Current Role at Stanford
Research Scholar, Stanford Cognitive and Systems Neuroscience Laboratory
Education & Certifications
Postdoctoral training, Stanford University, Computational Neuroscience
Ph.D., University of California Irvine, Psychology (Computational Cognitive Science) (2018)
M.A, University of California Irvine, Psychology (Computational Cognitive Science) (2015)
Diploma, UoL, Mathematics (2012)
MBA, Indian Institute of Management Calcutta, Finance, Systems (2003)
Bachelors, University of Mumbai, India, Electronics Engineering (2001)
Neurocognitive modeling of latent memory processes reveals reorganization of hippocampal-cortical circuits underlying learning and efficient strategies.
2021; 4 (1): 405
Efficient memory-based problem-solving strategies are a cardinal feature of expertise across a wide range of cognitive domains in childhood. However, little is known about the neurocognitive mechanisms that underlie the acquisition of efficient memory-based problem-solving strategies. Here we develop, to the best of our knowledge, a novel neurocognitive process model of latent memory processes to investigate how cognitive training designed to improve children's problem-solving skills alters brain network organization and leads to increased use and efficiency of memory retrieval-based strategies. We found that training increased both the use and efficiency of memory retrieval. Functional brain network analysis revealed training-induced changes in modular network organization, characterized by increase in network modules and reorganization of hippocampal-cortical circuits. Critically, training-related changes in modular network organization predicted performance gains, with emergent hippocampal, rather than parietal cortex, circuitry driving gains in efficiency of memory retrieval. Our findings elucidate a neurocognitive process model of brain network mechanisms that drive learning and gains in children's efficient problem-solving strategies.
View details for DOI 10.1038/s42003-021-01872-1
View details for PubMedID 33767350
Aberrant dynamics of cognitive control and motor circuits predict distinct restricted and repetitive behaviors in children with autism.
2021; 12 (1): 3537
Restricted and repetitive behaviors (RRBs) are a defining clinical feature of autism spectrum disorders (ASD). RRBs are highly heterogeneous with variable expression of circumscribed interests (CI), insistence of sameness (IS) and repetitive motor actions (RM), which are major impediments to effective functioning in individuals with ASD; yet, the neurobiological basis of CI, IS and RM is unknown. Here we evaluate a unified functional brain circuit model of RRBs and test the hypothesis that CI and IS are associated with aberrant cognitive control circuit dynamics, whereas RM is associated with aberrant motor circuit dynamics. Using task-free fMRI data from 96 children, we first demonstrate that time-varying cross-network interactions in cognitive control circuit are significantly reduced and inflexible in children with ASD, and predict CI and IS symptoms, but not RM symptoms. Furthermore, we show that time-varying cross-network interactions in motor circuit are significantly greater in children with ASD, and predict RM symptoms, but not CI or IS symptoms. We confirmed these results using cross-validation analyses. Moreover, we show that brain-clinical symptom relations are not detected with time-averaged functional connectivity analysis. Our findings provide neurobiological support for the validity of RRB subtypes and identify dissociable brain circuit dynamics as a candidate biomarker for a key clinical feature of ASD.
View details for DOI 10.1038/s41467-021-23822-5
View details for PubMedID 34112791
Anxiety and Stress Alter Decision-Making Dynamics and Causal Amygdala-Dorsolateral Prefrontal Cortex Circuits During Emotion Regulation in Children.
Anxiety and stress reactivity are risk factors for the development of affective disorders. However, the behavioral and neurocircuit mechanisms that potentiate maladaptive emotion regulation are poorly understood. Neuroimaging studies have implicated the amygdala and dorsolateral prefrontal cortex (DLPFC) in emotion regulation, but how anxiety and stress alter their context-specific causal circuit interactions is not known. Here, we use computational modeling to inform affective pathophysiology, etiology, and neurocircuit targets for early intervention.Forty-five children (10-11 years of age; 25 boys) reappraised aversive stimuli during functional magnetic resonance imaging scanning. Clinical measures of anxiety and stress were acquired for each child. Drift-diffusion modeling of behavioral data and causal circuit analysis of functional magnetic resonance imaging data, with a National Institute of Mental Health Research Domain Criteria approach, were used to characterize latent behavioral and neurocircuit decision-making dynamics driving emotion regulation.Children successfully reappraised negative responses to aversive stimuli. Drift-diffusion modeling revealed that emotion regulation was characterized by increased initial bias toward positive reactivity during viewing of aversive stimuli and increased drift rate, which captured evidence accumulation during emotion evaluation. Crucially, anxiety and stress reactivity impaired latent behavioral dynamics associated with reappraisal and decision making. Anxiety and stress increased dynamic casual influences from the right amygdala to DLPFC. In contrast, DLPFC, but not amygdala, reactivity was correlated with evidence accumulation and decision making during emotion reappraisal.Our findings provide new insights into how anxiety and stress in children impact decision making and amygdala-DLPFC signaling during emotion regulation, and uncover latent behavioral and neurocircuit mechanisms of early risk for psychopathology.
View details for DOI 10.1016/j.biopsych.2020.02.011
View details for PubMedID 32331823
Linear and nonlinear profiles of weak behavioral and neural differentiation of numerical operations in children with math learning difficulties.
Mathematical knowledge is constructed hierarchically during development from a basic understanding of addition and subtraction, two foundational and inter-related, but semantically distinct, numerical operations. Early in development, children show remarkable variability in their numerical problem-solving skills and difficulties in solving even simple addition and subtraction problems are a hallmark of math learning difficulties. Here, we use novel quantitative analyses to investigate whether less distinct representations are associated with poor problem-solving abilities in children during the early stages of math-skill acquisition. Crucially, we leverage dimensional and categorical analyses to identify linear and nonlinear neurobehavioral profiles of individual differences in math skills. Behaviorally, performance on the two different numerical operations was less differentiated in children with low math abilities, and lower problem-solving efficiency stemmed from weak evidence-accumulation during problem-solving. Children with low numerical abilities also showed less differentiated neural representations between addition and subtraction operations in multiple cortical areas, including the fusiform gyrus, intraparietal sulcus, anterior temporal cortex and insula. Furthermore, analysis of multi-regional neural representation patterns revealed significantly higher network similarity and aberrant integration of representations within a fusiform gyrus-intraparietal sulcus pathway important for manipulation of numerical quantity. These findings identify the lack of distinct neural representations as a novel neurobiological feature of individual differences in children's numerical problem-solving abilities, and an early developmental biomarker of low math skills. More generally, our approach combining dimensional and categorical analyses overcomes pitfalls associated with the use of arbitrary cutoffs for probing neurobehavioral profiles of individual differences in math abilities.
View details for DOI 10.1016/j.neuropsychologia.2021.107977
View details for PubMedID 34329664
A quantum probability account of individual differences in causal reasoning
JOURNAL OF MATHEMATICAL PSYCHOLOGY
2018; 87: 76-97
View details for DOI 10.1016/j.jmp.2018.09.003
View details for Web of Science ID 000453341900005