Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry

Application of a localized morphometrics approach to imaging-derived brain phenotypes for genotype-phenotype associations in pediatric mental health and neurodevelopmental disorders

Introduction

Quantitative global or regional brain imaging measurements, known as imaging-specific or -derived phenotypes (IDPs), are commonly used in genotype-phenotype association studies to explore the genomic architecture of the brain and how it may be affected by neurological diseases (e.g., Alzheimer's disease), mental health (e.g., depression), and neurodevelopmental disorders (e.g., attention-deficit hyperactivity disorder [ADHD]). For this purpose, medical images have been used as IDPs using a voxel-wise or global approach via principal component analysis. However, these methods have limitations related to multiple testing or the inability to isolate high variation regions, respectively.

Methods

To address these limitations, this study investigates a localized, principal component analysis-like approach for dimensionality reduction of cross-sectional T1-weighted MRI datasets utilizing diffeomorphic morphometry. This approach can reduce the dimensionality of images while preserving spatial information and enables the inclusion of spatial locality in the analysis. In doing so, this method can be used to explore morphometric brain changes across specific components and spatial scales of interest and to identify associations with genome regions in a multivariate genome-wide association study. For a first clinical feasibility study, this method was applied to data from the Adolescent Brain Cognitive Development (ABCD) study, including adolescents with ADHD (n = 1,359), obsessive-compulsive disorder (n = 1,752), and depression (n = 1,766).

Results

Meaningful associations of specific morphometric features with genome regions were identified with the data and corresponded to previous found brain regions in the respective mental health and neurodevelopmental disorder cohorts.

Discussion

In summary, the localized, principal component analysis-like approach can reduce the dimensionality of medical images while still being able to identify meaningful local brain region alterations that are associated with genomic markers across multiple scales. The proposed method can be applied to various image types and can be easily integrated in many genotype-phenotype association study setups.

Differential alterations in brain structural network organization during addiction between adolescents and adults

BACKGROUND

The adolescent brain may be susceptible to the influences of illicit drug use. While compensatory network reorganization is a unique developmental characteristic that may restore several brain disorders, its association with methamphetamine (MA) use-induced damage during adolescence is unclear.

METHODS

Using independent component (IC) analysis on structural magnetic resonance imaging data, spatially ICs described as morphometric networks were extracted to examine the effects of MA use on gray matter (GM) volumes and network module connectivity in adolescents (51 MA users v. 60 controls) and adults (54 MA users v. 60 controls).

RESULTS

MA use was related to significant GM volume reductions in the default mode, cognitive control, salience, limbic, sensory and visual network modules in adolescents. GM volumes were also reduced in the limbic and visual network modules of the adult MA group as compared to the adult control group. Differential patterns of structural connectivity between the basal ganglia (BG) and network modules were found between the adolescent and adult MA groups. Specifically, adult MA users exhibited significantly reduced connectivity of the BG with the default network modules compared to control adults, while adolescent MA users, despite the greater extent of network GM volume reductions, did not show alterations in network connectivity relative to control adolescents.

CONCLUSIONS

Our findings suggest the potential of compensatory network reorganization in adolescent brains in response to MA use. The developmental characteristic to compensate for MA-induced brain damage can be considered as an age-specific therapeutic target for adolescent MA users.

Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network

Schizophrenia is a highly heritable psychiatric disorder characterized by widespread functional and structural brain abnormalities. However, previous association studies between MRI and polygenic risk were mostly ROI-based single modality analyses, rather than identifying brain-based multimodal predictive biomarkers. Based on schizophrenia polygenic risk scores (PRS) from healthy white people within the UK Biobank dataset (N = 22,459), we discovered a robust PRS-associated brain pattern with smaller gray matter volume and decreased functional activation in frontotemporal cortex, which distinguished schizophrenia from controls with >83% accuracy, and predicted cognition and symptoms across 4 independent schizophrenia cohorts. Further multi-disease comparisons demonstrated that these identified frontotemporal alterations were most severe in schizophrenia and schizo-affective patients, milder in bipolar disorder, and indistinguishable from controls in autism, depression and attention-deficit hyperactivity disorder. These findings indicate the potential of the identified PRS-associated multimodal frontotemporal network to serve as a trans-diagnostic gene intermediated brain biomarker specific to schizophrenia.

Structural covariance networks in schizophrenia: A systematic review Part I

BACKGROUND

Schizophrenia is proposed as a disorder of dysconnectivity. However, examination of complexities of dysconnectivity has been challenging. Structural covariance networks (SCN) provide important insights into the nature of dysconnectivity. This systematic review examines the SCN studies that employed statistical approaches to elucidate covariation of regional morphometric variations.

METHODS

A systematic search of literature was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia. Fifty-two studies met the criteria.

RESULTS

Early SCN studies began using correlational structure of selected regions. Over the last 3 decades, methodological approaches have grown increasingly sophisticated from examining selected brain regions using correlation tests on small sample sizes to recent approaches that use advanced statistical methods to examine covariance structure of whole-brain parcellations on larger samples. Although the results are not fully consistent across all studies, a pattern of fronto-temporal, fronto-parietal and fronto-thalamic covariation is reported. Attempts to associate SCN alterations with functional connectivity, to differentiate between disease-related and neurodevelopment-related morphometric changes, and to develop "causality-based" models are being reported. Clinical correlation with outcome, psychotic symptoms, neurocognitive and social cognitive performance are also reported.

CONCLUSIONS

Application of advanced statistical methods are beginning to provide insights into interesting patterns of regional covariance including correlations with clinical and cognitive data. Although these findings appear similar to morphometric studies, SCNs have the advantage of highlighting topology of these regions and their relationship to the disease and associated variables. Further studies are needed to investigate neurobiological underpinnings of shared covariance, and causal links to clinical domains.

Shared sets of correlated polygenic risk scores and voxel-wise grey matter across multiple traits identified via bi-clustering

Neuropsychiatric disorders involve complex polygenic determinants as well as brain alterations. The combination of genetic inheritance and neuroimaging approaches could advance our understanding of psychiatric disorders. However, cross-disorder overlap is a current issue since psychiatric conditions share some neurogenetic correlates, symptoms, and brain effects. Exploring the impact of genetic risk on the brain across disorders could help understand commonalities across multiple psychopathologies. To do this, we first compute the linear relationship between PRS and voxel-wise grey matter volume to generate brain maps for five psychiatric and three control traits. Next, we use the biclustering approach to identify regions of the brain associated with polygenic risk scores in one or more traits. Our results demonstrate a significant overlap in brain regions connected to polygenic risk across psychiatric traits. Moreover, such brain domains are highly allied with the polygenic risk for non-psychiatric control traits. This multi-trait overlap characterizes the nonspecific relationship between neural anatomy and inherited risk factors in psychiatric conditions, and in some cases, the overlap in neural features linked to genetic risk for non-psychiatric attributes.Clinical Relevance-This study presents biclusters of multiple psychiatric and control traits. The analysis reported various brain regions, including cerebellum, cuneus, precuneus, fusiform, supplementary motor area, that show significant correlation with polygenic risk scores across diverse groups of psychiatric conditions and non-psychiatric control traits.

Neuromorphological Changes following Selection for Tameness and Aggression in the Russian Farm-Fox experiment

The Russian farm-fox experiment is an unusually long-running and well-controlled study designed to replicate wolf-to-dog domestication. As such, it offers an unprecedented window onto the neural mechanisms governing the evolution of behavior. Here we report evolved changes to gray matter morphology resulting from selection for tameness versus aggressive responses toward humans in a sample of 30 male fox brains. Contrasting with standing ideas on the effects of domestication on brain size, tame foxes did not show reduced brain volume. Rather, gray matter volume in both the tame and aggressive strains was increased relative to conventional farm foxes bred without deliberate selection on behavior. Furthermore, tame- and aggressive-enlarged regions overlapped substantially, including portions of motor, somatosensory, and prefrontal cortex, amygdala, hippocampus, and cerebellum. We also observed differential morphologic covariation across distributed gray matter networks. In one prefrontal-cerebellum network, this covariation differentiated the three populations along the tame-aggressive behavioral axis. Surprisingly, a prefrontal-hypothalamic network differentiated the tame and aggressive foxes together from the conventional strain. These findings indicate that selection for opposite behaviors can influence brain morphology in a similar way.SIGNIFICANCE STATEMENT Domestication represents one of the largest and most rapid evolutionary shifts of life on earth. However, its neural correlates are largely unknown. Here we report the neuroanatomical consequences of selective breeding for tameness or aggression in the seminal Russian farm-fox experiment. Compared with a population of conventional farm-bred control foxes, tame foxes show neuroanatomical changes in the PFC and hypothalamus, paralleling wolf-to-dog shifts. Surprisingly, though, aggressive foxes also show similar changes. Moreover, both strains show increased gray matter volume relative to controls. These results indicate that similar brain adaptations can result from selection for opposite behavior, that existing ideas of brain changes in domestication may need revision, and that significant neuroanatomical change can evolve very quickly, within the span of <100 generations.