001025672 001__ 1025672
001025672 005__ 20250203103301.0
001025672 037__ $$aFZJ-2024-03061
001025672 1001_ $$0P:(DE-Juel1)190448$$aSaberi, Amin$$b0$$eCorresponding author$$ufzj
001025672 1112_ $$a7th BigBrain Workshop$$cReykjavík$$d2023-10-04 - 2023-10-06$$wIceland
001025672 245__ $$aWhole-brain dynamical modeling of the adolescent developing brain
001025672 260__ $$c2023
001025672 3367_ $$033$$2EndNote$$aConference Paper
001025672 3367_ $$2DataCite$$aOther
001025672 3367_ $$2BibTeX$$aINPROCEEDINGS
001025672 3367_ $$2DRIVER$$aconferenceObject
001025672 3367_ $$2ORCID$$aLECTURE_SPEECH
001025672 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1714560077_3375$$xAfter Call
001025672 520__ $$aRegulation 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.
001025672 536__ $$0G:(DE-HGF)POF4-5252$$a5252 - Brain Dysfunction and Plasticity (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001025672 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x1
001025672 7001_ $$0P:(DE-Juel1)178756$$aWischnewski, Kevin$$b1$$ufzj
001025672 7001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b2$$ufzj
001025672 7001_ $$0P:(DE-Juel1)188324$$aSchaare, Lina$$b3$$ufzj
001025672 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b4$$ufzj
001025672 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b5$$ufzj
001025672 7001_ $$0P:(DE-Juel1)173843$$aValk, Sofie$$b6$$ufzj
001025672 909CO $$ooai:juser.fz-juelich.de:1025672$$pVDB
001025672 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)190448$$aForschungszentrum Jülich$$b0$$kFZJ
001025672 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)190448$$a Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig$$b0
001025672 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178756$$aForschungszentrum Jülich$$b1$$kFZJ
001025672 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)178756$$a HHU Düsseldorf$$b1
001025672 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178611$$aForschungszentrum Jülich$$b2$$kFZJ
001025672 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188324$$aForschungszentrum Jülich$$b3$$kFZJ
001025672 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)188324$$a Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig$$b3
001025672 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131880$$aForschungszentrum Jülich$$b4$$kFZJ
001025672 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b5$$kFZJ
001025672 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b5
001025672 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173843$$aForschungszentrum Jülich$$b6$$kFZJ
001025672 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)173843$$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,$$b6
001025672 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5252$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001025672 9141_ $$y2024
001025672 920__ $$lyes
001025672 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001025672 980__ $$aconf
001025672 980__ $$aVDB
001025672 980__ $$aI:(DE-Juel1)INM-7-20090406
001025672 980__ $$aUNRESTRICTED