Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Ali Khadem; Gholam Ali Hossein-Zadeh
Volume 8, Issue 1 , March 2014, , Pages 1-17
Abstract
In EEG/MEG datasets, the Volume Conduction (VC) artifact appears as instantaneous linear mixing of brain source activities on the channel measurements. A desired characteristic of an ideal EEG/MEG connectivity estimator (on sensor-space) is its robustness to VC artifact. This means that the VC of independent ...
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In EEG/MEG datasets, the Volume Conduction (VC) artifact appears as instantaneous linear mixing of brain source activities on the channel measurements. A desired characteristic of an ideal EEG/MEG connectivity estimator (on sensor-space) is its robustness to VC artifact. This means that the VC of independent brain sources must never lead to detection of significant connectivity among EEG/MEG channels. There has been no criterion in the literature so far that can compare the robustness levels of different (sensor-space) connectivity estimators against VC artifact. In this paper, a criterion called Robustness Index (RI) is proposed to compare the robustness levels of connectivity estimators to channel couplings which are modeled by instantaneous linear mixing of quasi-independent components. Since the VC effects have instantaneous linear mixing nature, we expect RI to rank the connectivity estimators according to their robustness levels to VC artifact. RI is used to rank seven functional connectivity estimators: the absolute value of Pearson Correlation Coefficient (CC), Mutual Information (MI), Magnitude Squared Coherence (Coh), (1:1) Phase Locking Value ((1:1)PLV), the absolute value of Imaginary part of Coherency (ImC), Phase Lag Index (PLI) and Weighted Phase Lag Index (WPLI). The results for simulated data and a real EEG dataset show the connectivity estimators that are theoretically robust to VC artifact (ImC, PLI and WPLI) yield RI values near %100 and have the highest ranks, as expected. Also, for the simulated models in which the true VC effects and brain sources are known, ranking the connectivity estimators by RI is consistent with their robustness levels against VC artifact. This supports the possibility of using RI as a tool for ranking the robustness levels of connectivity estimators against VC artifact for real EEG/MEG datasets.
Biomedical Image Processing / Medical Image Processing
Mohammad Reza Rezaeian; Gholam Ali Hossein-Zadeh; Hamid Soltanian Zadeh
Volume 8, Issue 1 , March 2014, , Pages 87-99
Abstract
Chemical exchange saturation transfer (CEST) is a new mechanism of contrast generation in magnetic resonance imaging (MRI) which differentiates molecule biomarkers via chemical shift. CEST MRI contrast mechanism is very complex and depends on radio frequency (RF) power and RF pulse shape. Two approaches ...
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Chemical exchange saturation transfer (CEST) is a new mechanism of contrast generation in magnetic resonance imaging (MRI) which differentiates molecule biomarkers via chemical shift. CEST MRI contrast mechanism is very complex and depends on radio frequency (RF) power and RF pulse shape. Two approaches have been used to saturate contrast agent (CA) protons: continuous wave CEST (CW-CEST) and pulsed CEST. To find the optimal RF pulse, numerical solution of Bloch-McConnell equations (BME) may be used. In this paperwe find the optimum values of RF pulse parameters that maximize the CEST contrast. Discrete pulses have lower specific absorption ratio (SAR) than CW RF pulses. However, since discretization is performed on continuous RF pulses, optimizing the continuous RF pulses leads to the optimization of discrete RF pulses. Therefore, in this paper, Rectangular, Gaussian and Fermi pulses are investigated as CW RF pulses. In this investigation, in addition to considering the SAR limitation, 60 dB approximation for the RF pulse amplitude is used. To compare the efficiency of pulses, their resultant flip angles (FA) are assumed equal. Efficiency of CW-CEST is investigated using two parameters, CEST ratio and SAR. According to these parametres, rectangular, Fermi and Gaussian RF pulses have the best performance respectively. Since implementation of rectangular RF is harder than Gaussian and Fermi RF pulses, Fermi and Gaussian RF pulses are desired. Our results suggest that it is possible to maximize CEST ratio by optimizing parameters of rectangular (with an amplitude of 5.7μT), Gaussian (σ about 0.7s) and Fermi (a-value about 0.3s) pulses. Results are verified by empirical formulation of CEST ratio.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Ali Khadem; Gholam Ali Hossein-Zadeh
Volume 6, Issue 1 , June 2012, , Pages 57-69
Abstract
Exploring the causal (delayed) brain relations is an important topic in the Neuroscience. The traditional estimators of brain causal (delayed) relations are mainly model-based and put restrictive assumptions on the brain dynamics. In the recent years, some nonparametric measures have been introduced ...
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Exploring the causal (delayed) brain relations is an important topic in the Neuroscience. The traditional estimators of brain causal (delayed) relations are mainly model-based and put restrictive assumptions on the brain dynamics. In the recent years, some nonparametric measures have been introduced to solve this problem. Among them, the most important one is Transfer Entropy (TE) which is based on the information theory and Conditional Mutual Information concept. However, in the presence of significant instantaneous relations that are observed extensively in the brain functional datasets, TE may estimate the causal (delayed) relations inaccurately. In this paper, two information theoretic based measures called Instantaneous Interaction (II) and Modified Transfer entropy (MTE) are introduced to estimate the instantaneous and causal (delayed) brain relations, respectively. MTE is used instead of TE whenever II is significant. These measures are evaluated on 3 simulated models and eyes-closed resting state EEG data. The simulation results show high ability of II to estimate the linear and nonlinear instantaneous relations. Also, based on the simulation results MTE outperforms TE to estimate causal (delayed) relations in presence of significant instantaneous relations (significant II). For the real EEG data, II detects a significant instantaneous relation between Posterior and Frontal EEG channels. Also MTE detects the information flow from Posterior EEG channels to Frontal ones more significantly than TE does. So in presence of significant instantaneous relations in the real EEG data, MTE outperforms TE.
Biomedical Image Processing / Medical Image Processing
Seyed Mohammad Shams; Gholam Ali Hossein-Zadeh; Mohammad Mehdi Karimi
Volume 1, Issue 1 , June 2007, , Pages 29-37
Abstract
In order to analyze the functional Magnetic Resonance Imaging (fMRI) data, the parameters of a nonlinear model for the hemodynamic system, so called Balloon model, were characterized and estimated. Two different approaches were applied to estimate these parameters. In the first step of both approaches, ...
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In order to analyze the functional Magnetic Resonance Imaging (fMRI) data, the parameters of a nonlinear model for the hemodynamic system, so called Balloon model, were characterized and estimated. Two different approaches were applied to estimate these parameters. In the first step of both approaches, the voxels which show neural activity were identified. Then, the parameters of the balloon model for these active voxels were estimated by both steepest descent algorithm, and through genetic algorithm. Proposed approaches were applied on experimental fMRI data and the parameters of nonlinear Balloon model were estimated for different brain voxels. Accuracy of these characterizations was assessed via comparing the measured time series at each voxel with the modeled time series. Also, it was shown that the results of the parameter-estimation are consistent with the results obtained from system characterization via Volterra Kernels (which were reported in previous studies). It was concluded that the suggested approaches could accomplish a nonlinear system characterization through numerical methods, whereas they avoid theoretical complexities and they have acceptable speed (especially steepest descent algorithm).
