Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns

In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or gray matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural, and functional brain connectivity networks. The aim of this study was to combine the morphological, structural, and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analyzing multiple types of relational data from the same objects simultaneously using graph- mining techniques. The main contribution of this research is the design, development, and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with gray matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with multiple sclerosis, and results show that several brain regions with a synchronized connectivity deterioration could be identified

Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes

BACKGROUND: We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. METHODS: Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups. RESULTS: Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. CONCLUSIONS: In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor

Long-Term Stability of Neuroaxonal Structure in Alemtuzumab-Treated Relapsing-Remitting Multiple Sclerosis Patients

Background: Patients with multiple sclerosis (MS) experience progressive thinning in optical coherence tomography (OCT) measures of neuroaxonal structure regardless of optic neuritis history. Few prospective studies have investigated the effects of disease-modifying therapies on neuroaxonal degeneration in the retina. Alemtuzumab is a monoclonal antibody shown to be superior to interferon β-1a in treating relapsing-remitting MS (RRMS). The purpose of this study was to assess the effects of alemtuzumab and first-line injectable treatments on OCT measures of neuroaxonal structure including peripapillary retinal nerve fiber layer (RNFL) thickness and combined ganglion cell-inner plexiform (GCIP) layer volume in RRMS patients followed up over 5 years. Methods: In this retrospective pilot study with prospectively collected double cohort data, spectral domain OCT measures of RNFL thickness and GCIP volume were compared between alemtuzumab-treated RRMS patients (N = 24) and RRMS patients treated with either interferon-β or glatiramer acetate (N = 21). Results: Over a median of 60 months (range 42-60 months), the alemtuzumab cohort demonstrated a change in the mean RNFL thickness (thinning from baseline) of -0.88 μm (95% confidence interval [CI] -2.63 to 0.86; P = 0.32) and mean GCIP volume of +0.013 mm (95% CI -0.006 to 0.032; P = 0.18). Over the same time period, the first-line therapy-treated cohort demonstrated greater degrees of RNFL thinning (mean change in RNFL thickness was -3.65 μm [95% CI -5.40 to -1.89; P = 0.0001]). There was also more prominent GCIP volume loss relative to baseline in the first-line therapy group (-0.052 mm [95% CI -0.070 to -0.034; P < 0.0001]). Conclusions: Alemtuzumab-treated patients with RRMS demonstrated relative stability of OCT-measured neuroaxonal structure compared with RRMS patients treated with either interferon-β or glatiramer acetate over a 5-year period. These findings, along with previous demonstration of improved brain atrophy rates, suggest that alemtuzumab may offer long-term preservation of neuroaxonal structure in patients with RRMS

Long-Term Stability of Neuroaxonal Structure in Alemtuzumab-Treated Relapsing-Remitting Multiple Sclerosis Patients

Background: Patients with multiple sclerosis (MS) experience progressive thinning in optical coherence tomography (OCT) measures of neuroaxonal structure regardless of optic neuritis history. Few prospective studies have investigated the effects of disease-modifying therapies on neuroaxonal degeneration in the retina. Alemtuzumab is a monoclonal antibody shown to be superior to interferon β-1a in treating relapsing-remitting MS (RRMS). The purpose of this study was to assess the effects of alemtuzumab and first-line injectable treatments on OCT measures of neuroaxonal structure including peripapillary retinal nerve fiber layer (RNFL) thickness and combined ganglion cell-inner plexiform (GCIP) layer volume in RRMS patients followed up over 5 years. Methods: In this retrospective pilot study with prospectively collected double cohort data, spectral domain OCT measures of RNFL thickness and GCIP volume were compared between alemtuzumab-treated RRMS patients (N = 24) and RRMS patients treated with either interferon-β or glatiramer acetate (N = 21). Results: Over a median of 60 months (range 42-60 months), the alemtuzumab cohort demonstrated a change in the mean RNFL thickness (thinning from baseline) of -0.88 μm (95% confidence interval [CI] -2.63 to 0.86; P = 0.32) and mean GCIP volume of +0.013 mm (95% CI -0.006 to 0.032; P = 0.18). Over the same time period, the first-line therapy-treated cohort demonstrated greater degrees of RNFL thinning (mean change in RNFL thickness was -3.65 μm [95% CI -5.40 to -1.89; P = 0.0001]). There was also more prominent GCIP volume loss relative to baseline in the first-line therapy group (-0.052 mm [95% CI -0.070 to -0.034; P < 0.0001]). Conclusions: Alemtuzumab-treated patients with RRMS demonstrated relative stability of OCT-measured neuroaxonal structure compared with RRMS patients treated with either interferon-β or glatiramer acetate over a 5-year period. These findings, along with previous demonstration of improved brain atrophy rates, suggest that alemtuzumab may offer long-term preservation of neuroaxonal structure in patients with RRMS

Dynamics and Predictors of Cognitive Impairment along the Disease Course in Multiple Sclerosis

(1) Background: The evolution and predictors of cognitive impairment (CI) in multiple sclerosis (MS) are poorly understood. We aimed to define the temporal dynamics of cognition throughout the disease course and identify clinical and neuroimaging measures that predict CI. (2) Methods: This paper features a longitudinal study with 212 patients who underwent several cognitive examinations at different time points. Dynamics of cognition were assessed using mixed-effects linear spline models. Machine learning techniques were used to identify which baseline demographic, clinical, and neuroimaging measures best predicted CI. (3) Results: In the first 5 years of MS, we detected an increase in the z-scores of global cognition, verbal memory, and information processing speed, which was followed by a decline in global cognition and memory (p < 0.05) between years 5 and 15. From 15 to 30 years of disease onset, cognitive decline continued, affecting global cognition and verbal memory. The baseline measures that best predicted CI were education, disease severity, lesion burden, and hippocampus and anterior cingulate cortex volume. (4) Conclusions: In MS, cognition deteriorates 5 years after disease onset, declining steadily over the next 25 years and more markedly affecting verbal memory. Education, disease severity, lesion burden, and volume of limbic structures predict future CI and may be helpful when identifying at-risk patients