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Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals
Avery EW, Yoo K, Rosenberg MD, Greene AS, Gao S, Na DL, Scheinost D, Constable TR et al.. Journal of cognitive neuroscience, 2019-10-29
Topic: Risk, prevention and care in population studies
Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n -back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.
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APA 7th
Avery, E. W., Yoo, K., Rosenberg, M. D., Greene, A. S., Gao, S., Na, D. L., et al. (2020). Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals. Journal of cognitive neuroscience. https://doi.org/10.1162/jocn_a_01487
Vancouver
Avery EW, Yoo K, Rosenberg MD, Greene AS, Gao S, Na DL, et al. Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals. Journal of cognitive neuroscience. 2020. doi:10.1162/jocn_a_01487. PMID: 31659926.
BibTeX
@article{avery2020-31659926,
author = {Avery, E. W. and Yoo, K. and Rosenberg, M. D. and Greene, A. S. and Gao, S. and Na, D. L. and Scheinost, D. and Constable, T. R. and others},
title = {{Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals}},
journal = {Journal of cognitive neuroscience},
year = {2020},
doi = {10.1162/jocn_a_01487},
pmid = {31659926},
url = {https://doi.org/10.1162/jocn_a_01487}
}
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Key findings
- Using n -back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy.
- Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models.
- The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.
Why it matters
Models built from whole-brain functional connectivity predicted working memory performance in new individuals, including older adults with mild cognitive impairment and Alzheimer's disease. This shows connectivity patterns relate to memory ability, but it is a prediction study and does not test any treatment or prevention.
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Study details
- Evidence strengthLow
How this study scored 44/100
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 (Journal of cognitive neuroscience) |
| Journal's citation record | 3 / 4 | Journal of Cognitive Neuroscience: journal h-index 305 (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 | 2020 — 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 | 5 / 7 | Cited 103 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 12.9 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 3 / 6 | Cited 6.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 43 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Marvin M. Chun: h-index 85 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Yale University: institution h-index 1,927 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31659926) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 44 / 100 | — |
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