Chantel Prat and Eric Shea-Brown, the 2018 February UWIN seminar speakers.Please join us for February’s UWIN seminar! This installment features a fascinating pair of short talks by UWIN faculty members Chantel Prat and Eric Shea-Brown:

  • “Neurometrics: Resting-state qEEG Predicts Second Language (L2) Learning as well as a Standardized Language Aptitude Test”
    Chantel Prat, Associate Professor, Department of Psychology, Institute for Learning & Brain Sciences, University of Washington
  • “Linking the statistics of network activity and network connectivity”
    Eric Shea-Brown, Assistant Professor, Department of Physiology & Biophysics, University of Washington

The seminar is on Wednesday, February 14th, 2018, at 3:30pm in Husky Union Building (HUB) 337.  Refreshments will be served prior to the talks.

——
Abstracts:

“Neurometrics: Resting-state qEEG Predicts Second Language (L2) Learning as well as a Standardized Language Aptitude Test” (Chantel Prat):

Decades of research using fMRI and EEG have shown that properties of network-level brain functioning at rest can be used to characterize individual differences in a variety of cognitive abilities. My current work explores the predictive utility of various characterizations of brain function using quantitative EEG (qEEG), resting-state fMRI, structural MRI, task-related fMRI, and psychometric tests of cognitive abilities for understanding individual differences in L2 learning. In the current talk, I’ll describe a study showing that 5 minutes of eyes-closed resting-state qEEG data can predict L2 learning as well, or better, than a standardized language aptitude test that takes a bit over an hour to administer. Future directions include the development and testing of neurometric assessment tools for predicting subsequent complex behaviors.


“Linking the statistics of network activity and network connectivity” (Eric Shea-Brown):

There is an avalanche of new data on the brain’s activity, revealing the collective dynamics of vast numbers of neurons. In principle, these collective dynamics can be of almost arbitrarily high dimension, with many independent degrees of freedom — and this may reflect powerful capacities for general computing or information. In practice, datasets reveal a range of outcomes, including collective dynamics of much lower dimension — and this may reflect the structure of tasks or latent variables. For what networks does each case occur? Our contribution to the answer is a new framework that links tractable statistical properties of network connectivity with the dimension of the activity that they produce. I’ll describe where we have succeeded, where we have failed, and the many avenues that remain.