The PREDICT-HD study has collected an impressive sample of healthy subjects with the marker for Huntington's Disease (HD), and a sample of controls without that marker. The analyses of cognitive, psychiatric, and motor function over time in this sample has provided evidence for a prodromal phase preceding clinical diagnosis of HD: In many of the domains being measured, subjects who are more than 15 years away from their predicted age of onset show little or no difference from controls, while subjects in the 9- 1 year window are already showing significant if subtle declines, and within 9 years of the predicted age of onset are showing large losses. While this is an impactful clarification of the prodromal phase of HD, it needs to be further clarified; the predicted age of onset as calculated by the number of CAG repeats in the Huntington gene is very precise when the number of repeats is high, but can lead to a very large window of several decades when the number is low. In this ancillary study, we apply multivariate techniques such as parallel independent components analysis (pICA) to the combined structural and genetic imaging data from the PREDICT sample.
In Aim 1, using a cross-sectional technique we will identify the genetic profiles which covary with disease-related patterns of gray matter loss.
In Aim 2, using a longitudinal sample we will identify the brain structure and genetic profiles which correlate with loss of motor and cognitive function.
In Aim 3, we will ensure that the PREDICT team is trained on these techniques and can apply them to its ongoing data collection, and that the results are incorporated into their data management system. The conclusion of this proposal places the effect of the CAG repeats within an initial context of genetic influences from the larger genome. We leverage the brain imaging measures to identify relevant profiles of genotypes within the HTT genetic network which accelerate or provide resilience to disease onset.
Preceding the clinical diagnosis of Huntington's disease (HD) there is evidence for decrease in functional abilities in several domains, as well as in brain volumes. What is not understood is the genetic influences which modulate this loss, in the context of having genetic marker for HD. We use the PREDICT-HD data to determine genetic correlates of brain structure and loss of function, to reduce the uncertainty in disease progression particularly in subjects whose genetic marker indicates a wide window of multiple decades in which they might develop the clinical diagnosis.
|Espinoza, Flor A; Turner, Jessica A; Vergara, Victor M et al. (2018) Whole-Brain Connectivity in a Large Study of Huntington's Disease Gene Mutation Carriers and Healthy Controls. Brain Connect 8:166-178|
|Liu, Jingyu; Ciarochi, Jennifer; Calhoun, Vince D et al. (2018) Genetics Modulate Gray Matter Variation Beyond Disease Burden in Prodromal Huntington's Disease. Front Neurol 9:190|
|Misiura, Maria B; Lourens, Spencer; Calhoun, Vince D et al. (2017) Cognitive Control, Learning, and Clinical Motor Ratings Are Most Highly Associated with Basal Ganglia Brain Volumes in the Premanifest Huntington's Disease Phenotype. J Int Neuropsychol Soc 23:159-170|
|Bradstreet, Lauren E; Hecht, Erin E; King, Tricia Z et al. (2017) Associations between autistic traits and fractional anisotropy values in white matter tracts in a nonclinical sample of young adults. Exp Brain Res 235:259-267|
|Ciarochi, Jennifer Ashley; Calhoun, Vince D; Lourens, Spencer et al. (2016) Patterns of Co-Occurring Gray Matter Concentration Loss across the Huntington Disease Prodrome. Front Neurol 7:147|
|Kim, Regina E Y; Lourens, Spencer; Long, Jeffrey D et al. (2015) Preliminary analysis using multi-atlas labeling algorithms for tracing longitudinal change. Front Neurosci 9:242|
|Plis, Sergey M; Hjelm, Devon R; Salakhutdinov, Ruslan et al. (2014) Deep learning for neuroimaging: a validation study. Front Neurosci 8:229|