Explore the brain with Nilearn
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1 Explore the brain with Nilearn Darya Chyzhyk Parietal team, INRIA, Paris-Saclay 2017
2 Content Concept MRI Data Decoding Functional connectivity Joblib Visualization Documentation
3 Concept
4 Concept How to start Data progress and development need more data public data, make more people working on data Soft Standardize procedure Interdisciplinary work Documentation justication help Motivation Make science open Promote research and collective work Visibility in the code Shared data
5 Concept How to start Data progress and development need more data public data, make more people working on data Soft Standardize procedure Interdisciplinary work Documentation justication help Motivation Make science open Promote research and collective work Visibility in the code Shared data
6 MRI
7 Data Clinical data (hospital) from private project Open data project: UK Biobank (500,000 participants), Human Connectome Project (HCP)... Autism Brain Imaging Data Exchange (ABIDE) Alzheimer's Disease Neuroimaging Initiative (ADNI) Fetching
8 Data Clinical data (hospital) from private project Open data project: UK Biobank (500,000 participants), Human Connectome Project (HCP) The Autism Brain Imaging Data Exchange (ABIDE) Alzheimer's Disease Neuroimaging Initiative (ADNI) Fetching
9 Fetching Structural, functional MRI, atlases
10 Decoding/Encoding Neuroimaging problem Decoding uses brain activity to predict information about the stimuli Encoding uses stimuli to predict activity in the brain
11 Decoding Haxby example 1 subject and two categories: faces and cats Decoding with SVM
12 Functional connectivity Functional connectivity is the connectivity between brain regions that share functional properties. FC can be dened as the temporal correlation between spatially remote neurophysiological events Time series extraction Correlation matrix Connectome
13 Functional connectivity Time series extraction
14 Functional connectivity Correlation matrix
15 Functional connectivity Connectome
16 Joblib Joblib is a set of tools to provide lightweight pipelining in Python: Avoid computing twice Parallel computing, n_jobs - the number of CPUs to use to do the computation
17 Visualization
18 Documentation User guide: description references Examples: notebook learn from working code
19 Conclusions Nilearn is advanced machine learning, pattern recognition and multivariate statistical techniques on neuroimaging data Python ecosystem Impact in comunity
20 Thank you! A. Abraham, L. Estève, E. Dohmatob, D. Bzdok, K. Dadi, A. Mensch, P. Gervais, V. Fritsch, S. Bougacha, B. Cipollini, M. Rahim, M. Perez-Guevara, K. J. Gorgolewski, Ó. Nájera, M. Eickenberg, A. Abadie, Y. Schwartz, A. Hoyos Idrobo, K. Shmelkov, F. Pedregosa, A. Mueller, J. Kossai, J. Grobler, A. Gramfort, M. Hanke, B. Thirion, and G. Varoquaux. Reference: Abraham, A.; Pedregosa, F.; Eickenberg, M.; Gervais, P.; Mueller, A.; Kossai, J.; Gramfort, A.; Thirion, B. & Varoquaux, G. Machine learning for neuroimaging with scikit-learn Frontiers in Neuroinformatics, 2014, 8, 14
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