scholarly journals Binaural summation of amplitude modulation involves weak interaural suppression

2018 ◽  
Author(s):  
D.H. Baker ◽  
G. Vilidaite ◽  
E. McClarnon ◽  
E. Valkova ◽  
A. Bruno ◽  
...  
Keyword(s):  
Amplitude Modulation ◽  
Modulation Depth ◽  
Visual Signal ◽  
Data Sets ◽  
Single Object ◽  
Ecological Constraints ◽  
Binaural Summation ◽  
Eeg Data ◽  
Signal Combination ◽  
The Brain

AbstractThe brain combines sounds from the two ears, but what is the algorithm used to achieve this summation of signals? Here we combine psychophysical amplitude modulation discrimination and steady-state electroencephalography (EEG) data to investigate the architecture of binaural combination for amplitude-modulated tones. Discrimination thresholds followed a ‘dipper’ shaped function of pedestal modulation depth, and were consistently lower for binaural than monaural presentation of modulated tones. The EEG responses were greater for binaural than monaural presentation of modulated tones, and when a masker was presented to one ear, it produced only weak suppression of the response to a signal presented to the other ear. Both data sets were well-fit by a computational model originally derived for visual signal combination, but with suppression between the two channels (ears) being much weaker than in binocular vision. We suggest that the distinct ecological constraints on vision and hearing can explain this difference, if it is assumed that the brain avoids over-representing sensory signals originating from a single object. These findings position our understanding of binaural summation in a broader context of work on sensory signal combination in the brain, and delineate the similarities and differences between vision and hearing.

scholarly journals EEG Correlates of Visual Signal Processing: Spectral Decomposition using ICA

2019 ◽  
Vol 8 (4) ◽  
pp. 582-586
Keyword(s):  
Signal Processing ◽  
Complex Systems ◽  
Biological Activities ◽  
Visual Signal ◽  
Data Sets ◽  
Ecg Signals ◽  
Functional Behavior ◽  
Eeg Data ◽  
Continuous Eeg ◽  
Enormous Amount

Studies on signal processing of biological activities is a way to ascertain the anatomical and functional behavior of complex systems. Many biomedical signals represent their significance for understanding the systems, like ECG signals of heart and EEG, MEG signals of brain’s electro- and magnetobehavior reveals their importance. Availability of enormous amount of EEG data, with unwanted noise and artifacts, from complex systems, is challenging to uncover the underlying dynamics of signals sources. Functional analysis of this data requires various methods to remove the artifacts and noise present in it and further investigation of the system dynamics. In this paper, we discussed the removal of artifacts and noise from brain’s EEG activity to achieve artifact rejections of continuous EEG data and to apply Independent Component Analysis (ICA) to analyze the event-related tasks. Cluster decomposed signal components allow us to visualize the independent components of any number of subjects and subject groups. Differentiating vast electrical activity of an abnormal subject in comparison with a normal subject is possible with the decomposing signal. ICA approach helps in correlating the event-related EEG signals for a subject in two or more conditions of the same experiment; hence, it suggests in creating the data sets of epochs for every condition.

Time consumption and quality of an automated fusion tool for SPECT and MRI images of the brain

2003 ◽  
Vol 42 (05) ◽  
pp. 215-219
Author(s):  
G. Platsch ◽  
A. Schwarz ◽  
K. Schmiedehausen ◽  
B. Tomandl ◽  
W. Huk ◽  
...  
Keyword(s):  
Data Sets ◽  
3D Mri ◽  
Clinical Routine ◽  
Fusion Procedure ◽  
3D Data ◽  
Mri Scans ◽  
2D Data ◽  
Time Required ◽  
The Brain ◽  
Manual Correction

Summary: Aim: Although the fusion of images from different modalities may improve diagnostic accuracy, it is rarely used in clinical routine work due to logistic problems. Therefore we evaluated performance and time needed for fusing MRI and SPECT images using a semiautomated dedicated software. Patients, material and Method: In 32 patients regional cerebral blood flow was measured using 99mTc ethylcystein dimer (ECD) and the three-headed SPECT camera MultiSPECT 3. MRI scans of the brain were performed using either a 0,2 T Open or a 1,5 T Sonata. Twelve of the MRI data sets were acquired using a 3D-T1w MPRAGE sequence, 20 with a 2D acquisition technique and different echo sequences. Image fusion was performed on a Syngo workstation using an entropy minimizing algorithm by an experienced user of the software. The fusion results were classified. We measured the time needed for the automated fusion procedure and in case of need that for manual realignment after automated, but insufficient fusion. Results: The mean time of the automated fusion procedure was 123 s. It was for the 2D significantly shorter than for the 3D MRI datasets. For four of the 2D data sets and two of the 3D data sets an optimal fit was reached using the automated approach. The remaining 26 data sets required manual correction. The sum of the time required for automated fusion and that needed for manual correction averaged 320 s (50-886 s). Conclusion: The fusion of 3D MRI data sets lasted significantly longer than that of the 2D MRI data. The automated fusion tool delivered in 20% an optimal fit, in 80% manual correction was necessary. Nevertheless, each of the 32 SPECT data sets could be merged in less than 15 min with the corresponding MRI data, which seems acceptable for clinical routine use.

Sources of the Scalp-Recorded Amplitude-Modulation Following Response

2002 ◽  
Vol 13 (04) ◽  
pp. 188-204 ◽  
Author(s):  
Shigeyuki Kuwada ◽  
Julia S. Anderson ◽  
Ranjan Batra ◽  
Douglas C. Fitzpatrick ◽  
Natacha Teissier ◽  
...  
Keyword(s):  
Animal Model ◽  
Amplitude Modulation ◽  
Single Unit ◽  
Modulation Frequency ◽  
Multiple Sources ◽  
High Modulation ◽  
Before And After ◽  
Single Unit Recordings ◽  
Composite Response ◽  
The Brain

The scalp-recorded amplitude-modulation following response (AMFR)” is gaining recognition as an objective audiometric tool, but little is known about the neural sources that underlie this potential. We hypothesized, based on our human studies and single-unit recordings in animals, that the scalp-recorded AMFR reflects the interaction of multiple sources. We tested this hypothesis using an animal model, the unanesthetized rabbit. We compared AMFRs recorded from the surface of the brain at different locations and before and after the administration of agents likely to enhance or suppress neural generators. We also recorded AMFRs locally at several stations along the auditory neuraxis. We conclude that the surface-recorded AMFR is indeed a composite response from multiple brain generators. Although the response at any modulation frequency can reflect the activity of more than one generator, the AMFRs to low and high modulation frequencies appear to reflect a strong contribution from cortical and subcortical sources, respectively.

scholarly journals Contribution of onset/offset information of modulation to amplitude modulation depth discrimination

2008 ◽  
Vol 123 (5) ◽  
pp. EL111-EL115 ◽  
Author(s):  
Derek R. Edwards ◽  
Jungmee Lee ◽  
Jennifer Andrews ◽  
Aileen Wong
Keyword(s):  
Amplitude Modulation ◽  
Modulation Depth ◽  
Depth Discrimination

scholarly journals Nervous Activity of the Brain in Five Dimensions

Biophysica ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 38-47
Author(s):  
Arturo Tozzi ◽  
James F. Peters ◽  
Norbert Jausovec ◽  
Arjuna P. H. Don ◽  
Sheela Ramanna ◽  
...  
Keyword(s):  
Fourier Analysis ◽  
Nervous Activity ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Data Sets ◽  
Spatial And Temporal Patterns ◽  
Five Dimensions ◽  
Four Dimensions ◽  
Scale Free ◽  
The Brain

The nervous activity of the brain takes place in higher-dimensional functional spaces. It has been proposed that the brain might be equipped with phase spaces characterized by four spatial dimensions plus time, instead of the classical three plus time. This suggests that global visualization methods for exploiting four-dimensional maps of three-dimensional experimental data sets might be used in neuroscience. We asked whether it is feasible to describe the four-dimensional trajectories (plus time) of two-dimensional (plus time) electroencephalographic traces (EEG). We made use of quaternion orthographic projections to map to the surface of four-dimensional hyperspheres EEG signal patches treated with Fourier analysis. Once achieved the proper quaternion maps, we show that this multi-dimensional procedure brings undoubted benefits. The treatment of EEG traces with Fourier analysis allows the investigation the scale-free activity of the brain in terms of trajectories on hyperspheres and quaternionic networks. Repetitive spatial and temporal patterns undetectable in three dimensions (plus time) are easily enlightened in four dimensions (plus time). Further, a quaternionic approach makes it feasible to identify spatially far apart and temporally distant periodic trajectories with the same features, such as, e.g., the same oscillatory frequency or amplitude. This leads to an incisive operational assessment of global or broken symmetries, domains of attraction inside three-dimensional projections and matching descriptions between the apparently random paths hidden in the very structure of nervous fractal signals.

Angiotensin Converting Enzyme 2 (ACE2) Expression in the Visual System

2021 ◽  
Author(s):  
James M. Hill ◽  
Christian Clement ◽  
L. Arceneaux ◽  
Walter Lukiw
Keyword(s):  
Signal Processing ◽  
Angiotensin Converting Enzyme ◽  
Occipital Lobe ◽  
Cell Types ◽  
Receptor Expression ◽  
Host Cells ◽  
Visual Signal ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
The Brain

Abstract Background: Multiple lines of evidence currently indicate that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)gains entry into human host cells via a high-affinity interaction with the angiotensin-converting enzyme 2 (ACE2) transmembrane receptor. Research has further shown the widespread expression of the ACE2 receptor on the surface of many different immune, non-immune and neural host cell types, and that SARS-CoV-2 has there markable capability to attack many different types of human-host cells simultaneously. One principal neuroanatomical region for highACE2 expression patterns occurs in the brainstem, an area of the brain containing regulatory centers for respiration, and this may in part explain the predisposition of many COVID-19 patients to respiratory distress. Early studies also indicated extensive ACE2 expression in the whole eye and the brain’s visual circuitry. In this study we analyzed ACE2 receptor expression at the mRNA and protein level in multiple cell types involved in human vision, including cell types of the external eye and several deep brain regions known to be involved in the processing of visual signals.Methods: ACE2 mRNA and protein analysis; multiple eye and brain cells and tissues; gamma32P-adenosine tri-phosphate ([γ-32P]dATP) radiolabeled probes; Northern analysis; ELISA.Results: The four main findings were: (i)that many different optical and neural cell types of the human visual system provide receptors essential for SARS-CoV-2 invasion; (ii)the remarkable ubiquity of ACE2 presence in cells of the eye and anatomical regions of the brain involved in visual signal processing; (iii)that ACE2 receptor expression in different ocular cell types and visual processing centers of the brain provide multiple compartments for SARS-CoV-2 infiltration; and (iv)a gradient of increasing ACE2 expression from the anterior surface of the eye to the visual signal processing areas of the occipital lobe and the primary visual neocortex.Conclusion: A gradient of ACE2 expression from the eye surface to the occipital lobe provide the SARS-CoV-2 virus a novel pathway from the outer eye into deeper anatomical regions of the brain involved in vision. These findings may explain, in part, the many recently reported neuro-ophthalmic manifestations of SARS-CoV-2infection in COVID-19 affected patients.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

scholarly journals Suppression of amplitude modulation induced by polarization mode dispersion using a multi-degree-of-freedom fiber filter

2018 ◽  
Vol 6 ◽  
Author(s):  
Rao Li ◽  
Youen Jiang ◽  
Zhi Qiao ◽  
Canhong Huang ◽  
Wei Fan ◽  
...  
Keyword(s):  
High Power ◽  
Amplitude Modulation ◽  
Transmission Spectrum ◽  
High Speed ◽  
Large Scale ◽  
Modulation Depth ◽  
Free Spectral Range ◽  
Polarization Mode Dispersion ◽  
Polarization Mode ◽  
Mode Dispersion

Polarization mode dispersion (PMD) in fibers for high-power lasers can induce significant frequency modulation to amplitude modulation (FM-to-AM) conversion. However, existing techniques are not sufficiently flexible to achieve efficient compensation for such FM-to-AM conversion. By analyzing the nonuniform transmission spectrum caused by PMD, we found that the large-scale envelope of the transmission spectrum has more serious impacts on the amount of AM. In order to suppress the PMD-induced FM-to-AM conversion, we propose a novel tunable spectral filter with multiple degrees of freedom based on a half-wave plate, a nematic liquid crystal, and an axis-rotated polarization-maintaining fiber. Peak wavelength, free spectral range (FSR), and modulation depth of the filter are decoupled and can be controlled independently, which is verified through both simulations and experiments. The filter is utilized to compensate for the PMD-induced FM-to-AM conversion in the front end of a high-power laser facility. The results indicate that, for a pulse with phase-modulation frequency of 22.82 GHz, the FM-to-AM conversion could be reduced from 18% to 3.2% within a short time and maintained below 6.5% for 3 h. The proposed filter is also promising for other applications that require flexible spectral control such as high-speed channel selection in optical communication networks.

scholarly journals Hyperspherical Manifold for EEG Signals of Epileptic Seizures

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Tahir Ahmad ◽  
Vinod Ramachandran
Keyword(s):  
Measure Theory ◽  
Epileptic Seizures ◽  
Physical Symptoms ◽  
Measure Data ◽  
Dimensional Manifold ◽  
Eeg Signals ◽  
Discrete Probability ◽  
Eeg Data ◽  
Manifold Theory ◽  
The Brain

The mathematical modelling of EEG signals of epileptic seizures presents a challenge as seizure data is erratic, often with no visible trend. Limitations in existing models indicate a need for a generalized model that can be used to analyze seizures without the need for apriori information, whilst minimizing the loss of signal data due to smoothing. This paper utilizes measure theory to design a discrete probability measure that reformats EEG data without altering its geometric structure. An analysis of EEG data from three patients experiencing epileptic seizures is made using the developed measure, resulting in successful identification of increased potential difference in portions of the brain that correspond to physical symptoms demonstrated by the patients. A mapping then is devised to transport the measure data onto the surface of a high-dimensional manifold, enabling the analysis of seizures using directional statistics and manifold theory. The subset of seizure signals on the manifold is shown to be a topological space, verifying Ahmad's approach to use topological modelling.

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