A multimodal approach to identify clinically relevant biomarkers to comprehensively monitor disease progression in a mouse model of pediatric neurodegenerative disease. (June 2020) - Johnson, Tyler B.; Brudvig, Jon J.; Lehtimäki, Kimmo K.; Cain, Jacob T.; White, Katherine A.; Bragge, Timo; Rytkönen, Jussi; Huhtala, Tuulia; Timm, Derek; Vihma, Maria; Puoliväli, Jukka T.; Poutiainen, Pekka; et al.
Highlights: Longitudinal neuroimaging reveals changes in Cln6 nclf mice that correlate with changes reported in patients with Batten disease. Brain region-specific degeneration in the cortex, striatum, hippocampus, and cerebellum results from CLN6 dysfunction in mice. CLN6 disease progression in mice leads to widespread brain metabolism defects and white matter loss as measured by FDG-PET and DTI imaging, respectively. Kinematic gait analysis combined with principal component analysis identifies sex-dependent gait variations that change dramatically with disease progression. Contrastive principal component analysis of longitudinal data shows progressive changes in Cln6 nclf mice that exceed those of any single phenotype with traditional metrics. Abstract: While research has accelerated the development of new treatments for pediatric neurodegenerative disorders, the ability to demonstrate the long-term efficacy of these therapies has been hindered by the lack of convincing, noninvasive methods for tracking disease progression both in animal models and in human clinical trials. Here, we unveil a new translational platform for tracking disease progression in an animal model of a pediatric neurodegenerative disorder, CLN6-Batten disease. Instead of looking at a handful of parameters or a single "needle in a haystack", we embrace the idea that disease progression, in mice and patients alike, is a diverse phenomenon best characterized by a combination of relevant biomarkers. Thus, we employed a multi-modal quantitative approach where 144 parameters were longitudinally monitored to allow for individual variability. We use a range of noninvasive neuroimaging modalities and kinematic gait analysis, all methods that parallel those commonly used in the clinic, followed by a powerful statistical platform to identify key progressive anatomical and metabolic changes that correlate strongly with the progression of pathological and behavioral deficits. This innovative, highly sensitive platform can be used as a powerful tool for preclinical studies on neurodegenerative diseases, and provides proof-of-principle for use as a potentially translatable tool for clinicians in the future.