Integrating Multimodal Neuroimaging and Genetics: A Structurally-Linked Sparse Canonical Correlation Analysis Approach DOI Creative Commons
Jiwon Chung, Sunghun Kim, Ji Hye Won

et al.

IEEE Journal of Translational Engineering in Health and Medicine, Journal Year: 2024, Volume and Issue: 12, P. 659 - 667

Published: Jan. 1, 2024

Neuroimaging genetics represents a multivariate approach aimed at elucidating the intricate relationships between high-dimensional genetic variations and neuroimaging data. Predominantly, existing methodologies revolve around Sparse Canonical Correlation Analysis (SCCA), framework we expand to 1) encompass multiple imaging modalities 2) promote simultaneous identification of structurally linked features across modalities. The brain regions were assessed using diffusion tensor imaging, which quantifies presence neuronal fibers, thereby grounding our in biologically well-founded prior knowledge within SCCA model. In proposed framework, leverage T1-weighted MRI functional (fMRI) time series data delineate both structural characteristics brain. Genetic variations, specifically single nucleotide polymorphisms (SNPs), are also incorporated as modality. Validation methodology was conducted simulated dataset large-scale normative from Human Connectome Project (HCP). Our demonstrated superior performance compared methods on revealed interpretable gene-imaging associations real dataset. Thus, lays groundwork for underpinnings structure function, providing novel insights into field neuroscience. code is available https://github.com/mungegg.

Language: Английский

A Unifying Hypothesis for the Genome Dynamics Proposed to Underly Neuropsychiatric Phenotypes DOI Open Access

George S. Gericke

Published: March 22, 2024

The sheer number of gene variants and the extent observed clinical molecular heterogeneity recorded in neuropsychiatric disorders (NPDs), could be due to magnified downstream effects initiated by a smaller group genomic higher order alterations response endogenous or environmental stress. Chromosomal common fragile sites (CFS) are functionally linked with microRNA’s, copy (CNVs), sub-microscopic deletions duplications DNA, rare single-nucleotide (SNVs/SNPs) small insertions/deletions (indels), as well chromosomal translocations, duplications, altered methylation, microRNA L1 transposon activity 3-D topology characteristics. These structural features have been various NPDs mostly isolated reports, usually only viewed areas harboring potential candidate genes interest. suggestion use level entry point, (the ‘fragilome’ associated features), activated central mechanism (‘stress’) for studying NPD genetics, has unify existing vast different observations this field. This approach may explain continuum findings distributed between affected unaffected individuals, clustering phenotypes overlapping comorbidities, extensive association certain other medical disorders.

Language: Английский

Citations

1
Polygenic scores for autism are associated with neurite density in adults and children from the general population DOI Creative Commons
Yuanjun Gu, Eva-Maria Stauffer, Saashi A. Bedford

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 13, 2024

Abstract Genetic variants linked to autism are thought change cognition and behaviour by altering the structure function of brain. Although a substantial body literature has identified structural brain differences in autism, it is unknown whether autism-associated common genetic changes cortical macro- micro-structure. We investigated this using neuroimaging data from adults (UK Biobank, N = 31,748) children (ABCD, 4,928). Using polygenic scores correlations we observe robust negative association between for magnetic resonance imaging derived phenotype neurite density (intracellular volume fraction) general population. This result consistent across both adults, cortex white matter tracts, confirmed correlations. There were no sex association. Mendelian randomisation analyses provide evidence causal relationship intracellular fraction, although should be revisited better powered instruments. Overall, study provides shared variant genetics density.

Language: Английский

Citations

1
Genetic architecture of brain morphology and overlap with neuropsychiatric traits DOI

Yi‐Jun Ge,

Yan Fu, Weikang Gong

et al.

Trends in Genetics, Journal Year: 2024, Volume and Issue: unknown

Published: May 1, 2024

Language: Английский

Citations

1
Identifying the genetic association between the cerebral cortex and fibromyalgia DOI
Aihui Liu, Jing Wang, Tianyu Jin

et al.

Cerebral Cortex, Journal Year: 2024, Volume and Issue: 34(8)

Published: Aug. 1, 2024

Abstract Fibromyalgia (FM) is a central sensitization syndrome that strongly associated with the cerebral cortex. This study used bidirectional two-sample Mendelian randomization (MR) analysis to investigate causality between FM and cortical surface area thickness of 34 brain regions. Inverse variance weighted (IVW) was as primary method for this study, sensitivity analyses further supported results. The forward MR revealed genetically determined thinner in parstriangularis (OR = 0.0567 mm, PIVW 0.0463), caudal middle frontal 0.0346 0.0433), rostral 0.0285 0.0463) FM. Additionally, reduced pericalcarine 0.9988 mm2, 0.0085) an increased risk Conversely, reverse indicated region (β −0.0035 0.0265), fusiform 0.0024 SE 0.0012, 0.0440), supramarginal −9.3938 0.0132), postcentral regions −6.3137 0.0360). Reduced gyrus shown have significant relationship prevalence causal analysis.

Language: Английский

Citations

1
Integrating Multimodal Neuroimaging and Genetics: A Structurally-Linked Sparse Canonical Correlation Analysis Approach DOI Creative Commons
Jiwon Chung, Sunghun Kim, Ji Hye Won

et al.

IEEE Journal of Translational Engineering in Health and Medicine, Journal Year: 2024, Volume and Issue: 12, P. 659 - 667

Published: Jan. 1, 2024

Neuroimaging genetics represents a multivariate approach aimed at elucidating the intricate relationships between high-dimensional genetic variations and neuroimaging data. Predominantly, existing methodologies revolve around Sparse Canonical Correlation Analysis (SCCA), framework we expand to 1) encompass multiple imaging modalities 2) promote simultaneous identification of structurally linked features across modalities. The brain regions were assessed using diffusion tensor imaging, which quantifies presence neuronal fibers, thereby grounding our in biologically well-founded prior knowledge within SCCA model. In proposed framework, leverage T1-weighted MRI functional (fMRI) time series data delineate both structural characteristics brain. Genetic variations, specifically single nucleotide polymorphisms (SNPs), are also incorporated as modality. Validation methodology was conducted simulated dataset large-scale normative from Human Connectome Project (HCP). Our demonstrated superior performance compared methods on revealed interpretable gene-imaging associations real dataset. Thus, lays groundwork for underpinnings structure function, providing novel insights into field neuroscience. code is available https://github.com/mungegg.

Language: Английский

Citations

1