Human multiscale brain connectome
We focus on building, validating and operating the human multiscale brain connectome and its variability. The main ambition is to develop personalised, biologically detailed brain network models capable of generating signals commonly used in human brain imaging, for applications in neuroscience and medicine.
By linking a multiscale human brain atlas to computational modelling integrating in-vivo and and ex-vivo data in the same brain reference framework, we aim to better understand the fundamental mechanisms of how the brain generates its behaviour and overcoming the challenges posed by inter-subject variability and neurodegeneracy.
Our goal is to increase the capacity of the neuroscientific community to model multiscale neural activity of human brain networks by building a conceptual, organisational and computational framework fully embedded in EBRAINS. Our vision is the translation of digital twin frameworks into clinical applications.
Mapping the connections inside the brain, from the brain network down to the neuron scale, is fundamental for the understanding of how the brain works, how it can be modelled and simulated, and how to develop neurological applications, including clinical ones. Bridging the gap between highly detailed human brain atlases and computational modelling, we are able to better understand how the brain generates its behaviour, laying the groundwork for future theoretical and applied neuroscience. Capitalising on many years of HBP work, we assemble components to integrated workflows involving a battery of tools (bottom-up, top-down) interoperating data, atlases, multiscale models, and simulators. Several Showcases provide examples of such workflows integrated in EBRAINS. We focus on, but are not limited to, the human brain, and the creation of workflows for personalised brain models based on individual data. The validity of the principles and concepts is demonstrated in applications to individual patient data for clinical translation such as in epilepsy, and in cohort data to understand human variability such as in healthy aging and pathology. Neuroethical interrogations guide the debate on the role of the digital twin brain in society.
We have demonstrated that fusion of (typically ex-vivo) high-resolution data and (in-vivo) individual data improves personalised brain modelling, developed workflows for adapting deep learning techniques to brain model inversion, and progressed towards translation of personalised brain modelling technologies to clinics and adoption by industry partners, such in the case for epilepsy treatment.
Hashemi et al. The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread. NeuroImage, Vol. 217 - 2020-08-01
Amunts et al. Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture. Science: Vol. 369, Issue 6506, pp. 988-992 - 2020-08-21
Casali et al (2020). Cellular-resolution mapping uncovers spatial adaptive filtering at the cerebellum input stage. Nature Communications Biology - 2020-03-15
Palesi et al. The Importance of Cerebellar Connectivity on Simulated Brain Dynamics. Frontiers in Cellular Neuroscience, Vol. 14 2020-07-31
Saggio et al. A taxonomy of seizure dynamotypes. eLife, Vol. 9 2020-07-21
Courtiol et al. Dynamical Mechanisms of Interictal Resting-State Functional Connectivity in Epilepsy. The Journal of Neuroscience, Vol. 40, No. 29 2020-07-15
Deco et al. Revisiting the global workspace orchestrating the hierarchical organization of the human brain. Nat Hum Behav. 2021 Jan 4.
Manninen T et al. Astrocyte-mediated spike-timing-dependent long-term depression modulates synaptic properties in the developing cortex. PLoS Comput Biol. 2020 Nov 10;16(11):e1008360.