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Ch.14 Using Spikes and Local Field
Potentials to Reveal Computational
Networks in Monkey Cortex
KRISTINA J. NIELSEN AND GREGOR RAINER
2009. 01. 13. Tue.
Shin Won-jin
Contents
• Introduction
• Measures of neural activity
– At the level of neurons
– At the level of networks
• Previous work on LFP and SUA/MUA
– Similarities btw LFP & SUA/MUA
– Differences btw LFP & SUA/MUA
• Combining LFP to SUA
– To reveal computational networks across the brain
Introduction
• Traditional neurophysiology: Singe-Unit Activity(SUA)
– To uncover the neural basis of cognition and action
– Ex) sensory systems, motor system
• Recent neurophysiology: Local Field Potential(LFP)
– A signal that reflects aggregate activity across populations of neurons
near the tip of the microelectrode
• SUA+LFP offers better insight into cortical brain mechanisms
Measures of Single Neural Activity(SUA)
1. Amplify and collect the comprehensive broadband electrical
signal
–
Using microelectrodes
2. Digitize at rate of 20kHz or higher
3. High-pass filtering to remove low-frequency components
4. Clustering to extract the times of action potentials
- High frequency band
-400Hz~3kHz
-MUA
-Low frequency band
-0Hz~300Hz
-LFP
MUA/SUA and LFP
• MUA(Multi-unit activity)
– Weighted average of the spiking activity within a sphere of about
200~300µm around the electrode tip
• c.f.) SUA(Single-unit activity)
– Represent local processing within a cortical column as well as the
long-range output that targets distant brain regions
• LFP
– Weighted average of the synaptic signals of a neuronal population
within 0.5~3mm of the electrode tip
– A measure of the local processing in a brain region as well as of the
inputs that the brain region receives
Similarities btw MUA/SUA and LFP(1/2)
• Similarities btw MUA/SUA and LFP
– In area V1, the initial cortical stage of visual processing
• Sensitive to the orientation of a grating pattern, as well as grating’s
contrast(LFP, SUA)
– In area MT, the visual cortex of motion perception
• Preferences for particular stimulus speeds and motion directions(LFP,
MUA)
• Performance in a speed discrimination task(LFP, MUA)
– Inferotemporal(IT) cortex, final stage of ventral visual processing
system
• Selectivity for complex objects(LFP, SUA)
• Tolerance to changes in an object’s position in space, as well as the
object’s size(LFP, SUA)
Similarities btw MUA/SUA and LFP(2/2)
• In M1 & SMA, primary & supplementary motor area
– Information about arm movements(LFP)
– Direction of an arm movement
Which hand is used for a task
Whether the monkey moves only one or both hands(SUA)
• In PRR & LIP in the posterior parietal cortex
– Maps for the direction of either arm or eye movements that the monkey
is intending to perform(SUA)
– Direction of planned arm and eye movements(LFP)
– Tuning widths for movement directions(LFP, SUA)
LFP in general shows responses properties
similar to that of the neurons recorded in the same brain region
Differences btw LFP & MUA/SUA(1/4)
• LFP pools signals over a larger neuronal population than the
other two signals
– Neurons contributing to the LFP signals have more diverse response
properties the ones contributing to either SUA or MUA
– Ex)
LFP is a poor predictor of the behavior of single neurons
LFP correlates better w/ the average signal of the neuronal population
Agreement in stimulus selectivity btw single neurons and the LFP signals recorded at the same electrode
Differences btw LFP & MUA/SUA(2/4)
• Some results cannot be explained by the previous assumption
– LFP is an average over a larger neuronal population than MUA/SUA
– Ex)
About 20% of the LFP site in MT are not visually responsive
Almost all MUA sites respond visually
• Local three-dimensional structure of cortex
Different sources generating LFP and SUA/MUA
Combined analyses of these signals
Differences btw LFP & MUA/SUA(3/4)
• In area V1
– SUA/MUA show strong adaptation effect
– LFP response remains elevated throughout the presentation duration
• In area M1 & SMA
– Correlation btw LFP and SUA is absent in one brain area but present in
another
• In the motor cortex, of predicting monkey behavior
– LFP can be used to successfully decode a movement direction
– About 50ms after this is possible based on SUA/MUA
– LFP+SUA/MUA results in higher decoding accuracy
Differences btw LFP & MUA/SUA(4/4)
• In LIP
– SUA & LFP can be used to predict the direction of an eye movement
– Only LFP can be used to decode the transition from planning an eye
movement to executing it
• In PRR
– SUA predict the direction of an eye or arm movement
– LFP distinguish btw eye and arm movement
Summary of Previous Works on
SUA/MUA & LFP
• LFP & SUA/MUA reflect different brain processes
– LFP reflects input and local processing to a brain region
– SUA/MUA represent the output of that region
• Similarities
– High degree of similarity btw the inputs to a brain region and it outputs
• Discrepancies
– Instances where input and output are not closely related
Combined analysis of LFP and SUA/MUA has the power to reveal
how different brain regions interact with each other to process information
Combining LFP to SUA
• Two monkeys were trained to discriminate btw natural scenes
• Determined the regions of each natural scene on which the
monkeys relied to perform the discrimination task
• Constructed unique stimulus sets for each monkey
– Diagnostic scene regions + 3 modifications
– Non-diagnostic scene regions + 3 modifications
• Probe the influence of diagnosticity on the responses of single
neurons and the LFP in the IT cortex
Responses of a Sample Single Neuron and Sample LFP site
Influence of Recording Position