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Satrajit S. Ghosh

Researcher at Harvard University

Publications -  189
Citations -  14959

Satrajit S. Ghosh is an academic researcher from Harvard University. The author has contributed to research in topics: Computer science & Neuroimaging. The author has an hindex of 44, co-authored 175 publications receiving 10291 citations. Previous affiliations of Satrajit S. Ghosh include Boston University & Massachusetts Institute of Technology.

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Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

TL;DR: The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient.
Posted ContentDOI

A multimodal cell census and atlas of the mammalian primary motor cortex

Ricky S. Adkins, +247 more
- 07 Oct 2021 - 
TL;DR: This study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps, and establishes a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

TL;DR: In this paper, the brain activation in patients with SAD was used as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT) intervention, and the results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions.
Journal ArticleDOI

Region of interest based analysis of functional imaging data.

TL;DR: The combined application of subject-specific ROI definition and region-level functional analysis is shown to appropriately compensate for intersubject anatomical variability, offering finer localization and increased sensitivity to task-related effects than standard techniques based on whole-brain normalization and voxel or cluster-levelfunctional analysis.