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Kim M, Lee J, Lee SY, Ha M, Park I, Jang J, Jang M, Park S, Kwon JS. Development of an eye-tracking system based on a deep learning model to assess executive function in patients with mental illnesses. Sci Rep 2024; 14:18186. [PMID: 39107349 PMCID: PMC11303748 DOI: 10.1038/s41598-024-68586-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Accepted: 07/25/2024] [Indexed: 08/10/2024] Open
Abstract
Patients with mental illnesses, particularly psychosis and obsessive‒compulsive disorder (OCD), frequently exhibit deficits in executive function and visuospatial memory. Traditional assessments, such as the Rey‒Osterrieth Complex Figure Test (RCFT), performed in clinical settings require time and effort. This study aimed to develop a deep learning model using the RCFT and based on eye tracking to detect impaired executive function during visuospatial memory encoding in patients with mental illnesses. In 96 patients with first-episode psychosis, 49 with clinical high risk for psychosis, 104 with OCD, and 159 healthy controls, eye movements were recorded during a 3-min RCFT figure memorization task, and organization and immediate recall scores were obtained. These scores, along with the fixation points indicating eye-focused locations in the figure, were used to train a Long Short-Term Memory + Attention model for detecting impaired executive function and visuospatial memory. The model distinguished between normal and impaired executive function, with an F1 score of 83.5%, and identified visuospatial memory deficits, with an F1 score of 80.7%, regardless of psychiatric diagnosis. These findings suggest that this eye tracking-based deep learning model can directly and rapidly identify impaired executive function during visuospatial memory encoding, with potential applications in various psychiatric and neurological disorders.
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Affiliation(s)
- Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-no, Chongno-gu, Seoul, 03080, Republic of Korea
| | - Jungha Lee
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Soo Yong Lee
- Kim Jaechul Graduate School of Artificial Intelligence, KAIST, Daejeon, Republic of Korea
| | - Minji Ha
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Inkyung Park
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Jiseon Jang
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Moonyoung Jang
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-no, Chongno-gu, Seoul, 03080, Republic of Korea
| | - Sunghyun Park
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-no, Chongno-gu, Seoul, 03080, Republic of Korea.
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea.
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Wolf A, Tripanpitak K, Umeda S, Otake-Matsuura M. Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review. Front Psychol 2023; 14:1197567. [PMID: 37546488 PMCID: PMC10399700 DOI: 10.3389/fpsyg.2023.1197567] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/19/2023] [Indexed: 08/08/2023] Open
Abstract
Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.
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Affiliation(s)
- Alexandra Wolf
- Cognitive Behavioral Assistive Technology (CBAT), Goal-Oriented Technology Group, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kornkanok Tripanpitak
- Cognitive Behavioral Assistive Technology (CBAT), Goal-Oriented Technology Group, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
| | - Satoshi Umeda
- Department of Psychology, Keio University, Tokyo, Japan
| | - Mihoko Otake-Matsuura
- Cognitive Behavioral Assistive Technology (CBAT), Goal-Oriented Technology Group, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
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Nij Bijvank JA, Hof SN, Prouskas SE, Schoonheim MM, Uitdehaag BMJ, van Rijn LJ, Petzold A. A novel eye-movement impairment in multiple sclerosis indicating widespread cortical damage. Brain 2023; 146:2476-2488. [PMID: 36535900 PMCID: PMC10232247 DOI: 10.1093/brain/awac474] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/04/2022] [Accepted: 11/22/2022] [Indexed: 11/04/2023] Open
Abstract
In multiple sclerosis, remyelination trials have yet to deliver success like that achieved for relapse rates with disease course modifying treatment trials. The challenge is to have a clinical, functional outcome measure. Currently, there are none that have been validated, other than visual evoked potentials in optic neuritis. Like vision, quick eye movements (saccades) are heavily dependent on myelination. We proposed that it is possible to extrapolate from demyelination of the medial longitudinal fasciculus in the brainstem to quantitative assessment of cortical networks governing saccadic eye movements in multiple sclerosis. We have developed and validated a double-step saccadic test, which consists of a pair of eye movements towards two stimuli presented in quick succession (the demonstrate eye movement networks with saccades protocol). In this single-centre, cross-sectional cohort study we interrogated the structural and functional relationships of double-step saccades in multiple sclerosis. Data were collected for double-step saccades, cognitive function (extended Rao's Brief Repeatable Battery), disability (Expanded Disability Status Scale) and visual functioning in daily life (National Eye Institute Visual Function Questionnaire). MRI was used to quantify grey matter atrophy and multiple sclerosis lesion load. Multivariable linear regression models were used for analysis of the relationships between double-step saccades and clinical and MRI metrics. We included 209 individuals with multiple sclerosis (mean age 54.3 ± 10.5 years, 58% female, 63% relapsing-remitting multiple sclerosis) and 60 healthy control subjects (mean age 52.1 ± 9.2 years, 53% female). The proportion of correct double-step saccades was significantly reduced in multiple sclerosis (mean 0.29 ± 0.22) compared to controls (0.45 ± 0.22, P < 0.001). Consistent with this, there was a significantly larger double-step dysmetric saccadic error in multiple sclerosis (mean vertical error -1.18 ± 1.20°) compared to controls (-0.54 ± 0.86°, P < 0.001). Impaired double-step saccadic metrics were consistently associated with more severe global and local grey matter atrophy (correct responses-cortical grey matter: β = 0.42, P < 0.001), lesion load (vertical error: β = -0.28, P < 0.001), progressive phenotypes, more severe physical and cognitive impairment (correct responses-information processing: β = 0.46, P < 0.001) and visual functioning. In conclusion, double-step saccades represent a robust metric that revealed a novel eye-movement impairment in individuals with multiple sclerosis. Double-step saccades outperformed other saccadic tasks in their statistical relationship with clinical, cognitive and visual functioning, as well as global and local grey matter atrophy. Double-step saccades should be evaluated longitudinally and tested as a potential novel outcome measure for remyelination trials in multiple sclerosis.
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Affiliation(s)
- Jenny A Nij Bijvank
- Amsterdam UMC, Department of Neurology, Vrije Universiteit Amsterdam, MS Centre and Neuro-ophthalmology Expertise Centre Amsterdam, Amsterdam Neuroscience, 1081 HZ Amsterdam, The Netherlands
- Amsterdam UMC, Department of Ophthalmology, Vrije Universiteit Amsterdam, Neuro-ophthalmology Expertise Centre Amsterdam, Amsterdam Neuroscience, 1081 HZ Amsterdam, The Netherlands
| | - Sam N Hof
- Amsterdam UMC, Department of Neurology, Vrije Universiteit Amsterdam, MS Centre and Neuro-ophthalmology Expertise Centre Amsterdam, Amsterdam Neuroscience, 1081 HZ Amsterdam, The Netherlands
| | - Stefanos E Prouskas
- Amsterdam UMC, Department of Anatomy and Neurosciences, Vrije Universiteit Amsterdam, MS Centre Amsterdam, Amsterdam Neuroscience, 1081 HZ Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Amsterdam UMC, Department of Anatomy and Neurosciences, Vrije Universiteit Amsterdam, MS Centre Amsterdam, Amsterdam Neuroscience, 1081 HZ Amsterdam, The Netherlands
| | - Bernard M J Uitdehaag
- Amsterdam UMC, Department of Neurology, Vrije Universiteit Amsterdam, MS Centre and Neuro-ophthalmology Expertise Centre Amsterdam, Amsterdam Neuroscience, 1081 HZ Amsterdam, The Netherlands
| | - Laurentius J van Rijn
- Amsterdam UMC, Department of Ophthalmology, Vrije Universiteit Amsterdam, Neuro-ophthalmology Expertise Centre Amsterdam, Amsterdam Neuroscience, 1081 HZ Amsterdam, The Netherlands
- Department of Ophthalmology, Onze Lieve Vrouwe Gasthuis, 1091 AC Amsterdam, The Netherlands
| | - Axel Petzold
- Amsterdam UMC, Department of Neurology, Vrije Universiteit Amsterdam, MS Centre and Neuro-ophthalmology Expertise Centre Amsterdam, Amsterdam Neuroscience, 1081 HZ Amsterdam, The Netherlands
- Amsterdam UMC, Department of Ophthalmology, Vrije Universiteit Amsterdam, Neuro-ophthalmology Expertise Centre Amsterdam, Amsterdam Neuroscience, 1081 HZ Amsterdam, The Netherlands
- Moorfields Eye Hospital, The National Hospital for Neurology and Neurosurgery and the Queen Square Institute of Neurology, UCL, London EC1V 2PD, UK
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Ionescu A, Ştefănescu E, Strilciuc Ş, Grad DA, Mureşanu D. Eyes on dementia: an overview of the interplay between eye movements and cognitive decline. J Med Life 2023; 16:642-662. [PMID: 37520470 PMCID: PMC10375353 DOI: 10.25122/jml-2023-0217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 04/30/2023] [Indexed: 08/01/2023] Open
Abstract
The economic and disease burden of dementia is forecasted to continue increasing. Considering its cognitive effects, timely diagnosis is important in developing a stage-based treatment plan and gathering data to support advocacy efforts and plan healthcare and social services. Eye-tracking technology has emerged as an efficient diagnostic tool in clinical practice and experimental studies. This review aimed to comprehensively analyze various aspects of eye-tracking technology, including pupillometry parameters, eye movements, eye-tracking devices, and neuropsychological tools. We conducted a systematic review retrieving articles published in the last ten years from six databases. Our results provide a complex overview for each included form of dementia/cognitive decline in terms of patient characteristics (age, sex-disaggregated by included pathologies), inclusion and exclusion criteria, devices, and neuropsychological tools. We also summarized findings on fixation stability tasks, saccadic evaluation, pupillometry, scene perception, object recognition, spatial memory, eye-tracking video tasks, and visual search. The eye-tracking method has become more common in cognitive assessments.
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Affiliation(s)
- Alec Ionescu
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- RoNeuro Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Emanuel Ştefănescu
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- RoNeuro Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Ştefan Strilciuc
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- RoNeuro Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Diana Alecsandra Grad
- RoNeuro Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
- Department of Public Health, Faculty of Political, Administrative and Communication Sciences, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Dafin Mureşanu
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- RoNeuro Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
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Kim KW, Wang Q, Koo SH, Shin BS. A single-center, randomized, parallel design study to evaluate the efficacy of donepezil in improving visuospatial abilities in patients with mild cognitive impairment using eye-tracker: the COG-EYE study protocol for a phase II trial. Trials 2022; 23:813. [PMID: 36167553 PMCID: PMC9513951 DOI: 10.1186/s13063-022-06781-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 09/18/2022] [Indexed: 11/15/2022] Open
Abstract
Background Cholinesterase inhibitors (ChEIs) decrease long-term cognitive decline in patients with Alzheimer’s disease (AD); however, there is little evidence that ChEIs affect cognitive test scores in patients with mild cognitive impairment (MCI). Conventional endpoints, such as cognitive tests or clinical rating scores, may lack the sensitivity to subtle treatment effects in participants with MCI. Therefore, there is an immediate need to refocus on direct physiological assessments to detect the effects of ChEIs in patients with MCI due to AD. Methods We propose a randomized controlled trial to evaluate the effect of donepezil, a ChEI, on patients with MCI due to AD. We plan to recruit 78 participants (39 in each arm) with MCI who had amyloid positron emission tomography (PET)-positive results for this open-label study. To evaluate subtle differences, we will measure eye-tracking metrics and digital pen data while participants perform the simplified Rey Complex Figure (RCFT) and clock drawing tests. The primary outcome is a change in the ratio of the number of fixations (working space/perceptual space) performed using the simplified RCFT, from baseline to 12 weeks, as assessed using eye-tracking metrics. The secondary outcomes are changes in general cognition, clinical severity, activities of daily living, and visuospatial function assessed using standard rating scores and digital pen data. The analyses of the primary and secondary outcomes will be based on the difference in changes during follow-up between the donepezil and control groups using the t-test or Mann–Whitney U test, as well as adjusting for baseline values. Discussion This study is designed to determine whether eye-tracking metrics can detect the effect of donepezil on visuospatial dysfunction more sensitively in patients with MCI. It is expected that multimodal data, such as eye-tracking and digital pen data, may provide helpful biomarkers for identifying subtle changes that are difficult to measure using conventional methods. Trial registration Clinical Research Information Service, Republic of Korea (CRIS, cris.nih.go.kr) KCT0006236. Registered on June 10, 2021.
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Affiliation(s)
- Ko Woon Kim
- Department of Neurology, Jeonbuk National University Medical School and Hospital, 20 Geonji-ro, Deokjin-gu, Jeonju, 54907, South Korea.,Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Qi Wang
- Jeonbuk National University Medical School, Jeonju, Korea
| | - Se Hee Koo
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Byoung-Soo Shin
- Department of Neurology, Jeonbuk National University Medical School and Hospital, 20 Geonji-ro, Deokjin-gu, Jeonju, 54907, South Korea. .,Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea.
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6
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Sun J, Liu Y, Wu H, Jing P, Ji Y. A novel deep learning approach for diagnosing Alzheimer's disease based on eye-tracking data. Front Hum Neurosci 2022; 16:972773. [PMID: 36158627 PMCID: PMC9500464 DOI: 10.3389/fnhum.2022.972773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to build an eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks.
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Affiliation(s)
- Jinglin Sun
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Yu Liu
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Hao Wu
- Tianjin Key Laboratory of Cerebrovascular and Neurodegenerative Diseases, Department of Neurology, Tianjin Dementia Institute, Tianjin Huanhu Hospital, Tianjin, China
| | - Peiguang Jing
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
- *Correspondence: Peiguang Jing
| | - Yong Ji
- Tianjin Key Laboratory of Cerebrovascular and Neurodegenerative Diseases, Department of Neurology, Tianjin Dementia Institute, Tianjin Huanhu Hospital, Tianjin, China
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Ryan JD, Wynn JS, Shen K, Liu ZX. Aging changes the interactions between the oculomotor and memory systems. NEUROPSYCHOLOGY, DEVELOPMENT, AND COGNITION. SECTION B, AGING, NEUROPSYCHOLOGY AND COGNITION 2022; 29:418-442. [PMID: 34856890 DOI: 10.1080/13825585.2021.2007841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/12/2021] [Indexed: 06/13/2023]
Abstract
The use of multi-modal approaches, particularly in conjunction with multivariate analytic techniques, can enrich models of cognition, brain function, and how they change with age. Recently, multivariate approaches have been applied to the study of eye movements in a manner akin to that of neural activity (i.e., pattern similarity). Here, we review the literature regarding multi-modal and/or multivariate approaches, with specific reference to the use of eyetracking to characterize age-related changes in memory. By applying multi-modal and multivariate approaches to the study of aging, research has shown that aging is characterized by moment-to-moment alterations in the amount and pattern of visual exploration, and by extension, alterations in the activity and function of the hippocampus and broader medial temporal lobe (MTL). These methodological advances suggest that age-related declines in the integrity of the memory system has consequences for oculomotor behavior in the moment, in a reciprocal fashion. Age-related changes in hippocampal and MTL structure and function may lead to an increase in, and change in the patterns of, visual exploration in an effort to upregulate the encoding of information. However, such visual exploration patterns may be non-optimal and actually reduce the amount and/or type of incoming information that is bound into a lasting memory representation. This research indicates that age-related cognitive impairments are considerably broader in scope than previously realized.
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Affiliation(s)
- Jennifer D Ryan
- Rotman Research Institute at Baycrest Health Sciences, Toronto, ON, Canada
- Departments of Psychology, Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Jordana S Wynn
- Department of Psychology, Harvard University, Cambridge MA, USA
| | - Kelly Shen
- Rotman Research Institute at Baycrest Health Sciences, Toronto, ON, Canada
| | - Zhong-Xu Liu
- Department of Behavioral Sciences, University of Michigan-Dearborn, Dearborn MI, USA
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Cid REC, Loewenstein DA. Salient Cognitive Paradigms to Assess Preclinical Alzheimer's Disease. Neurotherapeutics 2022; 19:89-98. [PMID: 35246818 PMCID: PMC9130422 DOI: 10.1007/s13311-022-01192-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2022] [Indexed: 01/03/2023] Open
Abstract
Despite the growing emphasis to identify early biological markers that can detect the progressive accumulation of brain pathology in the complex pathophysiologic cascade that occurs in Alzheimer's disease (AD), we continue to employ the same neuropsychological paradigms that were developed to detect dementia or frank cognitive impairment. It has become increasingly clear that we cannot expect to measure clinically meaningful change in relationship to these emerging preclinical biomarkers using these traditional cognitive assessment paradigms, nor will we advance the efforts to identify the earliest cognitive changes that emerge in AD. Over the last decade, a few novel promising cognitive assessment paradigms have emerged that have shown promise in identifying subtle cognitive deficits in AD which aids in early detection and monitoring of meaningful cognitive change over time. Some of these cognitive assessment paradigms are reviewed here, including semantic interference, semantic intrusion errors, memory binding, and binding of face and name associations. These paradigms may be useful for AD clinical trials focused on secondary prevention if there is sufficient rigor to suggest that they correlate with AD biomarkers, having robust sensitivity, specificity, and predictive utility among culturally and linguistically diverse populations at-risk for AD.
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Affiliation(s)
- Rosie E Curiel Cid
- Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1695 NW 9th Avenue, Florida, Miami, 33136, USA.
| | - David A Loewenstein
- Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1695 NW 9th Avenue, Florida, Miami, 33136, USA
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Odusami M, Maskeliūnas R, Damaševičius R, Krilavičius T. Analysis of Features of Alzheimer's Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics (Basel) 2021; 11:1071. [PMID: 34200832 PMCID: PMC8230447 DOI: 10.3390/diagnostics11061071] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/04/2021] [Accepted: 06/08/2021] [Indexed: 12/12/2022] Open
Abstract
One of the first signs of Alzheimer's disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer's Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity.
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Affiliation(s)
- Modupe Odusami
- Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania; (M.O.); (R.M.)
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania; (M.O.); (R.M.)
| | - Robertas Damaševičius
- Department of Applied Informatics, Vytautas Magnus University, 44248 Kaunas, Lithuania;
| | - Tomas Krilavičius
- Department of Applied Informatics, Vytautas Magnus University, 44248 Kaunas, Lithuania;
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