Biology subjects

Jadhav, K.

Publications and source records attributed to Jadhav, K..

3 recordsLinked to original sources

bioRxiv2021

Stress deceleration theory: chronic adolescent stress exposure results in decelerated neurobehavioral maturation

Normative development in adolescence indicates that the prefrontal cortex is still under development thereby unable to exert efficient top-down inhibitory control on subcortical regions such as the basolateral amygdala and the nucleus accumbens. This imbalance in the developmental trajectory between cortical and subcortical regions is implicated in expression of the prototypical impulsive, compulsive, reward seeking and risk-taking adolescent behavior. Here we demonstrate that a chronic mild unpredictable stress procedure during adolescence in male Wistar rats arrests the normal behavioral maturation such that they continue to express adolescent-like impulsive, hyperactive, and compulsive behaviors into late adulthood. This arrest in behavioral maturation is associated with the hypoexcitability of prelimbic cortex (PLC) pyramidal neurons and reduced PLC-mediated synaptic glutamatergic control of BLA and nucleus accumbens core (NAcC) neurons that lasts late into adulthood. At the same time stress exposure in adolescence results in the hyperexcitability of the BLA pyramidal neurons sending stronger glutamatergic projections to the NAcC. Chemogenetic reversal of the PLC hypoexcitability decreased compulsivity and improved the expression of goal-directed behavior in rats exposed to stress during adolescence, suggesting a causal role for PLC hypoexcitability in this stress-induced arrested behavioral development.

neuroscience↗

bioRxiv2021

Reversing anterior insular cortex neuronal hypoexcitability attenuates compulsive behavior in juvenile rats

Development of self-regulatory competencies during adolescence is partially dependent on normative brain maturation. Here we report that juvenile rats as compared to adults exhibit impulsive and compulsive-like behavioral traits, the latter being associated with lower expression of mRNA levels of the immediate early gene zif268 in the anterior insula (AI). This observation suggests that deficits in AI function in juvenile rats could explain their immature pattern of interoceptive cue integration in rational decision-making and compulsive phenotype. In support of this, here we report hypoexcitability of juvenile layer-V pyramidal neurons in the AI, concomitant with reduced glutamatergic synaptic input to these cells. Chemogenetic activation of the AI attenuated the compulsive trait suggesting that delayed maturation of the AI results in suboptimal integration of sensory and cognitive information in adolescents and this contributes to inflexible behaviors in specific conditions of reward availability.

neuroscience↗

bioRxiv2021

Addicted or not? A new machine learning-assisted tool for the diagnosis of addiction-like behavior in individual rats

BackgroundOver the last few decades there is a progressive transition from a categorical to a dimensional approach to psychiatric disorders. Especially in case of substance use disorders, the increased interest in the individual vulnerability to transition from controlled to compulsive drug seeking and taking warrants the development of novel dimension-based objective diagnostic or stratification tools. Here we drew on a multidimensional preclinical model of addiction, namely the 3-criteria model, previously developed to identify the neurobehavioural basis of the individual vulnerability to switch from control to compulsive drug taking, to test the potential interest of a machine-learning assisted classifier objectively to identify individual subjects as vulnerable or resistant to addiction. MethodsLarge behavioural datasets from several of our previous studies on addiction-like behaviour for cocaine or alcohol were fed to a variety of machine-learning algorithms (each consisting of an unsupervised-clustering method combined with a supervised-prediction algorithm) to develop a classifier that identifies resilient and vulnerable rats with high precision and reproducibility irrespective of the cohort to which they belong. ResultsA classifier based on K-median or K-mean-clustering (for cocaine or alcohol, respectively) followed by Artificial Neural Networks emerged as a highly reliable and accurate tool to predict if a single rat is vulnerable or resilient to addiction. Thus, each of the rats previously characterized as displaying 0 criterion (i.e., resilient) or 3 criteria (i.e., vulnerable) in individual cohorts were correctly labelled by this classifier. ConclusionThe present machine-learning-based classifier objectively labels single individuals as resilient or vulnerable to develop addiction-like behaviour in multisymptomatic preclinical models of cocaine or alcohol addiction-like behaviour in rats. This novel dimension-based classifier thereby increases the heuristic value and generalizability of these preclinical models while providing proof of principle for the deployment of similar tools for the future of diagnosis of psychiatric disorders.

neuroscience↗