Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns
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Individual learning performance of cognitive function is related to functional connections within 'task-activated' regions where activities increase during the corresponding cognitive tasks. On the other hand, since any brain region is connected with other regions and brain-wide networks, learning is characterized by modulations in connectivity between networks with different functions. Therefore, we hypothesized that learning performance is determined by functional connections among intrinsic networks that include both task-activated and less-activated networks. Subjects underwent resting-state functional MRI and a short period of training (80-90 min) in a working memory task on separate days. We calculated functional connectivity patterns of whole-brain intrinsic networks and examined whether a sparse linear regression model predicts a performance plateau from the individual patterns. The model resulted in highly accurate predictions (R(2) = 0.73, p = 0.003). Positive connections within task-activated networks, including the left fronto-parietal network, accounted for nearly half (48%) of the contribution ratio to the prediction. Moreover, consistent with our hypothesis, connections of the task-activated networks with less-activated networks showed a comparable contribution (44%). Our findings suggest that learning performance is potentially constrained by system-level interactions within task-activated networks as well as those between task-activated and less-activated networks.
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The training-dose categories were defined empirically from the distribution of training hours rather than from trajectory-based markers of learning, so the pattern should not be taken as direct evidence regarding fast versus slow learning phases in WM training.
“However, this pattern should not be taken as direct evidence regarding fast versus slow learning phases in WM training (Ericson & Klingberg, 2023; Yamashita et al., 2015), because our training-dose categories were defined empirically from the distribution of training hours rather than from trajectory-based markers of learning.”
From The asymmetry of working memory training transfer: A systematic review and meta-analysis (2026) · cites this paper as Yamashita et al., 2015
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Self-citation
- Authors
- Yamashita, M., Kawato, M., & Imamizu, H.
- Affiliations
- Advanced Telecommunications Research Institute International (JP)
- Corresponding author
- Yamashita, Masahiro
- Imamizu, Hiroshi
- Venue
- Scientific Reports
- Published
- 2015-01-05
- Volume
- 5
- Issue
- 1
- Pages
- 7622-7622
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5154600
- OpenAlex
- W2080879785
- Cited by
- 53
- References
- 44
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 2 · 2022: 7 · 2021: 6 · 2020: 7 · 2019: 5 · 2018: 8 · 2017: 7 · 2016: 6
- FWCI
- 4.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Institutional Repositories DataBase (IRDB) (publishedVersion)
- NAIST Digital Library (Nara Institute of Science and Technology) (submittedVersion)
- Funders
- Ministry of Education, Culture, Sports, Science and Technology (26120002)
- Ministry of Internal Affairs and Communications
- Japan Society for the Promotion of Science (26120002)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- working memory, learning plateau, intrinsic functional connectivity, resting-state fMRI, frontoparietal network, network interaction, individual differences in learning
- MeSH terms
- Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Memory, Short-Term, Psychomotor Performance, Radiography, Linear Models, Young Adult
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