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

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

Digital twin brain: a bridge between biological intelligence and artificial intelligence

In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems. Cutting-edge advancements in neuroscience research have revealed the intricate relationship between brain structure and function, while the success of artificial neural networks highlights the importance of network architecture. Now is the time to bring them together to better unravel how intelligence emerges from the brain's multiscale repositories. In this review, we propose the Digital Twin Brain (DTB) as a transformative platform that bridges the gap between biological and artificial intelligence. It consists of three core elements: the brain structure that is fundamental to the twinning process, bottom-layer models to generate brain functions, and its wide spectrum of applications. Crucially, brain atlases provide a vital constraint, preserving the brain's network organization within the DTB. Furthermore, we highlight open questions that invite joint efforts from interdisciplinary fields and emphasize the far-reaching implications of the DTB. The DTB can offer unprecedented insights into the emergence of intelligence and neurological disorders, which holds tremendous promise for advancing our understanding of both biological and artificial intelligence, and ultimately propelling the development of artificial general intelligence and facilitating precision mental healthcare

Measures of Resting State EEG Rhythms for Clinical Trials in Alzheimer’s Disease:Recommendations of an Expert Panel

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12Hz) and widespread delta (<4Hz) and theta (4-8Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes

Measures of resting state EEG rhythms for clinical trials in alzheimer's disease patients : recommendations of an expert panel

Background and Aim: Eyes-closed resting state electroencephalographic (rsEEG) rhythms reflect neurophysiological oscillatory mechanisms of synchronization/desynchronization of activity within neural populations of ascending reticular activating brain systems and thalamus-cortical circuits involved in quite vigilance regulation. Currently, they are not considered as biomarkers of Alzheimer’s disease (AD) in the amyloid, tau and neurodegeneration (ATN) Framework of Alzheimer’s Association and National Institute of Aging (AA-NIA). The Electrophysiology Professional Interest Area (EPIA) of AA and Global Brain Consortium endorsed this article written by a multidisciplinary Expert Panel to provide recommendations on candidate rsEEG measures for AD clinical trials. Method: The Panel revised the field literature and reached consensus about the rsEEG measures consistently associated with clinical phenotypes and neuroimaging markers of AD in previous international multicentric clinical trials. Most consistent findings: AD patients with mild cognitive impairment and dementia displayed reduced peak frequency, power, and paired-electrode “interrelatedness” in posterior alpha (8-12 Hz) rhythms and topographically widespread increases in delta (< 4 Hz) and theta (4-8 Hz) rhythms. Recommendations: (i) Careful multi-center standardization of instructions to patients, rsEEG recordings, and selection of artifact-free rsEEG periods; (ii) extraction of rsEEG power density and paired-electrode “interrelatedness” (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) rsEEG measures computed at delta, theta, and alpha frequency bands by validated open-access software platforms for replicability; (iii) valid use of those measures in stratification of AD patients and monitoring of disease progression and intervention; and iv) international initiatives to cross-validate rsEEG measures (including nonlinear) for disease monitoring and intervention