Bischoff(Grethe)-Arbib Basal Ganglia Modeling

1. Bischoff(Grethe)-Arbib Basal Ganglia Modeling Presented by James Bonaiuto

2. Amanda Bischoff (Grethe)’s Thesis • Models the basal ganglia (and some cortical areas) in three tasks: • Elbow flexion-extension • Reciprocal aiming • Sequential arm movements • Dopamine levels were modified to model the effects of Parkinson’s

3. Hypothesis on Basal Ganglia Function • Basal Ganglia • Indirect pathway – movement inhibition • Direct pathway – provides next sensory state to cortex • Cortex • Preparatory areas – project to indirect path • Movement-related areas – project to direct pathway

4. Model Overview - Cortex • Pre-SMA • Projects sequential information to SMA and indirect pathway • SMA • Contains information on the overall sequence • Keeps track of which movement is next • Project current movement to MC and direct pathway of basal ganglia • Project next movement to premovement population in MC and indirect pathway of basal ganglia • Motor Cortex • Carries out motor command • Handles fine-tuning of movement • Projects motor parameters to brainstem and direct pathway of basal ganglia

5. Next Sensory State Information • Why aren’t the basal ganglia responsible for movement initiation? • Crutcher & Alexander (1990) – movement related putamen neurons fire an average of 33 ms after the onset of a movement (after activation of MC – 56ms later, and SMA – 80 ms later) • Mink & Thach (1991b) – movement-related activity in GPe and GPi is also late • Turner & Anderson (1997) – GP neurons rarely change discharge before activity of agonist muscles

6. Basic Model • Segregated direct (movement)/indirect (preparation) pathways • Neat modeling trick: • To model up/down states of putamen neurons, the time constant is a sigmoid of the membrane potential • Same trick is used later to slowdown the cortex in the absence of dopamine

7. Elbow Flexion-Extension

8. Elbow Flexion-Extension - Results <Demonstration>

9. Reciprocal Aiming • Winstein et al. (1997) – Stylus tapping between two targets of varying sizes • Fitt’s Law – speed/accuracy tradeoff • ID=log2(2A/W) • MT=a+bID • Parkinson’s patients • Slower overall time • Constrained trajectory • Reached to smaller area of target • Predictions: • Slower speed is due to inability of BG to release inhibition of movement • Decrease in SMA and MC activity causes reduction in speed and variation of movement

10. Reciprocal Aiming - Model • Input: target positions in joint space • Problem when targets overlap in joint space • SMA_INH prepares upcoming movement – BG inhibits before appropriate • WTA– only fires in relation to movement in preparation • SMA_MVT receives info from both targets • Inhibition from SMA_INH – only responds to current target • MC_MVT • Encodes joint coordinates - converted to Cartesian space • Movement time calculated from firing rate

11. Reciprocal Aiming - Results • Normal - Qualitatively similar to Winstein et al.’s (1997) control data • 50% Dopamine • No contact with target, no pause between movements • Because neural part of model taking less time than arm • Hypothesis: slowdown in putamen function may cause slowdown in cortex too • Changed time constants of SMA and MC to depend on dopamine level • With dopamine depletion – takes longer for neurons to reach maximum and maximum is less than with dopamine (because of longer time constant) • Reduction in MC firing rates causes delays between movements • Caused restricted arm trajectory – lower velocity

12. Reciprocal Aiming Results SMA-Proper Motor Cortex

13. Reciprocal Aiming Results Putamen GPe STN GPi SNc

14. Reciprocal Aiming Results 50% Dopamine 20% Dopamine Normal

15. Sequential Arm Movements • Extends SMA module for a sequence of three movements • Tanji & Shima (1994) – SMA neurons selective for sequence order, others selective for movement no matter where it was in a sequence • Tanji & Mushiake (1996) - Pre-SMA active for visual stimuli – indicate sequence to be performed

16. Sequential Arm Movements - Model • Pre-SMA • Now selective for different sequence permutations • SMA • New population selective for different sequence permutations and subsequences • After the current movement begins, SMA_INH primes SMA_MVT for the next movement • MC_MVT needs to reach a threshold firing rate to produce target for movement generator • Hardcoded relationships between SMA_SEQ, SMA_MVT and SMA_INH

17. Sequential Arm Movements - Results SMA-Proper Motor Cortex • Seq123 and seq12 active until target 1 reached • Seq12 primes target 2 neurons in SMA_PROPER_INH and seq23 • Target 1 reached – seq23 reaches full activation • Seq23 primes target 3 neurons in SMA_PROPER_INH • Drop dopamine - seq123 is active longer • MC_MVT peaks for each movement lower than for previous one - each movement depends on activation from previous movement

18. Sequential Arm Movements - Results Putamen GPe STN GPi SNc

19. Sequential Arm Movements - Results • Reduce dopamine • Beginnings of pause between each submovement • Akinesia - took longer to initiate 1st movement • Bradykinesia – each movement take longer and longer • Indirect pathway is overactive (inhibits motor programs), direct pathway is less capable of responding to current motor command • Slower time constant and higher GPi inhibition -> SMA doesn’t know status of current motor program so doesn’t command the next movement Normal 50% Dopamine 20% Dopamine

20. Discussion • Can the same model do all three tasks? • Reciprocal aiming and flexion-extension can be cast as 2 movement sequences • Requires new weights for the SMA_SEQ12 and SMA_SEQ21 populations • How can these weights be learned? • The future work section lists the inclusion of cortico-STN projections • The GPR model includes these, but has an opposite take on the basal ganglia function (action selection) • Are these views reconcilable?