Please enable JavaScript.

Coggle requires JavaScript to display documents.

Correlation Studies - Coggle Diagram

- - - - remove longitudinal signal drift and instrument noise
      - :two: Correlation-based signal improvement
        
        remove motion artifacts
        
        HbO and HbR should be :heavy_minus_sign: correlated but become ⬆️ :heavy_plus_sign: correlated with motion artifacts
        
        :three: wavelet-based method employed to remove global physiological noise
        
        WTC used to identify globally co-varying time-frequency points (present consistently across entire dataset)
        
        k threshold of 0.5 :arrow_right: quantifies globality of physiological noise
  - - - :two: r-values we Fisher-z transformed
        
        :three: Identical task used as baseline & other 3 tasks Nonidentical, HC1, HC2 subtracted from baseline
        
        :four: one-sample t-test
        
        between-conditions :arrow_right: repeated-measures ANOVA
        
        FDR for all 15 channels to control multiple comparisons \((\alpha = 0.05)\)
  - - - participants randomly assigned leader (:red_circle: marker) & follower (:large_blue_circle: marker)
      - :two: Dyads completed 4 different drawing tasks in counterbalanced order with 6 trials each
        
        Nonidentical
        
        follower draws a symmetrical shape to leader (draws ⬇️ when leader draws ⬆️)
        
        Identical
        
        follower draws identical shape to leader
        
        Hum-Com 1
        
        participants told that they are co-drawing with :computer: but actually drawing with each other
        
        Hum-Com 2
        
        participants took turns to control marker when co-drawing with :computer:
- - - - oxygenated hemoglobin is the most sensitive parameter of regional cerebral blood flow
      - provides a robust correlation with the BOLD signal of fMRI
  - - - :two: Phase-Scrambling Permutation Test
        
        preprocessed time series for each pair randomly scrambled
        
        scrambled time series analysed using Pearson correlation analysis to form pseudo data
        
        Differences in correlation coefficient of pre-processed data & pseudo data
        
        conducted t-test with Bootstrap (1000 times) for each ROI channel
        
        :three: IBS analysis of 3 situations by
        using the repeated-measures ANOVA
        
        focusing on ROI channels that showed a significant difference between preprocessed NIRS data and pseudo data.
        
        situation-averaged IBS for experimental condition in each ROI also analyzed by ANOVA.