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@@ -50,7 +50,7 @@ In addition, you will also need to prepare an atlas-network file. This file shou
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The main pipeline comprises three steps, with scripts located in the ./src/ folder. Specifically, the first step (st1_ParamSt.m) initializes the parameters. The second step (st2_QPPanalysis.m) identifies and analyzes QPPs. The third step (st3_QPPFCvisual.m) visualizes QPPs and related results using the outputs from step 2. Note: When running the pipeline in Matlab, ensure that all scripts are executed within the ./src/ folder.
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<aname="section-2-1"></a>
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### 2.1 (Step 1) Run 'st1_ParamsSet.m'
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The following parameters need to be prespecified before running the script. A parameter file Params_`data`\_`ext`.mat will be generated and saved in the ./param/ folder after running this script.
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The following parameters need to be prespecified before running the script. A parameter file Params_`data`\_`ext`.mat will be generated and saved in the ./params/ folder after running this script.
| Filepath |`data`| the input filename |The input should has the filename `data`.mat |
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|`FCrs`| a (nP X 1) cell vector| Each cell includes the functional connectivity map after the QPP regression.|
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|`iROI2Net`|a (nroi X 1) vector| Sorted ROI network based on each network. E.g., QPPs{1}(iROI2Net,:) will return ROIs sorted QPP along functional networks.|
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Note: Similar to detected QPPs, the phase-adjusted QPP and related variables are saved in the following cell matrices, `QPPas`, `TMXas`, `METas`, `Cas`. All these parameters are saved in the `dataext`\_`*`_QPPs.mat file, where `*` depends on the prespecified parameters.
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Note: Similar to detected QPPs, the phase-adjusted QPP and related variables are saved in the following cell matrices, `QPPas`, `TMXas`, `METas`, `Cas`. All these parameters are saved in the `./results/dataext`\_`*`_QPPs.mat file, where `*` depends on the prespecified parameters.
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