Defining functional areas in individual human brains using resting functional connectivity MRI

By Alexander Li Cohen, Damien A. Fair, Nico U F Dosenbach, Francis M. Miezin, Donna L. Dierker, David C. Van Essen, Bradley L. Schlaggar, Steven E. Petersen. NeuroImage, 2008

613 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/24649556e617bdbb9f198277816f5321d83a5371

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In Cognitive Neuroscience

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Citations in the corpus

Probabilistic Maps of Visual Topography in Human Cortex. (2015, background): “1993), is a useful metric for assessing largescale cortical organization (Cohen et al. 2008; Yeo et al. 2011).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, neutral): “Incorporating spatial information can help these algorithms define spatially contiguous regions (Cohen et al., 2008; Craddock et al., 2012).”
A Review of Adversity, The Amygdala and the Hippocampus: A Consideration of Developmental Timing (2009, positive): “We argue that a developmental approach is necessary 2007; Fair and Cohen, 2008) and in defi ning functionally connected regions of interest in individual subjects (Cohen et al., 2008) a technique which would be well suited to the investigation of individual differences associated with stress.”
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion (2011, positive): “These ROIs were defined through several meta-analyses of task fMRI data, and with the fc-Mapping technique (Cohen et al., 2008; Nelson et al., 2010) applied to rs-fcMRI data (no subjects in the present study were used to define ROIs).”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, background): “, 2016b), edge detection (Cohen et al., 2008; Laumann et al., 2015; Gordon et al., 2016), region growing (Blumensath et al.”
Which fMRI clustering gives good brain parcellations? (2014, methodology): “We also omit edge-based parcellation methods, such as those described in Wig et al. (2013) or Cohen et al. (2008): while these are certainly useful to segment the cortical surface by revealing abrupt changes in the functional connectivity patterns when crossing region boundaries, they do not lend…”
The WU-Minn Human Connectome Project: An Overview (2013, methodology): “Several approaches to brain parcellation based on functional connectivity have been explored, including methods based on spatial gradients (Cohen et al., 2008; Smith et al., 2013), snowball sampling (Wig et al., 2013); and region-growing (Blumensath et al., 2013).”
Toward discovery science of human brain function (2010, methodology): “Specifically, we exploit a key property of functional connectivity maps, the presence of welldifferentiated borders between functionally distinct regions (45).”
Precision functional mapping of individual human brains (2017, positive): “Within-subject reliability of RSFC and derived measures First, each subject’s RSFC data was parcellated into discrete homogeneous parcels using the gradient-based parcellation technique (Cohen et al., 2008; Gordon et al., 2016; Laumann et al., 2015; Nelson et al., 2010; Wig et al., 2014).”
Functional network organization of the human brain (2011, methodology): “See Cohen et al. (2008) and Nelson et al. (2010a) for full conceptual and technical descriptions of fc-Mapping on cortical patches.”