Cognitive deficits represent a hallmark of neurodegenerative diseases but evaluating their progression is complex. Most current evaluations involve lengthy paper-and-pencil tasks which are subject to learning effects dependent on the mode of response (motor or verbal), the countries’ language or the examiners. To address these limitations, we hypothesized that applying neuroscience principles may offer a fruitful alternative. We thus developed the SelfCog, a digitized battery that tests motor, executive, visuospatial, language and memory functions in 15 minutes. All cognitive functions are tested according to the same paradigm, and a randomization algorithm provides a new test at each assessment with a constant level of difficulty. Here, we assessed its validity, reliability, and sensitivity to detect decline in early-stage Huntington’s disease in a prospective and international multilingual study (France, United Kingdom, and Germany). Fifty-one out of 85 participants with Huntington’s disease and 40 of 52 healthy controls included at baseline were followed up for one year. Assessments included a comprehensive clinical assessment battery including currently standard cognitive assessments alongside the SelfCog. We estimated associations between each of the clinical assessments and SelfCog using Spearman’s correlation and proneness to retest effects and sensitivity to decline through Linear Mixed Models. Longitudinal effect sizes were estimated for each cognitive score. Voxel-based morphometry and Tract-Based Spatial Statistics analyses were conducted to assess the consistency between performance on the SelfCog and MRI 3D-T1 and diffusion weighted imaging in a subgroup which underwent MRI at baseline and after 12 months. The SelfCog detected the decline of patients with Huntington’s disease in a one-year follow-up period with satisfactory psychometric properties. Huntington’s disease patients are correctly differentiated from controls. The SelfCog showed larger effect sizes than the classical cognitive assessments. Its scores were associated with grey and white matter damage at baseline and over one year. Given its good performance in longitudinal analyses of the Huntington’s disease cohort, it should likely become a very useful tool for measuring cognition in Huntington’s disease in the future. It highlights the value of moving the field along the neuroscience principles and eventually applying them to the evaluation of all neurodegenerative diseases.

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