Inferring network structure and local dynamics from neuronal patterns with quenched disorder

Year: 2020

Authors: Adam I., Cecchini G., Fanelli D., Kreuz T., Livi R., di Volo M., Mascaro Allegra AL., Conti E., Scaglione A., Silvestri L., Pavone FS.

Autors Affiliation: Univ Firenze, Dipartimento Ingn Informaz, Via S Marta 3, I-50139 Florence, Italy; Univ Firenze, Dipartimento Fis & Astron, Via G Sansone 1, I-50019 Sesto Fiorentino, Italy; Univ Firenze, CSDC, Via G Sansone 1, I-50019 Sesto Fiorentino, Italy; INFN Sez Firenze, Via G Sansone 1, I-50019 Sesto Fiorentino, Italy; CNR, Inst Complex Syst, Sesto Fiorentino, Italy; Univ Cergy Pontoise, CNRS, UMR 8089, Lab Phys Thaor & Modelisat, F-95302 Cergy Pontoise, France; CNR, Neurosci Inst IN, Via G Moruzzi 1, I-56125 Pisa, Italy; Univ Firenze, European Lab Nonlinear Spect LENS, Via N Carrara 1, I-50019 Sesto Fiorentino, Italy; CNR, Ist Nazl Ott INO, Via Nello Carrara 1, I-50019 Sesto Fiorentino, Italy.

Abstract: An inverse procedure is proposed and tested which aims at recovering the a priori unknown functional and structural information from global signals of living brains activity. To this end we consider a Leaky-Integrate and Fire (LIF) model with short term plasticity neurons, coupled via a directed network. Neurons are assigned a specific current value, which is heterogenous across the sample, and sets the firing regime in which the neuron is operating in. The aim of the method is to recover the distribution of incoming network degrees, as well as the distribution of the assigned currents, from global field measurements. The proposed approach to the inverse problem implements the reductionist Heterogenous Mean-Field approximation. This amounts in turn to organizing the neurons in different classes, depending on their associated degree and current. When tested again synthetic data, the method returns accurate estimates of the sought distributions, while managing to reproduce and interpolate almost exactly the time series of the supplied global field. Finally, we also applied the proposed technique to longitudinal widefield fluorescence microscopy data of cortical functionality in groups of awake Thy1-GCaMP6f mice. Mice are induced a photothrombotic stroke in the primary motor cortex and their recovery monitored in time. An all-to-all LIF model which accommodates for currents heterogeneity allows to adequately explain the recorded patterns of activation. Altered distributions in neuron excitability are in particular detected, compatible with the phenomenon of hyperexcitability in the penumbra region after stroke.

Journal/Review: CHAOS SOLITONS & FRACTALS

Volume: 140      Pages from: 110235  to: 110235

More Information: This project received funding from the European Union´s Horizon 2020 Research and Innovation Programme under Grant Agreements No. 720270 (HBP SGA1), 785907 (HBP SGA2) and 654148 (Laserlab-Europe), and from the EU programme H2020 EXCELLENT SCIENCE -European Research Council (ERC) under grant agreement ID No. 692943 (BrainBIT).
KeyWords: Network, Inverse problem, Neuronal dynamics, Leak Integrate andFire, Heterogenous Mean Field, Animal stroke models, Fluorescence microscopy
DOI: 10.1016/j.chaos.2020.110235

Citations: 6
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