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.

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.

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. As expected, task performance increased heart-rate and reduced the RMSSD, a cardiac index related to vagal tone. Across participants, higher heart rate during task was linked to increased activation in fronto-parietal regions, as well as to stronger deactivation in ventromedial prefrontal regions. Respiration frequency during task was associated with similar patterns, but in different regions than those identified for heart-rate. Finally, in a large set of regions, almost exclusively limited to the Default Mode Network, lower RMSSD was associated with greater deactivation, and furthermore, the vast majority of these regions were task-deactivated at the group level. Together, our findings show that inter-individual differences in ANS activity are strongly linked to task-induced deactivation. Importantly, our findings suggest that deactivation is a multifaceted construct potentially linked to ANS control, because distinct ANS measures correlate with deactivation in different regions. We discuss the implications for current theories of cortical control of the ANS and for accounts of deactivation, with particular reference to studies documenting a "failure to deactivate" in multiple clinical states.

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. In addition, we provide participants' eye gaze location as recorded simultaneously with fMRI, and an additional sample of 15 control participants whose eye gaze trajectories for the entire movie were recorded in a lab setting-to enable studies on attentional processes and comparative investigations on the potential impact of the stimulation setting on these processes.

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.g., as a template for generalization and rapid comparisons between environments. Using functional neuroimaging, we demonstrate that, in response to passively processed auditory inputs, functional integration patterns in the human brain track both the entropy and complexity of the auditory signal. Connectivity between several brain regions scaled monotonically with input entropy, suggesting sensitivity to uncertainty, whereas connectivity between other regions tracked entropy in a convex manner consistent with sensitivity to input complexity. These findings suggest that the human brain simultaneously tracks the uncertainty of sensory data and effectively models their environmental generators.

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. Participants were presented with rapid (3.3 Hz) auditory and visual series varying over four levels of entropy, while monitoring an infrequently changing fixation cross. For visual series, sensitivity to the magnitude of disorder was found in early visual cortex, the anterior cingulate, and the intraparietal sulcus. For auditory series, sensitivity was found in inferior frontal, lateral temporal, and supplementary motor regions implicated in speech perception and sequencing. Ventral premotor and central cingulate cortices were identified as possible candidates for modality-general uncertainty processing, exhibiting marginal sensitivity to disorder in both modalities. The right temporal pole differentiated the highest and lowest levels of disorder in both modalities, but did not show general sensitivity to the parametric manipulation of disorder. Our results indicate that neural sensitivity to input disorder relies largely on modality-specific systems embedded in extended sensory cortices, though uncertainty-related processing in frontal regions may be driven by both input modalities.

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. The metric that we develop quantifies the relative variance of the amplitude of local-maxima and local-minima in a BOLD time course (amplitude variance asymmetry; AVA). This metric reveals new properties of RSA activity, without relying on connectivity as a descriptive tool. We applied the AVA analysis to data from 3 different participant groups (2 adults, 1 child) collected from 3 different centers. The analyses show that AVA patterns a) identify 3 types of RSA profiles in adults' sensory systems b) differ in topology and pattern of dynamics in adults and children, and c) are stable across magnetic resonance scanners. Furthermore, children with higher IQ demonstrated more adult-like AVA patterns. These findings indicate that AVA reflects important and novel dimensions of brain development and RSA.

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. We predicted that changes in regularity would be coded for in systems previously associated with statistical coding (hippocampus and middle frontal regions) or event segmentation (posterior medial regions). Participants listened to a rapid train of four different tones whose order levels fluctuated over time. In an active task, participants monitored the tones and indicated when they perceived a change in regularity; in a passive task, they performed a concurrent visuo-motor task and could ignore the auditory input. Behavioral responses in the active task were used to define points of consensus between participants regarding changes in regularity. Activity in 7.5s epochs that preceded these order-change points was contrasted with activity during matched-length epochs where no participant indicated a change in order. We found that brain regions differentiating these two types of epochs matched those identified in prior research as mediating event segmentation in narratives and movies. These consisted mainly of medial posterior parietal and occipital regions, with limited involvement of temporal and lateral frontal cortices and no hippocampal involvement. In both tasks, order-change epochs were associated with a higher BOLD response than stable-order epochs, but the specific regions showing this pattern varied across tasks. We suggest that partitioning an input stream on the basis of statistical shifts constitutes a basic neural function underlying the ability to segment both semantic and non-semantic inputs. We further discuss the implications of these findings for neurobiological theories of statistical coding and event segmentation.

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. Here, we examined if sensitivity to order is manifested in both linear and non-linear BOLD response profiles, quantified the degree to which order-sensitive regions operate as a functional network, and evaluated these questions using a paradigm in which performance of the ongoing task could be completed without any attention to the stimulus whose order was manipulated. Participants listened to a 10-minute sequence of tones characterized by non-stationary order, and fMRI identified cortical regions sensitive to time-varying statistical features of this input. Activity in perisylvian regions was negatively correlated with input diversity, quantified via Shannon's Entropy. Activity in ventral premotor, lateral temporal, and insular regions was correlated linearly, parabolically, or via a step-function with the strength of transition constraints in the series, quantified via Markov Entropy. Granger-causality analysis revealed that order-sensitive regions form a functional network, with regions showing non-linear responses to order associated with more afferent connectivity than those showing linear responses. These findings identify networks that spontaneously code and respond to diverse aspects of order via multiple response profiles, and that play a central role in generating and gating predictive neural activity.

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. In this article, we describe two conceptual approaches to the treatment of ANS measures in the context of BOLD fMRI analysis. On the one hand, several research lines have treated ANS activity measures as noise, considering them as nothing but a confounding factor that reduces the power of fMRI analysis or its validity. Work in this line has developed powerful methods to remove ANS effects from the BOLD signal. On the other hand, a different line of work has made important progress in showing that ANS functions such as cardiac pulsation, heart rate variability and breathing rate could be considered as a theoretically meaningful component of the signal that is useful for understanding brain function. Work within this latter framework suggests that caution should be exercised when employing procedures to remove correlations between BOLD data and physiological measures. We discuss these two positions and the reasoning underlying them. Thereafter, we draw on the reviewed literature in presenting practical guidelines for treatment of ANS data, which are based on the premise that ANS data should be considered as theoretically meaningful information. This holds particularly when studying cortical systems involved in regulation, monitoring and/or generation of ANS activity, such as those involved in decision making, conflict resolution and the experience of emotion.

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. Several approaches have been proposed in the literature, generally based on the use of physiological recordings acquired during the functional scans, or on the extraction of the relevant information directly from the images. In this paper, the performances of the denoising approach based on general linear fitting of global signals of noninterest extracted from the functional scans were assessed. Results suggested that this approach is sufficiently accurate for the preprocessing of functional connectivity data.