Cortical activation associated with determination of depth order during transparent motion perception: A normalized integrative fMRI-MEG study.

When visual patterns drifting in different directions and/or at different speeds are superimposed on the same plane, observers perceive transparent surfaces on planes of different depths. This phenomenon is known as transparent motion perception. In this study, cortical activities were measured using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) to reveal the cortical dynamics associated with determination of depth order during transparent motion perception. In addition, offline eye movement measurements were performed to determine the latencies of the start of both pursuit eye movements and depth attention that are important in determination of the depth order. MEG and fMRI data were analyzed by a normalized integrative fMRI-MEG method that enables reconstruction of time-varying dipole moments of activated regions from MEG signals. Statistical analysis of fMRI data was performed to identify activated regions. The activated regions were used as spatial constraints for the reconstruction using the integrative fMRI-MEG method. We focused on the period between latencies (216-405 ms) determined by eye movement experiment, which are related to determination of the depth order. The results of integrative analysis revealed that significant neural activities were observed in the visual association area, the human middle temporal area, the intraparietal sulcus, the lateral occipital cortex, and the anterior cingulate cortex between 216 and 405 ms. These results suggest that initial eye movement and accompanying cortical activations during focused duration play an important role in determining the depth order during transparent motion perception.

A Visual Analytics Approach for Ecosystem Dynamics based on Empirical Dynamic Modeling.

An important approach for scientific inquiry across many disciplines involves using observational time series data to understand the relationships between key variables to gain mechanistic insights into the underlying rules that govern the given system. In real systems, such as those found in ecology, the relationships between time series variables are generally not static; instead, these relationships are dynamical and change in a nonlinear or state-dependent manner. To further understand such systems, we investigate integrating methods that appropriately characterize these dynamics (i.e., methods that measure interactions as they change with time-varying system states) with visualization techniques that can help analyze the behavior of the system. Here, we focus on empirical dynamic modeling (EDM) as a state-of-the-art method that specifically identifies causal variables and measures changing state-dependent relationships between time series variables. Instead of using approaches centered on parametric equations, EDM is an equation-free approach that studies systems based on their dynamic attractors. We propose a visual analytics system to support the identification and mechanistic interpretation of system states using an EDM-constructed dynamic graph. This work, as detailed in four analysis tasks and demonstrated with a GUI, provides a novel synthesis of EDM and visualization techniques such as brush-link visualization and visual summarization to interpret dynamic graphs representing ecosystem dynamics. We applied our proposed system to ecological simulation data and real data from a marine mesocosm study as two key use cases. Our case studies show that our visual analytics tools support the identification and interpretation of the system state by the user, and enable us to discover both confirmatory and new findings in ecosystem dynamics. Overall, we demonstrated that our system can facilitate an understanding of how systems function beyond the intuitive analysis of high-dimensional information based on specific domain knowledge.

Remote detected Low-Field MRI using an optically pumped atomic magnetometer combined with a liquid cooled pre-polarization coil.

Superconducting quantum interference devices are widely used in basic and clinical biomagnetic measurements such as low-field magnetic resonance imaging and magnetoencephalography primarily because they exhibit high sensitivity at low frequencies and have a wide bandwidth. The main disadvantage of these devices is that they require cryogenic coolants, which are rather expensive and not easily available. Meanwhile, with the advances in laser technology in the past few years, optically pumped atomic magnetometers (OPAMs) have been shown to be a good alternative as they can have adequate noise levels and are several millimeters in size, which makes them significantly easier to use. In this study, we used an OPAM module operating at a Larmor frequency of 5kHz to acquire NMR and MRI signals. This study presents these initial results as well as our initial attempts at imaging using this OPAM module. In addition, we have designed a liquid-cooled pre-polarizing coil that reduces the measurement time significantly.

Cortical neural activities associated with binocular rivalry: an EEG-fMRI integrative study.

Binocular rivalry occurs over time when each eye is simultaneously presented with different visual stimuli. We have addressed which brain regions are and the mechanisms involved in binocular rivalry using EEG and fMRI measurements of cortical activities during observations of competitive (rivalry) and cooperative (fusion) drifting vertical gratings. By applying an EEG-fMRI integrative method, we analyzed the time courses of multiple cortical sources of event-related potentials obtained under rivalry or fusion conditions. We detected significant cortical activities at bilateral MT+/V5 and the right prefrontal eye field in the rivalry condition; however, this may not reflect intrinsic alternation in binocular rivalry. Our findings suggest that novel integrative methods are necessary to investigate the distributed cortical network associated with binocular rivalry, through analysis of multiple cortical sources of event-related desynchronization and/or synchronization in certain frequency bands.