1
|
Clifford JO, Anand S, Tarpin-Bernard F, Bergeron MF, Ashford CB, Bayley PJ, Ashford JW. Episodic memory assessment: effects of sex and age on performance and response time during a continuous recognition task. Front Hum Neurosci 2024; 18:1304221. [PMID: 38638807 PMCID: PMC11024362 DOI: 10.3389/fnhum.2024.1304221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 03/08/2024] [Indexed: 04/20/2024] Open
Abstract
Introduction Continuous recognition tasks (CRTs) assess episodic memory (EM), the central functional disturbance in Alzheimer's disease and several related disorders. The online MemTrax computerized CRT provides a platform for screening and assessment that is engaging and can be repeated frequently. MemTrax presents complex visual stimuli, which require complex involvement of the lateral and medial temporal lobes and can be completed in less than 2 min. Results include number of correct recognitions (HITs), recognition failures (MISSes = 1-HITs), correct rejections (CRs), false alarms (FAs = 1-CRs), total correct (TC = HITs + CRs), and response times (RTs) for each HIT and FA. Prior analyses of MemTrax CRT data show no effects of sex but an effect of age on performance. The number of HITs corresponds to faster RT-HITs more closely than TC, and CRs do not relate to RT-HITs. RT-HITs show a typical skewed distribution, and cumulative RT-HITs fit a negative survival curve (RevEx). Thus, this study aimed to define precisely the effects of sex and age on HITS, CRs, RT-HITs, and the dynamics of RTs in an engaged population. Methods MemTrax CRT online data on 18,255 individuals was analyzed for sex, age, and distributions of HITs, CRs, MISSes, FAs, TC, and relationships to both RT-HITs and RT-FAs. Results HITs corresponded more closely to RT-HITs than did TC because CRs did not relate to RT-HITs. RT-FAs had a broader distribution than RT-HITs and were faster than RT-HITs in about half of the sample, slower in the other half. Performance metrics for men and women were the same. HITs declined with age as RT-HITs increased. CRs also decreased with age and RT-FAs increased, but with no correlation. The group over aged 50 years had RT-HITs distributions slower than under 50 years. For both age ranges, the RevEx model explained more than 99% of the variance in RT-HITs. Discussion The dichotomy of HITs and CRs suggests opposing cognitive strategies: (1) less certainty about recognitions, in association with slower RT-HITs and lower HIT percentages suggests recognition difficulty, leading to more MISSes, and (2) decreased CRs (more FAs) but faster RTs to HITs and FAs, suggesting overly quick decisions leading to errors. MemTrax CRT performance provides an indication of EM (HITs and RT-HITs may relate to function of the temporal lobe), executive function (FAs may relate to function of the frontal lobe), processing speed (RTs), cognitive ability, and age-related changes. This CRT provides potential clinical screening utility for early Alzheimer's disease and other conditions affecting EM, other cognitive functions, and more accurate impairment assessment to track changes over time.
Collapse
Affiliation(s)
- James O. Clifford
- Department of Psychology, College of San Mateo, San Mateo, CA, United States
| | - Sulekha Anand
- Department of Biological Sciences, San Jose State University, San Jose, CA, United States
| | | | - Michael F. Bergeron
- Department of Health Sciences, University of Hartford, West Hartford, CT, United States
| | - Curtis B. Ashford
- MemTrax, LLC, Redwood City, CA, United States
- CogniFit, LLC, Redwood City, CA, United States
| | - Peter J. Bayley
- VA Palo Alto Health Care System, Palo Alto, CA, United States
- Department of Psychiatry and Behavioral Sciences, Stanford, CA, United States
| | - John Wesson Ashford
- VA Palo Alto Health Care System, Palo Alto, CA, United States
- Department of Psychiatry and Behavioral Sciences, Stanford, CA, United States
| |
Collapse
|
2
|
Liu Y, Wu L, Chen W, Su F, Liu G, Zhou X, Ashford CB, Li F, Ashford JW, Pei Z, Xian W. The MemTrax memory test for detecting and assessing cognitive impairment in Parkinson's disease. Parkinsonism Relat Disord 2024; 120:106016. [PMID: 38325255 DOI: 10.1016/j.parkreldis.2024.106016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 01/07/2024] [Accepted: 01/28/2024] [Indexed: 02/09/2024]
Abstract
INTRODUCTION A valid, reliable, accessible measurement for the early detection of cognitive decline in patients with Parkinson's disease (PD) is in urgent demand. The objective of the study is to assess the clinical utility of the MemTrax Memory Test in detecting cognitive impairment in patients with PD. METHODS The MemTrax, a fast on-line cognitive screening tool based on continuous recognition task, and Montreal Cognitive Assessment (MoCA) were administered to 61 healthy controls (HC), 102 PD patients with normal cognition (PD-N), 74 PD patients with mild cognitive impairment (PD-MCI) and 52 PD patients with dementia (PD-D). The total percent correct (MTx- %C), average response time (MTx-RT), composite score (MTx-Cp) of MemTrax and the MoCA scores were comparatively analyzed. RESULTS The MoCA scores were similar between HC and PD-N, however, MTx- %C and MTx-Cp were lower in PD-N than HC(p < 0.05). MTx- %C, MTx-Cp and the MoCA scores were significantly lower in PD-MCI versus PD-N and in PD-D versus PD-MCI (p ≤ 0.001), while MTx-RT was statistically longer in PD-D versus PD-MCI (p ≤ 0.001). For PD groups, the MemTrax performance correlated with the MoCA scores. To detect PD-MCI, the optimal MTx- %C and MTx-Cp cutoff were 75 % and 50.0, respectively. To detect PD-D, the optimal MTx- %C, MTx-RT and MTx-Cp cutoff were 69 %, 1.341s and 40.6, respectively. CONCLUSION The MemTrax provides rapid, valid and reliable metrics for assessing cognition in PD patients which could be useful for identifying PD-MCI at early stage and monitoring cognitive function decline during the progression of disease.
Collapse
Affiliation(s)
- Yanmei Liu
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou, 510080, China
| | - Lei Wu
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou, 510080, China
| | - Weineng Chen
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou, 510080, China
| | - Fengjuan Su
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou, 510080, China
| | - Ganqiang Liu
- Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, No.66, Gongchang Road, Guangming District, Shenzhen, Guangdong, 518107, China
| | - Xianbo Zhou
- Center for Alzheimer's Research, Washington Institute of Clinical Research, Vienna, VA, USA; AstraNeura, Co., Ltd., Shanghai, China
| | | | - Feng Li
- Moore Threads Co., Ltd, China
| | - J Wesson Ashford
- Department of Psychiatry & Behavioral Sciences, Stanford University, War Related Illness & Injury Study Center, VA Palo Alto Health Care System, 3801 Miranda Ave., Palo Alto, CA, 94304, USA
| | - Zhong Pei
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou, 510080, China.
| | - Wenbiao Xian
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, No.58 Zhongshan Road 2, Guangzhou, 510080, China.
| |
Collapse
|
3
|
Ashford JW, Clifford JO, Bergeron MF. Advancing screening for cognitive impairment: the memtrax continuous recognition test. Aging (Albany NY) 2023; 15:5230-5231. [PMID: 37319322 PMCID: PMC10333071 DOI: 10.18632/aging.204828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/29/2023] [Indexed: 06/17/2023]
Affiliation(s)
- J. Wesson Ashford
- Department of Psychiatry and Behavioral Sciences and Alzheimer’s Center, Stanford University, Stanford, CA 94305, USA
- War Related Illness and Injury Study Center and Alzheimer’s Center, VA Palo Alto Health Care System, Palo Alto, CA 94304, USA
| | - James O. Clifford
- Department of Psychology, College of San Mateo, San Mateo, CA 94402, USA
| | - Michael F. Bergeron
- Department of Health Sciences, University of Hartford, West Hartford, CT 06117, USA
| |
Collapse
|
4
|
Ashford JW, Clifford JO, Anand S, Bergeron MF, Ashford CB, Bayley PJ. Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model. Front Aging Neurosci 2022; 14:1005298. [PMID: 36437986 PMCID: PMC9682919 DOI: 10.3389/fnagi.2022.1005298] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/17/2022] [Indexed: 07/24/2023] Open
Abstract
A critical issue in addressing medical conditions is measurement. Memory measurement is difficult, especially episodic memory, which is disrupted by many conditions. On-line computer testing can precisely measure and assess several memory functions. This study analyzed memory performances from a large group of anonymous, on-line participants using a continuous recognition task (CRT) implemented at https://memtrax.com. These analyses estimated ranges of acceptable performance and average response time (RT). For 344,165 presumed unique individuals completing the CRT a total of 602,272 times, data were stored on a server, including each correct response (HIT), Correct Rejection, and RT to the thousandth of a second. Responses were analyzed, distributions and relationships of these parameters were ascertained, and mean RTs were determined for each participant across the population. From 322,996 valid first tests, analysis of correctness showed that 63% of these tests achieved at least 45 correct (90%), 92% scored at or above 40 correct (80%), and 3% scored 35 correct (70%) or less. The distribution of RTs was skewed with 1% faster than 0.62 s, a median at 0.890 s, and 1% slower than 1.57 s. The RT distribution was best explained by a novel model, the reverse-exponential (RevEx) function. Increased RT speed was most closely associated with increased HIT accuracy. The MemTrax on-line memory test readily provides valid and reliable metrics for assessing individual episodic memory function that could have practical clinical utility for precise assessment of memory dysfunction in many conditions, including improvement or deterioration over time.
Collapse
Affiliation(s)
- J. Wesson Ashford
- War Related Illness and Injury Study Center, VA Palo Alto Health Care System, Palo Alto, CA, United States
- Department of Psychiatry and Behavioral Science, Stanford University, Palo Alto, CA, United States
| | - James O. Clifford
- Department of Psychology, College of San Mateo, San Mateo, CA, United States
| | - Sulekha Anand
- Department of Biological Sciences, San José State University, San Jose, CA, United States
| | - Michael F. Bergeron
- Department of Health Sciences, University of Hartford, West Hartford, CT, United States
| | | | - Peter J. Bayley
- War Related Illness and Injury Study Center, VA Palo Alto Health Care System, Palo Alto, CA, United States
- Department of Psychiatry and Behavioral Science, Stanford University, Palo Alto, CA, United States
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
AIM in Rehabilitation. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
7
|
Vichianin Y, Khummongkol A, Chiewvit P, Raksthaput A, Chaichanettee S, Aoonkaew N, Senanarong V. Accuracy of Support-Vector Machines for Diagnosis of Alzheimer's Disease, Using Volume of Brain Obtained by Structural MRI at Siriraj Hospital. Front Neurol 2021; 12:640696. [PMID: 34040575 PMCID: PMC8141708 DOI: 10.3389/fneur.2021.640696] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 03/12/2021] [Indexed: 11/30/2022] Open
Abstract
Background: The determination of brain volumes using visual ratings is associated with an inherently low accuracy for the diagnosis of Alzheimer's disease (AD). A support-vector machine (SVM) is one of the machine learning techniques, which may be utilized as a classifier for various classification problems. This study exploratorily investigated the accuracy of SVM classification models for AD subjects using brain volume and various clinical data as features. Methods: The study was designed as a retrospective chart review. A total of 201 eligible subjects were recruited from the Memory Clinic at Siriraj Hospital, Thailand. Eighteen cases were excluded due to incomplete MRI data. Subjects were randomly assigned to a training group (AD = 46, normal = 46) and testing group (AD = 45, normal = 46) for SVM modeling and validation, respectively. The results in terms of accuracy and a receiver operating characteristic curve analysis are reported. Results: The highest accuracy for brain volumetry (62.64%) was found using the hippocampus as a single feature. A combination of clinical parameters as features provided accuracy ranging between 83 and 90%. However, a combination of brain volumetry and clinical parameters as features to the SVM models did not improve the accuracy of the result. Conclusions: In our study, the use of brain volumetry as SVM features provided low classification accuracy with the highest accuracy of 62.64% using the hippocampus volume alone. In contrast, the use of clinical parameters [Thai mental state examination score, controlled oral word association tests (animals; and letters K, S, and P), learning memory, clock-drawing test, and construction-praxis] as features for SVM models provided good accuracy between 83 and 90%.
Collapse
Affiliation(s)
- Yudthaphon Vichianin
- Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Anutr Khummongkol
- Division of Neurology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Pipat Chiewvit
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Atthapon Raksthaput
- Division of Neurology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Sunisa Chaichanettee
- Division of Neurology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Nuttapol Aoonkaew
- Division of Neurology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Vorapun Senanarong
- Division of Neurology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| |
Collapse
|
8
|
Liu X, Chen X, Zhou X, Shang Y, Xu F, Zhang J, He J, Zhao F, Du B, Wang X, Zhang Q, Zhang W, Bergeron MF, Ding T, Ashford JW, Zhong L. Validity of the MemTrax Memory Test Compared to the Montreal Cognitive Assessment in the Detection of Mild Cognitive Impairment and Dementia due to Alzheimer's Disease in a Chinese Cohort. J Alzheimers Dis 2021; 80:1257-1267. [PMID: 33646151 DOI: 10.3233/jad-200936] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
BACKGROUND A valid, reliable, accessible, engaging, and affordable digital cognitive screen instrument for clinical use is in urgent demand. OBJECTIVE To assess the clinical utility of the MemTrax memory test for early detection of cognitive impairment in a Chinese cohort. METHODS The 2.5-minute MemTrax and the Montreal Cognitive Assessment (MoCA) were performed by 50 clinically diagnosed cognitively normal (CON), 50 mild cognitive impairment due to AD (MCI-AD), and 50 Alzheimer's disease (AD) volunteer participants. The percentage of correct responses (MTx-% C), the mean response time (MTx-RT), and the composite scores (MTx-Cp) of MemTrax and the MoCA scores were comparatively analyzed and receiver operating characteristic (ROC) curves generated. RESULTS Multivariate linear regression analyses indicated MTx-% C, MTx-Cp, and the MoCA score were significantly lower in MCI-AD versus CON and in AD versus MCI-AD groups (all with p≤0.001). For the differentiation of MCI-AD from CON, an optimized MTx-% C cutoff of 81% had 72% sensitivity and 84% specificity with an area under the curve (AUC) of 0.839, whereas the MoCA score of 23 had 54% sensitivity and 86% specificity with an AUC of 0.740. For the differentiation of AD from MCI-AD, MTx-Cp of 43.0 had 70% sensitivity and 82% specificity with an AUC of 0.799, whereas the MoCA score of 20 had 84% sensitivity and 62% specificity with an AUC of 0.767. CONCLUSION MemTrax can effectively detect both clinically diagnosed MCI and AD with better accuracy as compared to the MoCA based on AUCs in a Chinese cohort.
Collapse
Affiliation(s)
- 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
| | - Xinjie Chen
- 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
- SJN Biomed Ltd., Kunming, Yunnan, China.,Center for Alzheimer's Research, Washington Institute of Clinical Research, Vienna, VA, USA
| | - Yajun Shang
- Yunnan Provincial Clinical Research Center for Neurological Diseases, Yunnan, China.,Neurosurgery Department, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Fan Xu
- Department of Public Health, Chengdu Medical College, Sichuan, China
| | - Junyan Zhang
- Bothwin Clinical Study Consultant, Shanghai, China
| | - Jingfang He
- Bothwin Clinical Study Consultant, Shanghai, China
| | - Feng Zhao
- Department of Neurology, Dehong People's Hospital, Dehong, Yunnan, China
| | - Bo Du
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Xuan Wang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Qi Zhang
- SJN Biomed Ltd., Kunming, Yunnan, China
| | | | | | - Tao Ding
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - 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
| | - Lianmei Zhong
- Department of Neurology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.,Yunnan Provincial Clinical Research Center for Neurological Diseases, Yunnan, China
| |
Collapse
|
9
|
AIM in Rehabilitation. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_177-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
10
|
Bergeron MF, Landset S, Zhou X, Ding T, Khoshgoftaar TM, Zhao F, Du B, Chen X, Wang X, Zhong L, Liu X, Ashford JW. Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment. J Alzheimers Dis 2020; 77:1545-1558. [PMID: 32894241 PMCID: PMC7683062 DOI: 10.3233/jad-191340] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Background: The widespread incidence and prevalence of Alzheimer’s disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment. Objective: Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA). Methods: We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features. Results: Comparing the learners across selected combinations of MoCA scores and thresholds, Naïve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999). Conclusion: MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.
Collapse
Affiliation(s)
| | - Sara Landset
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
| | - Xianbo Zhou
- SJN Biomed LTD, Kunming, Yunnan, China.,Center for Alzheimer's Research, Washington Institute of Clinical Research, Washington, DC, USA
| | - Tao Ding
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Taghi M Khoshgoftaar
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
| | - Feng Zhao
- Department of Neurology, Dehong People's Hospital, Dehong, Yunnan, China
| | - Bo Du
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Xinjie Chen
- Department of Neurology, the First Affiliated Hospital of Kunming Medical University, Wuhua District, Kunming, Yunnan Province, China
| | - Xuan Wang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Lianmei Zhong
- Department of Neurology, the First Affiliated Hospital of Kunming Medical University, Wuhua District, Kunming, Yunnan Province, China
| | - Xiaolei Liu
- Department of Neurology, the First Affiliated Hospital of Kunming Medical University, Wuhua District, Kunming, Yunnan Province, China
| | - J Wesson Ashford
- War-Related Illness and Injury Study Center, VA Palo Alto Health Care System, Palo Alto, CA, USA.,Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, USA
| |
Collapse
|