DS&NI@UW

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

All research is becoming data-intensive research

Including neuroimaging...

Van Horn and Toga (2014)

The fourth paradigm of science

1. Empirical (experimental)

2. Theoretical (mathematical)

3. Simulation (computational)

4. Data-intensive (eScience)

Jim Gray

Our mission: "All across our campus, the process of discovery will increasingly rely on researchers’ ability to extract knowledge from vast amounts of data... In order to remain at the forefront, UW must be a leader in advancing these techniques and technologies, and in making [them] accessible to researchers in the broadest imaginable range of fields"

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."

(From a recent NIH BD2K RFA)

Incubator projects

Focused, intensive, collaborative projects

Data scientists + domain scientists

Results that wouldn't be possible otherwise

Data Science for Social Good

Urban @ UW

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

Project Leads: Anjana Sundaram, Neil Roche, Bill & Melinda Gates Foundation
DSSG Fellows: Joan Wang, Jason Portenoy, Fabliha Ibnat, Chris Suberlak
ALVA Students: Cameron Holt, Xilalit Sanchez
eScience Data Scientist Mentors: Ariel Rokem, Bryna Hazelton

Family Trajectories through Programs

http://tinyurl.com/dssg-homeless

Neuroimaging and Data Science

Normal behavior is supported by brain connectivity

Image from Catani and ffytche (2015)

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

Isotropic diffusion

Diffusion MRI

Anisotropic diffusion

Diffusion MRI

Modeling diffusion

Basser, Mattielo and Le Bihan (1994)

Diffusion statistics

Mean diffusivity

Fractional anisotropy

Principal diffusion direction

From diffusion to tracks

From diffusion to tracks

DIPY: Diffusion MRI in Python

Part of the NIPY community

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

Diffusion MRI: the challenge of validation

Algorithm 1

Algorithm 2

A statistical learning approach

In-vivo validation

Measurement #1

Measurement #2

Test-retest reliability

Model

Cross-validation

Rokem et al. (2015)

Dipy cross-validation API

http://tinyurl.com/dipy-wmm
(powered by http://mybinder.org)


				gtab = gradient_table(...)
				
				model = ReconstModel(gtab, ...)
				

				fit = model.fit(data, ...) # => ReconstFit
				

				prediction = fit.predict(gtab, ...)

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))

Rokem et al. (2015)

Corpus callosum

Corticospinal tract

Superior
longitudinal fasciculus

DTI

Crossing fiber model

Rokem et al. (2015)

When you've only measured once

k-fold cross-validation


				
				# Use a k of 2
				

				dti_pred = kfold_xval(dti_model, data, 2)

csd_pred = kfold_xval(csd_model, data, 2)

Algorithm 1

Algorithm 2

LiFE: Linear Fascicle Evaluation

Forward model from the tracks to the measured signal

Pestilli et al. (2014)

From diffusion to tracks

From tracks to diffusion

...

Pestilli et al. (2014)

Solve for


										>>> X.shape 

										   (10e8, 10e6)

Pestilli et al. (2014)


				
				
					fiber_model = life.FiberModel(gtab)
				

					fit = fiber_model.fit(data, tracks)
			

					prediction = fit.predict(gtab)
				

					optimized_tracks = tracks[fit.beta>0]