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Bio
I am an Electrical Engineer by training and have experience of working with many of its subfields during different stages of my career. During my PhD at ETH Zurich, I worked on the methods development for fMRI data analysis. My PhD supervisor was Prof. Dr. Markus Rudin and my cosupervisors were Prof. Dr. Klaas Enno Stephan (famous for DCM-SPM) and Prof. Dr. Irene Tracey (Director of FMRIB, Oxford – FSL group). As a cognitive and neuroscience scientist I had been developing and validating statistical tools and analysis methods for fMRI over the course of my PhD. Some of the tools that I worked with/validated for analysing rsfMRI data are: dynamic functional connectivity analysis, multivariate pattern analysis, dual regression, brain functional network analysis etc. Recently I m working on identifying the transient brain states using effective connectivity (Dynamic Causal Modelling). Furthermore I had also been working with application of machine learning algorithms to identify different brain states and use importance maps to identify the corresponding regions of interest. I have first hand experience of using Support Vector Machines, Random Forest, Deep Belief Networks and Convolution Neural Networks.
During my PhD, I also had the opportunity to collaborate with Asta Hadeberg at NTNU on ADHD patients and with Adeel Razi at UCL to work on spectral DCM. I also had the opportunity to spend a few months at Harvard Medical School under the supervision of Dr. Lino Becerra and Prof. Dr. David Borsook.
Currently I am working with Prof. Dr. Jorge Almeida on prosopagnosia patient where I have identified through probabilistic tractography analysis that the defects in structural connectivity tracts between OFA and FFA may lead to partially deformed faces. We are submitting this work for publication in Nature journal soon. I am also exploring the effects of tDCS on elderly population.
I am also working with Prof. John Gabrieli at MIT, a pioneer figure in the field of neuroscience. My interests are in developing treatment outcome prediction tools for psychiatric disorder patients using patient-specific neuroimaging data. Personalised (precision) medicine aims to identify reliable neuromarkers correlated with individual treatment response so as to identify, on a patient-by-patient basis, which intervention is most likely to benefit that patient. If successful, patients may be guided directly towards a treatment that is more effective for each of them individually (versus the nearly random choices that are now made in treatment selection). My project is to develop these personalized medicine tools for ADHD and SAD (social anxiety disorder) patients. Furthermore I am also developing an IQ score based on the neural signatures that is better predictor of performance in cognitive tasks.
Research Interests
Papers共 18 篇Author StatisticsCo-AuthorSimilar Experts
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bioRxiv (Cold Spring Harbor Laboratory) (2021)
Journal of the American Academy of Child & Adolescent Psychiatryno. 10 (2021)
Social Science Research Network (2021)
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