hctsa_DREADD

Analysis of DREADDs BOLD time-series data

Data preparation

You must have the following files downloaded or computed in the directories below:

• HCTSA_Control/HCTSA.mat
• HCTSA_Control/HCTSA_PVCre.mat
• HCTSA_LeftCTX/HCTSA.mat
• HCTSA_LeftCTX/HCTSA_PVCre.mat
• HCTSA_RightCTX/HCTSA.mat
• HCTSA_RightCTX/HCTSA_PVCre.mat

Data processing

Preparation of raw HCTSA data into different subsets for processing can be achieved by running:

DoAllProcessing()

This script does the following steps:

Splitting into files for specific sets of classes

For example, to make new files for classification of excitatory versus SHAM:

FilterDataset('Excitatory_SHAM');

Convert to baseline differences

Normalize features of each mouse across time relative to their baseline dynamics:

ConvertToBaselineDiffs('right','Excitatory_SHAM','subtract')

Label groups of time series for a given analysis

For right-hemisphere excitatory-sham analysis, corrects raw data, and data following subtraction of baseline features

[prePath,rawData,rawDataBL] = GiveMeLeftRightInfo('right','Excitatory_SHAM');
LabelDREADDSGroups(false,'right',rawData,'Excitatory_SHAM')
LabelDREADDSGroups(false,'right',rawDataBL,'Excitatory_SHAM')

Split by time point

For excitatory-sham differences:

SplitByTimePoint('Excitatory_SHAM',false)

For excitatory-sham differences (relative to baseline):

SplitByTimePoint('Excitatory_SHAM',true)

A reduced feature set

Can generate a reduced feature set using a custom similarity threshold here:

ProduceReducedFeatures()

Data analysis

A range of different analysis types in terms of labeling of classes of data:

• Excitatory_SHAM
• PVCre_SHAM
• Wild_SHAM
• Excitatory_PVCre
• PVCre_Wild
• Excitatory_Wild
• Excitatory_PVCre_SHAM
• Excitatory_PVCre_Wild_SHAM

How different are dynamics at a given time point

This uses 100 nulls (for speed) to compute the difference between excitatory and sham conditions at Delta.1 ('ts2-BL') in each of the three regions of interest (using all features):

FirstTimePointClassification('Excitatory_SHAM','all','ts2-BL',100)

Running at Delta.2:

FirstTimePointClassification('Excitatory_SHAM','all','ts3-BL',1000)

Which features are discriminatory

Individual time point:

E.g., characterize specific excitatory-sham differences in the injected region at Delta.1, using all features:

DiscriminativeFeatures('Excitatory_SHAM','right','all','ts2-BL')

And for PVCre versus control:

DiscriminativeFeatures('PVCre_SHAM','right','all','ts2-BL')

Across multiple time points:

This can be analyzed using ConsensusFeatures, which assumes that each measured time point is independent and looks for features that show consistent differences to SHAM across time. This assumption turns out to be very bad, so p-values obtained from this method are overly optimistic:

ConsensusFeatures('Excitatory_SHAM','right','all')

Feature score consistency

Are features selected for a given analysis consistent across all time points?

testStatCompareTime('Excitatory_SHAM','right','all')

Are Excitatory_SHAM features similar to PVCre_SHAM features (at individual time points)?

testStatCompareConditionTime('right','all')

Do consensus features selected in Excitatory_SHAM differ from those selected in PVCre_SHAM?

pValCompare

Can feature scores be measured relative to those in a control region to look for additional correlations in specific brain regions?

testStatRelativeControl('right','all')
testStatBar('all',false)

Plotting differences

Plot low-dimensional projections of the data:

LowDimProj('Excitatory_SHAM','right')

Plots for feature ID 33, distributions relative to baseline for Excitatory_SHAM in the right hemisphere region:

PlotConsensus(33,'Excitatory_SHAM','right')