Dynamics of neuronal firing correlation: modulation of "effective connectivity"

Aertsen, A; Gerstein, George L.; Habib, Muhammad; Palm, Günther

doi:10.1152/jn.1989.61.5.900

Cited by 1,014 publications

(796 citation statements)

References 20 publications

(3 reference statements)

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“…A very useful graphical display for this purpose is the joint poststimulus time histogram (JPSTH; Aertsen et al 1989). This extends the PSTH by counting the number of trials on which spikes occur for both neuron 1 at time u and neuron 2 at time v, with the aim of indicating the times (and relative time lags v Ϫ u) at which large numbers of joint spikes occur.…”

Section: The Tendency Of Two Neurons To Act Together May Be Displayedmentioning

confidence: 99%

Statistical Issues in the Analysis of Neuronal Data

Kass¹,

Ventura²,

Brown³

2005

Journal of Neurophysiology

251

239

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Kass, Robert E., Valérie Ventura, and Emery N. Brown. Statistical issues in the analysis of neuronal data. J Neurophysiol 94: 8 -25, 2005; doi:10.1152/jn.00648.2004. Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews wellestablished statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data. I N T R O D U C T I O NTechnical advances have made available new methods for collecting, storing, and manipulating electrophysiological data. Investigations may now not only characterize neuronal activity in anatomically well defined regions, but they can also examine dynamics of neuronal response and their relationship to behavior. Although elementary methods of data analysis [such as t-tests or visual examination of the peristimulus time histogram (PSTH)] remain useful for many purposes, the growing complexity of neuroscientific experiments, often examining subtle changes on a comparatively fine timescale, requires careful attention to statistical methods for data analysis. In this overview we discuss some of the fundamental data analytical issues that face researchers in neurophysiology, illustrating the general points with the problems of describing the evolution of a neuron's firing rate across time, finding accurate population codes, and assessing time-varying correlation between 2 neurons. In each case recent work has provided a statistical technique that outperforms previous methodology, boosting the scientific information as effectively as if the number of experimental trials, or the number of neurons, had been increased by a substantial factor. We also indicate some of the ways modern statistical procedures can accommodate important complexities, such as dynamic changes in temporal and spatial aspects of hippocampal place cell firing and trial-to-trial variability in cortical neurons recorded from behaving animals. Our review supplements the brief and general guidance offered by Curran-Everett and Benos (2004), and may be regarded as an update to the early work of Perkel et al. (1967a,b).The new field of computational neuroscience uses detailed biophysical models and artificial neural networks to study emergent behavior of neural systems and the way neural systems represent and transmit information (e.g...

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Section: The Tendency Of Two Neurons To Act Together May Be Displayedmentioning

confidence: 99%

Statistical Issues in the Analysis of Neuronal Data

Kass¹,

Ventura²,

Brown³

2005

Journal of Neurophysiology

251

239

View full text Add to dashboard Cite

show abstract

“…One approach to understanding the dynamic nature of connections between local and distant neural assemblies is the analysis of functional and effective connectivity (Friston et al, 1994): the former captures patterns of statistical dependence, whereas the latter attempts to extract networks of causal influences of one physiological time series over another (Aertsen et al, 1989). Several studies have demonstrated changes in functional connectivity patterns after brain tumor resection (Douw et al, 2008), recovery from stroke (Gerloff et al, 2006), and traumatic brain injury (Castellanos et al, 2010;Zouridakis et al, 2012), suggesting that functional connectivity graphs (FCGs) of brain activity are sensitive to changes due to brain insult.…”

Section: Introductionmentioning

confidence: 99%

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Antonakakis

Dimitriadis

Zervakis

et al. 2016

International Journal of Psychophysiology

122

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Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients.Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI.

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“…A particularly important application in this respect concerns their use as control measurements against which to test multiple single-unit spike data for the presence of dynamic spike synchronization phenomena (Gerstein and Aertsen, 1985;Aertsen et al, 1987Aertsen et al, , 1989Riehle et al, 1997;Grü n et al, 1999).…”

Section: Discussionmentioning

confidence: 99%

Single-trial estimation of neuronal firing rates: From single-neuron spike trains to population activity

Nawrot

Aertsen

Rotter

1999

Journal of Neuroscience Methods

123

108

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We present a method to estimate the neuronal firing rate from single-trial spike trains. The method, based on convolution of the spike train with a fixed kernel function, is calibrated by means of simulated spike trains for a representative selection of realistic dynamic rate functions. We derive rules for the optimized use and performance of the kernel method, specifically with respect to an effective choice of the shape and width of the kernel functions. An application of our technique to the on-line, single-trial reconstruction of arm movement trajectories from multiple single-unit spike trains using dynamic population vectors illustrates a possible use of the proposed method.

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Dynamics of neuronal firing correlation: modulation of "effective connectivity"

Cited by 1,014 publications

References 20 publications

Statistical Issues in the Analysis of Neuronal Data

Statistical Issues in the Analysis of Neuronal Data

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Single-trial estimation of neuronal firing rates: From single-neuron spike trains to population activity

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