Longitudinal mapping of structural disconnection and morphometric similarity in Multiple Sclerosis
Poster No:
1524
Submission Type:
Abstract Submission
Authors:
Mario Tranfa1, Alessandra Scaravilli1, Maria Petracca2, Marcello Moccia1, Mario Quarantelli3, Sirio Cocozza1, Arturo Brunetti1, Giuseppe Pontillo1
Institutions:
1University of Naples "Federico II", Naples, Italy, 2Sapienza University of Rome, Rome, Italy, 3National Research Council, Naples, Italy
First Author:
Co-Author(s):
Introduction:
Multiple sclerosis (MS) can be conceptualized as a network disorder. The accumulation of white matter (WM) demyelinating lesions leads to structural disconnection between gray matter (GM) regions (Rise et al. 2022), adding to other pathological processes that directly and indirectly damage the GM (i.e., microglial activation and cortical demyelination) (Calabrese et al. 2015). The resulting neurodegeneration (Tsagkas et al. 2020) disrupts the morphometric similarity patterns, ultimately subverting the hierarchical organization of the brain (Sebenius et al. 2023). Network-based approaches may represent a tool to overcome the "clinico-radiological paradox", that is the gap between the clinical status and the radiological severity assessed through conventional MRI biomarkers, such as WM lesion load and brain atrophy (Barkhof 2002). However, these methods require advanced MRI sequences that are not routinely acquired and long processing times, hampering their application in clinical practice.
Here, using conventional MRI and publicly available software, we assessed cross-sectional and longitudinal alterations of structural disconnection and morphometric similarity networks in MS, and tested whether they are sensitive to disease status and progression over time, and whether they could explain disease-related physical and cognitive disability.
Here, using conventional MRI and publicly available software, we assessed cross-sectional and longitudinal alterations of structural disconnection and morphometric similarity networks in MS, and tested whether they are sensitive to disease status and progression over time, and whether they could explain disease-related physical and cognitive disability.
Methods:
We retrospectively collected 3T structural brain MRIs of 461 MS patients (age=37.2±10.6y;F:M=324:137), corresponding to 1235 visits (mean follow-up time=1.9±2.0y, range=0.1-13.3y), and 55 healthy controls (age=42.4±15.7y;F:M=25:30). From 3D-T1w and FLAIR-T2w scans, WM lesions were automatically segmented and the brain was parcellated into 100 cortical (Schaefer atlas) and 14 subcortical (Aseg atlas) regions. For MS patients, subject-level WM masks were registered to the MNI space and used to compute networks of structural disconnection: using the Lesion Quantification Toolkit (Griffis et al. 2021), based on the HCP842 tractography atlas, disconnection between pairs of regions was estimated as the proportion of connecting streamlines passing through WM lesions. Likewise, with the Morphometric Inverse Divergence (MIND) method (Sebenius et al. 2023), we computed networks of morphometric similarity between cortical regions from 3D-T1w derived FreeSurfer outputs for both groups. Physical and cognitive disability were assessed with the expanded disability status scale (EDSS) and the symbol digit modalities test (SDMT), respectively. Via network-based statistics, the effect of time and clinical disability (and group, for MIND networks) were tested with linear mixed-effects models. Five-thousands permutations were used and statistical significance was set at p < 0.05 (FWER-corrected). Statistical analyses were carried out using R (version 4.1.2).
Results:
We identified a subnetwork of significant progressive structural disconnection (82 edges, pFWE=0.04), mainly comprising cortico-subcortical tracts. MIND networks were sensitive to disease status and progression over time, with distributed effects of decreased morphometric similarity in large subnetworks of 431 and 509 edges, respectively (pFWE<0.01). We observed associations of EDSS with structural disconnection and MIND subnetworks of 960 and 670 edges, respectively (pFWE<0.01, Figure 1). Similarly, SDMT was associated with structural disconnection and MIND subnetworks of 988 and 202 edges, respectively (pFWE<0.01, Figure 2).
·Figure 1. Matrix and node-level representation of the subnetworks of significant association between EDSS and structural disconnection (A) and morphometric similarity (B).
·Figure 2. Matrix and node-level representation of the subnetworks of significant association between SDMT and structural disconnection (A) and morphometric similarity (B).
Conclusions:
We have shown that structural disconnection and morphometric similarity networks, as assessed through conventional MRI, are sensitive to MS-related brain damage and its evolution over time. Moreover, they proved to be sensitive to physical and cognitive disability, potentially adding to established conventional MRI-derived measures as biomarkers of disease severity and progression. Extracting network measures from conventional MRI holds the potential for driving brain connectomics towards applicability in everyday clinical practice.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Modeling and Analysis Methods:
Classification and Predictive Modeling
Connectivity (eg. functional, effective, structural) 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Keywords:
ADULTS
Degenerative Disease
Demyelinating
DISORDERS
MRI
STRUCTURAL MRI
White Matter
Other - Network analysis
1|2Indicates the priority used for review
Provide references using author date format
Calabrese, M. (2015). «Exploring the Origins of Grey Matter Damage in Multiple Sclerosis». Nature Reviews Neuroscience 16 (3): 147–58. https://doi.org/10.1038/nrn3900.
Griffis, J. C., (2021). «Lesion Quantification Toolkit: A MATLAB Software Tool for Estimating Grey Matter Damage and White Matter Disconnections in Patients with Focal Brain Lesions». NeuroImage: Clinical 30: 102639. https://doi.org/10.1016/j.nicl.2021.102639.
Rise, H. H., (2022). «Brain Disconnectome Mapping Derived from White Matter Lesions and Serum Neurofilament Light Levels in Multiple Sclerosis: A Longitudinal Multicenter Study». NeuroImage: Clinical 35: 103099. https://doi.org/10.1016/j.nicl.2022.103099.
Sebenius, I., (2023). «Robust Estimation of Cortical Similarity Networks from Brain MRI». Nature Neuroscience 26 (8): 1461–71. https://doi.org/10.1038/s41593-023-01376-7.
Tsagkas, C. M. (2020). «Longitudinal Patterns of Cortical Thinning in Multiple Sclerosis». Human Brain Mapping 41 (8): 2198–2215. https://doi.org/10.1002/hbm.24940.