Gopikrishna Deshpande, Ph.D.

Affiliations: 
2007 Georgia Institute of Technology, Atlanta, GA 
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"Gopikrishna Deshpande"
Mean distance: 24.23 (cluster 40)
 

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Xiaoping Hu grad student 2007 Georgia Tech
 (Nonlinear and network characterization of brain function using functional MRI data.)
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Publications

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Zhao S, Rangaprakash D, Liang P, et al. (2019) Deterioration from healthy to mild cognitive impairment and Alzheimer's disease mirrored in corresponding loss of centrality in directed brain networks. Brain Informatics. 6: 8
Lanka P, Rangaprakash D, Dretsch MN, et al. (2019) Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets. Brain Imaging and Behavior
Strassberg LR, Waggoner LP, Deshpande G, et al. (2019) Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging. Journal of Visualized Experiments : Jove
Rangaprakash D, Dretsch MN, Katz JS, et al. (2019) Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma. Frontiers in Neuroscience. 13: 803
Syed MA, Yang Z, Rangaprakash D, et al. (2019) DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders. Neuroinformatics
McCormick M, Reyna VF, Ball K, et al. (2019) Neural Underpinnings of Financial Decision Bias in Older Adults: Putative Theoretical Models and a Way to Reconcile Them. Frontiers in Neuroscience. 13: 184
Dretsch MN, Rangaprakash D, Katz JS, et al. (2019) Strength and Temporal Variance of the Default Mode Network to Investigate Chronic Mild Traumatic Brain Injury in Service Members with Psychological Trauma. Journal of Experimental Neuroscience. 13: 1179069519833966
Zhao X, Rangaprakash D, Denney TS, et al. (2019) Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code. Data in Brief. 22: 570-573
Zhao X, Rangaprakash D, Yuan B, et al. (2018) Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning. Frontiers in Applied Mathematics and Statistics. 4
Wheelock MD, Rangaprakash D, Harnett NG, et al. (2018) Psychosocial stress reactivity is associated with decreased whole-brain network efficiency and increased amygdala centrality. Behavioral Neuroscience
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