J
James J. Pekar
Researcher at Kennedy Krieger Institute
Publications - 156
Citations - 22324
James J. Pekar is an academic researcher from Kennedy Krieger Institute. The author has contributed to research in topics: Functional magnetic resonance imaging & Resting state fMRI. The author has an hindex of 63, co-authored 153 publications receiving 20167 citations. Previous affiliations of James J. Pekar include McMaster University & University of Pennsylvania.
Papers
More filters
Journal ArticleDOI
Toward discovery science of human brain function
Bharat B. Biswal,Maarten Mennes,Xi-Nian Zuo,Suril Gohel,Clare Kelly,Steve M. Smith,Christian F. Beckmann,Jonathan S. Adelstein,Randy L. Buckner,Stan Colcombe,Anne Marie Dogonowski,Monique Ernst,Damien A. Fair,Michelle Hampson,Matthew J. Hoptman,James S. Hyde,Vesa Kiviniemi,Rolf Kötter,Shi-Jiang Li,Ching Po Lin,Mark J. Lowe,Clare E. Mackay,David J. Madden,Kristoffer Hougaard Madsen,Daniel S. Margulies,Helen S. Mayberg,Katie L. McMahon,Christopher S. Monk,Stewart H. Mostofsky,Bonnie J. Nagel,James J. Pekar,Scott Peltier,Steven E. Petersen,Valentin Riedl,Serge A.R.B. Rombouts,Bart Rypma,Bradley L. Schlaggar,Sein Schmidt,Rachael D. Seidler,Greg J. Siegle,Christian Sorg,Gao Jun Teng,Juha Veijola,Arno Villringer,Martin Walter,Lihong Wang,Xu Chu Weng,Susan Whitfield-Gabrieli,Peter C. Williamson,Christian Windischberger,Yu-Feng Zang,Hong Ying Zhang,F. Xavier Castellanos,F. Xavier Castellanos,Michael P. Milham +54 more
TL;DR: The 1000 Functional Connectomes Project (Fcon_1000) as discussed by the authors is a large-scale collection of functional connectome data from 1,414 volunteers collected independently at 35 international centers.
Journal ArticleDOI
A method for making group inferences from functional MRI data using independent component analysis
TL;DR: A novel approach for drawing group inferences using ICA of fMRI data is introduced, and its application to a simple visual paradigm that alternately stimulates the left or right visual field is presented.
Journal ArticleDOI
Tract Probability Maps in Stereotaxic Spaces: Analyses of White Matter Anatomy and Tract-Specific Quantification
Kegang Hua,Jiangyang Zhang,Setsu Wakana,Hangyi Jiang,Xin Li,Daniel S. Reich,Peter A. Calabresi,James J. Pekar,Peter C.M. van Zijl,Susumu Mori +9 more
TL;DR: This study created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects, and automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed.
Journal ArticleDOI
Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent
TL;DR: The results support the notion that the pre-SMA is critical for selection of appropriate behavior, whether selecting to execute an appropriate response or selecting to inhibit an inappropriate response.
Journal ArticleDOI
Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms.
TL;DR: A careful examination of some of the assumptions behind ICA methodologies is reported, examples of when applying ICA would provide difficult‐to‐interpret results, and suggestions for applying I CA to fMRI data especially when more than one task‐related component is present in the data are offered.