title: Voxelwise multivariate statistics and brain-wide machine learning using the full diffusion tensor.
abstract: In this paper, we propose to use the full diffusion tensor to perform brain-wide score prediction on diffusion tensor imaging (DTI) using the log-Euclidean framework., rather than the commonly used fractional anisotropy (FA). Indeed, scalar values such as the FA do not capture all the information contained in the diffusion tensor. Additionally, full tensor information is included in every step of the pre-processing pipeline: registration, smoothing and feature selection using voxelwise multivariate regression analysis. This approach was tested on data obtained from 30 children and adolescents with autism spectrum disorder and showed some improvement over the FA-only analysis.
authors: CEA, Neurospin, LNAO, Saclay, France.
authors: Anne-Laure
authors: AL
authors: Fouque
authors: Pierre
authors: P
authors: Fillard
authors: Anne
authors: A
authors: Bargiacchi
authors: Arnaud
authors: A
authors: Cachia
authors: Monica
authors: M
authors: Zilbovicius
authors: Benjamin
authors: B
authors: Thyreau
authors: Edith
authors: E
authors: Le Floch
authors: Philippe
authors: P
authors: Ciuciu
authors: Edouard
authors: E
authors: Duchesnay
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods; D002648:Child; D002659:Child Development Disorders, Pervasive / Q000473:pathology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D015999:Multivariate Analysis; D012984:Software