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.

Dynamic threat-reward neural processing under semi-naturalistic ecologically relevant scenarios.

Studies of affective neuroscience have typically employed highly controlled, static experimental paradigms to investigate the neural underpinnings of threat and reward processing in the brain. Yet our knowledge of affective processing in more naturalistic settings remains limited. Specifically, affective studies generally examine threat and reward features separately and under brief time periods, despite the fact that in nature organisms are often exposed to the simultaneous presence of threat and reward features for extended periods. To study the neural mechanisms of threat and reward processing under distinct temporal profiles, we created a modified version of the PACMAN game that included these environmental features. We also conducted two automated meta-analyses to compare the findings from our semi-naturalistic paradigm to those from more constrained experiments. Overall, our results revealed a distributed system of regions sensitive to threat imminence and a less distributed system related to reward imminence, both of which exhibited overlap yet neither of which involved the amygdala. Additionally, these systems broadly overlapped with corresponding meta-analyses, with the notable absence of the amygdala in our findings. Together, these findings suggest a shared system for salience processing that reveals a heightened sensitivity toward environmental threats compared to rewards when both are simultaneously present in an environment. The broad correspondence of our findings to meta-analyses, consisting of more tightly controlled paradigms, illustrates how semi-naturalistic studies can corroborate previous findings in the literature while also potentially uncovering novel mechanisms resulting from the nuances and contexts that manifest in such dynamic environments.

ezBIDS: Guided standardization of neuroimaging data interoperable with major data archives and platforms.

Data standardization promotes a common framework through which researchers can utilize others' data and is one of the leading methods neuroimaging researchers use to share and replicate findings. As of today, standardizing datasets requires technical expertise such as coding and knowledge of file formats. We present ezBIDS, a tool for converting neuroimaging data and associated metadata to the Brain Imaging Data Structure (BIDS) standard. ezBIDS contains four major features: (1) No installation or programming requirements. (2) Handling of both imaging and task events data and metadata. (3) Semi-automated inference and guidance for adherence to BIDS. (4) Multiple data management options: download BIDS data to local system, or transfer to OpenNeuro.org or to brainlife.io. In sum, ezBIDS requires neither coding proficiency nor knowledge of BIDS, and is the first BIDS tool to offer guided standardization, support for task events conversion, and interoperability with OpenNeuro.org and brainlife.io.

Impact of aversive affect on neural mechanisms of categorization decisions.

INTRODUCTION: Many theories contend that evidence accumulation is a critical component of decision-making. Cognitive accumulation models typically interpret two main parameters: a drift rate and decision threshold. The former is the rate of accumulation, based on the quality of evidence, and the latter is the amount of evidence required for a decision. Some studies have found neural signals that mimic evidence accumulators and can be described by the two parameters. However, few studies have related these neural parameters to experimental manipulations of sensory data or memory representations. Here, we investigated the influence of affective salience on neural accumulation parameters. High affective salience has been repeatedly shown to influence decision-making, yet its effect on neural evidence accumulation has been unexamined. METHODS: The current study used a two-choice object categorization task of body images (feet or hands). Half the images in each category were high in affective salience because they contained highly aversive features (gore and mutilation). To study such quick categorization decisions with a relatively slow technique like functional magnetic resonance imaging, we used a gradual reveal paradigm to lengthen cognitive processing time through the gradual "unmasking" of stimuli. RESULTS: Because the aversive features were task-irrelevant, high affective salience produced a distractor effect, slowing decision time. In visual accumulation regions of interest, high affective salience produced a longer time to peak activation. Unexpectedly, the later peak appeared to be the product of changes to both drift rate and decision threshold. The drift rate for high affective salience was shallower, and the decision threshold was greater. To our knowledge, this is the first demonstration of an experimental manipulation of sensory data or memory representations that changed the neural decision threshold. CONCLUSION: These findings advance our knowledge of the neural mechanisms underlying affective responses in general and the influence of high affective salience on object representations and categorization decisions.

A labeled Clinical-MRI dataset of Nigerian brains.

We describe a Magnetic Resonance Imaging (MRI) dataset from individuals from the African nation of Nigeria. The dataset contains pseudonymized structural MRI (T1w, T2w, FLAIR) data of clinical quality. Dataset contains data from 36 images from healthy control subjects, 32 images from individuals diagnosed with age-related dementia and 20 from individuals with Parkinson's disease. There is currently a paucity of data from the African continent. Given the potential for Africa to contribute to the global neuroscience community, this first MRI dataset represents both an opportunity and benchmark for future studies to share data from the African continent.

Neural segregation and integration of sensory, decision, and action processes during object categorization.

Neural and computational evidence suggests that perceptual decisions depend on an evidence accumulation process. The gradual reveal fMRI method, which prolongs a decision to match the slow temporal resolution of fMRI measurements, has classified dorsal visual stream regions as "Action" (alternatively, "Moment of Recognition" or "Commitment") and ventral visual stream regions as "Accumulator." Previous gradual reveal fMRI studies, however, only tested actions that were in response to decisions and, thus, related to evidence accumulation. To fully dissociate the contribution of sensory, decision, and motor components to Action and Accumulator regions in the dorsal and ventral visual streams, we extended the gradual reveal paradigm to also include responses made to cues where no decision was necessary. We found that the lateral occipital cortex in the ventral visual stream showed a highly selective Accumulator profile, whereas regions in the fusiform gyrus were influenced by action generation. Dorsal visual stream regions showed strikingly similar profiles as classical motor regions and also as regions of the salience network. These results suggest that the dorsal and ventral visual streams may appear highly segregated because they include a small number of regions that are highly selective for Accumulator or Action. However, the streams may be more integrated than previously thought and this integration may be accomplished by regions with graded responses that are less selective (i.e., more distributed).

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.

Social interaction recruits mentalizing and reward systems in middle childhood.

Social cognition develops in the context of reciprocal social interaction. However, most neuroimaging studies of mentalizing have used noninteractive tasks that may fail to capture important aspects of real-world mentalizing. In adults, social-interactive context modulates activity in regions linked to social cognition and reward, but few interactive studies have been done with children. The current fMRI study examines children aged 8-12 using a novel paradigm in which children believed they were interacting online with a peer. We compared mental and non-mental state reasoning about a live partner (Peer) versus a story character (Character), testing the effects of mentalizing and social interaction in a 2 × 2 design. Mental versus Non-Mental reasoning engaged regions identified in prior mentalizing studies, including the temporoparietal junction, superior temporal sulcus, and dorsomedial prefrontal cortex. Moreover, peer interaction, even in conditions without explicit mentalizing demands, activated many of the same mentalizing regions. Peer interaction also activated areas outside the traditional mentalizing network, including the reward system. Our results demonstrate that social interaction engages multiple neural systems during middle childhood and contribute further evidence that social-interactive paradigms are needed to fully capture how the brain supports social processing in the real world.