Human brain imaging studies of bistable perceptual phenomena revealed that frontal and parietal areas are activated during perceptual switches between the two conflicting percepts. However, these studies do not provide information about causality, i.e., whether activity reports a consequence or a cause of the perceptual change. Here we used functional magnetic resonance imaging to individually localize four parietal regions involved in perceptual switches during binocular rivalry in 15 subjects and subsequently disturbed their neural processing and that of a control site using 2 Hz repetitive transcranial magnetic stimulation (TMS) during binocular rivalry. We found that TMS over one of the sites, the right intraparietal sulcus (IPS), prolonged the periods of stable percepts. Additionally, the more lateralized the blood oxygen level-dependent signal was in IPS, the more lateralized the TMS effects were. Lateralization varied considerably across subjects, with a right-hemispheric bias. Control replay experiments rule out nonspecific effects of TMS on task performance, reaction times, or eye blinks. Our results thus demonstrate a causal, destabilizing, and individually lateralized effect of normal IPS function on perceptual continuity in rivalry. This is in accord with a role of IPS in perceptual selection, relating its role in rivalrous perception to that in attention.

Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential.

Grouping local elements into a holistic percept, also known as spatial binding, is crucial for meaningful perception. Previous studies have shown that neurons in early visual areas V1 and V2 can signal complex grouping-related information, such as illusory contours or object-border ownerships. However, relatively little is known about higher-level processes contributing to these signals and mediating global Gestalt perception. We used a novel bistable motion illusion that induced alternating and mutually exclusive vivid conscious experiences of either dynamic illusory contours forming a global Gestalt or moving ungrouped local elements while the visual stimulation remained the same. fMRI in healthy human volunteers revealed that activity fluctuations in two sites of the parietal cortex, the superior parietal lobe and the anterior intraparietal sulcus (aIPS), correlated specifically with the perception of the grouped illusory Gestalt as opposed to perception of ungrouped local elements. We then disturbed activity at these two sites in the same participants using transcranial magnetic stimulation (TMS). TMS over aIPS led to a selective shortening of the duration of the global Gestalt percept, with no effect on that of local elements. The results suggest that aIPS activity is directly involved in the process of spatial binding during effortless viewing in the healthy brain. Conscious perception of global Gestalt is therefore associated with aIPS function, similar to attention and perceptual selection.

Recent advances in MR technology have enabled increased spatial resolution for routine functional and anatomical imaging, which has created demand for software tools that are able to process these data. The availability of high-resolution data also raises the question of whether higher resolution leads to substantial gains in accuracy of quantitative morphometric neuroimaging procedures, in particular the cortical surface reconstruction and cortical thickness estimation. In this study we adapted the FreeSurfer cortical surface reconstruction pipeline to process structural data at native submillimeter resolution. We then quantified the differences in surface placement between meshes generated from (0.75 mm) isotropic resolution data acquired in 39 volunteers and the same data downsampled to the conventional 1 mm voxel size. We find that when processed at native resolution, cortex is estimated to be thinner in most areas, but thicker around the Cingulate and the Calcarine sulci as well as in the posterior bank of the Central sulcus. Thickness differences are driven by two kinds of effects. First, the gray-white surface is found closer to the white matter, especially in cortical areas with high myelin content, and thus low contrast, such as the Calcarine and the Central sulci, causing local increases in thickness estimates. Second, the gray-CSF surface is placed more interiorly, especially in the deep sulci, contributing to local decreases in thickness estimates. We suggest that both effects are due to reduced partial volume effects at higher spatial resolution. Submillimeter voxel sizes can therefore provide improved accuracy for measuring cortical thickness.

Our visual system's ability to group visual elements into meaningful entities and to separate them from others is referred to as scene segmentation. Visual motion often provides a powerful cue for this process as parallax or coherence can inform the visual system about scene or object structure. Here we tested the hypothesis that scene segmentation by motion cues relies on a common neural substrate in the parietal cortex. We used fMRI and a set of three entirely distinct motion stimuli to examine scene segmentation in the human brain. The stimuli covered a wide range of high-level processes, including perceptual grouping, transparent motion, and depth perception. All stimuli were perceptually bistable such that percepts alternated every few seconds while the physical stimulation remained constant. The perceptual states were asymmetric, in that one reflected the default (nonsegmented) interpretation, and the other the non-default (segmented) interpretation. We confirmed behaviorally that upon stimulus presentation, the default percept was always perceived first, before perceptual alternations ensued. Imaging results showed that across all stimulus classes perceptual scene-segmentation was associated with an increase of activity in the posterior parietal cortex together with a decrease of neural signal in the early visual cortex. This pattern of activation is compatible with predictive coding models of visual perception, and suggests that parietal cortex hosts a generic mechanism for scene segmentation. Making sense of cluttered visual scenes is crucial for everyday perception. An important cue to scene segmentation is visual motion: slight movements of scene elements give away which elements belong to the foreground or background or to the same object. We used three distinct stimuli that engage visual scene segmentation mechanisms based on motion. They involved perceptual grouping, transparent motion, and depth perception. Brain activity associated with all three mechanisms converged in the same parietal region with concurrent deactivation of early visual areas. The results suggest that posterior parietal cortex is a hub involved in structuring visual scenes based on different motion cues, and that feedback modulates early cortical processing in accord with predictive coding theory.

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Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μm at the group level for the simulated data, and below 200 μm at the single-subject level and below 100 μm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.

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