Percy Khushroo Mistry

All Publications

• Digital twins for understanding mechanisms of learning disabilities: Personalized deep neural networks reveal impact of neuronal hyperexcitability. bioRxiv : the preprint server for biology Strock, A., Mistry, P. K., Menon, V. 2024

  Abstract

  Learning disabilities affect a significant proportion of children worldwide, with far-reaching consequences for their academic, professional, and personal lives. Here we develop digital twins - biologically plausible personalized Deep Neural Networks (pDNNs) - to investigate the neurophysiological mechanisms underlying learning disabilities in children. Our pDNN reproduces behavioral and neural activity patterns observed in affected children, including lower performance accuracy, slower learning rates, neural hyper-excitability, and reduced neural differentiation of numerical problems. Crucially, pDNN models reveal aberrancies in the geometry of manifold structure, providing a comprehensive view of how neural excitability influences both learning performance and the internal structure of neural representations. Our findings not only advance knowledge of the neurophysiological underpinnings of learning differences but also open avenues for targeted, personalized strategies designed to bridge cognitive gaps in affected children. This work reveals the power of digital twins integrating AI and neuroscience to uncover mechanisms underlying neurodevelopmental disorders.

  View details for DOI 10.1101/2024.04.29.591409

  View details for PubMedID 38746231

• Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically inspired neural network. Nature communications Mistry, P. K., Strock, A., Liu, R., Young, G., Menon, V. 2023; 14 (1): 3843

  Abstract

  Number sense, the ability to decipher quantity, forms the foundation for mathematical cognition. How number sense emerges with learning is, however, not known. Here we use a biologically-inspired neural architecture comprising cortical layers V1, V2, V3, and intraparietal sulcus (IPS) to investigate how neural representations change with numerosity training. Learning dramatically reorganized neuronal tuning properties at both the single unit and population levels, resulting in the emergence of sharply-tuned representations of numerosity in the IPS layer. Ablation analysis revealed that spontaneous number neurons observed prior to learning were not critical to formation of number representations post-learning. Crucially, multidimensional scaling of population responses revealed the emergence of absolute and relative magnitude representations of quantity, including mid-point anchoring. These learnt representations may underlie changes from logarithmic to cyclic and linear mental number lines that are characteristic of number sense development in humans. Our findings elucidate mechanisms by which learning builds novel representations supporting number sense.

  View details for DOI 10.1038/s41467-023-39548-5

  View details for PubMedID 37386013

  View details for PubMedCentralID 6506249

• A neurodevelopmental shift in reward circuitry from mother's to nonfamilial voices in adolescence. The Journal of neuroscience : the official journal of the Society for Neuroscience Abrams, D. A., Mistry, P. K., Baker, A. E., Padmanabhan, A., Menon, V. 2022

  Abstract

  The social world of young children primarily revolves around parents and caregivers, who play a key role in guiding children's social and cognitive development. However, a hallmark of adolescence is a shift in orientation towards nonfamilial social targets, an adaptive process that prepares adolescents for their independence. Little is known regarding neurobiological signatures underlying changes in adolescents' social orientation. Using functional brain imaging of human voice processing in children and adolescents (ages 7-16), we demonstrate distinct neural signatures for mother's voice and nonfamilial voices across child and adolescent development in reward and social valuation systems, instantiated in nucleus accumbens and ventromedial prefrontal cortex. While younger children showed increased activity in these brain systems for mother's voice compared to nonfamilial voices, older adolescents showed the opposite effect with increased activity for nonfamilial compared to mother's voice. Findings uncover a critical role for reward and social valuative brain systems in the pronounced changes in adolescents' orientation towards nonfamilial social targets. Our approach provides a template for examining developmental shifts in social reward and motivation in individuals with pronounced social impairments, including adolescents with autism.Significance Statement:Children's social worlds undergo a transformation during adolescence. While socialization in young children revolves around parents and caregivers, adolescence is characterized by a shift in social orientation towards nonfamilial social partners. Here we show that this shift is reflected in neural activity measured from reward processing regions in response to brief vocal samples. When younger children hear their mother's voice, reward processing regions show greater activity compared to when they hear nonfamilial, unfamiliar voices. Strikingly, older adolescents show the opposite effect, with increased activity for nonfamilial compared to mother's voice. Findings identify the brain basis of adolescents' switch in social orientation towards nonfamilial social partners and provides a template for understanding neurodevelopment in clinical populations with social and communication difficulties.

  View details for DOI 10.1523/JNEUROSCI.2018-21.2022

  View details for PubMedID 35483917

• Neurocognitive modeling of latent memory processes reveals reorganization of hippocampal-cortical circuits underlying learning and efficient strategies. Communications biology Supekar, K., Chang, H., Mistry, P. K., Iuculano, T., Menon, V. 2021; 4 (1): 405

  Abstract

  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. Nature communications Supekar, K., Ryali, S., Mistry, P., Menon, V. 2021; 12 (1): 3537

  Abstract

  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. Biological psychiatry Warren, S. L., Zhang, Y. n., Duberg, K. n., Mistry, P. n., Cai, W. n., Qin, S. n., Bostan, S. N., Padmanabhan, A. n., Carrion, V. G., Menon, V. n. 2020

  Abstract

  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

• Space wandering in the rodent default mode network. Proceedings of the National Academy of Sciences of the United States of America Nghiem, T. E., Lee, B., Chao, T. H., Branigan, N. K., Mistry, P. K., Shih, Y. I., Menon, V. 2024; 121 (15): e2315167121

  Abstract

  The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here, we use multisite GCaMP (a genetically encoded calcium indicator) fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes-the retrosplenial cortex, cingulate cortex, and prelimbic cortex-as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and found that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.

  View details for DOI 10.1073/pnas.2315167121

  View details for PubMedID 38557177

• Space wandering in the rodent default mode network. bioRxiv : the preprint server for biology Nghiem, T. E., Lee, B., Chao, T. H., Branigan, N. K., Mistry, P. K., Shih, Y. I., Menon, V. 2023

  Abstract

  The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here we use multisite GCaMP fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes- the retrosplenial cortex, cingulate cortex, and prelimbic cortex- as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and discovered that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.

  View details for DOI 10.1101/2023.08.31.555793

  View details for PubMedID 37693501

  View details for PubMedCentralID PMC10491169

• A Multinomial Processing Tree Model of the 2-back Working Memory Task. Computational brain & behavior Lee, M. D., Mistry, P. K., Menon, V. 2022; 5 (3): 261-278

  Abstract

  The n-back task is a widely used behavioral task for measuring working memory and the ability to inhibit interfering information. We develop a novel model of the commonly used 2-back task using the cognitive psychometric framework provided by Multinomial Processing Trees. Our model involves three parameters: a memory parameter, corresponding to how well an individual encodes and updates sequence information about presented stimuli; a decision parameter corresponding to how well participants execute choices based on information stored in memory; and a base-rate parameter corresponding to bias for responding "yes" or "no". We test the parameter recovery properties of the model using existing 2-back experimental designs, and demonstrate the application of the model to two previous data sets: one from social psychology involving faces corresponding to different races (Stelter and Degner, British Journal of Psychology 109:777-798, 2018), and one from cognitive neuroscience involving more than 1000 participants from the Human Connectome Project (Van Essen et al., Neuroimage 80:62-79, 2013). We demonstrate that the model can be used to infer interpretable individual-level parameters. We develop a hierarchical extension of the model to test differences between stimulus conditions, comparing faces of different races, and comparing face to non-face stimuli. We also develop a multivariate regression extension to examine the relationship between the model parameters and individual performance on standardized cognitive measures including the List Sorting and Flanker tasks. We conclude by discussing how our model can be used to dissociate underlying cognitive processes such as encoding failures, inhibition failures, and binding failures.

  View details for DOI 10.1007/s42113-022-00138-1

  View details for PubMedID 37873549

  View details for PubMedCentralID PMC10593202

• Linear and nonlinear profiles of weak behavioral and neural differentiation of numerical operations in children with math learning difficulties. Neuropsychologia Chen, L., Iuculano, T., Mistry, P., Nicholas, J., Zhang, Y., Menon, V. 2021: 107977

  Abstract

  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 Mistry, P. K., Pothos, E. M., Vandekerckhove, J., Trueblood, J. S. 2018; 87: 76-97

  View details for DOI 10.1016/j.jmp.2018.09.003

  View details for Web of Science ID 000453341900005