Brain Activity and Complex MotionThe 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. Scribbling2. Prehension

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

CenterOut (CO) task and trajectories

An example of a scribbling trajectory

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

The final scribbling and CO trajectories

Similar Directional tuning during CO and scribbling movements

Different directional tuning during CO and scribbling movements

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

Possible causes for the difference in PDsOther movement parameters change between scribbling and CO: tangential velocityInitial position curvature of the segmentstangential accelerationThe differences are task related

Differences in PDs were not explained by variability in other movement parametersScribbling segments were divided based on high and low values of different movement parameters.PDs were obtained for each subgroup separatelyResults 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 STD2/13 (15%) cells with significantly different PDs due to variability in initial position.

The Free Tracing (FT) task & trajectories

PDs during FT and CO movements tended to be similarLower 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.

Summary and conclusionsDirectionally 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.

Brain Activity and Complex MotionMost 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?

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

Concatenating MovementsCompute the likelihood of change in firing rate for every cell.

Find which cells tend to change their firing rates just before: start of movement.

Concatenating MovementsCompute 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.

Concatenating MovementsCompute 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 .

Concatenating MovementsBUT cell assemblies overlap.

One needs to know who is firing AND who is quiet.

Problem with low firing rates and sparse sampling.

PrehensionIt 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.

Field Potential Oscillations in Posterior Parietal Cortex During Reaching and Grasping Movements

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, SegregatedConnections

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)

Task Setup & ProtocolTouch pad in center of workspace6 target locations X 3 prehension objects Controlled Sound & Light conditionsEpochs: Control, Signal, Set, Pre-Go, RT-MT, Hold.

movie

Prehension objects Plate: Finger oppositionBox: Power gripPrecision grip objectReaching pad

movie

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

Frequency domain: time resolved spectrum shows epoch-dependent changes in spectral compositionAlpha: 8-13 HzBeta: 13-30 HzGamma: 30-60 Hz

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

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

Great expectationsNon-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

Problem: Typical AIP data do not show beta oscillations (may show gamma oscillations) compareAlpha: 8-13 HzBeta: 13-30 HzGamma: 30-60 Hz

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

Between-Area coherograms

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

SummaryBeta 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)

Some neural mechanisms of cortico-cortical cooperation

QuestionDo, and how do, cortical areas coordinate their activity

Model systemPM (pre-motor) cortexDorsal PM reaching-related (Kalaska, Wise)Ventral PM grasping-related (Rizzolatti)

Temporal coordination hypothesisCrosstalk between areasAt behaviorally relevant time scalesModulated by context

TestsLocal field potential pair-wise correlationsSingle unit cross-correlations

LFP pair-wise correlations:the raw dataPREGORTMT

Zero-lag modulation by distance

Exponential decay with distance

Binned by distance

Modulation by behavior

Exponents coefficients differ

Significance2 way ANOVAdistance: ***epoch: ***interaction: ns

Rank test:

* < 0.05** < 0.01*** < 0.001

Spike-to-spike cross-correlations:the raw dataGO signalCue On

Zoom in

Same area, different electrodes

Same area, different electrodes

Same area, different electrodes

Cooperation during preparationCue OffGO signal

CC across areas

Movement specific cooperationMovement initiationMovement termination

Longer lags between areas

Statistics differ

Summary

Both local field potentials and single units seem to coordinate their activity across distances in a precise, context-related manner

Temporal coordination hypothesis supported

Brain Activity and Complex MotionConclusion

Life is tough

Extra Figures

Power in epochs (lumped): MIPRMS

PWR tot

Alpha

Beta

Gamma

Other

Power in epochs (lumped): AIPRMS

PWR tot

Alpha

Beta

Gamma

Other

The question stands in the center of this talk is .The motor tasks that we tested were the scribbling task which Moshe presented already, and the (next slide) Standard CO task from a central target to one out of six equally spaced peripheral targets. The CO task produced reaching movements with straight trajectories in those 6 directions and with bell shape velocity profiles and the scribbling task produced curved, continuous closed movements.We can see that the scribbling movement is very different than the CO movement. So in order to compare the neuronal activity between these two tasks, we wanted to extract pieces of scribbling movements that are most similar to the CO movements in terms of curvature and velocity profile. (next slide). Therefore we segmented the velocity profile between adjacent minima in order to get a bell-shape velocity profile. Here you can see the path that was presented before with its velocity profile, while the different colors represent different segments and the corresponding paths. You can see here that still some paths were curved and other were very short, therefore we had to clean further these segments. We filtered out all segments which had duration of less than 150 msec from start to peak velocity, whose average direction did not lie in the range of one of CO average directions +/- 2STDs. As a measure for curvature we used the standard deviation of the instantaneous direction. We filtered out all segments that lied on the high 1/3 of the distribution of these values.Since

This is the figure from K+S book, showing parallel pathways dedicated to R vs. G, running from V1 towards motor cortex, through PPC.

Recent anatomical studies support this model, yet stress the strong reciprocal connectivity between PM and PPC, in contrast to few PMv-PMd and SPL-IPL connections.

Workspace > objects > Trial description > movie if time The monkey presses TP in response to visual cue, then an object is shortly presented. After a delay, The monkey gets a visual go signal, and has to reach and grasp the object, and hold it grasped until another cue tells it to get back to the TP and get reward.

This figure also shows the 6 epochs used for the analysis we will show: CE, Signal, Set + Prego = 2 parts of the delay; RT+MT and Hold. Get used to the color coding From now - ResultsThese are all the traces of lfp from one object one direction in one MIP channel, together with behavioral events.

Explain axes, alignment on GO,

Scale bar = 100 microvolt.

Average (erp) in RED magnified times 5

Evoked response; seen around movements, less around Visual cue.

Oscillations are in beta range (~20 Hz),

Donoghue = Prego; Baker = Hold;

Stop! use frequency analysis to better characterize oscillations, but want some way to look at dynamics in time spectrogram. Spectrograms are FTs of short time segments. X axis is time in sec, Y is Frequency in Hz.

What we see are averages over trials of 1 object in 6 directions, when data was aligned on Go.

This is also an MIP ch, a different day.

The result is similar to what we saw in the time domain example: Pre-go and hold show shift towards beta (oscillations), whereas Signal ad Movement show shift towards alpha (erp). ***** STOP HERE !!! ***** We see that beta increase is not the same for each direction and can ask whether this activity is tuned. This is the same as with SU spike counts. For each epoch within each trial we get a number = activity in beta range; Vectorial sum of 6 direction averages makes the PD of the channel. Bootstrap method is used to determine significance of the tuning.

Here are the statistics of MIP channels from 18 rec. days.

Only statistically significant PDs are plotted for each epoch.

During CE almost nothing, during other epochs about quarter to third significantly tuned. The distribution is non uniform.

We do not see the beta oscillation shift.

We do see: decrease in total power before movement and increase during movement.

Gamma oscillations in this case (not typical) sharply tuned, during Hold.

So, we are now lowering our expectations

Coherence is a measure of constant phase relations between 2 signals within a frequency band, and is bounded between (0,1).Defined as squared CS divided by product of auto spectra of 2 signals. This is the frequency domain parallel of the CC function. This figure shows an average of coherence in whole trials (1 comb) in 3 pairs: within MIP, within AIP, and between areas.

It has been shown in time domain that LFP signals from close sites are highly correlated, and that the correlation decays with distance. We see here the same thing in the frequency domain.

The result is that coherence is strong within areas (Note the high peak in 20 Hz in MIP pair reflecting the oscillations), but weak between areas.

We have 2 excuses for the weak coherence (1) distance expectations (2) frequency content expectations. We now add (3) averaging out of short term effects. . In order to look more closely at this weak effect we used the coherogram time resolved coherence. The coherogram is like the specgram, a time resolved coherence estimate, where X is time in sec. and Y is frequency.

Peaks in coherence are observed around times of movement and visual presentation (note that data was aligned on GO) This is rather low than what we saw earlier for within area pairs, but is it statistically significant?

For this we used the test suggested by Rosenberg (1989)

What is plotted here is log of the p value for each time/freq. bin

The Significance test shows us again the 3 areas of significant coherence.

We see that between area coherence is stronger not in the time and not in the frequency where we saw the oscillations, but rather in times and frequencies that we saw the ERP phenomena

This result is (1) interesting and surprising (2) not compatible with the binding hypothesis

SWS eeg spectrum shifts to lower delta frequencies when sleep is deeper.

2 way anova (distance, epoch); *