P192 Identifying Cell-Type-Specific Alterations Underlying Schizophrenia-Related EEG Deficits Using a Multiscale Model of Auditory Thalamocortical Circuits
Scott McElroy*1,2, James Chen1,2, Nikita Novikov1,2,3, Pablo Fernández-López4, Carmen Paz Suárez-Araújo4, Christoph Metzner5, Daniel Javitt3, Sam Neymotin3, Salvador Dura-Bernal1,2,3
1Global Center for AI, Society and Mental Health, SUNY Downstate Health Sciences University, Brooklyn, United States of America
2Department of Physiology and Pharmacology,SUNY Downstate Health Sciences University, Brooklyn, United States of America
3Center for Biomedical Imaging & Neuromodulation, Nathan Kline Institute, Orangeburg, United States of America
4Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Gran Canaria, España
5Technische Universität Berlin, Berlin, Germany
*Email: scott.mcelroy@downstate.edu
Introduction
Schizophrenia is associated with cognitive deficits, including disruptions in sensory processing. Electroencephalography (EEG) studies have identified abnormalities in event-related potentials and cortical oscillations, particularly within the auditory system. Among the most well-established EEG biomarkers are the reduced 40 Hz Auditory Steady-State Response (ASSR) and impaired mismatch negativity (MMN). Understanding the neural mechanisms underlying these EEG deficits is critical for linking molecular and circuit-level alterations to cognitive dysfunctions in schizophrenia.Methods
We extended our computational model of auditory thalamocortical circuits to investigate the circuit-level mechanisms underlying schizophrenia-related EEG abnormalities1. The model simulates a cortical column with over 12,000 neurons and 30 million synapses, incorporating experimentally derived neuron densities, laminar organization, morphology, biophysics, and connectivity across multiple scales. Auditory inputs to the thalamus were modeled using a phenomenological cochlear representation, allowing for the reproduction of realistic physiological responses. Additionally, a more systematic approach to providing background network activity was implemented using Ornstein-Uhlenbeck (OU) processes to model time-varying, statistically independent somatic conductance injections.Results & Discussion
Our refinements enhance the physiological fidelity of EEG simulations, enabling improved replication of schizophrenia-related biomarkers. The integration of OU-modeled background activity ensures smoother, correlated variations in network input, leading to more biologically realistic fluctuations in neuronal dynamics. The OU process's mean and standard deviation are expressed as input conductance percentages for each cell type, linking them to intrinsic cellular properties. Additionally, we are developing an adaptive algorithm to dynamically calibrate population-specific OU parameters, ensuring model flexibility as it evolves. By incorporating experimentally observed molecular and genetic alterations, our model provides deeper insights into the neural basis of auditory processing deficits in schizophrenia and strengthens the link between cellular dysfunctions and EEG biomarkers.
Acknowledgements
This work is supported by NIBIB U24EB028998
References
1.10.1016/j.celrep.2023.113378
