Background Functional magnetic resonance imaging (fMRI) has revealed abnormal brain activity patterns in stroke patients, yet the genetic correlates underlying functional homotopy – defined as synchronized spontaneous activity between bilateral homologous brain regions – remain poorly characterized. This study investigates the genetic basis of voxel-mirrored homotopic connectivity (VMHC) abnormalities in stroke patients. Methods We analyzed resting-state fMRI data from 50 stroke patients and 50 healthy controls (HC) to quantify VMHC. Spatial transcriptome-neuroimaging correlations were established using the Allen Human Brain Atlas (AHBA) to identify VMHC-associated genes. Transcriptomic analyses combined pathway-centric functional annotation (DAVID) with protein-protein interaction (PPI) network modeling (STRING v12.0). Results Stroke patients exhibited significantly reduced VMHC in the rectus gyrus, superior temporal gyrus, middle occipital gyrus, cuneus, and right calcarine/left posterior cingulate gyrus (p < 0.05, GRF-corrected). VMHC alterations correlated positively and negatively with 1,198 genes each. Transcriptomic profiling revealed significant enrichment in synaptic vesicle trafficking, mitochondrial energy metabolism, and neuroinflammation-related pathways. PPI mapping uncovered multi-tiered networks with hub genes including BRCA1, CDK9, ACTB, and ATP6V1A/F involved in transcriptional regulation, cytoskeletal dynamics, and vesicular acidification. Conclusion This multimodal integration study elucidates polygenic correlates of post-stroke VMHC abnormalities, demonstrating that interhemispheric coordination may depend on synergistic interactions among functionally diverse gene clusters. Our findings provide a molecular framework for understanding post-stroke neural network reorganization and offer a link between functional neuroimaging phenotypes and gene expression, though all associations remain correlational and require mechanistic validation.
Ri-Bo Chen, Yu-Xuan He, Xin Huang et al.· Frontiers in Cellular Neuros...· 0 citations
BACKGROUND
Primary angle-closure glaucoma (PACG) damages retinal ganglion cells (RGCs) and is associated with neurodegeneration. This study used resting-state functional magnetic resonance imaging (fMRI) to analyze hemispheric lateralization and cooperative functional alterations in PACG. Machine learning assessed the classification efficacy of neuroimaging indicators, while integrated transcriptomics described spatial relationships between neuroimaging changes and genes, neurotransmitters, and cell types.
METHODS
Resting-state fMRI data were collected from 101 subjects (44 patients with PACG and 57 controls). Whole-brain maps of the Autonomy Index and connectivity of functional homotopic voxels (CFH) were constructed. Their classification efficacy was assessed using five machine learning classifiers. Partial Least Squares (PLS) spatial correlation analysis examined relationships between neuroimaging maps and gene-transcript profiles, cell type density, and neurotransmitter-receptor distribution maps.
RESULTS
PACG showed increased Autonomy Index in cerebellar Crus II and the right paracentral lobule, with widespread reductions in interhemispheric cooperation. Five machine learning models yielded classification results. Image-transcriptome analysis showed that positive Autonomy Index and CFH-positive gene features were enriched in synaptic signaling and neurodevelopment, whereas negative Autonomy Index and CFH-negative gene profiles correlated with neurodegeneration, metabolic dysfunction, and vascular-immune responses. Oligodendrocytes and endothelial cells showed spatial associations with the Autonomy Index and CFH. Brain lateralization and interhemispheric cooperation showed spatial correlations with VAChT topology and several neurotransmitter receptor/transporter maps.
CONCLUSION
PACG is characterized by enhanced cerebral lateralization and diminished interhemispheric cooperation. Explainable machine learning evaluated the classification efficacy of these imaging features. These findings provide a multimodal spatial-association framework relating PACG-related imaging alterations to normative molecular and cellular brain maps.
Jing-Wen Qiu, Yuan-Zhi He, Si-Xian Li et al.· Neuroscience· 0 citations
Stroke disrupts the brain's ability to process and integrate information over time, yet the underlying molecular mechanisms remain unclear. This study investigates post-stroke alterations in intrinsic neural timescales (INTs)-a measure of regional temporal integration-by combining resting-state fMRI, spatial transcriptomics, and PET-based neurochemical mapping. Fifty acute ischemic stroke patients (within 7 days of onset) and fifty matched healthy controls were examined. Voxel-wise and network-level analyses revealed significantly reduced INTs in temporoparietal and insular cortices, with pronounced network-level impairments in the visual and cerebellar systems. Using data from the Allen Human Brain Atlas, we identified gene expression patterns associated with these disruptions. Genes negatively associated with INT reductions were enriched for synaptic, mitochondrial, and neurodevelopmental pathways, while positively associated genes reflected immune signaling and nuclear transport. INT alterations also correlated with excitatory and inhibitory neuronal signatures and were spatially aligned with GABA-A receptor density. Importantly, INT reductions showed significant correlations with clinical assessments: whole-brain INT correlated negatively with NIHSS (r = -0.41) and positively with MoCA (r = 0.38) and FMA (r = 0.35); SMN INT correlated with FMA motor subscore (r = 0.44); and regional INT correlated with domain-specific NIHSS subscores (neglect: r = -0.42; language: r = -0.38). These findings position INT as a clinically meaningful systems-level correlate of stroke-induced dysfunction and highlight molecular pathways and neurotransmitter systems that may constrain temporal integration and recovery potential.
Shao-Gao Gui, Zhan-Xiang Hu, Yuan-Zhi He et al.· Brain Research· 0 citations
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