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A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C.. NeuroImage, 2017-04-13
Topic: Amyloid, tau and blood-test diagnosis
Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985-June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions.
Key findings
- Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo.
- The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression.
Why it matters
This review sorted brain-scan studies, found through Google Scholar and PubMed searches covering 1985 to June 2016, that used computer classification to tell Alzheimer's disease and mild cognitive impairment apart from other groups. It grouped them by scan type (structural MRI, functional MRI, diffusion imaging, FDG-PET and amyloid-PET) and by the brain features measured, such as hippocampal size, connectivity, glucose use and amyloid burden. The authors conclude these methods "show promise for individualized diagnosis and prediction of clinical progression"; that is promise, not a validated clinical test, and the review concerns diagnosis, not treatment.
Study details
- Evidence strengthLow
How this study scored 56/100
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.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NeuroImage) |
| Journal's citation record | 4 / 4 | NeuroImage: journal h-index 523 (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 | 2 / 5 | 2017 — within 10 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 | 6 / 7 | Cited 621 times (OpenAlex, 2026-10-10) |
| Citations per year | 5 / 5 | 56.5 citations a year over 11 year(s) since 2017 |
| Impact for its field and year | 6 / 6 | Cited 42.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 24 other studies in this library |
| Still being cited | 3 / 4 | Cited 163 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Christos A. Davatzikos: h-index 124 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Pennsylvania: institution h-index 1,859 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 28414186) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 56 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroimage.2017.03.057
- PubMed: 28414186
- Original source →
- Free full text: PubMed Central →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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
Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017). A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. NeuroImage. https://doi.org/10.1016/j.neuroimage.2017.03.057
Vancouver
Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. NeuroImage. 2017. doi:10.1016/j.neuroimage.2017.03.057. PMID: 28414186.
BibTeX
@article{rathore2017-28414186,
author = {Rathore, S. and Habes, M. and Iftikhar, M. A. and Shacklett, A. and Davatzikos, C.},
title = {{A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages}},
journal = {NeuroImage},
year = {2017},
doi = {10.1016/j.neuroimage.2017.03.057},
pmid = {28414186},
url = {https://doi.org/10.1016/j.neuroimage.2017.03.057}
}
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