scholarly journals Modeling brain dynamics after tumor resection using The Virtual Brain

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.

scholarly journals Modeling brain dynamics in brain tumor patients using The Virtual Brain

2018 ◽  
Author(s):  
Hannelore Aerts ◽  
Michael Schirner ◽  
Ben Jeurissen ◽  
Dirk Van Roost ◽  
Rik Achten ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Cognitive Performance ◽  
Network Topology ◽  
Large Scale ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Suitable Model ◽  
Tumor Patients ◽  
Structural Network ◽  
Optimized Model

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.

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

scholarly journals A DEEP LEARNING APPROACH TO ESTIMATING INITIAL CONDITIONS OF BRAIN NETWORK MODELS IN REFERENCE TO MEASURED FMRI DATA

2021 ◽  
Author(s):  
Amrit Kashyap ◽  
Sergey Plis ◽  
Michael Schirner ◽  
Petra Ritter ◽  
Shella Keilholz
Keyword(s):  
Deep Learning ◽  
Initial Conditions ◽  
Brain Activity ◽  
Network Models ◽  
Brain Network ◽  
Measured Data ◽  
Fmri Data ◽  
Short Term ◽  
Whole Brain ◽  
Neural Processes

Brain Network Models (BNMs) are a family of dynamical systems that simulate whole brain activity using neural mass models to represent local activity in different brain regions that influence each other via a global structural network. Research has been interested in using these network models to explain measured whole brain activity measured via resting state functional magnetic resonance imaging (rs-fMRI). Properties computed over longer periods of simulated and measured data such as average functional connectivity (FC), have shown to be comparable with similar properties estimated from measured rs-fMRI data. While this shows that these network models have similar properties over the dynamical landscape, it is unclear how well simulated trajectories compare with empirical trajectories on a timepoint-by-timepoint basis. Previous studies have shown that BNMs are able to produce relevant features at shorter timescales, but analysis of short-term trajectories or transient dynamics as defined by synchronized predictions from BNM made at the same timescale as the collected data has not yet been conducted. Relevant neural processes exist in the time frame of measurements and are often used in task fMRI studies to understand neural responses to behavioral cues. Therefore, it is important to investigate how much of these dynamics are captured by our current brain simulations. To test the nature of BNMs short term trajectories against observed data, we utilize a deep learning technique known as Neural ODE that based on an observed sequence of fMRI measurements, estimates the initial conditions such that the BNMs simulation is synchronized to produce the closest trajectory relative to the observed data. We test to see if the parameterization of a specific well studied BNM, the Firing Rate Model, calculated by maximizing its accuracy in reproducing observed short term trajectories matches with the parameterized model that produces the best average long-term metrics. Our results show that such an agreement between parameterization using long and short simulation analysis exists if also considering other factors such as the sensitivity in accuracy with relative to changes in structural connectivity. Therefore, we conclude that there is evidence that by solving for initial conditions, BNMs can be simulated in a meaningful way when comparing against measured data trajectories, although future studies are necessary to establish how BNM activity relate to behavioral variables or to faster neural processes during this time period.

scholarly journals Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

2020 ◽  
Author(s):  
Paul Triebkorn ◽  
Joelle Zimmermann ◽  
Leon Stefanovski ◽  
Dipanjan Roy ◽  
Ana Solodkin ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Network ◽  
Model Parameters ◽  
Network Modelling ◽  
Neural Populations ◽  
Global Coupling ◽  
The Brain

AbstractUsing The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters: conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain.Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.

scholarly journals Network Diffusion Modeling Explains Longitudinal Tau PET Data

2020 ◽  
Vol 14 ◽  
Author(s):  
Amelie Schäfer ◽  
Elizabeth C. Mormino ◽  
Ellen Kuhl
Keyword(s):  
Positron Emission Tomography ◽  
Clinical Symptoms ◽  
Dynamic Network ◽  
Network Models ◽  
Brain Regions ◽  
Brain Network ◽  
Emission Tomography ◽  
Model Parameters ◽  
Positron Emission ◽  
Network Diffusion

Alzheimer's disease is associated with the cerebral accumulation of neurofibrillary tangles of hyperphosphorylated tau protein. The progressive occurrence of tau aggregates in different brain regions is closely related to neurodegeneration and cognitive impairment. However, our current understanding of tau propagation relies almost exclusively on postmortem histopathology, and the precise propagation dynamics of misfolded tau in the living brain remain poorly understood. Here we combine longitudinal positron emission tomography and dynamic network modeling to test the hypothesis that misfolded tau propagates preferably along neuronal connections. We follow 46 subjects for three or four annual positron emission tomography scans and compare their pathological tau profiles against brain network models of intracellular and extracellular spreading. For each subject, we identify a personalized set of model parameters that characterizes the individual progression of pathological tau. Across all subjects, the mean protein production rate was 0.21 ± 0.15 and the intracellular diffusion coefficient was 0.34 ± 0.43. Our network diffusion model can serve as a tool to detect non-clinical symptoms at an earlier stage and make informed predictions about the timeline of neurodegeneration on an individual personalized basis.

scholarly journals LGG-32. CLINICAL OUTCOME OF PEDIATRIC GLIOMAS IN SINGLE INSTITUTION

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii372-iii372
Author(s):  
Hiroyuki Uchida ◽  
Nayuta Higa ◽  
Hajime Yonezawa ◽  
Tatsuki Oyoshi ◽  
Koji Yoshimoto
Keyword(s):  
Brain Tumor ◽  
Tumor Resection ◽  
Treatment Strategies ◽  
Pediatric Brain Tumor ◽  
University Hospital ◽  
Subependymal Giant Cell Astrocytoma ◽  
Histopathological Diagnosis ◽  
Tumor Patients ◽  
Who Grade ◽  
Pediatric Brain

Abstract Gliomas in children are rarer than in adult, then treatment strategies might vary from facility to facility. We report clinical features and outcome of pediatric glioma in our institution. Twenty-nine patients diagnosed with glioma, exclude ependymoma, 14 boys and 15 girls, among 98 pediatric brain tumor patients treated at Kagoshima University Hospital since 2006 were reviewed histopathology, extent of resection, adjuvant therapy and outcome, etc. Mean age at surgery was 10.4 (S.D. 5.6) years. Median follow-up period was 19.1 months. Histopathological diagnosis comprised 8 pilocytic astrocytoma, 3 ganglioglioma, 2 subependymal giant cell astrocytoma, 5 WHO grade Ⅱ astrocytoma, 8 glioblastoma, and desmoplastic infantile astrocytoma, anaplastic astrocytoma and astroblastoma were one case each. Tumor resection was performed in 24 cases, and 5 cases underwent biopsy. Chemotherapy was performed in 15 cases and irradiation was performed in 9 cases. Out of 5 WHO grade Ⅱ astrocytoma cases, 2 cases underwent biopsy following chemotherapy, 1 case underwent biopsy only and other 1 case underwent total resection. The four cases show long survival ranged from 71 to 136 months without irradiation. All of eight glioblastoma cases show poor prognosis ranged from 8.6 to 26.7 months regardless of chemo-radiotherapy. In management for pediatric brain tumor patients, irradiation is often laid over until recurrence. In WHO grade Ⅱ astrocytoma, the treatment strategy might be reasonable using appropriate chemotherapy even though biopsy cases.

scholarly journals Impact of Anticipated Awake Surgery on Psychooncological Distress in Brain Tumor Patients

2022 ◽  
Vol 11 ◽  
Author(s):  
Franziska Staub-Bartelt ◽  
Oliver Radtke ◽  
Daniel Hänggi ◽  
Michael Sabel ◽  
Marion Rapp
Keyword(s):  
Brain Tumor ◽  
Significant Influence ◽  
Tumor Resection ◽  
Statistical Analyses ◽  
Distress Thermometer ◽  
Awake Surgery ◽  
Anxiety And Depression ◽  
Cerebral Lesions ◽  
Tumor Patients ◽  
Significant Difference

BackgroundBrain tumor patients present high rates of distress, anxiety, and depression, in particular perioperatively. For resection of eloquent located cerebral lesions, awake surgery is the gold standard surgical method for the preservation of speech and motor function, which might be accompanied by increased psychological distress. The aim of the present study was to analyze if patients who are undergoing awake craniotomy suffer from increased prevalence or higher scores in distress, anxiety, or depression.MethodsPatients, who were electively admitted for brain tumor surgery at our neurooncological department, were perioperatively screened regarding distress, anxiety, and quality of life using three established self-assessment instruments (Hospital Anxiety and Depression Scale, distress thermometer, and European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30-BN20). Screening results were correlated regarding operation technique (awake vs. general anesthesia). Retrospective statistical analyses for nominal variables were conducted using chi-square test. Metric variables were analyzed using the Kruskal–Wallis test, the Mann–Whitney U-test, and independent-samples t-tests.ResultsData from 54 patients (26 male and 28 female) aged 29 to 82 years were available for statistical analyses. A total of 37 patients received primary resection and 17 recurrent tumor resection. Awake surgery was performed in 35 patients. There was no significant difference in awake versus non-awake surgery patients regarding prevalence (of distress (p = 0.465), anxiety (p = 0.223), or depression (p = 0.882). Furthermore, awake surgery had no significant influence on distress thermometer score (p = 0.668), anxiety score (p = 0.682), or depression score (p = 0.630) as well as future uncertainty (p = 0.436) or global health status (p = 0.943). Additionally, analyses revealed that primary or recurrent surgery also did not have any significant influence on the prevalence or scoring of the evaluated items.ConclusionAnalyses of our cohort’s data suggest that planned awake surgery might not have a negative impact on patients concerning the prevalence and severity of manifestation of distress, anxiety, or depression in psychooncological screening. Patients undergoing recurrent surgery tend to demonstrate increased distress, although results were not significant.

scholarly journals Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

eNeuro ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. ENEURO.0083-18.2018 ◽  
Author(s):  
Hannelore Aerts ◽  
Michael Schirner ◽  
Ben Jeurissen ◽  
Dirk Van Roost ◽  
Eric Achten ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Dynamics ◽  
Tumor Patients

scholarly journals Modeling brain activity in brain tumor patients and healthy controls: a proof-of-concept study.

2015 ◽  
Vol 9 ◽  
Author(s):  
Aerts Hannelore ◽  
Van Roost Dirk ◽  
Caeyenberghs Karen ◽  
Fias Wim ◽  
Achten Eric ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Activity ◽  
Healthy Controls ◽  
Proof Of Concept ◽  
Tumor Patients ◽  
Concept Study
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