Keith Worsley - PowerPoint PPT Presentation

Loading...

PPT – Keith Worsley PowerPoint presentation | free to download - id: 74c5d4-NDZiZ



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Keith Worsley

Description:

Correlation random fields, brain connectivity, and cosmology Keith Worsley Department of Mathematics and Statistics, and McConnell Brain Imaging Centre, – PowerPoint PPT presentation

Number of Views:64
Avg rating:3.0/5.0
Slides: 23
Provided by: hong158
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Keith Worsley


1
Correlation random fields, brain connectivity,
and cosmology
  • Keith Worsley
  • Department of Mathematics and Statistics, and
  • McConnell Brain Imaging Centre,
  • Montreal Neurological Institute,
  • McGill University

2
(No Transcript)
3
(No Transcript)
4
(No Transcript)
5
Savic et al. (2005). Brain response to putative
pheromones in homosexual men. Proceedings of the
National Academy of Sciences, 1027356-7361
6
fMRI data 120 scans, 3 scans each of hot, rest,
warm, rest, hot, rest,
T (hot warm effect) / S.d. t110 if no
effect
7
Scale space smooth X(t) with a range of filter
widths, s continuous wavelet transform adds an
extra dimension to the random field X(t, s)
Scale space, no signal
34
8
22.7
6
4
15.2
2
10.2
0
-2
6.8
-60
-40
-20
0
20
40
60
S FWHM (mm, on log scale)
One 15mm signal
34
8
22.7
6
4
15.2
2
10.2
0
-2
6.8
-60
-40
-20
0
20
40
60
t (mm)
15mm signal best detected with a 15mm smoothing
filter
8
Matched Filter Theorem ( Gauss-Markov Theorem)
to best detect a signal white noise, filter
should match signal
10mm and 23mm signals
34
8
22.7
6
4
15.2
2
10.2
0
-2
6.8
-60
-40
-20
0
20
40
60
S FWHM (mm, on log scale)
Two 10mm signals 20mm apart
34
8
22.7
6
4
15.2
2
10.2
0
-2
6.8
-60
-40
-20
0
20
40
60
t (mm)
But if the signals are too close together they
are detected as a single signal half way between
them
9
Scale space can even separate two signals at the
same location!
8mm and 150mm signals at the same location
10
5
0
-60
-40
-20
0
20
40
60
170
113.7
20
76
50.8
15
S FWHM (mm, on log scale)
34
10
22.7
15.2
5
10.2
6.8
-60
-40
-20
0
20
40
60
t (mm)
10
(No Transcript)
11
(No Transcript)
12
Expressive or not expressive (EXNEX)?
Male or female (GENDER)?
Correct bubbles
All bubbles
Image masked by bubbles as presented to the
subject
Correct / all bubbles
13
Fig. 1. Results of Experiment 1. (a) the raw
classification images, (b) the classification
images filtered with a smooth low-pass
(Butterworth) filter with a cutoff at 3 cycles
per letter, and (c) the best matches between the
filtered classification images and 11,284
letters, each resized and cut to fill a square
window in the two possible ways. For (b), we
squeezed pixel intensities within 2 standard
deviations from the mean.
Subject 1
Subject 2
Subject 3
14
(No Transcript)
15
threshold
threshold
threshold
threshold
16
BrainStat - the details
  • Jonathan Taylor, Stanford
  • Keith Worsley, McGill

17
What is BrainStat?
  • Based on FMRISTAT (Matlab)
  • Written in Python (open source)
  • Part of BrainPy (Poster 763 T-AM)
  • Concentrates on statistics
  • Analyses both magnitudes and delays (latencies)
  • P-values for peaks and clusters uses latest
    random field theory

18
Details
  • Input data is motion corrected and preferably
    slice timing corrected
  • Output is complete hierarchical mixed effects
    ReML analysis (local AR(p) errors at first stage)
  • Spatial regularization of (co)variance ratios
    chosen to target 100 df (Poster 610 M-PM)
  • P-values for peaks and clusters are best of
  • Bonferroni
  • random field theory
  • discrete local maxima (Poster 539 T-AM)

19
Methods
  • Slice timing and motion correction by FSL
  • AR(1) errors on each run
  • For each subject, 2 runs combined using fixed
    effects analysis
  • Spatial registration to 152 MNI by FSL
  • Subjects combined using mixed effects analysis
  • Repeated for all contrasts of both magnitudes and
    delays

20
(No Transcript)
21
(No Transcript)
22
Conclusions
  • Strong overall BOLD increase of 30.5
  • Substantial subject variability (sd ratio 8)
  • Evidence for greater BOLD response for different
    sentences (0.50.1)
  • Evidence for greater latency for different
    sentences (0.160.04 secs)
  • Event design is better for delays
  • Block design is better for overall magnitude
About PowerShow.com