University of Verona
Computer Science

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 5 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

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

P0647 - Studying intralesional axonal damage in MS white matter lesions with diffusion MRI biophysical models (ID 694)

Abstract

Background

Advanced diffusion-weighted MRI (DW-MRI) sequences, in combination with biophysical models, provide new information on the microstructural properties of the tissue.

Objectives

To investigate the differences in intra-axonal signal fraction (IASF) between perilesional normal-appearing white matter (pl-NAWM), white matter lesions (WML) without (rim-) and with paramagnetic rim (rim+) comparing eight biophysical diffusion models.

Methods

The study included 102 MS patients: RRMS: 66%, SPMS: 18%, PPMS: 16%, mean age 46±14; female 64%, disease duration 12.16±18.18 yrs, median EDSS: 2.5.

DW-MRI data were acquired with 1.8mm isotropic resolution and b-values [0, 700, 1000, 2000, 3000] s/mm2.

Lesion masks were generated with a deep-learning-based method and manually corrected if required; pl-NAWM was defined as a region of 3-voxels around each WML; 225 paramagnetic rim lesions were manually identified based on 3D EPI and 2330 were labelled as rim-.

The following microstructural models were applied: Ball and Stick, Ball and Rockets, AMICO-NODDI, SMT-NODDI, MCMDI, NODDIDA, CHARMED, Microstructure Bayesian approach.

Delta (WML - pl-NAWM) was calculated for each WML, and one-side Mann Whitney U was used to compare the delta between models, followed by Bonferroni to correct for multiple testing.

Mean difference and Cohen's d was used to assess differences between lesions with extensive axonal damage (rim+) and other WML (rim-).

Results

All models applied in this study reported low IASF in rim+ WML, medium IASF in rim- WML and relatively high IASF in pl-NAWM. However, a broad spectrum of IASF values was identified from the different models: relatively simple models such as Ball and Stick and CHARMED, showed low delta IASF within lesions, while MCMDI models reported the highest significant difference compared to other models (p<0.0001). The comparison between WML and pl-NAWM mean IASF across models showed that MCDMI exhibited the highest difference (mean 0.13, Cohen’s d 1.34). AMICO-NODDI and SMT-NODDI showed close results (mean difference 0.12/0.12 and Cohen’s d 1.46/1.51).

The models best discriminating IASF between rim+ and rim- lesions were MCMDI and NODIDDA (mean 0.08/0.07, Cohen’s d -0.69/-0.70).

Conclusions

We compared eight WM diffusion models for assessment of intralesional axonal damage in MS patients. The comparison between WML and pl-NAWM showed that robustness of the method, identified with SMT-based and NODDI-based models, it is crucial. For the comparison between lesions with a high level of damage (rim +) and other WML, the diffusivity estimation appeared to play an important role. The method which appeared both robust and able to estimate the diffusivity of the tissue was MCMDI, which performed best in both cases.

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