: 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. was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels.

Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective.

As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.

Variability in the analysis of a single neuroimaging dataset by many teams.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data.

Task-induced deactivation in diverse brain systems correlates with interindividual differences in distinct autonomic indices.

Neuroimaging research has shown that different cognitive tasks induce relatively specific activation patterns, as well as less task-specific deactivation patterns. Here we examined whether individual differences in Autonomic Nervous System (ANS) activity during task performance correlate with the magnitude of task-induced deactivation. In an fMRI study, participants performed a continuous mental arithmetic task in a task/rest block design, while undergoing combined fMRI and heart/respiration rate acquisitions using photoplethysmograph and respiration belt.

A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation.

Here we present an update of the studyforrest (http://studyforrest.org) dataset that complements the previously released functional magnetic resonance imaging (fMRI) data for natural language processing with a new two-hour 3 Tesla fMRI acquisition while 15 of the original participants were shown an audio-visual version of the stimulus motion picture. We demonstrate with two validation analyses that these new data support modeling specific properties of the complex natural stimulus, as well as a substantial within-subject BOLD response congruency in brain areas related to the processing of auditory inputs, speech, and narrative when compared to the existing fMRI data for audio-only stimulation.

Connectivity in the human brain dissociates entropy and complexity of auditory inputs.

Complex systems are described according to two central dimensions: (a) the randomness of their output, quantified via entropy; and (b) their complexity, which reflects the organization of a system's generators. Whereas some approaches hold that complexity can be reduced to uncertainty or entropy, an axiom of complexity science is that signals with very high or very low entropy are generated by relatively non-complex systems, while complex systems typically generate outputs with entropy peaking between these two extremes. In understanding their environment, individuals would benefit from coding for both input entropy and complexity; entropy indexes uncertainty and can inform probabilistic coding strategies, whereas complexity reflects a concise and abstract representation of the underlying environmental configuration, which can serve independent purposes, e.

Uncertainty in visual and auditory series is coded by modality-general and modality-specific neural systems.

Coding for the degree of disorder in a temporally unfolding sensory input allows for optimized encoding of these inputs via information compression and predictive processing. Prior neuroimaging work has examined sensitivity to statistical regularities within single sensory modalities and has associated this function with the hippocampus, anterior cingulate, and lateral temporal cortex. Here we investigated to what extent sensitivity to input disorder, quantified by Markov entropy, is subserved by modality-general or modality-specific neural systems when participants are not required to monitor the input.

Functional and developmental significance of amplitude variance asymmetry in the BOLD resting-state signal.

It is known that the brain's resting-state activity (RSA) is organized in low frequency oscillations that drive network connectivity. Recent research has also shown that elements of RSA described by high-frequency and nonoscillatory properties are non-random and functionally relevant. Motivated by this research, we investigated nonoscillatory aspects of the blood-oxygen-level-dependent (BOLD) RSA using a novel method for characterizing subtle fluctuation dynamics.

Neural systems mediating recognition of changes in statistical regularities.

Neuroimaging research has identified several brain systems sensitive to statistical regularities within environmental input. However, the continuous input impinging on sensory organs is rarely stationary and its degree of regularity may itself change over time. The goals of the current fMRI study were to identify systems sensitive to changes in statistical regularities within an ongoing stimulus, and determine to what extent sensitivity to such changes depends on intentional monitoring of order.

Multiple sensitivity profiles to diversity and transition structure in non-stationary input.

Recent formalizations suggest that the human brain codes for the degree of order in the environment and utilizes this knowledge to optimize perception and performance in the immediate future. However, the neural bases of how the brain spontaneously codes for order are poorly understood. It has been shown that activity in lateral temporal cortex and the hippocampus is linearly correlated with the order of short visual series under tasks requiring attention to the input and when series order is invariant over time.

The relationship between BOLD signal and autonomic nervous system functions: implications for processing of "physiological noise".

Functional magnetic resonance imaging (fMRI) research has revealed not only important aspects of the neural basis of cognitive and perceptual functions, but also important information on the relation between high-level brain functions and physiology. One of the central outstanding questions, given the features of the blood oxygenation level-dependent (BOLD) signal, is whether and how autonomic nervous system (ANS) functions are related to changes in brain states as measured in the human brain. A straightforward way to address this question has been to acquire external measurements of ANS activity such as cardiac and respiratory data, and examine their relation to the BOLD signal.

Images-based suppression of unwanted global signals in resting-state functional connectivity studies.

Correlated fluctuations of low-frequency fMRI signal have been suggested to reflect functional connectivity among the involved regions. However, large-scale correlations are especially prone to spurious global modulations induced by coherent physiological noise. Cardiac and respiratory rhythms are the most offending component, and a tailored preprocessing is needed in order to reduce their impact.