2009 Christine McLeavey & Jessica Tsang - Revision history

imported>Psych 204: /* References */

2009-12-11T17:56:48Z

References

← Older revision		Revision as of 17:56, 11 December 2009
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	[1] Smith, Steven M. et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage 31 (2006) 1487-1505.		[1] Smith, Steven M. et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage 31 (2006) 1487-1505.<br>
	[2] [http://www.fmrib.ox.ac.uk/fsl/tbss/index.html]		[2] [http://www.fmrib.ox.ac.uk/fsl/tbss/index.html TBSS Website]

	= Appendix I - Code =		= Appendix I - Code =

imported>Psych 204: /* References */

2009-12-11T17:55:58Z

References

← Older revision		Revision as of 17:55, 11 December 2009
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	[1] Smith, Steven M. et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage 31 (2006) 1487-1505.		[1] Smith, Steven M. et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage 31 (2006) 1487-1505.
			[2] [http://www.fmrib.ox.ac.uk/fsl/tbss/index.html]

	= Appendix I - Code =		= Appendix I - Code =

imported>Psych 204: /* Conclusions */

2009-12-11T17:34:47Z

Conclusions

← Older revision		Revision as of 17:34, 11 December 2009
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	= Conclusions =		= Conclusions =

	If these results are to be trusted, they would create a significant caveat for TBSS users. However, it must be emphasized that these are preliminary studies, and at this point it is quite likely that the problem lies in the algorithm used to shift the mean FA scores, or in some other part of our methodology. The next steps of this project would focus on verifying our methodology. Once we are more confident in this, we ~~could~~ start examining TBSS's abilities and limitations in finer detail. Additionally, we could use a similar approach to look at a comparable program (SPM/VBM).		If these results are to be trusted, they would create a significant caveat for TBSS users. However, it must be emphasized that these are preliminary studies, and at this point it is quite likely that the problem lies in the algorithm used to shift the mean FA scores, or in some other part of our methodology. The next steps of this project would focus on verifying our methodology. Once we are more confident in this, we can start examining TBSS's abilities and limitations in finer detail. Additionally, we could use a similar approach to look at a comparable program (SPM/VBM).

	=== Future Work ===		=== Future Work ===

imported>Psych 204: /* Shift FA in Group 1 */

2009-12-11T17:32:22Z

Shift FA in Group 1

← Older revision		Revision as of 17:32, 11 December 2009
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	== Shift FA in Group 1 ==		== Shift FA in Group 1 ==
	We determine from the mean FA scores and group standard deviations that in order to create a ~1.5 standard deviation shift in Group 1, we would need to lower the FA by 0.85%. Fortunately, both Arcuate and Occipital tracts have similar SD values, so we can apply a single shift for both. The following figures demonstrate the goal outcome of a script written to produce this FA shift.<br>		We determine from the mean FA scores and group standard deviations that in order to create a ~1.5 standard deviation shift in Group 1, we would need to lower the FA by 8.5%. Fortunately, both Arcuate and Occipital tracts have similar SD values, so we can apply a single shift for both. The following figures demonstrate the goal outcome of a script written to produce this FA shift.<br>

	[[File:Hypothetical_FA_Shift_arcuate.png \|300px\|thumb\|center\| Hypothetical Shift (Arcuate) ]]		[[File:Hypothetical_FA_Shift_arcuate.png \|300px\|thumb\|center\| Hypothetical Shift (Arcuate) ]]

imported>Psych 204: /* Tract-Based Spatial Statistics */

2009-12-11T17:29:02Z

Tract-Based Spatial Statistics

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	TBSS provides a popular alternative to Voxel-Based Morphometry (VBM) analysis. It uses the following algorithm:<br><br>		TBSS provides a popular alternative to Voxel-Based Morphometry (VBM) analysis. It uses the following algorithm:<br><br>
	1. Align multiple FA images using a voxelwise nonlinear registration. TBSS attempts to strike a middle ground between low-dimensional warping (keeping brain structure, but losing alignment) and high-dimensional warping (good alignment, but extreme warping of original images). Alignment is best when a single subject's FA image is chosen to be the target. TBSS exhaustively tests using every individual subject as a target for all the others, and then chooses the best result.<br>		1. Align multiple FA images using a voxelwise nonlinear registration. TBSS attempts to strike a middle ground between low-dimensional warping (keeping brain structure, but losing alignment) and high-dimensional warping (good alignment, but extreme warping of original images). Alignment is best when a single subject's FA image is chosen to be the target. TBSS exhaustively tests using every individual subject as a target for all the others, and then chooses the best result.<br>
	2. Create mean FA skeleton. TBSS averages the transformed FA images to create a mean FA image. Tract skeleton generation then aims to create a single line through the center of the tract. It does this by sorting through voxels perpendicular to the direction of the tract, and choosing the one with the highest FA as the center of the tract. This introduces the possibility that a strong neighbouring tract will mistakenly be included as the tract of interest, hence our considering the Arcuate Tract to be more difficult than the Occipital Tract. <br>		2. Create mean FA skeleton. TBSS averages the transformed FA images to create a mean FA image. Tract skeleton generation then aims to create a single line through the center of the tract. It does this by sorting through voxels perpendicular to the direction of the tract, and choosing the one with the highest FA as the center of the tract. <br>
	3. Project individual subjects' FA onto group skeleton. For each individual subject, TBSS looks in the perpendicular tract direction and chooses the voxels with the maximum FA value, assigning this to the skeleton voxel. This helps to fix any misalignment from step 1.<br>		3. Project individual subjects' FA onto group skeleton. For each individual subject, TBSS looks in the perpendicular tract direction and chooses the voxels with the maximum FA value, assigning this to the skeleton voxel. This helps to fix any misalignment from step 1. However, it introduces the possibility that a strong neighbouring tract will mistakenly be included as the tract of interest, hence our considering the Arcuate Tract to be more difficult than the Occipital Tract. <br>
	4. Run statistics to test for statistically significant FA differences between groups. Correct for multiple-comparisons, recognizing that by pure chance, statistically significant differences will be found if many voxels are compared. <br>		4. Run statistics to test for statistically significant FA differences between groups. Correct for multiple-comparisons, recognizing that by pure chance, statistically significant differences will be found if many voxels are compared. <br>

imported>Psych 204: /* Conclusions */

2009-12-11T17:25:24Z

Conclusions

← Older revision		Revision as of 17:25, 11 December 2009
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	= Conclusions =		= Conclusions =

	If these results are to be trusted, they would create a significant caveat for TBSS users. However, it must be emphasized that these are preliminary studies, and at this point it is quite likely that the problem lies in the algorithm used to shift the mean FA scores, or in some other part of our methodology.		If these results are to be trusted, they would create a significant caveat for TBSS users. However, it must be emphasized that these are preliminary studies, and at this point it is quite likely that the problem lies in the algorithm used to shift the mean FA scores, or in some other part of our methodology. The next steps of this project would focus on verifying our methodology. Once we are more confident in this, we could start examining TBSS's abilities and limitations in finer detail. Additionally, we could use a similar approach to look at a comparable program (SPM/VBM).

	=== Future Work ===		=== Future Work ===
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	2. Examine Mean FA histograms for Group 1 post-shift, to ensure they match predicted values. <br>		2. Examine Mean FA histograms for Group 1 post-shift, to ensure they match predicted values. <br>
	3. Repeat this experiment using other tracts, or using shifts in only part of a given tract. <br>		3. Repeat this experiment using other tracts, or using shifts in only part of a given tract. <br>
	4. Run similar tests using VBM and compare to results with TBSS. <br>		4. Run similar tests using SPM/VBM and compare to results with TBSS. <br>

	= References =		= References =

imported>Psych 204: /* Trouble Shooting */

2009-12-11T17:19:41Z

Trouble Shooting

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	== Trouble Shooting ==		== Trouble Shooting ==
	After some searching, we determined that the top part of the brain is missing in the TBSS results because in one subject, data from the top ~~part of the brain was also missing~~. TBSS would then throw out a section when it found it did not have data for every subject. We might exclude this subject from the study, however the tracts of interest are not near the missing data, and we elected to keep the subject in the study.		After some searching, we determined that the top part of the brain is missing in the TBSS results because the scan data from one subject was also missing data from the top. TBSS would then throw out a section when it found it did not have data for every subject. We might exclude this subject from the study in order to have nicer pictures, however the tracts of interest are not near the missing data, and we elected to keep the subject in the study.
	[[File:MissingTop.png \|300px\|thumb\|center\| Missing data at top of brain]]		[[File:MissingTop.png \|300px\|thumb\|center\| Missing data at top of brain]]

imported>Psych 204: /* Statistics */

2009-12-11T17:18:09Z

Statistics

← Older revision		Revision as of 17:18, 11 December 2009
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	== Statistics ==		== Statistics ==
	TBSS was able to detect differences in part of the Occipital Corpus Callosum tract, at a significance of p<.05. However, changes were made to the entire tract, and most of these changes were missed. ~~Also~~, the results shown here are uncorrected for multiple comparisons. This adjustment, accounting for the likelihood that many comparisons would yield some random points of statistical significance, would further lower the findings.		TBSS was able to detect differences in part of the Occipital portion of the Corpus Callosum tract, at a significance of p<.05. However, changes were made to the entire tract, and most of these changes were missed. Importantly, the results shown here are uncorrected for multiple comparisons. This adjustment, accounting for the likelihood that many comparisons would yield some random points of statistical significance, would further lower the statistical significance of the findings.
	[[File:Occipital_results.png \|300px\|thumb\|center\| Occipital Results]]		[[File:Occipital_results.png \|300px\|thumb\|center\| Occipital Results]]
	TBSS was basically unable to detect any difference in the Arcuate tract. It requires a setting of p<.2, uncorrected for multiple comparisons, before any signal in the Arcuate tract is seen. However, at this setting, these results can be attributed to randomness rather than a true signal, as indeed many other false positives throughout the brain are seen.		TBSS was basically unable to detect any difference in the Arcuate tract. It requires a setting of p<.2, uncorrected for multiple comparisons, before any signal in the Arcuate tract is seen. However, at this setting, these results can be attributed to randomness rather than a true signal, as indeed many other false positives throughout the brain are seen.

imported>Psych 204: /* Statistics */

2009-12-11T17:17:08Z

Statistics

← Older revision		Revision as of 17:17, 11 December 2009
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	== Statistics ==		== Statistics ==
			TBSS was able to detect differences in part of the Occipital Corpus Callosum tract, at a significance of p<.05. However, changes were made to the entire tract, and most of these changes were missed. Also, the results shown here are uncorrected for multiple comparisons. This adjustment, accounting for the likelihood that many comparisons would yield some random points of statistical significance, would further lower the findings.
	[[File:Occipital_results.png \|300px\|thumb\|center\| Occipital Results]]		[[File:Occipital_results.png \|300px\|thumb\|center\| Occipital Results]]
			TBSS was basically unable to detect any difference in the Arcuate tract. It requires a setting of p<.2, uncorrected for multiple comparisons, before any signal in the Arcuate tract is seen. However, at this setting, these results can be attributed to randomness rather than a true signal, as indeed many other false positives throughout the brain are seen.
	[[File:Arcuate_results.png \|300px\|thumb\|center\| Arcuate Results]]		[[File:Arcuate_results.png \|300px\|thumb\|center\| Arcuate Results]]

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

2009-12-11T17:05:49Z

Appendix I - Code and Data

← Older revision		Revision as of 17:05, 11 December 2009
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	[1] Smith, Steven M. et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage 31 (2006) 1487-1505.		[1] Smith, Steven M. et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage 31 (2006) 1487-1505.

	= Appendix I - Code ~~and Data~~ =		= Appendix I - Code =

	==Code==		==Code==
	[[Matlab Code for Reducing FA]]		[[Matlab Code for Reducing FA]]

	~~==Data==~~
	~~[[Media:MyDataZipFile.zip \| zip file with my data]]~~

	= Appendix II - Work partition =		= Appendix II - Work partition =
	Christine is new to fMRI work and did much of the brunt work - she divided the kids into two groups and showed the groups to be well matched statistically. She did the cleaning of the occipital tracts for each of the 55 kids and wrote the wiki page. Jessica provided the brains and experience and devised the way to reduce the FA in the tracts of one group, ran the results through TBSS, ran statistics on those results, and generally taught Christine the ropes.		Christine is new to fMRI work and did much of the brunt work - she divided the kids into two groups and showed the groups to be well matched statistically. She did the cleaning of the occipital tracts for each of the 55 kids and wrote the wiki page. Jessica provided the brains and experience and devised the way to reduce the FA in the tracts of one group, ran the results through TBSS, ran statistics on those results, and generally taught Christine the ropes.