Decoding post-stroke motor function from structural brain imaging.
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.
Sobell Department of Motor Neuroscience, Institute of Neurology, University College London, UK.
Jane M
JM
Rondina
Department of Data Science, EURECOM, France.
Maurizio
M
Filippone
Department of Statistics, University of Warwick, UK.
Mark
M
Girolami
Sobell Department of Motor Neuroscience, Institute of Neurology, University College London, UK.
Nick S
NS
Ward
D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D000068079:Motor Disorders / Q000175:diagnosis* / Q000209:etiology; D020521:Stroke / Q000150:complications / Q000000981:diagnostic imaging / Q000473:pathology*
Features extraction; Gaussian processes; Lesion load; Lesion patterns; Machine learning; Motor impairment; Multiple kernel learning; Patterns of lesion probability; Stroke