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Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory
Khazaee A, Ebrahimzadeh A, Babajani-Feremi A.. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2015-04-01
Topic: Brain imaging, connectivity and assessment
Objective Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation. Method In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD. Results Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%. Conclusion Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD. Significance Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease.
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APA 7th
Khazaee, A., Ebrahimzadeh, A., & Babajani-Feremi, A. (2015). Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. https://doi.org/10.1016/j.clinph.2015.02.060
Vancouver
Khazaee A, Ebrahimzadeh A, Babajani-Feremi A. Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 2015. doi:10.1016/j.clinph.2015.02.060. PMID: 25907414.
BibTeX
@article{khazaee2015-25907414,
author = {Khazaee, A. and Ebrahimzadeh, A. and Babajani-Feremi, A.},
title = {{Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory}},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
year = {2015},
doi = {10.1016/j.clinph.2015.02.060},
pmid = {25907414},
url = {https://doi.org/10.1016/j.clinph.2015.02.060}
}
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Key findings
- Objective Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases.
- Extracted network-based features were fed to different feature selection algorithms to choose most significant features.
- Results Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%.
- Conclusion Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD.
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Study details
- Evidence strengthLow
How this study scored 36/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 21 of the 58 points we could judge it on, which is 36 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 2015 — within 20 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 25907414) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 21 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 21 of the 58 points we could judge it on is 36 out of 100. |
| Total | 36 / 100 | — |
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