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Related Concept Videos

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ and tau...
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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: Jul 1, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Machine learning for missing data imputation in Alzheimer's research: predicting medial temporal lobe dynamic

Soodeh Moallemian1, Abolfazl Saghafi2, Rutvik Deshpande1

  • 1Center for Molecular & Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, United States.

Cognitive Neurodynamics
|February 13, 2026
PubMed
Summary

Accurate imputation methods are crucial for analyzing neuroimaging data with missing values. Advanced techniques like GAIN and MissForest significantly improve predictions of medial temporal lobe flexibility in aging brains.

Keywords:
Alzheimer’s diseaseMachine learningMissing completely at randomMissing value imputation

Related Experiment Videos

Last Updated: Jul 1, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Neuroscience
  • Biostatistics
  • Gerontology

Background:

  • Alzheimer's disease (AD) pathology precedes clinical symptoms.
  • Medial temporal lobe (MTL) dynamic flexibility may serve as an early AD biomarker.
  • Neuroimaging studies often face substantial data missingness.

Purpose of the Study:

  • To evaluate imputation strategies for estimating MTL dynamic flexibility in older adults.
  • To compare prediction accuracy across various imputation methods and regression models.
  • To identify optimal imputation techniques for high-missingness neuroimaging data.

Main Methods:

  • Utilized data from 656 older adults.
  • Compared complete-case analysis with five imputation strategies (MICE, GAIN, MissForest, MIWAE, ReMasker).
  • Assessed prediction accuracy using repeated 5-fold cross-validation with eight regression models.

Main Results:

  • Complete-case analysis showed limited performance.
  • All imputation methods improved accuracy over complete-case analysis.
  • MissForest and GAIN, especially with ensemble tree models, achieved the lowest prediction error and highest concordance gains.

Conclusions:

  • Robust imputation is essential for maximizing data utility in neuroimaging studies with missing data.
  • Advanced imputation techniques, particularly GAIN and MissForest, combined with ensemble tree models, enhance predictive reliability for MTL dynamic flexibility.
  • These findings support the use of sophisticated imputation for early AD biomarker research.