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Research Article: New Research | Novel Tools and Methods

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain 1

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Hannelore Aerts , Michael Schirner

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, Ben Jeurissen , Dirk Van Roost , Eric Achten , Petra Ritter

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and Daniele Marinazzo 1

Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, 9000, Belgium 2

Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin, Germany 3

Department of Neurology, Berlin Institute of Health, 10117, Berlin, Germany

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Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, 10117, Germany 5

Imec - Vision Lab, Department of Physics, University of Antwerp, Antwerp, 2610, Belgium

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Department of Neurosurgery, Ghent University Hospital, Ghent, 9000, Belgium

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Department of Neuroradiology, Ghent University Hospital, Ghent, 9000, Belgium

DOI: 10.1523/ENEURO.0083-18.2018 Received: 26 February 2018 Revised: 4 May 2018 Accepted: 8 May 2018 Published: 28 May 2018

Author contributions: DM and HA designed research; HA, DVR and EA acquired data; HA, MS, and BJ analyzed data; HA, MS and DM wrote the paper; PR provided analytic tools. Funding: Ghent University 01MR0210 01J10715

Funding: Belgian Science Policy P7-CEREBNET

The authors declare no competing financial interests. This project was supported by the Special Research Funds (BOF) of the University of Ghent (01MR0210 and 01J10715), as well as Grant P7/11 from the Interuniversity Attraction Poles Program of the Belgian Federal Government. Correspondence should be addressed to Daniele Marinazzo; Faculty of Psychology and Educational Sciences, Department of Data Analysis, Henri Dunantlaan 2, 9000 Gent, Belgium. E-mail: [email protected] Cite as: eNeuro 2018; 10.1523/ENEURO.0083-18.2018 Alerts: Sign up at eneuro.org/alerts to receive customized email alerts when the fully formatted version of this article is published.

Accepted manuscripts are peer-reviewed but have not been through the copyediting, formatting, or proofreading process. Copyright © 2018 Aerts et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

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Modeling brain dynamics in brain tumor patients using The Virtual Brain

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The virtual brain tumor patient

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Hannelore Aerts: Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, 9000 Ghent, Belgium Michael Schirner: Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, 10117 Berlin, Germany & Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany Ben Jeurissen: imec - Vision Lab, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium Dirk Van Roost: Department of Neurosurgery, Ghent University Hospital, 9000 Ghent, Belgium Eric Achten: Department of Neuroradiology, Ghent University Hospital, 9000 Ghent, Belgium Petra Ritter: Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, 10117 Berlin, Germany & Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, 10117 Berlin, Germany Daniele Marinazzo: Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, 9000 Ghent, Belgium Author contributions: DM and HA designed research; HA, DVR and EA acquired data; HA, MS, and BJ analyzed data; HA, MS and DM wrote the paper; PR provided analytic tools. Correspondence should be addressed to Prof. Dr. Daniele Marinazzo; Faculty of Psychology and Educational Sciences, Department of Data Analysis, Henri Dunantlaan 2, 9000 Gent, Belgium; [email protected] Number of Figures: 6 Number of Tables: 1 Number of Multimedia: 1

Number of words for Abstract: 238 Number of words for Significance Statement: 118 Number of words for Introduction: 707 Number of words for Discussion: 1762

Acknowledgements: We would like to thank Stephanie Bogaert and Pieter Vandemaele for their technical assistance in setting up the scanning protocols. Further, we are grateful to Stephanie Bogaert, Robby De Pauw, Iris Coppieters, Jeroen Kregel, Mireille Augustijn, Helena Verhelst, and Hannes Almgren for their help in acquiring the data, and to Kelly Berckmans for her help in creating the tumor masks. The authors declare no competing financial interests. This project was supported by the Special Research Funds (BOF) of the University of Ghent (01MR0210 and 01J10715), as well as Grant P7/11 from the Interuniversity Attraction Poles Program of the Belgian Federal Government.

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Abstract

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Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the

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lesion to spare during surgery. To this end, non-invasive neuroimaging techniques such as functional

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MRI and diffusion weighted imaging fiber tracking are currently employed. However, taking into

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account this information is often still insufficient, as the complex non-linear dynamics of the brain

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impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain

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network modeling carries the potential to bridge this gap by integrating neuroimaging data with

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biophysically based models to predict collective brain dynamics.

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As a first step in this direction, an appropriate computational model has to be selected, after which

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suitable model parameter values have to be determined. To this end, we simulated large-scale brain

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dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual

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Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced

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Wong-Wang model were individually optimized and compared between brain tumor patients and

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control subjects. In addition, the relationship between model parameters and structural network

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topology and cognitive performance was assessed.

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Results showed (1) significantly improved prediction accuracy of individual functional connectivity

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when using individually optimized model parameters; (2) local model parameters can differentiate

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between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy

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brain; and (3) interesting associations between individually optimized model parameters and structural

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network topology and cognitive performance.

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Significance statement

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Individualized medicine is increasingly put forward as an important means to advance medical care. In

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this regard, the neuroinformatics platform “The Virtual Brain” holds great promise, given its direct

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focus on simulation of subject-specific brain activity. Reliable prediction of patient-specific large-

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scale brain dynamics would open up the possibility to virtually lesion structural connectomes, making 2

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computational models unique predictive tools to investigate the impact of diverse structural

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connectivity alterations on brain functioning, including those purposefully induced by surgery. Results

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of this study establish the basis for this purpose, by demonstrating individual specificity of biophysical

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model parameters, differences in local model parameters dependent on distance from a tumor, and

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associations between model parameters and structural network topology and cognitive performance.

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Introduction

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Presurgical planning for brain tumor resection aims at delineating eloquent cortical areas and white

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matter pathways in the vicinity of the lesion to spare during surgery, in order to preserve essential

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brain functions. In brain tumor patients, localization of brain function often cannot be inferred from

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anatomical landmarks alone, since mass effects can distort normal topography or disease processes

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such as brain shift or plasticity can induce relocation of functions (Desmurget, Bonnetblanc, &

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Duffau, 2007). Therefore, non-invasive neuroimaging techniques such as functional MRI (fMRI) and

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diffusion weighted imaging (DWI) fiber tracking are currently employed to pre-operatively determine

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patient-specific important cortical areas and white matter tracts close to the lesion, to spare during

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surgery (Sunaert, 2006; Tieleman, Deblaere, Van Roost, Van Damme, & Achten, 2009).

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However, taking into account this pre-operative neuroimaging information is often still insufficient, as

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the complex non-linear dynamics of the brain impede straightforward prediction of functional outcome

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after surgical intervention. For example, fMRI cannot differentiate cortical areas that are essential for a

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particular function and which should be surgically preserved, from expendable areas which merely

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correlate with but are not essential for functionality (Duffau et al., 2003; Tharin & Golby, 2007).

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Large-scale brain network modeling carries the potential to bridge this gap by integrating

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neuroimaging data with biophysically based models to predict collective brain dynamics, from which

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functionality could be inferred. A future application of this framework could then entail the pre-

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surgical virtual exploration of the effects of different neurosurgical approaches based on individual

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patient data, to identify an optimal surgical strategy (Arsiwalla et al., 2015; Proix, Bartolomei, Guye,

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& Jirsa, 2017). 3

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As a first step in this direction, an appropriate computational model has to be selected, after which

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suitable model parameter values should be determined that lead to plausible brain dynamics. Different

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approaches can be used to simulate activity of nodes (i.e., brain areas) in large-scale brain network

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models varying from detailed spiking neuron models (Deco & Jirsa, 2012), over so-called neural mass

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or mean-field models that describe the collective activity of cell populations (Deco & Jirsa, 2012),

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down to abstract models such as the Ising model (Deco, Senden, & Jirsa, 2012; Haimovici,

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Tagliazucchi, Balenzuela, & Chialvo, 2013; Marinazzo et al., 2014; we refer the interested reader to

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Deco, Jirsa, Robinson, Breakspear, & Friston (2008) for an in-depth explanation of the general

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principle behind these approaches and the mechanism allowing a wide class of models to bridge

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temporal and spatial scales). In a clinical context, biologically interpretable dynamical models are of

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special interest, as their activity and parameters can allow inference of internal states and processes of

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the large-scale model, which cannot be measured with non-invasive neuroimaging techniques (Falcon

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et al., 2015; Schirner, McIntosh, Jirsa, Deco, & Ritter, 2018). Hence, they may provide an entry point

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for understanding brain disorders at a causal mechanistic level, which might lead to novel, more

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effective therapeutic interventions (Deco & Kringelbach, 2014; Proix et al., 2017). The first proof-of-

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concept studies have indicated that global and local biophysical model parameters derived from large-

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scale brain network modeling can be related to motor recovery after stroke (Falcon et al., 2015) and

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the generation and progression of epileptic seizures (Bernard & Jirsa, 2017; Jirsa, Stacey, Quilichini,

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Ivanov, & Bernard, 2014), demonstrating the potential clinical utility of computational modeling.

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In this study, we simulated large-scale brain dynamics in brain tumor patients and control subjects

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using The Virtual Brain (TVB) (Sanz Leon et al., 2013); an open-source neuroinformatics platform

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that enables the construction, simulation and analysis of brain network models. Local dynamics in

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each brain region were simulated using the Reduced Wong-Wang model (Deco et al., 2014), one of

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the more refined and biologically plausible models in the repertoire of TVB. Subsequently, local

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dynamics of all brain regions were coupled according to each subject’s empirical structural

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connectome to generate personalized virtual brain models.

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The objectives of this study then were (1) to evaluate the importance of constructing personalized

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virtual brain models using individually optimized model parameters and subject-specific structural

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connectomes; (2) to determine whether the individually optimized model parameter values differ

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between brain tumor patients and healthy controls; and (3) to elucidate the relationship between these

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model parameters on the one hand, and cognitive performance and structural network properties on the

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other hand.

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Materials and Methods

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Participants

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In this study we included patients who were diagnosed with either a glioma, developing from glial

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cells, or a meningioma, developing in the meninges (Fisher, Schwartzbaum, Wrensch, & Wiemels,

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2007). Both types of tumors can be described by their malignancy, based on the World Health

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Organization grading system. According to this system, grade I tumors are least malignant, whereas

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grade III (for meningioma) or IV (for glioma) tumors are most malignant. Hereby, malignancy relates

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to the speed with which the disease evolves, the extent to which the tumor infiltrates healthy brain

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tissue, and chances of recurrence or progression to higher grades of malignancy.

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Patients were recruited from a location which will be identified if the article is published between May

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2015 and October 2017. Patients were eligible if they (1) were at least 18 years old, (2) had a

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supratentorial meningioma (WHO grade I or II) or glioma (WHO grade II or III) brain tumor, (3) were

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able to complete neuropsychological testing, and (4) were medically approved to undergo MRI

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investigation. Partners were also asked to participate in the study to constitute a group of control

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subjects that suffer from comparable emotional distress as the patients.

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We collected data from 11 glioma patients (mean age 47.5y, SD = 11.3; 4 females), 14 meningioma

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patients (mean age 60.4y, SD = 12.3; 11 females), and 11 healthy partners (mean age 58.6y, SD =

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10.3; 4 females). Patient characteristics are described in Table 1. Testing took place at a location

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which will be identified if the article is published on the day before patients’ surgery. All participants 5

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received detailed study information and gave written informed consent prior to study enrollment. This

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research was approved by the Ethics Committee of a location which will be identified if the article is

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published. All data used for this study are publicly available at OpenNeuro under the name that will be

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identified if the article is published.

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MRI data acquisition

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From all participants, three types of MRI scans were obtained using a Siemens 3T Magnetom Trio

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MRI scanner with a 32-channel head coil. First, T1-MPRAGE anatomical images were acquired (160

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slices, TR = 1750 ms, TE = 4.18 ms, field of view = 256 mm, flip angle = 9°, voxel size 1 x 1 x 1 mm,

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TA = 4:05 min). Next, resting-state functional echo-planar imaging (EPI) data were obtained in an

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interleaved order (42 slices, TR = 2100ms, TE = 27 ms, field of view = 192 mm, flip angle = 90°,

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voxel size 3 x 3 x 3 mm, TA = 6:24 min). After the first 4 control subjects, 5 meningioma patients and

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2 glioma patients were scanned, the fMRI protocol was accidentally changed to a TR of 2400 ms

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resulting in a TA of 7:19 minutes. This has been taken care of in subsequent analyses by inclusion of

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an additional covariate. During the fMRI scan, participants were instructed to keep their eyes closed

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and not fall asleep. Finally, a multi-shell high-angular resolution diffusion-weighted MRI (DWI) scan

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was acquired (60 slices, TR = 8700 ms, TE = 110 ms, field of view = 240 mm, 101 diffusion

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directions, b-values of 0, 700, 1200, 2800 s/mm², voxel size 2.5 x 2.5 x 2.5 mm, TA = 15:14 min). In

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addition, two DWI b = 0 s/mm² images were collected with reversed phase-encoding blips for the

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purpose of correcting susceptibility induced distortions (Andersson, Skare, & Ashburner, 2003).

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MRI data preprocessing

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MRI data were preprocessed and subject-specific structural and functional connectivity matrices were

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extracted using a modified version of the TVB preprocessing pipeline (Schirner, Rothmeier, Jirsa,

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McIntosh, & Ritter, 2015). All steps are outlined below.

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Preprocessing of T1-weighted anatomical MRI data

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In

the

first

step,

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(http://surfer.nmr.mgh.harvard.edu) to obtain a subject-specific parcellation of each subject’s brain

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into 68 cortical regions (34 per hemisphere). T1-weighted data of all control subjects were subjected to

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the default recon-all processing pipeline, which includes the following steps: intensity normalization,

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skull stripping, removal of non-brain tissue, brain mask generation, cortical reconstruction,

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segmentation of subcortical white matter and deep gray matter volumetric structures, cortical

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tessellation of the gray matter/white matter and gray matter/pial boundary, and construction of a

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probabilistic atlas based cortical parcellation into 68 regions according to gyral and sulcal structure

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(Desikan et al., 2006; Fischl et al., 2004).

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As meningioma tumors generally exert pressure on the brain without infiltrating, our aim was to

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segment out the meningioma tumor before cortical reconstruction. However, visual inspection of the

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results showed this was done automatically by the recon-all processing pipeline of FreeSurfer in all

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but two meningioma patients. In the remaining two meningioma patients, who had very large lesions,

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manual edits were made.

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Glioma tumors, in contrast, generally do infiltrate the brain. To obtain a whole-brain parcellation

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scheme for these patients, two additional steps were carried out. First, glioma tumors were segmented

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using

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(https://github.com/CyclotronResearchCentre/USwithLesion, unpublished observations). Second, the

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Normalisation tool of the BCBtoolkit (Foulon et al., 2018) was used to produce an enantiomorphic

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filling of the affected area by symmetrically filling up the lesion mask with healthy tissue of the

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contralateral hemisphere (Nachev, Coulthard, Jäger, Kennard, & Husain, 2008). These “normalized”

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anatomical MRI data were then processed using the standard recon-all FreeSurfer processing pipeline.

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Resulting parcellations were visually inspected and manually corrected in two glioma patients.

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Functional MRI preprocessing

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Functional MRI (fMRI) data processing was carried out using FEAT (FMRI Expert Analysis Tool,

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version 6.00), part of FSL (FMRIB’s Software Library, http://www.fmrib.ox.ac.uk/fsl). Specifically,

the

high-resolution

Unified

anatomical

images

Segmentation

were

with

processed

using

FreeSurfer

Lesion

toolbox

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the following operations were applied: motion correction using MCFLIRT (Jenkinson, Bannister,

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Brady, & Smith, 2002), slice-timing correction, non-brain removal using BET (S. M. Smith, 2002),

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grand-mean intensity normalization of the entire 4D dataset by a single multiplicative factor, and high-

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pass temporal filtering (100-second high-pass filter). Next, the FreeSurfer cortical parcellation

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obtained in the previous step was mapped to the subject’s functional space. To this end, fMRI images

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were linearly registered to the subject’s high-resolution T1-weighted images using the epi_reg

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function of FSL FLIRT (Jenkinson et al., 2002; Jenkinson & Smith, 2001), after which the inverse of

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this transformation matrix was applied to transform the FreeSurfer parcellation scheme to the subject’s

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functional space. Average BOLD signal time series for each region were then generated by computing

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the spatial mean for all voxel time-series of each region. Lastly, functional connectivity (FC) matrices

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were constructed by calculating the Fisher z-transformed Pearson correlation coefficient between all

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pairs of region-wise aggregated BOLD time series.

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Diffusion-weighted MRI preprocessing

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Since all analyses of this study depend on the quality of the structural connectivity matrices, a state-of-

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the art pipeline was constructed for the preprocessing of DWI data and consecutive network

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construction, using a combination of FSL (FMRIB’s Software Library, http://www.fmrib.ox.ac.uk/fsl;

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version 5.0.9) and MRtrix3 (http://www.mrtrix.org; version 0.3.RC2). First, raw diffusion weighted

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MRI images were corrected for several artifacts. In particular, DWI images were denoised (MRtrix

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dwidenoise; Veraart et al., 2016), corrected for Gibbs ringing artifacts (MRtrix mrdegibbs; Kellner,

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Dhital, & Reisert, 2016), for motion and eddy currents (FSL eddy; Andersson & Sotiropoulos, 2016),

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for susceptibility induced distortions (FSL topup; Andersson, Skare, & Ashburner, 2003), and for bias

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field inhomogeneities (FSL FAST; Zhang, Brady, & Smith, 2001). Next, subjects’ high-resolution

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anatomical images were linearly registered to diffusion space with the epi_reg function of FSL FLIRT

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(Jenkinson et al., 2002; Jenkinson & Smith, 2001), and subsequently segmented into gray matter,

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white matter and cerebrospinal fluid (FSL FAST; Zhang, Brady, & Smith, 2001).

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Then, DWI images were intensity normalized across subjects and group average response functions

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were calculated. Specifically, response functions for each subject were estimated per b-value shell (b =

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0, 700, 1200 & 2800 s/mm²) and per tissue type (white matter, gray matter, cerebrospinal fluid) using

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the MRtrix3 script dwi2response dhollander (Dhollander, Raffelt, & Connelly, 2016). A scaling factor

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per subject was calculated by which the individual response functions could be multiplied in order to

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obtain the average response function across all subjects. DWI images were then initially normalized by

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dividing subjects’ DWI images by their corresponding scaling factor. After that, response functions

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per b-value shell and tissue type were recalculated for every subject and averaged across all subjects.

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This set of group average response functions were subsequently utilized in multi-shell multi-tissue

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constrained spherical deconvolution to estimate the fiber orientation distributions (MRtrix3

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msdwi2fod; Jeurissen, Tournier, Dhollander, Connelly, & Sijbers, 2014). In addition, tissue

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components from multi-tissue CSD were once more intensity normalized using MRtrix3 mtnormalise.

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Next, anatomically constrained probabilistic whole-brain fiber tracking (ACT) was performed using

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dynamic seeding generating 30 million streamlines per subject (MRtrix3 tckgen; R. E. Smith,

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Tournier, Calamante, & Connelly, 2015, 2012). Afterwards, spherical-deconvolution informed

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filtering of tractograms (SIFT) was applied to selectively filter out streamlines from the tractogram in

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order to improve the fit between the streamline reconstruction and the underlying diffusion images,

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retaining 7.5 million streamlines per subject (MRtrix3 sift; R. E. Smith, Tournier, Calamante, &

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Connelly, 2013). A structural connectivity (SC) matrix was then constructed by transforming the

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individual’s FreeSurfer parcellation scheme to diffusion space, and calculating the number of

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estimated tracts between any two brain regions (MRtrix3 tck2connectome). In addition, a distance

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matrix was constructed by calculating the average length of all streamlines connecting any two nodes

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(MRtrix3 tck2connectome). By using a proper high-order model and taking into account the full fiber

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orientation distribution function (through CSD), by taking into account the presence of non-white

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matter tissue (through multi-tissue CSD), by applying realistic individual anatomical priors (through

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ACT), and by ensuring fidelity of the tractograms to the data (through SIFT), it has been shown that

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the biological accuracy of tractograms can be vastly increased compared to those obtained with

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unfiltered unconstrained diffusion tensor tracking (Jeurissen, Descoteaux, Mori, & Leemans, 2017).

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Lastly, we thresholded and normalized the resulting SC matrices. Thresholding was carried out to

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minimize false-positive streamlines. Using an absolute threshold, setting to zero all connection

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weights smaller than 5, yielded a decaying degree distribution while ensuring all subjects’ network

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remained fully connected. This approach is similar to the one adopted by Collin, Kahn, De Reus,

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Cahn, & Van den Heuvel (2014). Normalization was performed by dividing all SC weights by a

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constant scalar across subjects (75.000 in our case: 7.5 million streamlines generated per subject / 100)

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to ensure all SC weights varied between 0 and 1, which was required for computational modeling in

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TVB.

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Computational modeling

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TVB (Sanz Leon et al., 2013) was utilized to simulate large-scale brain dynamics (see Figure 1 for

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visual overview of the workflow). In particular, local dynamics in each of the 68 cortical brain regions

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were simulated using the Reduced Wong-Wang model (Deco et al., 2014), implemented as highly

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optimized C code that allows for efficient parameter exploration (Schirner et al., 2018). In particular,

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each cortical region of the Desikan-Killiany brain atlas (Desikan et al., 2006) was modeled as a local

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network composed of interconnected excitatory and inhibitory neural mass models coupled by

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excitatory (NMDA) and inhibitory (GABA) connections (we refer the interested reader to Deco et al.

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(2014) for a more detailed description of this model). Excitatory neural mass models of all 68 brain

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regions were subsequently coupled according to the individual subject’s empirical structural

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connectome and weighted by a global scaling factor, to simulate large-scale brain dynamics.

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In order to optimize the fit between empirical and simulated functional connectivity, subject-specific

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parameter space explorations were conducted in which the global scaling parameter (G) was

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optimized. This parameter rescales each subject’s structural connectivity, which is given by relative

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values, to yield absolute interaction strengths. In particular, values of the global scaling parameter

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were varied from 0.01 to 3 in steps of 0.015. For each value of the global scaling parameter, individual 10

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resting-state blood-oxygen-level-dependent (BOLD) time series were generated with the same

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duration and sampling rate as the subject’s empirical resting-state fMRI acquisition, using the Balloon-

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Windkessel hemodynamic model (Friston, Mechelli, Turner, & Price, 2000). From these simulated

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time series, a functional connectivity matrix was computed by calculating the Fisher z-transformed

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Pearson correlation coefficient between all pairs of simulated BOLD time series. The value of the

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global scaling parameter that maximized the link-wise Pearson correlation between each individual's

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simulated and empirical functional connectivity matrix was then selected for further analyses.

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In addition, the feedback inhibition control parameters (Ji) – controlling the strengths of connections

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from inhibitory to excitatory mass models within each large-scale region i – were tuned in each

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iteration of the parameter space exploration, to clamp the average firing rate at ~3 Hz for each

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excitatory mass model. 3 Hz was chosen as attractor value, as it is the “intrinsic frequency” of an

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isolated neural mass model according to derivations from a spiking network (Deco et al., 2014). When

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multiple mass models are coupled, as in a virtual brain model, the firing rates increase due to the input

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from the global network. To get the firing rates back to a physiologically plausible average rate of 3

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Hz, the feedback inhibition control parameter Ji is increased until the population has this average firing

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rate. Thus, 3 Hz is the attractor value for each population, around which there is an ongoing

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fluctuation during the entire simulation time. Previous work has shown that tuning of these local

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model parameters significantly improves prediction of empirical functional connectivity (Deco et al.,

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2014). Note that only the global coupling scaling factor was used to maximize the fit with empirical

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functional connectivity, while the sole fitting target of the local inhibitory connection strengths were

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the average firing rates of excitatory mass models.

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Given the skewed distribution of local inhibitory connection strengths, median Ji values were

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computed per subject for further analyses. First, the median across the entire brain was computed

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(Jbrain). Second, median Ji was calculated in patients across a subset of tumor regions (Jtumor) to

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investigate possible local alterations in biophysical model parameters in the direct vicinity of the

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lesion. In glioma patients, tumor regions were defined as those cortical areas of the individual

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FreeSurfer parcellation that showed at least partial (i.e. minimum 1 voxel) overlap with the tumor 11

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mask. In meningioma patients, tumor regions consisted of regions that were (at least partially)

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displaced because of the tumor’s mass effect. To estimate which regions were displaced by the

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meningioma, patients’ anatomical images were transformed to MNI space (using FSL FLIRT with 12

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DOF), and this transformation was applied to their tumor mask. Then, the overlap between subjects’

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tumor mask in MNI space and the fsaverage Desikan-Killiany atlas (Desikan et al., 2006) in MNI

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space was calculated. Parcels that showed at least 1 voxel overlap with the tumor mask were denoted

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tumor nodes. Table 1-1 gives a graphical overview of the extent to which tumor nodes were affected in

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meningioma and glioma patients. Lastly, we also computed median local inhibitory connection

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strengths across healthy regions (Jnon-brain) to investigate possible distant lesion effects. In control

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subjects, this was the same as the whole-brain median Ji, whereas in tumor patients, the median across

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all non-tumor regions was calculated.

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Graph theory analysis

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Structural network topology was assessed with various graph theory metrics of integration (global

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efficiency, communicability), segregation (clustering coefficient, local efficiency, modularity), and

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centrality (degree, strength, betweenness centrality, participation coefficient), as well as graph density,

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using the Brain Connectivity Toolbox (Rubinov & Sporns, 2010). After inspecting the relationships

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among these graph theory metrics by linear correlation and principal component analysis, three

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distinct graph theory metrics were retained (|r|