Most scientists need software to perform their research (Barker et al., 2020 ; Carver et al., 2022 ; Hettrick, 2014 ; Hettrick et al., 2014 ; Switters & Osimo, 2019), and neuroscientists are no exception. Whether we work with reaction times, electrophysiological signals, or magnetic resonance imaging data, we rely on software to acquire, analyze, and statistically evaluate the raw data we obtain—or to generate such data if we work with simulations. In recent years, there has been a shift toward relying on free, open-source scientific software (FOSSS) for neuroscience data analysis (Poldrack et al., 2019), in line with the broader open science movement in academia (McKiernan et al., 2016) and wider industry trends (Eghbal, 2016). Importantly, FOSSS is typically developed by working scientists (not professional software developers), which sets up a precarious situation given the nature of the typical academic workplace wherein academics, especially in their early careers, are on short- and fixed-term contracts. In this paper, we argue that the existing ecosystem of neuroscientific open-source software is brittle, and discuss why and how the neuroscience community needs to come together to ensure a healthy software ecosystem to the benefit of all.

Electroencephalography (EEG) is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications, including brain-computer interfaces, epilepsy monitoring, and sleep staging. Advances in sensor technology have freed EEG from traditional laboratory settings, making low-cost ambulatory or at-home assessments of brain function possible. While ecologically valid brain assessments are becoming more practical, the impact of their reduced spatial resolution and susceptibility to noise remain to be investigated. This study set out to explore the potential of at-home EEG assessments for biomarker discovery using the brain age framework and four-channel consumer EEG data. We analyzed recordings from more than 5200 human subjects (18–81 years) during meditation and sleep, to predict age at the time of recording. With cross-validated R 2 scores between 0.3 - 0.5 , prediction performance was within the range of results obtained by recent benchmarks focused on laboratory-grade EEG. While age prediction was successful from both meditation and sleep recordings, the latter led to higher performance. Analysis by sleep stage uncovered that N2-N3 stages contained most of the signal. When combined, EEG features extracted from all sleep stages gave the best performance, suggesting that the entire night of sleep contains valuable age-related information. Furthermore, model comparisons suggested that information was spread out across electrodes and frequencies, supporting the use of multivariate modeling approaches. Thanks to our unique dataset of longitudinal repeat sessions spanning 153 to 529 days from eight subjects, we finally evaluated the variability of EEG-based age predictions, showing that they reflect both trait- and state-like information. Overall, our results demonstrate that state-of-the-art machine-learning approaches based on age prediction can be readily applied to real-world EEG recordings obtained during at-home sleep and meditation practice.

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Neuroscience studies face challenges in gathering large datasets, which limits the use of machine learning (ML) approaches. One possible solution is to incorporate additional data from large public datasets; however, data collected in different contexts often exhibit systematic differences called dataset shifts. Various factors, for example, site, device type, experimental protocol, or social characteristics, can lead to substantial divergence of brain signals that can hinder the success of ML across datasets. In this work, we focus on dataset shifts in recordings of brain activity using MEG and EEG. State-of-the-art predictive approaches on magneto- and electroencephalography (M/EEG) signals classically represent the data by covariance matrices. Model-based dataset alignment methods can leverage the geometry of covariance matrices, leading to three steps: re-centering, re-scaling, and rotation correction. This work explains theoretically how differences in brain activity, anatomy, or device configuration lead to certain shifts in data covariances. Using controlled simulations, the different alignment methods are evaluated. Their practical relevance is evaluated for brain age prediction on one MEG dataset (Cam-CAN, n = 646) and two EEG datasets (TUAB, n = 1385; LEMON, n = 213). Among the same dataset (Cam-CAN), when training and test recordings were from the same subjects but performing different tasks, paired rotation correction was essential ( δ R 2 = + 0.13 (rest-passive) or + 0.17 (rest-smt)). When in addition to different tasks we included unseen subjects, re-centering led to improved performance ( δ R 2 = + 0.096 for rest-passive, δ R 2 = + 0.045 for rest-smt). For generalization to an independent dataset sampled from a different population and recorded with a different device, re-centering was necessary to achieve brain age prediction performance close to within dataset prediction performance. This study demonstrates that the generalization of M/EEG-based regression models across datasets can be substantially enhanced by applying domain adaptation procedures that can statistically harmonize diverse datasets.

In this editorial we introduce a new non-profit open access journal, Imaging Neuroscience . In April 2023, editors of the journals NeuroImage and NeuroImage:Reports resigned, and a month later launched Imaging Neuroscience . NeuroImage had long been the leading journal in the field of neuroimaging. While the move to fully open access in 2020 represented a positive step toward modern academic practices, the publication fee was set to a level that the editors found unethical and unsustainable. The publisher of NeuroImage , Elsevier, was unwilling to reduce the fee after much discussion. This led us to launch Imaging Neuroscience with MIT Press, intended to replace NeuroImage as our field’s leading journal, but with greater control by the neuroimaging academic community over publication fees and adoption of modern and ethical publishing practices.