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

Previous works have demonstrated that iron tends to accumulate in deep brain nuclei during aging. However, in some structures (e.g. thalamus), this trend has been disputed.

Objectives

The main objective of this study was to quantify age-related changes in regional brain magnetic susceptibilities in healthy subjects.

Methods

Brain MRI was performed in 103 healthy individuals (62 females and 41 males) aged between 21 and 56 years. MRI was performed on a 3T scanner and included 3D MPRAGE T1-weighted images in sagittal plane, susceptibility weighted images (3D flow-compensated multi-echo GRE scan) for quantitative susceptibility mapping (QSM). QSM images were calculated using QSMbox package in MATLAB and co-registered with 3D MPRAGE in order to perform skull stripping (FSL BET), multi-atlas segmentation (MRICloud), and extraction of mean bulk magnetic susceptibility.

Results

Mean susceptibility (ppm) values ranged between 0.00 in total white matter and 0.95 in the substantia nigra. We found no significant differences between genders in magnetic susceptibilities in any of the examined structures. There was a positive correlation between age and magnetic susceptibility in the putamen (r=0.6443, p<0.0001), external globus pallidus (r= 0.5085, p<0.0001), red nucleus (r= 0.5015, p<0.0001), caudate (r=0.4525, p<0.0001), cerebral and cerebellar cortex (r= 0.4246, p<0.0001), substantia nigra (r=0.4060, p<0.0001), subthalamic nucleus (r= 0.3611, p=0.0002), internal globus pallidus (r= 0.2749, p=0.0049), and pulvinar (r= 0.2292, p=0.0199). Negative correlation was found in white matter (r= - 0.2792, p=0.0043) and thalamus (r= - 0.1947, p=0.0488).

Conclusions

We report evolution of regional brain magnetic susceptibilities in healthy subjects during aging. The results of this study define normal brain susceptibility values for age groups between 20 and 60 years; they may be used for iron level estimation in deep grey matter and as additional biomarkers of neurodegenerative diseases and multiple sclerosis. Our results suggest that iron content in external globus pallidus increases with aging, a finding that has previously been disputed. We demonstrate a positive age-related susceptibility increase in pulvinar while the opposite, i.e. gradual susceptibility decrease, was observed in other thalamic nuclei emphasising the necessity of separate analysis of these thalamic subregions. Gender does not relevantly impact susceptibility variations in brain tissue. The results of this study define normal brain iron levels which may be used as biomarkers of neurodegenerative and neuroinflammatory changes.

This study was supported by the Czech Ministry of Health grant (NV18-08-00062, 15-25602A, and RVO VFN64165), by the Charles University in Prague grant (PROGRES Q27) and by Roche company.