Introduc)on to fmri. Natalia Zaretskaya

Transcription

1 Introduc)on to fmri Natalia Zaretskaya

2 Content fmri signal fmri versus neural ac)vity A classical experiment: flickering checkerboard Preprocessing Univariate analysis Single- subject analysis Group analysis SoHware

3 fmri in comparison Spa)al resolu)on Temporal resolu)on

4 Some fmri study examples

5 Recommended reading!

6 fmri signal Hemodynamic response S)mulus Neural ac)vity increase Oxygen consump)on increase (CMR02) Cerebral blood flow increase (CBF) Cerebral blood volume increase (CBV) MR signal changes

7 Blood Oxygen Level Dependent signal (BOLD) Hemodynamic response S)mulus Neural ac)vity increase Oxygen consump)on increase (CMR02) Cerebral blood flow increase (CBF) Cerebral blood volume increase (CBV) MR signal changes BOLD signal

8 Neural ac)vity and energy consump)on in the primate brain Postsynap)c ac)vity: 75% Ac)on poten)als: 10% Ac)va)on neural excita)on Deac)va)on neural inhibi)on aher AXwell and Iadecola, TINS 2002 See also Logothe)s et al., 2001&2003

9 Hemodynamic response hxp://hirnforschung.kyb.mpg.de/en/methods/func)onal- magne)c- resonance- imaging- fmri.html

10 Hemodynamic response func)on (hrf) 0.12 HRF Brief s)mulus

11 Image types Func1onal/BOLD/ T2,T2*weighted Structural/anatomical/T1- weigted SagiXal view Coronal view Resolu)on 3x3x3 mm Transverse view Resolu)on: 1x1x1 mm

12 Image acquisi)on In- plane resolu)on 3x3mm Slice thickness 3mm Voxel size 3x3x3mm

13 Experimental design Condi1on of interest Checkerboard Control Gray background Finger tapping Rest Faces Houses Cola Pepsi

14 20 s on 20 s off A typical experiment Time (s) TR Time (s)

15 4D func)onal dataset 3D Repe))on )me (TR) )me Voxel 1me course Time (s)

16 Analysis steps Single- subject preprocessing Single- subject analysis (1 st level analysis) Group analysis (2 nd level analysis)

17 Mo)on correc)on Purpose: compensate for subject movement Time (s)

18 Mo)on correc)on Purpose: compensate for subject movement 6 parameter rigid body transform 3x 3x # volume

19 Slice- )me correc)on Purpose: compensate for the lag in slice acquisi)on ascending interleaved

20 Slice- )me correc)on Purpose: compensate for the lag in slice acquisi)on 2.5 s Before correc1on Time (s) AIer correc1on s Time (s)

21 Normaliza)on to common space Purpose1: compensate individual differences in brain shape for the group analysis hxp://publicdomainreview.org/collec)ons/phrenology- diagrams- from- vaughts- prac)cal- character- reader- 1902/

22 Templates & coordinate systems Purpose2: Unified coordinate system Talairach MNI hxp://imaging.mrc- cbu.cam.ac.uk/imaging/mnitalairach

23 Rigid transform Template 6 parameter rigid transform 3x 3x

24 Affine transform 12 parameter affine transform 3x 3x 3x 3x

25 Deforma)on field Warped image Nonlinear transform Source Template warping

26 Co- registra)on Subject 1 Template Group ac)va)on Subject 2 Subject n

27 Spa)al smoothing Purpose: increase the signal and suppress the noise 0 FWHM 5 FWHM 10 FWHM Full Max Full- Width/Half- max 2mm FWHM Half Max 5mm FWHM 10mm FWHM

28 More on smoothing later In the sohware sec)on!

29 Single- subject (first- level) analysis Univariate/voxel- wise analysis Ques)on: Which areas are ac)vated by the flickering checkerboard?

30 Checkerboard experiment Time (s) S1mulus func1on

31 Checkerboard experiment Time (s)

32 Simplest analysis Time (s)

33 Simplest analysis Time (s) T- test OFF ON

34 Simplest analysis Time (s) T- test OFF ON

35 General Linear Model (GLM) Build a predic)on about the signal Fit predic)on to actual data If it fits nicely there is ac)va)on!

36 Building predic)on Time (s) S1mulus func1on HRF Time (s) Regressor (signal predictor) convolu)on

37 Building regressors Time (s) S1mulus func1on HRF Time (s) convolu)on

38 General Linear Model Y(t) = β * X(t) + c + e(t) Measured data Modeled data (regressors) Constant offset Error (e.g. noise, confounds)

39 General linear model fit Y = Xβ + e β = (X T X) -1 X T y

40 Sta)s)cal inference (t- test) in GLM Voxel- wise t- sta)s)c t = β/se(β) 3D t- sta)s)c map 72,221 voxels Final result Is related to the variance of error Thresholding: deal with mul)ple comparison problem!

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42 What about two condi)ons? Condi)on A Condi)on B Time (s)

43 General Linear Model Y(t) = + + c + e(t) Measured data Modeled data (regressor 1) Modeled data (regressor 2) Error (e.g. noise, confounds)

44 Sta)s)cal inference (t- test) in GLM Voxel- wise t- sta)s)c t = (β1 β2) / SE(β1;β2) 3D t- sta)s)c map 72,221 voxels Final result Thresholding: deal with mul)ple comparison problem!

45 What about X condi)ons? Design Matrix Condi)ons of interest regressors Nuissance regressors )me Y = Xβ + e

46 Contrast: linear combina)on of betas Contrast vector: describes how beta- es1mates are combined ( weighted ) Contrast image: a 3D volume containing the result of beta- combina1ons Y(t) = β1*x1(t) + β2* X2(t) + c + e(t) [ 1-1 ] β1- β2

47 Group analysis (second- level analysis) Subject 1 contrast image Subject 2 contrast image Subject n contrast image Voxel- wise one- sample t- test Is our combina)o n of betas different from zero? 3D t- sta)s)c map Thresholding: deal with mul)ple comparison problem! Final result

48 GLM advantages Complex experimental designs Regress out uninteres)ng effects/ confounds Es)mate HRF shape ?

49 Different sohware, different philosophies Since 1994 hxp:// Since 1994 hxps://github.com/wanderine/broccoli/ Doug Greve hxp://afni.nimh.nih.gov/afni/ Since 2000 hxp://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ hxps://surfer.nmr.mgh.harvard.edu/fswiki/fsfast

50 Surface- based analysis advantages 14 mm FWHM Surface- based smoothing 5 mm apart in 3D 25 mm apart on surface Averaging with other )ssue types (WM, CSF) Averaging with other func)onal areas Surface- based inter- subject registra)on slide courtesy of Doug Greve

51 500 µm 0.6 mm isotropic voxels 3 mm isotropic voxels Weber et al., 2008

52 500 µm 0.6 mm isotropic voxels 3 mm isotropic voxels Weber et al., 2008

53 500 µm 0.6 mm isotropic voxels Thank you for your axen)on Also thanks to: Jon Polimeni Andreas Bartels Doug Greve for some of the slides and ideas 3 mm isotropic voxels Weber et al., 2008