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. 2018 Aug;24(8):1257-1267.
doi: 10.1038/s41591-018-0058-y. Epub 2018 Jun 18.

Low-frequency Cortical Activity Is a Neuromodulatory Target That Tracks Recovery After Stroke

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Free PMC article

Low-frequency Cortical Activity Is a Neuromodulatory Target That Tracks Recovery After Stroke

Dhakshin S Ramanathan et al. Nat Med. .
Free PMC article

Abstract

Recent work has highlighted the importance of transient low-frequency oscillatory (LFO; <4 Hz) activity in the healthy primary motor cortex during skilled upper-limb tasks. These brief bouts of oscillatory activity may establish the timing or sequencing of motor actions. Here, we show that LFOs track motor recovery post-stroke and can be a physiological target for neuromodulation. In rodents, we found that reach-related LFOs, as measured in both the local field potential and the related spiking activity, were diminished after stroke and that spontaneous recovery was closely correlated with their restoration in the perilesional cortex. Sensorimotor LFOs were also diminished in a human subject with chronic disability after stroke in contrast to two non-stroke subjects who demonstrated robust LFOs. Therapeutic delivery of electrical stimulation time-locked to the expected onset of LFOs was found to significantly improve skilled reaching in stroke animals. Together, our results suggest that restoration or modulation of cortical oscillatory dynamics is important for the recovery of upper-limb function and that they may serve as a novel target for clinical neuromodulation.

Figures

Figure 1
Figure 1. Low-frequency quasi-oscillatory (LFO) activity during a skilled forelimb reach task in healthy rats
a. Behavioral setup for skilled forelimb reach task with simultaneous neurophysiological recording. b. Fixed 32-channel micro-wire arrays were implanted in motor cortex. c. Z-scored firing rate changes (171 units from 4 rats) aligned to reach onset. d. Single trial example of brief low-frequency oscillatory activity during reaching (top: spike raster of all units in this example trial, middle: population peri-event time histogram for all spikes shown on top, bottom: z-scored raw LFP in gray and LFP filtered from 1.5 – 4 Hz in black from an example channel). This trial is representative example of trials that show high SFC and high power, as quantified subsequently. e. Mean spike-field coherence (SFC) across 171 units from 4 rats. f. Mean LFP power across 118 channels from 4 rats. g. 4 × 8 grid of electrodes from one animal, in actual spatial configuration, with 375 μm spacing in the y-direction and 250 μm spacing in the x-direction, plotting only power from 1.5 – 6 Hz, and from - .05 to 0.45 seconds from reach onset.
Figure 2
Figure 2. Stroke diminished LFO activity in M1
a. Experimental paradigm. After the MCA stroke, we continued recording neural activity from M1 during the reach task in same animals as Fig. 1. b. Histological section showing stroke and approximate location of electrodes from one animal. We performed a similar histological analysis in 4 animals to verify that there was some observable lesion resulting from the stroke. c. Pellet retrieval success rate before (mean 48.9%, SD 13.4%) and after (mean 12.4%, SD 13.8%) distal MCA stroke in 4 rats (2-sided paired t-test, t(3) = 5.77, *p = 0.010). d. Z-scored unit firing rate changes relative to reach onset (53 units from 4 rats). e. Single trial example of diminished LFO activity. Labeling convention is the same as Fig 1d. Bottom panel shows paw velocity in arbitrary units. This is representative of trials that show low SFC and LFP power, quantified in subsequent panels (g/h) f. Trial-by-trial low frequency LFP power decrease after stroke shown in an example channel, paralleled by decrease in success rate. Left: 1.5-4 Hz LFP power, middle: trial by trial success rate, right: success rate smoothed over 25 trials. Only trials in which rat reached and touched the pellet were included. This is representative of a channel that shows high power prior to stroke and low power after, as quantified in subsequent panels (g/h) g. Quantification of 1.5-4 Hz SFC before (n = 171 units) and after (n = 53 units) stroke in 4 rats. Thick lines show mean and shaded area is SEM. h. Quantification of changes in low frequency LFP power after stroke, comparing all paired channels (n = 101) from all 4 animals. Shaded area is SEM. i. Example grid of channels from the same rat as in Fig 1 and in the same scale. Channels with spiking activity are enclosed by black squares. Insets 1 and 2 show mean unit waveforms (shaded area is SEM) and inter-spike interval histograms from 2 selected channels. All 4 animals demonstrated a similar loss of low frequency power across channels after the stroke.
Figure 3
Figure 3. Restoration of LFOs in perilesional motor cortex tracked motor recovery
a. Experimental paradigm. b. Schematic showing location of stroke and electrode. c. Mean pellet retrieval success rate before stroke and during rehabilitation training sessions (n = 6, error bars show SEM, grey dots show mean of individual rats). Session 1 or S1 was 1 week post stroke for all. Each animal typically attempted 2 sessions of 25-35 trials each per day. d. Firing rate changes relative to reach onset in early (the first) and late (the last) sessions (for all units from all 6 rats). e. Example of increased LFO activity with rehabilitation, both at the level of spiking and LFP, in two trials with similar paw velocity. Labeling convention are the same as Fig 2e. f. Example channel from one animal showing trial by trial 1.5-4 Hz LFP power increase, along with success rate increase, over the course of rehabilitation training. Quantification of this effect across channels is in panels i/j. Labeling convention is the same as Fig 2f. Horizontal white lines separate training sessions. g-h. Mean SFC, calculated from units (n = 170 early, n = 219 late) in all 6 animals. Shaded area in h is SEM. i-j. Mean LFP power across channels (n = 176) from all 6 animals in early and late trials. Shaded area in j is SEM. k. Spatial topography of the low-frequency LFP power increase. Plot shows example channels from one animal. All 6 animals showed similar patterns of recovery, as quantified in panels i/j. l. Scatter showing significant correlation between restoration of low frequency power (mean 1.5-4Hz power from –0.25 to 0.75s around reach onset) and improvements on the motor task (r = 0.576, two-tailed Pearson's correlation, *p = 1.18e-7). Each x represents one session from one rat (n = 72 sessions), with values normalized for each animal to first session post-stroke.
Figure 4
Figure 4. Movement-related LFOs in sensorimotor cortex of a human stroke patient relative to non-stroke subjects
a. Left: center-out paradigm used in patients with ElectroCorticoGraphy (ECoG) recordings. In each trial, subjects were given a hold cue, followed by a “reach” cue (red) that indicated which target to move to. Right: example of trajectories in the stroke patient. Movement-related data was recorded from 2 subjects with no stroke (NS) and 1 stroke subject (SS). Analyses were collapsed across all movement directions in each subject. b. Placement of ECoG grid in the stroke subject, and location of stroke. Blue dots on image indicate where intracortical stimulation evoked hand movements. c. Event-related spectral power across sensorimotor electrodes from one intact subject, and the stroke subject. Power normalized to a baseline time period for each channel (activity prior to the hold-cue). This experiment was not repeated on a subsequent day. d. Temporal plot of mean low-frequency power (1.5-4 Hz) from sensorimotor electrodes in each of the 2 intact subjects (NS1, n = 18 electrodes, NS2, n = 16 electrodes and the stroke subject (n = 91 electrodes). Shaded error bars display SEM for each subject across electrodes. e. Spatiotemporal plot at the 3 time-points indicated in panel (d), demonstrating increase in low frequency power along the CS (sensorimotor strip) in the two healthy subjects, and absence of this power in the stroke subject. Z-score scale displayed to the right of the image is identical for all subjects and time points. Experiments were not repeated in these subjects.
Figure 5
Figure 5. LFO activity increased with Direct Current Stimulation (DCS) in acute (anesthetized) recording sessions
a. Recording and stimulation arrangement in acute experiments. b. LFP power before and during DCS shown in one session. Grey shaded area shows 1.5-4Hz frequency range. Thick lines in blue and red show mean and shaded areas show SEM. Inset shows 1.5-4Hz power in pre-DCS and during-DCS in all 11 sessions from 10 rats (mean and SEM shown in bar plots with individual values, two-tailed paired t-test, t(10) = -2.493, *p = 0.032). c. Spiking activity of the same neurons from a session before and during stimulation, showing increased coherent spiking during DCS. d. Mean SFC (dark red/blue line - conventions as previous) of 50 neurons from 10 rats. Shaded area represents SEM. 1.5-4Hz SFC (grey shaded area) increased with DCS (one-tailed paired t-test, t(49) = -1.727, *p = 0.045).
Figure 6
Figure 6. Task-dependent DCS improved motor function post-stroke
a. Cranial-screws placement for stimulation in relation to stroke lesion along with the ground screw. b. Pseudo-randomized stimulation design indicating the trial with either DC stimulation, a “sham-stim” control (stimulation turned on for only 200 ms), or no stimulation. c. Effects of DC vs. sham-stim on motor accuracy on the skilled forelimb reach task post-stroke. Bar plots demonstrate mean/SEM % improvement in accuracy, and lines show the effects in each animal (n = 7). We performed one-sample, two-sided t-test performed separately for the Stim (t(6) = 6.004, ***p = 9.6e-4) and Sham (t(6) = -0.77, p = 0.47) group, followed by a paired two-sample two-sided t-test to compare the effects between groups (t(6) = 4.91, p = 0.003)). d. Mean raw LFP trace (bold line, n = 70 trials stim off, n = 66 trials stim on) from one animal comparing DCS on vs. off; light grey lines show 6 example single trial traces. Dotted line indicates reach onset time. Quantification performed in next panel. e-f. Mean LFP power for all sessions (n = 13 stim on, n = 11 stim off sessions) across 4 animals. Bold line in f is the mean and the shaded area is SEM. g. Pseudo-randomized stimulation onset design depicting how a 1s stimulation was triggered in relation to reach onset. ΔT was negative if the stimulation occurred prior to reach onset, and it was positive if stimulation onset occurred after reach onset. h. Percentage accuracy as a function of ΔT (n = 4 animals). Shaded area displays SEM. (* indicates significant improvement in accuracy at ΔT between 500 - 400ms from the reach onset, t(3) = 9.035, *p = 0.046, after Bonferroni-Holm correction for 16 different time points). Grey line shows the mean 1.5-4Hz LFP from healthy animals, taken from Fig 1.

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References

    1. Churchland MM, Cunningham JP, Kaufman MT, Ryu SI, Shenoy KV. Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine? Neuron. 2010;68:387–400. - PMC - PubMed
    1. Churchland MM, et al. Neural population dynamics during reaching. Nature. 2012;487:51–56. - PMC - PubMed
    1. Hall TM, de Carvalho F, Jackson A. A Common Structure Underlies Low-Frequency Cortical Dynamics in Movement, Sleep, and Sedation. Neuron. 2014;83:1185–1199. - PMC - PubMed
    1. Bansal AK, Vargas-Irwin CE, Truccolo W, Donoghue JP. Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices. J Neurophysiol. 2011;105:1603–1619. - PMC - PubMed
    1. Stefanics G, et al. Phase Entrainment of Human Delta Oscillations Can Mediate the Effects of Expectation on Reaction Speed. J Neurosci. 2010;30:13578–13585. - PMC - PubMed

Advance Online Publication

    1. Wallstrom G, Liebner J, Kass RE. An Implementation of Bayesian Adaptive Regression Splines (BARS) in C with S and R Wrappers. J Stat Softw. 2008;26:1–21. - PMC - PubMed
    1. Yu BM, et al. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. In: Koller D, Schuurmans D, Bengio Y, Bottou L, editors. Advances in Neural Information Processing Systems 21. Curran Associates, Inc; 2009. pp. 1881–1888.
    1. Cowley BR, et al. DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity. J Neural Eng. 2013;10:066012. - PMC - PubMed
    1. Aarts E, Verhage M, Veenvliet JV, Dolan CV, Sluis S. van der A solution to dependency: using multilevel analysis to accommodate nested data. Nat Neurosci. 2014;17:491. - PubMed

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