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Oscillatory activity driven by stimuli picture evoked flicker stream are the dominant signals as illustrated by the frequency domain spectrum , most strongly represented by posterior electrodes in the topographic map . The present paper illustrates the usage of this FSS based approach for quantifying ssVEP-fMRI co-variation. Using a fusion algorithm that we have previously described (Ji et al., 2018), we applied the FSS preprocessing step to data from a 3-phase aversive conditioning paradigm that included a habituation, acquisition, and extinction phase. EEG and fMRI were recorded simultaneously while participants viewed periodically and rapidly phase-reversing Gabor patches (sine-wave gratings), evoking steady-state visual potentials .
Toward this overall goal, we record the simultaneous electroencephalogram and functional magnetic resonance imaging , characterizing one candidate mechanism, i.e., large-scale brain oscillations. The present report examines the use of Functional Source Separation as an optimization step in EEG-fMRI fusion that harnesses timing information to constrain the solutions that satisfy physiological assumptions. We applied this approach to the voxel-wise correlation of steady-state visual evoked potential amplitude and blood oxygen level-dependent imaging , across both time series.
In order to evaluate the performance of FSS optimization, the signal-to-noise ratio is determined by comparing the signal level during the stimuli on period with the signal level without stimulus. The pre-stimulus baseline from −1 to 0 s was set as the time interval of background/baseline noise, during which the participant was instructed to maintain fixation on a white cross, preceding the onset of the Gabor patch. In addition, a period from the inter-trial baseline (5.6–7 s computer hacker protective gloves steal data stock after stimulus onset) was also included as a manipulation check, quantifying the extent to which the extracted ssVEP oscillation attenuates after stimulus offset. Since this part of background information is blind to FSS optimization, it represents an intuitive test for neurophysiological validity. Where k is the parameter measuring the required minimum response, so as to define an admissible region where the optimization is only driven by J when response is greater than k.
- Here, we adopted the criteria of fastICA , aiming to minimize the Gaussianity of the results , since the observed mixed signals will tend to have more Gaussian amplitude distributions.
- Maps of binomial test result for FSS-ssVEP and BOLD in contrast to the ICA-ssVEP and BOLD , acquisition phase .
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For the present application, the maximum Rstim for each phase is computed separately with fastICA, and assigned to k. Since prior information about the sources can only be described by a non-differentiable function, simulated annealing is employed for global optimization (Kirkpatrick et al., 1983; Ingber, 2000). To ensure the stability and reliability of the optimization procedure, the contrast function for all three phases were added up and jointly optimized.
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On the other hand, noise such as alpha oscillations (generally in the range 8–13 Hz), or the absence of an ssVEP signal, may interfere with the optimization procedure, producing divergent results as shown for participant number 07. It should be noted that this study was limited in terms of sample size and in terms of signal-to-noise, low in some participants, despite the precise definition of ssVEPs in the frequency domain (Norcia et al., 2015). Furthermore, it is not within the scope of this manuscript to quantitatively compare many alternative pipelines to establish the benefits of FSS relative to other methods. Such analyses have been performed with other data types (Porcaro et al., 2010).
This information should not be considered complete, up to date, and is not intended to be used in place of a visit, consultation, or advice of a legal, medical, or any other professional. This is an open-access article distributed under the terms of the Creative Commons Attribution License . The use, distribution or reproduction in other forums is permitted, provided the original author and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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In addition, when comparing the scalp topographical distribution of FSS weights with ICA weights, the FSS with the focal occipital topography is physiologically more plausible than the distributed ICA topography. To illustrate the effects of preprocessing method, time course data of the averaged ssVEP epochs recovered by FSS across trials are overlapped with the results of ICA in Figure 3. The outline of the blue oscillations (FSS-ssVEP) overall shows more stability and less noise across time, including in the baseline segment, compared to the green envelope, which tends to be larger in the baseline segment and shows more temporal variation during the steady-state segment. Note that data from all phases were used in the joint optimization process. This will in some cases yield a less favorable signal-to-noise ratio compared to identifying optimal components for each single phase. However, a joint optimization across phases is expected to achieve more robust and more externally valid results, being based on more trials.
The data reported here include 40 total trials per phase per participant. Also, as you may have noticed, we have fallen foul to the dreaded injury curse, with some of our squad playing through the pain barrier, while others are now out of the line-up. Our superb medical team are working extra hard right now to get the guys ready for game action as quickly as possible. I get this bug on Ubuntu 18.10 with the kernel upgraded to 5.0.2 from the ubuntu repository. I do not hold a position in any crypto asset or cryptocurrency or blockchain company.
During the acquisition phase in which aversive learning occurs, we observed additional correlations between ssVEP and BOLD in the anterior cingulate cortex , precuneus, as well as the middle and superior temporal gyrus. Note that small but robust correlations between electrophysiological and hemodynamic measures during the same process have been consistently reported across different species, including human beings (see e.g., Boynton, 2011), for a discussion of this problem. Grand Mean time series of filtered (0.5–30 Hz) EEG time-series time locked to trial onset and averaged across all trials and subjects .
The FSS optimization tends to find a projection vector w that maximizes the above contrast function. Here, we adopted the criteria of fastICA , aiming to minimize the Gaussianity of the results , since the observed mixed signals will tend to have more Gaussian amplitude distributions. The function H accounts for prior information we have on sources, which give preference to solutions that tend to satisfy physiological assumptions. Parameter λ is used to weigh the two parts of the contrast function, chosen to be 10 for ssVEP recovery. To assess the statistical significance of the correlation results, we applied permutation tests to determining the threshold for rejecting the null hypothesis of no linear relation based on shuffled data, and assumed statistical significance if p-value is less than 0.05 for single participant. Given the small size of the present sample, the robustness of a given ssVEP-fMRI correlation across participants was quantified by measuring inter-subject overlap of voxels showing significant correlations after thresholding by a permutation test, using a binomial test.
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Neurorobotics is one of the most ambitious fields in robotics, driving integration of interdisciplinary data and knowledge. One of the most productive areas of interdisciplinary research in this area has been the implementation of biologically-inspired mechanisms in the development of autonomous systems. Specifically, enabling such systems to display adaptive behavior such as learning from good and bad outcomes, has been achieved by quantifying and understanding the neural mechanisms of the brain networks mediating adaptive behaviors in humans and animals. For example, associative learning from aversive or dangerous outcomes is crucial for an autonomous system, to avoid dangerous situations in the future. A body of neuroscience research has suggested that the neurocomputations in the human brain during associative learning involve re-shaping of sensory responses. The nature of these adaptive changes in sensory processing during learning however are not yet well enough understood to be readily implemented into on-board algorithms for robotics application.
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- Toward this overall goal, we record the simultaneous electroencephalogram and functional magnetic resonance imaging , characterizing one candidate mechanism, i.e., large-scale brain oscillations.
However, both ongoing and future research will establish and further optimize preprocessing steps for ssVEP-fMRI fusion. Figure 4 and Table 2 show the ssVEP-BOLD co-variation with a standard 4 s delay. For each participant, voxels that survived individual permutation-controlled thresholding were kept and submitted to a group-level binomial test. As expected, the ssVEP-BOLD correlation map contained extended visual cortical areas, including the calcarine fissure, cuneus, occipital gyrus, and fusiform gyrus. Additional areas of ssVEP-BOLD co-variation were seen in the postcentral cortex, the rolandic operculum, and superior temporal gyrus.
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During the acquisition block, participants were informed that they would intermittently feel a cutaneous electric shock during the experiment but were not instructed as to the contingencies of the shock administration. The extinction phase was also uninstructed, such that participants were not told that no more shocks were to be given. Each participant was instructed to remain still while in the scanner and to maintain fixation on the center of the screen. Cutaneous shocks were administered using an STMISO Stimulation Isolation Adapter (BIOPAC Systems, Inc., Goleta, CA) with MRI-compatible skin electrodes.
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The results showed the benefit of FSS for the extraction of robust ssVEP signals during simultaneous EEG-fMRI recordings. Applied to data from a 3-phase aversive conditioning paradigm, the correlation maps across the three phases show converging results, notably major overlapping areas in both primary and extended visual cortical regions, including calcarine sulcus, lingual cortex, and cuneus. In addition, during the acquisition phase when aversive learning occurs, we observed additional correlations between ssVEP and BOLD in the anterior cingulate cortex as well as the precuneus and superior temporal gyrus. To assess the spatial-temporal correlation between EEG and fMRI responses recorded simultaneously, the recovered ssVEP source was averaged across time segments of each fMRI scan, representing the intensity of neuronal activities, resulting in a time series with the same temporal resolution of the BOLD. As in the procedure of FSS optimization described in methods, the proper scaling of the source signals is non-recoverable, we are mostly interested in the predictability reflects the proportion of the variance in the BOLD signal that is linearly predicted from ssVEP time series.
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To this end, we calculated the probability of overlap at a given voxel under the null hypothesis that significant correlations occurred in different voxels and different participants. The resulting p-values were then corrected by the number of voxels considered overall, resulting in a threshold of 0.5 at a given voxel (i.e., five out of 10 participants showing permutation controlled, significant correlations at that voxel). These results were compared to the average correlation map, to establish the validity of this conservative statistical approach.
These structures included the anterior cingulate gyrus, known to be involved in fear acquisition and associative learning in general (e.g., Sehlmeyer et al., 2009; Fullana et al., 2016). As such, this finding further supports the validity of FSS-based preprocessing for ssVEP-fMRI fusion. The SNR index was evaluated for each participant and experimental phase separately, and compare with ICA results optimized for each phase separately. The data presented in this report were recorded from a differential aversive conditioning study in which Gabors of one orientation were occasionally paired with an electric shock (see Petro et al., 2017, for details). For the habituation block, participants were instructed that they would not feel any shock but to fixate on the patterns.
Following the previous work (Wang and Zheng, 2014; Ji et al., 2018), we use the bivariate case of cross multivariate correlation coefficient for correlation analysis between EEG and fMRI, which is equal to the absolute value of the cross correlation coefficient. The metabolic events are slower and temporally lagged to the neuro-electric activity . Based on the large body of research on stimulus-BOLD latency performed in the visual cortex (Buxton et al., 1998; Serences, 2004; Penny et al., 2011), we adopt the consensus setting of 4 s as the latency of BOLD relative to neural events. Contrasting the spatial distribution of ssVEP-fMRI correlation maps between FSS and ICA, as shown in Figure 5, that ICA yielded a less coherent, and overall noisier spatial pattern, with fewer voxels in visual areas displaying the expected effect.
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