Transcript Neurons BOLD

Class 3: Neurons  BOLD
2012 spring fMRI: theory & practice
Stimulus to BOLD
Source: Arthurs & Boniface, 2002, Trends in Neurosciences
BOLD signal
Source: Doug Noll’s primer
Neuron  BOLD?
Raichle, 2001, Nature
Vasculature
Source: Menon & Kim, TICS
Figure 6.8 Blood supply to the human cerebrum
Macro- vs. micro- vasculature
Macrovasculature:
vessels > 25 m radius
(cortical and pial veins)
 linear and oriented
 cause both magnitude
and phase changes
Capillary beds within the cortex.
Microvasculature:
vessels < 25 m radius
(venuoles and capillaries)
 randomly oriented
 cause only magnitude
changes
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
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)?
Figure 6.15 The change in diameter of arterioles following sciatic stimulation
Figure 6.16 Change in arteriole dilation as a function of distance from active neurons
Figure 7.12 Relative changes in cerebral blood flow and cerebral blood volume following
neuronal activity
BOLD Correlations
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
Source: Logothetis et al., 2001, Nature
Logothetis et al. (2001)
• combined BOLD fMRI and
electrophysiological recordings
• found that BOLD activity is more closely
related to LFPs than MUA
So there are still a lot to explore !!
Deoxygenated Blood  Signal Loss
Oxygenated blood?
No signal loss…
Deoxygenated blood?
Signal loss!!!
Images from Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
Figure 7.4 Changes in oxygenated and deoxygenated hemoglobin following neuronal
stimulation
Summary of BOLD signal generation
(A) under normal conditions,
oxygenated hemoglobin (Hb) is
converted
to
deoxygenated
hemoglobin at a constant rate
within the capillary bed. (B) But
when neurons become active, the
vascular system supplies more
oxygenated hemoglobin than is
needed by the neurons, through
an over-compensatory increase in
blood flow. This results in a
decrease in the amount of
deoxygenated hemoglobin and a
corresponding decrease in the
signal loss due to T2* effects,
leading to a brighter MR image
Figure Source, Huettel, Song & McCarthy, 2004,
Functional Magnetic Resonance Imaging
Figure 7.11 Schematic representations of the BOLD hemodynamic response
Hemodynamic Response Function
% signal change
= (point – baseline)/baseline
usually 0.5-3%
time to rise
signal begins to rise soon after stimulus begins
time to peak
initial dip
signal peaks 4-6 sec after stimulus begins
-more focal and potentially a better
measure
post stimulus undershoot
-somewhat elusive so far, not
signal suppressed after stimulation ends
everyone can find it
fMRI Measures the Population Activity
• population activity depends on
– how active the neurons are
– how many neurons are active
• manipulations that change the activity of many neurons a little have a
show bigger activation differences than manipulations that change the
activation of a few neurons a lot
– attention
•  activity
– learning
Verb generation
Verb generation after
15 min practice
•  activity
• fMRI may not
match single neuron
physiology results
Ideas from: Scannell & Young, 1999,
Proc Biol Sci
Raichle & Posner, Images of Mind cover image
Comparing Electrophysiolgy and BOLD
Data Source: Disbrow et al., 2000, PNAS
Figure Source, Huettel, Song & McCarthy, Functional Magnetic Resonance Imaging
The Concise Summary
We sort of understand this
(e.g., psychophysics,
neurophysiology)
We’re *&^%$#@ clueless here!
We sort of understand this (MR
Physics)
Bottom Line
• Despite all the caveats, questions and concerns, 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)
PET vs. fMRI
• fMRI does not require exposure to radiation
– fMRI can be repeated
• fMRI has better spatial and temporal resolution
– requires less averaging
– can resolve brief single events
• MRI is becoming very common; PET is specialized
• MRI can obtain anatomical and functional images within same session
• PET can resolve some areas of the brain better
• in PET, isotopes can tagged to many possible tracers (e.g., glucose or
dopamine)
• PET can provide more direct measures about metabolic processes