Revealing Computational Networks using Spikes and Local Field Potentials in Monkey Cortex

1. Ch.14 Using Spikes and Local Field Potentials to Reveal Computational Networks in Monkey CortexKRISTINA J. NIELSEN AND GREGOR RAINER 2009. 01. 13. Tue. ShinWon-jin

2. Contents • Introduction • Measures of neural activity • At the level of neurons • At the level of networks • Previous work on LFP and SUA/MUA • Similaritiesbtw LFP & SUA/MUA • Differences btw LFP & SUA/MUA • Combining LFP to SUA • To reveal computational networks across the brain

3. 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

4. Measures of Single Neural Activity(SUA) • Amplify and collect the comprehensive broadband electrical signal • Using microelectrodes • Digitize at rate of 20kHz or higher • High-pass filtering to remove low-frequency components • Clustering to extract the times of action potentials • Highfrequency band • 400Hz~3kHz • MUA • Low frequency band • 0Hz~300Hz • LFP

5. 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 aswell 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

6. Similarities btw MUA/SUA and LFP(1/2) • Similarities btw MUA/SUA and LFP • Inarea 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)

7. Similarities btw MUA/SUA and LFP(2/2) • In M1 & SMA, primary & supplementary motor area • Information about arm movements(LFP) • Direction of an arm movementWhich hand is used for a taskWhether 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

8. Differencesbtw LFP & MUA/SUA(1/4) • LFPpoolssignals 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

9. Differencesbtw 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 responsiveAlmost all MUA sites respond visually • Localthree-dimensional structure of cortex Different sources generating LFP and SUA/MUA Combined analyses of these signals

10. Differencesbtw LFP & MUA/SUA(3/4) • Inarea 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

11. Differencesbtw 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

12. 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

13. CombiningLFP 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

14. Responsesof a Sample Single Neuron and Sample LFP site

15. Influenceof Recording Position