Historically, the primary focus of studies of human white matter tracts has been on large tracts that connect anterior to posterior cortical regions. These include the superior longitudinal fasciculus (SLF), the inferior longitudinal fasciculus (ILF), and the inferior fronto-occipital fasciculus (IFOF). Recently, more refined and well understood tractography methods have facilitated the characterization of several tracts in the posterior of the human brain that connect dorsal to ventral cortical regions. These include the vertical occipital fasciculus (VOF), the posterior arcuate fasciculus (pArc), the temporo-parietal connection (TP-SPL), and the middle longitudinal fasciculus (MdLF). The addition of these dorso-ventral connective tracts to our standard picture of white matter architecture results in a more complicated pattern of white matter connectivity than previously considered. Dorso-ventral connective tracts may play a role in transferring information from superior horizontal tracts, such as the SLF, to inferior horizontal tracts, such as the IFOF and ILF. We present a full anatomical delineation of these major dorso-ventral connective white matter tracts (the VOF, pArc, TP-SPL, MdLF). We show their spatial layout and cortical termination mappings in relation to the more established horizontal tracts (SLF, IFOF, ILF, Arc) and consider standard values for quantitative features associated with the aforementioned tracts. We hope to facilitate further study on these tracts and their relations. To this end, we also share links to automated code that segments these tracts, thereby providing a standard approach to obtaining these tracts for subsequent analysis. We developed open source software to allow reproducible segmentation of the tracts: https://github.com/brainlife/Vertical_Tracts. Finally, we make the segmentation method available as an open cloud service on the data and analyses sharing platform brainlife.io. Investigators will be able to access these services and upload their data to segment these tracts.

We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.

We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.

Classical studies of attention have identified areas of parietal and frontal cortex as sources of attentional control. Recently, a ventral region in the macaque temporal cortex, the posterior infero-temporal dorsal area PITd, has been suggested as a third attentional control area. This raises the question of whether and how spatially distant areas coordinate a joint focus of attention. Here we tested the hypothesis that parieto-frontal attention areas and PITd are directly interconnected. By combining functional MRI with ex-vivo high-resolution diffusion MRI, we found that PITd and dorsal attention areas are all directly connected through three specific fascicles. These results ascribe a new function, the communication of attention signals, to two known fiber-bundles, highlight the importance of vertical interactions across the two visual streams, and imply that the control of endogenous attention, hitherto thought to reside in macaque dorsal cortical areas, is exerted by a dorso-ventral network.

Planned missing designs are becoming increasingly popular, but because there is no consensus on how to implement them in longitudinal research, we simulated longitudinal data to distinguish between strategies of assigning items to forms and of assigning forms to participants across measurement occasions. Using relative efficiency as the criterion, results indicate that balanced item assignment coupled with assigning different forms over time most often yields the optimal assignment method, but only if variables are reliable. We also address how practice effects can bias latent means. A second simulation demonstrates that (a) assigning different forms over time diminishes practice effects and (b) using planned-missing-data patterns as predictors of practice can remove bias altogether.