Biology subjects

Ramanathan, D. S.

Publications and source records attributed to Ramanathan, D. S..

6 recordsLinked to original sources

bioRxiv2025

Methylphenidate-induced reductions in choice impulsivity are associated with alterations in cortico-striatal beta oscillations.

Impulsive choice- preferring small, immediate rewards over larger, delayed reward- is a hallmark of many psychiatric disorders. Methylphenidate, a dopamine and noradrenaline transporter inhibitor, reduces impulsivity, but the neural mechanisms underlying this effect remain unclear. Identifying a neurophysiological signature of methylphenidates action could be used clinically to predict treatment response and guide drug development. To this end, we recorded local field potentials (LFP) from 32 electrodes spanning cortico-striatal circuits in rats performing a temporal discounting task, under saline or methylphenidate treatment conditions. Methylphenidate decreased beta frequency (15-30 Hz) power and connectivity for small, immediate rewards while simultaneously increasing beta power for large, delayed rewards. Reduced connectivity in cortico-striatal networks during small rewards predicted increased preference for delayed rewards. These findings suggest that methylphenidate influences discounting behavior by modulating reward-evoked beta oscillations in cortico-striatal networks. Thus, beta oscillations may serve as a translational biomarker of treatment responsivity to monoaminergic drugs, providing a circuit-level assay to measure and predict changes in impulsive behavior.

neuroscience↗

bioRxiv2023

Cognitive rehabilitation can improve brain injury-induced deficits in behavioral flexibility and impulsivity linked to impaired reward-feedback activity.

Traumatic brain injury (TBI) affects a large population, resulting in severe cognitive impairments. Although cognitive rehabilitation is an accepted treatment for some deficits, studies in patients are limited in ability to probe physiological and behavioral mechanisms. Therefore, animal models are needed to optimize strategies. Frontal TBI in a rat model results in robust and replicable cognitive deficits, making this an ideal candidate for investigating cognitive rehabilitation. In this study, we report three distinct frontal TBI experiments assessing behavior well into the chronic post-injury period using male Long-Evans rats. First, we evaluated the impact of frontal injury on local field potentials recorded simultaneously from 12 brain regions during a probabilistic reversal learning task (PbR). Next, rats were tested on reversal learning (PbR) or impulsivity (differential reinforcement of low-rate behavior: DRL) and half received salient cues associated with reinforcement contingencies as a form of "cognitive rehabilitation". After rehabilitation on the PbR task, brains were stained for markers of activity. On the DRL, cues were devalued to determine if beneficial effects persisted on impulsive behavior. TBI resulted in outcome salience deficits evident in task performance and reward-feedback signals occurring at beta frequencies in orbitofrontal cortex (OFC) and associated frontostriatal regions. Cognitive rehabilitation improved flexibility and increased OFC activity. Rehabilitation also reduced impulsivity, even after cues were degraded, which was partially mediated by improvements in timing behavior. The current study established a robust platform for investigating cognitive rehabilitation in animals and identified a strong role for dysfunctional OFC signaling after frontal TBI.

neuroscience↗

bioRxiv2022

Cortico-Striatal Beta-Oscillations as a Marker of Learned Reward Value

Single neuron correlates of reward value have been observed in brain regions along the cortico-striatal pathway including ventral striatum, orbital, and medial prefrontal cortex. Brain imaging studies in humans further validate these findings and suggest that value is represented in a network of brain regions opposed to a particular area. Neural activity oscillates at periodic frequencies to coordinate long-range communication in widespread, dynamic networks. To explore how oscillatory dynamics across brain regions may represent reward value, we measured local field potentials of male Long-Evans rats during three distinct behavioral tasks, each probing a different aspect of reward processing. Our goal was to use a data-driven approach to identify a common electrophysiology property associated with reward value. We found that reward-locked oscillations at beta frequencies, in both single units and local field potentials, were markers of positive reward valence. More importantly, Reward-locked beta-oscillations scaled with expected reward value on specific trial types and in a behaviorally relevant way across tasks. Oscillatory signatures of reward processing were observed throughout the cortico-striatal network including electrodes placed in orbitofrontal cortex, anterior insula, medial prefrontal cortex, ventral striatum, and amygdala. These data suggests that beta-oscillations reflect learned reward value in a distributed network, and this may serve as a stable and robust bio-marker for future studies.

animal behavior and cognition↗

bioRxiv2022

A Generalized Reinforcement Learning-Based Deep Neural Network (GRL-DNN) Agent Model for Diverse Cognitive Constructs

Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational applications for cognitive impairment, multiple cognitive functions are altered in a given individual. Hence it is important to study multiple cognitive abilities in the same subject or, in computational terms, model them using a single model. To this end, we propose a unified, reinforcement learning-based agent model comprising of systems for representation, memory, value computation and exploration. We successfully modelled the aforementioned cognitive tasks and show how individual performance can be mapped to model meta-parameters. This model has the potential to serve as a proxy for cognitively impaired conditions, and can be used as a clinical testbench on which therapeutic interventions can be simulated first before delivering to human subjects.

neuroscience↗

bioRxiv2021

Impaired sleep-dependent consolidation after stroke - interaction of slow waves, rehabilitation and GABA

Sleep is known to be important for promoting recovery after brain injuries such as stroke. Yet, it remains unclear how such injuries affect neural processing during sleep and how to precisely enhance sleep-dependent memory processing during recovery. Using an experimental model of focal cortical stroke in rats along with long-term electrophysiological monitoring of neural firing and sleep microarchitecture, here we show that sleep-dependent neural processing is altered after stroke induction. Specifically, we found that the precise coupling of spindles to global slow- oscillations (SO), a phenomenon that is known to be important for memory consolidation, appeared to be disrupted by a pathological increase in "isolated" local delta waves. The transition from this pathological to a more physiological sleep state - with both a reduction in isolated delta waves and increased spindle coupling to SO - was associated with sustained performance gains after task training during recovery. Interestingly, post-injury sleep processing could be pushed towards a more physiological state via a pharmacological reduction of tonic GABA. Together, our results suggest that sleep processing after cortical brain injuries may be impaired due to an increase in local delta waves and that restoration of physiological processing is important for recovery of task performance.

neuroscience↗

bioRxiv2020

Sleep spindles coordinate corticostriatal reactivations during the emergence of automaticity

Plasticity within the corticostriatal network is known to regulate the balance between behavioral flexibility and automaticity. Repeated training of an action has been shown to bias behavior towards automaticity, suggesting that training may trigger activity-dependent corticostriatal plasticity. However, surprisingly little is known about the natural activity patterns that may drive plasticity or when they occur during long-term training. Here we chronically monitored neural activity from primary motor cortex (M1) and the dorsolateral striatum (DLS) during both training and offline periods, i.e., time away from training including sleep, throughout the development of an automatic reaching action. We first show that blocking striatal NMDA receptors during offline periods prevents the emergence of behavioral consistency, a hallmark of automaticity. We then show that, throughout the development of an automatic reaching action, corticostriatal functional connectivity increases during offline periods. Such increases track the emergence of consistent behavior and predictable cross-area neural dynamics. We then identify sleep spindles during non-REM sleep (NREM) as uniquely poised to mediate corticostriatal plasticity during offline periods. We show that sleep spindles are periods of maximal corticostriatal transmission within offline periods, that sleep spindles in post-training NREM reactivate neurons across areas, and that sleep-spindle modulation in post-training NREM is linked to observable changes in spiking relationships between individual pairs of M1 and DLS neurons. Our results indicate that offline periods, in general, and sleep spindles, specifically, play an important role in regulating behavioral flexibility through corticostriatal network plasticity.

neuroscience↗