resting state fmri i from basics to advance applications
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NBML, Webinars, Second Meeting Resting State fMRI: I: From Basics to Advance Applications in in Hamed Ekhtiari, MD, PhD, National Brain Mapping Laboratory (NBML) Main Functional Neuroimaging Softwares tfMRI rfMRI dMRI sMRI Clinically


  1. NBML, Webinars, Second Meeting Resting State fMRI: I: From Basics to Advance Applications in in Hamed Ekhtiari, MD, PhD, National Brain Mapping Laboratory (NBML)

  2. Main Functional Neuroimaging Softwares

  3. tfMRI rfMRI dMRI sMRI

  4. Clinically Meaningful Image-Derived Phenotypes (IDPs)

  5. Automated Clinically Meaningful Image-Derived Phenotypes (IDPs)

  6. Automated Clinically Meaningful Image-Derived Phenotypes (IDPs) tfMRI rfMRI dMRI sMRI

  7. Automated Clinically Meaningful Image-Derived Phenotypes (IDPs) rfMRI

  8. 1771 Automated Image-Derived Phenotypes (IDPs) rfMRI

  9. ? Clinically Meaningful Automated Image-Derived Phenotypes (IDPs) rfMRI

  10. Automated Clinically Meaningful Image-Derived Phenotypes (IDPs) tfMRI rfMRI dMRI sMRI 1771 16 675 39

  11. 1100 Health Related Factors (11 Cat) 2501 IDPs (6 Cat)

  12. What is Resting Functional MRI? rfMRI 1771

  13. TR

  14. Task-based fMRI (Task>Rest) Rest Task Rest Task Rest Task Rest Task Rest What happens during REST? (Rest>Task)

  15. Default Mode Network (DMN)

  16. Lets do some calculations! 24

  17. 13 * 12 = ? 156 25

  18. 15 * 16 = ? 240 26

  19. 23 * 21 = ? 483 27

  20. 24 * 15 = ? 360 28

  21. Environmental Cues Executive Control

  22. Environmental Cues Executive Control

  23. Spontaneous Low Frequency Fluctuations during Rest Have Meaningful Signals

  24. How to Analyze These Time Series of Data (rfMRI)?

  25. Default Mode Network with Seeds in PCC (Cole, et al., 2010)

  26. Default Mode Network with Seeds in PCC (Cole, et al., 2010)

  27. (Cole, et al., 2010)

  28. Temporal Concatenation

  29. Temporal Concatenation

  30. From Group ICA to Individual Maps

  31. Beckmann, et al., 2005

  32. Amplitude of Low Frequency Fluctuation (ALFF) (Zang et al., 2007, 2008)

  33. Regional Homogeneity (ReHo) • Similarity of the time courses within usually 27 neighboring voxels • Measured with Kendall Coefficient of Concordance (KCC) (0-1) • Recorded as a value for the central voxel • Results in a voxel wise KCC values in the individualized maps 3 3 (Zang et al., 2004, Liu, et al., 2010)

  34. Now, What is this? rfMRI

  35. IDPs: 55 (out of 100) Nodes (Regional ALFF) and 1695 Edges (Connectivity)!

  36. Number Matters?

  37. What do you do with this database? 1100 Health Related Factors & 2501 Image-Derived Phenotypes (IDPs) tfMRI rfMRI sMRI dMRI 16 1771 675 39

  38. What do you do with this database? 1100 Health Related Factors & 2501 Image-Derived Phenotypes (IDPs) Levels of Data Analysis 1. Univariate Correlations (one by one) 2. Covariates of No Interest or Confounders (Age, Gender, and etc) 3. Data Driven Multivariate Analysis 4. Hypothesis Driven Analysis

  39. 1. Univariate Correlations (one by one) 2.8 million testing FDR ( P = 3.8 . 10 −5 ) and Bonf ( P =1.8 . 10 −8 )

  40. 1. Univariate Correlations

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