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{1381}
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ref: -0 tags: glassy carbon SU-8 pyrolysis CEC microelectrode stimulation stability platinum PEDOT date: 02-17-2017 00:05 gmt revision:2 [1] [0] [head]

A novel pattern transfer technique for mounting glassy carbon microelectrodes on polymeric flexible substrates

  • Use inert-atmosphere pyrolysis @ 900 - 1000 C of 20um SU-8 (which is aromatic) on a thermal oxide wafer.
  • Followed by spin & cure of PI.
  • Demonstrate strong carbonyl bonding of the glassy carbon with mechanical and FTIR testing.
  • Use of photosensitive PI allows through-vias to connect Cr/Au conductive traces.

PMID-28084398 Highly Stable Glassy Carbon Interfaces for Long-Term Neural Stimulation and Low-Noise Recording of Brain Activity

  • Use EIS to show superior charge-injection properties + stability of glassy carbon electrodes vs. Pt electrodes.
    • GC lasted > 5e6 pulses; Pt electrodes delaminated after 1e6 pulses.
    • Hydrogen bonding (above) clearly superior than neat PI-Pt interface
  • GC electrodes were, true to their name, glassy and much smoother than the platinum electrodes.
  • Further reduced impedance with PEDOT-PSS coating.
    • PEDOT-PSS coating on glassy carbon was, in their hands, far more stable than PEDOT-PSS on platinum.
  • All devices, GC, PEDOT:PSS, and Pt, had similar biocompatibility in their assay (figure 7)

{1253}
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ref: -0 tags: polyimide stieglitz stability date: 01-22-2017 05:35 gmt revision:1 [0] [head]

PMID-20144477 In vitro evaluation of the long-term stability of polyimide as a material for neural implants

  • PI degrades at 85C in PBS; otherwise, it's stable.
  • mechanical tests only; no electrical tests.
  • Durimide 7510 contains a photo-initiator and an adhesion promoter. Spin-coatable.
    • Adhesion can be inhibited with C4F8
    • notably softer.
  • Dupont Kapton is PMDA-ODA (phenol linkage in the amide); PI-2611 is BPDA-PPD (aromatic carbon-carbon in the dicarboxcylic acid). The latter resists water uptake better.

{895}
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ref: XindongLiu-2006.03 tags: neural recording electrodes stability cat parlene McCreery MEA date: 01-28-2013 02:50 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

IEEE-1605268 (pdf) Evaluation of the Stability of Intracortical Microelectrode Arrays

  • 35-50um IR electrodes, electrolytically sharpened at a 10 deg angle, with a 5um blunted tip.
  • Electrodes coated in parylene, and exposed at the tip with an eximer laser. Surface area of tip ~500um^2.
  • Sorted based on features (duration, pk-pk, ratio of + to -, ratio of + time to - time), followed by a demixing matrix (PCA?)
  • Did experiments in 25 cats with some task (for another paper?); got recordings for up to 800 days. Seems consistent with our results.
  • Neurons were stable (by their metrics) for up to 60 days.
  • sparse arrays showed stable recordings sooner than dense arrays, perhaps because they are larger and more qucikly become attached to the dura.
  • Electrodes were always unstable for the first 2-3 months. Stability index is as high as 30-40 days.
  • Average electrode yield was ~ 25%.
  • no histology.

____References____

Xindong Liu and McCreery, D.B. and Bullara, L.A. and Agnew, W.F. Evaluation of the stability of intracortical microelectrode arrays Neural Systems and Rehabilitation Engineering, IEEE Transactions on 14 1 91 -100 (2006)

{1081}
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ref: Hershey-2010.12 tags: DBS impulsivity STN feedback stability gonogo date: 02-22-2012 22:04 gmt revision:8 [7] [6] [5] [4] [3] [2] [head]

PMID-20855421[0] Mapping Go-No-Go performance within the subthalamic nucleus region.

  • Support the dorsal-ventral motor-cognitive model.
  • Only ventral subthalamic stimulation effected Go-No-Go accuracy.
    • Both ventral and dorsal stimulation showed positive motor effects.
  • On inhibition in the STN: (Aron and Poldrack 2006; Frank et al 2007).
    • Thought: if methamphetamine and L-Dopa have similar impulsivity / punding / hobbyism effects, why do they think that the function is localized exclusively in the STN? These behaviors seem a more general problem of dopamine disregulation. Meth heads presumably have intact STN. The pausing hypothesis (e.g. STN controls pausing in conflict situations) seems better to me (maybe); have to check rat results.
    • Such is the problem with taking one thing out of a feedback loop and assuming the resultant deficit corresponds with the original 'function' insofar as one can be assigned. Think if you adjust the coefficients on a filter -- it gets all F'ed, with minor projection onto the frequency response.
    • Low-order systems are less sensitive to drastic parameter adjustment, but still purpose is obscured in feedback systems.
    • See {1082}
  • STN DBS can lead to impaired withholding strong prepotent responses with strong response conflict
    • Such as the Stroop task (Jahanshahi et al 2000; Schroeder et al 2002; Witt et al 2004)
    • Stop signal task (Ray et al 2009)
    • Go-nogo tasks (Hershey et al 2004; Ballanger et al 2009).
    • Rats show the same deficit in inhibiting responses in strong conflict cases (Baunex et al 1995, 2001; Baunez and Robbins 1997).
  • Suggest that significant variability in treatment responses could be from the exact location of stimulation.
    • Ventral STN closer to SNr, and dorsal is closer to the ZI and thalamus.

____References____

[0] Hershey T, Campbell MC, Videen TO, Lugar HM, Weaver PM, Hartlein J, Karimi M, Tabbal SD, Perlmutter JS, Mapping Go-No-Go performance within the subthalamic nucleus region.Brain 133:Pt 12, 3625-34 (2010 Dec)

{1082}
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ref: -0 tags: feedback stability resonance butterworth matlab date: 01-22-2012 03:46 gmt revision:4 [3] [2] [1] [0] [head]

Just fer kicks, I tested what happens to low-order butterworth filters when you maladjust one of the feedback coefficients.

[B, A] = butter(2, 0.1);
[h, w] = freqz(B,A);
A(2) = A(2) * 0.9;
[h2, ~] = freqz(B,A);
hold off
subplot(1,2,1)
plot(w,abs(h))
hold on; plot(w,abs(h2), 'r')
title('10% change in one FB filter coef 2nd order butterworth')
xlabel('freq, rads / sec'); 
ylabel('filter response');

% do the same for a higher order filter. 
[B, A] = butter(3, 0.1);
[h, w] = freqz(B,A);
A(2) = A(2) * 0.9;
[h2, ~] = freqz(B,A);
subplot(1,2,2)
hold on
plot(w,abs(h), 'b')
plot(w,abs(h2), 'r')
title('10% change in one FB filter coef 3rd order butterworth')
xlabel('freq, rads / sec'); 
ylabel('filter response');

The filters show a resonant peak, even though feedback was reduced. Not surprising, really; a lot of systems will show reduced phase margin and will begin to oscillate when poles are moved. Does this mean that a given coefficient (anatomical area) is responsible for resonance? By itself, of course not; one can not extrapolate one effect from one manipulation in a feedback system, especially a higher-order feedback system.

This, of course hold in the mapping of digital (or analog) filters to pathology or anatomy. Pathology is likely reflective of how the loop is structured, not how one element functions (well, maybe).

For a paper, see {1083}

{1032}
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ref: Dickey-2009.08 tags: Utah probe MEA stability date: 01-05-2012 22:07 gmt revision:1 [0] [head]

PMID-19535480[0] Single-unit stability using chronically implanted multielectrode arrays.

  • We found that 57% of the original units were stable through 7 days, 43% were stable through 10 days, and 39% were stable through 15 days.
  • Still not that good. Actual neurons / synapses last .. well, the lifetime of an individual.

____References____

[0] Dickey AS, Suminski A, Amit Y, Hatsopoulos NG, Single-unit stability using chronically implanted multielectrode arrays.J Neurophysiol 102:2, 1331-9 (2009 Aug)

{669}
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ref: Pearlmutter-2009.06 tags: sleep network stability learning memory date: 02-05-2009 19:21 gmt revision:1 [0] [head]

PMID-19191602 A New Hypothesis for Sleep: Tuning for Criticality.

  • Their hypothesis: in the course of learning, the brain's networks move closer to instability, as the process of learning and information storage requires that the network move closer to instability.
    • That is, a perfectly stable network stores no information: output is the same independent of input; a highly unstable network can potentially store a lot of information, or be a very selective or critical system: output is highly sensitive to input.
  • Sleep serves to restore the stability of the network by exposing it to a variety of inputs, checking for runaway activity, and adjusting accordingly. (inhibition / glia? how?)
  • Say that when sleep is not possible, an emergency mechanism must com into play, namely tiredness, to prevent runaway behavior.
  • (From wikipedia:) a potentially serious side-effect of many antipsychotics is that they tend to lower a individual's seizure threshold. Recall that removal of all dopamine can inhibit REM sleep; it's all somehow consistent, but unclear how maintaining network stability and being able to move are related.