Object Selection by Automatic Spreading of Top-Down Attentional Signals in V1.

What is selected when attention is directed to a specific location of the visual field? Theories of object-based attention have suggested that when spatial attention is directed to part of an object, attention does not simply enhance the attended location but automatically spreads to enhance all locations that comprise the object. Here, we tested this hypothesis by reconstructing the distribution of attention from primary visual cortex (V1) population neuronal activity patterns in 24 human adults (17 female) using functional magnetic resonance imaging (fMRI) and population-based receptive field (prf) mapping. We find that attention spreads from a spatially cued location to the underlying object, and enhances all spatial locations that comprise the object. Importantly, this spreading was also evident when the object was not task relevant. These data suggest that attentional selection automatically operates at an object level, facilitating the reconstruction of coherent objects from fragmented representations in early visual cortex.SIGNIFICANCE STATEMENT Object perception is an astonishing feat of the visual system. When visual information about orientation, shape, and color enters through our eyes, it has yet to be integrated into a coherent representation of an object. But which visual features constitute a single object and which features belong to the background? The brain mechanisms underpinning object perception are yet to be understood. We now demonstrate that one candidate mechanism, the successive activation of all parts of an object, occurs in early visual cortex and results in a detailed representation of the object following Gestalt principles. Furthermore, our results suggest that object selection occurs automatically, without involving voluntary control.

Suppressed Sensory Response to Predictable Object Stimuli throughout the Ventral Visual Stream.

Prediction plays a crucial role in perception, as prominently suggested by predictive coding theories. However, the exact form and mechanism of predictive modulations of sensory processing remain unclear, with some studies reporting a downregulation of the sensory response for predictable input whereas others observed an enhanced response. In a similar vein, downregulation of the sensory response for predictable input has been linked to either sharpening or dampening of the sensory representation, which are opposite in nature. In the present study, we set out to investigate the neural consequences of perceptual expectation of object stimuli throughout the visual hierarchy, using fMRI in human volunteers. Participants of both sexes were exposed to pairs of sequentially presented object images in a statistical learning paradigm, in which the first object predicted the identity of the second object. Image transitions were not task relevant; thus, all learning of statistical regularities was incidental. We found strong suppression of neural responses to expected compared with unexpected stimuli throughout the ventral visual stream, including primary visual cortex, lateral occipital complex, and anterior ventral visual areas. Expectation suppression in lateral occipital complex scaled positively with image preference and voxel selectivity, lending support to the dampening account of expectation suppression in object perception.SIGNIFICANCE STATEMENT It has been suggested that the brain fundamentally relies on predictions and constructs models of the world to make sense of sensory information. Previous research on the neural basis of prediction has documented suppressed neural responses to expected compared with unexpected stimuli. In the present study, we demonstrate robust expectation suppression throughout the entire ventral visual stream, and underlying this suppression a dampening of the sensory representation in object-selective visual cortex, but not in primary visual cortex. Together, our results provide novel evidence in support of theories conceptualizing perception as an active inference process, which selectively dampens cortical representations of predictable objects. This dampening may support our ability to automatically filter out irrelevant, predictable objects.

Different Roles of Direct and Indirect Frontoparietal Pathways for Individual Working Memory Capacity.

The ability to temporarily store and manipulate information in working memory is a hallmark of human intelligence and differs considerably across individuals, but the structural brain correlates underlying these differences in working memory capacity (WMC) are only poorly understood. In two separate studies, diffusion MRI data and WMC scores were collected for 70 and 109 healthy individuals. Using a combination of probabilistic tractography and network analysis of the white matter tracts, we examined whether structural brain network properties were predictive of individual WMC. Converging evidence from both studies showed that lateral prefrontal cortex and posterior parietal cortex of high-capacity individuals are more densely connected compared with low-capacity individuals. Importantly, our network approach was further able to dissociate putative functional roles associated with two different pathways connecting frontal and parietal regions: a corticocortical pathway and a subcortical pathway. In Study 1, where participants were required to maintain and update working memory items, the connectivity of the direct and indirect pathway was predictive of WMC. In contrast, in Study 2, where participants were required to maintain working memory items without updating, only the connectivity of the direct pathway was predictive of individual WMC. Our results suggest an important dissociation in the circuitry connecting frontal and parietal regions, where direct frontoparietal connections might support storage and maintenance, whereas subcortically mediated connections support the flexible updating of working memory content.

Distractor-resistant Short-Term Memory Is Supported by Transient Changes in Neural Stimulus Representations.

Goal-directed behavior in a complex world requires the maintenance of goal-relevant information despite multiple sources of distraction. However, the brain mechanisms underlying distractor-resistant working or short-term memory (STM) are not fully understood. Although early single-unit recordings in monkeys and fMRI studies in humans pointed to an involvement of lateral prefrontal cortices, more recent studies highlighted the importance of posterior cortices for the active maintenance of visual information also in the presence of distraction. Here, we used a delayed match-to-sample task and multivariate searchlight analyses of fMRI data to investigate STM maintenance across three extended delay phases. Participants maintained two samples (either faces or houses) across an unfilled pre-distractor delay, a distractor-filled delay, and an unfilled post-distractor delay. STM contents (faces vs. houses) could be decoded above-chance in all three delay phases from occipital, temporal, and posterior parietal areas. Classifiers trained to distinguish face versus house maintenance successfully generalized from pre- to post-distractor delays and vice versa, but not to the distractor delay period. Furthermore, classifier performance in all delay phases was correlated with behavioral performance in house, but not face, trials. Our results demonstrate the involvement of distributed posterior, but not lateral prefrontal, cortices in active maintenance during and after distraction. They also show that the neural code underlying STM maintenance is transiently changed in the presence of distractors and reinstated after distraction. The correlation with behavior suggests that active STM maintenance is particularly relevant in house trials, whereas face trials might rely more strongly on contributions from long-term memory.

Preference for Audiovisual Speech Congruency in Superior Temporal Cortex.

Auditory speech perception can be altered by concurrent visual information. The superior temporal cortex is an important combining site for this integration process. This area was previously found to be sensitive to audiovisual congruency. However, the direction of this congruency effect (i.e., stronger or weaker activity for congruent compared to incongruent stimulation) has been more equivocal. Here, we used fMRI to look at the neural responses of human participants during the McGurk illusion--in which auditory /aba/ and visual /aga/ inputs are fused to perceived /ada/--in a large homogenous sample of participants who consistently experienced this illusion. This enabled us to compare the neuronal responses during congruent audiovisual stimulation with incongruent audiovisual stimulation leading to the McGurk illusion while avoiding the possible confounding factor of sensory surprise that can occur when McGurk stimuli are only occasionally perceived. We found larger activity for congruent audiovisual stimuli than for incongruent (McGurk) stimuli in bilateral superior temporal cortex, extending into the primary auditory cortex. This finding suggests that superior temporal cortex prefers when auditory and visual input support the same representation.

Predicting errors from reconfiguration patterns in human brain networks.

Task preparation is a complex cognitive process that implements anticipatory adjustments to facilitate future task performance. Little is known about quantitative network parameters governing this process in humans. Using functional magnetic resonance imaging (fMRI) and functional connectivity measurements, we show that the large-scale topology of the brain network involved in task preparation shows a pattern of dynamic reconfigurations that guides optimal behavior. This network could be decomposed into two distinct topological structures, an error-resilient core acting as a major hub that integrates most of the network's communication and a predominantly sensory periphery showing more flexible network adaptations. During task preparation, core-periphery interactions were dynamically adjusted. Task-relevant visual areas showed a higher topological proximity to the network core and an enhancement in their local centrality and interconnectivity. Failure to reconfigure the network topology was predictive for errors, indicating that anticipatory network reconfigurations are crucial for successful task performance. On the basis of a unique network decoding approach, we also develop a general framework for the identification of characteristic patterns in complex networks, which is applicable to other fields in neuroscience that relate dynamic network properties to behavior.

Observing, performing, and understanding actions: revisiting the role of cortical motor areas in processing of action words.

Language content and action/perception have been shown to activate common brain areas in previous neuroimaging studies. However, it is unclear whether overlapping cortical activation reflects a common neural source or adjacent, but distinct, sources. We address this issue by using multivoxel pattern analysis on fMRI data. Specifically, participants were instructed to engage in five tasks: (1) execute hand actions (AE), (2) observe hand actions (AO), (3) observe nonbiological motion (MO), (4) read action verbs, and (5) read nonaction verbs. A classifier was trained to distinguish between data collected from neural motor areas during (1) AE versus MO and (2) AO versus MO. These two algorithms were then used to test for a distinction between data collected during the reading of action versus nonaction verbs. The results show that the algorithm trained to distinguish between AE and MO distinguishes between word categories using signal recorded from the left parietal cortex and pre-SMA, but not from ventrolateral premotor cortex. In contrast, the algorithm trained to distinguish between AO and MO discriminates between word categories using the activity pattern in the left premotor and left parietal cortex. This shows that the sensitivity of premotor areas to language content is more similar to the process of observing others acting than to acting oneself. Furthermore, those parts of the brain that show comparable neural pattern for action execution and action word comprehension are high-level integrative motor areas rather than low-level motor areas.

Successor-like representation guides the prediction of future events in human visual cortex and hippocampus.

Human agents build models of their environment, which enable them to anticipate and plan upcoming events. However, little is known about the properties of such predictive models. Recently, it has been proposed that hippocampal representations take the form of a predictive map-like structure, the so-called successor representation (SR). Here, we used human functional magnetic resonance imaging to probe whether activity in the early visual cortex (V1) and hippocampus adhere to the postulated properties of the SR after visual sequence learning. Participants were exposed to an arbitrary spatiotemporal sequence consisting of four items (A-B-C-D). We found that after repeated exposure to the sequence, merely presenting single sequence items (e.g., - B - -) resulted in V1 activation at the successor locations of the full sequence (e.g., C-D), but not at the predecessor locations (e.g., A). This highlights that visual representations are skewed toward future states, in line with the SR. Similar results were also found in the hippocampus. Moreover, the hippocampus developed a coactivation profile that showed sensitivity to the temporal distance in sequence space, with fading representations for sequence events in the more distant past and future. V1, in contrast, showed a coactivation profile that was only sensitive to spatial distance in stimulus space. Taken together, these results provide empirical evidence for the proposition that both visual and hippocampal cortex represent a predictive map of the visual world akin to the SR.

Word contexts enhance the neural representation of individual letters in early visual cortex.

Visual context facilitates perception, but how this is neurally implemented remains unclear. One example of contextual facilitation is found in reading, where letters are more easily identified when embedded in a word. Bottom-up models explain this word advantage as a post-perceptual decision bias, while top-down models propose that word contexts enhance perception itself. Here, we arbitrate between these accounts by presenting words and nonwords and probing the representational fidelity of individual letters using functional magnetic resonance imaging. In line with top-down models, we find that word contexts enhance letter representations in early visual cortex. Moreover, we observe increased coupling between letter information in visual cortex and brain activity in key areas of the reading network, suggesting these areas may be the source of the enhancement. Our results provide evidence for top-down representational enhancement in word recognition, demonstrating that word contexts can modulate perceptual processing already at the earliest visual regions.

Lifetime cost-effectiveness of prophylactic implantation of a cardioverter defibrillator in patients with reduced left ventricular systolic function: results of Markov modelling in a European population.

AIMS: Current European guidelines recommend prophylactic implantation of cardioverter defibrillators (ICDs) in patients with a reduced left ventricular ejection fraction (LVEF) who are not in NYHA class IV and have reasonable life expectancy. Cost and benefit implications of this recommendation have not been reported from a European perspective. METHODS AND RESULTS: Markov modelling estimated lifetime costs and effects [life years (LY) and quality-adjusted LY (QALY) gained] of prophylactic ICD implantation vs. conventional treatment, among patients with a reduced LVEF. Efficacy was estimated from a meta-analysis of mortality rates in the six primary prevention trials with inclusion criteria matching ACC/AHA/ESC Class I or IIa recommendations. Direct medical costs were estimated using Belgian national references. Costs and effects were discounted at 3 and 1.5% per annum, respectively. Probabilistic sensitivity and scenario analyses estimated the uncertainty around the incremental cost-effectiveness ratio. An ICD implantation increased the lifetime direct costs by euro 46,413. Estimated mean LY/QALY gained were 1.88/1.57, respectively. Probabilistic analysis estimated mean lifetime cost per QALY gained as euro 31,717 (95% CI: euro 19,760-euro 61,316). Cost-effectiveness was influenced most by ICD efficacy, time to replacement, utility, and patient age at implantation. CONCLUSION: In a European healthcare setting, prophylactic ICD implantation may be cost-effective if current guidelines for patients with a reduced LVEF are followed.

Framing orientation selectivity.

The ongoing debate on the neural basis of orientation selectivity in the primary visual cortex continues.

Graph measures in task-based fMRI: Functional integration during read-out of visual and auditory information.

This study investigated how attending to auditory and visual information systematically changes graph theoretical measures of integration and functional connectivity between three network modules: auditory, visual, and a joint task core. Functional MRI BOLD activity was recorded while healthy volunteers attended to colour and/or pitch information presented within an audiovisual stimulus sequence. Network nodes and modules were based on peak voxels of BOLD contrasts, including colour and pitch sensitive brain regions as well as the dorsal attention network. Network edges represented correlations between nodes' activity and were computed separately for each condition. Connection strength was increased between the task and the visual module when participants attended to colour, and between the task and the auditory module when they attended to pitch. Moreover, several nodal graph measures showed consistent changes to attentional modulation in form of stronger integration of sensory regions in response to attention. Together, these findings corroborate dynamical adjustments of both modality-specific and modality-independent functional brain networks in response to task demands and their representation in graph theoretical measures.

Cued reactivation during slow-wave sleep induces brain connectivity changes related to memory stabilization.

Memory reprocessing following acquisition enhances memory consolidation. Specifically, neural activity during encoding is thought to be 'replayed' during subsequent slow-wave sleep. Such memory replay is thought to contribute to the functional reorganization of neural memory traces. In particular, memory replay may facilitate the exchange of information across brain regions by inducing a reconfiguration of connectivity across the brain. Memory reactivation can be induced by external cues through a procedure known as "targeted memory reactivation". Here, we analysed data from a published study with auditory cues used to reactivate visual object-location memories during slow-wave sleep. We characterized effects of memory reactivation on brain network connectivity using graph-theory. We found that cue presentation during slow-wave sleep increased global network integration of occipital cortex, a visual region that was also active during retrieval of object locations. Although cueing did not have an overall beneficial effect on the retention of cued versus uncued associations, individual differences in overnight memory stabilization were related to enhanced network integration of occipital cortex. Furthermore, occipital cortex displayed enhanced connectivity with mnemonic regions, namely the hippocampus, parahippocampal gyrus, thalamus and medial prefrontal cortex during cue sound presentation. Together, these results suggest a neural mechanism where cue-induced replay during sleep increases integration of task-relevant perceptual regions with mnemonic regions. This cross-regional integration may be instrumental for the consolidation and long-term storage of enduring memories.

Intelligence is associated with the modular structure of intrinsic brain networks.

General intelligence is a psychological construct that captures in a single metric the overall level of behavioural and cognitive performance in an individual. While previous research has attempted to localise intelligence in circumscribed brain regions, more recent work focuses on functional interactions between regions. However, even though brain networks are characterised by substantial modularity, it is unclear whether and how the brain's modular organisation is associated with general intelligence. Modelling subject-specific brain network graphs from functional MRI resting-state data (N = 309), we found that intelligence was not associated with global modularity features (e.g., number or size of modules) or the whole-brain proportions of different node types (e.g., connector hubs or provincial hubs). In contrast, we observed characteristic associations between intelligence and node-specific measures of within- and between-module connectivity, particularly in frontal and parietal brain regions that have previously been linked to intelligence. We propose that the connectivity profile of these regions may shape intelligence-relevant aspects of information processing. Our data demonstrate that not only region-specific differences in brain structure and function, but also the network-topological embedding of fronto-parietal as well as other cortical and subcortical brain regions is related to individual differences in higher cognitive abilities, i.e., intelligence.

Time-compressed preplay of anticipated events in human primary visual cortex.

Perception is guided by the anticipation of future events. It has been hypothesized that this process may be implemented by pattern completion in early visual cortex, in which a stimulus sequence is recreated after only a subset of the visual input is provided. Here we test this hypothesis using ultra-fast functional magnetic resonance imaging to measure BOLD activity at precisely defined receptive field locations in visual cortex (V1) of human volunteers. We find that after familiarizing subjects with a spatial sequence, flashing only the starting point of the sequence triggers an activity wave in V1 that resembles the full stimulus sequence. This preplay activity is temporally compressed compared to the actual stimulus sequence and remains present even when attention is diverted from the stimulus sequence. Preplay might therefore constitute an automatic prediction mechanism for temporal sequences in V1.

Mnemonic convergence in the human hippocampus.

The ability to form associations between a multitude of events is the hallmark of episodic memory. Computational models have espoused the importance of the hippocampus as convergence zone, binding different aspects of an episode into a coherent representation, by integrating information from multiple brain regions. However, evidence for this long-held hypothesis is limited, since previous work has largely focused on representational and network properties of the hippocampus in isolation. Here we identify the hippocampus as mnemonic convergence zone, using a combination of multivariate pattern and graph-theoretical network analyses of functional magnetic resonance imaging data from humans performing an associative memory task. We observe overlap of conjunctive coding and hub-like network attributes in the hippocampus. These results provide evidence for mnemonic convergence in the hippocampus, underlying the integration of distributed information into episodic memory representations.

McGurk illusion recalibrates subsequent auditory perception.

Visual information can alter auditory perception. This is clearly illustrated by the well-known McGurk illusion, where an auditory/aba/ and a visual /aga/ are merged to the percept of 'ada'. It is less clear however whether such a change in perception may recalibrate subsequent perception. Here we asked whether the altered auditory perception due to the McGurk illusion affects subsequent auditory perception, i.e. whether this process of fusion may cause a recalibration of the auditory boundaries between phonemes. Participants categorized auditory and audiovisual speech stimuli as /aba/, /ada/ or /aga/ while activity patterns in their auditory cortices were recorded using fMRI. Interestingly, following a McGurk illusion, an auditory /aba/ was more often misperceived as 'ada'. Furthermore, we observed a neural counterpart of this recalibration in the early auditory cortex. When the auditory input /aba/ was perceived as 'ada', activity patterns bore stronger resemblance to activity patterns elicited by /ada/ sounds than when they were correctly perceived as /aba/. Our results suggest that upon experiencing the McGurk illusion, the brain shifts the neural representation of an /aba/ sound towards /ada/, culminating in a recalibration in perception of subsequent auditory input.

Probabilistic clustering of the human connectome identifies communities and hubs.

A fundamental assumption in neuroscience is that brain function is constrained by its structural properties. This motivates the idea that the brain can be parcellated into functionally coherent regions based on anatomical connectivity patterns that capture how different areas are interconnected. Several studies have successfully implemented this idea in humans using diffusion weighted MRI, allowing parcellation to be conducted in vivo. Two distinct approaches to connectivity-based parcellation can be identified. The first uses the connection profiles of brain regions as a feature vector, and groups brain regions with similar connection profiles together. Alternatively, one may adopt a network perspective that aims to identify clusters of brain regions that show dense within-cluster and sparse between-cluster connectivity. In this paper, we introduce a probabilistic model for connectivity-based parcellation that unifies both approaches. Using the model we are able to obtain a parcellation of the human brain whose clusters may adhere to either interpretation. We find that parts of the connectome consistently cluster as densely connected components, while other parts consistently result in clusters with similar connections. Interestingly, the densely connected components consist predominantly of major cortical areas, while the clusters with similar connection profiles consist of regions that have previously been identified as the 'rich club'; regions known for their integrative role in connectivity. Furthermore, the probabilistic model allows quantification of the uncertainty in cluster assignments. We show that, while most clusters are clearly delineated, some regions are more difficult to assign. These results indicate that care should be taken when interpreting connectivity-based parcellations obtained using alternative deterministic procedures.