brainlife.io: a decentralized and open-source cloud platform to support neuroscience research.

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

Cognitive effects on experienced duration and speed of time, prospectively, retrospectively, in and out of lockdown.

Psychological time is influenced by multiple factors such as arousal, emotion, attention and memory. While laboratory observations are well documented, it remains unclear whether cognitive effects on time perception replicate in real-life settings. This study exploits a set of data collected online during the Covid-19 pandemic, where participants completed a verbal working memory (WM) task in which their cognitive load was manipulated using a parametric n-back (1-back, 3-back). At the end of every WM trial, participants estimated the duration of that trial and rated the speed at which they perceived time was passing. In this within-participant design, we initially tested whether the amount of information stored in WM affected time perception in opposite directions depending on whether duration was estimated prospectively (i.e., when participants attend to time) or retrospectively (i.e., when participants do not attend to time). Second, we tested the same working hypothesis for the felt passage of time, which may capture a distinct phenomenology. Third, we examined the link between duration and speed of time, and found that short durations tended to be perceived as fast. Last, we contrasted two groups of individuals tested in and out of lockdown to evaluate the impact of social isolation. We show that duration and speed estimations were differentially affected by social isolation. We discuss and conclude on the influence of cognitive load on various experiences of time.

brainlife.io: A decentralized and open source cloud platform to support neuroscience research.

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

The Blursday database as a resource to study subjective temporalities during COVID-19.

The COVID-19 pandemic and associated lockdowns triggered worldwide changes in the daily routines of human experience. The Blursday database provides repeated measures of subjective time and related processes from participants in nine countries tested on 14 questionnaires and 15 behavioural tasks during the COVID-19 pandemic. A total of 2,840 participants completed at least one task, and 439 participants completed all tasks in the first session. The database and all data collection tools are accessible to researchers for studying the effects of social isolation on temporal information processing, time perspective, decision-making, sleep, metacognition, attention, memory, self-perception and mindfulness. Blursday includes quantitative statistics such as sleep patterns, personality traits, psychological well-being and lockdown indices. The database provides quantitative insights on the effects of lockdown (stringency and mobility) and subjective confinement on time perception (duration, passage of time and temporal distances). Perceived isolation affects time perception, and we report an inter-individual central tendency effect in retrospective duration estimation.

Good scientific practice in EEG and MEG research: Progress and perspectives.

Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.

Does stroke volume influence heartbeat evoked responses?

We know surprisingly little on how heartbeat-evoked responses (HERs) vary with cardiac parameters. Here, we measured both stroke volume, or volume of blood ejected at each heartbeat, with impedance cardiography, and HER amplitude with magneto-encephalography, in 21 male and female participants at rest with eyes open. We observed that HER co-fluctuates with stroke volume on a beat-to-beat basis, but only when no correction for cardiac artifact was performed. This highlights the importance of an ICA correction tailored to the cardiac artifact. We also observed that easy-to-measure cardiac parameters (interbeat intervals, ECG amplitude) are sensitive to stroke volume fluctuations and can be used as proxies when stroke volume measurements are not available. Finally, interindividual differences in stroke volume were reflected in MEG data, but whether this effect is locked to heartbeats is unclear. Altogether, our results question assumptions on the link between stroke volume and HERs.

Statistical power: Implications for planning MEG studies.

Statistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated "experiments" using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the "signal condition", but not in the "noise condition", and detected significant differences at sensor level with classical paired t-tests across subjects, using amplitude, squared amplitude, and global field power (GFP) measures. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not. Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies and should be adapted according to the brain regions under study.

MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis.

The development of the Brain Imaging Data Structure (BIDS; Gorgolewski et al., 2016) gave the neuroscientific community a standard to organize and share data. BIDS prescribes file naming conventions and a folder structure to store data in a set of already existing file formats. Next to rules about organization of the data itself, BIDS provides standardized templates to store associated metadata in the form of Javascript Object Notation (JSON) and tab separated value (TSV) files. It thus facilitates data sharing, eases metadata querying, and enables automatic data analysis pipelines. BIDS is a rich system to curate, aggregate, and annotate neuroimaging databases.

Internal valence modulates the speed of object recognition.

Brain regions that process affect are strongly connected with visual regions, but the functional consequences of this structural organization have been relatively unexplored. How does the momentary affect of an observer influence perception? We induced either pleasant or unpleasant affect in participants and then recorded their neural activity using magnetoencephalography while they completed an object recognition task. We hypothesized, and found, that affect influenced the speed of object recognition by modulating the speed and amplitude of evoked responses in occipitotemporal cortex and regions important for representing affect. Furthermore, affect modulated functional interactions between affective and perceptual regions early during perceptual processing. These findings indicate that affect can serve as an important contextual influence on object recognition processes.

Spontaneous Neural Oscillations Bias Perception by Modulating Baseline Excitability.

The brain exhibits organized fluctuations of neural activity, even in the absence of tasks or sensory input. A prominent type of such spontaneous activity is the alpha rhythm, which influences perception and interacts with other ongoing neural activity. It is currently hypothesized that states of decreased prestimulus α oscillations indicate enhanced neural excitability, resulting in improved perceptual acuity. Nevertheless, it remains debated how changes in excitability manifest at the behavioral level in perceptual tasks. We addressed this issue by comparing two alternative models describing the effect of spontaneous α power on signal detection. The first model assumes that decreased α power increases baseline excitability, amplifying the response to both signal and noise, predicting a liberal detection criterion with no effect on sensitivity. The second model predicts that decreased α power increases the trial-by-trial precision of the sensory response, resulting in improved sensitivity. We tested these models in two EEG experiments in humans where we analyzed the effects of prestimulus α power on visual detection and discrimination using a signal detection framework. Both experiments provide strong evidence that decreased α power reflects a more liberal detection criterion, rather than improved sensitivity, consistent with the baseline model. In other words, when the task requires detecting stimulus presence versus absence, reduced α oscillations make observers more likely to report the stimulus regardless of actual stimulus presence. Contrary to previous interpretations, these results suggest that states of decreased α oscillations increase the global baseline excitability of sensory systems without affecting perceptual acuity. SIGNIFICANCE STATEMENT: Spontaneous fluctuations of brain activity explain why a faint sensory stimulus is sometimes perceived and sometimes not. The prevailing view is that heightened neural excitability, indexed by decreased α oscillations, promotes better perceptual performance. Here, we provide evidence that heightened neural excitability instead reflects a state of biased perception, during which a person is more likely to see a stimulus, whether or not it is actually present. Therefore, we propose that changes in neural excitability leave the precision of sensory processing unaffected. These results establish the link between spontaneous brain activity and the variability in human perception.

A practical guide to the selection of independent components of the electroencephalogram for artifact correction.

BACKGROUND: Electroencephalographic data are easily contaminated by signals of non-neural origin. Independent component analysis (ICA) can help correct EEG data for such artifacts. Artifact independent components (ICs) can be identified by experts via visual inspection. But artifact features are sometimes ambiguous or difficult to notice, and even experts may disagree about how to categorise a particular component. It is therefore important to inform users on artifact properties, and give them the opportunity to intervene. NEW METHOD: Here we first describe artifacts captured by ICA. We review current methods to automatically select artifactual components for rejection, and introduce the SASICA software, implementing several novel selection algorithms as well as two previously described automated methods (ADJUST, Mognon et al. Psychophysiology 2011;48(2):229; and FASTER, Nolan et al. J Neurosci Methods 2010;48(1):152). RESULTS: We evaluate these algorithms by comparing selections suggested by SASICA and other methods to manual rejections by experts. The results show that these methods can inform observers to improve rejections. However, no automated method can accurately isolate artifacts without supervision. The comprehensive and interactive plots produced by SASICA therefore constitute a helpful guide for human users for making final decisions. CONCLUSIONS: Rejecting ICs before EEG data analysis unavoidably requires some level of supervision. SASICA offers observers detailed information to guide selection of artifact ICs. Because it uses quantitative parameters and thresholds, it improves objectivity and reproducibility in reporting pre-processing procedures. SASICA is also a didactic tool that allows users to quickly understand what signal features captured by ICs make them likely to reflect artifacts.

Flicker-Induced Time Dilation Does Not Modulate EEG Correlates of Temporal Encoding.

In this study, we used EEG to investigate how visual stimulus dynamics (i.e. flicker) affect the mechanisms of duration perception. Previous studies have demonstrated that flickering visual stimuli are judged longer than equally long non-flickering stimuli. We tested whether this effect of flicker on duration judgments is mediated by changes in temporal encoding during the time interval. Here, temporal encoding refers to the perception of the unfolding of time throughout the temporal interval, also termed the "clock stage" in information processing models of interval timing. We hypothesized that if flicker mediates duration perception by affecting temporal encoding, then the dilation-effect should be reflected by neural correlates of temporal encoding. We presented flickering and steady stimuli in a duration bisection task and found that flicker dilated perceived duration. The EEG analysis allowed us to isolate a putative neural correlate of temporal encoding: a modulation of the amplitude of the contingent negative variation (CNV) by stimuli classified as "long" compared to physically identical stimuli classified as "short". However, flicker did not affect the CNV amplitude, suggesting that flicker does not dilate perceived duration by affecting temporal encoding. Possibly, flicker might affect only later stages of temporal processing such as interval comparison or decision making.

Invisible visual stimuli elicit increases in alpha-band power.

The cerebral cortex responds to stimuli of a wide range of intensities. Previous studies have demonstrated that undetectably weak somatosensory stimuli cause a functional deactivation or inhibition in somatosensory cortex. In the present study, we tested whether invisible visual stimuli lead to similar responses, indicated by an increase in EEG alpha-band power-an index of cortical excitability. We presented subliminal and supraliminal visual stimuli after estimating each participant's detection threshold. Stimuli consisted of peripherally presented small circular patches that differed in their contrast to a background consisting of a random white noise pattern. We demonstrate that subliminal and supraliminal stimuli each elicit specific neuronal response patterns. Supraliminal stimuli evoked an early, strongly phase-locked lower-frequency response representing the evoked potential and induced a decrease in alpha-band power from 400 ms on. By contrast, subliminal visual stimuli induced an increase of non-phase-locked power around 300 ms that was maximal within the alpha-band. This response might be due to an inhibitory mechanism, which reduces spurious visual activation that is unlikely to result from external stimuli.

Prestimulus neural oscillations inhibit visual perception via modulation of response gain.

The ongoing state of the brain radically affects how it processes sensory information. How does this ongoing brain activity interact with the processing of external stimuli? Spontaneous oscillations in the alpha range are thought to inhibit sensory processing, but little is known about the psychophysical mechanisms of this inhibition. We recorded ongoing brain activity with EEG while human observers performed a visual detection task with stimuli of different contrast intensities. To move beyond qualitative description, we formally compared psychometric functions obtained under different levels of ongoing alpha power and evaluated the inhibitory effect of ongoing alpha oscillations in terms of contrast or response gain models. This procedure opens the way to understanding the actual functional mechanisms by which ongoing brain activity affects visual performance. We found that strong prestimulus occipital alpha oscillations-but not more anterior mu oscillations-reduce performance most strongly for stimuli of the highest intensities tested. This inhibitory effect is best explained by a divisive reduction of response gain. Ongoing occipital alpha oscillations thus reflect changes in the visual system's input/output transformation that are independent of the sensory input to the system. They selectively scale the system's response, rather than change its sensitivity to sensory information.

Visual predictions in the orbitofrontal cortex rely on associative content.

Predicting upcoming events from incomplete information is an essential brain function. The orbitofrontal cortex (OFC) plays a critical role in this process by facilitating recognition of sensory inputs via predictive feedback to sensory cortices. In the visual domain, the OFC is engaged by low spatial frequency (LSF) and magnocellular-biased inputs, but beyond this, we know little about the information content required to activate it. Is the OFC automatically engaged to analyze any LSF information for meaning? Or is it engaged only when LSF information matches preexisting memory associations? We tested these hypotheses and show that only LSF information that could be linked to memory associations engages the OFC. Specifically, LSF stimuli activated the OFC in 2 distinct medial and lateral regions only if they resembled known visual objects. More identifiable objects increased activity in the medial OFC, known for its function in affective responses. Furthermore, these objects also increased the connectivity of the lateral OFC with the ventral visual cortex, a crucial region for object identification. At the interface between sensory, memory, and affective processing, the OFC thus appears to be attuned to the associative content of visual information and to play a central role in visuo-affective prediction.

A pool of pairs of related objects (POPORO) for investigating visual semantic integration: behavioral and electrophysiological validation.

Semantic processing of verbal and visual stimuli has been investigated in semantic violation or semantic priming paradigms in which a stimulus is either related or unrelated to a previously established semantic context. A hallmark of semantic priming is the N400 event-related potential (ERP)--a deflection of the ERP that is more negative for semantically unrelated target stimuli. The majority of studies investigating the N400 and semantic integration have used verbal material (words or sentences), and standardized stimulus sets with norms for semantic relatedness have been published for verbal but not for visual material. However, semantic processing of visual objects (as opposed to words) is an important issue in research on visual cognition. In this study, we present a set of 800 pairs of semantically related and unrelated visual objects. The images were rated for semantic relatedness by a sample of 132 participants. Furthermore, we analyzed low-level image properties and matched the two semantic categories according to these features. An ERP study confirmed the suitability of this image set for evoking a robust N400 effect of semantic integration. Additionally, using a general linear modeling approach of single-trial data, we also demonstrate that low-level visual image properties and semantic relatedness are in fact only minimally overlapping. The image set is available for download from the authors' website. We expect that the image set will facilitate studies investigating mechanisms of semantic and contextual processing of visual stimuli.

Early onset of neural synchronization in the contextual associations network.

Objects are more easily recognized in their typical context. However, is contextual information activated early enough to facilitate the perception of individual objects, or is contextual facilitation caused by postperceptual mechanisms? To elucidate this issue, we first need to study the temporal dynamics and neural interactions associated with contextual processing. Studies have shown that the contextual network consists of the parahippocampal, retrosplenial, and medial prefrontal cortices. We used functional MRI, magnetoencephalography, and phase synchrony analyses to compare the neural response to stimuli with strong or weak contextual associations. The context network was activated in functional MRI and preferentially synchronized in magnetoencephalography (MEG) for stimuli with strong contextual associations. Phase synchrony increased early (150-250 ms) only when it involved the parahippocampal cortex, whereas retrosplenial-medial prefrontal cortices synchrony was enhanced later (300-400 ms). These results describe the neural dynamics of context processing and suggest that context is activated early during object perception.