Cognitive Network Neuroscience
Open accessAbstract
Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.
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Connectivity-based approaches can provide direct insight into the nature of the underlying network dynamics that allow for behavioral adaptation.
“However, these approaches are increasingly complemented by connectivity-based approaches, which can provide direct insight into the nature of the underlying network dynamics that allow for behavioral adaptation (60).”
From Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015) · cites this paper as [60]
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Test-Retest Reliability of Graph Theory Measures of Structural Brain Connectivity
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Has brain imaging discovered anything new about how the brain works?
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Sorted by citations
Controllability of structural brain networks
Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., Medaglia, J. D., Vettel, J. M., Miller, M. B., Grafton, S. T., et al. (2015) · Nature Communications · doi:10.1038/ncomms9414 · Cited by 912 · see citing works
Self-citationDynamic reconfiguration of frontal brain networks during executive cognition in humans
Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., Schweiger, J. I., Grimm, O., Heinz, A., Tost, H., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422487112 · Cited by 791 · see citing works
Self-citationEmotion and the prefrontal cortex: an integrative review
Dixon, M. L., Thiruchselvam, R., Todd, R., & Christoff, K. (2017) · Psychological Bulletin · doi:10.1037/bul0000096 · Cited by 745 · see citing works
Normative brain size variation and brain shape diversity in humans
Reardon, P. K., Seidlitz, J., Vandekar, S., Liu, S., Patel, R., Park, M. T. M., Alexander-Bloch, A., Clasen, L. S., Blumenthal, J. D., Lalonde, F. M., et al. (2018) · Science · doi:10.1126/science.aar2578 · Cited by 255 · see citing works
Functional alignment with anatomical networks is associated with cognitive flexibility
Medaglia, J. D., Huang, W., Karuza, E. A., Kelkar, A., Thompson-Schill, S. L., Ribeiro, A., & Bassett, D. S. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0260-9 · Cited by 223 · see citing works
Self-citationFrom maps to multi-dimensional network mechanisms of mental disorders
Braun, U., Schaefer, A., Betzel, R. F., Tost, H., Meyer-Lindenberg, A., & Bassett, D. S. (2018) · Neuron · doi:10.1016/j.neuron.2017.11.007 · Cited by 177 · see citing works
Self-citationBrain Modularity: A Biomarker of Intervention-related Plasticity
Gallen, C. L., & D’Esposito, M. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.01.014 · Cited by 168 · see citing works
Optimal trajectories of brain state transitions
Gu, S., Betzel, R. F., Mattar, M. G., Cieslak, M., Delio, P. R., Grafton, S. T., Pasqualetti, F., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.003 · Cited by 183 · see citing works
Self-citationRich-club in the brain’s macrostructure: Insights from graph theoretical analysis
Kim, D.-J., & Min, B.-K. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.039 · Cited by 73 · see citing works
Medaglia, J. D., Satterthwaite, T. D., Kelkar, A., Ciric, R., Moore, T. M., Ruparel, K., Gur, R. C., Gur, R. E., & Bassett, D. S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.048 · Cited by 64 · see citing works
Self-citationFunctional Neuroimaging in Traumatic Brain Injury: From Nodes to Networks
Medaglia, J. D. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00407 · Cited by 63 · see citing works
Self-citationDoes mindfulness change the mind? A novel psychonectome perspective based on Network Analysis
Roca, P., Diez, G. G., Castellanos, N., & Vazquez, C. (2019) · PLOS ONE · doi:10.1371/journal.pone.0219793 · Cited by 37 · see citing works
- Authors
- Medaglia, J. D., Lynall, M.-E., & Bassett, D. S.
- Affiliations
- Moss Rehabilitation Hospital (US)
- University of Cambridge (GB)
- University of Pennsylvania (US)
- Venue
- Journal of Cognitive Neuroscience
- Publisher
- MIT Press - Journals
- Published
- 2015-08-01
- Volume
- 27
- Issue
- 8
- Pages
- 1471-1491
- ISSN
- 0898-929X (ISSN-L)
- 1530-8898
- PMC
- PMC4854276
- OpenAlex
- W2037716995
- Cited by
- 419
- References
- 189
- Citations by year
- 2026: 16 · 2025: 34 · 2024: 29 · 2023: 30 · 2022: 40 · 2021: 41 · 2020: 66 · 2019: 30 · 2018: 47 · 2017: 51 · 2016: 29 · 2015: 5
- FWCI
- 4.29
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Cognitive Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (T32HD071844)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- brain networks, structural connectivity, functional connectivity, resting-state networks, network dynamics, cognitive processes, sensory processing, language, emotions, attention, cognitive control, learning, memory
- MeSH terms
- Brain, Cognition, Humans, Models, Neurological, Neural Pathways, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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