Biology subjects

Bahuguna, J.

Publications and source records attributed to Bahuguna, J..

10 recordsLinked to original sources

bioRxiv2026

Maximum entropy model reveals frequent brain state switching in psychotic disorders in a multiversal brain function analysis

To design network-based treatments, it is critical to understand dynamic changes in brain networks over time. We comprehensively examined network structure using regional activation-only, pairwise-coactivation-only, and systems-level maximum entropy models (MEM) applied to the Human Connectome Project-Early Psychosis (HCP-EP) resting fMRI data (patients=109, controls=56). The MEM integrates regional activation levels and pairwise correlation-based functional connectivity (FC), providing the potential for better characterization of clinically meaningful brain states and how they change over time. Using the HCP/Glasser atlas to define brain regions within the default mode (DMN) and dorsal attention (DAN) networks, group differences in regional activation, graph measures of FC, and MEM features such as transition rates between minima in an energy landscape along with correlations with cognitive and psychopathological measures were examined. Psychosis was associated with reduced activation in several regions and multiple FC graph metric alterations. MEM demonstrated a wider variety of DMN and DAN activation/deactivation configurations, with more frequent switching between them, more pronounced network configuration differences and higher basin transition rates with reduced basin dwell times suggesting temporally unstable network configurations, meaning the brain cannot rely on any particular configuration for cognitive processing. Activation of only three regions correlated with working memory, but none of the FC metrics did. MEM features, in particular basin transitions and total energy, correlated negatively with working memory. Our findings suggest that MEM provides unique information, specifically attenuated inter-network connectivity with reduced stability of DMN and DAN brain states, to characterize networks as targets and their features as markers for novel treatment development.

neuroscience↗

bioRxiv2024

How cortico-basal ganglia-thalamic subnetworks can shift decision policies to maximize reward rate

All mammals exhibit flexible decision policies that depend, at least in part, on the cortico-basal ganglia-thalamic (CBGT) pathways. Yet understanding how the complex connectivity, dynamics, and plasticity of CBGT circuits translate into experience-dependent shifts of decision policies represents a longstanding challenge in neuroscience. Here we present the results of a computational approach to address this problem. Specifically, we simulated decisions driven by CBGT circuits under baseline, unrewarded conditions using a spiking neural network, and fit an evidence accumulation model to the resulting behavior. Using canonical correlation analysis, we then replicated the identification of three control ensembles (responsiveness, pliancy and choice) within CBGT circuits, with each of these subnetworks mapping to a specific configuration of the evidence accumulation process. We subsequently simulated learning in a simple two-choice task with one optimal (i.e., rewarded) target and found that feedback-driven dopaminergic plasticity on cortico-striatal synapses effectively manages the speed-accuracy tradeoff so as to increase reward rate over time. The learning-related changes in the decision policy can be decomposed in terms of the contributions of each control ensemble, whose influence is driven by sequential reward prediction errors on individual trials. Our results provide a clear and simple mechanism for how dopaminergic plasticity shifts subnetworks within CBGT circuits so as to maximize reward rate by strategically modulating how evidence is used to drive decisions. Author summaryThe task of selecting an action among multiple options can be framed as a process of accumulating streams of evidence, both internal and external, up to a decision threshold. A decision policy can be defined by the unique configuration of factors, such as accumulation rate and threshold height, that determine the dynamics of the evidence accumulation process. In mammals, this process is thought to be regulated by low dimensional subnetworks, called control ensembles, within the cortico-basal ganglia-thalamic (CBGT) pathways. These control ensembles effectively act by tuning specific aspects of evidence accumulation during decision making. Here we use simulations and computational analysis to show that synaptic plasticity at the cortico-striatal synapses, mediated by choice-related reward signals, adjusts CBGT control ensemble activity in a way that improves accuracy and reduces decision time to maximize the increase of reward rate during learning.

neuroscience↗

bioRxiv2024

Arkypallidal neurons in the external globus pallidus can mediate inhibitory control by altering competition in the striatum

Reactive inhibitory control is crucial for survival. Traditionally, this control in mammals was attributed solely to the hyperdirect pathway, with cortical control signals flowing unidirectionally from the subthalamic nucleus (STN) to basal ganglia output regions. Yet recent findings have put this model into question, suggesting that the STN is assisted in stopping actions through ascending control signals to the striatum mediated by the external globus pallidus (GPe). Here we investigate this suggestion by harnessing a biologically-constrained spiking model of the corticobasal ganglia-thalamic (CBGT) circuit that includes pallidostriatal pathways originating from arkypallidal neurons. Through a series of experiments probing the interaction between three critical inhibitory nodes (the STN, arkypallidal cells, and indirect path-way spiny projection neurons), we find that the GPe acts as a critical mediator of both ascending and descending inhibitory signals in the CBGT circuit. In particular, pallidostriatal pathways regulate this process by weakening the direct pathway dominance of the evidence accumulation process driving decisions, which increases the relative suppressive influence of the indirect pathway on basal ganglia output. These findings delineate how pallidostriatal pathways can facilitate action cancellation by managing the bidirectional flow of information within CBGT circuits.

neuroscience↗

bioRxiv2024

Subject-specific maximum entropy model of resting state fMRI shows diagnostically distinct patterns of energy state distributions

ObjectiveExisting neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. MethodMEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. ResultsThe model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. ConclusionsDiagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.

neuroscience↗

bioRxiv2023

Energy in functional brain states correlates with cognition in adolescent schizophrenia and healthy persons

Adolescent-onset schizophrenia (AOS) is rare, under-studied, and associated with more severe cognitive impairments and poorer outcomes than adult-onset schizophrenia. Neuroimaging has shown altered regional activations (first-order effects) and functional connectivity (second-order effects) in AOS compared to controls. The pairwise maximum entropy model (MEM) integrates first- and second-order factors into a single quantity called energy, which is inversely related to probability of occurrence of brain activity patterns. We take a combinatorial approach to study multiple brain-wide MEMs of task-associated components; hundreds of independent MEMs for various sub-systems are fit to 7 Tesla functional MRI scans. Acquisitions were collected from 23 AOS individuals and 53 healthy controls while performing the Penn Conditional Exclusion Test (PCET) for executive function, which is known to be impaired in AOS. Accuracy of PCET performance was significantly reduced among AOS compared to controls. A majority of the models showed significant negative correlation between PCET scores and the total energy attained over the fMRI. Across all instantiations, the AOS group was associated with significantly more frequent occurrence of states of higher energy, assessed with a mixed effects model. An example MEM instance was investigated further using energy landscapes, which visualize high and low energy states on a low-dimensional plane, and trajectory analysis, which quantify the evolution of brain states throughout this landscape. Both supported patient-control differences in the energy profiles. Severity of psychopathology was correlated positively with energy. The MEMs integrated representation of energy in task-associated systems can help characterize pathophysiology of AOS, cognitive impairments, and psychopathology.

neuroscience↗

bioRxiv2023

CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making

Here we introduce CBGTPy, a virtual environment for designing and testing goal-directed agents with internal dynamics that are modeled on the cortico-basal-ganglia-thalamic (CBGT) pathways in the mammalian brain. CBGTPy enables researchers to investigate the internal dynamics of the CBGT system during a variety of tasks, allowing for the formation of testable predictions about animal behavior and neural activity. The framework has been designed around the principle of flexibility, such that many experimental parameters in a decision making paradigm can be easily defined and modified. Here we demonstrate the capabilities of CBGTPy across a range of single and multi-choice tasks, highlighting the ease of set up and the biologically realistic behavior that it produces. We show that CBGTPy is extensible enough to apply to a range of experimental protocols and to allow for the implementation of model extensions with minimal developmental effort. Author summaryWe introduce a toolbox for producing biologically realistic simulations of the cortico-basal ganglia-thalamic dynamics during a variety of experimental tasks. The purpose is to foster the theory-experiment cycle, offering a tool for generating testable predictions of behavioral and neural responses that can be validated experimentally, in a framework that allows for simple updating as new experimental evidence emerges. We outline how our toolbox works and demonstrate its performance on a set of normative cognitive tasks.

neuroscience↗

bioRxiv2023

Interdependence patterns of multi-frequency oscillations predict visuomotor behavior

We show that sensorimotor behavior can be reliably predicted from single-trial EEG oscillations fluctuating in a coordinated manner across brain regions, frequency bands and movement time epochs. We define high-dimensional oscillatory portraits to capture the interdependence between basic oscillatory elements, quantifying oscillations occurring in single-trials at specific frequencies, locations and time epochs. We find that the general structure of the element-interdependence networks (effective connectivity) remains stable across task conditions, reflecting an intrinsic coordination architecture and responds to changes in task constraints by subtle but consistently distinct topological reorganizations. Trial categories are reliably and significantly better separated using oscillatory portraits, than from the information contained in individual oscillatory elements, suggesting an inter-element coordination-based encoding. Furthermore, single-trial oscillatory portrait fluctuations are predictive of fine trial-to-trial variations in movement kinematics. Remarkably, movement accuracy appears to be reflected in the capacity of the oscillatory coordination architecture to flexibly update as an effect of movement-error integration.

neuroscience↗

bioRxiv2022

Competition between action plans tracks with evidence accumulation during flexible decision-making

Changing your mind requires shifting the way streams of information lead to a decision. Using in silico experiments we show how the cortico-basal ganglia-thalamic (CBGT) circuits can feasibly implement shifts in the evidence accumulation process. When action contingencies change, dopaminergic plasticity redirects the balance of power, both within and between action representations, to divert the flow of evidence from one option to another. This finding predicts that when competition between action representations is highest, the rate of evidence accumulation is lowest. We then validate this prediction in a sample of homo sapiens as they perform an adaptive decision-making task while whole-brain hemodynamic responses are recorded. These results paint a holistic picture of how CBGT circuits manage and adapt the evidence accumulation process in mammals. One-sentence SummaryInteractions between cortical and subcortical circuits in the mammalian brain flexibly control the flow of information streams that drive decisions by shifting the balance of power both within and between action representations.

neuroscience↗

bioRxiv2019

Uncoupling the roles of firing rates and spike bursts in shaping the GPe-STN beta band oscillations.

The excess of 15-30 Hz ({beta}-band) oscillations in the basal ganglia is one of the key signatures of Parkinsons disease (PD). The STN-GPe network is integral to generation and modulation of {beta} band oscillations in basal ganglia. However, the role of changes in the firing rates and spike bursting of STN and GPe neurons in shaping these oscillations has remained unclear. In order to uncouple their effects, we studied the dynamics of STN-GPe network using numerical simulations. In particular, we used a neuron model, in which firing rates and spike bursting can be independently controlled. Using this model, we found that while STN firing rate is predictive of oscillations but GPe firing rate is not. The effect of spike bursting in STN and GPe neurons was state-dependent. That is, only when the network was operating in a state close to the border of oscillatory and non-oscillatory regimes, spike bursting had a qualitative effect on the {beta} band oscillations. In these network states, an increase in GPe bursting enhanced the oscillations whereas an equivalent proportion of spike bursting in STN suppressed the oscillations. These results provide new insights into the mechanisms underlying the transient {beta} bursts and how duration and power of {beta} band oscillations may be controlled by an interplay of GPe and STN firing rates and spike bursts. Author summaryThe STN-GPe network undergoes a change in firing rates as well as increased bursting during excessive {beta} band oscillations during Parkinsons disease. In this work we uncouple their effects by using a novel neuron model and show that presence of oscillations is contingent on the increase in STN firing rates, however the effect of spike bursting on oscillations depends on the network state. In a network state on the border of oscillatory and non-oscillatory regime, GPe spike bursting strengthens oscillations. The effect of spike bursting in the STN depends on the proportion of GPe neurons bursting. These results suggest a mechanism underlying a transient {beta} band oscillation bursts often seen in experimental data.

neuroscience↗

bioRxiv2016

Activity Dynamics and Signal Representation in Striatal Network Model with Distance-dependent Connectivity

Striatum is predominantly inhibitory and the main input nucleus of the basal ganglia. A functional characterization of its activity dynamics is crucial for understanding the mechanisms underlying phenomenon such as action selection and initiation. Here, we investigated the effects of the spatial connectivity structure on the emergence and maintenance of localized bumps of activity in large-scale striatal networks (~10,000 neurons). We show that in striatal network model in which the distance-dependent connection probability varies in a Gaussian fashion (Gaussian networks), the activity remains asynchronous irregular (AI) and spatially homogeneous, independent of the background input. By contrast, when the distance-dependent connectivity varies according to a Gamma distribution (Gamma networks), with short-range connectivity suppressed, a repertoire of activity dynamics can be observed: While weak background inputs induce spatially homogeneous AI activity, stronger background inputs induce stable, spatially localized activity bumps as in winner-take-all (WTA) dynamics. Interestingly, for intermediate background inputs, the networks exhibit spatially localized, but unstable activity bumps (Transition Activity, TA), resembling the experimentally observed neuronal assembly dynamics in the striatum.\n\nAmong the three main regimes of network activity (AI, WTA, TA) we found that in the AI and TA regimes, network dynamics are flexible and can be easily modified by external stimuli. Moreover, the dynamical state of the network returns to the baseline after the stimulus is removed. By contrast, the dynamics in the WTA state are rigid and can only be changed by very strong external stimuli. These results support the hypothesis that the flexibility of the striatal network state in response to stimuli is important for its normal function and the rigid network states (WTA) correspond to brain disorders such as Parkinsons disease, where the striatum looses its repertoire of dynamic states and is only receptive to very strong inputs.

neuroscience↗