<b>CircStat</b>: A<i>MATLAB</i>Toolbox for Circular Statistics

Berens, Philipp

doi:10.18637/jss.v031.i10

Cited by 2,657 publications

(2,496 citation statements)

References 27 publications

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“…Statistical analysis was performed with GraphPad Prism5, MATLAB and the Circular Statistics Toolbox by Philipp Berens 50 . Comparisons of firing rates were carried out with non-parametric tests, with the exception of the 2-way ANOVA analysis, for which an appropriate non-parametric equivalent is not available.…”

Section: Resultsmentioning

confidence: 99%

Behavior-dependent specialization of identified hippocampal interneurons

et al. 2012

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A large variety of GABAergic interneurons control information processing in hippocampal circuits governing the formation of neuronal representations. Whether distinct hippocampal interneuron types contribute differentially to information-processing during behavior is not known. We employed a novel technique for recording and labeling interneurons and pyramidal cells in drug-free, freely-moving rats. Recorded parvalbumin-expressing basket interneurons innervate somata and proximal pyramidal cell dendrites, whereas nitric-oxide-synthase-and neuropeptide-Y-expressing ivy cells provide synaptic and extrasynaptic dendritic modulation. Basket and ivy cells showed distinct spike timing dynamics, firing at different rates and times during theta and ripple oscillations. Basket but not ivy cells changed their firing rates during movement, sleep and quiet wakefulness, suggesting that basket cells coordinate cell assemblies in a behavioral state-contingent manner, whereas persistently-firing ivy cells might control network excitability and homeostasis. Different interneuron types provide GABA to specific subcellular domains at defined times and rates, thus differentially controlling network activity during behavior.GABAergic interneurons control information processing in cortical circuits as percussionists set the rhythm for a melody, or traffic lights regulate the movement of cars through a city. Interneurons generate oscillatory activity 1, 2 , synchronize the activity of pyramidal cells 3 and set time windows for synaptic integration 4 . A large diversity of interneuronal types is a hallmark of cortical circuits. Different domains of pyramidal cells, such as the soma, axoninitial-segment, proximal or distal dendrites 5 are innervated by distinct types of GABAergic interneuron. They also have distinct inputs and membrane properties 6-10 and show different firing patterns during network oscillations induced in vitro [11][12][13][14] or recorded in anesthetized animals 15 , indicating distinct roles for specific interneuron types. However, research on interneurons in drug-free animals that can freely change their behavior, has so far been limited to recordings from unidentified interneurons because of technical limitations. In the barrel cortex of head-restrained mice, groups of interneurons with distinct membrane dynamics during different behavioral states have been described 16,17 and in the hippocampus unidentified interneurons or interneurons belonging to heterogeneous groups expressing parvalbumin and/or somatostatin have been reported [18][19][20][21] to fire with different firing patterns during network oscillations. But, how do specific types of identified interneurons control the activity of cortical circuits in freely-moving animals? CouldCorrespondence and requests for materials should be addressed to D.L. (damien.lapray@pharm.ox.ac.uk) Results Identification of neurons recorded in freely-moving ratsWe recorded the activity of parvalbumin (PV)-expressing basket, ivy and pyramidal cells in the dorsal CA...

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Section: Resultsmentioning

confidence: 99%

Behavior-dependent specialization of identified hippocampal interneurons

et al. 2012

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“…This step allows us to find the source for a possible expanding wave in each cycle (step 2, Figure 1-figure supplement 2), about which the phase field is then evaluated to quantify the evidence for an expanding wave spatiotemporal organization (step 3, Figure 1-figure supplement 2). For this next step, we calculate the circular-linear correlation coefficient f;d (Jammalakadaka and Sengupta, 2001;Berens, 2009) between signal phase f and radial distance d from the source point in the original, unsmoothed phase field…”

Section: Detection Of Expanding Wavesmentioning

confidence: 99%

“…With the putative rotation center isolated in each oscillation cycle, we then proceed to calculate the circular-circular correlation coefficient f; between signal phase f and rotation angle (Fisher, 1993;Berens, 2009) …”

Section: Detection Of Rotating Wavesmentioning

confidence: 99%

“…To do this, we adopted a recentered averaging approach, shifting the vector field of propagation directions from the smoothed phase fields at each oscillation cycle to the putative rotation center (red dots, Figure 1-figure supplement 5), and taking the circular mean (Fisher, 1993;Berens, 2009) of propagation direction at each point. Performing the calculation in this way prevents regions with noise or high phase gradient magnitude from dominating the result.…”

Section: Average Vector Field Controlsmentioning

confidence: 99%

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Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night

Muller

Piantoni

Koller

et al. 2016

eLife

176

229

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During sleep, the thalamus generates a characteristic pattern of transient, 11-15 Hz sleep spindle oscillations, which synchronize the cortex through large-scale thalamocortical loops. Spindles have been increasingly demonstrated to be critical for sleep-dependent consolidation of memory, but the specific neural mechanism for this process remains unclear. We show here that cortical spindles are spatiotemporally organized into circular wave-like patterns, organizing neuronal activity over tens of milliseconds, within the timescale for storing memories in large-scale networks across the cortex via spike-time dependent plasticity. These circular patterns repeat over hours of sleep with millisecond temporal precision, allowing reinforcement of the activity patterns through hundreds of reverberations. These results provide a novel mechanistic account for how global sleep oscillations and synaptic plasticity could strengthen networks distributed across the cortex to store coherent and integrated memories.

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“…CNV amplitudes, prestimulus RMS amplitude for each band (delta, theta, alpha, and beta), and RMS band changes associated with stimulus onset, were examined in separate two-way MANOVAs, assessing amplitudes at nine core sites (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4) with the within-subjects factors of Topography and stimulus To assess the occurrence of preferred brain states, circular statistics were examined using the CircStat MATLAB toolbox (Berens, 2009). We report results of the Hodges-Ajne Omnibus test for non-uniformity of circular data.…”

Section: Statistical Analysesmentioning

confidence: 99%

Preferred EEG brain states at stimulus onset in a fixed interstimulus interval equiprobable auditory Go/NoGo task: A definitive study

Barry

Blasio

Pascalis

et al. 2014

International Journal of Psychophysiology

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This study examined the occurrence of preferred EEG phase states at stimulus onset in an equiprobable auditory Go/NoGo task with a fixed interstimulus interval, and their effects on the resultant event-related potentials (ERPs). We used a sliding short-time FFT decomposition of the EEG at Cz for each trial to assess prestimulus EEG activity in the delta, theta, alpha and beta bands. We determined the phase of each 2. Hz narrow-band contributing to these four broad bands at 125. ms before each stimulus onset, and for the first time, avoided contamination from poststimulus EEG activity. This phase value was extrapolated 125. ms to obtain the phase at stimulus onset, combined into the broad-band phase, and used to sort trials into four phase groups for each of the four broad bands. For each band, ERPs were derived for each phase from the raw EEG activity at 19 sites. Data sets from each band were separately decomposed using temporal Principal Components Analyses with unrestricted VARIMAX rotation to extract N1-1, PN, P2, P3, SW and LP components. Each component was analysed as a function of EEG phase at stimulus onset in the context of a simple conceptualisation of orthogonal phase effects (cortical negativity vs. positivity, negative driving vs. positive driving, waxing vs. waning). The predicted non-random occurrence of phase-defined brain states was confirmed. The preferred states of negativity, negative driving, and waxing were each associated with more efficient stimulus processing, as reflected in amplitude differences of the components. The present results confirm the existence of preferred brain states and their impact on the efficiency of brain dynamics in perceptual and cognitive processing. AbstractThis study examined the occurrence of preferred EEG phase states at stimulus onset in an equiprobable auditory Go/NoGo task with a fixed interstimulus interval, and their effects on the resultant event-related potentials (ERPs). We used a sliding short-time FFT decomposition of the EEG at Cz for each trial to assess prestimulus EEG activity in the delta, theta, alpha and beta bands. We determined the phase of each 2 Hz narrow-band contributing to these four broad bands at 125 ms before each stimulus onset, and for the first time, avoided contamination from postimulus EEG activity. This phase value was extrapolated 125 ms to obtain the phase at stimulus onset, combined into the broad-band phase, and used to sort trials into four phase groups for each of the four broad bands. For each band, ERPs were derived for each phase from the raw EEG activity at 19 sites. Data sets from each band were separately decomposed using temporal Principal Components Analyses with unrestricted Varimax rotation to extract N1-1, PN, P2, P3, SW and LP components. Each component was analysed as a function of EEG phase at stimulus onset in the context of a simple conceptualisation of orthogonal phase effects (cortical negativity vs. positivity, negative driving vs. positive driving, waxing vs. waning). The predicted nonrandom o...

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CircStat: AMATLABToolbox for Circular Statistics

Cited by 2,657 publications

References 27 publications

Behavior-dependent specialization of identified hippocampal interneurons

Behavior-dependent specialization of identified hippocampal interneurons

Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night

Preferred EEG brain states at stimulus onset in a fixed interstimulus interval equiprobable auditory Go/NoGo task: A definitive study

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