Background

Microstructural biophysical models reconstructed from advanced diffusion MRI (dMRI) data provide quantitative measures (qMs), which inform about the brain tissue microenvironment, based on different assumptions.

Objectives

To compare the sensitivity of available qMs to focal pathology in multiple sclerosis (MS), and to explore which qMs– or combinations of qMs – are best correlated with patients’ disability.

Methods

dMRI (1.8 mm isotropic resolution, 149 directions, b-values were 0, 700, 1000, 2000, 3000 s/mm²) was acquired from 67 relapsing-remitting and 33 progressive MS patients (median EDSS: 2.5). The qMs for the isotropic and intra-axonal compartments were derived from the following available models: Ball and Stick, NODDI, SMT-NODDI, MCMDI, NODDIDA, DIAMOND, Microstructure Bayesian approach (MB) and microstructure fingerprinting. In total, 13 qMs were included and subject-wise normalized within brain tissue (nqMs).

To identify the nqMs sensitive to focal pathology, an attention-based convolutional neural network (aCNN) was built to (a) classify randomly sampled WM lesion and perilesional WM patches and (b) generate attention weights (AWs) representing the relative importance of the qMs in the classification. Twenty patients were randomly selected in the test dataset (709 lesion patches and 746 perilesional WM patches), and the rest were in the cross-validation (CV) dataset (2925 lesion patches and 3176 perilesional WM patches). The performance metric was the area under the receiver operating characteristic curve (AUC). Because of the correlation between the nqMs, which may influence the relative AWs, we performed 10-fold CV and selected the nqMS that most contributed to the classification.

To assess which nqMS – or combination of nqMS was best correlated with EDSS, we used Spearman’s correlation coefficient (ρ) with two-sided 20000 permutation tests and followed by Bonferroni correction.

Results

The test AUC was 0.911 indicating the aCNN learned the right AWs to differentiate lesions and perilesional WM. The most discriminating nqMs included isotropic and intra-axonal compartments from MB, the neural density index (NDI) from the NODDI and the intra-axonal compartment from MCMDI.

The sum of isotropic and intra-axonal compartments of the MB (sMB) showed the strongest correlation with EDSS (ρ=-0.40,corr. p<0.0001) followed by the sum of sMB and NDI (ρ=-0.30,corr. p<0.05), and the sum of sMB and intra-axonal compartment from MCMDI (ρ=-0.32,corr. p<0.05). None of the selected nqMs as a single measure and their other combinations correlated with EDSS.

Conclusions

By performing aCNN-aided selection of the openly available WM quantitative measures, we have identified the measures most sensitive to MS focal pathology; furthermore, we have derived a new contrast that – by combining the measures of isotropic and intracellular diffusion – strongly correlated with patients’ disability.

Background

Neuromyelitis optica spectrum disease (NMOSD) and multiple sclerosis (MS) have a different pathophysiology. Accumulating evidence suggests that the choroid plexus plays a pivotal role in the pathogenesis of MS. However, MRI data comparing the choroid plexus volume between MS and NMOSD are scarce.

Objectives

To compare the choroid plexus volume in MS vs. NMOSD in vivo using high-resolution 3D MRI data. Migraine patients and healthy individuals served as control groups.

Methods

We included 95 MS patients [45% secondary progressive (SP); mean age 51.0±11.5 years; disease duration 20.8±10.4 years, 62% female; median Expanded Disability Status Scale (EDSS) 4.0], 43 NMOSD patients [28/43 anti-aquaporin 4 antibody positive; 11/43 anti-myelin oligodendrocyte glycoprotein antibody positive; 87% female; mean age 50.0±13.8 years; disease duration 6.8±7.3 years, median EDSS 3.0], 38 migraine patients [mean age 39±13 years, 79% female; 15/38 migraine with aura] and 65 healthy individuals [HCs, mean age 41±17 years, 48% female]. The choroid plexus of the lateral ventricles and T2-weighted (T2w) white matter lesions (WMLs) were segmented fully automated on T1-weighted (T1w) magnetization-prepared rapid gradient echo (MPRAGE) images and fluid attenuated inversion recovery sequences (FLAIR, voxel size of both sequences 1x1x1 mm³), respectively, using a supervised deep learning algorithm (multi-dimensional gated recurrent units). Total intracranial volume (TIV) and lateral ventricle volumes were assessed fully automated using Freesurfer. All outputs were reviewed and manually corrected (if necessary) using 3D-Slicer by trained raters who were blinded to the clinical information. Group differences were analyzed using multivariable generalized linear models (GLMs) adjusted for age, gender, TIV and lateral ventricle volume. Cohens’ d was used to calculate the standardized difference between the respective groups. Given p-values are adjusted for multiple comparisons (Bonferroni).

Results

Mean choroid plexus was larger in MS compared to NMOSD (1907±455 vs. 1467±408 µl; p<0.001, d=0.86), HCs (1663±424 µl; p=0.007, d=1.17) and migraine (1527±366 µl; p=0.02, d=0.72). There was no statistical difference in the choroid plexus volume between NMOSD, migraine and HCs. The choroid plexus was marginally larger in RRMS than SPMS (1959±482 vs. 1875±476 µl; p=0.28; d=0.17) and in untreated MS patients compared to MS patients on disease modifying therapy (2111±382 vs. 1876±459 µl; p=0.36). However, these differences did not reach statistical significance after correction for multiple comparisons. There was no association between the choroid plexus volume and total T2w WML volume in MS.

Conclusions

Patients with MS have larger choroid plexus than HCs, migraine and NMOSD patients. Further studies are warranted to investigate the respective roles of the choroid plexus in the pathogenesis of MS and NMOSD.