Uri Hertz

Extensive Cochleotopic Mapping of Human Auditory Cortical Fields Obtained with Phase-Encoding fMRI

The primary sensory cortices are characterized by a topographical mapping of basic sensory features which is considered to deteriorate in higher-order areas in favor of complex sensory features. Recently, however, retinotopic maps were also discovered in the higher-order visual, parietal and prefrontal cortices. The discovery of these maps enabled the distinction between visual regions, clarified their function and hierarchical processing. Could such extension of topographical mapping to high-order processing regions apply to the auditory modality as well? This question has been studied previously in animal models but only sporadically in humans, whose anatomical and functional organization may differ from that of animals (e.g. unique verbal functions and Heschls gyrus curvature). Here we applied fMRI spectral analysis to investigate the cochleotopic organization of the human cerebral cortex. We found multiple mirror-symmetric novel cochleotopic maps covering most of the core and high-order human auditory cortex, including regions considered non-cochleotopic, stretching all the way to the superior temporal sulcus. These maps suggest that topographical mapping persists well beyond the auditory core and belt, and that the mirror-symmetry of topographical preferences may be a fundamental principle across sensory modalities.

Disentangling unisensory and multisensory components in audiovisual integration using a novel multifrequency fMRI spectral analysis.

Events in the world are mediated through multiple sensory inputs which are processed separately in specific unisensory areas according to the division-of-labor principle, and then need to be integrated to create a unified percept. How this is done is still not clear. For instance, recent evidence showed crossmodal activation in primary areas. We developed a novel approach to study multisensory integration using multifrequency spectral analysis to investigate the processing of audio and visual streams in a multisensory context. Auditory and visual stimuli were delivered in the same experimental condition, each in different presentation frequencies, and thus could be detected by applying Fourier spectral analysis in their different presentation frequencies. The cochleotopic and retinotopic organization of primary auditory and visual areas were found to remain intact in spite of the multisensory context. Auditory responses were also found in the Precuneus, suggesting that it might be a new auditory area responsive to pure tone stimuli, and serving as one end of a novel sensory preference gradient stretching across POG to the calcarine sulcus. Additional audiovisual areal convergence was detected both in areas in the middle of sensory preference gradients, and in primary auditory areas. Interestingly, the in/out synchronization rate of the auditory and visual streams yielded a third interaction frequency, which could be analyzed independently to reveal higher-order audiovisual interaction responses. These results were detected in one compact and natural multisensory experimental condition, which has several advantages over previous approaches. The method can be further implemented to study any type of interaction, within and across sensory modalities.

Negative blood oxygenation level dependent homunculus and somatotopic information in primary motor cortex and supplementary motor area

A crucial attribute in movement encoding is an adequate balance between suppression of unwanted muscles and activation of required ones. We studied movement encoding across the primary motor cortex (M1) and supplementary motor area (SMA) by inspecting the positive and negative blood oxygenation level-dependent (BOLD) signals in these regions. Using periodic and event-related experiments incorporating the bilateral/axial movements of 20 body parts, we report detailed mototopic imaging maps in M1 and SMA. These maps were obtained using phase-locked analysis. In addition to the positive BOLD, significant negative BOLD was detected in M1 but not in the SMA. The negative BOLD spatial pattern was neither located at the ipsilateral somatotopic location nor randomly distributed. Rather, it was organized somatotopically across the entire homunculus and inversely to the positive BOLD, creating a negative BOLD homunculus. The neuronal source of negative BOLD is unclear. M1 provides a unique system to test whether the origin of negative BOLD is neuronal, because different arteries supply blood to different regions in the homunculus, ruling out blood-stealing explanations. Finally, multivoxel pattern analysis showed that positive BOLD in M1 and SMA and negative BOLD in M1 contain somatotopic information, enabling prediction of the moving body part from inside and outside its somatotopic location. We suggest that the neuronal processes underlying negative BOLD participate in somatotopic encoding in M1 but not in the SMA. This dissociation may emerge because of differences in the activity of these motor areas associated with movement suppression.

New Whole-Body Sensory-Motor Gradients Revealed Using Phase-Locked Analysis and Verified Using Multivoxel Pattern Analysis and Functional Connectivity

Topographic organization is one of the main principles of organization in the human brain. Specifically, whole-brain topographic mapping using spectral analysis is responsible for one of the greatest advances in vision research. Thus, it is intriguing that although topography is a key feature also in the motor system, whole-body somatosensory-motor mapping using spectral analysis has not been conducted in humans outside M1/SMA. Here, using this method, we were able to map a homunculus in the globus pallidus, a key target area for deep brain stimulation, which has not been mapped noninvasively or in healthy subjects. The analysis clarifies contradictory and partial results regarding somatotopy in the caudal-cingulate zone and rostral-cingulate zone in the medial wall and in the putamen. Most of the results were confirmed at the single-subject level and were found to be compatible with results from animal studies. Using multivoxel pattern analysis, we could predict movements of individual body parts in these homunculi, thus confirming that they contain somatotopic information. Using functional connectivity, we demonstrate interhemispheric functional somatotopic connectivity of these homunculi, such that the somatotopy in one hemisphere could have been found given the connectivity pattern of the corresponding regions of interest in the other hemisphere. When inspecting the somatotopic and nonsomatotopic connectivity patterns, a similarity index indicated that the pattern of connected and nonconnected regions of interest across different homunculi is similar for different body parts and hemispheres. The results show that topographical gradients are even more widespread than previously assumed in the somatosensory-motor system. Spectral analysis can thus potentially serve as a gold standard for defining somatosensory-motor system areas for basic research and clinical applications.

Flexibility and Stability in Sensory Processing Revealed Using Visual-to-Auditory Sensory Substitution

The classical view of sensory processing involves independent processing in sensory cortices and multisensory integration in associative areas. This hierarchical structure has been challenged by evidence of multisensory responses in sensory areas, and dynamic weighting of sensory inputs in associative areas, thus far reported independently. Here, we used a visual-to-auditory sensory substitution algorithm (SSA) to manipulate the information conveyed by sensory inputs while keeping the stimuli intact. During scan sessions before and after SSA learning, subjects were presented with visual images and auditory soundscapes. The findings reveal 2 dynamic processes. First, crossmodal attenuation of sensory cortices changed direction after SSA learning from visual attenuations of the auditory cortex to auditory attenuations of the visual cortex. Secondly, associative areas changed their sensory response profile from strongest response for visual to that for auditory. The interaction between these phenomena may play an important role in multisensory processing. Consistent features were also found in the sensory dominance in sensory areas and audiovisual convergence in associative area Middle Temporal Gyrus. These 2 factors allow for both stability and a fast, dynamic tuning of the system when required.

Intensity‐based masking: A tool to improve functional connectivity results of resting‐state fMRI

Seed‐based functional connectivity (FC) of resting‐state functional MRI data is a widely used methodology, enabling the identification of functional brain networks in health and disease. Based on signal correlations across the brain, FC measures are highly sensitive to noise. A somewhat neglected source of noise is the fMRI signal attenuation found in cortical regions in close vicinity to sinuses and air cavities, mainly in the orbitofrontal, anterior frontal and inferior temporal cortices. BOLD signal recorded at these regions suffers from dropout due to susceptibility artifacts, resulting in an attenuated signal with reduced signal‐to‐noise ratio in as many as 10% of cortical voxels. Nevertheless, signal attenuation is largely overlooked during FC analysis. Here we first demonstrate that signal attenuation can significantly influence FC measures by introducing false functional correlations and diminishing existing correlations between brain regions. We then propose a method for the detection and removal of the attenuated signal (“intensity‐based masking”) by fitting a Gaussian‐based model to the signal intensity distribution and calculating an intensity threshold tailored per subject. Finally, we apply our method on real‐world data, showing that it diminishes false correlations caused by signal dropout, and significantly improves the ability to detect functional networks in single subjects. Furthermore, we show that our method increases inter‐subject similarity in FC, enabling reliable distinction of different functional networks. We propose to include the intensity‐based masking method as a common practice in the pre‐processing of seed‐based functional connectivity analysis, and provide software tools for the computation of intensity‐based masks on fMRI data. Hum Brain Mapp 37:2407–2418, 2016.

Oxytocin Effect on Collective Decision Making: A Randomized Placebo Controlled Study.

Collective decision making often benefits both the individuals and the group in a variety of contexts. However, for the group to be successful, individuals should be able to strike a balance between their level of competence and their influence on the collective decisions. The hormone oxytocin has been shown to promote trust, conformism and attention to social cues. We wondered if this hormone may increase participants’ (unwarranted) reliance on their partners’ opinion, resulting in a reduction in collective benefit by disturbing the balance between influence and competence. To test this hypothesis we employed a randomized double-blind placebo-controlled design in which male dyads self-administered intranasal oxytocin or placebo and then performed a visual search task together. Compared to placebo, collective benefit did not decrease under oxytocin. Using an exploratory time dependent analysis, we observed increase in collective benefit over time under oxytocin. Moreover, trial-by-trial analysis showed that under oxytocin the more competent member of each dyad was less likely to change his mind during disagreements, while the less competent member showed a greater willingness to change his mind and conform to the opinion of his more reliable partner. This role-dependent effect may be mediated by enhanced monitoring of own and other’s performance level under oxytocin. Such enhanced social learning could improve the balance between influence and competence and lead to efficient and beneficial collaboration.

Stochastic satisficing account of confidence in uncertain value-based decisions

Every day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. uncertainty about waiting time in a queue, affects decisions and confidence when outcome is stochastic and continuous. How does one evaluate and choose between an option with unreliable but high expected reward, and an option with more certain but lower expected reward? Here we used an experimental design where two choices’ payoffs took continuous values, to examine the effect of outcome variance on decision and confidence. We found that our participants’ probability of choosing the good (high expected reward) option decreased when the good or the bad options’ payoffs were more variable. Their confidence ratings were affected by outcome variability, but only when choosing the good option. Unlike perceptual detection tasks, confidence ratings correlated only weakly with decisions’ time, but correlated with the consistency of trial-by-trial choices. Inspired by the satisficing heuristic, we propose a “stochastic satisficing” (SSAT) model for evaluating options with continuous uncertain outcomes. In this model, options are evaluated by their probability of exceeding an acceptability threshold, and confidence reports scale with the chosen option’s thus-defined satisficing probability. Participants’ decisions were best explained by an expected reward model, while the SSAT model provided the best prediction of decision confidence. We further tested and verified the predictions of this model in a second experiment. Our model and experimental results generalize the models of metacognition from perceptual detection tasks to continuous-value based decisions. Finally, we discuss how the stochastic satisficing account of decision confidence serves psychological and social purposes associated with the evaluation, communication and justification of decision-making.

Stochastic satisficing account of choice and confidence in uncertain value-based decisions

Every day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. waiting time in a queue, affects decisions when outcome is stochastic and continuous. For example, how does one choose between an option with unreliable but high expected reward, and an option with more certain but lower expected reward? Here we used an experimental design where two choices’ payoffs took continuous values, to examine the effect of outcome variance on decisions and confidence. Inconsistent with expected utility predictions, our participants’ probability of choosing the good option decreased when both better and worse options’ payoffs were more variable. Confidence ratings were affected by outcome variability only when choosing the good option. Inspired by the satisficing heuristic, we propose a “stochastic satisficing” (SSAT) model for choosing between options with continuous uncertain outcomes. In this model, decisions are made by comparing the available options’ probability of exceeding an acceptability threshold and confidence reports scale with the chosen option’s satisficing probability. The SSAT model best explained choice behaviour and most successfully predicted confidence ratings. We further tested the model’s prediction in a second experiment where choice and confidence behaviours were found to be consistent with the SSAT simulations. Our model and experimental results generalize the cognitive heuristic of satisficing to stochastic contexts and thus provide an account of bounded rationality in the face of uncertainty.

fMRI Dependent Components Analysis Reveals Dynamic Relations Between Functional Large Scale Cortical Networks

One of the major advantages of whole brain fMRI is the detection of large scale cortical networks. Dependent Components Analysis (DCA) is a novel approach designed to extract both cortical networks and their dependency structure. DCA is fundamentally different from prevalent data driven approaches, i.e. spatial ICA, in that instead of maximizing the independence of components it optimizes their dependency (in a tree graph structure, tDCA) depicting cortical areas as part of multiple cortical networks. Here tDCA was shown to reliably detect large scale functional networks in single subjects and in group analysis, by clustering non-noisy components on one branch of the tree structure. We used tDCA in three fMRI experiments in which identical auditory and visual stimuli were presented, but novelty information and task relevance were modified. tDCA components tended to include two anticorrelated networks, which were detected in two separate ICA components, or belonged in one component in seed functional connectivity. Although sensory components remained the same across experiments, other components changed as a function of the experimental conditions. These changes were either within component, where it encompassed other cortical areas, or between components, where the pattern of anticorrelated networks and their statistical dependency changed. Thus tDCA may prove to be a useful, robust tool that provides a rich description of the statistical structure underlying brain activity and its relationships to changes in experimental conditions. This tool may prove effective in detection and description of mental states, neural disorders and their dynamics.