Assessment of 3D Visual Discomfort Based on Dynamic Functional Connectivity Analysis with HMM in EEG.
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ABSTRACT:
SUBMITTER: Long Z
PROVIDER: S-EPMC9313185 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods.MethodsWe implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity.ResultsOur results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages.ConclusionsOverall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.
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Project description:In the study of brain functional connectivity networks, it is assumed that a network is built from a data window in which activity is stationary. However, brain activity is non-stationary over sufficiently large time periods. Addressing the analysis electroencephalograph (EEG) data, we propose a data segmentation method based on functional connectivity network structure. The goal of segmentation is to ensure that within a window of analysis, there is similar network structure. We designed an intuitive and flexible graph distance measure to quantify the difference in network structure between two analysis windows. This measure is modular: a variety of node importance indices can be plugged into it. We use a reference window versus sliding window comparison approach to detect changes, as indicated by outliers in the distribution of graph distance values. Performance of our segmentation method was tested in simulated EEG data and real EEG data from a drone piloting experiment (using correlation or phase-locking value as the functional connectivity strength metric). We compared our method under various node importance measures and against matrix-based dissimilarity metrics that use singular value decomposition on the connectivity matrix. The results show the graph distance approach worked better than matrix-based approaches; graph distance based on partial node centrality was most sensitive to network structural changes, especially when connectivity matrix values change little. The proposed method provides EEG data segmentation tailored for detecting changes in terms of functional connectivity networks. Our study provides a new perspective on EEG segmentation, one that is based on functional connectivity network structure differences.
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Project description:Properly constructed stereoscopic images are aligned vertically on the display screen, so on-screen binocular disparities are strictly horizontal. If the viewer's inter-ocular axis is also horizontal, he/she makes horizontal vergence eye movements to fuse the stereoscopic image. However, if the viewer's head is rolled to the side, the on-screen disparities now have horizontal and vertical components at the eyes. Thus, the viewer must make horizontal and vertical vergence movements to binocularly fuse the two images. Vertical vergence movements occur naturally, but they are usually quite small. Much larger movements are required when viewing stereoscopic images with the head rotated to the side. We asked whether the vertical vergence eye movements required to fuse stereoscopic images when the head is rolled cause visual discomfort. We also asked whether the ability to see stereoscopic depth is compromised with head roll. To answer these questions, we conducted behavioral experiments in which we simulated head roll by rotating the stereo display clockwise or counter-clockwise while the viewer's head remained upright relative to gravity. While viewing the stimulus, subjects performed a psychophysical task. Visual discomfort increased significantly with the amount of stimulus roll and with the magnitude of on-screen horizontal disparity. The ability to perceive stereoscopic depth also declined with increasing roll and on-screen disparity. The magnitude of both effects was proportional to the magnitude of the induced vertical disparity. We conclude that head roll is a significant cause of viewer discomfort and that it also adversely affects the perception of depth from stereoscopic displays.
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Project description:ObjectiveFunctional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis.MethodsWe introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1340 visually marked IEDs. Differences in network structure and strength were assessed.ResultsIEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure.ConclusionsIncreases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS.SignificanceDynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.
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