Exploring MGMT Methylation-Driven Structural Connectivity Changes in Insular Gliomas: A Tractography and Graph Theoretical Analysis

doi:10.21203/rs.3.rs-3588114/v1

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Research Article

Exploring MGMT Methylation-Driven Structural Connectivity Changes in Insular Gliomas: A Tractography and Graph Theoretical Analysis

https://doi.org/10.21203/rs.3.rs-3588114/v1

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Journal Publication

published 27 Dec, 2023

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Objectives

To investigate the correlation between methylation of the O-6-methylguanine-DNA methyltransferase (MGMT) promoter levels and structural connectivity in insular gliomas across hemispheres.

Methods

We analyzed 61 left insular glioma cases and 50 healthy controls, using differential tractography, correlational tractography, and graph theoretical analysis to investigate the correlation between structural connectivity and the methylation level.

Results

The differential tractography results revealed that in left insular glioma, the volume of affected inferior fronto-occipital fasciculus (IFOF, p = 0.012) and uncinate fascicle (UF, p = 0.030) significantly correlated with methylation levels. Correlational tractography results showed that the quantitative anisotropy (QA) value of peritumoral fiber tracts also exhibited a significant correlation with methylation levels (FDR < 0.05). On the other hand, in right insular glioma, anterior internal part of the reticular tract, IFOF, and thalamic radiation showed a significant correlation with methylation levels but at a different correlation direction from the left side (FDR < 0.05). The graph theoretical analysis showed that in the left insular gliomas, only the radius of graph was significantly lower in methylated MGMT group than unmethylated group (p = 0.047). No significant correlations between global properties and methylation levels were observed in insular gliomas on both sides.

Conclusion

MGMT methylation levels are significantly associated with structural connectivity changes in insular gliomas, displaying hemisphere-dependent differences.

MGMT promoter methylation

insular gliomas

differential tractography

correlational tractography

graph network

Glioma cells can inflict damage on adjacent white matter connections and tend to migrate along fiber tracts to distant locations, potentially harming neural networks^1,2. This complexity is accentuated by the tumors' heterogeneous nature, often manifesting divergent therapeutic responses³. One significant factor affecting glioma behavior is the methylation of the O-6-methylguanine-DNA methyltransferase (MGMT) promoter, termed the methylation level hereafter. It serves as an indicator for the treatment efficacy against diffuse gliomas and can forecast the outcomes of patients undergoing temozolomide (TMZ) treatment ^4,5. Furthermore, extensive research highlights the correlation between methylation levels and the invasive behavior of glioblastoma multiforme (GBM).^6–9. However, the influence exerted by MGMT methylation on the intricacies of brain connectivity, especially in insular gliomas, has yet to be comprehensively characterized.

Recent advancements in tractography have brought two novel tractography modalities: differential tractography and correlation tractography, to probe white matter connections with increased precision at individual and group level, respectively ^10,11. Differential tractography conducts a voxel-specific comparison of anisotropy between one individual patient and a group of controls and highlights only those changes resulting in distinct tractograms with abnormal segments¹⁰. Tractograms derived from this technique provide a direct quantification of neurodegenerative extents for subsequent analyses¹². On the other hand, correlational tractography, at a group level, pinpoints specific segments of neural pathways associated with a target variable, showcasing enhanced sensitivity and specificity compared to conventional tractography methods¹¹. Those novel techniques have been utilized to chart neural pathways and associate observations in conditions such as stroke, cognitive disorders, and neurodegenerative diseases^13–15. In addition to novel tractography, we also employed graph theory to investigate how changes in methylation levels influence long-range connections with more distant parts¹⁶. Graph analysis, rooted in network evaluation, captures brain networks through nodes, edges, and their interactions. Recent studies have documented changes in white matter connections and these network properties under pathological conditions^10,12,17. Harnessing these advanced techniques to examine this relationship in insular gliomas and their structural connections might provide insights into how molecular mechanisms affect neural connectivity patterns and their subsequent clinical consequences.

This retrospective study investigated the data of 61 insular glioma cases. We explored how affected fiber tract volumes, and the segment of the fiber tracts relate to methylation levels. Additionally, graph theoretical analysis revealed how methylation levels impact brain network variations across hemispheres. Our findings enhance our understanding of glioma pathophysiology and provide insights for therapeutic approaches in patients with insular gliomas.

Demographics

In this study, a retrospective analysis was conducted on 98 primary insular glioma cases that underwent preoperative DTI scanning and surgical treatment at our institution between July 2021 and July 2023. Cases that did not use pyrophosphate sequencing after surgery to determine MGMT methylation and had a midline shift greater than 1 cm in T1 image were excluded, resulting in a final inclusion of 61 patients (32 left and 29 right insular gliomas) in the study. Tumor dimensions were determined using T1 weighted images processed with the DSI-Studio software. Comparative analysis revealed no statistically significant disparities between the left and right insular gliomas regarding tumor size (p = 0.214), grade (p = 0.553), and methylation levels (p = 0.181).

We included 50 healthy controls (HCs) who underwent detailed questionnaires capturing demographic and health information. The Edinburgh Handedness Inventory ascertained the right-handedness of both patient cohorts and HCs. Upon statistical assessment, no notable differences in age (p = 0.243) or gender (p = 0.053) were observed between the insular glioma subsets and the HC group. These variables served as covariates in the subsequent analysis. Demographic specifics are elaborated in Table 1.

Table 1

Demographics and tumor profiles
	HC	Left insular glioma PT	Right insular glioma PT	p-value
Age (years)	41.08 ± 11.58	45.28 ± 10.98	43.97 ± 11.81	0.243
Sex				0.053^†
Male	24 (48.00%)	19 (59.38%)	22 (75.86%)
Female	26 (52.00%)	13 (40.62%)	7 (24.14%)
Tumor size (mm³)	/	45170.70 ± 29729.48	56597.37 ± 40984.13	0.214
Grades				0.553
2 Grade	/	23 (71.87%)	17 (58.62%)
3 Grade	/	3 (9.38%)	4 (13.79%)
4 Grade	/	6 (18.75%)	8 (27.59%)
MGMT promotor methylation level (%)	/	23.07 ± 16.47	17.41 ± 16.12	0.181
† This variable was accounted for as a covariate in the statistical analysis. HC = healthy control, PT = patients

MRI acquisition and preprocessing

All subjects received MRI scans with a Siemens Prisma 3.0 T scanner. The T1-weighted scans had parameters: 1 mm slice thickness, a 256 x 248 acquisition matrix for frequency and phase encoding, and a 96.875% phase field of view. The repetition time was 1600 ms, echo time 2.98 ms, inversion time 900 ms, and a 9° flip angle. The DTI used an optimized echo planar imaging (EPI) technique. Its parameters were: 1,760x1,760 mm field of view, 90° flip angle, b-values of 1,000 and 2,000 s/mm² over 30 diffusion directions, 64 ms echo time, 2,900 ms repetition time, and 2.5 mm slice thickness. Eddy current distortion was adjusted using FSL eddy. The diffusion imaging data was standardized using q-space diffeomorphic reconstruction (QSDR)^18,19.

Differential tractography

This highlighted areas where local fibers in our study subjects showed decreased anisotropic diffusion, indicating changes in fiber integrity. For this study, we set criteria for results filtering at percentile ranks below 40 for QA decrease and fiber length thresholds over 20mm. Our tracking employed a deterministic fiber-tracking algorithm found in DSI Studio software (http://dsi-studio.labsolver.org)²⁰. We produced a differential tractogram from 5,000,000 seed points in the white matter, with a random angular threshold between 15 to 90 degrees and a 1 mm step size. The identified paths, called affected tracts, pinpoint abnormal connectivity pathways. Subject-level false discovery rate (FDR) is calculated by dividing the volume of affected brain fibers in each patient by the total volume of affected fibers in both that patient and their corresponding HCs. This yields a group-level FDR value, which is determined by calculating the ratio of the combined volume of affected fibers across all patients to the sum of affected fiber volumes in the entire cohort, including both patients and HCs. Any data points with a subject-level FDR value exceeding this group-level threshold are considered non-significant and excluded from subsequent analysis¹⁰. In the patient group with significantly affected fiber tract volumes, correlation analyses are performed at both the whole-brain and specific fiber tract levels, assessing their relationship with MGMT promoter methylation levels while accounting for covariates such as age, sex, tumor size, and grade.

Correlation tractography

The spin density was sampled based on fiber directions from a standard atlas, aiding in the creation and evaluation of a local connectome matrix. We used deterministic fiber tracking and applied various 3.5 T thresholds to determine the correlation between fiber tracts and the MGMT promotor methylation level, adjusting for factors like age, sex, tumor size, and grade. After normalizing the QA-values, the tracts underwent 16 rounds of topology-informed pruning, using a 15-voxel distance as the selection criterion. An FDR below 0.05 signified a robust correlation between sections of the fiber tract and the MGMT promotor methylation level.

Graph Theoretical Analysis

Each patient's fiber tracts were tracked using DSI-Studio (Chen “陳” Aug 29 2023 version) to ensure consistent and dependable results from the graph theoretical analysis. The names of each type of fiber tract can be found in Supplementary Table 1. We used an advanced deterministic fiber-tracking algorithm, bolstered by refined techniques, to improve reproducibility^21,22. The alignment of the automated anatomical labeling (AAL) atlas to individual data was confirmed. From this, we constructed the structural connectome for each participant, applying a connectivity threshold of 0.001. The connectivity matrix was formed by counting the intersecting tracks, resulting in a unique 90 × 90 adjacency matrix for every participant, which depicted their brain's structural connectome matrix. Global structural connectivity was assessed through parameters such as global efficiency, small-worldness, radius of graph, and diameter of graph. Patients with an MGMT promoter methylation level greater than 10% were categorized into the methylation group, while those with a level less than 10% were defined as the non-methylation group²³.

Statistics

We used Chi-square tests to assess gender or grade differences between the two data groups. Independent t-tests or variance analysis analyzed disparities in variables between the groups. To examine the impact of MGMT methylation on global peroperties, we applied linear regression, accounting for covariates such as age, sex, tumor size, and grade. ANCOVA compared the global properties between the methylated MGMT group and the unmethylated MGMT group. P-values underwent correction via the Benjamini-Hochberg method. All statistical analyses and data visualizations were carried out using R version 4.2.2.

Differential tractography

Figure 1 presents the correlation between affected fiber tract volumes—derived via differential tractography—and MGMT promoter methylation levels for left and right insular gliomas. A total of 48 patients had an FDR value of less than 0.013, including 25 patients with left insular gliomas and 23 patients with right insular gliomas. The view showcases notable differences in the fiber tracts of clear brains in axial positions associated with MGMT promoter methylation levels categorize as "<10%", "10–30%", and ">30%". The accompanying scatter plot uses dot size to depict tumor volume, with the red hue, transitioning from light to dark, signifying glioma grades 2 through 4. At the whole-brain level, Fig. 1A shows the trend line that as the promoter methylation levels ascend, there's a progressive decline in the affected volume of fiber tracts in the left insular glioma (p = 0.048). Conversely, the right part of Fig. 1A showed no pronounced correlation (p = 0.117) in the right insular glioma.

The volume of affected fiber tracts with exhibiting a 40% drop in QA value are cataloged in Fig. 1B. Notably, both the left and right insular gliomas demonstrated that inferior fronto-occipital fascicle (IFOF), uncinate fasciculus (UF), and frontal aslant tract (FAT) had a relatively larger volume, leading us to probe their correlation with the MGMT promoter methylation level. In addition, the right insular glioma's ILF volume with significant variations was notably lower than the left's (p = 0.014). Other fiber bundles exhibited marginal volume variations (refer to Supplementary Table 1).

Figure 1C displays the correlations between volume of IFOF, UF, and FAT and the MGMT promoter methylation levels for the insular gliomas on both sides. The 3D brain visualizations for the three cases show decreasing sections of IFOF and UF associated with rising MGMT promoter methylation levels, while FAT remains relatively unchanged. Specifically, in the left insular glioma, a strong inverse correlation was evident between MGMT promoter methylation and certain IFOF (p = 0.012) and UF (p = 0.030) bundles. No such correlation was observed for FAT (p = 0.632). In the right insular glioma, the 3D visualizations did not exhibit clear correlations, echoed by the flattened trend line, suggesting no significant statistical relationship. Part D offers an in-depth statistical breakdown.

Correlational tractography

Figure 2 illustrates the fiber tracts with a significant correlation between their QA values and MGMT promoter methylation levels in left insular gliomas (A) and right insular gliomas (B). Areas with a strong positive correlation to methylation level were marked in red. For the left insular glioma in Fig. 2A, the anterior and medial portions of the IFOF, the region above the UF and beneath the anterior commissure and the corpus callosum forceps minor, the anteromedial segment of the extreme capsule, the posteromedial area of the corticospinal tract, and the inferomedial section of the superior corticostriatal tract exhibited QA values with a strong positive association with the methylation level. In contrast, the QA value of the opposing fornix had a notable negative link with the methylation level. Figure 2B portrays that in the right insular glioma, the methylation level negatively correlated with the QA values of the medial segment of the adjacent anterior thalamic radiation (ATR), the anteromedial part of the reticular tract, the anterior section of the frontal corticopontine tract, a minor segment at the upper front of the IFOF, the front portion of the superior thalamic radiation (STR), and the lower front segment of the corpus callosum body. No fiber tracts manifested a significant positive correlation.

Graph theoretical analysis

Figure 3 shows the analysis of the relationship between MGMT promoter methylation levels and global properties for both the left and right insular gliomas. The left column of Fig. 3A displays the correlation between the methylation level and properties. The size and grade of the left insular gliomas have a consistent distribution with the methylation level. The right column of Fig. 3A reveals differences between these parameters in the two groups. Notably, the methylated MGMT group exhibited a more substantial weighted graph radius (p = 0.047) than the unmethylated group. In Fig. 3B's left column, high-grade tumors on the right side predominantly matched with reduced methylation levels and displayed no significant differences in the four global properties. In Fig. 3C, specific statistical data is displayed.

Our study demonstrates significant differences in brain connectivity and methylation levels between the bilateral cerebral hemispheres in insular gliomas. We observed a conspicuous reduction in the volume and QA values of certain fiber tracts correlated with heightened methylation levels in left insular gliomas, but the pattern was opposite in the right. In terms of global properties, distinctions were discerned in the associations between insular gliomas of both hemispheres and methylation levels. These pioneering revelations propose that MGMT methylation might exert differential biological implications in insular gliomas contingent upon the cerebral hemisphere.

Our research highlights the intricate interplay between methylation levels and their impact on fiber tracts, the intricacies of which may be shaped by the tumor's hemispheric location. A previous study has been reported that MGMT was an inhibitory factor for GBM invasiveness⁶. Another study showed that MGMT-positive GBM cells manifest diminished angiogenic and tumorigenic capacities, revealing an inverse relationship between MGMT expression and invasiveness⁷. Nevertheless, the molecular imaging domain currently lacks a unanimous consensus on the link between MGMT promoter methylation rates and glioma invasiveness. Research by Ryoo et al.⁹ posits that GBMs with an unmethylated MGMT promoter demonstrate significantly heightened rCBV values than their methylated counterparts. Furthermore, two research endeavors identified that GBMs with MGMT promoter methylation displayed superior ADC values compared to those lacking said methylation^24,25. These results corroborate the relevant findings in our study regarding left-sided tumors. Previous studies confirmed that QA can offer insights into the integrity of the fiber tracts^26,27. The correlational tractography echoed with the results of differential tractography in left insular gliomas. Anatomically, insular gliomas predominantly emerge in the region demarcated by the UF/IFOF complex and the insular convexit²⁸. Remarkably, while damage to the UF, even in the dominant hemisphere, might be transient with potential recovery, impairments to the IFOF are long-lasting and can severely compromise semantic processing abilities²⁹. Contrastingly, a study by Hempel et al.³⁰ suggested that increased rCBV values in IDH-wild-type GBMs with methylated MGMT promoters compared to their unmethylated counterparts. A study by Pope et al.³¹ reveals that newly diagnosed GBMs treated with bevacizumab and exhibiting MGMT promoter methylation presented diminished ADC values. The outcomes derived from the correlational analysis pertaining to right insular gliomas resonate with the previously discussed perspectives. Evidently, with the augmentation in methylation levels, there may be a concomitant degradation in the integrity of some white matter tracts. Such deteriorations have the potential to adversely influence a spectrum of brain functions, encompassing cognitive, motor, and linguistic domains^32–35. Therefore, we speculate that a heightened level of methylation seemingly possesses the capability to safeguard, to a certain extent, the pertinent functions of the IFOF and the UF in patients with left insular gliomas. For patients with right insular gliomas, the outcome might be opposite. These findings enhance our understanding of the role of MGMT methylation in brain connectivity and necessitate the development of tailored therapeutic strategies based on the tumor's hemispheric location and its molecular profile.

There is no definitive conclusion regarding the influence of invasive molecular markers on the graph theoretical networks in patients with gliomas due to limited relevant research. An earlier study showed that IDH-wild-type tumors have lower global efficiency compared to IDH-mutant astrocytomas¹⁶. Our findings showed for the first time that methylation levels play a key role in influencing network connectivity and information propagation efficiency. Specifically, in left-side gliomas where the MGMT promoter is unmethylated, we observed a reduction in the radius of the graph. The graph's radius and diameter, representing the minimum and maximum shortest linkages between nodes respectively, shed light on the network's interconnectedness and potential information transmission lags³⁶. The result may contribute to an increase in network connectivity and the acceleration of information propagation in left un-methylated insular gliomas to get “network economy”. The hypothesis of 'network economy' is founded upon the compensatory mechanisms activated by the brain for optimizing resource allocation and enhancing the efficiency of information transmission^37,38. In the realm of neuronal connectivity, one essential driver for achieving “network economy” is the reduction of network wiring costs. This implies a conservation of time, space, and materials, necessitating that the majority of connections within the brain network will be short-range³⁷. This may be a protective response activated by the brain to counteract tumor invasion. Global efficiency measures the brain's ability to distribute and process information, which is crucial for complex cognitive functions. It is determined by assessing the average shortest path length between all pairs of nodes³⁹. Small-worldness quantifies the balance between clustered networks and efficient connections, reflecting the brain's adaptability and processing capacity³⁹. Interestingly, the relationship between methylation levels and global efficiency, the radius of the graph, the diameter of the graph, and small-worldness exhibits opposing trends in the bilateral cerebral hemispheres. We hypothesize that the observed variations in relational trends are potentially attributable to the combined effects of differences in tumor invasion patterns, lateralization of brain functions, and the distinct molecular landscapes that characterize left and right insular gliomas. These factors may collectively influence their interactions with brain networks and subsequent adaptive responses⁴⁰. The lack of significance could be due to the small number of patients included in this study.

This study presents several limitations. Primarily, the statistical non-significance observed in graph theoretical analysis, especially in right insular gliomas, potentially hints at an insufficient sample size. Moreover, the potential influence of unaccounted variables remains a concern. Lastly, our snapshot approach, though informative, may not capture the dynamic interplay between MGMT promoter methylation and glioma progression over time.

This study explores the intricate relationship between MGMT promoter methylation and its effects on insular gliomas, revealing distinct impacts based on tumor hemisphere. Left insular gliomas with higher methylation levels seem to protect vital white matter tracts like IFOF and UF, potentially preserving cognitive function. In contrast, right insular gliomas exhibit different patterns. These findings suggest MGMT methylation may modulate network connectivity and information propagation, with left insular gliomas adapting for functional integrity. Personalized therapies should consider tumor molecular and hemispheric profiles in future. Further research is needed, considering other genetic and epigenetic modifications in insular gliomas and their impact on adaptive brain mechanisms.

Acknowledgements

We would like to extend our heartfelt thanks to Dr. Yaou Liu, who serves as the Head of the Radiology Department at Beijing Tiantan Hospital, affiliated with Capital Medical University. His invaluable assistance in acquiring the imaging data was pivotal to the success of our study.

Funding

The National Natural Science Foundation of China (82172028) provided support for the work.

Ethical approval

The study has been evaluated and approved by the ethics community of Beijing Tiantan Hospital, Capital Medical University (KY 2020-146-02).

Conflict of interest

The authors declared that they have no conflicts of interest.

Authors' contributions

The conceptual foundation and structural layout of the research were led by JX, ZGH, and ZHD. Contributions to the data interpretation were made by CDY, XYS, GL, and BWX. ZCY was responsible for creating the figures and drafting the manuscript. The research was supervised by ZHD and SJS, who provided critical direction. FCY performed an exhaustive review, adding meaningful advice and recommendations. All contributing authors have examined and endorsed the finalized manuscript and jointly agree to its submission for journal publication.

Consent to participate.

Written informed consent for publication was obtained from all participants.

Consent to publish.

All subjects gave written informed consent for publication.

Hadjiabadi DH, Pung L, Zhang J, et al. Brain tumors disrupt the resting-state connectome. Neuroimage Clin. 2018;18:279-289. doi:10.1016/j.nicl.2018.01.026
Stoecklein VM, Stoecklein S, Galie F, et al. Resting-state fMRI detects alterations in whole brain connectivity related to tumor biology in glioma patients. Neuro Oncol. Sep 29 2020;22(9):1388-1398. doi:10.1093/neuonc/noaa044
Nicholson JG, Fine HA. Diffuse Glioma Heterogeneity and Its Therapeutic Implications. Cancer Discov. Mar 2021;11(3):575-590. doi:10.1158/2159-8290.CD-20-1474
Everhard S, Kaloshi G, Criniere E, et al. MGMT methylation: a marker of response to temozolomide in low-grade gliomas. Ann Neurol. Dec 2006;60(6):740-3. doi:10.1002/ana.21044
Dahlrot RH, Larsen P, Boldt HB, et al. Posttreatment Effect of MGMT Methylation Level on Glioblastoma Survival. J Neuropathol Exp Neurol. Jul 1 2019;78(7):633-640. doi:10.1093/jnen/nlz032
Chahal M, Abdulkarim B, Xu Y, et al. O6-Methylguanine-DNA methyltransferase is a novel negative effector of invasion in glioblastoma multiforme. Mol Cancer Ther. Nov 2012;11(11):2440-50. doi:10.1158/1535-7163.MCT-11-0977
Chahal M, Xu Y, Lesniak D, et al. MGMT modulates glioblastoma angiogenesis and response to the tyrosine kinase inhibitor sunitinib. Neuro Oncol. Aug 2010;12(8):822-33. doi:10.1093/neuonc/noq017
Knopp EA, Cha S, Johnson G, et al. Glial neoplasms: dynamic contrast-enhanced T2*-weighted MR imaging. Radiology. Jun 1999;211(3):791-8. doi:10.1148/radiology.211.3.r99jn46791
Ryoo I, Choi SH, Kim JH, et al. Cerebral blood volume calculated by dynamic susceptibility contrast-enhanced perfusion MR imaging: preliminary correlation study with glioblastoma genetic profiles. PLoS One. 2013;8(8):e71704. doi:10.1371/journal.pone.0071704
Yeh FC, Zaydan IM, Suski VR, et al. Differential tractography as a track-based biomarker for neuronal injury. Neuroimage. Nov 15 2019;202:116131. doi:10.1016/j.neuroimage.2019.116131
Yeh FC, Badre D, Verstynen T. Connectometry: A statistical approach harnessing the analytical potential of the local connectome. Neuroimage. Jan 15 2016;125:162-171. doi:10.1016/j.neuroimage.2015.10.053
Barrios-Martinez JV, Fernandes-Cabral DT, Abhinav K, et al. Differential tractography as a dynamic imaging biomarker: A methodological pilot study for Huntington's disease. Neuroimage Clin. 2022;35:103062. doi:10.1016/j.nicl.2022.103062
Sanchez-Catasus CA, Bohnen NI, D'Cruz N, Muller M. Striatal Acetylcholine-Dopamine Imbalance in Parkinson Disease: In Vivo Neuroimaging Study with Dual-Tracer PET and Dopaminergic PET-Informed Correlational Tractography. J Nucl Med. Mar 2022;63(3):438-445. doi:10.2967/jnumed.121.261939
Sanchez-Catasus CA, Bohnen NI, Yeh FC, D'Cruz N, Kanel P, Muller M. Dopaminergic Nigrostriatal Connectivity in Early Parkinson Disease: In Vivo Neuroimaging Study of (11)C-DTBZ PET Combined with Correlational Tractography. J Nucl Med. Apr 2021;62(4):545-552. doi:10.2967/jnumed.120.248500
Mattos DJS, Rutlin J, Hong X, Zinn K, Shimony JS, Carter AR. White matter integrity of contralesional and transcallosal tracts may predict response to upper limb task-specific training in chronic stroke. Neuroimage Clin. 2021;31:102710. doi:10.1016/j.nicl.2021.102710
Kesler SR, Noll K, Cahill DP, Rao G, Wefel JS. The effect of IDH1 mutation on the structural connectome in malignant astrocytoma. J Neurooncol. Feb 2017;131(3):565-574. doi:10.1007/s11060-016-2328-1
De Baene W, Rutten GJM, Sitskoorn MM. The Temporal Pattern of a Lesion Modulates the Functional Network Topology of Remote Brain Regions. Neural Plast. 2017;2017:3530723. doi:10.1155/2017/3530723
Yeh FC, Wedeen VJ, Tseng WY. Generalized q-sampling imaging. IEEE Trans Med Imaging. Sep 2010;29(9):1626-35. doi:10.1109/TMI.2010.2045126
Yeh FC, Tseng WY. NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction. Neuroimage. Sep 1 2011;58(1):91-9. doi:10.1016/j.neuroimage.2011.06.021
Yeh FC, Tang PF, Tseng WY. Diffusion MRI connectometry automatically reveals affected fiber pathways in individuals with chronic stroke. Neuroimage Clin. 2013;2:912-21. doi:10.1016/j.nicl.2013.06.014
Yeh FC, Verstynen TD, Wang Y, Fernandez-Miranda JC, Tseng WY. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS One. 2013;8(11):e80713. doi:10.1371/journal.pone.0080713
Yeh FC. Shape analysis of the human association pathways. Neuroimage. Dec 2020;223:117329. doi:10.1016/j.neuroimage.2020.117329
Radke J, Koch A, Pritsch F, et al. Predictive MGMT status in a homogeneous cohort of IDH wildtype glioblastoma patients. Acta Neuropathol Commun. Jun 5 2019;7(1):89. doi:10.1186/s40478-019-0745-z
Han Y, Yan LF, Wang XB, et al. Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis. BMC Cancer. Feb 21 2018;18(1):215. doi:10.1186/s12885-018-4114-2
Sunwoo L, Choi SH, Park CK, et al. Correlation of apparent diffusion coefficient values measured by diffusion MRI and MGMT promoter methylation semiquantitatively analyzed with MS-MLPA in patients with glioblastoma multiforme. J Magn Reson Imaging. Feb 2013;37(2):351-8. doi:10.1002/jmri.23838
Celtikci P, Fernandes-Cabral DT, Yeh FC, Panesar SS, Fernandez-Miranda JC. Generalized q-sampling imaging fiber tractography reveals displacement and infiltration of fiber tracts in low-grade gliomas. Neuroradiology. Mar 2018;60(3):267-280. doi:10.1007/s00234-018-1985-5
Faraji AH, Abhinav K, Jarbo K, et al. Longitudinal evaluation of corticospinal tract in patients with resected brainstem cavernous malformations using high-definition fiber tractography and diffusion connectometry analysis: preliminary experience. J Neurosurg. Nov 2015;123(5):1133-44. doi:10.3171/2014.12.JNS142169
Martino J, Mato D, de Lucas EM, et al. Subcortical anatomy as an anatomical and functional landmark in insulo-opercular gliomas: implications for surgical approach to the insular region. J Neurosurg. Oct 2015;123(4):1081-92. doi:10.3171/2014.11.JNS141992
Moritz-Gasser S, Herbet G, Duffau H. Mapping the connectivity underlying multimodal (verbal and non-verbal) semantic processing: a brain electrostimulation study. Neuropsychologia. Aug 2013;51(10):1814-22. doi:10.1016/j.neuropsychologia.2013.06.007
Hempel JM, Schittenhelm J, Klose U, et al. In Vivo Molecular Profiling of Human Glioma : Cross-Sectional Observational Study Using Dynamic Susceptibility Contrast Magnetic Resonance Perfusion Imaging. Clin Neuroradiol. Sep 2019;29(3):479-491. doi:10.1007/s00062-018-0676-2
Pope WB, Lai A, Mehta R, et al. Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma. AJNR Am J Neuroradiol. May 2011;32(5):882-9. doi:10.3174/ajnr.A2385
George K, J MD. Neuroanatomy, Thalamocortical Radiations. StatPearls. 2023.
Felten DL, O'Banion MK, Maida ME. Netter's atlas of neuroscience. Elsevier Health Sciences; 2015.
Waller R, Dotterer HL, Murray L, Maxwell AM, Hyde LW. White-matter tract abnormalities and antisocial behavior: A systematic review of diffusion tensor imaging studies across development. Neuroimage Clin. 2017;14:201-215. doi:10.1016/j.nicl.2017.01.014
Motomura K, Fujii M, Maesawa S, Kuramitsu S, Natsume A, Wakabayashi T. Association of dorsal inferior frontooccipital fasciculus fibers in the deep parietal lobe with both reading and writing processes: a brain mapping study. J Neurosurg. Jul 2014;121(1):142-8. doi:10.3171/2014.2.JNS131234
Zhu S, Hosni SI, Huang X, et al. A dynamical graph-based feature extraction approach to enhance mental task classification in brain-computer interfaces. Comput Biol Med. Feb 2023;153:106498. doi:10.1016/j.compbiomed.2022.106498
Fornito A, Zalesky A, Bullmore E. Fundamentals of brain network analysis. Academic press; 2016.
Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. Apr 13 2012;13(5):336-49. doi:10.1038/nrn3214
Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059-1069.
Gotts SJ, Jo HJ, Wallace GL, Saad ZS, Cox RW, Martin A. Two distinct forms of functional lateralization in the human brain. Proc Natl Acad Sci U S A. Sep 3 2013;110(36):E3435-44. doi:10.1073/pnas.1302581110

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Exploring MGMT Methylation-Driven Structural Connectivity Changes in Insular Gliomas: A Tractography and Graph Theoretical Analysis

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Abstract

Objectives

Methods

Results

Conclusion

Figures

Introduction

Methods

Demographics

MRI acquisition and preprocessing

Differential tractography

Correlation tractography

Graph Theoretical Analysis

Statistics

Results

Differential tractography

Correlational tractography

Graph theoretical analysis

Discussion

Declarations

Acknowledgements

Funding

Ethical approval

Conflict of interest

Authors' contributions

Consent to participate.

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1