Award Abstract # 0904625
Finding Structure in the Space of Activation Profiles in fMRI

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Initial Amendment Date: August 20, 2009
Latest Amendment Date: August 20, 2009
Award Number: 0904625
Award Instrument: Standard Grant
Program Manager: Kenneth Whang
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2009
End Date: August 31, 2013 (Estimated)
Total Intended Award Amount: $850,000.00
Total Awarded Amount to Date: $850,000.00
Funds Obligated to Date: FY 2009 = $850,000.00
History of Investigator:
  • Polina Golland (Principal Investigator)
    polina@csail.mit.edu
  • Nancy Kanwisher (Co-Principal Investigator)
Recipient Sponsored Research Office: Massachusetts Institute of Technology
77 MASSACHUSETTS AVE
CAMBRIDGE
MA  US  02139-4301
(617)253-1000
Sponsor Congressional District: 07
Primary Place of Performance: Massachusetts Institute of Technology
77 MASSACHUSETTS AVE
CAMBRIDGE
MA  US  02139-4301
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): E2NYLCDML6V1
Parent UEI: E2NYLCDML6V1
NSF Program(s): CRCNS-Computation Neuroscience
Primary Program Source: 01000910DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7327, 9102, 9215, HPCC
Program Element Code(s): 732700
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

This project will develop and validate a novel approach to modeling fMRI activations in rich experiments with multiple stimuli or tasks. Rather than rely on a spatial correspondence across subjects to identify robust activations, the proposed methods will employ a notion of functional consistency, removing the need to assume spatial alignment among functional areas in different subjects. The resulting models of fMRI activation will also naturally enable studies of anatomical variability in homologous functional regions across subjects. The motivation for this work comes from visual fMRI studies that present subjects with several categories of visual stimuli. As fMRI studies move towards more complex experiments that include more stimuli, the space of possible brain responses grows exponentially, presenting a serious challenge for analysis methods. Explicit representations of fMRI activation patterns that enable exploratory search in the space of possible brain responses are at the core of this project. Computational models of brain activity based on such representations will significantly enrich the utility of fMRI for investigating the functional organization of the brain.

The research team will develop computational methods for fMRI analysis naturally suited for experiments with a multitude of stimuli. The approach is to model the space of all possible activation profiles, to search for stable clusters of activation profiles, and to characterize functionally homogeneous sets of brain locations associated with these clusters. A natural extension of the model will not only identify stable activation profiles but also group stimuli based on the similarity of the evoked activation profiles in the brain. Furthermore, this approach will yield a model of spatial variability of the detected functional areas, leading to better functionally-guided registration algorithms. The methods will be validated in a set of empirical experiments with a large number of visual stimuli in object perception and recognition tasks. The fMRI studies in this project will produce new insights into the functional organization of the ventral pathway of the visual system.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 13)
D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland "Discovering structure in the space of fMRI selectivity profiles." NeuroImage , v.3(15) , 2010 , p.1085-1098
D. Lashkari, R. Sridharan, and P. Golland. "Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations." Advances in Neural Information Processing Systems , v.23 , 2010 , p.1252--126
D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N.G. Kanwisher, and P. Golland "Search for patterns of functional specificity in the brain: A nonparametric hierarchical Bayesian model for group fMRI data." NeuroImage , v.59(2) , 2012 , p.134
D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N.G. Kanwisher, and P. Golland. "Search for Patterns of Functional Specificity in the Brain: A Nonparametric Hierarchical Bayesian Model for Group fMRI Data." NeuroImage , v.59 , 2012 , p.1348-1368
D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. "Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation." Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis , 2010 , p.8 pa
E. Vul, D. Lashkari, P.-J. Hsieh, P. Golland, and N. Kanwisher. "Data-driven functional clustering reveals dominance of face, place, and body selectivity in the ventral visual pathway." Journal of Neurophysiology , v.108 , 2012 , p.2306-2322
E. Vul, D. Lashkari, P.-J. Hsieh, P. Golland, N.G. Kanwisher "Data-driven functional clustering reveals dominance of face, place, and body selectivity in the ventral visual pathway" Journal of Neurophysiology , v.108 , 2012 , p.2306-2322
Fedorenko, E., Duncan, J. & Kanwisher, N. "Language-Selective and Domain-General RegionsLie Side by Side within Broca?s Area." Current Biology , v.22 , 2012 , p.2059-2062
G. Chen, E. Fedorenko, N.G. Kanwisher, and P. Golland. "Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain." Proc. Neural Information Processing Systems (NIPS) Workshop on Machine Learning and Interpretation in Neuroimaging (MLNI) , v.LNAI 72 , 2012 , p.68-75
G. Langs, B.H. Menze, D. Lashkari, and P. Golland. "Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast" NeuroImage , v.56(2) , 2011 , p.497-507
G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. "Learning an Atlas of a Cognitive Process via Functional Geometry." In Proc. IPMI: International Conference on Information Processing and Medical Imaging , v.LNCS 68 , 2011 , p.135-146
(Showing: 1 - 10 of 13)

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