The past, present, and future of the brain imaging data structure (BIDS).

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.

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS).

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.

Individual risk attitudes arise from noise in neurocognitive magnitude representations.

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.

7T functional MRI finds no evidence for distinct functional subregions in the subthalamic nucleus during a speeded decision-making task.

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.

qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data.

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.

Separable pupillary signatures of perception and action during perceptual multistability.

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 fMRI reveals origins of feedforward and feedback activity within laminae of human ocular dominance columns.

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.

The functional microscopic neuroanatomy of the human subthalamic nucleus.

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 importance of standards for sharing of computational models and data.

The Target Article by Lee et al. (2019) 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.

A neural substrate of early response capture during conflict tasks in sensory areas.

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.

MP2RAGEME: T1 , T2* , and QSM mapping in one sequence at 7 tesla.

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.

Comparison of T2*-weighted and QSM contrasts in Parkinson's disease to visualize the STN with MRI.

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.

Comparing functional MRI protocols for small, iron-rich basal ganglia nuclei such as the subthalamic nucleus at 7 T and 3 T.

The basal ganglia (BG) form a network of subcortical nuclei. Functional magnetic resonance imaging (fMRI) in the BG could provide insight in its functioning and the underlying mechanisms of Deep 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. []: 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. Hum Brain Mapp 38:3226-3248, 2017. © 2017 Wiley Periodicals, Inc.

Sensory neural pathways revisited to unravel the temporal dynamics of the Simon effect: A model-based cognitive neuroscience approach.

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.

Towards a mechanistic understanding of the human subcortex.

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.

Transcranial Direct Current Stimulation Does Not Influence the Speed-Accuracy Tradeoff in Perceptual Decision-making: Evidence from Three Independent Studies.

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.

Different Ways of Linking Behavioral and Neural Data via Computational Cognitive Models.

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.

The Age-ility Project (Phase 1): Structural and functional imaging and electrophysiological data repository.

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.

The subcortical cocktail problem; mixed signals from the subthalamic nucleus and substantia nigra.

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.