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The human multiscale brain connectome and its variability – from synapses to large-scale networks and function
 

What we do

This Work Package develops biologically detailed human brain network models capable of generating brain signals as commonly measured in clinical and research settings. By linking a multiscale human brain atlas to computational modeling, the aim is to better understand the fundamental mechanisms of how the brain generates its behavior. 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.

In WP1, components created in the three-year work plans of HBP are being assembled to integrate workflows and produce scientific showcases demonstrating the added value of the integration in EBRAINS. These components involve a battery of tools (bottom-up, top-down) and workflows comprising data, atlases, multiscale models, and simulators. WP1 showcases are Showcase 1: Degeneracy in neuroscience - when is Big Data big enough? and Showcase 2: Improving epilepsy surgery with the Virtual Big Brain.

In WP1 we use ICT (Information and Communication Technology) simulation science to better understand brain function and improve treatments of the diseased brain. We build personalised brain models based on theories of network science and complex systems and apply these to real-world problems in neuroscience and medicine such as epilepsy and aging. The novel neurotechnologies establish workflows and solutions in EBRAINS, translated to clinics and adopted by industry partners.

How we're organized

To achieve the proposed objectives and produce the showcases, WP1 work plan is composed of 16 tasks in which 37 partners are contributing. These Tasks interact with each other as well as with other tasks from the science WPs and from the infrastructure WPs. The activities proposed in these tasks will release 46 Outputs associated to 6 deliverables.

WP1 Tasks and partners are displayed in the table below:

Task Title

Partners

T1.1 Human brain region-specific molecular and cellular data organisation

VU, JUELICH, IEM HAS, LENS, UEDIN, UPM, UKE, UBER, KNAW, IBEC, DZNE, UNIBAS

T1.3 Nested structural connectomes enriched with region-specific features

CEA, JUELICH, CNR, UDUS, KCL, LENS, KNAW, DZNE, UGA

T1.4: Human brain functional data collection and organisation

UCBL, INRIA, AMU, CHARITE, UGA

T1.5: Multiscale regional models of human cerebral cortex, hippocampus, cerebellum and basal ganglia

CNRS, CNR, INRIA, KI, KTH, HUJI, UNIPV, UPF, AMU, TUT

T1.6: Simulation of whole-brain network dynamics and its rhythmic activity, constrained by region-wide differences

UPF, CNRS, CNR, UNIPV, AMU

T1.7: Integrating population variance and individual predictability into human whole brain network models

UDUS, TAU, UPF, AMU, CHARITE

T1.8: Data integration and software interface with the multilevel human brain atlas

JUELICH, CEA, AMU

T1.9 Societal and ethical impact and implications of personalized brain modelling

UU, AMU

T1.10: Management, Coordination and Communication

AMU

T1.11: Technical coordination and scientific integration

AMU, UPM

T1.12 Model-free and model-based inference and validation workflows for causal brain network discovery (NetScovery)

AMU, IIC, UGA, IGM, UPF, CHARITE

T1.13 CEoI No.2 Brain atlas and simulation engine adapter construction

TBC

T1.14 Model-free and model-based inference and validation workflows for causal brain network discovery (NetScovery)

AMU, CRMBM, AP-HM

T1.15 Whole-bRaIn rodent SpikING neural NETworks (RisingNet)

UNIPV, CHARITE, POLIMI, CNRS, UNINA, KI

T1.16 Dysconnection syndromes and dynamic causal modelling

UCL

 

Publication highlights

  • 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. Article in Journal - NeuroImage, Vol. 217 - 2020-08-01
  • Amunts et al. Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture. Article in Journal - 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.