An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

By Rahul Desikan, Florent Ségonne, Bruce Fischl, Brian T. Quinn, Bradford C. Dickerson, Deborah Blacker, Randy L. Buckner, A. M. Dale, Ralph Paul Maguire, Bradley T. Hyman and 2 more. NeuroImage, 2006

13,059 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ad4e281293237bdb8efbbd639ac823536815f42b

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Citations in the corpus

The role of the nucleus accumbens and rostral anterior cingulate cortex in anhedonia: Integration of resting EEG, fMRI, and volumetric techniques (2009, methodology): “1 These ROIs were structurally defined by FreeSurfer’s automatic cortical and sub-cortical parcellation algorithms, which are highly reliable and Neural Correlates of Anhedonia compare favorably to manual methods (Desikan et al., 2006; Fischl et al., 2002; Fischl et al., 2004).”
Spectral Graph Convolutions for Population-based Disease Prediction (2017, methodology): “The mean time series for a set of regions extracted from the Harvard Oxford (HO) atlas [5] were computed and normalised to zero mean and unit variance.”
The Neuro Bureau ADHD-200 Preprocessed Repository (2016, methodology): “Images for four participants were excluded after visual inspection by Dr. Chu because they were determined to be of insuﬃcient quality for further processing.”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, neutral): “The term parcellated connectome refers to the matrix describing the connectivity between brain areas, as defined by an anatomical parcellation (Kötter and Wanke, 2005; Tzourio-Mazoyer et al., 2002; Desikan et al., 2006; Yeo et al., 2011; Bezgin et al., 2012); and the term dense connectome refers to ”
Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature (2010, methodology): “, 1998) may be more appropriate for the anatomy of the brain than classical volume based coordinate systems (Talairach and Tournoux, 1988): they provide better inter-subject averaging and allow the development of tools for automatically parcellating the cortex in a reproducible and accurate way (Des”
Proportional thresholding in resting‐state fMRI functional connectivity networks and consequences for patient‐control connectome studies: Issues and recommendations (2017, positive): “Next, tissue classi ﬁ cation and cortical segmentation was performed on the basis of the T1 scan, followed by parcellation of the cortex into 68 cortical areas using the Desikan-Killiany atlas (Desikan et al., 2006; Hagmann et al., 2008).”
White matter maturation reshapes structural connectivity in the late developing human brain (2010, methodology): “” In the low-resolution parcellation, 66 cortical regions of varying sizes were identified in each participant using an automated landmark-based algorithm (30).”
Sex differences in the adult human brain: Evidence from 5,216 UK Biobank participants (2017, methodology): “2007), according to the Desikan– Killiany atlas (Desikan et al. 2006).”
Schizophrenia, neuroimaging and connectomics (2012, methodology): “The most common approach has involved the use of an a priori anatomical parcellation, typically comprising ~10(2) regions (e.g., Tzourio-Mazoyer et al., 2002; Fig. 1; Desikan et al., 2006).”
Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults (2007, methodology): “…size normalization (Buckner et al., 2004), automated whole-brain segmentation (Fischl et al., 2002), automated labeling of cerebral cortex into regions of interest (Desikan et al., 2006), and large-deformation maps of brain structure shape (Csernansky, Wang, Joshi, Ratnanather, & Miller, 2004).”
Cortical Thickness or Grey Matter Volume? The Importance of Selecting the Phenotype for Imaging Genetics Studies (2009, neutral): “While the definitions of the regions of the (Desikan et al., 2006) atlas follow macroanatomic landmarks, not cytoarchitectonic, there is evidence suggesting that there is a substantial overlap between them (Fischl et al., 2008a), and the higher regional variability suggests that regional…”