Altered structural hub connectivity and its clinical relevance in glioma

Douw, Linda; Miller, Julie J.; Steenwijk,; Sm, Stufflebeam; Er, Gerstner

doi:10.1101/610618

Cited by 8 publications

(9 citation statements)

References 34 publications

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“…As we mentioned above, hub neuronal regions play a central role in the brain small-world network and can be clustered into a rich club. Glioma patients, however, have more hub-related connections in the contralateral hemisphere ( Yu et al, 2016 ; Douw et al, 2019 ; Yuan T. et al, 2020 ), while a slight difference in the ipsilateral hemisphere was found. These contralesional connections suggested the decompensation of structure and function and the particular redistribution of connectivity ( Crossley et al, 2014 ; Stam, 2014 ).…”

Section: Structural Connectome and The Alternations In Gliomasmentioning

confidence: 87%

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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Section: Structural Connectome and The Alternations In Gliomasmentioning

confidence: 87%

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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“…Extratumoural preand post-operative Neurite Density within the fronto-parietal and DMN had a protective factor in memory recovery that may be mediated by the prominent role of these networks in this cognitive domain. From the connectomic perspective, cognitive performance of brain tumour patients has been associated with hub-related structural connectivity within the DMN and fronto-parietal Network in hemispheres contralateral to the tumour 44 . By using connectomic metrics derived from both DTI and fMRI, Liu et al (2016) achieved a 75% accuracy when predicting survival of high-grade glioma patients 45 .…”

Section: Discussionmentioning

confidence: 99%

Memory recovery in relation to default mode network impairment and neurite density during brain tumor treatment

Romero-García

Suckling

Owen

et al. 2022

Journal of Neurosurgery

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OBJECTIVE The aim of this study was to test brain tumor interactions with brain networks, thereby identifying protective features and risk factors for memory recovery after resection. METHODS Seventeen patients with diffuse nonenhancing glioma (ages 22–56 years) underwent longitudinal MRI before and after surgery, and during a 12-month recovery period (47 MRI scans in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span, and backward digit span. Using structural MRI and neurite orientation dispersion and density imaging (NODDI) derived from diffusion-weighted images, the authors estimated lesion overlap and neurite density, respectively, with brain networks derived from normative data in healthy participants (somatomotor, dorsal attention, ventral attention, frontoparietal, and default mode network [DMN]). Linear mixed-effect models (LMMs) that regressed out the effect of age, gender, tumor grade, type of treatment, total lesion volume, and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery. RESULTS Memory recovery was not significantly associated with either the tumor location based on traditional lobe classification or the type of treatment received by patients (i.e., surgery alone or surgery with adjuvant chemoradiotherapy). Nonlocal effects of tumors were evident on neurite density, which was reduced not only within the tumor but also beyond the tumor boundary. In contrast, high preoperative neurite density outside the tumor but within the DMN was associated with better memory recovery (LMM, p value after false discovery rate correction [Pfdr] < 10−3). Furthermore, postoperative and follow-up neurite density within the DMN and frontoparietal network were also associated with memory recovery (LMM, Pfdr = 0.014 and Pfdr = 0.001, respectively). Preoperative tumor and postoperative lesion overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr = 0.002 and Pfdr < 10−4, respectively). CONCLUSIONS Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumors and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patients’ memory recovery.

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“…Extratumoural pre- and post-operative Neurite Density within the Fronto-parietal and DMN had a protective factor in memory recovery that may be mediated by the prominent role of these network in this cognitive domain. From the connectomic perspective, cognitive performance of brain tumour patients has been associated with hub-related structural connectivity within the DMN and Frontoparietal Network in hemispheres contralateral to the tumour[Douw et al, 2019]. By using connectomic metrics derived from both DTI and fMRI, Liu et al (2016) achieved a 75% accuracy when predicting survival of high-grade glioma patients[Liu et al, 2016].…”

Section: Discussionmentioning

confidence: 99%

Memory recovery is related to default mode network impairment and neurite density during brain tumours treatment

Romero-García

Suckling

Owen

et al. 2019

Preprint

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Surgical resection with adjuvant chemotherapy and radiotherapy are effective treatments to delay glioma progression and improve survival. Nevertheless, a large proportion of patients have treatment-induced cognitive deficits that dramatically reduce their life quality. Predicting the treatment-induced functional impairments is difficult due to the complex interlocking and diffusely spread networks that underpin different aspects of cognition. Here we investigated glioma interactions with brain networks in relation to cognitive recovery after surgical resection and during chemo-radiotherapy treatment. Seventeen patients with diffuse non-enhancing glioma (aged 22-56 years) were longitudinally MRI-scanned and cognitively assessed using a tablet-based screening tool before and after surgery, and during a 12-months recovery period. Using structural MRI and Neurite Orientation Dispersion and Density Imaging (NODDI) derived from diffusion-weighted images, we respectively estimated tumour overlap and Neurite Density (as an in-vivo proxy measure of axon and dendrite concentration) with brain networks and functional maps derived from normative data in healthy participants. We found that neither total lesion volume nor tumour location based on traditional lobular divisions were associated with memory or attention deficits. However, tumour and lesion overlap with the Default Mode Network (DMN), Attention Network and attention-related regions located in frontal and parietal cortex was associated with memory and attention deficits. This association was above and beyond the contributions of preoperative cognitive status and tumour volume (Linear Mixed Model, Pfdr<0.05). On the other hand, Neurite Density was reduced not only within the tumour, but also beyond the tumour boundary, revealing a distal effect with global consequences to brain networks. High preoperative Neurite Density outside the tumour, but within the Frontoparietal Network was associated with better memory and attention recovery. Moreover, postoperative and follow-up Neurite Density within the DMN, Frontoparietal and Attention Networks were associated with memory and attention improvements (Pfdr<0.05). We conclude that gliomas located on brain networks that are fundamental for cognitive processing mediate cognitive deficits and they exert a distal effect on Neurite Density in these networks that is also associated with cognitive recovery. Our work provides insights into the brain reorganisation that occurs due to the presence of a tumour and its subsequent removal, which has potential capability to predict cognitive outcomes. Understanding the pathophysiology underlying tumour related cognitive outcomes will be vital to the development of novel prognostic biomarkers, subgroup stratification in clinical trials, and individualised rehabilitation programmes.

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Altered structural hub connectivity and its clinical relevance in glioma

Cited by 8 publications

References 34 publications

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

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

Memory recovery in relation to default mode network impairment and neurite density during brain tumor treatment

Memory recovery is related to default mode network impairment and neurite density during brain tumours treatment

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