Functional MRI Shows Altered Deactivation and a Corresponding Decrease in Functional Connectivity of the Default Mode Network in Patients with Gliomas

Maniar, Y.M.; Peck, Kyung K.; Jenabi, Mehrnaz; Gene, Madeleine; Holodny, Andrei I.

doi:10.3174/ajnr.a7138

Cited by 9 publications

(14 citation statements)

References 37 publications

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“…Regarding the default mode network (DMN), the results reported in the literature are unable to provide a clear and unequivocal picture of the alterations. In fact, from recent studies, both decrement [31] and increment of the overall FC within such network [32], and with the other brain networks [34], were detected. However, it is to note that even where a global increase was found, local negative alterations in the posterior cingulate cortex (PCC) and the precuneus were observed [32].…”

Section: Default Mode Networkmentioning

confidence: 82%

“…In particular, the first applications of deep learning methods have been showing that such an approach can achieve excellent outcomes for the specific purpose of reconstructing tb-fMRI-like networks, starting from the rs-fMRI data. Nevertheless, it is also significant to note that ICA is already widely acknowledged, so much so that it is also used to isolate the functional networks in studies that do not focus on their identification but rather aim at characterizing their properties in terms of FC [29][30][31][32].…”

Section: Language Networkmentioning

confidence: 99%

“…However, it is to note that even where a global increase was found, local negative alterations in the posterior cingulate cortex (PCC) and the precuneus were observed [32]. Similarly, while some studies described a loss of FC in the ipsilateral hemisphere [31], others found the quantity increased in the lesioned side, at least in the case of right-sided gliomas [30]. Again, whether the FC increases [30,45] or decreases [46] in the contralateral hemisphere, and especially between the parahippocampal gyrus, the PCC, and the temporal regions, is debated.…”

Section: Default Mode Networkmentioning

confidence: 99%

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What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients?

et al. 2022

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Resting-state functional MRI has been increasingly implemented in imaging protocols for the study of functional connectivity in glioma patients as a sequence able to capture the activity of brain networks and to investigate their properties without requiring the patients’ cooperation. The present review aims at describing the most recent results obtained through the analysis of resting-state fMRI data in different contexts of interest for brain gliomas: the identification and localization of functional networks, the characterization of altered functional connectivity, and the evaluation of functional plasticity in relation to the resection of the glioma. An analysis of the literature showed that significant and promising results could be achieved through this technique in all the aspects under investigation. Nevertheless, there is room for improvement, especially in terms of stability and generalizability of the outcomes. Further research should be conducted on homogeneous samples of glioma patients and at fixed time points to reduce the considerable variability in the results obtained across and within studies. Future works should also aim at establishing robust metrics for the assessment of the disruption of functional connectivity and its recovery at the single-subject level.

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Section: Default Mode Networkmentioning

confidence: 82%

Section: Language Networkmentioning

confidence: 99%

Section: Default Mode Networkmentioning

confidence: 99%

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What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients?

et al. 2022

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“…Prior studies have reported a higher mean connectivity ( Otten et al, 2012 ) and brain-wide disrupted networks associated with tumor-related remodeling of the neurovasculature ( Hadjiabadi et al, 2018 ). In particular, several research groups measured the DMN in glioma by rs-fMRI and observed the increased integration of DMN in hippocampal areas, but the decreased integration in prefrontal and posterior cingulate cortex regions, and a trend of decreasing global connectivity with a higher WHO grade ( Esposito et al, 2012 ; Harris et al, 2014 ; Maniar et al, 2021 ). The decreased DMN integration and deactivation may lead to cognitive decline of patients.…”

Section: Functional Connectome and The Alternations In Gliomasmentioning

confidence: 99%

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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“…Then we proofread the scanning time point of each layer image, and the head motion correction was used to exclude subjects with excessive head movement. Next, we used the Echo-Planer Imaging (EPI) template of the Montreal Neurologic Institute (MNI) space to standardize the image registration to 3 mm × 3 mm × 3 mm (Brandman et al, 2021;Fan et al, 2021;Peng et al, 2021), and 1 https://www.fil.ion.ucl.ac.uk/spm/software/spm12 finally the Gaussian kernel with a half-height full-width value of 6 mm was used for spatial smoothing to reduce spatial noise (Maniar et al, 2021;van Buuren et al, 2021).…”

Section: Data Preprocessingmentioning

confidence: 99%

Identification of Methamphetamine Abstainers by Resting-State Functional Magnetic Resonance Imaging

Dong

Huang

et al. 2021

Front. Psychol.

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Methamphetamine (MA) can cause brain structural and functional impairment, but there are few studies on whether this difference will sustain on MA abstainers. The purpose of this study is to investigate the correlation of brain networks in MA abstainers. In this study, 47 people detoxified for at least 14 months and 44 normal people took a resting-state functional magnetic resonance imaging (RS-fMRI) scan. A dynamic (i.e., time-varying) functional connectivity (FC) is obtained by applying sliding windows in the time courses on the independent components (ICs). The windowed correlation data for each IC were then clustered by k-means. The number of subjects in each cluster was used as a new feature for individual identification. The results show that the classifier achieved satisfactory performance (82.3% accuracy, 77.7% specificity, and 85.7% sensitivity). We find that there are significant differences in the brain networks of MA abstainers and normal people in the time domain, but the spatial differences are not obvious. Most of the altered functional connections (time-varying) are identified to be located at dorsal default mode network. These results have shown that changes in the correlation of the time domain may play an important role in identifying MA abstainers. Therefore, our findings provide valuable insights in the identification of MA and elucidate the pathological mechanism of MA from a resting-state functional integration point of view.

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Functional MRI Shows Altered Deactivation and a Corresponding Decrease in Functional Connectivity of the Default Mode Network in Patients with Gliomas

Cited by 9 publications

References 37 publications

What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients?

What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients?

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

Identification of Methamphetamine Abstainers by Resting-State Functional Magnetic Resonance Imaging

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