University Hospital Basel and University of Basel
Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering

Author Of 1 Presentation

Imaging Oral Presentation

PS11.04 - Quantitative susceptibility mapping classifies white matter lesions with different myelin and axonal content and quantifies diffuse pathology in MS

Abstract

Background

Quantitative susceptibility mapping (QSM) identifies iron accumulation and myelin loss in smoldering white matter lesions (WMLs). Yet, QSM may be also used to provide a broader understanding of focal and diffuse MS pathology.

Objectives

To study QSM features across WMLs, to assess myelin and axonal loss in WMLs with different QSM features and to quantify QSM pathology in normal-appearing white and cortical grey matter (NAWM, NAGM).

Methods

Ninety-one MS patients (62 RRMS, 29 PMS) and 72 healthy controls (HC) underwent QSM, myelin water imaging (MWI) and multishell diffusion at 3T MRI. In WMLs, cortical lesions (CLs), NAWM and NAGM, we extracted mean QSM, myelin water fraction (MWF) and neurite density index (NDI). WMLs were classified into 5 groups according to their appearance on 3D-EPI QSM: (i) isointense; (ii) with hyperintense rim, Rim+ (iii); with hypointense rim relative to the lesion core, hypo Rim; (iv) hyperintense; (v) hypointense. Mann-Whitney and Kruskal-Wallis test with Dunn’s correction for multiple comparison were used to compare (a) lesion types and (b) specific lesions vs all other WMLs. Voxel-wise comparisons of NAWM QSM were performed using Threshold-Free Cluster Enhancement (TFCE) clustering. Cortical analysis of QSM NAGM and GM-HC was performed using FreeSurfer and compared using a General Linear model (GLM).

Results

Of 1136 WMLs in QSM maps, we detected: (i) 314 (27.6%), (ii) 183 (16.1%), (iii) 16 (1.41%), (iv) 577 (50.8%) and (v) 46 (4.05%) WML. All WML exhibited lower NDI than NAWM and WM-HC (P<0.0001). Isointense lesions exhibited higher NDI (P=0.0115) and MWF (P<0.0001) than other WMLs. Rim + and hyperintense lesions exhibited lower MWF than NAWM and WM-HC (P<0.0001). Rim + lesions showed lower MWF and NDI than other WML types (P<0.001). Hypo Rim+ lesions and hypointense lesions exhibited higher MWF than other WMLs (P=0.0006, P<0.05). Hyperintense lesions exhibited lower MWF than other WMLs types (P<0.01) except Rim+ lesions. TFCE and vertex-wise cortical surface analysis showed areas throughout the NA tissue, where QSM is either lower or higher compared to healthy tissue in HC and in PMS compared to RMS (P<0.01).

Conclusions

QSM is sensitive to diffuse and focal pathology with various myelin and axonal characteristics. We hypothesize that isointense WMLs show high repair activity, hypointense WMLs are remyelinated lesions and hyperintense WMLs are chronic inactive lesions. MRI-histopathology work is ongoing to confirm these findings.

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Author Of 6 Presentations

Machine Learning/Network Science Late Breaking Abstracts

LB1213 - Attention-based deep learning identifies a new microstructural diffusion MRI contrast sensitive to focal pathology and related to patient disability (ID 2074)

Speakers
Presentation Number
LB1213
Presentation Topic
Machine Learning/Network Science

Abstract

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.

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Imaging Poster Presentation

P0534 - Advanced magnetic resonance imaging for myelin and axonal density in MS: correlation with clinical disability and serum neurofilament levels (ID 1781)

Abstract

Background

Myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI) provide sensitive surrogate markers of myelin and axonal content in lesions and normal-appearing tissue. However, to date, there is scarce information about the relationship of these measures with (i) disability; and (ii) the axonal damage specific biomarker serum neurofilament light chain (sNfL).

Objectives

To explore the correlation of MWI and NODDI measures in MS lesions and in normal-appearing (NA) brain tissue with disability and sNfL.

Methods

Ninety-one MS patients (62 relapsing-remitting MS-RRMS and 29 progressive MS-PMS) underwent MWI and NODDI. Mean myelin water fraction (MWF) and neurite density index (NDI) were extracted in white matter lesions (WMLs), cortical lesions (CLs), NA white matter (NAWM) and cortical NA gray matter (CNAGM). For sNfL, a logarithmic transformation was applied to comply with normality assumption. Correlation studies between MRI measures, sNfL and EDSS were performed using linear models, with age and gender as covariates. The models were performed for the whole sample and for patients with clinical deficits only (EDSS >1).

Results

MWF and NDI did not correlate with EDSS when the entire cohort was considered (P>0.05). However, for those patients with clinical deficits (EDSS> 1), NDI in WMLs was associated with EDSS (NDI: P<0.01, beta=-10.00; N=74). We also found that MWF and NDI in WMLs were related to sNfL (MWF: P<0.01, beta=0.13; NDI: P<0.01, beta=-3.60). Again, this correlation was stronger in patients with EDSS>1 (MWF: P<0.01, beta=0.13; NDI: P <0.01, beta=-3.60).

Conclusions

Imaging surrogate markers of myelin and axon pathology in WML – and not in CLs and NA tissues - are correlated with disability and sNfL. Interestingly, associations between those imaging markers and disability/sNFL were more evident in patients with clinical deficits as compared to those without neurological deficits.

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Imaging Poster Presentation

P0561 - Comparison of different global network measures and tissue microstructural features to capture the ongoing brain damage in multiple sclerosis (ID 1284)

Speakers
Presentation Number
P0561
Presentation Topic
Imaging

Abstract

Background

Graph theory is used to study brain connectivity, i.e. connectomes, estimated with diffusion magnetic resonance imaging (dMRI). Previous studies have already investigated the correlation between some network measures and the Expansion Disability Status Scale (EDSS), which assesses the clinical worsening of multiple sclerosis (MS) patients.

Objectives

We investigated connectivity changes between healthy controls (HC) and relapsing remitting (RR) patients and tested whether such differences correlate with EDSS, comparing the effectiveness of various definitions of “connection strength” using different microstructural models.

Methods

dMRI was acquired for 67 HC (39F, 37±7yrs) and 49 RR (33F, 37±4yrs). Connectomes were created with deterministic tractography and weighting the connections by 1) number of streamlines (NOS) between grey-matter regions and, 2) mean value of quantitative scalar maps, estimated using state-of-the-art microstructural models, along the streamlines, notably: fractional anisotropy, FA; axial AD, radial RD and mean diffusivity MD; Intra Neurite and Isotropic Volume Fractions, ICVF and ISOVF; orientation dispersion, OD; Neurite volume fraction, INTRA; Extra-neurite transverse and mean diffusivity EXTRATRANS and EXTRAMD. We computed 5 network measures from each connectome: Density (ratio between actual and possible connections); Efficiency (capability of transferring and processing information); Modularity (network segregation); Clustering Coefficient (degree to which nodes tend to cluster together); Mean Strength (average of the sum of the edge weights connected to a node).

Results

The network measures that significantly differ between the 2 groups were: Efficiency for ICVF p=0.031, AD p<0.01, RD p<0.01, EXTRATRANS p=0.019 and MD p<0.01 connectomes; Clustering Coefficient for AD p=0.015, RD p=0.013, EXTRATRANS p=0.021 and MD p<0.01 connectomes; Mean Strength for ICVF p=0.019, INTRA p=0.037, AD p=0.011, RD p<0.01, EXTRATRANS p=0.014 and MD p<0.01 connectomes. Only Modularity significantly correlate with EDSS for NOS p=0.047, FA p=0.049, ICVF p=0.041 and INTRA p=0.030 connectomes. All tests accounted for age, sex and density as confounding factor.

Conclusions

The maps discriminating more HC from MS patients were AD, RD, MD and EXTRATRANS. The microstructure features along the tracts with the highest correlation to EDSS were those investigating axonal integrity (FA, ICVF and INTRA). Modularity was the metric most correlated with EDSS.

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Imaging Poster Presentation

P0580 - Focal inflammatory activity and lesion repair are associated with brain atrophy rates in MS patients (ID 1092)

Abstract

Background

The pathogenesis of neurodegeneration in multiple sclerosis (MS) is multifactorial and the determinants of brain atrophy rates are not completely understood.

Objectives

To investigate the association between annualized atrophy rate (AAR) of multiple brain measures (regional cortical thickness (CTh), volumes of basal ganglia, thalamus, white matter, gray matter, brain and brain parenchymal fraction (BPF)) and: (1) annualized rate of new and enlarging white matter lesions (WMLs); (2) annualized rate of resolved WMLs; (3) occurrence of progression independent of relapse activity (PIRA) during follow-up.

Methods

We included 1573 1.5T or 3T brain MRI scans from 378 patients of the Swiss MS Cohort Study (331 relapsing-remitting MS (RRMS), 27 clinically isolated syndrome (CIS), 11 secondary-progressive MS (SPMS), 9 primary-progressive MS (PPMS); 70% female; median age: 41.9 yrs; disease duration: 8.3 yrs; EDSS: 2.0; follow-up time: 4.0 yrs). Longitudinal changes in WMLs were obtained using an automated prototype (LeMan-PV). Brain volumes and CTh AARs were obtained using FreeSurfer longitudinal pipeline (v6.0) after WMLs filling. In patients fulfilling PIRA an EDSS progression had to be confirmed ≥6 months after the index event. Multivariable generalized linear models were used to model the association between AAR (dependent variable) and independent variables (1-3), correcting for age, sex, disease duration and baseline EDSS. p-values were adjusted for Bonferroni multiple comparison correction; for vertex-wise CTh analysis, Monte Carlo Z simulation was performed (cluster threshold p<0.05).

Results

We found positive associations between annualized rate of new and enlarging WMLs and (i) CTh AAR of 8 extensive clusters (bilateral frontal, temporal and occipital regions and right insula, all p<0.01) and (ii) AAR of: caudate bilaterally (p=0.02), white matter volume, brain volume and BPF (p<0.001 for all).

We also found a negative association between annualized rate of resolved WMLs and CTh AAR in 3 cortical clusters (right insula, precentral area and anterior cingulate region, all p<0.05); no associations with AAR of volumes emerged.

57 patients fulfilled PIRA whereas 295 experienced no EDSS progression events: no significant differences in AAR measures were found between these two groups.

Conclusions

In a large cohort of MS patients, with a median follow-up of 4 years, local radiological inflammatory and reparative activity were associated with AAR in multiple brain regions. PIRA did not seem to be related to increased AAR in any of the regions studied.

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Imaging Poster Presentation

P0595 - Investigating the relation between global structural network measures and serum neurofilament light in multiple sclerosis (ID 1325)

Speakers
Presentation Number
P0595
Presentation Topic
Imaging

Abstract

Background

Neurofilament light polypeptide (NfL) is a neurofilament protein highly expressed in myelinated axons. Increased serum NfL (sNfL) concentration indicates the presence of axonal damage in patients with multiple sclerosis (MS). Until now, the potential effects of this axonal damage on brain connectivity have never been investigated.

Objectives

We studied the relationship between active inflammation measured by sNFL and structural connectivity alterations detectable by global network metrics estimated with diffusion MRI.

Methods

Diffusion MRI, T1-weighted and FLAIR sequences were acquired on 74 patients (44F, 44.9±14.6yrs, 50 relapsing-remitting and 24 progressive) and sNfL levels were measured from blood samples in the same session. Volume of white-matter lesions was computed on FLAIR with an automatic in-house tool. To build the connectomes we 1) performed deterministic tractography on diffusion MRI, 2) segmented the grey matter in 85 regions using T1 images, and 3) quantified the connection strength of each pair of regions by counting the streamlines between them. From each connectome we extracted 5 global metrics: Density (ratio between actual and possible connections), Efficiency (capability of transferring and processing information); Modularity (network segregation); Clustering Coefficient (degree to which nodes tend to cluster together); Mean Strength (average of the sum of the edge weights connected to a node). Since discrepancies in density may affect other metrics, we first tested its correlation with sNFL, then we performed partial correlations of the last 4 metrics with sNFL using age, sex and density as covariates.

Results

We found negative correlation between density and sNfL (R=-0.252 p=0.05) indicating that high axonal damage is associated with reduced number of connections. Efficiency and mean strength showed a strong anti-correlation with sNfL (R=-0.325 p=0.011 and R=-0.475 p<0.001), while modularity and clustering coefficient seemed not related to axonal damage (R=0.183 p=0.162 and R=-0.215 p=0.099). Finally, a positive association with sNfL was found for both the lesions volume and the Expansion Disability Status Scale (p=0.011 R=0.323 and p=0.038 R=0.267), confirming previous results.

Conclusions

We showed that high values of sNfL are associated with global connectivity damage (reduced number of connections, efficiency and mean strength) confirming the utility of network-based connectivity metrics to assess MS disease impact.

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Imaging Poster Presentation

P0624 - Quantitative multiparametric 3T-MRI of postmortem multiple sclerosis whole brains (ID 1583)

Abstract

Background

Postmortem MRI provides precious insights into the relation of MRI metrics to pathoanatomical features of multiple sclerosis (MS) and can help to understand the basis of damage and repair.

Objectives

To investigate the respective features of MS lesions in the cortex and in the white matter using multiparametric postmortem MR imaging at 3T and identify discriminant characteristics of white matter lesion subgroups.

Methods

We scanned three fixed brains of secondary-progressive MS patients (mean disease duration 15.3 years) on a standard clinical 3T-MRI scanner with following sequences: Magnetization Transfer Saturation (MTsat), T1-relaxometry (T1-rt), Myelin Water Fraction (MWF) and Diffusion Tensor - Fractional Anisotropy (DTI-FA). We compared these metrics between (i) cortical lesions (CL, n=118) and normal-appearing grey matter (NAGM, n=186) and (ii) white matter lesions (WML, n=140) and normal-appearing white matter (NAWM, n=53) using a Mann-Whitney U test. Then, we analyzed the differences between different subgroups of WML (periventricular lesions -PVL-, n=38, WM part of leukocortical lesions -WMLCL-, n=36, subcortical lesions -SCL-, n=66, and areas of “dirty white matter” -DWM-, n=15) by performing a Kruskal-Wallis test and a Mann-Whitney U tests for direct comparison. Bonferroni correction for multiple-testing was applied.

Results

CL exhibited lower MTsat (p<0.001), higher T1-rt (p<0.001) and MWF (p<0.01) than normal appearing cortical tissue. WML showed lower MTsat (p<0.001), higher T1-rt (p<0.001), and lower MWF (p<0.001) than normal appearing white matter. DTI-FA did not differ between CL/WML and NAWM/NAGM. MTsat values were lower in the PVL (p<0.001) and higher in the DWM (p<0.001) in comparison to all other lesion subgroups. T1-rt were higher in PVL (p<0.001) compared to the other lesion subgroups. MWF values were higher in DWM and SCL (p<0.01), not statistically different between PVL and WMLCL. DTI-FA values were lower in WMLCL in comparison to all other subgroups (p<0.01) and did not differ between the other categories.

Conclusions

Postmortem MRI metrics in WML/CL as well as in different subgroups of WML, are compatible with myelin damage and tissue destruction. Interestingly, MWF was higher in CL than in NAGM, which might correspond to a predominance of “myelin blistering” pathology in the cortex. Ongoing work aims to directly correlate our findings with detailed histopathological characterization including electron microscopy of myelin damage.

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