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Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease
Khazaee A, Ebrahimzadeh A, Babajani-Feremi A.. Brain imaging and behavior, 2016-09-01
Topic: Amyloid, tau and blood-test diagnosis
The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer's disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.
Key findings
- In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions.
- Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.
Why it matters
Combining graph measures from resting-state fMRI with machine learning classified healthy controls, mild cognitive impairment, and Alzheimer's disease with reported accuracies of 88.4% for three groups and 72.0% for identifying MCI. The authors state this approach may assist in diagnosis, not treatment, cure, or prevention.
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Study details
- Participants89
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Brain imaging and behavior) |
| Journal's citation record | 1 / 4 | Brain Imaging and Behavior: journal h-index 89 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2016 — within 20 year(s) of the newest study here |
| Sample size | 1 / 4 | 89 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 214 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 17.8 citations a year over 12 year(s) since 2016 |
| Impact for its field and year | 4 / 6 | Cited 9.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 2 / 4 | Cited 31 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Abbas Babajani‐Feremi: h-index 23 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University of Tennessee Health Science Center: institution h-index 654 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 26363784) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 45 / 100 | — |
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Khazaee, A., Ebrahimzadeh, A., & Babajani-Feremi, A. (2016). Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease. Brain imaging and behavior. https://doi.org/10.1007/s11682-015-9448-7
Vancouver
Khazaee A, Ebrahimzadeh A, Babajani-Feremi A. Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease. Brain imaging and behavior. 2016. doi:10.1007/s11682-015-9448-7. PMID: 26363784.
BibTeX
@article{khazaee2016-26363784,
author = {Khazaee, A. and Ebrahimzadeh, A. and Babajani-Feremi, A.},
title = {{Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease}},
journal = {Brain imaging and behavior},
year = {2016},
doi = {10.1007/s11682-015-9448-7},
pmid = {26363784},
url = {https://doi.org/10.1007/s11682-015-9448-7}
}
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