<html><head><style type='text/css'>p { margin: 0; }</style></head><body><div style='font-family: arial,helvetica,sans-serif; font-size: 10pt; color: #000000'>Yida Wang will present his research seminar/general exam on Friday January 13 <br>at 9:30AM in Room 302 (note room). The members of his committee are: Kai Li <br>(advisor), Moses Charikar, and Nicholas
Turk-Browne (Psychology, Neuroscience).<br>Everyone is invited to attend his talk, and those faculty members wishing to remain <br>for the oral exam following are welcome to do so. His abstract and reading list <br>follow below.<br>-------------------------------<br><div style="color:#000;font-weight:normal;font-style:normal;text-decoration:none;font-family:Helvetica,Arial,sans-serif;font-size:12pt;">Computing and decoding brain full pairwise correlation matrices<br>
<br>
<div class="moz-text-html" lang="x-western">
Human brain imaging such as Functional Magnetic Resonance Imaging
(fMRI) has made transformational impacts on neuroscience. However,
due to technology limitations in the past, researchers have
constrained their analyses by making assumptions that are biased
or false, leading to missed opportunities for science discovery,
and in the worst case, incorrect inferences.Realizing that the
processes in the brain may depend on widely distributed
interactions among regions, we propose a novel method of analyzing
the brain by its full correlation structure and show the
effectiveness of the correlation analysis. The full correlation
matrices lead to dramatic increase of data amount by at most
extracting 7 orders of magnitude more information. With careful
data computing, allocating and appropriate feature selection, we
design a parallel tool to get the full correlation matrices in
distribution and analyze them efficiently via classification to
show the effectiveness of connectivity analysis. The critical
voxels picked by our tool well match the existing theory of
cognitive neuroscience in terms of location. And based on the
selected voxels, experimental results demonstrate that the
correlation based classification gets a comparable accuracy as
traditional activation based classification within a subject, and
is able to attain substantially higher accuracy than activation
when analyzing across subjects, which, for the first time, shows
the stability of brain correlation structures among different
subjects.<br>
<br>
This work is advised by Prof. Moses Charikar and Prof. Kai Li at
Princeton Computer Science Department, and Prof. Nicholas
Turk-Browne and Prof. Jonathan Cohen at Princeton Neuroscience
Institute.<br>
<br>
Reading List:<br>
Book:<br>
Modern Operating Systems, 3rd Edition, Andrew Tanenbaum.<br>
<br>
Computer Architecture: A Quantitative Approach, 4th Edition, John
Hennessy and David Patterson.<br>
<br>
Paper:<br>
Goto, K. and Van De Geijn, R. 2008. Anatomy of high-performance
matrix multiplication. ACM Trans. Math. Softw. 34, 3.<br>
<br>
J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on
Large Clusters, Proc. 6th Symp. Operating System Design and
Implementation (OSDI), Usenix Assoc., 2004, pp. 137-150.<br>
<br>
Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave,
Justin Ma, Murphy McCauley, Michael Franklin, Scott Shenker, and
Ion Stoica. Resilient distributed datasets: A fault-tolerant
abstraction for in-memory cluster computing. Technical Report
UCB/EECS-2011-82, EECS Department, University of California,
Berkeley, Jul 2011.<br>
<br>
Pereira F, Mitchell T M and Botvinick M. Machine learning
classifiers and fMRI: A tutorial overview Neuroimage. 2008<br>
<br>
Norman, K. A., Polyn, S. M., Detre, G. J. and Haxby, J. V. Beyond
mind-reading: multi-voxel pattern analysis of fMRI data. Trends
Cogn. Sci. 10, 424-430 (2006).<br>
<br>
Jacob Y, Rapson A, Kafri M, Baruchi I, Hendler T, Jacob EB.
Revealing voxel correlation cliques by functional holography
analysis of fMRI. Journal of Neuroscience Methods 2010; 191:
126-137.<br>
<br>
Smith SM. Overview of fMRI analysis. The British Journal of
Radiology, 77 (2004), 167-175.<br>
<br>
Kriegeskorte N, Goebel R, Bandettini P. Information-based
functional brain mapping. Proceedings of the National Academy of
Sciences 2006; 103(10): 3863-3868.<br>
<br>
Haxby JV, Guntupalli JS, Connolly AC, Halchenko YO, Conroy BR,
Gobbini MI, Hanke M, Ramadge PJ. A Common, High-Dimensional Model
of the Representational Space in Human Ventral Temporal Cortex.
Neuron, 72(2), 2011.<br></div></div><br></div></body></html>