2026 American Academy of Neurology Abstract Website

Objective:

Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a network-level neurodegenerative disease involving distributed disruptions across structural and functional systems. While previous studies have often examined white matter integrity or functional connectivity in isolation, the nature of structure–function coupling and its reorganization in ALS remains poorly understood.

Background:

In our previous pilot Connectivity in ALS (CoALS)1 we explored structural and functional network topology in a small cohort of ALS subjects compared with age-matched control data obtained from an open-source database.This study is a follow up study using multimodal imaging in our own subjects and control population to assess connectivity differences.

Design/Methods:

We conducted a multimodal connectomic analysis in ALS patients and matched controls, integrating cortical thickness–based structural covariance networks, diffusion MRI tractography, and resting-state and task-based functional MRI. Graph-theoretical metrics were derived, and cross-modal structure–function correspondence was quantified using ROI-wise correlation analyses.

Results:

ALS participants showed preserved global network topology but evidence of selective reorganization, particularly within motor and interhemispheric pathways. Cortical covariance networks exhibited minimal association with functional dynamics, whereas diffusion-derived white matter connectivity remained closely aligned with functional organization. This structure–function coupling was maintained or even enhanced during task performance, suggesting adaptive reconfiguration rather than uniform disconnection.

Conclusions:

Structure–function coupling in ALS is not globally diminished but reorganized, with robust white matter–functional relationships coexisting alongside weak cortical covariance–functional associations. These findings refine the traditional disconnection model and highlight the utility of multimodal metrics for understanding disease mechanisms and developing biomarkers for progression and therapeutic response.