Brain Modularity: A Biomarker of Intervention-related Plasticity
Open accessAbstract
Interventions using methods such as cognitive training and aerobic exercise have shown potential to enhance cognitive abilities. However, there is often pronounced individual variability in the magnitude of these gains. Here, we propose that brain network modularity, a measure of brain subnetwork segregation, is a unifying biomarker of intervention-related plasticity. We present work from multiple independent studies demonstrating that individual differences in baseline brain modularity predict gains in cognitive control functions across several populations and interventions, spanning healthy adults to patients with clinical deficits and cognitive training to aerobic exercise. We believe that this predictive framework provides a foundation for developing targeted, personalized interventions to improve cognition.
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Default mode and cingulo frontal patterns predict remission to medication and can guide selection between CBT and pharmacotherapy.
“default mode and cingulo frontal patterns predict remission to medication and can guide selection between CBT and pharmacotherapy [370,371]”
From Synaptic Plasticity—Intrinsic Excitability and Antidepressant Discovery (2026) · cites this paper as [370]
The “Additional Work Supporting a Brain Modularity-Plasticity Relationship” section reports that baseline default and visual network modularity predicts reduced OCD symptom severity after CBT. The “Concluding Remarks and Future Directions” section proposes that modularity could eventually guide personalized interventions. Neither passage supports cingulo-frontal patterns predicting medication remission or choosing between CBT and pharmacotherapy; page numbers were not available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredDLPFC-to-sgACC and broader salience network coupling stratify rTMS outcomes.
“Dorsolateral prefrontal cortex (DLPFC)-to-subgenual anterior cingulate cortex (sgACC) and broader salience network coupling stratify rTMS outcomes”
From Synaptic Plasticity—Intrinsic Excitability and Antidepressant Discovery (2026) · cites this paper as [370]
The 'Additional Work Supporting a Brain Modularity-Plasticity Relationship' section discusses network modularity predicting outcomes after cognitive training and other interventions, but not rTMS outcomes. Searches for DLPFC–sgACC coupling, salience-network coupling, and transcranial magnetic stimulation found no passage addressing the claimed stratification. (LLM, conf=0.76)
Experimental — accuracy not yet measuredHigher baseline rACC theta predicts greater symptom improvement across modalities and differentiates responders.
“Higher baseline rACC theta predicts greater symptom improvement across modalities and differentiates responders”
From Synaptic Plasticity—Intrinsic Excitability and Antidepressant Discovery (2026) · cites this paper as [370]
The sections on cognitive and exercise training report that baseline brain network modularity predicts cognitive gains, while the Additional Work section mentions modularity predicting symptom-severity reductions after OCD therapy. Neither those passages nor searches for rACC theta and treatment response found evidence about baseline rACC theta, symptom improvement across modalities, or differentiating responders. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe EEG spectral slope is a proxy for excitation to inhibition balance; it flattens with cortical disinhibition and steepens as inhibition strengthens.
“the EEG spectral slope, a proxy for excitation to inhibition balance, flattens with cortical disinhibition and steepens as inhibition strengthens”
From Synaptic Plasticity—Intrinsic Excitability and Antidepressant Discovery (2026) · cites this paper as [370]
The paper’s “A Network Perspective for Studying Cognitive Plasticity” section mentions frontal alpha power as a possible predictor of learning, and its “Additional Work Supporting a Brain Modularity-Plasticity Relationship” section discusses modularity measured from resting EEG. Neither passage addresses EEG spectral slope, excitation–inhibition balance, or how the slope changes with disinhibition or stronger inhibition. Searches for those terms and for aperiodic EEG power found no supporting passage in our copy; page numbers were unavailable. (LLM, conf=0.76)
Experimental — accuracy not yet measuredGreater baseline evidence of disrupted connectivity is predictive of subsequent behavioral improvements in markers of cognitive control.
“In addition, a growing body of work suggests that greater baseline evidence of disrupted connectivity is predictive of subsequent behavioral improvements in markers of cognitive control (55, 56).”
From The Role of Exercise in Management of Mental Health Disorders: An Integrative Review (2021) · cites this paper as [55]
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Lim, J., Lee, J., Woo, C., Song, J., Oh, M. S., Yu, K., & Lee, B. (2020) · Journal of Clinical Neurology · doi:10.3988/jcn.2020.16.1.116 · Cited by 6 · see citing works
The uniqueness of the individual functional connectome
Horien, C., Scheinost, D., & Constable, R. T. (2019) · Connectomics · doi:10.1016/b978-0-12-813838-0.00004-2 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [67]Schlosser G and Wagner GP, eds (2004) Modularity in Development and Evolution, University of Chicago Press
Cited by
Sorted by citations
The role of PFC networks in cognitive control and executive function
Menon, V., & D’Esposito, M. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01152-w · Cited by 654 · see citing works
Self-citationThe Role of Exercise in Management of Mental Health Disorders: An Integrative Review
Smith, P. J., & Merwin, R. M. (2021) · Annual Review of Medicine · doi:10.1146/annurev-med-060619-022943 · Cited by 360 · see citing works
Dose-Response Matters! - A Perspective on the Exercise Prescription in Exercise-Cognition Research
Herold, F., Müller, P., Gronwald, T., & Müller, N. G. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02338 · Cited by 196 · see citing works
Leveraging technology to personalize cognitive enhancement methods in aging
Ziegler, D. A., Anguera, J. A., Gallen, C. L., Hsu, W.-Y., Wais, P. E., & Gazzaley, A. (2022) · Nature Aging · doi:10.1038/s43587-022-00237-5 · Cited by 50 · see citing works
Self-citationSato, S. D., Shah, V. A., Fettrow, T., Hall, K. G., Tays, G. D., Cenko, E., Roy, A., Clark, D. J., Ferris, D. P., Hass, C. J., et al. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121155 · Cited by 6 · see citing works
Synaptic Plasticity—Intrinsic Excitability and Antidepressant Discovery
Tanaka, M. (2026) · Biomedicines · doi:10.3390/biomedicines14061265
- Authors
- Gallen, C. L., & D’Esposito, M.
- Affiliations
- University of California, San Francisco (US)
- University of California, Berkeley (US)
- Corresponding author
- Gallen, Courtney L.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 23
- Issue
- 4
- Pages
- 293-304
- ISSN
- 1364-6613 (ISSN-L)
- PMC
- PMC6750199
- OpenAlex
- W2920611415
- Cited by
- 168
- References
- 90
- Citations by year
- 2026: 13 · 2025: 30 · 2024: 29 · 2023: 19 · 2022: 27 · 2021: 23 · 2020: 20 · 2019: 7
- FWCI
- 2.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 348 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Trends in Cognitive Sciences (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Science Foundation (1808384)
- NIH (NS79698)
- VA Research Administration
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- brain network modularity, brain network segregation, cognitive training, aerobic exercise, cognitive control, individual differences, biomarkers, cognitive enhancement
- MeSH terms
- Cognitive Remediation, Brain, Exercise Therapy, Humans, Nerve Net, Neuronal Plasticity, Biomarkers, Executive Function, Cognitive Dysfunction
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.
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