Brain Activity and Complex Motion

1. Brain Activity and Complex Motion The usual way of studying brain activities is in a “well controlled” environment with ability to repeat each condition many times. In motor physiology this is very often done by studying single unit activity in relation to a reaching movement. What can we see with more natural motion? 1. Scribbling 2. Prehension

2. Do motor cortical neurons change their preferred directions when switching between different motor tasks ?

3. Center–Out (CO) task and trajectories

4. An example of a scribbling trajectory

5. Extracting “CO like” segments out of the scribbling trajectory. • Velocity profile was segmented between adjacent minima • Segments were cleaned based on duration from start to peak velocity, average direction and STD of instantaneous direction within segment.

6. The final scribbling and CO trajectories

7. Similar Directional tuning during CO and scribbling movements

8. Different directional tuning during CO and scribbling movements

9. PDs during CO and scribbling tended to be different In 13 out of 20 cells (65%) PDs during scribbling were significantly different than PDs during reaching movements.

10. Possible causes for the difference in PDs • Other movement parameters change between scribbling and CO: • tangential velocity • Initial position • curvature of the segments • tangential acceleration • The differences are task related

11. Differences in PDs were not explained by variability in other movement parameters • Scribbling segments were divided based on high and low values of different movement parameters. • PDs were obtained for each subgroup separately • Results from PDs comparison: • No significantly different PDs due to variability in peak velocity and peak acceleration. • 1/13 (8%) cells with significantly different PDs due to variability in segments’ directional STD • 2/13 (15%) cells with significantly different PDs due to variability in initial position.

12. The Free Tracing (FT) task & trajectories

13. PDs during FT and CO movements tended to be similar • Lower differences of PDs between CO and FT relative to CO and scribbling PD differences. • Only 10 out of 49 cells (20%) had significantly different PDs during FT and CO movements.

14. Summary and conclusions • Directionally tuned cells during both CO and scribbling movements tended to have different preferred directions during each type of movement. • These differences were not explained by the variability in various movement parameters. • These differences were less frequent when the monkey alternated between CO and FT tasks. • Therefore directional tuning of motor cortical cells is not only movement but also task dependent.

15. Brain Activity and Complex Motion Most of what we do or perceive is compositional. We compose sounds into phonemes; phonemes into words; words into sentences;… What are the neuronal correlates of these properties? In scribbling – what happens when two pieces of motion are concatenated?

16. Concatenating Movements (Tishby + Gat) Compute the likelihood of change in firing rate for all cells.

17. Concatenating Movements Compute the likelihood of change in firing rate for every cell. Find which cells tend to change their firing rates just before: start of movement.

18. Concatenating Movements Compute the likelihood of change in firing rate for every cell. Find which cells tend to change their firing rates just before: start of movement. peak tangential velocity.

19. Concatenating Movements Compute the likelihood of change in firing rate for every cell. Find which cells tend to change their firing rates just before: start of movement. peak tangential velocity. trough in “ “ .

20. Concatenating Movements BUT “cell assemblies” overlap. One needs to know who is firing AND who is quiet. Problem with low firing rates and sparse sampling.

21. Prehension It is still unclear what happens neuronally when 1 element of motion is concatenated to another. Compositionality can manifest itself also by combining elements in parallel (like lines to a figure). In motor systems that happens, for instance, during prehension: We can pick any object from any location. Thus, all combinations of grasping and reaching may be combined.

22. Field Potential Oscillations in Posterior Parietal CortexDuring Reaching and Grasping Movements

23. “Reaching & Grasping are Mediated by Separate Parieto-Premotor Channels” (Kandel, Schwartz & Jessell, 4th Ed.) Reaching – MIP & MDP(Andersen), Area 5 (Kalaska) PMdc (Wise, Kalaska) Grasping - AIP (Sakata) F5 in PMv (Rizzolatti) Unit properties: Directional Tuning Object Specificity Bidirectional, Segregated Connections

24. Objectives • Train monkeys to reach & grasp various objects in various directions. • Record simultaneously from “Reaching- related” and “Grasping-related” areas. • Search for signs/mechanisms of inter-area coordination. • This talk: focus on LFP oscillations, which were suggested as a binding mechanism for distributed representations (Singer & Gray, 1995)

25. Task Setup & Protocol • Touch pad in center of workspace • 6 target locations X 3 prehension objects • Controlled Sound & Light conditions • Epochs: Control, Signal, Set, Pre-Go, RT-MT, Hold. movie

27. Prehension objects Plate: Finger opposition Box: Power grip Precision grip object Reaching pad

28. movie

29. Time Domain: LFP traces show task dependent modulation, including oscillations

30. Frequency domain: time resolved spectrum shows epoch-dependent changes in spectral composition Alpha: 8-13 Hz Beta: 13-30 Hz Gamma: 30-60 Hz

31. Beta oscillations in SPL show directional selectivity, with non-uniform PD distribution

32. This is very different from tuning of MU spikes in the same area

33. Great expectations • Non-Uniform tuning distribution exists Both in Oscillations and in RMS of signal. • This is consistent with Motor Cortex results (Donchin et al., 2001). • We are ready to look at between-area effects (Coherence). • BUT …

34. Problem: Typical AIP data do not show beta oscillations (may show gamma oscillations) Alpha: 8-13 Hz Beta: 13-30 Hz Gamma: 30-60 Hz compare

35. Within and between area coherence: a measure of coordination? d=.78mm d=1.53mm d=14.77mm

36. Between-Area coherograms

37. Significant coherence is related (time & frequency-wise) to Evoked Potential phenomena, not beta/gamma oscillations

38. Summary • Beta Oscillations very frequent in SPL, Gamma Oscillations are less frequent, & more in IPL. • Our results do not comply with previously suggested explanations / functions of oscillations: (1) Fast oscillations are signs of focused attention states (Murthy & Fetz, 1996). This is the reverse of SWS. (2) Motor cortex beta oscillations are useful for efficient motor output state, in contrast to high processing capacity (Baker et al., 1999). (3) Gamma oscillations serve to bind distributed cortical representations (Singer & Gray, 1995)

39. Some neural mechanisms of cortico-cortical cooperation

40. Question • Do, and how do, cortical areas coordinate their activity • Model system • PM (pre-motor) cortex • Dorsal PM – ‘reaching-related’ (Kalaska, Wise) • Ventral PM – ‘grasping-related’ (Rizzolatti)

41. Temporal coordination hypothesis • Crosstalk between areas • At behaviorally relevant time scales • Modulated by context • Tests • Local field potential pair-wise correlations • Single unit cross-correlations

42. LFP pair-wise correlations:the raw data PREGO RTMT

43. Zero-lag modulation by distance

44. Exponential decay with distance

45. Binned by distance

46. Modulation by behavior

47. Exponents coefficients differ

48. Significance 2 way ANOVA distance: *** epoch: *** interaction: ns Rank test: * < 0.05 ** < 0.01 *** < 0.001

49. Spike-to-spike cross-correlations:the raw data ‘GO’ signal Cue On

50. Zoom in…