Max - Revision history

imported>Psych204B: /* Study Design */

2012-03-16T04:10:46Z

Study Design

← Older revision		Revision as of 04:10, 16 March 2012
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	[[File: Study Design.png \| Figure 0: Study Design]]		[[File: Study Design.png \| Figure 0: Study Design]]

	Faces, body parts, and other objects were shown to participants in the scanner for 1,000 ms per stimulus. Some stimuli were repeated (6 times) and others were presented only once. Repeated trials had an average of 20.44 trials between repeats. Trial types (repeated vs. non-repeated vs. blank vs. scrambled image) were counterbalanced and the repeated trials were counterbalanced within trial type.		Faces, body parts, and other objects were shown to participants in the scanner for 1,000 ms per stimulus. Some stimuli were repeated (6 times) and others were presented only once. Repeated trials had an average of 20.44 trials between repeats. Trial types (repeated vs. non-repeated vs. blank vs. scrambled image) were counterbalanced and the repeated trials were counterbalanced within trial type. Subjects also performed a categorization task during the study, pressing a different button for each stimulus category.
	~~Repeated and nonrepeated images~~
	~~Average of 20~~.~~44 trials between repeats~~
	~~Counterbalanced type of trial for the (n – 1)st trial~~
	~~Counterbalanced categories within types~~
	~~Categorization task~~


	== Scan Properties ==		== Scan Properties ==

imported>Psych204B at 04:09, 16 March 2012

2012-03-16T04:09:59Z

← Older revision		Revision as of 04:09, 16 March 2012
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	== Study Design ==		== Study Design ==

	[[File: Study Design.~~jpg~~ \| Figure 0: Study Design]]		[[File: Study Design.png \| Figure 0: Study Design]]

	Faces, body parts, and other objects were shown to participants in the scanner for 1,000 ms per stimulus. Some stimuli were repeated (6 times) and others were presented only once. Repeated trials had an average of 20.44 trials between repeats. Trial types (repeated vs. non-repeated vs. blank vs. scrambled image) were counterbalanced and the repeated trials were counterbalanced within trial type.		Faces, body parts, and other objects were shown to participants in the scanner for 1,000 ms per stimulus. Some stimuli were repeated (6 times) and others were presented only once. Repeated trials had an average of 20.44 trials between repeats. Trial types (repeated vs. non-repeated vs. blank vs. scrambled image) were counterbalanced and the repeated trials were counterbalanced within trial type.

imported>Psych204B at 04:09, 16 March 2012

2012-03-16T04:09:39Z

← Older revision		Revision as of 04:09, 16 March 2012
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	= Methods =		= Methods =

			== Study Design ==

			[[File: Study Design.jpg \| Figure 0: Study Design]]

			Faces, body parts, and other objects were shown to participants in the scanner for 1,000 ms per stimulus. Some stimuli were repeated (6 times) and others were presented only once. Repeated trials had an average of 20.44 trials between repeats. Trial types (repeated vs. non-repeated vs. blank vs. scrambled image) were counterbalanced and the repeated trials were counterbalanced within trial type.
			Repeated and nonrepeated images
			Average of 20.44 trials between repeats
			Counterbalanced type of trial for the (n – 1)st trial
			Counterbalanced categories within types
			Categorization task


			== Scan Properties ==

	=== MR acquisition ===		=== MR acquisition ===

imported>Psych204B: /* Conclusions */

2012-03-16T04:05:51Z

Conclusions

← Older revision		Revision as of 04:05, 16 March 2012
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	== Multi-Voxel Patterns ==		== Multi-Voxel Patterns ==
			The multi-voxel patterns validate and extend the GLM results. In the face-selective ROIs, we see more correlated activation between non-repeated and repeated face trials, but no correlations between face patterns and other patterns. Across the other ROIs, we see similar specific adaptation effects for body parts (face-selective ROI #2 and body part-selective ROI) and cars (face-selective ROI #2). We also see some mixed evidence of common patterns of activation across non-repeated categories as shown by the weaker correlations between body parts, cars, and houses.

	~~The~~ multi-voxel patterns ~~validate~~ and ~~extend~~ the ~~GLM results~~. ~~In the face-selective ROIs~~, ~~we see more correlated activation between non~~-~~repeated and repeated face trials~~, ~~but no correlations between face patterns~~ and ~~other patterns~~. ~~Across~~ the ~~other ROIs, we see similar specific adaptation effects for body parts (face-selective ROI #2~~ and ~~body part-selective ROI)~~ and ~~cars (face-selective ROI #2). We also see some mixed evidence of common patterns of activation across non-repeated categories as shown by~~ the ~~weaker correlations between body parts, cars, and houses~~.		== General Conclusions ==
			In this exploratory study, I examined several different ways to fit predictors to an ROI timecourse and their effects on predictor magnitudes, variance explained, and rank ordering of activations. Moreover, I attempted to understand the data on a finer spatial scale by scrutinizing multi-voxel patterns. While several more analyses would be necessary to test the statistical and pragmatic significance of the adaptation effects found here, this project begins to demonstrate the importance of choices made in the stream of data analysis. fMRI datasets are rich, complex, noisy, multi-componential, and vast. The same dataset can tell very different stories depending on the types of analyses performed and extracting that story is both responsibility of and opportunity for the researcher.

	= References - Resources and related work =		= References - Resources and related work =

imported>Psych204B: /* References - Resources and related work */

2012-03-16T03:56:27Z

References - Resources and related work

← Older revision		Revision as of 03:56, 16 March 2012
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	= References - Resources and related work =		= References - Resources and related work =
			[1] Repetition and the brain: neural models of stimulus specific effects.
			Kalanit Grill-Spector, Rik Henson and Alex Martin. Trends in cognitive science. 2006 Jan;10(1):14-23. Epub Nov. 28th, 2005.

			[2] fMRI-adaptation and category selectivity in human ventral temporal cortex: Regional differences across time scales.
			Weiner, KS., Sayres, R., Vinberg J., Grill-Spector, K. J Neurophysiol. 2010 Apr 7. [Epub ahead of print] 103(6):3349-3365

imported>Psych204B: /* Multi-Voxel Patterns */

2012-03-16T03:54:09Z

Multi-Voxel Patterns

← Older revision		Revision as of 03:54, 16 March 2012
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	== Multi-Voxel Patterns ==		== Multi-Voxel Patterns ==

	The multi-voxel patterns validate and extend the GLM results. In the face-selective ROIs, we see more correlated activation between non-repeated and repeated face trials, but no correlations between face patterns and other patterns. Across the other ROIs, we see similar specific adaptation effects for body parts (face-selective ROI #2 and body part-selective ROI) and cars (face-selective ROI #2).		The multi-voxel patterns validate and extend the GLM results. In the face-selective ROIs, we see more correlated activation between non-repeated and repeated face trials, but no correlations between face patterns and other patterns. Across the other ROIs, we see similar specific adaptation effects for body parts (face-selective ROI #2 and body part-selective ROI) and cars (face-selective ROI #2). We also see some mixed evidence of common patterns of activation across non-repeated categories as shown by the weaker correlations between body parts, cars, and houses.

	We also see some common patterns of activation across non-repeated categories as ~~evidenced~~ by

	= References - Resources and related work =		= References - Resources and related work =

imported>Psych204B: /* Multi-Voxel Patterns */

2012-03-16T03:52:30Z

Multi-Voxel Patterns

← Older revision		Revision as of 03:52, 16 March 2012
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	== Multi-Voxel Patterns ==		== Multi-Voxel Patterns ==

	The multi-voxel patterns validate and extend the GLM results. In the face-selective ROIs, we see more correlated activation between non-repeated and repeated face trials, but no correlations between face patterns and other patterns. Across the other ROIs, we see similar specific adaptation effects for		The multi-voxel patterns validate and extend the GLM results. In the face-selective ROIs, we see more correlated activation between non-repeated and repeated face trials, but no correlations between face patterns and other patterns. Across the other ROIs, we see similar specific adaptation effects for body parts (face-selective ROI #2 and body part-selective ROI) and cars (face-selective ROI #2).

			We also see some common patterns of activation across non-repeated categories as evidenced by

	= References - Resources and related work =		= References - Resources and related work =

imported>Psych204B: /* Multi-Voxel Patterns */

2012-03-16T03:50:51Z

Multi-Voxel Patterns

← Older revision		Revision as of 03:50, 16 March 2012
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	== Multi-Voxel Patterns ==		== Multi-Voxel Patterns ==

			The multi-voxel patterns validate and extend the GLM results. In the face-selective ROIs, we see more correlated activation between non-repeated and repeated face trials, but no correlations between face patterns and other patterns. Across the other ROIs, we see similar specific adaptation effects for

	= References - Resources and related work =		= References - Resources and related work =

imported>Psych204B: /* Standard GLM vs. Deconvolution */

2012-03-16T03:48:06Z

Standard GLM vs. Deconvolution

← Older revision		Revision as of 03:48, 16 March 2012
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	= Conclusions =		= Conclusions =

	== ~~Standard GLM~~ vs. Deconvolution ==		== HRFs vs. Deconvolution ==
			Comparing across the different HRFs, apparent differences arise in the overall magnitude of effects (far lower betas for the Dale & Buckner HRF), rank ordering of effects (non-repeated body parts vs. repeated faces, for example), and variance explained (Dale & Buckner does far worse). The magnitude of adaptation effects is much greater for some HRFs than others.

			Deconvolution explains far more variance than any of the fitted HRFs (35% vs. 19%), and as we can see from the average time course, seems to model a very clean HRF, in which the rise, peak, and undershoot occur at the same time for each trial.

	== Multi-Voxel Patterns ==		== Multi-Voxel Patterns ==

	= References - Resources and related work =		= References - Resources and related work =

imported>Psych204B: /* Conclusions */

2012-03-16T03:38:37Z

Conclusions

← Older revision		Revision as of 03:38, 16 March 2012
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	= Conclusions =		= Conclusions =

	~~Here is where you say what your results mean~~.		== Standard GLM vs. Deconvolution ==

			== Multi-Voxel Patterns ==

	= References - Resources and related work =		= References - Resources and related work =