Biology subjects

Bharmauria, V.

Publications and source records attributed to Bharmauria, V..

12 recordsLinked to original sources

bioRxiv2026

Linear and Nonlinear Oscillatory Functional Brain Networks in Euthymic Bipolar Disorder Classification

Functional brain network (FBN) dysconnectivity has been repeatedly reported in bipolar disorder (BD). However, it remains unclear how this dysconnectivity manifests from the perspective of oscillatory FBNs, that is, which network measures and frequency bands most reliably capture this alteration. Moreover, it is unknown whether this dysconnection is predominantly expressed through linear or nonlinear interactions. Here, we investigated properties of oscillatory FBNs in individuals with euthymic BD. Networks were constructed using linear and nonlinear connectivity measures applied to source-localized resting-state electroencephalography (EEG) current density signals. We then quantified whole-FBN and nodal features using conventional and spectral graph theory methods to characterize disorder-related network mechanisms and evaluate their potential as biomarkers. Significant group differences between BD and control groups were observed in the theta and alpha1 bands. Dynamical whole-FBN alterations were detected primarily in linear oscillatory FBNs, with reduced Shannon entropy and energy in the BD group. These effects were replicated using machine learning, achieving 85% classification accuracy with entropy and energy as the most informative features. In contrast, nodal-level differences emerged mainly in nonlinear FBNs, revealing increased centrality in frontal, and decreased centrality across temporal, and limbic regions. These findings emphasize distinct, frequency-specific roles of linear and nonlinear oscillatory FBNs in BD, with global dysconnectivity reflected in linear FBNs and local alterations captured by nonlinear connectivity. Moreover, network measures related to synchronization stability and complexity more effectively capture BD-related dysconnectivity, suggesting that dynamic features of oscillatory FBNs may serve as potential biomarkers for BD.

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bioRxiv2025

High-Frequency Spinal Cord Stimulation Reorganizes Cortical Cross-Frequency Coupling in a Region- and Time-Dependent Manner

Pain management strategies have progressed beyond traditional pharmacologic and physical interventions, integrating advanced neuromodulation techniques such as deep brain stimulation, peripheral nerve stimulation, and high-frequency spinal cord stimulation (hSCS). Despite its clinical efficacy, the supraspinal mechanisms underlying hSCS remain poorly understood. Prior work in sheep demonstrated that hSCS modulates gamma ({gamma}) band (70-150 Hz) activity in the primary somatosensory and association cortices, implicating cortical involvement in pain modulation. Given, the interaction between low and high oscillations, we hypothesized that hSCS modulates {gamma} activity in a region- and time-dependent manner through specific coupling with theta ({square}) rhythms (4-8 Hz). Using 96-channel subdural electrocorticography (ECoG), we computed {square}-{gamma} phase-amplitude coupling (PAC) and the corresponding modulation index (MI) to quantify the effects of hSCS. While the preferred {square}phase of {gamma} activity remained consistent across conditions and regions, MI increased significantly post-stimulation--most prominently in the association cortex, where robust -{gamma} phase locking was observed. In contrast, the somatosensory cortex exhibited weaker and more variable locking. Temporally, both cortices demonstrated an early, rapid increase in MI post-hSCS, accompanied by a shift (association) and attenuation (somatosensory) of the secondary peak. These findings reveal distinct regional and temporal dynamics in PAC following hSCS and suggest complementary roles of somatosensory and association cortices in processing neuromodulatory input. hSCS appears to reorganize cortical cross-frequency interactions, supporting its role in reorganizing functional network dynamics relevant to sensory processing and the subjective pain experience.

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bioRxiv2024

Distinct mechanisms of visual and sound adaptation in the cat visual cortex

Sensory areas exhibit modular selectivity to stimuli, but they can also respond to features outside of their basic modality. Several studies have shown cross-modal plastic modifications between visual and auditory cortices; however, the exact mechanisms of these modifications are yet not completely known. To this aim, we investigated the effect of 12 minutes of visual vs. sound adaptation [forceful application of a non-optimal stimulus to a neuron(s) under observation] on the infra- and supra-granular primary visual neurons (V1) of the cat (Felis catus). Previous reports showed that both protocols induced orientation tuning shifts, but sound increased the bandwidths. Here, we compared visual vs. sound adaptation effects, specifically analysing the raw tuning curves by computing the area under the curve (AUC) on a trial-by-trial basis. We report that sound adaptation elicited broader tuning curves accompanied with increased variance in the supra- and infra-granular layers, compared with visual adaptation. These findings suggest unique modulation of dendritic structure by distinct adaptation protocols, resulting in disparate tunings. We suggest that broader tuning curves after sound adaptation may keep the visual cortex prepared across a spectrum of abstract representations that match with visual stimuli.

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bioRxiv2023

Adaptation-induced sharpening of orientation tuning curves in the mouse visual cortex

Orientation selectivity is an emergent property of visual neurons across species with columnar and non-columnar organization of the visual cortex. To compute the orientation selectivity of a neuron, a tuning function is fit on the raw responses of a neuron and then a measure, termed orientation selectivity index (OSI), is derived from this fitted curve to determine the tuning of the neuron. Previously, it has been shown that adaptation (a protocol where a neuron under observation is presented a non-optimal stimulus for a specific time) has varying effects on the tuning properties of neurons, such as, orientation, spatial frequency, motion etc. The emergence of OSI is more established in columnar cortices than the non-columnar ones. However, how adaptation impinges upon the OSI of the latter has not been systematically investigated. Here, in the mouse primary visual cortex (V1), we show that a 12-min adaptation protocol sharpens the OSI (tuning) of the visual neurons, underlying a specific dendritic neural mechanism, potentially facilitating the learning of novel features.

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bioRxiv2023

Multiunit frontal eye field activity codes the visuomotor transformation, but not gaze prediction or target memory, in a delayed saccade task

Single-unit (SU) activity - action potentials isolated from one neuron -- has traditionally been employed to relate neuronal activity to behavior. However, recent investigations have shown that multi-unit (MU) activity - ensemble neural activity recorded within the vicinity of one microelectrode - may also contain accurate estimations of task-related neural population dynamics. Here, using a well-established model-fitting approach, we compared the spatial codes of SU response fields with corresponding MU response fields recorded from the frontal eye fields (FEF) in head-unrestrained monkeys (Macaca mulatta) during a memory-guided saccade task. We focused on characterizing the visuomotor transformation from Target-in-Eye coordinates to future Gaze-in-Eye coordinates (Sajad et al., 2015). Most SU visual response fields coded targets (with some predicting Gaze), whereas the MU population only coded targets. Most SU motor responses coded Gaze, but many still retained a target code. In contrast, MU motor activity predominantly coded Gaze with very little target coding. Finally, both SU and MU populations showed a progressive transition through intermediate Target-to-Gaze codes during the delay period, but the MU activity showed a smoother transition. These results confirm the theoretical and practical potential of MU activity recordings as a biomarker for fundamental sensorimotor transformations (e.g., Target-to-Gaze coding in the oculomotor system), while also highlighting the importance of SU activity for coding more cognitive (e.g., predictive / memory) aspects of sensorimotor behavior. SIGNIFICANCE STATEMENTMulti-unit recordings (undifferentiated signals from several neurons) are relatively easy to record and provide a simplified estimate of neural dynamics, but it is not clear which single-unit signals are retained, amplified, or lost. Here, we compared single-/multi-unit activity from a well-defined structure (the frontal eye fields) and behavior (memory-delay saccade task), tracking their spatial codes through time. The progressive transformation from target to gaze coding observed in single-unit activity was retained in multi-unit activity, but gaze prediction (in the visual response) and target memory (in the motor response) were lost. This suggests that multi-unit activity provides an excellent biomarker for healthy sensorimotor transformations, at the cost of missing more subtle cognitive signals.

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bioRxiv2021

Integration of allocentric and egocentric visual information in a convolutional / multilayer perceptron network model of goal-directed gaze shifts

Allocentric (landmark-centered) and egocentric (eye-centered) visual codes are fundamental for spatial cognition, navigation, and goal-directed movement. Neuroimaging and neurophysiology suggest these codes are segregated initially, but then reintegrated in frontal cortex for movement control. We created and validated a theoretical framework for this process using physiologically constrained inputs and outputs. To implement a general framework, we integrated a Convolutional Neural Network (CNN) of the visual system with a Multilayer Perceptron (MLP) model of the sensorimotor transformation. The network was trained on a task where a landmark shifted relative to the saccade target. These visual parameters were input to the CNN, the CNN output and initial gaze position to the MLP, and a decoder transformed MLP output into saccade vectors. Decoded saccade output replicated idealized training sets with various allocentric weightings, and actual monkey data where the landmark shift had a partial influence (R2 = 0.8). Furthermore, MLP output units accurately simulated prefrontal response field shifts recorded from monkeys during the same paradigm. In summary, our model replicated both the general properties of the visuomotor transformations for gaze and specific experimental results obtained during allocentric-egocentric integration, suggesting it can provide a general framework for understanding these and other complex visuomotor behaviors.

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bioRxiv2021

Gaze-Related Activity in Primate Frontal Cortex Predicts and Mitigates Spatial Uncertainty

A remarkable feature of primate behavior is the ability to predict future events based on past experience and current sensory cues. To understand how the brain plans movements in the presence of unstable cues, we recorded gaze-related activity in the frontal cortex of two monkeys engaged in a quasi-predictable cue-conflict task. Animals were trained to look toward remembered visual targets in the presence of a landmark that shifted with fixed amplitude but randomized direction. As simulated by a probabilistic model based on known physiology/behavior, gaze end points assumed a circular distribution around the target, mirroring the possible directions of the landmark shift. This predictive strategy was reflected in frontal cortex activity (especially supplementary eye fields), which anticipated future gaze distributions before the actual landmark shift. In general, these results implicate prefrontal cortex in the predictive integration of environmental cues and their learned statistical properties to mitigate spatial uncertainty.

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bioRxiv2020

Landmark-Centered Coding in Frontal Cortex Visual Responses

Visual landmarks influence spatial cognition [1-3], navigation [4,5] and goal-directed behavior [6-8], but their influence on visual coding in sensorimotor systems is poorly understood [6,9-11]. We hypothesized that visual responses in frontal cortex control gaze areas encode potential targets in an intermediate gaze-centered / landmark-centered reference frame that might depend on specific target-landmark configurations rather than a global mechanism. We tested this hypothesis by recording neural activity in the frontal eye fields (FEF) and supplementary eye fields (SEF) while head-unrestrained macaques engaged in a memory-delay gaze task. Visual response fields (the area of visual space where targets modulate activity) were tested for each neuron in the presence of a background landmark placed at one of four oblique configurations relative to the target stimulus. 102 of 312 FEF and 43 of 256 SEF neurons showed spatially tuned response fields in this task. We then fit these data against a mathematical continuum between a gaze-centered model and a landmark-centered model. When we pooled data across the entire dataset for each neuron, our response field fits did not deviate significantly from the gaze-centered model. However, when we fit response fields separately for each target-landmark configuration, the best fits shifted (mean 37% / 40%) toward landmark-centered coding in FEF / SEF respectively. This confirmed an intermediate gaze / landmark-centered mechanism dependent on local (configuration-dependent) interactions. Overall, these data show that external landmarks influence prefrontal visual responses, likely helping to stabilize gaze goals in the presence of variable eye and head orientations. HighlightsO_LIPrefrontal visual responses recorded in the presence of visual landmarks C_LIO_LIResponse fields showed intermediate gaze / landmark-centered organization C_LIO_LIThis influence depended on specific target-landmark configurations C_LI

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bioRxiv2020

Spatiotemporal coding in the macaque supplementary eye fields: landmark influence in the target-to-gaze transformation

Eye-centered (egocentric) and landmark-centered (allocentric) visual signals influence spatial cognition, navigation and goal-directed action, but the neural mechanisms that integrate these signals for motor control are poorly understood. A likely candidate for ego / allocentric integration in the gaze control system is the supplementary eye fields (SEF), a mediofrontal structure with high-level executive functions, spatially tuned visual / motor response fields, and reciprocal projections with the frontal eye fields (FEF). To test this hypothesis, we trained two head-unrestrained animals to saccade toward a remembered visual target in the presence of a visual landmark that shifted during the delay, causing gaze end points to shift partially in the same direction. 256 SEF neurons were recorded, including 68 with spatially tuned response fields. Model fits to the latter established that, like the FEF and superior colliculus, spatially tuned SEF responses primarily showed an egocentric (eye-centered) target-to-gaze position transformation. However, the landmark shift influenced this default egocentric transformation: during the delay, motor neurons (with no visual response) showed a transient but unintegrated shift (i.e., not correlated with the target-to-gaze transformation), whereas during the saccade-related burst visuomotor neurons showed an integrated shift (i.e., correlated with the target-to-gaze transformation). This differed from our simultaneous FEF recordings (Bharmauria et al., 2020), which showed a transient shift in visuomotor neurons, followed by an integrated response in all motor responses. Based on these findings and past literature, we propose that prefrontal cortex incorporates landmark-centered information into a distributed, eye-centered target-to-gaze transformation through a reciprocal prefrontal circuit.

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bioRxiv2019

Integration of eye-centered and landmark-centered codes in frontal eye field gaze responses

The visual system is thought to separate egocentric and allocentric representations, but behavioral experiments show that these codes are optimally integrated to influence goal-directed movements. To test if frontal cortex participates in this integration process, we recorded primate frontal eye field (FEF) activity during a cue-conflict memory delay saccade task. To dissociate egocentric and allocentric coordinates, we surreptitiously shifted a visual landmark during the delay period, causing saccades to deviate by 37% in the same direction. To assess the cellular mechanisms, we fit neural response fields against an egocentric (eye centered target-to-gaze) continuum, and an allocentric shift (eye-to-landmark centered) continuum. Initial visual responses best fit target position. Motor responses (after the landmark shift) predicted future gaze position but embedded within the motor code was a 29% shift toward allocentric coordinates. This shift appeared transiently in memory-related visuomotor activity, and then reappeared in motor activity before saccades. Notably, fits along the egocentric and allocentric shift continua were initially independent, but became correlated just before the motor burst. Overall, these results implicate frontal cortex in the integration of egocentric and allocentric visual information for goal-directed action, and demonstrate the cell-specific, temporal progression of signal multiplexing for this process in the gaze system.

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bioRxiv2019

Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

To characterize differences between different state-related brain networks, statistical graph theory approaches have been employed to identify informative, topological properties. However, dynamical properties have been studied little in this regard. Our goal here was to introduce spectral graph theory as a reliable approach to determine dynamic properties of functional brain networks and to find how topological versus dynamical features differentiate between such networks. To this goal, 45 participants performed no task with eyes open (EO) or closed (EC) while electroencephalography data were recorded. These data were used to create weighted adjacency matrices for each condition (rest state EO and rest state EC). Then, using the spectral graph theory approach and Shannon entropy, we identified dynamical properties for weighted graphs, and we compared these features with topological aspects of graphs. The results showed that spectral graph theory can distinguish different state-dependent neural networks with different synchronies. On the other hand, correlation analysis indicated that although dynamical and topological properties of random networks are completely independent, these network features can be related in the case of brain generated graphs. In conclusion, the spectral graph theory approach can be used to make inferences about various state-related brain networks, for healthy and clinical populations.\n\nAuthor SummeryBy considering functional communications across different brain regions, a complex network is achieved that is known as functional brain network. Topologically, this network is constructed by different nodes (activity of brain regions or signals over recording electrodes) and different edges (similarity, correlation or phase difference between nodes). Paths, clusters, hubs, and centrality of nodes are examples of topological properties of these networks. However, synchrony and stability of functional brain networks can not be revealed by consideration of topological properties. Alternatively, spectral graph theory (SGT) can demonstrate the dynamic, synchrony and stability of graphs. But this approach has been studied little in brain network analysis. Here, we employed SGT, as well as topological methods, to investigate which approaches are more reliable to find differences between distinct state-related brain networks. On the other hand, we investigated correlations between topology and dynamic in different type of networks (brain generated and random networks). We found that SGT measures can clearly distinguish between distinct state-related brain networks and it can reveal synchrony and complexity of these networks. Also, results show that although dynamic and topology of random-generated graph are completely independent, these properties exhibit several correlations in the case of functional brain networks.

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bioRxiv2018

Sound induces change in orientation preference of V1 neurons: Audio-visual cross-influence

In the cortex, demarcated unimodal sensory regions often respond to unforeseen sensory stimuli and exhibit plasticity. The goal of the current investigation was to test evoked responses of primary visual cortex (V1) neurons when an adapting auditory stimulus is applied in isolation. Using extracellular recordings in anesthetized cats, we demonstrate that, unlike the prevailing observation of only slight modulations in the firing rates of the neurons, sound imposition in isolation entirely shifted the peaks of orientation tuning curves of neurons in both supra- and infragranular layers of V1. Our results suggest that neurons specific to either layer dynamically integrate features of sound and modify the organization of the orientation map of V1. Intriguingly, these experiments present novel findings that the mere presentation of a prolonged auditory stimulus may drastically recalibrate the tuning properties of the visual neurons and highlight the phenomenal neuroplasticity of V1 neurons.\n\nHighlightsO_LIProlonged application of sound shifts preferred orientation of neurons in area 17 of cats.\nC_LIO_LIOrientation tuning shifts in the individual neurons in supra- and infragranular layers of V1.\nC_LIO_LISupra- and infragranular layers of V1 operate together but also as distinct compartments following sound adaptation.\nC_LIO_LIVisual cortex is specifically and dynamically impacted by non-visual stimuli.\nC_LIO_LISound adaptation certainly modifies the organization of orientation maps in V1.\nC_LI

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