Time Series Analysis - Revision history

Chris: /* FreeSurfer */

2022-06-28T16:51:26Z

FreeSurfer

← Older revision		Revision as of 12:51, 28 June 2022
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	If the intention is to explore correlations among regional time series or a functional connectivity analysis, you should additionally consider preprocessing the data to remove some artefacts that may influence your results, and could cause problems down the road that might necessitate a complete re-analysis.		If the intention is to explore correlations among regional time series or a functional connectivity analysis, you should additionally consider preprocessing the data to remove some artefacts that may influence your results, and could cause problems down the road that might necessitate a complete re-analysis.

	=== ~~Surface~~ Parcellation and Functional ~~Analysis~~ ===		=== [[Detrending FreeSurfer Data\|Detrending]] ===
	The ~~first~~ precondition for these analyses in surface space is that the anatomical data must be processed in [[FreeSurfer#Structural_Preprocessing \| FreeSurfer]], and a [[FreeSurfer#Functional_Analysis \| functional analysis]] must be run on the BOLD data. These data are stored in voxel-space .mgz files. Rather than explore the time-series of activations for each voxel individually (there are many thousands of them), we will instead be aggregating over groups of voxels in [[:Category:ROI\|regions of interest]].		The second step is to ''detrend'' your functional data, which is to remove a linear signal drift from your data.

			Over the course of a run, there can be a linear drift in the signal in different regions of the brain. There are many possible causes for this that have nothing to do with any interesting aspect of your data -- in other words, this linear drift is a nuisance artifact because it introduces spurious correlations between two unrelated time series.

			You can see this for yourself in a quick experiment you could whip up in Excel: take two vectors of 100 randomly generated numbers (e.g., <code>randbetween(1,99))</code>. They should be uncorrelated. Now add 1, 2, 3, ... , 99, 100 to the values in each vector. This simulates a linear trend in the data. You shouldn't be surprised to find that the two vectors are now highly and positively correlated!

			=== Parcellation Using Anatomical and Functional Regions ===
			The second precondition for these analyses in surface space is that the anatomical data must be processed in [[FreeSurfer#Structural_Preprocessing \| FreeSurfer]], and a [[FreeSurfer#Functional_Analysis \| functional analysis]] must be run on the BOLD data. These data are stored in voxel-space .mgz files. Rather than explore the time-series of activations for each voxel individually (there are many thousands of them), we will instead be aggregating over groups of voxels in [[:Category:ROI\|regions of interest]].

	==== Anatomical (Atlas-Based) Parcellation ====		==== Anatomical (Atlas-Based) Parcellation ====
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	Significant activation clusters are also a good choice for time series analysis, as they allow analyses of time series that are restricted to those brain regions that are reliably associated with a task or experimental condition. Care must be taken in the selection of an appropriate contrast to avoid biasing the data (a.k.a. cherry-picking), making your results a foregone conclusion. For example, if you used significant clusters in an A>B contrast as ROIs, you shouldn't be surprised to find that the time series data extracted from these regions has a greater mean signal during blocks of Condition A than during blocks of Condition B. In general, the functional contrast used to generate ROIs should be orthogonal or equally weighted with respect to the conditions you wish to discriminate between in your time series analysis. For example, if your experimental task has 2 conditions, A and B, a good choice for a functional ROI might be task vs. baseline.		Significant activation clusters are also a good choice for time series analysis, as they allow analyses of time series that are restricted to those brain regions that are reliably associated with a task or experimental condition. Care must be taken in the selection of an appropriate contrast to avoid biasing the data (a.k.a. cherry-picking), making your results a foregone conclusion. For example, if you used significant clusters in an A>B contrast as ROIs, you shouldn't be surprised to find that the time series data extracted from these regions has a greater mean signal during blocks of Condition A than during blocks of Condition B. In general, the functional contrast used to generate ROIs should be orthogonal or equally weighted with respect to the conditions you wish to discriminate between in your time series analysis. For example, if your experimental task has 2 conditions, A and B, a good choice for a functional ROI might be task vs. baseline.

	~~Label files~~ can be generated from significant clusters identified in group-level contrasts. Oftentimes, these clusters will have a range of cortical extents (i.e., big and small clusters). We typically subdivide regions so that they are of more comparable size. ~~Instructions on how~~ to ~~do this~~, ~~and how to map clusters from group-level analyses (which are carried out on the fsaverage or some other template surface) to each individual's native surface can be found~~ in the ~~description for~~ [[Freesurfer Subparcellation]].		===== Fine Tuning Parcellations =====
			Regions can be generated from significant clusters identified in group-level contrasts. Oftentimes, these clusters will have a range of cortical extents (i.e., big and small clusters). Likewise, many anatomical region definitions vary in size. We typically subdivide regions so that they are of more comparable size. We have developed tools to work with surface ROI definitions, described in more detail in the [[Freesurfer Subparcellation]] page.
	~~=== [[Detrending FreeSurfer Data\|Detrending]] ===~~
	~~The second step is to ''detrend'' your functional data, which is to remove a linear signal drift from your data.~~

	Over the course of a run, there can be a linear drift in the signal in different regions of the brain. There are many possible causes for this that have nothing to do with any interesting aspect of your data -- in other words, this linear drift is a nuisance artifact because it introduces spurious correlations between two unrelated time series.

	You can see this for yourself in a quick experiment you could whip up in Excel: take two vectors of 100 randomly generated numbers (e.g., <code>randbetween(1,99))</code>. They should be uncorrelated. Now add 1, 2, 3, ... , 99, 100 to the values in each vector. This simulates a linear trend in the data. You shouldn't be surprised to find that the two vectors are now highly and positively correlated!

	=== [[Time Series Data in Surface Space \| Extract Timecourse Data to Text File]] ===		=== [[Time Series Data in Surface Space \| Extract Timecourse Data to Text File]] ===

Chris: /* Functional Connectivity Overview of Steps */

2022-05-19T17:52:29Z