Merge pull request #2 from adasegroup/seminar2

Seminar2

.gitignore

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+data
+100206
+sub-100206_MNI159_space.nii.gz
+subject_name0GenericAffine.mat
+subject_name1InverseWarp.nii.gz
+subject_name1Warp.nii.gz
+subject_nameInverseWarped.nii.gz
+subject_nameWarped.nii.gz
+T1_fs_preprocessed.nii
+.vscode

README.md

@@ -11,10 +11,17 @@ This course is specifically aimed at MSc and PhD students with basic knowledge o
 ### The list of the current seminars published (will be updated with time):
 * SEMINAR 0 (04.09) BASH for Engeneering pipelines, CometML
  * Before seminar you are to install Docker https://www.docker.com/
-* SEMINAR 1 (04.09) EEG analysis, Machine Learning in EEG
-* SEMINAR 2 (04.09) MRI data analysis, sources, databases, tools
-  * Before the seminar you are to install FreeSurfer https://surfer.nmr.mgh.harvard.edu/
-* SEMINAR 3 (04.09) Machine Learning for structural MRI data analysis
+* SEMINAR 1 (07.09, 11.09) EEG analysis, Machine Learning in EEG
+* SEMINAR 2 (14.09) MRI data analysis, sources, databases, tools 
+  * Before seminar, please, do the following:
+    1) Download data from link https://drive.google.com/drive/folders/1P0ZhS1EoDY6fncnJb7foNFPjY5uoN6r0?usp=sharing
+    2) Clone repository to your local machine
+    3) Run docker locally and ensure it working with command `docker run hello-world`
+    4) In terminal: `cd to NEUROML/seminar2`
+    5) Type command `docker build -t neuroml/seminar2:0.0.1` and wait for successfull build
+    6) Run `docker run --rm -it -v /directory/to/downloaded/data:/workspace/data -p 8080:8080 neuroml/seminar2:0.0.1`
+    7) Open browser (preferebly Chrome) -> localhost:8080
+* SEMINAR 3 (18.09) Machine Learning for structural MRI data analysis
   * Before the seminar you are to get an account and granted access here https://db.humanconnectome.org/data/projects/HCP_1200
 * SEMINAR 4 (04.09) fMRI data preprocessing, analysis, GLM
 * SEMINAR 5 (04.09) Functional connectivity analysis and Machine Learning modelling

seminar2/.ipynb_checkpoints/Dockerfile-checkpoint

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+FROM poldracklab/fmriprep:1.1.8
+
+ENV DEBIAN_FRONTEND noninteractive
+
+RUN apt-get update -qq && \
+        apt-get install --yes --no-install-recommends \
+        curl g++ wget ca-certificates libxt6 libxrandr2 patch unzip zip pigz jq cmake moreutils time bzip2
+
+RUN wget https://ftpmirror.gnu.org/parallel/parallel-20190222.tar.bz2 \
+    && bzip2 -dc parallel-20190222.tar.bz2 | tar xvf - \
+    && cd parallel-20190222 \
+    && ./configure && make && make install \
+    && mkdir -p /workspace
+
+ENV TERM xterm-color \
+    LANG=C.UTF-8 \
+    LC_ALL=C.UTF-8 \
+    HOME /workspace
+
+WORKDIR /workspace
+
+RUN pip install nipype nilearn sklearn plumbum \
+    && sync
+
+RUN pip install --upgrade pip \
+    && pip install jupyter jupyterlab \
+    && sync
+
+
+COPY license.txt /opt/freesurfer/license.txt
+
+EXPOSE 8080
+
+ENTRYPOINT ["/usr/local/miniconda/bin/jupyter", "lab", "--ip=0.0.0.0", "--port 8080", "--allow-root", "--NotebookApp.token=''" "--notebook-dir='/workdir'"]

seminar2/.ipynb_checkpoints/README-checkpoint.md

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+# NEUROML2020:
+## Neuroimaging and Machine Learning for Biomedicine
+This is a repository containing seminars and lecture materials for the Skoltech course on Machine Learning in Neuroimaging data, Fall 2020.
+
+Course by NeuroML ADASE lab: http://adase.group/neuro/ 
+
+## Course description:
+This course is specifically aimed at MSc and PhD students with basic knowledge of Machine Learning techniques pursuing further growth in neuroimaging data analysis, either in clinical practice or in neuroscience research. The course will provide you with training in the aspects of human neuroimaging methods, data properties and applied Machine Learning techniques. The course is focused on brain biophysics, scanning techniques and methods of data analysis. Students will develop a broad set of skills that are essential to study brain function, brain pathology and solve biomedical tasks with state-of-the-art Machine Learning and Computer Vision techniques.​
+
+
+### The list of the current seminars published (will be updated with time):
+* SEMINAR 0 (04.09) BASH for Engeneering pipelines, CometML
+ * Before seminar you are to install Docker https://www.docker.com/
+* SEMINAR 1 (04.09) EEG analysis, Machine Learning in EEG
+* SEMINAR 2 (04.09) MRI data analysis, sources, databases, tools 
+  * Before the seminar you are to install FreeSurfer https://surfer.nmr.mgh.harvard.edu/
+* SEMINAR 3 (04.09) Machine Learning for structural MRI data analysis
+  * Before the seminar you are to get an account and granted access here https://db.humanconnectome.org/data/projects/HCP_1200
+* SEMINAR 4 (04.09) fMRI data preprocessing, analysis, GLM
+* SEMINAR 5 (04.09) Functional connectivity analysis and Machine Learning modelling
+* SEMINAR 6 (04.09) Deep Learning models and fMRI data analysis
+* SEMINAR 7 (04.09) Advanced topics: ML engeneering pipelines, domain adaptation, brain visualization 
+
+#### Datasets used (please get a personal account and complete data use agreement):
+* Human Connectome Project https://db.humanconnectome.org/data/projects/HCP_1200
+* UCLA Consortium for Neuropsychiatric Phenomics LA5c Study https://openneuro.org/datasets/ds000030/versions/1.0.0
+* Autism Brain Imaging Data Exchange http://fcon_1000.projects.nitrc.org/indi/abide/
+
+#### Software used (please get a personal account and complete usage agreement):
+* FreeSurfer https://surfer.nmr.mgh.harvard.edu/
+* FmriPrep https://fmriprep.org/en/stable/
+* Docker https://www.docker.com/

seminar2/.ipynb_checkpoints/error-checkpoint.log

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+mghRead(/home/yarkin/code/NEUROML2020/data/fs_preproc/100206/T1.mgz, -1): could not open file

seminar2/.ipynb_checkpoints/multiproc-checkpoint.sh

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+#! /bin/bash
+
+export SUBJECTS_DIR=/output
+export input_path=/data/raw
+
+ids=()
+
+for dirname in /${input_path}/*; do
+
+    subj_id=${dirname#/${input_path}/}    # remove "/input/sub-"
+    subj_id=${subj_id%/}             # remove trailing "/"
+
+    if [[ ! -d "/output/$subj_id" ]]; then
+        ids+=( "$subj_id" )
+    fi
+done
+
+printf 'Found ID: %s\n' "${ids[@]}" >&2
+
+printf '%s\n' "${ids[@]}" | parallel --jobs 10 --timeout 250% --progress recon-all -expert /input/expert.opts -s {.} -i /${input_path}/{.}/unprocessed/3T/T1w_MPR1/{.}_3T_T1w_MPR1.nii.gz -all -qcache