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BOLD fMRI John VanMeter, Ph.D. Center for Functional and Molecular Imaging Georgetown University Medical Center. Outline. BOLD contrast fMRI conceptually Relationship between BOLD contrast and hemodynamics History of BOLD contrast - PowerPoint PPT Presentation
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BOLD fMRI
John VanMeter, Ph.D.Center for Functional and Molecular Imaging
Georgetown University Medical Center
Outline• BOLD contrast fMRI conceptually• Relationship between BOLD contrast
and hemodynamics • History of BOLD contrast• Relationship between neuronal glucose
metabolism and blood flow• Theories and properties of BOLD
contrast mechanisms
Neuronal Activity and Blood Flow Changes: Initial Hypothesis
• Roy and Sherrington hypothesized that local neuronal activity is related to regional changes in both cerebral blood flow and metabolism (1890).
• “There are, then, two more or less distinct mechanisms for controlling the cerebral circulation, viz. - firstly, an intrinsic one by which the blood supply of the various parts of the brain can be varied locally in accordance with local requirements, and secondly, an extrinsic, viz. - the vasomotor nervous system…”
Roy and Sherrington’s Experiments
“… the increase in the volume of the brain which results from stimulation of the sensory nerves is mainly if not entirely due to passive or elastic distension of its vessels as a result of the blood-pressure in the systemic arteries.”
History of BOLD fMRI• Initial discovery of magnetic properties
of blood by Linus Pauling and graduate student Charles Coryell (1936):– Magnetic properties of a blood cell
(hemoglobin) depends on whether it has an oxygen molecule
– With oxygen zero magnetic moment– Without oxygen sizeable magnetic
moment
Initial In Vivo Measurement of Neuronal Activity
• Initial techniques used PET (positron emission tomography)
• PET uses injection of a radiotracers which are variants of physiological molecules that include a radio isotope
• FDG (2-fluoro-2deoxy-D-glucose) for glucose metabolism
• H2015 for blood flow
Functional Imaging - PET• Sokoloff demonstrated that rCBF (blood
flow) increases in visual cortex in proportion to photic stimulation using PET (1961).
• Demonstrated “coupling” between blood flow and metabolism (1981).
Relationship Between Glucose Metabolism and Blood Flow
• Sokoloff (1981) used autoradiography
• Measured both glucose metabolism and blood flow
• 39 brain regions in rat brain
• Correlation r=0.95• Slope m=2.6
First MRI-based Measurement of Neuronal Activity
• Belliveau (1990) used MRI contrast agent Gadolinium as an exogenous tracer
• Gadolinium locally disrupts MRI signal
• Perfusion weighted imaging (PWI)
Oxy- vs. Deoxy- Hemoglobin
• Oxygenated hemoglobin (Hb) is diamagnetic (zero magnetic moment)
• Deoxygenated hemoglobin (dHb) is paramagnetic (magnetic moment)
• Magnetic susceptibility of dHb is about 20% greater than Hb
• Magnetic susceptibility affects rate of dephasing - T2 and T2* contrast!
T1 & T2 Contrast Versus Oxygenated Hemoglobin
Demonstration of BOLD Contrast
• Seiji Ogawa (1990) manipulates oxygen content of air breathed by rats
• Results in variation of oxygenated state of blood
• Demonstrates effect on T2* contrast to make images of blood vessels
Ogawa’s Images of Blood Vessels Based on Oxygen Content
Pure oxygen
Normal Air
Magnetic Susceptibility Greater on T2* than T2 Images
OxygenatedHemoglobin
DeoxygenatedHemoglobin
Spin GradientEcho (T2) Echo (T2*)
Oxygenation vs Local Field Changes
Bandettini and Wong. Int. J. Imaging Systems and Technology. 6:133 (1995)
First fMRI BOLD in Human• Kwong (1992)
demonstrated first BOLD-contrast fMRI in human visual cortex
Blood Flow vs BOLD Changes
• Kwong also showed how changes in BOLD corresponded to changes in blood flow
• Important to show that BOLD and blood are related
Build Up to BOLD Contrast• Hypothesis of relationship between blood flow and activity
(Roy & Sherrington, 1890)• Discovery of differential magnetic properties of oxygenated
and deoxygenated hemoglobin (Pauling, 1936)• Blood flow increases with activity (Sokoloff, 1961)• Blood flow correlated with glucose metabolism (Sokoloff,
1981)• Demonstration of blood flow measured using MRI with an
exogenous tracer (Belliveau, 1990)• Demonstration of effect of dHb on T2* contrast (Ogawa,
1990) use of blood as an endogenous tracer• Generation of first BOLD images (Ogawa, 1990)• First BOLD images in humans (Kwong, 1992)
Basic Model of Relationship Between BOLD fMRI & Neuronal Activity
WHY DOESMRI SIGNALINCREASE?
Disparity Between Blood Flow & Oxygen Consumption
• Fox & Raichle conducted PET experiments to measure glucose metabolism (CMRglu), blood flow (CBF), and rate of oxygen metabolism (CMRO2)
• Measured percent change between visual stimulation and rest
• Increase in CBF=50%, CMRglu=51%• But increase in CMRO2 is only 5%!!• Implies anaerobic metabolism of glucose
Disparity & MRI Signal Increase
• Upshot of Fox & Raichle: much more oxygen (CBF) is supplied than is used (CMRO2)
• While neuronal activity results in more deoxygenated hemoglobin much more oxygenated hemoglobin flows in flushing out deoxygenated hemoglobin
• Result is a decrease in dHB and thus an increase in MRI signal
• But there’s uncoupling of glucose metabolism and oxygen metabolism - WHY?
Uncoupling Problematic• Fox & Raichle data nicely explains why MRI
signal increases with neuronal activity• But a new problem is presented:
uncoupling of glucose and oxygen metabolism
• We expect a 6:1 ratio of oxygen-to-glucose (OGI) for aerobic glycolysis but F&R saw about 1:10
• Implication is anaerobic glycolysis is used
Theories to Explain Uncoupling Found by Fox & Raichle
1. Watering the Garden for the Sake of One Thirsty Flower
2. Astrocyte-Neuron Lactate Shuttle Model
3. Transit Time and Oxygen Extraction
Separate Measurement of Oxy & Deoxy Hemoglobin
• Malonek & Grinvald used optical imaging to measure Hb and dHb separately during visual stimulation
dHb spatially focal and co-located to neuronal activity
Hb more widely distributed
Implications of Differences in Concentration of Hb & dHb
• Rapid increase in dHb implies oxidative metabolism initially
• High spatial correspondence between initial dHb increase and neuronal activity
• Coarse spatial correspondence and greater extent of delivery of Hb
Theories to Explain Uncoupling Found by Fox & Raichle
1. Watering the Garden for the Sake of One Thirsty Flower
2. Astrocyte-Neuron Lactate Shuttle Model3. Transit Time and Oxygen Extraction
(extended to Balloon Model)4. Aerobic glycolysis already near max at
rest thus activity requires quick increase in energy via anaerobic glycolysis (Prichard, 1991)
Watering the Garden• According to this model uncoupling
observed by Fox & Raichle does not imply anaerobic glycolysis
• Instead Malonek & Grinvald’s data shows huge excess of freshly oxygenated hemoglobin spread over a wide area displacing deoxygenated hemoglobin
• But CMRglu wasn’t measured; still haven’t explained why Fox & Raichle gets a 1:10 versus expected 6:1 OGI
Astrocyte-Neuron Lactate Shuttle Model
• Initially anaerobic glycolysis occurs producing excess glutamate (consistent with Fox & Raichle)
• Glutamate taken up by astrocyte to prevent toxicity and converted to glutamine which neuron can reuse
• Delicate balance is achieved by astrocyte through intake of Na+ produced by sodium-potassium pump of neuron
• Astrocyte uses 2 ATP molecules• Great because that’s all the ATP available!• But where’s the ATP for the neuron?
ANLS Model (cont’d)• Astrocyte dumps resulting lactate, which diffuses
into neuron that turns into pyruvate and into TCA cycle to give neuron 36 ATP molecules for neuron’s energy
• Thus, we’re back to aerobic glycolysis, which requires 6 molecules of oxygen
• Model hypothesizes early anaerobic followed by aerobic glycolysis
• Support for this comes from Mintun (2002) who showed uncoupling only occurs with initial onset of stimulus then coupling is reestablished with continued stimulation
Astrocyte-Neuron Lactate Shuttle Model
Transit Time and Oxygen Extraction
• Disputes that uncoupling implies anaerobic glycolysis as does Watering the Garden
• Model is based on limited time for extraction of oxygen due to increase in velocity of blood flow with neuronal activity
Transit Time and Oxygen Extraction
• Model proposed by Buxton (1998) rests on four assumptions:
1. Increased blood flow accomplished by increase in velocity as opposed pumping more blood through more capillaries
2. Virtually all oxygen is metabolized3. But not all of the glucose is metabolized4. Extraction of oxygen from blood by neurons
is limited and proportional to transit timeTransit time - how long it takes for blood to pass
through a given area
Transit Time and Oxygen Extraction
• Wouldn’t limited time for extraction of oxygen due to increase in velocity of blood also limit glucose availability?
• Buxton - well actually glucose availability is even more limited than oxygen but less than half that is extracted is actually used…
• Data from Gjedde (2002) supports glucose part
Balloon Model
Buxton et al. Neuroimage 2004
• No uncoupling of CBF and CMRO2; difference between CBF and CMRO2 lowers oxygen extraction fraction (E) [Fick Principle]
• Initial increase in blood flow increases blood volume (ballooning of venous capillary to accommodate)
• Predicts an initial dip in BOLD signal
Uncoupling Problem• Debate continues to this day• Uncoupling problem important to
understanding the fundamental basis of fMRI signal
• fMRI is an indirect measure of blood flow and is not directly tied to glucose metabolism or even oxygen metabolism
• Relationship between mechanisms of metabolism and blood flow is important to understanding how closely related BOLD and blood flow are to neuronal activity
Implications of Theories for Uncoupling
• “Watering the Garden” model posits widespread distribution of CBF increase poor fMRI spatial resolution
• “Transit Time” model implies excess oxygen rich blood passing over area of activity getting into venous system poor fMRI spatial resolution
• Both imply a “Draining Vein” problem with dHb flowing downstream of area of activity
• Frahm (1994) asked “Brain or Vein?”• Uncoupling issue remains unresolved
Physiological Mechanisms for Regulation of Blood Flow
• How is blood flow controlled?• Arterioles well upstream need to respond to
produce local changes in blood flow• Mechanism for accomplishing this is largely
unknown• Possible candidates include nitrous oxide
synthesis, potassium accumulation, generation of lactate, or acetylcholine activity
Initial Dip• Studies used very
short TR (100ms) and visual stimulus for 10s at 4T or higher
• Menon (1995) found Initial Dip in fMRI signal before expected increase
• There’s also a post stimulus undershoot
Spatial Extent of Initial Dip• Voxels with initial dip were more
spatially restricted and localized to gray matter around calcarine sulcus
Implications of Initial Dip• Menon suggested dip is directly related to
oxygen extraction and thus more closely related to neuronal activity
• But dip could also result from temporary decrease in blood flow or increase in blood volume
• Initial dip if it occurs is contradictory with anaerobic glycolysis - Why?
• Balloon model predicts increase in blood volume and thus consistent with initial dip but for a different reason than Menon posits
HDR (Hemodynamic Response)HRF (Hemodynamic Response Function)
• Change in MR signal related to neuronal activity (HRF)
• Has multiple components– Changes delayed by 1-2
sec (lags activity)– Initial dip (not always
seen)– Influx of Hb greater than
needed for activity– 5-6 sec time to peak– Undershoot follows ~6s
after peak
Typical HDR for Long Stimulus (Block)
• Peak is sustained with prolonged stimulation
• Block is also referred to as an epoch
• Brief stimulus is referred to as an event
Undershoot• Arises from rapid
return to baseline of CBF but delayed return of CBV
• Delay in CBV return to baseline results in an accumulation of dHb
BOLD vs Neuronal Activity• Logothetis, et al., 2001
recorded LFP, MUA, SUA, and BOLD simultaneously
• BOLD response best explained by changes in LFP
• Suggests BOLD reflects “incoming input and local processing rather than spiking activity”
• ”The BOLD contrast mechanism directly directly reflects the neural responses elicited by a stimulus.”
Open Questions about Basis of BOLD fMRI
• Uncoupling problem - Why does it occur? To what extent?
• Is there an Initial Dip? What causes the dip? Is it more localized than the expected signal increase?
• What about “Draining Veins”?• How does the arterial system upstream
know when and by how much to increase blood flow?
Factors Affecting BOLD Signal
• Physiology– Cerebral blood flow (baseline and change)– Metabolic oxygen consumption– Cerebral blood volume
• Equipment– Static field strength– Field homogeneity (e.g. shim dependent T2*)
• Pulse sequence– Gradient vs spin echo– Echo time, repeat time, flip angle– Resolution
Physiological Baseline• Baseline CBF changes (up for hypercapnia, down for
hypocapnia) • But CBF CMRO2 unchanged (probably) (Brown et al JCBFM 2003)• BOLD response (probably)
Cohen et al JCBFM 2002
Spatial & Temporal Properties of BOLD
• Spatial resolution - ability to distinguish unique changes in activity from one location to the next
• Temporal resolution - ability to distinguish changes across time
• Linearity vs Nonlinearity - does combined response to 2 or more events with short ISI (inter-stimulus interval) lead to sum in BOLD response?
Problems With Increasing Spatial Resolution
• Increased spatial resolution results in smaller voxels– Fewer protons so less MRI signal– Less dHb thus more noise in BOLD fMRI
signal– Degree of activation varies by brain region
with greater activation in sensorimotor areas and less in frontal and association cortices
• Smaller voxels ultimately make detecting changes harder
Spatial vs Temporal Resolution
• Acquisition time per slice goes up as voxel size goes down– Number of phase encode lines
increases thus more time required to cover k-space
• Decreasing slice thickness will require increasing number of slices to maintain same coverage again increasing acquisition time
Designing an fMRI Protocol• Tradeoffs
– Increased spatial resolution requires• Increased TR (scan time)• Less coverage (fewer slices)
– Increased temporal resolution requires• Decreased spatial resolution (larger voxels)• Less coverage (fewer slices)• Reducing amount of k-space acquired (less SNR)
– Increased SNR (signal-to-noise ration) requires• Decreased spatial resolution and/or• Increased scan time via averaging
Signal to Noise Ratio
Spatial Resolution
TemporalResolution
(f)MRI Image Acquisition Constraints
Partial Volume Effects• Any given voxel will be a
mix of tissue types• Boundaries with sulci will
include CSF
• Both can lead to a reduction in overall fMRI BOLD signal
Spatial Correspondence
Theoretical Lower Bound on Spatial Resolution
• Ultimately determined by the size of capillaries– 1mm in length– ~100 microns between capillaries– Theoretical lower bound for any
hemodynamic based measurement is 100 microns
Mapping Ocular Dominance Columns
• Menon, 1997 presented visual stimulus to alternating eyes
• Expect to see side-by-side alternating areas of activation in V1 corresponding to columns first shown by Hubel & Wiesel
• Acquired at 4T using a single slice with 547m x 547m resolution
fMRI of Ocular Dominance Columns
Ocular Dominance Columns - Take 2
• Cheng, 2001 used 4T with 470m2 resolution, single slice
• Each slice required 32-RF pulses to get enough SNR (averaging), scan time for 1 slice was 10s!
• Stimulus was 2min monocular presentation of light interspersed with 1min darkness
Replication Within Subject
Ocular Dominance Columns - Take 3
fMRI Data Processing & Spatial Resolution
• Typical processing includes– Motion correction which will reslice
the data (reslicing of data requires averaging of voxels to reformat data)
– Spatial Normalization (transforming into atlas space) again reslices data
– Spatial smoothing (blurring)• Net result is reduction in effective
spatial resolution
Temporal Resolution• TR in fMRI refers to time needed to collect one
volume of data• Long TR (>3s) good for detecting differences
in activation but not differences in HRF (hemodynamic response function) characteristics– Where is activity occurring?
• Shorter TR (<2s) gives better estimate of differences in HRF characteristics – What are the differences in activity between two
stimuli activating in the same area?
“Jitter”Interleaved Stimulus Presentation
• Instead of locking stimulus presentation to the TR jitter it
• Effectively gives more data on HRF curve than locked to the TR
• Thus, effective temporal resolution is increased
BOLD as a Psychophysical Measure
Duration of Cognitive Processing & BOLD Response• Psychophysical experiments looking at mental
rotation have shown that the greater the differences in angle between two figures the longer the response time
• What happens to BOLD response?
BOLD Response Duration Increases
Timing Between Brain Regions
• Move joystick from one target to another
• Measured reaction time and difference in onset time of BOLD response different brain regions
V1-SMA differences suggests decision pathway
SMA-M1 flatness suggests simple execution
Latency of BOLD Response• Examination of the
latency (time to onset) in voxels with significant activation– Blue shortest– Yellow longest
• Output from V1 (slices a & c) feeds fusiform gyrus (slices b & d)
• As hoped response delayed in fusiform relative to V1
Linearity of Hemodynamic Response?
• Linearity would imply– there is an additive effect of two stimuli
presented close enough in time– HRF scales with stimulus intensity– HRF response to two or more stimuli
equal summation of response to individual stimuli
• Under what conditions is HRF linear?
Linearity of HRF - Theoretical
• Give two stimuli close in time
• Is the HRF for the second equal to the first?
Nonlinearity Via Attenuation - Theoretical
• Or is there some attenuation (reduction) in the response to the 2nd stimulus?
• Refractory effects - change in response to 2nd stimulus based on presence of first?
Does HRF Scale with Stimulus Magnitude?
Superposition of HRF ?
Evidence for Linearity• Boynton, 1996• Presented
several short stimuli for various durations
• Found response scaled with contrast
• Found good correspondence between actual response and predicted thus linearity held
Superposition• Boynton found good correspondence
between predicted and actual measured response
• However, when 2 or more 3s stimuli presented - got smaller than predicted response
• Attributed to adaptation of neurons leading to reduced activity
• Support for linearity & superposition when stimuli >3s
Response to Multiple Trials• Dale & Buckner,
1997• Three identical
trials presented• ISI was either 2s or
5s• Each trial gives
additive effect
Separation of Response to Multiple Trials
• Recovered HRF for 2nd and 3rd trials quite closely match that of the 1st for 5s ISI
• Again at shorter ISI’s of 2s results were reduced amplitude and greater latency
• Evidence of nonlinearity at short ISI’s
HRF as a Function of Interstimulus Interval
• Huettel, 2000 used visual stimuli separated by a variable amount of time
• Found reduction in amplitude of response and increase in latency as ISI decreased
Linearity of HRF and Refractory Period
• Linearity seems to hold for combinations of stimuli with ISI’s 5-6s or longer
• Much evidence of a refractory period during which additional presentation of stimuli produces smaller and delayed response
• Is this bad? Can we take advantage of this?
fMRI Adaptation (fMRI-A)• Grill-Spector & Mallach, 2001• Presented same face with different sizes,
positions, shading, and angles• Response in fusiform was reduced during
conditions where size and position was varied• Signal recovered when shading or angle was
varied!• Conclusion - fusiform recognizes identity
regardless of size or position but treats shading and angle changes as ‘different’ face
fMRI Adaptation• Top graph - release of
response to attributes other than color thus this area preferentially responds to changes in physical characteristics
• Bottom graph - release of response only to vehicle type thus this area preferentially responds to complex object categories
Summary• fMRI BOLD signal arises from changes in
oxygenated state of blood• Blood flow is primary means for delivering oxygen
and glucose to neurons for production of energy• Aerobic and anaerobic glycolysis implies different
amounts of ATP (energy) production and oxygen requirements; important for understanding how well BOLD relates to neuronal activity
• Definitive linkage of BOLD, blood flow and neuronal energy metabolism still elusive
• Properties of BOLD signal