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Riad R, Lunven M, Titeux H, Cao XN, Hamet Bagnou J, Lemoine L, Montillot J, Sliwinski A, Youssov K, Cleret de Langavant L, Dupoux E, Bachoud-Lévi AC. Predicting clinical scores in Huntington's disease: a lightweight speech test. J Neurol 2022; 269:5008-5021. [PMID: 35567614 PMCID: PMC9363375 DOI: 10.1007/s00415-022-11148-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/19/2022] [Accepted: 04/18/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES Using brief samples of speech recordings, we aimed at predicting, through machine learning, the clinical performance in Huntington's Disease (HD), an inherited Neurodegenerative disease (NDD). METHODS We collected and analyzed 126 samples of audio recordings of both forward and backward counting from 103 Huntington's disease gene carriers [87 manifest and 16 premanifest; mean age 50.6 (SD 11.2), range (27-88) years] from three multicenter prospective studies in France and Belgium (MIG-HD (ClinicalTrials.gov NCT00190450); BIO-HD (ClinicalTrials.gov NCT00190450) and Repair-HD (ClinicalTrials.gov NCT00190450). We pre-registered all of our methods before running any analyses, in order to avoid inflated results. We automatically extracted 60 speech features from blindly annotated samples. We used machine learning models to combine multiple speech features in order to make predictions at individual levels of the clinical markers. We trained machine learning models on 86% of the samples, the remaining 14% constituted the independent test set. We combined speech features with demographics variables (age, sex, CAG repeats, and burden score) to predict cognitive, motor, and functional scores of the Unified Huntington's disease rating scale. We provided correlation between speech variables and striatal volumes. RESULTS Speech features combined with demographics allowed the prediction of the individual cognitive, motor, and functional scores with a relative error from 12.7 to 20.0% which is better than predictions using demographics and genetic information. Both mean and standard deviation of pause durations during backward recitation and clinical scores correlated with striatal atrophy (Spearman 0.6 and 0.5-0.6, respectively). INTERPRETATION Brief and examiner-free speech recording and analysis may become in the future an efficient method for remote evaluation of the individual condition in HD and likely in other NDD.
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Affiliation(s)
- Rachid Riad
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France.,Laboratoire de Sciences Cognitives et Psycholinguistique, CNRS 8554, PSL University, 29 rue d'Ulm, 75005, Paris, France.,INRIA, Cognitive Machine Learning Team, 2 Rue Simone IFF, 75012, Paris, France.,EHESS, 54 boulevard Raspail, 75006, Paris, France
| | - Marine Lunven
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Hadrien Titeux
- Laboratoire de Sciences Cognitives et Psycholinguistique, CNRS 8554, PSL University, 29 rue d'Ulm, 75005, Paris, France.,INRIA, Cognitive Machine Learning Team, 2 Rue Simone IFF, 75012, Paris, France.,EHESS, 54 boulevard Raspail, 75006, Paris, France
| | - Xuan-Nga Cao
- Laboratoire de Sciences Cognitives et Psycholinguistique, CNRS 8554, PSL University, 29 rue d'Ulm, 75005, Paris, France.,INRIA, Cognitive Machine Learning Team, 2 Rue Simone IFF, 75012, Paris, France.,EHESS, 54 boulevard Raspail, 75006, Paris, France
| | - Jennifer Hamet Bagnou
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Laurie Lemoine
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Justine Montillot
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Agnes Sliwinski
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Katia Youssov
- Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Laurent Cleret de Langavant
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Emmanuel Dupoux
- Laboratoire de Sciences Cognitives et Psycholinguistique, CNRS 8554, PSL University, 29 rue d'Ulm, 75005, Paris, France.,INRIA, Cognitive Machine Learning Team, 2 Rue Simone IFF, 75012, Paris, France.,EHESS, 54 boulevard Raspail, 75006, Paris, France
| | - Anne-Catherine Bachoud-Lévi
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France. .,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France. .,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France. .,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France.
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Godkin FE, Turner E, Demnati Y, Vert A, Roberts A, Swartz RH, McLaughlin PM, Weber KS, Thai V, Beyer KB, Cornish B, Abrahao A, Black SE, Masellis M, Zinman L, Beaton D, Binns MA, Chau V, Kwan D, Lim A, Munoz DP, Strother SC, Sunderland KM, Tan B, McIlroy WE, Van Ooteghem K. Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease. J Neurol 2021; 269:2673-2686. [PMID: 34705114 PMCID: PMC8548705 DOI: 10.1007/s00415-021-10831-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD). METHODS Thirty-nine participants with CVD, Alzheimer's disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson's disease, or amyotrophic lateral sclerosis (median age 68 (45-83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance. RESULTS Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17-22 min/day), with significant differences between some locations (p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear (p < 0.001) and there was a small but significant increase in non-wear over the collection period (p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners. CONCLUSION A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.
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Affiliation(s)
- F Elizabeth Godkin
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Erin Turner
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Youness Demnati
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Adam Vert
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Angela Roberts
- School of Communication Sciences and Disorders, Elborn College, Western University, London, ON, Canada.,Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
| | - Richard H Swartz
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Kyle S Weber
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Vanessa Thai
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kit B Beyer
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Benjamin Cornish
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Agessandro Abrahao
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sandra E Black
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mario Masellis
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Lorne Zinman
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Malcolm A Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Vivian Chau
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Donna Kwan
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Andrew Lim
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Douglas P Munoz
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Kelly M Sunderland
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - William E McIlroy
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Karen Van Ooteghem
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
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