Bridging structure and function with brain network modeling

High-throughput neuroimaging technology enables rapid acquisition of vast amounts of structural and functional data on multiple spatial and temporal scales. While novel methods to extract information from these data are continuously developed, there is no principled approach for the systematic integration of distinct experimental results into a common theoretical framework, yet. The central result of this dissertation is a biophysically-based framework for brain network modeling that links structural and functional data across scales and modalities and integrates them with dynamical systems theory. Specifically, the publications in this thesis i. introduce an automated pipeline that extracts structural and functional information from multimodal imaging data to construct and constrain brain models, ii. link whole-brain models with empirical EEG-fMRI (simultaneous electroencephalography and functional magnetic resonance imaging) data to integrate neural signals with simulated activity, iii. propose a framework for reverse-engineering neurophysiological dynamics and mechanisms underlying commonly observed features of neural activity, iv. document a software module that makes users acquainted with theory and practice of brain modeling, v. associate aging with structural and functional connectivity and vi. examine how parcellation size and short-range connectivity affect model dynamics. Taken together, these results form a novel approach that enables reverse-engineering of neurophysiological processes and mechanisms on the basis of biophysically-based brain models.Zusammenfassung Hochdurchsatzverfahren zur neuronalen Bildgebung ermöglichen die schnelle Erfassung großer Mengen an strukturellen und funktionellen Daten über verschiedenen räumlichen und zeitlichen Skalen. Obwohl ständig neue Methoden zur Verarbeitung der in diesen Daten enthaltenen Informationen entwickelt werden gibt es bisher kein systematisches Verfahren um experimentelle Ergebnisse in einem gemeinsamen theoretischen Rahmenwerk zu integrieren und zu verknüpfen. Das Hauptergebnis dieser Dissertation ist ein biophysikalisch basiertes Gehirn- Netzwerkmodell das strukturelle und funktionelle Daten über verschiedene Skalen und Modalitäten hinweg verknüpft und mit dynamischer Systemtheorie vereint. Die hier zusammengefassten Publikationen i. stellen eine automatische Software-Pipeline vor die strukturelle und funktionelle Informationen aus multimodalen Bilddaten extrahiert um Gehirnmodelle zu konstruieren und zu parametrisieren, ii. verknüpfen Ganzhi rnmodel le mi t empi r i schen EEG- fMRT ( s imul tane Elektroenzephalographie und funktionelle Magnetresonanztomographie) Daten um neuronale Signale mit simulierter Aktivität zu integrieren, iii. schlagen ein Rahmenwerk vor um neurophysiologische Dynamiken und Mechanismen die häufig beobachteten Eigenschaften neuronaler Aktivität zu Grunde liegen zu rekonstruieren, iv. dokumentieren ein Software-Modul das Benutzer mit Theorie und Praxis der Gehirnmodellierung vertraut macht, v. assoziieren Alterungsprozesse mit struktureller und funktioneller Konnektivität und vi. untersuchen wie Gehirn-Parzellierung und lokale Konnektivität die Modelldynamik beeinflussen. Zusammengenommen ergibt sich ein neuartiges Verfahren das die Rekonstruktion neurophysiologischer Prozesse und Mechanismen ermöglicht und mit dessen Hilfe neuronale Aktivität auf verschiedenen räumlichen und zeitlichen Skalen anhand biophysikalisch basierter Modelle vorhersagt werden kann

Modeling brain dynamics in brain tumor patients using the virtual brain

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive 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 nonlinear 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 that 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

An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data

AbstractLarge amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface

A macaque connectome for large-scale network simulations in TheVirtualBrain

© 2019, The Author(s). Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data

Nine years of online mentoring for secondary school girls in STEM: an empirical comparison of three mentoring formats

Online mentoring can be useful for supporting girls in science, technology, engineering, and mathematics (STEM). Yet, little is known about the differential effects of various online mentoring formats. We examine the general and relative effectiveness of three online mentoring formats, one‐on‐one mentoring, many‐to‐many group mentoring, and a hybrid form of the two. All three formats were implemented in different years in the Germany‐wide online‐only mentoring program, CyberMentor, whose platform enables communication and networking between up to 800 girls (in grades 5–13) and 800 women (STEM professionals) each year. We combined longitudinal mentee data for all first‐year participants (N = 4017 girls, Mage = 14.15 years) from 9 consecutive mentoring years to evaluate and compare the three mentoring formats. Overall, all formats effected comparable increases in mentees’ STEM activities and certainty about career plans. However, mentees’ communication behavior and networking behavior on the mentoring platform differed between the three formats. Mentees in the hybrid mentoring format showed the most extensive STEM‐related communication and networking on the platform. We also analyzed the explanatory contributions of STEM‐related communication and networking on interindividual differences in the developmental trajectories of mentees’ STEM activities, elective intentions in STEM, and certainty about career plans, for each format separately

New Function for an Old Enzyme: NEP Deficient Mice Develop Late-Onset Obesity

BACKGROUND: According to the World Health Organization (WHO) there is a pandemic of obesity with approximately 300 million people being obese. Typically, human obesity has a polygenetic causation. Neutral endopeptidase (NEP), also known as neprilysin, is considered to be one of the key enzymes in the metabolism of many active peptide hormones. METHODOLOGY/PRINCIPAL FINDINGS: An incidental observation in NEP-deficient mice was a late-onset excessive gain in body weight exclusively from a ubiquitous accumulation of fat tissue. In accord with polygenetic human obesity, mice were characterized by deregulation of lipid metabolism, higher blood glucose levels, with impaired glucose tolerance. The key role of NEP in determining body mass was confirmed by the use of the NEP inhibitor candoxatril in wild-type mice that increased body weight due to increased food intake. This is a peripheral and not a central NEP action on the switch for appetite control, since candoxatril cannot cross the blood-brain barrier. Furthermore, we demonstrated that inhibition of NEP in mice with cachexia delayed rapid body weight loss. Thus, lack in NEP activity, genetically or pharmacologically, leads to a gain in body fat. CONCLUSIONS/SIGNIFICANCE: In the present study, we have identified NEP to be a crucial player in the development of obesity. NEP-deficient mice start to become obese under a normocaloric diet in an age of 6-7 months and thus are an ideal model for the typical human late-onset obesity. Therefore, the described obesity model is an ideal tool for research on development, molecular mechanisms, diagnosis, and therapy of the pandemic obesity

Chemical Approaches to Synthetic Drug Delivery Systems for Systemic Applications

Poor water solubility and low bioavailability of active pharmaceutical ingredients (APIs) are major causes of friction in the pharmaceutical industry and represent a formidable hurdle for pharmaceutical drug development. Drug delivery remains the major challenge for the application of new small-molecule drugs as well as biopharmaceuticals. The three challenges for synthetic delivery systems are: (i) controlling drug distribution and clearance in the blood; (ii) solubilizing poorly water-soluble agents, and (iii) selectively targeting specific tissues. Although several polymer-based systems have addressed the first two demands and have been translated into clinical practice, no targeted synthetic drug delivery system has reached the market. This Review is designed to provide a background on the challenges and requirements for the design and translation of new polymer-based delivery systems. This report will focus on chemical approaches to drug delivery for systemic applications

Modeling brain dynamics after tumor resection using The Virtual Brain

Brain 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, simulation of the activity of neural populations connected according to the white matter fibers, producing personalized brain network models, has been introduced as a promising tool for this purpose. The Virtual Brain provides a robust open source framework to implement these models. 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", mimicking patient's actual surgery by removing white matter fibers in the resection mask and simulating again neural activity on this new connectome. 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. Concerning the virtual neurosurgery analyses, use of the pre-surgery model implemented on the virtually resected structural connectome resulted in improved similarity with post-surgical empirical functional connectivity in some patients, but negligible improvement in others. These findings reveal interesting avenues for increasing interactions between computational neuroscience and neuro-oncology, as well as important limitations that warrant further investigation

Brain simulation as a cloud service: The Virtual Brain on EBRAINS

open access articleThe Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic con- version of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collabo- ration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation

Online mentoring for girls in secondary education to increase participation rates of women in STEM: A long‐term follow‐up study on later university major and career choices

AbstractAn important first step in talent development in science, technology, engineering, and mathematics (STEM) is getting individuals excited about STEM. Females, in particular, are underrepresented in many STEM fields. Since girls’ interest in STEM declines in adolescence, interventions should begin in secondary education at the latest. One appropriate intervention is (online) mentoring. Although its short‐term effectiveness has been demonstrated for proximal outcomes during secondary education (e.g., positive changes in elective intentions in STEM), studies of the long‐term effectiveness of STEM mentoring provided during secondary education—especially for real‐life choices of university STEM majors and professions—are lacking. In our study, we examine females’ real‐life decisions about university majors and entering professions made years after they had participated in an online mentoring program (CyberMentor) during secondary education. The program's proximal positive influence on girls’ elective intentions in STEM and certainty about career plans during secondary education had previously been demonstrated in several studies with pre–post‐test waitlist control group designs. Specifically, we compared the choices that former mentees (n = 410) made about university majors and entering professions several years after program participation with (1) females of their age cohort and (2) females of a group of girls comparably interested in STEM who had signed up for the program but then not participated (n = 71). Further, we examined the explanatory contribution to these later career‐path‐relevant, real‐life choices based on (1) mentees’ baseline conditions prior to entering the program (e.g., elective intentions in STEM), (2) successful 1‐year program participation, and (3) multiyear program participation. Findings indicate positive long‐term effects of the program in all areas investigated.</p