An improved deep residual network prediction model for the early diagnosis of Alzheimer's disease

H Sun, A Wang, W Wang, C Liu - Sensors, 2021 - mdpi.com
The early diagnosis of Alzheimer's disease (AD) can allow patients to take preventive
measures before irreversible brain damage occurs. It can be seen from cross-sectional …

A machine learning classifier for predicting stable MCI patients using gene biomarkers

RH Lin, CC Wang, CW Tung - International Journal of Environmental …, 2022 - mdpi.com
Alzheimer's disease (AD) is a neurodegenerative disorder with an insidious onset and
irreversible condition. Patients with mild cognitive impairment (MCI) are at high risk of …

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross‐sectional and longitudinal magnetic resonance imaging data

A Pérez‐Millan, J Contador… - Human brain …, 2023 - Wiley Online Library
Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of
dementia with partly overlapping, symptoms and brain signatures. There is a need to …

Early MCI‐to‐AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

S Minhas, A Khanum, A Alvi, F Riaz… - Computational …, 2021 - Wiley Online Library
In Alzheimer's disease (AD) progression, it is imperative to identify the subjects with mild
cognitive impairment before clinical symptoms of AD appear. This work proposes a …

Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI

L Zhang, J Wu, L Wang, L Wang, DC Steffens… - … Conference on Medical …, 2023 - Springer
Brain structural MRI has been widely used for assessing future progression of cognitive
impairment (CI) based on learning-based methods. Previous studies generally suffer from …

Analysis of functional connectivity in depression based on a weighted hyper-network method

X Shao, W Kong, S Sun, N Li, X Li… - Journal of Neural …, 2023 - iopscience.iop.org
Objective. Brain connectivity network is a vital tool to reveal the interaction between different
brain regions. Currently, most functional connectivity methods can only capture pairs of …

MRI-based model for MCI conversion using deep zero-shot transfer learning

F Ren, C Yang, YA Nanehkaran - The Journal of Supercomputing, 2023 - Springer
This study describes a deep zero-shot transfer learning model (DZTLM) for predicting mild
cognitive impairment (MCI) in patients with Alzheimer's disease (AD). The proposed DZTLM …

Distinguishing patients with MRI-negative temporal lobe epilepsy from normal controls based on individual morphological brain network

W Zhang, Y Duan, L Qi, Z Li, J Ren, N Nangale… - Brain Topography, 2023 - Springer
Abstract Temporal Lobe Epilepsy (TLE) is the most common subtype of focal epilepsy and
the most refractory to drug treatment. Roughly 30% of patients do not have easily identifiable …

Multivariate pattern analysis of medical imaging-based Alzheimer's disease

M Alarjani, B Almarri - Frontiers in Medicine, 2024 - frontiersin.org
Alzheimer's disease (AD) is a devastating brain disorder that steadily worsens over time. It is
marked by a relentless decline in memory and cognitive abilities. As the disease progresses …

Analysis of hippocampus evolution patterns and prediction of conversion in mild cognitive impairment using multivariate Morphometry Statistics

L Zhang, Y Fu, Z Zhao, Z Cong… - Journal of …, 2022 - content.iospress.com
Background: Mild cognitive impairment (MCI), which is generally regarded as the prodromal
stage of Alzheimer's disease (AD), is associated with morphological changes in brain …