Data_Sheet_1_Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity.PDF

Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.</p

ROC curves of the mean diffusion (MD), axial (λa) and radial (λr) diffusivities, NAA/Cho and NAA/Cr ratios in RR (A), SP (B), PP (C) and all-MS patient groups (D).

<p>ROC curves of the mean diffusion (MD), axial (λa) and radial (λr) diffusivities, NAA/Cho and NAA/Cr ratios in RR (A), SP (B), PP (C) and all-MS patient groups (D).</p

Correlations between radial diffusivity (λr) and NAA/Cr ratio with the ROI T2-LL in RR, PP and all-MS patient groups. (NS = Not significant).

<p>Correlations between radial diffusivity (λr) and NAA/Cr ratio with the ROI T2-LL in RR, PP and all-MS patient groups. (NS = Not significant).</p

Demographics and group characteristics.

<p>Values are expressed as mean (Standard deviation).</p><p>RR: relapsing remitting; SP: secondary progressive; PP: primary progressive; EDSS: expanded disability status scale; ROI T2-LL: T2-lesion load corresponding to the lesion volume measured within the region of interest (ROI); Brain T2-LL: T2-lesion load corresponding to the lesion volume measured in the whole brain.</p

Areas under ROC curves analysis of DTI and MRSI derived measures in different groups of MS patients.

<p>Statistical significance (*p<0.05; **p<0.01; ***p<0.001) when comparing areas under ROC curves (AUC) of the following MR metrics: MD: mean diffusivity, λr: radial diffusivity, NAA: N-acetylaspartate, Cr: creatine, RR: relapsing remitting; SP: secondary progressive; PP: primary progressive.</p

Correlation rates (r) between diffusion and metabolic measures and the ROI T2-LL in different groups of MS patients.

<p>Correlation significance (*p<0.05; **p<0.01; ***p<0.001).</p><p>ROI T2-LL: T2-lesion load corresponding to the lesion volume within the region of interest, MD: mean diffusion, FA: fraction of anisotropy, λa: axial diffusivity, λr: radial diffusivity, NAA: N-acetylaspartate, Cr: creatine, RR: relapsing remitting; SP: secondary progressive; PP: primary progressive.</p

Diffusion and metabolic measures in MS patients and control subjects groups.

<p>Values (Mean ± SD) of mean diffusion (MD), fraction of anisotropy (FA), axial (λa) and radial (λr) diffusivities, N-acetylaspartate (NAA), choline (Cho) and creatine (Cr) (*p<0.05; **p<0.01; ***p<0.001 when compared to controls). RR: relapsing remitting; SP: secondary progressive; PP: primary progressive.</p

Detection of longitudinal variations by applying the “mean” and “histogram” methods.

<p><b>(A)</b> On the left CST of Patient1 between W1 and W8 time-points, detecting a change in two preexisting lesions (L1, L2); <b>(B)</b> On the right IFOF of Patient2 between W1 and W6 detecting a new lesion; <b>(C)</b> On the right IFOF of Patient2 between W1 and W7 detecting a change in two preexisting lesions (L1, L2) and the apparition of a new lesion (L3). Lesions are shown on FLAIR images. Fiber-subsets labeled as “unchanged” (green) and “changed” (red) are shown on top of FLAIR images.</p

Global overview of the “histogram” approach.

<p>As first step <b>(A1)</b> the histogram of the data extracted from time point <i>i</i> and time-point <i>i+p</i> in the same cross-section are fitted using Gaussian mixture model. As second step <b>(A2)</b> our method detects a pathological longitudinal variation between the two time-points in the histogram. The obtained threshold value γ is then used to differentiate between “changed” and “unchanged” fibers <b>(B)</b>. Plotted FA signal profile of the two subset of fiber and cross-sectional view of the labeled fibers <b>(C)</b>.</p

Iterative analysis of the “changed” fiber-subset of Patient1’s left CST (A) and of Patient2’s right IFOF (B) at different time-points.

<p><b>(A)</b> Detection of a new lesion (L1) at W6 and at W8, and a preexisting lesion at W7, evolving by contaminating the CST). <b>(B)</b> Detection of a preexisting lesion (L4) and a new lesion (L5) at W6, both evolving in size and degree of FA alteration at W7, and remaining unchanged at W8.</p