Vincon, N. | bioRxiv Science

bioRxiv2025

Three cortical streams for somatosensory information processing

AbstractThe somatosensory cortex processes information hierarchically, transforming sensory input into appropriate responses. This hierarchy, in turn, provides a fundamental principle for the organization of anatomical and functional properties across the somatosensory cortex. While the local somatosensory hierarchy has been studied, a comprehensive model that fully illustrates somatosensory information transmission in fine detail remains lacking. In this study, we examine multimodal connectivity patterns of the entire macaque somatosensory cortex by integrating the information from receptor covariance (RC) and structural (SC) or functional connectivity (FC). Our findings not only reveal the hierarchical relationships but also propose a model of somatosensory processing streams. In this model, area 3bl serves as the initial cortical stage for somatosensory signals, projecting to areas 3al, 1, and 2. From there, somatosensory signals follow three major pathways: ventrally to the SII complex, medially to the medial SI and TSA, and posteriorly to somatosensory association areas in the parietal lobe. Further analysis shows that RC is not only closely linked to SC and FC but in addition displays unique characteristics that likely relate to the hierarchical processing across sensory modalities. This study deepens our understanding of brain connectivity patterns across different modalities and links the structural, chemoarchitectonic, and functional organization of the macaque somatosensory cortex.

neuroscience↗

bioRxiv2022

A Deep Learning solution for triaging patient with cancer from their predicted mutational status using histopathological images

The presence of mutation in cancer can be associated with a response to a targeted cancer therapy. Therefore it has become an important information while it helps giving a more specialized and more efficient treatment for each patient. Detection of mutation is routinely made by DNA-sequencing diagnostic tests. Recently, a new strategy of deep learning based method of prediction of mutation from histopathological images has shown promising results. However it is still unknown whether these methods can be useful as screening tests aside from sequencing methods for efficient population diagnosis. Here we developed our own deep learning based prediction pipeline for the detection of mutation in breast, lung, colon and ovarian cancer and find clinically relevant genes that have easy-to-detect mutational status, and especially TP53 which can be detected in these four tumors. We then propose 3 potential screening strategies and show the effectiveness of our deep learning pipeline for optimizing diagnosis in the patient population.

bioinformatics↗