics pipelines have been applied to ISH images to determine expression intensity and pattern. To better capture the complex patterns of gene expression in the human cerebral cortex, we applied a machine learning approach. We propose gene re-identification as a contrastive learning task to compute representations of ISH images. We train our model on a ISH dataset of ~1,000 genes obtained from postmortem samples from 42 individuals. This model reaches a gene re-identification rate of 38.3%, a 13x improvement over random chance. We find that the learned embeddings predict expression intensity and pattern. To test generalization, we generated embeddings in a second dataset that assayed the expression of 78 genes in 53 individuals. In this set of images, 60.2% of genes are re-identified, suggesting the model is robust. Importantly, this dataset assayed expression in individuals diagnosed with schizophrenia. Gene and donor-specific embeddings from the model predict schizophrenia diagnosis at levels similar to that reached with demographic information. Mutations in the most discriminative gene, SCN4B, may help understand cardiovascular associations with schizophrenia and its treatment. We have publicly released our source code, embeddings, and models to spur further application to spatial transcriptomics. In summary, we propose and evaluate gene re-identification as a machine learning task to represent ISH gene expression images.

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bioRxiv2020

Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes

Autism Spectrum Disorder (ASD) is characterized by substantial, yet highly heterogeneous abnormalities in functional brain connectivity. However, the origin and significance of this phenomenon remain unclear. To unravel ASD connectopathy and relate it to underlying etiological heterogeneity, we carried out a bi-center cross-etiological investigation of fMRI-based connectivity in the mouse, in which specific ASD-relevant mutations can be isolated and modelled minimizing environmental contributions. By performing brain-wide connectivity mapping across 16 mouse mutants, we show that different ASD-associated etiologies cause a broad spectrum of connectional abnormalities in which diverse, often diverging, connectivity signatures are recognizable. Despite this heterogeneity, the identified connectivity alterations could be classified into four subtypes characterized by discrete signatures of network dysfunction. Our findings show that etiological variability is a key determinant of connectivity heterogeneity in ASD, hence reconciling conflicting findings in clinical populations. The identification of etiologically-relevant connectivity subtypes could improve diagnostic label accuracy in the non-syndromic ASD population and paves the way for personalized treatment approaches.

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bioRxiv2020

Integration of Brain and Behavior Measures for Identification of Data-Driven Groups Cutting Across Children with ASD, ADHD, or OCD

Autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD) are clinically and biologically heterogeneous neurodevelopmental disorders (NDDs). The objective of the present study was to integrate brain imaging and behavioral measures to identify new brain-behavior subgroups cutting across these disorders. A subset of the data from the Province of Ontario Neurodevelopmental Disorder (POND) Network including participants with different NDDs (aged 6-16 years) that underwent cross-sectional T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scanning on the same 3T scanner, and behavioral/cognitive assessments was used. Similarity Network Fusion was applied to integrate cortical thickness, subcortical volume, white matter fractional anisotropy (FA), and behavioral measures in 176 children with ASD, ADHD or OCD with complete data that passed quality control. Normalized mutual information (NMI) was used to determine top contributing model features. Bootstrapping, out-of-model outcome measures and supervised machine learning were each used to examine stability and evaluate the new groups. Cortical thickness in socio-emotional and attention/executive networks and inattention symptoms comprised the top ten features driving participant similarity and differences between four transdiagnostic groups. Subcortical volumes (pallidum, nucleus accumbens, thalamus) were also different among groups, although white matter FA showed limited differences. Features driving participant similarity remained stable across resampling, and the new groups showed significantly different scores on everyday adaptive functioning. Our findings open the possibility of studying new data-driven groups that represent children with NDDs more similar to each other than others within their own diagnostic group. Such new groups can be evaluated longitudinally for prognostic utility and could be stratified for clinical trials targeted toward each groups unique brain and behavioral profiles.

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bioRxiv2019

Ultra-high field imaging of hippocampal neuroanatomy in first episode psychosis demonstrates receptor-specific morphometric patterning

ObjectiveThe hippocampus is considered a putative marker in schizophrenia with early volume deficits of select subfields. Certain subregions are thought to be more vulnerable due to a glutamate-driven mechanism of excitotoxicity, hypermetabolism, and then degeneration. Here, we explored whether hippocampal anomalies in first-episode psychosis (FEP) correlate with glutamate receptor density via a serotonin receptor proxy by leveraging structural neuroimaging, spectroscopy (MRS), and gene expression.\n\nMethodsHigh field 7T brain MR images were collected from 27 control, 41 FEP participants, along with 1H-MRS measures of glutamate. Automated methods were used to delineate the hippocampus and atlases of the serotonin receptor system were used to map receptor density across the hippocampus and subfields. We used gene expression data from the Allen Human Brain Atlas to test for correlations between serotonin and glutamate receptor genes.\n\nResultsWe found reduced hippocampal volumes in FEP, replicating previous findings. Amongst the subfields, CA4-dentate gyrus showed greatest reductions. Gene expression analysis indicated 5-HTR1A and 5-HTR4 receptor subtypes as predictors of AMPA and NMDA receptor expression, respectively. Volumetric differences in the subfields correlated most strongly with 5-HT1A (R=0.64, p=4.09E-03) and 5-HT4 (R=0.54, p=0.02) densities as expected, and replicated using previously published data from two FEP studies. Measures of individual structure-receptor alignment were derived through normative modeling of hippocampal shape and correlations to receptor distributions, termed Receptor-Specific Morphometric Signatures (RSMS). Right-sided 5-HT4 RSMS was correlated with glutamate (R=0.357, p=0.048).\n\nConclusionsWe demonstrate glutamate-driven hippocampal remodeling in FEP through a receptor-density gated mechanism, thus providing a mechanistic explanation of how redox dysregulation affects brain structure and symptomatic heterogeneity in schizophrenia.

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bioRxiv2018

Longitudinally Mapping Childhood Socioeconomic Status Associations with Cortical and Subcortical Morphology

Childhood socioeconomic status (SES) impacts cognitive development and mental health, but its association with structural brain development is not yet well-characterized. Here, we analyzed 1243 longitudinally-acquired structural MRI scans from 623 youth to investigate the relation between SES and cortical and subcortical morphology between ages 5 and 25 years. We found positive associations between SES and total volumes of the brain, cortical sheet, and four separate subcortical structures. These associations were developmentally fixed rather than age-dependent. Surface-based shape analysis revealed that higher SES is associated with areal expansion of (i) lateral prefrontal, anterior cingulate, lateral temporal, and superior parietal cortices and (ii) ventrolateral thalamic, and medial amygdalo-hippocampal sub-regions. Meta-analyses of functional imaging data indicate that cortical correlates of SES are centered on brain systems subserving sensorimotor functions, language, memory, and emotional processing. We further show that anatomical variation within a subset of these cortical regions partially mediates the positive association between SES and IQ. Finally, we identify neuroanatomical correlates of SES that exist above and beyond accompanying variation in IQ. Our findings clarify the spatiotemporal patterning of SES-related neuroanatomical variation and inform ongoing efforts to dissect the causal pathways underpinning observed associations between childhood SES and regional brain anatomy.

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bioRxiv2018

Carriage of Supernumerary Sex Chromosomes Decreases the Volume and Alters the Shape of Limbic Structures.

Sex chromosome aneuploidy (SCA) enhances risk for several psychiatric disorders associated with the limbic system, including mood and autism spectrum disorders. These patients provide a powerful genetics-first model for understanding the biological basis of psychopathology. Additionally, these disorders are frequently sex-biased in prevalence, further suggesting an etiological role for sex chromosomes. To clarify how limbic anatomy varies across sex and sex chromosome complement, we characterized amygdala and hippocampus structure in a uniquely large sample of patients carrying supernumerary sex chromosomes (n = 132) and typically developing controls (n=166). After correction for sex-differences in brain size, karyotypically normal males (XY) and females (XX) did not differ in volume or shape of either structure. In contrast, all SCAs were associated with lowered amygdala volume relative to gonadally-matched controls. This effect was robust to three different methods for total brain volume correction, including an allometric analysis that derived normative scaling rules for these structures in a separate, typically developing population (n = 79). Hippocampal volume was insensitive to SCA after correction for total brain volume. However, surface-based analysis revealed that SCA, regardless of specific karyotype, was consistently associated with a spatially specific pattern of shape change in both amygdala and hippocampus. In particular, SCA was accompanied by contraction around the basomedial nucleus of the amygdala and an area within the hippocampal surface that cuts across hippocampal subfields. These results demonstrate the power of SCA as a model to understand how copy number variation can precipitate changes in brain systems relevant to psychiatric disease.

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