Transcript Local integration 2

Chapter 4:
Local integration 2: Neural
correlates of the BOLD signal
Overview
• Introduce some of the basic principles of fMRI
• Explain how fMRI throws up a local integration
challenge
• Survey some influential recent experiments on
the neural correlates of the BOLD signal
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
PET
• PET measures cerebral blood flow by tracking the flow of water
labeled with a radioactive isotope
• Basic assumption – local blood flow within the brain is related to
cognitive function
• Cognitive activity  increased cellular activity  increased
blood flow
• The correlation between cognitive function and blood flow has been
well documented since 19th century
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Blood flow and fMRI
• fMRI measures levels of blood oxygenation, not blood
flow
• deoxygenated hemoglobin disrupts magnetic
fields, while oxygenated hemoglobin does not
• Levels of blood oxygenation provide an indirect measure
of blood flow
• oxygen consumption is not proportional to blood
supply (unlike glucose)
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Blood flow and fMRI
• Cognitive activity
correlated with
• Increased cellular activity
correlated with
• Increase blood oxygen levels [because supply exceeds
demand]
• BOLD contrast is the contrast between oxygenated and
deoxygenated blood
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Integration?
• How do we move from coarse-grained correlations
between blood flow and cognitive activity to an
understanding of how cognitive activity takes place
• We want to know not just where cognitive
activity is happening, but how it is happening
• Requires calibrating imaging data with data
about neural activity
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Problem of levels
• Neuroimaging allows us to identify which brain areas are
active when subjects perform particular tasks
• But there is a difference between
• Localizing cognitive activity
• Explaining or modeling cognitive activity
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Bridging to the neural level
Brain areas
• anatomically/functionally identifiable
Neural networks/populations
• standardly studied through computational
models – behavior of populations of artificial
neurons
Individual neurons/small groups of neurons
• can be studied through single/multi unit
recordings
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Integration question
• What is the neural activity that generates the BOLD
contrast?
• necessary first step in building neural network
models
• requires building bridges between different
levels of organization and different
technologies/tools
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Single unit recording
• Using microelectrodes to investigate
– how neurons respond to sensory inputs
– how neurons discharge when motor acts are
performed
• Microelectrode recordings of interest to cognitive scientists are
typically extracellular
– intracellular recording very difficult in living
animals
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Schematic neuron
• Dendrites transmit
electrostimulation from other
neurons
• If the combined effect of this
stimulation exceeds a
threshold, then the neuron
generates an action potential
• This action potential is
transmitted via the axon
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Single unit recording
• Monkey’s head held immobile
• Microelectrode tip (< 10 m)
inserted near neuron
• can detect firing of a single
neuron (action potential)
• high spatial and temporal
resolution
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Mirror neurons
• Area F5 of macaque monkey
(premotor cortex) contains
visuomotor neurons
• Sensitive to different types of
action (e.g. grasping vs
tearing)
• Some fire both when the
monkey performs an action
and when the monkey
observes the action being
performed
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
2 levels of organization
Large-scale neural activity, revealed by fMRI
• ways of identifying specialization in neural
areas, as a function of blood oxygen levels
Fine-grained receptivity of individual neurons, as revealed
in single-unit recordings
The large-scale activity results from the collective activity
of large numbers of individual neurons – but how?
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Neural correlate of BOLD signal
Two possibilities
• BOLD signal is correlated with the firing rates of
populations of neurons
• BOLD signal is correlated with the inputs to
neurons
[These are not equivalent, because neurons only fire when
inputs reach a threshold]
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Rees, Friston,
and Koch 2000
FMRI data on
motion
perception
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Calibrating with single-unit data
(Rees et al. 2000)
• fMRI results show linear relationship between strength of
BOLD signal in V5 and coherence of moving stimulus
• Likewise, single neurons in V5 of macaque cortex are linearly
related with motion coherence in their preferred direction
• Authors propose linear relationship between strength of BOLD
signal and average firing rates of neurons
9 spikes per second for each % of BOLD
contrast
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Logothetis et al 2001
• Logothetis and his team measured the strength of the
BOLD signal in monkey primary visual cortex at the same
time as using microelectrodes to measure 2 types of
neural activity
• spiking activity of neurons near electrode tip
• local field potentials
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Local field potential (LFP)
• Electrophysiological signal representing synaptic activity
at the dendrites
• Corresponds to input to the neuron (and integrative
processing)
• Slow oscillatory wave
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Measuring LFP
• LFP can be measured using the same microelectrodes
as measure spiking/firing activity
• Since LFP is a lower frequency signal it can be isolated
through a low-pass filter
• The LFP recorded at a single microelectrode represents
dendritic activity in neurons within a few mm of the
electrode tip
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Logothetis et al. 2000
• Anaesthetized monkey presented with rotating
checkerboard pattern
• Compared evolution of BOLD signal with LFP and spiking
signals
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Take home message
Good news:
• Logothetis experiments show how to build a
bridge between BOLD signal and activity of
individual neurons/small populations of neurons
Bad news:
• The neural correlates of the BOLD signal is not
the dimension of neural activity most frequently
measured in single neuron studies
• We don’t know much about the connection
between LFP and cognition
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010