title: Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning.
abstract: Changes in brain morphology and white matter lesions are two hallmarks of multiple sclerosis (MS) pathology, but their variability beyond volumetrics is poorly characterized. To further our understanding of complex MS pathology, we aim to build a statistical model of brain images that can automatically discover spatial patterns of variability in brain morphology and lesion distribution. We propose building such a model using a deep belief network (DBN), a layered network whose parameters can be learned from training images. In contrast to other manifold learning algorithms, the DBN approach does not require a prebuilt proximity graph, which is particularly advantageous for modeling lesions, because their sparse and random nature makes defining a suitable distance measure between lesion images challenging. Our model consists of a morphology DBN, a lesion DBN, and a joint DBN that models concurring morphological and lesion patterns. Our results show that this model can automatically discover the classic patterns of MS pathology, as well as more subtle ones, and that the parameters computed have strong relationships to MS clinical scores.
authors.forename: Tom
authors.initials: T
authors.lastname: Brosch
authors.forename: Youngjin
authors.initials: Y
authors.lastname: Yoo
authors.forename: David K B
authors.initials: DK
authors.lastname: Li
authors.forename: Anthony
authors.initials: A
authors.lastname: Traboulsee
authors.forename: Roger
authors.initials: R
authors.lastname: Tam
mesh_terms: D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008959:Models, Neurological; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000473:pathology*; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity