Modeling the impact of lesions in the human brain
Open accessAbstract
Lesions of anatomical brain networks result in functional disturbances of brain systems and behavior which depend sensitively, often unpredictably, on the lesion site. The availability of whole-brain maps of structural connections within the human cerebrum and our increased understanding of the physiology and large-scale dynamics of cortical networks allow us to investigate the functional consequences of focal brain lesions in a computational model. We simulate the dynamic effects of lesions placed in different regions of the cerebral cortex by recording changes in the pattern of endogenous ("resting-state") neural activity. We find that lesions produce specific patterns of altered functional connectivity among distant regions of cortex, often affecting both cortical hemispheres. The magnitude of these dynamic effects depends on the lesion location and is partly predicted by structural network properties of the lesion site. In the model, lesions along the cortical midline and in the vicinity of the temporo-parietal junction result in large and widely distributed changes in functional connectivity, while lesions of primary sensory or motor regions remain more localized. The model suggests that dynamic lesion effects can be predicted on the basis of specific network measures of structural brain networks and that these effects may be related to known behavioral and cognitive consequences of brain lesions.
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Simulated dynamic effects of lesions varied in size and spatial pattern depending on lesion location, with midline, temporoparietal junction, and superior frontal gyrus lesions tending to have the greatest effect on neural dynamics.
“Simulated dynamic effects of lesions varied in size and spatial pattern depending on the lesion location, with lesions in the midline as well as those involving the temporoparietal junction and superior frontal gyrus tending to have the greatest effect on neural dynamics.”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [4]
In general, lesions reduced functional connectivity, with effects most pronounced in the ipsilateral hemisphere, but also extending nonlocally to the contralateral hemisphere.
“In general, lesions reduced functional connectivity, with effects most pronounced in the ipsilateral hemisphere, but also extending in a nonlocal manner to the contralateral hemisphere.”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [4]
The extent that alterations of the structural network produced dynamic consequences was most accurately predicted by injury to the DMN rather than by the degree or strength of connections directly incorporated by the lesion.
“the extent that alterations of the structural network produced dynamic consequences was most accurately predicted by injury to the DMN rather than, for instance, the degree or strength of connections directly incorporated by the lesion.”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [4]
Simulation experiments using structure-function models predict that purely focal and transient perturbations in activity result in diffuse network reorganization affecting distant brain systems.
“simulation experiments using structure-function models have predicted that even purely focal and transient perturbations in activity should result in diffuse network reorganization that affects distant brain systems (Alstott et al., 2009; Honey and Sporns, 2008; Misic et al., 2015).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Alstott et al., 2009
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- Authors
- Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L., & Sporns, O.
- Affiliations
- Indiana University Bloomington (US)
- Black Dog Institute (AU)
- École Polytechnique (FR)
- Corresponding author
- Sporns, Olaf
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2009-06-12
- Volume
- 5
- Issue
- 6
- Pages
- e1000408
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2688028
- OpenAlex
- W2088905367
- Cited by
- 561
- References
- 51
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 28 · 2023: 34 · 2022: 40 · 2021: 42 · 2020: 28 · 2019: 33 · 2018: 28 · 2017: 54 · 2016: 47 · 2015: 30 · 2014: 39 · 2013: 38 · 2012: 31
- FWCI
- 13.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- IRIS (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- SERVAL (Université de Lausanne) (submittedVersion, other-oa)
- Funders
- National Science Foundation
- James S. McDonnell Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Université de Lausanne
- 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
- brain lesions, functional connectivity, resting-state brain activity, cortical networks, structural connectivity, temporoparietal junction, network metrics, computational models of the brain, hemispheric lateralization
- MeSH terms
- Algorithms, Brain Diseases, Brain Mapping, Cerebral Cortex, Computer Simulation, Humans, Models, Neurological, Nerve Net, Reproducibility of Results
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.
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