Toward discovery science of human brain function

By Bharat B. Biswal, Maarten Mennes, Xi-Nian Zuo, Suril R. Gohel, Clare Kelly, Steve M. Smith, Christian F. Beckmann, J. Adelstein, Randy L. Buckner, Stanley J. Colcombe and 44 more. Proceedings of the National Academy of Sciences of the United States of America, 2010

3,011 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/85e7d96dfd5efcbb0711bcc89039ba0d1dbda64e

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Citations in the corpus

ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI (2015, methodology): “…in imaging research [e.g., the Genetic Frontotemporal dementia Initiative (GenFI), Rohrer et al., 2013; 1000 Functional Connectome Project, Biswal et al., 2010; ADHD 200 Consortium dataset (The ADHD-200 Consortium, 2012); and Autism Brain Imaging Data Exchange (ABIDE), Di Martino et al.,…”
Building a Science of Individual Differences from fMRI. (2016, methodology): “It is now well established that resting-state functional connectivity (RSFC) can extract networks in the brain that subserve shared psychological functions [145].”
The Brain Basis of Positive and Negative Affect: Evidence from a Meta-Analysis of the Human Neuroimaging Literature. (2016, methodology): “Using the logic of large-scale brain organization that is available from neuroimaging studies (Smith et al. 2009; Biswal et al. 2010; Poldrack 2010; Yeo et al. 2011), this third hypothesis is avalence-general “affectiveworkspace”hypothesis (Barrett and Bliss-Moreau 2009).”
Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study (2012, methodology): “Numerous investigations have used this approach to describe alterations in intrinsic networks related to basic demographic factors (e.g., Biswal et al., 2010; Allen et al., 2011) or neurodegenerative and psychiatric disorders (e.g., Calhoun et al., 2008; Jafri et al., 2008; Filippini et al., 2009)…”
Big Data for Health (2015, background): “IMG 1414 subjects Network Analysis Resting state of neural fMRI data [132]”
Adolescent neurodevelopment. (2013, background): “Magnetic resonance imaging (MRI) and other imaging technologies have proved valuable for detailing size of (3; 4) and connectivity across (5; 6) brain regions at different ages, as well as for indexing relative changes in regional activation patterns during performance of target risk-taking, decisio”
Resting state fMRI: A review of methods and clinical applications (2012, neutral): “Studies suggest that RSNs can be detected reliably across imaging sessions(52,53) and across different subjects,(10,53) though there may be some loci of variability between subjects.(52) In particular, Chou et al(54) exam-”
An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data (2012, methodology): “As rsfc-MRI has evolved to become an important tool for examining brain networks in health and disease (Biswal et al., 2010; Fox and Greicius, 2010; Glahn et al., 2010; Seeley et al., 2009; Yeo et al., 2011; Zhou et al., 2010), it is of critical importance to understand how best to model and…”
The future of FMRI connectivity (2012, background): “31 For example the “1000 functional connectomes” project (aka “KFC”) (Biswal et al., 2010), created by Mike Milham.”
The human connectome: Origins and challenges (2013, background): “are so-called resting-state networks that have been shown to display relatively stable anatomical distributions and functional attributes, and that can be reliably extracted (Biswal et al., 2010).”
Networks of anatomical covariance (2013, neutral): “…et al., 2006; Stam, 2004; Stam et al., 2007) or fMRI (Achard and Bullmore, 2007, 2006; Bassett and Bullmore, 2006; Bellec et al., 2010; Biswal et al., 2010; Eguiluz et al., 2005; Greicius et al., 2003, 2004; He et al., 2009a; Honey et al., 2007, 2009; Salvador et al., 2005a, 2005b;…”
Distributed Weight Consolidation: A Brain Segmentation Case Study (2018, methodology): “We train networks using 956 sMRI volumes collected by the Human Connectomme Project (HCP) [27], 1,136 sMRI volumes collected by the Nathan Kline Institute (NKI) [20], and 183 sMRI volumes collected by the Buckner Laboratory [1].”