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Basic physiology of fMRI: signal and noise
Overview
• Brief overview of fMRI • Contrast mechanisms
– Source of the fMRI signal
• Sources of Noise (& corrections) – Motion
– Cardiac
– Respiration
Brain activity during cued finger tapping
Measuring Brain Function with MRI
Task
Neuronal Activity
Red Blood Cells
How can we see brain function with MRI?
red blood cells
oxygenated
deoxygenated
L. Pauling, C. D. Coryell, Proc.Natl. Acad. Sci. USA 22, 210-216, 1936.
Oxygenated and deoxygenated red blood cells have different magnetic properties
diamagnetic
paramagnetic
Detecting “activation”
Capillaries!
Veins!Arteries!
Capillaries!
Veins!Arteries!
χ1 χ2 χ2 χ2
ΔS =
0
0.2
0.4
0.6
0.8
1
1.2
0 50 100 150 200
M0a e-t/T2*a - M0r e-t/T2*r
time
Sig
nal
ΔS S0
= M0a
M0r e-tΔR2* - 1
Measuring Brain Function with MRI
Task
t
t
task Hemodynamics Neuronal Activity
Red Blood Cells
Task Blood Flow deoxy-Hb MRI signal
BOLD = Blood Oxygenation Level Dependent
The Blood Oxygenation Level Dependent Response
Task
neuronal activity
BF
deoxy-Hb
MR signal
The Blood Oxygenation Level Dependent Response
Task
neuronal activity
BF
HbO2 deoxy-Hb
MR signal
CMRO2 BV - - +
0 20 40 60
Time (sec)
Sig
nal
0 5 10 15 Time (sec)
Sig
nal
Temporal dynamics of fMRI signal change
Delay
Activation amplitude
Arbitrary Scale
+ 2 sec
- 2 sec
0 sec 0 10 20 30 40 50
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Num
ber o
f Vox
els
Delay (sec)
t
task fMRI signal
0 10 s
Functional Magnetic Resonance Imaging (fMRI)
Typical MRI t
Functional MRI
Look for changes in MRI signal over time
Task Blood Flow MRI signal deoxy-Hb
Brain activity during cued finger tapping
t
t
task
Map the changes in MRI signal over time
Functional MRI
t
t
Task-related Neuronal Activity
Physiologic noise (cardiac, respiration)
Subject motion
Scanner instabilities Image reconstruction errors
Spontaneous Neuronal Activity
Hemodynamics resting state
Problem: Subject Motion
motion
t
t
t
Signal changes occur at edges
t
Correcting for subject motion
• Image registration
t = 0 t = 1
• Prevent Motion
• Regress out motion-related changes
Problem: motion during the volume
Problem: motion during the volume
slices are actually acquired like this… expected acquisition:
Problem: motion during the volume
Potential solution: Prospective motion correction
slice tracks with the motion
• C.C. Lee, et al., Magn. Res. Med., 39, 1998. • H.A. Ward, et al., Magn. Res. Med., 43, 2000. • O. Speck, et al., Magma 19, 2006.
Physiological noise
0 40 80 120 160 200 240 280 320 time (s)
Cardiac
M.S. Dagli et al., NeuroImage 9, 1999
Respiration
time 00 TIME = 110 s
ΔΦ =90 o 400
200
B0
Field Map
Figure courtesy of J. Bodurka
Can filtering (<0.1Hz) reduce cardiac noise?
0 5 10 15 20 time (s)
Sign
al
Cardiac fluctuation (1.1 Hz) Sampled every TR (2s)
0 0.5 1 1.5 2 Frequency (Hz)
0
0.1
0.2
0.3
Pow
er
• Not always: Aliasing
Correction of physiological noise
RETROICOR (G. Glover et al., Magn. Reson. Med. 44, 2000.)
Pulse Oximeter
0 1 2 3 4 5 6 7 8 9 10 time (s)
Respiration
MR Images
φc
φr
sin( φc )
cos( φc )
sin( 2φc )
cos( 2φc )
sin( φr )
cos( φr )
sin( 2φr )
cos( 2φr )
Additional Regressors:
Correction of physiological noise
time
Sig
nal
Reshuffle the data based on its cardiac or respiration phase
0 1 2 3 4 5 6
Sig
nal
phase
Correction: Cardiac + Respiration
X. Hu et al., Magn. Res. Med. 1995
At what stage should physio correction be done?
• Preprocessing – Motion correction – Physiological noise correction – Slice time correction – Nuisance Regression – Spatial Smoothing – Convert to percent signal change – Temporal filter
• Define ROI (seed) • Average EPI time course over ROI • Regression T.B. Jones et al., Neuroimage 42, 2008
Physio. fluctations can occur at low frequencies
Variations in breathing during rest ΔBOLD signal
PETCO2
BOLD (GM)
BOLD (WM)
R.G. Wise et al., NeuroImage 21, 2004
ΔPETCO2 correlated w/ ΔBOLD
Resting fluctuations in respiration
RVT = Respiration Volume per Time
50 100 150 200 250 300 350
0
6
|Z|
group (n=11)
RVT-related changes
RVT
Heart Rate
Birn et al. (unpublished)
RVHRcor (Respiration Volume + Heart Rate)
C. Chang et al., Neuroimage 2009
Nuisance Regression
H.J. Jo et al., NeuroImage 52, 2010
The effect of global signal regression
The global signal is highly correlated w/ RVT
-1
-0.5
0
0.5
1
CC
: RV
T ~
glob
al s
igna
l
without global regression
with global regression
Connectivity maps
0.5
0.3
-0.1
0
(separate study, n = 18)
Global Signal Regression
K. Murphy, et al., Neuroimage, 2008
Can introduce anti-correlations
Global Signal Regression
Z.S. Saad, et al., Brain Connectivity, 2012
Can alter group differences …under certain conditions
Nuisance Regression
H.J. Jo et al., NeuroImage 52, 2010
PSTCor (Phase-shifted Soft Tissue Corr.)
J.S. Anderson et al., HBM 32, 2011
“FIX” (FMRIB’s ICA-based X-noisifier)
L. Griffanti, et al., Neuroimage 2014
ICA-AROMA
ICA-AROMA
R.H.R. Pruim et al., NeuroImage 112 (2015)
Tools to correct for physiological noise
• Filtering - Works only if TR is short enough (< 400ms)
• IMPACT (Chuang et al., 2001) – if TR short enough
• Retrospective correction (k-space) (X. Hu et al., 1995)
• RETROICOR (Glover et al., 2000)
• CORSICA (V. Pelbarg, et al., 2007)
• PESTICA (E.B. Beall, et al., 2007)
• RVTcor (R.M. Birn, et al., 2006) • RVHRcor (C. Chang, et al., 2009) • ANATICOR (H-J. Jo, et al., ) • APPLECOR, PEARCOR (M Marx, et al., 2013) • PSTCor (J.S. Anderson, et al., 2011) • CompCor (Y. Behzadi, et al., 2007)
• FIX (L. Griffanti, et al., 2014)
• ICA-AROMA (###, et al., 2015)
Should we perform physio corrections?
• Reduces fluctuations related to heart beat and respiration – Reduce false positives – Reduce false negatives
• Some physiological fluctuations are
associated with neuronal activity – E.g. Heart rate variability
This is still an open question in the field
Signal / Physiologic Noise
Tem
pora
l Sig
nal t
o N
oise
Rat
io
TSNR vs SNR
Signal-to-Noise Ratio
J. Bodurka
TSNR vs SNR
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