Award Abstract #0613595
Multimodal Dynamic Imaging of Human Brain Activity

NSF Org:	IIS Division of Information & Intelligent Systems
Initial Amendment Date:	September 20, 2006
Latest Amendment Date:	February 26, 2008
Award Number:	0613595
Award Instrument:	Continuing grant
Program Manager:	Kenneth C. Whang IIS Division of Information & Intelligent Systems CSE Directorate for Computer & Information Science & Engineering
Start Date:	October 1, 2006
Expires:	March 31, 2010 (Estimated)
Awarded Amount to Date:	$695298
Investigator(s):	Scott Makeig smakeig@ucsd.edu (Principal Investigator) Kenneth Kreutz-Delgado (Co-Principal Investigator) Bhaskar Rao (Co-Principal Investigator)
Sponsor:	University of California-San Diego Office of Contract & Grant Admin La Jolla, CA 92093 858/534-0246
NSF Program(s):	SLC ACTIVITIES, CRCNS
Field Application(s):	0104000 Information Systems, 0116000 Human Subjects
Program Reference Code(s):	OTHR, HPCC, 9218, 7327, 0000
Program Element Code(s):	T884, 7704, 7327

ABSTRACT

he central active challenge we are constantly addressing in daily life is to correctly assess the intent of others ('What is she trying to do? ...') and the import of sensory events ('What - good or bad - may happen now? ...') based on active perception ('It looks to me like she is trying to ...') and retrieved associations (''And she was the one who ...'). The corresponding problem for cognitive neuroscience is to identify, ideally from non-invasive brain activity recordings, those patterns of distributed brain activity that accompany and support active human cognition and behavior. This problem has two parts: First, -What patterns of distributed brain dynamics follow from, accompany, and predict specific world events and subject behavior? -To fully understand the experience and behavior of subjects in performing a given task, we must take into account both the import of each task event to the subject and the intent of each of behavioral event. These factors cannot be known directly, but they may be accurately guessed or inferred, in many cases, from detailed recordings of subject behavior and from the specific historical context in which each recorded environmental or behavioral event occurs. In the case of electroencephalographic (EEG) and/or magnetoencephalographic (MEG) signals recorded non-invasively from the human scalp, a second part of the problem remains -Which brain areas generate the identified signal patterns?'

The usual approach to analyzing electromagnetic scalp data has been to separate recorded events and behavior into a few simple categories, to average the recorded brain dynamics time locked to each event category, and then to apply physical inverse source estimation methods to scalp maps of peaks in the resulting averages. This project will explore using new machine learning methods, including advanced independent component analysis (ICA) and sparse Bayesian learning (SBL) methods, to jointly model the recorded task event, subject behavior, and brain dynamic data recorded in a complex learning task. The project has two goals: First, to identify patterns in unaveraged EEG and/or MEG data that reliably accompany subject behavior in specific contexts, and second to determine the exact areas of the subject's cortical mantle that locally synchonize their electromagnetic activities to produce the identified scalp patterns. If successful, the project will enhance the value of noninvasive electromagnetic brain imaging for identifying and measuring, with high temporal and spatial resolution, complex, distributed patterns of locally synchronous cortical activity that support active human cognition.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Akalin Acar Z, Makeig S.  "Realistic Modeling of the Human Head from Available Data.,"  13th Annual Meeting of the Organization for Human Brain Mapping,  2007, 

JA Palmer; K Kreutz-Delgado; BD Rao; S Makeig.  "Modeling and Estimation of Dependent Subspaces with Non-Radially Symmetric and Skewed Densities,"  Proceedings of the 7th International Symposium on Independent Component Analysis, Lecture Notes in Computer Science, Springer,,  2007, 

JA Palmer; S Makeig; K Kreutz-Delgado; BD Rao.  "Newton Method for the ICA Mixture Model,"  Proceedings of the 33rd IEEE International Conference on Acoustics and Signal Processing (ICASSP 2008,  2007,  p. 1805.

Makeig, S, Ramírez, RR, Weber D, Wipf D, Dale C, Simpson G.  "Current density distributions of independent sources during directed spatial attention computed by sparse Bayesian learning.,"  Biomag 2006 Proceedings, 15th International Conference on Biomagnetism,  2006, 

Palmer J, Kreutz-Delgado K, Rao B, Makeig S.  "Modeling and Estimation of Dependent Subspaces with Non-Radially Symmetric and Skewed Densities.,"  Proceedings of ICA 2007, the 7th International Conference on Independent Component Analysis and Signal Separation,  2007, 

Palmer JA, Kreutz-Delgado K, Makeig S.  "Super-Gaussian Mixture Source Model for ICA,"  Proceedings of the 6th International Symposium on Independent Component Analysis,  2006, 

Ramírez RR, Makeig S.  "Neuroelectromagnetic source imaging using multiscale geodesic neural bases and sparse Bayesian learning.,"  12th Annual Meeting of the Organization for Human Brain Mapping,  2007, 

Ramírez RR, Makeig S.  "Neuroelectromagnetic source imaging of spatiotemporal brain dynamical patterns using frequency-domain independent vector analysis (IVA) and geodesic sparse Bayesian learning (gSBL).,"  13th Annual Meeting of the Organization for Human Brain Mapping,  2007, 

Ramírez RR, Wipf D, Rao B, Makeig S.  "Sparse Bayesian learning for estimating the spatial orientations and extents of distributed sources.,"  Biomag 2006 Proceedings, 15th International Conference on Biomagnetism,  2006, 

Wipf D, Palmer J, Rao B, Kreutz-Delgado K.  "Performance evaluation of latent variable models with sparse priors.,"  Proceedings of the 32nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP),  2007, 

Wipf D, Palmer JA, Rao B.  "Relating Bayesian Methods for Neuroelectromagnetic Source Localization.,"  Biomag 2006 Proceedings, 15th International Conference on Biomagnetism,  2006, 

Wipf D, Ramírez RR, Palmer JA, Makeig S, Rao BD.  "Analysis of empirical Bayesian methods for neuroelectromagnetic source localization.,"  Advances in Neural Information Processing Systems,  v.19,  2006, 

Wipf DP, Rao BD.  "An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem.,"  IEEE Transactions on Signal Processing,  v.55(7),  2007,  p. 3704.


(Showing: 1 - 13 of 13).

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