001017849 001__ 1017849
001017849 005__ 20240313094903.0
001017849 037__ $$aFZJ-2023-04363
001017849 041__ $$aEnglish
001017849 1001_ $$0P:(DE-Juel1)165321$$aPronold, Jari$$b0
001017849 1112_ $$a2nd Cologne Neuroscience Day$$cCologne$$d2023-10-26 - 2023-10-26$$wGermany
001017849 245__ $$aMulti-Scale Spiking Network Model of Human Cerebral Cortex
001017849 260__ $$c2023
001017849 3367_ $$033$$2EndNote$$aConference Paper
001017849 3367_ $$2BibTeX$$aINPROCEEDINGS
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001017849 500__ $$aReferences: [1] Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ. Brain Struct Funct. 2018;223(3):1409–35.[2] Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M, et al.  PLOS Comput Biol. 2018;14(10):e1006359.[3] Potjans TC, Diesmann M. Cerebral Cortex. 2014;24(3):785–806.[4] Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, et al. Neuroimage. 2013;80:62–79[5] Mohan H, Verhoog MB, Doreswamy KK, Eyal G, Aardse R, et al. Cerebral Cortex. 2015;25(12):4839–53.[6] Minxha J, Adolphs R, Fusi S, Mamelak AN, Rutishauser U. Science. 2020;368(6498).
001017849 520__ $$aBackground: The structure of the brain plays a crucial role in shaping its activity. However, the link between structural connectivity and observed neuronal activity remains incompletely understood. Previous research utilizing a large-scale spiking network model of leaky integrate-and-fire neurons has addressed this question for macaque cortex [1,2]. Here, a similar framework is employed to investigate human cortex in a model that links the cortical network structure to the resting-state activity of neurons, populations, layers, and areas.Objectives: The objective of this study is to investigate the link between structural connectivity and observed neuronal activity in human cortex using a large-scale spiking network model, and to create a platform for multi-scale in silico studies of human cortex.Materials and Methods: The model includes all 34 areas in a single hemisphere of human cortex according to the Desikan-Killiany parcellation. Our approach integrates cortical data on architecture, morphology, and connectivity into a multi-scale framework for predicting neuron connections. Each cortical area is represented by a 1 $mm^2$ layered microcircuit adapted from [3] with the full density of neurons and synapses. Inter-area connectivity relies on diffusion tensor imaging data [4] and the determination of laminar patterns of synaptic connectivity takes into account human neuron morphology data [5]. The model comprises 4 million neurons and 50 billion synapses, simulated with the NEST simulator on the supercomputer JURECA-DC. Results and Conclusions: Simulations of the model with uniform synaptic weights reveal a state with asynchronous and irregular activity that deviates from experimental recordings in terms of spiking activity and inter-area functional connectivity. Increasing inter-area synapse strength enables the model to capture both microscopic and macroscopic resting-state activity of human cortex measured via electrophysiological recordings and fMRI [6]. Furthermore, the model reveals rapid propagation of the effects of a single-spike perturbation across the entire network. This suggests individual spikes play a role in fast sensory processing and behavioral responses in the cortical network. Overall, the model serves as a basis for the investigation of multi-scale structure-dynamics relationships in human cortex.
001017849 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001017849 536__ $$0G:(GEPRIS)313856816$$aDFG project 313856816 - SPP 2041: Computational Connectomics (313856816)$$c313856816$$x1
001017849 536__ $$0G:(GEPRIS)347572269$$aDFG project 347572269 - Heterogenität von Zytoarchitektur, Chemoarchitektur und Konnektivität in einem großskaligen Computermodell der menschlichen Großhirnrinde (347572269)$$c347572269$$x2
001017849 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x3
001017849 536__ $$0G:(DE-Juel1)jinb33_20220812$$aBrain-Scale Simulations (jinb33_20220812)$$cjinb33_20220812$$fBrain-Scale Simulations$$x4
001017849 7001_ $$0P:(DE-HGF)0$$aMeegen, Alexander van$$b1
001017849 7001_ $$0P:(DE-Juel1)180364$$aVollenbröker, Hannah$$b2
001017849 7001_ $$0P:(DE-Juel1)190767$$aShimoura, Renan$$b3$$eCorresponding author$$ufzj
001017849 7001_ $$0P:(DE-HGF)0$$aSenden, Mario$$b4
001017849 7001_ $$0P:(DE-HGF)0$$aHilgetag, Claus C.$$b5
001017849 7001_ $$0P:(DE-Juel1)145578$$aBakker, Rembrandt$$b6$$ufzj
001017849 7001_ $$0P:(DE-Juel1)138512$$avan Albada, Sacha$$b7$$ufzj
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001017849 9141_ $$y2023
001017849 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
001017849 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
001017849 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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