Biology subjects

Nagarajan, S.

Publications and source records attributed to Nagarajan, S..

42 records · Page 3Linked to original sources

bioRxiv2019

Spectral Graph Theory of Brain Oscillations

The relationship between the brains structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectomes Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled non-linear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30Hz) activity estimated from source localized scalp magneto-encephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. Significance StatementThe relationship between the brains structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectomes Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics.

neuroscience↗

bioRxiv2019

The FACTS model of speech motor control: fusing state estimation and task-based control

We present a new computational model of speech motor control: the Feedback-Aware Control of Tasks in Speech or FACTS model. This model is based on a state feedback control architecture, which is widely accepted in non-speech motor domains. The FACTS model employs a hierarchical observer-based architecture, with a distinct higher-level controller of speech tasks and a lower-level controller of speech articulators. The task controller is modeled as a dynamical system governing the creation of desired constrictions in the vocal tract, based on the Task Dynamics model. Critically, both the task and articulatory controllers rely on an internal estimate of the current state of the vocal tract to generate motor commands. This internal state estimate is derived from initial predictions based on efference copy of applied controls. The resulting state estimate is then used to generate predictions of expected auditory and somatosensory feedback, and a comparison between predicted feedback and actual feedback is used to update the internal state prediction. We show that the FACTS model is able to qualitatively replicate many characteristics of the human speech system: the model is robust to noise in both the sensory and motor pathways, is relatively unaffected by a loss of auditory feedback but is more significantly impacted by the loss of somatosensory feedback, and responds appropriately to externally-imposed alterations of auditory and somatosensory feedback. The model also replicates previously hypothesized trade-offs between reliance on auditory and somatosensory feedback in speech motor control and shows for the first time how this relationship may be mediated by acuity in each sensory domain. These results have important implications for our understanding of the speech motor control system in humans.

neuroscience↗

bioRxiv2018

Identifiability in connectome based neural mass models

The spatial-temporal patterns of neuronal dynamics emerge from the network of coordinated brain regions, this structure-function relationship of the brain can be described mathematically by biophysical models of coupled brain regions connected by white matter tractography. Implementations of such models have focused on reproducing functional connectivity extracted from functional magnetic resonance imaging (fMRI), but these efforts are limited by the temporal resolution of fMRI data and the reduction of time course recordings into phenomenological functional connectivity maps. Here, we optimize parameters of a neural mass model (NMM) to best fit region-wise power spectra across the whole brain estimated from source localized electroencephalography (EEG). NMM models with global parameters were not able to fully reproduce region-wise power spectra, with or without the inclusion of structural connectivity information. In contrast, without the inclusion of structural connectivity information, independent oscillators at each brain region are able to reproduce region-wise power spectra. But the addition of structural connectivity and transmission delays to the NMM does not improve overall power spectra fit. Connectome-based NMM implementations with regional parameters lead to high dimensional network models that produce non-unique results. Inherent parameter identifiability problem in network models poses challenges for using such models as diagnostic tools for neurological diseases.

neuroscience↗

bioRxiv2018

Establishing a causal role for medial prefrontal cortex in reality monitoring

Reality monitoring is defined as the ability to distinguish internally self-generated information from externally-derived information. Functional imaging studies have consistently found that the medial prefrontal cortex (mPFC) is a key brain region subserving reality monitoring. The aim of this study was to determine a causal role for mPFC in reality monitoring using navigated repetitive transcranial magnetic stimulation (nrTMS). In a subject-blinded sham-controlled crossover design, healthy individuals received either active or sham nrTMS targeting mPFC. Active modulation of mPFC using nrTMS at a frequency of 10 Hz, significantly improved identification of both self-generated and externally-derived information during reality monitoring, when compared to sham or baseline. Targeted excitatory modulation of mPFC also improved positive mood ratings, reduced negative mood ratings and increased overall alertness/arousal. These results establish optimal nrTMS dosing parameters that maximized tolerability/comfort and induced significant neuromodulatory effects in the mPFC target. Importantly, this is a proof-of-concept study that establishes the mPFC as a novel brain target that can be stimulated with nrTMS to causally impact both mood and higher-order reality monitoring.

neuroscience↗

bioRxiv2018

Beta-band activity in medial prefrontal cortex predicts source memory encoding and retrieval accuracy

Reality monitoring is defined as the ability to distinguish internally self-generated information from externally-derived information. The medial prefrontal cortex (mPFC) is a key brain region subserving reality monitoring and has been shown to be activated specifically during the retrieval of self-generated information. However, it is unclear if mPFC is activated during the encoding of self-generated information into memory. If so, it is important to understand whether successful retrieval of self-generated information critically depends on enhanced neural activity within mPFC during initial encoding of this self-generated information.\n\nWe used magnetoencephalographic imaging (MEGI) to determine the timing and location of cortical activity during a reality-monitoring task involving self generated contextual source memory encoding and retrieval. We found both during encoding and retrieval of self-generated information, when compared to externally-derived information, mPFC showed significant task induced oscillatory power modulation in the beta-band. During initial encoding of self-generated information, greater mPFC beta-band power reductions occurred within a time window of -700ms to -500ms prior to vocalization, activity in mPFC that was not observed during encoding of externally-derived information. This mPFC activity during encoding of self-generated information predicted subsequent retrieval accuracy of self-generated information. Beta-band activity in mPFC was also observed during the initial retrieval of self-generated information within a time window of 300 to 500ms following stimulus onset and correlated with accurate retrieval performance of self-generated information. Together, these results further highlight the importance of mPFC in mediating the initial generation and awareness of participants internal thoughts.

neuroscience↗

bioRxiv2018

Bidirectional Control of Coronary Vascular Resistance by Eicosanoids via a Novel GPCR

Arachidonic acid metabolites epoxyeicosatrienoates (EETs) and hydroxyeicosatetraenoates (HETEs) are important regulators of myocardial blood flow and coronary vascular resistance (CVR), but their mechanisms of action are not fully understood. We identified G protein-coupled receptor 39 (GPR39) as a microvascular smooth muscle cell (mVSMC) receptor antagonistically regulated by two endogenous eicosanoids: 15-HETE, which stimulates GPR39 to increase mVSMC intracellular calcium and augment microvascular CVR, and 14,15-EET, which inhibits these actions. Furthermore, zinc ion acts as an allosteric modulator of GPR39 to potentiate the efficacy of the two ligands. Our findings will have a major impact on understanding the roles of eicosanoids in cardiovascular physiology and disease, and provide an opportunity for the development of novel GPR39-targeting therapies for cardiovascular disease.\n\nOne Sentence SummaryGPR39 is a microvascular smooth muscle cell receptor regulated by two vasoactive eicosanoids with opposing actions.

physiology↗