2009 Jimmy Chion - Revision history

imported>Psych 204: /* Appendix I - Code */

2009-12-11T06:28:50Z

Appendix I - Code

← Older revision		Revision as of 06:28, 11 December 2009
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	= Appendix I - Code =		= Appendix I - Code =

	[[Media:~~code ICM~~.zip \| zip file with code]]		[[Media:codeJimmyChion.zip \| zip file with code]]

	= Appendix II - Work partition =		= Appendix II - Work partition =
	To give you a sense of the dynamic, Nathan did a lot of explaining to me, gave me some papers and the steps on how it should be coded. I coded up the first approach. He debugged it. We worked on it together and beefed it up, and then we worked on the second approach together (but it was mostly Nathan because he knew what mrVista functions to use). All in all, I learned a lot more working with Nathan than I would have by myself.		To give you a sense of the dynamic, Nathan did a lot of explaining to me, gave me some papers and the steps on how it should be coded. I coded up the first approach. He debugged it. We worked on it together and beefed it up, and then we worked on the second approach together (but it was mostly Nathan because he knew what mrVista functions to use). All in all, I learned a lot more working with Nathan than I would have by myself.

imported>Psych 204: /* Appendix I - Code */

2009-12-11T06:27:29Z

Appendix I - Code

← Older revision		Revision as of 06:27, 11 December 2009
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	= Appendix I - Code =		= Appendix I - Code =

	[[Media:~~Code_ICM~~.zip \| zip file with code]]		[[Media:code ICM.zip \| zip file with code]]

	= Appendix II - Work partition =		= Appendix II - Work partition =
	To give you a sense of the dynamic, Nathan did a lot of explaining to me, gave me some papers and the steps on how it should be coded. I coded up the first approach. He debugged it. We worked on it together and beefed it up, and then we worked on the second approach together (but it was mostly Nathan because he knew what mrVista functions to use). All in all, I learned a lot more working with Nathan than I would have by myself.		To give you a sense of the dynamic, Nathan did a lot of explaining to me, gave me some papers and the steps on how it should be coded. I coded up the first approach. He debugged it. We worked on it together and beefed it up, and then we worked on the second approach together (but it was mostly Nathan because he knew what mrVista functions to use). All in all, I learned a lot more working with Nathan than I would have by myself.

imported>Psych 204: /* Appendix I - Code */

2009-12-11T06:27:03Z

Appendix I - Code

← Older revision		Revision as of 06:27, 11 December 2009
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	= Appendix I - Code =		= Appendix I - Code =

	[[Media:~~code_ICM~~.zip \| zip file with code]]		[[Media:Code_ICM.zip \| zip file with code]]

	= Appendix II - Work partition =		= Appendix II - Work partition =
	To give you a sense of the dynamic, Nathan did a lot of explaining to me, gave me some papers and the steps on how it should be coded. I coded up the first approach. He debugged it. We worked on it together and beefed it up, and then we worked on the second approach together (but it was mostly Nathan because he knew what mrVista functions to use). All in all, I learned a lot more working with Nathan than I would have by myself.		To give you a sense of the dynamic, Nathan did a lot of explaining to me, gave me some papers and the steps on how it should be coded. I coded up the first approach. He debugged it. We worked on it together and beefed it up, and then we worked on the second approach together (but it was mostly Nathan because he knew what mrVista functions to use). All in all, I learned a lot more working with Nathan than I would have by myself.

imported>Psych 204: /* Appendix I - Code */

2009-12-11T06:26:33Z

Appendix I - Code

← Older revision		Revision as of 06:26, 11 December 2009
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	= Appendix I - Code =		= Appendix I - Code =

	[[Media:~~corrMap~~.m \| zip file with code]]		[[Media:code_ICM.zip \| zip file with code]]

	= Appendix II - Work partition =		= Appendix II - Work partition =
	To give you a sense of the dynamic, Nathan did a lot of explaining to me, gave me some papers and the steps on how it should be coded. I coded up the first approach. He debugged it. We worked on it together and beefed it up, and then we worked on the second approach together (but it was mostly Nathan because he knew what mrVista functions to use). All in all, I learned a lot more working with Nathan than I would have by myself.		To give you a sense of the dynamic, Nathan did a lot of explaining to me, gave me some papers and the steps on how it should be coded. I coded up the first approach. He debugged it. We worked on it together and beefed it up, and then we worked on the second approach together (but it was mostly Nathan because he knew what mrVista functions to use). All in all, I learned a lot more working with Nathan than I would have by myself.

imported>Wandell at 23:48, 10 December 2009

2009-12-10T23:48:17Z

← Older revision		Revision as of 23:48, 10 December 2009
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	Back to [[Psych204-Projects-2009 \|Psych 204 Projects 2009]]		Back to [[Psych204-Projects-2009 \|Psych 204 Projects 2009]]

	~~'''Intersubject correlations mapping'''~~

	Multisubject analysis remains a core problem in neuroimaging due to the greater variance in cortical activity is less well-defined areas of the brain. This project correlated an ROI in one subject's brain to multiple ROIs in another brain. In our first exploration, Dr. Nathan Witthoft and I wrote MATLAB code to find, for a given voxel in subject A, the ROI in subject B which held the voxel with the highest correlation. We iterated across all voxels in subject A's ROI and found the mode of where the highest correlations were from. The simple correlations were then mapped in various ways. In our second exploration, we wrote code to map (on a voxel by voxel basis) the average correlations of an ROI in subject A to the ROIs in subject B. Our code provides a tool to help quantify the similarity or dissimilarity of ROIs in different subjects.		Multisubject analysis remains a core problem in neuroimaging due to the greater variance in cortical activity is less well-defined areas of the brain. This project correlated an ROI in one subject's brain to multiple ROIs in another brain. In our first exploration, Dr. Nathan Witthoft and I wrote MATLAB code to find, for a given voxel in subject A, the ROI in subject B which held the voxel with the highest correlation. We iterated across all voxels in subject A's ROI and found the mode of where the highest correlations were from. The simple correlations were then mapped in various ways. In our second exploration, we wrote code to map (on a voxel by voxel basis) the average correlations of an ROI in subject A to the ROIs in subject B. Our code provides a tool to help quantify the similarity or dissimilarity of ROIs in different subjects.

	= ~~Background =~~		= Intersubject correlations mapping =

	~~== Research =~~=

	== The problem ==		== The problem ==

imported>Wandell at 23:47, 10 December 2009

2009-12-10T23:47:38Z

← Older revision		Revision as of 23:47, 10 December 2009
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	Back to [[Psych204-Projects-2009 \|Psych 204 Projects 2009]]		Back to [[Psych204-Projects-2009 \|Psych 204 Projects 2009]]

			'''Intersubject correlations mapping'''

	Multisubject analysis remains a core problem in neuroimaging due to the greater variance in cortical activity is less well-defined areas of the brain. This project correlated an ROI in one subject's brain to multiple ROIs in another brain. In our first exploration, Dr. Nathan Witthoft and I wrote MATLAB code to find, for a given voxel in subject A, the ROI in subject B which held the voxel with the highest correlation. We iterated across all voxels in subject A's ROI and found the mode of where the highest correlations were from. The simple correlations were then mapped in various ways. In our second exploration, we wrote code to map (on a voxel by voxel basis) the average correlations of an ROI in subject A to the ROIs in subject B. Our code provides a tool to help quantify the similarity or dissimilarity of ROIs in different subjects.		Multisubject analysis remains a core problem in neuroimaging due to the greater variance in cortical activity is less well-defined areas of the brain. This project correlated an ROI in one subject's brain to multiple ROIs in another brain. In our first exploration, Dr. Nathan Witthoft and I wrote MATLAB code to find, for a given voxel in subject A, the ROI in subject B which held the voxel with the highest correlation. We iterated across all voxels in subject A's ROI and found the mode of where the highest correlations were from. The simple correlations were then mapped in various ways. In our second exploration, we wrote code to map (on a voxel by voxel basis) the average correlations of an ROI in subject A to the ROIs in subject B. Our code provides a tool to help quantify the similarity or dissimilarity of ROIs in different subjects.

imported>Wandell: /* Project: Intersubject correlations mapping */

2009-12-10T23:47:19Z

Project: Intersubject correlations mapping

← Older revision		Revision as of 23:47, 10 December 2009
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	Multisubject analysis remains a core problem in neuroimaging due to the greater variance in cortical activity is less well-defined areas of the brain. This project correlated an ROI in one subject's brain to multiple ROIs in another brain. In our first exploration, Dr. Nathan Witthoft and I wrote MATLAB code to find, for a given voxel in subject A, the ROI in subject B which held the voxel with the highest correlation. We iterated across all voxels in subject A's ROI and found the mode of where the highest correlations were from. The simple correlations were then mapped in various ways. In our second exploration, we wrote code to map (on a voxel by voxel basis) the average correlations of an ROI in subject A to the ROIs in subject B. Our code provides a tool to help quantify the similarity or dissimilarity of ROIs in different subjects.		Multisubject analysis remains a core problem in neuroimaging due to the greater variance in cortical activity is less well-defined areas of the brain. This project correlated an ROI in one subject's brain to multiple ROIs in another brain. In our first exploration, Dr. Nathan Witthoft and I wrote MATLAB code to find, for a given voxel in subject A, the ROI in subject B which held the voxel with the highest correlation. We iterated across all voxels in subject A's ROI and found the mode of where the highest correlations were from. The simple correlations were then mapped in various ways. In our second exploration, we wrote code to map (on a voxel by voxel basis) the average correlations of an ROI in subject A to the ROIs in subject B. Our code provides a tool to help quantify the similarity or dissimilarity of ROIs in different subjects.

	~~= Project: Intersubject correlations mapping =~~

	= Background =		= Background =

imported>Wandell at 23:47, 10 December 2009

2009-12-10T23:47:07Z

← Older revision		Revision as of 23:47, 10 December 2009
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	Back to [[Psych204-Projects-2009 \|Psych 204 Projects 2009]]		Back to [[Psych204-Projects-2009 \|Psych 204 Projects 2009]]

			Multisubject analysis remains a core problem in neuroimaging due to the greater variance in cortical activity is less well-defined areas of the brain. This project correlated an ROI in one subject's brain to multiple ROIs in another brain. In our first exploration, Dr. Nathan Witthoft and I wrote MATLAB code to find, for a given voxel in subject A, the ROI in subject B which held the voxel with the highest correlation. We iterated across all voxels in subject A's ROI and found the mode of where the highest correlations were from. The simple correlations were then mapped in various ways. In our second exploration, we wrote code to map (on a voxel by voxel basis) the average correlations of an ROI in subject A to the ROIs in subject B. Our code provides a tool to help quantify the similarity or dissimilarity of ROIs in different subjects.

	= Project: Intersubject correlations mapping =		= Project: Intersubject correlations mapping =
	Multisubject analysis remains a core problem in neuroimaging due to the greater variance in cortical activity is less well-defined areas of the brain. This project correlated an ROI in one subject's brain to multiple ROIs in another brain. In our first exploration, Dr. Nathan Witthoft and I wrote MATLAB code to find, for a given voxel in subject A, the ROI in subject B which held the voxel with the highest correlation. We iterated across all voxels in subject A's ROI and found the mode of where the highest correlations were from. The simple correlations were then mapped in various ways. In our second exploration, we wrote code to map (on a voxel by voxel basis) the average correlations of an ROI in subject A to the ROIs in subject B. Our code provides a tool to help quantify the similarity or dissimilarity of ROIs in different subjects.
	~~<br>~~

	= Background =		= Background =

	== Research ==		== Research ==


	== The problem ==		== The problem ==

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

2009-12-10T23:46:16Z

References - Resources and related work

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

	~~References:~~<br>		<ol>
			<li>
	Kriegeskorte, N. Mur, M. & Bandettini, P (2008). Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience. Front Syst Neurosci (2) 4.		Kriegeskorte, N. Mur, M. & Bandettini, P (2008). Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience. Front Syst Neurosci (2) 4.
	http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2605405/		http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2605405/ </li>
			<li>
	Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004). Intersubject Synchronization of Cortical Activity During Natural Vision.Science (303) 1634-1640		Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., & Malach, R. (2004). Intersubject Synchronization of Cortical Activity During Natural Vision.Science (303) 1634-1640 </li>
			</ol>
	Software: mrVista		Software: mrVista

imported>Psych 204: /* Results */

2009-12-09T10:13:44Z

Results

← Older revision		Revision as of 10:13, 9 December 2009
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	These are example outputs using actual subject data.		These are example outputs using actual subject data.
	[[File:exampleoutput.jpg \|thumb\|600px\|center\|Row is subject kw and column is subject jc. Each row represents the histogram of voxels that picked the ROI on that column. Take the histogram in the figure below and convert that into a percentage and then make that into one row of this chart.]]		[[File:exampleoutput.jpg \|thumb\|600px\|center\|Row is subject kw and column is subject jc. Each row represents the histogram of voxels that picked the ROI on that column. Take the histogram in the figure below and convert that into a percentage and then make that into one row of this chart.]]
	[[File:examplehistogram.jpg \|thumb\|300px\|~~left~~\| Note: not the same subjects as previous figure. The histogram shows which from which ROI in subject B the voxels in subject A correlated with most. The ROI in subject A is the title of the graph.]]		[[File:examplehistogram.jpg \|thumb\|300px\|center\| Note: not the same subjects as previous figure. The histogram shows which from which ROI in subject B the voxels in subject A correlated with most. The ROI in subject A is the title of the graph.]]
	[[File:al_lFF-dyall.jpg \|thumb\|300px\|~~right~~\| Produced from the second approach. This figure shows subject A's left PPA to subject B's brain. The green ROIs are PPA and the pink ones are FFa. the blue and white are retrosplenial. I've included them because they appear using the same contrast (places>objects) that is used to define the PPA]]		[[File:al_lFF-dyall.jpg \|thumb\|300px\|center\| Produced from the second approach. This figure shows subject A's left PPA to subject B's brain. The green ROIs are PPA and the pink ones are FFa. the blue and white are retrosplenial. I've included them because they appear using the same contrast (places>objects) that is used to define the PPA]]
	[[File:alLPPA_dy.jpg \|thumb\|300px\|~~right~~\| Subject A's left FFA mapped to B's brain. Picks out the face areas much better]]		[[File:alLPPA_dy.jpg \|thumb\|300px\|center\| Subject A's left FFA mapped to B's brain. Picks out the face areas much better]]

	= Conclusions =		= Conclusions =