MRI Markers and Functional Performance in Patients With CIS and MS: A Cross-Sectional Study

Abstract

Introduction: Brain atrophy is a widely accepted marker of disease severity with association to clinical disability in multiple sclerosis (MS). It is unclear to which extent this association reflects common age effects on both atrophy and function.Objective: To explore how functional performance in gait, upper extremities and cognition is associated with brain atrophy in patients with Clinically Isolated Syndrome (CIS) and relapsing-remitting MS (RRMS), controlling for effects of age and sex.Methods: In 27 patients with CIS, 59 with RRMS (EDSS ≤3) and 63 healthy controls (HC), 3T MRI were analyzed for T2 lesion count (T2C), volume (T2V) and brain volumes [normalized brain volume (NBV), gray matter volume (NGMV), white matter volume (NWMV), thalamic volume (NThalV)]. Functional performance was measured with short maximum walking speed (SMSW speed), 9-hole peg test (9HPT) and symbol digit modalities test (SDMT). Linear regression models were created for functional variables with stepwise inclusion of age, sex and MR imaging markers.Results: CIS differed from HC only in T2C and T2V. RRMS differed from HC in NBV, NGMV and NThalV, T2C and T2V, but not in NWMV. A strong association with age was seen in HC, CIS and RRMS groups for NBV (r = −0.5 to −0.6) and NGMV (r = −0.6 to −0.8). Associations with age were seen in HC and RRMS but not CIS for NThalV (r = −0.3; r = −0.5), T2C (rs = 0.3; rs = 0.2) and T2V (rs = 0.3; rs = 0.3). No effect of age was seen on NWMV. Correlations of functional performance with age in RRMS were seen for SMSW speed, 9HPTand SDMT (r = −0.27 to −0.46). Regression analyses yielded significant models only in the RRMS group for 9HPT, SMSW speed and EDSS. These included NBV, NGMV, NThalV, NWMV, logT2V, age and sex as predictors. NThalV was the only MRI variable predicting a functional measure (9HPTr) with a higher standardized beta than age and sex (R2 = 0.36, p < 1e-04).Conclusion: Thalamic atrophy was a stronger predictor of hand function (9HPT) in RRMS, than age and sex. This underlines the clinical relevance of thalamic atrophy and the relevance of hand function as a clinical marker even in mildly disabled patients.

Multiple sclerosis impairment scale and brain MRI in secondary progressive multiple sclerosis

“…In our study, we only found correlations of T25FW with the volume of thalamus and mean upper cervical spinal cord area. Thalamic volume has also previously been correlated with 9‐HPT in RRMS patients 45,46 . In our SPMS cohort, we found a correlation of 9‐HPT with thalamic, corpus callosum, mean upper cervical spinal cord area, and total lesion volumes.…”

“…Thalamic volume has also previously been correlated with 9-HPT in RRMS patients. 45,46 In our SPMS cohort, we found a correlation of 9-HPT with thalamic, corpus callosum, mean upper cervical spinal cord area, and total lesion volumes. This indicates that hand function may be highly complex and dependent on multiple cerebral areas.…”

The Impact of Intracortical Lesions on Volumes of Subcortical Structures in Multiple Sclerosis

“…3,4 Thalamic atrophy was associated with fatigue, 5,6 cognitive impairments, 7,8 decrease of walking speed, 9 and higher disability. 10,11 Measuring thalamic volume may be a good surrogate biomarker for clinical trials and, probably, routine practice. 4,11,12 Thalamic atrophy in MS can be related to either primary damage in the thalamus or distant damage of afferent or efferent thalamic fibers.…”

Gait abnormalities in minimally disabled people with Multiple Sclerosis: A 3D-motion analysis study

“…The major limitation of the present study is represented by the limited MRI assessment. We have not considered the thalamic atrophy that has proved to occur early in the disease course (Azevedo et al, 2018) Indeed, Rasche et al (2018) have recently showed that thalamic volume, but not other brain volumes (normalized brain volume, white matter volume, gray matter volume) correlates with a functional measure of motor dexterity (9-hole peg test). However, data are lacking on the possible correlation between MRI measure and specific gait parameters assessed by a very refined approach such as GA.…”