Biology subjects

Douw, L.

Publications and source records attributed to Douw, L..

11 recordsLinked to original sources

bioRxiv2026

Experimental Framework to Investigate Glioma in Organotypic Human Cortex

Background Gliomas are primary brain tumors that integrate into the surrounding brain via neuron-glioma synapses and disrupt normal neuronal functioning. Recent work links tumor-brain connectivity to clinical measures such as patients' functional status. However, few experimental systems allow the visualization of tumor and resident brain cell architecture together in human tissue at the scale needed to connect cellular organization to clinical outcomes. To address this gap, we developed an experimental framework for investigating the neuron-glioma network in organotypic human cortex from glioma patients, designed to support future correlation of cellular architecture to clinical outcomes such as functional status and survival. Methods Brain tissue samples containing cortex from seven glioma patients were collected during tumor resection and cultured for seven days as organotypic slices. We established several multiplexed immunohistochemistry panels to stain for glioma cells, various types of resident brain cells including neurons and cells from the oligodendrocyte lineage, as well as axonal networks and myelination patterns. Results We demonstrated the presence of tumor cells in all samples using SOX2, a tumor cell marker. NeuN+ neuronal cells were also successfully identified, as were cells from the oligodendrocyte lineage using SOX10 (pan-lineage marker), BCAS1 (pre-myelinating oligodendrocyte marker), and ASPA (mature oligodendrocyte marker). The neuronal network and myelin were successfully visualized using MAP2 (neurons and dendrites), SMI312 (axons), and PLP1 (myelin). Conclusions High-quality visualization of tumor cells, resident brain cells and the (myelinated) neuronal network in organotypic human cortex from glioma patients is possible after seven days of culturing. Future studies could further optimize culture conditions and should study stability of all cellular components in longer term slice cultures. Preliminary analyses indicate that cellular measures derived from human organotypic slices can be linked to patients' functional status, demonstrating the capacity to generate data suitable for large-scale investigation of the effects of glioma-brain network architecture on clinical outcomes.

neuroscience↗

bioRxiv2023

Pseudo-craniotomy of a whole-brain model reveals tumor-induced alterations to neuronal dynamics in glioma patients

Brain tumors can induce pathological changes in neuronal dynamics both on a local and global level. Here, we use a whole-brain modeling approach to investigate these pathological alterations in neuronal activity. By fitting a Hopf whole-brain model to empirical functional connectivity, we demonstrate that phase correlations are largely determined by the ratio of interregional coupling strength and intraregional excitability. Furthermore, we observe considerable differences in interregional-versus-intraregional dynamics between glioma patients and healthy controls, both on an individual and population-based level. In particular, we show that local tumor pathology induces shifts in the global brain dynamics by promoting the contribution of interregional interactions. Our approach demonstrates that whole-brain models provide valuable insights for understanding glioma-associated alterations in functional connectivity.

neuroscience↗

bioRxiv2023

Emergence of High-Order Functional Hubs in the Human Brain

Network theory is often based on pairwise relationships between nodes, which is not necessarily realistic for modeling complex systems. Importantly, it does not accurately capture non-pairwise interactions in the human brain, often considered one of the most complex systems. In this work, we develop a multivariate signal processing pipeline to build high-order networks from time series and apply it to resting-state functional magnetic resonance imaging (fMRI) signals to characterize high-order communication between brain regions. We also propose connectivity and signal processing rules for building uniform hypergraphs and argue that each multivariate interdependence metric could define weights in a hypergraph. As a proof of concept, we investigate the most relevant three-point interactions in the human brain by searching for high-order "hubs" in a cohort of 100 individuals from the Human Connectome Project. We find that, for each choice of multivariate interdependence, the high-order hubs are compatible with distinct systems in the brain. Additionally, the high-order functional brain networks exhibit simultaneous integration and segregation patterns qualitatively observable from their high-order hubs. Our work hereby introduces a promising heuristic route for hypergraph representation of brain activity and opens up exciting avenues for further research in high-order network neuroscience and complex systems.

neuroscience↗

bioRxiv2022

Hub types and their translational actions in research meetings: a conversation analytic approach

Translational processes that facilitate effective interdisciplinary communication remain a black box. Here, we aimed to operationalize translational practices by combining conversation analysis and network science to analyze interdisciplinary research meetings. We applied conversation analysis to transcribed meetings, and identified chair, clarifying, skeptical, expert, connecting, and practical actions. For each meeting, we constructed a network ( meetome) with team members as nodes, and the number of consecutive speaking turns between members as links. We found that the relative occurrence of role-specific actions correlated with network measures. We discuss how awareness of interactional roles within meetings may help to implement a translational approach in interdisciplinary research teams.

scientific communication and education↗

bioRxiv2022

Modeling of individual neurophysiological brain connectivity

IntroductionComputational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical connections in the brain. However, group averaged data is often used in this context and it remains unclear whether individual predictions of FC patterns using this approach can be made. Here, we assess the value of using individualized structural data for simulation of individual whole-brain FC. MethodsThe Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC) obtained from diffusion weighted imaging. Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using both phase-based (phase lag index (PLI), phase locking value (PLV)) and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze the goodness-of-fit of different metrics for individual predictions. Prediction of individual FC was compared against the prediction of group averaged FC. We further tested whether SC of a different participant could equally well predict a participants FC pattern. ResultsThe AEC provided a significantly better match between individually simulated and empirical FC than phase-based metrics. Simulations with individual SC provided higher correlations between simulated and empirical FC compared to using the group-averaged SC. However, using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants own SC. DiscussionThis work underlines the added value of FC simulations based on individual instead of group-averaged SC, and could aid in a better understanding of mechanisms underlying individual functional network trajectories in neurological disease. Impact statementIn this work, we investigated how well individual empirical functional connectivity can be simulated using the individuals structural connectivity matrix combined with neural mass modeling. Our research highlights the potential added value of using individual simulations of functional connectivity, and could aid in a better understanding of mechanisms underlying individual functional network trajectories in neurological disease. Moreover, individualized prediction of disease trajectories could enhance patient care and may provide better treatment options.

neuroscience↗

bioRxiv2021

Multimodal multilayer network centrality relates to executive functioning

Executive functioning is a higher-order cognitive process that is thought to depend on a brain network organization facilitating network integration across specialized subnetworks. The frontoparietal network (FPN), a subnetwork that has diverse connections to other brain modules, seems pivotal to this integration, and a more central role of regions in the FPN has been related to better executive functioning. Brain networks can be constructed using different modalities: diffusion MRI (dMRI) can be used to reconstruct structural networks, while resting-state fMRI (rsfMRI) and magnetoencephalography (MEG) yield functional networks. These networks are often studied in a unimodal way, which cannot capture potential complementary or synergistic modal information. The multilayer framework is a relatively new approach that allows for the integration of different modalities into one network of networks. It has already yielded promising results in the field of neuroscience, having been related to e.g. cognitive dysfunction in Alzheimers disease. Multilayer analyses thus have the potential to help us better understand the relation between brain network organization and executive functioning. Here, we hypothesized a positive association between centrality of the FPN and executive functioning, and we expected that multimodal multilayer centrality would supersede unilayer centrality in explaining executive functioning. We used dMRI, rsfMRI, MEG, and neuropsychological data obtained from 33 healthy adults (age range 22-70 years) to construct eight modality-specific unilayer networks (dMRI, fMRI, and six MEG frequency bands), as well as a multilayer network comprising all unilayer networks. Interlayer links in the multilayer network were present only between a nodes counterpart across layers. We then computed and averaged eigenvector centrality of the nodes within the FPN for every uni- and multilayer network and used multiple regression models to examine the relation between uni- or multilayer centrality and executive functioning. We found that higher multilayer FPN centrality, but not unilayer FPN centrality, was related to better executive functioning. To further validate multilayer FPN centrality as a relevant measure, we assessed its relation with age. Network organization has been shown to change across the life span, becoming increasingly efficient up to middle age and regressing to a more segregated topology at higher age. Indeed, the relation between age and multilayer centrality followed an inverted-U shape. These results show the importance of FPN integration for executive functioning as well as the value of a multilayer framework in network analyses of the brain. Multilayer network analysis may particularly advance our understanding of the interplay between different brain network aspects in clinical populations, where network alterations differ across modalities. HighlightsO_LIMultimodal neuroimaging and neurophysiology data were collected in healthy adults C_LIO_LIMultilayer frontoparietal centrality was positively associated with executive functioning C_LIO_LIUnilayer (unimodal) centralities were not associated with executive functioning C_LIO_LIThere was an inverted-U relationship between multilayer centrality and age C_LI

neuroscience↗

bioRxiv2021

A hands-on tutorial on network and topological neuroscience

The brain is an extraordinarily complex system that facilitates the efficient integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pair-wise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks, and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses data from the 1000 Functional Connectomes Project. Moreover, we would like to highlight one part of our notebook that is solely dedicated to realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pair-wise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.

neuroscience↗

bioRxiv2021

Neuronal morphology and physiology, functional brain networks and memory in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) patients are at risk of memory deficits, which have been linked to functional network disturbances, particularly of integration of the default mode network (DMN). However, the cellular substrates of functional network integration are unknown. We leverage a unique cross-scale dataset of therapy-resistant TLE patients, who underwent fMRI, MEG and/or neuropsychological testing before neurosurgery. fMRI and MEG underwent atlas-based connectivity analyses. Functional network centrality of the lateral middle temporal gyrus, part of the DMN, was used as a measure of local network integration. Subsequently, non-pathological cortical tissue from this region was used for single cell morphological and electrophysiological patch-clamp analysis, assessing integration in terms of total dendritic length and action potential rise speed. As could be hypothesized, greater network centrality related to better memory performance. Moreover, greater network centrality correlated with more integrative properties at the cellular level across patients. We conclude that individual differences in cognitively relevant functional network integration of a DMN region are mirrored by differences in cellular integrative properties of this region in TLE patients. These findings connect previously separate scales of investigation, increasing translational insight into focal pathology and large-scale network disturbances in TLE.

neuroscience↗

bioRxiv2019

Resting-state and task-based centrality of dorsolateral prefrontal cortex predict resilience to inhibitory repetitive transcranial magnetic stimulation

BackgroundRepetitive transcranial magnetic stimulation (rTMS) is used to investigate normal brain function in healthy participants and as a treatment for brain disorders. Various subject factors can influence individual response to rTMS, including brain network properties.\n\nObjective/HypothesisA previous study by our group showed that virtually lesioning the left dorsolateral prefrontal cortex (dlPFC; important for cognitive flexibility) using inhibitory rTMS reduced performance on a set-shifting task. We aimed to determine whether this behavioural response was related to topological features of pre-TMS resting-state and task-based functional networks.\n\nMethodsInhibitory (1Hz) rTMS was applied to the left dlPFC in 16 healthy participants, and to the vertex in 17 participants as a control condition. Participants performed a set-shifting task during fMRI at baseline and directly after a single rTMS session 1-2 weeks later. Functional network topology measures were calculated from resting-state and task-based fMRI scans using graph theoretical analysis.\n\nResultsThe dlPFC-stimulated group, but not the vertex group, showed reduced set shifting performance after rTMS associated with lower task-based betweenness centrality of the dlPFC at baseline (p=.030) and a smaller reduction in task-based betweenness centrality after rTMS (p=.024). Reduced repeat trial accuracy after rTMS was associated with higher baseline resting state node strength of the dlPFC (p=.017).\n\nConclusionsOur results suggest that behavioural response to inhibitory rTMS to the dlPFC is dependent on baseline functional network features. Individuals with more globally integrated stimulated regions show greater resilience to inhibitory rTMS, while individuals with more locally well-connected regions show greater vulnerability.\n\nHighlightsO_LIFunctional brain network properties predict behavioural response to 1Hz DLPFC rTMS\nC_LIO_LIGlobally integrated stimulated regions are resilient to effects of inhibitory rTMS\nC_LIO_LISegregated, locally well-connected regions are vulnerable to inhibitory rTMS\nC_LIO_LIChange in performance after rTMS correlates with change in network properties\nC_LI

neuroscience↗

bioRxiv2019

Altered structural hub connectivity and its clinical relevance in glioma

Background and PurposeStructural network analysis of diffusion imaging is increasingly used to study neurological disease, its pathophysiology and symptoms. We therefore evaluate structural hub connectivity in glioma patients and its association with molecular subtype and clinical status.\n\nMaterials and MethodsUsing retrospective diffusion imaging, structural connectivity was investigated in 65 newly diagnosed glioma patients (36 males; mean age 52 {+/-} 14 years) and 60 healthy controls (23 males; mean age 50 {+/-} 7 years). Probabilistic tractography was performed between 39 cortical nodes per hemisphere. In patients, tumors were drawn in to exclude each tumor-containing voxel from analysis. As previous connectomic research in glioma and other neurological diseases has shown particular importance of hub nodes and connections, the numbers of connections between hubs, hubs and non-hubs, and non-hubs were calculated for each hemisphere separately. Clinical and molecular characteristics were assessed as part of routine clinical care. Group differences in connectivity and its associations with performance and molecular subtypes were tested non-parametrically through Mann-Whitney U-tests, corrected for multiple comparisons.\n\nResultsGlioma patients had more hub-related connections in the hemisphere contralateral to the tumor (hub-hub P = 0.002, hub-non-hub P = 0.005), despite being comparable to controls in terms of total and ipsilateral connections. Within patients, hub-related connectivity related to performance status (P = 0.009) and molecular subtype (P = 0.045).\n\nConclusionWe present experimental evidence for the relevance of structural connectomics as a tool to pick up on the clinical impact of glioma on the rest of the brain.

cancer biology↗

bioRxiv2018

Topological phase transitions in functional brain networks

Functional brain networks are often constructed by quantifying correlations among brain regions. Their topological structure includes nodes, edges, triangles and even higher-dimensional objects. Topological data analysis (TDA) is the emerging framework to process datasets under this perspective. In parallel, topology has proven essential for understanding fundamental questions in physics. Here we report the discovery of topological phase transitions in functional brain networks by merging concepts from TDA, topology, geometry, physics, and network theory. We show that topological phase transitions occur when the Euler entropy has a singularity, which remarkably coincides with the emergence of multidimensional topological holes in the brain network. Our results suggest that a major alteration in the pattern of brain correlations can modify the signature of such transitions, and may point to suboptimal brain functioning. Due to the universal character of phase transitions and noise robustness of TDA, our findings open perspectives towards establishing reliable topological and geometrical biomarkers of individual and group differences in functional brain network organization.

neuroscience↗