Using machine learning to evaluate large-scale brain networks in patients with brain tumors: Traditional and non-traditional eloquent areas Article Swipe
Alexis A. Morell
,
Daniel G. Eichberg
,
Ashish H. Shah
,
Evan Luther
,
Victor M. Lu
,
Michael Kader
,
Dominique Higgins
,
Martín A. Merenzon
,
Nitesh V. Patel
,
Ricardo J. Komotar
,
Michael E. Ivan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/noajnl/vdac142
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/noajnl/vdac142
Background Large-scale brain networks and higher cognitive functions are frequently altered in neuro-oncology patients, but comprehensive non-invasive brain mapping is difficult to achieve in the clinical setting. The objective of our study is to evaluate traditional and non-traditional eloquent areas in brain tumor patients using a machine-learning platform. Methods We retrospectively included patients who underwent surgery for brain tumor resection at our Institution. Preoperative MRI with T1-weighted and DTI sequences were uploaded into the Quicktome platform. We categorized the integrity of nine large-scale brain networks: language, sensorimotor, visual, ventral attention, central executive, default mode, dorsal attention, salience and limbic. Network integrity was correlated with preoperative clinical data. Results One-hundred patients were included in the study. The most affected network was the central executive network (49%), followed by the default mode network (43%) and dorsal attention network (32%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (3.42 vs 2.19, P < .001), compared to patients without deficits. Furthermore, we found that patients without neurologic deficits had an average 2.19 networks affected and 1.51 networks at-risk, with most of them being related to non-traditional eloquent areas (P < .001). Conclusion Our results show that large-scale brain networks are frequently affected in patients with brain tumors, even when presenting without evident neurologic deficits. In our study, the most commonly affected brain networks were related to non-traditional eloquent areas. Integrating non-invasive brain mapping machine-learning techniques into the clinical setting may help elucidate how to preserve higher-order cognitive functions associated with those networks.
Related Topics
Concepts
Default mode network
Task-positive network
Medicine
Brain tumor
Salience (neuroscience)
Cognition
Psychology
Neuroscience
Pathology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/noajnl/vdac142
- OA Status
- gold
- Cited By
- 20
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4297859923
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4297859923Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/noajnl/vdac142Digital Object Identifier
- Title
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Using machine learning to evaluate large-scale brain networks in patients with brain tumors: Traditional and non-traditional eloquent areasWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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Alexis A. Morell, Daniel G. Eichberg, Ashish H. Shah, Evan Luther, Victor M. Lu, Michael Kader, Dominique Higgins, Martín A. Merenzon, Nitesh V. Patel, Ricardo J. Komotar, Michael E. IvanList of authors in order
- Landing page
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https://doi.org/10.1093/noajnl/vdac142Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1093/noajnl/vdac142Direct OA link when available
- Concepts
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Default mode network, Task-positive network, Medicine, Brain tumor, Salience (neuroscience), Cognition, Psychology, Neuroscience, PathologyTop concepts (fields/topics) attached by OpenAlex
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20Total citation count in OpenAlex
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2025: 3, 2024: 9, 2023: 8Per-year citation counts (last 5 years)
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52Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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