Ecole d'ingénieur et centre de recherche en Sciences du numérique

Decoding post-stroke motor function from structural brain imaging

Rondina, Jane M; Filippone, Maurizio; Girolami, Mark; Ward, Nick S

NeuroImage: Clinical, Volume 12, August 2016

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.

Document Doi Bibtex

Titre:Decoding post-stroke motor function from structural brain imaging
Mots Clés:Stroke, Motor impairment, Lesion patterns, Machine learning, Gaussian proc esses, Multiple kernel learning, Features extraction, Patterns of lesion probability, L esion load
Type:Journal
Langue:English
Ville:
Date:
Département:Data Science
Eurecom ref:4979
Copyright: © Elsevier. Personal use of this material is permitted. The definitive version of this paper was published in NeuroImage: Clinical, Volume 12, August 2016 and is available at : http://dx.doi.org/10.1016/j.nicl.2016.07.014
Bibtex: @article{EURECOM+4979, doi = {http://dx.doi.org/10.1016/j.nicl.2016.07.014}, year = {2016}, month = {08}, title = {{D}ecoding post-stroke motor function from structural brain imaging}, author = {{R}ondina, {J}ane {M} and {F}ilippone, {M}aurizio and {G}irolami, {M}ark and {W}ard, {N}ick {S}}, journal = {{N}euro{I}mage: {C}linical, {V}olume 12, {A}ugust 2016}, url = {http://www.eurecom.fr/publication/4979} }
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