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Sunday, July 19 • 8:00pm - 9:00pm
P135: A cortical model examining mismatch negativity deficits in schizophrenia

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Meeting ID: 845 3747 7335

Password: CNS2020

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Gili Karni
, Christoph Metzner

Recent advances in computational modeling, genome-wide association studies, neuroimaging, and theoretical neuroscience pose better opportunities to study neuropsychiatric disorders, such as schizophrenia (SZC)[4]. However, despite a repeated examination of its well-characterized phenotypes, our understanding of SZC's neurophysiological biomarkers or cortical dynamics remain elusive.

This study presents a biophysical spiking neuron model of perceptual inference, based on the predictive coding framework [1]. The model, implemented in NetPyNE [6], incorporates various single-cell models of both excitatory and inhibitory neurons [2,8], mimicking the circuits of the primary auditory cortex. This model allows for the exploration of the effects bio- genetic variants (expressed via ion-channels or synaptic mechanism alterations, see[5]) have on auditory mismatch negativity (MMN) deficits, a common biomarker for SZC [3]. More particularly, the model distinguishes between repetition suppression and prediction error and examines their respective contribution to the MMN. The first part of this report establishes the model's explanatory power using two well-known paradigms: the oddball paradigm and the cascade paradigm. Both can reproduce the electrophysiological measures of the MMN among healthy subjects. Later, via tuning the parameters of single-neuron equations or the network's synaptic weights, the model exhibits the expected LFP changes associated with SZC [7].

Therefore, this model enables exploring how biogenetic alterations affect the underlying components of the observed MMN deficits. Novel, yet preliminary, predictions are presented and suggested future steps for validations are listed. This model could support studies exploring genetic effects on the MMN (or other aspects of predictive coding) in the auditory cortex.


References 
[1]Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012). Canonical microcircuits for predictive coding. Neuron, 76(4), 695-711.
[2]Beeman, D. (2018). Comparison with human layer 2/3 pyramidal cell dendritic morphologies. Poster session presented at the meeting of Society for Neuroscience, San Diego.
[3]Garrido, M. I., Kilner, J. M., Stephan, K. E., & Friston, K. J. (2009). The mismatch negativity: a review of underlying mechanisms. Clinical neurophysiology, 120(3), 453-463.
[4]Krystal, John H., et al. "Computational psychiatry and the challenge of schizophrenia." (2017): 473-475.
[5]Mäki-Marttunen, T., Halnes, G., Devor, A., et al. (2016). Functional effects of schizophrenia-linked genetic variants on intrinsic single-neuron excitability: a modeling study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(1), 49-59.
[6]Dura-Bernal S, Suter B, Gleeson P, Cantarelli M, Quintana A, Rodriguez F, ... &; McDougal R.
NetPyNE: a tool for data-driven multiscale modeling of brain circuits. bioRxiv 2018, 461137.
[7]Michie, P. T., Malmierca, M. S., Harms, L., & Todd, J. (2016). The neurobiology of MMN and implications for schizophrenia. Biological psychology, 116, 90-97.
[8]Vierling-Claassen, D., Cardin, J., Moore, C. I., & Jones, S. R. (2010). Computational modeling of distinct neocortical oscillations driven by cell- type selective optogenetic drive: separable resonant circuits controlled by low-threshold spiking and fast-spiking interneurons. Frontiers in human neuroscience, 4, 198.

Speakers
avatar for Gili Karni

Gili Karni

Minerva schools at KGI
I am curious about the emergence of human intelligence. Specifically, I am fascinated by the flexibility, speed, and resilience of human learning and how it informs decision making. I hope to explore the algorithms underlying these processes using computational methods.Besides research... Read More →



Sunday July 19, 2020 8:00pm - 9:00pm CEST
Slot 06