scholarly journals Comparison of human population receptive field estimates between scanners and the effect of temporal filtering

2019 ◽  
Author(s):  
Catherine Morgan ◽  
D Samuel Schwarzkopf
Keyword(s):  
Receptive Field ◽  
Goodness Of Fit ◽  
Time Course ◽  
Signal To Noise Ratio ◽  
3 Tesla ◽  
Frequency Noise ◽  
Temporal Filtering ◽  
Low Pass ◽  
Cortical Magnification ◽  
Tesla Scanner

AbstractPopulation receptive field (pRF) analysis with functional magnetic resonance imaging (fMRI) is an increasingly popular method for mapping visual field representations and estimating the spatial selectivity of voxels in human visual cortex. However, the multitude of experimental setups and processing methods used makes comparisons of results between studies difficult. Here, we show that pRF maps acquired in the same three individuals using comparable scanning parameters on a 1.5 and a 3 Tesla scanner located in two different countries are very similar. As expected, the signal-to-noise ratio for the 3 Tesla data was superior; critically, however, estimates of pRF size and cortical magnification did not reveal any systematic differences between the sites. Moreover, we tested the effect of low-pass filtering of the time series on pRF estimates. Unsurprisingly, filtering enhanced goodness-of-fit, presumably by removing high-frequency noise. However, there was no substantial increase in the number of voxels containing meaningful retinotopic signals after low-pass filtering. Importantly, filtering also increased estimates of pRF size in the early visual areas which could substantially skew interpretations of spatial tuning properties. Our results therefore suggest that pRF estimates are generally comparable between scanners of different field strengths, but temporal filtering should be used with caution.PrecisPopulation Receptive Field mapping performed with similar protocols at two different sites, a 1.5T MRI scanner in London, and a 3T scanner in Auckland, yielded comparable results. Temporal filtering of the fMRI time course increased concordance of modelled pRFs, but introduced a bias in pRF size.

scholarly journals Comparison of human population receptive field estimates between scanners and the effect of temporal filtering

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 1681
Author(s):  
Catherine Morgan ◽  
D. Samuel Schwarzkopf
Keyword(s):  
Receptive Field ◽  
Goodness Of Fit ◽  
Signal To Noise Ratio ◽  
3 Tesla ◽  
Frequency Noise ◽  
Temporal Filtering ◽  
Visual Areas ◽  
Low Pass ◽  
Cortical Magnification ◽  
Tesla Scanner

Background: Population receptive field (pRF) analysis with functional magnetic resonance imaging (fMRI) is an increasingly popular method for mapping visual field representations and estimating the spatial selectivity of voxels in human visual cortex. However, the multitude of experimental setups and processing methods used makes comparisons of results between studies difficult. Methods: Here, we compared pRF maps acquired in the same three individuals using comparable scanning parameters on a 1.5 and a 3 Tesla scanner located in two different countries. We also tested the effect of low-pass filtering of the time series on pRF estimates. Results: As expected, the signal-to-noise ratio for the 3 Tesla data was superior; critically, however, estimates of pRF size and cortical magnification did not reveal any systematic differences between the sites. Unsurprisingly, low-pass filtering enhanced goodness-of-fit, presumably by removing high-frequency noise. However, there was no substantial increase in the number of voxels containing meaningful retinotopic signals after low-pass filtering. Importantly, filtering also increased estimates of pRF size in the early visual areas which could substantially skew interpretations of spatial tuning properties. Conclusion: Our results therefore suggest that pRF estimates are generally comparable between scanners of different field strengths, but temporal filtering should be used with caution.

scholarly journals Comparison of human population receptive field estimates between scanners and the effect of temporal filtering

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1681
Author(s):  
Catherine Morgan ◽  
D. Samuel Schwarzkopf
Keyword(s):  
Receptive Field ◽  
Goodness Of Fit ◽  
Signal To Noise Ratio ◽  
3 Tesla ◽  
Frequency Noise ◽  
Temporal Filtering ◽  
Visual Areas ◽  
Low Pass ◽  
Cortical Magnification ◽  
Tesla Scanner

Background: Population receptive field (pRF) analysis with functional magnetic resonance imaging (fMRI) is an increasingly popular method for mapping visual field representations and estimating the spatial selectivity of voxels in human visual cortex. However, the multitude of experimental setups and processing methods used makes comparisons of results between studies difficult. Methods: Here, we compared pRF maps acquired in the same three individuals using comparable scanning parameters on a 1.5 and a 3 Tesla scanner located in two different countries. We also tested the effect of low-pass filtering of the time series on pRF estimates. Results: As expected, the signal-to-noise ratio for the 3 Tesla data was superior; critically, however, estimates of pRF size and cortical magnification did not reveal any systematic differences between the sites. Unsurprisingly, low-pass filtering enhanced goodness-of-fit, presumably by removing high-frequency noise. However, there was no substantial increase in the number of voxels containing meaningful retinotopic signals after low-pass filtering. Importantly, filtering also increased estimates of pRF size in the early visual areas which could substantially skew interpretations of spatial tuning properties. Conclusion: Our results therefore suggest that pRF estimates are generally comparable between scanners of different field strengths, but temporal filtering should be used with caution.

Modelling of Human Flicker Detection at Various Light Levels

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 129-129
Author(s):  
J Rovamo ◽  
A Raninen
Keyword(s):  
Lateral Inhibition ◽  
Goodness Of Fit ◽  
Matched Filter ◽  
Detection Efficiency ◽  
Temporal Frequency ◽  
Dark Light ◽  
Temporal Filtering ◽  
Neural Noise ◽  
Temporal Noise ◽  
Low Pass

Root-mean-square flicker sensitivity was measured within 0.2 – 2500 phot td and 0.5 – 30 Hz for a small spot with an equiluminous surround using computer graphics and a 2AFC method. In temporal noise, flicker sensitivity as a function of temporal frequency had a low-pass shape at all illuminances. Without temporal noise, the flicker sensitivity functions showed a band-pass shape at high illuminances, but changed to a low-pass shape at low illuminances. Our data are well described (goodness of fit 88%) by a model comprising: (i) low-pass temporal filtering by the modulation transfer function (MTF) of the photoreceptors ( R), (ii) high-pass temporal filtering by the MTF of the neural visual pathways ( P) resulting from lateral inhibition, (iii) addition of the temporal equivalent ( Nit) of internal neural noise, and (iv) detection by a temporal matched filter, provided that we take into account the fact that receptor responses become weaker and slower with decreasing illuminance despite adaptation. In our model detection efficiency was \eta( f)=0.148 f−0.568, Nit=46.3 μs and P( f) equal to f, where f is flicker frequency. In addition, R= R0[1+( f/ fc)2]−3, where R0=(1+24.1/ I)−0.5, fc=6.33 I0.172, and I is retinal illuminance. The model indicates that for the detection of a flickering spot in cone vision (i) the strength of lateral inhibition is independent of light level and (ii) quantal noise and dark light always remain insignificant sources of noise. (Mathematical expressions may not appear as intended)

scholarly journals ECG Signal Denoising and Reconstruction Based on Basis Pursuit

2021 ◽  
Vol 11 (4) ◽  
pp. 1591
Author(s):  
Ruixia Liu ◽  
Minglei Shu ◽  
Changfang Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Heart Diseases ◽  
Gaussian White Noise ◽  
Basis Pursuit ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Penalty Term ◽  
Ecg Signals ◽  
Alternating Direction ◽  
Low Pass

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE).

scholarly journals Assessing the usefulness of acute physiological responses following resistance exercise: sensitivity, magnitude of change, and time course of measures

2019 ◽  
Vol 44 (3) ◽  
pp. 309-319 ◽  
Author(s):  
Joshua S. Jackman ◽  
Phillip G. Bell ◽  
Simone Gill ◽  
Ken van Someren ◽  
Gareth W. Davison ◽  
...  
Keyword(s):  
Resistance Exercise ◽  
Time Course ◽  
Signal To Noise Ratio ◽  
Physiological Responses ◽  
High Sensitivity ◽  
Maximal Voluntary Isometric Contraction ◽  
Reactive Protein ◽  
Adaptation Processes ◽  
Exercise Stimulus ◽  
Metalloproteinase 9

A variety of strategies exist to modulate the acute physiological responses following resistance exercise aimed at enhancing recovery and/or adaptation processes. To assess the true impact of these strategies, it is important to know the ability of different measures to detect meaningful change. We investigated the sensitivity of measures used to quantify acute physiological responses to resistance exercise and constructed a physiological profile to characterise the magnitude of change and the time course of these responses. Eight males accustomed to regular resistance exercise performed experimental sessions during a “control week”, void of an exercise stimulus. The following week, termed the “exercise week”, participants repeated this sequence of experimental sessions, and they also performed a bout of lower-limb resistance exercise following the baseline assessments. Assessments were conducted at baseline and at 2, 6, 24, 48, 72, and 96 h after the intervention. On the basis of the signal-to-noise ratio, the most sensitive measures were maximal voluntary isometric contraction, 20-m sprint, countermovement jump peak force, rate of force development (100–200 ms), muscle soreness, Daily Analysis Of Life Demands For Athletes part B, limb girth, matrix metalloproteinase-9, interleukin-6, creatine kinase, and high-sensitivity C-reactive protein with ratios >1.5. Clear changes in these measures following resistance exercise were determined via magnitude-based inferences. These findings highlight measures that can detect real changes in acute physiological responses following resistance exercise in trained individuals. Researchers investigating strategies to manipulate acute physiological responses for recovery and/or adaptation can use these measures, as well as the recommended sampling points, to be confident that their interventions are making a worthwhile impact.

Temporal Dynamics of Shape Analysis in Macaque Visual Area V2

2004 ◽  
Vol 92 (5) ◽  
pp. 3030-3042 ◽  
Author(s):  
Jay Hegdé ◽  
David C. Van Essen
Keyword(s):  
Cortical Neurons ◽  
Time Course ◽  
Temporal Dynamics ◽  
Visual Stimulation ◽  
Signal To Noise Ratio ◽  
Stimulus Onset ◽  
Visual Area ◽  
Response Modulation ◽  
Population Response ◽  
Area V2

The firing rate of visual cortical neurons typically changes substantially during a sustained visual stimulus. To assess whether, and to what extent, the information about shape conveyed by neurons in visual area V2 changes over the course of the response, we recorded the responses of V2 neurons in awake, fixating monkeys while presenting a diverse set of static shape stimuli within the classical receptive field. We analyzed the time course of various measures of responsiveness and stimulus-related response modulation at the level of individual cells and of the population. For a majority of V2 cells, the response modulation was maximal during the initial transient response (40–80 ms after stimulus onset). During the same period, the population response was relatively correlated, in that V2 cells tended to respond similarly to specific subsets of stimuli. Over the ensuing 80–100 ms, the signal-to-noise ratio of individual cells generally declined, but to a lesser degree than the evoked-response rate during the corresponding time bins, and the response profiles became decorrelated for many individual cells. Concomitantly, the population response became substantially decorrelated. Our results indicate that the information about stimulus shape evolves dynamically and relatively rapidly in V2 during static visual stimulation in ways that may contribute to form discrimination.

Receptive Field Size and Response Latency Are Correlated Within the Cat Visual Thalamus

2005 ◽  
Vol 93 (6) ◽  
pp. 3537-3547 ◽  
Author(s):  
Chong Weng ◽  
Chun-I Yeh ◽  
Carl R. Stoelzel ◽  
Jose-Manuel Alonso
Keyword(s):  
Receptive Field ◽  
Spatial Frequency ◽  
Response Latency ◽  
Time Course ◽  
Frequency Tuning ◽  
Receptive Fields ◽  
Cortical Cell ◽  
Field Size ◽  
Receptive Field Size ◽  
Spatial Frequency Tuning

Each point in visual space is encoded at the level of the thalamus by a group of neighboring cells with overlapping receptive fields. Here we show that the receptive fields of these cells differ in size and response latency but not at random. We have found that in the cat lateral geniculate nucleus (LGN) the receptive field size and response latency of neighboring neurons are significantly correlated: the larger the receptive field, the faster the response to visual stimuli. This correlation is widespread in LGN. It is found in groups of cells belonging to the same type (e.g., Y cells), and of different types (i.e., X and Y), within a specific layer or across different layers. These results indicate that the inputs from the multiple geniculate afferents that converge onto a cortical cell (approximately 30) are likely to arrive in a sequence determined by the receptive field size of the geniculate afferents. Recent studies have shown that the peak of the spatial frequency tuning of a cortical cell shifts toward higher frequencies as the response progresses in time. Our results are consistent with the idea that these shifts in spatial frequency tuning arise from differences in the response time course of the thalamic inputs.

Hebb-Type Dynamics is Sufficient to Account for the Inverse Magnification Rule in Cortical Somatotopy

1990 ◽  
Vol 2 (1) ◽  
pp. 71-84 ◽  
Author(s):  
Kamil A. Grajski ◽  
Michael M. Merzenich
Keyword(s):  
Computer Simulation ◽  
Receptive Field ◽  
Cortical Neuron ◽  
Inverse Relationship ◽  
Unit Area ◽  
Skin Surface ◽  
Field Size ◽  
Synaptic Modification ◽  
Somatotopic Map ◽  
Cortical Magnification

The inverse magnification rule in cortical somatotopy is the experimentally derived inverse relationship between cortical magnification (area of somatotopic map representing a unit area of skin surface) and receptive field size (area of restricted skin surface driving a cortical neuron). We show by computer simulation of a simple, multilayer model that Hebb-type synaptic modification subject to competitive constraints is sufficient to account for the inverse magnification rule.

scholarly journals Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

2021 ◽  
pp. 1-15
Author(s):  
Poovarasan Selvaraj ◽  
E. Chandra
Keyword(s):  
Real Time ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Time Delay Estimation ◽  
Variational Model ◽  
Speech Signals ◽  
Frequency Noise ◽  
Intrinsic Mode Functions ◽  
Real Time Applications

The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

Sign in / Sign up

Export Citation Format

Share Document