Arango-Cabrera, R.

Balaguer-Ballester, Emili

Mateos-Aparicio, P.

Sanchez-Vives, M.V.

Bournemouth University Research Online

Emergence and Propagation of Asynchronous States ofSpontaneous Cortical ActivityRomán Arango1, Pedro Mateos-Aparicio2, Maria V. Sanchez-Vives2,3† and Emili Balaguer-Ballester1†(1) Department of Computing and Informatics, Faculty of Science and Technology, Bournemouth University, UK.(2) Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. (3) ICREA, Barcelona, Spain.† Joint last author.30th Annual Computational Neuroscience Meeting, Online, 5th July 2021Slow Oscillations (SO) as The Default Activity Pattern of the Cerebral Cortex [1]PhenomenologyI Multiscale Slow Oscillations (≤1Hz):. from the neuronal level, to the whole brain (slowwaves), through the local network level.I Emergent activity under functionaldisconnection:. NREM sleep, deep anaesthesia or cortical slicesKey Dynamical FeaturesI Relaxation-oscillator behaviour:. Intrinsic fluctuations between two alternatingmetastable attractors [5]: UP and DOWN states.I Spatio-temporal propagation:. The travelling UP/DOWN wavefront revealsproperties of the underlying network [4].Figure 1:Simultaneous LFP (top) and intracellular (bottom) recordings from theauditory cortex of the anaesthetized rat, exhibiting slow oscillations [2].Further Properties of the SO Cortical StateI Low Connectivity: resilience to perturbances [3].I Facilitation of the transition towards more connected,awake-like states (AS).I UP States: Model of circuit attractor implementingcomputation and acting as a window intoconciousness.→ SO is a promising paradigm to study the cortical function and the emergence of conciousness.MotivationHow such a globally synchronized regime (the SO) gives rise to largely decorrelated awake states is an openquestion [5, 6, 7]. We aim at:I Characterising and detecting the various states emerging from the SO regimeI In particular, how the emergence of asynchrony is spatially orchestrated by the local network.Experimental Model and Cortical Slice RecordingsExtracellular recordings in coronal cortical slices of the ferret’s primary visual cortexFrom SO to an Awake-LikeState (AS) [3, 8]Pharmacological ModulationsI addition of Carbachol (0.5 µM) +Norepinephrine (50 µM)I reduction of extracellular Calcium(from 1 mM to 0.8 mM)→ Experimental model to explorethe transitions from the SO statetowards an awake-like, largelyasynchronous state: emulatingthe transition fromunconciousness to consiousness.Figure 2:Nissl-stained ferret’s V1 cortical slice depicting cor-tical layers and the location of the multi-electrode array. Elec-trodes will ideally lie on different layers (supra- and infra-ganular), across different cortical columns [4].Figure 3:16-channelflexible multi-electrodearray used for therecordings [9].LFP and MUAExtracellular Recordings are usually decomposed into Local FieldPotentials (LFP) and Multi-Unit Activity (MUA):I LFP results from afferent neuronal activity (e.g., from thesummation of EPSP), as captured by the low-frequencyband (<200Hz) of the extracellular recordings.I Only units in the vicinity of the electrode contribute to theMUA (ie, efferent activity), represented in the highfrequencies of the recording.Estimating the MUATheoretical motivation: high-frequency spectralcomponents of the population firing rate are asymptoticallyproportional to the individual firing rates of the neuronsinvolved [10].→ The MUA may be estimated as the relative powerchange of the high frequencies (200-1500 Hz) of theextracellular recordings.Figure 4:Signals obtained from an electrode’s ECR during the SO regime. Fromtop to bottom: ECR, LFP, raw MUA and energy-preserving MUA.Figure 5:Same as previous figure during the Awake-Like (AS) regime. Note thechange of magnitude order over the MUA signals.Decomposition of the Awake-Like Regime into Synchronous/Asynchronous PeriodsI Periods of MUA are deemed asynchronous whenever their spectral features do not significantly differ fromtheir intrinsic background noise.I To further validate this method, we devised a complementary strategy to detect synchronous activity, whichprovides in addition significance level thresholds.Figure 6:Representative detection example of asynchronous and synchronous states over multi-channel logMUA signals. Synchronous periods detected at thestated significance level α = 6.31 · 10−4 are shown in red; asynchronous periods within a confidence level CL = 99.95%, in green; in blue those periods that areover-classified; and in black, the not-classified.Spatial Correlation of Oscillatory Activity During the ASMUA’s Power SpectraFigure 7:Averaged power spectra of thelogMUA of individual channels. Log-log-space plots are displayed by MEA nodes.Dashed lines hint at their background en-ergy.A consistent main peak (1.482 Hz± 0.135 Hz, n = 5) in all chan-nels across different slices in es-timated logMUA’s power spectraover entire AS recordings (300 s)reflects the time-average of inter-mittent surges of oscillations.→ Significant differences both in peak’s frequency andpower between intra- v inter-column location, irrespectiveof the layer.Figure 8:Effects of the layout spatial organisation over the main spectral peak’sfrequency (right) and normalised power (left). Each dot represents the marginalmean of the corresponding feature for each slice under one level of the 2-level locationfactor. (*) p < 0.05, (**) p < 0.01, (***) p < 0.001.Spatial Clustering of Spectral DensitiesFigure 9:Spatial organisation of mean oscillatory activity assessed by the similarity of normalised power spectra. Top left, similarity heat-map of all 16 channels’spectra; the darker the matrix entry, the more similar the spectra. Bottom left, statistical assessment of the median similarity distance under three distinctorganisational levels (node, column and layer). Each dot represents the similarity between two power spectra. Grey dots for inter-group comparisons, red forintra-group. (***) p < 0.001. Top right, dendrogram representation of the similarity matrix. Four non-degenerated clusters were obtained, whose colour-codingmatches that of the MEA schematic below (bottom right).→ Power spectra tend to form clusters that broadly encompass cortical columns, as intra-column vinter-column similarity tests show.→ The laminar level proves not to be significant to account for spectral similarity.Global Spatio-Temporal Coherence of AsynchronicityFigure 10:Spatio-temporal coherence of asynchronous states’ occurrence. Top: isolated async states, aggregated by node (as their colour indicates) and overlappedfor all 6 nodes. Bottom: similarity matrices for each level of aggregation: channel (left), node (centre) and column (right). Top row shows the normalised Jaccardcoefficients, measuring the ratio between the intersection and union of two sets. Normalisation of the Jaccard coefficients is required to compare asynchronouspatterns of aggregates with very disparate synchronicity tenors. Bottom row shows the corresponding surrogate p-values (N = 10000, for column and nodeaggregates; N=1000 for the individual channel interactions).I Channels within the same node or the same column show the highest degree of co-occurrence of asynchronous periods.I Some evidences of transient trans-columnar asynchrony co-occurrence.→This is the first step towards our proof that transitions between synchronous and asynchronous statespropagate underlying a columnar organisation.ConclusionsI In the AS, an excited SO-like activity cohabits with periods of asynchrony of uneven duration and irregularoccurrence [5].I The spatio-temporal interplay of these states depends on the structural organisation of the cortical network:. the firing rate intensity is dictated by the layer [11]. the oscillatory activity is sustained at the column level [12]. sync/async alternation propagates across the whole sliceReferences[1] Sanchez-Vives MV, et al. (2017) Neuron 94(5):993-1001.[2] Sanchez-Vives MV, et al. (2014) Arch Ital Biol. 152(2-3):147-55.[3] D’Andola M, et al. (2017) Cereb Cortex, 112:105-113.[4] Capone C, et al. (2017) Cereb Cortex, 28:1-17.[5] Tort-Colet N, et al. (2021) Cell Reports 35:109270[6] Dasilva M, et al. (2021) Neuroimage 224:117415.[7] Barbero-Castillo A, et al. (2021) Adv Sci (Weinh) 2021 May 21:e2005027.[8] Barbero-Castillo A, et al. (2021) J Neurosci, 41(23):5029-5044.[9] Illa X, et al. (2015) In: SPIE Microtechnologies, Int Soc Opt and Phot, p. 951803.[10] Mattia M, et al. (2002) Phys Rev E, 66 051917.[11] Senzai Y, et al. (2019) Neuron 101(3):500–513.[12] Rebollo B, et al. (2021) Sci Adv 7(10):eabc7772.balaguerlab.github.io http://www.sanchez-vives.org/ rcabrera@bournemouth.ac.uk

Emergence and propagation of asynchronous states of spontaneous cortical activity

http://eprints.bournemouth.ac.uk/35797/1/Poster_CNS2021_RomanArango.pdf

Emergence and propagation of asynchronous states of spontaneous cortical activity

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