Energy-Based Hierarchical Clustering of Cortical Slow Waves in Multi-Electrode Recordings

Chronic full-band recordings with graphene microtransistors as neural interfaces for discrimination of brain states

“…This change in complexity is associated with changes in network excitability, in the spatiotemporal patterns of wave propagation and in the spectral content of the signal. 8,18–20 The majority of the existing studies, however, use AC-coupled passive electrodes that filter out the DC component of the signal. 2…”

“…It is well known that the spectral components of the signal can be used to discriminate between different brain states. 11 Accordingly, we fitted a linear regression model to explain the relationship between the anesthesia level, expressed here as isoflurane concentration [% in O 2 ] delivered to the animal, and the spectral content of the signal in five frequency bands defined as follow: delta [0.5-4] Hz, theta [4][5][6][7] Hz, alpha [7][8][9][10][11][12][13][14][15] Hz, beta [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz and gamma Hz. We computed the power spectral density (PSD) of subsequent 3-s segments derived from the raw signal using Welch's method with Hanning windows.…”

“…This change in complexity is associated with changes in network excitability, in the spatiotemporal patterns of wave propagation and in the spectral content of the signal. 8,[18][19][20] The majority of the existing studies, however, use AC-coupled passive electrodes that filter out the DC component of the signal. 2 Here, we aimed to investigate whether the full-band recordings achievable with arrays of gSGFETs would reveal novel information about brain states and levels of anesthesia that may be lost in traditional high-pass recordings.…”

A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

“…Moreover, it is generally unclear which observables are relevant for the local cortical function or higher cognitive functions (e.g., memory consolidation). The typically reported properties are thus often heuristic and include, for example, transition slopes, 35 phase velocity, 7 , 32 wave type classification, 44 , 45 , 46 , 47 , 48 source/sink location and propagation patterns, 49 , 50 excitability, 37 , 51 , 52 and event frequency. 53 Thus, we here focus on common observables that can be extracted from different measurement modalities (i.e., planarity, interwave intervals, velocity, and direction).…”

Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

“…The properties that are typically reported are thus often heuristic and include, for example, transition slopes (Chan et al, 2015), b) recorded anesthetized GCaMP6f mice with wide-field fluorescence microscopy (Celotto et al, 2020), c) distributed network of cortical columns of LIF with Spike Frequency Adaptation neurons (Pastorelli et al, 2019), d) one-dimensional multi-layer thalamo-cortical model with one-and two-compartment neuron models using Hodgkin-Huxley kinetics (Bazhenov et al, 2002), e) 2D balanced conductance-based spiking neural network model (Keane and Gong, 2015), f) multi-electrode recording in ferret cortical slices (Capone et al, 2019b), g) human HD-EEG during first sleep episode of the night (Massimini et al, 2004), h) human ECoG recording during sleep (Muller et al, 2016), i) intracranial depth EEG in sleeping human subjects (Nir et al, 2011), j) intracranial depth EEG in humans during sleep (Botella-Soler et al, 2012). (Ruiz-Mejias et al, 2011), phase velocity (Massimini et al, 2004;Muller et al, 2016), wave type classification (Camassa et al, 2021;Denker et al, 2018b;Pazienti et al, 2022;Roberts et al, 2019;Townsend et al, 2015), source/sink location and propagation patterns (Huang et al, 2010;Liang et al, 2021), excitability (De Bonis et al, 2019Mattia and Sanchez-Vives, 2012;Ruiz-Mejias et al, 2016), event frequency (Capone et al, 2022), and others. Thus, we here also focus on such common observables (i.e., planarity, inter-wave intervals, velocity, and direction).…”