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Updated: Apr 11, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Connectivity in ALS II (CoALS II): a study of structural and functional connectivity in ALS
Vijay Renga1,2, Charlotte A Jeffreys2, Gina E Kersey2
1Department of Neurology, Medical University of South Carolina, Charleston, SC, United States.
Background:
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.
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. A comprehensive 104-node parcellation scheme based on the Desikan-Killiany atlas was employed.
Results:
ALS participants showed preserved global network topology (p > 0.05 for efficiency and small-worldness) 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 (p = 0.005), 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.

