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
TLDR
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.Abstract:
As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.read more
Citations
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Journal ArticleDOI
Tracking Whole-Brain Connectivity Dynamics in the Resting State
Elena A. Allen,Eswar Damaraju,Sergey M. Plis,Erik B. Erhardt,Tom Eichele,Vince D. Calhoun,Vince D. Calhoun +6 more
TL;DR: In this article, the authors describe an approach to assess whole-brain functional connectivity dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices.
Journal ArticleDOI
The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery
TL;DR: This Perspective uses the term "chronnectome" to describe metrics that allow a dynamic view of coupling and focuses on multivariate approaches developed in the group and review a number of approaches with an emphasis on matrix decompositions such as principle component analysis and independent component analysis.
Journal ArticleDOI
The cognitive neuroscience of ageing
TL;DR: Current trends and unresolved issues in the cognitive neuroscience of ageing are discussed and it is less clear how age differences in brain activity relate to cognitive performance.
Journal ArticleDOI
Behavioral interpretations of intrinsic connectivity networks
Angela R. Laird,P. Mickle Fox,Simon B. Eickhoff,Jessica A. Turner,Kimberly L. Ray,D. Reese McKay,David C. Glahn,Christian F. Beckmann,Stephen M. Smith,Peter T. Fox +9 more
TL;DR: This work presents a full functional explication of intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes.
Journal ArticleDOI
Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia
Eswar Damaraju,Elena A. Allen,Elena A. Allen,Aysenil Belger,Judith M. Ford,Judith M. Ford,Sarah McEwen,Daniel H. Mathalon,Daniel H. Mathalon,Bryon A. Mueller,Godfrey D. Pearlson,Steven G. Potkin,Adrian Preda,Jessica A. Turner,Jatin G. Vaidya,T.G.M. van Erp,Vince D. Calhoun,Vince D. Calhoun +17 more
TL;DR: The results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.
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