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Anatomical heterogeneity in low-grade and high-grade gliomas: A multiscale perspective
Shengpeng Liang1, Nuo Dong2, Yumin Chen2
1Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangdong-Hong Kong Joint Laboratory for Psychiatric Disorders, Guangdong Province Key Laboratory of Psychiatric Disorders, Guangdong Basic Research Center of Excellence for Integrated Traditional and Western Medicine for Qingzhi Diseases, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China; Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Background:
Low-grade gliomas (LGGs) and high-grade gliomas (HGGs) often exhibit distinct spatial distributions, a phenomenon that remains incompletely understood. Based on previous research, we hypothesized that functional networks, neurotransmitters, and isocitrate dehydrogenase-1 (IDH-1) status characterize the spatial patterns of LGG and HGG.
Methods:
We analyzed 399 patients diagnosed with primary gliomas. First, we generated glioma frequency maps based on tumor grade, neurotransmitters, and IDH-1 status and constructed a brain functional connectivity network to explore heterogeneity in glioma location. Second, all tumor masks were mirror-symmetrized onto the brain's left hemisphere to facilitate feature extraction. We performed independent component analysis on merged four-dimensional files using Multivariate Exploratory Linear Optimized Decomposition into Independent Component (MELODIC), identifying four IDH-1 wild-type lesion covariance networks (IDHwt-LCNs) and three IDH-1 mutant lesion covariance networks (IDHmut-LCNs) with distinct spatial distributions, and analyzing correlation between the neurotransmitter levels and the IDH-wt/mut specific LCNs. Finally, we compared 42 white matter fibers extracted using XTRACT with 39 functional brain connectivity networks from the multi-subject dictionary learning (MSDL) atlas, revealing significant associations among the frontal aslant tract (FAT) and the intraparietal sulcus (IPS).
Results:
Our findings revealed high anatomical heterogeneity between LGG and HGG. Moreover, the high node strength played a critical role in the distinct spatial distribution of glioma. Significant correlations were observed between glioma frequency maps and dopaminergic, cholinergic, μ-opioid, and serotonergic neurotransmission. Furthermore, IDHwt/mut-LCNs analysis demonstrated that IDH-1 status influences glioma distribution, involving key brain structures. Lastly, we also found significant correlations between IDHwt/mut-LCNs and the neurotransmission of dopaminergic, cholinergic, μ-opioid, and serotonergic systems.
Conclusion:
Our study highlighted the mechanisms by which functional networks, neurotransmitter systems, and IDH-1 status collectively contribute to the anatomical heterogeneity observed in LGG and HGG.
