help
help > Extract connectivity values of specific ROIs?
Oct 10, 2017 12:10 PM | chibi
Extract connectivity values of specific ROIs?
Hello Alfonso and community,
I want to analyze the effect of obesity on the brain and cognition (test scores) and differences in functional connectivity.
If I want to examine the differences in the functional connectivity between obese and lean subjects regarding their test results AND control for age and sex, would I use the following setup and contrast?
Obese Subjects: [1 1 1 1 1 1 0 0 0 0]
Leans Subjects: [0 0 0 0 0 0 1 1 1 1]
ScoreObese: [12 14 18 9 7 0 0 0 0 0]
ScoreLean: [0 0 0 0 0 15 20 14 10 8]
Age:[60 89 72 91 80 72 ...]
Sex:[1 2 1 2 1 2 ...]
Contrast [0 0 1 -1 0 0]
And I also have difficulties putting the results in words, does it mean positive ROI-to-ROI-connections correlate with higher test scores in the obese group?
Also, in similar studies observing the effect of obesity on functional connectivity, they mostly used functional connectivity values for single regions and not connections (e.g. "participants with obesity showed significantly greater connectivity strength in the salience network", "higher baseline BMI was associated with less DMN connectivity" etc.).
Is it possible to make similar statements with CONN as well and extract connectivity values for specific regions?
I want to analyze the effect of obesity on the brain and cognition (test scores) and differences in functional connectivity.
If I want to examine the differences in the functional connectivity between obese and lean subjects regarding their test results AND control for age and sex, would I use the following setup and contrast?
Obese Subjects: [1 1 1 1 1 1 0 0 0 0]
Leans Subjects: [0 0 0 0 0 0 1 1 1 1]
ScoreObese: [12 14 18 9 7 0 0 0 0 0]
ScoreLean: [0 0 0 0 0 15 20 14 10 8]
Age:[60 89 72 91 80 72 ...]
Sex:[1 2 1 2 1 2 ...]
Contrast [0 0 1 -1 0 0]
And I also have difficulties putting the results in words, does it mean positive ROI-to-ROI-connections correlate with higher test scores in the obese group?
Also, in similar studies observing the effect of obesity on functional connectivity, they mostly used functional connectivity values for single regions and not connections (e.g. "participants with obesity showed significantly greater connectivity strength in the salience network", "higher baseline BMI was associated with less DMN connectivity" etc.).
Is it possible to make similar statements with CONN as well and extract connectivity values for specific regions?