Increasing participant diversity in AD research: Plans for digital screening, blood testing, and a community‐engaged approach in the Alzheimer's Disease Neuroimaging Initiative 4

Weiner, Michael W.; Veitch, Dallas P.; Miller, Melanie; Aisen, Paul S.; Albala, Bruce; Beckett, Laurel A.; Green, Robert C.; Harvey, Danielle; Jack, Clifford R.; Landau, Susan; Morris, John C.; Nosheny, Rachel L.; Okonkwo, Ozioma C.; Perrin, Richard J.; Petersen, Ronald C.; Rivera‐Mindt, Monica; Saykin, Andrew J.; Shaw, Leslie M.; Toga, Arthur W.; Tosun, Duygu; Trojanowski, John Q.

doi:10.1002/alz.12797

Cited by 49 publications

(42 citation statements)

References 47 publications

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“…Fourth, although the patients with AD in this study were clinically diagnosed based on examinations such as magnetic resonance imaging and single‐photon emission computerized tomography, other diseases (e.g. argyrophilic grain disease, frontotemporal lobar degeneration, and Lewy body disease) could not be ruled out completely 1,94–97 . Likewise, as no biological or biophysical examinations were performed for the HCs, it is possible that these participants had asymptomatic diseases, including small vessel disease 98–102 .…”

Section: Discussionmentioning

confidence: 98%

“…argyrophilic grain disease, frontotemporal lobar degeneration, and Lewy body disease) could not be ruled out completely. 1,[94][95][96][97] Likewise, as no biological or biophysical examinations were performed for the HCs, it is possible that these participants had asymptomatic diseases, including small vessel disease. [98][99][100][101][102] Last, while Universal Dependencies has been designed to facilitate multilingual parser development [50][51][52] and has recently become available for the Japanese language, some linguistic issues among different languages remain unsolved.…”

Section: Future Directionsmentioning

confidence: 99%

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Language patterns in Japanese patients with Alzheimer disease: A machine learning approach

Momota

Liang

Horigome

et al. 2023

Psychiatry Clin Neurosci

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Aim:The authors applied natural language processing and machine learning to explore the disease-related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro-American languages. Methods:The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add-on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part-of-speech and dependency was tagged and counted to create features such as tagfrequency and tag-to-tag transition-frequency. Each feature's importance was computed during the 100-fold repeated random subsampling validation and averaged. Results:The model resulted in an accuracy of 0.84 (SD = 0.06), and an area under the curve of 0.90 (SD = 0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part-of-speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words-related features were lower among the patients, whereas those with stagnationrelated features were higher. Conclusion:The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical-semantic decline known as 'empty speech', which is regarded as a characteristic of AD.

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Section: Discussionmentioning

confidence: 98%

Section: Future Directionsmentioning

confidence: 99%

Language patterns in Japanese patients with Alzheimer disease: A machine learning approach

Momota

Liang

Horigome

et al. 2023

Psychiatry Clin Neurosci

View full text Add to dashboard Cite

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“…There is a need for additional studies to develop algorithms with the ethnicity and demographic characteristics of subjects in mind (Bae et al, 2020). An important step is the ADNI4 project which aims to enroll 50 to 66% unrepresented populations in 2022 using new biofluid and technologies (Weiner et al, 2023). Until then, generalization will remain a challenge.…”

Section: Challenges Of Ai Generalization and Data Shortagementioning

confidence: 99%

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

Mirkin

Albensi

2023

Front. Aging Neurosci.

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Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

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“…Neuroimaging Initiative (ADNI), aims to use online screening on 20,000 participants before enrichment for further phenotyping 50 . Ours is the first to directly benchmark online cognitive testing against in person testing in its ability to predict brain morphology.…”

Section: Associations Between Cognition Assessments and Cortical Morp...mentioning

confidence: 99%

Can an online battery match in-person cognitive testing in predicting age-related cortical changes?

Thienel

Borne

Faucher

et al. 2023

Preprint

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Understanding how cognition and brain structure change across the lifespan is crucial for gaining insight into the healthy ageing process, as well as identifying early signs of neurodegenerative changes. In our recent prospective study of healthy ageing in midlife and older adults1, we compared the association of two cognitive batteries with age-related variability in brain morphology. Our findings revealed that online cognitive testing, which is more cost-effective, demonstrated comparable association to sulcal width as comprehensive in person assessment. In person cognitive testing shows a significantly stronger correlation with sulcal width when compared to online testing, although the difference is numerically minor. In addition, it was found that both cognitive assessment assays showed a more pronounced age-related decline in individuals with Aβ burden. These findings suggest that online assessment is able to detect accelerated cognitive ageing comparably to the in-person assay in our preclinical sample, even in the early stages of Aβ accumulation before significant structural brain changes occur. Taken together with their greater cost effectiveness, online cognitive testing could lead to more equitable early detection and intervention for neurodegenerative diseases.

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Increasing participant diversity in AD research: Plans for digital screening, blood testing, and a community‐engaged approach in the Alzheimer's Disease Neuroimaging Initiative 4

Cited by 49 publications

References 47 publications

Language patterns in Japanese patients with Alzheimer disease: A machine learning approach

Language patterns in Japanese patients with Alzheimer disease: A machine learning approach

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

Can an online battery match in-person cognitive testing in predicting age-related cortical changes?

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