Biology subjects

Balasubramani, P. P.

Publications and source records attributed to Balasubramani, P. P..

6 recordsLinked to original sources

bioRxiv2026

Operations Near the Edge of cognitive decline: Behavioral and Neural Correlates of cognitive load in Mild cognitive impairment

MotivationEarly detection of cognitive strain is essential for identifying Mild Cognitive Impairment (MCI) and its progression to more severe neurodegenerative diseases. This study investigates cognitive strain in MCI by examining both behavioral and neural correlates during a real-world task. MethodsTwenty-four older adults (M=69, 14 male, 10 female), including 11 with MCI and 13 healthy controls (MMSE >22/30), performed a spatial navigation task under varying memory load. We recorded brain activity using electroencephalography (EEG) and a mismatch negativity (MMN) paradigm to capture error-related potentials (ErrPs), which reflect cognitive load and processing errors. ResultsBoth groups showed a decline in working memory performance as cognitive load increased ({beta} = -41.39, SE = 10.73, t(90) = -3.86, p < .001). MCI participants, however, exhibited more efficient use of non-intrinsic (allocentric) reference frames under high cognitive load ({beta} = 2.47, SE = 0.95, t(82) = 2.61, p = .009). MCI individuals also displayed larger ErrPs in central brain regions, indicating greater neural resource allocation. Additionally, MCI participants showed a decrease in parietal-to-frontal theta dominance under high load ({beta} = -0.68, SE = 0.32, t(90) = -2.12, p = .037), and reduced fronto-parietal connectivity. DiscussionThese findings suggest that MCI participants experience greater cognitive strain, evidenced by increased neural resource allocation and strategy-shifting under load. This heightened strain may serve as an early marker of MCI progression, offering potential for early detection of pathological changes.

neuroscience↗

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

Disentangling reward processes underlying payoff maximization from individual differences in gain frequency bias and reinforcement learning

Choice selection strategies and decision making are typically investigated using multiple-choice gambling paradigms that require participants to maximize reward payoff. However, research shows that performance in such paradigms suffers from individual biases towards the frequency of gains to choose smaller local gains over larger longer term gain, also referred to as melioration. Here, we developed a simple two-choice reward task, implemented in 186 healthy human adult subjects across the adult lifespan to understand the behavioral, computational, and neural bases of payoff maximization versus melioration. The observed reward choice behavior on this task was best explained by a reinforcement learning model of differential future reward prediction. Simultaneously recorded and source-localized electroencephalography (EEG) showed that diminished theta-band activations in the right rostral anterior cingulate cortex (rACC) correspond to greater reward payoff maximization, specifically during the presentation of cumulative reward information at the end of each task trial. Notably, these activations (greater rACC theta) predicted depressed mood symptoms, thereby showcasing a reward processing marker of potential clinical utility. Significance StatementThis study presents cognitive, computational and neural (EEG-based) analyses of a rapid reward-based decision-making task. The research has the following three highlights. 1) It teases apart two core aspects of reward processing, i.e. long term expected value maximization versus immediate gain frequency melioration based choice behavior. 2) It models reinforcement learning based behavioral differences between individuals showing that observed performance is best explained by differential extents of reward prediction. 3) It investigates neural correlates in 186 healthy human subjects across the adult lifespan, revealing specific theta band cortical source activations in right rostral anterior cingulate as correlates for maximization that further predict depressed mood across subjects.

neuroscience↗

bioRxiv2020

Mapping Cognitive Brain Functions at Scale

A fundamental set of cognitive abilities enable humans to efficiently process goal-relevant information, suppress irrelevant distractions, maintain information in working memory, and act flexibly in different behavioral contexts. Yet, studies of human cognition and their underlying neural mechanisms usually evaluate these cognitive constructs in silos, instead of comprehensively in-tandem within the same individual. Here, we developed a scalable, mobile platform, "BrainE" (short for Brain Engagement), to rapidly assay several essential aspects of cognition simultaneous with wireless electroencephalography (EEG) recordings. Using BrainE, we rapidly assessed five aspects of cognition including (1) selective attention, (2) response inhibition, (3) working memory, (4) flanker interference and (5) emotion interference processing, in 102 healthy young adults. We evaluated stimulus encoding in all tasks using the EEG neural recordings, and isolated the cortical sources of the spectrotemporal EEG dynamics. Additionally, we used BrainE in a two-visit study in 24 young adults to investigate the reliability of the neuro-cognitive data as well as its plasticity to transcranial magnetic stimulation (TMS). We found that stimulus encoding on multiple cognitive tasks could be rapidly assessed, identifying common as well as distinct task processes in both sensory and cognitive control brain regions. Event related synchronization (ERS) in the theta (3-7 Hz) and alpha (8-12 Hz) frequencies as well as event related desynchronization (ERD) in the beta frequencies (13-30 Hz) were distinctly observed in each task. The observed ERS/ERD effects were overall anticorrelated. The two-visit study confirmed high test-retest reliability for both cognitive and neural data, and neural responses showed specific TMS protocol driven modulation. We also show that the global cognitive neural responses are sensitive to mental health symptom self-reports. This first study with the BrainE platform showcases its utility in studying neuro-cognitive dynamics in a rapid and scalable fashion. HighlightsO_LIRapid and scalable EEG recordings reveal common and distinct cortical activations across five core cognitive tasks. C_LIO_LIData acquired across visits one-week-apart show high test-retest reliability for both cognitive and neural measurements. C_LIO_LIEvoked neural responses during emotion interference processing demonstrate specific short-term plasticity driven by type of neurostimulation. C_LIO_LICognitively evoked neural responses are sensitive to variations in mental health symptoms. C_LI

neuroscience↗

bioRxiv2018

Orbitofrontal neuron ensembles contribute to inhibitory control

Stopping, or inhibition, is a form of self-control that is a core part of adaptive behavior. We hypothesize that inhibition commands originate, in part, from the orbitofrontal cortex (OFC). We recorded activity of OFC neurons in macaques performing a stop signal task. Decoding analyses revealed a clear difference in ensemble responses that distinguish successful from failed inhibition that begins after the stop signal and before the stop signal reaction time. We also found a different and unrelated ensemble pattern that distinguishes successful from failed stopping before the beginning of the trial. These signals were distinct from, and orthogonal to, value encoding, which was also observed in these neurons. The timing of the early and late signals was, respectively, consistent with the idea that OFC contributes both proactively and reactively to inhibition. These results support the view, inspired by anatomy, that OFC gathers diverse sensory inputs to compute early-stage executive signals.

neuroscience↗

bioRxiv2017

Bipolar oscillations between positive and negative mood states in a computational model of Basal Ganglia

Bipolar disorder is characterized by mood swings - oscillations between manic and depressive states. The swings (oscillations) mark the length of an episode in a patients mood cycle (period), and can vary from hours to years. The proposed modeling study uses decision making framework to investigate the role of basal ganglia network in generating bipolar oscillations. In this model, the basal ganglia system performs a two-arm bandit task in which one of the arms leads to a positive outcome, while the other leads to a negative outcome. In healthy conditions, the model chooses positive action and avoids negative one, whereas under bipolar conditions, the model exhibits slow oscillations in its choice of positive or negative outcomes, reminiscent of bipolar oscillations. The model is cast at three levels of abstraction: 1) a two-dimensional dynamical system model, 2) a phenomenological basal ganglia model, 3) a detailed network model of basal ganglia. Phase-plane analyses on the simple reduced dynamical system with two variables reveal the essential parameters that generate pathological bipolar-like oscillations. Phenomenological and network models of the basal ganglia extend that logic, and interpret bipolar oscillations in terms of the activity of dopaminergic and serotonergic projections on the cortico-basal ganglia network dynamics. The networks dysfunction, specifically in terms of reward and risk sensitivity, is shown to be responsible for the pathological bipolar oscillations. The study proposes a computational model that explores the effects of impaired serotonergic neuromodulation on the dynamics of the cortico basal ganglia network, and relates this impairment to abstract mood states (manic and depressive episodes) and oscillations of bipolar disorder.

neuroscience↗