Marcus Grueschow

Odor quality coding and categorization in human posterior piriform cortex

Efficient recognition of odorous objects universally shapes animal behavior and is crucial for survival. To distinguish kin from nonkin, mate from nonmate and food from nonfood, organisms must be able to create meaningful perceptual representations of odor qualities and categories. It is currently unknown where and in what form the brain encodes information about odor quality. By combining functional magnetic resonance imaging (fMRI) with multivariate (pattern-based) techniques, we found that spatially distributed ensemble activity in human posterior piriform cortex (PPC) coincides with perceptual ratings of odor quality, such that odorants with more (or less) similar fMRI patterns were perceived as more (or less) alike. We did not observe these effects in anterior piriform cortex, amygdala or orbitofrontal cortex, indicating that ensemble coding of odor categorical perception is regionally specific for PPC. These findings substantiate theoretical models emphasizing the importance of distributed piriform templates for the perceptual reconstruction of odor object quality.

Perceptual learning and decision making in human medial frontal cortex

The dominant view that perceptual learning is accompanied by changes in early sensory representations has recently been challenged. Here we tested the idea that perceptual learning can be accounted for by reinforcement learning involving changes in higher decision-making areas. We trained subjects on an orientation discrimination task involving feedback over 4 days, acquiring fMRI data on the first and last day. Behavioral improvements were well explained by a reinforcement learning model in which learning leads to enhanced readout of sensory information, thereby establishing noise-robust representations of decision variables. We find stimulus orientation encoded in early visual and higher cortical regions such as lateral parietal cortex and anterior cingulate cortex (ACC). However, only activity patterns in the ACC tracked changes in decision variables during learning. These results provide strong evidence for perceptual learning-related changes in higher order areas and suggest that perceptual and reward learning are based on a common neurobiological mechanism.

Neural Oscillations and Synchronization Differentially Support Evidence Accumulation in Perceptual and Value-Based Decision Making

Organisms make two types of decisions on a regular basis. Perceptual decisions are determined by objective states of the world (e.g., melons are bigger than apples), whereas value-based decisions are determined by subjective preferences (e.g., I prefer apples to melons). Theoretical accounts suggest that both types of choice involve neural computations accumulating evidence for the choice alternatives; however, little is known about the overlap or differences in the processes underlying perceptual versus value-based decisions. We analyzed EEG recordings during a paradigm where perceptual- and value-based choices were based on identical stimuli. For both types of choice, evidence accumulation was evident in parietal gamma-frequency oscillations, whereas a similar frontal signal was unique for value-based decisions. Fronto-parietal synchronization of these signals predicted value-based choice accuracy. These findings uncover how decisions emerge from topographic- and frequency-specific oscillations that accumulate distinct aspects of evidence, with large-scale synchronization as a mechanism integrating these spatially distributed signals.

The precision of value-based choices depends causally on fronto-parietal phase coupling

Which meal would you like today, chicken or pasta? For such value-based choices, organisms must flexibly integrate various types of sensory information about internal states and the environment to transform them into actions. Recent accounts suggest that these choice-relevant processes are mediated by information transfer between functionally specialized but spatially distributed brain regions in parietal and prefrontal cortex; however, it remains unclear whether such fronto-parietal communication is causally involved in guiding value-based choices. We find that transcranially inducing oscillatory desynchronization between the frontopolar and -parietal cortex leads to more inaccurate choices between food rewards while leaving closely matched perceptual decisions unaffected. Computational modelling shows that this exogenous manipulation leads to imprecise value assignments to the choice alternatives. Thus, our study demonstrates that accurate value-based decisions critically involve coherent rhythmic information transfer between fronto-parietal brain areas and establishes an experimental approach to non-invasively manipulate the precision of value-based choices in humans.

Automatic versus Choice-Dependent Value Representations in the Human Brain

The subjective values of choice options can impact on behavior in two fundamentally different types of situations: first, when people explicitly base their actions on such values, and second, when values attract attention despite being irrelevant for current behavior. Here we show with functional magnetic resonance imaging (fMRI) that these two behavioral functions of values are encoded in distinct regions of the human brain. In the medial prefrontal cortex, value-related activity is enhanced when subjective value becomes choice-relevant, and the magnitude of this increase relates directly to the outcome and reliability of the value-based choice. In contrast, activity in the posterior cingulate cortex represents values similarly when they are relevant or irrelevant for the present choice, and the strength of this representation predicts attentional capture by choice-irrelevant values. Our results suggest that distinct components of the brains valuation network encode value in context-dependent manners that serve fundamentally different behavioral aims.

Brain Network Mechanisms Underlying Motor Enhancement by Transcranial Entrainment of Gamma Oscillations.

Gamma and beta oscillations are routinely observed in motor-related brain circuits during movement preparation and execution. Entrainment of gamma or beta oscillations via transcranial alternating current stimulation (tACS) over primary motor cortex (M1) has opposite effects on motor performance, suggesting a causal role of these brain rhythms for motor control. However, it is largely unknown which brain mechanisms characterize these changes in motor performance brought about by tACS. In particular, it is unclear whether these effects result from brain activity changes only in the targeted areas or within functionally connected brain circuits. Here we investigated this issue by applying gamma-band and beta-band tACS over M1 in healthy humans during a visuomotor task and concurrent functional magnetic resonance imaging (fMRI). Gamma tACS indeed improved both the velocity and acceleration of visually triggered movements, compared with both beta tACS and sham stimulation. Beta tACS induced a numerical decrease in velocity compared with sham stimulation, but this was not statistically significant. Crucially, gamma tACS induced motor performance enhancements correlated with changed BOLD activity in the stimulated M1. Moreover, we found frequency- and task-specific neural compensatory activity modulations in the dorsomedial prefrontal cortex (dmPFC), suggesting a key regulatory role of this region in motor performance. Connectivity analyses revealed that the dmPFC interacted functionally with M1 and with regions within the executive motor system. These results suggest a role of the dmPFC for motor control and show that tACS-induced behavioral changes not only result from activity modulations underneath the stimulation electrode but also reflect compensatory modulation within connected and functionally related brain networks. More generally, our results illustrate how combined tACS–fMRI can be used to resolve the causal link between cortical rhythms, brain systems, and behavior. SIGNIFICANCE STATEMENT Recent research has suggested a causal role for gamma oscillations during movement preparation and execution. Here we combine transcranial alternating current stimulation (tACS) with functional magnetic resonance imaging (fMRI) to identify the neural mechanisms that accompany motor performance enhancements triggered by gamma tACS over the primary motor cortex. We show that the tACS-induced motor performance enhancements correlate with changed neural activity in the stimulated area and modulate, in a frequency- and task-specific manner, the neural activity in the dorsomedial prefrontal cortex. This suggests a regulatory role of this region for motor control. More generally, we show that combined tACS–fMRI can elucidate the causal link between brain oscillations, neural systems, and behavior.

BOLD responses in human V1 to local structure in natural scenes: Implications for theories of visual coding.

In this study we tested predictions of two important theories of visual coding, contrast energy and sparse coding theory, on the dependence of population activity level and metabolic demands on spatial structure of the visual input. With carefully calibrated displays we find that in humans neither the V1 blood oxygenation level dependent (BOLD) response nor the initial visually evoked fields in magnetoencephalography (MEG) are sensitive to phase perturbations in photographs of natural scenes. As a control, we quantitatively show that the applied phase perturbations decrease sparseness (kurtosis) of our stimuli but preserve their root mean square (RMS) contrast. Importantly, we show that the lack of sensitivity of the V1 population response level to phase perturbations is not due to a lack of sensitivity of our methods because V1 responses were highly sensitive to variations of image RMS contrast. Our results suggest that the transition from a sparse to a distributed neural code in the early visual system induced by reducing image sparseness has negligible consequences for population metabolic cost. This result imposes a novel and important empirical constraint on quantitative models of sparse coding: Population metabolic rate and population activation level is sensitive to second order statistics (RMS contrast) of the input but not to its spatial phase and fourth order statistics (kurtosis).

Sensory perceptions of individuals exposed to the static field of a 7T MRI: A controlled blinded study

To determine the subjective experience of subjects undergoing 7T magnetic resonance imaging (MRI) compared to a mock scanner with no magnetic field.

Attentional Bias towards Positive Emotion Predicts Stress Resilience

There is extensive evidence for an association between an attentional bias towards emotionally negative stimuli and vulnerability to stress-related psychopathology. Less is known about whether selective attention towards emotionally positive stimuli relates to mental health and stress resilience. The current study used a modified Dot Probe task to investigate if individual differences in attentional biases towards either happy or angry emotional stimuli, or an interaction between these biases, are related to self-reported trait stress resilience. In a nonclinical sample (N = 43), we indexed attentional biases as individual differences in reaction time for stimuli preceded by either happy or angry (compared to neutral) face stimuli. Participants with greater attentional bias towards happy faces (but not angry faces) reported higher trait resilience. However, an attentional bias towards angry stimuli moderated this effect: The attentional bias towards happy faces was only predictive for resilience in those individuals who also endorsed an attentional bias towards angry stimuli. An attentional bias towards positive emotional stimuli may thus be a protective factor contributing to stress resilience, specifically in those individuals who also endorse an attentional bias towards negative emotional stimuli. Our findings therefore suggest a novel target for prevention and treatment interventions addressing stress-related psychopathology.

Fast construction of voxel-level functional connectivity graphs

BackgroundGraph-based analysis of fMRI data has recently emerged as a promising approach to study brain networks. Based on the assessment of synchronous fMRI activity at separate brain sites, functional connectivity graphs are constructed and analyzed using graph-theoretical concepts. Most previous studies investigated region-level graphs, which are computationally inexpensive, but bring along the problem of choosing sensible regions and involve blurring of more detailed information. In contrast, voxel-level graphs provide the finest granularity attainable from the data, enabling analyses at superior spatial resolution. They are, however, associated with considerable computational demands, which can render high-resolution analyses infeasible. In response, many existing studies investigating functional connectivity at the voxel-level reduced the computational burden by sacrificing spatial resolution.MethodsHere, a novel, time-efficient method for graph construction is presented that retains the original spatial resolution. Performance gains are instead achieved through data reduction in the temporal domain based on dichotomization of voxel time series combined with tetrachoric correlation estimation and efficient implementation.ResultsBy comparison with graph construction based on Pearson’s r, the technique used by the majority of previous studies, we find that the novel approach produces highly similar results an order of magnitude faster.ConclusionsIts demonstrated performance makes the proposed approach a sensible and efficient alternative to customary practice. An open source software package containing the created programs is freely available for download.