The integration of structural and functional magnetic resonance imaging (MRI) measures a significantly improved prediction of long-term clinical deterioration in patients with multiple sclerosis (MS), according to study results published in Neurology: Neuroimmunology & Neuroinflammation.
There is currently an active debate about the power of conventional MRI in predicting the clinical course of MS. Efforts have been made in recent years to improve the predictive value of MRI in MS by examining clinically relevant brain compartments, including gray matter (GM), strategic white matter (WM), and the spinal cord. In addition, the mapping of functional and structural brain networks was also considered to be potentially clinically relevant.
To date, the combined predictive value of functional and structural network techniques in predicting clinical deterioration in patients with MS is not fully understood. An Italian team of researchers attempted to fill this research gap by evaluating the integration of functional and structural network MRI measurements to predict worsening clinical disability over a median of 6.4 years in patients with MS.
In the study, researchers obtained 3D T1-weighted and functional MRI scans at rest on 233 patients with MS from a prospective hospital cohort and 77 healthy controls. The MS cohort included 157 patients with relapsing-remitting (RR) disease (RRMS), 59 with secondary progressive (SP) MS (SPMS) and 17 with primary progressive (PP) MS.
The patients in the study underwent a neurological evaluation both at the start of the study and after a median follow-up of 6.4 years. The study researchers rated the Expanded Disability Status Scale (EDSS) score, disease-modifying treatment (DMT) changes, and the incidence of clinical relapses.
In the follow-up period, the MS patients were then classified as clinically stable or deteriorated based on the changes in disability. In addition, the study researchers rated the frequency of SPMS conversion in patients with RRMS.
In addition to global brain volumetry, researchers performed an independent component analysis to identify the primary patterns in functional connectivity (FC) and GM networks.
The median follow-up EDSS score was 4.0 (median EDSS score change 0.5; P <0.0001). About 45% (n = 105) of MS patients experienced clinical deterioration after the median follow-up period of 6.4 years, while the remaining 128 patients had stable disease.
In addition, 16% (n = 26) of patients who initially had RRMS experienced disease conversion to SPMS. Factors independently associated with transition to SPMS included baseline disability, normalized GM volume, and GM atrophy (false detection rate P-range = 0.01-0.09; out of the bag [OOB] Accuracy, 0.84).
Normalized GM and brain volumes, increased FC of the left precentral gyrus in the sensorimotor network (SMN), decreased FC between default mode networks and GM atrophy in the frontoparietal network were identified as predictors of clinical deterioration in a treatment-adapted random forest model (OOB Accuracy, 0.74).
Inclusion of network MRI variables (baseline EDSS score, normalized GM volume, GM sensorimotor network 1, and DMT change) improved the prediction of disability deterioration (P = 0.009) and SPMS conversion (OOB- AUC, 0.84; 95% CI, 0.76.). -0.91; P = .02).
One limitation of this study was the lack of baseline and follow-up cognitive assessments, an important limitation considering that cognitive changes can significantly affect patient functioning. Therefore, the examiners indicated that they had a deterioration in other clinically important scores than the EDSS, such as: B. the multiple sclerosis functional composite, could not assess.
The study researchers suggest that “the added value of other MRI and serological biomarkers, such as.”
Disclosure: Several authors stated links to the pharmaceutical industry. For a full list of the details, see the original article.
Rocca MA, Valsasina P, Meani A, et al. Network Damage Predicts Clinical Worsening in Multiple Sclerosis: A 6.4-Year Study. Neurol Neuroimmunol Neuroinflamm. 2021; 8 (4): e1006. doi: 10.1212 / NXI.0000000000001006