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Decoding post-stroke motor function from structural brain imaging

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Title: Decoding post-stroke motor function from structural brain imaging
Authors: Rondina, JM
Filippone, M
Girolami, M
Ward, NS
Item Type: Journal Article
Abstract: Clinical research based on neuroimaging data has benefited from machine learning methods, which have the ability to provide individualized predictions and to account for the interaction among units of information in the brain. Application of machine learning in structural imaging to investigate diseases that involve brain injury presents an additional challenge, especially in conditions like stroke, due to the high variability across patients regarding characteristics of the lesions. Extracting data from anatomical images in a way that translates brain damage information into features to be used as input to learning algorithms is still an open question. One of the most common approaches to capture regional information from brain injury is to obtain the lesion load per region (i.e. the proportion of voxels in anatomical structures that are considered to be damaged). However, no systematic evaluation has yet been performed to compare this approach with using patterns of voxels (i.e. considering each voxel as a single feature). In this paper we compared both approaches applying Gaussian Process Regression to decode motor scores in 50 chronic stroke patients based solely on data derived from structural MRI. For both approaches we compared different ways to delimit anatomical areas: regions of interest from an anatomical atlas, the corticospinal tract, a mask obtained from fMRI analysis with a motor task in healthy controls and regions selected using lesion-symptom mapping. Our analysis showed that extracting features through patterns of voxels that represent lesion probability produced better results than quantifying the lesion load per region. In particular, from the different ways to delimit anatomical areas compared, the best performance was obtained with a combination of a range of cortical and subcortical motor areas as well as the corticospinal tract. These results will inform the appropriate methodology for predicting long term motor outcomes from early post-stroke structural brain imaging.
Issue Date: 2-Aug-2016
Date of Acceptance: 30-Jul-2016
URI: http://hdl.handle.net/10044/1/66128
DOI: https://dx.doi.org/10.1016/j.nicl.2016.07.014
ISSN: 2213-1582
Publisher: Elsevier
Start Page: 372
End Page: 380
Journal / Book Title: NeuroImage: Clinical
Volume: 12
Copyright Statement: © 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Science & Technology
Life Sciences & Biomedicine
Neuroimaging
Neurosciences & Neurology
Stroke
Motor impairment
Lesion patterns
Machine learning
Gaussian processes
Multiple kernel learning
Features extraction
Patterns of lesion probability
Lesion load
SUPPORT VECTOR REGRESSION
UNIFIED SEGMENTATION
STROKE PATIENTS
RECOVERY
IMPAIRMENT
MRI
PREDICTION
INTEGRITY
VARIABLES
IMAGES
Brain
Female
Humans
Machine Learning
Magnetic Resonance Imaging
Male
Middle Aged
Motor Disorders
Publication Status: Published
Online Publication Date: 2016-08-02
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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