Sebastian M. Frank

Vestibular and visual responses in human posterior insular cortex

The central hub of the cortical vestibular network in humans is likely localized in the region of posterior lateral sulcus. An area characterized by responsiveness to visual motion has previously been described at a similar location and named posterior insular cortex (PIC). Currently it is not known whether PIC processes vestibular information as well. We localized PIC using visual motion stimulation in functional magnetic resonance imaging (fMRI) and investigated whether PIC also responds to vestibular stimuli. To this end, we designed an MRI-compatible caloric stimulation device that allowed us to stimulate bithermally with hot temperature in one ear and simultaneously cold temperature in the other or with warm temperatures in both ears for baseline. During each trial, participants indicated the presence or absence of self-motion sensations. We found activation in PIC during periods of self motion when vestibular stimulation was carried out with minimal visual input. In combined visual-vestibular stimulation area PIC was activated in a similar fashion during congruent and incongruent stimulation conditions. Our results show that PIC not only responds to visual motion but also to vestibular stimuli related to the sensation of self motion. We suggest that PIC is part of the cortical vestibular network and plays a role in the integration of visual and vestibular stimuli for the perception of self motion.

Neural Mechanisms of Feature Conjunction Learning: Enduring Changes in Occipital Cortex After A Week of Training

Most visual activities, whether reading, driving, or playing video games, require rapid detection and identification of learned patterns defined by arbitrary conjunctions of visual features. Initially, such detection is slow and inefficient, but it can become fast and efficient with training. To determine how the brain learns to process conjunctions of visual features efficiently, we trained participants over eight consecutive days to search for a target defined by an arbitrary conjunction of color and location among distractors with a different conjunction of the same features. During each training session, we measured brain activity with functional magnetic resonance imaging (fMRI). The speed of visual search for feature conjunctions improved dramatically within just a few days. These behavioral improvements were correlated with increased neural responses to the stimuli in visual cortex. This suggests that changes in neural processing in visual cortex contribute to the speeding up of visual feature conjunction search. We find evidence that this effect is driven by an increase in the signal‐to‐noise ratio (SNR) of the BOLD signal for search targets over distractors. In a control condition where target and distractor identities were exchanged after training, learned search efficiency was abolished, suggesting that the primary improvement was perceptual learning for the search stimuli, not task‐learning. Moreover, when participants were retested on the original task after nine months without further training, the acquired changes in behavior and brain activity were still present, showing that this can be an enduring form of learning and neural reorganization. Hum Brain Mapp 35:1201–1211, 2014.

Visual-vestibular processing in the human Sylvian fissure

Unlike other sensory systems, the cortical organization of the human vestibular system is not well established. A central role is assumed for the region of the posterior Sylvian fissure, close to the posterior insula. At this site, activation during vestibular stimulation has been observed in previous imaging studies and labeled as the parieto-insular vestibular cortex area (PIVC). However, vestibular responses are found in other parts of the Sylvian fissure as well, including a region that is referred to as the posterior insular cortex (PIC). The anatomical and functional relationship between PIC and PIVC is still poorly understood, because both areas have never been compared in the same participants. Therefore, to better understand the apparently more complex organization of vestibular cortex in the Sylvian fissure, we employed caloric and visual object motion stimuli during functional magnetic resonance imaging and compared location and function of PIVC and PIC in the same participants. Both regions responded to caloric vestibular stimulation, but only the activation pattern in right PIVC reliably represented the direction of the caloric stimulus. Conversely, activity in PIVC was suppressed during stimulation with visual object motion, whereas PIC showed activation. Area PIC is located at a more posterior site in the Sylvian fissure than PIVC. Our results suggest that PIVC and PIC should be considered separate areas in the vestibular Sylvian network, both in terms of location and function.

Multisensory Integration in Self Motion Perception

Self motion perception involves the integration of visual, vestibular, somatosensory and motor signals. This article reviews the findings from single unit electrophysiology, functional and structural magnetic resonance imaging and psychophysics to present an update on how the human and non-human primate brain integrates multisensory information to estimate ones position and motion in space. The results indicate that there is a network of regions in the non-human primate and human brain that processes self motion cues from the different sense modalities.

An MRI-compatible caloric stimulation device for the investigation of human vestibular cortex

BACKGROUND Self-motion perception involves the integration of vestibular, visual, somatosensory and other sensory cues. The neural responses to caloric vestibular stimulation (CVS) in humans have been investigated with functional magnetic resonance imaging (fMRI). NEW METHOD We developed an fMRI-compatible, bithermal caloric stimulation device for repeated CVS. Tempered water is pumped via a closed-loop tube-system to one or both ear canals. Water temperature transmits to the surface of the ear canal via a small glass-pod. For our purposes we used hot (47-49°C), cold (5-7.5°C), or warm for baseline (30-32.5°C). The pods are integrated in the MRI ear protection and connected to water influx and efflux tubes. With our device we can apply multiple vestibular stimulation and baseline trials consecutively. Control measurements indicate that the applied temperatures are stable across trials. MRI-signal differences due to water flow and water temperature are restricted to the area surrounding the pod and are unlikely to intrude into brain tissue. RESULTS Vestibular stimulation with our device elicits caloric nystagmus when no central fixation is presented. We validated our system by conducting a CVS experiment during fMRI-scanning. Participants indicated the presence or absence of a self-motion sensation. Periods of self motion yielded activation in the cortical vestibular network including putative human parieto-insular vestibular cortex (PIVC). COMPARISON WITH EXISTING METHODS Our closed-loop device eliminates many problems associated with caloric stimulation during fMRI. CONCLUSIONS Our device allows researchers to explore neural responses to CVS and those evoked by combined sensory stimulation.

Cross-Modal Attention Effects in the Vestibular Cortex during Attentive Tracking of Moving Objects

The midposterior fundus of the Sylvian fissure in the human brain is central to the cortical processing of vestibular cues. At least two vestibular areas are located at this site: the parietoinsular vestibular cortex (PIVC) and the posterior insular cortex (PIC). It is now well established that activity in sensory systems is subject to cross-modal attention effects. Attending to a stimulus in one sensory modality enhances activity in the corresponding cortical sensory system, but simultaneously suppresses activity in other sensory systems. Here, we wanted to probe whether such cross-modal attention effects also target the vestibular system. To this end, we used a visual multiple-object tracking task. By parametrically varying the number of tracked targets, we could measure the effect of attentional load on the PIVC and the PIC while holding the perceptual load constant. Participants performed the tracking task during functional magnetic resonance imaging. Results show that, compared with passive viewing of object motion, activity during object tracking was suppressed in the PIVC and enhanced in the PIC. Greater attentional load, induced by increasing the number of tracked targets, was associated with a corresponding increase in the suppression of activity in the PIVC. Activity in the anterior part of the PIC decreased with increasing load, whereas load effects were absent in the posterior PIC. Results of a control experiment show that attention-induced suppression in the PIVC is stronger than any suppression evoked by the visual stimulus per se. Overall, our results suggest that attention has a cross-modal modulatory effect on the vestibular cortex during visual object tracking. SIGNIFICANCE STATEMENT In this study we investigate cross-modal attention effects in the human vestibular cortex. We applied the visual multiple-object tracking task because it is known to evoke attentional load effects on neural activity in visual motion-processing and attention-processing areas. Here we demonstrate a load-dependent effect of attention on the activation in the vestibular cortex, despite constant visual motion stimulation. We find that activity in the parietoinsular vestibular cortex is more strongly suppressed the greater the attentional load on the visual tracking task. These findings suggest cross-modal attentional modulation in the vestibular cortex.

Neural correlates of context-dependent feature conjunction learning in visual search tasks

Many perceptual learning experiments show that repeated exposure to a basic visual feature such as a specific orientation or spatial frequency can modify perception of that feature, and that those perceptual changes are associated with changes in neural tuning early in visual processing. Such perceptual learning effects thus exert a bottom‐up influence on subsequent stimulus processing, independent of task‐demands or endogenous influences (e.g., volitional attention). However, it is unclear whether such bottom‐up changes in perception can occur as more complex stimuli such as conjunctions of visual features are learned. It is not known whether changes in the efficiency with which people learn to process feature conjunctions in a task (e.g., visual search) reflect true bottom‐up perceptual learning versus top‐down, task‐related learning (e.g., learning better control of endogenous attention). Here we show that feature conjunction learning in visual search leads to bottom‐up changes in stimulus processing. First, using fMRI, we demonstrate that conjunction learning in visual search has a distinct neural signature: an increase in target‐evoked activity relative to distractor‐evoked activity (i.e., a relative increase in target salience). Second, we demonstrate that after learning, this neural signature is still evident even when participants passively view learned stimuli while performing an unrelated, attention‐demanding task. This suggests that conjunction learning results in altered bottom‐up perceptual processing of the learned conjunction stimuli (i.e., a perceptual change independent of the task). We further show that the acquired change in target‐evoked activity is contextually dependent on the presence of distractors, suggesting that search array Gestalts are learned. Hum Brain Mapp 37:2319–2330, 2016.

Caudate nucleus reactivity predicts perceptual learning rate for visual feature conjunctions

Useful information in the visual environment is often contained in specific conjunctions of visual features (e.g., color and shape). The ability to quickly and accurately process such conjunctions can be learned. However, the neural mechanisms responsible for such learning remain largely unknown. It has been suggested that some forms of visual learning might involve the dopaminergic neuromodulatory system (Roelfsema et al., 2010; Seitz and Watanabe, 2005), but this hypothesis has not yet been directly tested. Here we test the hypothesis that learning visual feature conjunctions involves the dopaminergic system, using functional neuroimaging, genetic assays, and behavioral testing techniques. We use a correlative approach to evaluate potential associations between individual differences in visual feature conjunction learning rate and individual differences in dopaminergic function as indexed by neuroimaging and genetic markers. We find a significant correlation between activity in the caudate nucleus (a component of the dopaminergic system connected to visual areas of the brain) and visual feature conjunction learning rate. Specifically, individuals who showed a larger difference in activity between positive and negative feedback on an unrelated cognitive task, indicative of a more reactive dopaminergic system, learned visual feature conjunctions more quickly than those who showed a smaller activity difference. This finding supports the hypothesis that the dopaminergic system is involved in visual learning, and suggests that visual feature conjunction learning could be closely related to associative learning. However, no significant, reliable correlations were found between feature conjunction learning and genotype or dopaminergic activity in any other regions of interest.

The parieto-insular vestibular cortex in humans: more than a single area?

Here, we review the structure and function of a core region in the vestibular cortex of humans that is located in the midposterior Sylvian fissure and referred to as the parieto-insular vestibular cortex (PIVC). Previous studies have investigated PIVC by using vestibular or visual motion stimuli and have observed activations that were distributed across multiple anatomical structures, including the temporo-parietal junction, retroinsula, parietal operculum, and posterior insula. However, it has remained unclear whether all of these anatomical areas correspond to PIVC and whether PIVC responds to both vestibular and visual stimuli. Recent results suggest that the region that has been referred to as PIVC in previous studies consists of multiple areas with different anatomical correlates and different functional specializations. Specifically, a vestibular but not visual area is located in the parietal operculum, close to the posterior insula, and likely corresponds to the nonhuman primate PIVC, while a visual-vestibular area is located in the retroinsular cortex and is referred to, for historical reasons, as the posterior insular cortex area (PIC). In this article, we review the anatomy, connectivity, and function of PIVC and PIC and propose that the core of the human vestibular cortex consists of at least two separate areas, which we refer to together as PIVC+. We also review the organization in the nonhuman primate brain and show that there are parallels to the proposed organization in humans.

Learning efficient visual search for stimuli containing diagnostic spatial configurations and color-shape conjunctions

Visual search is often slow and difficult for complex stimuli such as feature conjunctions. Search efficiency, however, can improve with training. Search for stimuli that can be identified by the spatial configuration of two elements (e.g., the relative position of two colored shapes) improves dramatically within a few hundred trials of practice. Several recent imaging studies have identified neural correlates of this learning, but it remains unclear what stimulus properties participants learn to use to search efficiently. Influential models, such as reverse hierarchy theory, propose two major possibilities: learning to use information contained in low-level image statistics (e.g., single features at particular retinotopic locations) or in high-level characteristics (e.g., feature conjunctions) of the task-relevant stimuli. In a series of experiments, we tested these two hypotheses, which make different predictions about the effect of various stimulus manipulations after training. We find relatively small effects of manipulating low-level properties of the stimuli (e.g., changing their retinotopic location) and some conjunctive properties (e.g., color-position), whereas the effects of manipulating other conjunctive properties (e.g., color-shape) are larger. Overall, the findings suggest conjunction learning involving such stimuli might be an emergent phenomenon that reflects multiple different learning processes, each of which capitalizes on different types of information contained in the stimuli. We also show that both targets and distractors are learned, and that reversing learned target and distractor identities impairs performance. This suggests that participants do not merely learn to discriminate target and distractor stimuli, they also learn stimulus identity mappings that contribute to performance improvements.