Chapter 4: Local integration 2: Neural correlates of the BOLD signal

Chapter 4:Local integration 2: Neural correlates of the BOLD signal

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


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


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)


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


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


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


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


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


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


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


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


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


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?


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]


Rees, Friston, and Koch 2000

FMRI data on motion perception


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


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


Local field potential (LFP)

• Electrophysiological signal representing synaptic activity at the dendrites

• Corresponds to input to the neuron (and integrative processing)

• Slow oscillatory wave


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


Logothetis et al. 2000

• Anaesthetized monkey presented with rotating checkerboard pattern

• Compared evolution of BOLD signal with LFP and spiking signals


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

Chapter 4: Local integration 2: Neural correlates of the BOLD signal

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