P231 Emotional network modelling: whole brain simulations of fear conditioning in humans
Dianela A Osorio-Becerra1, Andrea Fusari1, Ashika Roy2, Danilo Benozzo1, Andreas Frick2, Egidio D’Angelo1, Fulvia Palesi1,Claudia Casellato1
1Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
2Department of Medical Sciences, Uppsala University, Uppsala, Sweden
p { line-height: 115%; text-align: left; orphans: 2; widows: 2; margin-bottom: 0.1in; direction: ltr; background: transparent }a:link { color: #467886; text-decoration: underline }
*Email: claudia.casellato@unipv.it
Introduction
Emotion in mammals involves complex brain networks [1,2], for which it is critical to identify the specific regional connectivity and microcircuit properties. We couple in-silico brain dynamics in whole-brain simulations by The Virtual Brain – TVB [3] with experimental fear conditioning data in humans. These include MRI (DWI, resting-state and task-dependent fMRI) and fear-related behavioural measurements (skin conductance responses (SCR), a biomarker of emotional arousal [4]). This work represents a preliminary exploration on how data-driven subject-specific models of brain dynamics could predict emotional behaviour.
Methods
Data come from 17 healthy subjects. The fMRI is acquired at TR 3 s. The mean SCR is extracted for CS+ (conditioned stimulus paired with the unconditioned stimulus, US) and CS− (neutral stimulus), both during acquisition and extinction (acq_csp, acq_csm, ext_csp, ext_csm), and for US (electric shock) during acquisition (acq_us). The SCR usually increases with the paired CS/US pattern presentation, and it decreases when CS is no longer followed by US (extinction).
In the model, a fear-TVB network was defined selecting the regions involved, with each node represented by a reduced Wong-Wang model [5] and using the subject-specific structural connectivity from DWI. Then, the fear-TVB network was optimized in terms of global and local connection parameters, by maximizing the match between the subject-specific experimental functional connectivity matrices (static and dynamic – expFC and expFCD), obtained from resting-state fMRI, and the simulated ones (simFC and simFCD). Finally, these parameters were correlated with the subject-specific SCRs.
Results
The fear-TVB network was reconstructed using 88 nodes, including the amygdala, cerebellum, periaqueductal gray, and parts of the limbic system. The TVB parameters - i.e. global couplingGand three synaptic parameters (excitatory NMDA strengthJNMDA, inhibitory GABA strengthJi, recurrent excitationw+) - were extracted during the optimization process, see Fig.1. By correlating TVB parameters with SCR measures, a positive correlation betweenGandacq_us(ρ=0.37)emerged. However, higher correlation was found when considering sex separately, reinforcing the existing literature on this field.
Discussion
These findings suggest that individual differences in resting-state neural dynamics influence fear acquisition, with distinct mechanisms supporting US processing and conditioned fear discrimination. Although it is the first time that a correlation between network dynamics and fear responses is revealed, the relationship between global connectivity strength and fear responses is still weak, more data and closer understanding of the underlying network is needed. The next step is to use the fMRI data along fear conditioning trials by defining a time-dependent subject-specific TVB parameter space, which may correlate with the corresponding time-dependent fear responses.
Figure 1. a)Fear network b)Anterior and posterior views, 88 nodes (frontal, prefrontal, limbic, parietal, temporal, occipital, deep ganglia, brainstem and cerebellum) c)Violin plots of fear-measures and TVB parameters d)Exp and sim FC matrices, mean across subjects, each element is the Pearson Correlation Coefficient e)Exp vs sim: PCC for FC and the Kolmogorov–Smirnov distance for FCD, one point for subject
Acknowledgements
European Union's Horizon 2020 research under the Marie Sklodowska-Curie grant agreement No. 956414 for "Cerebellum and Emotional Networks", and #NEXTGENERATIONEU, by the Ministry of University and Research, National Recovery and Resilience Plan, project MNESYS (PE0000006)-A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022).
References
[1]https://doi.org/10.3389/fnsys.2023.1185752
[2]https://doi.org/10.1146/annurev.neuro.23.1.155
[3]https://doi.org/10.3389/fninf.2013.00010
[4]https://doi.org/10.1177/1094428116681073[5]https://doi.org/10.1523/JNEUROSCI.5068-13.201
