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Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

 

The functional and computational properties of brain areas are determined, in large part, by their connectivity profiles. Advances in neuroimaging and network neuroscience allow us to characterize the human brain noninvasively, but a comprehensive understanding of the human brain demands an account of the anatomy of brain connections. Long-range anatomical connections are instantiated by white matter, which itself is organized into tracts. These tracts are often disrupted by central nervous system disorders, and they can be targeted by neuromodulatory interventions, such as deep brain stimulation. Here, we characterized the connections, morphology, traversal, and functions of the major white matter tracts in the brain. There are major discrepancies across different accounts of white matter tract anatomy, hindering our attempts to accurately map the connectivity of the human brain. However, we are often able to clarify the source(s) of these discrepancies through careful consideration of both histological tract-tracing and diffusion-weighted tractography studies. In combination, the advantages and disadvantages of each method permit novel insights into brain connectivity. Ultimately, our synthesis provides an essential reference for neuroscientists and clinicians interested in brain connectivity and anatomy, allowing for the study of the association of white matter’s properties with behavior, development, and disorders.

 

Abstract Tractography has created new horizons for researchers to study brain connectivity in vivo. However, tractography is an advanced and challenging method that has not been used so far for medical data analysis at a large scale in comparison to other traditional brain imaging methods. This work allows tractography to be used for large scale and high-quality medical analytics. BUndle ANalytics (BUAN) is a fast, robust, and flexible computational framework for real-world tractometric studies. BUAN combines tractography and anatomical information to analyze the challenging datasets and identifies significant group differences in specific locations of the white matter bundles. Additionally, BUAN takes the shape of the bundles into consideration for the analysis. BUAN compares the shapes of the bundles using a metric called bundle adjacency which calculates shape similarity between two given bundles. BUAN builds networks of bundle shape similarities that can be paramount for automating quality control. BUAN is freely available in DIPY. Results are presented using publicly available Parkinson’s Progression Markers Initiative data. 

We describe a dataset of processed data with associated reproducible preprocessing pipeline collected from two collegiate athlete groups and one non-athlete group. The dataset shares minimally processed diffusion-weighted magnetic resonance imaging (dMRI) data, three models of the diffusion signal in the voxel, full-brain tractograms, segmentation of the major white matter tracts as well as structural connectivity matrices. There is currently a paucity of similar datasets openly shared. Furthermore, major challenges are associated with collecting this type of data. The data and derivatives shared here can be used as a reference to study the effects of long-term exposure to collegiate athletics, such as the effects of repetitive head impacts. We use advanced anatomical and dMRI data processing methods publicly available as reproducible web services at brainlife.io.

 

Endogenous attention is the cognitive function that selects the relevant pieces of sensory information to achieve goals and it is known to be controlled by dorsal fronto-parietal brain areas. Here we expand this notion by identifying a control attention area located in the temporal lobe. By combining a demanding behavioral paradigm with functional neuroimaging and diffusion tractography, we show that like fronto-parietal attentional areas, the human posterior inferotemporal cortex exhibits significant attentional modulatory activity. This area is functionally distinct from surrounding cortical areas, and is directly connected to parietal and frontal attentional regions. These results show that attentional control spans three cortical lobes and overarches large distances through fiber pathways that run orthogonally to the dominant anterior-posterior axes of sensory processing, thus suggesting a different organizing principle for cognitive control.