Publications

Conscious perception alternates between the two eyes’ images during binocular rivalry. How hierarchical processes in our brain interact to resolve visual competition to generate conscious perception remains unclear. Here we investigated the mesoscale neural circuitry for binocular rivalry in human cortical and subcortical areas using high-resolution functional MRI at 7 Tesla. Eye-specific response modulation in binocular rivalry was strongest in the superficial layers of V1 ocular dominance columns (ODCs), and more synchronized in the superficial and deep layers. The intraparietal sulcus (IPS) generated stronger eye-specific response modulation and increased effective connectivity to the early visual cortex during binocular rivalry compared to monocular “replay” simulations. Although there was no evidence of eye-specific rivalry modulation in the lateral geniculate nucleus (LGN) of the thalamus, strong perceptual rivalry modulation can be found in its parvocellular (P) subdivision. Finally, IPS and ventral pulvinar showed robust perceptual rivalry modulation and increased connectivity to the early visual cortex. These findings demonstrate that local interocular competition arises from lateral mutual inhibition between V1 ODCs, and feedback signals from IPS to visual cortex and visual thalamus further synchronize and resolve visual competition to generate conscious perception.

We perceive a stable visual world across eye movements, despite the drastic retinal transients these movements produce. To explain visual spatial stability, it has been suggested that the brain encodes the location of attended visual stimuli in an external, or spatiotopic, reference frame. However, spatiotopy is seemingly at odds with the fundamental retinotopic organization of visual inputs. Here, we probe the spatial reference frame of vision using ultra-high-field (7T) fMRI and single-voxel population receptive field mapping, while independently manipulating both gaze direction and spatial attention. Participants performed visual tasks that manipulated spatial attention and gaze direction to dissociate retinal from real-world stimulus positions. Our findings show that population receptive fields shift with gaze, consistent with a fully retinotopic reference frame for visual-spatial processing, even when using Bayesian decoding of BOLD responses. These results suggest that vision relies on a retinotopic frame of reference throughout the visual cortex, irrespective of spatial attention deployment.

Risk preferences—the willingness to accept greater uncertainty to achieve larger potential rewards—determine many aspects of our lives and are often interpreted as a stable individual trait. However, this perspective does not explain why risk preferences can vary substantially across contexts and even across repetitions of the same decisions. We provide modeling and neural evidence that contextual shifts and moment-to-moment fluctuations in risk preferences emerge from Bayesian inference on noisy magnitude representations in parietal cortex. In our fMRI study, participants chose between safe and risky options that were either held in working memory or visible on the screen. Risk aversion increased for large payoffs and risk-seeking for small payoffs when options were in working memory, explained by Bayesian central tendency biases. Behavioural and neural data, including population-receptive field modelling, demonstrated that noisier intraparietal representations led to less consistent and less risk-neutral choices, suggesting reliance on prior beliefs about payoffs. These findings highlight that risk preferences and their fluctuations are mechanistically rooted in perceptual inference on noisy parietal magnitude representations, with broad implications for economic, psychological, and neuroscience theories of risk-taking behavior.

Risk preferences have traditionally been considered as stable traits that reflect subjective-valuation processes in prefrontal areas. More recently, however, it has been suggested that risk preferences may also be shaped by how choice problems are perceived and processed in perceptual brain regions. Specifically, the acuity of the parietal approximate number system (ANS), which encodes payoff magnitudes for different choice options, has been shown to correlate with both risk preferences and choice consistency. However, this correlational relationship leaves open the question whether parietal magnitude representations in fact causally underlie choice. Here, we provide direct evidence for such a key causal role of parietal magnitude representations in economic choice, using continuous theta-burst transcranial magnetic stimulation (cTBS), combined with functional MRI (fMRI) and numerical population receptive field (nPRF) modeling. Our stimulation protocol targeted numerosity-tuned regions in the right parietal cortex, identified through nPRF modeling of individual fMRI data (n=35; within-subject design). The stimulation successfully perturbed neural processing, as evidenced by decreased amplitude of numerical magnitude-tuned responses and less accurate multivariate decoding of presented magnitudes from unseen data that were not used for model fitting. In line with a perceptual account of risky choice, the reduction in neural information capacity was also reflected in noisier behavioral responses. Moreover, a computational cognitive model fitted to choice behavior revealed that perturbing the ANS specifically increased the noisiness of small-magnitude representations. This perturbation made small magnitudes to be perceived as larger than they actually are, leading to more risk-seeking behavior. Finally, individual estimates of the cTBS effect on cognitive noise correlated with the corresponding decrease in amplitude of numerical magnitude-tuned fMRI responses, further solidifying the role of the parietal ANS in economic choice. In conclusion, our study demonstrates that the precision of parietal magnitude representations causally influences economic decision-making, with noisier encoding promoting biased risk-taking as formalized in recent perceptual models of risky choice.

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Adaptive behaviour requires the selection of relevant, and the ignoring of irrelevant visual information. Previous work has shown that prior information about target (i.e., relevant) and non-target (i.e., irrelevant) objects facilitates such selection, presumably by enabling observers to create a working memory template for or against such objects. Using a cued visual search task, here we aimed to investigate whether, and how, target and non-target templates differ in terms of neural representation, as measured through fMRI activity patterns. Two hypotheses were tested: (1) while target representations are activated, non-target representations are suppressed prior to search, and thus these representations differ, and (2) target and non-target templates share similar initial representations but involve different control signals in anticipation of, or upon encountering the search display. Behaviourally, both target and non-target cues facilitated search compared to baseline. Using multivariate pattern and representational similarity analyses, the findings revealed little support for the suppression hypothesis. Posterior brain regions primarily coded for the category content of the template and did so similarly for target and non-target information. Frontal cortical areas, particularly the lateral prefrontal cortex and frontal eye fields, instead showed sensitivity to the status of the template, regardless of the object category it belonged to. These results are most consistent with the hypothesis that prior to selection, target and non-target information are represented similarly in terms of content, but differ in terms of the associated control signals.

Humans are generally risk averse, preferring smaller certain over larger uncertain outcomes. Economic theories usually explain this by assuming concave utility functions. Here, we provide evidence that risk aversion can also arise from relative underestimation of larger monetary payoffs, a perceptual bias rooted in the noisy logarithmic coding of numerical magnitudes. We confirmed this with psychophysics and functional magnetic resonance imaging, by measuring behavioural and neural acuity of magnitude representations during a magnitude perception task and relating these measures to risk attitudes during separate risky financial decisions. Computational modelling indicated that participants use similar mental magnitude representations in both tasks, with correlated precision across perceptual and risky choices. Participants with more precise magnitude representations in parietal cortex showed less variable behaviour and less risk aversion. Our results highlight that at least some individual characteristics of economic behaviour can reflect capacity limitations in perceptual processing rather than processes that assign subjective values to monetary outcomes.

The subthalamic nucleus (STN) is a small, subcortical brain structure. It is a target for deep brain stimulation, an invasive treatment that reduces motor symptoms of Parkinson's disease. Side effects of DBS are commonly explained using the tripartite model of STN organization, which proposes three functionally distinct subregions in the STN specialized in cognitive, limbic, and motor processing. However, evidence for the tripartite model exclusively comes from anatomical studies and functional studies using clinical patients. Here, we provide the first experimental tests of the tripartite model in healthy volunteers using ultra-high field 7 Tesla (T) functional magnetic resonance imaging (fMRI). Thirty-four participants performed a random-dot motion decision-making task with a difficulty manipulation and a choice payoff manipulation aimed to differentially affect cognitive and limbic networks. Moreover, participants responded with their left and right index finger, differentially affecting motor networks. We analysed BOLD signal in three subregions of the STN along the dorsolateral–ventromedial axis, identified using manually delineated high resolution anatomical images and based on a previously published atlas. Using these paradigms, all segments responded equally to the experimental manipulations, and the tasks did not provide evidence for the tripartite model.

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.

The pupil provides a rich, non-invasive measure of the neural bases of perception and cognition and has been of particular value in uncovering the role of arousal-linked neuromodulation, which alters both cortical processing and pupil size. But pupil size is subject to a multitude of influences, which complicates unique interpretation. We measured pupils of observers experiencing perceptual multistability—an ever-changing subjective percept in the face of unchanging but inconclusive sensory input. In separate conditions, the endogenously generated perceptual changes were either task-relevant or not, allowing a separation between perception-related and task-related pupil signals. Perceptual changes were marked by a complex pupil response that could be decomposed into two components: a dilation tied to task execution and plausibly indicative of an arousal-linked noradrenaline surge, and an overlapping constriction tied to the perceptual transient and plausibly a marker of altered visual cortical representation. Constriction, but not dilation, amplitude systematically depended on the time interval between perceptual changes, possibly providing an overt index of neural adaptation. These results show that the pupil provides a simultaneous reading on interacting but dissociable neural processes during perceptual multistability, and suggest that arousal-linked neuromodulator release shapes action but not perception in these circumstances.

Ultra-high field MRI can functionally image the cerebral cortex of human subjects at the submillimeter scale of cortical columns and laminae. Here, we investigate both in concert, by imaging ocular dominance columns (ODCs) in primary visual cortex (V1) across different cortical depths. We ensured that putative ODC patterns in V1 (a) are stable across runs, sessions, and scanners located in different continents, (b) have a width (~1.3 mm) expected from post-mortem and animal work and (c) are absent at the retinotopic location of the blind spot. We then dissociated the effects of bottom-up thalamo-cortical input and attentional feedback processes on activity in V1 across cortical depth. Importantly, the separation of bottom-up information flows into ODCs allowed us to validly compare attentional conditions while keeping the stimulus identical throughout the experiment. We find that, when correcting for draining vein effects and using both model-based and model-free approaches, the effect of monocular stimulation is largest at deep and middle cortical depths. Conversely, spatial attention influences BOLD activity exclusively near the pial surface. Our findings show that simultaneous interrogation of columnar and laminar dimensions of the cortical fold can dissociate thalamocortical inputs from top-down processing, and allow the investigation of their interactions without any stimulus manipulation.

Quantitative magnetic resonance imaging generates images of meaningful physical or chemical variables measured in physical units that allow quantitative comparisons between tissue regions and among subjects scanned at the same or different sites. Here, we show that we can acquire quantitative T1, T2*, and quantitative susceptibility mapping (QSM) information in a single acquisition, using a multi-echo (ME) extension of the second gradient-echo image of the MP2RAGE sequence. This combination is called MP2RAGE ME, or MP2RAGEME. The simultaneous acquisition results in large time savings, perfectly coregistered data, and minimal image quality differences compared to separately acquired data. Following a correction for residual transmit B1+-sensitivity, quantitative T1, T2*, and QSM values were in excellent agreement with those obtained from separately acquired, also B1+-corrected, MP2RAGE data and ME gradient echo data. The quantitative values from reference regions of interests were also in very good correspondence with literature values. From the MP2RAGEME data, we further derived a multiparametric cortical parcellation, as well as a combined arterial and venous map. In sum, our MP2RAGEME sequence has the benefit in large time savings, perfectly coregistered data and minor image quality differences.

Studies that aim to understand the neural correlates of response conflicts commonly probe frontal brain areas associated with controlled inhibition and decision processes. However, untimely fast conflict errors happen even before these top-down processes are engaged. The dual-route model proposes that during conflict tasks, as soon as the stimulus is presented, two early processes are immediately at play. The task-relevant and task-irrelevant processes generate either compatible responses, when all stimulus features align, or incompatible responses, when stimulus features are in conflict. We aimed to find a neural substrate of these two processes by means of relating the quality of the representation of stimulus features in visual and somatosensory brain areas to responses in conflict tasks. Participants were scanned using fMRI while performing somatosensory and visual Simon tasks. The fMRI data were then analysed using a MVPA in early visual and somatosensory cortices. In agreement with our hypotheses, results suggest that the sensory representation of the task-relevant and task-irrelevant features drive erroneous trials. These results demonstrate that traces of response conflicts can arise already in sensory brain areas and that the quality of the representations in these regions can account for an early response capture by irrelevant stimulus features.

The subthalamic nucleus (STN) is successfully used as a surgical target for deep brain stimulation in the treatment of movement disorders. Interestingly, the internal structure of the STN is still incompletely understood. The objective of the present study was to investigate three-dimensional (3D) immunoreactivity patterns for 12 individual protein markers for GABA-ergic, serotonergic, dopaminergic as well as glutamatergic signaling. We analyzed the immunoreactivity using optical densities and created a 3D reconstruction of seven postmortem human STNs. Quantitative modeling of the reconstructed 3D immunoreactivity patterns revealed that the applied protein markers show a gradient distribution in the STN. These gradients were predominantly organized along the ventromedial to dorsolateral axis of the STN. The results are of particular interest in view of the theoretical underpinning for surgical targeting, which is based on a tripartite distribution of cognitive, limbic and motor function in the STN.

The target article by Lee et al. (in review) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, including their inputs and outputs, is also essential to improving the reproducibility of model-based analyses. We outline an ongoing effort (within the context of the Brain Imaging Data Structure community) to develop standards for the sharing of the structure of computational models and their outputs.

The human subcortex is a densely populated part of the brain, of which only 7% of the individual structures are depicted in standard MRI atlases. In vivo MRI of the subcortex is challenging owing to its anatomical complexity and its deep location in the brain. The technical advances that are needed to reliably uncover this ‘terra incognita’ call for an interdisciplinary human neuroanatomical approach. We discuss the emerging methods that could be used in such an approach and the incorporation of the data that are generated from these methods into model-based cognitive neuroscience frameworks.

The basal ganglia (BG) form a network of subcortical nuclei. Functional magnetic resonanceimaging (fMRI) in the BG could provide insight in its functioning and the underlying mechanisms ofDeep Brain Stimulation (DBS). However, fMRI of the BG with high specificity is challenging, because the nuclei are small and variable in their anatomical location. High resolution fMRI at field strengths of 7 Tesla (T) could help resolve these challenges to some extent. A set of MR protocols was developed for functional imaging of the BG nuclei at 3 T and 7 T. The protocols were validated using a stop-signal reaction task (Logan et al. [1984]: J Exp Psychol: Human Percept Perform 10:276–291). Compared with sub-millimeter 7 T fMRI protocols aimed at cortex, a reduction of echo time and spatial resolution was strictly necessary to obtain robust Blood Oxygen Level Dependent (BOLD) sensitivity in the BG. An fMRI protocol at 3 T with identical resolution to the 7 T showed no robust BOLD sensitivity in any of the BG nuclei. The results suggest that the subthalamic nucleus, as well as the substantia nigra, red nucleus, and the internal and external parts of the globus pallidus show increased activation in failed stop trials compared with successful stop and go trials.

The subthalamic nucleus (STN) plays a crucial role in the surgical treatment of Parkinson’s disease (PD). Studies investigating optimal protocols for STN visualization using state of the art magnetic resonance imaging (MRI) techniques have shown that susceptibility weighted images, which display the magnetic susceptibility distribution, yield better results than T1-weighted, T2-weighted, and T2*-weighted contrasts. However, these findings are based on young healthy individuals, and require validation in elderly individuals and persons suffering from PD. Using 7T MRI, the present study set out to investigate which MRI contrasts yielded the best results for STN visualization in 12 PD patients and age-matched healthy controls (HC). We found that STNs were more difficult to delineate in PD as reflected by a lower inter-rater agreement when compared to HCs. No STN size differences were observed between the groups. Analyses of quantitative susceptibility mapping (QSM) images showed a higher inter-rater agreement reflected by increased Dice-coefficients. The location of the center of mass of the STN was not affected by contrast. Overall, contrast-to-noise ratios (CNR) were higher in QSM than in T2*-weighted images. This can at least partially, explain the higher inter-rater agreement in QSM. The current results indicate that the calculation of QSM contrasts contributes to an improved visualization of the entire STN. We conclude that QSM contrast is the preferred choice for the visualization of the STN in persons with PD as well as in aging HC.

The Simon task is one of the most prominent interference tasks and has been extensively studied in experimental psychology and cognitive neuroscience. Despite years of research, the underlying mechanism driving the phenomenon and its temporal dynamics are still disputed. Within the framework of the review, we adopt a model-based cognitive neuroscience approach. We first go over key findings in the literature of the Simon task, discuss competing qualitative cognitive theories and the difficulty of testing them empirically. We then introduce sequential sampling models, a particular class of mathematical cognitive process models. Finally, we argue that the brain architecture accountable for the processing of spatial ('where') and non-spatial ('what') information, could constrain these models. We conclude that there is a clear need to bridge neural and behavioral measures, and that mathematical cognitive models may facilitate the construction of this bridge and work towards revealing the underlying mechanisms of the Simon effect.

Our understanding of the complex interplay between structural and functional organisation of brain networks is being advanced by the development of novel multi-modal analyses approaches. The Age-ility Project (Phase 1) data repository offers open access to structural MRI, diffusion MRI, and resting-state fMRI scans, as well as resting-state EEG recorded from the same community participants (n = 131, 15–35 y, 66 male). Raw imaging and electrophysiological data as well as essential demographics are made available via the NITRC website. All data have been reviewed for artifacts using a rigorous quality control protocol and detailed case notes are provided.

In perceptual decision-making tasks, participants are usually assumed to apply only a single cognitive strategy throughout the course of a task. Variability in observed behavior (e.g., reaction times) is explained as the result of variability in the same cognitive process that gave rise to the observed behavior. For example, in most theories of perceptual decision-making, it is assumed that variability in reaction times is the result of the variability in the amount of information the stimulus provides, the efficiency of information processing, the amount of response caution, and the speed of the motor response (Gold and Shadlen, 2007; Brown and Heathcote, 2008; Ratcliff and McKoon, 2008; Forstmann et al., 2015). Such theories assume that during every trial of a perceptual decision-making task, stimulus information is processed and used to guide a choice. This assumption could be challenged by hypothesizing that, on a subset of trials, participants are guessing instead of using the stimulus information provided. Such a guessing strategy seems plausible in speeded decision-making, where a response has to be given very quickly. In a recent publication in The Journal of Neuroscience, Noorbaloochi and colleagues (2015) suggest that, under such circumstances, participants are likely to use mixed task strategies.

Cognitive neuroscientists sometimes apply formal models to investigate how the brain implements cognitive processes. These models describe behavioral data in terms of underlying, latent variables linked to hypothesized cognitive processes. A goal of model-based cognitive neuroscience is to link these variables to brain measurements, which can advance progress in both cognitive and neuroscientific research. However, the details and the philosophical approach for this linking problem can vary greatly. We propose a continuum of approaches that differ in the degree of tight, quantitative, and explicit hypothesizing. We describe this continuum using four points along it, which we dub qualitative structural, qualitative predictive, quantitative predictive, and single model linking approaches. We further illustrate by providing examples from three research fields (decision making, reinforcement learning, and symbolic reasoning) for the different linking approaches.

In perceptual decision-making tasks, people balance the speed and accuracy with which they make their decisions by modulating a response threshold. Neuroimaging studies suggest that this speed–accuracy tradeoff is implemented in a corticobasal ganglia network that includes an important contribution from the pre-SMA. To test this hypothesis, we used anodal transcranial direct current stimulation (tDCS) to modulate neural activity in pre-SMA while participants performed a simple perceptual decision-making task. Participants viewed a pattern of moving dots and judged the direction of the global motion. In separate trials, they were cued to either respond quickly or accurately. We used the diffusion decision model to estimate the response threshold parameter, comparing conditions in which participants received sham or anodal tDCS. In three independent experiments, we failed to observe an influence of tDCS on the response threshold. Additional, exploratory analyses showed no influence of tDCS on the duration of nondecision processes or on the efficiency of information processing. Taken together, these findings provide a cautionary note, either concerning the causal role of pre-SMA in decision-making or on the utility of tDCS for modifying response caution in decision-making tasks.

The subthalamic nucleus and the directly adjacent substantia nigra are small and important structures in the basal ganglia. Functional magnetic resonance imaging studies have shown that the subthalamic nucleus and substantia nigra are selectively involved in response inhibition, conflict processing, and adjusting global and selective response thresholds. However, imaging these nuclei is complex, because they are in such close proximity, they can vary in location, and are very small relative to the resolution of most fMRI sequences. Here, we investigated the consistency in localization of these nuclei in BOLD fMRI studies, comparing reported coordinates with probabilistic atlas maps of young human participants derived from ultra-high resolution 7T MRI scanning. We show that the fMRI signal reported in previous studies is likely not unequivocally arising from the subthalamic nucleus but represents a mixture of subthalamic nucleus, substantia nigra, and surrounding tissue. Using a simulation study, we also tested to what extent spatial smoothing, often used in fMRI preprocessing pipelines, influences the mixture of BOLD signals. We propose concrete steps how to analyze fMRI BOLD data to allow inferences about the functional role of small subcortical nuclei like the subthalamic nucleus and substantia nigra.

The subthalamic nucleus (STN) is an important node of the cortico-basal ganglia network and the main target of deep brain stimulation (DBS) in Parkinson's disease. Histological studies have revealed an inhomogeneous iron distribution within the STN, which has been related to putative subdivisions within this nucleus. Here, we investigate the iron distribution in more detail using quantitative susceptibility mapping (QSM), a novel magnetic resonance imaging (MRI) contrast mechanism. QSM allows for detailed assessment of iron content in both in vivo and postmortem tissue. Twelve human participants and 7 postmortem brain samples containing the STN were scanned using ultra-high field 7 Tesla (T) MRI. Iron concentrations were found to be higher in the medial-inferior tip of the STN. Using quantitative methods we show that the increase of iron concentration towards the medial-inferior tip is of a gradual rather than a discrete nature.

In this study parsimonious language models were used to construct word clouds of the proceedings of the European Parliament. Multiple design choices had to be made and are discussed. Important features are stemming during tokenization, including bigrams into the word cloud and multilingualism. Also, the original parsimonious language models were extended with an additional term dampening unigrams that already occurred in the word cloud. This algorithm was tested in a small user study, using proceedings of the University of Amsterdam Science faculty's student council. Members of this council had to give their preference for multiple word clouds constructed using either parsimonious language models or simple Term Frequencies (TF) with stop words. 68% over 29% (p <;60; 0.05, two-tailed paired t-test) preferred the word clouds constructed using parsimonious language models. Beside the system design, further technical findings, the social significance of applying word clouds to political data and possibilities for future work are discussed.