  Longitudinal fMRI datasets hold great promise for the study of
neurodegenerative diseases, but realizing their potential depends on extracting
accurate fMRI-based brain measures in individuals over time. This is especially
true for rare, heterogeneous and/or rapidly progressing diseases, which often
involve small samples whose functional features may vary dramatically across
subjects and over time, making traditional group-difference analyses of limited
utility. One such disease is ALS, which results in extreme motor function loss
and eventual death. Here, we analyze a rich longitudinal dataset containing 190
motor task fMRI scans from 16 ALS patients and 22 age-matched HCs. We propose a
novel longitudinal extension to our cortical surface-based spatial Bayesian
GLM, which has high power and precision to detect activations in individuals.
Using a series of longitudinal mixed-effects models to subsequently study the
relationship between activation and disease progression, we observe an inverted
U-shaped trajectory: at relatively mild disability we observe enlarging
activations, while at higher disability we observe severely diminished
activation, reflecting progression toward complete motor function loss. We
observe distinct trajectories depending on clinical progression rate, with
faster progressors exhibiting more extreme hyper-activation and subsequent
hypo-activation. These differential trajectories suggest that initial
hyper-activation is likely attributable to loss of inhibitory neurons. By
contrast, earlier studies employing more limited sampling designs and using
traditional group-difference analysis approaches were only able to observe the
initial hyper-activation, which was assumed to be due to a compensatory
process. This study provides a first example of how surface-based spatial
Bayesian modeling furthers scientific understanding of neurodegenerative
disease.

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