B R AIN COMMUNICATIONS https://doi.org/10.1093/braincomms/fcac194 BRAIN COMMUNICATIONS 2022: Page 1 of 12 | 1 A novel framework to estimate cognitive impairment via finger interaction with digital devices Ashley A. Holmes, 1, * Shikha Tripathi, 2, * Emily Katz, 1 Ijah Mondesire-Crump, 1 Rahul Mahajan, 1,3 Aaron Ritter, 4 Teresa Arroyo-Gallego 1† and Luca Giancardo 2,† * These authors contributed equally to this work. † These authors contributed equally to this work. Measuring cognitive function is essential for characterizing brain health and tracking cognitive decline in Alzheimer’s Disease and other neurodegenerative conditions. Current tools to accurately evaluate cognitive impairment typically rely on a battery of question- naires administered during clinical visits which is essential for the acquisition of repeated measurements in longitudinal studies. Previous studies have shown that the remote data collection of passively monitored daily interaction with personal digital devices can measure motor signs in the early stages of synucleinopathies, as well as facilitate longitudinal patient assessment in the real-world scenario with high patient compliance. This was achieved by the automatic discovery of patterns in the time series of keystroke dy- namics, i.e. the time required to press and release keys, by machine learning algorithms. In this work, our hypothesis is that the typing patterns generated from user-device interaction may refect relevant features of the effects of cognitive impairment caused by neuro- degeneration. We use machine learning algorithms to estimate cognitive performance through the analysis of keystroke dynamic pat- terns that were extracted from mechanical and touchscreen keyboard use in a dataset of cognitively normal (n = 39, 51% male) and cognitively impaired subjects (n = 38, 60% male). These algorithms are trained and evaluated using a novel framework that integrates items from multiple neuropsychological and clinical scales into cognitive subdomains to generate a more holistic representation of multifaceted clinical signs. In our results, we see that these models based on typing input achieve moderate correlations with verbal memory, non-verbal memory and executive function subdomains [Spearman’s ρ between 0.54 (P < 0.001) and 0.42 (P < 0.001)] and a weak correlation with language/verbal skills [Spearman’s ρ 0.30 (P < 0.05)]. In addition, we observe a moderate correlation between our typing-based approach and the Total Montreal Cognitive Assessment score [Spearman’s ρ 0.48 (P < 0.001)]. Finally, we show that these machine learning models can perform better by using our subdomain framework that integrates the information from multiple neuropsychological scales as opposed to using the individual items that make up these scales. Our results support our hypothesis that typing patterns are able to refect the effects of neurodegeneration in mild cognitive impairment and Alzheimer’s disease and that this new subdomain framework both helps the development of machine learning models and improves their interpretability. 1 nQ Medical, Cambridge, MA 02142, USA 2 Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA 3 Division of Neurocritical Care, Department of Neurology, Brigham & Women’s Hospital, Boston, MA 02115, USA 4 Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland Clinic, Las Vegas, NV 89106, USA Correspondence to: Teresa Arroyo-Gallego nQ Medical, 245 Main Street 2nd Floor Cambridge, MA 02142, USA E-mail: gallego@nq-medical.com Received December 03, 2021. Revised May 11, 2022. Accepted July 25, 2022. Advance access publication July 28, 2022 © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Downloaded from https://academic.oup.com/braincomms/article/4/4/fcac194/6651151 by guest on 19 September 2022