Background

Recent studies have described Multiple Sclerosis (MS) as a disconnection syndrome (Rocca et al. 2015). Modelling disconnectomes using brain networks enables to quantify connectivity loss using graph analysis. To build structural connectomes, high-quality diffusion Magnetic Resonance Imaging (dMRI) and robust tractography algorithms are typically required. However, high-quality dMRI is rarely acquired in clinical workups due to time constraints.

Objectives

We propose to use a tractography atlas to extract brain connectivity loss in response to lesions without requiring dMRI, and to model structural disconnectomes with brain graphs. Topological graph features are proposed as new radiological biomarkers and their relation with Total Lesion Volume (TLV) and Expanded Disability Status Scale (EDSS) are studied.

Methods

589 MS patients (159 males, age 28±8yo, EDSS 2.40±1.22, TLV 13.0±14.6mL) underwent MRI at 3T (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). Acquisition protocols included T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) and fluid-attenuated inversion recovery (FLAIR).

Lesions were segmented using LeMan-PV, a prototype lesion segmentation algorithm (Fartaria et al. 2016). The lesion masks were registered to standard MNI space and overlapped with the HCP842 tractography atlas (Yeh et al. 2018). Streamlines passing through lesions were isolated to define the affected connectivity.

The disconnectome graph was built using brain regions from the Brainnetome atlas (Fan et al. 2016) as nodes, whilst edges were weighted by the percent of unaffected streamlines connecting two nodes relative to the atlas connectivity. Topological features were extracted from the disconnectome graph and their Spearman’s correlations with TLV and EDSS were computed.

Results

Transitivity (T) and global efficiency (GE) decreased for larger TLV (R=-0.42 and R=-0.78), whereas the average shortest path length (PL) increased (R=0.78). When looking at correlations with EDSS, T (R=-0.17), GE (R=-0.24) and PL (R=0.23) showed stronger associations than lesion count (R=0.14) but were comparable to TLV (R=0.23). All correlations were significant (p<0.001).

Conclusions

We proposed an atlas-based disconnectome model which allowed to study connectivity loss in MS patients without requiring dMRI. Overall, patients showed a lower small-worldness and efficiency for larger TLV and worse disability. These observations were consistent with previous studies on diffusion-based connectomes and open new avenues of research for routine clinical data.

Background

Regional brain atrophy is a sensitive disability marker for MS patients. A previous study has shown that atrophy of the corpus callosum is an early marker for disease progression. However, the relationship between diffuse pathology in specific brain regions and the course of regional atrophy development remains poorly understood.

Objectives

To investigate quantitative T1 maps and entropy (amount of T1 inhomogeneity) in regional brain structures from diagnostic MRI (performed at disease onset) of MS patients and compare these findings with healthy controls (HC).

Methods

Fifty MS patients and 102 HC were examined on a 3T MRI scanner (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). The MRI protocol comprised 3D MP2RAGE, 3D MPRAGE, 3D FLAIR and 3D DIR. The calculation of T1 maps, brain structure segmentations and brain volume measurements were obtained from a single MP2RAGE scan. Lesion segmentation masks were obtained using the LeManPV prototype software (Siemens Healthcare, Erlangen, Germany). We evaluated T1 maps from normal-appearing white matter (excluding lesions) in the corpus callosum, the brain lobes, brainstem and cerebellum, as well as from normal-appearing gray matter (excluding lesions) in the thalami, basal ganglia, and cortical gray matter. We calculated median regional T1 relaxation times, T1 entropy and volume for the above-mentioned structures for the early-MS group and 50 age- and sex-matched HC subjects. Statistical comparison was performed using t-tests.

Results

The median T1 of the corpus callosum in the early MS group was 838 ms (SD 38.5), with entropy 8.42 (SD 0.24); compared to 810 ms (SD 25.2) and 8.23 (SD 0.13) in the HC group. Statistically significant differences were found in T1 times and entropy between the groups (p<0.001); volumes were, however, not statistically different. Smaller but also statistically significant differences in T1 maps and entropy were found for white matter of the brain lobes (p<0.001). Thalami volumes showed statistically significant differences between groups, but not median T1 times (MS group 1055 ms, SD 32.6 vs. HC 1049 ms, SD 21.2).

Conclusions

Pathology of the normal-appearing white matter in T1 relaxometry can already be detected at MS disease onset. In particular, corpus callosum T1 times were considerably higher at clinical onset of MS compared to HC. We hypothesize that early microstructural changes detected at disease onset lead to evolution of regional brain atrophy.

Background

Recent studies have described Multiple Sclerosis (MS) as a disconnection syndrome (Rocca et al. 2015). Modelling disconnectomes using brain networks enables to quantify connectivity loss using graph analysis. To build structural connectomes, high-quality diffusion Magnetic Resonance Imaging (dMRI) and robust tractography algorithms are typically required. However, high-quality dMRI is rarely acquired in clinical workups due to time constraints.

Objectives

We propose to use a tractography atlas to extract brain connectivity loss in response to lesions without requiring dMRI, and to model structural disconnectomes with brain graphs. Topological graph features are proposed as new radiological biomarkers and their relation with Total Lesion Volume (TLV) and Expanded Disability Status Scale (EDSS) are studied.

Methods

589 MS patients (159 males, age 28±8yo, EDSS 2.40±1.22, TLV 13.0±14.6mL) underwent MRI at 3T (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). Acquisition protocols included T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) and fluid-attenuated inversion recovery (FLAIR).

Lesions were segmented using LeMan-PV, a prototype lesion segmentation algorithm (Fartaria et al. 2016). The lesion masks were registered to standard MNI space and overlapped with the HCP842 tractography atlas (Yeh et al. 2018). Streamlines passing through lesions were isolated to define the affected connectivity.

The disconnectome graph was built using brain regions from the Brainnetome atlas (Fan et al. 2016) as nodes, whilst edges were weighted by the percent of unaffected streamlines connecting two nodes relative to the atlas connectivity. Topological features were extracted from the disconnectome graph and their Spearman’s correlations with TLV and EDSS were computed.

Results

Transitivity (T) and global efficiency (GE) decreased for larger TLV (R=-0.42 and R=-0.78), whereas the average shortest path length (PL) increased (R=0.78). When looking at correlations with EDSS, T (R=-0.17), GE (R=-0.24) and PL (R=0.23) showed stronger associations than lesion count (R=0.14) but were comparable to TLV (R=0.23). All correlations were significant (p<0.001).

Conclusions

We proposed an atlas-based disconnectome model which allowed to study connectivity loss in MS patients without requiring dMRI. Overall, patients showed a lower small-worldness and efficiency for larger TLV and worse disability. These observations were consistent with previous studies on diffusion-based connectomes and open new avenues of research for routine clinical data.