Using big data methods to understand Alzheimer’s disease

Warren, Samuel L.; Moustafa, Ahmed A.; Alashwal, Hany

doi:10.1016/b978-0-12-821334-6.00004-1

Cited by 1 publication

(3 citation statements)

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“…In the literature, there are multiple approaches to diagnosing AD using neuroimaging, traditional biomarkers, and neuropsychological methods 12 . Each of these fields has significant strengths and limitations when diagnosing AD.…”

Section: Introductionmentioning

confidence: 99%

“… 4 , 5 There are treatments available that can treat some of the symptoms of AD and temporarily slow the progression of the disease; however, there is currently no treatment that can stop the progression of AD. 6 One of the biggest reasons for the dearth of effective treatment is the state of current AD diagnoses. In Australia, AD is commonly diagnosed using psychological tests and clinical observations (eg, interviews and pen‐and‐paper scales).…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, there are multiple approaches to diagnosing AD using neuroimaging, traditional biomarkers, and neuropsychological methods. 12 Each of these fields has significant strengths and limitations when diagnosing AD. Namely, traditional biomarkers can directly measure AD symptoms (eg, beta‐amyloid and tau) but are often invasive and vary in accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review

Warren

Moustafa

2022

Journal of Neuroimaging

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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 example, 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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