chapter 4: local integration 2: neural correlates of the bold signal
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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. PET. - PowerPoint PPT PresentationTRANSCRIPT
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Chapter 4:Local integration 2: Neural correlates of the BOLD signal
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
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
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
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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
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
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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)
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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?
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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]
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Rees, Friston, and Koch 2000
FMRI data on motion perception
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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
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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
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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
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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
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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
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Take home messageGood 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