Biology subjects

Kulkarni, K. R.

Publications and source records attributed to Kulkarni, K. R..

8 recordsLinked to original sources

bioRxiv2025

Dopamine and serotonin transients predict depressive symptom relief following deep brain stimulation of human subcallosal cingulate cortex

Recent advances in deep brain stimulation (DBS) of the subcallosal cingulate (SCC) show promise in mitigating the symptoms of treatment-resistant depression (TRD) in humans1-3. Monoamines, such as dopamine and serotonin, mediate the effects of pharmacological treatments of depression. However, their roles in recovery following DBS remain elusive, largely due to technical limitations of measuring these neurotransmitters in the living human brain. Here, by leveraging machine learning-enhanced electrochemistry4-7, we show that dopamine and serotonin signaling following DBS to the SCC predicted later depressive symptom relief in humans with TRD. We found that both dopamine and serotonin levels increased following subtherapeutic intraoperative SCC stimulation, with each neurotransmitter showing selective responses to distinct decision-making tasks. Furthermore, acute dopamine increases predicted later mood improvements during a social decision-making task, while serotonin enhancement predicted faster responses during a non-social learning task longitudinally. Critically, changes in dopamine and serotonin levels during the social decision-making task jointly predicted depressive symptom remission at 6-month follow-up. These findings illustrate the contribution of both dopamine and serotonin signaling in predicting behavioral improvement and depressive symptom remission in humans with TRD. Such neurochemical plasticity may serve as potential mechanistic biomarkers for SCC DBS mechanism and TRD treatment response. Significance statementO_LIDopamine and serotonin levels increased following acute DBS to the SCC in humans. C_LIO_LIAcute dopamine and serotonin changes predicted later mood and response speed changes. C_LIO_LISustained TRD recovery was predicted by acute increases in both dopamine and serotonin estimates. C_LI

neuroscience↗

bioRxiv2025

Multi-Class Classification of Cannabis and Alcohol Use Disorder: Identifying Common and Substance-Specific Neural Circuits.

Machine learning approaches have advanced the identification of neural signatures of substance use, particularly through case-control comparisons and network-level analyses. However, most studies have focused on single substances in isolation, making it difficult to directly compare shared and distinct network computations and limiting their generalizability, given that many individuals engage in polysubstance use. Here, we introduce an explainable, connectivity-based multiclass classification framework that models distributed brain network organization to delineate both shared and substance-specific mechanisms of addiction. This design enables direct comparison among cannabis users, alcohol users, and controls, revealing network computations that uniquely differentiate each group. Using functional connectivity features from cue-induced craving tasks, we classified cannabis users (n=166), alcohol users (n=101), and healthy controls (n=238), achieving high out-of-sample accuracy (87% for cannabis, 69% for alcohol, and 73% for controls). Functional connectivity-based models consistently outperformed activation-based models, highlighting the importance of inter-regional network properties for biomarker development. Network analysis further revealed dorsal prefrontal, cingulate, and precuneus hubs consistent with meta-analytic craving networks and substance-specific connectivity motifs enhanced prefrontal coupling in cannabis users and greater insular-striatal integration in alcohol users. These results demonstrate that distinct network configurations define different substance-use profiles, advancing interpretable biomarkers for addiction neuroscience.

neuroscience↗

bioRxiv2024

Human Substantia Nigra Neurons Encode Reward Expectations

Dopamine (DA) signals originating from substantia nigra (SN) neurons are centrally involved in the regulation of motor and reward processing. DA signals behaviorally relevant events where reward outcomes differ from expectations (reward prediction errors, RPEs). RPEs play a crucial role in learning optimal courses of action and in determining response vigor when an agent expects rewards. Nevertheless, how reward expectations, crucial for RPE calculations, are conveyed to and represented in the dopaminergic system is not fully understood, especially in the human brain where the activity of DA neurons is difficult to study. One possibility, suggested by evidence from animal models, is that DA neurons explicitly encode reward expectations. Alternatively, they may receive RPE information directly from upstream brain regions. To address whether SN neuron activity directly reflects reward expectation information, we directly examined the encoding of reward expectation signals in human putative DA neurons by performing single-unit recordings from the SN of patients undergoing neurosurgery. Patients played a two-armed bandit decision- making task in which they attempted to maximize reward. We show that neuronal firing rates (FR) of putative DA neurons during the reward expectation period explicitly encode reward expectations. First, activity in these neurons was modulated by previous trial outcomes, such that FR were greater after positive outcomes than after neutral or negative outcome trials. Second, this increase in FR was associated with shorter reaction times, consistent with an invigorating effect of DA neuron activity during expectation. These results suggest that human DA neurons explicitly encode reward expectations, providing a neurophysiological substrate for a signal critical for reward learning.

neuroscience↗

bioRxiv2023

A generalizable computational mechanism underlying the interaction between momentary craving and decision-making

Substance craving and maladaptive choices are intertwined across addictive disorders. However, the computational mechanisms connecting craving and decision-making remain elusive. Here, we tested a hypothesis that momentary craving and value-based decision-making influence each other during substance-related reinforcement learning. We measured momentary craving as two groups of human participants (alcohol drinkers and cannabis users; total n=132) performed a reinforcement learning task in which they received group-specific addictive cue or monetary rewards. Using computational modeling, we found that, across both groups, momentary craving biased learning rate related to substance-associated prediction errors (RPEs), but not monetary RPEs. Additionally, expected values and RPEs jointly influenced elicited craving across reward types and participant groups. Alcohol and cannabis users also differed in the extent to which their craving and decision-making influenced each other, suggesting important computational divergence between the two groups. Finally, regressions incorporating model-derived parameters best predicted substance use severity in the alcohol, but not cannabis group, supporting the utility of using these model-based parameters in making clinical predictions for selective substance groups. Together, these findings provide a computational mechanism for the interaction between substance craving and maladaptive choices that is generalizable across addictive domains.

neuroscience↗

bioRxiv2022

Neural patterns differentiate traumatic from sad autobiographical memories in PTSD

For people with post-traumatic stress disorder (PTSD), recalling traumatic memories often displays as intrusions that differ profoundly from processing of regular negative memories. These mnemonic features fueled theories speculating a qualitative divergence in cognitive state linked with traumatic memories. Yet to date, little empirical evidence supports this view. Here, we examined neural activity of PTSD patients who were listening to narratives depicting their own memories. An inter-subject representational similarity analysis of cross-subject semantic content and neural patterns revealed a differentiation in hippocampal representation by narrative type: Semantically similar sad autobiographical memories elicited similar neural representations across participants. By contrast, within the same individuals, semantically thematically similar trauma memories were not represented similarly. Furthermore, we were able to decode memory type from hippocampal multivoxel patterns. Finally, individual symptom severity modulated semantic representation of the traumatic narratives in the posterior cingulate cortex. Taken together, these findings suggest that traumatic memories are a qualitatively divergent cognitive entity.

neuroscience↗

bioRxiv2021

An interpretable connectivity-based decoding model for classification of chronic marijuana use

BackgroundPsychiatric neuroimaging typically proceeds with one of two approaches: encoding models, which aim to model neural mechanisms, or decoding models, which aim to predict behavioral or clinical characteristics from brain imaging data. In this study, we seek to combine these aims by developing interpretable decoding models that offer both accurate prediction and novel neural insights. We demonstrate the effectiveness of this combined approach in a case study of chronic marijuana use. MethodsChronic marijuana (MJ) users (n=195) and non-using healthy controls (n=128) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic MJ users and non-users based on task-evoked, whole-brain functional connectivity. We then used graph theoretic analyses to identify predictive functional connectivities among brain regions that contributed most substantially to the classification of chronic marijuana use. ResultsWe obtained high (~80% out-of-sample) accuracy across four different classification models, demonstrating that task-evoked, whole-brain functional connectivity can successfully differentiate chronic marijuana users from non-users. Subsequent network analyses revealed key predictive regions (e.g., anterior cingulate cortex, dorsolateral prefrontal cortex, and precuneus) that are often implicated in neuroimaging studies of substance use disorders, as well as some key exceptions. We also identified a core set of networks of brain regions that contributed to successful classification, comprised of many of the same predictive regions. ConclusionsOur dual aims of accurate prediction and interpretability were successful, producing a predictive model that also provides interpretability at the neural level. This novel approach may complement other predictive-exploratory approaches for a more complete understanding of neural mechanisms in drug use and other neuropsychiatric disorders.

neuroscience↗

bioRxiv2017

Global connectivity of the frontoparietal cognitive control network is related to depression symptoms in undiagnosed individuals

We all vary in our mental health, even among healthy (undiagnosed) individuals. Understanding this variability may reveal factors driving the onset of mental illness, as well as factors driving sub-clinical mental health problems that can still influence quality of life. To better understand the large-scale brain network mechanisms underlying this variability in mental health we examined the relationship between mental health symptoms and resting-state functional connectivity patterns in cognitive control systems. The frontoparietal cognitive control network (FPN) consists of flexible hubs that can regulate distributed systems depending on current goals, and dysfunction in the FPN has been identified in a variety of psychiatric disorders. Alterations in FPN connectivity may influence mental health by disrupting the ability to regulate symptoms in a goal-directed manner. This suggests that the FPN may play an important role in the promotion and maintenance of mental health generally. Here we test the hypothesis that disruptions in FPN connectivity are related to mental health (depression) symptoms even among healthy individuals. This hypothesis is consistent with a general role of FPN in the regulation of mental health symptoms. We found that depression symptoms were negatively correlated with between-network global connectivity (BGC) of the FPN as well as the default mode network (DMN). This suggests that decreased connectivity between the FPN (and, separately, DMN) and the rest of the brain is related to increased depression symptoms among undiagnosed individuals. These findings complement previous clinical studies to support the hypothesis that global FPN connectivity contributes to the regulation of mental health symptoms across both mentally healthy and unhealthy individuals.

neuroscience↗

bioRxiv2017

Cognitive task information is transferred between brain regions via resting-state network topology

Resting-state network connectivity has been associated with a variety of cognitive abilities, yet it remains unclear how these connectivity properties might contribute to the neurocognitive computations underlying these abilities. We developed a new approach - information transfer mapping - to test the hypothesis that resting-state functional network topology describes the computational mappings between brain regions that carry cognitive task information. Here we report that the transfer of diverse, task-rule information in distributed brain regions can be predicted based on estimated activity flow through resting-state network connections. Further, we find that these task-rule information transfers are coordinated by global hub regions within cognitive control networks. Activity flow over resting-state connections thus provides a large-scale network mechanism for cognitive task information transfer and global information coordination in the human brain, demonstrating the cognitive relevance of resting-state network topology.

neuroscience↗