Barts-MS rose-tinted-odometer: ★ (seeing orange; halfway between red and yellow)
Do you want to know how badly damaged your MS brain is or would you prefer to put your head in the sand and ignore it? This is a dilemma facing a large number of you. Do you ask your neurologist if you have exaggerated or accelerated brain atrophy? Do you ask to have cognitive screening to see how good or bad your cognition is?
Another metric that is likely to enter clinical practice in the future is a metric to assess how well your brain’s functional network is working. The brain is like multiple computers working together in parallel. The brains’ computers or functional domains work together in harmony as a functional network. If you acquire enough lesions and damage the functional network the brain stops working as well and efficiently as it should. This manifests as cognitive fatigue and cognitive problems. It takes so much more mental effort to get the brain’s damaged functional network to perform well, which is why it causes fatigue.
The study below shows that in pwMS with damage to the brain measured using both structure (loss of volume) and function (loss of connectivity) do poorly; i.e. they were more likely to become secondary progressive over the next 6 years. Are you surprised by these results? It is amazing how accurate these MRI metrics were in being able to predict who would become progressive or not.
The message from this and other studies is simple, MS damage begets MS damage. This is why we have to diagnose and treat MS as early as possible and if necessary as effectively as possible. Once damage accumulates it is irreversible and when it is detected it represents a sick brain, which then continues to be shredded by the processes that drive smouldering MS.
Rocca et al. Network Damage Predicts Clinical Worsening in Multiple Sclerosis: A 6.4-Year Study. Neurol Neuroimmunol Neuroinflamm. 2021 May 21;8(4):e1006.
Objective: In multiple sclerosis (MS), clinical impairment is likely due to both structural damage and abnormal brain function. We assessed the added value of integrating structural and functional network MRI measures to predict 6.4-year MS clinical disability deterioration.
Methods: Baseline 3D T1-weighted and resting-state functional MRI scans were obtained from 233 patients with MS and 77 healthy controls. Patients underwent a neurologic evaluation at baseline and at 6.4-year median follow-up (interquartile range = 5.06-7.51 years). At follow-up, patients were classified as clinically stable/worsened according to disability changes. In relapsing-remitting (RR) MS, secondary progressive (SP) MS conversion was evaluated. Global brain volumetry was obtained. Furthermore, independent component analysis identified the main functional connectivity (FC) and gray matter (GM) network patterns.
Results: At follow-up, 105/233 (45%) patients were clinically worsened; 26/157 (16%) patients with RRMS evolved to SPMS. The treatment-adjusted random forest model identified normalized GM and brain volumes, decreased FC between default-mode networks, increased FC of the left precentral gyrus in the sensorimotor network (SMN), and GM atrophy in the fronto-parietal network (false discovery rate [FDR]-corrected p = range 0.01-0.09) as predictors of clinical worsening (out-of-bag [OOB] accuracy = 0.74). An expected contribution of baseline disability was also present (FDR-p = 0.01). Baseline disability, normalized GM volume, and GM atrophy in the SMN (FDR-p = range 0.01-0.09) were independently associated with SPMS conversion (OOB accuracy = 0.84). At receiver operating characteristic analysis, including network MRI variables improved disability worsening (p = 0.05) and SPMS conversion (p = 0.02) prediction.
Conclusions: Integration of MRI network measures helped determining the relative contributions of global/local GM damage and functional reorganization to clinical deterioration in MS.
General Disclaimer: Please note that the opinions expressed here are those of Professor Giovannoni and do not necessarily reflect the positions of the Barts and The London School of Medicine and Dentistry nor Barts Health NHS Trust.