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“Subtraction” Experiments

“Subtraction” Experiments. 1985-1993’ish: the PET era 1993-2000: the fMRI “localization” era What’s next… beyond localization…. Topics. “Modern” fMRI Event-related designs Power of Linearity Region-of-interest analysis. Disadvantages to fMRI “Blocked Designs”.

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“Subtraction” Experiments

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  1. “Subtraction” Experiments • 1985-1993’ish: the PET era • 1993-2000: the fMRI “localization” era • What’s next… beyond localization…

  2. Topics • “Modern” fMRI • Event-related designs • Power of Linearity • Region-of-interest analysis

  3. Disadvantages to fMRI“Blocked Designs” • One condition for extended periods of time (e.g., 20 s)

  4. Blocked-design alternative:“event-related design” • Measure fMRI response to individual events/trials Idealized Response In practice there is LOTS of noise Solution: average lots of trials together <matlab demo>

  5. Blocked-design alternative:“event-related design” • Measure fMRI response to individual events Time 20 s • Analysis: • For each voxel in the brain calculate an event-related average for each condition. • If this is a “face-selective voxel” what will the average look like: • for the face condition? • for the place condition?

  6. Advantages/Disadvantages to widely-spaced event-related designs? • Advantages: • Expectation/adjustment of effort • Behavior-dependent response • Remembered vs. forgotten items • Complex designs • Attention cueing • Disadvantages: • Very few trials • Lots of time in the scanner • Only a few conditions • More boring than you can believe • Still a localization experiment (so far, more to come…) • Did the voxel respond or not respond

  7. Topics • “Modern” fMRI • Event-related designs • Power of Linearity • Region-of-interest analysis

  8. Why can we do more than localization experiments? • The fMRI signal is approximately linear. • Signal is proportional to neural activity: more neural activity = more fMRI signal. • Read the Heeger paper on website for evidence of linearity.

  9. Linearity: law of superposition

  10. Possible alternatives to linearity “Compressive nonlinearity” “Step function” fMRI signal fMRI signal 0 0 50 100 50 100 Neural activity Neural activity “Linear” fMRI signal 0 50 100 Neural activity

  11. Linearity implications • (1) Allows us to talk about the “amount” of activity. • A small response vs. a large response is meaningful. • No difference in response is meaningful. • Applies to blocked or event-related designs. • The “blunt-electrode” approach to fMRI. • Parametric designs (p 61-62 text) • Not just that an area is involved (localization), but how it is involved.

  12. How to measure “amount” of fMRI response 20s 20s Visual Blank Visual Blank Visual Blank Expected fMRI response in visual areas

  13. Linearity implications • (1) Allows us to talk about the “amount” of activity. • A small response vs. a large response is meaningful. • No difference in response is meaningful. • Applies to blocked or event-related designs. • The “blunt-electrode” approach to fMRI. • Parametric designs (p 61-62 text) • Not just that an area is involved (localization), but how it is involved. Behavior Area of interest Hippocampus Prefrontal cortex Amygdala Parietal cortex “Face area” (RT, % correct) fMRI Signal Memory load Conflict Fear Attention Familiarity Parameter Parameter

  14. Linearity implications • (2) Through data averaging and counterbalancing, we can have closely spaced trials. • Instead of 1 trial every 20 seconds, 1 trial every 2 seconds. • “Rapid event-related designs”. Time (s) 1 s

  15. Linearity: law of superposition

  16. Topics • “Modern” fMRI • Event-related designs • Power of Linearity • Region-of-interest analysis

  17. Region-of-interest Analysis • If you know where to look… • (1) Define the area(s) that you want to analyze in detail. • (2) Specifically test your ROI.

  18. fMRI Design • “Localizer” Scan • Block design comparing faces with other objects • This scan will be used to define our ROI’s • “Experimental” Scans • Present a variety of conditions that test a specific hypothesis about the ROI

  19. Advantages of ROI designs • Much more powerful (statistically) • Have a predefined hypothesis about where to expect an effect. • Can use much more sensitive statistical tests. • Make claims about specific functional areas of the brain (between-subject variability in anatomical locations).

  20. Summarize • Linearity allows us to: • Measure the amount of neural activity (parametric studies) • Rapid event-related designs • Localization still useful • ROI analyses

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