Biology subjects

Venkadesh, S.

Publications and source records attributed to Venkadesh, S..

8 recordsLinked to original sources

bioRxiv2026

Cortical asymmetry and its developmental change have distinct relationships with brain connectivity

The two cerebral hemispheres specialize while remaining interconnected. Does greater structural asymmetry imply less connectivity, and does connectivity also relate to how asymmetry changes during development? We addressed these questions in three independent cohorts spanning birth to 22 years, measuring asymmetry as the magnitude of the left-right difference in association tract overlap across 74 homotopic cortical region pairs. The regional pattern of asymmetry was similar across cohorts. In all three cohorts, more asymmetric regions had lower connectional density to cortical and non-cortical targets. However, involvement of the corpus callosum, the major pathway connecting the hemispheres, showed no consistent inverse relationship with regional asymmetry across cohorts. Instead, callosal involvement conditioned the relationship between mean regional asymmetry and its age-related change. In the two older cohorts, greater mean asymmetry accompanied more positive age effects among regions with appreciable callosal overlap, whereas this relationship was weak or negative among regions with little callosal overlap (standardized interactions +0.42 and +0.56). The pattern replicated across the two cohorts at matched ages, and longitudinal region-mean estimates in the Adolescent Brain Cognitive Development cohort were similar in direction and magnitude. In the youngest cohort, regions converged toward the mean, narrowing the between-region spread by 12%, with no detected callosal moderation. More asymmetric regions have lower overall connectional density, whereas callosal involvement helps explain how regional asymmetry changes with age.

neuroscience↗

bioRxiv2026

Reproducibility of Diffusion, Shape, and Connectivity Metrics Across Scanners: Implications for Multi-Site Tractography

Multi-site diffusion MRI studies face scanner-induced variability that can obscure biological signal. Harmonization methods such as ComBat have been developed to address this, but have been evaluated primarily on diffusion scalar metrics. Whether scanner reproducibility differs across fundamentally distinct tract-derived representations has not been systematically compared. Here, we compared the reproducibility of three metric families (diffusion, shape, and connectivity) across 36 association tracts using the MASiVar dataset (5 subjects, 4 scanners, 27 sessions). We assessed intraclass correlation coefficients (ICC) and multivariate subject discrimination at baseline, under dimensionality reduction, and after ComBat harmonization. At baseline, shape metrics showed the highest reproducibility (median ICC 0.69), followed by connectivity (0.49) and diffusion (0.34). Shape and connectivity achieved comparable subject discrimination (both 1.75), significantly exceeding diffusion (1.23). ComBat harmonization improved all families but harmonized diffusion (0.58) remained below unharmonized shape (0.69), indicating that metric family selection remains consequential even after harmonization. Under low-dimensional representation, connectivity showed the largest gains (ICC 0.86, subject discrimination 3.0), exceeding other families at any dimensionality. Analysis of principal component loadings identified a small number of cortical regions per tract (median 6) that capture 95% of the reproducible connectivity signal, providing a per-tract reference for selecting the most informative regions in future multi-site studies. These findings indicate that the choice of which tract-derived metrics to analyze in multi-site studies deserves at least as much consideration as how to harmonize them.

neuroscience↗

bioRxiv2025

Cross-species brain circuitry from diffusion MRI tractography and mouse viral tracing

Understanding how brain networks support cognition requires knowing not only which regions are connected but the direction in which neuronal signals propagate. However, directed connectivity cannot be measured non-invasively in primates or humans, limiting systems-level inference. Here we integrate projection polarity from [~]1,200 mouse viral tracing experiments with species-specific diffusion MRI tractography to construct directed connectomes across mouse, marmoset, macaque, and human. Using a common cross-species atlas, we developed a path efficiency metric that balances projection strength against axonal length and applied shortest-path algorithms to quantify directional influence. This framework revealed conserved and divergent organization: the entorhinal-hippocampal projection was the most efficient in all species; humans showed strengthened anterior insula-superior temporal pathways; macaques exhibited peak inferior temporal outflows; and marmosets maintained disproportionately strong olfactory influence. These results provide a scalable and ethically feasible approach for modeling directed neuronal signaling across species.

neuroscience↗

bioRxiv2025

A common cross-species atlas of cortical gray matter

Translational neuroscience requires consistent anatomical frameworks to compare brain organization across species despite differences in size and specialization. Existing atlases are species-specific, limiting cross-species analyses. Here we developed a hierarchical common atlas delineating homologous cortical and subcortical gray matter regions across mouse, rat, marmoset, rhesus macaque, and human, built upon population-averaged minimal deformation templates and uniform tissue segmentation. The atlas was validated through cross-atlas Dice similarity against established human parcellations, cross-scale containment against species-specific atlases, cross-species geometric consistency of regional positioning, and comparison of independent mouse and marmoset invasive tracer connectivity, with a per-region homology confidence index quantifying the strength of each regional assignment. Cross-species tracer comparison revealed a structured gradient of correspondence, with sensorimotor connections showing strong conservation and association connections showing progressive divergence. This freely available atlas provides a unified coordinate system for comparative neuroscience, enabling quantitative evaluation of where cross-species correspondence holds and where species-specific divergence emerges.

neuroscience↗

bioRxiv2022

A Simple Model of Cortical Intraregional Metastability

Transient synchronization of bursting activity in neural networks, which occurs in patterns of metastable phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neural circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, emerges in an inhibitory neural population when the neurons intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neural population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of neural oscillations and the features of emergent metastability. In addition, a novel metric is formulated to characterize collective transitions in metastable networks. Finally, we discuss a blueprint to model the whole-brain resting-state dynamics using our scalable representation of intraregional network metastability.

neuroscience↗

bioRxiv2020

Itinerant complexity in networks of intrinsically bursting neurons

Active neurons can be broadly classified by their intrinsic oscillation patterns into two classes characterized by periodic spiking or periodic bursting. Here we show that networks of identical bursting neurons with inhibitory pulsatory coupling exhibit itinerant dynamics. Using the relative phases of bursts between neurons, we numerically demonstrate that the network exhibits endogenous transitions among multiple modes of transient synchrony. This is true even for bursts consisting of two spikes. In contrast, our simulations reveal that identical singlet-spiking neurons do not exhibit such complexity in the network. These results suggest a role for bursting dynamics in realizing itinerant complexity in neural circuits.

neuroscience↗

bioRxiv2019

Simple models of quantitative firing phenotypes in hippocampal neurons: comprehensive coverage of intrinsic diversity

Patterns of periodic voltage spikes elicited by a neuron help define its dynamical identity. Experimentally recorded spike trains from various neurons show qualitatively distinguishable features such as delayed spiking, spiking with/without frequency adaptation, and intrinsic bursting. Moreover, the input-dependent responses of a neuron not only show different quantitative features, such as higher spike frequency for a stronger input current injection, but can also exhibit qualitatively different responses, such as spiking and bursting under different input conditions, thus forming a complex phenotype of responses. In a previous work, Hippocampome.org, a comprehensive knowledgebase of hippocampal neuron types, systematically characterized various spike pattern phenotypes experimentally identified from 120 neuron types/subtypes. In this paper, we present a comprehensive set of simple phenomenological models that quantitatively reproduce the diverse and complex phenotypes of hippocampal neurons. In addition to point-neuron models, we created compact multi-compartment models with up to four compartments, which will allow spatial segregation of synaptic integration in network simulations. Electrotonic compartmentalization observed in our compact multi-compartment models is qualitatively consistent with experimental observations. Furthermore, we observed that adding dendritic compartments to point-neuron models, in general, allowed soma to reproduce features of bursting patterns and abrupt non-linearities in some frequency adapting patterns slightly more accurately. This work maps 120 neuron types/subtypes in the rodent hippocampus to a low-dimensional model space and adds another dimension to the knowledge accumulated in Hippocampome.org. Computationally efficient representations of intrinsic dynamics, along with other pieces of knowledge available in Hippocampome.org, provide a biologically realistic platform to explore the dynamical interactions of various types at the mesoscopic level.\n\nAuthor SummaryThe neurons in the hippocampus show enormous diversity in their intrinsic activity patterns. A comprehensive characterization of various intrinsic types using a neuronal modeling system is necessary to simulate biologically realistic networks of brain regions. Morphologically detailed neuronal modeling frameworks often limit the scalability of such network simulations due to the specification of hundreds of rules governing each neurons intrinsic dynamics. In this work, we have accomplished a comprehensive mapping of experimentally identified intrinsic dynamics in a simple modeling system with only two governing rules. We have created over a hundred point-neuron models that reflect the intrinsic differences among the hippocampal neuron types both qualitatively and quantitatively. In addition, we compactly extended our point-neurons to include up to four compartments, which will allow anatomically finer-grained connections among the neurons in a network. Our compact model representations, which are freely available in Hippocampome.org, will allow future researchers to investigate dynamical interactions among various intrinsic types and emergent integrative properties using scalable, yet biologically realistic network simulations.

neuroscience↗

bioRxiv2017

Firing pattern classification and phenotyping in a knowledge base of hippocampal neuron types

Systematically organizing the anatomical, molecular, and physiological properties of cortical neurons is important for understanding their computational functions. Hippocampome.org defines 122 neuron types in the rodent hippocampal formation (dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex) based on their somatic, axonal, and dendritic locations, putative excitatory/inhibitory outputs, molecular marker expression, and biophysical properties such as time constant and input resistance. Here we augment the electrophysiological data of this knowledge base by collecting, quantifying, and analyzing the firing responses to depolarizing current injections for every hippocampal neuron type from available published experiments. We designed and implemented objective protocols to classify firing patterns based on both transient and steady-state activity. Specifically, we identified 5 transients (delay, adapting spiking, rapidly adapting spiking, transient stuttering, and transient slow-wave bursting) and 4 steady states (non-adapting spiking, persistent stuttering, persistent slow-wave bursting, and silence). By characterizing the set of all firing responses reported for hippocampal neurons, this automated classification approach revealed 9 unique families of firing pattern phenotypes while distinguishing potential new neuronal subtypes. Several novel statistical associations also emerged between firing responses and other electrophysiological properties, morphological features, and molecular marker expression. The firing pattern parameters, complete experimental conditions (including solution and stimulus details), digitized spike times, exact reference to the original empirical evidence, and analysis scripts are released open-source through Hippocampome.org for all neuron types, greatly enhancing the existing search and browse capabilities. This information, collated online in human-and machine-accessible form, will help design and interpret both experiments and hippocampal model simulations.\n\nSignificance StatementComprehensive characterization of nerve cells is essential for understanding signal processing in biological neuronal networks. Firing patterns are important identification characteristics of neurons and play crucial roles in information coding in neural systems. Building upon the comprehensive knowledge base Hippocampome.org, we developed and implemented automated protocols to classify all known firing responses exhibited by each neuron type of the rodent hippocampus based on analysis of transient and steady-state activity. This approach identified the most distinguishing elements of every firing phenotype and revealed previously unnoticed statistical associations of firing responses with other electrophysiological, morphological, and molecular properties. The resulting data, freely released online, constitute a powerful resource for designing and interpreting experiments as well as developing and testing hippocampal models.

neuroscience↗