Stoecklein, S. | Search bioRxiv

bioRxiv2026

Gliomas preferentially develop within the action-mode network

Gliomas tend to arise in specific brain regions and may integrate into functional circuits, suggesting they could be regulated by brain activity. However, it remains unclear whether glioma growth is related to system-level brain networks. Analyzing neuroimaging data from three datasets including 1,310 patients with cerebral gliomas, we identified and replicated a functionally connected glioma network, which overlaps with the action-mode network (AMN), somatomotor network (SMN), and action-related subcortical regions. Resting-state functional connectivity (RSFC) of the AMN successfully predicted the location of glioma occurrence in two independent datasets with complex tumor distributions. Remarkably, no patient had a glioma entirely outside the AMN, and over 89% of patients exhibited gliomas with at least 50% overlap with the network. Moreover, the spatial overlap between glioma location and the AMN demonstrated significant prognostic value in survival analyses, with higher AMN-tumor overlap associated with poorer overall survival. Notably, the acetylcholine transporter, a key player in glioma pathogenesis that drives transcriptional reprogramming, showed an expression pattern overlapping with the AMN. Meta-analytic annotations further linked the glioma network to processes of action initiation, execution, and feedback. These findings indicate that gliomas preferentially arise in circuits involved in action and highlight the central role of the AMN in glioma pathophysiology and growth.

neuroscience↗

bioRxiv2024

How to measure functional connectivity using resting-state fMRI? A comprehensive empirical exploration of different connectivity metrics

BackgroundFunctional connectivity in the context of functional magnetic resonance imaging is typically quantified by Pearso[n]s or partial correlation between regional time series of the blood oxygenation level dependent signal. However, a recent interdisciplinary methodological work proposes more than 230 different metrics to measure similarity between different types of time series. ObjectiveHence, we systematically evaluated how the results of typical research approaches in functional neuroimaging vary depending on the functional connectivity metric of choice. We further explored which metrics most accurately detect neural decline induced by age and malignant brain tumors, aiming to initiate a debate on how best assessing brain connectivity in functional neuroimaging research. MethodsWe addressed both research questions using four independent neuroimaging datasets, comprising multimodal data from a total of 1187 individuals. We analyzed resting-state functional sequences to calculate functional connectivity using 20 representative metrics from four distinct mathematical domains. We further used T1- and T2-weighted images to compute regional brain volumes, diffusion-weighted imaging data to build structural connectomes, and pseudo-continuous arterial spin labeling to measure regional brain perfusion. ResultsFirst, our results demonstrate that the results of typical functional neuroimaging approaches differ fundamentally depending on the functional connectivity metric of choice. Second, we show that correlational and distance metrics are most appropriate to cover neural decline induced by age. In this context, partial correlation performs worse than other correlational metrics. Third, our findings suggest that the FC metric of choice depends on the utilized scanning parameters, the regions of interest, and the individual investigated. Lastly, beyond the major objective of this study, we provide evidence in favor of brain perfusion measured via pseudo-continuous arterial spin labeling as a robust neural entity mirroring age-related neural and cognitive decline. ConclusionOur empirical evaluation supports a recent theoretical functional connectivity framework. Future functional imaging studies need to comprehensively define the study-specific theoretical property of interest, the methodological property to assess the theoretical property, and the confounding property that may bias the conclusions.

neuroscience↗