Topographic connectivity reveals task-dependent retinotopic processing throughout the human brain.

The human visual system is organized as a hierarchy of maps that share the topography of the retina. Known retinotopic maps have been identified using simple visual stimuli under strict fixation, conditions different from everyday vision which is active, dynamic, and complex. This means that it remains unknown how much of the brain is truly visually organized. Here I demonstrate widespread stable visual organization beyond the traditional visual system, in default-mode network and hippocampus. Detailed topographic connectivity with primary visual cortex during movie-watching, resting-state, and retinotopic-mapping experiments revealed that visual-spatial representations throughout the brain are warped by cognitive state. Specifically, traditionally visual regions alternate with default-mode network and hippocampus in preferentially representing the center of the visual field. This visual role of default-mode network and hippocampus would allow these regions to interface between abstract memories and concrete sensory impressions. Together, these results indicate that visual-spatial organization is a fundamental coding principle that structures the communication between distant brain regions.

Divisive normalization unifies disparate response signatures throughout the human visual hierarchy.

Neural processing is hypothesized to apply the same mathematical operations in a variety of contexts, implementing so-called canonical neural computations. Divisive normalization (DN) is considered a prime candidate for a canonical computation. Here, we propose a population receptive field (pRF) model based on DN and evaluate it using ultra-high-field functional MRI (fMRI). The DN model parsimoniously captures seemingly disparate response signatures with a single computation, superseding existing pRF models in both performance and biological plausibility. We observe systematic variations in specific DN model parameters across the visual hierarchy and show how they relate to differences in response modulation and visuospatial information integration. The DN model delivers a unifying framework for visuospatial responses throughout the human visual hierarchy and provides insights into its underlying information-encoding computations. These findings extend the role of DN as a canonical computation to neuronal populations throughout the human visual hierarchy.

A selection and targeting framework of cortical locations for line-scanning fMRI.

Depth-resolved functional magnetic resonance imaging (fMRI) is an emerging field growing in popularity given the potential of separating signals from different computational processes in cerebral cortex. Conventional acquisition schemes suffer from low spatial and temporal resolutions. Line-scanning methods allow depth-resolved fMRI by sacrificing spatial coverage to sample blood oxygenated level-dependent (BOLD) responses at ultra-high temporal and spatial resolution. For neuroscience applications, it is critical to be able to place the line accurately to (1) sample the right neural population and (2) target that neural population with tailored stimuli or tasks. To this end, we devised a multi-session framework where a target cortical location is selected based on anatomical and functional properties. The line is then positioned according to this information in a separate second session, and we tailor the experiment to focus on the target location. Anatomically, the precision of the line placement was confirmed by projecting a nominal representation of the acquired line back onto the surface. Functional estimates of neural selectivities in the line, as quantified by a visual population-receptive field model, resembled the target selectivities well for most subjects. This functional precision was quantified in detail by estimating the distance between the visual field location of the targeted vertex and the location in visual cortex (V1) that most closely resembled the line-scanning estimates; this distance was on average ~5.5 mm. Given the dimensions of the line, differences in acquisition, session, and stimulus design, this validates that line-scanning can be used to probe local neural sensitivities across sessions. In summary, we present an accurate framework for line-scanning MRI; we believe such a framework is required to harness the full potential of line-scanning and maximize its utility. Furthermore, this approach bridges canonical fMRI experiments with electrophysiological experiments, which in turn allows novel avenues for studying human physiology non-invasively.

Towards functional spin-echo BOLD line-scanning in humans at 7T.

OBJECTIVE: Neurons cluster into sub-millimeter spatial structures and neural activity occurs at millisecond resolutions; hence, ultimately, high spatial and high temporal resolutions are required for functional MRI. In this work, we implemented a spin-echo line-scanning (SELINE) sequence to use in high spatial and temporal resolution fMRI. MATERIALS AND METHODS: A line is formed by simply rotating the spin-echo refocusing gradient to a plane perpendicular to the excited slice and by removing the phase-encoding gradient. This technique promises a combination of high spatial and temporal resolution (250 µm, 500 ms) and microvascular specificity of functional responses. We compared SELINE data to a corresponding gradient-echo version (GELINE). RESULTS: We demonstrate that SELINE showed much-improved line selection (i.e. a sharper line profile) compared to GELINE, albeit at the cost of a significant drop in functional sensitivity. DISCUSSION: This low functional sensitivity needs to be addressed before SELINE can be applied for neuroscientific purposes.

Representation of Contralateral Visual Space in the Human Hippocampus.

The initial encoding of visual information primarily from the contralateral visual field is a fundamental organizing principle of the primate visual system. Recently, the presence of such retinotopic sensitivity has been shown to extend well beyond early visual cortex to regions not historically considered retinotopically sensitive. In particular, human scene-selective regions in parahippocampal and medial parietal cortex exhibit prominent biases for the contralateral visual field. Here, we used fMRI to test the hypothesis that the human hippocampus, which is thought to be anatomically connected with these scene-selective regions, would also exhibit a biased representation of contralateral visual space. First, population receptive field (pRF) mapping with scene stimuli revealed strong biases for the contralateral visual field in bilateral hippocampus. Second, the distribution of retinotopic sensitivity suggested a more prominent representation in anterior medial portions of the hippocampus. Finally, the contralateral bias was confirmed in independent data taken from the Human Connectome Project (HCP) initiative. The presence of contralateral biases in the hippocampus, a structure considered by many as the apex of the visual hierarchy, highlights the truly pervasive influence of retinotopy. Moreover, this finding has important implications for understanding how visual information relates to the allocentric global spatial representations known to be encoded therein.SIGNIFICANCE STATEMENT Retinotopic encoding of visual information is an organizing principle of visual cortex. Recent work demonstrates this sensitivity in structures far beyond early visual cortex, including those anatomically connected to the hippocampus. Here, using population receptive field (pRF) modeling in two independent sets of data we demonstrate a consistent bias for the contralateral visual field in bilateral hippocampus. Such a bias highlights the truly pervasive influence of retinotopy, with important implications for understanding how the presence of retinotopy relates to more allocentric spatial representations.

Visual Working Memory Adapts to the Nature of Anticipated Interference.

Visual working memory has been proven to be relatively robust against interference. However, little is known on whether such robust coding is obligatory, or can be flexibly recruited depending on its expected usefulness. To address this, participants remembered both the color and orientation of a grating. During the maintenance, we inserted a secondary color/orientation memory task, interfering with the primary task. Crucially, we varied the expectations of the type of interference by varying the probability of the two types of intervening task. Behavioral data indicate that to-be-remembered features for which interference is expected are bolstered, whereas to-be-remembered features for which no interference is expected are left vulnerable. This was further supported by fMRI data obtained from visual cortex. In conclusion, the flexibility of visual working memory allows it to strengthen memories for which it anticipates the highest risk of interference.

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.

Visual Organization of the Default Network.

The default network (DN) is a brain network with correlated activities spanning frontal, parietal, and temporal cortical lobes. The DN activates for high-level cognition tasks and deactivates when subjects are actively engaged in perceptual tasks. Despite numerous observations, the role of DN deactivation remains unclear. Using computational neuroimaging applied to a large dataset of the Human Connectome Project (HCP) and to two individual subjects scanned over many repeated runs, we demonstrate that the DN selectively deactivates as a function of the position of a visual stimulus. That is, we show that spatial vision is encoded within the DN by means of deactivation relative to baseline. Our results suggest that the DN functions as a set of high-level visual regions, opening up the possibility of using vision-science tools to understand its putative function in cognition and perception.

Learning in Visual Regions as Support for the Bias in Future Value-Driven Choice.

Reinforcement learning can bias decision-making toward the option with the highest expected outcome. Cognitive learning theories associate this bias with the constant tracking of stimulus values and the evaluation of choice outcomes in the striatum and prefrontal cortex. Decisions however first require processing of sensory input, and to date, we know far less about the interplay between learning and perception. This functional magnetic resonance imaging study (N = 43) relates visual blood oxygen level-dependent (BOLD) responses to value beliefs during choice and signed prediction errors after outcomes. To understand these relationships, which co-occurred in the striatum, we sought relevance by evaluating the prediction of future value-based decisions in a separate transfer phase where learning was already established. We decoded choice outcomes with a 70% accuracy with a supervised machine learning algorithm that was given trial-by-trial BOLD from visual regions alongside more traditional motor, prefrontal, and striatal regions. Importantly, this decoding of future value-driven choice outcomes again highlighted an important role for visual activity. These results raise the intriguing possibility that the tracking of value in visual cortex is supportive for the striatal bias toward the more valued option in future choice.

Correction: How pupil responses track value-based decision-making during and after reinforcement learning.

[This corrects the article DOI: 10.1371/journal.pcbi.1006632.].

Dopaminergic medication reduces striatal sensitivity to negative outcomes in Parkinson's disease.

Reduced levels of dopamine in Parkinson's disease contribute to changes in learning, resulting from the loss of midbrain neurons that transmit a dopaminergic teaching signal to the striatum. Dopamine medication used by patients with Parkinson's disease has previously been linked to behavioural changes during learning as well as to adjustments in value-based decision-making after learning. To date, however, little is known about the specific relationship between dopaminergic medication-driven differences during learning and subsequent changes in approach/avoidance tendencies in individual patients. Twenty-four Parkinson's disease patients ON and OFF dopaminergic medication and 24 healthy controls subjects underwent functional MRI while performing a probabilistic reinforcement learning experiment. During learning, dopaminergic medication reduced an overemphasis on negative outcomes. Medication reduced negative (but not positive) outcome learning rates, while concurrent striatal blood oxygen level-dependent responses showed reduced prediction error sensitivity. Medication-induced shifts in negative learning rates were predictive of changes in approach/avoidance choice patterns after learning, and these changes were accompanied by systematic striatal blood oxygen level-dependent response alterations. These findings elucidate the role of dopamine-driven learning differences in Parkinson's disease, and show how these changes during learning impact subsequent value-based decision-making.

Cross-Task Contributions of Frontobasal Ganglia Circuitry in Response Inhibition and Conflict-Induced Slowing.

Why are we so slow in choosing the lesser of 2 evils? We considered whether such slowing relates to uncertainty about the value of these options, which arises from the tendency to avoid them during learning, and whether such slowing relates to frontosubthalamic inhibitory control mechanisms. In total, 49 participants performed a reinforcement-learning task and a stop-signal task while fMRI was recorded. A reinforcement-learning model was used to quantify learning strategies. Individual differences in lose-lose slowing related to information uncertainty due to sampling, and independently, to less efficient response inhibition in the stop-signal task. Neuroimaging analysis revealed an analogous dissociation: subthalamic nucleus (STN) BOLD activity related to variability in stopping latencies, whereas weaker frontosubthalamic connectivity related to slowing and information sampling. Across tasks, fast inhibitors increased STN activity for successfully canceled responses in the stop task, but decreased activity for lose-lose choices. These data support the notion that fronto-STN communication implements a rapid but transient brake on response execution, and that slowing due to decision uncertainty could result from an inefficient release of this "hold your horses" mechanism.

How pupil responses track value-based decision-making during and after reinforcement learning.

Cognition can reveal itself in the pupil, as latent cognitive processes map onto specific pupil responses. For instance, the pupil dilates when we make decisions and these pupil size fluctuations reflect decision-making computations during and after a choice. Surprisingly little is known, however, about how pupil responses relate to decisions driven by the learned value of stimuli. This understanding is important, as most real-life decisions are guided by the outcomes of earlier choices. The goal of this study was to investigate which cognitive processes the pupil reflects during value-based decision-making. We used a reinforcement learning task to study pupil responses during value-based decisions and subsequent decision evaluations, employing computational modeling to quantitatively describe the underlying cognitive processes. We found that the pupil closely tracks reinforcement learning processes independently across participants and across trials. Prior to choice, the pupil dilated as a function of trial-by-trial fluctuations in value beliefs about the to-be chosen option and predicted an individual's tendency to exploit high value options. After feedback a biphasic pupil response was observed, the amplitude of which correlated with participants' learning rates. Furthermore, across trials, early feedback-related dilation scaled with value uncertainty, whereas later constriction scaled with signed reward prediction errors. These findings show that pupil size fluctuations can provide detailed information about the computations underlying value-based decisions and the subsequent updating of value beliefs. As these processes are affected in a host of psychiatric disorders, our results indicate that pupillometry can be used as an accessible tool to non-invasively study the processes underlying ongoing reinforcement learning in the clinic.

Porcupine: A visual pipeline tool for neuroimaging analysis.

The field of neuroimaging is rapidly adopting a more reproducible approach to data acquisition and analysis. Data structures and formats are being standardised and data analyses are getting more automated. However, as data analysis becomes more complicated, researchers often have to write longer analysis scripts, spanning different tools across multiple programming languages. This makes it more difficult to share or recreate code, reducing the reproducibility of the analysis. We present a tool, Porcupine, that constructs one's analysis visually and automatically produces analysis code. The graphical representation improves understanding of the performed analysis, while retaining the flexibility of modifying the produced code manually to custom needs. Not only does Porcupine produce the analysis code, it also creates a shareable environment for running the code in the form of a Docker image. Together, this forms a reproducible way of constructing, visualising and sharing one's analysis. Currently, Porcupine links to Nipype functionalities, which in turn accesses most standard neuroimaging analysis tools. Our goal is to release researchers from the constraints of specific implementation details, thereby freeing them to think about novel and creative ways to solve a given problem. Porcupine improves the overview researchers have of their processing pipelines, and facilitates both the development and communication of their work. This will reduce the threshold at which less expert users can generate reusable pipelines. With Porcupine, we bridge the gap between a conceptual and an implementational level of analysis and make it easier for researchers to create reproducible and shareable science. We provide a wide range of examples and documentation, as well as installer files for all platforms on our website: https://timvanmourik.github.io/Porcupine. Porcupine is free, open source, and released under the GNU General Public License v3.0.

Multistable Perception and the Role of the Frontoparietal Cortex in Perceptual Inference.

A given pattern of optical stimulation can arise from countless possible real-world sources, creating a dilemma for vision: What in the world actually gives rise to the current pattern? This dilemma was pointed out centuries ago by the astronomer and mathematician Ibn Al-Haytham and was forcefully restated 150 years ago when von Helmholtz characterized perception as unconscious inference. To buttress his contention, von Helmholtz cited multistable perception: recurring changes in perception despite unchanging sensory input. Recent neuroscientific studies have exploited multistable perception to identify brain areas uniquely activated in association with these perceptual changes, but the specific roles of those activations remain controversial. This article provides an overview of theoretical models of multistable perception, a review of recent neuroimaging and brain stimulation studies focused on mechanisms associated with these perceptual changes, and a synthesis of available evidence within the context of current notions about Bayesian inference that find their historical roots in von Helmholtz's work.

Decision-related pupil dilation reflects upcoming choice and individual bias.

A number of studies have shown that pupil size increases transiently during effortful decisions. These decision-related changes in pupil size are mediated by central neuromodulatory systems, which also influence the internal state of brain regions engaged in decision making. It has been proposed that pupil-linked neuromodulatory systems are activated by the termination of decision processes, and, consequently, that these systems primarily affect the postdecisional brain state. Here, we present pupil results that run contrary to this proposal, suggesting an important intradecisional role. We measured pupil size while subjects formed protracted decisions about the presence or absence ("yes" vs. "no") of a visual contrast signal embedded in dynamic noise. Linear systems analysis revealed that the pupil was significantly driven by a sustained input throughout the course of the decision formation. This sustained component was larger than the transient component during the final choice (indicated by button press). The overall amplitude of pupil dilation during decision formation was bigger before yes than no choices, irrespective of the physical presence of the target signal. Remarkably, the magnitude of this pupil choice effect (yes > no) reflected the individual criterion: it was strongest in conservative subjects choosing yes against their bias. We conclude that the central neuromodulatory systems controlling pupil size are continuously engaged during decision formation in a way that reveals how the upcoming choice relates to the decision maker's attitude. Changes in brain state seem to interact with biased decision making in the face of uncertainty.

Interregional alpha-band synchrony supports temporal cross-modal integration.

In a continuously changing environment, time is a key property that tells us whether information from the different senses belongs together. Yet, little is known about how the brain integrates temporal information across sensory modalities. Using high-density EEG combined with a novel psychometric timing task in which human subjects evaluated durations of audiovisual stimuli, we show that the strength of alpha-band (8-12 Hz) phase synchrony between localizer-defined auditory and visual regions depended on cross-modal attention: during encoding of a constant 500 ms standard interval, audiovisual alpha synchrony decreased when subjects attended audition while ignoring vision, compared to when they attended both modalities. In addition, alpha connectivity during a variable target interval predicted the degree to which auditory stimulus duration biased time estimation while attending vision. This cross-modal interference effect was estimated using a hierarchical Bayesian model of a psychometric function that also provided an estimate of each individual's tendency to exhibit attention lapses. This lapse rate, in turn, was predicted by single-trial estimates of the stability of interregional alpha synchrony: when attending to both modalities, trials with greater stability in patterns of connectivity were characterized by reduced contamination by lapses. Together, these results provide new insights into a functional role of the coupling of alpha phase dynamics between sensory cortices in integrating cross-modal information over time.

Computational model links normalization to chemoarchitecture in the human visual system.

A goal of cognitive neuroscience is to provide computational accounts of brain function. Canonical computations-mathematical operations used by the brain in many contexts-fulfill broad information-processing needs by varying their algorithmic parameters. A key question concerns the identification of biological substrates for these computations and their algorithms. Chemoarchitecture-the spatial distribution of neurotransmitter receptor densities-shapes brain function. Here, we propose that local variations in specific receptor densities implement algorithmic modulations of canonical computations. To test this hypothesis, we combine mathematical modeling of brain responses with chemoarchitecture data. We compare parameters of divisive normalization obtained from 7-tesla functional magnetic resonance imaging with receptor density maps obtained from positron emission tomography. We find evidence that serotonin and γ-aminobutyric acid receptor densities are the biological substrate for algorithmic modulations of divisive normalization in the human visual system. Our model links computational and biological levels of vision, explaining how canonical computations allow the brain to fulfill broad information-processing needs.

Quantitative MRI at 7-Tesla reveals novel frontocortical myeloarchitecture anomalies in major depressive disorder.

Whereas meta-analytical data highlight abnormal frontocortical macrostructure (thickness/surface area/volume) in Major Depressive Disorder (MDD), the underlying microstructural processes remain uncharted, due to the use of conventional MRI scanners and acquisition techniques. We uniquely combined Ultra-High Field MRI at 7.0 Tesla with Quantitative Imaging to map intracortical myelin (proxied by longitudinal relaxation time T1) and iron concentration (proxied by transverse relaxation time T2*), microstructural processes deemed particularly germane to cortical macrostructure. Informed by meta-analytical evidence, we focused specifically on orbitofrontal and rostral anterior cingulate cortices among adult MDD patients (N = 48) and matched healthy controls (HC; N = 10). Analyses probed the association of MDD diagnosis and clinical profile (severity, medication use, comorbid anxiety disorders, childhood trauma) with aforementioned microstructural properties. MDD diagnosis (p's < 0.05, Cohen's D = 0.55-0.66) and symptom severity (p's < 0.01, r = 0.271-0.267) both related to decreased intracortical myelination (higher T1 values) within the lateral orbitofrontal cortex, a region tightly coupled to processing negative affect and feelings of sadness in MDD. No relations were found with local iron concentrations. These findings allow uniquely fine-grained insights on frontocortical microstructure in MDD, and cautiously point to intracortical demyelination as a possible driver of macroscale cortical disintegrity in MDD.

Robust high spatio-temporal line-scanning fMRI in humans at 7T using multi-echo readouts, denoising and prospective motion correction.

BACKGROUND: Functional magnetic resonance imaging (fMRI), typically using blood oxygenation level-dependent (BOLD) contrast weighted imaging, allows the study of brain function with millimeter spatial resolution and temporal resolution of one to a few seconds. At a mesoscopic scale, neurons in the human brain are spatially organized in structures with dimensions of hundreds of micrometers, while they communicate at the millisecond timescale. For this reason, it is important to develop an fMRI method with simultaneous high spatial and temporal resolution. Line-scanning promises to reach this goal at the cost of volume coverage. NEW METHOD: Here, we release a comprehensive update to human line-scanning fMRI. First, we investigated multi-echo line-scanning with five different protocols varying the number of echoes and readout bandwidth while keeping the TR constant. In these, we compared different echo combination approaches in terms of BOLD activation (sensitivity) and temporal signal-to-noise ratio. Second, we implemented an adaptation of NOise reduction with DIstribution Corrected principal component analysis (NORDIC) thermal noise removal for line-scanning fMRI data. Finally, we tested three image-based navigators for motion correction and investigated different ways of performing fMRI analysis on the timecourses which were influenced by the insertion of the navigators themselves. RESULTS: The presented improvements are relatively straightforward to implement; multi-echo readout and NORDIC denoising together, significantly improve data quality in terms of tSNR and t-statistical values, while motion correction makes line-scanning fMRI more robust. COMPARISON WITH EXISTING METHODS: Multi-echo acquisitions and denoising have previously been applied in 3D magnetic resonance imaging. Their combination and application to 1D line-scanning is novel. The current proposed method greatly outperforms the previous line-scanning acquisitions with single-echo acquisition, in terms of tSNR (4.0 for single-echo line-scanning and 36.2 for NORDIC-denoised multi-echo) and t-statistical values (3.8 for single-echo line-scanning and 25.1 for NORDIC-denoised multi-echo line-scanning). CONCLUSIONS: Line-scanning fMRI was advanced compared to its previous implementation in order to improve sensitivity and reliability. The improved line-scanning acquisition could be used, in the future, for neuroscientific and clinical applications.