Abstract
Given a simple noun such as apple, and a question such as "Is it edible?," what processes take place in the human brain? More specifically, given the stimulus, what are the interactions between (groups of) neurons (also known as functional connectivity) and how can we automatically infer those interactions, given measurements of the brain activity? Furthermore, how does this connectivity differ across different human subjects? In this work, we show that this problem, even though originating from the field of neuroscience, can benefit from big data techniques; we present a simple, novel good-enough brain model, or GeBM in short, and a novel algorithm Sparse-SysId, which are able to effectively model the dynamics of the neuron interactions and infer the functional connectivity. Moreover, GeBM is able to simulate basic psychological phenomena such as habituation and priming (whose definition we provide in the main text). We evaluate GeBM by using real brain data. GeBM produces brain activity patterns that are strikingly similar to the real ones, where the inferred functional connectivity is able to provide neuroscientific insights toward a better understanding of the way that neurons interact with each other, as well as detect regularities and outliers in multisubject brain activity measurements.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 216-229 |
| Number of pages | 14 |
| Journal | Big Data |
| Volume | 2 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 2014 |
Bibliographical note
Funding Information:Research was supported by grants NSF IIS-1247489, NSF IIS-1247632, NSF CDI 0835797, NIH/NICHD 12165321, DARPA FA87501320005, and IARPA FA865013C7360, and by Google. This work was also supported in part by a fellowship to Alona Fyshe from the Multimodal Neuroimaging Training Program funded by NIH grants T90DA022761 and R90DA023420. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding parties. We would also like to thank Erika Laing and the UPMC Brain Mapping Center for their help with the data and visualization of our results. Finally, we would like to thank Gustavo Sudre for collecting and sharing the data, and Dr. Brian Murphy for valuable conversations.
Funding Information:
Research was supported by grants NSF IIS-1247489, NSF IIS- 1247632, NSF CDI 0835797, NIH/NICHD 12165321, DARPA FA87501320005, and IARPA FA865013C7360, and by Google. This work was also supported in part by a fellowship to Alona Fyshe from the Multimodal Neuroimaging Training Program funded by NIH grants T90DA022761 and R90DA023420.
Publisher Copyright:
© Mary Ann Liebert, Inc. 2014.