Software Demonstrations

Statistical analysis of functional MRI (fMRI) data requires correct control for multiple comparisons. Most often this is done using cluster-extent based thresholding. One of the drawbacks of this method is that it requires the setting of an arbitrary cluster-forming threshold (usually Z > 3.1) that determines the size/shape of the clusters used for further analysis. Once this threshold is set, researchers are not allowed, statistically, to redo the analysis with a different threshold. Together with the knowledge that the correct interpretation of a significant cluster is: 'there is at least one active voxel in this cluster' and not 'all voxels in this cluster are active', this leads to a (statistically) suboptimal way of analysing fMRI data.

Recent advances in statistics have led to a new range of methods based on the True Discovery Proportion (TDP) [1-3]. These methods estimate the lower bound of the number of truly active voxels, i.e.TDP, within a cluster, for any cluster in the data, as many times a researcher wants, with full control of the family-wise error rate. They do not require the setting of an arbitrary threshold as the TDP provides a simultaneous bound on the number of active voxels over all possible clusters.

In practice, these methods thus give the researcher almost unlimited freedom in selecting and analysing clusters. One can use different thresholds for different clusters, calculate the TDP for an a priori defined cluster, or search for clusters with at least a certain TDP level. This flexibility requires software that goes beyond what is available in current statistical analysis packages like FSL or SPM. 

The NIH BRAIN Initiative Connectivity across Scales (CONNECTS) program was launched in 2023 to produce wiring diagrams of mammalian brains at unprecedented resolutions. Here we report on data standardization, integration, and visualization efforts in the center for Large-scale Imaging of Neural Circuits (LINC), a CONNECTS-funded consortium focused on the macaque and human brain. The LINC center will scale up novel optical and X-ray microscopy techniques to image a large brain volume (~12/360 cc in macaque/human) that contains cortico-subcortical projections targeted by neuromodulation for motor and psychiatric disorders. This volume will be imaged with: polarization-sensitive optical coherence tomography (PS-OCT; Liu, 2023) at 6μm; lightsheet microscopy (LSM; Voleti, 2019) at 0.6μm; and hierarchical phase-contrast tomography (HiP-CT; Walsh, 2021) at 0.87-17μm. Mesoscopic diffusion MRI (dMRI; Huang, 2021) in the same brains will provide the link to noninvasive neuroimaging. 

The analysis of moment-to-moment changes in co-activation patterns (CAPs) in functional MRI (fMRI) has been useful for studying dynamic properties of neural activity. This method is based on clustering fMRI time-frames into several recurrent spatial patterns within and across subjects [1]. Studies have also focused on quantifying properties of the temporal organization of CAPs, such as fractional occupancy [2]. The analyses of co-activations are computationally intensive, requiring the clustering of high-dimensional data concatenated over subjects. Further, while a variety of analytic choices are involved in studying CAPs, the field lacks a unified open-source platform to allow a robust feature selection required for reproducible mappings of brain and behavioral measurements. 

The hippocampus is a unique cortical structure, key to understanding brain function and plasticity. Unravelling its complex organization requires the integration of multiscale data, linking molecular features to macroscale hierarchies. Gene and cell type expression are fundamental microscale phenotypes, and their profiling can provide a reference description of how microstructural features are distributed across the brain. Post-mortem samples, however, are often discontinuous and have limited spatial coverage, thus potentially overlooking fine-grained information. Here, we charted gene and cell type expression patterns within the hippocampus with unprecedented resolution. 

Neural Mass Models (NMMs) are essential tools for exploring the complex interactions among neuronal populations. However, classical models such as the Jansen and Rit NMM (JR-NMM) are constrained by oversimplified modularization and fixed conduction delays. These limitations hinder their ability to accurately represent brain dynamics, simulate neurological disorders (e.g., epilepsy), and model large-scale network interactions. Building on our previous work on Distributed-Delay NMMs (DD-NMMs) (Fig. 1), we now enhance this framework with biologically plausible distributed delays informed by axonal properties. Furthermore, we extend its utility to include sensitivity analyses and integration of realistic physiological mechanisms, such as electrophysiology, neurotransmitters, and chemoreceptor dynamics, enabling multi-modal studies of brain activity (e.g., EEG/MEG and fMRI). 

Accurate electrode localization is essential for neuroimaging applications, including scalp EEG and stereo-EEG (sEEG). For EEG, precise knowledge of electrode positions facilitates cortical current density mapping and source localization[1]. In sEEG, accurate electrode contact localization enables seizure onset zone (SOZ) identification, which is critical for epilepsy surgery. Traditional methods such as manual digitization or landmark-based techniques are labor-intensive, prone to human error, and require expert involvement. This study introduces automated EEG and sEEG electrode localization workflows within Brainstorm[2], an open-source neuroimaging platform, enhancing accuracy, efficiency, and accessibility for both research and clinical applications.