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Related Experiment Video

Updated: Oct 11, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Information Flow Pattern in Early Mild Cognitive Impairment Patients.

Haijuan He1, Shuang Ding1, Chunhui Jiang1

  • 1Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Xinjiang, China.

Frontiers in Neurology
|December 3, 2021
PubMed
Summary

Brain information flow is disrupted in early mild cognitive impairment (EMCI). These patterns effectively differentiate EMCI patients from healthy controls and predict neurological status.

Keywords:
early mild cognitive impairmentinformation flowresting state functional MRIsupport vector classificationsupport vector regression

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Biomarkers

Background:

  • Early mild cognitive impairment (EMCI) represents a preclinical stage of dementia, necessitating reliable diagnostic and predictive tools.
  • Understanding brain network dynamics is crucial for early detection and intervention in neurodegenerative diseases.

Purpose of the Study:

  • To investigate brain information flow patterns in EMCI patients.
  • To assess the potential of these patterns for differentiating and predicting EMCI.

Main Methods:

  • Utilized resting-state functional MRI data from 49 EMCI patients and 40 healthy controls (HCs) from the Alzheimer's Disease Neuroimaging Initiative.
  • Employed non-parametric multiplicative regression-Granger causality analysis (NPMR-GCA) to calculate preferred information flow directions and indices.
  • Applied support vector classification and lasso regression for differentiation and prediction analyses.

Main Results:

  • EMCI patients exhibited disturbed preferred information flow directions within key brain networks (DMN, ECN, SMN, VN) compared to HCs.
  • Altered preferred information flow indices were observed in specific brain regions, including the thalamus, posterior cingulate, and precentral gyrus.
  • Information flow directions achieved 80% classification accuracy for differentiating EMCI from HC and showed strong predictive ability for cognitive and biomarker measures (r > 0.7).

Conclusions:

  • EMCI is characterized by a disturbed brain information flow pattern.
  • These information flow measures can aid clinicians in identifying EMCI patients.
  • The findings suggest information flow analysis is a valuable tool for assessing the neurological state in EMCI.