Increased Stimulus Expectancy Triggers Low-frequency Phase Reset during Restricted Vigilance

Temporal cues can be used to selectively attend to relevant information during abundant sensory stimulation. However, such cues differ vastly in the accuracy of their temporal estimates, ranging from very predictable to very unpredictable. When cues are strongly predictable, attention may facilitate selective processing by aligning relevant incoming information to high neuronal excitability phases of ongoing low-frequency oscillations. However, top–down effects on ongoing oscillations when temporal cues have some predictability, but also contain temporal uncertainties, are unknown. Here, we experimentally created such a situation of mixed predictability and uncertainty: A target could occur within a limited time window after cue but was always unpredictable in exact timing. Crucially to assess top–down effects in such a mixed situation, we manipulated target probability. High target likelihood, compared with low likelihood, enhanced delta oscillations more strongly as measured by evoked power and intertrial coherence. Moreover, delta phase modulated detection rates for probable targets. The delta frequency range corresponds with half-a-period to the target occurrence window and therefore suggests that low-frequency phase reset is engaged to produce a long window of high excitability when event timing is uncertain within a restricted temporal window.

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Design for Calibratability of a N-Integer Low-Frequency Phase-Locked Loop

2018 Conference on Design of Circuits and Integrated Systems (DCIS) ◽

10.1109/dcis.2018.8681479 ◽

2018 ◽

Author(s):

Rui Moutinho Teixeira ◽

Jose Machado da Silva

Keyword(s):

Low Frequency ◽

Phase Locked Loop ◽

Frequency Phase

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Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

International Journal of Image and Graphics ◽

10.1142/s0219467821500236 ◽

2020 ◽

pp. 2150023

Author(s):

Priya R. Kamath ◽

Kedarnath Senapati ◽

P. Jidesh

Keyword(s):

High Frequency ◽

Low Frequency ◽

Relevant Information ◽

Detection Algorithm ◽

Two Dimensional ◽

Sar Images ◽

S Transform ◽

Processing Step ◽

Frequency Components ◽

Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

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Top-down control of visual cortex by the frontal eye fields through oscillatory realignment

Nature Communications ◽

10.1038/s41467-021-21979-7 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Domenica Veniero ◽

Joachim Gross ◽

Stephanie Morand ◽

Felix Duecker ◽

Alexander T. Sack ◽

...

Keyword(s):

Visual Cortex ◽

Transcranial Magnetic Stimulation ◽

Visual Perception ◽

Magnetic Stimulation ◽

Single Pulse ◽

Frontal Eye Fields ◽

Phase Reset ◽

Top Down ◽

Visual Areas ◽

Beta Frequency

AbstractVoluntary allocation of visual attention is controlled by top-down signals generated within the Frontal Eye Fields (FEFs) that can change the excitability of lower-level visual areas. However, the mechanism through which this control is achieved remains elusive. Here, we emulated the generation of an attentional signal using single-pulse transcranial magnetic stimulation to activate the FEFs and tracked its consequences over the visual cortex. First, we documented changes to brain oscillations using electroencephalography and found evidence for a phase reset over occipital sites at beta frequency. We then probed for perceptual consequences of this top-down triggered phase reset and assessed its anatomical specificity. We show that FEF activation leads to cyclic modulation of visual perception and extrastriate but not primary visual cortex excitability, again at beta frequency. We conclude that top-down signals originating in FEF causally shape visual cortex activity and perception through mechanisms of oscillatory realignment.

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Auditory Imagery Modulates Frequency-specific Areas in the Human Auditory Cortex

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00280 ◽

2013 ◽

Vol 25 (2) ◽

pp. 175-187 ◽

Cited By ~ 17

Author(s):

Jihoon Oh ◽

Jae Hyung Kwon ◽

Po Song Yang ◽

Jaeseung Jeong

Keyword(s):

Auditory Cortex ◽

High Frequency ◽

Low Frequency ◽

Primary Auditory Cortex ◽

Auditory Imagery ◽

Neural Responses ◽

Top Down ◽

Visual Areas ◽

Control Functional ◽

Response Area

Neural responses in early sensory areas are influenced by top–down processing. In the visual system, early visual areas have been shown to actively participate in top–down processing based on their topographical properties. Although it has been suggested that the auditory cortex is involved in top–down control, functional evidence of topographic modulation is still lacking. Here, we show that mental auditory imagery for familiar melodies induces significant activation in the frequency-responsive areas of the primary auditory cortex (PAC). This activation is related to the characteristics of the imagery: when subjects were asked to imagine high-frequency melodies, we observed increased activation in the high- versus low-frequency response area; when the subjects were asked to imagine low-frequency melodies, the opposite was observed. Furthermore, we found that A1 is more closely related to the observed frequency-related modulation than R in tonotopic subfields of the PAC. Our findings suggest that top–down processing in the auditory cortex relies on a mechanism similar to that used in the perception of external auditory stimuli, which is comparable to early visual systems.

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Modulation of Neurocardiac Function by Oesophageal Stimulation in Humans

Clinical Science ◽

10.1042/cs0920167 ◽

1997 ◽

Vol 92 (2) ◽

pp. 167-174 ◽

Cited By ~ 42

Author(s):

Gervais Tougas ◽

Markad Kamath ◽

Geena Watteel ◽

Debbie Fitzpatrick ◽

Ernest L. Fallen ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Power Spectrum ◽

High Frequency ◽

Low Frequency ◽

Sensory Stimulation ◽

Sensory Stimuli ◽

Autonomic Reflexes ◽

Low Frequency Power ◽

Beat Heart

1. The heart and the oesophagus have similar sensory pathways, and sensations originating from the oesophagus are often difficult to differentiate from those of cardiac origin. We hypothesized that oesophageal sensory stimuli could alter neurocardiac function through autonomic reflexes elicited by these oesophageal stimuli. In the present study, we examined the neurocardiac response to oesophageal stimulation and the effects of electrical and mechanical oesophageal stimulation on the power spectrum of beat-to-beat heart rate variability in male volunteers. 2. In 14 healthy volunteers, beat-to-beat heart rate variability was compared at rest and during oesophageal stimulation, using either electrical (200 μs, 16 mA, 0.2 Hz) or mechanical (0.5 s, 14 ml, 0.2 Hz) stimuli. The power spectrum of beat-to-beat heart rate variability was obtained and its low- and high-frequency components were determined. 3. Distal oesophageal stimulation decreased heart rate slightly (both electrical and mechanical) (P < 0.005), and markedly altered heart rate variability (P < 0.001). Both electrical and mechanical oesophageal stimulation increased the absolute and normalized area of the high-frequency band within the power spectrum (P < 0.001), while simultaneously decreasing the low-frequency power (P < 0.005). 4. In humans, oesophageal stimulation, whether electrical or mechanical, appears to amplify respiratory-driven cardiac vagoafferent modulation while decreasing sympathetic modulation. The technique provides access to vagoafferent fibres and thus may yield useful information on the autonomic effects of visceral or oesophageal sensory stimulation.

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Analysis of stimulus-related activity in rat auditory cortex using complex spectral coefficients

Journal of Neurophysiology ◽

10.1152/jn.00187.2013 ◽

2013 ◽

Vol 110 (3) ◽

pp. 621-639 ◽

Cited By ~ 1

Author(s):

Bryan M. Krause ◽

Matthew I. Banks

Keyword(s):

Low Frequency ◽

Phase Changes ◽

Induced Response ◽

Top Down ◽

Time Frequency ◽

State Dependent ◽

Spectral Coefficients ◽

The Mean ◽

Sensory Responses ◽

Mean Variance

The neural mechanisms of sensory responses recorded from the scalp or cortical surface remain controversial. Evoked vs. induced response components (i.e., changes in mean vs. variance) are associated with bottom-up vs. top-down processing, but trial-by-trial response variability can confound this interpretation. Phase reset of ongoing oscillations has also been postulated to contribute to sensory responses. In this article, we present evidence that responses under passive listening conditions are dominated by variable evoked response components. We measured the mean, variance, and phase of complex time-frequency coefficients of epidurally recorded responses to acoustic stimuli in rats. During the stimulus, changes in mean, variance, and phase tended to co-occur. After the stimulus, there was a small, low-frequency offset response in the mean and modest, prolonged desynchronization in the alpha band. Simulations showed that trial-by-trial variability in the mean can account for most of the variance and phase changes observed during the stimulus. This variability was state dependent, with smallest variability during periods of greatest arousal. Our data suggest that cortical responses to auditory stimuli reflect variable inputs to the cortical network. These analyses suggest that caution should be exercised when interpreting variance and phase changes in terms of top-down cortical processing.

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Multimodal Medical Image Fusion using Guided Filter in NSCT Domain

Biomedical & Pharmacology Journal ◽

10.13005/bpj/1566 ◽

2018 ◽

Vol 11 (4) ◽

pp. 1937-1946

Author(s):

Nancy Mehta ◽

Sumit Budhiraja

Keyword(s):

Image Fusion ◽

Medical Image ◽

Similarity Index ◽

Low Frequency ◽

Structural Similarity ◽

Relevant Information ◽

Phase Congruency ◽

Edge Preserving ◽

Medical Image Fusion ◽

Fused Image

Multimodal medical image fusion aims at minimizing the redundancy and collecting the relevant information using the input images acquired from different medical sensors. The main goal is to produce a single fused image having more information and has higher efficiency for medical applications. In this paper modified fusion method has been proposed in which NSCT decomposition is used to decompose the wavelet coefficients obtained after wavelet decomposition. NSCT being multidirectional,shift invariant transform provide better results.Guided filter has been used for the fusion of high frequency coefficients on account of its edge preserving property. Phase congruency is used for the fusion of low frequency coefficients due to its insensitivity to illumination contrast hence making it suitable for medical images. The simulated results show that the proposed technique shows better performance in terms of entropy, structural similarity index, Piella metric. The fusion response of the proposed technique is also compared with other fusion approaches; proving the effectiveness of the obtained fusion results.

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Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/267 ◽

2019 ◽

Cited By ~ 4

Author(s):

Bingbing Xu ◽

Huawei Shen ◽

Qi Cao ◽

Keting Cen ◽

Xueqi Cheng

Keyword(s):

Heat Kernel ◽

Supervised Learning ◽

Low Frequency ◽

Weighted Average ◽

Relevant Information ◽

Semisupervised Learning ◽

Heat Diffusion ◽

Graph Structure ◽

Convolutional Networks ◽

Benchmark Datasets

Graph convolutional networks gain remarkable success in semi-supervised learning on graph-structured data. The key to graph-based semisupervised learning is capturing the smoothness of labels or features over nodes exerted by graph structure. Previous methods, spectral methods and spatial methods, devote to defining graph convolution as a weighted average over neighboring nodes, and then learn graph convolution kernels to leverage the smoothness to improve the performance of graph-based semi-supervised learning. One open challenge is how to determine appropriate neighborhood that reflects relevant information of smoothness manifested in graph structure. In this paper, we propose GraphHeat, leveraging heat kernel to enhance low-frequency filters and enforce smoothness in the signal variation on the graph. GraphHeat leverages the local structure of target node under heat diffusion to determine its neighboring nodes flexibly, without the constraint of order suffered by previous methods. GraphHeat achieves state-of-the-art results in the task of graph-based semi-supervised classification across three benchmark datasets: Cora, Citeseer and Pubmed.

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Signature patterns for top-down and bottom-up information processing via cross-frequency coupling in macaque auditory cortex

10.1101/403980 ◽

2018 ◽

Author(s):

Christian D. Márton ◽

Makoto Fukushima ◽

Corrie R. Camalier ◽

Simon R. Schultz ◽

Bruno B. Averbeck

Keyword(s):

Information Processing ◽

Auditory Cortex ◽

Predictive Coding ◽

Low Frequency ◽

Information Need ◽

Top Down ◽

Bottom Up ◽

Low Frequencies ◽

Frequency Coupling ◽

Cross Frequency Coupling

AbstractPredictive coding is a theoretical framework that provides a functional interpretation of top-down and bottom up interactions in sensory processing. The theory has suggested that specific frequency bands relay bottom-up and top-down information (e.g. “γ up, β down”). But it remains unclear whether this notion generalizes to cross-frequency interactions. Furthermore, most of the evidence so far comes from visual pathways. Here we examined cross-frequency coupling across four sectors of the auditory hierarchy in the macaque. We computed two measures of cross-frequency coupling, phase-amplitude coupling (PAC) and amplitude-amplitude coupling (AAC). Our findings revealed distinct patterns for bottom-up and top-down information processing among cross-frequency interactions. Both top-down and bottom-up made prominent use of low frequencies: low-to-low frequency (θ, α, β) and low frequency-to-high γ couplings were predominant top-down, while low frequency-to-low γ couplings were predominant bottom-up. These patterns were largely preserved across coupling types (PAC and AAC) and across stimulus types (natural and synthetic auditory stimuli), suggesting they are a general feature of information processing in auditory cortex. Moreover, our findings showed that low-frequency PAC alternated between predominantly top-down or bottom-up over time. Altogether, this suggests sensory information need not be propagated along separate frequencies upwards and downwards. Rather, information can be unmixed by having low frequencies couple to distinct frequency ranges in the target region, and by alternating top-down and bottom-up processing over time.1SignificanceThe brain consists of highly interconnected cortical areas, yet the patterns in directional cortical communication are not fully understood, in particular with regards to interactions between different signal components across frequencies. We employed a a unified, computationally advantageous Granger-causal framework to examine bi-directional cross-frequency interactions across four sectors of the auditory cortical hierarchy in macaques. Our findings extend the view of cross-frequency interactions in auditory cortex, suggesting they also play a prominent role in top-down processing. Our findings also suggest information need not be propagated along separate channels up and down the cortical hierarchy, with important implications for theories of information processing in the brain such as predictive coding.

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