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About me
brainlife applications that I developed
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This app generates a subject-specific parcellation of subcortical areas using the CIT168 parcellation.
An automated segmentation of major white matter tracts from an input whole brain tractography. Utilizes a WMQL-like approach to segment streamlines, as documented in Bullock et al. 2019. Makes extensive use of the wmaTools library.
This app computes and plots the degree-based ring averages for microperimetry measurements. It makes use of OCT_scripts. It outputs radar plots, along with a table indicating the returned value for each degree of visual eccentricity.
This app creates a streamline density mask (NIfTI format) for each structure labled in a classification structure and saves it to disk. You can then use this data to compare overlap between tracts, the general course taken, or even cortical intersections (e.g. endpoint mappings).
A collection of three papers that I wrote in the summer of 2018 for my PhD program’s qualifying examinations
An interactive database and framework for cataloging accounts of white matter tracts and their anatomy throughout the literature.
A user-friendly, binder-enabled jupyter notebook for performing anatomy-based segmentations.
A website/collection of jupyter notebooks designed to provide a comprehensive exploration of concepts related to white matter segmentation.
A collection of code and scripts for processing, analyzing and visualizing data from Optical Coherence Tomography (OCT) and Macular Integrity Assessment (MAIA), at both the individual and group levels.
GitHub action for using the CiteAs API to generate a software citation ACKNOWLEDGEMENTS.md document automatically from a requirements.txt file.
Matlab-based toolkit for the segmentation and analysis of streamline tractography.
Pythonic refactoring of wma_Tools toolkit for the segmentation and analysis of streamline tractography.
A dockerized, brainlife.io application to track the anterior limb of the internal capsule using ensemble tractography.
Brainlife.io application for classifying streamlines into known anatomical tracts.
Published in NIH NCBI GenBank, 2004
A mutated sequence from oryza sativa, from the 2004 NCSU SCIBLS program
Recommended citation: N/A https://www.ncbi.nlm.nih.gov/nuccore/AY660000.1
Published in International Workshop on Shape in Medical Imaging, 2018
In this paper, we propose to use the Laplace-Beltrami (LB) spectrum as a descriptor of the shape of white matter tracts.
Recommended citation: Kitchell, L., Bullock, D., Hayashi, S., & Pestilli, F. (2018, September). Shape analysis of white matter tracts via the Laplace-Beltrami spectrum. In International Workshop on Shape in Medical Imaging (pp. 195-206). Springer, Cham. https://par.nsf.gov/servlets/purl/10073355
Published in 2019 IEEE 16th International Symposium on Biomedical Imaging, 2019
…we extend a state-of-the-art example-based method based on the Linear Assignment Problem (LAP) by including prior anatomical information within the optimization process.
Recommended citation: Bertò, G., Avesani, P., Pestilli, F., Bullock, D., Caron, B., & Olivetti, E. (2019, April). Anatomically-Informed Multiple Linear Assignment Problems for White Matter Bundle Segmentation. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 135-138). IEEE. https://arxiv.org/pdf/1907.07077
Published in Scientific data, 2019
The Open Diffusion Data Derivatives (O3D) repository.
Recommended citation: Avesani, P., McPherson, B., Hayashi, S., Caiafa, C. F., Henschel, R., Garyfallidis, E., ... & Pestilli, F. (2019). The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Scientific data, 6(1), 1-13. https://osf.io/y82n7/download
Published in Brain Structure and Function, 2019
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.
Recommended citation: Bullock, D., Takemura, H., Caiafa, C.F. et al. Associative white matter connecting the dorsal and ventral posterior human cortex. Brain Struct Funct 224, 2631–2660 (2019). https://doi.org/10.1007/s00429-019-01907-8 https://par.nsf.gov/servlets/purl/10111131
Published in PsyArXiv, 2020
…we examined the baseline differences in collegiate athletic participants by using two models of the diffusion-weighted magnetic resonance imaging signal (the Diffusion Tensor and NODDI model)… For both models, athletes were found to have consistently higher measures of microstructure than controls.
Recommended citation: Caron, B., Bullock, D., Kitchell, L., McPherson, B. C., Kellar, D. A., Cheng, H., ... & Pestilli, F. (2020). Advanced mapping of the human white matter microstructure better separates elite sports participation. PsyArXiv. https://psyarxiv.com/fvk5r/download?format=pdf
Published in Scientific reports, 2020
BUndle ANalytics (BUAN) is a fast, robust, and flexible computational framework for real-world tractometric studies.
Recommended citation: Chandio, B. Q., Risacher, S. L., Pestilli, F., Bullock, D., Yeh, F. C., Koudoro, S., ... & Garyfallidis, E. (2020). Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Scientific reports, 10(1), 1-18. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7555507/pdf/41598_2020_Article_74054.pdf
Published in NeuroImage, 2021
In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model.
Recommended citation: Bertò, G., Bullock, D., Astolfi, P., Hayashi, S., Zigiotto, L., Annicchiarico, L., ... & Olivetti, E. (2021). Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation. NeuroImage, 224, 117402. https://www.researchgate.net/profile/Giulia-Berto/publication/339216328_Classifyber_a_robust_streamline-based_linear_classifier_for_white_matter_bundle_segmentation/links/5e47e70f458515072d9f1b79/Classifyber-a-robust-streamline-based-linear-classifier-for-white-matter-bundle-segmentation.pdf
Published in Nature Communications, 2021
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.
Recommended citation: Sani, I., Stemmann, H., Caron, B., Bullock, D., Stemmler, T., Fahle, M., ... & Freiwald, W. A. (2021). The human endogenous attentional control network includes a ventro-temporal cortical node. Nature communications, 12(1), 1-16. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7810878/pdf/41467_2020_Article_20583.pdf
Published in Scientific Data, 2021
We describe a dataset of processed data with associated reproducible preprocessing pipeline collected from two collegiate athlete groups and one non-athlete group.
Recommended citation: Caron, B., Stuck, R., McPherson, B. et al. Collegiate athlete brain data for white matter mapping and network neuroscience. Sci Data 8, 56 (2021). https://doi.org/10.1038/s41597-021-00823-z https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7878753/pdf/41597_2021_Article_823.pdf
Published in Indiana University ProQuest Dissertations Publishing, 2021
Although the [white matter] architecture has been studied for hundreds of years, much is still unknown. This has hindered efforts to associate characteristics of the white matter with human behavior, development, and disorders. Here, we seek to ameliorate this.
Recommended citation: Bullock, D. (2021). Computational Segmentation of White Matter Anatomy: Methods, Insights, and Standards. https://www.proquest.com/docview/2566003752/fulltextPDF/93E6DF93FFA241EFPQ/1
Published in Journal of Vision, 2021
We propose a model that posits that the development of PVP white matter is related to the flow of neural activity from the ventral visual stream and to the dorsal visual stream.
Recommended citation: Vinci-Booher, S., Caron, B., Bullock, D., James, K., & Pestilli, F. (2021). A model of the development of major white matter pathways within and between ventral and dorsal visual streams. Journal of Vision, 21(9), 2698-2698. https://doi.org/10.1167/jov.21.9.2698
Published in NeuroImage, 2021
42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects.
Recommended citation: Schilling, K. G., Rheault, F., Petit, L., Hansen, C. B., Nath, V., Yeh, F. C., ... & Descoteaux, M. (2021). Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?. NeuroImage, 243, 118502. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855321/pdf/nihms-1777494.pdf
Published in Cerebral Cortex, 2022
Here, we analyzed intracerebral EEG from 11 epileptic patients viewing a stimulus sequence beginning with a neutral face with direct gaze. An overlap analysis of posterior white matter tractography endpoints (from 1066 healthy brains) relative to active intracerebral electrodes in the 11 patients showed likely involvement of both dorsal and ventral posterior white matter pathways.
Recommended citation: Babo-Rebelo, M., Puce, A., Bullock, D., Hugueville, L., Pestilli, F., Adam, C., ... & George, N. (2022). Visual information routes in the posterior dorsal and ventral face network studied with intracranial neurophysiology and white matter tract endpoints. Cerebral Cortex, 32(2), 342-366. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754371/pdf/bhab212.pdf
Published in Cerebral Cortex, 2022
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.
Recommended citation: Daniel N Bullock, Elena A Hayday, Mark D Grier, Wei Tang, Franco Pestilli, Sarah R Heilbronner, A taxonomy of the brain’s white matter: twenty-one major tracts for the 21st century, Cerebral Cortex, Volume 32, Issue 20, 15 October 2022, Pages 4524–4548, https://doi.org/10.1093/cercor/bhab500 https://psyarxiv.com/fvk5r/download?format=pdf
Published in Brain Structure and Function, 2022
We used reproducible, cloud-computing methods and diffusion imaging from adults and children (ages 5-8 years) to compare PVP development to that of tracts within the ventral and dorsal pathways. Overall, results suggest a potential role for the PVP in the development of the dorsal visual stream that may be related to its ability to facilitate interactions between ventral and dorsal streams during learning.
Recommended citation: Vinci-Booher, S., Caron, B., Bullock, D., James, K., & Pestilli, F. (2022). Development of white matter tracts between and within the dorsal and ventral streams. Brain Structure and Function, 227(4), 1457-1477. https://www.biorxiv.org/content/10.1101/2021.01.27.428423.full.pdf
Published in Current Research in Neurobiology, 2022
Despite their respective receptor specificities, both clozapine (CLZ) and deschloroclozapine (DCZ) have partial affinity for a variety of endogenous receptors and can induce dose-specific changes even in naïve animals. This complicates their use as actuator ligands for Designer Receptors Exclusively Activated by Designer Drugs (DREADDs). In this study we aimed to examine the effects of these compounds on resting-state functional connectivity (rs-FC) and intrinsic neural timescales (INTs) in drug-naïve, non-human primates.
Recommended citation: Adriana K. Cushnie, Daniel N. Bullock, Ana M. G. Manea, Wei Tang, Jan Zimmermann, Sarah R.Heilbronner, The use of chemogenetic actuator ligands in nonhuman primate DREADDs-fMRI, Current Research in Neurobiology, in press, https://doi.org/10.1016/j.crneur.2022.100072 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860110/pdf/main.pdf
Published in arXiv, 2023
…Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. this http URL automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here this http URLs technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility…
Recommended citation: Hayashi, S., Caron, B. A., Heinsfeld, A. S., Vinci-Booher, S., McPherson, B., Bullock, D. N., ... & Pestilli, F. (2023). brainlife. io: A decentralized and open source cloud platform to support neuroscience research. ArXiv.Current Research in Neurobiology, revision under review, https://doi.org/10.48550/arXiv.2306.02183 https://arxiv.org/ftp/arxiv/papers/2306/2306.02183.pdf
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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