Reading Development project - Revision history

Chris: /* Time Series Extraction */

2020-05-16T18:47:45Z

Time Series Extraction

← Older revision		Revision as of 14:47, 16 May 2020
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	The 2mm-smoothed data were detrended using detrend_wmcsf.sh to remove linear trend, motion and wm and csf signal as nuisance regressors:		The 2mm-smoothed data were detrended using detrend_wmcsf.sh to remove linear trend, motion and wm and csf signal as nuisance regressors:
	detrend_wmcsf.sh fmcpr.siemens.sm2 fsaverage `cat fssubs`		detrend_wmcsf.sh fmcpr.siemens.sm2 fsaverage `cat fssubs`
	The surfacetimecourses.sh shell script was used to extract time series in fsaverage space to plaintext files. We used the ?h.rdrdcompact.annot annotation file, which can be found in the /ubfs/openfmri/label folder.		The surfacetimecourses.sh shell script was used to extract time series in fsaverage space to plaintext files. We used the ?h.rdrdcompact.annot annotation file, which can be found in the /ubfs/openfmri/label folder:
			surfacetimecourses.sh tdrdcompact fmcpr.siemens.sm2 fsaverage `cat fssubs`

	==Functional Connectivity Estimation==		==Functional Connectivity Estimation==

Chris: /* Time Series Extraction */

2020-05-16T18:44:18Z

Time Series Extraction

← Older revision		Revision as of 14:44, 16 May 2020
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	The 2mm-smoothed data were detrended using detrend_wmcsf.sh to remove linear trend, motion and wm and csf signal as nuisance regressors:		The 2mm-smoothed data were detrended using detrend_wmcsf.sh to remove linear trend, motion and wm and csf signal as nuisance regressors:
	detrend_wmcsf.sh fmcpr.siemens.sm2 fsaverage `cat fssubs`		detrend_wmcsf.sh fmcpr.siemens.sm2 fsaverage `cat fssubs`
	The surfacetimecourses.sh shell script was used to extract time series in fsaverage space to plaintext files.		The surfacetimecourses.sh shell script was used to extract time series in fsaverage space to plaintext files. We used the ?h.rdrdcompact.annot annotation file, which can be found in the /ubfs/openfmri/label folder.

	==Functional Connectivity Estimation==		==Functional Connectivity Estimation==

Chris: /* Time Series Extraction */

2020-05-15T23:08:39Z

Time Series Extraction

← Older revision		Revision as of 19:08, 15 May 2020
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	Masks were subdivided along template anatomical boundaries using custom scripts intersection clusters with template region boundaries. These regions were further subdivided using mris_divide_whatever.		Masks were subdivided along template anatomical boundaries using custom scripts intersection clusters with template region boundaries. These regions were further subdivided using mris_divide_whatever.
	==Time Series Extraction==		==Time Series Extraction==
	The 2mm-smoothed data were detrended using detrend_wmcsf.sh to remove linear trend, motion and wm and csf signal as nuisance regressors.		The 2mm-smoothed data were detrended using detrend_wmcsf.sh to remove linear trend, motion and wm and csf signal as nuisance regressors:
	detrend_wmcsf.sh fmcpr.siemens.sm2 fsaverage `cat fssubs`		detrend_wmcsf.sh fmcpr.siemens.sm2 fsaverage `cat fssubs`
	The surfacetimecourses.sh shell script was used to extract time series in fsaverage space to plaintext files.		The surfacetimecourses.sh shell script was used to extract time series in fsaverage space to plaintext files.

Chris: /* Time Series Extraction */

2020-05-15T23:07:46Z

Time Series Extraction

← Older revision		Revision as of 19:07, 15 May 2020
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	Masks were subdivided along template anatomical boundaries using custom scripts intersection clusters with template region boundaries. These regions were further subdivided using mris_divide_whatever.		Masks were subdivided along template anatomical boundaries using custom scripts intersection clusters with template region boundaries. These regions were further subdivided using mris_divide_whatever.
	==Time Series Extraction==		==Time Series Extraction==
	The 2mm-smoothed data were detrended using detrend_wmcsf.sh to remove linear trend, motion and wm and csf signal as nuisance regressors. The surfacetimecourses.sh shell script was used to extract time series in fsaverage space to plaintext files.		The 2mm-smoothed data were detrended using detrend_wmcsf.sh to remove linear trend, motion and wm and csf signal as nuisance regressors.
			detrend_wmcsf.sh fmcpr.siemens.sm2 fsaverage `cat fssubs`
			The surfacetimecourses.sh shell script was used to extract time series in fsaverage space to plaintext files.

	==Functional Connectivity Estimation==		==Functional Connectivity Estimation==
	Both Pearson R and cross-mutual information (XMI) was used to calculate a functional connectivity matrix for each functional run (8 runs/participant) using a custom MATLAB script. Why both Pearson R and XMI? Well, I was curious whether one might be better than the other. You do end up with somewhat different values either way.		Both Pearson R and cross-mutual information (XMI) was used to calculate a functional connectivity matrix for each functional run (8 runs/participant) using a custom MATLAB script. Why both Pearson R and XMI? Well, I was curious whether one might be better than the other. You do end up with somewhat different values either way.

Chris: /* Preprocessing */

2020-05-15T23:04:11Z

Preprocessing

← Older revision		Revision as of 19:04, 15 May 2020
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	28 Children (14 TD, 14 RD) with either strong or weak (incl. dyslexia diagnosis) reading skills and no other diagnosis and data from 2 time points. Children were scored according to Z score on 3 reading measures (WATT, Pseudoword decoding, something else), with an additional -1 or -0.5 assigned for a clinical dyslexia diagnosis (-1) or reading difficulty diagnosis (-.5). I selected the children with the top and bottom 14 summed scores.		28 Children (14 TD, 14 RD) with either strong or weak (incl. dyslexia diagnosis) reading skills and no other diagnosis and data from 2 time points. Children were scored according to Z score on 3 reading measures (WATT, Pseudoword decoding, something else), with an additional -1 or -0.5 assigned for a clinical dyslexia diagnosis (-1) or reading difficulty diagnosis (-.5). I selected the children with the top and bottom 14 summed scores.
	==Preprocessing==		==Preprocessing==
	Anatomical surfaces reconstructed using T1w images at both early and late time points. Functional data from VV runs (word and nonword) preprocessed with both 6mm and 2mm blurring, Siemens slice time.		Anatomical surfaces reconstructed using T1w images at both early and late time points. Functional data from VV runs (word and nonword) preprocessed with both 6mm and 2mm blurring, Siemens slice time:
			smoothing=2
			nohup preproc-sess -sf fssubs -surface fsaverage lhrh -per-run -fsd bold -sliceorder siemens -fwhm ${smoothing} &

	==Functional Masking Using GLMA==		==Functional Masking Using GLMA==
	A GLMA was carried out, contrasting all lexical vs. baseline to create group-level functional masks for TD and RD (p<.001, cluster P<.05).		A GLMA was carried out, contrasting all lexical vs. baseline to create group-level functional masks for TD and RD (p<.001, cluster P<.05).

Chris at 17:15, 13 May 2020

2020-05-13T17:15:20Z

← Older revision		Revision as of 13:15, 13 May 2020
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	===Alternative Approaches===		===Alternative Approaches===
	I had two options for generating the normalized XMI matrices. My first intuition was to normalize the XMI values within each matrix individually. This meant that the connectivity values were scaled with reference to the rest of the connectivity values for that participant for that run. It also occurred to me that I might instead normalize the XMI values globally, with respect to all XMI values occurring in all runs for all participants. I ended up writing a script that generated the FC patterns using normalized and scaled values computed individually and globally as two separate pattern files. It turned out that this was a lucky happenstance, because using both sets of patterns together lead to much better performance than either of them individually. I'm not sure whether that is because I doubled the amount of training data or because the two methods of pattern generation bolstered each other to give the models a better picture of the feature relations.		I had two options for generating the normalized XMI matrices. My first intuition was to normalize the XMI values within each matrix individually. This meant that the connectivity values were scaled with reference to the rest of the connectivity values for that participant for that run. It also occurred to me that I might instead normalize the XMI values globally, with respect to all XMI values occurring in all runs for all participants. I ended up writing a script that generated the FC patterns using normalized and scaled values computed individually and globally as two separate pattern files. It turned out that this was a lucky happenstance, because using both sets of patterns together lead to much better performance than either of them individually. I'm not sure whether that is because I doubled the amount of training data or because the two methods of pattern generation bolstered each other to give the models a better picture of the feature relations.

			I also had the option of splitting up the time series and computing connectivity within the first and second-half of the run as a way of both doubling the data and also allowing consistency to influence training (i.e., if a participant uses wildly different networks in the first and second halves of the run, this might be both informative but also a challenge for the network to learn).
			==Model Training==
			4-layer feedforward networks were created. The number of hidden units in the first hidden layer was set to be log2 of the number of inputs. The size of the 2 subsequent hidden layers was set to half the size of the first hidden layer. There were two 1-unit output layers, classifying reading skill (RD=0 vs TD=1) and whether the pattern came from a pseudoword (0) or a word (1) run.
			Training began with the full set of connectivity values, with the exception of roughly 250 connections that were identified during the FC calculation step as being between two subregions of the same functional cluster because they seem less interesting and may be more influenced by things like regional blood flow.

Chris: /* MATLAB FC Pseudocode */

2020-05-13T17:03:05Z

MATLAB FC Pseudocode

← Older revision		Revision as of 13:03, 13 May 2020
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	merge lh and rh into a single ts matrix		merge lh and rh into a single ts matrix
	eliminate timepoints at spikes (3SD from mean)		eliminate timepoints at spikes (3SD from mean)
	initialize empty adjacency ~~matrix~~ '''A'''		initialize empty adjacency matrices '''A''' and '''R'''
	for each ordered pair of regions (i,j) in upper triangle of A:		for each ordered pair of regions (i,j) in upper triangle of A:
	[http://stats.stackexchange.com/questions/179674/number-of-bins-when-computing-mutual-information calculate the number of bins] required for the cross-mutual-information calculation		[http://stats.stackexchange.com/questions/179674/number-of-bins-when-computing-mutual-information calculate the number of bins] required for the cross-mutual-information calculation

Chris: /* MATLAB FC Pseudocode */

2020-05-13T17:02:36Z

MATLAB FC Pseudocode

← Older revision		Revision as of 13:02, 13 May 2020
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	[http://stats.stackexchange.com/questions/179674/number-of-bins-when-computing-mutual-information calculate the number of bins] required for the cross-mutual-information calculation		[http://stats.stackexchange.com/questions/179674/number-of-bins-when-computing-mutual-information calculate the number of bins] required for the cross-mutual-information calculation
	calculate the cross-mutual-information for time series in regions i and j and store in '''A[i,j]'''		calculate the cross-mutual-information for time series in regions i and j and store in '''A[i,j]'''
	calculate the pearson correlation for time series in regions i and j and store in '''A[i,j]'''		calculate the pearson correlation for time series in regions i and j and store in '''R[i,j]'''
	append A to collection of adjacency matrices for S		append A and R to collection of adjacency matrices for S
	save all adjacency matrices for S to a .mat file		save all adjacency matrices for S to a .mat file

Chris: /* Functional Connectivity Estimation */

2020-05-13T17:01:58Z

Functional Connectivity Estimation

← Older revision		Revision as of 13:01, 13 May 2020
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	The 2mm-smoothed data were detrended using detrend_wmcsf.sh to remove linear trend, motion and wm and csf signal as nuisance regressors. The surfacetimecourses.sh shell script was used to extract time series in fsaverage space to plaintext files.		The 2mm-smoothed data were detrended using detrend_wmcsf.sh to remove linear trend, motion and wm and csf signal as nuisance regressors. The surfacetimecourses.sh shell script was used to extract time series in fsaverage space to plaintext files.
	==Functional Connectivity Estimation==		==Functional Connectivity Estimation==
	~~Cross~~-mutual information (XMI) was used to calculate a functional connectivity matrix for each functional run (8 runs/participant) using a custom MATLAB script.		Both Pearson R and cross-mutual information (XMI) was used to calculate a functional connectivity matrix for each functional run (8 runs/participant) using a custom MATLAB script. Why both Pearson R and XMI? Well, I was curious whether one might be better than the other. You do end up with somewhat different values either way.
	===MATLAB FC Pseudocode===		===MATLAB FC Pseudocode===
	for each subject S:		for each subject S:
Line 25:		Line 25:
	[http://stats.stackexchange.com/questions/179674/number-of-bins-when-computing-mutual-information calculate the number of bins] required for the cross-mutual-information calculation		[http://stats.stackexchange.com/questions/179674/number-of-bins-when-computing-mutual-information calculate the number of bins] required for the cross-mutual-information calculation
	calculate the cross-mutual-information for time series in regions i and j and store in '''A[i,j]'''		calculate the cross-mutual-information for time series in regions i and j and store in '''A[i,j]'''
			calculate the pearson correlation for time series in regions i and j and store in '''A[i,j]'''
	append A to collection of adjacency matrices for S		append A to collection of adjacency matrices for S
	save all adjacency matrices for S to a .mat file		save all adjacency matrices for S to a .mat file

	==FC Pattern Generation==		==FC Pattern Generation==
	Upper triangle of XMI matrix was extracted as a vector and normalized (Z Score) and scaled to fall between 0 and 1. The resulting distribution was very positively skewed so the square root of these values was calculated to make the distribution more normal. The scaled FC vectors for each of the 8 runs per participant were tagged for lexicality (word=1, pseudoword=0) at element ''n-2'', and reading skill (RD=0, TD=1) at element ''n-1'' and the decimal equivalent of the lexicality+reading skill code (e.g., 10 = 2) at element n, which was necessary to allow the k-folds cross-validation to balance the category distributions. This generated a matrix of 224 (28 subjects * 8 runs) by 6789 (6786 connections + lexicality code + group code + combined code). The matrix was written to a single .csv file.		Upper triangle of XMI matrix was extracted as a vector and normalized (Z Score) and scaled to fall between 0 and 1. The resulting distribution was very positively skewed so the square root of these values was calculated to make the distribution more normal. The scaled FC vectors for each of the 8 runs per participant were tagged for lexicality (word=1, pseudoword=0) at element ''n-2'', and reading skill (RD=0, TD=1) at element ''n-1'' and the decimal equivalent of the lexicality+reading skill code (e.g., 10 = 2) at element n, which was necessary to allow the k-folds cross-validation to balance the category distributions. This generated a matrix of 224 (28 subjects * 8 runs) by 6789 (6786 connections + lexicality code + group code + combined code). The matrix was written to a single .csv file.
	===Alternative Approaches===		===Alternative Approaches===
	I had two options for generating the normalized XMI matrices. My first intuition was to normalize the XMI values within each matrix individually. This meant that the connectivity values were scaled with reference to the rest of the connectivity values for that participant for that run. It also occurred to me that I might instead normalize the XMI values globally, with respect to all XMI values occurring in all runs for all participants. I ended up writing a script that generated the FC patterns using normalized and scaled values computed individually and globally as two separate pattern files. It turned out that this was a lucky happenstance, because using both sets of patterns together lead to much better performance than either of them individually. I'm not sure whether that is because I doubled the amount of training data or because the two methods of pattern generation bolstered each other to give the models a better picture of the feature relations.		I had two options for generating the normalized XMI matrices. My first intuition was to normalize the XMI values within each matrix individually. This meant that the connectivity values were scaled with reference to the rest of the connectivity values for that participant for that run. It also occurred to me that I might instead normalize the XMI values globally, with respect to all XMI values occurring in all runs for all participants. I ended up writing a script that generated the FC patterns using normalized and scaled values computed individually and globally as two separate pattern files. It turned out that this was a lucky happenstance, because using both sets of patterns together lead to much better performance than either of them individually. I'm not sure whether that is because I doubled the amount of training data or because the two methods of pattern generation bolstered each other to give the models a better picture of the feature relations.

Chris: /* FC Pattern Generation */

2020-05-02T04:14:29Z

FC Pattern Generation

← Older revision		Revision as of 00:14, 2 May 2020
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	save all adjacency matrices for S to a .mat file		save all adjacency matrices for S to a .mat file
	==FC Pattern Generation==		==FC Pattern Generation==
	Upper triangle of XMI matrix was extracted as a vector and normalized (Z Score) and scaled to fall between 0 and 1. The resulting distribution was very positively skewed so the square root of these values was calculated to make the distribution more normal. The scaled FC vectors for each of the 8 runs per participant were tagged for lexicality (word=1, pseudoword=0) at element ''n-1'', and reading skill (RD=0, TD=1) at element ''n''. This generated a matrix of 224 (28 subjects * 8 runs) by ~~6788~~ (6786 connections + lexicality code + group code). The matrix was written to a single .csv file.		Upper triangle of XMI matrix was extracted as a vector and normalized (Z Score) and scaled to fall between 0 and 1. The resulting distribution was very positively skewed so the square root of these values was calculated to make the distribution more normal. The scaled FC vectors for each of the 8 runs per participant were tagged for lexicality (word=1, pseudoword=0) at element ''n-2'', and reading skill (RD=0, TD=1) at element ''n-1'' and the decimal equivalent of the lexicality+reading skill code (e.g., 10 = 2) at element n, which was necessary to allow the k-folds cross-validation to balance the category distributions. This generated a matrix of 224 (28 subjects * 8 runs) by 6789 (6786 connections + lexicality code + group code + combined code). The matrix was written to a single .csv file.
			===Alternative Approaches===
			I had two options for generating the normalized XMI matrices. My first intuition was to normalize the XMI values within each matrix individually. This meant that the connectivity values were scaled with reference to the rest of the connectivity values for that participant for that run. It also occurred to me that I might instead normalize the XMI values globally, with respect to all XMI values occurring in all runs for all participants. I ended up writing a script that generated the FC patterns using normalized and scaled values computed individually and globally as two separate pattern files. It turned out that this was a lucky happenstance, because using both sets of patterns together lead to much better performance than either of them individually. I'm not sure whether that is because I doubled the amount of training data or because the two methods of pattern generation bolstered each other to give the models a better picture of the feature relations.