Phase reorganization leads to transient β-LFP spatial wave patterns in motor cortex

The final paper from my thesis is, at long last, published [get PDF]. 

We studied traveling waves observed in electrical Local Field Potential (LFP) signals in primate motor cortex. We found that the structure of traveling waves in beta LFP oscillations was more complex than previously appreciated. 

Previous theoretical work noted that traveling waves in the brain do not always reflect "true" traveling waves, like the ripples from throwing a stone into water: They can also arise from common inputs arriving at different times, or from transient spatial reorganization of coupled oscillators.

We sought to clarify which of these scenarios is consistent with beta-LFP traveling waves in motor cortex. Our previous work looking at the role of single neurons suggested that the waves may be phase waves in coupled oscillators, rather than traveling pulses from a defined source.

This study supports the phase wave scenario. We also discovered a rich variety of spatial structures not previously reported, such as spiral and radiating waves and more complex structures. 

Many thanks to Carlos Vargas-Irwin, John P. Donoghue, and Wilson Truccolo. This work can be cited as

Rule, M.E., Vargas-Irwin, C., Donoghue, J.P. and Truccolo, W., 2018. Phase reorganization leads to transient β-LFP spatial wave patterns in motor cortex during steady-state movement preparation. Journal of neurophysiology, 119(6), pp.2212-2228. 

Figure 3, surveying the variety of patterns:

Fig. 3. Transient beta-local field potential (β-LFP) oscillations exhibit a rich variety of spatiotemporal wave patterns. In addition to traveling plane waves, beta spatiotemporal dynamics showed synchronous states, radiating and rotating waves, and other more complex wave patterns. Each example was taken from the 4×4-mm² area sampled by the 10×10 multielectrode array in ventral premotor cortex area of subject S. Missing electrodes were interpolated from nearest neighbors. Average phase-delay maps were computed by wrapping Hilbert phases at the median frequency of the wave event before computing the average analytic signal. The mean analytic signal was smoothed at a 2-mm scale to generate the phase-delay maps pictured. The smoothed Hilbert phase (φ) was differentiated to extract critical points from the wave dynamics, shown as a blue dot for a radiating wave and red dots for rotating waves. Spatially synchronized states were detected as patterns where the angular distribution of analytic signals was concentrated as shown by the first (top to bottom) example. Plane-wave states were detected as spatial patterns where the angular distribution of the phase gradient (∇φ) direction was concentrated, as shown by the second example.

Optogenetic stimulation shifts the excitability of cerebral cortex from type I to type II

Our new paper, Heitmann et al. [get PDF], is finally out! It's a collaboration between the theoretical neuroscientists Stewart Heitmann and Bard Ermentrout at the University of Pittsburgh, and the Truccolo lab at Brown University. 

This work could help us understand what happens when we stimulate cerebral cortex in primates using optogenetics. Modeling how the brain responds to stimulation is important for learning how to use this new technology to control neural activity.

Optogenetic stimulation elicits gamma (~50 Hz) oscillations, the amplitude of which grows with the intensity of light stimulation. However, traveling waves away from the stimulation site also emerge. It's difficult to reconcile oscillatory and traveling-wave dynamics in neural field models, but Heitmann et al. arrive at a surprising and testable prediction: 

The observed effects can be explained by paradoxical recruitment of inhibition at low levels of stimulation, which changes cortex from a wave-propagating medium to an oscillator. 

At higher stimulation levels, excitation overwhelms inhibition, giving rise to the observed gamma oscillations. 

Many thanks to Stewart Heitmann, Wilson Truccolo, and Bard Ermentrout. The paper can be cited as:

Heitmann, S., Rule, M., Truccolo, W. and Ermentrout, B., 2017. Optogenetic stimulation shifts the excitability of cerebral cortex from type I to type II: oscillation onset and wave propagation. PLoS computational biology, 13(1), p.e1005349.

 

 

Collective neural dynamics in primate motor cortex

As of the 29th of May, 2016, I officially have a Ph.D. in neuroscience! The thesis, Collective Neural Dynamics in Primate Motor Cortex, is available from the Brown University library [PDF].

I studied how single-neuron activity relates to large-scale collective neural dynamics during movement planning and execution. The thesis covers three research projects, which have been (will be) published as stand-alone papers:

• Chapter 2, pp. 88-121: Contribution of LFP dynamics to spiking variability in motor cortex during movement execution. read more...
  

Rule, M.E., Vargas-Irwin, C., Donoghue, J.P. and Truccolo, W., 2015. Contribution of LFP dynamics to single-neuron spiking variability in motor c ortex during movement execution. Frontiers in systems neuroscience, 9, p.89.

• Chapter 3, pp. 122:168: Dissociation between single-neuron spiking β-rhythmicity and transient β-LFP oscillations during movement preparation in primate motor cortex. read more…
  

Rule, M.E., Vargas-Irwin, C.E., Donoghue, J.P. and Truccolo, W., 2017. Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex. Journal of neurophysiology, 117(4), pp.1524-1543.

• Chapter 4, pp. 169-213: Phase diversity and spatiotemporal wavedynamics in primate motor cortex local field potentials. read more…

Rule, M.E., Vargas-Irwin, C., Donoghue, J.P. and Truccolo, W., 2018. Phase reorganization leads to transient β-LFP spatial wave patterns in motor cortex during steady-state movement preparation. Journal of neurophysiology, 119(6), pp.2212-2228.

The introduction contains background on primate motor cortex (Chapter 1, pp. 7-88), including its constituent areas, how they connect with the rest of the brain, and how neurons connect to each-other within each area. It surveys what is known (as of 2016) about motor cortex population dynamics, LFP oscillations, and spatiotemporal waves. The section on statistical methods (Chapter 1.5, pp. 61-87) provides background for signal processing to extract single-neuron spikes and LFPs from multi-electrode array recordings. It also covers how to apply Generalized Linear Point-Process Models (PP-GLM) to analyze spiking neural data.

I'd also like to share two new illustrations from the introduction not published elsewhere:

Figure 1.1 

(high resolution PDF, SVG)

 

Figure 1.1: Anatomy of visually-guided reaching and grasping. During visually guided reaching and grasping, the arm and hand area of M1 coordinates with the dorsal and ventral premotor areas PMd and PMv. In this illustration, reciprocally connected motor and parietal areas are shaded in common colors. Premotor areas receive segregated streams of visual information from parietal cortex. Area PMd receives information about spatial geometry important for reaching from dorsal parietal areas (shaded in blue). Area PMv receives information about object geometry important for grasping from the parietal areas shaded in orange. Area M1 also receives feedback from somatosensory cortex (areas 3a,1,2, shaded in grey). Connections between parietal and premotor cortex are taken from Tanné-Gariépy et al. (2002), and anatomical boundaries of premotor areas are taken from Dum and Strick (2002).

Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution

My first Ph.D. publication is out! Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution explores how the activity of individual neurons in motor cortex is related to population activity, as measured by electrical Local Field Potentials (LFPs). 

[get PDF] 

How do the actions of individual cells combine to create the emergent dynamics that underlie perception, thought, and action? To answer this question, we should study populations of single neurons, and ask how their activity is related to measures of collective population dynamics. This study was a collaboration between the Truccolo and Donoghue labs, and looked at neural population recordings from primate motor cortex during movement.

We found that the activity of single cells was tightly coupled to population activity as measured by LFPs, and that both of these signals were closely realated to movement. This suggest that, during movement execution, collective dynamics reflected in motor cortex LFPs mostly reflect the sensorimotor processes directly controlling movement output. It also suggests that primary motor cortex isn't engaged in other activities like cognition or future planning, while executing movements.

Importantly, we considered both past and future movement in this analysis, and found that single neurons and LFPs both contain information about recent and upcoming movements. This is consistent with the view that motor cortex acts as a dynamical pattern generator.

Many thanks to Carlos Vargas-Irwin, John P. Donoghue, and Wilson Truccolo. The article is open access, and you can also grab the PDF from Github. The paper can be cited as:

Rule, M.E., Vargas-Irwin, C., Donoghue, J.P. and Truccolo, W., 2015. Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution. Frontiers in systems neuroscience, 9, p.89. 

 

Figure 4. Breakdown of LFP predictive power by frequency band and LFP feature. Box-plots over the population of isolated units (all sessions combined) showing the predictive power of models based on phase, amplitude, or analytic signal features in isolation from each of eight LFP bands. To better assess the individual predictive power of each LFP feature, models were fitted for each feature separately. Certain features, such as the instantaneous phase and analytic signal for the 0.3–2 Hz band, as well as the analytic signal amplitude modulation above 100 Hz, consistently predict spiking across all animals and areas.

Motor cortex LFP spatiotemporal dynamics in a cued grasp with instructed delay task

Update: Portions of these notes have now been published in the Journal of Neurophysiology as  "Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex" and "Phase reorganization leads to transient β-LFP spatial wave patterns in motor cortex during steady-state movement preparation".

[get notes as PDF]

Task-locked modulations in neural activity

The Cued Grasp with Instructed Delay (CGID) task reliably elicits task-locked activity in all three motor areas (M1, PMd, PMv).

• Consistent with prior literature, the movement period of the CGID task is marked by slow motor evoked potentials (Fig. 2), increased single-unit firing rates (Fig. 3), and beta suppression (Fig. 4).
• Beta oscillations are enhanced during the first four seconds of the task, although there are some differences between subjects.
• The average level of beta-LFP synchrony is correlated with beta-LFP power, and varies across phases of the task.
• We find no evidence of task-locked phase resetting of beta LFP oscillations
• The spatiotemporal structure of beta-LFP waves is correlated with amplitude and synchrony, with lower amplitudes reflecting more complex wave structures, and higher amplitudes as more synchronous.

Figure 1: The CGID task reliably elicits evoked potentials, which correlate with beta suppression. In subject S, beta power is strongest in the first second before object presentation. In subject R, beta oscillations are more variable, with somewhat stronger power between the grip and go cues. In both animals, high beta power appears to correspond to periods of higher beta synchrony, and larger phase gradient directionality, a measure of how much LFP activity resembles a plane wave. Conversely, increases in the average magnitude of the Hilbert phase gradient, which summarizes how quickly beta phase changes over the array, and in the number of critical points in the Hilbert phase gradient, which summarizes the complexity of the beta spatiotemporal wave patterns, correspond to periods of beta suppression.