A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli

2018 ◽  
Vol 63 (2) ◽  
pp. 163-175
Author(s):  
Savita V. Raut ◽  
Dinkar M. Yadav
Keyword(s):  
Signal Analysis ◽  
Geometric Mean ◽  
Frequency Component ◽  
Neural Response ◽  
Fmri Signal ◽  
Decomposition Models ◽  
Selection Framework ◽  
Fmri Analysis ◽  
Comparative Results ◽  
Voxel Selection

AbstractThis paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.

Voxel Selection Framework in Multi-Voxel Pattern Analysis of fMRI Data for Prediction of Neural Response to Visual Stimuli

2014 ◽  
Vol 33 (4) ◽  
pp. 925-934 ◽  
Author(s):  
Chun-An Chou ◽  
Kittipat Kampa ◽  
Sonya H. Mehta ◽  
Rosalia F. Tungaraza ◽  
W. Art Chaovalitwongse ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Visual Stimuli ◽  
Neural Response ◽  
Fmri Data ◽  
Selection Framework ◽  
Voxel Selection

scholarly journals Electrical discharge phenomenon of electrode gaps in oil insulation

2018 ◽  
Vol 197 ◽  
pp. 11005
Author(s):  
Jannus Maurits Nainggolan ◽  
MK Iwa Ganiwa ◽  
Chairul Hudaya ◽  
Amien Rahardjo
Keyword(s):  
Acoustic Emission ◽  
Signal Analysis ◽  
Electrical Discharge ◽  
Frequency Component ◽  
Solid Layer ◽  
Insulating Material ◽  
Emission Signal ◽  
Plane Electrode ◽  
Maximum Value ◽  
Ae Signal

An electrical discharge is a phenomenon of ionization of an insulating material. Ionization can occur when the stress applied to the insulating material begins to close to the maximum value of stress can be restrained. In this study, a high voltage was given on a point-plane electrode that would produce ionization (discharge) on the gap of the electrode. The point-plane electrode was placed in an iron tank containing oil insulation. The distance of a gap between the electrodes varies from 2 mm to 4 mm. Then, the signal from the occurrence of electrical discharge was capture using an acoustic emission (AE) sensor placed on the outside of the tank wall. The detected acoustic emission signal was amplified with a 40 dB amplifier, so the signal would be easier to analyze. At the other condition, a solid layer of insulation with a thickness of 4 mm would also be placed on the gap the electrode. The result of the signal analysis showed small differences in the intensity of the detected AE signal at all the distance of electrode gaps. The main frequency component of the detected AE signal at all electrode gaps was several hundred kilohertz.

scholarly journals Experimental Study on the Measurement of Water Bottom Vibration Induced by Underwater Drilling Blasting

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Gu Wenbin ◽  
Chen Jianghai ◽  
Wang Zhenxiong ◽  
Wang Zhihua ◽  
Liu Jianqing ◽  
...  
Keyword(s):  
Experimental Study ◽  
Field Experiment ◽  
Frequency Band ◽  
Signal Analysis ◽  
Low Frequency ◽  
Ground Vibration ◽  
Frequency Component ◽  
Vibration Signals ◽  
Attenuation Laws ◽  
Water Bottom

Due to the lack of proper instrumentations and the difficulties in underwater measurements, the studies about water bottom vibration induced by underwater drilling blasting are seldom reported. In order to investigate the propagation and attenuation laws of blasting induced water bottom vibration, a water bottom vibration monitor was developed with consideration of the difficulties in underwater measurements. By means of this equipment, the actual water bottom vibration induced by underwater drilling blasting was measured in a field experiment. It shows that the water bottom vibration monitor could collect vibration signals quite effectively in underwater environments. The followed signal analysis shows that the characteristics of water bottom vibration and land ground vibration induced by the same underwater drilling blasting are quite different due to the different geological environments. The amplitude and frequency band of water bottom vibration both exceed those of land ground vibration. Water bottom vibration is mainly in low-frequency band that induced by blasting impact directly acts on rock. Besides the low-frequency component, land vibration contains another higher frequency band component that induced by followed water hammer wave acts on bank slope.

scholarly journals FMRI Signal Analysis Using Empirical Mean Curve Decomposition

2013 ◽  
Vol 60 (1) ◽  
pp. 42-54 ◽  
Author(s):  
Fan Deng ◽  
Dajiang Zhu ◽  
Jinglei Lv ◽  
Lei Guo ◽  
Tianming Liu
Keyword(s):  
Signal Analysis ◽  
Fmri Signal

scholarly journals Axial variation of deoxyhemoglobin density as a source of the low-frequency time lag structure in blood oxygenation level-dependent signals

2019 ◽  
Author(s):  
Toshihiko Aso ◽  
Shinnichi Urayama ◽  
Fukuyama Hidenao ◽  
Toshiya Murai
Keyword(s):  
Time Lag ◽  
Biological Significance ◽  
Low Frequency ◽  
Frequency Component ◽  
Real Space ◽  
Blood Oxygenation ◽  
Vascular Tree ◽  
Physiological Basis ◽  
Fmri Signal ◽  
Lag Structure

AbstractPerfusion-related information is reportedly embedded in the low-frequency component of a blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal. The blood-propagation pattern through the cerebral vascular tree is detected as an interregional lag variation of spontaneous low-frequency oscillations (sLFOs). Mapping of this lag, or phase, has been implicitly treated as a projection of the vascular tree structure onto real space. While accumulating evidence supports the biological significance of this signal component, the physiological basis of the “perfusion lag structure,” a requirement for an integrative resting-state fMRI-signal model, is lacking. In this study, we conducted analyses furthering the hypothesis that the sLFO is not only largely of systemic origin, but also essentially intrinsic to blood, and hence behaves as a virtual tracer. By summing the small fluctuations of instantaneous phase differences between adjacent vascular regions, a velocity response to respiratory challenges was detected. Regarding the relationship to neurovascular coupling, the removal of the whole lag structure, which can be considered as an optimized global-signal regression, resulted in a reduction of inter-individual variance while preserving the fMRI response. Examination of the T2* and S0, or non-BOLD, components of the fMRI signal revealed that the lag structure is deoxyhemoglobin dependent, while paradoxically presenting a signal-magnitude reduction in the venous side of the cerebral vasculature. These findings provide insight into the origin of BOLD sLFOs, suggesting that they are highly intrinsic to the circulating blood.

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