Biophysics of BOLD and common image artefacts

1
Biophysics of BOLD and common image artefacts

• Rhodri Cusack

• Overview
• Biophysics of BOLD signal
• - Neural gt Hemodynamic gt MRI
• Complications at each level
• - and what to do about them

2
Stimulus to signal
Arthurs Boniface (2002)
3
(No Transcript)
4
What aspect of neuronal activity determines BOLD
response??

• LFP (Logothetis et al, 2001)
• Simultaneous BOLD FMRI electrophysiological
  recording
• Measured Local Field Potentials (pre-synaptic,
  input) and Multi Unit Activity (spiking, output)
• Both provided reasonable fit to BOLD activity,
  but LFPs better
• Energy use/oxygen consumption (Hoge et al, 1999)
• Used MRI to measure CBF oxygenation
• Found linear relationship
• Neurotransmitter (Attwell Iadecola, 2002)
• Assessment of energy use by different processes
• Spiking very energy expensive
• Most used by postsynaptic currents action
  potentials
• Argue hemodynamic response is driven by
  neurotransmitter signalling and not local energy
  use
• Glutamate/GABA?

5
(No Transcript)
6
Hemodynamic response (HDR)

• Increase in volume
• Increase in volume throughout system, from
  arterioles to capillaries, venules veins
• Fastest and greatest response in arterioles
  (Vanzetta, Hildenshen Grinwald, 2005)
• Large increase in flow
• Oxygenation
• Initial de-oxygenation in capillaries (Vanzetta
  et al, 2005)
• Then, flow increase leads to a increase in
  oxygenation relative to the baseline state (i.e.,
  OEF decreases, Ogawa et al, 1990 Bandettini et
  al, 1997)

7
Influences on spatial distribution of HDR

• Spatial characteristics influenced by
• Neurovascular coupling (Malonek Grinvald, 1995)
• Vascular plumbing structure
• Intracortical vessels
• Pial network
• Larger vessels
• Size of region activated (Turner, 2002)
• Larger regions may give signal from veins further
  away

8
Influences on temporal profile of HDR

• Temporal characteristics influenced by
• By neurovascular coupling in arterioles/capillarie
  s
• Flow times
• Function of vessel size
• Blood velocity proportional to radius much of
  delay in small vessels
• Mixing due to laminar flow within vessels (de
  Zwart, 2005)
• Should include diffusion (Parrot)
• Other effects (e.g., vessel size dependence of
  post-stimulus adaptation Mandeville et al,
  1999 Yacoub et al, 2006)

9
The Balloon Model
Similar elements incorporated into SPMs DCM
forward model
Buxton, Uludag, Dubowitz, Liu (2004)
10
(No Transcript)
11
Hemodynamic-MRI coupling

• HDR affects MRI signal through several mechanisms
  (Hoogenraad et al, 2001)
• Extravascular reduced field gradients around
  venules veins
• Reduced deoxygenation causes magnetic property
  (susceptibility) of blood to become more similar
  to surrounding tissue
• Reduced field gradients in GM CSF
• Intravascular change in T2
• Reduced phase mismatch between signal from inside
  and outside venules veins
• Change in blood volume
• Affected by parameters of MR acqusition
• Field strength (higher field more sensitivity,
  particularly to smaller vessels/capillaries)
  (Haacke, 1994 Yacoub et al, 2003)
• Gradient echo vs. spin echo latter insensitive
  to large vessels less signal, but more
  spatially specific (Lee et al, 1999 Zhao et al,
  2006)

12
(No Transcript)
13
(No Transcript)
14
Non-linearities in the BOLD signal
measured
linear
BOLD Response
Signal
Stimulus timing
0.25 s
0.5 s
1 s
2 s
20 s
Brief stimuli produce larger responses than
expected Degree of non-linearity varies across
space Neural in origin
Slide adapted from Bandettini Birn, Saad
Bandettini (2001)
15
(No Transcript)
16
(No Transcript)
17
Vascular artefacts

• Vascular structure can affect signal at many
  levels
• Vascular signal
• Larger in magnitude
• More noisy

• Group level
• Large variability in venous structure reduces
  chance of bias
• May be more important in case studies or MVPA/
  functional localiser studies

Cusack et al (in preparation)
18
(No Transcript)
19
Fields should be flat

• MRI scanners apply a strong magnetic field
• 3 Tesla at the WBIC
• Ideally, field should be homogeneous
• Easy to arrange when it is empty, but ruined as
  soon as a head is put in
• Different materials act to strengthen (para) or
  weaken (dia) magnetic fields

With Shaihan Malik
20
0
0.27
-0.27
Field strength relative to 3 Tesla(parts per
million)
21
Tackling inhomogeneneity artefacts

• Distortion
• Optimise acquisition
• Acquire fieldmaps undistort
• Distortion by movement interaction
• Put movement parameters into model
• Use Realign unwarp (Andersson, 2001)
• Dropout
• Optimise acquisition parameters (e.g., TE, slice
  orientation, voxel size)
• Z-shimming
• Use spin echo (Schwarzbauer)
• Passive shimming (Wilson, Jezzard and colleagues
  Cusack et al, 2004)
• Choose the right subjects

22
Fieldmap undistortion evaluation by eye
UndistortedEPI mean
UndistortedEPI mean
Structural
EPI mean
Structural
EPI mean
Cusack, Brett Osswald (2004)
23
Quantitative evaluation - shape match to
structural scan - Measure similarity in shapes
between structural scan and EPIs before and after
undistortion - Use mutual information statistic
Cusack, Brett Osswald (2004)
24
Overlap across subjects
Cusack, Brett Osswald (2004)
25
Undistortion on data from Siemens Trio

• Shimming apply corrective field gradients
• Much better on modern machines
• Higher order shims optimised automatically
• Shims only optimised for volume being acquired in
  EPI
• Acquire smaller volumes
• Undistortion most important for regions near
  inhomogeneities

26
Comparative studies

• Watch out when comparing to other models
• Field inhomogeneities vary dramatically
• Human vs. sphinx vs. supine

27
Passive shimming
EPI
BOLD
Wilson colleagues, FMRIB, Oxford Cusack et al
(2005)
28
Optimising parameters to reduce dropout
From Rik Henson
29
Summary

• BOLD FMRI involves a complicated set of couplings
• Be careful when interpreting effects, or
  comparing FMRI with other imaging modalities
• It can fail in many ways
• Optimise acquisition and analysis
• Perform proper quality control