Quantitative brainstem and spinal MRI in amyotrophic lateral sclerosis: implications for predicting noninvasive ventilation needs.

BACKGROUND: Respiratory complications resulting from motor neurons degeneration are the primary cause of death in amyotrophic lateral sclerosis (ALS). Predicting the need for non-invasive ventilation (NIV) in ALS is important for advance care planning and clinical trial design. The aim of this study was to assess the potential of quantitative MRI at the brainstem and spinal cord levels to predict the need for NIV during the first six months after diagnosis. METHODS: Forty-one ALS patients underwent MRI and spirometry shortly after diagnosis. The need for NIV was monitored according to French health guidelines for 6 months. The performance of four regression models based on: clinical variables, brainstem structures volumes, cervical spinal measurements, and combined variables were compared to predict the need for NIV within this period. RESULTS: Both the clinical model (R2 = 0.28, AUC = 0.85, AICc = 42.67, BIC = 49.8) and the brainstem structures' volumes model (R2 = 0.30, AUC = 0.85, AICc = 40.13, BIC = 46.99) demonstrated good predictive performance. In addition, cervical spinal cord measurements model similar performance (R2 = 0.338, AUC = 0.87, AICc = 37.99, BIC = 44.49). Notably, the combined model incorporating predictors from all three models yielded the best performance (R2 = 0.60, AUC = 0.959, AICc = 36.38, BIC = 44.8). These findings are supported by observed positive correlations between brainstem volumes, cervical (C4/C7) cross-sectional area, and spirometry-measured lung volumes. CONCLUSIONS: Our study shows that brainstem volumes and spinal cord area are promising measures to predict respiratory intervention needs in ALS.

Comparison of spinal magnetic resonance imaging and classical clinical factors in predicting motor capacity in amyotrophic lateral sclerosis.

BACKGROUND: Motor capacity is crucial in amyotrophic lateral sclerosis (ALS) clinical trial design and patient care. However, few studies have explored the potential of multimodal MRI to predict motor capacity in ALS. This study aims to evaluate the predictive value of cervical spinal cord MRI parameters for motor capacity in ALS compared to clinical prognostic factors. METHODS: Spinal multimodal MRI was performed shortly after diagnosis in 41 ALS patients and 12 healthy participants as part of a prospective multicenter cohort study, the PULSE study (NCT00002013-A00969-36). Motor capacity was assessed using ALSFRS-R scores. Multiple stepwise linear regression models were constructed to predict motor capacity at 3 and 6 months from diagnosis, based on clinical variables, structural MRI measurements, including spinal cord cross-sectional area (CSA), anterior-posterior, and left-to-right cross-section diameters at vertebral levels from C1 to T4, and diffusion parameters in the lateral corticospinal tracts (LCSTs) and dorsal columns. RESULTS: Structural MRI measurements were significantly correlated with the ALSFRS-R score and its sub-scores. And as early as 3 months from diagnosis, structural MRI measurements fit the best multiple linear regression model to predict the total ALSFRS-R (R2 = 0.70, p value = 0.0001) and arm sub-score (R2 = 0.69, p value = 0.0002), and combined with DTI metric in the LCST and clinical factors fit the best multiple linear regression model to predict leg sub-score (R2 = 0.73, p value = 0.0002). CONCLUSIONS: Spinal multimodal MRI could be promising as a tool to enhance prognostic accuracy and serve as a motor function proxy in ALS.