Modeling brain dynamics in brain tumor patients using The Virtual Brain

AbstractPresurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, non-invasive neuroimaging techniques such as functional MRI and diffusion weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex non-linear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics.As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed.Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

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Modeling brain dynamics after tumor resection using The Virtual Brain

10.1101/752931 ◽

2019 ◽

Author(s):

Hannelore Aerts ◽

Michael Schirner ◽

Thijs Dhollander ◽

Ben Jeurissen ◽

Eric Achten ◽

...

Keyword(s):

Brain Tumor ◽

Network Model ◽

Brain Activity ◽

Tumor Resection ◽

Network Models ◽

Brain Network ◽

Brain Dynamics ◽

Model Parameters ◽

Tumor Patients ◽

Healthy Control

AbstractBrain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, computational modeling of brain activity using so-called brain network models has been introduced as a promising tool for this purpose. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first “virtual neurosurgery” analyses to evaluate the potential of brain network modeling in predicting brain dynamics after tumor resection.We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. In addition, we identify several robust associations between individually optimized model parameters, structural network topology and cognitive performance from pre-to post-operative assessment. Concerning the virtual neurosurgery analyses, we obtain promising results in some patients, whereas the predictive accuracy of the currently applied model is poor in others. These findings reveal interesting avenues for future research, as well as important limitations that warrant further investigation.

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Multidimensional Associations between Cognition and Connectome Organization in Temporal Lobe Epilepsy

10.1101/675884 ◽

2019 ◽

Author(s):

Raúl Rodríguez-Cruces ◽

Boris C. Bernhardt ◽

Luis Concha

Keyword(s):

Temporal Lobe Epilepsy ◽

Cognitive Performance ◽

Temporal Lobe ◽

Network Topology ◽

Large Scale ◽

Cognitive Impairments ◽

Structural Connectivity ◽

Multivariate Techniques ◽

Structural Network ◽

Multiple Domains

AbstractObjectiveTemporal lobe epilepsy (TLE) is known to affect large-scale structural networks and cognitive function in multiple domains. The study of complex relations between structural network organization and cognition requires comprehensive analytical methods and a shift towards multivariate techniques. The current work sought to identify multidimensional associations between cognitive performance and structural network topology in TLE.MethodsWe studied 34 drug-resistant TLE patients and 25 age- and sex-matched healthy controls. All participants underwent a comprehensive neurocognitive battery and multimodal MRI, allowing for large-scale connectomics, and morphological evaluation of subcortical and neocortical regions. Using canonical correlation analysis, we identified a multivariate mode that links cognitive performance to a brain structural network. Our approach was complemented by bootstrap-based clustering to derive cognitive subtypes and associated patterns of macroscale connectome anomalies.ResultsBoth methodologies provided converging evidence for a close coupling between cognitive impairments across multiple domains and large-scale structural network compromise. Cognitive classes presented with an increasing gradient of abnormalities (increasing cortical and subcortical atrophy and less efficient white matter connectome organization in patients with increasing degrees of cognitive impairments). Notably, network topology characterized better the cognitive performance than morphometric measures. Thus, connectome characteristics featured as important markers of network reorganization and loss of inter-regional connectivity.ConclusionsThe multivariate approach emphasized the close interplay between cognitive impairment and large-scale network anomalies in TLE. Our findings contribute to understand the complexity of structural connectivity regulating the heterogeneous cognitive deficits found in epilepsy

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OMIC-02. COGNITIVE DEFICITS AND ALTERED FUNCTIONAL BRAIN NETWORK ORGANIZATION IN PEDIATRIC BRAIN TUMOR PATIENTS

Neuro-Oncology ◽

10.1093/neuonc/noab090.149 ◽

2021 ◽

Vol 23 (Supplement_1) ◽

pp. i37-i37

Author(s):

Benjamin Seitzman ◽

Hari Anandarajah ◽

Alana McMichael ◽

Hongjie Gu ◽

Dennis Barbour ◽

...

Keyword(s):

Brain Tumor ◽

Cognitive Performance ◽

Pediatric Brain Tumor ◽

Functional Network ◽

Network Organization ◽

Tumor Patients ◽

Functional Brain Network ◽

Pediatric Brain ◽

Insight Into

Abstract Pediatric brain tumor survivors experience significant cognitive sequelae from their diagnosis and treatment. The exact mechanisms of cognitive injury are poorly understood, and validated predictors of long-term cognitive outcome are lacking. Large-scale, distributed brain systems provide a window into brain organization and function that may yield insight into these mechanisms and outcomes. We evaluated functional network architecture, cognitive performance, and brain-behavior relationships in pediatric brain tumor patients. Patients ages 8–18 years old with diagnosis of a brain tumor underwent awake resting state functional Magnetic Resonance Imaging during regularly scheduled clinical visits and were tested with the National Institutes of Health Toolbox Cognition Battery. Age- and sex-matched typically developing children were used as controls. We observed that functional network organization was significantly altered in patients compared to controls (p < 0.001), with the integrity of the dorsal attention network particularly affected (p < 0.0001). Moreover, patients demonstrated significant impairments in multiple domains of cognitive performance, including attention (p < 0.0001). Finally, a significant amount of variance (R squared = 0.52, F = 3.2, p < 0.05) of age-adjusted total composite scores from the Toolbox was explained by changes in segregation between the dorsal attention and default mode networks. Our results suggest that changes in functional network organization may provide insight into long-term changes in cognitive function in pediatric brain tumor patients.

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Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

eNeuro ◽

10.1523/eneuro.0083-18.2018 ◽

2018 ◽

Vol 5 (3) ◽

pp. ENEURO.0083-18.2018 ◽

Cited By ~ 14

Author(s):

Hannelore Aerts ◽

Michael Schirner ◽

Ben Jeurissen ◽

Dirk Van Roost ◽

Eric Achten ◽

...

Keyword(s):

Brain Tumor ◽

Brain Dynamics ◽

Tumor Patients

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Modeling brain dynamics in brain tumor patients using The Virtual Brain

Frontiers in Neuroscience ◽

10.3389/conf.fnins.2018.95.00030 ◽

2018 ◽

Vol 12 ◽

Author(s):

Hannelore Aerts ◽

Michael Schirner ◽

Ben Jeurissen ◽

Dirk Van Roost ◽

Eric Achten ◽

...

Keyword(s):

Brain Tumor ◽

Brain Dynamics ◽

Tumor Patients

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Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients

BMC Neuroscience ◽

10.1186/1471-2202-11-103 ◽

2010 ◽

Vol 11 (1) ◽

Cited By ~ 91

Author(s):

Linda Douw ◽

Edwin van Dellen ◽

Marjolein de Groot ◽

Jan J Heimans ◽

Martin Klein ◽

...

Keyword(s):

Brain Tumor ◽

Network Topology ◽

Brain Connectivity ◽

Theta Band ◽

Tumor Patients

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Task-evoked reconfiguration of the fronto-parietal network is associated with cognitive performance in brain tumor patients

Brain Imaging and Behavior ◽

10.1007/s11682-019-00189-2 ◽

2019 ◽

Vol 14 (6) ◽

pp. 2351-2366

Author(s):

Wouter De Baene ◽

Martijn J. Jansma ◽

Irena T. Schouwenaars ◽

Geert-Jan M. Rutten ◽

Margriet M. Sitskoorn

Keyword(s):

Brain Tumor ◽

Functional Connectivity ◽

Cognitive Performance ◽

Cognitive Deficits ◽

Memory Task ◽

Tumor Patients ◽

Cognitive Domains ◽

Functional Connectivity Strength ◽

The Mean ◽

Combined Data

Abstract In healthy participants, the strength of task-evoked network reconfigurations is associated with cognitive performance across several cognitive domains. It is, however, unclear whether the capacity for network reconfiguration also plays a role in cognitive deficits in brain tumor patients. In the current study, we examined whether the level of reconfiguration of the fronto-parietal (‘FPN’) and default mode network (‘DMN’) during task execution is correlated with cognitive performance in patients with different types of brain tumors. For this purpose, we combined data from a resting state and task-fMRI paradigm in patients with a glioma or meningioma. Cognitive performance was measured using the in-scanner working memory task, as well as an out-of-scanner cognitive flexibility task. Task-evoked changes in functional connectivity strength (defined as the mean of the absolute values of all connections) and in functional connectivity patterns within and between the FPN and DMN did not differ significantly across meningioma and fast (HGG) and slowly growing glioma (LGG) patients. Across these brain tumor patients, a significant and positive correlation was found between the level of task-evoked reconfiguration of the FPN and cognitive performance. This suggests that the capacity for FPN reconfiguration also plays a role in cognitive deficits in brain tumor patients, as was previously found for normal cognitive performance in healthy controls.

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Brain connectivity dynamics: Multilayer network switching rate predicts brain performance

10.1101/403105 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mangor Pedersen ◽

Andrew Zalesky ◽

Amir Omidvarnia ◽

Graeme D. Jackson

Keyword(s):

Large Scale ◽

Brain Connectivity ◽

Brain Networks ◽

Brain Dynamics ◽

Model Parameters ◽

Brain Areas ◽

Multilayer Network ◽

Time Resolved ◽

Brain Functions ◽

Human Connectome Project

AbstractLarge-scale brain dynamics measures repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood and whether switching between these states is important for behavior has been little studied. Our aim here is to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved functional MRI (fMRI) connectivity from one-hour long data recordings in 1003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data was used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching which we define as the rate at which each brain region transits between different fMRI networks. We found i) an inverse relationship between network switching and connectivity dynamics –defined as the difference in variance between time-resolved fMRI connectivity signals and phase randomized surrogates–; ii) brain connectivity was lower during intervals of network switching; iii) brain areas with frequent network switching had greater temporal complexity; iv) brain areas with high network switching were located in association cortices; and v) using cross-validated Elastic Net regression, network switching predicted inter-subject variation in working memory performance, planning/reasoning and amount of sleep. Our findings shed new light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

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