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The brain generates a representation of our environment by integrating signals from a common source, but segregating signals from different sources. This fMRI study investigated how the brain arbitrates between perceptual integration and segregation based on top-down congruency expectations and bottom-up stimulus-bound congruency cues. Participants were presented audiovisual movies of phonologically congruent, incongruent or McGurk syllables that can be integrated into an illusory percept (e.g. “ti” percept for visual «ki» with auditory /pi/). They reported the syllable they perceived. Critically, we manipulated participants' top-down congruency expectations by presenting McGurk stimuli embedded in blocks of congruent or incongruent syllables. Behaviorally, participants were more likely to fuse audiovisual signals into an illusory McGurk percept in congruent than incongruent contexts. At the neural level, the left inferior frontal sulcus (lIFS) showed increased activations for bottom-up incongruent relative to congruent inputs. Moreover, lIFS activations were increased for physically identical McGurk stimuli, when participants segregated the audiovisual signals and reported their auditory percept. Critically, this activation increase for perceptual segregation was amplified when participants expected audiovisually incongruent signals based on prior sensory experience. Collectively, our results demonstrate that the lIFS combines top-down prior (in)congruency expectations with bottom-up (in)congruency cues to arbitrate between multisensory integration and segregation.

•There is a rising interest in openness and reproducibility in neuroimaging.•Despite its clear benefits, adoption is slow and information is scattered.•We provide an integrated review of open resources covering the full research lifecycle.•Consortium of 20+ experts across neuroimaging modalities (MRI, PET, MEG, EEG).•List of ca. 300 resources supporting openness and reproducibility in neuroimaging. Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

In our environment, our senses are bombarded with a myriad of signals, only a subset of which is relevant for our goals. Using sub-millimeter-resolution fMRI at 7T, we resolved BOLD-response and activation patterns across cortical depth in early sensory cortices to auditory, visual and audiovisual stimuli under auditory or visual attention. In visual cortices, auditory stimulation induced widespread inhibition irrespective of attention, whereas auditory relative to visual attention suppressed mainly central visual field representations. In auditory cortices, visual stimulation suppressed activations, but amplified responses to concurrent auditory stimuli, in a patchy topography. Critically, multisensory interactions in auditory cortices were stronger in deeper laminae, while attentional influences were greatest at the surface. These distinct depth-dependent profiles suggest that multisensory and attentional mechanisms regulate sensory processing via partly distinct circuitries. Our findings are crucial for understanding how the brain regulates information flow across senses to interact with our complex multisensory world.

How does sensory experience shape the development of the visual brain? To answer this eluding question, we examine brain responses to visual categories in a rare group of cataract-reversal individuals who experienced a short transient period of early blindness. Encoding of low-level visual properties is impaired in the early visual cortex (EVC) of cataract-reversal participants, whereas categorical responses in downstream ventral occipito-temporal cortex (VOTC) are preserved. In controls, degrading visual input to mimic the visual deficits of cataracts produces cascading disruptions extending from EVC to VOTC, unlike in the cataract group. A deep neural network trained on altered visual input reproduces this dissociation, supporting the brain findings. These results demonstrate that while EVC is permanently affected by early deprivation, categorical coding in VOTC shows resilience, highlighting different sensitive periods for specific brain regions and computations. This study shows that transient blindness at birth leaves lasting effects on early visual functions, while higher visual regions encoding categories remain unaffected, revealing different sensitive periods for different functions in vision.

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets, serving not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data, also known as PET-BIDS, and share several open-access datasets curated following PET-BIDS along with tools for conversion, validation and analysis of PET-BIDS datasets.

Inconsistencies in survey-based (eg, questionnaire) data collection across biomedical, clinical, behavioral, and social sciences pose challenges to research reproducibility. ReproSchema is an ecosystem that standardizes survey design and facilitates reproducible data collection through a schema-centric framework, a library of reusable assessments, and computational tools for validation and conversion. Unlike conventional survey platforms that primarily offer graphical user interface-based survey creation, ReproSchema provides a structured, modular approach for defining and managing survey components, enabling interoperability and adaptability across diverse research settings. This study examines ReproSchema's role in enhancing research reproducibility and reliability. We introduce its conceptual and practical foundations, compare it against 12 platforms to assess its effectiveness in addressing inconsistencies in data collection, and demonstrate its application through 3 use cases: standardizing required mental health survey common data elements, tracking changes in longitudinal data collection, and creating interactive checklists for neuroimaging research. We describe ReproSchema's core components, including its schema-based design; reusable assessment library with >90 assessments; and tools to validate data, convert survey formats (eg, REDCap [Research Electronic Data Capture] and Fast Healthcare Interoperability Resources), and build protocols. We compared 12 platforms-Center for Expanded Data Annotation and Retrieval, formr, KoboToolbox, Longitudinal Online Research and Imaging System, MindLogger, OpenClinica, Pavlovia, PsyToolkit, Qualtrics, REDCap, SurveyCTO, and SurveyMonkey-against 14 findability, accessibility, interoperability, and reusability (FAIR) principles and assessed their support of 8 survey functionalities (eg, multilingual support and automated scoring). Finally, we applied ReproSchema to 3 use cases-NIMH-Minimal, the Adolescent Brain Cognitive Development and HEALthy Brain and Child Development Studies, and the Committee on Best Practices in Data Analysis and Sharing Checklist-to illustrate ReproSchema's versatility. ReproSchema provides a structured framework for standardizing survey-based data collection while ensuring compatibility with existing survey tools. Our comparison results showed that ReproSchema met 14 of 14 FAIR criteria and supported 6 of 8 key survey functionalities: provision of standardized assessments, multilingual support, multimedia integration, data validation, advanced branching logic, and automated scoring. Three use cases illustrating ReproSchema's flexibility include standardizing essential mental health assessments (NIMH-Minimal), systematically tracking changes in longitudinal studies (Adolescent Brain Cognitive Development and HEALthy Brain and Child Development), and converting a 71-page neuroimaging best practices guide into an interactive checklist (Committee on Best Practices in Data Analysis and Sharing). ReproSchema enhances reproducibility by structuring survey-based data collection through a structured, schema-driven approach. It integrates version control, manages metadata, and ensures interoperability, maintaining consistency across studies and compatibility with common survey tools. Planned developments, including ontology mappings and semantic search, will broaden its use, supporting transparent, scalable, and reproducible research across disciplines.

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular neuroimaging technique that measures cortical hemodynamic activity in a non-invasive and portable fashion. Although the fNIRS community has been successful in disseminating open-source processing tools and a standard file format (SNIRF), reproducible research and sharing of fNIRS data amongst researchers has been hindered by a lack of standards and clarity over how study data should be organized and stored. This problem is not new in neuroimaging, and it became evident years ago with the proliferation of publicly available neuroimaging datasets. To solve this critical issue, the neuroimaging community created the Brain Imaging Data Structure (BIDS) that specifies standards for how datasets should be organized to facilitate sharing and reproducibility of science. Currently, BIDS supports dozens of neuroimaging modalities including MRI, EEG, MEG, PET, and many others. In this paper, we present the extension of BIDS for NIRS data alongside tools that may assist researchers in organizing existing and new data with the goal of promoting public disseminations of fNIRS datasets.

We present an extension to the Brain Imaging Data Structure (BIDS) for motion data. Motion data is frequently recorded alongside human brain imaging and electrophysiological data. The goal of Motion-BIDS is to make motion data interoperable across different laboratories and with other data modalities in human brain and behavioral research. To this end, Motion-BIDS standardizes the data format and metadata structure. It describes how to document experimental details, considering the diversity of hardware and software systems for motion data. This promotes findable, accessible, interoperable, and reusable data sharing and Open Science in human motion research.

Parkinson’s disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to cohort selection or image analysis. In this context, an evaluation of the robustness of such biomarkers to variations in the data processing workflow is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to fully reproduce (reimplementing the experiments with the same methods, including data collection from the same database) and replicate (different data and/or method) the models described in (Nguyen et al., 2021) to predict individual’s PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson’s Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in (Nguyen et al., 2021) and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. Different criteria were used to evaluate the reproduction attempt and compare the results with the original ones. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study ( R 2 > 0), which is consistent with its findings. In addition, we performed a partial reproduction using derived data provided by the authors of the original study, and we obtained results that were close to the original ones. The challenges encountered while attempting to reproduce (fully and partially) and replicating the original work are likely explained by the complexity of neuroimaging studies, in particular in clinical settings. We provide recommendations to further facilitate the reproducibility of such studies in the future.

The Brain Imaging Data Structure (BIDS) is an increasingly adopted standard for organizing scientific data and metadata. It facilitates easier and more straightforward data sharing and reuse. BIDS currently encompasses several biomedical imaging and non-imaging techniques, and as more research groups begin to use it, additional experimental techniques are being incorporated into the standard, allowing diverse experimental methods to be stored within the same cohesive structure. Here, we present an extension for magnetic resonance spectroscopy (MRS) data, termed MRS-BIDS.