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ref: Brown-2003.04 tags: PD oscillations DBS date: 02-22-2012 16:06 gmt revision:4 [3] [2] [1] [0] [head]

PMID-12671940[0] Oscillatory nature of human basal ganglia activity: relationship to the pathophysiology of Parkinson's disease.

  • Break oscillations into > 30 Hz and < 60 Hz bands.
    • these two are inversely affected by movement, and inversely affected by dopamine treatment.
    • This seems inconsistent with the other literature. (?)
  • Lesions of the GPi improve dyskinesias, without deleterout effects on motor function.
  • The tact assumption from the MPTP monkey research is that synchronization and bursting is an abnormal phenomena.
    • Also suggest that synchronization may be a means of 'binding' or increasing the salience of input.
  • The degree of synchronization increases during non-oscillating periods in PD patients treated with the dopamine agonist apomorphine.
  • [27] PMID-11431506 and there is one report of locking in patients without tremor.
    • In both nuclei, APO increased the overall proportion of spikes in burst discharges (as detected with Poisson "surprise" analysis), and a greater proportion of cells with an irregular discharge pattern was observed.
    • During the OFF state, more than 15% of neurons tested (STN = 93, GPi = 63) responded to passive movement of two or more joints. After APO, this proportion decreased significantly to 7% of STN cells and 4% of GPi cells (STN = 28, GPi = 26).
    • Concurrent with a reduction in limb tremor, the percentage of cells with tremor-related activity (TCs) was found to be significantly reduced from 19 to 6% in the STN and 14 to 0% in the GPi following APO administration.
  • [31] PMID-9990083 there is evidence that human STN and GPi units firing at tremor frequency show only transient periods of locking to peripheral tremor
    • We found that GPi tremor-related activity at a given site could fluctuate between states of synchronization and independence with respect to upper limb tremor. Consistent with this finding, some paired recording sites within GPi showed periods of transient synchronization. These observations support the hypothesis of independent tremor-generating circuits whose coupling can fluctuate over time.
  • Monkeys treated with MPTP show pronounced increases in synchrony at < 30 Hz.
  • Levodopa markedly increases > 60 Hz coherence between GP & midline EEG.
    • "The synchronization of single units in STN or GPi at high frequencies has not been demonstrated in microelectrode studies to date. (2002).
    • HF coherence is found between SNT and GPi, and these structures w SMA.
  • Stimulation of the pallidium and enteopeduncular nucleus in cats at 3-10Hz leds to synchronization of the EEG and gradual slowing and eventual cessation of spontaneous movements.
    • Could this abnormal, low-frequency, synchronous oscillatory activity in GPi and its input STN act, by means of the thalamus, to hold the motor cortex in a low-frequency antikinetic state in Parkinson’s disease? [7].
  • There is a disappearance of > 60 Hz oscillations in the STN with drowsiness [29].
  • Complex movements are particuarly difficult for PD patients.
  • Human GPi neurons normally fire at 85 to 140 Hz -- so 130 Hz stimulation may entrain them.

Conclusion: he really thinks that there is a strong dichotomy between HF, pro-kinetic, and MF, anti-kinetic oscillations.

____References____

[0] Brown P, Oscillatory nature of human basal ganglia activity: relationship to the pathophysiology of Parkinson's disease.Mov Disord 18:4, 357-63 (2003 Apr)

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ref: Brown-2001.12 tags: EMG ECoG motor control human coherence dopamine oscillations date: 01-19-2012 21:41 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-11765129[0] Cortical network resonance and motor activity in humans.

  • good review.
  • No coherence between ECoG and eMG below 12 Hz; frequency coherence around 18 Hz.
    • This seen only in high-resolution ECoG; lower resolution signals blurs the sharp peak.
  • Striking narrowband frequency of coherence.
  • ECoG - ECoG coherence not at same frequency as EMG-ECoG.
  • Marked task-dependence of these coherences, e.g. for wrist extension and flexion they observed similar EMG/ECoG coherences; for different tasks using the same muscles, different patterns of coherence.
  • Pyramidal cell discharge tends to be phase-locked to oscillations in the local field potential (Murthy and Fetz 1996)
    • All synchronization must ultimately be through spikes, as LFPs are not transmitted down the spinal cord.
  • Broadband coherence is pathological // they note it occurred during cortical myclonus (box 2)
  • Superficial chattering pyramidal cells (!!) firing bursts of frequency at 20 to 80 Hz, interconnected to produce spike doublets (Jefferys 1996).
  • Dopamine restores coherence between EMG and ECoG in a PD patient.

____References____

[0] Brown P, Marsden JF, Cortical network resonance and motor activity in humans.Neuroscientist 7:6, 518-27 (2001 Dec)

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ref: Brown-2007 tags: Kalman filter BMI Black spike_sorting Donoghue date: 01-06-2012 00:07 gmt revision:1 [0] [head]

From Uncertain Spikes to Prosthetic Control a powerpoint presentation w/ good overview of all that the Brown group has done

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ref: Brown-2008.03 tags: microstimulation recording artifact supression MEA ICMS date: 12-28-2011 20:43 gmt revision:3 [2] [1] [0] [head]

IEEE-4464125 (pdf) Stimulus-Artifact Elimination in a Multi-Electrode System

  • Stimulate and record on the same electrode within 3ms; record on adjacent electrodes within 500us.
  • Target at MEAs, again.
  • Notes that very small charge mismatches of 1% or less, which is common and acceptable in traditional analog circuit designs, generates an artifact that saturates the neural amp signal chain.
  • for stimulating & recording on the same electrode, the the residual charge must be brought down to 1/1e5 the stimulating charge (or less).
  • paper follows upon {833} -- shared author, Blum -- especially in the idea of using active feedback to cancel artifact charge & associated voltage.
  • target the active feedback for keeping all amplifier out of saturation.
  • vary highpass filter poles during artifact supression (!)
  • bias currents of 1fA on the feedback highpass stage. yikes.

Brown EA, Ross JD, Blum RA, Yoonkey N, Wheeler BC, and DeWeerth SP (2008) Stimulus-Artifact Elimination in a Multi-Electrode System. IEEE TRans. Biomed. Circuit Sys. 2. 10-21

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ref: Brown-2007.09 tags: motor force field learning vision date: 02-20-2009 00:28 gmt revision:1 [0] [head]

PMID-17855611 Motor Force Field Learning Influences Visual Processing of Target Motion

  • as you can see from the title - this is an interesting result.
  • learning to compensate for forces applied to the hand influenced how participants predicted target motion for interception.
  • subjects were trained on a robotic manipulandum that applied different force fields; they had to use the manipulandum to hit a accelerating target.
  • There were 3 force feilds: rightward, leftward, and null. Target accelerated left to right. Subjects with the rightward force field hit more targets than the null, and these more targets than the leftward force field. Hence motor knowledge of the environment (associated accelerations, as if there were wind or water current...) influenced how motion was perceived and acted upon.
    • perhaps there is a simple explanation for this (rather than their evolutionary information-sharing hypothesis): there exists a network that serves to convert visual-spatial coordinates into motor plans, and later muscle activations. The presence of a force field initially only affects the motor/muscle control parts of the ctx, but as training continues, the changes are propagated earlier into the system - to the visual system (or at least the visual-planning system). But this is a complicated system, and it's hard to predict how and where adaptation occurs.

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ref: Brown-2001.11 tags: Huntingtons motor_learning intentional implicit cognitive deficits date: 0-0-2007 0:0 revision:0 [head]

PMID-11673321 http://brain.oxfordjournals.org/cgi/content/full/124/11/2188 :

  • 16 genetically-confirmed Huntington's patients (and matched controls) trained on a task using trial and error learning (intentional), and implicit learning (unintentional).
  • the task setup was simple: they had to press one of four keys arranged in a cross (with center) either in response to commands or while guessing a sequence of a few keys.
  • Within the random, commanded task there was a sequence that could/should be noticed.
  • Huntington's patients performed worse on the intentional learning segment, but comparably on the implicit learning / implicit sequence awareness, though the latter test seems rather weak to me.