A structural brain network of genetic vulnerability to psychiatric illness

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Antiphospholipid Antibodies in Mental Disorders

Thrombotic events striking the central nervous system are clinical criteria for the antiphospholipid syndrome (APS). Besides these, neuropsychiatric non-APS criteria manifestations are increasingly described in patients with persistently positive antiphospholipid antibodies (aPL). Among these are psychiatric manifestations. Animal models mainly describe hyperactive behavior and anxiety associated with hippocampal abnormalities. Cases of associations with psychosis, mood disorders, bipolarity, anxiety, obsessive-compulsive behavior, and depression have been reported but are still rare. Systematic human clinical association studies are concordant with a risk of psychosis, depression (simple to major), and anxiety disorders, but these are limited and of inconstant methodological quality. Brain imaging in patients, also insufficiently investigated, shows early signs of hypoperfusion and of subtle diffuse white matter changes compatible with an alteration of the axonal structure and changes in the myelin sheath. Direct interactions of aPL with the brain cells, both on cell lines and on animal and human brain biopsies, targeting both glial cells, astrocytes, and neurons, can be demonstrated. These clusters of arguments make the association between psychiatric diseases and aPL increasingly plausible. However, a considerable amount of clinical research must still be performed in accordance with the highest standards of methodological quality. The therapeutic management of this association, in terms of both prevention and cure, currently remains unresolved.

The Influence of Maternal and Paternal Parenting on Adolescent Brain Structure

BACKGROUND

Adolescents raised in families with different maternal and paternal parenting combinations exhibit variations in neurocognition and psychopathology; however, whether neural differences exist remains unexplored. This study used a longitudinal twin sample to delineate how different parenting combinations influence adolescent brain structure and to elucidate the genetic contribution.

METHODS

A cohort of 216 twins participated in parenting assessments during early adolescence and underwent magnetic resonance imaging scanning during middle adolescence. We utilized latent profile analysis to distinguish between various maternal and paternal parenting profiles and subsequently investigated their influences on brain anatomy. Biometric analysis was applied to assess genetic influences on brain structure, and associations with internalizing symptoms were explored.

RESULTS

In early adolescence, 4 parenting profiles emerged, which were characterized by levels of harshness and hostility in one or both parents. Compared with adolescents in "catparent" families (low harshness/hostility in both parents), those raised in "tigermom" families (harsh/hostile mother only) exhibited a smaller nucleus accumbens volume and larger temporal cortex surface area; those in "tigerdad" families demonstrated larger thalamus volumes; and those in "tigerparent" families displayed smaller volumes in the midanterior corpus callosum. Genetic risk factors contributed significantly to the observed brain structural heterogeneity and internalizing symptoms. However, the influences of parenting profiles and brain structure on internalizing symptoms were not significant.

CONCLUSIONS

The findings underscore distinct brain structural features linked to maternal and paternal parenting combinations, particularly in terms of subcortical volume and cortical surface area. This study suggests an interdependent role of maternal and paternal parenting in shaping adolescent neurodevelopment.

The genetic landscape of substance use disorders

Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.

The schizophrenia syndrome, circa 2024: What we know and how that informs its nature

With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.

Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review

BACKGROUND AND HYPOTHESIS

Schizophrenia is highly heritable, with a polygenic effect of many genes conferring risk. Evidence on whether cumulative risk also predicts alterations in brain morphology and function is inconsistent. This systematic review examined evidence for schizophrenia polygenic risk score (sczPRS) associations with commonly used magnetic resonance imaging (MRI) measures. We expected consistent evidence to emerge for significant sczPRS associations with variation in structure and function, specifically in frontal, temporal, and insula cortices that are commonly implicated in schizophrenia pathophysiology.

STUDY DESIGN

In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies published between January 2013 and March 2022. Studies were screened against predetermined criteria and National Institutes of Health (NIH) quality assessment tools.

STUDY RESULTS

In total, 57 studies of T1-weighted structural, diffusion, and functional MRI were included (age range = 9-80 years, Nrange = 64-76 644). We observed moderate, albeit preliminary, evidence for higher sczPRS predicting global reductions in cortical thickness and widespread variation in functional connectivity, and to a lesser extent, region-specific reductions in frontal and temporal volume and thickness. Conversely, sczPRS does not predict whole-brain surface area or gray/white matter volume. Limited evidence emerged for sczPRS associations with diffusion tensor measures of white matter microstructure in a large community sample and smaller cohorts of children and young adults. These findings were broadly consistent across community and clinical populations.

CONCLUSIONS

Our review supports the hypothesis that schizophrenia is a disorder of disrupted within and between-region brain connectivity, and points to specific whole-brain and regional MRI metrics that may provide useful intermediate phenotypes.

Disrupted white matter structural networks in individuals with alcohol dependence

Previous diffusion tensor imaging (DTI) studies have demonstrated widespread white matter microstructure damage in individuals with alcoholism. However, very little is known about the alterations in the topological architecture of white matter structural networks in alcohol dependence (AD). This study included 67 AD patients and 69 controls. The graph theoretical analysis method was applied to examine the topological organization of the white matter structural networks, and network-based statistics (NBS) were employed to detect structural connectivity alterations. Compared to controls, AD patients exhibited abnormal global network properties characterized by increased small-worldness, normalized clustering coefficient, clustering coefficient, and shortest path length; and decreased global efficiency and local efficiency. Further analyses revealed decreased nodal efficiency and degree centrality in AD patients mainly located in the default mode network (DMN), including the precuneus, anterior cingulate and paracingulate gyrus, median cingulate and paracingulate gyrus, posterior cingulate gyrus, and medial part of the superior frontal gyrus. Furthermore, based on NBS approaches, patients displayed weaker subnetwork connectivity mainly located in the region of the DMN. Additionally, altered network metrics were correlated with intelligence quotient (IQ) scores and global assessment function (GAF) scores. Our results may reveal the disruption of whole-brain white matter structural networks in AD individuals, which may contribute to our comprehension of the underlying pathophysiological mechanisms of alcohol addiction at the level of white matter structural networks.

Schizophrenia Polygenic Risk During Typical Development Reflects Multiscale Cortical Organization

Background

Schizophrenia is widely recognized as a neurodevelopmental disorder. Abnormal cortical development in otherwise typically developing children and adolescents may be revealed using polygenic risk scores for schizophrenia (PRS-SCZ).

Methods

We assessed PRS-SCZ and cortical morphometry in typically developing children and adolescents (3-21 years, 46.8% female) using whole-genome genotyping and T1-weighted magnetic resonance imaging (n = 390) from the PING (Pediatric Imaging, Neurocognition, and Genetics) cohort. We contextualized the findings using 1) age-matched transcriptomics, 2) histologically defined cytoarchitectural types and functionally defined networks, and 3) case-control differences of schizophrenia and other major psychiatric disorders derived from meta-analytic data of 6 ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) working groups, including a total of 12,876 patients and 15,670 control participants.

Results

Higher PRS-SCZ was associated with greater cortical thickness, which was most prominent in areas with heightened gene expression of dendrites and synapses. PRS-SCZ-related increases in vertexwise cortical thickness were mainly distributed in association cortical areas, particularly the ventral attention network, while relatively sparing koniocortical type cortex (i.e., primary sensory areas). The large-scale pattern of cortical thickness increases related to PRS-SCZ mirrored the pattern of cortical thinning in schizophrenia and mood-related psychiatric disorders derived from the ENIGMA consortium. Age group models illustrate a possible trajectory from PRS-SCZ-associated cortical thickness increases in early childhood toward thinning in late adolescence, with the latter resembling the adult brain phenotype of schizophrenia.

Conclusions

Collectively, combining imaging genetics with multiscale mapping, our work provides novel insight into how genetic risk for schizophrenia affects the cortex early in life.

Acute blood biomarker profiles predict cognitive deficits 6 and 12 months after COVID-19 hospitalization

Post-COVID cognitive deficits, including 'brain fog', are clinically complex, with both objective and subjective components. They are common and debilitating, and can affect the ability to work, yet their biological underpinnings remain unknown. In this prospective cohort study of 1,837 adults hospitalized with COVID-19, we identified two distinct biomarker profiles measured during the acute admission, which predict cognitive outcomes 6 and 12 months after COVID-19. A first profile links elevated fibrinogen relative to C-reactive protein with both objective and subjective cognitive deficits. A second profile links elevated D-dimer relative to C-reactive protein with subjective cognitive deficits and occupational impact. This second profile was mediated by fatigue and shortness of breath. Neither profile was significantly mediated by depression or anxiety. Results were robust across secondary analyses. They were replicated, and their specificity to COVID-19 tested, in a large-scale electronic health records dataset. These findings provide insights into the heterogeneous biology of post-COVID cognitive deficits.

Robust estimation of cortical similarity networks from brain MRI

Structural similarity is a growing focus for magnetic resonance imaging (MRI) of connectomes. Here we propose Morphometric INverse Divergence (MIND), a new method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on n > 11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry and more correlated with tract-tracing measures of axonal connectivity. MIND networks derived from human T1-weighted MRI were more sensitive to age-related changes than MSNs or networks derived by tractography of diffusion-weighted MRI. Gene co-expression between cortical areas was more strongly coupled to MIND networks than to MSNs or tractography. MIND network phenotypes were also more heritable, especially edges between structurally differentiated areas. MIND network analysis provides a biologically validated lens for cortical connectomics using readily available MRI data.

A transdiagnostic network for psychiatric illness derived from atrophy and lesions

Psychiatric disorders share neurobiology and frequently co-occur. This neurobiological and clinical overlap highlights opportunities for transdiagnostic treatments. In this study, we used coordinate and lesion network mapping to test for a shared brain network across psychiatric disorders. In our meta-analysis of 193 studies, atrophy coordinates across six psychiatric disorders mapped to a common brain network defined by positive connectivity to anterior cingulate and insula, and by negative connectivity to posterior parietal and lateral occipital cortex. This network was robust to leave-one-diagnosis-out cross-validation and specific to atrophy coordinates from psychiatric versus neurodegenerative disorders (72 studies). In 194 patients with penetrating head trauma, lesion damage to this network correlated with the number of post-lesion psychiatric diagnoses. Neurosurgical ablation targets for psychiatric illness (four targets) also aligned with the network. This convergent brain network for psychiatric illness may partially explain high rates of psychiatric comorbidity and could highlight neuromodulation targets for patients with more than one psychiatric disorder.

School performance and genetic propensities for educational attainment and depression in the etiology of self-harm: a Danish population-based study

BACKGROUND

Poor school performance is linked to higher risks of self-harm. The association might be explained through genetic liabilities for depression or educational attainment. We investigated the association between school performance and self-harm in a population-based sample while assessing the potential influence of polygenic risk scores (PRSs) for depression (PRSMDD) and for educational attainment (PRSEDU).

METHOD

We conducted a follow-up study of individuals born 1987-98 and followed from age 18 until 2016. The total sample consisted of a case group (23,779 diagnosed with mental disorders; schizophrenia, bipolar disorder, depression, autism, and attention deficit hyperactivity disorder (ADHD) and a randomly sampled comparison group (n = 10,925). Genome-wide data were obtained from the Neonatal Screening Biobank and information on school performance, family psychiatric history, and socioeconomic status from national administrative registers.

RESULTS

Individuals in the top PRSMDD decile were at higher self-harm risk in the case group (IRR: 1.30; 95% CI 1.15-1.46), whereas individuals in the top PRSEDU decile were at lower self-harm risk (IRR: 0.63; 95% CI: 0.55-0.74). Poorer school performance was associated with higher self-harm risk in persons diagnosed with any mental disorder (IRR: 1.69; 95% CI: 1.44-1.99) and among the comparison group (IRR: 7.93; 95% CI: 4.47-15.18). Observed effects of PRSMDD and PRSEDU on self-harm risk were strongest for individuals with poor school performance.

CONCLUSION

Associations between PRSMDD and self-harm risk and between PRSEDU and self-harm risk were found. Nevertheless, these polygenic scores seem currently of limited clinical utility for identifying individuals at high self-harm risk.

Overlapping brain correlates of superior cognition among children at genetic risk for Alzheimer's disease and/or major depressive disorder

Early life adversity (ELA) tends to accelerate neurobiological ageing, which, in turn, is thought to heighten vulnerability to both major depressive disorder (MDD) and Alzheimer's disease (AD). The two conditions are putatively related, with MDD representing either a risk factor or early symptom of AD. Given the substantial environmental susceptibility of both disorders, timely identification of their neurocognitive markers could facilitate interventions to prevent clinical onset. To this end, we analysed multimodal data from the Adolescent Brain and Cognitive Development study (ages 9-10 years). To disentangle genetic from correlated genetic-environmental influences, while also probing gene-adversity interactions, we compared adoptees, a group generally exposed to substantial ELA, with children raised by their biological families via genetic risk scores (GRS) from genome-wide association studies. AD and MDD GRSs predicted overlapping and widespread neurodevelopmental alterations associated with superior fluid cognition. Specifically, among adoptees only, greater AD GRS were related to accelerated structural maturation (i.e., cortical thinning) and higher MDD GRS were linked to delayed functional neurodevelopment, as reflected in compensatory brain activation on an inhibitory control task. Our study identifies compensatory mechanisms linked to MDD risk and highlights the potential cognitive benefits of accelerated maturation linked to AD vulnerability in late childhood.

Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

BACKGROUND

Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects.

METHODS

This study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory-based network analysis was used to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices.

RESULTS

Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate-corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis.

CONCLUSIONS

We found shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results showed a compromised brain communication across a spectrum of major psychiatric disorders.

Structural alterations of the insula in depression patients - A 7-Tesla-MRI study

INTRODUCTION

The insular cortex is part of a network of highly connected cerebral "rich club" - regions and has been implicated in the pathophysiology of various psychiatric and neurological disorders, of which major depressive disease is one of the most prevalent. "Rich club" vulnerability can be a contributing factor in disease development. High-resolution structural subfield analysis of insular volume in combination with cortical thickness measurements and psychological testing might elucidate the way in which the insula is changed in depression.

MATERIAL AND METHODS

High-resolution structural images of the brain were acquired using a 7T-MRI scanner. The mean grey matter volume and cortical thickness within the insular subfields were analysed using voxel-based morphometry (VBM) and surface analysis techniques respectively. Insular subfields were defined according to the Brainnetome Atlas for VBM - and the Destrieux-Atlas for cortical thickness - analysis. Thirty-three patients with confirmed major depressive disease, as well as thirty-one healthy controls matched for age and gender, were measured. The severity of depression in MDD patients was measured via a BDI-II score and objective clinical assessment (AMDP). Intergroup statistical analysis was performed using ANCOVA. An intragroup multivariate regression analysis of patient psychological test results was calculated. Corrections for multiple comparisons was performed using FDR.

RESULTS

Significant differences between groups were observed in the left granular dorsal insula according to VBM-analysis. AMDP-scores positively correlated with cortical thickness in the right superior segment of the circular insular sulcus.

CONCLUSIONS

The combination of differences in grey matter volume between healthy controls and patients with a positive correlation of cortical thickness with disease severity underscores the insula's role in the pathogeneses of MDD. The connectivity hub insular cortex seems vulnerable to disruption in context of affective disease.

Associations between brain imaging and polygenic scores of mental health and educational attainment in children aged 9-11

Psychiatric disorders are highly heritable and polygenic, and many have their peak onset in late childhood and adolescence, a period of tremendous changes. Although the neurodevelopmental antecedents of mental illness are widely acknowledged, research in youth population cohorts is still scarce, preventing our progress towards the early characterization of these disorders. We included 7,124 children (9-11 years old) from the Adolescent Brain and Cognitive Development Study to map the associations of structural and diffusion brain imaging with common genetic variants and polygenic scores for psychiatric disorders and educational attainment. We used principal component analysis to derive imaging components, and calculated their heritability. We then assessed the relationship of imaging components with genetic and clinical psychiatric risk with univariate models and Canonical correlation analysis (CCA). Most imaging components had moderate heritability. Univariate models showed limited evidence and small associations of polygenic scores with brain structure at this age. CCA revealed two significant modes of covariation. The first mode linked higher polygenic scores for educational attainment with less externalizing problems and larger surface area. The second mode related higher polygenic scores for schizophrenia, bipolar disorder, and autism spectrum disorder to higher global cortical thickness, smaller white matter volumes of the fornix and cingulum, larger medial occipital surface area and smaller surface area of lateral and medial temporal regions. While cross-validation suggested limited generalizability, our results highlight the potential of multivariate models to better understand the transdiagnostic and distributed relationships between mental health and brain structure in late childhood.

Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency

Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.

Obsessive compulsive symptom dimensions are linked to altered white-matter microstructure in a community sample of youth

Obsessive-compulsive symptoms (OCS) are common in school-aged children and predict the development of obsessive compulsive disorder (OCD). White-matter abnormalities have been described in OCD, but the white matter correlates of OCS in the developing brain are unclear. Some correlates of OCS (or a diagnosis of OCD) may reflect correlates of a transdiagnostic or even general psychopathology factor. We examined these questions in a large sample of typically developing youth (N = 1208), using a hierarchical analysis of fixel-based white matter measures in relation to OCS and general psychopathology. General psychopathology was associated with abnormalities in the posterior corpus callosum and forceps major in an age-dependent manner, suggesting altered maturation (specifically, hypermaturation in younger subjects). A unidimensional measure of OCS did not associate with any white-matter abnormalities, but analysis of separate OCS dimensions (derived from factor analysis within this sample) revealed the 'Bad Thoughts' dimension to associate with white-matter abnormalities in dorsal parietal white-matter and descending corticospinal tracts, and the 'Symmetry' dimension to associate with abnormalities in the anterior corpus callosum. Repetition/checking and Symmetry OCS were additionally associated with posterior abnormalities overlapping with the correlates of general psychopathology. Contamination symptoms had no white-matter correlates. Secondary analysis of fractional anisotropy (FA) revealed distinct white-matter abnormalities, suggesting that fixel-based and FA analyses identify distinct features of white matter relevant to psychopathology. These findings suggest that OCS dimensions correlate with dissociable abnormalities in white matter, implicating separable networks. Future studies should examine these white-matter signatures in a longitudinal framework.

Brain Mapping of Behavioral Domains Using Multi-Scale Networks and Canonical Correlation Analysis

Simultaneous mapping of multiple behavioral domains into brain networks remains a major challenge. Here, we shed some light on this problem by employing a combination of machine learning, structural and functional brain networks at different spatial resolutions (also known as scales), together with performance scores across multiple neurobehavioral domains, including sensation, motor skills, and cognition. Provided by the Human Connectome Project, we make use of three cohorts: 640 participants for model training, 160 subjects for validation, and 200 subjects for model performance testing thus enhancing prediction generalization. Our modeling consists of two main stages, namely dimensionality reduction in brain network features at multiple scales, followed by canonical correlation analysis, which determines an optimal linear combination of connectivity features to predict multiple behavioral performance scores. To assess the differences in the predictive power of each modality, we separately applied three different strategies: structural unimodal, functional unimodal, and multimodal, that is, structural in combination with functional features of the brain network. Our results show that the multimodal association outperforms any of the unimodal analyses. Then, to answer which human brain structures were most involved in predicting multiple behavioral scores, we simulated different synthetic scenarios in which in each case we completely deleted a brain structure or a complete resting state network, and recalculated performance in its absence. In deletions, we found critical structures to affect performance when predicting single behavioral domains, but this occurred in a lesser manner for prediction of multi-domain behavior. Overall, our results confirm that although there are synergistic contributions between brain structure and function that enhance behavioral prediction, brain networks may also be mutually redundant in predicting multidomain behavior, such that even after deletion of a structure, the connectivity of the others can compensate for its lack in predicting behavior.

The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification

Structural magnetic resonance imaging (MRI) features have played an increasingly crucial role in discriminating patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from normal controls (NC). However, the large number of structural MRI studies only extracted low-level neuroimaging features or simply concatenated multitudinous features while ignoring the interregional covariate information. The appropriate representation and integration of multilevel features will be preferable for the precise discrimination in the progression of AD. In this study, we proposed a novel inter-coupled feature representation method and built an integration model considering the two-level (the regions of interest (ROI) level and the network level) coupled features based on structural MRI data. For the intra-coupled interactions about the network-level features, we performed the ROI-level (intra- and inter-) coupled interaction within each network by feature expansion and coupling learning. For the inter-coupled interaction of the network-level features, we measured the coupled relationships among different networks via Canonical correlation analysis. We evaluated the classification performance using coupled feature representations on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results showed that the coupled integration model with hierarchical features achieved the optimal classification performance with an accuracy of 90.44% for AD and NC groups, with an accuracy of 87.72% for the MCI converter (MCI-c) and MCI non-converter (MCI-nc) groups. These findings suggested that our two-level coupled interaction representation of hierarchical features has been the effective means for the precise discrimination of MCI-c from MCI-nc groups and, therefore, helpful in the characterization of different AD courses.

Cell type-specific manifestations of cortical thickness heterogeneity in schizophrenia

Brain morphology differs markedly between individuals with schizophrenia, but the cellular and genetic basis of this heterogeneity is poorly understood. Here, we sought to determine whether cortical thickness (CTh) heterogeneity in schizophrenia relates to interregional variation in distinct neural cell types, as inferred from established gene expression data and person-specific genomic variation. This study comprised 1849 participants in total, including a discovery (140 cases and 1267 controls) and a validation cohort (335 cases and 185 controls). To characterize CTh heterogeneity, normative ranges were established for 34 cortical regions and the extent of deviation from these ranges was measured for each individual with schizophrenia. CTh deviations were explained by interregional gene expression levels of five out of seven neural cell types examined: (1) astrocytes; (2) endothelial cells; (3) oligodendrocyte progenitor cells (OPCs); (4) excitatory neurons; and (5) inhibitory neurons. Regional alignment between CTh alterations with cell type transcriptional maps distinguished broad patient subtypes, which were validated against genomic data drawn from the same individuals. In a predominantly neuronal/endothelial subtype (22% of patients), CTh deviations covaried with polygenic risk for schizophrenia (sczPRS) calculated specifically from genes marking neuronal and endothelial cells (r = -0.40, p = 0.010). Whereas, in a predominantly glia/OPC subtype (43% of patients), CTh deviations covaried with sczPRS calculated from glia and OPC-linked genes (r = -0.30, p = 0.028). This multi-scale analysis of genomic, transcriptomic, and brain phenotypic data may indicate that CTh heterogeneity in schizophrenia relates to inter-individual variation in cell-type specific functions. Decomposing heterogeneity in relation to cortical cell types enables prioritization of schizophrenia subsets for future disease modeling efforts.

Neuroimaging brain growth charts: A road to mental health

Mental disorders are common health concerns and contribute to a heavy global burden on our modern society. It is challenging to identify and treat them timely. Neuroimaging evidence suggests the incidence of various psychiatric and behavioral disorders is closely related to the atypical development of brain structure and function. The identification and understanding of atypical brain development provide chances for clinicians to detect mental disorders earlier, perhaps even prior to onset, and treat them more precisely. An invaluable and necessary method in identifying and monitoring atypical brain development are growth charts of typically developing individuals in the population. The brain growth charts can offer a series of standard references on typical neurodevelopment, representing an important resource for the scientific and medical communities. In the present paper, we review the relationship between mental disorders and atypical brain development from a perspective of normative brain development by surveying the recent progress in the development of brain growth charts, including four aspects on growth chart utility: 1) cohorts, 2) measures, 3) mechanisms, and 4) clinical translations. In doing so, we seek to clarify the challenges and opportunities in charting brain growth, and to promote the application of brain growth charts in clinical practice.

Giant ankyrin-B mediates transduction of axon guidance and collateral branch pruning factor sema 3A

Variants in the high confident autism spectrum disorder (ASD) gene ANK2 target both ubiquitously expressed 220 kDa ankyrin-B and neurospecific 440 kDa ankyrin-B (AnkB440) isoforms. Previous work showed that knock-in mice expressing an ASD-linked Ank2 variant yielding a truncated AnkB440 product exhibit ectopic brain connectivity and behavioral abnormalities. Expression of this variant or loss of AnkB440 caused axonal hyperbranching in vitro, which implicated AnkB440 microtubule bundling activity in suppressing collateral branch formation. Leveraging multiple mouse models, cellular assays, and live microscopy, we show that AnkB440 also modulates axon collateral branching stochastically by reducing the number of F-actin-rich branch initiation points. Additionally, we show that AnkB440 enables growth cone (GC) collapse in response to chemorepellent factor semaphorin 3 A (Sema 3 A) by stabilizing its receptor complex L1 cell adhesion molecule/neuropilin-1. ASD-linked ANK2 variants failed to rescue Sema 3A-induced GC collapse. We propose that impaired response to repellent cues due to AnkB440 deficits leads to axonal targeting and branch pruning defects and may contribute to the pathogenicity of ANK2 variants.

White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis

Subtle alterations in white matter microstructure are observed in youth at clinical high risk (CHR) for psychosis. However, the timing of these changes and their relationships to the emergence of psychosis remain unclear. Here, we track the evolution of white matter abnormalities in a large, longitudinal cohort of CHR individuals comprising the North American Prodrome Longitudinal Study (NAPLS-3). Multi-shell diffusion magnetic resonance imaging data were collected across multiple timepoints (1-5 over 1 year) in 286 subjects (aged 12-32 years): 25 CHR individuals who transitioned to psychosis (CHR-P; 61 scans), 205 CHR subjects with unknown transition outcome after the 1-year follow-up period (CHR-U; 596 scans), and 56 healthy controls (195 scans). Linear mixed effects models were fitted to infer the impact of age and illness-onset on variation in the fractional anisotropy of cellular tissue (FAT) and the volume fraction of extracellular free water (FW). Baseline measures of white matter microstructure did not differentiate between HC, CHR-U and CHR-P individuals. However, age trajectories differed between the three groups in line with a developmental effect: CHR-P and CHR-U groups displayed higher FAT in adolescence, and 4% lower FAT by 30 years of age compared to controls. Furthermore, older CHR-P subjects (20+ years) displayed 4% higher FW in the forceps major (p < 0.05). Prospective analysis in CHR-P did not reveal a significant impact of illness onset on regional FAT or FW, suggesting that transition to psychosis is not marked by dramatic change in white matter microstructure. Instead, clinical high risk for psychosis-regardless of transition outcome-is characterized by subtle age-related white matter changes that occur in tandem with development.

Psychopathy

Psychopathy is a personality disorder characterized by a constellation of affective, interpersonal, lifestyle and antisocial features whose antecedents can be identified in a subgroup of young people showing severe antisocial behaviour. The prevalence of psychopathy in the general population is thought to be ~1%, but is up to 25% in prisoners. The aetiology of psychopathy is complex, with contributions of both genetic and environmental risk factors, and gene-environment interactions and correlations. Psychopathy is characterized by structural and functional brain abnormalities in cortical (such as the prefrontal and insular cortices) and subcortical (for example, the amygdala and striatum) regions leading to neurocognitive disruption in emotional responsiveness, reinforcement-based decision-making and attention. Although no effective treatment exists for adults with psychopathy, preliminary intervention studies targeting key neurocognitive disturbances have shown promising results. Given that psychopathy is often comorbid with other psychiatric disorders and increases the risk of physical health problems, educational and employment failure, accidents and criminality, the identification of children and young people at risk for this personality disorder and preventative work are important. Indeed, interventions that target the antecedents of psychopathic features in children and adolescents have been found to be effective.