Biology subjects

Shevchenko, V.

Publications and source records attributed to Shevchenko, V..

5 recordsLinked to original sources

bioRxiv2026

Early sensory deprivation drives local reorganization of sensory integration within a conserved global hierarchy

The human brain processes sensory information through a hierarchical system, from primary to higher-level regions, integrating inputs across modalities to support perception and cognition. While early sensory loss triggers widespread neuroplastic changes, its impact on integration across the cortical hierarchy remains unclear. Here, we examined the cortical reorganization of individuals with early blindness and deafness using a sensory integration framework that quantifies how brain regions prioritize different sensory inputs across the hierarchy. We found that early sensory deprivation drives highly localized reorganization adjacent to the deprived primary cortical areas: extrastriate cortex in early blindness and the superior temporal cortex in early deafness. These findings were further corroborated by analysis of the functional gradients, which found reorganization within these sensory regions. Notably, the hierarchy was largely preserved across groups. However, the sensory integration framework uniquely detected reorganization in language-related regions in deaf individuals with knowledge of a visual communication system known as cued speech. The specific differences between early deaf and hearing individuals remained restricted to superior temporal cortex. Together, our findings demonstrate that early sensory deprivation drives targeted reorganization adjacent to the affected primary sensory cortex, while preserving the overall hierarchy of cortical integration.

neuroscience↗

bioRxiv2025

Spatial layout of visual specialization is shaped by competing default mode and sensory networks

Understanding how the brain encodes information started with a map but turned into a maze: paths multiplied; boundaries blurred. Neurons tuned to specific features are not confined to single regions, but distributed across the cortex. Retinotopy, once thought limited to early visual areas, now appears in over 20 cortical regions--from the tip of the occipital cortex to the shore of the lateral frontal cortex. To describe and understand these complex mosaics of functional specialization, we focus on the spatial influences that shape their emergence across the cortical sheet. To this end, we developed Spatial Component Decomposition (SCD), a sparse dictionary learning framework that locates sources of spatial influence without relying on prior assumptions from systems neuroscience. Applied to MRI data capturing retinotopic maps, SCD reveals a dominant linear gradient extending over 60 mm from V1 and covering all the known posterior visual areas. Yet, it also revealed systematic competition from other primary sensory areas and default mode transmodal hubs. These suppressive influences shape the cortical embedding of visual information, even during purely visual tasks. Our results suggest that functional specialization emerges from spatial competition between representational systems--not just from feedforward inputs.

neuroscience↗

bioRxiv2025

The intrinsic cortical geometry of reading

How does the brain support the complex processes that allow us to read? Using predictive modeling we establish that visual and association cortex are closer together in individuals with stronger oral reading ability. These findings indicate that large-scale cortical geometry provides a scaffold that supports the coordinated processing required to read.

neuroscience↗

bioRxiv2024

Individual brain activity patterns during task are predicted by distinct resting-state networks that may reflect local neurobiological features

Understanding how individual cortical features shape functional brain organization offers a promising framework for examining the principles of cognitive specialization in the human brain. This study explores the relationship between various cortical characteristics--i.e resting-state functional connectivity, structural connectivity, microstructure, morphology, and geometry--and the layout of task-specific functional activations. We employ linear models to predict the functional layout of the cortex at the individual level from each of these feature modalities. Our findings demonstrate that resting-state component loadings predict individual task activations, consistently across hemispheres and independent datasets. Whereas the first few components provide a common space for functional activations across tasks, predictive higher-order component loadings demonstrated task-specificity. Cortical microstructure/morphology was notably predictive of activation strength in the occipital cortex, highlighting its relevance for cortical functional specialization. By relating resting state components to a set of reference maps of cortical organization, we identify associations that suggest possible neurobiological underpinnings of specific cognitive functions. The remaining feature modalities were only predictive of group-level functional activations. These results advance our understanding of how distinct cortical features may contribute to functional specialization, guiding future inquiry into the organization of cognitive functions on the cortex.

neuroscience↗

bioRxiv2024

A Comparative Machine Learning Study of Connectivity-Based Biomarkers of Schizophrenia

Functional connectivity holds promise as a biomarker of psychiatric disorders. Yet, its high dimensionality, combined with small sample sizes in clinical research, increases the risk of overfitting when the aim is prediction. Recently, low-dimensional representations of the connectome such as macroscale cortical gradients and gradient dispersion have been proposed, with studies noting consistent gradient and dispersion differences in psychiatric conditions. However, it is unknown which of these derived measures has the highest predictive capacity and how they compare to raw connectivity. Our study evaluates which connectome features -- functional connectivity, gradients, or gradient dispersion -- best identify schizophrenia. Figure 1 summarizes this work. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=41 SRC="FIGDIR/small/573898v1_fig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@12c0fdorg.highwire.dtl.DTLVardef@13c4702org.highwire.dtl.DTLVardef@59f8d7org.highwire.dtl.DTLVardef@e00f56_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Overview of the methods and main outcome of the paper. Schematic images: Flaticon.com. NC: neurotypical controls, SCZ: patients with schizophrenia. C_FIG Surprisingly, our findings indicate that functional connectivity outperforms its low-dimensional derivatives such as cortical gradients and gradient dispersion in identifying schizophrenia. Additionally, we demonstrated that the edges which contribute the most to classification performance are the ones connecting primary sensory regions.

neuroscience↗