Combining Magnetization Transfer Ratio MRI and Quantitative Measures of Walking Improves the Identification of
Nora E Fritz1,2,3,4, Erin M Edwards4, Jennifer Keller1
1Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD 21205, USA.
Abstract:
Multiple sclerosis (MS) impacts balance and walking function, resulting in accidental falls. History of falls and clinical assessment are commonly used for fall prediction, yet these measures have limited predictive validity. Falls are multifactorial; consideration of disease-specific pathology may be critical for improving fall prediction in MS. The objective of this study was to examine the predictive value of clinical measures (i.e., walking, strength, sensation) and corticospinal tract (CST) MRI measures, both discretely and combined, to fall status in MS. Twenty-nine individuals with relapsing-remitting MS (mean ± SD age: 48.7 ± 11.5 years; 17 females; Expanded Disability Status Scale (EDSS): 4.0 (range 1-6.5); symptom duration: 11.9 ± 8.7 years; 14 fallers) participated in a 3T brain MRI including diffusion tensor imaging and magnetization transfer ratio (MTR) and clinical tests of walking, strength, sensation and falls history. Clinical measures of walking were significantly associated with CST fractional anisotropy and MTR. A model including CST MTR, walk velocity and vibration sensation explained >31% of the variance in fall status (R2 = 0.3181) and accurately distinguished 73.8% fallers, which was superior to stand-alone models that included only MRI or clinical measures. This study advances the field by combining clinical and MRI measures to improve fall prediction accuracy in MS.
Insights
Predicting falls in multiple sclerosis (MS) is improved by combining clinical assessments with advanced MRI measures of the corticospinal tract (CST). This approach offers superior accuracy over traditional methods for identifying individuals at risk of falling.
Area of Science:
- Neurology
- Radiology
- Rehabilitation Medicine
Background:
- Multiple sclerosis (MS) significantly impairs balance and walking, leading to falls.
- Current fall prediction methods using fall history and clinical assessments have limited accuracy.
- Falls in MS are complex, necessitating the inclusion of disease-specific pathology for better prediction.
Purpose of the Study:
- To evaluate the predictive capability of clinical measures (walking, strength, sensation) and corticospinal tract (CST) magnetic resonance imaging (MRI) for fall status in MS.
- To determine if combining clinical and CST MRI measures enhances fall prediction accuracy compared to using either alone.
Main Methods:
- Twenty-nine individuals with relapsing-remitting MS underwent 3T brain MRI (diffusion tensor imaging, magnetization transfer ratio [MTR]).
- Clinical assessments included tests of walking, strength, sensation, and falls history.
- Statistical models were used to assess the association between clinical and MRI measures with fall status.
Main Results:
- Clinical walking measures correlated significantly with CST fractional anisotropy and MTR.
- A combined model incorporating CST MTR, walk velocity, and vibration sensation explained over 31% of the variance in fall status.
- This integrated model correctly identified 73.8% of fallers, outperforming models using only MRI or clinical data.
Conclusions:
- Combining CST MTR and specific clinical measures (walk velocity, vibration sensation) significantly improves fall prediction accuracy in MS.
- This multimodal approach offers a more robust method for identifying individuals with MS at higher risk of falls.
- Integrating neuroimaging biomarkers with clinical assessments is crucial for advancing fall prediction in MS.
