liang, y. | bioRxiv Science

bioRxiv2025

Investigating the Mechanisms and Potential Therapeutic Targets of Vestibular Migraine

BackgroundVestibular Migraine (VM) is a complex neurological disorder with recurrent headaches and various vestibular symptoms. Although it severely affects patients quality of life, its causes and pathophysiology are still unclear, and effective treatments are scarce. The lack of data emphasizes the need for bioinformatics to find key genes and pathways in VM, which could help develop new diagnostic and treatment methods. MethodThe GSE109558 dataset was acquired from the Gene Expression Omnibus (GEO) database. To identify VM - related differentially expressed genes, screening was carried out through limma and Weighted Correlation Network Analysis (WGCNA).The functional analysis of VM - related differentially expressed genes was conducted using three bioinformatics approaches: Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA). Feature selection was further refined using lasso regression and random forest. Also, CIBERSORT was utilized to analyze the infiltration of immune cells, and Spearmans correlation analysis was employed to explore the correlations between diagnostic differentially expressed genes and immune cells.Finally, the Comparative Toxicogenomics Database (CTD) was utilized to search for corresponding drugs, and molecular docking was performed to explore potential therapeutic targets. ResultSix key feature genes (CABIN1, IFIT3, HEATR1, ARHGDIA, RAB11FIP4, and ZNF444) were identified as potential diagnostic markers for VM. Among these, CABIN1 demonstrated the highest diagnostic potential based on ROC curve performance, highlighting its promise as a diagnostic biomarker.Functional annotation of DEGs revealed their enrichment in biological processes related to inflammation, calcium ion channel regulation, and other pathways likely involved in VM pathophysiology. Through the CTD, drugs like Acetaminophen, bisphenol A, and Phenylephrine were identified. Molecular docking simulation was used to explore their potential therapeutic mechanisms for VM. ConclusionThis study offers important insights into the molecular mechanisms of VM and identifies six key feature genes, with CABIN1 standing out as a potential diagnostic marker.These findings pave the way for further research to validate the diagnostic and therapeutic implications of these genes and pathways.

genetics↗

bioRxiv2022

Imaging the Neural Substrate of Trigeminal Neuralgia Pain Using Deep Learning

Trigeminal neuralgia (TN) is a severe and disabling facial pain condition and is characterized by intermittent, severe, electric shock-like pain in one (or more) trigeminal subdivisions. This pain can be triggered by an innocuous stimulus or can be spontaneous. Presently available therapies for TN include both surgical and pharmacological management; however, the lack of a known etiology for TN contributes to the unpredictable response to treatment and the variability in long-term clinical outcomes. Given this, a range of peripheral and central mechanisms underlying TN pain remain to be understood. We acquired functional magnetic resonance imaging (fMRI) data from TN patients who (1) rested comfortably in the scanner during a resting state session and (2) rated their pain levels in real time using a calibrated tracking ball-controlled scale in a pain tracking session. Following data acquisition, the data was analyzed using the conventional correlation analysis and two artificial intelligence (AI)-inspired deep learning methods: convolutional neural network (CNN) and graph convolutional neural network (GCNN). Each of the three methods yielded a set of brain regions related to the generation and perception of pain in TN. There were six regions that were identified by all three methods, including the superior temporal cortex, the insula, the fusiform, the precentral gyrus, the superior frontal gyrus, and the supramarginal gyrus. Additionally, 17 regions, including dorsal anterior cingulate cortex(dACC) and the thalamus, were identified by at least two of the three methods. Collectively, these 23 regions represent signature centers of TN pain and provide target areas for future studies relating to central mechanisms of TN.

neuroscience↗