Updating temporal expectancy of an aversive event engages striatal plasticity under amygdala control

Dallérac, Glenn; Graupner, Michael; Knippenberg, J.M.J.; Martinez, Raquel Chacon Ruiz; Tavares, Tatiane Ferreira; Tallot, Lucille; Massioui, Nicole El; Verschueren, Anna; Höhn, Sophie; Boulanger-Bertolus, Julie; Reyes, Alex D.; LeDoux, Joseph E.; Schafe, Glenn E.; Díaz-Mataix, Lorenzo; Doyère, Valérie

doi:10.1038/ncomms13920

Cited by 37 publications

(55 citation statements)

References 66 publications

(90 reference statements)

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“…We tested DAR models on three different datasets, with invasive recordings in humans and rodents: 1. A rodent local field potential (LFP) recording in the dorso-medial striatum from [Dallérac et al, 2017](supplementary figure 2), (1800 seconds down-sampled at 333 Hz) 2. A rodent LFP recording in the hippocampus from [Khodagholy et al, 2015] collected during rapid eye movement (REM) sleep (100 seconds down-sampled at 625 Hz) 3.…”

Section: Empirical Datasetsmentioning

confidence: 99%

Non-linear Auto-Regressive Models for Cross-Frequency Coupling in Neural Time Series

Tour

Tallot

Grabot

et al. 2017

Preprint

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We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model "goodness of fit" via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. Author SummaryNeural oscillations synchronize information across brain areas at various anatomical and temporal scales. Of particular relevance, slow fluctuations of brain activity have been shown to affect high frequency neural activity, by regulating the excitability level of neural populations. Such cross-frequency-coupling can take several forms. In the most frequently observed type, the power of high frequency activity is time-locked to a specific phase of slow frequency oscillations, yielding phase-amplitude-coupling (PAC). Even when readily observed in neural recordings, such non-linear coupling is particularly challenging to formally characterize. Typically, neuroscientists use band-pass filtering and Hilbert transforms with ad-hoc correlations. Here, we explicitly address current limitations and propose an alternative probabilistic signal modeling approach, for which statistical inference is fast and well-posed. To statistically model PAC, we propose to use non-linear auto-regressive models which estimate the spectral modulation of a signal conditionally to a driving signal. This conditional spectral analysis enables easy model selection and clear hypothesis-testing by using the likelihood of a given model. We demonstrate the advantage of the model-based approach on three datasets acquired in rats and in humans. We further provide novel neuroscientific insights on previously reported PAC phenomena, capturing two mechanisms in PAC: influence of amplitude and directionality estimation.

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Section: Empirical Datasetsmentioning

confidence: 99%

Non-linear Auto-Regressive Models for Cross-Frequency Coupling in Neural Time Series

Tour

Tallot

Grabot

et al. 2017

Preprint

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“…One task that provides an ideal window into behavioral flexibility is interval timing, which requires participants to estimate an interval of several seconds via a motor response. Across species, interval timing requires the prefrontal cortex and striatum (Coull et al, 2011; Dallérac et al, 2017; Emmons et al, 2017, 2016; Matell and Meck, 2004; Merchant and de Lafuente, 2014). Work from our group and others has shown that both prefrontal and striatal neurons encode temporal information via ‘time-related ramping’ activity—or monotonic changes in firing rate over a temporal interval (Bakhurin et al, 2017; Donnelly et al, 2015; Emmons et al, 2017; Kim et al, 2018; Narayanan, 2016; Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Temporal learning among prefrontal and striatal ensembles

Emmons

Tunes

Choi

et al. 2020

Preprint

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1Behavioral flexibility requires the prefrontal cortex and striatum. Here, we investigate neuronal 2 ensembles in the medial frontal cortex (MFC) and the dorsomedial striatum (DMS) during one 3 form of behavioral flexibility: learning a new temporal interval. We studied corticostriatal 4 neuronal activity as rodents trained to respond after a 12-second fixed interval (FI12) learned to 5 respond at a shorter 3-second fixed interval (FI3). On FI12 trials, we discovered time-related 6 ramping was reduced in the MFC but not in the DMS in two-interval vs. one-interval sessions. 7 We also found that more DMS neurons than MFC neurons exhibited differential interval-related 8 activity on the first day of two-interval performance. Finally, MFC and DMS ramping was 9 similar with successive days of two-interval performance but DMS temporal decoding increased 10 on FI3 trials. These data suggest that the MFC and DMS play distinct roles during temporal 11 learning and provide insight into corticostriatal circuits. 13 Behavioral flexibility requires learning to adapt to uncertainty. Two forebrain structures 14 critical for flexibility are the prefrontal cortex and striatum (Fuster, 2008; Kehagia et al., 2010). 15 Prefrontal cortical neurons densely innervate the striatum (Gabbott et al., 2005; Wall et al., 2013) 16 and disruptions of either structure profoundly impact the learning of new goals, rules, and 17 strategies (Hart et al., 2018; Ragozzino, 2007). Dysfunctional corticostriatal circuits and 18 connectivity are implicated in a range of psychiatric and neurological disorders (Deutch, 1993; 19 Shepherd, 2013). However, the relative roles of prefrontal and striatal networks during 20 behavioral flexibility are unclear. 21One task that provides an ideal window into behavioral flexibility is interval timing, 22 which requires participants to estimate an interval of several seconds via a motor response. 23Across species, interval timing requires the prefrontal cortex and striatum (Coull et al., 2011; Merchant and de 25 Lafuente, 2014). Work from our group and others has shown that both prefrontal and striatal 26 neurons encode temporal information via 'time-related ramping' activity-or monotonic changes 27 in firing rate over a temporal interval (Bakhurin et al., 2017; Donnelly et al., 2015; Emmons et 28 al., 2017; Kim et al., 2018; Narayanan, 2016; Wang et al., 2018). Our past work suggested that 29 ramping activity in neurons of the medial frontal cortex (MFC) and the dorsomedial striatum 30 (DMS) is very similar, with ~40% of neurons in each area exhibiting such activity (Emmons et 31 al., 2017). We have also found that MFC inactivation attenuates DMS ramping (Emmons et al., 32 2019, 2017) and that MFC stimulation is sufficient to increase DMS ramping (Emmons et al., 33 2019). These data suggest that DMS ramping is closely linked to MFC ramping and suggest the 34 hypothesis that MFC and DMS ensembles respond similarly as animals learn new temporal 35 intervals. By contrast, recordings from prima...

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“…It is closely related to cognitive mechanisms such as attention, high-level visual processing and motor control. The first signal (LFPcortical) is recorded in the rat cortex [17], while the second one (LFP-striatal) is recorded in the rat striatum [41]. Figure 8 shows samples from these two data sets.…”

Section: Local Field Potential Datamentioning

confidence: 99%

Generalized Convolutional Sparse Coding With Unknown Noise

Wang

Kwok

2020

IEEE Trans. on Image Process.

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Convolutional sparse coding (CSC) can learn representative shift-invariant patterns from multiple kinds of data. However, existing CSC methods can only model noises from Gaussian distribution, which is restrictive and unrealistic. In this paper, we propose a general CSC model capable of dealing with complicated unknown noise. The noise is now modeled by Gaussian mixture model, which can approximate any continuous probability density function. We use the expectation-maximization algorithm to solve the problem and design an efficient method for the weighted CSC problem in maximization step. The crux is to speed up the convolution in the frequency domain while keeping the other computation involving weight matrix in the spatial domain. Besides, we simultaneously update the dictionary and codes by nonconvex accelerated proximal gradient algorithm without bringing in extra alternating loops. The resultant method obtains comparable time and space complexity compared with existing CSC methods. Extensive experiments on synthetic and real noisy biomedical data sets validate that our method can model noise effectively and obtain high-quality filters and representation.

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Updating temporal expectancy of an aversive event engages striatal plasticity under amygdala control

Cited by 37 publications

References 66 publications

Non-linear Auto-Regressive Models for Cross-Frequency Coupling in Neural Time Series

Non-linear Auto-Regressive Models for Cross-Frequency Coupling in Neural Time Series

Temporal learning among prefrontal and striatal ensembles

Generalized Convolutional Sparse Coding With Unknown Noise

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