The Voxelwise Encoding Model framework (VEM) is a powerful approach for functional brain mapping. In the VEM framework, features are extracted from the stimulus (or task) and used in an encoding model to predict brain activity. If the encoding model is able to predict brain activity in some part of the brain, then one may conclude that some information represented in the features is also encoded in the brain. In VEM, a separate encoding model is fitted on each spatial sample (i.e., each voxel). VEM has many benefits compared to other methods for analyzing and modeling neuroimaging data. Most importantly, VEM can use large numbers of features simultaneously, which enables the analysis of complex naturalistic stimuli and tasks. Therefore, VEM can produce high-dimensional functional maps that reflect the selectivity of each voxel to large numbers of features. Moreover, because model performance is estimated on a separate test dataset not used during fitting, VEM minimizes overfitting and inflated Type I error confounds that plague other approaches, and the results of VEM generalize to new subjects and new stimuli. Despite these benefits, VEM is still not widely used in neuroimaging, partly because no tutorials on this method are available currently. To demystify the VEM framework and ease its dissemination, this paper presents a series of hands-on tutorials accessible to novice practitioners. The VEM tutorials are based on free open-source tools and public datasets, and reproduce the analysis presented in previously published work.

Functional connectivity (FC) computed using functional magnetic resonance imaging (fMRI) of the gray matter (GM) regions of the human brain has been successfully used to find reliable markers of healthy and clinical populations. Approximately 50% of the human brain consists of white matter (WM), and previous studies have shown the presence of blood oxygen level-dependent (BOLD) signals in the WM. However, current FC analysis by researchers is limited to GM regions of the brain, and fMRI data from WM are typically not analyzed. Here, we present the White Matter Functional Networks (WhiFuN) Toolbox specifically designed for WM-FC analysis, incorporating preprocessing steps that minimize signal contamination due to GM, optimized methods for extracting meaningful WM signals, and dedicated statistical and visualization tools for WM-FC. WhiFuN is based on SPM12 preprocessing and contains statistical tools for group-level analyses. WhiFuN provides an intuitive graphical user interface allowing users to execute all steps from preprocessing to final group level analyses and does not require prior knowledge of computer programming. To demonstrate the features and capabilities of WhiFuN, 98 healthy controls from the publicly available HCP 100 unrelated dataset were used to identify sex differences in WM-FC. We found significant WM-FC sex differences between the left body of the corpus callosum (CC) and the WM-FN that included the left and right posterior corona radiata and the left and right posterior thalamic region. WhiFuN will provide a platform for the neuroimaging community, offering new dimensions to elucidate human brain function as an integrated system of both GM and WM.

Monte Carlo simulation is extensively utilized in functional magnetic resonance imaging (MRI) research to examine the behavior of an MR sequence in the presence of diffusion within complex microstructures. These simulations necessitate a substantial number of diffusing particles and time steps to be modeled to achieve convergence and produce robust and reliable results, which is computationally intensive. Incorporating additional parameters to enhance the realism of the simulations further intensifies this computational burden, particularly when simulating steady-state sequences, which require a long period of time to be simulated. To address this, we present SpinWalk, a high-performance Monte Carlo simulator for functional MRI. SpinWalk is free and open-source software, designed to offer a high-performance framework for facilitating the simulation of custom sequences. SpinWalk enables popular sequences in functional MRI to be efficiently simulated and ensures that results can be consistently reproduced. Key sequence and tissue parameters can be set, making SpinWalk flexible in examining different factors that contribute in signal formation. This versatility is demonstrated by replicating simulations from several previous studies, including GRE, SE, bSSFP, GRASE, and STE sequences. Performance evaluations demonstrate that SpinWalk can significantly reduce computation times, making it feasible to perform extensive simulations within a reasonable time frame.

Many methods for the forward modeling of the blood-oxygenation level-dependent (BOLD) effect have been created and analyzed to elucidate the mechanisms of BOLD functional MRI (fMRI) techniques and to expand on the potential of the transverse relaxation time (T 2 *) in quantitative MRI. Simulations of this nature can be difficult to implement without prior experience, and differences made by methodological choices can be unclear, which provides a significant barrier of entry into the field. In this paper, we present BOLDsωimsuite, a toolbox for forward modeling of the BOLD effect, which collects many of the principal methods used in the literature into a single coherent package. Implemented as a Python package, simulations are made using scripts by combining various simulation components, thereby providing flexibility in methodological choices. The goal of this toolbox is to provide an open-source, reproducible simulation software suite that is adaptable for different MRI applications, and to which additional features can be added by the user with relative ease. This paper first provides an overview of the methods available in the package and how these methods can be constructed from the toolbox’s modular code components. Then, a brief theoretical explanation of each simulation component is given, supported by the relevant contributors. Next, sample simulations and analyzes that can be created using the package are presented to display its features. Finally, recommendations regarding computational requirements are included to help users choose the best simulation methods to fit their needs. This package has many use cases and significantly reduces methodological barriers to forward modeling. It can also be a good learning tool for MR physics as well as a powerful tool to promote reproducible science.

We propose the Gaussian-Linear Hidden Markov model (GLHMM), a generalisation of different types of HMMs commonly used in neuroscience. In short, the GLHMM is a general framework where linear regression is used to flexibly parameterise the Gaussian state distribution, thereby accommodating a wide range of uses—including unsupervised, encoding, and decoding models. GLHMM is available as a Python toolbox with an emphasis on statistical testing and out-of-sample prediction—that is, aimed at finding and characterising brain–behaviour associations. The toolbox uses a stochastic variational inference approach, enabling it to handle large data sets at reasonable computational time. The GLHMM can work with various types of data, including animal recordings or non-brain data, and is suitable for a broad range of experimental paradigms. For demonstration, we show examples with fMRI, local field potential, electrocorticography, magnetoencephalography, and pupillometry.

Normative models of brain structure estimate the effects of covariates such as age and sex using large samples of healthy controls. These models can then be applied to, for example, smaller clinical cohorts to distinguish disease effects from other covariates. However, these advanced statistical modelling approaches can be difficult to access, and processing large healthy cohorts is computationally demanding. Thus, accessible platforms with pre-trained normative models are needed. We present such a platform for brain morphology analysis as an open-source web application https://cnnplab.shinyapps.io/BrainMoNoCle/ , with six key features: (i) user-friendly web interface, (ii) individual and group outputs, (iii) multi-site analysis, (iv) regional and whole-brain analysis, (v) integration with existing tools, and (vi) featuring multiple morphology metrics. Using a diverse sample of 3,276 healthy controls across 21 sites, we pre-trained normative models on various metrics. We validated the models with a small sample of individuals with bipolar disorder, showing outputs that aligned closely with existing literature only after applying our normative modelling. Using a cohort of people with temporal lobe epilepsy, we showed that individual-level abnormalities were in line with seizure lateralisation. Finally, with the ability to investigate multiple morphology measures in the same framework, we found that biological covariates are better explained in specific morphology measures, and for applications, only some measures are sensitive to the disease process. Our platform offers a comprehensive framework to analyse brain morphology in clinical and research settings. Validations confirm the superiority of normative models and the advantage of investigating a range of brain morphology metrics together.

Pooling multi-site datasets is the dominant trend to expand sample sizes in neuroimaging field, thereby enhancing statistical power and reproducibility of research findings. Nevertheless, the heterogeneity derived from aggregating data from various imaging sites obstructs efficient inferences. Our recent study thoroughly assessed methods for harmonizing multi-site resting-state fMRI images, accelerating progress and providing initial application instructions. Despite this advancement, the removal of such site effects generally necessitates a certain level of programming expertise. In our effort to streamline the harmonization of site effects using advanced methodologies, we are pleased to introduce the DPABI Harmonization module. This versatile tool, allowing agnostic to specific analysis methods, integrates a range of techniques, including the state-of-the-art Subsampling Maximum-mean-distance Algorithms (SMA, recommended), ComBat/CovBat, linear models, and invariant conditional variational auto-encoder (ICVAE). It equips neuroscientists with an easy-to-use and transparent harmonization workflow, ensuring the feasibility of post-hoc analysis for multi-site studies.

Currently, whole-brain vertex-wise analyses on brain surfaces commonly require specially configured operating systems/environments to run and are largely inaccessible to R users. As such, these analyses are inconvenient to execute and inaccessible to many aspiring researchers. To address these limitations, we present VertexWiseR, a user-friendly R package, to run cortical and hippocampal surface vertex-wise analyses, in just about any computer, requiring minimal technical expertise and computational resources. The package allows cohort-wise anatomical surface data to be highly compressed into a single, compact, easy-to-share file. Users can then run a range of vertex-wise statistical analyses with that single file without requiring a special operating system/environment and direct access to the preprocessed file directories. This enables the user to easily take the analyses “offline”, which would be highly appropriate and conducive in classroom settings. This R package includes a conventional suite of tools for extracting, manipulating, analyzing, and visualizing vertex-wise data, and is designed to be easy for beginners to use. Furthermore, it also contains novel or advanced functionalities such as hippocampal surface analyses, meta-analytic decoding, threshold-free cluster enhancement, and mixed-effects models that would appeal to experienced researchers as well. In the current report, we showcase these functionalities in the analyses of two publicly accessible datasets. Overall, our R package opens up new frontiers for the R’s user base/community and makes such neuroimaging analyses accessible to the masses.

FMRI data are noisy, complicated to acquire, and typically go through many steps of processing before they are used in a study or clinical practice. Being able to visualize and understand the data from the start through the completion of processing, while being confident that each intermediate step was successful, is challenging. AFNI’s afni_proc.py is a tool to create and run a processing pipeline for FMRI data. With its flexible features, afni_proc.py allows users to both control and evaluate their processing at a detailed level. It has been designed to keep users informed about all processing steps: it does not just process the data, but also first outputs a fully commented processing script that the users can read, query, interpret, and refer back to. Having this full provenance is important for being able to understand each step of processing; it also promotes transparency and reproducibility by keeping the record of individual-level processing and modeling specifics in a single, shareable place. Additionally, afni_proc.py creates pipelines that contain several automatic self-checks for potential problems during runtime. The output directory contains a dictionary of relevant quantities that can be programmatically queried for potential issues and a systematic, interactive quality control (QC) HTML. All of these features help users evaluate and understand their data and processing in detail. We describe these and other aspects of afni_proc.py here using a set of task-based and resting-state FMRI example commands.

Magnetic resonance imaging (MRI) is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large datasets for potential poor-quality outliers can be a challenge. We present AIDAqc, a machine-learning-assisted automated Python-based command-line tool for small animal MRI quality assessment. Quality control features include signal-to-noise ratio (SNR), temporal SNR, and motion. All features are automatically calculated and no regions of interest are needed. Automated outlier detection for a given dataset combines the interquartile range and the machine-learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. To evaluate the reliability of individual quality control metrics, a simulation of noise (Gaussian, salt and pepper, speckle) and motion was performed. In outlier detection, single scans with induced artifacts were successfully identified by AIDAqc. AIDAqc was challenged in a large heterogeneous dataset collected from 19 international laboratories, including data from mice, rats, rabbits, hamsters, and gerbils, obtained with different hardware and at different field strengths. The results show that the manual inter-rater agreement (mean Fleiss Kappa score 0.17) is low when identifying poor-quality data. A direct comparison of AIDAqc results, therefore, showed only low-to-moderate concordance. In a manual post hoc validation of AIDAqc output, precision was high (>70%). The outlier data can have a significant impact on further postprocessing, as shown in representative functional and structural connectivity analysis. In summary, this pipeline optimized for small animal MRI provides researchers with a valuable tool to efficiently and effectively assess the quality of their MRI data, which is essential for improved reliability and reproducibility.

The analysis of electroencephalography (EEG)/magnetoencephalography (MEG) functional connectivity has become an important tool in neuroscience. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, that is, after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved by enforcing multithreading and high efficiency for all computations, so that repeated computations were avoided wherever possible, which allows for a major speed-up in the case of overlapping intervals. We present comprehensive performance evaluations of these implementations proving the online capability for the computation of large all-to-all functional connectivity networks. As a proof of principle, we demonstrate the feasibility of online functional connectivity estimation in the evaluation of somatosensory evoked brain activity