Index correlation
The measures correlation allows you to verify the level of dependence between a measure with an external file (typically an index, but it can also be an external measure) and how likely this value is statistically significant.
After the main directory, you have to select the group which you want to study and what kind of spatial analysis you want to analyze between:
- Areas, to study one of the macroareas (frontal area, temporal area, central area, parietal area or occipital area)
- Total, to study the single locations
- Asymmetry, to study the differences on the measure between the right and the left neural hemisphere
- Global, to study the overall measure value
Before to start the analysis, you also have to select the conservativeness level:
- the minimum let you to use an alpha value equal to 0.05 (so, a comparison can be significant if the relative p-value is less than this value)
- the maximum reduce this value, by considering it as equal to 0.05 divided by the number of different comparisons, for example if will be compared n frequency bands and m locations, you will have a p-value equals to:
Now you can choose the whished measure and the external file, and you can RUN your analysis.
Note that the external file has to be a matrix with the studied subjects'names on the first column, and the index value (or the external measure value) on the second column:
| 508 | 2 |
|---|---|
| 509 | 4 |
| 510 | 5 |
| 513 | 12 |
| 516 | 3 |
| 521 | 9 |
Athena will returns 2 figures which include the p-value and the Spearman's coefficient ρ (rho), respectively.
Furthermore, the toolbox will also show a figure for each analyzed parameter, which represents the scattered plot between the two measures and the representation of the relationship between them.
Each figure will present the p-value and the rho value, and the result of the significance check (significant if the analysis has a statistical significant result, not significant otherwise).
You can also Now you can continue your correlation analysis, or return to the previous interface to discover other statistical analysis.