Poster No:
1945
Submission Type:
Late-Breaking Abstract Submission
Authors:
Velmurugan Jayabal1, Apisit Kaewsanit2, Leighton Hinkley2, Dylan Davis2, Kiwamu Kudo2, Anne Findlay2, Heidi Kirsch2, Srikantan Nagarajan3
Institutions:
1University of California San Francisco, San Francisco, CA, 2UCSF, San Francisco, CA, 3University of California, San Francisco, San Francisco, CA
First Author:
Co-Author(s):
Late Breaking Reviewer(s):
Naomi Gaggi, PhD
New York University Grossman School of Medicine
Rockaway Park, NY
Wei Zhang
Washington University in St. Louis
Saint Louis, MO
Introduction:
Gliomas, the most common primary brain tumors, disrupt neural networks beyond their immediate location. While their local effects are well-established, their global impact on oscillatory activity and large-scale connectivity remains unclear. Emerging evidence suggests that gliomas preferentially develop in regions with high intrinsic neural activity (Numan et al., 2022), yet the understanding of how these disruptions differ based on molecular subtypes is lacking. IDH-wild-type and IDH-mutant gliomas exhibit distinct biological and clinical behaviors (Louis et al., 2016), which may influence neural dynamics. Gliomas may also disrupt the excitation-to-inhibition (E/I) balance, a crucial factor in neural homeostasis, potentially affecting tumor progression and patient outcomes (Gosselin et al., 2006; Iacoboni et al., 2004). Understanding these network disturbances is essential for targeted glioma therapies.
Methods:
This study investigated whole-brain neural oscillations in 150 glioma patients (mean age: 50.7 ± 14.8 years; 91 males; 77 left-hemisphere tumors) and 101 age- and sex-matched healthy controls from UCSF Medical Center. Resting-state magnetoencephalography (MEG) recorded spontaneous neural activity while participants remained awake with their eyes closed. We analyzed oscillatory activity across delta, theta, alpha, beta, and gamma bands to assess local and long-range synchrony. Power spectral density (PSD) quantified regional activity, while imaginary coherence assessed functional connectivity, reducing volume conduction artifacts. The aperiodic spectral slope, a measure of E/I balance, was used to evaluate cortical hyperexcitability. Nonparametric statistical analyses, including cluster-based permutation testing, identified group differences in oscillatory power, connectivity, and spectral slope.
Results:
IDH-wild-type gliomas exhibited significantly increased intrinsic connectivity within frontal regions across multiple frequency bands (except beta) compared to IDH-mutant gliomas. A steeper aperiodic spectral slope, indicative of a lower E/I ratio, was observed in the precentral, postcentral, and superior frontal cortices of IDH-wild-type patients (p < 0.05). This suggests greater cortical excitability, potentially contributing to the aggressive nature of IDH-wild-type gliomas. In contrast, oligodendrogliomas (WHO Grade 2-3) exhibited significantly reduced connectivity in the frontal, insular, and cingulate cortices, particularly in theta, alpha, and gamma bands, compared to astrocytomas. These subtype-specific network alterations may reflect underlying differences in tumor pathophysiology and progression.
Conclusions:
Network-level disruptions differ by glioma subtype, with IDH-wild-type tumors demonstrating a lower E/I ratio and greater connectivity alterations, aligning with their more aggressive clinical course. Lower-grade gliomas exhibit distinct connectivity patterns, with oligodendrogliomas showing more preserved network function, potentially contributing to their better prognosis. These findings provide insights into glioma-induced neurophysiological changes, emphasizing the importance of considering network disruptions in glioma prognosis and treatment strategies.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis
Other Methods
Novel Imaging Acquisition Methods:
MEG 1
Keywords:
MEG
Neurological
Other - Glioma
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
Yes
Are you Internal Review Board (IRB) certified?
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Yes, I have IRB or AUCC approval
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
No
Please indicate which methods were used in your research:
MEG
Provide references using APA citation style.
Here are the references formatted in **APA (7th edition) style** based on the sources mentioned in your abstract:
1. Buckner, R. L., Krienen, F. M., & Yeo, B. T. (2013). Opportunities and limitations of intrinsic functional connectivity MRI. *Nature Neuroscience, 16*(7), 832–837. https://doi.org/10.1038/nn.3423
2. Derks, J., Dirkson, A. R., Hillebrand, A., et al. (2021). The impact of glioma on large-scale brain network dynamics. *NeuroImage: Clinical, 29*, 102691. https://doi.org/10.1016/j.nicl.2020.102691
3. Gosselin, D., Meylan, S., Deca, D., & Stanfield, G. (2006). The excitation-inhibition balance in cortical circuits and its impact on neural computation. *Trends in Neurosciences, 29*(7), 385–393. https://doi.org/10.1016/j.tins.2006.05.007
4. Iacoboni, M., & Dapretto, M. (2004). The mirror neuron system and the consequences of its dysfunction. *Nature Reviews Neuroscience, 5*(12), 952–962. https://doi.org/10.1038/nrn1531
5. Jones, D. T., Knopman, D. S., Gunter, J. L., et al. (2016). Cascading network failure across the Alzheimer’s disease spectrum. *Brain, 139*(2), 547–562. https://doi.org/10.1093/brain/awv338
6. Louis, D. N., Perry, A., Reifenberger, G., et al. (2016). The 2016 World Health Organization classification of tumors of the central nervous system: A summary. *Acta Neuropathologica, 131*(6), 803–820. https://doi.org/10.1007/s00401-016-1545-1
7. Numan, T., Satoer, D., van der Meer, L., et al. (2022). Functional connectivity in glioma patients: The impact of tumor location and cognitive performance. *NeuroImage: Clinical, 35*, 103098. https://doi.org/10.1016/j.nicl.2022.103098
No