Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Groupwise Structural Parcellation of the Whole Cortex: A Logistic Random Effects Model Based Approach

Institution:
1Université Côte d'Azur, Inria, France.
2Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Elsevier Science
Publication Date:
Apr-2018
Journal:
Neuroimage
Volume Number:
170
Pages:
307-20
Citation:
Neuroimage. 2018 Apr 15;170:307-20.
PubMed ID:
28161314
PMCID:
PMC5538957
Keywords:
Statistical clustering models, Structural connectivity, Structural parcellation, Tractography
Appears in Collections:
NAC, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
Generated Citation:
Gallardo G., Wells III W.M., Deriche R., Wassermann D. Groupwise Structural Parcellation of the Whole Cortex: A Logistic Random Effects Model Based Approach. Neuroimage. 2018 Apr 15;170:307-20. PMID: 28161314. PMCID: PMC5538957.
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Current theories hold that brain function is highly related to long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcellation based on extrinsic connectivity remains challenging. Current parcellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity of the cortex. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient parceling technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with structural and functional parcellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with the motor strip mapping included in the Human Connectome Project data.