Finding maximally disconnected subnetworks with shortest path tractography
| Autor: | Matthew Cieslak, Clint Greene, Lukas J. Volz, Christian Grefkes, Scott T. Grafton, L. Hensel, Kenneth Rose |
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| Rok vydání: | 2019 |
| Předmět: |
Male
Computer science FODR Fiber orientation distribution reorientation ODFs Orientation distribution functions Disconnection computer.software_genre lcsh:RC346-429 0302 clinical medicine Voxel Brain injury QA Quantitative anisotropy Human Connectome Project 05 social sciences GQI Generalized q-sampling imaging Regular Article Middle Aged White Matter Stroke Diffusion Tensor Imaging CSD Constrained spherical deconvolution Neurology FODs Fiber orientation distributions Connectome lcsh:R858-859.7 Female Tractography Graphs Algorithms Connectomes CST Corticospinal tract Brain networks DW-MRI Diffusion-weighted magnetic resonance imaging Cognitive Neuroscience education Lesion symptom mapping DTI Diffusion tensor imaging Neuroimaging Disconnectome HARDI High angular resolution diffusion imaging lcsh:Computer applications to medicine. Medical informatics 050105 experimental psychology Diffusion MRI Spatial normalization CLSM Connectome-based lesion symptom mapping 03 medical and health sciences DWI Diffusion weighted image Humans 0501 psychology and cognitive sciences Radiology Nuclear Medicine and imaging PSFs Point spread functions lcsh:Neurology. Diseases of the nervous system Aged business.industry Pattern recognition Hyperintensity SyGN Symmetric group wise normalization GFA Generalized fractional anisotropy Corticospinal tract Neurology (clinical) Artificial intelligence VLSM Voxel-based lesion symptom mapping Nerve Net business computer HCP Human connectome project 030217 neurology & neurosurgery |
| Zdroj: | NeuroImage : Clinical NeuroImage: Clinical, Vol 23, Iss, Pp-(2019) |
| ISSN: | 2213-1582 |
| Popis: | 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. Highlights • Significantly accelerated shortest path tractography approach for constructing connectomes and disconnectomes. • New algorithm extracts the subnetwork containing cortical connections and regions with maximal shared connectivity loss. • The size of the maximally disconnected subnetwork quantifies the extent of disconnection and correlates with stroke measures. • Fast and accurate approach for visualizing and analyzing the disconnected portion of a patient's structural connectome. |
| Databáze: | OpenAIRE |
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