TY  - CONF
AU  - Pronold, Jari
AU  - Meegen, Alexander van
AU  - Vollenbröker, Hannah
AU  - Shimoura, Renan
AU  - Senden, Mario
AU  - Goulas, Alexandros
AU  - Hilgetag, Claus C.
AU  - Bakker, Rembrandt
TI  - Multi-Scale Spiking Network Model of Human Cerebral Cortex
M1  - FZJ-2023-02823
PY  - 2023
N1  - References: [1] Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ. Multi-scale account of the network structure of macaque visual cortex. Brain Struct Funct. 2018;223(3):1409–35.[2] Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M, et al. A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas.  PLOS Comput Biol. 2018;14(10):e1006359.[3] Von Economo C, Koskinas GN. Cellular structure of the human cerebral cortex. Karger Medical and Scientific Publishers; 2009.[4] Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K, Wu-Minn HCP Consortium. The WU-Minn human connectome project: an overview. Neuroimage. 2013;80:62–79.[5] Potjans TC, Diesmann M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cerebral Cortex. 2014;24(3):785–806.[6] Mohan H, Verhoog MB, Doreswamy KK, Eyal G, Aardse R, et al. Dendritic and axonal architecture of individual pyramidal neurons across layers of adult human neocortex. Cerebral Cortex. 2015;25(12):4839–53.[7] Minxha J, Adolphs R, Fusi S, Mamelak AN, Rutishauser U. Flexible recruitment of memory-based choice representations by the human medial frontal cortex. Science. 2020;368(6498).[8] Lamme VAF, Roelfsema PR. The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci. 2000; 23:571–579.
AB  - The structure of the brain plays a crucial role in shaping its activity. However, the link between structural connectivity and observed neuronal activity remains not fully 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]. In this study, we employ the same framework to investigate human cortex and present a large-scale spiking network model that links the cortical network structure to the resting-state activity of neurons, populations, layers, and areas.Our approach integrates data on cortical architecture, cellular morphologies, and local and cortico-cortical connectivity into a multi-scale framework to predict connection probabilities between neurons based on their types and locations within areas and layers. We represent each cortical area with a 1 mm2 area-specific microcircuit incorporating the full density of neurons and synapses. For this first model version, the laminar thicknesses and neuron densities are derived from the von Economo and Koskinas atlas [3]. The connectivity on the area level is informed by diffusion tensor imaging (DTI) data [4], while predictions on laminar connectivity patterns are derived from predictive connectomics based on macaque data that express regularities of laminar connectivity patterns as a function of cortical architecture. We use the Potjans and Diesmann [5] model as a basis for the local connectivity, scaling it according to cytoarchitectonic data. To map inter-area synapses to target cells, which may have their cell body in a different layer compared to the synapse location, we assign synapses in proportion to the layer- and cell-type-specific dendritic lengths determined from human neuron morphologies [6]. The model contains approximately 4 million neurons and 50 billion synapses and is simulated on JURECA-DC using the NEST simulator.Simulations of the model reveal a state with asynchronous and irregular activity that deviates from experimental recordings in terms of spiking activity and inter-area functional connectivity. By increasing the strength of the inter-area synapses, a state is reached that  captures aspects of both microscopic and macroscopic resting-state activity of human cortex measured via electrophysiological recordings from medial frontal cortex and fMRI [7]. Furthermore, we used our model to track the effect of a single additional spike through the large-scale network. We find that a single-spike perturbation spreads rapidly across the entire network within 50-75 ms, comparable to visual response latencies in macaque cortex [8], suggesting that the cortical network allows individual spikes to play a role in fast sensory processing and behavioral responses. Overall, the model serves as a basis for the investigation of multi-scale structure-dynamics relationships in human cortex.
T2  - 32nd Annual Computational Neuroscience Meeting CNS*2023
CY  - 15 Jul 2023 - 19 Jul 2023, Leipzig (Germany)
Y2  - 15 Jul 2023 - 19 Jul 2023
M2  - Leipzig, Germany
LB  - PUB:(DE-HGF)24
DO  - DOI:10.34734/FZJ-2023-02823
UR  - https://juser.fz-juelich.de/record/1009486
ER  -