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1. Heritability and interindividual variability of regional structure-function coupling

   https://doi.org/10.1038/s41467-021-25184-4  

   Gu, Zijin; Jamison, Keith Wakefield; Sabuncu, Mert Rory; Kuceyeski, Amy (August 2021, Nature Communications)

   Abstract White matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors. 

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2. Forming Connections: Functional Brain Connectivity is Associated With Executive Functioning Abilities in Early Childhood

   https://doi.org/10.1111/desc.13604  

   Kelsey, Caroline; Kamenetskiy, Adelia; Mulligan, Kaitlin; Tiras, Carly; Kent, Michaela; Bayet, Laurie; Richards, John; Bosquet_Enlow, Michelle; Nelson, Charles A (March 2025, Developmental Science)

   Functional magnetic resonance imaging (fMRI) studies with adults provide evidence that functional brain networks, including the default mode network and frontoparietal network, underlie executive functioning (EF). However, given the challenges of using fMRI with infants and young children, little work has assessed the developmental trajectories of these networks or their associations with EF at key developmental stages. More recently, functional near‐infrared spectroscopy (fNIRS) has emerged as a promising neuroimaging tool which can provide information on cortical functional networks and can be more easily implemented with young children. Children (N= 207;n= 116 male;n= 167 White) had fNIRS data recorded at infancy, 3, 5, and 7 years of age while watching a 2‐min nonsocial video. At 3, 5, and 7 years, children completed behavioral assessments and parents completed questionnaires to assess child EF abilities. Results showed that, although early functional brain network connectivity was not associated with later functional brain connectivity, EF was concurrently and longitudinally associated with functional connectivity levels in both networks. Overall, these results inform the understanding of early emerging neural underpinnings of regulatory abilities and point to considerable change in the composition of functional brain networks and a conservation of function across development. 

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3. Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer’s disease

   https://doi.org/10.1186/s12920-022-01323-8  

   Kim, Mansu; Wu, Ruiming; Yao, Xiaohui; Saykin, Andrew J.; Moore, Jason H.; Shen, Li (December 2022, BMC Medical Genomics)

   Abstract Background Alzheimer’s disease (AD) is a complex neurodegenerative disorder and the most common type of dementia. AD is characterized by a decline of cognitive function and brain atrophy, and is highly heritable with estimated heritability ranging from 60 to 80 $$\%$$ % . The most straightforward and widely used strategy to identify AD genetic basis is to perform genome-wide association study (GWAS) of the case-control diagnostic status. These GWAS studies have identified over 50 AD related susceptibility loci. Recently, imaging genetics has emerged as a new field where brain imaging measures are studied as quantitative traits to detect genetic factors. Given that many imaging genetics studies did not involve the diagnostic outcome in the analysis, the identified imaging or genetic markers may not be related or specific to the disease outcome. Results We propose a novel method to identify disease-related genetic variants enriched by imaging endophenotypes, which are the imaging traits associated with both genetic factors and disease status. Our analysis consists of three steps: (1) map the effects of a genetic variant (e.g., single nucleotide polymorphism or SNP) onto imaging traits across the brain using a linear regression model, (2) map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model, and (3) detect SNP-diagnosis association via correlating the SNP effects with the diagnostic effects on the brain-wide imaging traits. We demonstrate the promise of our approach by applying it to the Alzheimer’s Disease Neuroimaging Initiative database. Among 54 AD related susceptibility loci reported in prior large-scale AD GWAS, our approach identifies 41 of those from a much smaller study cohort while the standard association approaches identify only two of those. Clearly, the proposed imaging endophenotype enriched approach can reveal promising AD genetic variants undetectable using the traditional method. Conclusion We have proposed a novel method to identify AD genetic variants enriched by brain-wide imaging endophenotypes. This approach can not only boost detection power, but also reveal interesting biological pathways from genetic determinants to intermediate brain traits and to phenotypic AD outcomes. 

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4. A multivariate to multivariate approach for voxel‐wise genome‐wide association analysis

   https://doi.org/10.1002/sim.10101  

   Wu, Qiong; Zhang, Yuan; Huang, Xiaoqi; Ma, Tianzhou; Hong, L Elliot; Kochunov, Peter; Chen, Shuo (August 2024, Statistics in Medicine)

   The joint analysis of imaging‐genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel‐wise genome‐wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)‐voxel pairs. We attempt to identify underlying organized association patterns of SNP‐voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose abi‐cliquegraph structure (ie, a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP‐voxelbi‐cliquesand an inference model for statistical testing. We further provide theoretical results to guarantee the accuracy of our computational algorithms and statistical inference. We validate our method by extensive simulation studies, and then apply it to the whole genome genetic and voxel‐level white matter integrity data collected from 1052 participants of the human connectome project. The results demonstrate multiple genetic loci influencing white matter integrity measures on splenium and genu of the corpus callosum. 

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5. Brain signaling becomes less integrated and more segregated with age

   https://doi.org/10.1162/netn_a_00389  

   Razban, Rostam M; Antal, Botond B; Dill, Ken A; Mujica-Parodi, Lilianne R (May 2024, Network neuroscience)

   The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global vs. local signaling patterns. However, there is no consensus for how to best define what the two states look like. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, Pint and Pseg, from functional MRI data. We find that integration/segregation decreases/increases with age across three databases, and changes are consistent with weakened connection strength among regions rather than topological connectivity based on structural and diffusion MRI data. 

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