Nicole - Revision history

imported>Psych204B at 06:55, 15 March 2012

2012-03-15T06:55:11Z

← Older revision		Revision as of 06:55, 15 March 2012
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	<br>		<br>

	In this interactive figure, ArtRepair displays four graphs of various features of the data. First, the global average signal is plotted in arbitrary scanner units in the top graph. The second plot conveys similar information but instead displays this intensity in terms of standard deviations away from the mean signal intensity of the run. The final two ~~groups~~ display motion information~~, as~~ translational and rotational motion in the third graph and as overall scan-to-scan movement in the fourth graph.		In this interactive figure, ArtRepair displays four graphs of various features of the data. First, the global average signal is plotted in arbitrary scanner units in the top graph. The second plot conveys similar information but instead displays this intensity in terms of standard deviations away from the mean signal intensity of the run. The final two plots display motion information; translational and rotational motion is shown in the third graph and overall scan-to-scan movement is plotted in the fourth graph.

	The user can select the threshold at which to repair volumes using the “Up” and “Down” buttons located in the lower left corner of the figure. As shown in the images here, I selected a motion threshold of 0.5mm/TR, which corresponds to a threshold of 0.9 SD in this particular dataset. One could imagine that researchers studying patient populations or children may prefer a more lenient motion threshold. This same figure output generated for the second block of the subject with the most motion is displayed below:		The user can select the threshold at which to repair volumes using the “Up” and “Down” buttons located in the lower left corner of the figure. As shown in the images here, I selected a motion threshold of 0.5mm/TR, which corresponds to a threshold of 0.9 SD in this particular dataset. One could imagine that researchers studying patient populations or children may prefer a more lenient motion threshold. This same figure output generated for the second block of the subject with the most motion is displayed below:
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	<br>		<br>

			Volumes flagged as outliers can be repaired using one of three methods: interpolation, despiking, or replacing with the mean. In interpolation, the volumes in question are replaced with the average of the nearest uncorrected neighbors. Despiking also interpolates from neighbor volumes, but instead averages the two nearest neighbors regardless of their outlier status. Finally, replacing each outlier volume with the mean signal intensity can be used to statistically remove these volumes from future analyses. For this project, I selected interpolation as the best way to treat large chunks of consecutive outlier volumes.. As such, a new series of volumes was generated with corrections applied to the outlier volumes denoted with red lines in the images above. Additionally, ArtRepair outputs a vector of scans to deweight (shown with green lines above) that can be used in future level 1 analyses or in fixed effects group level analyses.

	To ensure quality control, I created movies of both uncorrected and corrected data. To see these movies, please follow the links below. Note the significant reduction in motion and intensity artifacts after the application of ArtRepair’s interpolation methods.		To ensure quality control, I created movies of both the uncorrected and corrected data. To see these movies, please follow the links below. Note the significant reduction in motion and intensity artifacts after the application of ArtRepair’s interpolation methods.

	[http://www.stanford.edu/~hallinen/204B_videos/uncorrected.m4v \| Uncorrected, realigned raw images.]		[http://www.stanford.edu/~hallinen/204B_videos/uncorrected.m4v \| Uncorrected, realigned raw images.]

imported>Psych204B at 06:47, 15 March 2012

2012-03-15T06:47:03Z

← Older revision		Revision as of 06:47, 15 March 2012
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	<br>		<br>

	In this interactive figure, ArtRepair displays four graphs of various features of the data. First, the global average signal is plotted in arbitrary scanner units in the top graph. The second plot conveys similar information but instead displays this intensity in terms of standard deviations away from the mean signal intensity of the run. The final two groups display motion information, as translational and rotational motion in the third graph and as overall scan-to-scan movement in the fourth graph.		In this interactive figure, ArtRepair displays four graphs of various features of the data. First, the global average signal is plotted in arbitrary scanner units in the top graph. The second plot conveys similar information but instead displays this intensity in terms of standard deviations away from the mean signal intensity of the run. The final two groups display motion information, as translational and rotational motion in the third graph and as overall scan-to-scan movement in the fourth graph.

	The user can select the threshold at which to repair volumes using the “Up” and “Down” buttons located in the lower left corner of the figure. As shown in the images here, I selected a motion threshold of 0.5mm/TR, which corresponds to a threshold of 0.9 SD in this particular dataset. One could imagine that researchers studying patient populations or children may prefer a more lenient motion threshold. This same figure output generated for the second block of the subject with the most motion is displayed below:		The user can select the threshold at which to repair volumes using the “Up” and “Down” buttons located in the lower left corner of the figure. As shown in the images here, I selected a motion threshold of 0.5mm/TR, which corresponds to a threshold of 0.9 SD in this particular dataset. One could imagine that researchers studying patient populations or children may prefer a more lenient motion threshold. This same figure output generated for the second block of the subject with the most motion is displayed below:

imported>Psych204B at 06:46, 15 March 2012

2012-03-15T06:46:32Z

← Older revision		Revision as of 06:46, 15 March 2012
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	=== Interpolation using motion correction algorithms ===		=== Interpolation using motion correction algorithms ===
	To identify and correct motion outliers, the Artifact Repair toolbox ([http://cibsr.stanford.edu/tools/human-brain-project/artrepair-software.html \|Art Repair]) was used in conjunction with SPM. The following graphic is produced by the ArtRepair software’s feature “Bad Volumes: Detect and Repair”:		To identify and correct motion outliers, the Artifact Repair toolbox ([http://cibsr.stanford.edu/tools/human-brain-project/artrepair-software.html \|Art Repair]) was used in conjunction with SPM. The following graphic is produced by the ArtRepair software’s feature “Bad Volumes: Detect and Repair”:
			<br>
	[[File: ArtRepair_201_block1_SPMmask.jpg\|400px\| Motion during study block 1.]]		[[File: ArtRepair_201_block1_SPMmask.jpg\|400px\| Motion during study block 1.]]
	<br>		<br>

	In this interactive figure, ArtRepair displays four graphs of various features of the data. First, the global average signal is plotted in arbitrary scanner units in the top graph. The second plot conveys similar information but instead displays this intensity in terms of standard deviations away from the mean signal intensity of the run. The final two groups display motion information, as translational and rotational motion in the third graph and as overall scan-to-scan movement in the fourth graph.		In this interactive figure, ArtRepair displays four graphs of various features of the data. First, the global average signal is plotted in arbitrary scanner units in the top graph. The second plot conveys similar information but instead displays this intensity in terms of standard deviations away from the mean signal intensity of the run. The final two groups display motion information, as translational and rotational motion in the third graph and as overall scan-to-scan movement in the fourth graph.

imported>Psych204B at 06:45, 15 March 2012

2012-03-15T06:45:03Z

← Older revision		Revision as of 06:45, 15 March 2012
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	=== Interpolation using motion correction algorithms ===		=== Interpolation using motion correction algorithms ===
			To identify and correct motion outliers, the Artifact Repair toolbox ([http://cibsr.stanford.edu/tools/human-brain-project/artrepair-software.html \|Art Repair]) was used in conjunction with SPM. The following graphic is produced by the ArtRepair software’s feature “Bad Volumes: Detect and Repair”:
	[[File: ArtRepair_201_block1_SPMmask.jpg\|400px\| Motion during study block 1.]]		[[File: ArtRepair_201_block1_SPMmask.jpg\|400px\| Motion during study block 1.]]
	<br>		<br>
			In this interactive figure, ArtRepair displays four graphs of various features of the data. First, the global average signal is plotted in arbitrary scanner units in the top graph. The second plot conveys similar information but instead displays this intensity in terms of standard deviations away from the mean signal intensity of the run. The final two groups display motion information, as translational and rotational motion in the third graph and as overall scan-to-scan movement in the fourth graph.

			The user can select the threshold at which to repair volumes using the “Up” and “Down” buttons located in the lower left corner of the figure. As shown in the images here, I selected a motion threshold of 0.5mm/TR, which corresponds to a threshold of 0.9 SD in this particular dataset. One could imagine that researchers studying patient populations or children may prefer a more lenient motion threshold. This same figure output generated for the second block of the subject with the most motion is displayed below:

	[[File: ArtRepair_201_block2_SPMmask.jpg\|400px\| Motion during study block 2.]]		[[File: ArtRepair_201_block2_SPMmask.jpg\|400px\| Motion during study block 2.]]
	<br>		<br>


	To ensure quality control, I created movies of both uncorrected and corrected data. To see these movies, please follow the links below. Note the significant reduction in motion and intensity artifacts after the application of ArtRepair’s interpolation methods.		To ensure quality control, I created movies of both uncorrected and corrected data. To see these movies, please follow the links below. Note the significant reduction in motion and intensity artifacts after the application of ArtRepair’s interpolation methods.

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	== MR Analysis ==		== MR Analysis ==
	The MR data were analyzed using SPM software tools in MATLAB: [http://www.fil.ion.ucl.ac.uk/spm/]~~. To identify and correct motion outliers, the Artifact Repair toolbox ([http://cibsr.stanford.edu/tools/human-brain-project/artrepair-software.html \|Art Repair]) was used in conjunction with SPM~~.		The MR data were analyzed using SPM software tools in MATLAB: [http://www.fil.ion.ucl.ac.uk/spm/].

imported>Psych204B at 06:34, 15 March 2012

2012-03-15T06:34:22Z

← Older revision		Revision as of 06:34, 15 March 2012
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	[http://www.stanford.edu/~hallinen/204B_videos/uncorrected.m4v \| Uncorrected, realigned raw images.]		[http://www.stanford.edu/~hallinen/204B_videos/uncorrected.m4v \| Uncorrected, realigned raw images.]

	~~<br>~~
	[http://www.stanford.edu/~hallinen/204B_videos/corrected.m4v \| Realigned raw images that have been motion corrected.]		[http://www.stanford.edu/~hallinen/204B_videos/corrected.m4v \| Realigned raw images that have been motion corrected.]





	= Methods =		= Methods =
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	To accommodate future group analyses, each subjects’ data was transformed into MNI template space. MNI is an abbreviation for [http://en.wikipedia.org/wiki/Montreal_Neurological_Institute Montreal Neurological Institute].		To accommodate future group analyses, each subjects’ data was transformed into MNI template space. MNI is an abbreviation for [http://en.wikipedia.org/wiki/Montreal_Neurological_Institute Montreal Neurological Institute].
	First, each subject’s high resolution T1-weighted anatomical scan from each subject to an MNI template (‘Coregister’) and segmenting the grey and white matter (‘Segment’). Each subjects’ functional data was then smoothed and normalized by applying the same transformation as was applied to his or her T1 scan (‘Normalize’).		First, each subject’s high resolution T1-weighted anatomical scan from each subject to an MNI template (‘Coregister’) and segmenting the grey and white matter (‘Segment’). Each subjects’ functional data was then smoothed at a kernel of 6mm and normalized by applying the same transformation as was applied to his or her T1 scan (‘Normalize’).

	[[File:PreprocPipeline.jpg \| Preprocessing pipeline with addition of ART repair step.]]		[[File:PreprocPipeline.jpg \| Preprocessing pipeline with addition of ART repair step.]]
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	= Group Level Analyses =		= Group Level Analyses =
			One-sample t-tests were used to analyze several contrasts at the group level. Of interest in the images below is the contrast between using the formula and rest (activations indicate regions that are significantly more active during formula processing over rest). These analyses were modeled using random effects (each subjects’ level 1 statistics were computed separately and then analyzed at the group level). Because this study is exploratory, a relatively low T-threshold of 2.0 was used in the following images and no cluster-thresholding was applied. These data are displayed on the standard MNI-152 template as they have been smoothed and normalized to the template.

	== No Motion Correction ==		== No Motion Correction ==
	[[File:uncorrected.png \|500px\| Group level parameter map with no motion correction.]]		[[File:uncorrected.png \|500px\| Group level parameter map with no motion correction.]]
			<br>
			Here, the group level activations of all subjects with no motion correction are displayed in orange.

	== Using Motion Correction ==		== Using Motion Correction ==
	[[File:corrected_overlay.png \|500px\| Group level parameter map with motion correction.]]		[[File:corrected_overlay.png \|500px\| Group level parameter map with motion correction.]]
			<br>
			In this image, the group level statistics have been computed using the motion-corrected level-1 data of the participant with the most severe motion outliers. These activations are overlaid in blue and share many regions in common with the uncorrected activations (displayed in orange).

	== ~~Dropping A~~ Subject ~~With~~ Too Much Motion ==		== Excluding a Subject with Too Much Motion ==
	[[File:all_overlays.png \|500px\| Group level parameter map with motion subject excluded.]]		[[File:all_overlays.png \|500px\| Group level parameter map with motion subject excluded.]]
			<br>
			Finally, this image shows the result of excluding the subject with the most motion from the dataset; only 6 of the 7 total subjects’ data are included in this analysis. These results, overlaid in green, are clearly more spatially constrained than the other two analyses. This comparison highlights the potential of one subject to affect group analyses of a small sample size.

	= Conclusions =		= Conclusions =

imported>Psych204B at 06:18, 15 March 2012

2012-03-15T06:18:26Z

← Older revision		Revision as of 06:18, 15 March 2012
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	To ensure quality control, I created movies of both uncorrected and corrected data. To see these movies, please follow the links below. Note the significant reduction in motion and intensity artifacts after the application of ArtRepair’s interpolation methods.		To ensure quality control, I created movies of both uncorrected and corrected data. To see these movies, please follow the links below. Note the significant reduction in motion and intensity artifacts after the application of ArtRepair’s interpolation methods.

	[http://www.stanford.edu/~hallinen/uncorrected.m4v \| Uncorrected, realigned raw images.]		[http://www.stanford.edu/~hallinen/204B_videos/uncorrected.m4v \| Uncorrected, realigned raw images.]

	<br>		<br>
	[http://www.stanford.edu/~hallinen/corrected.m4v \| Realigned raw images that have been motion corrected.]		[http://www.stanford.edu/~hallinen/204B_videos/corrected.m4v \| Realigned raw images that have been motion corrected.]

imported>Psych204B at 06:17, 15 March 2012

2012-03-15T06:17:08Z

← Older revision		Revision as of 06:17, 15 March 2012
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	[[File: ArtRepair_201_block2_SPMmask.jpg\|400px\| Motion during study block 2.]]		[[File: ArtRepair_201_block2_SPMmask.jpg\|400px\| Motion during study block 2.]]
	<br>		<br>
	~~[[File: Uncorrected_attempt2~~.~~m4v\|400px\| Uncorrected~~, ~~realigned raw images~~.]]		To ensure quality control, I created movies of both uncorrected and corrected data. To see these movies, please follow the links below. Note the significant reduction in motion and intensity artifacts after the application of ArtRepair’s interpolation methods.

	[[~~File~~: ~~Corrected_attempt2~~.m4v~~\|400px~~\| Realigned raw images that have been motion corrected.]]		[http://www.stanford.edu/~hallinen/uncorrected.m4v \| Uncorrected, realigned raw images.]

			<br>
			[http://www.stanford.edu/~hallinen/corrected.m4v \| Realigned raw images that have been motion corrected.]

imported>Psych204B at 06:08, 15 March 2012

2012-03-15T06:08:53Z

← Older revision		Revision as of 06:08, 15 March 2012
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	[[File: ArtRepair_201_block2_SPMmask.jpg\|400px\| Motion during study block 2.]]		[[File: ArtRepair_201_block2_SPMmask.jpg\|400px\| Motion during study block 2.]]
			<br>
			[[File: Uncorrected_attempt2.m4v\|400px\| Uncorrected, realigned raw images.]]

			[[File: Corrected_attempt2.m4v\|400px\| Realigned raw images that have been motion corrected.]]





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	== No Motion Correction ==		== No Motion Correction ==
	[[File:level1_uncorrected.png \|500px\| Subject-level parameter map with no motion correction.]]		[[File:level1_uncorrected.png \|500px\| Subject-level parameter map with no motion correction.]]
			<br>
	In this image, the contrast between formula use and rest is shown in orange. This analysis has been conducted using data that have been preprocessed but not motion corrected. The widespread activation patterns and large portions of activation outside of the brain and on the brain stem are artifacts of the subject’s motion.		In this image, the contrast between formula use and rest is shown in orange. This analysis has been conducted using data that have been preprocessed but not motion corrected. The widespread activation patterns and large portions of activation outside of the brain and on the brain stem are artifacts of the subject’s motion.

	== Using Motion Correction ==		== Using Motion Correction ==
	[[File:level1_correctedOverlay.png \|500px\| Subject-level parameter map with motion correction.]]		[[File:level1_correctedOverlay.png \|500px\| Subject-level parameter map with motion correction.]]
			<br>
	Now, the same contrast (formula use – rest) is shown for motion corrected files in blue. This image illustrates the effects of motion correction on single subject analyses. Although some false positives still remain, the activation is more concentrated as the outlier volumes have been repaired using interpolation.		Now, the same contrast (formula use – rest) is shown for motion corrected files in blue. This image illustrates the effects of motion correction on single subject analyses. Although some false positives still remain, the activation is more concentrated as the outlier volumes have been repaired using interpolation.

imported>Psych204B at 06:06, 15 March 2012

2012-03-15T06:06:16Z

← Older revision		Revision as of 06:06, 15 March 2012
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	== No Motion Correction ==		== No Motion Correction ==
	[[File:level1_uncorrected.png \|500px\| Subject-level parameter map with no motion correction.]]		[[File:level1_uncorrected.png \|500px\| Subject-level parameter map with no motion correction.]]
			In this image, the contrast between formula use and rest is shown in orange. This analysis has been conducted using data that have been preprocessed but not motion corrected. The widespread activation patterns and large portions of activation outside of the brain and on the brain stem are artifacts of the subject’s motion.

	== Using Motion Correction ==		== Using Motion Correction ==
	[[File:level1_correctedOverlay.png \|500px\| Subject-level parameter map with motion correction.]]		[[File:level1_correctedOverlay.png \|500px\| Subject-level parameter map with motion correction.]]
			Now, the same contrast (formula use – rest) is shown for motion corrected files in blue. This image illustrates the effects of motion correction on single subject analyses. Although some false positives still remain, the activation is more concentrated as the outlier volumes have been repaired using interpolation.

	= Group Level Analyses =		= Group Level Analyses =
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	== Dropping A Subject With Too Much Motion ==		== Dropping A Subject With Too Much Motion ==
	[[File:all_overlays.png \|500px\| Group level parameter map with motion subject excluded.]]		[[File:all_overlays.png \|500px\| Group level parameter map with motion subject excluded.]]

imported>Psych204B at 06:01, 15 March 2012

2012-03-15T06:01:20Z

← Older revision		Revision as of 06:01, 15 March 2012
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	=== Interpolation using motion correction algorithms ===		=== Interpolation using motion correction algorithms ===

			[[File: ArtRepair_201_block1_SPMmask.jpg\|400px\| Motion during study block 1.]]
			<br>

			[[File: ArtRepair_201_block2_SPMmask.jpg\|400px\| Motion during study block 2.]]


	= Methods =		= Methods =