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Related Concept Videos

Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

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Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
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Related Experiment Video

Updated: May 5, 2026

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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PyCaret machine learning library with three preprocessing steps after eLORETA source estimation predicts Alzheimer's

Yasunori Aoki1,2,3, Rei Takahashi1,2, Roberto D Pascual-Marqui4

  • 1Department of Psychiatry, Nippon Life Hospital, Osaka, Japan.

Neuroimage. Reports
|January 21, 2026
PubMed
Summary
This summary is machine-generated.

Early Alzheimer's detection is possible using electroencephalography (EEG) and machine learning. This study shows that a linear discriminant analysis model applied to eLORETA EEG data can accurately identify Alzheimer's disease (AD) and mild cognitive impairment (MCIAD) even before symptoms appear.

Keywords:
Alzheimer's diseaseElectroencephalography (EEG)Exact low-resolution electromagnetic tomography (eLORETA)Linear discriminant analysisMachine learningPyCaret

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline.
  • Pathological changes in AD begin decades before clinical symptoms manifest, making early diagnosis challenging.
  • Current diagnostic methods often struggle to differentiate early AD and mild cognitive impairment due to AD (MCIAD) from normal aging.

Purpose of the Study:

  • To develop and validate an accurate method for early detection of Alzheimer's disease (AD) and MCIAD.
  • To identify reliable biomarkers in electroencephalography (EEG) data for pre-symptomatic AD detection.
  • To leverage advanced source estimation and machine learning techniques for improved diagnostic accuracy.

Main Methods:

  • Utilized exact low-resolution brain electromagnetic tomography (eLORETA) for EEG source estimation.
  • Applied machine learning, specifically linear discriminant analysis (LDA) via the PyCaret library, for classification.
  • Employed preprocessing steps including subject-wise normalization, age-difference correction, and log-transformation on eLORETA data.

Main Results:

  • The LDA model achieved 100.0% accuracy in distinguishing AD patients from healthy subjects.
  • The model demonstrated high accuracy (96.4%) in identifying MCIAD patients.
  • Identified specific patterns of cortical electrical activity (delta, theta, alpha, beta bands) associated with AD progression in distinct brain regions (DLPFC, PCC).

Conclusions:

  • The LDA model applied to eLORETA-processed EEG data is a promising tool for early and pre-symptomatic detection of Alzheimer's disease.
  • This approach can identify physiological features of AD in EEG data before the onset of clinical symptoms.
  • The combination of eLORETA and PyCaret offers a significant contribution to the early diagnosis of AD, aiding in timely intervention and management.