Connectome spectrum electromagnetic tomography: A method to reconstruct electrical brain source networks at high-spatial resolution.

Connectome spectrum electromagnetic tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal. These results demonstrate that CSET offers high spatio-temporal accuracy, enabling neuroscientists to extend their research beyond the current limitations of low sampling frequency in functional MRI and the poor spatial resolution of M/EEG.

The functional role of spatial anisotropies in ensemble perception.

BACKGROUND: The human brain can rapidly represent sets of similar stimuli by their ensemble summary statistics, like the average orientation or size. Classic models assume that ensemble statistics are computed by integrating all elements with equal weight. Challenging this view, here, we show that ensemble statistics are estimated by combining parafoveal and foveal statistics in proportion to their reliability. In a series of experiments, observers reproduced the average orientation of an ensemble of stimuli under varying levels of visual uncertainty. RESULTS: Ensemble statistics were affected by multiple spatial biases, in particular, a strong and persistent bias towards the center of the visual field. This bias, evident in the majority of subjects and in all experiments, scaled with uncertainty: the higher the uncertainty in the ensemble statistics, the larger the bias towards the element shown at the fovea. CONCLUSION: Our findings indicate that ensemble perception cannot be explained by simple uniform pooling. The visual system weights information anisotropically from both the parafovea and the fovea, taking the intrinsic spatial anisotropies of vision into account to compensate for visual uncertainty.

The effects of visual distractors on serial dependence.

Attractive serial dependence occurs when perceptual decisions are attracted toward previous stimuli. This effect is mediated by spatial attention and is most likely to occur when similar stimuli are attended at nearby locations. Attention, however, also involves the suppression of distracting information and of spatial locations where distracting stimuli have frequently appeared. Although distractors form an integral part of our visual experience, how they affect the processing of subsequent stimuli is unknown. Here, in two experiments, we tested serial dependence from distractor stimuli during an orientation adjustment task. We interleaved adjustment trials with a discrimination task requiring observers to ignore a peripheral distractor randomly appearing on half of the trials. Distractors were either similar to the adjustment probe (Experiment 1) or differed in spatial frequency and contrast (Experiment 2) and were shown at predictable or random locations in separate blocks. The results showed that the distractor caused considerable attentional capture in the discrimination task, with observers likely using proactive strategies to anticipate distractors at predictable locations. However, there was no evidence that the distractors affected the perceptual stream leading to positive serial dependence. Instead, they left a weak repulsive trace in Experiment 1 and more generally interfered with the effect of the previous adjustment probe in the serial dependence task. We suggest that this repulsive bias may reflect the operation of mechanisms involved in attentional suppression.

Tuning perception and decisions to temporal context.

Recent work suggests that serial dependence, where perceptual decisions are biased toward previous stimuli, arises from the prior that sensory input is temporally correlated. However, existing studies have mostly used random stimulus sequences that do not involve such temporal consistencies. Here, we manipulated the temporal statistics of visual stimuli to examine the role of true temporal correlations in serial dependence. In two experiments, observers reproduced the orientation of the last stimulus in a sequence, while we varied temporal correlations in the stimulus features at two timescales: stimulus history within the trial and decision history across trials. We found a clear dissociation: increasing temporal correlation in the stimulus history led to adaptation-like repulsive biases, whereas increasing temporal correlation in the decision history reduced attractive biases. Thus, we suggest that temporal correlation enhances the discriminative ability of the visual system, revealing the fundamental role of the broader temporal context.

Pre-stimulus alpha activity modulates long-lasting unconscious feature integration.

Pre-stimulus alpha (α) activity can influence perception of shortly presented, low-contrast stimuli. The underlying mechanisms are often thought to affect perception exactly at the time of presentation. In addition, it is suggested that α cycles determine temporal windows of integration. However, in everyday situations, stimuli are usually presented for periods longer than ∼100 ms and perception is often an integration of information across space and time. Moving objects are just one example. Hence, the question is whether α activity plays a role also in temporal integration, especially when stimuli are integrated over several α cycles. Using electroencephalography (EEG), we investigated the relationship between pre-stimulus brain activity and long-lasting integration in the sequential metacontrast paradigm (SQM), where two opposite vernier offsets, embedded in a stream of lines, are unconsciously integrated into a single percept. We show that increases in α power, even 300 ms before the stimulus, affected the probability of reporting the first offset, shown at the very beginning of the SQM. This effect was mediated by the systematic slowing of the α rhythm that followed the peak in α power. No phase effects were found. Together, our results demonstrate a cascade of neural changes, following spontaneous bursts of α activity and extending beyond a single moment, which influences the sensory representation of visual features for hundreds of milliseconds. Crucially, as feature integration in the SQM occurs before a conscious percept is elicited, this also provides evidence that α activity is linked to mechanisms regulating unconscious processing.

Attractive and repulsive serial dependence: The role of task relevance, the passage of time, and the number of stimuli.

Visual decisions are attracted toward features of previous stimuli. This phenomenon, termed serial dependence, has been related to a mechanism that integrates present visual input with stimuli seen up to 10 to 15 s in the past. It is believed that this mechanism is "temporally tuned" and the effect of prior stimuli fades with time. Here, we investigated whether the temporal window of serial dependence is influenced by the number of stimuli shown. Observers performed an orientation adjustment task where the interval between the past and present stimulus and the number of intervening stimuli varied. First, we found that the direction-repulsive or attractive-and duration of the effect of a past stimulus depends on whether the past stimulus was relevant to behavior. Second, we show that the number of stimuli, and not only the passage of time, plays a role: The effect of a stimulus at a fixed interval depends on the number of other stimuli shown after. Our results demonstrate that neither a single mechanism nor a general tuning window can fully capture the complexity of serial dependence.

The role of secondary features in serial dependence.

Recent work indicates that visual features are processed in a serially dependent manner: The decision about a stimulus feature in the present is influenced by the features of stimuli seen in the past, leading to serial dependence. It remains unclear, however, under which conditions serial dependence is influenced by secondary features of the stimulus. Here, we investigate whether the color of a stimulus influences serial dependence in an orientation adjustment task. Observers viewed a sequence of oriented stimuli that randomly changed color (red or green) and reproduced the orientation of the last stimulus in the sequence. In addition, they had to either detect a certain color in the stimulus (Experiment 1) or discriminate the color of the stimulus (Experiment 2). We found that color does not influence serial dependence for orientation, and that observers were biased by previous orientations independently of changes or repetitions in the stimulus color. This occurred even when observers were explicitly asked to discriminate the stimuli based on their color. Together, our two experiments indicate that when the task involves a single elementary feature such as orientation, serial dependence is not modulated by changes in other features of the stimulus.

Stronger serial dependence in the depth plane than the fronto-parallel plane between realistic objects: Evidence from virtual reality.

Visual estimates of stimulus features are systematically biased toward the features of previously encountered stimuli. Such serial dependencies have often been linked to how the brain maintains perceptual continuity. However, serial dependence has mostly been studied for simple two-dimensional stimuli. Here, we present the first attempt at examining serial dependence in three dimensions with natural objects, using virtual reality (VR). In Experiment 1, observers were presented with 3D virtually rendered objects commonly encountered in daily life and were asked to reproduce their orientation. The rotation plane of the object and its distance from the observer were manipulated. Large positive serial dependence effects were observed, but most notably, larger biases were observed when the object was rotated in depth, and when the object was rendered as being further away from the observer. In Experiment 2, we tested the object specificity of serial dependence by varying object identity from trial to trial. Similar serial dependence was observed irrespective of whether the test item was the same object, a different exemplar from the same object category, or a different object from a separate category. In Experiment 3, we manipulated the retinal size of the stimulus in conjunction with its distance. Serial dependence was most strongly modulated by retinal size, rather than VR depth cues. Our results suggest that the increased uncertainty added by the third dimension in VR increases serial dependence. We argue that investigating serial dependence in VR will provide potentially more accurate insights into the nature and mechanisms behind these biases.

Serial dependence in visual perception: A review.

How does the visual system represent continuity in the constantly changing visual input? A recent proposal is that vision is serially dependent: Stimuli seen a moment ago influence what we perceive in the present. In line with this, recent frameworks suggest that the visual system anticipates whether an object seen at one moment is the same as the one seen a moment ago, binding visual representations across consecutive perceptual episodes. A growing body of work supports this view, revealing signatures of serial dependence in many diverse visual tasks. Yet, the variety of disparate findings and interpretations calls for a more general picture. Here, we survey the main paradigms and results over the past decade. We also focus on the challenge of finding a relationship between serial dependence and the concept of "object identity," taking centuries-long history of research into account. Among the seemingly contrasting findings on serial dependence, we highlight common patterns that may elucidate the nature of this phenomenon and attempt to identify questions that are unanswered.

Unlocking crowding by ensemble statistics.

In crowding,1,2,3,4,5,6,7 objects that can be easily recognized in isolation appear jumbled when surrounded by other elements.8 Traditionally, crowding is explained by local pooling mechanisms,3,6,9,10,11,12,13,14,15 but many findings have shown that the global configuration of the entire stimulus display, rather than local aspects, determines crowding.8,16,17,18,19,20,21,22,23,24,25,26,27,28 However, understanding global configurations is challenging because even slight changes can lead from crowding to uncrowding and vice versa.23,25,28,29 Unfortunately, the number of configurations to explore is virtually infinite. Here, we show that one does not need to know the specific configuration of flankers to determine crowding strength but only their ensemble statistics, which allow for the rapid computation of groups within the stimulus display.30,31,32,33,34,35,36,37 To investigate the role of ensemble statistics in (un)crowding, we used a classic vernier offset discrimination task in which the vernier was flanked by multiple squares. We manipulated the orientation statistics of the squares based on the following rationale: a central square with an orientation different from the mean orientation of the other squares stands out from the rest and groups with the vernier, causing strong crowding. If, on the other hand, all squares group together, the vernier is the only element that stands out, and crowding is weak. These effects should depend exclusively on the perceived ensemble statistics, i.e., on the mean orientation of the squares and not on their individual orientations. In two experiments, we confirmed these predictions.

Feature distribution learning by passive exposure.

Humans can rapidly estimate the statistical properties of groups of stimuli, including their average and variability. But recent studies of so-called Feature Distribution Learning (FDL) have shown that observers can quickly learn even more complex aspects of feature distributions. In FDL, observers learn the full shape of a distribution of features in a set of distractor stimuli and use this information to improve visual search: response times (RT) are slowed if the target feature lies inside the previous distractor distribution, and the RT patterns closely reflect the distribution shape. FDL requires only a few trials and is markedly sensitive to different distribution types. It is unknown, however, whether our perceptual system encodes feature distributions automatically and by passive exposure, or whether this learning requires active engagement with the stimuli. In two experiments, we sought to answer this question. During an initial exposure stage, participants passively viewed a display of 36 lines that included one orientation singleton or no singletons. In the following search display, they had to find an oddly oriented target. The orientations of the lines were determined either by a Gaussian or a uniform distribution. We found evidence for FDL only when the passive trials contained an orientation singleton. Under these conditions, RT's decreased as a function of the orientation distance between the target and the mean of the exposed distractor distribution. These results suggest that passive exposure to a distribution of visual features can affect subsequent search performance, but only if a singleton appears during exposure to the distribution.

Structure supports function: Informing directed and dynamic functional connectivity with anatomical priors.

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections. Despite this intrinsic relationship between structural connectivity (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited. Here, we propose a new adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. We show that, particularly under conditions of low signal-to-noise ratio, SC priors can help to refine estimates of directed FC, promoting sparse functional networks that combine information from structure and function. In addition, the proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new tool for multimodal imaging in the context of dynamic and directed FC analysis.

Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes.

We describe the multimodal neuroimaging dataset VEPCON (OpenNeuro Dataset ds003505). It includes raw data and derivatives of high-density EEG, structural MRI, diffusion weighted images (DWI) and single-trial behavior (accuracy, reaction time). Visual evoked potentials (VEPs) were recorded while participants (n = 20) discriminated briefly presented faces from scrambled faces, or coherently moving stimuli from incoherent ones. EEG and MRI were recorded separately from the same participants. The dataset contains raw EEG and behavioral data, pre-processed EEG of single trials in each condition, structural MRIs, individual brain parcellations at 5 spatial resolutions (83 to 1015 regions), and the corresponding structural connectomes computed from fiber count, fiber density, average fractional anisotropy and mean diffusivity maps. For source imaging, VEPCON provides EEG inverse solutions based on individual anatomy, with Python and Matlab scripts to derive activity time-series in each brain region, for each parcellation level. The BIDS-compatible dataset can contribute to multimodal methods development, studying structure-function relations, and to unimodal optimization of source imaging and graph analyses, among many other possibilities.

Selective attention involves a feature-specific sequential release from inhibitory gating.

Selective attention is a fundamental cognitive mechanism that allows our brain to preferentially process relevant sensory information, while filtering out distracting information. Attention is thought to flexibly gate the communication of irrelevant information through top-down alpha-rhythmic (8-12 Hz) functional connections, which influence early visual processing. However, the dynamic effects of top-down influence on downstream visual processing remain unknown. Here, we used electroencephalography to investigate local and network effects of selective attention while subjects attended to distinct features of identical stimuli. We found that attention-related changes in the functional brain network organization emerge shortly after stimulus onset, accompanied by an overall decrease of functional connectivity. Signatures of attentional selection were evident from a sequential release from alpha-band parietal gating in feature-selective areas. The directed connectivity paths and temporal evolution of this release from gating were consistent with the sensory effect of each feature, providing a neural basis for how visual processing quickly prioritizes relevant information in functionally specialized areas.

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity.

The functional organization of neural processes is constrained by the brain's intrinsic structural connectivity, i.e., the connectome. Here, we explore how structural connectivity can improve the representation of brain activity signals and their dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI, and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. These harmonic modes are known as connectome harmonics. Here we describe brain activity signal as a time-varying combination of connectome harmonics. We term this description as the connectome spectrum of the signal. We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterizes fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that a smaller number of dimensions capture the differences between low-level and high-level visual processing in the connectome spectrum. Also, we demonstrate that connectome harmonics capture more sensitively the topological properties of brain activity. In summary, this work provides statistical, functional, and topological evidence indicating that the description of brain activity in terms of structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning.

Predictive brain: Addressing the level of representation by reviewing perceptual hysteresis.

In recent years, the idea that the prediction of sensory input is one of the major computational goals of the nervous system led to the development of several large-scale theories of brain functioning, such as different versions of the Bayesian approach to brain functions, predictive coding theories of cognition and the Free-energy principle. During the years, various empirical phenomena have been re-interpreted within such frames, and have been considered as consequences of predictive processing. Here we focus on perceptual hysteresis, or serial dependence, as an exemplary case. We unravel a potential gap in the predictive frameworks and raise the idea that alternative explanations of this effect can solve this issue, as they address the type of cognitive and neural representations involved.

The anisotropic field of ensemble coding.

Human observers can accurately estimate statistical summaries from an ensemble of multiple stimuli, including the average size, hue, and direction of motion. The efficiency and speed with which statistical summaries are extracted suggest an automatic mechanism of ensemble coding that operates beyond the capacity limits of attention and memory. However, the extent to which ensemble coding reflects a truly parallel and holistic mode of processing or a non-uniform and biased integration of multiple items is still under debate. In the present work, we used a technique, based on a Spatial Weighted Average Model (SWM), to recover the spatial profile of weights with which individual stimuli contribute to the estimated average during mean size adjustment tasks. In a series of experiments, we derived two-dimensional SWM maps for ensembles presented at different retinal locations, with different degrees of dispersion and under different attentional demands. Our findings revealed strong spatial anisotropies and leftward biases in ensemble coding that were organized in retinotopic reference frames and persisted under attentional manipulations. These results demonstrate an anisotropic spatial contribution to ensemble coding that could be mediated by the differential activation of the two hemispheres during spatial processing and scene encoding.

Serial dependence does not originate from low-level visual processing.

Perception depends not only on the current sensory input but also on the preceding history of stimuli. In serial dependence (SD), for example, the orientation of a Gabor patch is mistakenly reported as more similar to previous trials than it actually is. This bias is typically observed for moderate orientation differences (<45°) and extends over a few trials in the past. It is hotly debated whether SD originates at perceptual or post-perceptual, e.g., decisional, stages. Here, we provide evidence for the latter hypothesis. We presented Gabor patches with different spatial frequencies or Gabors intermingled with dot patterns. Even though stimuli were perceptually clearly dissimilar, we found robust SD effects arguing against any perceptual account. These findings suggest a re-evaluation of current models and theoretical accounts of SD.