From arokem at gmail.com Tue Jan 14 17:50:49 2020 From: arokem at gmail.com (Ariel Rokem) Date: Tue, 14 Jan 2020 14:50:49 -0800 Subject: [Neuroimaging] NeuroHackademy 2020 : call for applications Message-ID: We are happy to announce a call for applications to participate in Neurohackademy 2020! This two-week hands-on workshop held at the University of Washington eScience Institute in Seattle, July 27th - August 7th, 2020, focuses on tools and techniques used to analyze human neuroscience data, on methods used to extract information from large datasets of publicly available data (such as the Human Connectome Project, OpenfMRI, etc.), and on tools for making human neuroscience research open and reproducible. Neurohackademy sessions in the first week will include lectures and tutorials on data science, machine learning, data visualization, and data resources. The second week will be devoted to participant-directed activities: guided work on team projects, hackathon sessions, and breakout sessions on topics of interest. For more details and a preliminary list of instructors, see: https://neurohackademy.org/ We are now accepting applications to participate at https://neurohackademy.org/apply/ Ideally, applicants should have some prior experience with programming and with neuroscience data analysis, but we welcome applications from participants with a variety of relevant backgrounds. Accepted applicants will be asked to pay a fee of $200 upon final registration. This fee will include participation in the course, accommodation in the UW dorms, and two meals a day (breakfast and lunch), for the duration of the course. A limited number of fee waivers and travel grants will be available. We encourage students with financial need and students from groups that are underrepresented in neuroimaging and data science to apply for these grants (see application form for details). We may also be able to support participants who need childcare during their participation in the course. Important dates: March 1st: Application deadline April 2nd: Notification of acceptance April 20th: Final registration deadline -------------- next part -------------- An HTML attachment was scrubbed... URL: From icbsii at bme.ssn.edu.in Sun Jan 12 10:51:49 2020 From: icbsii at bme.ssn.edu.in (icbsii bme) Date: Sun, 12 Jan 2020 21:21:49 +0530 Subject: [Neuroimaging] Call for Papers - IEEE ICBSII 2020 Message-ID: Dear all Greetings The Department of Biomedical Engineering, SSN College of Engineering, Kalavakkam is organizing *2020 IEEE Sixth International Conference on Biosignals, Images, and Instrumentation (ICBSII 2020)* during *February 27 - 28, 2020.* ICBSII 2020 is an opportunity for medical engineering professionals from around the world who are involved in the field of healthcare to submit their original research papers which have not been submitted elsewhere in other conference/journals. *Track 1:* Bio - Signal Processing *Track 2:* Medical Imaging Processing *Track 3: *Medical Data Analytics *Track 4:* Medical Instrumentation All registered and presented papers in the conference will be forwarded for inclusion in IEEE Digital Xplore. Conference website: *www.icbsii.com * IEEE Approval link: *https://conferences.ieee.org/conferences_events/conferences/conferencedetails/49132 * Authors can submit their manuscripts through the following link, * https://easychair.org/conferences/?conf=icbsii2020 * Submission guidelines:* http://www.icbsii.com/submission.html * *Important Dates:* - Deadline for Full Paper Submission: *January 25, 2020 (Extended) * - Acceptance Intimation: *February 05, 2020* - Submission of Camera Ready Paper: *February 10, 2020* - Deadline for Registration: *February 08, 2020* - Conference Date: *February 27 - 28, 2020* Please find the attached brochure. We also request you to kindly circulate this information to your friends and colleagues. -- With Regards, ICBSII-2019, Organizing Team, Department of Biomedical Engineering, SSN College of Engineering, Kalavakkam, Chennai. -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: ICBSII 2020 Brochure.pdf Type: application/pdf Size: 383924 bytes Desc: not available URL: From garyfallidis at gmail.com Wed Jan 15 13:12:32 2020 From: garyfallidis at gmail.com (Eleftherios Garyfallidis) Date: Wed, 15 Jan 2020 13:12:32 -0500 Subject: [Neuroimaging] ANN: DIPY 1.1.1 - a powerful release Message-ID: We are excited to announce a new release of DIPY: DIPY 1.1.1 is out! In addition: a) A new 5 day workshop available during March 16-20 to learn the theory and applications of the hundreds of methods available in DIPY 1.1.1 Intense! See the exquisite program here . *b) Given the need for a myriad of new DIPY derivative projects, DIPY moved to its own organization in GitHub. **Long live DIPY! * *And therefore, *https://github.com/dipy/dipy* supersedes https://github.com/nipy/dipy The old link will be available as a redirect link for the next 6 months.* c) Please support us by *citing** DIPY* in your papers using the following DOI: 10.3389/fninf.2014.00008 otherwise the DIPY citation police will find you. ;) DIPY 1.1.1 (Friday, 10 January 2020) This release received contributions from 11 developers (the full release notes are at: https://dipy.org/documentation/1.1.1./release_notes/release1.1/). Thank you all for your contributions and feedback! Please click here to check API changes. Highlights of this release include: - New module for deep learning DIPY.NN (uses TensorFlow 2.0). - Improved DKI performance and increased utilities. - Non-linear and RESTORE fits from DTI compatible now with DKI. - Numerical solutions for estimating axial, radial and mean kurtosis. - Added Kurtosis Fractional Anisotropy by Glenn et al. 2015. - Added Mean Kurtosis Tensor by Hansen et al. 2013. - Nibabel minimum version is 3.0.0. - Azure CI added and Appveyor CI removed. - New command line interfaces for LPCA, MPPCA and Gibbs Unringing. - New MTMS CSD tutorial added. - Horizon refactored and updated to support StatefulTractograms. - Speeded up all cython modules by using a smarter configuration setting. - All tutorials updated to API changes and 2 new tutorials added. - Large documentation update. - Closed 126 issues and merged 50 pull requests. Note: - Have in mind that DIPY stopped supporting Python 2 after version 0.16.0. All major Python projects have switched to Python 3. It is time that you switch too. To upgrade or install DIPY Run the following command in your terminal: pip install --upgrade dipy or conda install -c conda-forge dipy This version of DIPY depends on nibabel (3.0.0+). For visualization you need FURY (0.4.0+). Questions or suggestions? For any questions go to http://dipy.org, or send an e-mail to dipy at python.org We also have an instant messaging service and chat room available at https://gitter.im/nipy/dipy On behalf of the DIPY developers, Eleftherios Garyfallidis, Ariel Rokem, Serge Koudoro https://dipy.org/contributors -------------- next part -------------- An HTML attachment was scrubbed... URL: From esmairi.adel at gmail.com Wed Jan 22 03:42:50 2020 From: esmairi.adel at gmail.com (adel esmairi) Date: Wed, 22 Jan 2020 09:42:50 +0100 Subject: [Neuroimaging] read MGH info using Nibabel Message-ID: Hello, I have a preprocessed mgz files, i can check the information by running mrc_info. But i didn't find the same information when i used the python library *ninbabel*. My problem is i can't get *talairach* information. Who can help me please ? -------------- next part -------------- An HTML attachment was scrubbed... URL: From christophe at pallier.org Wed Jan 22 06:35:15 2020 From: christophe at pallier.org (Christophe Pallier) Date: Wed, 22 Jan 2020 12:35:15 +0100 Subject: [Neuroimaging] Are the values returned by nilearn NiftiMapsMasker.fit() averages, sums, weigthed or not? Message-ID: Dear all, I am using a NiftiMapsMasker object to extract data from stats maps. I expected it to compute the average values of the voxels defined by the non-null voxels in the masks. It seems that I am wrong: 1. when the mask are not binarized, the returned values are different than if the mask is not binarized. I suspect the niftimapsmasker is weighting by the voxel values from the masks. 2. It seems to be computing the sum rather than the average of the values within the masks, as the following code shows: Can anyone confirm or disconfirm? --- import numpy as np import nibabel as nib from nilearn.input_data import NiftiMapsMasker ROIs = ['bin_entire-roi.nii', 'bin_part1-roi.nii', 'bin_part2-roi.nii'] # get the size of masks (in voxels) imgs = [nib.load(f) for f in ROIs] data = [i.get_data() for i in imgs ] sizes = [np.count_nonzero(d) for d in data] # extract contrast values inside the ROIs images = ['contrast.nii.gz'] masker = NiftiMapsMasker(ROIs) values = masker.fit_transform(images) print("values (whole, part1, part2)", end=':') print(values[0]) print("sum of part1 and part2", end=':') print(np.sum(values[0][1:])) print("weighted mean of part 1 and part2:", end=':') print((values[0][1]*sizes[1] + values[0][2]*sizes[2])/(sizes[1] + sizes[2])) --- values (whole, part1, part2):[ -6.95688541 32.0976529 -39.05453831] sum of part1 and part2:-6.956885409069663 weighted mean of part 1 and part2::-30.661250335048116 -- Christophe Pallier PS: I have a cat who loves to walk on my keybxxxxxxxxxxxxxxxxxxxxxxx and she often presses the send key before I am finished writing the email. From christophe at pallier.org Wed Jan 22 10:09:25 2020 From: christophe at pallier.org (Christophe Pallier) Date: Wed, 22 Jan 2020 16:09:25 +0100 Subject: [Neuroimaging] flipping left-right side of images Message-ID: Dear all , I am trying to flip maks images which are in MNI space. I have tried the naive approach of flipping the data array in voxel space, but the results is not satisfactory (the result is not symmetrical to the original) --- from numpy import flip img = nib.load('mask.nii') data = img.get_data() nib.save(nib.Nifti1Image(np.flip(data, axis=0), affine=img.affine), 'test.nii') --- I also tried to hack the affine matrix, switching the sign of the first element, and using nilearn.image.resample_img, to create a new image, but with bad result. Any suggestion? From SyedQasim.Abbas at latrobe.edu.au Wed Jan 29 00:28:31 2020 From: SyedQasim.Abbas at latrobe.edu.au (Syed Qasim Abbas) Date: Wed, 29 Jan 2020 05:28:31 +0000 Subject: [Neuroimaging] Cerebellum and Brainstem Removal Message-ID: Hi, I am trying to accomplish cerebellum and brainstem removal from my image dataset in python. Unfortunately, I am not able to find any concrete information about cerebellum removal procedure. Can anyone suggest me some tool or procedure to accomplish the stated task. Thanks in anticipation Regards Qasim -------------- next part -------------- An HTML attachment was scrubbed... URL: