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  1. Oscillations in Mammalian Sensorimotor Processing Diane Whitmer Dissertation Defense Division of Biological Sciences September 29, 2008

  2. Overview • Oscillations in the Rat Vibrissa System A. How can rats use their whiskers to locate objects? B. Does hippocampal theta drive whisking? • Visually cued Finger Movements in Human Epilepsy Patients A. What is the neural signature of movements? B. (How) Should intracranial signals be un-mixed? **ICA** III. Conclusions and Next Steps

  3. Overview • Oscillations in the Rat Vibrissa System A. How can rats use their whiskers to locate objects? B. Does hippocampal theta drive whisking? • Visually cued Finger Movements in Human Epilepsy Patients A. What is the neural signature of movements? B. (How) Should intracranial signals be un-mixed? III. Conclusions and Next Steps

  4. Rats engage in exploratory “whisking” Berg & Kleinfeld, 2003

  5. The Rat Vibrissae Pathway Kolb and Tees, 1990 Brecht et al., 1997 Deschenes et al., 2001

  6. Coding strategies for object localization Small or no movements Mehta, Whitmer et al., 2007

  7. Coding strategies for object localization Small or no movements Whisker movements Mehta, Whitmer et al., 2007

  8. Behavioral testing of whisking Restraint Bar Nose Sensor Reward Inlet Valve Position Sensor Lever Water Fountain Reward Outlet Vacuum

  9. Mehta, Whitmer et al., 2007

  10. Mehta, Whitmer et al., 2007

  11. Responses from a Testing Session Mehta, Whitmer et al., 2007

  12. Responses Latencies Mehta, Whitmer et al., 2007

  13. Coding strategies for object localization Mehta, Whitmer et al., 2007

  14. Significance for the Rat Vibrissa System • Vibrissa process sensory information about What and Where • Results from discrimination of location in rostral-caudal plane suggests overall scheme for position in 3-d space: Ahissar & Knutsen, 2008

  15. Overview • Oscillations in the Rat Vibrissa System A. How can rats use their whiskers to locate objects? Information about the location of the whisker is combined with contact information. B. Does hippocampal theta drive whisking? • Visually cued Finger Movements in Human Epilepsy Patients A. What is the neural signature of movements? B. (How) Should intracranial signals be un-mixed? III. Conclusions and Next Steps

  16. Overview • Oscillations in the Rat Vibrissa System A. What is the significance of phase in the whisking cycle? Information about the location of the whisker is combined with contact information. B. Does hippocampal theta drive whisking? • Visually cued Finger Movements in Human Epilepsy Patients A. What is the neural signature of movements? B. (How) Should intracranial signals be un-mixed? III. Conclusions and Next Steps

  17. Hippocampal theta rhythm is associated with voluntary movement in the rat • running • jumping • exploratory head movements • swimming Vanderwolf, 1969

  18. Are these two signals phase-locked? Berg, Whitmer, Kleinfeld, 2006

  19. Coherence quantifies phase-locking Berg, Whitmer, Kleinfeld, 2006

  20. Coherence quantifies phase-locking Berg, Whitmer, Kleinfeld, 2006

  21. Trial to trial variability of coherence between whisking and hippocampal theta Berg, Whitmer, Kleinfeld, 2006

  22. Trial to trial variability of coherence between whisking and hippocampal theta Berg, Whitmer, Kleinfeld, 2006

  23. Coherence between whisking and hippocampal theta is not significant Berg, Whitmer, Kleinfeld, 2006

  24. Overview • Oscillations in the Rat Vibrissa System A. What is the significance of phase in the whisking cycle? Information about the location of the whisker is combined with contact information.B. Does hippocampal theta drive whisking? NO • Visually cued Finger Movements in Human Epilepsy Patients A. What is the neural signature of movements? B. (How) Should intracranial signals be un-mixed? III. Conclusions and Next Steps

  25. Overview • Oscillations in the Rat Vibrissa System A. What is the significance of phase in the whisking cycle? Information about the location of the whisker is combined with contact information. B. Does hippocampal theta drive whisking? NO. • Visually cued Finger Movements in Human Epilepsy Patients A. What is the neural signature of movements? B. (How) Should intracranial signals be un-mixed? III. Conclusions and Next Steps

  26. Scales of measurement of electrophysiological brain signals Churchland & Sejnowski, 1992

  27. Jasper & Penfield, 1949 Electroencephalography (EEG) recordings

  28. CSF Cocktail Party EEG Independent Component Analysis of EEG Data Makeig, Bell, Jung & Sejnowski, 1996

  29. Independent Component Analysis x = A s (theory) x: recorded channel data s: actual underlying sources A: “mixing matrix” Assume that sources are: • Statistically independent • Volume conduction instantaneous (no time delays) • Sources mix linearly to produce channel data • Spatially stationary

  30. 1. ICA separates EEG data into different brain rhythms that are modulated during this working memory task. Voltage Onton & Makeig, 2006

  31. 2. The maps from projecting independent components onto the electrodes produce biologically plausible patterns (dipoles) Onton & Makeig, 2006

  32. CSF EEG Intracranial EEG (iEEG) ICA of iEEG Standard EEG ICA of EEG ??

  33. Is ICA useful for the interpretation of intracranial data? Three Ways to Assess: 1. Are the time series of intracranial channels statistically independent? (Control) 2. Do independent component maps appear consistent with anatomically and/or functionally linked brain regions? 3. Does ICA separate functionally distinct brain processes? a. Pathological signals? b. Event-related dynamics?

  34. Patient Electrode Locations Inter-hemispheric fissure Right frontal & lateral lobe Orbital Frontal Surface Mesial temporal lobe Lateral temporal lobe

  35. Visually Cued Finger Movement Task Stimulus Key-press Beep Next Stimulus Time (msec) 0 epoch: 2 sec ISI: 1.570 sec Task Design 10 Trials Per Finger Per Condition (N = 400) Block Design: L pic - R pic - L word - R word L pic - R pic - L word - R word Finger presentation randomized within a block right ring

  36. ICA of Intracranial Data Example Channel (black) and Component (blue) Time Series Grid 24 Grid 25 Grid 26 Grid 27 Grid 28 Grid 29 IC1 IC2 IC3 IC4 IC5 IC6

  37. ICA of Intracranial Data Reduction in pairwise mutual information from channels to components

  38. ICA of Intracranial Data Reduction in pairwise mutual information from channels to components

  39. Independent component map are consistent with anatomically and/or functionally linked brain regions Focal Diffuse Complex

  40. = FIRDA: frontal intermittent rhythmic delta, reportedly synchronous Right lateral frontal Grid Lateral Temporal Strips ICA separates pathological “FIRDA” acivity Strips in anterior frontal interhemispheric fissure Orbital Frontal Surface strip Mesial Temporal Strips

  41. IC3 Epileptic “Frontal Intermittent Rhythmic Delta Activity” (FIRDA)

  42. Jasper & Penfield, 1949 Cortical signatures of movement Jasper & Andrews, 1936

  43. Jasper & Penfield, 1949 Miller et al, 2007 Cortical signatures of movement Alpha/beta power decrease Jasper & Andrews, 1936

  44. Jasper & Penfield, 1949 Miller et al, 2007 Cortical signatures of movement Gamma power increase Alpha/beta power decrease Jasper & Andrews, 1936

  45. Mu blocking on Grid24 Finger movement Finger movement Grid24 log spectral power Finger movement

  46. ICA finds components with classic movement-related dynamics Finger movement Finger movement IC18, 89% of Grid24 Finger movement IC18 captures classic event-related spectral changes and mu blocking associated with finger movement

  47. Independent components identify components in overlapping brain areas with different dynamics

  48. ICA is useful for the interpretation of intracranial data Three Ways to Assess: 1. ICA finds a set of time series that are more statistically independent than the sensor data (sanity check) 2. Independent component maps appear consistent with anatomically linked brain regions 3. ICA separates functionally distinct brain processes: a. Pathological signals b. Event-related dynamics

  49. Next Steps for ICA of Intracranial Data • ICA for the interpretation of cognitive task data for which the dynamics are not known in advance