A whole brain fMRI atlas generated via spatially constrained spectral clustering

By R. Craddock, George Andrew James, Paul E. Holtzheimer, Xiaoping P. Hu, Helen S. Mayberg. 2011

1,668 citations recorded by the source corpus.

Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto‐architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data‐driven method for generating an ROI atlas by parcellating whole brain resting‐state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade‐offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard‐Oxford, Eickoff‐Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, methodology): “As illustrated in Figure 5, parcellations generated by the gwMRF model have comparable visuotopic alignment with parcellations from fully automatic approaches (Craddock et al. 2012; Shen et al. 2013; Gordon et al. 2016), but worse alignment than the semi-automatic multimodal approach (Glasser et al.”
From brain maps to cognitive ontologies: informatics and the search for mental structure (2015, methodology): “The earliest work in this domain focused primarily on decoding of specific stimulus or task features within a single individual, but subsequent work has shown that one can decode large-scale psychological functions from fMRI data in a way that generalizes across individuals (Mourão-Miranda et al.…”
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives (2018, methodology): “The CC200 functional parcellation atlas [8] of the brain, totaling 200 regions, was used to estimate the brain connectivity matrix.”
Tracking whole-brain connectivity dynamics in the resting state. (2014, positive): “Future work could consider higher model order ICA or clustering to delineate regions that are more functionally homogenous (Craddock et al. 2012); however, as the number of nodes grows, it will become increasingly difficult to estimate the covariance matrix from short windows.”
The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis (2012, methodology): “The residual BOLD data was then analyzed using the spatially constrained spectral clustering method (Craddock et al., 2012) in order to derive 188 gray matter/subcortical/cerebellar ROIs that were spatially contiguous and maximally functionally homogenous across subjects.”
A Convergent Functional Architecture of the Insula Emerges Across Imaging Modalities (2012, positive): “Leveraging this insight, studies have demonstrated that data-driven clustering methods based on iFC measures can partition the brain into distinct functional systems, thus revealing its functional “building blocks” (e.g., Bellec et al., 2010; Craddock et al., 2011; Nelson et al., 2010a,b; Yeo et al.”
The effect of scan length on the reliability of resting-state fMRI connectivity estimates (2013, positive): “In functional MRI, the DC has been used to assess the commonality of connectivity values from different scans of the same individual that survive a given threshold, zt (Craddock et al., 2012).”
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample (2017, methodology): “…smoothed (8 mm FWHM Gaussian kernel); for region of interest (ROI) analysis, applied masks from anatomically labelled template images (Harvard-Oxford atlas, Desikan et al., 2006; Craddock et al., 2012; in separate sub-branches) to extract mean regional GM andWM signal values for each participant.”
Advancing functional connectivity research from association to causation (2019, background): “This encompasses a range of possibilities: small anatomical entities, such as neurons or microcolumns, or larger parcels of neural tissue, whose boundaries are determined cytoarchitectonically or otherwise 10,50–52 .”
The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture (2016, neutral): “…(Brodmann 1909; Von Economo and Koskinas 1925; Zilles and Amunts 2010), while whole-brain parcellation studies based on rfMRI or dMRImay be realized by either global border detection (Wig et al. 2014; Gordon et al. 2016) or global clustering (Craddock et al. 2012; Moreno-Dominguez et al. 2014).”
Reduced default mode network functional connectivity in patients with recurrent major depressive disorder (2019, positive): “, Craddock’s 200 functional clustering atlas (18), with 48 DMN ROIs] confirmed our results, except that the effect of symptom severity in recurrent MDDs became insignificant (t = 1.”
Deriving reproducible biomarkers from multi‐site resting‐state data: An Autism‐based example (2016, background): “, 2010) of functional connectivity on a thousand individuals, and Craddock (Craddock et al., 2012), a multiscale functional atlas computed using constrained spectral clustering, to study the impact of the number of regions.”