- ONCE YOU HAVE DATA
- quality control
- preprocessing
- Analysis
- Robustness checks
- Computational neuroscience
- Laminar and high-resolution MRI
- Meta analysis ( ??? )
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https://en.wikibooks.org/wiki/Neuroimaging_Data_Processing/Data_Quality
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MRIQC MRI quality control. A BIDS app that runs a pipeline to assess the quality of your data.
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the PCP Quality Assessment Protocol is another BIDS app based on the protocol of [the connectome project data}(http://preprocessed-connectomes-project.org/quality-assessment-protocol/)
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Qoala-t for QA for freesurfer segmentations also with an online shinyapp
OHBM 2019 session on quality control by Pradeep
There are some ready made pipeline as BIDS apps that already exist and have been tested. Using them might save you time and make your results more reproducible.
- AFNI based
- HCP Pipelines: a set of tools (primarily, but not exclusively, shell scripts) for processing MRI images for the Human Connectome Project.
- fMRIprep
- The NeuroImaging Analysis Kit: NIAK is a library of pipelines for the preprocessing and mining of large functional neuroimaging data.
- Automatic Analysis: is a pipeline system for neuroimaging written primarily in Matlab. It robustly supports recent versions of SPM, as well as selected functions from other software packages. The goal is to facilitate automatic, flexible, and replicable neuroimaging analyses through a comprehensive pipeline system.
- nipypelines
- Configurable Pipeline for the Analysis of Connectomes: C-PAC is a software for performing high-throughput preprocessing and analysis of functional connectomes data using high-performance computers.
There is also an OPPNI for Optimization of Preprocessing Pipelines for NeuroImaging.
ART repair
ART
fmridenoise
https://cibsr.stanford.edu/tools/human-brain-project/artrepair-software.html
http://technicalfmri.blogspot.com/2018/02/physiological-monitoring-and-recording.html?m=1
http://blogs.discovermagazine.com/neuroskeptic/2014/07/06/fmri-scanning-dead/ http://blogs.discovermagazine.com/neuroskeptic/2013/11/26/head-movement-bad-news-neuroscience/ http://blogs.discovermagazine.com/neuroskeptic/2012/08/07/brains-in-motion-are-bad-for-neuroscience/
- a FAQ article on the GLM by Cyril Pernet with matlab code to go through
- see the section on percent signal change to better understand how to report results
- orthogonalization of regressors can be a bit hard to wrap your head aroudnd at first but Jeanette Mumford ( ??? ) has great paper on the topic with a jupyter notebook.
SPM tool box for
Analytical flexibility is a big problem in neuroimaging most likely the source of a lot of false positive results.
If several analysis are attempted it can be good to have ways to decide amongst them. There is bad way to do like the one described in the overfitting toolbox.
But there are better ways to do it:
- The MACS SPM toolbox by Joram Soch
- ( ??? )
http://blogs.discovermagazine.com/neuroskeptic/2014/06/21/fmri-mvpa-crack-neural-code/
https://distill.pub/2016/misread-tsne/
- Jo Etzel has a great blog if you want to know more about multivariate analysis: MVPA meandering
Cross-validation : what, which and how? by Pradeep Reedy Raamana at OHBM 2018 (30 min)
What can we say about weight maps from linear decoding models? https://www.pathlms.com/ohbm/courses/8246/sections/12542/video_presentations/116081
Neuroimaging toolboxes for representation similarity analysis (RSA), support vector machine (SVM) and others...
I know almost nothing about resting state but I have been told this site is worth having a look at.
- Course/Tutorials
- Tools ( ??? )
fmridenoise neuropycon
https://www.kaggle.com/chrisfilo/diffusion-weighted-imaging-dwi-analysis-with-dipy#
the salmon http://neuroskeptic.blogspot.com/2009/09/fmri-gets-slap-in-face-with-dead-fish.html
imager's fallacy http://blog.efpsa.org/2014/12/17/a-psychologists-guide-to-reading-a-neuroimaging-paper/
All Resolution Inference: Increasing Spatial Specificity of fMRI with Valid Circular Inference https://www.pathlms.com/ohbm/courses/8246/sections/12541/video_presentations/115959
http://blogs.discovermagazine.com/neuroskeptic/2011/09/11/neuroscience-fails-stats-101/
Probabilistic Treshold-free Cluster Enhancement pTFCE (probabilistic TFCE) is a cluster-enahncement method to improve detectability of neuroimaging signal. It performs topology-based belief boosting by integrating cluster information into voxel-wise statistical inference.
In case you do not remember how random field theory works to correct for multiple comparison, check this.
https://brainder.org/2011/09/05/fdr-corrected-fdr-adjusted-p-values/#comment-15388
A primer on permutation testing (not only) for MVPA by Carsten Allefeld (36 min)
The prevalence test
https://nikokriegeskorte.org/tag/encoding-models/
Python based pRF analysis.
A pRF analysis toolbox called the Seriously Annoying Matlab SuRFer from Sam Schwarzkopf.
Non neuroimaging cases
- multiverse analysis
- specification curves presented in a talk at SPSP [here](https://www.youtube.com/watch?v=g75jstZidX0
- vibration of effects
This paper comes with some material to apply bayesian decoding analysis to neuronal data can be of interest.
As someone said on twitter there is a cottage industry of blog posts trying to understand/explain this:
- https://medium.com/@solopchuk/intuitions-on-predictive-coding-and-the-free-energy-principle-3fc5bcedc754
- http://romainbrette.fr/what-is-computational-neuroscience-xxix-the-free-energy-principle/
- https://kaiu.me/2017/06/23/deep-active-inference-for-artificial-general-intelligence/
- http://slatestarcodex.com/2018/03/04/god-help-us-lets-try-to-understand-friston-on-free-energy
- http://www.aliannajmaren.com/2017/07/27/how-to-read-karl-friston-in-the-original-greek/
And a tutorial
A tutorial series on DCM by Kevin Aquino [6 hrs]
Renzo Hubert is keeping track of the most recent development of laminar MRI via twitter but also on his blog. He also curates laminar-fMRI related talks on his Youtube channel or papers in this google spreahsheet.
- This blog post has a list of most of the softwares that are related to laminar fMRI.
- A more recent tool not listed in there for creating equivolumetric surfaces.
In terms of functional data there is high-res VASO (CBV) dataset here.
For anatomical data you can have a look here.
BID conference
- A talk by Tom Nichols at OHBM 2018 for an overview
- A practical by Camille Maumet at OHBM 2018 on meta-analysis: [slides] ( ??? )
- A talk on ALE and brainmap https://www.pathlms.com/ohbm/courses/8246/sections/12542/video_presentations/116066
NiMARE is a Python library for coordinate- and image-based meta-analysis. Chris Gorgolewski wrote a tutorial on how to use it.
https://github.com/NeuroVault/metanalysis_examples
For coordinate based meta-analysis:
For image based meta-analysis:
- IBMA is the Image-Based Meta-Analysis toolbox for SPM.