41,107 research outputs found
Analytical methods and experimental approaches for electrophysiological studies of brain oscillations
Brain oscillations are increasingly the subject of electrophysiological studies probing their role in the functioning and dysfunction of the human brain. In recent years this research area has seen rapid and significant changes in the experimental approaches and analysis methods. This article reviews these developments and provides a structured overview of experimental approaches, spectral analysis techniques and methods to establish relationships between brain oscillations and behaviour
Discrete structure of the brain rhythms
Neuronal activity in the brain generates synchronous oscillations of the
Local Field Potential (LFP). The traditional analyses of the LFPs are based on
decomposing the signal into simpler components, such as sinusoidal harmonics.
However, a common drawback of such methods is that the decomposition primitives
are usually presumed from the onset, which may bias our understanding of the
signal's structure. Here, we introduce an alternative approach that allows an
impartial, high resolution, hands-off decomposition of the brain waves into a
small number of discrete, frequency-modulated oscillatory processes, which we
call oscillons. In particular, we demonstrate that mouse hippocampal LFP
contain a single oscillon that occupies the -frequency band and a
couple of -oscillons that correspond, respectively, to slow and fast
-waves. Since the oscillons were identified empirically, they may
represent the actual, physical structure of synchronous oscillations in
neuronal ensembles, whereas Fourier-defined "brain waves" are nothing but
poorly resolved oscillons.Comment: 17 pages, 9 figure
Recommended from our members
A mission synthesis algorithm for fatigue damage analysis
This paper presents a signal processing based algorithm, the Mildly Nonstationary Mission Synthesis
(MNMS), which produces a short mission signal from long records of experimental data. The
algorithm uses the Discrete Fourier Transform, Orthogonal Wavelet Transform and bump reinsertion
procedures. In order to observe the algorithm effectiveness a fatigue damage case study was
performed for a vehicle lower suspension arm using signals containing tensile and compressive
preloading. The mission synthesis results were compared to the original road data in terms of both the
global signal statistics and the fatigue damage variation as a function of compression ratio. Three
bump reinsertion methods were used and evaluated. The methods differed in the manner in which
bumps (shock events) from different wavelet groups (frequency bands) were synchronised during the
reinsertion process. One method, based on time synchronised section reinsertion, produced the best
results in terms of mission signal kurtosis, crest factor, root-mean-square level and power spectral
density. For improved algorithm performance, bump selection was identified as the main control
parameter requiring optimisation
Analysis of Dynamic Brain Imaging Data
Modern imaging techniques for probing brain function, including functional
Magnetic Resonance Imaging, intrinsic and extrinsic contrast optical imaging,
and magnetoencephalography, generate large data sets with complex content. In
this paper we develop appropriate techniques of analysis and visualization of
such imaging data, in order to separate the signal from the noise, as well as
to characterize the signal. The techniques developed fall into the general
category of multivariate time series analysis, and in particular we extensively
use the multitaper framework of spectral analysis. We develop specific
protocols for the analysis of fMRI, optical imaging and MEG data, and
illustrate the techniques by applications to real data sets generated by these
imaging modalities. In general, the analysis protocols involve two distinct
stages: `noise' characterization and suppression, and `signal' characterization
and visualization. An important general conclusion of our study is the utility
of a frequency-based representation, with short, moving analysis windows to
account for non-stationarity in the data. Of particular note are (a) the
development of a decomposition technique (`space-frequency singular value
decomposition') that is shown to be a useful means of characterizing the image
data, and (b) the development of an algorithm, based on multitaper methods, for
the removal of approximately periodic physiological artifacts arising from
cardiac and respiratory sources.Comment: 40 pages; 26 figures with subparts including 3 figures as .gif files.
Originally submitted to the neuro-sys archive which was never publicly
announced (was 9804003
Speckle interferometry
We have presented the basic mathematical treatment of interferometry in the
optical domain. Its applications in astronomical observations using both the
single aperture, as well as the diluted apertures are described in detail. We
have also described about the shortcomings of this technique in the presence of
Earth's atmosphere. A short descriptions of the atmospheric turbulence and its
effect on the flat wavefront from a stellar source is given. The formation of
speckle which acts as carrier of information is defined. Laboratory experiments
with phase modulation screens, as well as the resultant intensity distributions
due to point source are demonstrated. The experimental method to freeze the
speckles, as well as data processing techniques for both Fourier modulus and
Fourier phase are described. We have also discussed the technique of the
aperture synthesis using non-redundant aperture masks at the pupil plane of the
telescope, emphasizing set on the comparison with speckle interferometry. The
various methods of image restoration and their comparisons are also discussed.
Finally, we have touched upon certain astrophysical problems which can be
tackled with the newly developed speckle interferometer using the 2.34 meter
Vainu Bappu Telescope (VBT), situated at the Vainu Bappu Observatory (VBO),
Kavalur, India.Comment: 32 pages tex files including figure
On the mechanism of response latencies in auditory nerve fibers
Despite the structural differences of the middle and inner ears, the latency pattern in auditory nerve fibers to an identical sound has been found similar across numerous species. Studies have shown the similarity in remarkable species with distinct cochleae or even without a basilar membrane. This stimulus-, neuron-, and species- independent similarity of latency cannot be simply explained by the concept of cochlear traveling waves that is generally accepted as the main cause of the neural latency pattern.
An original concept of Fourier pattern is defined, intended to characterize a feature of temporal processing—specifically phase encoding—that is not readily apparent in more conventional analyses. The pattern is created by marking the first amplitude maximum for each sinusoid component of the stimulus, to encode phase information. The hypothesis is that the hearing organ serves as a running analyzer whose output reflects synchronization of auditory neural activity consistent with the Fourier pattern.
A combined research of experimental, correlational and meta-analysis approaches is used to test the hypothesis. Manipulations included phase encoding and stimuli to test their effects on the predicted latency pattern. Animal studies in the literature using the same stimulus were then compared to determine the degree of relationship.
The results show that each marking accounts for a large percentage of a corresponding peak latency in the peristimulus-time histogram. For each of the stimuli considered, the latency predicted by the Fourier pattern is highly correlated with the observed latency in the auditory nerve fiber of representative species.
The results suggest that the hearing organ analyzes not only amplitude spectrum but also phase information in Fourier analysis, to distribute the specific spikes among auditory nerve fibers and within a single unit.
This phase-encoding mechanism in Fourier analysis is proposed to be the common mechanism that, in the face of species differences in peripheral auditory hardware, accounts for the considerable similarities across species in their latency-by-frequency functions, in turn assuring optimal phase encoding across species. Also, the mechanism has the potential to improve phase encoding of cochlear implants
Detecting the harmonics of oscillations with time-variable frequencies
A method is introduced for the spectral analysis of complex noisy signals containing several frequency components. It enables components that are independent to be distinguished from the harmonics of nonsinusoidal oscillatory processes of lower frequency. The method is based on mutual information and surrogate testing combined with the wavelet transform, and it is applicable to relatively short time series containing frequencies that are time variable. Where the fundamental frequency and harmonics of a process can be identified, the characteristic shape of the corresponding oscillation can be determined, enabling adaptive filtering to remove other components and nonoscillatory noise from the signal. Thus the total bandwidth of the signal can be correctly partitioned and the power associated with each component then can be quantified more accurately. The method is first demonstrated on numerical examples. It is then used to identify the higher harmonics of oscillations in human skin blood flow, both spontaneous and associated with periodic iontophoresis of a vasodilatory agent. The method should be equally relevant to all situations where signals of comparable complexity are encountered, including applications in astrophysics, engineering, and electrical circuits, as well as in other areas of physiology and biology
Higher-Order Properties of Analytic Wavelets
The influence of higher-order wavelet properties on the analytic wavelet
transform behavior is investigated, and wavelet functions offering advantageous
performance are identified. This is accomplished through detailed investigation
of the generalized Morse wavelets, a two-parameter family of exactly analytic
continuous wavelets. The degree of time/frequency localization, the existence
of a mapping between scale and frequency, and the bias involved in estimating
properties of modulated oscillatory signals, are proposed as important
considerations. Wavelet behavior is found to be strongly impacted by the degree
of asymmetry of the wavelet in both the frequency and the time domain, as
quantified by the third central moments. A particular subset of the generalized
Morse wavelets, recognized as deriving from an inhomogeneous Airy function,
emerge as having particularly desirable properties. These "Airy wavelets"
substantially outperform the only approximately analytic Morlet wavelets for
high time localization. Special cases of the generalized Morse wavelets are
examined, revealing a broad range of behaviors which can be matched to the
characteristics of a signal.Comment: 15 pages, 6 Postscript figure
- …