S
Srinivas Rachakonda
Researcher at Georgia Institute of Technology
Publications - 38
Citations - 3520
Srinivas Rachakonda is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Resting state fMRI & Functional magnetic resonance imaging. The author has an hindex of 20, co-authored 36 publications receiving 2888 citations. Previous affiliations of Srinivas Rachakonda include The Mind Research Network.
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Journal ArticleDOI
A Baseline for the Multivariate Comparison of Resting-State Networks
Elena A. Allen,Erik B. Erhardt,Eswar Damaraju,William Gruner,William Gruner,Judith M. Segall,Judith M. Segall,Rogers F. Silva,Rogers F. Silva,Martin Havlicek,Martin Havlicek,Srinivas Rachakonda,Jill Fries,Ravi Kalyanam,Ravi Kalyanam,Andrew M. Michael,Arvind Caprihan,Jessica A. Turner,Jessica A. Turner,Tom Eichele,Steven Adelsheim,Angela D. Bryan,Angela D. Bryan,Juan R. Bustillo,Vincent P. Clark,Vincent P. Clark,Sarah W. Feldstein Ewing,Francesca M. Filbey,Francesca M. Filbey,Corey C. Ford,Kent E. Hutchison,Kent E. Hutchison,Rex E. Jung,Rex E. Jung,Kent A. Kiehl,Kent A. Kiehl,Piyadasa W. Kodituwakku,Yuko M. Komesu,Andrew R. Mayer,Andrew R. Mayer,Godfrey D. Pearlson,John P. Phillips,John P. Phillips,Joseph Sadek,Michael Stevens,Ursina Teuscher,Ursina Teuscher,Robert J. Thoma,Vince D. Calhoun +48 more
TL;DR: A multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing is introduced and is demonstrated by identifying the effects of age and gender on the resting-state networks of 603 healthy adolescents and adults.
Journal ArticleDOI
Comparison of multi-subject ICA methods for analysis of fMRI data.
Erik B. Erhardt,Srinivas Rachakonda,Edward J. Bedrick,Elena A. Allen,Tulay Adali,Vince D. Calhoun,Vince D. Calhoun +6 more
TL;DR: Comparisons of subject‐specific, spatial concatenation, and group data mean subject‐level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject‐ specific and group Data mean subject-level PCA are preferred because of well‐estimated TCs and SMs.
Journal ArticleDOI
Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia.
Qingbao Yu,Erik B. Erhardt,Jing Sui,Yuhui Du,Hao He,R Devon Hjelm,Mustafa S. Çetin,Mustafa S. Çetin,Srinivas Rachakonda,Robyn L. Miller,Godfrey D. Pearlson,Vince D. Calhoun +11 more
TL;DR: A new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data is developed and applied to healthy controls and patients with schizophrenia, indicating that SZs show decreased variance in the dynamic graph metrics.
Journal ArticleDOI
Patterns of Gray Matter Abnormalities in Schizophrenia Based on an International Mega-analysis
Cota Navin Gupta,Vince D. Calhoun,Vince D. Calhoun,Vince D. Calhoun,Srinivas Rachakonda,Jiayu Chen,Veena Patel,Jingyu Liu,Judith M. Segall,Barbara Franke,Marcel P. Zwiers,Alejandro Arias-Vasquez,Jan K. Buitelaar,Simon E. Fisher,Simon E. Fisher,Guillén Fernández,Theo G.M. van Erp,Steven G. Potkin,Judith M. Ford,Daniel H. Mathalon,Sarah McEwen,Hyo Jong Lee,Bryon A. Mueller,Douglas N. Greve,Ole A. Andreassen,Ingrid Agartz,Ingrid Agartz,Randy L. Gollub,Scott R. Sponheim,Stefan Ehrlich,Stefan Ehrlich,Lei Wang,Godfrey D. Pearlson,David C. Glahn,Emma Sprooten,Andrew R. Mayer,Julia M. Stephen,Rex E. Jung,José M. Cañive,José M. Cañive,Juan R. Bustillo,Jessica A. Turner,Jessica A. Turner +42 more
TL;DR: This mega-analysis confirms that the commonly found GMC loss in patients with schizophrenia in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset.
Journal ArticleDOI
Dynamic functional network connectivity reveals unique and overlapping profiles of insula subdivisions.
Jason S. Nomi,Kristafor Farrant,Eswar Damaraju,Srinivas Rachakonda,Vince D. Calhoun,Vince D. Calhoun,Lucina Q. Uddin +6 more
TL;DR: The d‐FNC analysis revealed that the most frequently occurring dynamic state mirrored the cognition‐emotion‐interoception division observed from the s‐F NC analysis, with less frequently occurring states showing overlapping and unique subdivision connectivity profiles.