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Livingston G, Huntley J, Liu KY, Costafreda SG, Selbæk G, Alladi S, Ames D, Banerjee S, Burns A, Brayne C, Fox NC, Ferri CP, Gitlin LN, Howard R, Kales HC, Kivimäki M, Larson EB, Nakasujja N, Rockwood K, Samus Q, Shirai K, Singh-Manoux A, Schneider LS, Walsh S, Yao Y, Sommerlad A, Mukadam N. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet 2024; 404:572-628. [PMID: 39096926 DOI: 10.1016/s0140-6736(24)01296-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 04/08/2024] [Accepted: 06/16/2024] [Indexed: 08/05/2024]
Affiliation(s)
- Gill Livingston
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK.
| | - Jonathan Huntley
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Kathy Y Liu
- Division of Psychiatry, University College London, London, UK
| | - Sergi G Costafreda
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK
| | - Geir Selbæk
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway; Geriatric Department, Oslo University Hospital, Oslo, Norway
| | - Suvarna Alladi
- National Institute of Mental Health and Neurosciences, Bangalore, India
| | - David Ames
- National Ageing Research Institute, Melbourne, VIC, Australia; University of Melbourne Academic Unit for Psychiatry of Old Age, Melbourne, VIC, Australia
| | - Sube Banerjee
- Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | | | - Carol Brayne
- Cambridge Public Health, University of Cambridge, Cambridge, UK
| | - Nick C Fox
- The Dementia Research Centre, Department of Neurodegenerative Disease, University College London, London, UK
| | - Cleusa P Ferri
- Health Technology Assessment Unit, Hospital Alemão Oswaldo Cruz, São Paulo, Brazil; Department of Psychiatry, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Laura N Gitlin
- College of Nursing and Health Professions, AgeWell Collaboratory, Drexel University, Philadelphia, PA, USA
| | - Robert Howard
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK
| | - Helen C Kales
- Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California, Sacramento, CA, USA
| | - Mika Kivimäki
- Division of Psychiatry, University College London, London, UK; Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Eric B Larson
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Noeline Nakasujja
- Department of Psychiatry College of Health Sciences, Makerere University College of Health Sciences, Makerere University, Kampala City, Uganda
| | - Kenneth Rockwood
- Centre for the Health Care of Elderly People, Geriatric Medicine, Dalhousie University, Halifax, NS, Canada
| | - Quincy Samus
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins Bayview, Johns Hopkins University, Baltimore, MD, USA
| | - Kokoro Shirai
- Graduate School of Social and Environmental Medicine, Osaka University, Osaka, Japan
| | - Archana Singh-Manoux
- Division of Psychiatry, University College London, London, UK; Université Paris Cité, Inserm U1153, Paris, France
| | - Lon S Schneider
- Department of Psychiatry and the Behavioural Sciences and Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Sebastian Walsh
- Cambridge Public Health, University of Cambridge, Cambridge, UK
| | - Yao Yao
- China Center for Health Development Studies, School of Public Health, Peking University, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Andrew Sommerlad
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK
| | - Naaheed Mukadam
- Division of Psychiatry, University College London, London, UK; Camden and Islington NHS Foundation Trust, London, UK
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2
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Xu Q, Kim Y, Chung K, Schulz P, Gottlieb A. Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers. JMIR Form Res 2024; 8:e55575. [PMID: 39024003 PMCID: PMC11294783 DOI: 10.2196/55575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/15/2024] [Accepted: 06/08/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI). OBJECTIVE This study aimed to use data collected from fitness trackers to predict MCI status. METHODS In this pilot study, fitness trackers were worn by 20 participants: 12 patients with MCI and 8 age-matched controls. We collected physical activity, heart rate, and sleep data from each participant for up to 1 month and further developed a machine learning model to predict MCI status. RESULTS Our machine learning model was able to perfectly separate between MCI and controls (area under the curve=1.0). The top predictive features from the model included peak, cardio, and fat burn heart rate zones; resting heart rate; average deep sleep time; and total light activity time. CONCLUSIONS Our results suggest that a longitudinal digital biomarker differentiates between controls and patients with MCI in a very cost-effective and noninvasive way and hence may be very useful for identifying patients with very early AD who can benefit from clinical trials and new, disease-modifying therapies.
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Affiliation(s)
- Qidi Xu
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Yejin Kim
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Karen Chung
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Paul Schulz
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Assaf Gottlieb
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States
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3
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Samuel G, Anderson GM, Lucivero F, Lucassen A. Why digital innovation may not reduce healthcare's environmental footprint. BMJ 2024; 385:e078303. [PMID: 38830688 DOI: 10.1136/bmj-2023-078303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Affiliation(s)
- Gabrielle Samuel
- Department of Global Health and Social Medicine, King's College London, London, UK
| | - Geoffrey M Anderson
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | | | - Anneke Lucassen
- Centre for Personalised Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
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4
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Lee JY, Lim MCX, Koh RY, Tsen MT, Chye SM. Blood-based therapies to combat neurodegenerative diseases. Metab Brain Dis 2024; 39:985-1004. [PMID: 38842660 DOI: 10.1007/s11011-024-01368-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 05/31/2024] [Indexed: 06/07/2024]
Abstract
Neurodegeneration, known as the progressive loss of neurons in terms of their structure and function, is the principal pathophysiological change found in the majority of brain-related disorders. Ageing has been considered the most well-established risk factor in most common neurodegenerative diseases, such as Parkinson's disease (PD) and Alzheimer's disease (AD). There is currently no effective treatment or cure for these diseases; the approved therapeutic options to date are only for palliative care. Ageing and neurodegenerative diseases are closely intertwined; reversing the aspects of brain ageing could theoretically mitigate age-related neurodegeneration. Ever since the regenerative properties of young blood on aged tissues came to light, substantial efforts have been focused on identifying and characterizing the circulating factors in the young and old systemic milieu that may attenuate or accentuate brain ageing and neurodegeneration. Later studies discovered the superiority of old plasma dilution in tissue rejuvenation, which is achieved through a molecular reset of the systemic proteome. These findings supported the use of therapeutic blood exchange for the treatment of degenerative diseases in older individuals. The first objective of this article is to explore the rejuvenating properties of blood-based therapies in the ageing brains and their therapeutic effects on AD. Then, we also look into the clinical applications, various limitations, and challenges associated with blood-based therapies for AD patients.
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Affiliation(s)
- Jia Yee Lee
- School of Health Science, International Medical University, 57000, Kuala Lumpur, Malaysia
| | - Mervyn Chen Xi Lim
- School of Health Science, International Medical University, 57000, Kuala Lumpur, Malaysia
| | - Rhun Yian Koh
- Division of Applied Biomedical Science and Biotechnology, School of Health Science, International Medical University, No. 126, Jalan Jalil Perkasa 19, Bukit Jalil, 57000, Kuala Lumpur, Malaysia
| | - Min Tze Tsen
- Division of Applied Biomedical Science and Biotechnology, School of Health Science, International Medical University, No. 126, Jalan Jalil Perkasa 19, Bukit Jalil, 57000, Kuala Lumpur, Malaysia
| | - Soi Moi Chye
- Division of Applied Biomedical Science and Biotechnology, School of Health Science, International Medical University, No. 126, Jalan Jalil Perkasa 19, Bukit Jalil, 57000, Kuala Lumpur, Malaysia.
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Bruinsma J, Visser LNC, Abaci A, Rosenberg A, Diaz A, Hanke S, Crutzen R, Mangialasche F, Kivipelto M, Thunborg C. Social activities in multidomain dementia prevention interventions: insights from practice and a blueprint for the future. Front Psychiatry 2024; 15:1386688. [PMID: 38832328 PMCID: PMC11146203 DOI: 10.3389/fpsyt.2024.1386688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 05/01/2024] [Indexed: 06/05/2024] Open
Abstract
Introduction Social activities are important for health and act as a driver of cognitive reserve during aging. In this perspective paper, we describe challenges and outline future (research) endeavors to establish better operationalization of social activities in multidomain interventions to prevent dementia. Body We first address the lack of conceptual clarity, which makes it difficult to measure engagement in social activities. Second, drawing from our experience with the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER), we discuss social activities in multidomain dementia prevention interventions. Using qualitative data from the Multimodal Preventive Trial for Alzheimer's Disease (MIND-ADmini), we reflect on participant experiences with social activities. Third, we address the potential and challenges of digital solutions in promoting social activities in interventions for dementia prevention. Finally, we share insights from a workshop on digital technology, where we consulted with individuals with and without cognitive impairment who have been involved in three European projects (i.e., EU-FINGERS, Multi-MeMo, and LETHE). Discussion Based on these insights, we advocate for research that strengthens and accelerates the integration of social activities into multidomain interventions for dementia prevention. We propose several ways to achieve this: (a) by conducting mixed methods research to formulate a broadly accepted definition and instructions to measure social activities; (b) by focusing on promoting engagement in social activities beyond the intervention setting; and (c) by exploring the needs and preferences of older adults towards digitally-supported interventions and co-design of new technologies that enrich in-person social activities.
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Affiliation(s)
- Jeroen Bruinsma
- Department of Health Promotion, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Leonie N. C. Visser
- Department of Medical Psychology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
- Public Health Research Institute, Quality of Care/Personalized Medicine, Amsterdam, Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam University Medical Center (UMC), Amsterdam, Netherlands
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Alara Abaci
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Anna Rosenberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Ana Diaz
- Alzheimer Europe, Senningerberg, Luxembourg
| | - Sten Hanke
- Department of Applied Informatics, Institute of eHealth, FH Joanneum - University of Applied Sciences, Graz, Austria
| | - Rik Crutzen
- Department of Health Promotion, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Francesca Mangialasche
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Medical Unit Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Miia Kivipelto
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Medical Unit Aging, Karolinska University Hospital, Stockholm, Sweden
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- The Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, United Kingdom
| | - Charlotta Thunborg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Medical Unit Aging, Karolinska University Hospital, Stockholm, Sweden
- Department of Caring Sciences, Faculty of Health and Occupational Studies, University of Gävle, Gävle, Sweden
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DesRuisseaux LA, Gereau Mora M, Suchy Y. Computerized assessment of executive functioning: Validation of the CNS Vital Signs executive functioning scores in a sample of community-dwelling older adults. Clin Neuropsychol 2024:1-23. [PMID: 38763890 DOI: 10.1080/13854046.2024.2354953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 05/09/2024] [Indexed: 05/21/2024]
Abstract
Objective: Computerized assessment of cognitive functioning has gained significant popularity over recent years, yet options for clinical assessment of executive functioning (EF) are lacking. One computerized testing platform, CNS Vital Signs (CNS-VS), offers tests designed to measure EF but requires further validation. The goal of the present study was to validate CNS-VS executive scores against standard clinical measures of EF. We also sought to determine whether a modified CNS-VS composite score that included variables purported to measure inhibition, switching, and working memory would outperform the currently available CNS-VS Executive Function Index. Method: A sample of 73 cognitively healthy older adults completed four tests from the Delis-Kaplan Executive Function System, the Digit Span subtest from the Wechsler Adult Intelligence Scale-fourth edition, and three CNS-VS tasks purported to measure inhibition, switching, and working memory. Results: Performances on the CNS-VS tests were predicted by performances on standard paper-and-pencil measures. Although the currently available CNS-VS Executive Function Index predicted unique variance in a well-validated paper-and-pencil EF composite score, our Modified CNS-VS EF composite accounted for unique variance above and beyond the original CNS-VS Executive Function Index, while the reverse was not true. Conclusions: The present results support the construct validity of CNS-VS EF tests but also suggest that modifications to their current composite scores would improve the prediction of EF performance.
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Affiliation(s)
| | | | - Yana Suchy
- Department of Psychology, University of Utah, Salt Lake City, UT, USA
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Piera-Jiménez J, Dedeu T, Pagliari C, Trupec T. Strengthening primary health care in Europe with digital solutions. Aten Primaria 2024; 56:102904. [PMID: 38692228 PMCID: PMC11070233 DOI: 10.1016/j.aprim.2024.102904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 05/03/2024] Open
Abstract
This article provides an in-depth analysis of digital transformation in European primary healthcare (PHC). It assesses the impact of digital technology on healthcare delivery and management, highlighting variations in digital maturity across Europe. It emphasizes the significance of digital tools, especially during the COVID-19 pandemic, in enhancing accessibility and efficiency in healthcare. It discusses the integration of telehealth, remote monitoring, and e-health solutions, showcasing their role in patient empowerment and proactive care. Examples are included from various countries, such as Greece's ePrescription system, Lithuania's adoption of remote consultations, Spain's use of risk stratification solutions, and the Netherlands' advanced use of telemonitoring solutions, to illustrate the diverse implementation of digital solutions in PHC. The article offers insights into the challenges and opportunities of embedding digital technologies into a multidisciplinary healthcare framework, pointing towards future directions for PHC in Europe.
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Affiliation(s)
- Jordi Piera-Jiménez
- Catalan Health Service, Barcelona, Spain; Digitalization for the Sustainability of the Healthcare System (DS3), IDIBELL, Barcelona, Spain; Faculty of Informatics, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Barcelona, Spain.
| | - Toni Dedeu
- WHO European Centre for Primary Health Centre, Almaty, Kazakhstan
| | - Claudia Pagliari
- Usher Institute and Edinburgh Global Health Academy, The University of Edinburgh, Edinburgh, United Kingdom
| | - Tatjana Trupec
- Care and Public Health Research Institute, Maastricht University, The Netherlands; School of Medicine, University of Zagreb, Croatia
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8
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Harris C, Tang Y, Birnbaum E, Cherian C, Mendhe D, Chen MH. Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies. Arch Clin Neuropsychol 2024; 39:290-304. [PMID: 38520381 DOI: 10.1093/arclin/acae016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 02/16/2024] [Indexed: 03/25/2024] Open
Abstract
Compared with other health disciplines, there is a stagnation in technological innovation in the field of clinical neuropsychology. Traditional paper-and-pencil tests have a number of shortcomings, such as low-frequency data collection and limitations in ecological validity. While computerized cognitive assessment may help overcome some of these issues, current computerized paradigms do not address the majority of these limitations. In this paper, we review recent literature on the applications of novel digital health approaches, including ecological momentary assessment, smartphone-based assessment and sensors, wearable devices, passive driving sensors, smart homes, voice biomarkers, and electronic health record mining, in neurological populations. We describe how each digital tool may be applied to neurologic care and overcome limitations of traditional neuropsychological assessment. Ethical considerations, limitations of current research, as well as our proposed future of neuropsychological practice are also discussed.
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Affiliation(s)
- Che Harris
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
| | - Yingfei Tang
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
| | - Eliana Birnbaum
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Christine Cherian
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Dinesh Mendhe
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Michelle H Chen
- Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
- Department of Neurology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA
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9
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Yamada Y, Shinkawa K, Kobayashi M, Nemoto M, Ota M, Nemoto K, Arai T. Distinct eye movement patterns to complex scenes in Alzheimer's disease and Lewy body disease. Front Neurosci 2024; 18:1333894. [PMID: 38646608 PMCID: PMC11026598 DOI: 10.3389/fnins.2024.1333894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 03/22/2024] [Indexed: 04/23/2024] Open
Abstract
Background Alzheimer's disease (AD) and Lewy body disease (LBD), the two most common causes of neurodegenerative dementia with similar clinical manifestations, both show impaired visual attention and altered eye movements. However, prior studies have used structured tasks or restricted stimuli, limiting the insights into how eye movements alter and differ between AD and LBD in daily life. Objective We aimed to comprehensively characterize eye movements of AD and LBD patients on naturalistic complex scenes with broad categories of objects, which would provide a context closer to real-world free viewing, and to identify disease-specific patterns of altered eye movements. Methods We collected spontaneous viewing behaviors to 200 naturalistic complex scenes from patients with AD or LBD at the prodromal or dementia stage, as well as matched control participants. We then investigated eye movement patterns using a computational visual attention model with high-level image features of object properties and semantic information. Results Compared with matched controls, we identified two disease-specific altered patterns of eye movements: diminished visual exploration, which differentially correlates with cognitive impairment in AD and with motor impairment in LBD; and reduced gaze allocation to objects, attributed to a weaker attention bias toward high-level image features in AD and attributed to a greater image-center bias in LBD. Conclusion Our findings may help differentiate AD and LBD patients and comprehend their real-world visual behaviors to mitigate the widespread impact of impaired visual attention on daily activities.
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Affiliation(s)
- Yasunori Yamada
- Digital Health, IBM Research, Tokyo, Japan
- Department of Psychiatry, Division of Clinical Medicine, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | | | - Masatomo Kobayashi
- Digital Health, IBM Research, Tokyo, Japan
- Department of Psychiatry, Division of Clinical Medicine, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Miyuki Nemoto
- Department of Psychiatry, Division of Clinical Medicine, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Miho Ota
- Department of Psychiatry, Division of Clinical Medicine, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Kiyotaka Nemoto
- Department of Psychiatry, Division of Clinical Medicine, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Tetsuaki Arai
- Department of Psychiatry, Division of Clinical Medicine, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
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10
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Mattke S, Tang Y, Hanson M. Expected wait times for access to a disease-modifying Alzheimer's treatment in England: A modelling study. J Health Serv Res Policy 2024; 29:69-75. [PMID: 37931615 DOI: 10.1177/13558196231211141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
OBJECTIVES We previously analysed the preparedness to deliver a disease-modifying Alzheimer's treatment in the United Kingdom and predicted substantial wait times. This study updates the prediction for the National Health Service (NHS) in England, using an improved model and newer data. METHODS We reviewed published data on capacity for diagnosis of cognitive impairment combined with expert input and constructed a model for wait times to access from 2023 to 2043. The model tracks patients from initial evaluation in primary care, cognitive testing by a dementia specialist, confirmatory biomarker testing with positron emission tomography (PET) scans or examination of cerebrospinal fluid and infusion delivery. Capacity for specialist visits and PET scans are assumed to be capacity constrained, and cerebrospinal fluid testing and infusion delivery to be scalable. RESULTS Capacity constraints were projected to result in substantial wait times: patients referred to specialists based on a brief cognitive test, which is the current standard of care, would expect an overall initial wait times of 56 months in 2023, increasing to 129 months in 2029 and then falling slowly to around 100 months. Use of a blood test for the confirmation of Alzheimer's pathology as an additional triage step, would reduce wait times to around 17 to 25 months. DISCUSSION The NHS England lacks capacity to provide timely access to a disease-modifying treatment, which is estimated to result in significant wait times and potentially avoidable disease progression. Better diagnostic tools at initial evaluation may reduce delays.
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Affiliation(s)
- Soeren Mattke
- University of Southern California, Los Angeles, CA, United States
| | - Yu Tang
- University of Southern California, Los Angeles, CA, United States
| | - Mark Hanson
- University of Southern California, Los Angeles, CA, United States
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11
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Benge JF, Aguirre A, Scullin MK, Kiselica A, Hilsabeck RC, Paydarfar D, Thomaz E, Douglas M. Digital Methods for Performing Daily Tasks Among Older Adults: An Initial Report of Frequency of Use and Perceived Utility. Exp Aging Res 2024; 50:133-154. [PMID: 36739553 PMCID: PMC11250545 DOI: 10.1080/0361073x.2023.2172950] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 01/22/2023] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Digital technologies permit new ways of performing instrumental activities of daily living (iADLs) for older adults, but these approaches are not usually considered in existing iADL measures. The current study investigated how a sample of older adults report using digital versus analog approaches for iADLs. METHOD 248 older adults completed the Digital and Analog Daily Activities Survey, a newly developed measure of how an individual performs financial, navigation, medication, and other iADLs. RESULTS The majority of participants reported regularly using digital methods for some iADLs, such as paying bills (67.7%) and using GPS (67.7%). Low digital adopters were older than high adopters (F(2, 245) = 12.24, p < .001), but otherwise the groups did not differ in terms of gender, years of education, or history of neurological disorders. Participants who used digital methods relatively more than analog methods reported greater levels of satisfaction with their approach and fewer daily errors. CONCLUSIONS Many older adults have adopted digital technologies for supporting daily tasks, which suggests limitations to the validity of current iADL assessments. By capitalizing on existing habits and enriching environments with new technologies, there are opportunities to promote technological reserve in older adults in a manner that sustains daily functioning.
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Affiliation(s)
- Jared F Benge
- Department of Neurology, University of Texas at Austin, Austin, TX, USA
- Mulva Clinic for the Neurosciences, University of Texas at Austin, Austin, TX, USA
| | - Alyssa Aguirre
- Department of Neurology, University of Texas at Austin, Austin, TX, USA
- Mulva Clinic for the Neurosciences, University of Texas at Austin, Austin, TX, USA
| | - Michael K Scullin
- Department of Psychology and Neurosciences, Baylor University, Waco, TX, USA
| | - Andrew Kiselica
- Department of Health Psychology, University of Missouri, Columbia, MO, USA
| | - Robin C Hilsabeck
- Department of Neurology, University of Texas at Austin, Austin, TX, USA
- Mulva Clinic for the Neurosciences, University of Texas at Austin, Austin, TX, USA
| | - David Paydarfar
- Department of Neurology, University of Texas at Austin, Austin, TX, USA
- Mulva Clinic for the Neurosciences, University of Texas at Austin, Austin, TX, USA
| | - Edison Thomaz
- Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, USA
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12
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Masanneck L, Pawlitzki MG, Meuth SG. [Digital medicine in neurological research-Between hype and evidence]. DER NERVENARZT 2024; 95:230-235. [PMID: 38095660 DOI: 10.1007/s00115-023-01581-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/31/2023] [Indexed: 03/06/2024]
Abstract
BACKGROUND The rapid advancement of digital medicine and health technologies in neurology offers both significant potential and challenges. This article outlines fundamental aspects of digital medicine related to neurological research and highlights application examples of digital technologies in neurological research. AIM To provide a comprehensive overview of current digital developments in neurology and their impact on neurological research. MATERIAL AND METHODS In this narrative review articles from various sources and references related to digital medicine and health technologies in neurology were compiled and analyzed. RESULTS AND DISCUSSION The data presented indicate that digital health technologies and digital therapeutics have the potential to decisively shape neurological care and research; however, it is emphasized that a critical evaluation and evidence-based approach to these technologies are essential to determine their actual value in neurology.
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Affiliation(s)
- Lars Masanneck
- Klinik für Neurologie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland.
- Hasso-Plattner-Institut, Potsdam, Deutschland.
| | - Marc G Pawlitzki
- Klinik für Neurologie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
| | - Sven G Meuth
- Klinik für Neurologie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland.
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Chudzik A, Śledzianowski A, Przybyszewski AW. Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases. SENSORS (BASEL, SWITZERLAND) 2024; 24:1572. [PMID: 38475108 DOI: 10.3390/s24051572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/16/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
Neurodegenerative diseases (NDs) such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores "digital biomarkers" (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.
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Affiliation(s)
- Artur Chudzik
- Polish-Japanese Academy of Information Technology, Faculty of Computer Science, 86 Koszykowa Street, 02-008 Warsaw, Poland
| | - Albert Śledzianowski
- Polish-Japanese Academy of Information Technology, Faculty of Computer Science, 86 Koszykowa Street, 02-008 Warsaw, Poland
| | - Andrzej W Przybyszewski
- Polish-Japanese Academy of Information Technology, Faculty of Computer Science, 86 Koszykowa Street, 02-008 Warsaw, Poland
- UMass Chan Medical School, Department of Neurology, 65 Lake Avenue, Worcester, MA 01655, USA
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Wang J, Zhou Z, Cheng S, Zhou L, Sun X, Song Z, Wu Z, Lu J, Qin Y, Wang Y. Dual-task turn velocity - a novel digital biomarker for mild cognitive impairment and dementia. Front Aging Neurosci 2024; 16:1304265. [PMID: 38476660 PMCID: PMC10927999 DOI: 10.3389/fnagi.2024.1304265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 02/13/2024] [Indexed: 03/14/2024] Open
Abstract
Background Disorders associated with cognitive impairment impose a significant burden on both families and society. Previous studies have indicated that gait characteristics under dual-task as reliable markers of early cognitive impairment. Therefore, digital gait detection has great potential for future cognitive screening. However, research on digital biomarkers based on smart devices to identify cognitive impairment remains limited. The aim of this study is to explore digital gait biomarkers by utilizing intelligent wearable devices for discriminating mild cognitive impairment and dementia. Methods This study included 122 subjects (age: 74.7 ± 7.7 years) diagnosed with normal cognition (NC, n = 38), mild cognitive impairment (MCI, n = 42), or dementia (n = 42). All subjects underwent comprehensive neuropsychological assessments and cranial Magnetic Resonance Imaging (MRI). Gait parameters were collected using validated wearable devices in both single-task and dual-task (DT). We analyzed the ability of gait variables to predict MCI and dementia, and examined the correlations between specific DT-gait parameters and sub-cognitive functions as well as hippocampal atrophy. Results Our results demonstrated that dual-task could significantly improve the ability to predict cognitive impairment based on gait parameters such as gait speed (GS) and stride length (SL). Additionally, we discovered that turn velocity (TV and DT-TV) can be a valuable novel digital marker for predicting MCI and dementia, for identifying MCI (DT-TV: AUC = 0.801, sensitivity 0.738, specificity 0.842), and dementia (DT-TV: AUC = 0.923, sensitivity 0.857, specificity 0.842). The correlation analysis and linear regression analysis revealed a robust association between DT-TV and memory function, as well as the hippocampus atrophy. Conclusion This study presents a novel finding that DT-TV could accurately identify varying degrees of cognitive impairment. DT-TV is strongly correlated with memory function and hippocampus shrinkage, suggests that it can accurately reflect changes in cognitive function. Therefore, DT-TV could serve as a novel and effective digital biomarker for discriminating cognitive impairment.
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Affiliation(s)
- Jing Wang
- Department of Geriatrics, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zheping Zhou
- Department of Geriatrics, Affiliated Changshu Hospital of Nantong University, Changshu, China
| | - Shanshan Cheng
- Department of Geriatrics, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Li Zhou
- Department of Nutritional Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaoou Sun
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Ziyang Song
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhiwei Wu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jinhua Lu
- Department of Geriatrics, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yiren Qin
- Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yueju Wang
- Department of Geriatrics, The First Affiliated Hospital of Soochow University, Suzhou, China
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Rykov YG, Patterson MD, Gangwar BA, Jabar SB, Leonardo J, Ng KP, Kandiah N. Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment. BMC Med 2024; 22:36. [PMID: 38273340 PMCID: PMC10809621 DOI: 10.1186/s12916-024-03252-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/09/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI. METHODS We used the dataset collected from a 10-week single-arm clinical trial in older adults (50-70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors' data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using "leave-one-subject-out" and "leave-one-interval-out" cross-validation. RESULTS The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61. CONCLUSIONS Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.
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Affiliation(s)
| | | | | | | | - Jacklyn Leonardo
- Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Kok Pin Ng
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Nagaendran Kandiah
- Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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Popp Z, Low S, Igwe A, Rahman MS, Kim M, Khan R, Oh E, Kumar A, De Anda‐Duran I, Ding H, Hwang PH, Sunderaraman P, Shih LC, Lin H, Kolachalama VB, Au R. Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research. J Am Heart Assoc 2024; 13:e031247. [PMID: 38226518 PMCID: PMC10926806 DOI: 10.1161/jaha.123.031247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.
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Affiliation(s)
- Zachary Popp
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Spencer Low
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Akwaugo Igwe
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Md Salman Rahman
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Minzae Kim
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Raiyan Khan
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Emily Oh
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ankita Kumar
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ileana De Anda‐Duran
- Department of EpidemiologyTulane University School of Public Health & Tropical MedicineNew OrleansLAUSA
| | - Huitong Ding
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Phillip H. Hwang
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Preeti Sunderaraman
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Ludy C. Shih
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMA
| | - Vijaya B. Kolachalama
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Rhoda Au
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
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Erickson CM, Wexler A, Largent EA. Alzheimer's in the modern age: Ethical challenges in the use of digital monitoring to identify cognitive changes. Inform Health Soc Care 2024; 49:1-13. [PMID: 38116960 PMCID: PMC11001527 DOI: 10.1080/17538157.2023.2294203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Pushes toward earlier detection of Alzheimer's disease (AD)-related cognitive changes are creating interest in leveraging technologies, like cellphones, that are already widespread and well-equipped for data collection to facilitate digital monitoring for AD. Studies are ongoing to identify and validate potential "digital biomarkers" that might indicate someone has or is at risk of developing AD dementia. Digital biomarkers for AD have potential as a tool in aiding more timely diagnosis, though more robust research is needed to support their validity and utility. While there are grounds for optimism, leveraging digital monitoring and informatics for cognitive changes also poses ethical challenges, related to topics such as algorithmic bias, consent, and data privacy and security. As we confront the modern era of Alzheimer's disease, individuals, companies, regulators and policymakers alike must prepare for a future in which our day-to-day interactions with technology in our daily life may identify AD-related cognitive changes.
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Affiliation(s)
- Claire M Erickson
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Anna Wexler
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Emily A Largent
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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Mattke S, Gustavsson A, Jacobs L, Kern S, Palmqvist S, Eriksdotter M, Skoog I, Winblad B, Wimo A, Jönsson L. Estimates of Current Capacity for Diagnosing Alzheimer's Disease in Sweden and the Need to Expand Specialist Numbers. J Prev Alzheimers Dis 2024; 11:155-161. [PMID: 38230728 PMCID: PMC10995070 DOI: 10.14283/jpad.2023.94] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 06/05/2023] [Indexed: 01/18/2024]
Abstract
BACKGROUND The emergence of disease-modifying Alzheimer's (AD) treatments provides new hope to patients and families but concerns have been raised about the preparedness of healthcare systems to provide timely access to such treatments because of a combination of a complex diagnostic process and a large prevalent pool. OBJECTIVES We assess the preparedness of Sweden, a high-income country known for its dementia-friendly policies, to diagnose AD patients eligible for treatment within a six-month window, given current capacity for specialist evaluations and biomarker testing. We calculate the investment requirements for Sweden to achieve this target over a timeframe of 20 years. DESIGN Desk research to identify data for population, mortality, disease burden, cost of services and current capacity, expert consultation to inform assumptions about patient journey, and use of a Markov model to predict waiting times. The model simulates the patients' journey through different evaluation stages: initial evaluation by a primary care specialist, neurocognitive testing by an AD specialist, and confirmatory biomarker testing with PET scanning or cerebrospinal fluid (CSF) testing. The model assumes specialist appointments and PET scans are capacity constrained, and patients progress from cognitively normal to MCI and from MCI to dementia in the resulting waiting times. MEASUREMENTS Projected waiting times for diagnosis of eligibility for disease-modifying Alzheimer's treatment from 2023 to 2042 assuming current capacity, assuming 20% of Swedish residents aged 60 years and above would seek an evaluation for cognitive decline. Investments required to scale capacity up to reach target of providing diagnosis within six months on average. RESULTS Initial average waiting times for AD specialist appointments would be around 21 months in 2023 and remain around 55 months through 2042, as demand would continue to outstrip supply throughout the 20-year model horizon. Waiting times for biomarker testing would be stable at less than four weeks, as patients would be held up in the queue for their first specialist consultations, and use of CSF testing is widely accepted in Sweden. An additional 25% of AD specialists would have to be added above the current growth trend to reduce waiting times to less than 6 months at an average annual cost of approximately 805 million SEK. The increased cost of volume of biomarker testing would amount to about 106 million SEK per year. CONCLUSIONS At current capacity, the Swedish healthcare system is unable to provide timely diagnosis of patients eligible for disease-modifying AD treatment. Although future diagnostic technologies, such as digital cognitive assessments and blood tests for the AD pathology, might decrease demand for capacity-constrained services, substantial investments will be required to meet a target of less than six months of waiting time for a diagnosis.
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Affiliation(s)
- S Mattke
- Soeren Mattke, University of Southern California, 635 Downey Way, #505N, Los Angeles, CA 90089, Mobile: +1 202 468 5797,
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Gupta N, Kasula V, Sanmugananthan P, Panico N, Dubin AH, Sykes DAW, D'Amico RS. SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies. World Neurosurg X 2024; 21:100247. [PMID: 38033718 PMCID: PMC10682285 DOI: 10.1016/j.wnsx.2023.100247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/24/2023] [Indexed: 12/02/2023] Open
Abstract
Background/objective Recent technological advances have allowed for the development of smart wearable devices (SmartWear) which can be used to monitor various aspects of patient healthcare. These devices provide clinicians with continuous biometric data collection for patients in both inpatient and outpatient settings. Although these devices have been widely used in fields such as cardiology and orthopedics, their use in the field of neurosurgery and neurology remains in its infancy. Methods A comprehensive literature search for the current and future applications of SmartWear devices in the above conditions was conducted, focusing on outpatient monitoring. Findings Through the integration of sensors which measure parameters such as physical activity, hemodynamic variables, and electrical conductivity - these devices have been applied to patient populations such as those at risk for stroke, suffering from epilepsy, with neurodegenerative disease, with spinal cord injury and/or recovering from neurosurgical procedures. Further, these devices are being tested in various clinical trials and there is a demonstrated interest in the development of new technologies. Conclusion This review provides an in-depth evaluation of the use of SmartWear in selected neurological diseases and neurosurgical applications. It is clear that these devices have demonstrated efficacy in a variety of neurological and neurosurgical applications, however challenges such as data privacy and management must be addressed.
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Affiliation(s)
- Nithin Gupta
- Campbell University School of Osteopathic Medicine, Lillington, NC, USA
| | - Varun Kasula
- Campbell University School of Osteopathic Medicine, Lillington, NC, USA
| | | | | | - Aimee H. Dubin
- Campbell University School of Osteopathic Medicine, Lillington, NC, USA
| | - David AW. Sykes
- Department of Neurosurgery, Duke University Medical School, Durham, NC, USA
| | - Randy S. D'Amico
- Lenox Hill Hospital, Department of Neurosurgery, New York, NY, USA
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Robin J, Xu M, Kaufman LD, Simpson W, McCaughey S, Tatton N, Wolfus C, Ward M. Development of a Speech-based Composite Score for Remotely Quantifying Language Changes in Frontotemporal Dementia. Cogn Behav Neurol 2023; 36:237-248. [PMID: 37878468 PMCID: PMC10683975 DOI: 10.1097/wnn.0000000000000356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 04/07/2023] [Indexed: 10/27/2023]
Abstract
BACKGROUND Changes to speech and language are common symptoms across different subtypes of frontotemporal dementia (FTD). These changes affect the ability to communicate, impacting everyday functions. Accurately assessing these changes may help clinicians to track disease progression and detect response to treatment. OBJECTIVE To determine which aspects of speech show significant change over time and to develop a novel composite score for tracking speech and language decline in individuals with FTD. METHOD We recruited individuals with FTD to complete remote digital speech assessments based on a picture description task. Speech samples were analyzed to derive acoustic and linguistic measures of speech and language, which were tested for longitudinal change over the course of the study and were used to compute a novel composite score. RESULTS Thirty-six (16 F, 20 M; M age = 61.3 years) individuals were enrolled in the study, with 27 completing a follow-up assessment 12 months later. We identified eight variables reflecting different aspects of language that showed longitudinal decline in the FTD clinical syndrome subtypes and developed a novel composite score based on these variables. The resulting composite score demonstrated a significant effect of change over time, high test-retest reliability, and a correlation with standard scores on various other speech tasks. CONCLUSION Remote digital speech assessments have the potential to characterize speech and language abilities in individuals with FTD, reducing the burden of clinical assessments while providing a novel measure of speech and language abilities that is sensitive to disease and relevant to everyday function.
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Affiliation(s)
- Jessica Robin
- Winterlight Labs, Incorporated, Toronto, Ontario, Canada
| | - Mengdan Xu
- Winterlight Labs, Incorporated, Toronto, Ontario, Canada
| | | | - William Simpson
- Winterlight Labs, Incorporated, Toronto, Ontario, Canada
- Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, Ontario, Canada
| | | | | | | | - Michael Ward
- Alector, Incorporated, San Francisco, California
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Winchester LM, Harshfield EL, Shi L, Badhwar A, Khleifat AA, Clarke N, Dehsarvi A, Lengyel I, Lourida I, Madan CR, Marzi SJ, Proitsi P, Rajkumar AP, Rittman T, Silajdžić E, Tamburin S, Ranson JM, Llewellyn DJ. Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia. Alzheimers Dement 2023; 19:5860-5871. [PMID: 37654029 PMCID: PMC10840606 DOI: 10.1002/alz.13390] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 09/02/2023]
Abstract
With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.
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Affiliation(s)
| | - Eric L Harshfield
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, Cambridge, UK
| | - Liu Shi
- Novo Nordisk Research Centre Oxford (NNRCO), Headington, UK
| | - AmanPreet Badhwar
- Département de Pharmacologie et Physiologie, Institut de Génie Biomédical, Faculté de Médecine, Université de Montréal, Montreal, Canada
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Natasha Clarke
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Amir Dehsarvi
- School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Imre Lengyel
- Wellcome-Wolfson Institute of Experimental Medicine, Queen's University, Belfast, UK
| | - Ilianna Lourida
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute at Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Petroula Proitsi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Anto P Rajkumar
- Institute of Mental Health, Mental Health and Clinical Neurosciences academic unit, University of Nottingham, Nottingham, UK, Mental health services of older people, Nottinghamshire healthcare NHS foundation trust, Nottingham, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Edina Silajdžić
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Janice M Ranson
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | - David J Llewellyn
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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22
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Hazan J, Liu KY, Fox NC, Howard R. Online clinical tools to support the use of new plasma biomarker diagnostic technology in the assessment of Alzheimer's disease: a narrative review. Brain Commun 2023; 5:fcad322. [PMID: 38090277 PMCID: PMC10715781 DOI: 10.1093/braincomms/fcad322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/11/2023] [Accepted: 11/23/2023] [Indexed: 02/15/2024] Open
Abstract
Recent advances in new diagnostic technologies for Alzheimer's disease have improved the speed and precision of diagnosis. However, accessing the potential benefits of this technology poses challenges for clinicians, such as deciding whether it is clinically appropriate to order a diagnostic test, which specific test or tests to order and how to interpret test results and communicate these to the patient and their caregiver. Tools to support decision-making could provide additional structure and information to the clinical assessment process. These tools could be accessed online, and such 'e-tools' can provide an interactive interface to support patients and clinicians in the use of new diagnostic technologies for Alzheimer's disease. We performed a narrative review of the literature to synthesize information available on this research topic. Relevant studies that provide an understanding of how these online tools could be used to optimize the clinical utility of diagnostic technology were identified. Based on these, we discuss the ways in which e-tools have been used to assist in the diagnosis of Alzheimer's disease and propose recommendations for future research to aid further development.
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Affiliation(s)
- Jemma Hazan
- Division of Psychiatry, University College London, London W1T 7BN, UK
| | - Kathy Y Liu
- Division of Psychiatry, University College London, London W1T 7BN, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London, W1T 7NF, UK
| | - Robert Howard
- Division of Psychiatry, University College London, London W1T 7BN, UK
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23
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Narasimhan R, Gopalan M, Sikkandar MY, Alassaf A, AlMohimeed I, Alhussaini K, Aleid A, Sheik SB. Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers. SENSORS (BASEL, SWITZERLAND) 2023; 23:8867. [PMID: 37960568 PMCID: PMC10647614 DOI: 10.3390/s23218867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/22/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023]
Abstract
Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer's disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point. The activity dataset from the Oregon Center for Aging and Technology (ORCATECH) includes measures representing various domains such as walk, sleep, etc. Each sensor-captured activity measure is constructed as a time series, and a variety of summary statistics is computed. The similarity between one individual's activity time series and that of the remaining individuals is also computed as distance measures. The long short-term memory (LSTM) recurrent neural network is trained with time series statistics and distance measures for the prediction modeling, and performance is evaluated by classification accuracy. The model outcomes are explained using the SHapley Additive exPlanations (SHAP) framework. LSTM model trained using the time series statistics and distance measures outperforms other modeling scenarios, including baseline classifiers, with an overall prediction accuracy of 83.84%. SHAP values reveal that sleep-related features contribute the most to the prediction of the cognitive stage at the future time point, and this aligns with the findings in the literature. Findings from this study not only demonstrate that a practical, less expensive, longitudinal monitoring of older adults' activity routines can benefit immensely in modeling AD progression but also unveil the most contributing features that are medically applicable and meaningful.
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Affiliation(s)
- Rajaram Narasimhan
- Centre for Sensors and Process Control, Hindustan Institute of Technology and Science, Chennai 603103, India;
| | - Muthukumaran Gopalan
- Centre for Sensors and Process Control, Hindustan Institute of Technology and Science, Chennai 603103, India;
| | - Mohamed Yacin Sikkandar
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia; (A.A.); (I.A.)
| | - Ahmad Alassaf
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia; (A.A.); (I.A.)
| | - Ibrahim AlMohimeed
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia; (A.A.); (I.A.)
| | - Khalid Alhussaini
- Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi Arabia; (K.A.); (A.A.)
| | - Adham Aleid
- Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi Arabia; (K.A.); (A.A.)
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24
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Ohm DT, Rhodes E, Bahena A, Capp N, Lowe M, Sabatini P, Trotman W, Olm CA, Phillips J, Prabhakaran K, Rascovsky K, Massimo L, McMillan C, Gee J, Tisdall MD, Yushkevich PA, Lee EB, Grossman M, Irwin DJ. Neuroanatomical and cellular degeneration associated with a social disorder characterized by new ritualistic belief systems in a TDP-C patient vs. a Pick patient. Front Neurol 2023; 14:1245886. [PMID: 37900607 PMCID: PMC10600461 DOI: 10.3389/fneur.2023.1245886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/15/2023] [Indexed: 10/31/2023] Open
Abstract
Frontotemporal dementia (FTD) is a spectrum of clinically and pathologically heterogenous neurodegenerative dementias. Clinical and anatomical variants of FTD have been described and associated with underlying frontotemporal lobar degeneration (FTLD) pathology, including tauopathies (FTLD-tau) or TDP-43 proteinopathies (FTLD-TDP). FTD patients with predominant degeneration of anterior temporal cortices often develop a language disorder of semantic knowledge loss and/or a social disorder often characterized by compulsive rituals and belief systems corresponding to predominant left or right hemisphere involvement, respectively. The neural substrates of these complex social disorders remain unclear. Here, we present a comparative imaging and postmortem study of two patients, one with FTLD-TDP (subtype C) and one with FTLD-tau (subtype Pick disease), who both developed new rigid belief systems. The FTLD-TDP patient developed a complex set of values centered on positivity and associated with specific physical and behavioral features of pigs, while the FTLD-tau patient developed compulsive, goal-directed behaviors related to general themes of positivity and spirituality. Neuroimaging showed left-predominant temporal atrophy in the FTLD-TDP patient and right-predominant frontotemporal atrophy in the FTLD-tau patient. Consistent with antemortem cortical atrophy, histopathologic examinations revealed severe loss of neurons and myelin predominantly in the anterior temporal lobes of both patients, but the FTLD-tau patient showed more bilateral, dorsolateral involvement featuring greater pathology and loss of projection neurons and deep white matter. These findings highlight that the regions within and connected to anterior temporal lobes may have differential vulnerability to distinct FTLD proteinopathies and serve important roles in human belief systems.
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Affiliation(s)
- Daniel T. Ohm
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Emma Rhodes
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Alejandra Bahena
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Noah Capp
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - MaKayla Lowe
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Philip Sabatini
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Winifred Trotman
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher A. Olm
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Jeffrey Phillips
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Karthik Prabhakaran
- Penn Image Computing and Science Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Katya Rascovsky
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Lauren Massimo
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Corey McMillan
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - James Gee
- Penn Image Computing and Science Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - M. Dylan Tisdall
- Center for Advanced Magnetic Resonance Imaging and Spectroscopy, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Paul A. Yushkevich
- Penn Image Computing and Science Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Edward B. Lee
- Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - David J. Irwin
- Penn Digital Neuropathology Laboratory, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
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25
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Palermo F, Chen Y, Capstick A, Fletcher-Loyd N, Walsh C, Kouchaki S, True J, Balazikova O, Soreq E, Scott G, Rostill H, Nilforooshan R, Barnaghi P. TIHM: An open dataset for remote healthcare monitoring in dementia. Sci Data 2023; 10:606. [PMID: 37689815 PMCID: PMC10492790 DOI: 10.1038/s41597-023-02519-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 08/30/2023] [Indexed: 09/11/2023] Open
Abstract
Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressing and treating already established health conditions that affect PLWD. Managing these conditions continuously can inform better decision-making earlier for higher-quality care management for PLWD. The Technology Integrated Health Management (TIHM) project developed a new digital platform to routinely collect longitudinal, observational, and measurement data, within the home and apply machine learning and analytical models for the detection and prediction of adverse health events affecting the well-being of PLWD. This work describes the TIHM dataset collected during the second phase (i.e., feasibility study) of the TIHM project. The data was collected from homes of 56 PLWD and associated with events and clinical observations (daily activity, physiological monitoring, and labels for health-related conditions). The study recorded an average of 50 days of data per participant, totalling 2803 days.
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Affiliation(s)
- Francesca Palermo
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Yu Chen
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Alexander Capstick
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Nan Fletcher-Loyd
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Chloe Walsh
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
- Surrey and Borders Partnership NHS Trust, Leatherhead, KT22 7AD, UK
| | - Samaneh Kouchaki
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
- University of Surrey, Guildford, GU2 7XH, UK
| | - Jessica True
- Surrey and Borders Partnership NHS Trust, Leatherhead, KT22 7AD, UK
| | - Olga Balazikova
- Surrey and Borders Partnership NHS Trust, Leatherhead, KT22 7AD, UK
| | - Eyal Soreq
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Gregory Scott
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Helen Rostill
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
- Surrey and Borders Partnership NHS Trust, Leatherhead, KT22 7AD, UK
| | - Ramin Nilforooshan
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
- Surrey and Borders Partnership NHS Trust, Leatherhead, KT22 7AD, UK
| | - Payam Barnaghi
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK.
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK.
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26
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Yao Y, Huang W, Chen J, Liu X, Bai L, Chen W, Cheng Y, Ping J, Marks TJ, Facchetti A. Flexible and Stretchable Organic Electrochemical Transistors for Physiological Sensing Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2209906. [PMID: 36808773 DOI: 10.1002/adma.202209906] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Flexible and stretchable bioelectronics provides a biocompatible interface between electronics and biological systems and has received tremendous attention for in situ monitoring of various biological systems. Considerable progress in organic electronics has made organic semiconductors, as well as other organic electronic materials, ideal candidates for developing wearable, implantable, and biocompatible electronic circuits due to their potential mechanical compliance and biocompatibility. Organic electrochemical transistors (OECTs), as an emerging class of organic electronic building blocks, exhibit significant advantages in biological sensing due to the ionic nature at the basis of the switching behavior, low driving voltage (<1 V), and high transconductance (in millisiemens range). During the past few years, significant progress in constructing flexible/stretchable OECTs (FSOECTs) for both biochemical and bioelectrical sensors has been reported. In this regard, to summarize major research accomplishments in this emerging field, this review first discusses structure and critical features of FSOECTs, including working principles, materials, and architectural engineering. Next, a wide spectrum of relevant physiological sensing applications, where FSOECTs are the key components, are summarized. Last, major challenges and opportunities for further advancing FSOECT physiological sensors are discussed.
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Affiliation(s)
- Yao Yao
- School of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, P. R. China
- Innovation Platform of Micro/Nano Technology for Biosensing, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 311200, P. R. China
- Department of Chemistry and the Materials Research Center, Northwestern University, Sheridan Road, Evanston, IL, 60208, USA
| | - Wei Huang
- Department of Chemistry and the Materials Research Center, Northwestern University, Sheridan Road, Evanston, IL, 60208, USA
- School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, Sichuan, 611731, P. R. China
| | - Jianhua Chen
- Department of Chemistry and the Materials Research Center, Northwestern University, Sheridan Road, Evanston, IL, 60208, USA
| | - Xiaoxue Liu
- School of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, P. R. China
- Innovation Platform of Micro/Nano Technology for Biosensing, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 311200, P. R. China
| | - Libing Bai
- School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, Sichuan, 611731, P. R. China
| | - Wei Chen
- School of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, P. R. China
| | - Yuhua Cheng
- School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, Sichuan, 611731, P. R. China
| | - Jianfeng Ping
- School of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, P. R. China
- Innovation Platform of Micro/Nano Technology for Biosensing, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 311200, P. R. China
| | - Tobin J Marks
- Department of Chemistry and the Materials Research Center, Northwestern University, Sheridan Road, Evanston, IL, 60208, USA
| | - Antonio Facchetti
- Department of Chemistry and the Materials Research Center, Northwestern University, Sheridan Road, Evanston, IL, 60208, USA
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, 60174, Sweden
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27
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Osborne OM, Naranjo O, Heckmann BL, Dykxhoorn D, Toborek M. Anti-amyloid: An antibody to cure Alzheimer's or an attitude. iScience 2023; 26:107461. [PMID: 37588168 PMCID: PMC10425904 DOI: 10.1016/j.isci.2023.107461] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023] Open
Abstract
For more than a century, clinicians have been aware of the devastating neurological condition called Alzheimer's disease (AD). AD is characterized by the presence of abnormal amyloid protein plaques and tau tangles in the brain. The dominant hypothesis, termed the amyloid hypothesis, attributes AD development to excessive cleavage and accumulation of amyloid precursor protein (APP), leading to brain tissue atrophy. The amyloid hypothesis has greatly influenced AD research and therapeutic endeavors. However, despite significant attention, a complete understanding of amyloid and APP's roles in disease pathology, progression, and cognitive impairment remains elusive. Recent controversies and several unsuccessful drug trials have called into question whether amyloid is the only neuropathological factor for treatment. To accomplish disease amelioration, we argue that researchers and clinicians may need to take a compounding approach to target amyloid and other factors in the brain, including traditional pharmaceuticals and holistic therapies.
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Affiliation(s)
- Olivia M. Osborne
- Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Oandy Naranjo
- Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Bradlee L. Heckmann
- Department of Immunology, University of South Florida Morsani College of Medicine, Tampa, FL 33602, USA
- Byrd Alzheimer’s Center, University of South Florida Health Neuroscience Institute, Tampa, FL 33613, USA
- Department of Molecular Medicine, University of South Florida Morsani College of Medicine, Tampa, FL 33602, USA
- Asha Therapeutics, Tampa, FL, USA
| | - Derek Dykxhoorn
- Dr. John T. Macdonald Foundation Department of Human Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Michal Toborek
- Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
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28
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Mancioppi G, Rovini E, Fiorini L, Zeghari R, Gros A, Manera V, Robert P, Cavallo F. Mild cognitive impairment identification based on motor and cognitive dual-task pooled indices. PLoS One 2023; 18:e0287380. [PMID: 37531347 PMCID: PMC10395992 DOI: 10.1371/journal.pone.0287380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 06/05/2023] [Indexed: 08/04/2023] Open
Abstract
OBJECTIVE This study investigates the possibility of adopting motor and cognitive dual-task (MCDT) approaches to identify subjects with mild cognitive impairment (MCI) and subjective cognitive impairment (SCI). METHODS The upper and lower motor performances of 44 older adults were assessed using the SensHand and SensFoot wearable system during three MCDTs: forefinger tapping (FTAP), toe-tapping heel pin (TTHP), and walking 10 m (GAIT). We developed five pooled indices (PIs) based on these MCDTs, and we included them, along with demographic data (age) and clinical scores (Frontal Assessment Battery (FAB) scores), in five logistic regression models. RESULTS Models which consider cognitively normal adult (CNA) vs MCI subjects have accuracies that range from 67% to 78%. The addition of clinical scores stabilised the accuracies, which ranged from 85% to 89%. For models which consider CNA vs SCI vs MCI subjects, there are great benefits to considering all three regressors (age, FAB score, and PIs); the overall accuracies of the three-class models range between 50% and 59% when just PIs and age are considered, whereas the overall accuracy increases by 18% when all three regressors are utilised. CONCLUSION Logistic regression models that consider MCDT PIs and age have been effective in distinguishing between CNA and MCI subjects. The inclusion of clinical scores increased the models' accuracy. Particularly high performances in distinguishing among CNA, SCI, and MCI subjects were obtained by the TTHP PI. This study suggests that a broader framework for MCDTs, which should encompass a greater selection of motor tasks, could provide clinicians with new appropriate tools.
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Affiliation(s)
- Gianmaria Mancioppi
- The Department of Industrial Engineering, University of Florence, Florence, Italy
| | - Erika Rovini
- The Department of Industrial Engineering, University of Florence, Florence, Italy
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Pisa, Italy
| | - Laura Fiorini
- The Department of Industrial Engineering, University of Florence, Florence, Italy
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Pisa, Italy
| | - Radia Zeghari
- The CoBTeK, Université Côte d'Azur (UCA), Nice, France
- Nice University Hospital, Public Health Department, Côte d'Azur University, Nice, France
| | - Auriane Gros
- The CoBTeK, Université Côte d'Azur (UCA), Nice, France
- Association Innovation Alzheimer, Nice, France
- Department of Speech Therapy (Departement d'Orthophonie, DON), Université Côte d'Azur, Nice, France
- Centre Hospitalier Universitaire de Nice, Service Clinique Gériatrique du Cerveau et du Mouvement, Centre Mémoire Ressources et Recherche, Université Côte d'Azur, Nice, France
| | - Valeria Manera
- The CoBTeK, Université Côte d'Azur (UCA), Nice, France
- Association Innovation Alzheimer, Nice, France
- Department of Speech Therapy (Departement d'Orthophonie, DON), Université Côte d'Azur, Nice, France
| | - Philippe Robert
- The CoBTeK, Université Côte d'Azur (UCA), Nice, France
- Association Innovation Alzheimer, Nice, France
- Centre Hospitalier Universitaire de Nice, Service Clinique Gériatrique du Cerveau et du Mouvement, Centre Mémoire Ressources et Recherche, Université Côte d'Azur, Nice, France
| | - Filippo Cavallo
- The Department of Industrial Engineering, University of Florence, Florence, Italy
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Pisa, Italy
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29
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Wolf A, Tripanpitak K, Umeda S, Otake-Matsuura M. Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review. Front Psychol 2023; 14:1197567. [PMID: 37546488 PMCID: PMC10399700 DOI: 10.3389/fpsyg.2023.1197567] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/19/2023] [Indexed: 08/08/2023] Open
Abstract
Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.
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Affiliation(s)
- Alexandra Wolf
- Cognitive Behavioral Assistive Technology (CBAT), Goal-Oriented Technology Group, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kornkanok Tripanpitak
- Cognitive Behavioral Assistive Technology (CBAT), Goal-Oriented Technology Group, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
| | - Satoshi Umeda
- Department of Psychology, Keio University, Tokyo, Japan
| | - Mihoko Otake-Matsuura
- Cognitive Behavioral Assistive Technology (CBAT), Goal-Oriented Technology Group, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
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Ghosal R, Varma VR, Volfson D, Hillel I, Urbanek J, Hausdorff JM, Watts A, Zipunnikov V. Distributional data analysis via quantile functions and its application to modeling digital biomarkers of gait in Alzheimer's Disease. Biostatistics 2023; 24:539-561. [PMID: 36519565 PMCID: PMC10544806 DOI: 10.1093/biostatistics/kxab041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 09/10/2021] [Accepted: 10/19/2021] [Indexed: 07/20/2023] Open
Abstract
With the advent of continuous health monitoring with wearable devices, users now generate their unique streams of continuous data such as minute-level step counts or heartbeats. Summarizing these streams via scalar summaries often ignores the distributional nature of wearable data and almost unavoidably leads to the loss of critical information. We propose to capture the distributional nature of wearable data via user-specific quantile functions (QF) and use these QFs as predictors in scalar-on-quantile-function-regression (SOQFR). As an alternative approach, we also propose to represent QFs via user-specific L-moments, robust rank-based analogs of traditional moments, and use L-moments as predictors in SOQFR (SOQFR-L). These two approaches provide two mutually consistent interpretations: in terms of quantile levels by SOQFR and in terms of L-moments by SOQFR-L. We also demonstrate how to deal with multi-modal distributional data via Joint and Individual Variation Explained using L-moments. The proposed methods are illustrated in a study of association of digital gait biomarkers with cognitive function in Alzheimers disease. Our analysis shows that the proposed methods demonstrate higher predictive performance and attain much stronger associations with clinical cognitive scales compared to simple distributional summaries.
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Affiliation(s)
- Rahul Ghosal
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Vijay R Varma
- National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, MD, USA
| | - Dmitri Volfson
- Neuroscience Analytics, Computational Biology, Takeda, Cambridge, MA, USA
| | - Inbar Hillel
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Jacek Urbanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, Department of Physical Therapy, Sackler Faculty of Medicine, and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, and Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Amber Watts
- Department of Psychology, University of Kansas, Lawrence, KS, USA
| | - Vadim Zipunnikov
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Miller MI, Shih LC, Kolachalama VB. Machine Learning in Clinical Trials: A Primer with Applications to Neurology. Neurotherapeutics 2023; 20:1066-1080. [PMID: 37249836 PMCID: PMC10228463 DOI: 10.1007/s13311-023-01384-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2023] [Indexed: 05/31/2023] Open
Abstract
We reviewed foundational concepts in artificial intelligence (AI) and machine learning (ML) and discussed ways in which these methodologies may be employed to enhance progress in clinical trials and research, with particular attention to applications in the design, conduct, and interpretation of clinical trials for neurologic diseases. We discussed ways in which ML may help to accelerate the pace of subject recruitment, provide realistic simulation of medical interventions, and enhance remote trial administration via novel digital biomarkers and therapeutics. Lastly, we provide a brief overview of the technical, administrative, and regulatory challenges that must be addressed as ML achieves greater integration into clinical trial workflows.
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Affiliation(s)
- Matthew I Miller
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Ludy C Shih
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA.
- Department of Computer Science and Faculty of Computing & Data Sciences, Boston University, Boston, MA, 02115, USA.
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Martínez-Nicolás I, Martínez-Sánchez F, Ivanova O, Meilán JJG. Reading and lexical-semantic retrieval tasks outperforms single task speech analysis in the screening of mild cognitive impairment and Alzheimer's disease. Sci Rep 2023; 13:9728. [PMID: 37322073 PMCID: PMC10272227 DOI: 10.1038/s41598-023-36804-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
Age-related cognitive impairment have increased dramatically in recent years, which has risen the interes in developing screening tools for mild cognitive impairment and Alzheimer's disease. Speech analysis allows to exploit the behavioral consequences of cognitive deficits on the patient's vocal performance so that it is possible to identify pathologies affecting speech production such as dementia. Previous studies have further shown that the speech task used determines how the speech parameters are altered. We aim to combine the impairments in several speech production tasks in order to improve the accuracy of screening through speech analysis. The sample consists of 72 participants divided into three equal groups of healthy older adults, people with mild cognitive impairment, or Alzheimer's disease, matched by age and education. A complete neuropsychological assessment and two voice recordings were performed. The tasks required the participants to read a text, and complete a sentence with semantic information. A stepwise linear discriminant analysis was performed to select speech parameters with discriminative power. The discriminative functions obtained an accuracy of 83.3% in simultaneous classifications of several levels of cognitive impairment. It would therefore be a promising screening tool for dementia.
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Affiliation(s)
| | | | - Olga Ivanova
- Faculty of Philology, University of Salamanca, 37008, Salamanca, Spain
| | - Juan J G Meilán
- Faculty of Psychology, University of Salamanca, 37008, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, 37007, Salamanca, Spain
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ZhuParris A, de Goede AA, Yocarini IE, Kraaij W, Groeneveld GJ, Doll RJ. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115243. [PMID: 37299969 DOI: 10.3390/s23115243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
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Affiliation(s)
- Ahnjili ZhuParris
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Annika A de Goede
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
| | - Iris E Yocarini
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- The Netherlands Organisation for Applied Scientific Research (TNO), Anna van Buerenplein 1, 2595 DA, Den Haag, The Netherlands
| | - Geert Jan Groeneveld
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Robert Jan Doll
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
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Anda-Duran ID, Hwang PH, Popp ZT, Low S, Ding H, Rahman S, Igwe A, Kolachalama VB, Lin H, Au R. Matching science to reality: how to deploy a participant-driven digital brain health platform. FRONTIERS IN DEMENTIA 2023; 2:1135451. [PMID: 38706716 PMCID: PMC11067045 DOI: 10.3389/frdem.2023.1135451] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Introduction Advances in digital technologies for health research enable opportunities for digital phenotyping of individuals in research and clinical settings. Beyond providing opportunities for advanced data analytics with data science and machine learning approaches, digital technologies offer solutions to several of the existing barriers in research practice that have resulted in biased samples. Methods A participant-driven, precision brain health monitoring digital platform has been introduced to two longitudinal cohort studies, the Boston University Alzheimer's Disease Research Center (BU ADRC) and the Bogalusa Heart Study (BHS). The platform was developed with prioritization of digital data in native format, multiple OS, validity of derived metrics, feasibility and usability. A platform including nine remote technologies and three staff-guided digital assessments has been introduced in the BU ADRC population, including a multimodal smartphone application also introduced to the BHS population. Participants select which technologies they would like to use and can manipulate their personal platform and schedule over time. Results Participants from the BU ADRC are using an average of 5.9 technologies to date, providing strong evidence for the usability of numerous digital technologies in older adult populations. Broad phenotyping of both cohorts is ongoing, with the collection of data spanning cognitive testing, sleep, physical activity, speech, motor activity, cardiovascular health, mood, gait, balance, and more. Several challenges in digital phenotyping implementation in the BU ADRC and the BHS have arisen, and the protocol has been revised and optimized to minimize participant burden while sustaining participant contact and support. Discussion The importance of digital data in its native format, near real-time data access, passive participant engagement, and availability of technologies across OS has been supported by the pattern of participant technology use and adherence across cohorts. The precision brain health monitoring platform will be iteratively adjusted and improved over time. The pragmatic study design enables multimodal digital phenotyping of distinct clinically characterized cohorts in both rural and urban U.S. settings.
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Affiliation(s)
- Ileana De Anda-Duran
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Phillip H. Hwang
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - Zachary Thomas Popp
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Spencer Low
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Huitong Ding
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Salman Rahman
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Akwaugo Igwe
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Vijaya B. Kolachalama
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Computer Science and Faculty of Computing & Data Sciences, Boston University, Boston, MA, United States
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Rhoda Au
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
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35
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Ford E, Milne R, Curlewis K. Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2023; 13:e1492. [PMID: 38439952 PMCID: PMC10909482 DOI: 10.1002/widm.1492] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 03/06/2024]
Abstract
Dementia poses a growing challenge for health services but remains stigmatized and under-recognized. Digital technologies to aid the earlier detection of dementia are approaching market. These include traditional cognitive screening tools presented on mobile devices, smartphone native applications, passive data collection from wearable, in-home and in-car sensors, as well as machine learning techniques applied to clinic and imaging data. It has been suggested that earlier detection and diagnosis may help patients plan for their future, achieve a better quality of life, and access clinical trials and possible future disease modifying treatments. In this review, we explore whether digital tools for the early detection of dementia can or should be deployed, by assessing them against the principles of ethical screening programs. We conclude that while the importance of dementia as a health problem is unquestionable, significant challenges remain. There is no available treatment which improves the prognosis of diagnosed disease. Progression from early-stage disease to dementia is neither given nor currently predictable. Available technologies are generally not both minimally invasive and highly accurate. Digital deployment risks exacerbating health inequalities due to biased training data and inequity in digital access. Finally, the acceptability of early dementia detection is not established, and resources would be needed to ensure follow-up and support for those flagged by any new system. We conclude that early dementia detection deployed at scale via digital technologies does not meet standards for a screening program and we offer recommendations for moving toward an ethical mode of implementation. This article is categorized under:Application Areas > Health CareCommercial, Legal, and Ethical Issues > Ethical ConsiderationsTechnologies > Artificial Intelligence.
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Affiliation(s)
- Elizabeth Ford
- Department of Primary Care and Public HealthBrighton and Sussex Medical SchoolBrightonUK
| | - Richard Milne
- Kavli Centre for Ethics, Science and the PublicUniversity of CambridgeCambridgeUK
- Engagement and SocietyWellcome Connecting ScienceCambridgeUK
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36
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Vrahatis AG, Skolariki K, Krokidis MG, Lazaros K, Exarchos TP, Vlamos P. Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094184. [PMID: 37177386 PMCID: PMC10180573 DOI: 10.3390/s23094184] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 05/15/2023]
Abstract
Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.
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Affiliation(s)
- Aristidis G Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Konstantina Skolariki
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Marios G Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Konstantinos Lazaros
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Themis P Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
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Morrone CD, Raghuraman R, Hussaini SA, Yu WH. Proteostasis failure exacerbates neuronal circuit dysfunction and sleep impairments in Alzheimer's disease. Mol Neurodegener 2023; 18:27. [PMID: 37085942 PMCID: PMC10119020 DOI: 10.1186/s13024-023-00617-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/29/2023] [Indexed: 04/23/2023] Open
Abstract
Failed proteostasis is a well-documented feature of Alzheimer's disease, particularly, reduced protein degradation and clearance. However, the contribution of failed proteostasis to neuronal circuit dysfunction is an emerging concept in neurodegenerative research and will prove critical in understanding cognitive decline. Our objective is to convey Alzheimer's disease progression with the growing evidence for a bidirectional relationship of sleep disruption and proteostasis failure. Proteostasis dysfunction and tauopathy in Alzheimer's disease disrupts neurons that regulate the sleep-wake cycle, which presents behavior as impaired slow wave and rapid eye movement sleep patterns. Subsequent sleep loss further impairs protein clearance. Sleep loss is a defined feature seen early in many neurodegenerative disorders and contributes to memory impairments in Alzheimer's disease. Canonical pathological hallmarks, β-amyloid, and tau, directly disrupt sleep, and neurodegeneration of locus coeruleus, hippocampal and hypothalamic neurons from tau proteinopathy causes disruption of the neuronal circuitry of sleep. Acting in a positive-feedback-loop, sleep loss and circadian rhythm disruption then increase spread of β-amyloid and tau, through impairments of proteasome, autophagy, unfolded protein response and glymphatic clearance. This phenomenon extends beyond β-amyloid and tau, with interactions of sleep impairment with the homeostasis of TDP-43, α-synuclein, FUS, and huntingtin proteins, implicating sleep loss as an important consideration in an array of neurodegenerative diseases and in cases of mixed neuropathology. Critically, the dynamics of this interaction in the neurodegenerative environment are not fully elucidated and are deserving of further discussion and research. Finally, we propose sleep-enhancing therapeutics as potential interventions for promoting healthy proteostasis, including β-amyloid and tau clearance, mechanistically linking these processes. With further clinical and preclinical research, we propose this dynamic interaction as a diagnostic and therapeutic framework, informing precise single- and combinatorial-treatments for Alzheimer's disease and other brain disorders.
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Affiliation(s)
- Christopher Daniel Morrone
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, M5T 1R8, Canada.
| | - Radha Raghuraman
- Taub Institute, Columbia University Irving Medical Center, 630W 168th Street, New York, NY, 10032, USA
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, 630W 168th Street, New York, NY, 10032, USA
| | - S Abid Hussaini
- Taub Institute, Columbia University Irving Medical Center, 630W 168th Street, New York, NY, 10032, USA.
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, 630W 168th Street, New York, NY, 10032, USA.
| | - Wai Haung Yu
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, M5T 1R8, Canada.
- Geriatric Mental Health Research Services, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, M5T 1R8, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada.
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Robin J, Xu M, Balagopalan A, Novikova J, Kahn L, Oday A, Hejrati M, Hashemifar S, Negahdar M, Simpson W, Teng E. Automated detection of progressive speech changes in early Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12445. [PMID: 37361261 PMCID: PMC10286224 DOI: 10.1002/dad2.12445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/21/2023] [Accepted: 04/27/2023] [Indexed: 06/28/2023]
Abstract
Speech and language changes occur in Alzheimer's disease (AD), but few studies have characterized their longitudinal course. We analyzed open-ended speech samples from a prodromal-to-mild AD cohort to develop a novel composite score to characterize progressive speech changes. Participant speech from the Clinical Dementia Rating (CDR) interview was analyzed to compute metrics reflecting speech and language characteristics. We determined the aspects of speech and language that exhibited significant longitudinal change over 18 months. Nine acoustic and linguistic measures were combined to create a novel composite score. The speech composite exhibited significant correlations with primary and secondary clinical endpoints and a similar effect size for detecting longitudinal change. Our results demonstrate the feasibility of using automated speech processing to characterize longitudinal change in early AD. Speech-based composite scores could be used to monitor change and detect response to treatment in future research. HIGHLIGHTS Longitudinal speech samples were analyzed to characterize speech changes in early AD.Acoustic and linguistic measures showed significant change over 18 months.A novel speech composite score was computed to characterize longitudinal change.The speech composite correlated with primary and secondary trial endpoints.Automated speech analysis could facilitate remote, high frequency monitoring in AD.
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Affiliation(s)
| | - Mengdan Xu
- Winterlight Labs Inc.TorontoOntarioCanada
| | - Aparna Balagopalan
- Winterlight Labs Inc.TorontoOntarioCanada
- Massachusetts Institute of TechnologyCambridgeMassachusettsUSA
- Present address:
Genentech, Inc.South San FranciscoCaliforniaUSA
| | | | - Laura Kahn
- Present address:
Genentech, Inc.South San FranciscoCaliforniaUSA
- Present address:
ReCode Therapeutics, Menlo ParkCaliforniaUSA
| | - Abdi Oday
- Present address:
Genentech, Inc.South San FranciscoCaliforniaUSA
| | - Mohsen Hejrati
- Present address:
Genentech, Inc.South San FranciscoCaliforniaUSA
| | | | | | | | - Edmond Teng
- Present address:
Genentech, Inc.South San FranciscoCaliforniaUSA
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Faisal MAA, Chowdhury MEH, Mahbub ZB, Pedersen S, Ahmed MU, Khandakar A, Alhatou M, Nabil M, Ara I, Bhuiyan EH, Mahmud S, AbdulMoniem M. NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04557-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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40
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Lanzi AM, Saylor AK, Fromm D, Liu H, MacWhinney B, Cohen ML. DementiaBank: Theoretical Rationale, Protocol, and Illustrative Analyses. AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2023; 32:426-438. [PMID: 36791255 PMCID: PMC10171844 DOI: 10.1044/2022_ajslp-22-00281] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/01/2022] [Accepted: 11/25/2022] [Indexed: 05/12/2023]
Abstract
PURPOSE Dementia from Alzheimer's disease (AD) is characterized primarily by a significant decline in memory abilities; however, language abilities are also commonly affected and may precede the decline of other cognitive abilities. To study the progression of language, there is a need for open-access databases that can be used to build algorithms to produce translational models sensitive enough to detect early declines in language abilities. DementiaBank is an open-access repository of transcribed video/audio data from communicative interactions from people with dementia, mild cognitive impairment (MCI), and controls. The aims of this tutorial are to (a) describe the newly established standardized DementiaBank discourse protocol, (b) describe the Delaware corpus data, and (c) provide examples of automated linguistic analyses that can be conducted with the Delaware corpus data and describe additional DementiaBank resources. METHOD The DementiaBank discourse protocol elicits four types of discourse: picture description, story narrative, procedural, and personal narrative. The Delaware corpus currently includes data from 20 neurotypical adults and 33 adults with MCI from possible AD who completed the DementiaBank discourse protocol and a cognitive-linguistic battery. Language samples were video- and audio-recorded, transcribed, coded, and uploaded to DementiaBank. The protocol materials and transcription programs can be accessed for free via the DementiaBank website. RESULTS Illustrative analyses show the potential of the Delaware corpus data to help understand discourse metrics at the individual and group levels. In addition, they highlight analyses that could be used across TalkBank's other clinical banks (e.g., AphasiaBank). Information is also included on manual and automatic speech recognition transcription methods. CONCLUSIONS DementiaBank is a shared online database that can facilitate research efforts to address the gaps in knowledge about language changes associated with MCI and dementia from AD. Identifying early language markers could lead to improved assessment and treatment approaches for adults at risk for dementia.
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Affiliation(s)
- Alyssa M. Lanzi
- Department of Communication Sciences and Disorders, University of Delaware, Newark
- Delaware Center for Cognitive Aging Research, University of Delaware, Newark
| | - Anna K. Saylor
- Department of Communication Sciences and Disorders, University of Delaware, Newark
| | - Davida Fromm
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA
| | | | - Brian MacWhinney
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA
| | - Matthew L. Cohen
- Department of Communication Sciences and Disorders, University of Delaware, Newark
- Delaware Center for Cognitive Aging Research, University of Delaware, Newark
- Center for Health Assessment Research and Translation, University of Delaware, Newark
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Hampel H, Gao P, Cummings J, Toschi N, Thompson PM, Hu Y, Cho M, Vergallo A. The foundation and architecture of precision medicine in neurology and psychiatry. Trends Neurosci 2023; 46:176-198. [PMID: 36642626 PMCID: PMC10720395 DOI: 10.1016/j.tins.2022.12.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/18/2022] [Accepted: 12/14/2022] [Indexed: 01/15/2023]
Abstract
Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.
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Affiliation(s)
- Harald Hampel
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA.
| | - Peng Gao
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yan Hu
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Min Cho
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Andrea Vergallo
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
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Amini S, Hao B, Zhang L, Song M, Gupta A, Karjadi C, Kolachalama VB, Au R, Paschalidis IC. Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach. Alzheimers Dement 2023; 19:946-955. [PMID: 35796399 PMCID: PMC10148688 DOI: 10.1002/alz.12721] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 03/20/2022] [Accepted: 05/18/2022] [Indexed: 11/06/2022]
Abstract
INTRODUCTION Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia. METHODS A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects' neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants' demographic characteristics. RESULTS Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively. DISCUSSION The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.
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Affiliation(s)
- Samad Amini
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Boran Hao
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Lifu Zhang
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Mengting Song
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Aman Gupta
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Cody Karjadi
- Framingham Heart Study, Boston University, Boston, Massachusetts, USA
| | - Vijaya B. Kolachalama
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, USA
- Department of Computer Science, Boston University, Boston, Massachusetts, USA
| | - Rhoda Au
- Framingham Heart Study, Boston University, Boston, Massachusetts, USA
- Departments of Anatomy & Neurobiology, Neurology, and Epidemiology, Boston University School of Medicine and School of Public Health, Boston, Massachusetts, USA
| | - Ioannis Ch. Paschalidis
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, USA
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Evidence from ClinicalTrials.gov on the growth of Digital Health Technologies in neurology trials. NPJ Digit Med 2023; 6:23. [PMID: 36765123 PMCID: PMC9918454 DOI: 10.1038/s41746-023-00767-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 01/24/2023] [Indexed: 02/12/2023] Open
Abstract
Digital Health Technologies (DHTs) such as connected sensors offer particular promise for improving data collection and patient empowerment in neurology research and care. This study analyzed the recent evolution of the use of DHTs in trials registered on ClinicalTrials.gov for four chronic neurological disorders: epilepsy, multiple sclerosis, Alzheimer's, and Parkinson's disease. We document growth in the collection of both more established digital measures (e.g., motor function) and more novel digital measures (e.g., speech) over recent years, highlighting contexts of use and key trends.
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Wang Y, Chen T, Wang C, Ogihara A, Ma X, Huang S, Zhou S, Li S, Liu J, Li K. A New Smart 2-Min Mobile Alerting Method for Mild Cognitive Impairment Due to Alzheimer's Disease in the Community. Brain Sci 2023; 13:brainsci13020244. [PMID: 36831787 PMCID: PMC9954272 DOI: 10.3390/brainsci13020244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 01/29/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
The early identification of mild cognitive impairment (MCI) due to Alzheimer's disease (AD), in an early stage of AD can expand the AD warning window. We propose a new capability index evaluating the spatial execution process (SEP), which can dynamically evaluate the execution process in the space navigation task. The hypothesis is proposed that there are neurobehavioral differences between normal cognitive (NC) elderly and AD patients with MCI reflected in digital biomarkers captured during SEP. According to this, we designed a new smart 2-min mobile alerting method for MCI due to AD, for community screening. Two digital biomarkers, total mission execution distance (METRtotal) and execution distance above the transverse obstacle (EDabove), were selected by step-up regression analysis. For the participants with more than 9 years of education, the alerting efficiency of the combination of the two digital biomarkers for MCI due to AD could reach 0.83. This method has the advantages of fast speed, high alerting efficiency, low cost and high intelligence and thus has a high application value for community screening in developing countries. It also provides a new intelligent alerting approach based on the human-computer interaction (HCI) paradigm for MCI due to AD in community screening.
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Affiliation(s)
- Yujia Wang
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Tong Chen
- Department of Neurology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China
| | - Chen Wang
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Atsushi Ogihara
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Department of Health Sciences and Social Welfare, Faculty of Human Sciences, Waseda University, Tokorozawa 359-1162, Japan
| | - Xiaowen Ma
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Shouqiang Huang
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Siyu Zhou
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
- School of Public Health, Hangzhou Normal University, Hangzhou 311121, China
| | - Shuwu Li
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Jiakang Liu
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Kai Li
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou 310053, China
- Correspondence:
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Lorenzon N, Musoles-Lleó J, Turrisi F, Gomis-González M, De La Torre R, Dierssen M. State-of-the-art therapy for Down syndrome. Dev Med Child Neurol 2023. [PMID: 36692980 DOI: 10.1111/dmcn.15517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/04/2022] [Accepted: 12/19/2022] [Indexed: 01/25/2023]
Abstract
In the last decade, an important effort was made in the field of Down syndrome to find new interventions that improve cognition. These therapies have added to the traditional symptomatic treatments and to the drugs for treating Alzheimer disease in the general population repurposed for Down syndrome. Defining next-generation therapeutics will involve biomarker-based therapeutic decision-making, and preventive and multimodal interventions. However, translation of specific findings into effective therapeutic strategies has been disappointingly slow and has failed in many cases at the clinical level, leading to reduced credibility of mouse studies. This is aggravated by a tendency to favour large-magnitude effects and highly significant findings, leading to high expectations but also to a biased view of the complex pathophysiology of Down syndrome. Here, we review some of the most recent and promising strategies for ameliorating the cognitive state of individuals with Down syndrome. We studied the landscape of preclinical and clinical studies and conducted a thorough literature search on PubMed and ClinicalTrials.gov for articles published between June 2012 and August 2022 on therapies for ameliorating cognitive function in individuals with Down syndrome. We critically assess current therapeutic approaches, why therapies fail in clinical trials in Down syndrome, and what could be the path forward. We discuss some intrinsic difficulties for translational research, and the need for a framework that improves the detection of drug efficacy to avoid discarding compounds too early from the companies' pipelines.
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Affiliation(s)
- Nicola Lorenzon
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Juanluis Musoles-Lleó
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Federica Turrisi
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain.,Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute, Barcelona, Spain
| | - Maria Gomis-González
- Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute, Barcelona, Spain
| | - Rafael De La Torre
- Universitat Pompeu Fabra, Barcelona, Spain.,Integrative Pharmacology and Systems Neurosciences Research Group, Neurosciences Research Program, Hospital del Mar Medical Research Institute, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Mara Dierssen
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Raras, Barcelona, Spain
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Bayat S, Roe CM, Schindler S, Murphy SA, Doherty JM, Johnson AM, Walker A, Ances BM, Morris JC, Babulal GM. Everyday Driving and Plasma Biomarkers in Alzheimer's Disease: Leveraging Artificial Intelligence to Expand Our Diagnostic Toolkit. J Alzheimers Dis 2023; 92:1487-1497. [PMID: 36938737 PMCID: PMC10133181 DOI: 10.3233/jad-221268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
BACKGROUND Driving behavior as a digital marker and recent developments in blood-based biomarkers show promise as a widespread solution for the early identification of Alzheimer's disease (AD). OBJECTIVE This study used artificial intelligence methods to evaluate the association between naturalistic driving behavior and blood-based biomarkers of AD. METHODS We employed an artificial neural network (ANN) to examine the relationship between everyday driving behavior and plasma biomarker of AD. The primary outcome was plasma Aβ42/Aβ40, where Aβ42/Aβ40 < 0.1013 was used to define amyloid positivity. Two ANN models were trained and tested for predicting the outcome. The first model architecture only includes driving variables as input, whereas the second architecture includes the combination of age, APOE ɛ4 status, and driving variables. RESULTS All 142 participants (mean [SD] age 73.9 [5.2] years; 76 [53.5%] men; 80 participants [56.3% ] with amyloid positivity based on plasma Aβ42/Aβ40) were cognitively normal. The six driving features, included in the ANN models, were the number of trips during rush hour, the median and standard deviation of jerk, the number of hard braking incidents and night trips, and the standard deviation of speed. The F1 score of the model with driving variables alone was 0.75 [0.023] for predicting plasma Aβ42/Aβ40. Incorporating age and APOE ɛ4 carrier status improved the diagnostic performance of the model to 0.80 [>0.051]. CONCLUSION Blood-based AD biomarkers offer a novel opportunity to establish the efficacy of naturalistic driving as an accessible digital marker for AD pathology in driving research.
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Affiliation(s)
- Sayeh Bayat
- Department of Biomedical Engineering, University of Calgary, Calgary, Canada
- Department of Geomatics Engineering, University of Calgary, Calgary, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | | | - Suzanne Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Samantha A. Murphy
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jason M. Doherty
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ann M. Johnson
- Center for Clinical Studies, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexis Walker
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Beau M. Ances
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ganesh M. Babulal
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Institute of Public Health, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Faculty of Humanities, University of Johannesburg, South Africa
- Department of Clinical Research and Leadership, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
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Bachman SL, Blankenship JM, Busa M, Serviente C, Lyden K, Clay I. Capturing Measures That Matter: The Potential Value of Digital Measures of Physical Behavior for Alzheimer's Disease Drug Development. J Alzheimers Dis 2023; 95:379-389. [PMID: 37545234 PMCID: PMC10578291 DOI: 10.3233/jad-230152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2023] [Indexed: 08/08/2023]
Abstract
Alzheimer's disease (AD) is a devastating neurodegenerative disease and the primary cause of dementia worldwide. Despite the magnitude of AD's impact on patients, caregivers, and society, nearly all AD clinical trials fail. A potential contributor to this high rate of failure is that established clinical outcome assessments fail to capture subtle clinical changes, entail high burden for patients and their caregivers, and ineffectively address the aspects of health deemed important by patients and their caregivers. AD progression is associated with widespread changes in physical behavior that have impacts on the ability to function independently, which is a meaningful aspect of health for patients with AD and important for diagnosis. However, established assessments of functional independence remain underutilized in AD clinical trials and are limited by subjective biases and ceiling effects. Digital measures of real-world physical behavior assessed passively, continuously, and remotely using digital health technologies have the potential to address some of these limitations and to capture aspects of functional independence in patients with AD. In particular, measures of real-world gait, physical activity, and life-space mobility captured with wearable sensors may offer value. Additional research is needed to understand the validity, feasibility, and acceptability of these measures in AD clinical research.
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Affiliation(s)
| | | | - Michael Busa
- Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA
- Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Corinna Serviente
- Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA
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Li K, Ma X, Chen T, Xin J, Wang C, Wu B, Ogihara A, Zhou S, Liu J, Huang S, Wang Y, Li S, Chen Z, Xu R. A new early warning method for mild cognitive impairment due to Alzheimer's disease based on dynamic evaluation of the "spatial executive process". Digit Health 2023; 9:20552076231194938. [PMID: 37654709 PMCID: PMC10467230 DOI: 10.1177/20552076231194938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/28/2023] [Indexed: 09/02/2023] Open
Abstract
Objective Mild cognitive impairment (MCI) due to Alzheimer's disease (AD), as an early stage of AD, is an important point for early warning of AD. Neuropathological studies have shown that AD pathology in pre-dementia patients involves the hippocampus and caudate nucleus, which are responsible for controlling cognitive mechanisms such as the spatial executive process (SEP). The aim of this study is to design a new method for early warning of MCI due to AD by dynamically evaluating SEP. Methods We designed fingertip interaction handwriting digital evaluation paradigms and analyzed the dynamic trajectory of fingertip interaction and image data during "clock drawing" and "repetitive writing" tasks. Extracted fingertip interaction digital biomarkers were used to assess participants' SEP disorders, ultimately enabling intelligent diagnosis of MCI due to AD. A cross-sectional study demonstrated the predictive performance of this new method. Results We enrolled 30 normal cognitive (NC) elderly and 30 MCI due to AD patients, and clinical research results showed that there may be neurobehavioral differences between the two groups in digital biomarkers captured during SEP. The early warning performance for MCI due to AD of this new method (areas under the curve (AUC) = 0.880) is better than that of the Minimum Mental State Examination (MMSE) neuropsychological scale (AUC = 0.856) assessed by physicians. Conclusion Patients with MCI due to AD may have SEP disorders, and this new method based on dynamic evaluation of SEP will provide a novel human-computer interaction and intelligent early warning method for home and community screening of MCI due to AD.
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Affiliation(s)
- Kai Li
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
- Joint Laboratory of Police Health Smart Surveillance, Zhejiang Police College, Hangzhou, China
| | - Xiaowen Ma
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Tong Chen
- Department of Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Junyi Xin
- School of Information Engineering, Hangzhou Medical College, Hangzhou, China
| | - Chen Wang
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Bo Wu
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Computer Science, Tokyo University of Technology, Hachioji City, Tokyo, Japan
| | - Atsushi Ogihara
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Health Sciences and Social Welfare, Faculty of Human Sciences, Waseda University, Tokorozawa, Japan
| | - Siyu Zhou
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Public health, Hangzhou Normal University, Hangzhou, China
| | - Jiakang Liu
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shouqiang Huang
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yujia Wang
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuwu Li
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zeyuan Chen
- Joint Laboratory of Police Health Smart Surveillance, Zhejiang Police College, Hangzhou, China
- School of International Studies and Cooperation, Zhejiang Police College, Hangzhou, China
| | - Runlong Xu
- School of Information Engineering, Hangzhou Medical College, Hangzhou, China
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Tacchino A, Podda J, Bergamaschi V, Pedullà L, Brichetto G. Cognitive rehabilitation in multiple sclerosis: Three digital ingredients to address current and future priorities. Front Hum Neurosci 2023; 17:1130231. [PMID: 36908712 PMCID: PMC9995764 DOI: 10.3389/fnhum.2023.1130231] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/09/2023] [Indexed: 02/25/2023] Open
Abstract
Multiple sclerosis (MS) is a neurological chronic disease with autoimmune demyelinating lesions and one of the most common disability causes in young adults. People with MS (PwMS) experience cognitive impairments (CIs) and clinical evidence shows their presence during all MS stages even in the absence of other symptoms. Cognitive rehabilitation (CR) aims at reducing CI and improving PwMS' awareness of cognitive difficulties faced in their daily living. More defined cognitive profiles, easier treatment access and the need to transfer intervention effects into everyday life activities are aims of utmost relevance for CR in MS. Currently, advanced technologies may pave the way to rethink CR in MS to address the priority of more personalized and effective, accessible and ecological interventions. For this purpose, digital twins, tele-cognitive-rehabilitation and metaverse are the main candidate digital ingredients. Based on scientific evidences, we propose digital twin technology to enhance MS cognitive phenotyping; tele-cognitive-rehabilitation to make feasible the cognitive intervention access to a larger number of PwMS; and metaverse to represent the best choice to train real-world dual- and multi-tasking deficits in virtual daily life environments. Moreover, multi-domain high-frequency big-data collected through tele-cognitive-assessment, tele-cognitive-rehabilitation, and metaverse may be merged to refine artificial intelligence algorithms and obtain increasingly detailed patient's cognitive profile in order to enhance intervention personalization. Here, we present how these digital ingredients and their integration could be crucial to address the current and future needs of CR facilitating the early detection of subtle CI and the delivery of increasingly effective treatments.
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Affiliation(s)
- Andrea Tacchino
- Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy
| | - Jessica Podda
- Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy
| | - Valeria Bergamaschi
- AISM Rehabilitation Center Liguria, Italian Multiple Sclerosis Society (AISM), Genoa, Italy
| | - Ludovico Pedullà
- Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy
| | - Giampaolo Brichetto
- Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy.,AISM Rehabilitation Center Liguria, Italian Multiple Sclerosis Society (AISM), Genoa, Italy
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50
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Golini E, Rigamonti M, Raspa M, Scavizzi F, Falcone G, Gourdon G, Mandillo S. Excessive rest time during active phase is reliably detected in a mouse model of myotonic dystrophy type 1 using home cage monitoring. Front Behav Neurosci 2023; 17:1130055. [PMID: 36935893 PMCID: PMC10017452 DOI: 10.3389/fnbeh.2023.1130055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/17/2023] [Indexed: 03/06/2023] Open
Abstract
Myotonic dystrophy type 1 (DM1) is a dominantly inherited neuromuscular disease caused by the abnormal expansion of CTG-repeats in the 3'-untranslated region of the Dystrophia Myotonica Protein Kinase (DMPK) gene, characterized by multisystemic symptoms including muscle weakness, myotonia, cardio-respiratory problems, hypersomnia, cognitive dysfunction and behavioral abnormalities. Sleep-related disturbances are among the most reported symptoms that negatively affect the quality of life of patients and that are present in early and adult-onset forms of the disease. DMSXL mice carry a mutated human DMPK transgene containing >1,000 CTGrepeats, modeling an early onset, severe form of DM1. They exhibit a pathologic neuromuscular phenotype and also synaptic dysfunction resulting in neurological and behavioral deficits similar to those observed in patients. Additionally, they are underweight with a very high mortality within the first month after birth presenting several welfare issues. To specifically explore sleep/rest-related behaviors of this frail DM1 mouse model we used an automated home cage-based system that allows 24/7 monitoring of their activity non-invasively. We tested male and female DMSXL mice and their wild-type (WT) littermates in Digital Ventilated Cages (DVCR) assessing activity and rest parameters on day and night for 5 weeks. We demonstrated that DMSXL mice show reduced activity and regularity disruption index (RDI), higher percentage of zero activity per each hour and longer periods of rest during the active phase compared to WT. This novel rest-related phenotype in DMSXL mice, assessed unobtrusively, could be valuable to further explore mechanisms and potential therapeutic interventions to alleviate the very common symptom of excessive daytime sleepiness in DM1 patients.
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Affiliation(s)
- Elisabetta Golini
- Institute of Biochemistry and Cell Biology (IBBC), National Research Council (CNR), Monterotondo, Italy
| | - Mara Rigamonti
- Tecniplast S.p.A., Buguggiate, Italy
- *Correspondence: Mara Rigamonti,
| | - Marcello Raspa
- Institute of Biochemistry and Cell Biology (IBBC), National Research Council (CNR), Monterotondo, Italy
| | - Ferdinando Scavizzi
- Institute of Biochemistry and Cell Biology (IBBC), National Research Council (CNR), Monterotondo, Italy
| | - Germana Falcone
- Institute of Biochemistry and Cell Biology (IBBC), National Research Council (CNR), Monterotondo, Italy
| | - Genevieve Gourdon
- Sorbonne Université, INSERM, Institut de Myologie, Centre de Recherche en Myologie, Paris, France
| | - Silvia Mandillo
- Institute of Biochemistry and Cell Biology (IBBC), National Research Council (CNR), Monterotondo, Italy
- Silvia Mandillo,
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