Dissociation of structural and functional connectomic coherence in glioma patients

Jütten, Kerstin; Weninger, Leon; Mainz, Verena; Gauggel, Siegfried; Binkofski, Ferdinand; Wiesmann, Martin; Merhof, Dorit; Clusmann, Hans; Na, Chuh‐Hyoun

doi:10.1038/s41598-021-95932-5

Cited by 17 publications

(21 citation statements)

References 52 publications

(83 reference statements)

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“…Within these ROIs, the SC and FC was computed to analyze between-group connectomic differences for whole brain and the Default-Mode Network (DMN). 2 In this paper, we analyzed for the DMN only the resting-state FC and diffusion-MRI-based SC. SC comprised edge-weight (EW) and fractional anisotropy (FA) in IDHmut and IDHwt patients and healthy controls.…”

Section: Methodsmentioning

confidence: 99%

Dynamical graph networks may aid in phenotyping prognostically different brain tumor types

Meyer‐Baese

Juetten

Meyer-Baese

et al. 2022

Emerging Topics in Artificial Intelligence (ETAI) 2022

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Diffuse infiltrative glioma are considered as a systemic brain disorder and produce alterations on cerebral functional and structural integrity beyond the tumor location. These alterations are the result of the dynamic interplay between large-scale neural circuits. Describing the nature of these interactions has been a challenging task yet important for glioma disease evolution. Modern dynamic graph network theory techniques and control theory applied to these structural and functional networks opens a new research avenue for understanding the dynamical properties and differences between healthy controls and glioma patients. It has been shown that controllability is relevant for providing the mechanistic explanation of how the brain navigates between cognitive states. We believe that it is also relevant for describing the connectomic alterations in glioma and the differences among subtypes and healthy controls. The nodes that are needed to control these networks and influence them to any state are called driver nodes. We determined the driver nodes of the Default-Mode Network (DMN) for resting-state functional connectivity (FC) and diffusion-MRI-based structural connectivity (SC) (comprising edge-weight (EW) and fractional anisotropy (FA)) networks in isodehydrogenase mutated (IDHmut) and wildtype (IDHwt) patients and healthy controls. Our results show that healthy controls have a better controllability for both FC and SC, and that structural connectomic dynamical aberrations are more pronounced in glioma patients than functional connectomic alterations.

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Section: Methodsmentioning

confidence: 99%

Dynamical graph networks may aid in phenotyping prognostically different brain tumor types

Meyer‐Baese

Juetten

Meyer-Baese

et al. 2022

Emerging Topics in Artificial Intelligence (ETAI) 2022

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“…An MRI examination was applied using a 3T Siemens Prisma MRI scanner equipped with a standard 20-channel head coil. The detailed scanning protocol was described in previous studies [ 29 , 36 ] and comprised the following pulse sequences: a sagittal 3D T1 magnetization-prepared rapid acquisition gradient echo sequence; a contrast-enhanced, T1-weighted turbo inversion recovery magnitude dark-fluid sequence; a T2-weighted TIRM dark-fluid scan, as well as a fluid attenuation inversion recovery sequence. In addition, RS-fMRI was implemented using echo planar imaging.…”

Section: Methodsmentioning

confidence: 99%

“…Functional preprocessing was performed using SPM12 as implemented in MATLAB 9.3 (Matlab, MA, USA). A detailed description of the image preprocessing protocol can be found in previous studies [ 29 , 36 ], and included semi-automatic tumor lesion segmentation using the ITK-SNAP software (masking T1 hypo- and T2-FLAIR hyperintensities for gliomas grade II-III, as well as T1 hypointensities and contrast-enhancing tumors for glioblastomas). The tumor volume was then calculated by counting the number of voxels in the tumor segmentation mask and corresponded to the volume in mm 3 .…”

Section: Methodsmentioning

confidence: 99%

Self-Referential Processing and Resting-State Functional MRI Connectivity of Cortical Midline Structures in Glioma Patients

Jütten

Forster

et al. 2022

Brain Sciences

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Metacognition has only scarcely been investigated in brain tumor patients. It is unclear if and how the tumor-lesioned brain might be able to maintain an adequate sense-of-self. As cortical midline structures (CMS) are regarded as essential for self-referential mental activity, we investigated resting-state fMRI connectivity (FC) of CMS to the default-mode network (DMN) and to the whole brain, comparing glioma patients and matched controls. Subjects furthermore performed a trait judgement (TJ), a trait recall task (TR), and neuropsychological testing. In the TJ, adjectives had to be ascribed as self- or non-self-describing, assessing the self-serving effect (SSE), a normally observed bias for positive traits. In the TR, the mnemic neglect effect (MNE), a memory advantage for positive traits, was tested. The groups were compared and partial correlations between FC and test metrics were analyzed. Although patients were significantly impaired in terms of verbal memory, groups did not differ in the SSE or the MNE results, showing preserved metacognitive abilities in patients. FC of CMS to the DMN was maintained, but was significantly decreased to whole brain in the patients. FC of the dorsomedial prefrontal cortex (DMPFC) to whole brain was correlated with the MNE in patients. Preserving the DMPFC in therapeutic interventions might be relevant for maintaining self-related verbal information processing in the memory domain in glioma patients.

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“…More specifically, if a hub bears the throughput beyond its threshold, it could lead to catastrophic system collapse. For the whole brain level, network analysis showed decreased global and local connectivity efficiency in the ipsilesional hemisphere ( Fekonja et al, 2021 ); Jutten et al (2021) investigated fractional anisotropy (FA) of dMRI and achieved similar results of structural connectivity and systemic microstructural WM dis-integrity, especially in the IDH-wild-type (IDH-wt) group due to the worse biological attributes ( Eckel-Passow et al, 2015 ). In addition, patients with IDH-wt and a greater lesion volume have significantly lower connectivity efficiency and cognitive capability ( Kesler et al, 2017 ).…”

Section: Structural Connectome and The Alternations In Gliomasmentioning

confidence: 96%

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review

Chen

Teng

et al. 2022

Front. Neurosci.

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In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural–functional coupling of glioma. Additionally, the brain–computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.

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Dissociation of structural and functional connectomic coherence in glioma patients

Cited by 17 publications

References 52 publications

Dynamical graph networks may aid in phenotyping prognostically different brain tumor types

Dynamical graph networks may aid in phenotyping prognostically different brain tumor types

Self-Referential Processing and Resting-State Functional MRI Connectivity of Cortical Midline Structures in Glioma Patients

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review

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