Multiple sclerosis lesions that impair memory map to a connected memory circuit

Kletenik, Isaiah; Cohen, Alexander L.; Glanz, Bonnie I.; Ferguson, Michael; Tauhid, Shahamat; Li, Jing; Drew, William; Polgar‐Turcsanyi, Mariann; Palotai, Miklós; Siddiqi, Shan; Marshall, Gad A.; Chitnis, Tanuja; Bakshi, Rohit; Fox, Michael

doi:10.1007/s00415-023-11907-8

Cited by 4 publications

(3 citation statements)

References 76 publications

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“…This challenges the traditional view of the white matter BOLD signal as a mere artefact and supports recent studies – including LNM analyses of white matter lesions in multiple sclerosis – demonstrating that it contains biologically meaningful information. 61–63…”

Section: Discussionmentioning

confidence: 99%

“…This challenges the traditional view of the white matter BOLD signal as a mere artefact and supports recent studies -including LNM analyses of white matter lesions in multiple sclerosis -demonstrating that it contains biologically meaningful information. [61][62][63] Conclusion WMH-related brain network connectivity measures significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume or epidemiological factors. Our findings highlight the contribution of WMH disconnectivity, particularly in attention-related brain regions, to vascular cognitive impairment.…”

Section: Strengths and Limitationsmentioning

confidence: 94%

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Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients

Petersen,

Coenen,

DeCarli

et al. 2024

Preprint

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Introduction: White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enabling the inference of brain networks disconnected by lesions, represents a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. Methods & results: We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Conclusion: WMH-related brain network disconnectivity significantly improves the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

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Section: Discussionmentioning

confidence: 99%

Section: Strengths and Limitationsmentioning

confidence: 94%

Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients

Petersen,

Coenen,

DeCarli

et al. 2024

Preprint

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“…9 Such atlas-based approaches have demonstrated substantial agreement with individual tractography-based disconnectomes, 10 offering an alternative perspective for evaluating the impact of MS lesions. Disconnection metrics correlate with physical disability 11 and systemic biomarkers of axonal damage in MS, 12 and disruption of specific brain subnetworks are linked to MS symptoms such as reduced information processing speed, 13 memory dysfunction, 14 or depression. 15 Similarly, single-subject GM networks can be built from anatomical MRI by estimating a set of morphological properties (e.g., volume, thickness, curvature) within each GM region and computing the similarities between regions.…”

Section: Introductionmentioning

confidence: 99%

Mapping Structural Disconnection and Morphometric Similarity Alterations in Multiple Sclerosis

Tranfa,

Petracca,

Moccia

et al. 2024

Preprint

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Whilst multiple sclerosis (MS) can be conceptualized as a network disorder, brain network analyses are typically dependent on advanced MRI sequences not commonly acquired in clinical practice. Here, we used conventional MRI to assess cross-sectional and longitudinal modifications of structural disconnection and morphometric similarity networks in people with MS (pwMS), along with their relationship with clinical disability.In this longitudinal monocentric study, 3T structural MRI scans of pwMS and healthy controls (HC) were retrospectively analysed. Physical and cognitive disabilities were assessed with the expanded disability status scale (EDSS) and the symbol digit modalities test (SDMT), respectively. Demyelinating lesions were automatically segmented on 3D-T1w and FLAIR images and, based on normative tractography data, the corresponding masks were used to compute pairwise structural disconnection between atlas-defined brain regions (100 cortical and 14 subcortical). Using the Morphometric Inverse Divergence (MIND) method, we built matrices of morphometric similarity between cortical regions based on FreeSurfer surface reconstruction. Using network-based statistics (NBS) and its prediction-based extension NBS-predict, we tested whether subject-level connectomes were associated with disease status, progression, clinical disability, and long-term confirmed disability progression (CDP), independently from global lesion burden and atrophy. The coupling between structural disconnection and morphometric similarity was assessed at different scales.We studied 461 pwMS (age 37.2±10.6 years, F/M 324/137), corresponding to 1235 visits (mean follow-up time 1.9±2.0 years, range 0.1-13.3 years), and 55 HC (age 42.4±15.7 years; F/M 25/30). Long-term clinical follow-up was available for 285 pwMS (mean follow-up time 12.4±2.8 years), 127 of whom (44.6%) exhibited CDP. At baseline, structural disconnection in pwMS was mostly centered around the thalami and cortical sensory and association hubs, while morphometric similarity was extensively disrupted (pFWE<0.01). EDSS was related to fronto-thalamic disconnection (pFWE<0.01) and disrupted morphometric similarity around the left perisylvian cortex (pFWE0.02), whilst SDMT was associated with cortico-subcortical disconnection in the left hemisphere (pFWE<0.01). Longitudinally, both structural disconnection and morphometric similarity disruption significantly progressed (pFWE0.04 andpFWE<0.01), correlating with EDSS increase (rho 0.07, p 0.02 and rho 0.11,p<0.001), whilst baseline disconnection predicted long-term CDP with nearly 60% accuracy (p0.03). On average, structural disconnection and morphometric similarity were positively associated at both the edge (rho 0.18,p<0.001) and node (rho 0.16,p<0.001) levels.Structural disconnection and morphometric similarity networks, as assessed through conventional MRI, are sensitive to MS-related brain damage and its progression. They explain disease-related clinical disability and predict its long-term evolution independently from global lesion burden and atrophy, potentially adding to established MRI measures as network-based biomarkers of disease severity and progression.

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Cognitive impairment in multiple sclerosis: from phenomenology to neurobiological mechanisms

Jellinger

2024

J Neural Transm

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Multiple sclerosis lesions that impair memory map to a connected memory circuit

Cited by 4 publications

References 76 publications

Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients

Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients

Mapping Structural Disconnection and Morphometric Similarity Alterations in Multiple Sclerosis

Cognitive impairment in multiple sclerosis: from phenomenology to neurobiological mechanisms

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