The Association Between Functional Assessment and Structural Brain Biomarkers in an Ethnically Diverse Sample With Normal Cognition, Mild Cognitive Impairment, or Dementia.

Objective: To investigate the association between the functional activities questionnaire (FAQ) and brain biomarkers (bilateral hippocampal volume [HV], bilateral entorhinal volume [ERV], and entorhinal cortical thickness [ERT]) in cognitively normal (CN) individuals, mild cognitive impairment (MCI), or dementia.

Method: In total, 226 participants (137 females; mean age = 71.76, SD = 7.

Gaussian discriminative component analysis for early detection of Alzheimer's disease: A supervised dimensionality reduction algorithm.

Background: Using multiple modalities of biomarkers, several machine leaning-based approaches have been proposed to characterize patterns of structural, functional and metabolic differences discernible from multimodal neuroimaging data for Alzheimer's disease (AD). Current investigations report several studies using binary classification often augmented with local feature selection methods, while fewer other studies address the challenging problem of multiclass classification.

New Method: To assess the merits of each of these research directions, this study introduces a supervised Gaussian discriminative component analysis (GDCA) algorithm, which can effectively delineate subtle changes of early mild cognitive impairment (EMCI) group in relation to the cognitively normal control (CN) group.

A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging.

Background: Diagnosis of early mild cognitive impairment (EMCI) as a prodromal stage of Alzheimer's disease (AD) with its delineation from the cognitively normal (CN) group remains a challenging but essential step for the planning of early treatment. Although several studies have focused on the MCI diagnosis, this study introduces the early stage of MCI to assess more thoroughly the earliest signs of disease manifestation and progression.

New Method: We used random forest feature selection model with a Gaussian-based algorithm to perform method evaluation.

A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.

Predicting the progression of Alzheimer's Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the development of novel machine learning algorithms. This study proposes a novel machine learning algorithm for predicting the progression of Alzheimer's disease using a distributed multimodal, multitask learning method. More specifically, each individual task is defined as a regression model, which predicts cognitive scores at a single time point.

Types of errors on a semantic interference task in mild cognitive impairment and dementia.

Objective: This research aimed to determine whether qualitative analysis of different types of intrusion errors on a verbal cognitive task was useful in detecting subtle cognitive impairment in preclinical stages prior to the progression to dementia.

Method: Different types of semantic intrusions on the Loewenstein-Acevedo Scales of Semantic Interference and Learning (LASSI-L) were compared across 160 individuals diagnosed as cognitively normal (CN), amnestic Mild Cognitive Impairment (aMCI), and dementia. The sample included Hispanics and non-Hispanic European Americans.