001     1025672
005     20250203103301.0
037 _ _ |a FZJ-2024-03061
100 1 _ |a Saberi, Amin
|0 P:(DE-Juel1)190448
|b 0
|e Corresponding author
|u fzj
111 2 _ |a 7th BigBrain Workshop
|c Reykjavík
|d 2023-10-04 - 2023-10-06
|w Iceland
245 _ _ |a Whole-brain dynamical modeling of the adolescent developing brain
260 _ _ |c 2023
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a Other
|2 DataCite
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a LECTURE_SPEECH
|2 ORCID
336 7 _ |a Conference Presentation
|b conf
|m conf
|0 PUB:(DE-HGF)6
|s 1714560077_3375
|2 PUB:(DE-HGF)
|x After Call
520 _ _ |a Regulation of cortical microcircuits is crucial for optimal neural processing. Adolescence involves substantial macro- and microscale changes in the brain, including maturation of cortical microcircuits. Evidence from animal studies suggests a calibration of cortical microcircuits and excitation-to-inhibition (E-I) ratio during adolescence. However, in-vivo measurement of cortical microcircuits in the human developing brain is challenging, and therefore the supporting in-vivo evidence on maturation of E-I ratio in humans is limited. Whole-brain dynamical modeling is a promising approach that enables mechanistic inferences about hidden brain features, such as estimated properties of cortical microcircuits and E-I ratio. Here, we used whole-brain dynamical modeling to study age-related changes of whole-brain model parameters during adolescence.We simulated cortical activity based on a mean-field model of excitatory and inhibitory neuronal ensembles in regions connected based on subject-specific or group-averaged structural connectomes. The fit of simulations to empirical resting-state functional images of each subject was evaluated based on comparison of simulated and empirical functional connectivity as well as functional connectivity dynamics matrices. We identified optimal model parameters for each subject using covariance matrix adaptation evolution strategy as well as GPU-accelerated grid search of the whole parameter space. Based on the simulations performed with the optimal parameters, we calculated the regional E-I ratios in the simulation as their time-averaged simulated excitatory firing rates. We observed region-specific changes of E-I ratio with age, which was decreased in parietal and frontal regions and increased in occipital regions. In addition, we observed association of grey-white matter contrast with E-I ratio in specifc regions. Following, we aim to increase regional specificity of the simulations by introducing heterogeneity in the model parameters based on biological maps of receptors as well as myelo- and cytoarchitecture.Overall, we present a whole-brain modeling approach to estimate E-I ratio in developing adolescents which revealed region-specific changes of E-I ratio with age and its links to cortical microstructure.
536 _ _ |a 5252 - Brain Dysfunction and Plasticity (POF4-525)
|0 G:(DE-HGF)POF4-5252
|c POF4-525
|f POF IV
|x 0
536 _ _ |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)
|0 G:(DE-Juel1)JL SMHB-2021-2027
|c JL SMHB-2021-2027
|x 1
700 1 _ |a Wischnewski, Kevin
|0 P:(DE-Juel1)178756
|b 1
|u fzj
700 1 _ |a Jung, Kyesam
|0 P:(DE-Juel1)178611
|b 2
|u fzj
700 1 _ |a Schaare, Lina
|0 P:(DE-Juel1)188324
|b 3
|u fzj
700 1 _ |a Popovych, Oleksandr
|0 P:(DE-Juel1)131880
|b 4
|u fzj
700 1 _ |a Eickhoff, Simon
|0 P:(DE-Juel1)131678
|b 5
|u fzj
700 1 _ |a Valk, Sofie
|0 P:(DE-Juel1)173843
|b 6
|u fzj
909 C O |o oai:juser.fz-juelich.de:1025672
|p VDB
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)190448
910 1 _ |a Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig
|0 I:(DE-HGF)0
|b 0
|6 P:(DE-Juel1)190448
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)178756
910 1 _ |a HHU Düsseldorf
|0 I:(DE-HGF)0
|b 1
|6 P:(DE-Juel1)178756
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)178611
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)188324
910 1 _ |a Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig
|0 I:(DE-HGF)0
|b 3
|6 P:(DE-Juel1)188324
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)131880
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)131678
910 1 _ |a HHU Düsseldorf
|0 I:(DE-HGF)0
|b 5
|6 P:(DE-Juel1)131678
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 6
|6 P:(DE-Juel1)173843
910 1 _ |a Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf,
|0 I:(DE-HGF)0
|b 6
|6 P:(DE-Juel1)173843
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5252
|x 0
914 1 _ |y 2024
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-7-20090406
|k INM-7
|l Gehirn & Verhalten
|x 0
980 _ _ |a conf
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-7-20090406
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21