A computational neuropsychiatry lab dedicated to developing computational methods for a better understanding of individual differences in brain functioning in healthy and patient populations
Characterizing spatio- temporal dynamics in brain activity to develop person- and disorder-centric biomarkers. Learn more ...
Understanding the role of brain dynamics for optimized learning and performance in individual and team settings. Learn more ...
Developing methods that use network science, machine learning, and signal processing for better understanding of brain dynamics. Learn more ...
I am a computational neuroscientist who is trained in machine learning, neuroscience and psychiatry. The overarching goal of my research is to develop reliable computational methods that will allow for characterizing and modeling temporal dynamics of brain activity, without averaging data in either space or time. I firmly believe that the spatiotemporal richness in brain activity might hold the key to finding the person- and disorder-centric biomarkers. Currently, funded by a career development award (K99/R00; NIMH) and a young investigator award (NARSAD; Brain & Behavior Foundation), I am developing methods to model the temporal dynamics of brain activity in individuals with fragile X syndrome and healthy controls.Linkedin Twitter Web page
There is a rapidly growing momentum in the field directed towards quantifying the fluctuations in intrinsic (at rest) brain activity or connectivity over time. Although several innovative methods are already proposed, they usually invariably average (or smooth) the data in space (by using seed- or network-based approaches) or time (e.g., by using a sliding-window), thereby perhaps not examining these fluctuations in their entirety. Funded by an NIMH K99/R00 Career Development Award and a NARSAD Young Investigator Award, we are developing new computational methods that could allow for an understanding of brain's dynamical landscape without arbitrarily averaging the spatiotemporal data.
In Neuroscience, large amounts of data are being collected at several scales and much of these 'big data' essentially encodes relations or interconnections between different entities. The size and complexity of these data present challenges and opportunities to develop sophisticated methods for analysis and visualization. We believe that Network Science can answer this call by providing a unique framework of methods, tools and theories to characterize and predict the behavior of one of the most complex system - the brain. We actively develop and apply network science methods to better understand the underlying mechanistic aspects of neurodevelopmental disorders.
The human brain is highly complex, and even a simple task involves several factors, e.g., attention, emotion, memory, learning, decision making, physiological complexity of the brain itself, and the environment. Hence, it is currently impossible to create a single computational model to account for all aspects of the brain. In fact, it is impractical to model even a single factor completely. However, the goal of computational modeling is not necessarily to build absolutely correct theories. Some of the most successful models are based on approximate and relatively crude underlying assumptions. Rather, the goal is to enable progress in understanding a particular complex phenomenon. The complex phenomena is simulated based on an initial formulation, of a theory, generating novel hypothesis from this simulation, collecting new data based on these predictions, and then refining the model (and the underlying theory itself). The process thereby implements the classic cycle of theory development, testing, and revision to advance the field. We actively use computational modeling of neuroimaging data (EEG/fMRI/fNIRS) to not just tie together neural activity and behavioral findings, but also advance our understanding of brain processes by providing formulations and testable predictions about the underlying mechanisms.
Creativity is widely recognized as an essential skill for entrepreneurial success and adaptation to daily-life demands. However, we know little about the neural changes associated with creative capacity. We are actively involved in doing a series of studies to better understand:
|24. X-Chromosome Effects on Attention Networks: Insights from Imaging Resting-State Networks in Turner Syndrome (in-press) Cerebral Cortex|
|23. Identification of biotypes in Attention-Deficit/Hyperactivity Disorder, a report from a randomized, controlled trial. (in-press) Personalized Medicine in Psychiatry|
|22. Compensatory Hyper-Connectivity in Developing Brains of Young Children with Type 1 Diabetes. (2016) Diabetes|
|21. Changes in Brain Activation Associated with Spontaneous Improvization and Figural Creativity After Design-Thinking-Based Training: A Longitudinal fMRI Study (2016) Cerebral Cortex|
|20. Understanding the influence of personality on dynamic social gesture processing: An fMRI study. (2016) Neuropsychologia|
|19. Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics. (2016) Cerebral Cortex|
|18. Sex differences in neural and behavioral signatures of cooperation revealed by fNIRS hyperscanning. (2016) Scientific Reports|
|17. Surface-based morphometry reveals distinct cortical thickness and surface area profiles in Williams syndrome. (2016) Am J Med Genet Part B|
|16. Mean-field thalamocortical modeling of longitudinal EEG acquired during intensive meditation training. (2015) NeuroImage|
|15. Pictionary-based fMRI paradigm to study the neural correlates of spontaneous improvisation and figural creativity. (2015) Scientific Reports|
|14. Estimating individual contribution from group-based structural correlation networks. (2015) NeuroImage|
|13. Neural correlates of self-injurious behavior in Prader–Willi syndrome. (2015) Human Brain Mapping|
|12. Examining the neural correlates of emergent equivalence relations in fragile X syndrome. (2015) Psychiatry Research: Neuroimaging|
|11. Creativity training enhances goal-directed attention and information processing. (2014) Thinking Skills and Creativity|
|10. Revealing the neural networks associated with processing of natural social interaction and the related effects of actor-orientation and face-visibility. (2014) NeuroImage|
|9. Early signs of anomalous neural functional connectivity in healthy offspring of parents with bipolar disorder. (2014) Bipolar Disorders|
|8. Targeted intervention to increase creative capacity and performance: A randomized controlled pilot study. (2014) Thinking Skills and Creativity|
|7. Intensive training induces longitudinal changes in meditation state-related EEG oscillatory activity. (2012) Frontiers in Human Neuroscience|
|6. Behavioral, neuroimaging, and computational evidence for perceptual caching in repetition priming. (2010) Brain Research|
|5. Memory Processes in Perceptual Decision Making. (2008) Proceedings of the Cognitive Science Society|
|4. A Computational Model of the Motivation-learning Interface. (2007) Proceedings of the Cognitive Science Society|
|3. Autonomous Learning of Stable Quadruped Locomotion. (2007) Lecture Notes in Computer Science|
|2. System Identification for the Hodgkin-Huxley Model using Artificial Neural Networks. (2007) Neural Networks (IJCNN)|
|1. Optimization of association rule mining using improved genetic algorithms. (2004) Systems, Man and Cybernetics (IEEE International Conference)|
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For any queries related to research and/or participation send an email to email@example.com