Amyloid-based brain simulation of Alzheimer’s disease with The Virtual Brain

Einführung. Unsere Erkenntnisse über die zugrundeliegenden Mechanismen, über Biomarker und mögliche kausale Therapien der Alzheimer-Krankheit sind nach wie vor unzureichend. In dieser Arbeit präsentieren wir ein computergestütztes Multiskalen- Gehirnmodell, welches das mikroskopische Phänomen des veränderten Gleichgewichts zwischen Exzitation und Inhibition mit der makroskopischen Beobachtung der Verlangsamung in der Elektroenzephalographie bei Alzheimer- Krankheit verknüpft. Methoden. Die Neuroinformatik-Plattform The Virtual Brain (TVB; thevirtualbrain.org) bietet die Möglichkeit für standardisierte Simulationen der Dynamik des gesamten Gehirns auf der Basis struktureller Konnektivität. Als neues Konzept verknüpfen wir nun das Protein Amyloid-Beta (Abeta) aus der Positronenemissionstomographie (PET) mit dem Phänomen der Übererregbarkeit bei der Alzheimer-Krankheit. Basierend auf einem standardisierten gesundem Konnektom und individuellen PET-basierten Verteilungen von Abeta virtualisieren wir einzelne Gehirne bei Patienten mit Alzheimer- Krankheit, leichter kognitiver Beeinträchtigung (MCI) und altersangepassten gesunden Kontrollen (HC) unter Verwendung von Daten aus der ADNI-3-Datenbank (http: //adni.lni.usc.edu). Die individuelle Abeta-Belastung wird auf eine regionale Veränderung des Gleichgewichts zwischen Exzitation und Inhibition übertragen, die zu lokaler Übererregung führt. Wir analysieren simulierte Elektroenzephalogramme (EEG) und regionale neuronale Aktivität. Ergebnisse. Das bekannte Phänomen der EEG-Verlangsamung bei Patienten mit Alzheimer-Krankheit konnte in unseren Simulationen reproduziert werden. Wir konnten weiterhin zeigen, dass die Heterogenität der Abeta-Verteilung (mit einigen stark betroffenen Regionen) wichtig ist, um zu einer Verlangsamung des EEGs zu führen. Die beobachteten spektralen Phänomene bei der Alzheimer-Krankheit waren hauptsächlich in den wichtigen Netzwerkknotenpunkten (Hubs) zu beobachten, unabhängig von der räumlichen Lokalisierung von Abeta. Wir präsentieren außerdem eine Strategie der virtuellen Therapie mit Memantin durch Modellierung seines N- Methyl-D-Aspartat (NMDA) -Rezeptor-Antagonismus in TVB. Dieser Ansatz ergab eine mögliche Reversibilität in silico der beobachteten EEG-Verlangsamung in virtuellen AD-Gehirnen. Diskussion. Wir liefern einen Proof-of-Concept mit einem neuartigen mechanistischen virtuellen Gehirnmodell der Alzheimer-Krankheit, das zeigt, wie TVB die Simulation von makroskopischen Phänomenen ermöglicht, die durch mikroskopische Merkmale im menschlichen Gehirn verursacht werden.Introduction. Our knowledge on the underlying mechanisms as well as biomarkers and disease-modifying treatments of Alzheimer’s disease still remains poor. In this work, I present a computational multi-scale brain model which links the micro-scale phenomenon of changed Excitation-Inhibition-balance to macro-scale observation of slowing in electroencephalography in Alzheimer’s disease. Methods. The neuroinformatics platform The Virtual Brain (TVB; thevirtualbrain.org) is a tool for standardized large-scale structural connectivity-based simulations of whole brain dynamics. As a novelty, we connect the protein amyloid beta (Abeta) from positron emission tomography (PET) to the phenomenon of hyperexcitability in Alzheimer’s disease. Based on an averaged healthy connectome and individual PET derived distributions of Abeta, we virtualize individual brains in patients with Alzheimer’s disease, mild cognitive impairment and in age-matched healthy controls using data from the ADNI-3 database (http://adni.lni.usc.edu). The individual Abeta burden is transferred to a regional change in Excitation-Inhibition balance, leading to local hyperexcitation. We analyze simulated electroencephalograms (EEG) and regional neural activity. Results. The known phenomenon of EEG slowing in Alzheimer’s disease could be reproduced in our simulations. We could show that the heterogeneity of the Abeta distribution (with some highly affected regions) is important to lead to the EEG slowing. The observed spectral phenomena in Alzheimer’s disease were mainly observable in the network hubs, independent of the spatial localization of Abeta. We present moreover a strategy of virtual therapy with memantine by modeling N-methyl-D- aspartate (NMDA) receptor antagonism in TVB. This approach turned out potential reversibility of the observed EEG slowing in virtual Alzheimer’s disease brains. Discussion. We provide proof-of-concept with a novel mechanistic virtual brain model of Alzheimer’s disease, which shows how TVB enables the simulation of large-scale phenomena caused by micro-scale features in human brains

Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease

Introduction: While the prevalence of neurodegenerative diseases associated with dementia such as Alzheimer's disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modeling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment. Methods: The Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics. We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics. As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta). We construct personalized virtual brains based on an averaged healthy connectome and individual PET derived distributions of Abeta in patients with mild cognitive impairment (MCI, N = 8) and Alzheimer's Disease (AD, N = 10) and in age-matched healthy controls (HC, N = 15) using data from ADNI-3 data base (http://adni.loni.usc.edu). In the personalized virtual brains, individual Abeta burden modulates regional Excitation-Inhibition balance, leading to local hyperexcitation with high Abeta loads. We analyze simulated regional neural activity and electroencephalograms (EEG). Results: Known empirical alterations of EEG in patients with AD compared to HCs were reproduced by simulations. The virtual AD group showed slower frequencies in simulated local field potentials and EEG compared to MCI and HC groups. The heterogeneity of the Abeta load is crucial for the virtual EEG slowing which is absent for control models with homogeneous Abeta distributions. Slowing phenomena primarily affect the network hubs, independent of the spatial distribution of Abeta. Modeling the N-methyl-D-aspartate (NMDA) receptor antagonism of memantine in local population models, reveals potential functional reversibility of the observed large-scale alterations (reflected by EEG slowing) in virtual AD brains. Discussion: We demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains

Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain

Deep brain stimulation (DBS) has been successfully applied in various neurodegenerative diseases as an effective symptomatic treatment. However, its mechanisms of action within the brain network are still poorly understood. Many virtual DBS models analyze a subnetwork around the basal ganglia and its dynamics as a spiking network with their details validated by experimental data. However, connectomic evidence shows widespread effects of DBS affecting many different cortical and subcortical areas. From a clinical perspective, various effects of DBS besides the motoric impact have been demonstrated. The neuroinformatics platform The Virtual Brain (TVB) offers a modeling framework allowing us to virtually perform stimulation, including DBS, and forecast the outcome from a dynamic systems perspective prior to invasive surgery with DBS lead placement. For an accurate prediction of the effects of DBS, we implement a detailed spiking model of the basal ganglia, which we combine with TVB via our previously developed co-simulation environment. This multiscale co-simulation approach builds on the extensive previous literature of spiking models of the basal ganglia while simultaneously offering a whole-brain perspective on widespread effects of the stimulation going beyond the motor circuit. In the first demonstration of our model, we show that virtual DBS can move the firing rates of a Parkinson's disease patient's thalamus - basal ganglia network towards the healthy regime while, at the same time, altering the activity in distributed cortical regions with a pronounced effect in frontal regions. Thus, we provide proof of concept for virtual DBS in a co-simulation environment with TVB. The developed modeling approach has the potential to optimize DBS lead placement and configuration and forecast the success of DBS treatment for individual patients

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

The 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 conversion 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 collaboration 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

Whole-brain modeling of the differential influences of amyloid-beta and tau in Alzheimer’s disease

Abstract Background Alzheimer’s disease is a neurodegenerative condition associated with the accumulation of two misfolded proteins, amyloid-beta (A $$\beta$$ β ) and tau. We study their effect on neuronal activity, with the aim of assessing their individual and combined impact. Methods We use a whole-brain dynamic model to find the optimal parameters that best describe the effects of A $$\beta$$ β and tau on the excitation-inhibition balance of the local nodes. Results We found a clear dominance of A $$\beta$$ β over tau in the early disease stages (MCI), while tau dominates over A $$\beta$$ β in the latest stages (AD). We identify crucial roles for A $$\beta$$ β and tau in complex neuronal dynamics and demonstrate the viability of using regional distributions to define models of large-scale brain function in AD. Conclusions Our study provides further insight into the dynamics and complex interplay between these two proteins, opening the path for further investigations on biomarkers and candidate therapeutic targets in-silico

Scale-Free Functional Brain Networks Exhibit Increased Connectivity, Are More Integrated and Less Segregated in Patients with Parkinson’s Disease following Dopaminergic Treatment

Dopaminergic treatment (DT), the standard therapy for Parkinson’s disease (PD), alters the dynamics of functional brain networks at specific time scales. Here, we explore the scale-free functional connectivity (FC) in the PD population and how it is affected by DT. We analyzed the electroencephalogram of: (i) 15 PD patients during DT (ON) and after DT washout (OFF) and (ii) 16 healthy control individuals (HC). We estimated FC using bivariate focus-based multifractal analysis, which evaluated the long-term memory (H(2)) and multifractal strength (ΔH15) of the connections. Subsequent analysis yielded network metrics (node degree, clustering coefficient and path length) based on FC estimated by H(2) or ΔH15. Cognitive performance was assessed by the Mini Mental State Examination (MMSE) and the North American Adult Reading Test (NAART). The node degrees of the ΔH15 networks were significantly higher in ON, compared to OFF and HC, while clustering coefficient and path length significantly decreased. No alterations were observed in the H(2) networks. Significant positive correlations were also found between the metrics of H(2) networks and NAART scores in the HC group. These results demonstrate that DT alters the multifractal coupled dynamics in the brain, warranting the investigation of scale-free FC in clinical and pharmacological studies

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

International audienceDespite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain ( www.thevirtualbrain.org ), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD

Brain simulation augments machine‐learning–based classification of dementia

ABSTRACT Introduction Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi‐modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD). Methods We enhance large‐scale whole‐brain simulation in TVB with a cause‐and‐effect model linking local amyloid beta (Aβ) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB‐simulated local field potentials (LFP) for ML classification. Results The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1‐score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the AD‐typical spatial distribution. Discussion The cause‐and‐effect implementation of local hyperexcitation caused by Aβ can improve the ML–driven classification of AD and demonstrates TVB's ability to decode information in empirical data using connectivity‐based brain simulation

Brain simulation augments machine-learning-based classification of dementia

ABSTRACT INTRODUCTION Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer’s disease. METHODS We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local Amyloid β PET with altered excitability. We use PET and MRI data from 33 participants of Alzheimer’s Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for machine-learning classification. RESULTS The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the Alzheimer’s-typical spatial distribution. DISCUSSION The cause-and-effect implementation of local hyperexcitation caused by Amyloid β can improve the machine-learning-driven classification of Alzheimer’s and demonstrates TVB’s ability to decode information in empirical data employing connectivity-based brain simulation. RESEARCH IN CONTEXT SYSTEMATIC REVIEW . Machine-learning has been proven to augment diagnostics of dementia in several ways. Imaging-based approaches enable early diagnostic predictions. However, individual projections of long-term outcome as well as differential diagnosis remain difficult, as the mechanisms behind the used classifying features often remain unclear. Mechanistic whole-brain models in synergy with powerful machine learning aim to close this gap. INTERPRETATION . Our work demonstrates that multi-scale brain simulations considering Amyloid β distributions and cause-and-effect regulatory cascades reveal hidden electrophysiological processes that are not readily accessible through measurements in humans. We demonstrate that these simulation-inferred features hold the potential to improve diagnostic classification of Alzheimer’s disease. FUTURE DIRECTIONS . The simulation-based classification model needs to be tested for clinical usability in a larger cohort with an independent test set, either with another imaging database or a prospective study to assess its capability for long-term disease trajectories