The advent of ultra-high field functional magnetic resonance imaging (fMRI) has greatly facilitated submillimeter resolution acquisitions (voxel volume below (1 mm³)), allowing the investigation of cortical columns and cortical depth dependent (i.e. laminar) structures in the human brain. Advanced data analysis techniques are essential to exploit the information in high resolution functional measures. In this article, we use recent, exemplary 9.4 T human functional and anatomical data to review the advantages and disadvantages of (1) pooling high resolution data across regions of interest for cortical depth profile analysis, (2) pooling across cortical depths for mapping patches of cortex while discarding depth-dependent (i.e. columnar) effects, and (3) isotropic sampling without pooling to assess individual voxel’s responses. A set of cortical depth meshes may be a solution to sampling information tangentially while keeping correspondence across depths. For quantitative analysis of the spatial organization in fine-grained structures, a cortical grid approach is advantageous. We further extend this general framework by combining it with a previously introduced cortical layer volume-preserving (equi-volume) approach. This framework can readily accommodate the research questions which allow for spatial smoothing within or across layers. We demonstrate and discuss that equi-volume sampling yields a slight advantage over equidistant sampling given the current limitations of fMRI voxel size, participant motion, coregistration and segmentation. Our 9.4 T human anatomical and functional data indicate the advantage over lower fields including 7 T and demonstrate the practical applicability of T2* and T2-weighted fMRI acquisitions.

The specific contents of human consciousness rely on the activity of specialized neurons in cerebral cortex. We hypothesized that the conscious experience of a specific visual motion axis is reflected in response amplitudes of direction-selective clusters in the human motion complex. Using submillimeter fMRI at ultrahigh field (7 T) we identified fine-grained clusters that were tuned to either horizontal or vertical motion presented in an unambiguous motion display. We then recorded their responses while human observers reported the perceived axis of motion for an ambiguous apparent motion display. Although retinal stimulation remained constant, subjects reported recurring changes between horizontal and vertical motion percepts every 7 to 13 s. We found that these perceptual states were dissociatively reflected in the response amplitudes of the identified horizontal and vertical clusters. We also found that responses to unambiguous motion were organized in a columnar fashion such that motion preferences were stable in the direction of cortical depth and changed when moving along the cortical surface. We suggest that activity in these specialized clusters is involved in tracking the distinct conscious experience of a particular motion axis.

Visual mental imagery is the quasi-perceptual experience of “seeing in the mind’s eye”. While a tight correspondence between imagery and perception in terms of subjective experience is well established, their correspondence in terms of neural representations remains insufficiently understood. In the present study, we exploit the high spatial resolution of functional magnetic resonance imaging (fMRI) at 7T, the retinotopic organization of early visual cortex, and machine-learning techniques to investigate whether visual imagery of letter shapes preserves the topographic organization of perceived shapes. Sub-millimeter resolution fMRI images were obtained from early visual cortex in six subjects performing visual imagery of four different letter shapes. Predictions of imagery voxel activation patterns based on a population receptive field-encoding model and physical letter stimuli provided first evidence in favor of detailed topographic organization. Subsequent visual field reconstructions of imagery data based on the inversion of the encoding model further showed that visual imagery preserves the geometric profile of letter shapes. These results open new avenues for decoding, as we show that a denoising autoencoder can be used to pretrain a classifier purely based on perceptual data before fine-tuning it on imagery data. Finally, we show that the autoencoder can project imagery-related voxel activations onto their perceptual counterpart allowing for visually recognizable reconstructions even at the single-trial level. The latter may eventually be utilized for the development of content-based BCI letter-speller systems.

There is a long-standing debate about the neurocognitive implementation of mental imagery. One form of mental imagery is the imagery of visual motion, which is of interest due to its naturalistic and dynamic character. However, so far only the mere occurrence rather than the specific content of motion imagery was shown to be detectable. In the current study, the application of multi-voxel pattern analysis to high-resolution functional data of 12 subjects acquired with ultra-high field 7 T functional magnetic resonance imaging allowed us to show that imagery of visual motion can indeed activate the earliest levels of the visual hierarchy, but the extent thereof varies highly between subjects. Our approach enabled classification not only of complex imagery, but also of its actual contents, in that the direction of imagined motion out of four options was successfully identified in two thirds of the subjects and with accuracies of up to 91.3% in individual subjects. A searchlight analysis confirmed the local origin of decodable information in striate and extra-striate cortex. These high-accuracy findings not only shed new light on a central question in vision science on the constituents of mental imagery, but also show for the first time that the specific sub-categorical content of visual motion imagery is reliably decodable from brain imaging data on a single-subject level.

Many patients with Disorders of Consciousness (DOC) are misdiagnosed for a variety of reasons. These patients typically cannot communicate. Because such patients are not provided with the needed tools, one of their basic human needs remains unsatisfied, leaving them truly locked in to their bodies. This chapter first reviews current methods and problems of diagnoses and assistive technology for communication, supporting the view that advances in both respects are needed for patients with DOC. The authors also discuss possible solutions to these problems and introduce emerging developments based on EEG (Electroencephalography), fMRI (Functional Magnetic Resonance Imaging), and fNIRS (Functional Near-Infrared Spectroscopy) that have been validated with patients and healthy volunteers.

Brain-computer interfaces offer a way to communicate for people with severe motor and speech disabilities. However, successful current letter speller implementations require perception-driven paradigms (EEG) or cognitively demanding tasks (fMRI, fNIRS) which are not directly linked to letters visualized in the mind's eye. A more natural, content-based, BCI speller system immediately decoding imagined letters from associated brain activity is desirable. In the current study, we take the first steps towards such a BCI and offer new insights into the neural underpinnings of visual mental imagery, a process which is considered one of the main sources of human cognitive complexity. We demonstrate for the first time the feasibility to reconstruct visual field images which carry recognizable content of imagined letter shapes. Using submillimeter resolution fMRI

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Interindividual differences play a crucial role in research on mental imagery. The inherently private nature of imagery does not allow for the same experimental control that is possible in perception research. Even when there are precise instructions subjects will differ in their particular imagery strategy and, hence, show different brain activations. Here, we show results of a time-resolved searchlight analysis for 12 individual subjects who perform a visual motion imagery task. The data show the spatial and temporal extent of brain areas and time windows that allow for a successful decoding of the direction of imagined motion out of four options. Accuracy maps for six different time windows are shown for every individual subject and are made freely available on NeuroVault. These data accompany the findings in the publication “Decoding the direction of imagined visual motion using 7 T ultra-high field fMRI” (Emmerling et al., 2016) [1].