Jody Culham
Brain and Mind Institute
Department of Psychology
University of Western Ontario
http://www.fmri4newbies.com/
What Neuroscientists Can and Cannot
Learn from fMRI
Last Update: January 18, 2012
Last Course: Psychology 9223, W2010, University of Western Ontario
Section 1
The BOLD Signal
Deoxygenated Blood  Signal Loss
Oxygenated blood?
•
Diamagnetic
•
Doesn’t distort
surrounding
magnetic field
•
No signal loss…
rat breathing pure oxygen
rat breathing normal air (less oxygen than pure oxygen)
Deoxygenated blood?
• Paramagnetic
• Distorts surrounding
magnetic field
• Signal loss !!!
Images from Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
based on two papers from Ogawa et al., 1990, both in Magnetic Resonance in Medicine
Hemoglobin (Hb)
Figure Source, Huettel, Song & McCarthy, 2004,
Functional Magnetic Resonance Imaging
BOLD Time Course
Blood Oxygenation Level-Dependent Signal
BOLD Response
(% signal change)
Positive BOLD response
3
2
Overshoot
1
Initial
Dip
0
Post-stimulus
Undershoot
Time
Stimulus
Perhaps it should be BDLD?
Blood DE-oxygenation level-dependent signal?
• Technically, “BOLD” is a misnomer
• The fMRI signal is dependent on deoxygenation
rather than oxygenation per se
• The more deoxy-Hb there is the lower the signal
fMRI
Signal
Amount of deoxy-Hb
“BDLD” idea from Bruce Pike, MNI
Deoxy-Hb Concentration
Deoxy-Hb
3
2
1
0
Time
Stimulus
Initial Dip (Hypo-oxic Phase)
• Transient increase in oxygen consumption, before
change in blood flow
– Menon et al., 1995; Hu, et al., 1997
• Smaller amplitude than main BOLD signal
– 10% of peak amplitude (e.g., 0.1% signal change)
• Potentially more spatially specific
– Oxygen utilization may be more closely associated with
neuronal activity than positive response
Slide modified from
Duke course
Rise (Hyperoxic Phase)
• Results from vasodilation of arterioles, resulting in a
large increase in cerebral blood flow
• Inflection point can be used to index onset of
processing
Slide modified from
Duke course
Peak – Overshoot
• Over-compensatory response
– More pronounced in BOLD signal measures than flow
measures
• Overshoot found in blocked designs with extended
intervals
– Signal saturates after ~10s of stimulation
Slide modified from
Duke course
Sustained Response
• Blocked design analyses rest upon presence of
sustained response
– Comparison of sustained activity vs. baseline
– Statistically simple, powerful
• Problems
– Difficulty in identifying magnitude of activation
– Little ability to describe form of hemodynamic response
Slide modified from
Duke course
Undershoot
• Cerebral blood flow more locked to stimuli than
cerebral blood volume
– Increased blood volume with baseline flow leads to decrease
in MR signal
• More frequently observed for longer-duration stimuli
(>10s)
– Short duration stimuli may not evidence
– May remain for 10s of seconds
Slide modified from
Duke course
Evolution of BOLD Response
Hu et al., 1997, MRM
Trial to Trial Variability
Huettel, Song & McCarthy, 2004,
Functional Magnetic Resonance Imaging
Variability of HRF Between Subjects
Aguirre, Zarahn & D’Esposito, 1998
• HRF shows considerable variability between subjects
different subjects
• Within subjects, responses are more consistent, although there is still
some variability between sessions
same subject, same session
same subject, different session
Variability of HRF Between Areas
Possible caveat: HRF may also vary between areas, not just subjects
• Buckner et al., 1996:
• noted a delay of .5-1 sec between visual and prefrontal regions
• vasculature difference?
• processing latency?
• Bug or feature?
• Menon & Kim – mental chronometry
Buckner et al., 1996
Variability Between Subjects/Areas
• greater variability
between subjects than
between regions
• deviations from
canonical HRF cause
false negatives (Type II
errors)
• Consider including a
run to establish subjectspecific HRFs from
robust area like M1
Handwerker et al., 2004, Neuroimage
Sampling Rate
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
Linearity of BOLD response
Linearity:
“Do things add up?”
red = 2 - 1
green = 3 - 2
Sync each trial response
to start of trial
Not quite linear
but good enough!
Source: Dale & Buckner, 1997
Section 2
From Neurons to BOLD
40
0
-55
-70
Refractory period
Time (ms)
BOLD Signal Change (%)
Voltage (mV)
From Neurons to BOLD
Positive BOLD Response
1
0
Undershoot
Time (s)
• Any similarity in the shapes of the curves for
action potentials and the BOLD response is
purely coincidental (but still kinda cool)
Stimulus to BOLD
Source: Arthurs & Boniface, 2002, Trends in Neurosciences
Neural Networks
Post-Synaptic Potentials
•
•
The inputs to a neuron (post-synaptic potentials) increase (excitatory PSPs) or
decrease (inhibitory PSPs) the membrane voltage
If the summed PSPs at the axon hillock push the voltage above the threshold, the
neuron will fire an action potential
What does electrophysiology measure?
Raw microelectrode signal
Filter out low frequencies  Action Potentials (APs)
Filter out high frequencies  Local Field Potentials (LFPs)
Source: http://www.cin.uni-tuebingen.de/research/methods-in-neuroscience/networks.php
BOLD Correlations
24 s stimulus
12 s stimulus
4 s stimulus
Source: Logothetis et al., 2001, Nature
Local Field Potentials (LFP)
• reflect post-synaptic potentials
• similar to what EEG (ERPs) and MEG
measure
Multi-Unit Activity (MUA)
• reflects action potentials
• similar to what most electrophysiology
measures
Logothetis et al. (2001)
• combined BOLD fMRI and
electrophysiological recordings
• found that BOLD activity is more closely
related to LFPs than MUA
Even Simple Circuits Aren’t Simple
gray matter
(dendrites, cell bodies
& synapses)
Lower tier area
(e.g., thalamus)
white matter
(axons)
Will BOLD activation from the blue voxel reflect:
Middle tier area
(e.g., V1, primary
visual cortex)
• output of the black neuron (action potentials)?
• excitatory input (green synapses)?
• inhibitory input (red synapses)?
Higher tier area
(e.g., V2, secondary
visual cortex)
• inputs from the same layer (which constitute ~80% of
synapses)?
• feedforward projections (from lower-tier areas)?
…
• feedback projections (from higher-tier areas)?
Comparing Electrophysiolgy and BOLD
Data Source: Disbrow et al., 2000, PNAS
Figure Source, Huettel, Song & McCarthy, Functional Magnetic Resonance Imaging
fMRI Measures the Population Activity
Ideas from: Scannell & Young, 1999,
Proc Biol Sci
fMRI for Dummies
Effects of Practice
Verb generation
Verb generation
after 15 min practice
Raichle & Posner, Images of Mind cover image
Bug or feature?
• fMRI adaptation enables us to study the tuning of
neurons
fMRI for Dummies
Stimulus to BOLD
Source: Arthurs & Boniface, 2002, Trends in Neurosciences
Vasculature: Brain vs. Vein
Source: Menon & Kim, TICS
Contents of a Voxel
Capillary beds within the cortex
Source: Duvernoy, Delon &
Vannson, 1981, Brain Research
Bulletin
Source: Logothetis, 2008, Nature
“Brain vs. Vein”
• large vessels produce BOLD activation further from the true site of activation
than small vessels (especially problematic for high-resolution fMRI)
• large vessels line the sulci and make it hard to tell which bank of a sulcus
the activity arises from
• the % signal change in large vessels can be considerably higher than in
small vessels (e.g., 10% vs. 2%)
• activation in large vessels occurs later than in small ones
Source: Ono et al., 1990, Atlas of the Cerebral Sulci
Vessel Valves
Source: Harrison et al. (2002). Cerebral Cortex.
Dilation of Arterioles
vasodilation could be induced by either
electrical stimulation or release of Ca2+
Time
stim
max dilation ~3-6 s
after stim
• biggest changes in arteriole dilation occurred near stimulation;
however, effects could also be observed several mm upstream
Source: Adapted from Takano et al., 2006, Nat Neurosci, by Huettel, et al., 2nd ed.
Upstream Effects
arteriole
veins
• biggest changes in arteriole dilation occurred near stimulation;
however, effects could also be observed several mm upstream
Source: Adapted from Iadecola et al., 1997, J Neurophysiol, by Huettel, et al., 2nd ed.
Don’t Trust Sinus Activity
•
You will sometimes see bogus “activity” in the sagittal sinus
Energy Budget
Data Source: Atwell & Laughlin, 2001, J. Cereb. Blood Flow Metab.
Figure Source, Huettel, Song & McCarthy, Functional Magnetic Resonance Imaging
The Forgotten Brain Cells
Common (i.e., Wrong) Wisdom
“Glial cells are probably not essential for processing information”
(Kandel, Schwartz & Jessell, Principles of Neural Science 3rd Ed.)
Tripartite Synapse
•
Astrocytes are adjacent to both
synapses and blood vessels
–
•
well poised to adjust vascular response to
neural activity
Astrocytes outnumber neurons by at
least 10:1 and comprise ~50% of the
total CNS volume

Astrocytes perform a number of
critically important functions:
1.
2.
3.
4.
Neurotransmitter uptake and recycling
Neurometabolic regulation
Cerebrovascular regulation
Release of signaling molecules
(“gliotransmitters”)
Source: Figley & Stroman, 2011, EJN
Glycolysis
Source: Raichle, 2001, Nature
Vasoactive Substances
• substances that cause the vessels to dilate
• potassium ions (K+)
– move from intra- to extra-cellular space during synaptic
activity
• adenosine
– increases with high metabolic activity
• nitric oxide
– released by local and distant activation
• gap junctions
• calcium (Ca2+)
– triggered by neuronal activation
• dopamine
Information Source: Huettel, Song & McCarthy, 2nd ed.
What about inhibitory synapses?
• GABA = inhibitory neurotransmitter
 hyperpolarization (IPSP)
• less metabolically demanding than excitatory
(glutamatergic) activity
• GABA can be taken up presynaptically rather than
recycled through astrocytes
• Therefore, neurotransmission at inhibitory synapses
likely influences the BOLD signal less than at
excitatory synapses
Not Just Neurons
Leopold, 2009, based on data of Sirotin & Das, 2009, Nature
Sirotin & Das, 2009
• awake macaque monkey sees tiny light in dark room
– red: keep tight fixation; green: relax
– timing of red-green is periodic
•
measure blood flow in area of peripheral visual cortex
– away from foveal representation of fixation point
– on some trials visual stimuli were presented to activate the measured area
Non-Neuronal Effects
Leopold, 2009, based on data of Sirotin & Das, 2009, Nature
Sirotin & Das, 2009
• two components to blood flow in visual cortex (V1)
1. related to neuronal responses to visual stimuli
2. related anticipation of neural events
Properties of Predictive Response
• response follows expected trial timing
– when trial timing is changed, monkey performs correctly but
this response persists for a few trials
• occurs even without stimulation
• correlated with pupil diameter
– is it just general arousal?
• visual cortex response does not occur with predictive
sequence of auditory events
– suggests it’s more regionally specific than general arousal
• occurs in arterial signal
Stimulus to BOLD
Source: Arthurs & Boniface, 2002, Trends in Neurosciences
Gradient Echo vs. Spin Echo
Gradient Echo
• high SNR
• strong contribution of vessels
Spin Echo
• lower SNR
• weaker contribution of vessels
Source: Logothetis, 2008, Nature
The Concise Summary
We sort of understand this
(e.g., psychophysics,
neurophysiology)
We’re *&^%$#@ clueless here!
We sort of understand this
(MR Physics)
Is the fMRI Sky Falling?
Don’t Panic
• BOLD imaging is well correlated with results from other
methods
• BOLD imaging can resolve activation at a fairly small
scale (e.g., retinotopic mapping)
• PSPs and action potentials are correlated so either way,
it’s getting at something meaningful
• even if BOLD activation doesn’t correlate completely with
electrophysiology, that doesn’t mean it’s wrong
– may be picking up other processing info (e.g., PSPs, synchrony)
– maybe anticipatory changes in blood flow are interesting too
Section 3
Spatial Limits of fMRI
fMRI in the Big Picture
What Limits Spatial Resolution
• noise
– smaller voxels have lower SNR
• head motion
– the smaller your voxels, the more contamination head motion
induces
• temporal resolution
– the smaller your voxels, the longer it takes to acquire the same
volume
• 4 mm x 4 mm at 16 slices/sec
• OR 1 mm x 1 mm at 1 slice/sec
• vasculature
– depends on pulse sequences
• e.g., spin echo sequences reduce contributions from large vessels
– some preprocessing techniques may reduce contribution of large
vessels (Menon, 2002, MRM)
Ocular Dominance Columns
• Columns on the order of ~0.5 mm have been
observed with fMRI
Submillimeter Resolution
vein
Stria of Gennari
(Layer IV)
Spin Echo
Functional
(activation
localized to
Layer IV)
Gradient Echo
Functional
(superficial activation
includes vessels)
Spin Echo
Anatomical
Gradient Echo
Anatomical
Goenze, Zappe & Logothetis, 2007, Magnetic Resonance Imaging
• anaesthetized monkey; 4.7 T; contrast agent (MION)
• ~0.3 x 0.3 x 2 mm
Voxel Size
non-isotropic
non-isotropic
isotropic
3x3x6
= 54 mm3
3x3x3
= 27 mm3
2.1 x 2.1 x 6
= 27 mm3
e.g., SNR = 100
e.g., SNR = 71
e.g., SNR = 71
In general, larger voxels buy you more SNR.
EXCEPT when the activated region does not fill the voxel (partial voluming)
Partial Voluming
• The fMRI signal occurs in gray matter (where the
synapses and dendrites are)
• If your voxel includes white matter (where the axons
are), fluid, or space outside the brain, you effectively
water down your signal
fMRI for Dummies
Partial Voluming
Partial volume effects: The combination, within a single voxel, of signal
contributions from two or more distinct tissue types or functional regions
(Huettel, Song & McCarthy, 2004)
This voxel contains mostly gray matter
This voxel contains mostly white matter
This voxel contains both gray and white
matter. Even if neurons within the voxel
are strongly activated, the signal may be
washed out by the absence of activation
in white matter.
Partial voluming becomes more of a
problem with larger voxel sizes
Worst case scenario: A 22 cm x 22 cm x
22 cm voxel would contain the whole
brain
The Initial Dip
• The initial dip seems to have better spatial specificity
• However, it’s often called the “elusive initial dip” for a reason