Journal (Sort by date)

  • Wu, Y., Hong, Y., Feng, Y., Shen, D., Yap, P.-T., 2019. Mitigating Gyral Bias in Cortical Tractography via Asymmetric Fiber Orientation Distributions. Medical Image Analysis. (Best paper award, 2 runners-up)

  • Yue, L., Hu, D., Zhang, H., Wen, J., Wu, Y., Li, W., Sun, L., Li, X., Wang, J., Li, G. and Wang, T., 2021. Prediction of 7‐year’s conversion from subjective cognitive decline to mild cognitive impairment. Human brain mapping, 42(1), pp.192-203.

  • Li, G., Liu, Y., Zheng, Y., Wu, Y., Li, D., Liang, X., Chen, Y., Cui, Y., Yap, P.T., Qiu, S. and Zhang, H., 2021. Multiscale neural modeling of resting-state fMRI reveals executive-limbic malfunction as a core mechanism in major depressive disorder. NeuroImage: Clinical, 31, p.102758.

  • Wu, Y., Zhang, F., Makris, N., Ning, Y., Norton, I., She, S., Peng, H., Rathi, Y., Feng, Y., Wu, H., others, 2018d. Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder. NeuroImage 181, 16–29.

  • Wu, Y., Feng, Y., Li, F., Gao C., 2015. A Novel Fiber Orientation Distribution Reconstruction Method Based on Dictionary Basis Function Framework. Chinese Journal of Biomedical Engineering, (3), p.6.

  • Zhang, F., Wu, Y., Norton, I., Rathi, Y., Golby, A.J., O’Donnell, L.J., 2019b. Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering. Human brain mapping.

  • Zhang, Fan, Wu, Y., Norton, I., Rigolo, L., Rathi, Y., Makris, N., O’Donnell, L.J., 2018. An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. Neuroimage 179, 429–447.

  • Huynh, K.M., Xu, T., Wu, Y., Wang, X., Chen, G., Wu, H., Thung, K.H., Lin, W., Shen, D. and Yap, P.T., 2020. Probing Tissue Microarchitecture of the Baby Brain via Spherical Mean Spectrum Imaging. IEEE Transactions on Medical Imaging.

  • Feng, Y., Wu, Y., Rathi, Y., Westin, C.-F., 2015. Sparse deconvolution of higher order tensor for fiber orientation distribution estimation. Artificial intelligence in medicine 65, 229–238.

  • Feng, Y., Wu, Y., G, Zhang., R Liang., 2015. High Order Tensor diffusion magnetic resonance sparse imaging based on compressed sensing. Pattern Recognition and Artificial Intelligence, 28(8):710-719.

  • Huynh, K.M., Chen, G., Wu, Y., Shen, D., Yap, P.-T., 2019a. Multi-Site Harmonization of Diffusion MRI Data via Method of Moments. IEEE transactions on medical imaging.

  • Nath, V., Schilling, K.G., Parvathaneni, P., Huo, Y., Blaber, J.A., Hainline, A.E., etc., Wu, Y., Barakovic, M., Romascano, D., Rafael‐Patino, J., Frigo, M. and Girard, G., 2019. Tractography reproducibility challenge with empirical data (traced): The 2017 ismrm diffusion study group challenge. Journal of Magnetic Resonance Imaging.

  • Maier-Hein, K.H., Neher, P.F., Houde, J.C., Côté, M.A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.C., Lin, Y.C. ,ect., Wu, Y., Ji, Q. and Reddick, W.E., 2017. The challenge of mapping the human connectome based on diffusion tractography. Nature communications, 8(1), p.1349.

  • Zhang, H., Palaniyappan, L., Wu, Y., Cong, E., Wu, C., Ding, L., Jin, F., Qiu, M., Huang, Y., Wu, Y. and Wang, J., 2020. The concurrent disturbance of dynamic functional and structural brain connectome in major depressive disorder: the prefronto-insular pathway. Journal of Affective Disorders.

  • Jin, L., Zeng, Q., He, J., Feng, Y., Zhou, S. and Wu, Y., 2019. A ReliefF-SVM-based method for marking dopamine-based disease characteristics: A study on SWEDD and Parkinson’s disease. Behavioural brain research, 356, pp.400-407.

  • Yue, L., Hu, D., Zhang, H., Wen, J., Wu, Y., Wang, T., Shen, D. and Xiao, S., 2019. Prediction of 7-year progression from subjective cognitive decline to MCI: evidence from the china longitudinal ageing study (CLAS). Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 15(7), pp.P1396-P1397.

  • Zhou, S., Jin, L., He, J., Zeng, Q., Wu, Y., Cao, Z. and Feng, Y., 2018. Distributed performance of white matter properties in chess players: A DWI study using automated fiber quantification. Brain research, 1700, pp.9-18.

  • Xu, T., Feng, Y., Wu, Y., Zeng, Q., Zhang, J., He, J. and Zhuge, Q., 2017. A Novel Richardson-Lucy Model with Dictionary Basis and Spatial Regularization for Isolating Isotropic Signals. PloS one, 12(1), p.e0168864.

  • Xu, Y., Feng, Y., Niu, Y. and Wu, Y., 2014. Estimation of fiber orientation distribution with non-negative constrained higher order tensor deconvolution. Journal of Systems Science and Mathematical Sciences, (7), p.4.

  • Hong, Y., Chang, W.T., Chen, G., Wu, Y., Lin, W., Shen, D. and Yap, P.T., 2020. Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE). arXiv preprint arXiv:2002.10908.

  • Yue, L., Hu, D., Zhang, H., Wen, J., Wu, Y., Li, W., Sun, L., Li, X., Wang, J., Li, G. and Wang, T., 2020. Prediction of 7-year’s Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment. medRxiv.

  • Li, G., Liu, Y., Zheng, Y., Wu, Y., Li, D., Liang, X., Chen, Y., Cui, Y., Yap, P.T., Qiu, S. and Zhang, H., 2020. Multiscale Neural Modeling of Resting-state fMRI Reveals Executive-Limbic Malfunction as a Core Mechanism in Major Depressive Disorder. medRxiv.

Overview

This project uses the GitHub Flow for collaboration. The codebase contains Python code, Jinja2-based HTML pages, Sass stylesheets and Javascript code.

  • nox is used for automating development tasks.

  • Gulp-based build pipeline is used to process the Sass and Javascript files.

  • sphinx-autobuild is used to provide live-reloading pages when working on the theme.

  • pre-commit is used for running the linters.

Initial Setup

To work on this project, you need to have git 2.17+, Python 3.6+ and NodeJS 12.

  • Clone this project using git:

    git clone https://github.com/pradyunsg/furo.git
cd furo

  • Install the project’s dependencies:

    npm install
pip install nox

You’re all set for working on this project.

Commands

Code Linting

nox -s lint

Run the linters, as configured with pre-commit.

Local Development Server

nox -s docs-live

Serve this project’s documentation locally, using sphinx-autobuild. This will open the generated documentation page in your browser.

The server also watches for changes made to the documentation (docs/) or theme (src/), which will trigger a rebuild. Once the build is completed, server will automagically reload any open pages using livereload.

Tip

My workflow, when I’m working on this theme, is along the lines of:

  • Run this command, and wait for the browser window to open.

  • alt+tab gets me back to my text editor.

  • Make changes to some files and save those changes.

  • alt+tab switches to the browser.

  • After a small delay, the change is reflected in the browser.

  • If I want to make more changes, alt+tab and I’m back to my text editor.

  • Repeat the previous 4 steps until happy.

- @pradyunsg

Documentation Generation

nox -s docs

Generate the documentation for Furo into the build/docs folder. This (mostly) does the same thing as nox -s docs-live, except it invokes sphinx-build instead of sphinx-autobuild.

Release process

  • Update the changelog

  • Run nox -s release

  • Once that command succeeds, you’re done!

Installing directly from GitHub

There are times when you might want to install the in-development version of Furo (mostly for testing that a fix actually does fix things).

Furo cannot be installed directly using pip with the Git repository directly. This is because the Git repository does not contained the compiled CSS/JS. The distributions on PyPI have the compiled assets (because they’re platform agnostic and plain text).

# Clone the repository
git clone https://github.com/pradyunsg/furo.git
cd furo
# Build the static assets
npm install
./node_modules/.bin/gulp build
# Install with pip
pip install .