2013 - ACM Senior Member
Satrajit S. Ghosh spends much of his time researching Neuroimaging, Software, Neuroscience, Image processing and Data mining. His studies deal with areas such as Field, Metadata and Cognitive neuroscience as well as Neuroimaging. He has included themes like Brain morphometry, Data processing, Data science and Pattern recognition in his Software study.
In general Neuroscience study, his work on Cognition, Default mode network, Sensory system and Broca's area often relates to the realm of Premotor cortex, thereby connecting several areas of interest. His Image processing study combines topics in areas such as Pipeline, Preprocessor, Inference and Interpretability. His Data mining research is multidisciplinary, incorporating perspectives in Workflow and Thesaurus.
His primary areas of investigation include Neuroimaging, Neuroscience, Artificial intelligence, Pattern recognition and Functional magnetic resonance imaging. The concepts of his Neuroimaging study are interwoven with issues in Metadata, Data mining, Data model, Software and Data science. His biological study spans a wide range of topics, including Visualization, Data processing and Data structure.
His Neuroscience study frequently draws connections to other fields, such as Speech production. In his research, Embedding and Functional connectivity is intimately related to Resting state fMRI, which falls under the overarching field of Artificial intelligence. His work carried out in the field of Functional magnetic resonance imaging brings together such families of science as Clinical psychology and Brain function.
The scientist’s investigation covers issues in Neuroimaging, Neuroscience, Artificial intelligence, Anxiety and Functional magnetic resonance imaging. The study incorporates disciplines such as Computer graphics, Image processing, Software, Software tool and Data science in addition to Neuroimaging. His studies in Image processing integrate themes in fields like Data mining, Thesaurus, Pipeline, Interpretability and Workflow.
In the field of Neuroscience, his study on Thalamus, Auditory system and Cerebellum overlaps with subjects such as In vivo and Process. His Artificial intelligence research includes themes of Machine learning, Receiver operating characteristic, Data model and Pattern recognition. Satrajit S. Ghosh interconnects Neurophysiology, Audiology and Active listening in the investigation of issues within Functional magnetic resonance imaging.
His scientific interests lie mostly in Neuroscience, Neuroimaging, Preprocessor, Image processing and Artificial intelligence. Many of his studies on Neuroscience involve topics that are commonly interrelated, such as Gradient based algorithm. His Neuroimaging research incorporates themes from Voxel and Data science.
His Preprocessor research is multidisciplinary, incorporating elements of Data mining, Thesaurus, Interpretability, Pipeline and Workflow. Satrajit S. Ghosh works mostly in the field of Image processing, limiting it down to topics relating to Inference and, in certain cases, Artificial neural network, Segmentation and Deep learning, as a part of the same area of interest. His research in Artificial intelligence focuses on subjects like Pattern recognition, which are connected to Data structure, Software, Neural correlates of consciousness and Functional magnetic resonance imaging.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Neural modeling and imaging of the cortical interactions underlying syllable production.
Frank H. Guenther;Satrajit S. Ghosh;Jason A. Tourville.
Brain and Language (2006)
Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python.
Krzysztof J. Gorgolewski;Christopher D. Burns;Cindee M. Madison;Dav Clark.
Frontiers in Neuroinformatics (2011)
Situating the default-mode network along a principal gradient of macroscale cortical organization
Daniel S. Margulies;Satrajit S. Ghosh;Satrajit S. Ghosh;Alexandros Goulas;Marcel Falkiewicz.
Proceedings of the National Academy of Sciences of the United States of America (2016)
fMRIPrep: a robust preprocessing pipeline for functional MRI
Oscar Esteban;Christopher J. Markiewicz;Ross W. Blair;Craig A. Moodie.
Nature Methods (2019)
NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain
Krzysztof J. Gorgolewski;Krzysztof J. Gorgolewski;Gael Varoquaux;Gabriel Rivera;Yannick Schwarz.
Frontiers in Neuroinformatics (2015)
Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience
John D.E. Gabrieli;John D.E. Gabrieli;Satrajit S. Ghosh;Satrajit S. Ghosh;Susan Whitfield-Gabrieli.
The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.
Krzysztof J. Gorgolewski;Tibor Auer;Vince D. Calhoun;R. Cameron Craddock.
Scientific Data (2016)
Evaluation of volume-based and surface-based brain image registration methods
Arno Klein;Satrajit S. Ghosh;Brian Avants;B.T.T. Yeo.
Data sharing in neuroimaging research
Jean-Baptiste Poline;Jean-Baptiste Poline;Janis L. Breeze;Satrajit S. Ghosh;Krzysztof J. Gorgolewski.
Frontiers in Neuroinformatics (2012)
Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging
Oliver Doehrmann;Satrajit S. Ghosh;Frida E. Polli;Gretchen O. Reynolds.
JAMA Psychiatry (2013)
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