Abstract
Electrical measurements of aggregate neural activity, such as local field potentials (LFPs) or electroencephalograms (EEGs), can capture oscillations of neural activity over a wide range of frequencies and are widely used to study brain function and dysfunction. However, relatively little is known about how to relate features of such aggregate neural recordings to the functional and anatomical configurations of the underlying neural circuits that produce them. An important neural circuit parameter which has profound effects on neural network dynamics and neural function is the ratio between excitation and inhibition (E:I), which has been found to be atypical in many neuropsychiatric conditions. Here we used simulations of recurrent networks of point-like leaky integrate-and-fire (LIF) neurons to study how to infer parameters such as the E:I ratio or the magnitude of the external input of the network from aggregate electrical measures. We used approximations (or proxies), validated in previous work, to generate realistic LFPs and EEGs from simulations of such networks. We computed different spectral features from simulated neural mass signals, such as the 1/f spectral power law or the Hurst exponent (H), and studied how these features changed when we changed the E:I ratio or the strength of the external input of the network model. We discuss how different spectral features of aggregate signals relate to the E:I ratio or the strength of the external input and outline our efforts to fit our model, in future work, to multiple measures extracted from empirical recordings of aggregate neural activity.
This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie (grant agreement No. 893825 to P.M.C), the NIH Brain Initiative (grants U19NS107464 to S.P. and NS108410 to S.P.) and the Simons Foundation (SFARI Explorer 602849 to S.P.).
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References
Barbieri, F., Mazzoni, A., Logothetis, N.K., Panzeri, S., Brunel, N.: Stimulus dependence of local field potential spectra: experiment versus theory. J. Neurosci. 34, 14589–14605 (2014)
Bosl, W., Tierney, A., Tager-Flusberg, H., Nelson, C.: EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med. 9(1), 1–16 (2011)
Bosl, W.J., Loddenkemper, T., Nelson, C.A.: Nonlinear EEG biomarker profiles for autism and absence epilepsy. Neuropsychiatric Electrophysiol. 3(1), 1–22 (2017)
Brunel, N., Wang, X.J.: What determines the frequency of fast network oscillations with irregular neural discharges? i. synaptic dynamics and excitation-inhibition balance. J. Neurophysiol. 90(1), 415–430 (2003)
Buzsáki, G., Anastassiou, C.A., Koch, C.: The origin of extracellular fields and currents - EEG, ECoG. LFP and spikes. Nat. Rev. Neurosci. 13(6), 407–420 (2012)
Buzsáki, G., Wang, X.J.: Mechanisms of gamma oscillations. Ann. Rev. Neurosci. 35, 203–225 (2012)
Cavallari, S., Panzeri, S., Mazzoni, A.: Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks. Front. Neural Circuits 8, 12 (2014)
Colgin, L.L., et al.: Frequency of gamma oscillations routes flow of information in the hippocampus. Nature 462(7271), 353–357 (2009)
Donoghue, T., et al.: Parameterizing neural power spectra into periodic and aperiodic components. Nat. Neurosci. 23(12), 1655–1665 (2020)
Einevoll, G.T., Kayser, C., Logothetis, N.K., Panzeri, S.: Modelling and analysis of local field potentials for studying the function of cortical circuits. Nat. Rev. Neurosci. 14(11), 770–785 (2013)
Gao, R., Peterson, E.J., Voytek, B.: Inferring synaptic excitation/inhibition balance from field potentials. Neuroimage 158, 70–78 (2017)
Herrmann, C., Demiralp, T.: Human EEG gamma oscillations in neuropsychiatric disorders. Clin. Neurophysiol. 116(12), 2719–2733 (2005)
Mahmud, M., Vassanelli, S.: Processing and analysis of multichannel extracellular neuronal signals: state-of-the-art and challenges. Front. Neurosci. 10, 248 (2016)
Martínez-Cañada, P., Ness, T.V., Einevoll, G.T., Fellin, T., Panzeri, S.: Computation of the electroencephalogram (EEG) from network models of point neurons. PLoS Comput. Biol. 17(4), e1008893 (2021)
Mazzoni, A., Brunel, N., Cavallari, S., Logothetis, N.K., Panzeri, S.: Cortical dynamics during naturalistic sensory stimulations: experiments and models. J. Physiol. Paris 105(1), 2–15 (2011)
Mazzoni, A., Lindén, H., Cuntz, H., Lansner, A., Panzeri, S., Einevoll, G.T.: Computing the Local Field Potential (LFP) from integrate-and-fire network models. PLoS Comput. Biol. 11(12), e1004584 (2015)
Mazzoni, A., Panzeri, S., Logothetis, N.K., Brunel, N.: Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons. PLoS Comput. Biol. 4(12), e1000239 (2008)
Mazzoni, A., Whittingstall, K., Brunel, N., Logothetis, N.K., Panzeri, S.: Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model. NeuroImage 52(3), 956–972 (2010)
Pesaran, B., et al.: Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nat. Neurosci. 21(7), 903–919 (2018)
Rubenstein, J., Merzenich, M.M.: Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav. 2(5), 255–267 (2003)
da Silva, F.L.: EEG and MEG: relevance to neuroscience. Neuron 80(5), 1112–1128 (2013)
Sohal, V.S., Rubenstein, J.L.: Excitation-inhibition balance as a framework for investigating mechanisms in neuropsychiatric disorders. Mol. Psychiatry 24(9), 1248–1257 (2019)
Trakoshis, S., et al.: Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women. eLife 9, e55684 (2020)
Wang, J., Barstein, J., Ethridge, L.E., Mosconi, M.W., Takarae, Y., Sweeney, J.A.: Resting state EEG abnormalities in autism spectrum disorders. J. Neurodevelopmental Disord. 5(1), 1–14 (2013)
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Martínez-Cañada, P., Panzeri, S. (2021). Spectral Properties of Local Field Potentials and Electroencephalograms as Indices for Changes in Neural Circuit Parameters. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds) Brain Informatics. BI 2021. Lecture Notes in Computer Science(), vol 12960. Springer, Cham. https://doi.org/10.1007/978-3-030-86993-9_11
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