Software Demonstration

For the development of new imaging and reconstruction methods, it is essential to have a convenient way to quickly view and compare images. While many professional viewers exist for medical images, they fail to streamline the workflow for developers. It is often inconvenient to transfer the files from the reconstruction computer to the device with the imaging software installed. Moreover, software is often limited to specific operating systems and supports only a small subset of image formats. Therefore, we developed a medical image viewer extension for Visual Studio Code (vscode) based on the NiiVue project [1] for simple and fast viewing of local and remote files. This viewer is primarily targeted towards developers of reconstruction pipelines, but also supports a wide range of imaging workflows in vscode. 

Functional Magnetic Resonance Imaging (fMRI), a pivotal tool for neuroscientific research, leverages blood oxygenation levels to infer neural activity. However, its reliance on hemodynamic responses also renders it sensitive to various physiological processes affecting blood oxygenation. This dual nature presents both challenges and opportunities: while these physiological factors can introduce confounds in interpreting neural signals [1], they simultaneously offer valuable insights into essential human functions encompassing cognition, emotion, and health [2-4]. To this end, we underscore the necessity of acquiring concurrent physiological data such as cardiac and respiratory activity, gas exchange metrics (O2/CO2 levels), and skin conductance. Adoption of concurrent physiological signals is growing within the neuroimaging community, reflecting a broader appreciation of physiological dynamics in brain imaging studies. Emphasizing the critical role of physiological monitoring in fMRI data quality, physiopy is a dynamic, collaborative initiative designed to streamline the integration of physiological data with fMRI research. The foundation of physiopy rests on four key pillars: (1) Accessible Software Suite: Offering a range of user-friendly software tools specifically tailored for efficient physiological data processing, (2) Comprehensive Documentation: Ensuring clarity and ease of use through detailed guides and instructional materials, (3) Community-Driven Practices: Fostering a culture of shared knowledge and collaborative development of best practices, and (4) Engaged Community: Cultivating an active network of users, developers, and researchers, all united by a shared interest in the integration of physiology within neuroimaging research. 

Large-scale neuroimaging datasets are vital for brain-behavior studies, but the reliability of statistical results depends on its data quality. Therefore, protocol compliance becomes indispensable, emphasizing the need for accurate data acquisition for each subject across sites and scanners. Manual protocol compliance is impractical especially for massive datasets, necessitating an automated approach for minimizing non-compliance.

We have demonstrated the pervasive lack of compliance in large-scale datasets [1] using our open-source tool mrQA, revealing a substantial non-compliance rate of up to 60%, even though the initial exploration focused on a limited subset of parameters.

mrQA can now inspect many more parameters to generate a comprehensive compliance report. Apart from ensuring that all subjects were acquired accurately for each sequence (horizontal audit), mrQA also checks if related sequences acquired within a session are compatible with each other (vertical audit) as shown in Figure 1. With the integration of deeper checks with additional parameters, it becomes apparent that more issues may emerge, emphasizing the need for rigorous monitoring practices. We also explore patterns of non-compliance across scanner vendors, models, and sites such that appropriate strategies can be adopted to minimize such issues at MR imaging centers. 

Although electroencephalography (EEG) was the first functional human brain monitoring modality (1926-), EEG data analysis long lagged in adapting new data analysis approaches – both in neurology, where visual pattern recognition applied to the raw scalp signal data is still the dominant approach, and in cognitive neuroscience where event-related potential (ERP) averages of individual scalp channel signals, collected from relatively small numbers of participants, long remained the predominant research measure. These methods, however, leave unrevealed much information about brain function contained in the data, and also cannot exploit consistencies in complex data that can only be identified in and extracted from large to very large data collections using new statistical and machine learning methods. Sharing neuroelectromagnetic (NEM) data is critical to leveraging public research investment and to supporting rigor and reproducibility in funded research. Several funding bodies require data sharing. Data sharing also allows researchers to use modern research tools to evaluate new data in a new way, by directly comparing it directly to ever accumulating stores of shared data collected in related or compatible paradigms.
Here we report initial results of building NEMAR (nemar.org), a large, publicly available human neuroelectromagnetic (NEM) data, tools, and compute resource tightly linked to a freely available high-performance computing resource, the Neuroscience Gateway (NSG). Our goal is to build a widely used and scientifically productive open resource for archiving, sharing, and further analysis and meta-analysis of NEM data. 

The increasing popularity of the Brain Imaging Data Structure (BIDS) specification for neuroimaging data (Gorgolewski et al., 2016) has led to a flourishing of workflows known as BIDS Apps (Gorgolewski et al., 2017). With a standardized data structure enforced by BIDS, a wide variety of workflows can be easily applied to neuroimaging datasets from any arbitrary source. This not only simplifies app development, but makes it easier to share generic analysis code for future replication. Nevertheless, even with the BIDS specification, a wide diversity of data formatting persists, especially for derived files. This makes it challenging to design BIDS Apps consuming derivative files with nonstandard naming conventions. To ensure workflows remain generic even in the absence of specific naming conventions, we present snakebids, a Python library leveraging pybids to create generic interfaces between BIDS Apps and datasets. 

By embedding data within geometric structures, visualisation provides a gateway to understanding and communicating complex information. In neuroimaging, embedding data in brain geometries improves the tractability of high-dimensional data; visualisation software (e.g., [1-3]) is thus critical in brain mapping. However, the geometries that structure neuroimaging datasets can be highly heterogeneous. MR data, for example, are often reconstructed as image intensity values in a regularly sampled three-dimensional Euclidean volume. By contrast, the convoluted sheet of the mammalian cerebral cortex can be modeled as a two-dimensional Riemannian manifold, which software suites approximate as a polygon mesh[4-5]. Maps of brain connectivity have the intrinsic topology of a graph, with vertices that can be embedded either in physical coordinates or algorithmically in a low-dimensional space[3,6]. Measures of brain function further extend these geometries in the dimension of time.

Existing software has often addressed this heterogeneity by implementing separate plotting routines for different geometries. Here, we introduce the software library hyve (the hypercoil[7] visualisation engine) to implement an alternative compositional approach. Under our approach, users construct a new visualisation protocol by composing an abstract base plotting routine with a chain of functional atoms called primitives (Fig 1). Each functional primitive imbues the base routine with a distinct functionality, forming the basis of a modular, flexible, and extensible system for building reusable plotting protocols.