Biology subjects

Soheili-Nezhad, S.

Publications and source records attributed to Soheili-Nezhad, S..

10 recordsLinked to original sources

bioRxiv2026

Population-wide variation in connectopic organization of cerebral language hubs

The uniquely human capacity for language is supported by a distributed brain network with cerebral cortical hubs in the left inferior frontal gyrus (LIFG) and left superior temporal sulcus (LSTS). Individual differences in the graded spatial organization of function across these regions may provide sensitive phenotypes for behavioural and genetic association analyses, to better understand the biological underpinnings of language. In 41,437 UK Biobank participants we applied connectopic mapping to the LIFG and LSTS. This approach maps spatial changes across a region in terms of connectivity with the rest of the brain, based on resting state functional imaging. On average, we found that Brodmann Area (BA) 44, BA 45 and the posterior LSTS were strongly connected to the superior and inferior parietal lobe and the dorsal attention and frontoparietal networks, whereas BA 47 and the medial LSTS were strongly connected to superior frontal areas BA 8B and BA 9, and the default mode network more broadly. However, there was extensive population-wide variation in these graded connectopic patterns, which significantly mediated the effects of polygenic scores for reading ability and educational attainment on performance on a vocabulary task. Furthermore we identified 3 genomic loci displaying multivariate association patterns with these connectopic maps, one of which coincides with a long non-coding RNA, LINC01165, with potential relevance in human evolution. Overall, connectopic mapping produced fewer significant genetic association findings and lower heritability (range from 0.3 to 9.4%) than previous parcel-based approaches for mapping language network connectivity. These observations suggest higher genetic complexity and/or prominent roles for environmental or random developmental effects on these connectopic maps.

neuroscience↗

bioRxiv2025

Integrating brain structure and function for the neurobiology and genetics of language

Brain structure and function have largely been studied separately in relation to the neurobiology and genetics of language. Here we used linked independent component analysis to integrate language network functional connectivity with brain volumetric and white matter structure in 32,677 UK Biobank participants, followed by analysis of behavioural, neurobiological and genetic correlates of the derived multimodal structure-function imaging components. Stronger functional connectivity between brain language areas was associated with increased volume of parts of the cerebellum and motor cortex, together with smaller ventricles and sensory parietal and occipital areas. The brain structure-function language components mediated an association between vocabulary level and polygenic scores for reading ability. We report 18 genomic loci associated with brain structure-function language components. Single-nucleotide polymorphism (SNP)-based heritability estimates for these components were 23-30%, and there was significant enrichment of heritability in primate-conserved genomic loci and fetal brain human-gained enhancer elements. This study revealed that structural correlates of functional language network connectivity extend well beyond previously defined language areas of the brain, and highlights the value of multimodal brain phenotyping for human neurogenetic discovery.

neuroscience↗

bioRxiv2023

Imaging genetics of language network functional connectivity reveals links with language-related abilities, dyslexia and handedness

Language is supported by a distributed network of brain regions with a particular contribution from the left hemisphere. A multi-level understanding of this network requires studying its genetic architecture. We used resting-state imaging data from 29,681 participants (UK Biobank) to measure connectivity between 18 left-hemisphere regions involved in multimodal sentence-level processing, as well as their right-hemisphere homotopes, and interhemispheric connections. Multivariate genome-wide association analysis of this total network, based on genetic variants with population frequencies > 1%, identified 14 genomic loci, of which three were also associated with asymmetry of intrahemispheric connectivity. Polygenic dispositions to lower language-related abilities, dyslexia and left-handedness were associated with generally reduced leftward asymmetry of functional connectivity. Exome-wide association analysis based on rare, protein-altering variants (frequencies[≤] 1%) suggested 7 additional genes. These findings shed new light on genetic contributions to language network organization and related behavioural traits.

genomics↗

bioRxiv2023

Exome-wide analysis implicates rare protein-altering variants in human handedness

Handedness is a manifestation of brain hemispheric specialization. Left-handedness occurs at increased rates in neurodevelopmental disorders. Genome-wide association studies have identified common genetic effects on handedness or brain asymmetry, which mostly involve variants outside protein-coding regions and may affect gene expression. Implicated genes include several that encode tubulins (microtubule components) or microtubule-associated proteins. Here we examined whether left-handedness is also influenced by rare coding variants (frequencies [≤] 1%), using exome data from 38,043 left-handed and 313,271 right-handed individuals (UK Biobank). The beta-tubulin gene TUBB4B showed exome-wide significant association, with a rate of rare coding variants 2.7 times higher in left-handers (0.076%) than right-handers (0.028%). The TUBB4B variants were mostly heterozygous missense changes, but included two frameshifts found only in left-handers. Other TUBB4B variants have been linked to sensorineural and/or ciliopathic disorders, but not the variants found here. Among genes previously implicated in autism or schizophrenia by exome screening, DSCAM and FOXP1 showed evidence for rare coding variant association with left-handedness. The exome-wide heritability of left-handedness due to rare coding variants was 0.91%. This study revealed a role for rare, protein-altering variants in left-handedness, providing further evidence for the involvement of microtubules and disorder-relevant genes.

genetics↗

bioRxiv2022

Reproducibility of Principal and Independent Genomic Components of Brain Structure and Function

IntroductionThe highly polygenic and pleiotropic nature of behavioural traits, psychiatric disorders, and structural and functional brain phenotypes complicate mechanistic interpretation of related genome-wide association study (GWAS) signals, such that the underlying causal biological processes remain obscure. We propose the novel method of genomic principal and independent component analysis (PCA, ICA) to decompose a large set of univariate GWAS statistics of multimodal brain traits into more interpretable latent genomic components. Here we introduce this new method and evaluate its various analytic parameters and reproducibility across independent samples. MethodsTwo releases of GWAS summary statistics from the UK biobank (UKB), with 11,086 and 22,138 participants respectively, were retrieved from the Oxford BIG-40 server. GWAS summary statistics were clumped resulting in n=165,364 single nucleotide polymorphisms (SNP) and m=2,240 imaging derived phenotypes (IDPs). Both genome-wide beta-values and their corresponding, standard-error scaled z-values were decomposed using multivariate exploratory linear optimised decomposition into independent components (MELODIC). We evaluated variance explained at multiple dimensions up to 200. We tested the reproducibility of output of dimensions 5, 10, 25, and 50 by computing Pearsons correlation between component loadings, and Fisher Exact tests on overlap of the top SNP loadings across samples. Reproducibility statistics of the original raw and z-transformed univariate GWAS served as benchmarks. We also inspected the clustering of genomic components across neuroimaging modalities using t-SNE. ResultsThe first five PCs derived from z-transformed GWAS captured 31.9% of the variance across SNP effect sizes, while 200 PCs increased the variance explained to 79.6%. Reproducibility of 10-dimensional PCs and ICs showed the best balance between model complexity and robustness, and variance explained (PCs: |rz-max|=0.33, |rraw-max|=0.30; ICs: |rz-max|=0.23, |rraw-max|=0.19), with decreasing model stability and reproducibility at higher dimensions. Both genomic PC and IC reproducibilities improved substantially relative to mean univariate GWAS reproducibility up to a dimension of 10. Genomic components clustered along neuroimaging modalities. ConclusionOur results indicate that these novel methods of genomic ICA and PCA decompose genetic effects on IDPs from raw GWAS statistics with high reproducibility by taking advantage of the inherent pleiotropic patterns. These findings encourage further applications of genomic ICA and PCA as fully data-driven methods to effectively reduce the dimensionality, enhance the signal to noise ratio, and improve interpretability of high-dimensional multi-trait genome-wide analyses.

neuroscience↗

bioRxiv2021

Longitudinal changes of ADHD symptoms in association with white matter microstructure: a tract-specific fixel-based analysis

BackgroundVariation in the longitudinal course of childhood attention deficit/hyperactivity disorder (ADHD) coincides with neurodevelopmental maturation of brain structure and function. Prior work has attempted to determine how alterations in white matter (WM) relate to changes in symptom severity, but much of that work has been done in smaller cross-sectional samples using voxel-based analyses. Using standard diffusion-weighted imaging (DWI) methods, we previously showed WM alterations were associated with ADHD symptom remission over time in a longitudinal sample of probands, siblings, and unaffected individuals. Here, we extend this work by further assessing the nature of these changes in WM microstructure by including an additional follow-up measurement (aged 18 - 34 years), and using the more physiologically informative fixel-based analysis (FBA). MethodsData were obtained from 139 participants over 3 clinical and 2 follow-up DWI waves, and analyzed using FBA in regions-of-interest based on prior findings. We replicated previously reported significant models and extended them by adding another time-point, testing whether changes in combined ADHD and hyperactivity-impulsivity (HI) continuous symptom scores are associated with fixel metrics at follow-up. ResultsClinical improvement in HI symptoms over time was associated with more fiber density at follow-up in the left corticospinal tract (lCST) (tmax=1.092, standardized effect[SE]=0.044, pFWE=0.016). Improvement in combined ADHD symptoms over time was associated with more fiber cross-section at follow-up in the lCST (tmax=3.775, SE=0.051, pFWE=0.019). Conclusions: Aberrant white matter development involves both lCST micro- and macrostructural alterations, and its path may be moderated by preceding symptom trajectory.

neuroscience↗

bioRxiv2021

Independent Genomic Sources of Brain Structure and Function

IntroductionThe last decade has seen a surge in well powered genome-wide association studies (GWASs) of complex behavioural traits, disorders, and more recently, of brain structural and functional neuroimaging features. However, the extreme polygenicity of these complex traits makes it difficult to translate the GWAS signal into mechanistic biological insights. We postulate that the covariance of SNP-effects across many brain features, as be captured by latent genomic components of SNP effect sizes. These may partly reflect the concerted multi-locus genomic effects through known molecular pathways and protein-protein interactions. Here, we test the feasibility of a new data-driven method to derive such latent components of genome-wide effects on more than thousand neuroimaging derived traits, and investigate their utility in interpreting the complex biological processes that shape the GWAS signal. MethodsWe downloaded the GWAS summary statistics of 3,143 brain imaging-derived phenotypes (IDPs) from the UK Biobank, provided by the Oxford Brain Imaging Genetics (BIG) Server (Elliott et al. 2018). Probabilistic independent component analysis (ICA) was used to extract two hundred independent genomic components from the matrix of SNP-effect sizes. We qualitatively describe the distribution of the latent components loadings in the neuroimaging and the genomic dimensions. Gene-wide statistics were calculated for each genomic component. We tested the genomic components enrichment for molecular pathways using MSigDB, and for single-cell RNA-sequencing of adult and foetal brain cells. Results200 components explained 80% of the variance in SNP-effects sizes. Each MRI modality and data processing method projected the imaging data into a clearly distinct cluster in the genomic component embedded space. Among the 200 genomic components, 157 were clearly driven by a single locus, while 39 were highly polygenic. Together, these 39 components were significantly enriched for 2,274 MSigDB gene sets (fully corrected for multiple testing across gene-sets and components). Several components were sensitive to molecular pathways, single cell expression profiles, and brain traits in patterns consistent with knowledge across these biological levels. To illustrate this, we highlight a component that implicated axonal regeneration pathways, which was specifically enriched for gene expression in oligodendrocyte precursors, microglia and astrocytes, and loaded highly on white matter neuroimaging traits. We highlight a second component that implicated synaptic function and neuron projection organization pathways that was specifically enriched for neuronal cell transcriptomes. ConclusionWe propose genomic ICA as a new method to identify latent genetic factors influencing brain structure and function by multimodal MRI. The derived latent genomic dimensions are highly sensitive to known molecular pathways and cell-specific gene expression profiles. Genomic ICA may help to disentangle the many different biological routes by which the genome defines the inter-individual variation of the brain. Future research is aimed at using this method to profile individual subjects genomic data along the new latent dimensions and evaluating the utility of these dimensions in stratifying heterogeneous patient populations.

neuroscience↗

bioRxiv2020

Associations between ADHD symptom remission and white matter microstructure: a longitudinal analysis

BackgroundAttention-deficit hyperactivity disorder (ADHD) is associated with white matter (WM) microstructure. Our objective was to investigate how WM microstructure is longitudinally related to symptom remission in adolescents and young adults with ADHD. MethodsWe obtained diffusion-weighted imaging (DWI) data from 99 participants at two time points (mean age baseline: 16.91 years, mean age follow-up: 20.57 years). We used voxel-wise Tract-Based Spatial Statistics (TBSS) with permutation-based inference to investigate associations of inattention (IA) and hyperactivity-impulsivity (HI) symptom change with fractional anisotropy (FA) at baseline, follow-up, and change between time points. ResultsRemission of combined HI and IA symptoms was significantly associated with reduced FA at follow-up in the left superior longitudinal fasciculus and the left corticospinal tract (CST) (PFWE=0.038 and PFWE=0.044, respectively), mainly driven by an association between HI remission and follow-up CST FA (PFWE=0.049). There was no significant association of combined symptom decrease with FA at baseline or with changes in FA between the two assessments. ConclusionsIn this longitudinal DWI study of ADHD using dimensional symptom scores, we show that greater symptom decrease is associated with lower follow-up FA in specific WM tracts. Altered FA thus may appear to follow, rather than precede, changes in symptom remission. Our findings indicate divergent WM developmental trajectories between individuals with persistent and remittent ADHD, and support the role of prefrontal and sensorimotor tracts in the remission of ADHD.

neuroscience↗

bioRxiv2019

Reduced fronto-striatal volume in ADHD in two cohorts across the lifespan

ObjectiveNeuroimaging studies have associated Attention-Deficit/Hyperactivity Disorder (ADHD) with altered brain anatomy. However, small and heterogeneous study samples, and the use of region-of-interest and tissue-specific analyses have limited the consistency and replicability of these effects. The present study uses a fully data-driven multivariate approach to investigate alterations in both gray and white matter simultaneously, and capture neuroanatomical features associated with ADHD in two large, independent, demographically different cohorts.\n\nMethodsThe study comprised two ADHD cohorts with structural magnetic resonance imaging data: the Dutch NeuroIMAGE cohort (n=890, average age 17.2 years, discovery sample) and the Brazilian IMpACT cohort (n=180, average age 44.2 years, cross validation sample). Using independent component analysis of whole-brain morphometry images in the NeuroIMAGE cohort, 375 independent components of neuroanatomical variations were extracted and assessed their association with ADHD. Afterwards, ADHD-associated components were cross validated in the Brazilian IMpACT cohort.\n\nResultsIn both discovery (corrected- p=0.020) and validation (p=0.033) cohorts, ADHD diagnosis was significantly associated with reduced brain volume in a component mapping to frontal lobes, striatum, and their interconnecting white-matter tracts. The most pronounced case-control differences were localized in white matter adjacent to the orbitofrontal cortex.\n\nConclusionIndependent component analysis is a sensitive approach to uncover neuroanatomical alterations in ADHD and avoid bias attributable to a priori region-of-interest based methods. Current results provide further evidence for the role of the fronto-striatal circuit in ADHD. The fact that the two cohorts are from different continents and comprising different age ranges highlights the robustness of the findings.

neuroscience↗

bioRxiv2017

Amygdalar atrophy as the genetically mediated hub of limbic degeneration in Alzheimer’s disease

Pharmacological progress, basic science and medical practice can benefit from objective biomarkers that assist in early diagnosis and prognostic stratification of diseases. In the field of Alzheimers disease (AD), the clinical presentation of early stage dementia may not fulfill any diagnostic criteria for years, and quantifying structural brain changes by magnetic resonance imaging (MRI) has shown promise in the discovery of sensitive biomarkers. Although hippocampal atrophy is often used as an in vivo measure of AD, data-driven neuroimaging has revealed complex patterns of regional brain vulnerability that may not perfectly map to anatomical boundaries. In addition to aiding diagnosis, decoding genetic influences on neuroimaging measures of the disease can enlighten molecular mechanisms of the underlying pathology in living patients and guide the therapeutic design.\n\nHere, we aimed to extract a data-driven MRI feature of brain atrophy in AD by decomposing structural neuroimages using independent component analysis (ICA), a method for performing unbiased computational search in high dimensional data spaces. Our study of the AD Neuroimaging Initiative dataset (n=1,100 subjects) revealed a disease-vulnerable feature with a network-like topology, comprising amygdala, hippocampus, fornix and the inter-connecting white-matter tracts of the limbic system. Whole-genome sequencing identified a nonsynonymous variant (rs34173062) in SHARPIN, a gene coding for a synaptic protein, as a significant modifier of this new MRI feature (p=2.1x10-10). The risk variant was brought to replication in the UK Biobank dataset (n=8,428 subjects), where it was associated with reduced cortical thickness in areas co-localizing with those of the discovery sample (left entorhinal cortex p=0.002, right entorhinal cortex p=8.6x10-4; same direction), as well as with the history of AD in both parents (p=2.3x10-6; same direction).\n\nIn conclusion, our study shows that ICA can transform voxel-wise volumetric measures of the brain into a data-driven feature of neurodegeneration in AD. Structure of the limbic system, as the most vulnerable focus of brain atrophy in AD, is affected by genetic variability of SHARPIN. The elevated risk of dementia in carriers of the minor allele supports engagement of SHARPIN in the disease pathways, and its role in neurotransmitter receptor scaffolding and integrin signaling may inform on new molecular mechanisms of AD pathophysiology.\n\nAbbreviationsAlzheimers disease (AD), genome-wide association study (GWAS), independent component analysis (ICA), mild cognitive impairment (MCI), medial temporal circuit (MTC), single-nucleotide polymorphism (SNP), tensor-based morphometry (TBM)

neuroscience↗