Cerebrovascular reactivity (CVR) imaging is used to assess the vasodilatory capacity of cerebral blood vessels. While blood flow (CVR CBF ), blood velocity (CVR v ), and preferably blood volume changes (CVR CBV ) are used to represent physiological CVR, quantifying these measures is fraught with acquisition challenges in humans. Consequently, blood oxygenation level-dependent (BOLD)-MRI CVR (CVR BOLD ) is the most widely used MRI-based CVR method, even though it arguably provides the most indirect estimation of CVR. In this paper, we sought to holistically address the quantitative capacity and shortcomings of CVR BOLD . To do so, we developed a CVR BOLD simulation framework and, together with data from the CVR BOLD literature, addressed whether and to what extent CVR BOLD accurately reflects CVR, and with which parameters CVR BOLD varies most. In short, we show the following: CVR BOLD does not necessarily correspond to physiological measures of CVR and depends on physiological (e.g., hematocrit) and acquisition (e.g., field strength) parameters; CVR BOLD is dependent on the stimulus protocol (e.g., breath-holding vs. controlled hypercapnia) chosen to elicit a vasoactive response; resting-state CVR BOLD does not necessarily reflect breath-hold CVR BOLD , likely due to confounding neuronal activity; in stenotic disease and steal physiology, CVR BOLD results from a combination of factors which do not necessarily reflect the underlying CVR. We are confident that this work will provide researchers and clinicians with invaluable insights and advance the field of cerebrovascular imaging by enabling more accurate quantification of CVR in both health and disease.

Dynamic Causal Models (DCMs) in functional Magnetic Resonance Imaging (fMRI) decipher causal interactions, known as Effective Connectivity, among neuronal populations. However, their utility is often constrained by computational limitations, restricting analysis to a small subset of interacting brain areas, typically fewer than 10, thus lacking scalability. While the regression DCM (rDCM) has emerged as a faster alternative to traditional DCMs, it is not without its limitations, including the linearization of DCM terms, reliance on a fixed Hemodynamic Response Function (HRF), and an inability to accommodate modulatory influences. In response to these challenges, we propose a novel hybrid approach named Transformer encoder DCM decoder (TREND), which combines a Transformer encoder with state-of-the-art physiological DCM (P-DCM) as decoder. This innovative method addresses the scalability issue while preserving the nonlinearities inherent in DCM equations. Through extensive simulations, we validate TREND’s efficacy by demonstrating its ability to accurately predict effective connectivity values with dramatically reduced computational time relative to original P-DCM even in networks comprising up to, for instance, 100 interacting brain regions. Furthermore, we showcase TREND on an empirical fMRI dataset demonstrating the superior accuracy and/or speed of TREND compared with other DCM variants. In summary, by amalgamating P-DCM with Transformer, we introduce and validate a pioneering approach for determining effective connectivity values among brain regions, extending its applicability seamlessly to large-scale brain networks.

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues, and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research.