Resting-state connectivity stratifies premanifest Huntington’s disease by longitudinal cognitive decline rate

Abstract

Patient stratification is critical for the sensitivity of clinical trials at early stages of neurodegenerative disorders. in Huntington's disease (HD), genetic tests make cognitive, motor and brain imaging measurements possible before symptom manifestation (pre-HD). We evaluated pre-HD stratification models based on single visit resting-state functional MRi (rs-fMRi) data that assess observed longitudinal motor and cognitive change rates from the multisite Track-On HD cohort (74 pre-HD, 79 control participants). We computed longitudinal performance change on 10 tasks (including visits from the preceding tRAcK-HD study when available), as well as functional connectivity density (fcD) maps in single rs-fMRi visits, which showed high test-retest reliability. We assigned pre-HD subjects to subgroups of fast, intermediate, and slow change along single tasks or combinations of them, correcting for expectations based on aging; and trained FCD-based classifiers to distinguish fast-from slow-progressing individuals. For robustness, models were validated across imaging sites. Stratification models distinguished fast-from slow-changing participants and provided continuous assessments of decline applicable to the whole pre-HD population, relying on previously-neglected white matter functional signals. these results suggest novel correlates of early deterioration and a robust stratification strategy where a single MRI measurement provides an estimate of multiple ongoing longitudinal changes.

Classification of Huntington’s Disease Stage with Features Derived from Structural and Diffusion-Weighted Imaging

“…As such, supervised methods have been explored within the framework of prodromal and early-stage biomarkers in pathological neurodegeneration [ 35 ], with the ultimate goal of achieving a valuable assessment of novel neuroprotective therapies. Yet, only a few studies have focused on Huntington’s disease, either to differentiate between clinical groups from cross-sectional data [ 17 , 18 , 21 , 36 ], or to predict clinical outcomes from longitudinal data [ 37 , 38 ].…”

Baseline Neuroimaging Predicts Decline to Dementia From Amnestic Mild Cognitive Impairment

“…Model performance was evaluated across 10 permutations of two-level nested stratified cross-validation ( Lindquist et al, 2017 ; Varoquaux et al, 2017 ; Polosecki et al, 2020 ). To elaborate, we began by splitting the data into randomized folds and performed an outer cross-validation loop consisting of k iterations.…”

Cell therapy in Huntington's disease: Taking stock of past studies to move the field forward

“…90 When this study commenced, no cognitive assessments were universally accepted except the UHDRS cognitive score, which also lacks sensitivity, and TFC was discounted consider- 94 Likewise future trials should capitalize on volumetric MRI and connectivity measures. 95,96 Additional trial design measures might include enrichment strategies to recruit patients predicted to have faster rates of progression based on neuroimaging analyses, genetic polymorphisms, clinical profiles, or retest effects 85,[97][98][99][100] to increase the likelihood of demonstrating treatment efficacy with small cohorts over shorter durations.…”