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This wiki describe the main functionality of the MATLAB class `PSM` (Probabilistic Stimulation Map). This class has for main goal to create abstraction of how maps are computed in order to focus on the results.
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# 2008_BetterMaps
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MATLAB Class to handle Probabilistic Stimulation Map (PSM). It currently includes the following algorithms:
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| Algorithm | Features | Images | Threshold | Stat. tests | Null-hypothesis | False positive correction | Sweetspot |
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| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
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| Nguyen, 2019 | Activated voxels coord., weights, scores, VTA index | n, mean, scoresArray | None | Wilcoxon (exact) | 0 | Benjamini-Hochberg (Genovese, 2002) | 10 percentile of significantMean |
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| Dembek, 2019 | Activated voxels coord., weights, scores, VTA index, stim. amplitude, MSSA¹ | n, mean, scoresArray, h0 | 15 | Wilcoxon (approx.) | MSSA¹ | Permutation Tests | largest cluster |
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| Reich, 2019 | Activated voxels coord., weights, scores, VTA index | n, mean, scoresArray, h0 | None | two samples t-test | scores of VTA excluding voxel | None | largest cluster |
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¹ Mean score of the VTA with same amplitude
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# Requirements
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Several algorithms featured in the class are programmed in python, so make sure the environment embedded in the repository is working on your computer. |