Biology subjects

Duraivel, S.

Publications and source records attributed to Duraivel, S..

4 recordsLinked to original sources

bioRxiv2026

Monosynaptic connections link functionally similar regions in human cortex

The functional organization of human cortex has been mapped in considerable detail1-5, yet the axonal connections linking these areas remain largely unknown. This gap precludes inferring either the computational pathways through cortical networks or the principles governing why each area connects to its particular target regions. Here, in patients undergoing intracranial monitoring for epilepsy, we used concurrent electrical stimulation and functional magnetic resonance imaging (es-fMRI), validated previously against tracer studies in nonhuman primates6, to map anatomical connectivity of cortical sites across the whole brain, and combined these maps with task and resting-state fMRI in the same individuals. Es-fMRI revealed four main findings. First, connectivity followed an asymmetric functional-similarity principle: connected sites tended to share similar functional profiles, but, because connectivity is sparse, most pairs of functionally similar sites were not connected. Second, although connectivity also declines with distance7, the functional profile explained three times as much variance in connectivity as distance did, and long-range connections were the most functionally specific. Third, es-fMRI revealed direct monosynaptic links between established functional regions, including between the fusiform face area (FFA)2 and the temporoparietal junction (TPJ)3 for social cognition. Fourth, resting-state functional connectivity (rsFC) of a stimulation site was only weakly correlated with that sites es-fMRI connectivity. Together, these results provide a first principled link between anatomical connectivity and functional organization in the human cortex, and establish es-fMRI as a scalable approach to building a tracer-grade connectome of the human brain.

neuroscience↗

bioRxiv2026

Partition Coefficients Reveal Changes in Properties of Low-Contrast Biomolecular Condensates

Biomolecular condensates are domains within cells with distinct compositions, held together by intermolecular cohesion. They are implicated in a variety of cellular processes, and in vitro studies have revealed the molecular driving forces that underly their condensation. However, in vitro condensates do not capture essential features of cellular condensates. In particular, enrichment of proteins, quantified by partition coefficients, is often exaggerated in these simplified systems. We show that the addition of free amino acids and other small molecules to model condensates can bring their partition coefficients within physiological range. In this limit, where there is low biochemical contrast between condensates and their surroundings, we observe striking changes to condensate behavior. Such low-contrast condensates exhibit large fluctuations in shape and composition and show enhanced sensitivity to changes in their environment. These behaviors reflect dramatic shifts to their material properties, including interfacial tension, rheology, and chemical susceptibilities. We note remarkable similarities in these effects across seemingly unrelated two-phase fluid systems. To explain these trends, we reformulate classic models of critical phenomena in terms of partition coefficients. This framework simplifies application of theory to experiments with near-critical fluids and suggests new experimental approaches for assessing condensate physiology in live cells.

biophysics↗

bioRxiv2025

Shared latent representations of speech production for cross-patient speech decoding

Speech brain-computer interfaces (BCIs) can restore communication in individuals with neuromotor disorders who are unable to speak. However, current speech BCIs limit patient usability and successful deployment by requiring large volumes of patient-specific data collected over long periods of time. A promising solution to facilitate usability and accelerate their successful deployment is to combine data from multiple patients. This has proven difficult, however, due to differences in user neuroanatomy, varied placement of electrode arrays, and sparse sampling of targeted anatomy. Here, by aligning patient-specific neural data to a shared latent space, we show that speech BCIs can be trained on data combined across patients. Using canonical correlation analysis and high-density micro-electrocorticography (ECoG), we uncovered shared neural latent dynamics with preserved micro-scale speech information. This approach enabled cross-patient decoding models to achieve improved performance relative to patient-specific models facilitated by the high resolution and broad coverage of ECoG. Our findings support future speech BCIs that are more accurate and rapidly deployable, ultimately improving the quality of life for people with impaired communication from neuromotor disorders.

neuroscience↗

bioRxiv2022

Accurate speech decoding requires high-resolution neural interfaces

Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One promising solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed novel, high-resolution, micro-electrocorticographic (ECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57x higher spatial resolution and 48% higher signal-to-noise ratio compared to standard invasive recordings. This increased signal quality improved phoneme decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show for the first time that ECoG can enable high-quality speech decoding, demonstrating its ability to improve neural interfaces for neural speech prostheses.

neuroscience↗