“Brain age” predicts disability accumulation in multiple sclerosis

Brier, Matthew R.; Li, Zhuocheng; Ly, Maria; Karim, Helmet; Liang, Leda; Du, Wei; McCarthy, John E.; Cross, Anne H.; Benzinger, Tammie L.S.; Naismith, Robert T.; Chahin, Salim

doi:10.1002/acn3.51782

Cited by 16 publications

(1 citation statement)

References 34 publications

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“…The brain-age paradigm offers a window into this problem and has been previously used to characterize neurodegeneration in multiple sclerosis due to its sensitivity to brain ageing-like patterns. 5–7,24,25 In line with previous studies, our results confirmed that, when observed through the lens of healthy ageing, the brains of PwMS look older than normal (around eight years on average), suggesting that at least some of the disease-related variance in brain structure can be effectively modelled in terms of premature/accelerated brain ageing.…”

Section: Discussionsupporting

confidence: 91%

Disentangling neurodegeneration from ageing in multiple sclerosis: the brain-predicted disease duration gap

Pontillo,

Prados,

Colman

et al. 2024

Preprint

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Disentangling brain ageing from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. Here, we statistically modelled disease duration (DD) in PwMS as a function of brain MRI scans and evaluated whether the brain-predicted DD gap (i.e., the difference between predicted and actual duration) could complement the brain-age gap as a DD-adjusted global measure of multiple sclerosis-specific brain damage.In this retrospective study, we used 3D T1-weighted brain MRI scans of PwMS (i) from a large multicentric dataset (n = 4,392) for age and DD modelling, and (ii) from a monocentric longitudinal cohort of patients with early multiple sclerosis (n = 252 patients, 749 sessions) for clinical validation. We trained and tested a deep learning model based on a 3D DenseNet architecture to predict DD from minimally pre-processed brain MRI scans, while age predictions were obtained with the previously validated DeepBrainNet algorithm. Model predictions were scrutinised to assess the influence of lesions and brain volumes, while the DD gap metric was biologically and clinically validated within a linear model framework assessing its relationship with brain-age gap values and with physical disability measured with the Expanded Disability Status Scale (EDSS).Our model predicted DD better than chance (mean absolute error = 5.63 years, R2= 0.34) and was nearly orthogonal to the brain-age model, as suggested by the very weak correlation between DD gap and brain-age gap values (r= 0.06). DD predictions were influenced by spatially distributed variations in brain volume, and, unlike brain-predicted age, were sensitive to the presence of lesions (mean difference between unfilled and filled scans: 0.55 ± 0.57 years,p< 0.001). The DD gap metric significantly explained EDSS scores (β = 0.060,p< 0.001), adding to brain-age gap values (ΔR2= 0.012,p< 0.001). Longitudinally, increasing annualised DD gap was associated with greater annualised EDSS changes (r= 0.50,p< 0.001), with a significant incremental contribution in explaining disability worsening compared to changes of the brain-age gap alone (ΔR2= 0.064,p< 0.001).The brain-predicted DD gap metric appears to be sensitive to multiple sclerosis-related lesions and brain atrophy, adding to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally. Potentially, it may be used as a multiple sclerosis-specific biomarker of disease severity and progression.

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Section: Discussionsupporting

confidence: 91%

Disentangling neurodegeneration from ageing in multiple sclerosis: the brain-predicted disease duration gap

Pontillo,

Prados,

Colman

et al. 2024

Preprint

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Artificial Intelligence and Multiple Sclerosis

Amin,

Martínez-Heras,

Ontaneda

et al. 2024

Curr Neurol Neurosci Rep

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In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.

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