Remi Gau

The occipital cortex of people born blind massively enhances its response to sounds, but the brain circuitry supporting such crossmodal plasticity remains elusive. Here we capitalized on ultra-high-field fMRI (7T) coupled with sub-millimetre BOLD and VASO acquisitions to infer whether motion-related information is processed via feedforward or feedback pathways, probing responses to visual motion in sighted participants and auditory motion in both sighted and early blind individuals. By identifying circuitry from layer-dependent activity of the middle temporal cortex (hMT+/V5), we observed that moving, but not static, sounds selectively elicited a feedforward response in the middle layers of hMT+/V5 of blind people, analogous to visual motion processing in sighted individuals. Furthermore, we observed that hMT+/V5 shows enhanced auditory motion-selective connectivity with the Planum Temporale in the sighted and with the cuneus in blind people. These findings reveal that hMT+/V5 implements a feedforward motion selective response profile in sighted and blind individuals, with its sensory input shifting from vision to audition in the absence of visual experience.

Electromyography (EMG) is fundamental to clinical assessment, rehabilitation, neuromuscular research, and human-machine interfaces. Despite decades of use, no widely adopted standard exists for organizing and sharing EMG data, limiting reusability and large-scale data aggregation. We present EMG-BIDS, an extension to the Brain Imaging Data Structure (BIDS) that standardizes the organization of EMG recordings. EMG-BIDS addresses challenges unique to EMG, including diverse electrode types (surface or intramuscular, single channel to high-density arrays), heterogeneous electrode placements across anatomical locations, montages (e.g., monopolar or bipolar sensor designs), and the critical need for transparent documentation of sensor positioning. The specification introduces hierarchical coordinate systems that link local electrode grids to anatomical landmarks, enabling precise and reproducible placement documentation. EMG-BIDS is now part of BIDS as of version 1.11.0, supported by existing tools, including MNE-BIDS and EEGLAB. We demonstrate the specification through public datasets, including high-density surface EMG recordings. EMG-BIDS provides the foundation for FAIR (Findable, Accessible, Interoperable, Reusable) EMG data sharing, enabling meta-analyses, multi-site studies, and machine learning applications that require standardized, well-documented datasets.

Neuroimaging data management and processing are tedious and error-prone, prompting reproducibility concerns. Globally, studies with heterogeneous infrastructure and governance policies lead to eclectic data processing and sharing, necessitating standardization of data workflows to ensure reusability and comparability of multi-centric datasets. The Nipoppy neuroinformatics framework facilitates such standardization by combining specification, protocol, and software to manage study-level data workflows. With its adoption, researchers can share standardized, derived datasets enabling efficient, reproducible, and inclusive research.

This preregistration template guides researchers who wish to preregister their EEG projects, more specifically studies investigating event-related potentials (ERPs) in the sensor space.

This preregistration template guides researchers who wish to preregister their EEG projects, more specifically studies investigating event-related potentials (ERPs) in the sensor space.

Recognizing emotion expressions from facial and vocal signals is crucial for optimal social interactions. We comprehensively characterized regions within the face and voice processing networks to probe their contribution to unimodal and crossmodal emotion representation. Using multivariate pattern classification analyses, we found that emotional expressions could be reliably decoded from the dominant sensory modality in all individually defined face- and voice-selective regions. Emotion expressions from the non-dominant modality (vocal expressions in the face networks, and vice versa) could also be decoded in all areas except the occipital face areas. A shared neural code for facial and vocal emotions is implemented in the temporal voice areas (TVA), the posterior superior temporal sulcus (pSTS) and the precentral gyrus (PCG). The simultaneous presentation of congruent facial and vocal expressions elicited distinct activity patterns across most regions, highlighting that multisensory inputs reshape brain responses relative to unisensory stimulation across the entire face and voice brain network. These findings suggest that face and voice-selective regions broadly encode emotion expressions within and even across the senses, relying on a multisensory gradient that converges in temporo-frontal regions where their distributed multisensory responses align to create a supramodal representation of specific emotion expressions.

The choices we make during the recording, preprocessing and analysis of event‐related potentials (ERP) data can affect study outcomes. As such, it is critical that they are transparently reported to allow for reproducibility. Yet, systematic reviews of reporting practices in the field have shown that journal articles often do not meet this goal and that existing reporting guidelines have not resulted in a sufficient improvement in reporting transparency. An easier workflow for transparently documenting pipelines used in regular journal articles is needed. The ARTEM‐IS (Agreed Reporting Template for EEG Methodology—International Standard) initiative is working towards addressing this issue by building dynamic, interactive web applications that support documenting information required by existing publication guidelines in the form of a standardized metadata template. Completing an ARTEM‐IS form results in a human‐reader‐friendly PDF or DOCX and a machine‐readable JSON summary of methodological information. This level of specificity surpasses conventional article methods sections, ensuring fewer omissions and ambiguities. These can be used as supplements to a publication, as a memory aid when writing a paper, or as records that allow easier metadata extraction. Here, we present the ARTEM‐IS for ERP, which supports describing a typical ERP study, including most of its core methodological aspects (study description, experimental design, hardware, data acquisition, pre‐processing, measurement, visualization, additional comments). We discuss the current functionalities of ARTEM‐IS for ERP, its development via a grassroots collaborative initiative, and potential extensions (e.g., including complex designs or statistical analyses). In doing so, we highlight how widespread adoption of ARTEM‐IS can benefit researchers, reviewers, and the broader scientific community by improving transparency, reducing reporting errors, and expediting rigorous replication efforts.

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.

The choices we make during the recording, preprocessing and analysis of event-related potentials (ERP) data can affect study outcomes. As such, it is critical that they are transparently reported to allow for reproducibility. Yet, systematic reviews of reporting practices in the field have shown that journal articles often do not meet this goal and that existing reporting guidelines have not resulted in a sufficient improvement to reporting transparency. An easier workflow for transparently documenting pipelines used in regular journal articles is needed. The ARTEM-IS (Agreed Reporting Template for EEG Methodology - International Standard) initiative is working towards addressing this issue by building dynamic, interactive web applications that support documenting information required by existing publication guidelines in the form of a standardised metadata template. Completing an ARTEM-IS form results in a human-reader-friendly PDF or DOCX and a machine-readable JSON summary of methodological information. This level of specificity surpasses conventional article methods sections, ensuring fewer omissions and ambiguities. These can be used as supplements to a publication, as a memory aid when writing a paper, or as records that allow easier metadata extraction. Here, we present the ARTEM-IS for ERP, which supports describing a typical ERP study, including most of its core methodological aspects (study description, experimental design, hardware, data acquisition, pre-processing, measurement, visualisation, additional comments). We discuss the current functionalities of ARTEM-IS for ERP, its development via a grassroots collaborative initiative, and potential extensions (e.g., including complex designs or statistical analyses). In doing so, we highlight how widespread adoption of ARTEM-IS can benefit researchers, reviewers, and the broader scientific community by improving transparency, reducing reporting errors, and expediting rigorous replication efforts.

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.