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Grigas O, Damaševičius R, Maskeliūnas R. Positive Effect of Super-Resolved Structural Magnetic Resonance Imaging for Mild Cognitive Impairment Detection. Brain Sci 2024; 14:381. [PMID: 38672031 DOI: 10.3390/brainsci14040381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
This paper presents a novel approach to improving the detection of mild cognitive impairment (MCI) through the use of super-resolved structural magnetic resonance imaging (MRI) and optimized deep learning models. The study introduces enhancements to the perceptual quality of super-resolved 2D structural MRI images using advanced loss functions, modifications to the upscaler part of the generator, and experiments with various discriminators within a generative adversarial training setting. It empirically demonstrates the effectiveness of super-resolution in the MCI detection task, showcasing performance improvements across different state-of-the-art classification models. The paper also addresses the challenge of accurately capturing perceptual image quality, particularly when images contain checkerboard artifacts, and proposes a methodology that incorporates hyperparameter optimization through a Pareto optimal Markov blanket (POMB). This approach systematically explores the hyperparameter space, focusing on reducing overfitting and enhancing model generalizability. The research findings contribute to the field by demonstrating that super-resolution can significantly improve the quality of MRI images for MCI detection, highlighting the importance of choosing an adequate discriminator and the potential of super-resolution as a preprocessing step to boost classification model performance.
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Affiliation(s)
- Ovidijus Grigas
- Faculty of Informatics, Kaunas University of Technology, 50254 Kaunas, Lithuania
| | - Robertas Damaševičius
- Faculty of Informatics, Kaunas University of Technology, 50254 Kaunas, Lithuania
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Rytis Maskeliūnas
- Faculty of Informatics, Kaunas University of Technology, 50254 Kaunas, Lithuania
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Chico-Garcia JL, Sainz-Amo R, Monreal E, Rodriguez-Jorge F, Sainz de la Maza S, Masjuan J, Villar LM, Costa-Frossard França L. Passive assessment of tapping speed through smartphone is useful for monitoring multiple sclerosis. Mult Scler Relat Disord 2024; 86:105595. [PMID: 38598952 DOI: 10.1016/j.msard.2024.105595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/06/2024] [Accepted: 03/27/2024] [Indexed: 04/12/2024]
Abstract
INTRODUCTION Continuously acquired smartphone keyboard interactions may be useful to monitor progression in multiple sclerosis (MS). We aimed to study the correlation between tapping speed (TS), measured as keys/s, and baseline disability scales in patients with MS. METHODS Single-center prospective study in patients with MS. We passively assessed TS during first week, measured by an "in house" smartphone application. Reliability was assessed by intraclass correlation coefficient (ICC). Correlations between median and maximum keys/s of first week of assessment and baseline disability measures were explored. RESULTS One-hundred three patients were included: 62.1 % women, with a median (IQR) age of 47 (40.4-54.8) years-old and an EDSS score of 3.0 (2.0-4.0). Distribution by MS subtypes was: 77.7 % relapsing-remitting MS (RRMS), 17.5 % secondary-progressive MS (SPMS) and 4.9 % primary-progressive MS (PPMS). ICC during first week was 0.714 (p < 0.00001). Both median and maximum keys/s showed a negative correlation with Expanded Disability Status Score, 9-hole peg test and timed 25-foot walk and a positive correlation with Processing Speed Test CogEval® raw and Z-score. Median and maximum keys/s were lower in patients diagnosed with SPMS than in RRMS. Both measures of tapping speed were associated with MS phenotype independently of age. CONCLUSION TS measured through our application is reliable and correlates with baseline disability scales.
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Affiliation(s)
- Juan Luis Chico-Garcia
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain; Alcala University, Alcalá de Henares, Spain.
| | - Raquel Sainz-Amo
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain; Alcala University, Alcalá de Henares, Spain
| | - Enric Monreal
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain; Alcala University, Alcalá de Henares, Spain
| | - Fernando Rodriguez-Jorge
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain; Alcala University, Alcalá de Henares, Spain
| | - Susana Sainz de la Maza
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain; Alcala University, Alcalá de Henares, Spain
| | - Jaime Masjuan
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain; Alcala University, Alcalá de Henares, Spain
| | - Luisa María Villar
- Alcala University, Alcalá de Henares, Spain; Department of Immunology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain
| | - Lucienne Costa-Frossard França
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain; Alcala University, Alcalá de Henares, Spain
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Park JH. Non-equivalence of sub-tasks of the Rey-Osterrieth Complex Figure Test with convolutional neural networks to discriminate mild cognitive impairment. BMC Psychiatry 2024; 24:166. [PMID: 38413893 PMCID: PMC10900783 DOI: 10.1186/s12888-024-05622-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/18/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND The Rey-Osterrieth Complex Figure Test (RCFT) is a tool to evaluate cognitive function. Despite its usefulness, its scoring criteria are as complicated as its figure, leading to a low reliability. Therefore, this study aimed to determine the feasibility of using the convolutional neural network (CNN) model based on the RCFT as a screening tool for mild cognitive impairment (MCI) and investigate the non-equivalence of sub-tasks of the RCFT. METHODS A total of 354 RCFT images (copy and recall conditions) were obtained from 103 healthy controls (HCs) and 74 patients with amnestic MCI (a-MCI). The CNN model was trained to predict MCI based on the RCFT-copy and RCFT-recall images. To evaluate the CNN model's performance, accuracy, sensitivity, specificity, and F1-score were measured. To compare discriminative power, the area under the curve (AUC) was calculated by the receiver operating characteristic (ROC) curve analysis. RESULTS The CNN model based on the RCFT-recall was the most accurate in discriminating a-MCI (accuracy: RCFT-copy = 0.846, RCFT-recall = 0.872, MoCA-K = 0.818). Furthermore, the CNN model based on the RCFT could better discriminate MCI than the MoCA-K (AUC: RCFT-copy = 0.851, RCFT-recall = 0.88, MoCA-K = 0.848). The CNN model based on the RCFT-recall was superior to the RCFT-copy. CONCLUSION These findings suggest the feasibility of using the CNN model based on the RCFT as a surrogate for a conventional screening tool for a-MCI and demonstrate the superiority of the CNN model based on the RCFT-recall to the RCFT-copy.
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Affiliation(s)
- Jin-Hyuck Park
- Department of Occupational Therapy, College of Medical Science, Soonchunhyang University, Room 1401, College of Medical Science, 22 Soonchunhyang-ro, Shinchang-myeon, Asan, Chungcheongnam-do, 31538, Republic of Korea.
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Zhang J, Zhao YJ, Wang JY, Cui H, Li S, Meng X, Cai RY, Xie J, Sun SY, Yao Y, Li J. Comprehensive assessment of fine motor movement and cognitive function among older adults in China: a cross-sectional study. BMC Geriatr 2024; 24:118. [PMID: 38297201 PMCID: PMC10832076 DOI: 10.1186/s12877-024-04725-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/19/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Fine motor skills are closely related to cognitive function. However, there is currently no comprehensive assessment of fine motor movement and how it corresponds with cognitive function. To conduct a complete assessment of fine motor and clarify the relationship between various dimensions of fine motor and cognitive function. METHODS We conducted a cross-sectional study with 267 community-based participants aged ≥ 60 years in Beijing, China. We assessed four tests performance and gathered detailed fine motor indicators using Micro-Electro-Mechanical System (MEMS) motion capture technology. The wearable MEMS device provided us with precise fine motion metrics, while Chinese version of the Montreal Cognitive Assessment (MoCA) was used to assess cognitive function. We adopted logistic regression to analyze the relationship between fine motor movement and cognitive function. RESULTS 129 (48.3%) of the participants had cognitive impairment. The vast majority of fine motor movements have independent linear correlations with MoCA-BJ scores. According to logistic regression analysis, completion time in the Same-pattern tapping test (OR = 1.033, 95%CI = 1.003-1.063), Completion time of non-dominant hand in the Pieces flipping test (OR = 1.006, 95%CI = 1.000-1.011), and trajectory distance of dominant hand in the Pegboard test (OR = 1.044, 95%CI = 1.010-1.068), which represents dexterity, are related to cognitive impairment. Coordination, represented by lag time between hands in the Same-pattern tapping (OR = 1.663, 95%CI = 1.131-2.444), is correlated with cognitive impairment. Coverage in the Dual-hand drawing test as an important indicator of stability is negatively correlated with cognitive function (OR = 0.709, 95%CI = 0.6501-0.959). Based on the above 5-feature model showed consistently high accuracy and sensitivity at the MoCA-BJ score (ACU = 0.80-0.87). CONCLUSIONS The results of a comprehensive fine-motor assessment that integrates dexterity, coordination, and stability are closely related to cognitive functioning. Fine motor movement has the potential to be a reliable predictor of cognitive impairment.
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Affiliation(s)
- Jie Zhang
- Department of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Ye-Jing Zhao
- Department of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | | | - Han Cui
- Department of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Shaojie Li
- China Center for Health Development Studies, Peking University, Beijing, China
| | - Xue Meng
- Office of the National Clinical Research Center for Geriatric Diseases, Beijing Hospital, Institute of Geriatric Medicine, National Center of Gerontology, Chinese Academy of Medical Sciences, Beijing, P. R. China
| | - Rui-Yu Cai
- Department of Geriatric Medicine, Zhucheng People's Hospital, Weifang City, Shandong Province, China
| | - Juan Xie
- Geriatric Department, Hefei First People's Hospital, Hefei, China
| | - Su-Ya Sun
- Department of Geriatrics, Tangshan Gong Ren Hospital, Tangshan, Hebei, China
| | - Yao Yao
- China Center for Health Development Studies, Peking University, Beijing, China.
| | - Jing Li
- Department of Geriatric Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P. R. China.
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Li R, Wang X, Lawler K, Garg S, St George RJ, Bindoff AD, Bartlett L, Roccati E, King AE, Vickers JC, Bai Q, Alty J. Brief webcam test of hand movements predicts episodic memory, executive function, and working memory in a community sample of cognitively asymptomatic older adults. Alzheimers Dement (Amst) 2024; 16:e12520. [PMID: 38274411 PMCID: PMC10809289 DOI: 10.1002/dad2.12520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 12/01/2023] [Accepted: 12/05/2023] [Indexed: 01/27/2024]
Abstract
INTRODUCTION Low-cost simple tests for preclinical Alzheimer's disease are a research priority. We evaluated whether remote unsupervised webcam recordings of finger-tapping were associated with cognitive performance in older adults. METHODS A total of 404 cognitively-asymptomatic participants (64.6 [6.77] years; 70.8% female) completed 10-second finger-tapping tests (Tasmanian [TAS] Test) and cognitive tests (Cambridge Neuropsychological Test Automated Battery [CANTAB]) online at home. Regression models including hand movement features were compared with null models (comprising age, sex, and education level); change in Akaike Information Criterion greater than 2 (ΔAIC > 2) denoted statistical difference. RESULTS Hand movement features improved prediction of episodic memory, executive function, and working memory scores (ΔAIC > 2). Dominant hand features outperformed nondominant hand features for episodic memory (ΔAIC = 2.5), executive function (ΔAIC = 4.8), and working memory (ΔAIC = 2.2). DISCUSSION This brief webcam test improved prediction of cognitive performance compared to age, sex, and education. Finger-tapping holds potential as a remote language-agnostic screening tool to stratify community cohorts at risk for cognitive decline.
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Affiliation(s)
- Renjie Li
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
- School of ICTUniversity of TasmaniaHobartTasmaniaAustralia
| | - Xinyi Wang
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - Katherine Lawler
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
- School of Allied HealthHuman Services and SportLa Trobe UniversityMelbourneVictoriaAustralia
| | - Saurabh Garg
- School of ICTUniversity of TasmaniaHobartTasmaniaAustralia
| | | | - Aidan D. Bindoff
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - Larissa Bartlett
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - Eddy Roccati
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - Anna E. King
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - James C. Vickers
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
| | - Quan Bai
- School of ICTUniversity of TasmaniaHobartTasmaniaAustralia
| | - Jane Alty
- Wicking Dementia Research and Education CentreUniversity of TasmaniaHobartTasmaniaAustralia
- School of MedicineUniversity of TasmaniaHobartTasmaniaAustralia
- Neurology DepartmentRoyal Hobart HospitalHobartTasmaniaAustralia
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Cabrera-León Y, Báez PG, Fernández-López P, Suárez-Araujo CP. Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review. J Alzheimers Dis 2024; 98:793-823. [PMID: 38489188 DOI: 10.3233/jad-231271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Background The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too. Background Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future. Methods Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected. Results The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field. Conclusions The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.
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Affiliation(s)
- Ylermi Cabrera-León
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
| | - Patricio García Báez
- Departamento de Ingeniería Informática y de Sistemas, Escuela Superior de Ingeniería y Tecnología, Universidad de La Laguna, San Cristóbal de La Laguna, Canary Islands, Spain
| | - Pablo Fernández-López
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
| | - Carmen Paz Suárez-Araujo
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
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7
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Rudd KD, Lawler K, Callisaya ML, Alty J. Investigating the associations between upper limb motor function and cognitive impairment: a scoping review. GeroScience 2023; 45:3449-3473. [PMID: 37337026 PMCID: PMC10643613 DOI: 10.1007/s11357-023-00844-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/26/2023] [Indexed: 06/21/2023] Open
Abstract
Upper limb motor function is a potential new biomarker of cognitive impairment and may aid discrimination from healthy ageing. However, it remains unclear which assessments to use. This study aimed to explore what methods have been used and to describe associations between upper limb function and cognitive impairment. A scoping review was conducted using PubMed, CINAHL and Web of Science. A systematic search was undertaken, including synonyms for key concepts 'upper limb', 'motor function' and 'cognitive impairment'. Selection criteria included tests of upper limb motor function and impaired cognition in adults. Analysis was by narrative synthesis. Sixty papers published between 1998 and 2022, comprising 41,800 participants, were included. The most common assessment tasks were finger tapping, Purdue Pegboard Test and functional tasks such as writing. Protocols were diverse in terms of equipment used and recording duration. Most participants were recruited from clinical settings. Alzheimer's Disease was the most common cause of cognitive impairment. Results were mixed but, generally, slower speed, more errors, and greater variability in upper limb movement variables was associated with cognitive impairment. This review maps the upper limb motor function assessments used and summarises the available evidence on how these associate with cognitive impairment. It identifies research gaps and may help guide protocols for future research. There is potential for upper limb motor function to be used in assessments of cognitive impairment.
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Affiliation(s)
- Kaylee D Rudd
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Tasmania, Australia
| | - Katherine Lawler
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Tasmania, Australia
- School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, Victoria, Australia
| | - Michele L Callisaya
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
- Peninsula Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Jane Alty
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Tasmania, Australia.
- School of Medicine, University of Tasmania, Hobart, Tasmania, Australia.
- Neurology Department, Royal Hobart Hospital, Hobart, Tasmania, Australia.
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Parsapoor M. AI-based assessments of speech and language impairments in dementia. Alzheimers Dement 2023; 19:4675-4687. [PMID: 37578167 DOI: 10.1002/alz.13395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 06/03/2023] [Accepted: 06/05/2023] [Indexed: 08/15/2023]
Abstract
Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI-powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets.
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Affiliation(s)
- Mahboobeh Parsapoor
- Centre de Recherche Informatique de Montréal: CRIM, Montreal, Quebec, Canada
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Nguyen TM, Leow AD, Ajilore O. A Review on Smartphone Keystroke Dynamics as a Digital Biomarker for Understanding Neurocognitive Functioning. Brain Sci 2023; 13:959. [PMID: 37371437 DOI: 10.3390/brainsci13060959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
Can digital technologies provide a passive unobtrusive means to observe and study cognition outside of the laboratory? Previously, cognitive assessments and monitoring were conducted in a laboratory or clinical setting, allowing for a cross-sectional glimpse of cognitive states. In the last decade, researchers have been utilizing technological advances and devices to explore ways of assessing cognition in the real world. We propose that the virtual keyboard of smartphones, an increasingly ubiquitous digital device, can provide the ideal conduit for passive data collection to study cognition. Passive data collection occurs without the active engagement of a participant and allows for near-continuous, objective data collection. Most importantly, this data collection can occur in the real world, capturing authentic datapoints. This method of data collection and its analyses provide a more comprehensive and potentially more suitable insight into cognitive states, as intra-individual cognitive fluctuations over time have shown to be an early manifestation of cognitive decline. We review different ways passive data, centered around keystroke dynamics, collected from smartphones, have been used to assess and evaluate cognition. We also discuss gaps in the literature where future directions of utilizing passive data can continue to provide inferences into cognition and elaborate on the importance of digital data privacy and consent.
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Affiliation(s)
- Theresa M Nguyen
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Alex D Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Li A, Li J, Zhang D, Wu W, Zhao J, Qiang Y. Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening. Front Hum Neurosci 2023; 17:1183457. [PMID: 37144160 PMCID: PMC10151757 DOI: 10.3389/fnhum.2023.1183457] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 03/27/2023] [Indexed: 05/06/2023] Open
Abstract
Introduction Advances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients' behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment. Methods We collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers. Results The experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands. Discussion It was found that patients' classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.
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Affiliation(s)
- Aoyu Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jingwen Li
- School of Computer Science, Xijing University, Xian, China
| | - Dongxu Zhang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Wei Wu
- Department of Clinical Laboratory, Affiliated People’s Hospital of Shanxi Medical University, Shanxi Provincial People’s Hospital, Taiyuan, China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
- *Correspondence: Yan Qiang,
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12
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Chen MH, Leow A, Ross MK, DeLuca J, Chiaravalloti N, Costa SL, Genova HM, Weber E, Hussain F, Demos AP. Associations between smartphone keystroke dynamics and cognition in MS. Digit Health 2022; 8:20552076221143234. [PMID: 36506490 PMCID: PMC9730018 DOI: 10.1177/20552076221143234] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
Objective Examine the associations between smartphone keystroke dynamics and cognitive functioning among persons with multiple sclerosis (MS). Methods Sixteen persons with MS with no self-reported upper extremity or typing difficulties and 10 healthy controls (HCs) completed six weeks of remote monitoring of their keystroke dynamics (i.e., how they typed on their smartphone keyboards). They also completed a comprehensive neuropsychological assessment and symptom ratings about fatigue, depression, and anxiety at baseline. Results A total of 1,335,787 keystrokes were collected, which were part of 30,968 typing sessions. The MS group typed slower (P < .001) and more variably (P = .032) than the HC group. Faster typing speed was associated with better performance on measures of processing speed (P = .016), attention (P = .022), and executive functioning (cognitive flexibility: P = .029; behavioral inhibition: P = .002; verbal fluency: P = .039), as well as less severe impact from fatigue (P < .001) and less severe anxiety symptoms (P = .007). Those with better cognitive functioning and less severe symptoms showed a stronger correlation between the use of backspace and autocorrection events (P < .001). Conclusion Typing speed may be sensitive to cognitive functions subserved by the frontal-subcortical brain circuits. Individuals with better cognitive functioning and less severe symptoms may be better at monitoring their typing errors. Keystroke dynamics have the potential to be used as an unobtrusive remote monitoring method for real-life cognitive functioning among persons with MS, which may improve the detection of relapses, evaluate treatment efficacy, and track disability progression.
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Affiliation(s)
- 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,Michelle H Chen, Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick,
NJ 08901, USA.
Alex Leow, Department of Psychiatry, University of Illinois at Chicago, 1601 W. Taylor St., SPHPI MC 912, Chicago, IL 60612, USA.
| | - Alex Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Mindy K Ross
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - John DeLuca
- Kessler Foundation, East Hanover, NJ, USA,Department of Physical Medicine and Rehabilitation, New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | - Nancy Chiaravalloti
- Kessler Foundation, East Hanover, NJ, USA,Department of Physical Medicine and Rehabilitation, New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | - Silvana L Costa
- Kessler Foundation, East Hanover, NJ, USA,Department of Physical Medicine and Rehabilitation, New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | - Helen M Genova
- Kessler Foundation, East Hanover, NJ, USA,Department of Physical Medicine and Rehabilitation, New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | - Erica Weber
- Kessler Foundation, East Hanover, NJ, USA,Department of Physical Medicine and Rehabilitation, New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | - Faraz Hussain
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Alexander P Demos
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
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Whelan R, Barbey FM, Cominetti MR, Gillan CM, Rosická AM. Developments in scalable strategies for detecting early markers of cognitive decline. Transl Psychiatry 2022; 12:473. [PMID: 36351888 PMCID: PMC9645320 DOI: 10.1038/s41398-022-02237-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 11/10/2022] Open
Abstract
Effective strategies for early detection of cognitive decline, if deployed on a large scale, would have individual and societal benefits. However, current detection methods are invasive or time-consuming and therefore not suitable for longitudinal monitoring of asymptomatic individuals. For example, biological markers of neuropathology associated with cognitive decline are typically collected via cerebral spinal fluid, cognitive functioning is evaluated from face-to-face assessments by experts and brain measures are obtained using expensive, non-portable equipment. Here, we describe scalable, repeatable, relatively non-invasive and comparatively inexpensive strategies for detecting the earliest markers of cognitive decline. These approaches are characterized by simple data collection protocols conducted in locations outside the laboratory: measurements are collected passively, by the participants themselves or by non-experts. The analysis of these data is, in contrast, often performed in a centralized location using sophisticated techniques. Recent developments allow neuropathology associated with potential cognitive decline to be accurately detected from peripheral blood samples. Advances in smartphone technology facilitate unobtrusive passive measurements of speech, fine motor movement and gait, that can be used to predict cognitive decline. Specific cognitive processes can be assayed using 'gamified' versions of standard laboratory cognitive tasks, which keep users engaged across multiple test sessions. High quality brain data can be regularly obtained, collected at-home by users themselves, using portable electroencephalography. Although these methods have great potential for addressing an important health challenge, there are barriers to be overcome. Technical obstacles include the need for standardization and interoperability across hardware and software. Societal challenges involve ensuring equity in access to new technologies, the cost of implementation and of any follow-up care, plus ethical issues.
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Affiliation(s)
- Robert Whelan
- School of Psychology, Trinity College Dublin, Dublin, Ireland. .,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
| | - Florentine M. Barbey
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,Cumulus Neuroscience Ltd, Dublin, Ireland
| | - Marcia R. Cominetti
- grid.8217.c0000 0004 1936 9705Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland ,grid.411247.50000 0001 2163 588XDepartment of Gerontology, Universidade Federal de São Carlos, São Carlos, Brazil
| | - Claire M. Gillan
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Anna M. Rosická
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland
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Holmes AA, Tripathi S, Katz E, Mondesire-Crump I, Mahajan R, Ritter A, Arroyo-Gallego T, Giancardo L. A novel framework to estimate cognitive impairment via finger interaction with digital devices. Brain Commun 2022; 4:fcac194. [PMID: 35950091 PMCID: PMC9356723 DOI: 10.1093/braincomms/fcac194] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 05/11/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Measuring cognitive function is essential for characterizing brain health and tracking cognitive decline in Alzheimer’s Disease and other neurodegenerative conditions. Current tools to accurately evaluate cognitive impairment typically rely on a battery of questionnaires administered during clinical visits which is essential for the acquisition of repeated measurements in longitudinal studies. Previous studies have shown that the remote data collection of passively monitored daily interaction with personal digital devices can measure motor signs in the early stages of synucleinopathies, as well as facilitate longitudinal patient assessment in the real-world scenario with high patient compliance. This was achieved by the automatic discovery of patterns in the time series of keystroke dynamics, i.e. the time required to press and release keys, by machine learning algorithms. In this work, our hypothesis is that the typing patterns generated from user-device interaction may reflect relevant features of the effects of cognitive impairment caused by neurodegeneration. We use machine learning algorithms to estimate cognitive performance through the analysis of keystroke dynamic patterns that were extracted from mechanical and touchscreen keyboard use in a dataset of cognitively normal (n = 39, 51% male) and cognitively impaired subjects (n = 38, 60% male). These algorithms are trained and evaluated using a novel framework that integrates items from multiple neuropsychological and clinical scales into cognitive subdomains to generate a more holistic representation of multifaceted clinical signs. In our results, we see that these models based on typing input achieve moderate correlations with verbal memory, non-verbal memory and executive function subdomains [Spearman’s ρ between 0.54 (P < 0.001) and 0.42 (P < 0.001)] and a weak correlation with language/verbal skills [Spearman’s ρ 0.30 (P < 0.05)]. In addition, we observe a moderate correlation between our typing-based approach and the Total Montreal Cognitive Assessment score [Spearman’s ρ 0.48 (P < 0.001)]. Finally, we show that these machine learning models can perform better by using our subdomain framework that integrates the information from multiple neuropsychological scales as opposed to using the individual items that make up these scales. Our results support our hypothesis that typing patterns are able to reflect the effects of neurodegeneration in mild cognitive impairment and Alzheimer’s disease and that this new subdomain framework both helps the development of machine learning models and improves their interpretability.
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Affiliation(s)
| | - Shikha Tripathi
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston , Houston, TX 77030 , USA
| | | | | | - Rahul Mahajan
- nQ Medical , Cambridge, MA 02142 , USA
- Division of Neurocritical Care, Department of Neurology, Brigham & Women’s Hospital , Boston, MA 02115 , USA
| | - Aaron Ritter
- Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland Clinic , Las Vegas, NV 89106 , USA
| | | | - Luca Giancardo
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston , Houston, TX 77030 , USA
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15
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Teh SK, Rawtaer I, Tan HP. Predictive Accuracy of Digital Biomarker Technologies for Detection of Mild Cognitive Impairment and Pre-Frailty Amongst Older Adults: A Systematic Review and Meta-Analysis. IEEE J Biomed Health Inform 2022; 26:3638-3648. [PMID: 35737623 DOI: 10.1109/jbhi.2022.3185798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Digital biomarker technologies coupled with predictive models are increasingly applied for early detection of age-related potentially reversible conditions including mild cognitive impairment (MCI) and pre-frailty (PF). We aimed to determine the predictive accuracy of digital biomarker technologies to detect MCI and PF with systematic review and meta-analysis. A computer-assisted search on major academic research databases including IEEE-Xplore was conducted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were adopted reporting in this study. Summary receiver operating characteristic curve based on random-effect bivariate model was used to evaluate overall sensitivity and specificity for detection of the respective age-related conditions. A total of 43 studies were selected for final systematic review and meta-analysis. 26 studies reported on detection of MCI with sensitivity and specificity of 0.48-1.00 and 0.55-1.00, respectively. On the other hand, there were 17 studies that reported on the detection of PF with reported sensitivity of 0.53-1.00 and specificity of 0.61-1.00. Meta-analysis further revealed pooled sensitivities of 0.84 (95% CI: 0.79-0.88) and 0.82 (95% CI: 0.74-0.88) for in-home detection of MCI and PF, respectively, while pooled specificities were 0.85 (95% CI: 0.80-0.89) and 0.82 (95% CI: 0.75-0.88), respectively. Besides MCI, and PF, in this work during systematic review, we also found one study which reported a sensitivity of 0.93 and a specificity of 0.57 for detection of cognitive frailty (CF). The meta-analytic result, for the first time, quantifies the predictive efficacy of digital biomarker technologies for detection of MCI and PF. Additionally, we found the number of studies for detection of CF to be notably lower, indicating possible research gaps to explore predictive models on digital biomarker technology for detection of CF.
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Abstract
While people 65 years of age and older represent 16% of the population in the United States, they account for >40% of surgical procedures performed each year. Maintaining brain health after anesthesia and surgery is not only important to our patients, but it is also an increasingly important patient safety imperative for the specialty of anesthesiology. Aging is a complex process that diminishes the reserve of every organ system and often results in a patient who is vulnerable to the stress of surgery. The brain is no exception, and many older patients present with preoperative cognitive impairment that is undiagnosed. As we age, a number of changes occur in the human brain, resulting in a patient who is less resilient to perioperative stress, making older adults more susceptible to the phenotypic expression of perioperative neurocognitive disorders. This review summarizes the current scientific and clinical understanding of perioperative neurocognitive disorders and recommends patient-centered, age-focused interventions that can better mitigate risk, prevent harm, and improve outcomes for our patients. Finally, it discusses the emerging topic of sleep and cognitive health and other future frontiers of scientific inquiry that might inform clinical best practices.
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Affiliation(s)
- Susana Vacas
- From the Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Cecilia Canales
- From the Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Stacie G Deiner
- Department of Anesthesiology, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire
| | - Daniel J Cole
- From the Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
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17
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Alfalahi H, Khandoker AH, Chowdhury N, Iakovakis D, Dias SB, Chaudhuri KR, Hadjileontiadis LJ. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Sci Rep 2022; 12:7690. [PMID: 35546606 PMCID: PMC9095860 DOI: 10.1038/s41598-022-11865-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/25/2022] [Indexed: 12/12/2022] Open
Abstract
The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson's disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82-0.90, I2 = 79.49%) and 0.83 (CI 0.79-0.87, I2 = 83.45%) for PD, 0.83 (95% CI 0.65-1.00, I2 = 79.10%) and 0.87 (95% CI 0.80-0.93, I2 = 0%) for psychomotor impairment, and 0.85 (95% CI 0.74-0.96, I2 = 50.39%) and 0.82 (95% CI 0.70-0.94, I2 = 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a "co-creation" approach that stems from mechanistic explanations of patients' characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.
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Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Nayeefa Chowdhury
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Cruz Quebrada, 1499-002, Lisbon, Portugal
| | - K Ray Chaudhuri
- Parkinson's Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE5 9RS, United Kingdom
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
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Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J. Applications of Artificial Intelligence to aid detection of dementia: a scoping review on current capabilities and future directions. J Biomed Inform 2022; 127:104030. [DOI: 10.1016/j.jbi.2022.104030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/21/2022] [Accepted: 02/12/2022] [Indexed: 12/17/2022]
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Ojakäär T, Koychev I. Secondary Prevention of Dementia: Combining Risk Factors and Scalable Screening Technology. Front Neurol 2021; 12:772836. [PMID: 34867762 PMCID: PMC8634660 DOI: 10.3389/fneur.2021.772836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 10/11/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is the most common cause of dementia. Over a third of dementia cases are estimated to be due to potentially modifiable risk factors, thus offering opportunities for both identification of those most likely to be in early disease as well as secondary prevention. Diabetes, hypertension and chronic kidney failure have all been linked to increased risk for AD and dementia and through their high prevalence are particularly apt targets for initiatives to reduce burden of AD. This can take place through targeted interventions of cardiovascular risk factors (shown to improve cognitive outcomes) or novel disease modifying treatments in people with confirmed AD pathology. The success of this approach to secondary prevention depends on the availability of inexpensive and scalable methods for detecting preclinical and prodromal dementia states. Developments in blood-based biomarkers for Alzheimer's disease are rapidly becoming a viable such method for monitoring large at-risk groups. In addition, digital technologies for remote monitoring of cognitive and behavioral changes can add clinically relevant data to further improve personalisation of prevention strategies. This review sets the scene for this approach to secondary care of dementia through a review of the evidence for cardiovascular risk factors (diabetes, hypertension and chronic kidney disease) as major risk factors for AD. We then summarize the developments in blood-based and cognitive biomarkers that allow the detection of pathological states at the earliest possible stage. We propose that at-risk cohorts should be created based on the interaction between cardiovascular and constitutional risk factors. These cohorts can then be monitored effectively using a combination of blood-based biomarkers and digital technologies. We argue that this strategy allows for both risk factor reduction-based prevention programmes as well as for optimisation of any benefits offered by current and future disease modifying treatment through rapid identification of individuals most likely to benefit from them.
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Affiliation(s)
| | - Ivan Koychev
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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Smith E, Storch EA, Vahia I, Wong STC, Lavretsky H, Cummings JL, Eyre HA. Affective Computing for Late-Life Mood and Cognitive Disorders. Front Psychiatry 2021; 12:782183. [PMID: 35002802 PMCID: PMC8732874 DOI: 10.3389/fpsyt.2021.782183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 11/29/2021] [Indexed: 12/20/2022] Open
Abstract
Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.
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Affiliation(s)
- Erin Smith
- The PRODEO Institute, San Francisco, CA, United States.,Organisation for Economic Co-operation and Development (OECD), Paris, France.,Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, United States.,Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Eric A Storch
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Ipsit Vahia
- Division of Geriatric Psychiatry, McLean Hospital, Boston, MA, United States.,Division of Geriatric Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Stephen T C Wong
- Systems Medicine and Biomedical Engineering Houston Methodist, Houston, TX, United States
| | - Helen Lavretsky
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jeffrey L 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, United States
| | - Harris A Eyre
- The PRODEO Institute, San Francisco, CA, United States.,Organisation for Economic Co-operation and Development (OECD), Paris, France.,Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.,Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States.,IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Deakin University, Geelong, VIC, Australia
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