ORCID Profile
0000-0001-6473-1791
Current Organisations
Bond University
,
Curtin University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 27-07-2021
Publisher: Elsevier
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 13-02-2023
DOI: 10.1007/S12144-023-04281-1
Abstract: In this study, we investigated the ability of commonly used neuropsychological tests to detect cognitive and functional decline across the Alzheimer’s disease (AD) continuum. Moreover, as preclinical AD is a key area of investigation, we focused on the ability of neuropsychological tests to distinguish the early stages of the disease, such as in iduals with Subjective Memory Complaints (SMC). This study included 595 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset who were cognitively normal (CN), SMC, mild cognitive impairment (MCI early or late stage), or AD. Our cognitive measures included the Rey Auditory Verbal Learning Test (RAVLT), the Everyday Cognition Questionnaire (ECog), the Functional Abilities Questionnaire (FAQ), the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog), the Montreal Cognitive Assessment scale (MoCA), and the Trail Making test (TMT-B). Overall, our results indicated that the ADAS-13, RAVLT (learning), FAQ, ECog, and MoCA were all predictive of the AD progression continuum. However, TMT-B and the RAVLT (immediate and forgetting) were not significant predictors of the AD continuum. Indeed, contrary to our expectations ECog self-report (partner and patient) were the two strongest predictors in the model to detect the progression from CN to AD. Accordingly, we suggest using the ECog (both versions), RAVLT (learning), ADAS-13, and the MoCA to screen all stages of the AD continuum. In conclusion, we infer that these tests could help clinicians effectively detect the early stages of the disease (e.g., SMC) and distinguish the different stages of AD.
Publisher: Elsevier
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 17-02-2022
DOI: 10.1007/S44202-022-00031-9
Abstract: While there is a multitude of studies on mild cognitive impairment (MCI more than 80,000 articles), subjective memory complaints (SMC) have received less attention as a prodromal stage of Alzheimer’s disease (AD less than 2000 articles). In this perspective review article, we argue that SMC should also be considered as another risk factor for the development of AD, and perhaps a pre-MCI condition. This recognition of SMC could help clinicians to identify in iduals at risk of developing dementia and could provide protective treatment for them. Accordingly, in this perspective article, we review key studies that outline the nature of SMC, discuss how SMC is measured, explore SMC in MCI, introduce some approaches to SMC treatment, and we discuss future directions for SMC research. Overall, we argue that, like MCI, there should be more research on SMC as a risk factor for developing AD. Consequentially, we aim to highlight the need for further research on SMC and the condition’s role as a potential neuroprotector against AD (e.g., early-stage marker).
Publisher: Wiley
Date: 18-10-2023
DOI: 10.1111/JON.13063
Abstract: Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and clinical observations. However, these diagnoses are not perfect, and additional diagnostic tools (e.g., MRI) can help improve our understanding of AD as well as our ability to detect the disease. Accordingly, a large amount of research has been invested into innovative diagnostic methods for AD. Functional MRI (fMRI) is a form of neuroimaging technology that has been used to diagnose AD however, fMRI is incredibly noisy, complex, and thus lacks clinical use. Nonetheless, recent innovations in deep learning technology could enable the simplified and streamlined analysis of fMRI. Deep learning is a form of artificial intelligence that uses computer algorithms based on human neural networks to solve complex problems. For ex le, in fMRI research, deep learning models can automatically denoise images and classify AD by detecting patterns in participants’ brain scans. In this systematic review, we investigate how fMRI (specifically resting‐state fMRI) and deep learning methods are used to diagnose AD. In turn, we outline the common deep neural network, preprocessing, and classification methods used in the literature. We also discuss the accuracy, strengths, limitations, and future direction of fMRI deep learning methods. In turn, we aim to summarize the current field for new researchers, suggest specific areas for future research, and highlight the potential of fMRI to aid AD diagnoses.
No related grants have been discovered for Samuel Warren.