Non-linear auto-regressive models for cross-frequency coupling in neural time series

Tour, Tom Dupré la; Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Wassenhove, Virginie van; Grenier, Yves; Gramfort, Alexandre

doi:10.1371/journal.pcbi.1005893

Cited by 57 publications

(48 citation statements)

References 95 publications

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“…The network simulation was generated as follows: first, a source S 0 was coupled to a target with a CFIT. We adapted an example of [27] to simulate a more complex scenario with a non-sinusoidal driver, since in nonlinear systems such as the brain, perfect sinusoidal are often an exception [28]. The CFIT coupling was between f 1 = 6 Hz and f 2 = 80 Hz.…”

Section: Evaluation Of the Soso Algorithm (Ii) On Examplementioning

confidence: 99%

Measuring spectrally-resolved information transfer for sender- and receiver-specific frequencies

Pinzuti

Wollsdtadt

Gutknecht

et al. 2020

Preprint

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Information transfer, measured by transfer entropy, is a key component of distributed computation. It is therefore important to understand the pattern of information transfer in order to unravel the distributed computational algorithms of a system. Since in many natural systems distributed computation is thought to rely on rhythmic processes a frequency resolved measure of information transfer is highly desirable. Here, we present a novel algorithm, and its efficient implementation, to identify separately frequencies sending and receiving information in a network. Our approach relies on the invertible maximum overlap discrete wavelet transform (MODWT) for the creation of surrogate data in the computation of transfer entropy and entirely avoids filtering of the original signals. The approach thereby avoids well-known problems due to phase shifts or the ineffectiveness of filtering in the information theoretic setting. We also show that measuring frequency-resolved information transfer is a partial information decomposition problem that cannot be fully resolved to date and discuss the implications of this issue. Last, we evaluate the performance of our algorithm on simulated data and apply it to human magnetoencephalography (MEG) recordings and to local field potential recordings in the ferret. In human MEG we demonstrate top-down information flow in temporal cortex from very high frequencies (above 100Hz) to both similarly high frequencies and to frequencies around 20Hz, i.e. a complex spectral configuration of cortical information transmission that has not been described before. In the ferret we show that the prefrontal cortex sends information at low frequencies (4-8 Hz) to early visual cortex (V1), while V1 receives the information at high frequencies (> 125 Hz). Author SummarySystems in nature that perform computations typically consist of a large number of relatively simple but interacting parts. In human brains, for example, billions of neurons work together to enable our cognitive abilities. This well-orchestrated teamwork requires information to be exchanged very frequently. In many cases this exchange February 8, 2020 1/37 happens rhythmically and, therefore, it seems beneficial for our understanding of physical systems if we could link the information exchange to specific rhythms. We here present a method to determine which rhythms send, and which rhythms receive information. Since many rhythms can interact at both sender and receiver side, we show that the interpretation of results always needs to consider that the above problem is tightly linked to partial information decomposition -an intriguing problem from information theory only solved recently, and only partly. We applied our novel method to information transfer in the human inferior temporal cortex, a brain region relevant for object perception, and unexpectedly found information transfer originating at very high frequencies at 100Hz and then forking to be received at both similarly high but also much lower frequencies around 20Hz. These results ...

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Section: Evaluation Of the Soso Algorithm (Ii) On Examplementioning

confidence: 99%

Measuring spectrally-resolved information transfer for sender- and receiver-specific frequencies

Pinzuti

Wollsdtadt

Gutknecht

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…However, since the model is based only on the driver's value, it does not disentangle the ascending phase from the descending phase of the driver. To fix this issue and obtain phase invariance, the parametrization can be improved using a complex-valued driver x = x re + jx im [13]. The parametrization is now:…”

Section: Driven Autoregressive Modelsmentioning

confidence: 99%

“…Our work builds upon driven AR (DAR) models [12], which have been used in particular to estimate cross-frequency coupling (CFC) in neural time-series [13]. In a word, CFC is an inter-frequency coupling phenomenon observed in electrophysiology signals, that is believed to play a central role in functional interactions between neural ensembles [14].…”

Section: Introductionmentioning

confidence: 99%

“…A limitation of DAR models is the assumption that the driver is given. In practice, the driver is obtained by filtering an exogenous time-series, which requires to search for filter parameters over a grid of values [13].…”

Section: Introductionmentioning

confidence: 99%

“…For the former, we propose a fast optimization scheme based on quasi-Newton L-BFGS algorithm [15]. For the latter, we refer the reader to [13]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Driver Estimation in Non-Linear Autoregressive Models

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Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

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Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time-averaged approaches and sophisticated algorithms that can capture the time-varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word 'chronnectome' (integration of the Greek word 'Chronos', which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex

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Non-linear auto-regressive models for cross-frequency coupling in neural time series

Cited by 57 publications

References 95 publications

Measuring spectrally-resolved information transfer for sender- and receiver-specific frequencies

Measuring spectrally-resolved information transfer for sender- and receiver-specific frequencies

Driver Estimation in Non-Linear Autoregressive Models

Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

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