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Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world
Hallquist MN, Hillary FG.. Network neuroscience (Cambridge, Mass.), 2018-10-01
Topic: Risk, prevention and care in population studies
Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer's disease or depression have adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.
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APA 7th
Hallquist, M. N., & Hillary, F. G. (2019). Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world. Network neuroscience (Cambridge, Mass.). https://doi.org/10.1162/netn_a_00054
Vancouver
Hallquist MN, Hillary FG. Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world. Network neuroscience (Cambridge, Mass.). 2019. doi:10.1162/netn_a_00054. PMID: 30793071.
BibTeX
@article{hallquist2019-30793071,
author = {Hallquist, M. N. and Hillary, F. G.},
title = {{Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2019},
doi = {10.1162/netn_a_00054},
pmid = {30793071},
url = {https://doi.org/10.1162/netn_a_00054}
}
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Key findings
- Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences.
- Third, few graph metrics were reported in common, precluding meta-analyses.
- Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure).
- Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons.
- Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.
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Study details
- Study designmeta-analysis
- Evidence strengthHigh
How this study scored 69/100
Published in a peer-reviewed journal, and it is a meta-analysis pooling many studies. Scored zero for sample size, effect reported and studied in people 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 40 of the 58 points we could judge it on, which is 69 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Network neuroscience (Cambridge, Mass.)) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 12 / 12 | A meta-analysis pooling many studies |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2019 — 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 | A review of other studies — randomisation is not credited here |
| 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 30793071) |
| Free to read | 2 / 2 | Free to read |
| Points | 40 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 40 of the 58 points we could judge it on is 69 out of 100. |
| Total | 69 / 100 | — |
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