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Barata F, Shim J, Wu F, Langer P, Fleisch E. The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers. JAMIA Open 2024; 7:ooae027. [PMID: 38596697 PMCID: PMC11000821 DOI: 10.1093/jamiaopen/ooae027] [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: 06/21/2023] [Revised: 01/22/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024] Open
Abstract
Objectives We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers. Materials and Methods The Bitemporal Lens Model integrates the change-point model, focusing on critical disease-specific parameters, and the recurrent-pattern model, emphasizing lifestyle and behavioral patterns, for early risk identification. Results By incorporating both the change-point and recurrent-pattern models, the Bitemporal Lens Model offers a comprehensive approach to preventive healthcare, enabling a more nuanced understanding of individual health trajectories, demonstrated through its application in cardiovascular disease prevention. Discussion We explore the benefits of the Bitemporal Lens Model, highlighting its capacity for personalized risk assessment through the integration of two distinct lenses. We also acknowledge challenges associated with handling intricate data across dual temporal dimensions, maintaining data integrity, and addressing ethical concerns pertaining to privacy and data protection. Conclusion The Bitemporal Lens Model presents a novel approach to enhancing preventive healthcare effectiveness.
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Affiliation(s)
- Filipe Barata
- Centre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland
| | - Jinjoo Shim
- Centre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland
| | - Fan Wu
- Centre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland
| | - Patrick Langer
- Centre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland
| | - Elgar Fleisch
- Centre for Digital Health Interventions, ETH Zurich, Zürich, Zürich, 8092, Switzerland
- Centre for Digital Health Interventions, University of St. Gallen, St. Gallen, St. Gallen, 9000, Switzerland
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Smokovski I, Steinle N, Behnke A, Bhaskar SMM, Grech G, Richter K, Niklewski G, Birkenbihl C, Parini P, Andrews RJ, Bauchner H, Golubnitschaja O. Digital biomarkers: 3PM approach revolutionizing chronic disease management - EPMA 2024 position. EPMA J 2024; 15:149-162. [PMID: 38841615 PMCID: PMC11147994 DOI: 10.1007/s13167-024-00364-6] [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: 04/11/2024] [Accepted: 04/23/2024] [Indexed: 06/07/2024]
Abstract
Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide. Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs. Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large. DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs.
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Affiliation(s)
- Ivica Smokovski
- University Clinic of Endocrinology, Diabetes and Metabolic Disorders, Skopje, North Macedonia
- Faculty of Medical Sciences, University Goce Delcev, Stip, North Macedonia
| | - Nanette Steinle
- Veteran Affairs Capitol Health Care Network, Linthicum, MD USA
- University of Maryland School of Medicine, Baltimore, MD USA
| | - Andrew Behnke
- Endocrinology Section, Carilion Clinic, Roanoke, VA USA
- Virginia Tech Carilion School of Medicine, Roanoke, VA USA
| | - Sonu M. M. Bhaskar
- Department of Neurology, Division of Cerebrovascular Medicine and Neurology, National Cerebral and Cardiovascular Centre (NCVC), Suita, Osaka Japan
- Department of Neurology & Neurophysiology, Liverpool Hospital, Ingham Institute for Applied Medical Research and South Western Sydney Local Health District, Sydney, NSW Australia
- NSW Brain Clot Bank, Global Health Neurology Lab & NSW Health Pathology, Sydney, NSW Australia
| | - Godfrey Grech
- Department of Pathology, Faculty of Medicine & Surgery, University of Malta, Msida, Malta
| | - Kneginja Richter
- Faculty of Medical Sciences, University Goce Delcev, Stip, North Macedonia
- CuraMed Tagesklinik Nürnberg GmbH, Nuremberg, Germany
- Technische Hochschule Nürnberg GSO, Nuremberg, Germany
- University Clinic for Psychiatry and Psychotherapy, Paracelsus Medical University, Nuremberg, Germany
| | - Günter Niklewski
- University Clinic for Psychiatry and Psychotherapy, Paracelsus Medical University, Nuremberg, Germany
| | - Colin Birkenbihl
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Paolo Parini
- Cardio Metabolic Unit, Department of Medicine Huddinge, and Department of Laboratory Medicine, Karolinska Institute, and Medicine Unit of Endocrinology, Theme Inflammation and Ageing, Karolinska University Hospital, Stockholm, Sweden
| | - Russell J. Andrews
- Nanotechnology & Smart Systems Groups, NASA Ames Research Center, Aerospace Medical Association, Silicon Valley, CA USA
| | - Howard Bauchner
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA
| | - Olga Golubnitschaja
- Predictive, Preventive and Personalized (3P) Medicine, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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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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Teng Z. Novel Development and Prospects in Pathogenesis, Diagnosis, and Therapy of Alzheimer's Disease. J Alzheimers Dis Rep 2024; 8:345-354. [PMID: 38405339 PMCID: PMC10894614 DOI: 10.3233/adr-230130] [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: 09/13/2023] [Accepted: 12/29/2023] [Indexed: 02/27/2024] Open
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease with cognitive decline and behavioral dysfunction. AD will become a global public health concern due to its increasing prevalence brought on by the severity of global aging. It is critical to understand the pathogenic mechanisms of AD and investigate or pursue a viable therapy strategy in clinic. Amyloid-β (Aβ) accumulation and abnormally hyperphosphorylated tau protein are the main regulating variables in the pathological phase of AD. And neuroinflammation brought on by activated microglia was found to be one risk factor contributing to changes in Aβ and tau pathology. It is important to investigate the unique biomarkers of early diagnosis and advanced stage, which may help to elucidate the specific pathological process of AD and provide potential novel therapeutic targets or preventative measures.
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Affiliation(s)
- Zenghui Teng
- Medical Faculty, Institute of Neuro- and Sensory Physiology, Heinrich-Heine-University Düsseldorf, Germany
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Etekochay MO, Amaravadhi AR, González GV, Atanasov AG, Matin M, Mofatteh M, Steinbusch HW, Tesfaye T, Praticò D. Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer's Disease. J Alzheimers Dis 2024; 99:1-20. [PMID: 38640152 DOI: 10.3233/jad-231135] [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: 04/21/2024]
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for AD, such as neuroimaging approaches. Neuroimaging techniques, including positron emission tomography and magnetic resonance imaging, have revolutionized the field by providing valuable insights into the structural and functional alterations in the brains of individuals with AD. These imaging modalities enable the detection of early biomarkers such as amyloid-β plaques and tau protein tangles, facilitating early and precise diagnosis. Furthermore, the emerging technologies encompassing blood-based biomarkers and neurochemical profiling exhibit promising results in the identification of specific molecular signatures for AD. The integration of machine learning algorithms and artificial intelligence has enhanced the predictive capacity of these diagnostic tools when analyzing complex datasets. In this review article, we will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD. These advancements hold immense potential for early detection and intervention, thereby paving the way for personalized therapeutic strategies and ultimately augmenting the quality of life for individuals affected by AD.
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Affiliation(s)
| | - Amoolya Rao Amaravadhi
- Internal Medicine, Malla Reddy Institute of Medical Sciences, Jeedimetla, Hyderabad, India
| | - Gabriel Villarrubia González
- Expert Systems and Applications Laboratory (ESALAB), Faculty of Science, University of Salamanca, Salamanca, Spain
| | - Atanas G Atanasov
- Department of Biotechnology and Nutrigenomics, Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Poland
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Maima Matin
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Mohammad Mofatteh
- School of Medicine, Dentistry, and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Harry Wilhelm Steinbusch
- Department of Cellular and Translational Neuroscience, School for Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Netherlands
| | - Tadele Tesfaye
- CareHealth Medical Practice, Jimma Road, Addis Ababa, Ethiopia
| | - Domenico Praticò
- Alzheimer's Center at Temple, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
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Muurling M, de Boer C, Hinds C, Atreya A, Doherty A, Alepopoulos V, Curcic J, Brem AK, Conde P, Kuruppu S, Morató X, Saletti V, Galluzzi S, Vilarino Luis E, Cardoso S, Stukelj T, Kramberger MG, Roik D, Koychev I, Hopøy AC, Schwertner E, Gkioka M, Aarsland D, Visser PJ. Feasibility and usability of remote monitoring in Alzheimer's disease. Digit Health 2024; 10:20552076241238133. [PMID: 38601188 PMCID: PMC11005503 DOI: 10.1177/20552076241238133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 02/22/2024] [Indexed: 04/12/2024] Open
Abstract
Introduction Remote monitoring technologies (RMTs) can measure cognitive and functional decline objectively at-home, and offer opportunities to measure passively and continuously, possibly improving sensitivity and reducing participant burden in clinical trials. However, there is skepticism that age and cognitive or functional impairment may render participants unable or unwilling to comply with complex RMT protocols. We therefore assessed the feasibility and usability of a complex RMT protocol in all syndromic stages of Alzheimer's disease and in healthy control participants. Methods For 8 weeks, participants (N = 229) used two activity trackers, two interactive apps with either daily or weekly cognitive tasks, and optionally a wearable camera. A subset of participants participated in a 4-week sub-study (N = 45) using fixed at-home sensors, a wearable EEG sleep headband and a driving performance device. Feasibility was assessed by evaluating compliance and drop-out rates. Usability was assessed by problem rates (e.g., understanding instructions, discomfort, forgetting to use the RMT or technical problems) as discussed during bi-weekly semi-structured interviews. Results Most problems were found for the active apps and EEG sleep headband. Problem rates increased and compliance rates decreased with disease severity, but the study remained feasible. Conclusions This study shows that a highly complex RMT protocol is feasible, even in a mild-to-moderate AD population, encouraging other researchers to use RMTs in their study designs. We recommend evaluating the design of individual devices carefully before finalizing study protocols, considering RMTs which allow for real-time compliance monitoring, and engaging the partners of study participants in the research.
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Affiliation(s)
- Marijn Muurling
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Casper de Boer
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Chris Hinds
- Nuffield Department of Population Health, University of Oxford Big Data Institute, Oxford, UK
| | - Alankar Atreya
- Nuffield Department of Population Health, University of Oxford Big Data Institute, Oxford, UK
| | - Aiden Doherty
- Nuffield Department of Population Health, University of Oxford Big Data Institute, Oxford, UK
| | - Vasilis Alepopoulos
- Information Technologies Institute, Center for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece
| | | | - Anna-Katharine Brem
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Old Age Psychiatry, University Hospital of Old Age Psychiatry, University of Bern, Bern, Switzerland
| | - Pauline Conde
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sajini Kuruppu
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Xavier Morató
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Valentina Saletti
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Samantha Galluzzi
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Estefania Vilarino Luis
- Centre de la mémoire, Université de Genève (UNIGE), Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Sandra Cardoso
- Faculdade de Medicina da, Universidade de Lisboa, Lisbon, Portugal
| | - Tina Stukelj
- Department of Neurology, University Medical Center Ljubljana and Medical faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Milica Gregorič Kramberger
- Department of Neurology, University Medical Center Ljubljana and Medical faculty, University of Ljubljana, Ljubljana, Slovenia
- Division of Clinical Geriatrics, Department of Neurobiology, Department of Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Dora Roik
- Department of Geriatric Psychiatry, Central Institute for Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg,
Germany
| | - Ivan Koychev
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ann-Cecilie Hopøy
- Department of Old Age Psychiatry, Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Emilia Schwertner
- Division of Clinical Geriatrics, Department of Neurobiology, Department of Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Faculty of Psychology, SWPS University of Social Sciences and Humanities, Krakow, Poland
| | - Mara Gkioka
- Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI – AUTh), Balkan Center, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dag Aarsland
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Old Age Psychiatry, Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Division of Clinical Geriatrics, Department of Neurobiology, Department of Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
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Muurling M, de Boer C, Vairavan S, Harms RL, Chadha AS, Tarnanas I, Luis EV, Religa D, Gjestsen MT, Galluzzi S, Ibarria Sala M, Koychev I, Hausner L, Gkioka M, Aarsland D, Visser PJ, Brem AK. Augmented reality versus standard tests to assess cognition and function in early Alzheimer's disease. NPJ Digit Med 2023; 6:234. [PMID: 38110486 PMCID: PMC10728213 DOI: 10.1038/s41746-023-00978-6] [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: 05/17/2023] [Accepted: 11/29/2023] [Indexed: 12/20/2023] Open
Abstract
Augmented reality (AR) apps, in which the virtual and real world are combined, can recreate instrumental activities of daily living (IADL) and are therefore promising to measure cognition needed for IADL in early Alzheimer's disease (AD) both in the clinic and in the home settings. The primary aim of this study was to distinguish and classify healthy controls (HC) from participants with AD pathology in an early AD stage using an AR app. The secondary aims were to test the association of the app with clinical cognitive and functional tests and investigate the feasibility of at-home testing using AR. We furthermore investigated the test-retest reliability and potential learning effects of the task. The digital score from the AR app could significantly distinguish HC from preclinical AD (preAD) and prodromal AD (proAD), and preAD from proAD, both with in-clinic and at-home tests. For the classification of the proAD group, the digital score (AUCclinic_visit = 0.84 [0.75-0.93], AUCat_home = 0.77 [0.61-0.93]) was as good as the cognitive score (AUC = 0.85 [0.78-0.93]), while for classifying the preAD group, the digital score (AUCclinic_visit = 0.66 [0.53-0.78], AUCat_home = 0.76 [0.61-0.91]) was superior to the cognitive score (AUC = 0.55 [0.42-0.68]). In-clinic and at-home tests moderately correlated (rho = 0.57, p < 0.001). The digital score was associated with the clinical cognitive score (rho = 0.56, p < 0.001). No learning effects were found. Here we report the AR app distinguishes HC from otherwise healthy Aβ-positive individuals, both in the outpatient setting and at home, which is currently not possible with standard cognitive tests.
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Affiliation(s)
- Marijn Muurling
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
| | - Casper de Boer
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Srinivasan Vairavan
- Janssen Research & Development, LLC, 1125 Trenton Harbourton Road, Titusville, NJ, 08560, USA
| | | | | | - Ioannis Tarnanas
- Altoida Inc., Washington, DC, USA
- Trinity College Dublin, Global Brain Health Institute - GHBI, Dublin, Ireland
| | - Estefania Vilarino Luis
- Centre de la mémoire, Université de Genève (UNIGE), Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Dorota Religa
- Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Stockholm, Sweden
| | - Martha Therese Gjestsen
- Centre for Age-related Medicine, Stavanger University Hospital, Stavanger, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Samantha Galluzzi
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Marta Ibarria Sala
- Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain
| | - Ivan Koychev
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Lucrezia Hausner
- Central Institute for Mental Health, Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Mara Gkioka
- Alzheimer Hellas and Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI - AUTh), Balkan Center, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dag Aarsland
- Centre for Age-related Medicine, Stavanger University Hospital, Stavanger, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Anna-Katharine Brem
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- University Hospital of Old Age Psychiatry, University of Bern, Bern, Switzerland
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8
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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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Alfalahi H, Dias SB, Khandoker AH, Chaudhuri KR, Hadjileontiadis LJ. A scoping review of neurodegenerative manifestations in explainable digital phenotyping. NPJ Parkinsons Dis 2023; 9:49. [PMID: 36997573 PMCID: PMC10063633 DOI: 10.1038/s41531-023-00494-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/16/2023] [Indexed: 04/03/2023] Open
Abstract
Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.
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Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, University of Lisbon, Lisbon, Portugal
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Kallol Ray Chaudhuri
- Parkinson Foundation, International Center of Excellence, King's College London, Denmark Hills, London, UK
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
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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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Tarnanas I, Tsolaki M. Making Pre-screening for Alzheimer's Disease (AD) and Postoperative Delirium Among Post-Acute COVID-19 Syndrome (PACS) a National Priority: The Deep Neuro Study. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1424:41-47. [PMID: 37486477 DOI: 10.1007/978-3-031-31982-2_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
SARS-CoV-2 effects on cognition are a vibrant area of active research. Many researchers suggest that COVID-19 patients with severe symptoms leading to hospitalization sustain significant neurodegenerative injury, such as encephalopathy and poor discharge disposition. However, despite some post-acute COVID-19 syndrome (PACS) case series that have described elevated neurodegenerative biomarkers, no studies have been identified that directly compared levels to those in mild cognitive impairment, non-PACS postoperative delirium patients after major non-emergent surgery, or preclinical Alzheimer's disease (AD) patients that have clinical evidence of Alzheimer's without symptoms. According to recent estimates, there may be 416 million people globally on the AD continuum, which include approximately 315 million people with preclinical AD. In light of all the above, a more effective application of digital biomarker and explainable artificial intelligence methodologies that explored amyloid beta, neuronal, axonal, and glial markers in relation to neurological complications in-hospital or later outcomes could significantly assist progress in the field. Easy and scalable subjects' risk stratification is of utmost importance, yet current international collaboration initiatives are still challenging due to the limited explainability and accuracy to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials. In this open letter, we propose the administration of selected digital biomarkers previously discovered and validated in other EU-funded studies to become a routine assessment for non-PACS preoperative cognitive impairment, PACS neurological complications in-hospital, or later PACS and non-PACS improvement in cognition after surgery. The open letter also includes an economic analysis of the implications for such national-level initiatives. Similar collaboration initiatives could have existing pre-diagnostic detection and progression prediction solutions pre-screen the stage before and around diagnosis, enabling new disease manifestation mapping and pushing the field into unchartered territory.
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Affiliation(s)
- Ioannis Tarnanas
- Altoida Inc, Washington, DC, USA.
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
- Atlantic Fellow for Equity in Brain Health, Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA.
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Santiago, Chile.
| | - Magda Tsolaki
- Greek Association of Alzheimer's Disease and Related Disorders (GAADRD), Thessaloniki, Greece
- 1st University Department of Neurology UH "AHEPA", School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI - AUTh) Balkan Center, Buildings A & B, Aristotle University of Thessaloniki, Thessaloniki, Greece
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12
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Skirrow C, Meszaros M, Meepegama U, Lenain R, Papp KV, Weston J, Fristed E. Validation of a Remote and Fully Automated Story Recall Task to Assess for Early Cognitive Impairment in Older Adults: Longitudinal Case-Control Observational Study. JMIR Aging 2022; 5:e37090. [PMID: 36178715 PMCID: PMC9568813 DOI: 10.2196/37090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 07/07/2022] [Accepted: 07/13/2022] [Indexed: 01/23/2023] Open
Abstract
Background Story recall is a simple and sensitive cognitive test that is commonly used to measure changes in episodic memory function in early Alzheimer disease (AD). Recent advances in digital technology and natural language processing methods make this test a candidate for automated administration and scoring. Multiple parallel test stimuli are required for higher-frequency disease monitoring. Objective This study aims to develop and validate a remote and fully automated story recall task, suitable for longitudinal assessment, in a population of older adults with and without mild cognitive impairment (MCI) or mild AD. Methods The “Amyloid Prediction in Early Stage Alzheimer’s disease” (AMYPRED) studies recruited participants in the United Kingdom (AMYPRED-UK: NCT04828122) and the United States (AMYPRED-US: NCT04928976). Participants were asked to complete optional daily self-administered assessments remotely on their smart devices over 7 to 8 days. Assessments included immediate and delayed recall of 3 stories from the Automatic Story Recall Task (ASRT), a test with multiple parallel stimuli (18 short stories and 18 long stories) balanced for key linguistic and discourse metrics. Verbal responses were recorded and securely transferred from participants’ personal devices and automatically transcribed and scored using text similarity metrics between the source text and retelling to derive a generalized match score. Group differences in adherence and task performance were examined using logistic and linear mixed models, respectively. Correlational analysis examined parallel-forms reliability of ASRTs and convergent validity with cognitive tests (Logical Memory Test and Preclinical Alzheimer’s Cognitive Composite with semantic processing). Acceptability and usability data were obtained using a remotely administered questionnaire. Results Of the 200 participants recruited in the AMYPRED studies, 151 (75.5%)—78 cognitively unimpaired (CU) and 73 MCI or mild AD—engaged in optional remote assessments. Adherence to daily assessment was moderate and did not decline over time but was higher in CU participants (ASRTs were completed each day by 73/106, 68.9% participants with MCI or mild AD and 78/94, 83% CU participants). Participants reported favorable task usability: infrequent technical problems, easy use of the app, and a broad interest in the tasks. Task performance improved modestly across the week and was better for immediate recall. The generalized match scores were lower in participants with MCI or mild AD (Cohen d=1.54). Parallel-forms reliability of ASRT stories was moderate to strong for immediate recall (mean rho 0.73, range 0.56-0.88) and delayed recall (mean rho=0.73, range=0.54-0.86). The ASRTs showed moderate convergent validity with established cognitive tests. Conclusions The unsupervised, self-administered ASRT task is sensitive to cognitive impairments in MCI and mild AD. The task showed good usability, high parallel-forms reliability, and high convergent validity with established cognitive tests. Remote, low-cost, low-burden, and automatically scored speech assessments could support diagnostic screening, health care, and treatment monitoring.
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Affiliation(s)
| | | | | | | | - Kathryn V Papp
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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13
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Tarnanas I, Tsolaki M. Making pre-screening for Alzheimer's disease (AD) and Postoperative delirium among post-acute COVID-19 syndrome - (PACS) a national priority: The Deep Neuro Study. OPEN RESEARCH EUROPE 2022; 2:98. [PMID: 37767224 PMCID: PMC10521085 DOI: 10.12688/openreseurope.15005.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/16/2022] [Indexed: 09/29/2023]
Abstract
SARS-CoV-2 effects on cognition is a vibrant area of active research. Many researchers suggest that COVID-19 patients with severe symptoms leading to hospitalization, sustain significant neurodegenerative injury, such as encephalopathy and poor discharge disposition. However, despite some post-acute COVID-19 syndrome (PACS) case series that have described elevated neurodegenerative biomarkers, no studies have been identified that directly compared levels to those in mild cognitive impairment, non-PACS postoperative delirium patients after major non-emergent surgery or preclinical Alzheimer's Disease (AD) patients, that have clinical evidence of Alzheimer's without symptoms. According to recent estimates, there may be 416 million people globally on the AD continuum, which include approximately 315 million people with preclinical AD. In light of all the above, a more effective application of digital biomarker and explainable artificial intelligence methodologies that explored amyloid beta, neuronal, axonal, and glial markers in relation to neurological complications in-hospital or later outcomes could significantly assist progress in the field. Easy and scalable subjects' risk stratification is of utmost importance, yet current international collaboration initiatives are still challenging due to the limited explainability and accuracy to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials. In this open letter, we propose the administration of selected digital biomarkers previously discovered and validated in other EU funded studies to become a routine assessment for non-PACS preoperative cognitive impairment, PACS neurological complications in-hospital or later PACS and non-PACS improvement in cognition after surgery. The open letter also includes an economic analysis of the implications for such national level initiatives. Similar collaboration initiatives could have existing prediagnostic detection and progression prediction solutions pre-screen the stage before and around diagnosis, enabling new disease manifestation mapping and pushing the field into unchartered territory.
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Affiliation(s)
- Ioannis Tarnanas
- Altoida Inc, Washington DC, 20003, USA
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Atlantic Fellow for Equity in Brain Health, University of California San Francisco, San Francisco, USA
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Magda Tsolaki
- Greek Association of Alzheimer's Disease and Related Disorders (GAADRD), Thessaloniki, Greece
- 1st University Department of Neurology UH “AHEPA”, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI - AUTh) Balkan Center, Aristotle University of Thessaloniki, Buildings A & B, Thessaloniki, Greece
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14
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Seixas AA, Rajabli F, Pericak-Vance MA, Jean-Louis G, Harms RL, Tarnanas I. Associations of digital neuro-signatures with molecular and neuroimaging measures of brain resilience: The altoida large cohort study. Front Psychiatry 2022; 13:899080. [PMID: 36061297 PMCID: PMC9435312 DOI: 10.3389/fpsyt.2022.899080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 07/06/2022] [Indexed: 01/08/2023] Open
Abstract
Background Mixed results in the predictive ability of traditional biomarkers to determine cognitive functioning and changes in older adults have led to misdiagnosis and inappropriate treatment plans to address mild cognitive impairment and dementia among older adults. To address this critical gap, the primary goal of the current study is to investigate whether a digital neuro signature (DNS-br) biomarker predicted global cognitive functioning and change over time relative among cognitively impaired and cognitive healthy older adults. The secondary goal is to compare the effect size of the DNS-br biomarker on global cognitive functioning compared to traditional imaging and genomic biomarkers. The tertiary goal is to investigate which demographic and clinical factors predicted DNS-br in cognitively impaired and cognitively healthy older adults. Methods We conducted two experiments (Study A and Study B) to assess DNS for brain resilience (DNS-br) against the established FDG-PET brain imaging signature for brain resilience, based on a 10 min digital cognitive assessment tool. Study A was a semi-naturalistic observational study that included 29 participants, age 65+, with mild to moderate mild cognitive impairment and AD diagnosis. Study B was also a semi-naturalistic observational multicenter study which included 496 participants (213 mild cognitive impairment (MCI) and 283 cognitively healthy controls (HC), a total of 525 participants-cognitively healthy (n = 283) or diagnosed with MCI (n = 213) or AD (n = 29). Results DNS-br total score and majority of the 11 DNS-br neurocognitive subdomain scores were significantly associated with FDG-PET resilience signature, PIB ratio, cerebral gray matter and white matter volume after adjusting for multiple testing. DNS-br total score predicts cognitive impairment for the 80+ individuals in the Altoida large cohort study. We identified a significant interaction between the DNS-br total score and time, indicating that participants with higher DNS-br total score or FDG-PET in the resilience signature would show less cognitive decline over time. Conclusion Our findings highlight that a digital biomarker predicted cognitive functioning and change, which established biomarkers are unable to reliably do. Our findings also offer possible etiologies of MCI and AD, where education did not protect against cognitive decline.
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Affiliation(s)
- Azizi A. Seixas
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Farid Rajabli
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Margaret A. Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Girardin Jean-Louis
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, United States
| | | | - Ioannis Tarnanas
- Altoida Inc., Washington, DC, United States
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
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15
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Rutkowski TM, Abe MS, Tokunaga S, Komendzinski T, Otake-Matsuura M. Dementia Digital Neuro-biomarker Study from Theta-band EEG Fluctuation Analysis in Facial and Emotional Identification Short-term Memory Oddball Paradigm. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4056-4059. [PMID: 36086235 DOI: 10.1109/embc48229.2022.9871991] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
An efficient machine learning (ML) implementation in the so-called 'AI for social good' domain shall contribute to dementia digital neuro-biomarker development for early-onset prognosis of a possible cognitive decline. We report encouraging initial developments of wearable EEG-derived theta-band fluctuations examination and a successive classification embracing a time-series complexity examination with a multifractal detrended fluctuation analysis (MFDFA) in the face or emotion video-clip identification short-term oddball memory tasks. We also report findings from a thirty-five elderly volunteer pilot study that EEG responses to instructed to ignore (inhibited) oddball paradigm stimulation results in more informative MFDFA features, leading to better machine learning classification results. The reported pilot project showcases vital social assistance of artificial intelligence (AI) application for an early-onset dementia prognosis. Clinical Relevance- This introduces a candidate for an objective digital neuro-biomarker from theta-band EEG recorded by a wearable for a plausible replacement of biased 'paper & pencil' tests for a mild cognitive impairment (MCI) evaluation.
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Kobayashi M, Yamada Y, Shinkawa K, Nemoto M, Nemoto K, Arai T. Automated Early Detection of Alzheimer's Disease by Capturing Impairments in Multiple Cognitive Domains with Multiple Drawing Tasks. J Alzheimers Dis 2022; 88:1075-1089. [PMID: 35723100 PMCID: PMC9484124 DOI: 10.3233/jad-215714] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Automatic analysis of the drawing process using a digital tablet and pen has been applied to successfully detect Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most studies focused on analyzing individual drawing tasks separately, and the question of how a combination of drawing tasks could improve the detection performance thus remains unexplored. OBJECTIVE We aimed to investigate whether analysis of the drawing process in multiple drawing tasks could capture different, complementary aspects of cognitive impairments, with a view toward combining multiple tasks to effectively improve the detection capability. METHODS We collected drawing data from 144 community-dwelling older adults (27 AD, 65 MCI, and 52 cognitively normal, or CN) who performed five drawing tasks. We then extracted motion- and pause-related drawing features for each task and investigated the statistical associations of the features with the participants' diagnostic statuses and cognitive measures. RESULTS The drawing features showed gradual changes from CN to MCI and then to AD, and the changes in the features for each task were statistically associated with cognitive impairments in different domains. For classification into the three diagnostic categories, a machine learning model using the features from all five tasks achieved a classification accuracy of 75.2%, an improvement by 7.8% over that of the best single-task model. CONCLUSION Our results demonstrate that a common set of drawing features from multiple drawing tasks can capture different, complementary aspects of cognitive impairments, which may lead to a scalable way to improve the automated detection of AD and MCI.
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Ashford JW, Schmitt FA, Bergeron MF, Bayley PJ, Clifford JO, Xu Q, Liu X, Zhou X, Kumar V, Buschke H, Dean M, Finkel SI, Hyer L, Perry G. Now is the Time to Improve Cognitive Screening and Assessment for Clinical and Research Advancement. J Alzheimers Dis 2022; 87:305-315. [DOI: 10.3233/jad-220211] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Alzheimer’s disease (AD) is the only cause of death ranked in the top ten globally without precise early diagnosis or effective means of prevention or treatment. Further, AD was identified as a pandemic [1] well before COVID-19 was dubbed a 21st century pandemic [2]. And now, with the realization of the prominent secondary impacts of pandemics, there is a growing, widespread recognition of the tremendous magnitude of the impending burden from AD in an aging world population in the coming decades [3]. This appreciation has amplified the growing and pressing need for a new, efficacious, and practical platform to detect and track cognitive decline, beginning in the preliminary (prodromal) phases of the disease, sensitively, accurately, effectively, reliably, efficiently, and remotely [4–7]. Moreover, the parallel necessity of clarifying and understanding risk factors, developing successful prevention strategies [8–17], and discovering and monitoring viable and effective treatments could all benefit from accurate and efficient screening and assessment platforms. Modern recognition of AD [18] as a common affliction of the elderly began in 1968 with a paper by Blessed, Tomlinson, & Roth [19] in which two tests, one a brief assessment of cognitive function and the other a measure of daily function, demonstrated impairment which was associated with the postmortem counts of neurofibrillary tangles, composed mainly of microtubule-associated protein-tau (tau), in the brain, though not to senile plaques, composed mainly of amyloid-β (Aβ). Even in more recent analyses, the tangles correspond with the severity of dementia more than the plaques [20, 21]. Since 1960, a plethora of cognitive tests, paper and pencil [22, 23], simple screening models [24], and computerized [25–27], have been developed to assess the dysfunction associated with AD. However, there has been limited application of Modern Test Theory, which includes Item Characteristic Curve Analysis, used in the technological development of such tools [28–31], along with widespread failure to understand the underlying AD pathological process to guide test development [32, 33]. The lack of such development has likely been a major contributor to the failure of the field to develop timely screening approaches for AD [34, 35], inaccurate assessment of the progression of AD [36], and even now, failure to find an effective approach to stopping AD.
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Affiliation(s)
- J. Wesson Ashford
- War Related Illness and Injury Study Center, VA Palo Alto HCS, Palo Alto, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Medical, Scientific, Memory Screening Advisory Board, Alzheimer’s Foundation of American (AFA), New York, USA
| | - Frederick A. Schmitt
- Medical, Scientific, Memory Screening Advisory Board, Alzheimer’s Foundation of American (AFA), New York, USA
- Departments of Neurology, Psychiatry, Neurosurgery, Psychology, Behavioral Science; Sanders-Brown Center on Aging, Spinal Cord & Brain Injury Research Center, University of Kentucky, Sanders-Brown Center on Aging, Lexington, KY, USA
| | | | - Peter J. Bayley
- War Related Illness and Injury Study Center, VA Palo Alto HCS, Palo Alto, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Medical, Scientific, Memory Screening Advisory Board, Alzheimer’s Foundation of American (AFA), New York, USA
| | | | - Qun Xu
- Health Management Center, Department of Neurology, Renji Hospital of Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaolei Liu
- Department of Neurology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Yunnan Provincial Clinical Research Center for Neurological Diseases, Yunnan, China
| | - Xianbo Zhou
- Center for Alzheimer’s Research, Washington Institute of Clinical Research, Vienna, VA, USA
- Zhongze Therapeutics, Shanghai, China
| | | | - Herman Buschke
- Medical, Scientific, Memory Screening Advisory Board, Alzheimer’s Foundation of American (AFA), New York, USA
- The Saul R. Korey Department of Neurology and Dominick P. Purpura Department of Neuroscience, Lena and Joseph Gluck Distinguished Scholar in Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Margaret Dean
- Medical, Scientific, Memory Screening Advisory Board, Alzheimer’s Foundation of American (AFA), New York, USA
- Geriatric Division, Internal Medicine, Texas Tech Health Sciences Center, Amarillo, TX, USA
| | - Sanford I. Finkel
- Medical, Scientific, Memory Screening Advisory Board, Alzheimer’s Foundation of American (AFA), New York, USA
- University of Chicago Medical School, Chicago, IL, USA
| | - Lee Hyer
- Medical, Scientific, Memory Screening Advisory Board, Alzheimer’s Foundation of American (AFA), New York, USA
- Gateway Behavioral Health, Mercer University, School of Medicine, Savannah, GA, USA
| | - George Perry
- Medical, Scientific, Memory Screening Advisory Board, Alzheimer’s Foundation of American (AFA), New York, USA
- Brain Health Consortium, Department Biology and Chemistry, University of Texas at San Antonio, San Antonio, TX, USA
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Liepelt-Scarfone I, Ophey A, Kalbe E. Cognition in prodromal Parkinson's disease. PROGRESS IN BRAIN RESEARCH 2022; 269:93-111. [PMID: 35248208 DOI: 10.1016/bs.pbr.2022.01.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
One characteristic of Parkinson's disease (PD) is a prodromal phase, lasting many years during which both pre-clinical motor and non-motor symptoms occur. Around one-fifth of patients with PD manifest mild cognitive impairment at time of clinical diagnosis. Thus, important challenges are to define the time of onset of cognitive dysfunction in the prodromal phase of PD, and to define its co-occurrence with other specific characteristics. Evidence for cognitive change in prodromal PD comes from various study designs, including both longitudinal and cross-sectional approaches with different target groups. These studies support the concept that changes in global cognitive function and alterations in executive functions occur, and that these changes may be present up to 6 years before clinical PD diagnosis. Notably, this evidence led to including global cognitive impairment as an independent prodromal marker in the recently updated research criteria of the Movement Disorder Society for prodromal PD. Knowledge in this field, however, is still at its beginning, and evidence is sparse about many aspects of this topic. Further longitudinal studies including standardized assessments of global and domain-specific cognitive functions are needed to gain further knowledge about the first appearance, the course, and the interaction of cognitive deficits with other non-motor symptoms in prodromal stage PD. Treatment approaches, including non-pharmacological interventions, in individuals with prodromal PD might help to prevent or delay cognitive dysfunction in early PD.
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Affiliation(s)
- Inga Liepelt-Scarfone
- German Center for Neurodegenerative Diseases (DZNE) and Hertie Institute for Clinical Brain Research, Department of Neurodegenerative Diseases, University of Tübingen, Tübingen, Germany; IB-Hochschule, Stuttgart, Germany.
| | - Anja Ophey
- Medical Psychology, Neuropsychology and Gender Studies, Center for Neuropsychological Diagnostics and Intervention (CeNDI), University Hospital Cologne and Medical Faculty of the University of Cologne, Cologne, Germany
| | - Elke Kalbe
- Medical Psychology, Neuropsychology and Gender Studies, Center for Neuropsychological Diagnostics and Intervention (CeNDI), University Hospital Cologne and Medical Faculty of the University of Cologne, Cologne, Germany
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19
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Tarnanas I, Vlamos P, Harms DR. Can detection and prediction models for Alzheimer's Disease be applied to Prodromal Parkinson's Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures. OPEN RESEARCH EUROPE 2022; 1:146. [PMID: 37645162 PMCID: PMC10445877 DOI: 10.12688/openreseurope.14216.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/04/2022] [Indexed: 08/31/2023]
Abstract
Parkinson's disease (PD) is the fastest growing neurodegeneration and has a prediagnostic phase with a lot of challenges to identify clinical and laboratory biomarkers for those in the earliest stages or those 'at risk'. Despite the current research effort, further progress in this field hinges on the more effective application of digital biomarker and artificial intelligence applications at the prediagnostic stages of PD. It is of the highest importance to stratify such prediagnostic subjects that seem to have the most neuroprotective benefit from drugs. However, current initiatives to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials are still challenging due to the limited accuracy and explainability of existing prediagnostic detection and progression prediction solutions. In this brief paper, we report on a novel digital neuro signature (DNS) for prodromal-PD based on selected digital biomarkers previously discovered on preclinical Alzheimer's disease. (AD). Our preliminary results demonstrated a standard DNS signature for both preclinical AD and prodromal PD, containing a ranked selection of features. This novel DNS signature was rapidly repurposed out of 793 digital biomarker features and selected the top 20 digital biomarkers that are predictive and could detect both the biological signature of preclinical AD and the biological mechanism of a-synucleinopathy in prodromal PD. The resulting model can provide physicians with a pool of patients potentially eligible for therapy and comes along with information about the importance of the digital biomarkers that are predictive, based on SHapley Additive exPlanations (SHAP). Similar initiatives could clarify the stage before and around diagnosis, enabling the field to push into unchartered territory at the earliest stages of the disease.
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Affiliation(s)
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory (BiHELab), Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu, Greece
| | | | - The RADAR-AD Consortium
- Altoida Inc., Washington DC, Washington, DC (DC), 20003, USA
- Bioinformatics and Human Electrophysiology Laboratory (BiHELab), Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu, Greece
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