Data Science meets Neuroscience at the University – The verical occipital fasciculus - a century old controversy – Resolved through computational neuroanatomy!
Data Science meets Neuroscience at the University – The verical occipital fasciculus - a century old controversy – Resolved through computational neuroanatomy!
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2015-11-16-viscog
On Github arokem / 2015-11-16-viscog
Vision and Cognition Lab UW Psychology November 16, 2015
Data Science meets Neuroscience at the University
Ariel Rokem, University of Washington eScience Institute
Follow along at http://arokem.github.io/2015-11-16-viscog
The fourth paradigm of science
1. Empirical (experimental)
2. Theoretical (mathematical)
3. Simulation (computational)
4. Data-intensive (eScience)
Data Science?
Data Science?
Programming and software engineering
Data management
Statistics and machine learning
Data visualization and communication
A focus on reproducibility and openess
New role for data scientists
Facilitate data-intensive research in different fields (inter- and cross- disciplinary)
Focus on methodology
Focus on reproducibility
Contribute to openly available tools, rather than/in addition to peer-reviewed publications
"Career paths for data scientists that recognize and reward contributions in methodology, computation, or development of tools are important."
Incubator projects
Focused, intensive, collaborative projects
Data scientists + domain scientists
Results that wouldn't be possible otherwise
Data Science for Social Good
Inspired by DSSG program at U Chicago, GA Tech
10-week internship program
16 DSSG fellows/students
6 high-school students from ALVA program
4 projects (+project leads!)
+ Data scientist mentors
Predictors of Permanent Housing for Homeless Families
Neuroimaging and Data Science
Normal behavior is supported by brain connectivity
Not just passive cables
Brain connections change with development
Individual differences account for differences in behaviour
Adapt with learning
This has clinical significance
Diffusion MRI
Diffusion MRI
Diffusion MRI
Modeling diffusion
Diffusion statistics
From diffusion to tracks
From diffusion to tracks
From diffusion to tracks
DIPY: Diffusion MRI in Python
Started in 2009 by Eleftherios Garyfallidis
Contributors from at least six different countries and many different labs
Why Python?
The lingua franca of reproducible computational science
Open source
Easy to learn
Come learn Python!
January 7th-8th, the WRF Data Science Studio (Physics/Astronomy building)Why Python?
The lingua franca of reproducible computational science
Open source
Easy to learn
Phenomenal ecosystem of open-source tools
The scipy & nipy ecosystem
The scipy & nipy ecosystem
The scipy & nipy ecosystem
The scipy & nipy ecosystem
Diffusion MRI: the challenge of validation
A statistical learning approach
Dipy cross-validation API
http://tinyurl.com/dipy-wmm (powered by http://mybinder.org)model = ReconstModel(gtab, ...)
fit = model.fit(data, ...) # => ReconstFit
For example
model = dti.TensorModel(gtab)
fit = model.fit(data1)
prediction = fit.predict(gtab)
RMSE = np.sqrt(\ np.mean((prediction - data2) ** 2), -1))
rRMSE = RMSE / np.sqrt(\ np.mean((data1 - data2) ** 2), -1))
When you've only measured once
# Use a k of 2
dti_pred = kfold_xval(dti_model, data, 2)
csd_pred = kfold_xval(csd_model, data, 2)
LiFE: Linear Fascicle Evaluation
Forward model from the tracks to the measured signal
From diffusion to tracks
From tracks to diffusion
fiber_model = life.FiberModel(gtab)
fit = fiber_model.fit(data, tracks)
prediction = fit.predict(gtab)
optimized_tracks = tracks[fit.beta>0]
The verical occipital fasciculus - a century old controversy
The verical occipital fasciculus - a century old controversy
Resolved through computational neuroanatomy!
The VOF is strategically located
To transmit information between dorsal and ventral visual areas
Summary
The eScience Institute
The Dipy project
In vivo validation through statistical learning
Come visit the Data Science Studio!