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Hierarchical statistical analysis of fMRI data across runs/sessions/subjects/studies using BRAINSTAT/FMRISTAT. Jonathan Taylor, Stanford Keith Worsley, McGill. What is BRAINSTAT / FMRISTAT ?. FMRISTAT is a Matlab fMRI stats analysis package BRAINSTAT is a Python version Main components:

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slide1

Hierarchical statistical analysis of fMRI data across runs/sessions/subjects/studiesusing BRAINSTAT/FMRISTAT

Jonathan Taylor, Stanford

Keith Worsley, McGill

what is brainstat fmristat
What is BRAINSTAT / FMRISTAT ?
  • FMRISTAT is a Matlab fMRI stats analysis package
  • BRAINSTAT is a Python version
  • Main components:
    • FMRILM: Linear model, AR(p) errors, bias correction, smoothing of autocorrelation to boost degrees of freedom*
    • MULTISTAT: Mixed effects linear model, ReML estimation, EM algorithm, smoothing of random/fixed effects sd to boost degrees of freedom*
      • Key idea: IN: effect, sd, df, fwhm, OUT: effect, sd, df, fwhm
    • STAT_SUMMARY: best of Bonferroni, non-isotropic random field theory, DLM (Discrete Local Maxima)*

*new theoretical results

  • Treats magnitudes and delays in the same way
slide3

FMRILM: smoothing of temporal autocorrelation

  • Variability in acor lowers df
  • Df depends
  • on contrast
  • Smoothing acor brings df back up:

dfacor = dfresidual(2 + 1)

1 1 2 acor(contrast of data)2

dfeffdfresidualdfacor

FWHMacor2 3/2

FWHMdata2

= +

Hot-warm stimulus

Hot stimulus

FWHMdata = 8.79

Residual df = 110

Residual df = 110

100

100

Target = 100 df

Target = 100 df

Contrast of data, acor = 0.79

50

Contrast of data, acor = 0.61

50

dfeff

dfeff

0

0

0

10

20

30

0

10

20

30

FWHM = 10.3mm

FWHM = 12.4mm

FWHMacor

FWHMacor

multistat smoothing of random fixed fx sd

400

300

200

100

0

0

20

40

Infinity

MULTISTAT: smoothing of random/fixed FX sd

FWHMratio2 3/2

FWHMdata2

e.g. dfrandom = 3,

dffixed = 4  110

= 440,

FWHMdata = 8mm:

dfratio = dfrandom(2 + 1)

1 1 1

dfeffdfratiodffixed

= +

fixed effects

analysis,

dfeff = 440

dfeff

FWHM

= 19mm

Target = 100 df

random effects

analysis,

dfeff = 3

FWHMratio

slide5

STAT_SUMMARY

High FWHM: use Random Field Theory

Low FWHM:

use Bonferroni

In between: use Discrete Local Maxima (DLM)

0.12

Gaussian

T, 20 df

T, 10 df

0.1

Random Field Theory

Bonferroni

0.08

DLM

can ½

P-value

when

FWHM

~3 voxels

0.06

P-value

0.04

Discrete Local Maxima

True

0.02

Bonferroni, N=Resels

0

0

1

2

3

4

5

6

7

8

9

10

FWHM of smoothing kernel (voxels)

slide6

STAT_SUMMARY

High FWHM: use Random Field Theory

Low FWHM:

use Bonferroni

In between: use Discrete Local Maxima (DLM)

Bonferroni

4.7

4.6

4.5

True

T, 10 df

4.4

Random Field Theory

4.3

T, 20 df

Discrete Local Maxima (DLM)

4.2

Gaussianized threshold

4.1

Gaussian

4

3.9

Bonferroni, N=Resels

3.8

3.7

0

1

2

3

4

5

6

7

8

9

10

FWHM of smoothing kernel (voxels)

slide8

STAT_SUMMARY example: single run, hot-warm

Detected by BON and

DLM but not by RFT

Detected by DLM,

but not by BON or RFT

slide9

0.6

0.4

0.2

0

-0.2

-0.4

-5

0

5

10

15

20

25

t (seconds)

Estimating the delay of the response

  • Delay or latency to the peak of the HRF is approximated by a linear combination of two optimally chosen basis functions:

delay

basis1

basis2

HRF

shift

  • HRF(t + shift) ~ basis1(t)w1(shift) + basis2(t)w2(shift)
  • Convolve bases with the stimulus, then add to the linear model
example fiac data
Example: FIAC data
  • 16 subjects
  • 4 runs per subject
    • 2 runs: event design
    • 2 runs: block design
  • 4 conditions
    • Same sentence, same speaker
    • Same sentence, different speaker
    • Different sentence, same speaker
    • Different sentence, different speaker
  • 3T, 200 frames, TR=2.5s
response
Response
  • Events
  • Blocks

Beginning of block/run

design matrix for block expt
Design matrix for block expt
  • B1, B2 are basis functions for magnitude and delay:
1 st level analysis
1st level analysis
  • Motion and slice time correction (using FSL)
  • 5 conditions
  • Smoothing of temporal autocorrelation to control the effective df (new!)
slide14

Efficiency

  • Sd of contrasts (lower is better) for a single run, assuming additivity of responses
  • For the magnitudes, event and block have similar efficiency
  • For the delays, event is much better.
2 nd level analysis
2nd level analysis
  • Analyse events and blocks separately
  • Register contrasts to Talairach (using FSL)
    • Bad registration on 2 subjects - dropped
  • Combine 2 runs using fixed FX
  • Combine remaining 14 subjects using random FX
    • 3 contrasts × event/block × magnitude/delay = 12
  • Threshold using best of Bonferroni, random field theory, and discrete local maxima (new!)

3rd level analysis

slide21

Event

Block

Magnitude

Delay

events vs blocks for delays in different same sentence
Events vs blocks for delaysin different – same sentence
  • Events: 0.14±0.04s; Blocks: 1.19±0.23s
  • Both significant, P<0.05 (corrected) (!?!)
  • Answer: take a look at blocks:

Greater

magnitude

Different sentence

(sustained interest)

Best fitting block

Same sentence

(lose interest)

Greater

delay

slide24

Magnitude increase for

  • Sentence, Event
  • Sentence, Block
  • Sentence, Combined
  • Speaker, Combined

at (-54,-14,-2)

slide25

Magnitude decrease for

  • Sentence, Block
  • Sentence, Combined

at (-54,-54,40)

slide26

Delay increase for

  • Sentence, Event

at (58,-18,2)

inside the region where all

conditions are activated

conclusions
Conclusions
  • Greater %BOLD response for
    • different – same sentences (1.08±0.16%)
    • different – same speaker (0.47±0.0.8%)
  • Greater latency for
    • different – same sentences (0.148±0.035 secs)
slide28

z=-12

z=2

z=5

1

2

3

1,4

3

3

3

3

1

The main effects of sentence repetition (in red) and of speaker repetition (in blue).1: Meriaux et al, Madic; 2: Goebel et al, Brain voyager; 3: Beckman et al, FSL; 4: Dehaene-Lambertz et al, SPM2.

Brainstat:

combined

block and

event,

threshold

at T>5.67,

P<0.05.