Genetic Neuroimaging

The human fetal cerebral cortex undergoes genetically orchestrated gyrification (cortical folding) during the prenatal period. Recent fetal brain MRI advancements allow spatiotemporal quantification of macro- and microstructures during cortical folding (Figure 1a). Researchers aim to understand the relationship between morphological changes and underlying microstructural alterations associated with cortical folding (Garcia et al., 2021).
Transcriptomic technologies revealed regional gene expression patterns during prenatal development (Miller et al., 2014, Li et al., 2018). Pioneering studies by (Huang et al., 2013) and (Vasung et al., 2021) correlated MRI measurements with relevant genes using ex-vivo and in-vivo fetal brains but fell short in describing cortical folding. Therefore, we aim to comprehensively characterize spatiotemporal macro- and microstructural development of the fetal cerebral cortex by integrating in-utero fetal structural MRI, diffusion MRI, and gene expression. 

Identifying the degree to which the genetic architecture of brain structure and function derived from brain imaging features overlap with other brain-based traits is important for understanding overlapping neurobiological mechanisms. Pre-assembled pipelines for post-GWAS analyses, such as CTG-VL[1] can help with efficient processing, but have not been developed to specifically test for genetic relationships and enrichment against brain-related traits. Toolboxes from abagen[2] and ENIGMA[3] integrate brain gene-expression across different atlases but do not take GWAS summary statistics as input. Here we present GiNi (Genetic interrogator for Neuroimaging) a Python-based command line tool, to streamline a comprehensive collection of statistical genetic relationships between multiple brain-based traits. We include heritability estimates, global and local genetic correlation, and causal genetic associations and enrichment across various brain regions, tissues and cell types. 

Adolescence is a pivotal phase in human development marked by significant transformations in brain and behavior. As individuals transition from childhood to adulthood, the brain undergoes dynamic changes in its structure and function, influencing cognitive, emotional, and social behaviors. The development of the adolescent brain and behavior is intricately shaped by a complex interplay of genetic factors and environmental influences^1,2. Yet, how genetic variation influences the multifaceted relationship between the brain and behavior remains understudied.
Copy number variations (CNVs) represent a notable source of genetic variation. This class of genetic mutations is defined as either a deletion or duplication of sequences of nucleotides more than 1000 base pairs long^3,4. It is increasingly recognized that many CNVs exert far-reaching consequences throughout the body, making them a sharp imaging-genetics tool for interrogating the effects of genetic modifications on brain physicality and behavioral differentiation^5,6. We thus hypothesize that CNVs shape the complex brain-behavior relationship. 

Oscillations in neuronal brain activity play a crucial role in information processing and have been studied extensively as biological markers of human behavior and psychopathology [1]. A century ago, in 1924, Hans Berger's discovery marked the inception of a transformative era in neuroscience, leading to crucial advancements in our understanding of brain function and the corresponding behavioral phenomena [2]. Twin studies have demonstrated that individual differences in EEG oscillations are strongly driven by genetic factors [3]. However, our understanding of their molecular genetic architecture is still very limited. Here, we conducted a genome-wide association study (GWAS) of resting-state EEG oscillations to discover associated genomic loci and to examine the pleiotropic relationships with other complex traits, i.e., the links with brain structure and mental illness. 

Depression, a growing global concern with a prevalence surpassing 5%(Collins, Patel et al. 2011), gravely impairs the wellbeing and quality of life of affected individuals and posing substantial societal burdens(Herrman, Patel et al. 2022). The limited success in achieving consistent remission is intricately linked to our incomplete understanding of its pathogenesis(Yuan, Yang et al. 2023). Unraveling these elusive mechanisms is paramount, setting the stage for more effective therapeutic interventions.
While several studies have delved into the association between plasma proteins and depression(Zhang, Guo et al. 2022), their insights, albeit valuable, are constrained by small sample sizes or limited proteomic scope. Thus, it is crucial to explore the profiling protein dysregulations prior to depression onset using large biobanks. Besides, given that depression arises from a sophisticated interplay of biological and environmental elements(Yuan, Yang et al. 2023), examining these proteins within diverse biological and environmental factors and understanding their linked pathways is essential. 

Recent imaging-genetics research has demonstrated that heritable MRI-derived brain structural features show important genetic overlaps with brain function and psychopathology [1,2,3]. Yet, while the brain forms a genetically-coordinated network, existing work on the genetics of brain structure has focused on structural features at the global or regional level. As such, the genetics of network-based measures of brain structure remain largely unknown.

In this work, we conducted hundreds of genome-wide association studies (GWAS) to comprehensively characterize the genetics of structural similarity networks in the brain. Specifically, using N>30,000 subjects from the UK Biobank, we studied the genetics of Morphometric INverse Divergence (MIND), a robust and biologically-validated method to construct structural similarity networks from MRI [4]. We identified 109 independent genomic regions associated with MIND, many of which were not associated with the structural feature from which the networks were derived.

We observed positive genetic correlations between MIND network edges and the corresponding edges from functional connectivity (FC) networks, offering new evidence for a shared genetic basis for brain structure and function. Moreover, we identified putative causal relationships between MIND and functional connectivity that were specific to the association cortex.

Finally, we observed evidence for local genetic correlations between MIND network connectivity and schizophrenia, identifying specific genes such as CACNA1c that may disproportionately contribute to the shared genetic basis of brain connectivity and mental illness.