Universität zu Köln

Greene, Clint, Cieslak, Matthew, Volz, Lukas J. ORCID: 0000-0002-0161-654X, Hensel, Lukas, Grefkes, Christian ORCID: 0000-0002-1656-720X, Rose, Ken and Grafton, Scott T. (2019). Finding maximally disconnected subnetworks with shortest path tractography. NeuroImage-Clin., 23. OXFORD: ELSEVIER SCI LTD. ISSN 2213-1582

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Abstract

Connectome-based lesion symptom mapping (CLSM) can be used to relate disruptions of brain network connectivity with clinical measures. We present a novel method that extends current CLSM approaches by introducing a fast reliable and accurate way for computing disconnectomes, i.e. identifying damaged or lesioned connections. We introduce a new algorithm that finds the maximally disconnected subgraph containing regions and region pairs with the greatest shared connectivity loss. After normalizing a stroke patient's segmented MRI lesion into template space, probability weighted structural connectivity matrices are constructed from shortest paths found in white matter voxel graphs of 210 subjects from the Human Connectome Project. Percent connectivity loss matrices are constructed by measuring the proportion of shortest-path probability weighted connections that are lost because of an intersection with the patient's lesion. Maximally disconnected subgraphs of the overall connectivity loss matrix are then derived using a computationally fast greedy algorithm that closely approximates the exact solution. We illustrate the approach in eleven stroke patients with hemiparesis by identifying expected disconnections of the corticospinal tract (CST) with cortical sensorimotor regions. Major disconnections are found in the thalamus, basal ganglia, and inferior parietal cortex. Moreover, the size of the maximally disconnected subgraph quantifies the extent of cortical disconnection and strongly correlates with multiple clinical measures. The methods provide a fast, reliable approach for both visualizing and quantifying the disconnected portion of a patient's structural connectome based on their routine clinical MRI, without reliance on concomitant diffusion weighted imaging. The method can be extended to large databases of stroke patients, multiple sclerosis or other diseases causing focal white matter injuries helping to better characterize clinically relevant white matter lesions and to identify biomarkers for the recovery potential of individual patients.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Greene, Clint UNSPECIFIED UNSPECIFIED UNSPECIFIED Cieslak, Matthew UNSPECIFIED UNSPECIFIED UNSPECIFIED Volz, Lukas J. UNSPECIFIED orcid.org/0000-0002-0161-654X UNSPECIFIED Hensel, Lukas UNSPECIFIED UNSPECIFIED UNSPECIFIED Grefkes, Christian UNSPECIFIED orcid.org/0000-0002-1656-720X UNSPECIFIED Rose, Ken UNSPECIFIED UNSPECIFIED UNSPECIFIED Grafton, Scott T. UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-160637
DOI:	10.1016/j.nicl.2019.101903
Journal or Publication Title:	NeuroImage-Clin.
Volume:	23
Date:	2019
Publisher:	ELSEVIER SCI LTD
Place of Publication:	OXFORD
ISSN:	2213-1582
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language WHITE-MATTER CONNECTIVITY; LESION LOAD; CONNECTOME; STROKE; MOTOR; NETWORKS; RECOVERY Multiple languages Neuroimaging Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/16063