Projects:ModelingFunctionalActivationPatterns - Revision history

Georgehchen: /* Literature */

2012-11-28T19:59:59Z

Literature

Georgehchen at 19:55, 28 November 2012

2012-11-28T19:55:35Z

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2012-11-28T19:51:44Z

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2012-11-28T19:51:33Z

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2012-11-28T19:48:46Z

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Georgehchen: Created page with ' Back to NA-MIC Collaborations, MIT Algorithms, NOTOC = Modeling Functional Activation Patterns =…'

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Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= Modeling Functional Activation Patterns =

For a given cognitive task such as language processing, the location of
corresponding functional regions in the brain may vary across subjects
relative to anatomy. We present a probabilistic generative model that accounts
for such variability as observed in functional magnetic resonance imaging
(fMRI) data. We relate our approach to
sparse coding that estimates a basis consisting of functional regions in the
brain. Individual fMRI data is represented as a weighted sum of these
functional regions that undergo deformations. We demonstrate the proposed
method on a language fMRI study. Our method identified activation regions
that agree with known literature on language processing and established
correspondences among activation regions across subjects, producing more
robust group-level effects than anatomical alignment alone.

= Description =

== Experiments ==

== Conclusion ==

We developed a model that accounts for spatial variability of functional
activation regions in the brain via deformations of weighted dictionary
elements. Learning model parameters and estimating deformations yield
correspondences of functional activation regions in the brain across subjects.
We demonstrate our model in a language fMRI study, which contains substantial
variability. We plan to validate the detected parcels using data from
different fMRI language experiments.

== Literature ==

= Key Investigators =

MIT: George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, and Polina Golland

= Publications =

@@ Line 121: / Line 121: @@
 Cortex 20(1), 130–140, 2010.
-[4] E. Fedorenko, P.J. Hsieh, A. Nieto-Casta ̃on, S. Whitfield-Gabrieli, N. Kanwisher.
+[4] E. Fedorenko, P.J. Hsieh, A. Nieto-Castanon, S. Whitfield-Gabrieli, N. Kanwisher.
 "New method for fMRI investigations of language: Defining ROIs functionally
 in individual subjects". Neurophysiology 104, 1177–1194, 2010.

← Older revision		Revision as of 19:55, 28 November 2012
Line 130:		Line 130:

	= Publications =		= Publications =
		+
		+	[http://www.na-mic.org/publications/pages/display?search=Projects%3AModelingFunctionalActivationPatterns&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked\| NA-MIC Publications Database on Modeling Functional Activation Patterns]

@@ Line 40: / Line 40: @@
 We train our model on an fMRI study of 82 subjects reading sentences and
 pronounceable non-words [4]. First, we apply the standard
-fMRI general linear model for the sentences vs.~non-words contrast.
+fMRI general linear model for the sentences vs. non-words contrast.
 We apply our model to subjects' t-statistic maps
 thresholded at p-value=0.01. These images are pre-aligned using the subjects'
 anatomical MRI scans via affine registration.
-[[File:Georgehc disc group parcel supports.png|800px|thumb|center|]]
+[[File:Georgehc disc group parcel supports.png|800px|thumb|center|
-[[File:Georgehc disc wrfx.png|800px|thumb|center|]]
+[[File:Georgehc disc wrfx.png|800px|thumb|center|
 Fig. 1(a)
@@ Line 93: / Line 111: @@
 in fMRI activation location: a bayesian hierarchical spatial model". Biometrics 65(4),
 –1051, 2009.
 [2] B. Thirion, P. Pinel, A. Tucholka, A. Roche, P. Ciuciu, J.F. Mangin, J.B. Poline.
@@ Line 99: / Line 116: @@
 reliability of fMRI group studies". IEEE Transactions in Medical Imaging 26(9),
 –1269, 2007.
 [3] M.R. Sabuncu, B.D. Singer, B. Conroy, R.E. Bryan, P.J. Ramadge, J.V. Haxby.
 "Function-based intersubject alignment of human cortical anatomy". Cerebral
 Cortex 20(1), 130–140, 2010.
 [4] E. Fedorenko, P.J. Hsieh, A. Nieto-Casta ̃on, S. Whitfield-Gabrieli, N. Kanwisher.
 "New method for fMRI investigations of language: Defining ROIs functionally
 in individual subjects". Neurophysiology 104, 1177–1194, 2010.
 = Key Investigators =

@@ Line 1: / Line 1: @@
- Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
+[[File:Example.jpg]] Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
 __NOTOC__
 = Modeling Functional Activation Patterns =
@@ Line 45: / Line 45: @@
 anatomical MRI scans via affine registration.
 Fig. 1(a)

← Older revision		Revision as of 19:48, 28 November 2012
Line 37:		Line 37:

	== Experiments ==		== Experiments ==
		+
		+	We train our model on an fMRI study of 82 subjects reading sentences and
		+	pronounceable non-words [4]. First, we apply the standard
		+	fMRI general linear model for the sentences vs.~non-words contrast.
		+	We apply our model to subjects' t-statistic maps
		+	thresholded at p-value=0.01. These images are pre-aligned using the subjects'
		+	anatomical MRI scans via affine registration.
		+
		+
		+	Fig. 1(a)
		+	shows the spatial support of the final learned
		+	dictionary elements on four slices.
		+	Fig. 1(b) illustrates some
		+	of the dictionary elements extracted by the algorithm. The dictionary elements
		+	include regions previously reported as indicative of lexical and structural
		+	language processing [4], namely portions of the temporal lobes,
		+	the right cerebellum, and the left frontal lobe. There are also dictionary
		+	elements corresponding to the medial prefrontal cortex, the posterior
		+	cingulate, and the precuneus.
		+
		+	To evaluate the quality of the estimated alignment, we apply the estimated
		+	deformation to held-out time course data for each subject and perform standard
		+	weighted random effects analysis. We then look at significance
		+	values within the support of each dictionary element. Importantly, for drawing
		+	conclusions on the group-level parcels defined by the estimated dictionary
		+	elements, within each parcel, it is the peak and regions around the peak that
		+	are of interest rather than the full support of the dictionary element. Thus,
		+	to quantify the advantage of our method, within each dictionary element, we
		+	compare the top 25% highest significance values for our method versus those
		+	of anatomical alignment (Fig. 2). We observe that accounting for
		+	functional variability via deformations results in substantially higher peak
		+	significance values within the estimated group-level parcels, suggesting
		+	better overlap of these functional activation regions across subjects. On
		+	average, our method improves the significance of group analysis by roughly an
		+	order of magnitude when evaluating the top 25% significance values. Even if
		+	we look at the top 50% of significance values in each dictionary element, the
		+	results remain similar to those in Fig. 2.

	== Conclusion ==		== Conclusion ==

@@ Line 17: / Line 17: @@
 = Description =
 == Experiments ==
@@ Line 31: / Line 49: @@
 == Literature ==
 = Key Investigators =

Projects:ModelingFunctionalActivationPatterns - Revision history

Georgehchen: /* Literature */

Georgehchen at 19:55, 28 November 2012

Georgehchen at 19:53, 28 November 2012

Georgehchen at 19:51, 28 November 2012

Georgehchen at 19:51, 28 November 2012

Georgehchen at 19:48, 28 November 2012

Georgehchen at 19:38, 28 November 2012

Georgehchen: Created page with ' Back to NA-MIC Collaborations, MIT Algorithms, __NOTOC__ = Modeling Functional Activation Patterns =…'

Georgehchen: Created page with ' Back to NA-MIC Collaborations, MIT Algorithms, NOTOC = Modeling Functional Activation Patterns =…'