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Related Concept Videos

Alzheimer's Disease: Overview01:26

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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.
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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification.

Yuhuan Hu1, Caiyun Wen2, Guoquan Cao2

  • 1Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

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|May 5, 2022
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Summary
This summary is machine-generated.

Early Alzheimer's disease (AD) diagnosis is crucial. Combining structural MRI (sMRI) and functional MRI (rs-fMRI) with deep learning improves AD detection accuracy, showing potential for better patient outcomes.

Keywords:
Alzheimer's diseaseClassificationDeep learningMultimodalityrs-fMRIsMRI

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Early diagnosis and intervention are vital for Alzheimer's disease (AD) management.
  • Current diagnostic methods using structural MRI (sMRI) and resting-state fMRI (rs-fMRI) show promise but often assume fixed time lags, limiting detailed brain region relationship analysis.
  • Dynamic changes in brain signal propagation delays necessitate advanced methods for accurate functional network construction.

Purpose of the Study:

  • To develop and evaluate a deep learning-based Granger causality estimator for constructing brain functional networks.
  • To integrate sMRI and rs-fMRI data for improved classification of Alzheimer's disease (AD) from healthy controls.
  • To assess the diagnostic performance of combined sMRI and rs-fMRI features using a Support Vector Machine (SVM) classifier.

Main Methods:

  • Utilized a deep learning approach, specifically long short-term memory networks, for Granger causality estimation to model brain connectivity with dynamic time lags.
  • Analyzed cerebral cortex properties using sMRI and graph metrics of functional networks using rs-fMRI data.
  • Extracted optimal feature subsets from both modalities and trained an SVM classifier to differentiate AD patients (n=27) from healthy controls (n=20).

Main Results:

  • The SVM classifier achieved high diagnostic accuracies: 87.23% for sMRI features, 78.72% for rs-fMRI features, and 91.49% for combined sMRI and rs-fMRI features.
  • The integration of sMRI and rs-fMRI data significantly improved AD classification compared to using either modality alone.
  • The study demonstrates the potential of combining sMRI and rs-fMRI for more effective identification of AD.

Conclusions:

  • Integrating sMRI and rs-fMRI provides complementary information, enhancing the accuracy of Alzheimer's disease diagnosis.
  • The deep learning-based Granger causality approach offers a more nuanced analysis of brain connectivity by accounting for changing time lags.
  • This multimodal neuroimaging approach holds significant promise for improving early detection and clinical management of Alzheimer's disease.