Data-driven disease subgrouping in ALS: a multicenter cerebral functional connectivity study.

Area of Science:

• Neuroimaging
• Neurology
• Biomarker Discovery

Background:

• Amyotrophic Lateral Sclerosis (ALS) is clinically heterogeneous, making early functional impairment detection challenging.
• Current clinical evaluation methods lack consensus and objectivity, hindering understanding of disease progression.
• Neuroimaging offers a potential objective approach to identify and evaluate ALS disease stage-specific subgroups.

Purpose of the Study:

• To compare a data-driven neuroimaging approach with clinical criteria for stratifying ALS patients.
• To assess the utility of functional connectivity in identifying disease stage-specific ALS subgroups.
• To evaluate neuroimaging as a potential objective biomarker for ALS patient stratification and monitoring.

Main Methods:

• Evaluated cerebral functional connectivity in 174 ALS patients and 165 controls using resting-state fMRI.
• Subgrouped patients using data-driven hierarchical clustering and three clinical criteria (Riluzole/Edaravone trial criteria, median progression rate).
• Assessed associations between functional connectivity and clinical function (cognitive, motor, lung).

Main Results:

• Both data-driven and clinical approaches identified two subgroups with differing clinical characteristics.
• The data-driven approach revealed greater spatial extents of cerebral connectivity alterations.
• Observed clinical and connectivity differences were dependent on the stratification method used.

Conclusions:

• The data-driven approach using functional MRI shows promise as an objective biomarker for ALS patient stratification.
• This method can identify alterations in both clinical and cerebral function related to disease stage.
• Potential applications include clinical trial enrichment and monitoring disease and therapeutic progression.