Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example.

Background: Deep-learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g.

Common Neuropsychiatric S ymptoms in Alzheimer's Disease, Mild Cognitive Impairment, and Subjective Memory Complaints: A Unified Framework.

The Alzheimer's disease (AD) continuum is a unique spectrum of cognitive impairment that typically involves the stages of subjective memory complaints (SMC), mild cognitive impairment (MCI), and AD dementia. Neuropsychiatric symptoms (NPS), such as apathy, anxiety, stress, and depression, are highly common throughout the AD continuum. However, there is a dearth of research on how these NPS vary across the AD continuum, especially SMC.

An Alzheimer's disease category progression sub-grouping analysis using manifold learning on ADNI.

Many current statistical and machine learning methods have been used to explore Alzheimer's disease (AD) and its associated patterns that contribute to the disease. However, there has been limited success in understanding the relationship between cognitive tests, biomarker data, and patient AD category progressions. In this work, we perform exploratory data analysis of AD health record data by analyzing various learned lower dimensional manifolds to separate early-stage AD categories further.

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

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.

Harnessing forgetfulness: can episodic-memory tests predict early Alzheimer's disease?

A rapid increase in the number of patients with Alzheimer's disease (AD) is expected over the next decades. Accordingly, there is a critical need for early-stage AD detection methods that can enable effective treatment strategies. In this study, we consider the ability of episodic-memory measures to predict mild cognitive impairment (MCI) to AD conversion and thus, detect early-stage AD.