Independent Components of Neural Activity Carry Information on Individual Populations

Głąbska, Helena; Potworowski, Jan; Łęski, Szymon; Wójcik, Daniel K.

doi:10.1371/journal.pone.0105071

Cited by 40 publications

(54 citation statements)

References 33 publications

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“…Because there are potentially more populations detected by neural recordings than can possibly be parsed, it has been unclear how ICA would behave when faced with real neural data. Recent simulations in Gła̧bska et al (2014) and Makarov et al, (2010) show that ICA tends to group similar populations together, which is consistent with our results. With more recordings, ICA would be able to pick out more sources at finer resolution (Agarwal et al, 2014b; Jun et al, 2017).…”

Section: Discussionsupporting

confidence: 93%

Subcortical theta oscillations alternate with high amplitude neocortical population within synchronized states

Krull

Sakata

Toyoizumi

2018

Preprint

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Synchronized states are marked by large-amplitude low-frequency oscillations in the cortex. These states can be seen during quiet waking or slow-wave sleep. Within synchronized states, previous studies have noted a plethora of different types of activity, including delta oscillations (0.5-4 Hz) and slow oscillations (<1 Hz) in the cortex and large- and small-irregular activity in the hippocampus. However, it is not still fully characterized how neural populations contribute to the synchronized state. Here we apply independent component analysis (ICA) to parse which populations are involved in different kinds of cortical activity, and find two populations that alternate throughout synchronized states. One population broadly affects cortical deep layers, and is associated with larger amplitude slower cortical activity. The other population exhibits theta-frequency oscillations that are not easily observed in raw field potential recordings. These theta oscillations apparently come from below the cortex, suggesting hippocampal origin, and are associated with smaller amplitude faster cortical activity. Relative involvement of these two alternating populations may indicate different modes of operation within synchronized states.

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Section: Discussionsupporting

confidence: 93%

Subcortical theta oscillations alternate with high amplitude neocortical population within synchronized states

Krull

Sakata

Toyoizumi

2018

Preprint

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“…An obvious question is whether the present successful estimation of network parameters from LFPs will extend to more complex network models with more than three parameters specifying the connections like in the Brunel network. Of particular interest here is multilayered cortical network models where several neuronal populations contribute to the LFP signal (Reimann et al, 2013; Głąbska et al, 2014; Tomsett et al, 2015; Głąbska et al, 2016; Hagen et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Estimation of neural network model parameters from local field potentials (LFPs)

Skaar

Stasik

Hagen

et al. 2019

Preprint

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1Most modeling in systems neuroscience has been descriptive where neural 2 representations, that is, 'receptive fields', have been found by statistically 3 correlating neural activity to sensory input. In the traditional physics approach 4 to modelling, hypotheses are represented by mechanistic models based on the 5 underlying building blocks of the system, and candidate models are validated 6 by comparing with experiments. Until now validation of mechanistic cortical 7 network models has been based on comparison with neuronal spikes, found 8 from the high-frequency part of extracellular electrical potentials. In this 9 * Joint first author † Joint first author ‡ Corresponding author: gaute.einevoll@nmbu.no the signal, the local field potential (LFP), can be used to infer properties of the 11 neuronal network. In particular, we asked the question whether the LFP can 12 be used to accurately estimate synaptic connection weights in the underlying 13 network. We considered the thoroughly analysed Brunel network comprising 14 an excitatory and an inhibitory population of recurrently connected integrate-15 and-fire (LIF) neurons. This model exhibits a high diversity of spiking 16 network dynamics depending on the values of only three synaptic weight 17 parameters. The LFP generated by the network was computed using a hybrid 18 scheme where spikes computed from the point-neuron network were replayed 19on biophysically detailed multicompartmental neurons. We assessed how 20 accurately the three model parameters could be estimated from power spectra 21 of stationary 'background' LFP signals by application of convolutional neural 22 nets (CNNs). All network parameters could be very accurately estimated, 23 suggesting that LFPs indeed can be used for network model validation. 24 Significance statement 25 Most of what we have learned about brain networks in vivo have come from the 26 measurement of spikes (action potentials) recorded by extracellular electrodes. 27The low-frequency part of these signals, the local field potential (LFP), 28 contains unique information about how dendrites in neuronal populations 29 integrate synaptic inputs, but has so far played a lesser role. To investigate 30 whether the LFP can be used to validate network models, we computed LFP 31 signals for a recurrent network model (the Brunel network) for which the 32 3 ground-truth parameters are known. By application of convolutional neural 33 nets (CNNs) we found that the synaptic weights indeed could be accurately 34 estimated from 'background' LFP signals, suggesting a future key role for 35 LFP in development of network models. 36 1 Introduction 37 The traditional physics approach to modeling typically involves four steps: 38 (i) A hypothesis is formulated in terms of a candidate mechanistic mathemat-39 ical model, that is, a model based on interactions between building blocks of 40 the system, (ii) predictions of experimentally measurable quantities are calcu-41 lated from the model, (iii) the predictions are compared with exper...

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“…It is therefore absolutely necessary to disentangle the temporal fluctuations produced by each of the contributing pathways in order to establish a quantitative relationship with the activity of a single afferent population. So far, the only approaches providing acceptable separation that maintains complete temporal resolution of the mixing sources are the spatial discrimination techniques (Makarova et al, 2011; Fernández-Ruiz and Herreras, 2013; Martín-Vázquez et al, 2013, 2016; Benito et al, 2014, 2016; Głąbska et al, 2014; Schomburg et al, 2014). …”

Section: The Evil Triad: Baseline Polarity and A Cocktail Partymentioning

confidence: 99%

Local Field Potentials: Myths and Misunderstandings

Herreras

2016

Front. Neural Circuits

251

244

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The intracerebral local field potential (LFP) is a measure of brain activity that reflects the highly dynamic flow of information across neural networks. This is a composite signal that receives contributions from multiple neural sources, yet interpreting its nature and significance may be hindered by several confounding factors and technical limitations. By and large, the main factor defining the amplitude of LFPs is the geometry of the current sources, over and above the degree of synchronization or the properties of the media. As such, similar levels of activity may result in potentials that differ in several orders of magnitude in different populations. The geometry of these sources has been experimentally inaccessible until intracerebral high density recordings enabled the co-activating sources to be revealed. Without this information, it has proven difficult to interpret a century's worth of recordings that used temporal cues alone, such as event or spike related potentials and frequency bands. Meanwhile, a collection of biophysically ill-founded concepts have been considered legitimate, which can now be corrected in the light of recent advances. The relationship of LFPs to their sources is often counterintuitive. For instance, most LFP activity is not local but remote, it may be larger further from rather than close to the source, the polarity does not define its excitatory or inhibitory nature, and the amplitude may increase when source's activity is reduced. As technological developments foster the use of LFPs, the time is now ripe to raise awareness of the need to take into account spatial aspects of these signals and of the errors derived from neglecting to do so.

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Independent Components of Neural Activity Carry Information on Individual Populations

Cited by 40 publications

References 33 publications

Subcortical theta oscillations alternate with high amplitude neocortical population within synchronized states

Subcortical theta oscillations alternate with high amplitude neocortical population within synchronized states

Estimation of neural network model parameters from local field potentials (LFPs)

Local Field Potentials: Myths and Misunderstandings

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