Network Damage Predictive of MS Disease Course, Study Finds

A new analysis may give clinicians a more in-depth understanding of an individual patient’s multiple sclerosis (MS) disease course.

Structural and functional measures obtained by MRI can help clinicians improve their predictions of clinical deterioration in patients with multiple sclerosis (MS), according to new research.

The study was published in the Neurology Neuroimmunology & Neuroinflammation and is based on more than 200 patients with MS.

The authors explained that MS is a highly variable disease, and the availability of disease-modifying drugs make it more essential than ever to be able to characterize a particular patient’s MS.

Traditionally, they noted, conventional MRI and clinical evaluations were used to guide treatment decisions, although it is not clear that such measures hold significant value in terms of predicting patient disease progression. More recently, MRI has been used to assess clinically relevant compartments such as gray matter (GM), strategic white matter (WM) tracks, and the spinal cord, the authors noted.

“This helped demonstrate that in patients with established MS and in progressive MS (PMS), GM damage plays a major role in explaining long-term disability and cognitive decline,” they wrote.

The investigators wondered, however, whether structural and functional mapping of the brain might lend additional insights into likely disease paths, noting that disability accumulation in MS may be related to impaired interactions between different parts of the brain.

“In this study, we hypothesized that integrating structural and functional network information may help to identify specific circuits being critical for clinical deterioration in heterogeneous diseases such as MS and may improve prediction of the subsequent disease course, in terms of disability increase and evolution to a more severe clinical phenotype,” they wrote.

To test their hypothesis, they took baseline 3D T1-weighted and resting-state functional MRI scans from 233 patients with MS and 77 controls. Study enrollees were given a neurologic evaluation both at baseline and at a median follow-up of 6.4 years. They were also classified in terms of their clinical disease progression at follow-up, including diagnosis as relapsing-remitting MS (RRMS) and secondary PMS (SPMS), as applicable. Global brain volumetry was assessed, and independent component analysis was used to track main functional connectivity and GM network patterns, the investigators said.

Nearly half of the patients (45%) saw clinical decline by follow-up, and 16% of the 157 patients with RRMS saw their disease evolve into SPMS over the same time frame, the authors reported.

A treatment-adjusted random forest (RF) model identified 4 factors as predictors of clinical worsening: normalized GM and brain volumes, decreased functional connectivity between default-mode networks, increased functional connectivity of the left precentral gyrus in the sensorimotor network (SMN), and GM atrophy in the fronto-parietal network, the investigators said.

Three factors were independently linked with SPMS conversion: baseline disability, normalized GM volume, and GM atrophy in the SMN, they found.

The suthors wrote that one of the main strengths of their study was the assessment of network-specific MRI measures.

“This was a rewarding strategy to ameliorate prediction of MS disease evolution, because both functional and structural network MRI abnormalities were found to be informative of subsequent clinical deterioration at medium-term, and inclusion of network MRI metrics in RF models significantly improved prediction performance,” they said.

The investigators said future studies should examine the added value of other MRI and serologic biomarkers, including WM network damage and demyelination/remyelination indices.

Reference

Rocca MA, Valsasina P, Meani A, et al. Network damage predicts clinical worsening in multiple sclerosis: a 6.4-year study. Neurol Neuroimmunol Neuroinflamm. Published online May 21, 2021. doi:10.1212/NXI.0000000000001006