Let
Maximum lag
A time series
Consquently,
Above two conditions form the weakly stationary.
Let
Let
Max lag
Originally for autocorrelated time-series data.
Modified for cross-correlated time-series data.
Max lag
Distance cross correlation
Multiscale graph cross correlation
Procedure: Given number of blocks size
For each block
Choose
Concatenate the chosen blocks to form a new time series
Compute
Compute
Independent data
Linear dependence
Non-linear dependence
Data dependent on lag 3.
Resting state(?) fMRI data
n=1200 frames,
22 brain regions, each part of some large-scale brain network.
- Default Mode Network (DMN)
- Dorsal Attention Network (dAtt)
- Ventral Attention Network (vAtt)
- Visual Network (Visual)
- FrontoParietal Network (FP)
- Limbic Network (Limbic)
- Somatomotor Network (SM)
Network | Shorthand | Activation? |
---|---|---|
Default Mode | DMN | When resting |
Dorsal Attention | dAtt | Selecting stimuli relevant to a goal |
Ventral Attention | vAtt | Detecting and redirecting attention to relevant stimuli |
Visual | Visual | Analyzing the various components of the visual scene |
FrontoParietal | FP | Making decisions in the context of goal-driven behaviour |
Limbic | Limbic | Emotion, consciousness, motivation and long-term memory |
Somatomotor | SM | Detecting somatosensory stimuli, movement |
Network | Shorthand | Activation? |
---|---|---|
Default Mode | DMN | When resting |
Dorsal Attention | dAtt | Selecting stimuli relevant to a goal |
Ventral Attention | vAtt | Detecting and redirecting attention to relevant stimuli |
Visual | Visual | Analyzing the various components of the visual scene |
FrontoParietal | FP | Making decisions in the context of goal-driven behaviour |
Limbic | Limbic | Emotion, consciousness, motivation and long-term memory |
Somatomotor | SM | Detecting somatosensory stimuli, movement |
- Analyzing each component individually can lead to wrong inference.