Changes in science practices are often perceived to be slow.It took about 10 years from the Collins and Tabak editorial on scientific reproducibility in 2014 [1] to see data management mandates implemented by US funding agencies [2].However, open science practices have seen a sharp increase in adoption over the last few years, supported by policy (for example, those by the European Commission or the 2022 White House Office of Science and Technology Policy (OSTP) memoAU : Pleasecheckandconfirmthattheplacementofclosingparenthesisinthesenten ) [3,4] as well as new generations of digital tools and scientists who are embedding open values in their research practices.In this faster-paced open science environment, universities are key to fostering adoption among researchers.Universities drive implementation by advancing best practices and accounting for the needs and norms of diverse departments and disciplines.Universities are positioned to catalyze adoption of open practices through their academic evaluation processes, particularly, recruitment, tenure, and promotion.The capacity of researchers and instructors to engage with data and software scholarship will shape the next generation of students and scientists, and universities will play a crucial role in nurturing those skills by rewarding such contributions and expertise among their faculty.The ways in which promotion and tenure committees operate vary significantly across universities and departments.While committees often have the capability to evaluate the rigor and quality of articles and monographs in their scientific field, assessment with respect to practices concerning research data and software is a recent development and one that can be harder to implement, as there are few guidelines to facilitate the process.More specifically, the guidelines given to tenure and promotion committees often reference data and software in general terms, with some notable exceptions such as guidelines in [5] and are almost systematically trumped by other factors such as the number and perceived impact of journal publications.The core issue is that many colleges establish a scholarship versus service dichotomy: Peer-reviewed articles or monographs published by university presses are considered

The Canadian Open Neuroscience Platform (CONP) takes a multifaceted approach to enabling open neuroscience, aiming to make research, data, and tools accessible to everyone, with the ultimate objective of accelerating discovery. Its core infrastructure is the CONP Portal, a repository with a decentralized design, where datasets and analysis tools across disparate platforms can be browsed, searched, accessed, and shared in accordance with FAIR principles. Another key piece of CONP infrastructure is NeuroLibre, a preprint server capable of creating and hosting executable and fully reproducible scientific publications that embed text, figures, and code. As part of its holistic approach, the CONP has also constructed frameworks and guidance for ethics and data governance, provided support and developed resources to help train the next generation of neuroscientists, and has fostered and grown an engaged community through outreach and communications. In this manuscript, we provide a high-level overview of this multipronged platform and its vision of lowering the barriers to the practice of open neuroscience and yielding the associated benefits for both individual researchers and the wider community.

Neuroimaging study results can vary significantly depending on the datasets and processing pipelines utilized by researchers to run their analyses, contributing to reproducibility issues. These issues are compounded by the fact that there are a large variety of seemingly equivalent tools and methodologies available to researchers for processing neuroimaging data. Here we present NeuroCI, a novel software framework that allows users to evaluate the variability of their results across multiple pipelines and datasets. NeuroCI makes use of Continuous Integration (CI), a software engineering technique, to facilitate the reproducibility of computational experiments by launching a series of automated tests when code or data is added to a repository. However, unlike regular CI services, our CI-based framework uses distributed computation and storage to meet the large memory and storage requirements of neuroimaging pipelines and datasets. Moreover, the framework's modular design enables it to continuously ingest pipelines and datasets provided by the user, and to compute and visualize results across multiple different pipelines and datasets. This allows researchers and practitioners to quantify the variability and reliability of results in their domain across a large range of computational methods.

Moving beyond static text and illustrations is a central challenge for scientific publishing in the 21st century.As early as 1995, Donoho and Buckheit paraphrased John Claerbout that "an article about [a] computational result is advertising, not scholarship.The actual scholarship is the full software environment, code and data, that produced the result" [1].Awareness of this problem has only grown over the last 25 years; nonetheless, scientific publishing infrastructures remain remarkably resistant to change [2].Even as these infrastructures have largely stagnated, the internet has ushered in a transition "from the wet lab to the web lab" [3].New expectations have emerged in this shift, but these expectations must play against the reality of currently available infrastructures and associated sociological pressures.Here, we compare current scientific publishing norms against those associated with online content more broadly, and we argue that meeting the "Claerbout challenge" of providing the full software environment, code, and data supporting a scientific result will require open infrastructure development to create environments for authoring, reviewing, and accessing interactive research objects.

Over the past year, I (M.B.) have been investigating the landscape of code-sharing in academic journals across different research fields.At the end of my PhD, I made the choice to share code that reproduces figures from one of my papers [1], and since then, I've been involved in several open-source projects (qMRLab and AxonDeepSeg) and initiatives dealing with open science in publishing (NeuroLibre and Canadian Open Neuroscience Platform).Recently, following an editorial by N.S. on reproducibility and the future of MRI research [2], we wrote a blog post presenting an analysis of the open-source landscape for the journal Magnetic Resonance in Medicine (MRM), which broadly focuses on MRI research for medical applications.These findings provided a snapshot of the current state of the open-source landscape for that journal (e.g., most used coding language is still MATLAB) and some insights into new trends (12% of the articles shared code that reproduced figures).In this editorial, we examine the open-source landscape of PLOS Computational Biology.PLOS Computational Biology is inherently different from MRM not only because of the difference in research topics, but also because it's an openaccess journal that focuses primarily on computational studies.The broad questions that were of interest are the following:• What percentage of PLOS Computational Biology publications claim to share code?• If they share code, what coding languages do they use?• Where do the authors typically host their code?• How many publications share scripts that reproduce some or all of the figures from their paper? Details of analysisTo perform this analysis, all the articles published in PLOS Computational Biology from January to December 2019 were downloaded.A script was then executed to search for all the articles that contained one of a list of keywords that may hint at containing code/data.Following that, all the articles that matched keywords were compiled into a Google Sheet file and manually searched inside each of those articles to determine if the code they used was actually shared.The external links in the articles were then examined to see if they (1) shared code; (2) see which languages the code used;(3) where they hosted their code; and (4) if the code aimed to reproduce any of the figures.See Table 1 for an overview of results.Overall, 41% of the articles published in PLOS Computational Biology reported sharing some code.It is possible that the rate is even slightly higher, as some articles that reported

The cerebral cortex is connected to sub-cortical structures through multiple hierarchically organized descending and ascending pathways. Cortical representations of subcortical neural activity reflect embedded complex spatiotemporal dynamics. Past studies extensively examined the molecular, cellular and circuit properties of intracortical and cortico-subcortical pathways. It remains poorly understood how the spiking activity of a cortical or subcortical neuron is associated with cortex-wide network dynamics. In this study, we use simultaneously recorded mesoscale calcium imaging and behavior video as multi-modality for predicting neuron spiking on awake mice. A novel group patch wise classification and asymptotic model is proposed to address the ultra imbalanced spikes prediction problem. Experiments demonstrate the efficacy of the proposed classification and asymptotic predicting strategies. Our empirical results reveal that the spike activities of neurons were associated with distinct cortical calcium signals and behavior information,which was observed that ongoing neural cortical activity encodes a high-dimensional latent signal of behavior and sensory.

To map the neural substrate of mental function, cognitive neuroimaging relies on controlled psychological manipulations that engage brain systems associated with specific cognitive processes. In order to build comprehensive atlases of cognitive function in the brain, it must assemble maps for many different cognitive processes, which often evoke overlapping patterns of activation. Such data aggregation faces contrasting goals: on the one hand finding correspondences across vastly different cognitive experiments, while on the other hand precisely describing the function of any given brain region. Here we introduce a new analysis framework that tackles these difficulties and thereby enables the generation of brain atlases for cognitive function. The approach leverages ontologies of cognitive concepts and multi-label brain decoding to map the neural substrate of these concepts. We demonstrate the approach by building an atlas of functional brain organization based on 30 diverse functional neuroimaging studies, totaling 196 different experimental conditions. Unlike conventional brain mapping, this functional atlas supports robust reverse inference: predicting the mental processes from brain activity in the regions delineated by the atlas. To establish that this reverse inference is indeed governed by the corresponding concepts, and not idiosyncrasies of experimental designs, we show that it can accurately decode the cognitive concepts recruited in new tasks. These results demonstrate that aggregating independent task-fMRI studies can provide a more precise global atlas of selective associations between brain and cognition.

In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.

There is a recent interest in using functional magnetic resonance imaging (fMRI) for decoding more naturalistic, cognitive states, in which subjects perform various tasks in a continuous, self-directed manner. In this setting, the set of brain volumes over the entire task duration is usually taken as a single sample with connectivity estimates, such as Pearson's correlation, employed as features. Since covariance matrices live on the positive semidefinite cone, their elements are inherently inter-related. The assumption of uncorrelated features implicit in most classifier learning algorithms is thus violated. Coupled with the usual small sample sizes, the generalizability of the learned classifiers is limited, and the identification of significant brain connections from the classifier weights is nontrivial. In this paper, we present a Riemannian approach for connectivity-based brain decoding. The core idea is to project the covariance estimates onto a common tangent space to reduce the statistical dependencies between their elements. For this, we propose a matrix whitening transport, and compare it against parallel transport implemented via the Schild's ladder algorithm. To validate our classification approach, we apply it to fMRI data acquired from twenty four subjects during four continuous, self-driven tasks. We show that our approach provides significantly higher classification accuracy than directly using Pearson's correlation and its regularized variants as features. To facilitate result interpretation, we further propose a non-parametric scheme that combines bootstrapping and permutation testing for identifying significantly discriminative brain connections from the classifier weights. Using this scheme, a number of neuro-anatomically meaningful connections are detected, whereas no significant connections are found with pure permutation testing.

Spontaneous brain activity, as observed in functional neuroimaging, has been shown to display reproducible structure that expresses brain architecture and carries markers of brain pathologies. An important view of modern neuroscience is that such large-scale structure of coherent activity reflects modularity properties of brain connectivity graphs. However, to date, there has been no demonstration that the limited and noisy data available in spontaneous activity observations could be used to learn full-brain probabilistic models that generalize to new data. Learning such models entails two main challenges: i) modeling full brain connectivity is a difficult estimation problem that faces the curse of dimensionality and ii) variability between subjects, coupled with the variability of functional signals between experimental runs, makes the use of multiple datasets challenging. We describe subject-level brain functional connectivity structure as a multivariate Gaussian process and introduce a new strategy to estimate it from group data, by imposing a common structure on the graphical model in the population. We show that individual models learned from functional Magnetic Resonance Imaging (fMRI) data using this population prior generalize better to unseen data than models based on alternative regularization schemes. To our knowledge, this is the first report of a cross-validated model of spontaneous brain activity. Finally, we use the estimated graphical model to explore the large-scale characteristics of functional architecture and show for the first time that known cognitive networks appear as the integrated communities of functional connectivity graph.

Schizophrenia is a complex psychiatric disorder that has eluded a characterization in terms of local abnormalities of brain activity, and is hypothesized to affect the collective, ``emergent working of the brain. We propose a novel data-driven approach to capture emergent features using functional brain networks [Eguiluzet al] extracted from fMRI data, and demonstrate its advantage over traditional region-of-interest (ROI) and local, task-specific linear activation analyzes. Our results suggest that schizophrenia is indeed associated with disruption of global, emergent brain properties related to its functioning as a network, which cannot be explained by alteration of local activation patterns. Moreover, further exploitation of interactions by sparse Markov Random Field classifiers shows clear gain over linear methods, such as Gaussian Naive Bayes and SVM, allowing to reach 86% accuracy (over 50% baseline - random guess), which is quite remarkable given that it is based on a single fMRI experiment using a simple auditory task.

Group studies of functional magnetic resonance imaging datasets are usually based on the computation of the mean signal across subjects at each voxel (random effects analyses), assuming that all subjects have been set in the same anatomical space (normalization). Although this approach allows for a correct specificity (rate of false detections), it is not very efficient for three reasons: i) its underlying hypotheses, perfect coregistration of the individual datasets and normality of the measured signal at the group level are frequently violated; ii) the group size is small in general, so that asymptotic approximations on the parameters distributions do not hold; iii) the large size of the images requires some conservative strategies to control the false detection rate, at the risk of increasing the number of false negatives. Given that it is still very challenging to build generative or parametric models of intersubject variability, we rely on a rule based, bottom-up approach: we present a set of procedures that detect structures of interest from each subject's data, then search for correspondences across subjects and outline the most reproducible activation regions in the group studied. This framework enables a strict control on the number of false detections. It is shown here that this analysis demonstrates increased validity and improves both the sensitivity and reliability of group analyses compared with standard methods. Moreover, it directly provides information on the spatial position correspondence or variability of the activated regions across subjects, which is difficult to obtain in standard voxel-based analyses.

The classical approach of within-subject analysis in event-related functional magnetic resonance imaging (fMRI) first relies on (i) a detection step to localize which parts of the brain are activated by a given stimulus type, and then on (ii) an estimation step to recover the temporal dynamics of the brain response. To date, specially in region-based analysis, the two questions have been addressed separately while intrinsically connected to each other. This situation motivates the need for new methods in neuroimaging that go beyond this unsatisfactory trade-off. In this paper, we propose a generalization of a region based Bayesian detection-estimation approach that addresses (i)-(ii) simultaneously as a bilinear inverse problem. The proposed extension relies on a 2-class Gamma-Gaussian prior mixture modeling to classify the voxels of the brain region either as activated or unactivated. Our approach provides both a spatial activity map and a HRF estimation using Monte Carlo Markov chain (MCMC) techniques. Results show that this novel mixture model yields lower false positive rates and a better sensitivity in comparison with a 2-class Gaussian mixture

The acquisition of brain images in fMRI yields rich topographic information about the functional structure of the brain. However, these descriptions are limited by strong inter-subject variability. A recent approach to represent the gross functional architecture across the population as seen in fMRI consists in automatically defining accross-subjects brain parcels. This technique yields large-scale inter-subject correspondences while allowing some spatial relaxation in the alignment of the brains. We address here the open question of an optimal parameterization (number of parcels) of brain parcellations using information theoretic criteria and cross-validation. Moreover, a finer analysis of variance components enables us to better characterize intra- and inter-subject variability sources in parcellation models.

In functional magnetic resonance imaging (fMRI), the hemodynamic response function (HRF) represents the impulse response of the neurovascular system. Its identification is essential for a better understanding of cerebral activity since it provides a typical time course of the response to a stimulus in a given region of interest (ROI). The authors have developed an HRF estimation method based on a single time course (Ciuciu, P. et al., IEEE Trans. Medical Imaging, vol.22, no.10, p.1235-51, 2003). We now propose an extension that takes the spatial homogeneity of the HRF into account. Our hypothesis, based on biological results, is that a ROI can be characterized by a single HRF but varying magnitude in space. Our goal is to estimate those magnitudes that could then be interpreted as a correlate of the neural response. We are thus faced with a semi-blind deconvolution inverse problem since the time arrivals of the neural response are known - they correspond to stimuli timing. To cope with this issue, we introduce specific prior information about the HRF and the neural response. Finally, we develop an MCMC approach to approximate the posterior mean estimates of unknown quantities. Simulation results show the improvement brought by our formulation compared to our earlier approach.

In this paper, we propose a new representation of the cortical surface that may be used to study the cortex folding process and to recover some putative stable anatomical landmarks called sulcal roots usually buried in the depth of adult brains. This representation is a primal sketch derived from a scale space computed for the mean curvature of the cortical surface. This scale-space stems from a diffusion equation geodesic to the cortical surface. The primal sketch is made up of objects defined from mean curvature minima and saddle points. The resulting sketch aims first at highlighting significant elementary cortical folds, second at representing the fold merging process during brain growth. The relevance of the framework is illustrated by the study of central sulcus sulcal roots from antenatal to adult age. Some results are proposed for ten different brains. Some preliminary results are also provided for superior temporal sulcus.

This paper deals with the estimation of the blood oxygen level-dependent response to a stimulus, as measured in functional magnetic resonance imaging (fMRI) data. A precise estimation is essential for a better understanding of cerebral activations. The most recent works have used a nonparametric framework for this estimation, considering each brain region as a system characterized by its impulse response, the so-called hemodynamic response function (HRF). However, the use of these techniques has remained limited since they are not well-adapted to real fMRI data. Here, we develop a threefold extension to previous works. We consider asynchronous event-related paradigms, account for different trial types and integrate several fMRI sessions into the estimation. These generalizations are simultaneously addressed through a badly conditioned observation model. Bayesian formalism is used to model temporal prior information of the underlying physiological process of the brain hemodynamic response. By this way, the HRF estimate results from a tradeoff between information brought by the data and by our prior knowledge. This tradeoff is modeled with hyperparameters that are set to the maximum-likelihood estimate using an expectation conditional maximization algorithm. The proposed unsupervised approach is validated on both synthetic and real fMRI data, the latter originating from a speech perception experiment.

The authors propose a new method for the analysis of brain activation images that aims at detecting activated volumes rather than pixels. The method is based on Poisson process modeling, hierarchical description, and multiscale detection (MSD). Its performances have been assessed using both Monte Carlo simulated images and experimental PET brain activation data. As compared to other methods, the MSD approach shows enhanced sensitivity with a controlled overall type I error, and has the ability to provide an estimate of the spatial limits of the detected signals. It is applicable to any kind of difference image for which the spatial autocorrelation function can be approximated by a stationary Gaussian function.