TY - JOUR
T1 - Networks of microstructural damage predict disability in multiple sclerosis
AU - Colato, Elisa
AU - Prados, Ferran
AU - Stutters, Jonathan
AU - Bianchi, Alessia
AU - Narayanan, Sridar
AU - Arnold, Douglas L.
AU - Wheeler-Kingshott, Claudia
AU - Barkhof, Frederik
AU - Ciccarelli, Olga
AU - Chard, Declan T.
AU - Eshaghi, Arman
N1 - Funding Information: This investigation was supported (in part) by (an) award(s) from the International Progressive MS Alliance, award reference number PA-1412-02420. We are grateful to all the IPMSA investigators who have contributed trial data to this study as part of EPITOME: Enhancing Power of Intervention Trials Through Optimised MRI End points network. DTC, FB, FP and OC are supported by the NIHR biomedical research centre at UCLH. We are grateful to all the MS-SMART investigators. MS-SMART was an investigator-led project sponsored by University College London (UCL), Edinburgh Clinical Trials Unit (ECTU), QSMSC NMR/MRI analysis centre, NIHR Efficacy and Mechanism Evaluation (EME) Programme, MS Clinical Trials Network (MS CTN), the UK Multiple Sclerosis (MS) Society and the US National MS Society. Data collection and sharing for part of this project was provided by the Human Connectome Project (HCP; Principal Investigators: Bruce Rosen, MD, PhD, Arthur W Toga, PhD, Van J Weeden, MD). Funding Information: HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH) and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of NeuroImaging at the University of Southern California. Publisher Copyright: © Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Background: Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods. Methods: We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures. Results: We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001). Conclusions: GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials.
AB - Background: Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods. Methods: We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures. Results: We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001). Conclusions: GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials.
KW - brain mapping
KW - image analysis
KW - multiple sclerosis
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85166752833&partnerID=8YFLogxK
U2 - https://doi.org/10.1136/jnnp-2022-330203
DO - https://doi.org/10.1136/jnnp-2022-330203
M3 - Article
C2 - 37468305
SN - 0022-3050
VL - 94
SP - 992
EP - 1003
JO - Journal of Neurology, Neurosurgery and Psychiatry
JF - Journal of Neurology, Neurosurgery and Psychiatry
IS - 12
M1 - jnnp-2022-330203
ER -