From suprajasankari at gmail.com Mon Mar 5 05:56:37 2018 From: suprajasankari at gmail.com (SJ JV) Date: Mon, 5 Mar 2018 19:56:37 +0900 Subject: [Neuroimaging] LeaveOneOut cross validation Message-ID: Hi In order to do LOOCV for PLSRegression on a set of n correlation matrices like: pls = PLSRegression(n_components = 3) cv=LeavePLabelOut([1,1,1,1,1], p=2) cv_scores = cross_val_score(pls, correlationmatrices, y=[1,1,1,1,1], cv=cv) Output labels are either 0 or 1 as shown in y. The above does not work. What's the correct way to implement LOOCV for PLSRegression in the above case ? Thank you S.V -- U -------------- next part -------------- An HTML attachment was scrubbed... URL: From julien at editx.eu Thu Mar 1 08:36:42 2018 From: julien at editx.eu (Julien Carlier) Date: Thu, 1 Mar 2018 14:36:42 +0100 Subject: [Neuroimaging] Python Challenge Online - 30 Questions Message-ID: Hello, Cisco & Dimension Data organize a Python Challenge on EDITx. It is a good way to test your skills & have fun. -> https://editx.eu/it-challenge/python-challenge-2018-cisco-and-dimension-data Rules are simple: 15 minutes online, 30 questions & 3 jokers. Everyone is allowed to participate but only belgian residents can win the prizes. Do not hesitate to share it with people that might be interested. Feedbacks are welcome :) Regards, Julien -- Frankenstraat - Rue des Francs 79 1000 Brussels -------------- next part -------------- An HTML attachment was scrubbed... URL: From greynell at gmail.com Wed Mar 7 11:26:09 2018 From: greynell at gmail.com (=?UTF-8?Q?Gabriel_Reyn=C3=A9s?=) Date: Wed, 7 Mar 2018 17:26:09 +0100 Subject: [Neuroimaging] Extract max values from fit.transform() Message-ID: This is a question related to a previous subject " [Neuroimaging] Use of atlas to compute Z-Scores ". I have an image of Z-Scores values. I want to extract the maximum Z-score from each region based on an atlas. So far, I use: aal = datasets.fetch_atlas_aal('SPM12') masker = input_data.NiftiLabelsMasker(aal.maps, standardize = True) region_data = masker.fit_transform([path_to_ZScore_image]) The problem is that region_data is the mean value. Is there some way to extract the maximum value? Or another specific function (min, median...)? Thanks in advance! Gabriel -------------- next part -------------- An HTML attachment was scrubbed... URL: From christophe at pallier.org Wed Mar 7 11:57:19 2018 From: christophe at pallier.org (Christophe Pallier) Date: Wed, 7 Mar 2018 17:57:19 +0100 Subject: [Neuroimaging] Extract max values from fit.transform() In-Reply-To: References: Message-ID: > The problem is that region_data is the mean value. Is there some way to extract the maximum value? Or another specific function (min, median...)? I do not know if it is possible with masker.fit_transform, however you can do it in a few steps: - by using get_data() on the z-image to get a 3d array. - then, for each ROI, by masking the data with the ROI mask as a boolean array, and then use any numpy function on the result. Note that the shape of the z-image and the mask must be exactly the same (there is no resampling). Hope this helps, Christophe On Wed, Mar 7, 2018 at 5:26 PM, Gabriel Reyn?s wrote: > This is a question related to a previous subject " [Neuroimaging] Use of > atlas to compute Z-Scores > > ". > > I have an image of Z-Scores values. I want to extract the maximum Z-score > from each region based on an atlas. > > So far, I use: > > aal = datasets.fetch_atlas_aal('SPM12') > masker = input_data.NiftiLabelsMasker(aal.maps, standardize = True) > region_data = masker.fit_transform([path_to_ZScore_image]) > > > The problem is that region_data is the mean value. Is there some way to > extract the maximum value? Or another specific function (min, median...)? > > > Thanks in advance! > > > > Gabriel > > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > > -- -- Christophe Pallier INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145, 91191 Gif-sur-Yvette Cedex, France Tel: 00 33 1 69 08 79 34 Personal web site: http://www.pallier.org Lab web site: http://www.unicog.org -------------- next part -------------- An HTML attachment was scrubbed... URL: From ludob60 at gmail.com Mon Mar 19 06:39:28 2018 From: ludob60 at gmail.com (ludovico coletta) Date: Mon, 19 Mar 2018 11:39:28 +0100 Subject: [Neuroimaging] Fundend PhD scholarship @IIT/CIMeC (Rovereto) Message-ID: Dear all, The Functional Neuroimaging lab at the Istituto Italiano di Tecnologia ( https://www.iit.it/research/lines/functional-neuroimaging), Rovereto (Italy), invites applications for one PhD scholarship to investigate the dynamics of functional connectivity under resting-conditions, and upon cell-type selective neurostimulation. The successful candidate will have a MSc in neuroscience, biotechnology, computer science, physics, or equivalent. Proficiency in image processing and analysis (Matlab, R, Python), and/or in vivo electrophysiology is highly recommended. This four-year studentship aims to provide the student with a thorough training in conducting research at the interface of biomedical imaging, computational image analysis, and experimental neuroscience. The studentship is part of the international doctoral school in cognitive and brain sciences, in partnership with the University of Trento (http://web.unitn.it/en/cimec/). Final admission to the doctoral school entails a competitive selection process, as per the school regulations ( http://web.unitn.it/en/drcimec/10140/admission-doctoral-school-cognitive-and-brain-sciences ). The Istituto Italiano di Tecnologia (IIT) is a private law Foundation, created with the objective of promoting Italy's technological development and higher education in science and technology. Research at IIT is interdisciplinary and addresses basic and applied science through the development of novel technical applications. The Functional Neuroimaging lab is located a the Center for Neuroscience and Cognitive Sciences (CNCS) @UNITN in Rovereto, Italy, one of the research nodes set up by IIT. The CNCS is an interdisciplinary research center dedicated to the investigation of the brain at multiple scales. Please send your application (full CV, two academic referees, copy of master degree thesis, statement of research interest) by email to alessandro.gozzi at iit.it *no later than May 26th, 2018*. -------------- next part -------------- An HTML attachment was scrubbed... URL: From ryuvaraj at ntu.edu.sg Thu Mar 22 05:05:22 2018 From: ryuvaraj at ntu.edu.sg (Yuvaraj Rajamanickam (Dr)) Date: Thu, 22 Mar 2018 09:05:22 +0000 Subject: [Neuroimaging] Final call for papers & tutorials: PRNI (Pattern Recognition in Neuroimaging) 2018. Message-ID: <3E9B0165C01BA047A1AFFBA5B9161C415E3730C1@EXCHMBOX31.staff.main.ntu.edu.sg> FINAL CALL FOR PAPERS ******* please accept our apologies for cross-posting ******* -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- FIRST CALL FOR PAPERS AND TUTORIALS PRNI 2018: 8th International Workshop on Pattern Recognition in Neuroimaging to be held 12-14 June 2018 at the National University of Singapore, Singapore www.prni.org - @PRNIworkshop - www.facebook.com/PRNIworkshop/ ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The 8th International Workshop on Pattern Recognition in Neuroimaging (PRNI) will be held at the Centre for Life Sciences Auditorium, National University of Singapore, Singapore on June 12-14, 2018.Pattern recognition techniques are an important tool for neuroimaging data analysis. These techniques are helping to elucidate normal and abnormal brain function, cognition and perception, anatomical and functional brain architecture, biomarkers for diagnosis and personalized medicine, and as a scientific tool to decipher neural mechanisms underlying human cognition. The International Workshop on Pattern Recognition in Neuroimaging (PRNI) aims to: (1) foster dialogue between developers and users of cutting-edge analysis techniques in order to find matches between analysis techniques and neuroscientific questions; (2) showcase recent methodological advances in pattern recognition algorithms for neuroimaging analysis; and (3) identify challenging neuroscientific questions in need of new analysis approaches. Authors should prepare full papers with a maximum length of 4 pages (two column IEEE style) for double-blind review. The manuscript submission deadline is 04 April 2018, 11:59 pm SGT. Accepted manuscripts will be assigned either to an oral or poster sessions; all accepted manuscripts will be included in the workshop proceedings. Similarly to previous years, in addition to full length papers PRNI will also accept short abstracts (500 words excluding the title, abstract, tables, figure and data legends, and references) for poster presentation. Finally, this year PRNI has an open call for tutorial proposals. A tutorial can take a form of 2h, 4h or whole day event aimed at demonstrating a computational technique, software tool, or specific concept. Tutorial proposals featuring hands on demonstrations and promoting diversity (e.g. gender, background, institution) will be preferred. PRNI will cover conference registration fees for up to two tutors per accepted program. The submission deadline is also 04 April 2018, 11:59 pm SGT. All the accepted papers will be published in IEEE Xplore digital library Please see www.prni.org and follow @PRNIworkshop and www.facebook.com/PRNIworkshop/ for news and details. ________________________________ CONFIDENTIALITY: This email is intended solely for the person(s) named and may be confidential and/or privileged. If you are not the intended recipient, please delete it, notify us and do not copy, use, or disclose its contents. Towards a sustainable earth: Print only when necessary. Thank you. -------------- next part -------------- An HTML attachment was scrubbed... URL: From samuel.garcia at cnrs.fr Fri Mar 23 17:04:03 2018 From: samuel.garcia at cnrs.fr (Samuel Garcia) Date: Fri, 23 Mar 2018 22:04:03 +0100 Subject: [Neuroimaging] [ANN] python-neo 0.6.0 Message-ID: Dear all, I am totally happy to announce the released of neo 0.6.0 Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats. Major changes: * Introduced |neo.rawio|: a low-level reader for various data formats * Added continuous integration for all IOs using CircleCI (previously only |neo.core| was tested, using Travis CI) * Moved the test file repository to https://web.gin.g-node.org/NeuralEnsemble/ephy_testing_data - this makes it easier for people to contribute new files for testing Best, Samuel -------------- next part -------------- An HTML attachment was scrubbed... URL: From denis.engemann at gmail.com Fri Mar 23 17:16:21 2018 From: denis.engemann at gmail.com (Denis-Alexander Engemann) Date: Fri, 23 Mar 2018 21:16:21 +0000 Subject: [Neuroimaging] [ANN] python-neo 0.6.0 In-Reply-To: References: Message-ID: Comgrats! We?ll soon benefit from this release in MNE:) Denis On Fri 23 Mar 2018 at 22:08, Samuel Garcia wrote: > Dear all, > I am totally happy to announce the released of neo 0.6.0 > > > Neo is a package for representing > electrophysiology data in Python, together with support for reading a wide > range of neurophysiology file formats. > Major changes: > > - Introduced neo.rawio: a low-level reader for various data formats > - Added continuous integration for all IOs using CircleCI (previously > only neo.core > > was tested, using Travis CI) > - Moved the test file repository to > https://web.gin.g-node.org/NeuralEnsemble/ephy_testing_data - this > makes it easier for people to contribute new files for testing > > Best, > > Samuel > > > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > -------------- next part -------------- An HTML attachment was scrubbed... URL: From pinghongyeh at gmail.com Thu Mar 29 12:02:23 2018 From: pinghongyeh at gmail.com (Ping-Hong Yeh) Date: Thu, 29 Mar 2018 12:02:23 -0400 Subject: [Neuroimaging] [DIPY] propagator anisotropy estimation using MAP(L)MRI In-Reply-To: References:

Message-ID: Hi Rutger, We have some bad PA maps created using default settings, and I would like to hear your opinions on improving the fitting. Attached are the screenshots of PA_GCV, norm_laplacian, L_opt and PA_laplacian_weighted0.2 maps. I am currently running the fitting using 0.05 for the minimum bound of the GCV, but I am not sure if that would help. In order to do comparisons between controls and disease population, we need to make sure that the same fitting parameters are applied for the MAPMRI fitting for avoiding any biases. Do you have suggestions regarding this matter? Thank you. Ping On Tue, Jan 23, 2018 at 7:42 AM, Rutger Fick wrote: > Hi Ping, > > Salt and pepper noise is not a good sign (I just didn't see it so much on > the second set of slices you sent). To spot badly estimated voxels is > typically pretty easy - RTOP and many others can have negative or huge > values, which typically come from oscillations in the signs extrapolation. > You can often see these as bright spots in the laplacian norm. > > If you go through the data and see that salt and pepper noise corresponds > to unusually high norms, Increasing the laplacian minimum weight in the > code as i told you wil usually resolve it (or fixing it to a value like > 0.05 or 0.1 or something, see what works without overdoing it). > > Best, > Rutger > > > > > On 23 Jan 2018 03:06, "Ping-Hong Yeh" wrote: > > Hi Rutger, > > Thank you very much for the detailed reply. > > I guess i do not need to worry about those salt-pepper dots? > > Would you recommend output laplacian norm and laplacian_weighted maps and > go through the images for each data set? Any tips for realizing something > really goes wrong when looking at the propagator anisotropy map? > > Best, > > Ping > > > On Jan 22, 2018 6:55 PM, "Rutger Fick" wrote: > >> Hi Ping, >> >> In my experience, badly estimated voxels typically have super high >> laplacian norm and very low estimated laplacian weight (lopt). >> Looking at these results I would say things actually look pretty good! >> >> Getting the best results is always tricky finding a balance of optimally >> regularizing: not fitting the noise but also not over-regularizing, which >> is why the GCV option is nice. >> But, in rare cases it does mess up. So, if you want to give the GCV a bit >> less freedom to go low (to be on the safe side) you can increase the >> minimum bound of the GCV optimization in line 2272 of the code. >> >> There's many ways to speed up the code I gave you if you want to put in >> the effort ;-) Using parallel processing is not standardly implemented in >> dipy, but maybe you can hack it somehow. >> You can also set the laplacian_weight = 0.1 or something to avoid GCV, >> but it won't make a huge difference. I only ever used this code to do >> research - so speed was not really a concern. >> >> Anyway, hope this all helped! Let me know if everything works out, >> >> Best, >> Rutger >> >> On 19 January 2018 at 22:03, Ping-Hong Yeh wrote: >> >>> Hi Rutger, >>> >>> Attached please find the snapshot of norm_of_laplacian_signal, lopt, >>> and pa maps of the same data set i used earlier. >>> >>> BTW, is there a way to speed up the mapmri_pa processing? Will the >>> OpenMP help? >>> >>> Thank you, >>> >>> ping >>> >>> On Fri, Jan 19, 2018 at 1:25 PM, Rutger Fick >>> wrote: >>> >>>> Hi Ping, >>>> >>>> So far, so good. >>>> In my opinion the TORTOISE PA reconstruction looks a bit >>>> flat/overregularized - but then again I don't know what kind of >>>> regularization they implemented for themselves. >>>> The PA of the implementation I gave you seems to give more consistent >>>> contrast for different tissue configurations - which is a good - but looks >>>> like it under-regularizes in some individual voxels (the salt-pepper noise >>>> in the PA/RTOP). >>>> >>>> To check if this is the case, can you show me the >>>> mapfit_L.norm_of_laplacian_signal() and mapfit_L.lopt maps? >>>> >>>> Rutger >>>> >>>> >>>> >>>> >>>> On 19 January 2018 at 17:43, Ping-Hong Yeh >>>> wrote: >>>> >>>>> Hi Rutger, >>>>> >>>>> Just give you an update of the results (see the attached snapshots) >>>>> using GCV weighted and Laplacian regularization for MAPMRI >>>>> estimation. >>>>> >>>>> The other PA mapping was calculated using TORTOISE. I have also >>>>> attached RTOP mapping calculated using DIPY with and without GCV >>>>> weighted and Laplacian regularization. >>>>> >>>>> Comparing to the TORTOISE, PA values in the one using GCV weighted >>>>> and Laplacian regularization method are relatively smaller, >>>>> particularly over the regions with the less dense white matter. >>>>> >>>>> For RTOP images, I am not sure whether GCV weighted and Laplacian >>>>> regularization method outperforms the one without using GCV weighted >>>>> and Laplacian regularization. >>>>> >>>>> Any comments? >>>>> Thank you, >>>>> >>>>> ping >>>>> >>>>> On Wed, Jan 17, 2018 at 7:48 PM, Rutger Fick >>>>> wrote: >>>>> >>>>>> Hi Ping, >>>>>> >>>>>> If it's still running and gave only that error then probably it was >>>>>> just a single voxel that failed and the rest is working. However, I >>>>>> recommend you first try to fit a smaller dataset (just a few voxels or a >>>>>> single slice) just to check the results make sense. >>>>>> >>>>>> I should mention that the code I gave you is slower than Dipy's >>>>>> public version for reasons I won't get into, so don't worry if you have to >>>>>> wait longer than before. >>>>>> >>>>>> Best, >>>>>> Rutger >>>>>> >>>>>> On 18 Jan 2018 00:58, "Ping-Hong Yeh" wrote: >>>>>> >>>>>>> Hi Rutger, >>>>>>> >>>>>>> Thanks again for the prompt reply. >>>>>>> >>>>>>> Adding "mask" to mapmri have fixed the error; however, another error >>>>>>> shows up, >>>>>>> >>>>>>> mapfit_L = map_model_L.fit(data,mask=data[..., 0]>0) >>>>>>> dipy/core/geometry.py:129: RuntimeWarning: invalid value encountered >>>>>>> in true_divide >>>>>>> theta = np.arccos(z / r) >>>>>>> dipy/reconst/mapmri_pa.py:364: UserWarning: The MAPMRI positivity >>>>>>> constraint depends on CVXOPT (http://cvxopt.org/). CVXOPT is >>>>>>> licensed under the GPL (see: http://cvxopt.org/copyright.html) and >>>>>>> you may be subject to this license when using the positivity constraint. >>>>>>> warn(w_s) >>>>>>> dipy/reconst/mapmri_pa.py:413: UserWarning: Optimization did not >>>>>>> find a solution >>>>>>> warn('Optimization did not find a solution') >>>>>>> Error: Couldn't find per display information >>>>>>> >>>>>>> >>>>>>> It is still running though. Should i stop the running? >>>>>>> >>>>>>> Thank you. >>>>>>> ping >>>>>>> >>>>>>> On Tue, Jan 16, 2018 at 7:18 PM, Rutger Fick >>>>>>> wrote: >>>>>>> >>>>>>>> Hi Ping, >>>>>>>> >>>>>>>> Reading the error messages, it looks like you're fitting a masked >>>>>>>> voxel. The following error: >>>>>>>> >>>>>>>> /Library/Python/2.7/site-packages/dipy/reconst/mapmri_pa.py:389: >>>>>>>> RuntimeWarning: invalid value encountered in divide >>>>>>>> data = np.asarray(data / data[self.gtab.b0s_mask].mean()) >>>>>>>> >>>>>>>> says you're dividing by either zero or NaN, which means your b0 >>>>>>>> value of that voxel was zero (or you had no b0 values possibly). Note that >>>>>>>> mapmri needs at least one b0 measurement. >>>>>>>> I recommend you check if it works when you fit a voxel that you >>>>>>>> know for sure is in white matter. If it works, you can do something like >>>>>>>> map_model_L.fit(data, mask=data[..., 0]>0) to use a mask that only >>>>>>>> fits if the first measured DWI is positive (assuming your first measurement >>>>>>>> is a b0). >>>>>>>> >>>>>>>> Best, >>>>>>>> Rutger >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> On 16 January 2018 at 23:46, Ping-Hong Yeh >>>>>>>> wrote: >>>>>>>> >>>>>>>>> Hi Rutger, >>>>>>>>> >>>>>>>>> I got an error running the map_model.fit using mapmri_pa. Here is >>>>>>>>> the scripts i used, >>>>>>>>> >>>>>>>>> >>>>>>>>> map_model_L = mapmri_pa.MapmriModel(gtab, >>>>>>>>> radial_order=radial_order, >>>>>>>>> laplacian_regularization=True, # >>>>>>>>> this regularization enhances reproducibility of estimated q-space indices >>>>>>>>> by imposing smoothness >>>>>>>>> laplacian_weighting="GCV", # this >>>>>>>>> makes it use generalized cross-validation to find the best regularization >>>>>>>>> weight >>>>>>>>> positivity_constraint=True) # this >>>>>>>>> ensures the estimated PDF is positive >>>>>>>>> >>>>>>>>> mapfit_L = map_model_L.fit(data) >>>>>>>>> >>>>>>>>> , and the error message, >>>>>>>>> >>>>>>>>> >>>>>>>>> /Library/Python/2.7/site-packages/dipy/core/geometry.py:129: >>>>>>>>> RuntimeWarning: invalid value encountered in true_divide >>>>>>>>> theta = np.arccos(z / r) >>>>>>>>> /Library/Python/2.7/site-packages/dipy/reconst/mapmri_pa.py:364: >>>>>>>>> UserWarning: The MAPMRI positivity constraint depends on CVXOPT (http: >>>>>>>>> xopt.org/). CVXOPT is licensed under the GPL (see: >>>>>>>>> http://cvxopt.org/copyright.html) and you may be subject to this >>>>>>>>> license when using positivity constraint. >>>>>>>>> warn(w_s) >>>>>>>>> /Library/Python/2.7/site-packages/dipy/reconst/mapmri_pa.py:389: >>>>>>>>> RuntimeWarning: invalid value encountered in divide >>>>>>>>> data = np.asarray(data / data[self.gtab.b0s_mask].mean()) >>>>>>>>> /Library/Python/2.7/site-packages/dipy/reconst/mapmri_pa.py:413: >>>>>>>>> UserWarning: Optimization did not find a solution >>>>>>>>> warn('Optimization did not find a solution') >>>>>>>>> /Library/Python/2.7/site-packages/dipy/reconst/mapmri_pa.py:444: >>>>>>>>> UserWarning: Optimization did not find a solution >>>>>>>>> warn('Optimization did not find a solution') >>>>>>>>> Traceback (most recent call last): >>>>>>>>> File "", line 1, in >>>>>>>>> File "/Library/Python/2.7/site-pack >>>>>>>>> ages/dipy/reconst/multi_voxel.py", line 33, in new_fit >>>>>>>>> fit_array[ijk] = single_voxel_fit(self, data[ijk]) >>>>>>>>> File "/Library/Python/2.7/site-pack >>>>>>>>> ages/dipy/reconst/mapmri_pa.py", line 465, in fit >>>>>>>>> coef_iso = coef_iso / sum(coef_iso * self.Bm_iso) >>>>>>>>> UnboundLocalError: local variable 'coef_iso' referenced before >>>>>>>>> assignment >>>>>>>>> >>>>>>>>> >>>>>>>>> Any suggestions? >>>>>>>>> >>>>>>>>> Thank you. >>>>>>>>> >>>>>>>>> ping >>>>>>>>> >>>>>>>>> On Fri, Jan 12, 2018 at 6:24 PM, Rutger Fick < >>>>>>>>> fick.rutger at gmail.com> wrote: >>>>>>>>> >>>>>>>>>> Hi Ping, >>>>>>>>>> >>>>>>>>>> Attached is the mapmri code that also has PA, just put it in the >>>>>>>>>> dipy/reconst/ folder (where also the current mapmri.py file is) and run >>>>>>>>>> "python setup.py install" from dipy's main folder. That should make it >>>>>>>>>> usable in the same way as the current mapmri module. >>>>>>>>>> Note that its based on an old implementation that still works >>>>>>>>>> with the "cvxopt" optimizer package, so you'll have to install cvxopt to >>>>>>>>>> make it run. >>>>>>>>>> >>>>>>>>>> I recommend you use the model with both laplacian regularization >>>>>>>>>> and positivity constraint, this give the best results in general. >>>>>>>>>> >>>>>>>>>> from dipy.reconst import mapmri_pa >>>>>>>>>> mapmod = mapmri_pa.MapmriModel(gtab, >>>>>>>>>> laplacian_regularization=True, # >>>>>>>>>> this regularization enhances reproducibility of estimated q-space indices >>>>>>>>>> by imposing smoothness >>>>>>>>>> laplacian_weighting="GCV", # this >>>>>>>>>> makes it use generalized cross-validation to find the best regularization >>>>>>>>>> weight >>>>>>>>>> positivity_constraint=True) # >>>>>>>>>> this ensures the estimated PDF is positive >>>>>>>>>> mapfit = mapmod.fit(data) >>>>>>>>>> pa = mapfit.pa() >>>>>>>>>> >>>>>>>>>> Aside from the original MAPMRI citation for Ozarslan et al. >>>>>>>>>> (2013), note that the relevant citation for dipy's laplacian-regularized >>>>>>>>>> MAP-MRI implementation is [1]. >>>>>>>>>> [1] Fick, Rutger HJ, et al. "MAPL: Tissue microstructure >>>>>>>>>> estimation using Laplacian-regularized MAP-MRI and its application to HCP >>>>>>>>>> data." *NeuroImage* 134 (2016): 365-385. >>>>>>>>>> >>>>>>>>>> Hope it helps and let me know if you need anything else, >>>>>>>>>> Rutger >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> On 12 January 2018 at 21:48, Ping-Hong Yeh >>>>>>>>> > wrote: >>>>>>>>>> >>>>>>>>>>> Hi Roger, >>>>>>>>>>> >>>>>>>>>>> Thanks for the prompt reply. >>>>>>>>>>> May I have the code for estimating PA? >>>>>>>>>>> >>>>>>>>>>> Ping >>>>>>>>>>> >>>>>>>>>>> On Jan 12, 2018 3:21 PM, "Rutger Fick" >>>>>>>>>>> wrote: >>>>>>>>>>> >>>>>>>>>>>> Hi Ping, >>>>>>>>>>>> >>>>>>>>>>>> MAPL is just a name for using laplacian-regularized MAP-MRI. If >>>>>>>>>>>> you're using the dipy mapmri implementation then you're using MAPL by >>>>>>>>>>>> default. >>>>>>>>>>>> From a fitted mapmri model you can estimate overall >>>>>>>>>>>> non-gaussianity using fitted_model.ng(), and parallel and perpendicular >>>>>>>>>>>> non-Gaussianity using ng_parallel() and ng_perpendic >>>>>>>>>>>> perpendicularular(). >>>>>>>>>>>> Propagator Anisotropic is not included in the current dipy >>>>>>>>>>>> implementation. However, I do have a personal version of dipy's mapmri >>>>>>>>>>>> implementation that includes it, if you're interested. >>>>>>>>>>>> >>>>>>>>>>>> Best, >>>>>>>>>>>> Rutger >>>>>>>>>>>> >>>>>>>>>>>> On 12 January 2018 at 16:49, Ping-Hong Yeh < >>>>>>>>>>>> pinghongyeh at gmail.com> wrote: >>>>>>>>>>>> >>>>>>>>>>>>> Hi DIPY users, >>>>>>>>>>>>> >>>>>>>>>>>>> I would like to know the way of estimating non-Gaussian and >>>>>>>>>>>>> PA, mentioned in the Avram et al. ?Clinical feasibility of >>>>>>>>>>>>> using mean apparent propagator (MAP) MRI to characterize brain tissue >>>>>>>>>>>>> microstructure? paper, using MAPMRI or MAPL model. >>>>>>>>>>>>> >>>>>>>>>>>>> Thank you. >>>>>>>>>>>>> >>>>>>>>>>>>> Ping >>>>>>>>>>>>> >>>>>>>>>>>>> _______________________________________________ >>>>>>>>>>>>> Neuroimaging mailing list >>>>>>>>>>>>> Neuroimaging at python.org >>>>>>>>>>>>> https://mail.python.org/mailman/listinfo/neuroimaging >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> _______________________________________________ >>>>>>>>>>>> Neuroimaging mailing list >>>>>>>>>>>> Neuroimaging at python.org >>>>>>>>>>>> https://mail.python.org/mailman/listinfo/neuroimaging >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>> _______________________________________________ >>>>>>>>>>> Neuroimaging mailing list >>>>>>>>>>> Neuroimaging at python.org >>>>>>>>>>> https://mail.python.org/mailman/listinfo/neuroimaging >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>> >>>>>>>>>> _______________________________________________ >>>>>>>>>> Neuroimaging mailing list >>>>>>>>>> Neuroimaging at python.org >>>>>>>>>> https://mail.python.org/mailman/listinfo/neuroimaging >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>>> _______________________________________________ >>>>>>>>> Neuroimaging mailing list >>>>>>>>> Neuroimaging at python.org >>>>>>>>> https://mail.python.org/mailman/listinfo/neuroimaging >>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>>> _______________________________________________ >>>>>>>> Neuroimaging mailing list >>>>>>>> Neuroimaging at python.org >>>>>>>> https://mail.python.org/mailman/listinfo/neuroimaging >>>>>>>> >>>>>>>> >>>>>>> >>>>>>> _______________________________________________ >>>>>>> Neuroimaging mailing list >>>>>>> Neuroimaging at python.org >>>>>>> https://mail.python.org/mailman/listinfo/neuroimaging >>>>>>> >>>>>>> >>>>>> _______________________________________________ >>>>>> Neuroimaging mailing list >>>>>> Neuroimaging at python.org >>>>>> https://mail.python.org/mailman/listinfo/neuroimaging >>>>>> >>>>>> >>>>> >>>>> _______________________________________________ >>>>> Neuroimaging mailing list >>>>> Neuroimaging at python.org >>>>> https://mail.python.org/mailman/listinfo/neuroimaging >>>>> >>>>> >>>> >>>> _______________________________________________ >>>> Neuroimaging mailing list >>>> Neuroimaging at python.org >>>> https://mail.python.org/mailman/listinfo/neuroimaging >>>> >>>> >>> >>> _______________________________________________ >>> Neuroimaging mailing list >>> Neuroimaging at python.org >>> https://mail.python.org/mailman/listinfo/neuroimaging >>> >>> >> >> _______________________________________________ >> Neuroimaging mailing list >> Neuroimaging at python.org >> https://mail.python.org/mailman/listinfo/neuroimaging >> >> > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > > > > _______________________________________________ > Neuroimaging mailing list > Neuroimaging at python.org > https://mail.python.org/mailman/listinfo/neuroimaging > > -------------- next part -------------- An HTML attachment was scrubbed... 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