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Brain network efficiency is influenced by the pathologic source of corticobasal syndrome
Medaglia JD, Huang W, Segarra S, Olm C, Gee J, Grossman M, Ribeiro A, McMillan CT et al.. Neurology, 2017-08-04
Topic: Brain proteins, microglia and lab models
Objective To apply network-based statistics to diffusion-weighted imaging tractography data and detect Alzheimer disease vs non-Alzheimer degeneration in the context of corticobasal syndrome. Methods In a cross-sectional design, pathology was confirmed by autopsy or a pathologically validated CSF total tau-to-β-amyloid ratio (T-tau/Aβ). Using structural MRI data, we identify association areas in fronto-temporo-parietal cortex with reduced gray matter density in corticobasal syndrome (n = 40) relative to age-matched controls (n = 40).
Key findings
- Methods In a cross-sectional design, pathology was confirmed by autopsy or a pathologically validated CSF total tau-to-β-amyloid ratio (T-tau/Aβ).
- Using structural MRI data, we identify association areas in fronto-temporo-parietal cortex with reduced gray matter density in corticobasal syndrome (n = 40) relative to age-matched controls (n = 40).
- Using these fronto-temporo-parietal regions of interest, we construct structural brain networks in clinically similar subgroups of individuals with Alzheimer disease (n = 21) or non-Alzheimer pathology (n = 19) by linking these regions by the number of white matter streamlines identified in a deterministic tractography analysis of diffusion tensor imaging data.
- We characterize these structural networks using 5 graph-based statistics, and assess their relative utility in classifying underlying pathology with leave-one-out cross-validation using a supervised support vector machine.
Why it matters
This study used brain imaging to look at how the source of nerve cell damage affects brain network efficiency in people with corticobasal syndrome, distinguishing Alzheimer's from non-Alzheimer's degeneration. It is a cross-sectional study and does not test any treatment or cure.
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Study details
- Study designcross-sectional
- Participants40
- Evidence strengthLow
How this study scored 54/100
Published in a leading specialty journal, and it is a snapshot of people at one point in time. Scored zero for effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Neurology) |
| Journal's citation record | 4 / 4 | Neurology: journal h-index 525 (OpenAlex, 2026-10-29) |
| Study design | 4 / 12 | A snapshot of people at one point in time |
| 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 | 1 / 4 | 40 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 | 3 / 7 | Cited 34 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.1 citations a year over 11 year(s) since 2017 |
| Impact for its field and year | 2 / 6 | Cited 2.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 0 / 4 | Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Danielle S. Bassett: h-index 114 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Massachusetts Institute of Technology: institution h-index 2,557 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 28779011) |
| Free to read | 2 / 2 | Free to read |
| Total | 54 / 100 | — |
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Read the original
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. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Medaglia, J. D., Huang, W., Segarra, S., Olm, C., Gee, J., Grossman, M., et al. (2017). Brain network efficiency is influenced by the pathologic source of corticobasal syndrome. Neurology. https://doi.org/10.1212/wnl.0000000000004324
Vancouver
Medaglia JD, Huang W, Segarra S, Olm C, Gee J, Grossman M, et al. Brain network efficiency is influenced by the pathologic source of corticobasal syndrome. Neurology. 2017. doi:10.1212/wnl.0000000000004324. PMID: 28779011.
BibTeX
@article{medaglia2017-28779011,
author = {Medaglia, J. D. and Huang, W. and Segarra, S. and Olm, C. and Gee, J. and Grossman, M. and Ribeiro, A. and McMillan, C. T. and others},
title = {{Brain network efficiency is influenced by the pathologic source of corticobasal syndrome}},
journal = {Neurology},
year = {2017},
doi = {10.1212/wnl.0000000000004324},
pmid = {28779011},
url = {https://doi.org/10.1212/wnl.0000000000004324}
}
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