m8ta
You are not authenticated, login.
text: sort by
tags: modified
type: chronology
{1074}
hide / / print
ref: Gubellini-2009.09 tags: DBS PD 2009 review historical microstimulation ICMS chronaxie rheobase date: 02-22-2012 14:33 gmt revision:11 [10] [9] [8] [7] [6] [5] [head]

PMID-19559747[0] Deep brain stimulation in neurological diseases and experimental models: from molecule to complex behavior.

  • Wow, DBS has been used since the 1950s for localization of lesion targets; in the 1960's was discovered to alleviate tremor; 70s and 80s targeted at the cerebellum for treatimng movement disorders or epilepsy.
  • Extensive list of all the other studies & their stimulation protocols.
  • Large mylenated fibers have chronaxies ranging aruond 30-200 us, while cell bodies and dendrites this value is around 1-10ms. (Rank, 1975).
    • Lapique: minimum energy is a/b, where b is the rhreobase (the minimal electric current of infinite duration that results in an action potential), and chronaxie is the minimum time over which an electric current double the strength of the rheobase needs to be applied in order ti stimulate a nerve cell.
    • Q(t)t=U rh(1+t cht) \frac{Q(t)}{t} = U_{rh}(1 + \frac{t_ch}{t}) where U rhU_{rh} is the rheobase and t cht_{ch} is the chronaxie.
    • you can simplify this to: I th=I rh(1+t cht) I_{th} = I_{rh} (1 + \frac{t_{ch}}{t}) where I rhI_{rh} is the rheobase current and I thI_{th} is the threshold current (Irnich, 2002).
  • Measurements of chronaxie in VIM and GPi found values of 60-75us, hence DBS effects are likely mediated through the activation of afferent and efferent axons. (Holsheimer et al 2000a, 2000b)
    • In line with these findings, cortical stimulation also results in the activation of afferent and efferent axons (Nowak and Bullier, 1998a, 1998b PMID-9504844).
    • Ustim can result in cell body hyperpolarization coupled with action potential initiation in the axon (McIntyre and Grill, 1999; Nowak and Bullier 1998a b).
  • Stimulation depends on the direction of the electric field, obviously. When the axons and E\vec{E} are ||.
  • Acute stimulation is different from chronic DBS (as used in patients); it may be a mistake to extrapolate conclusions.
    • DBS electrodes become encapsulated, and current delivered hence decreases.
  • Strong placebo effect of just the DBS surgery.
    • Implantation of electrodes alone had therapeutic benefit in 6-mo trial. (Mann et al 2009).
  • mean lead impedance is 400-120 ohms in clinical DBS leads, PT-IR.
    • platinum is relatively non-toxic to the brain when compared to metals such as gold or rhodium.
  • If stimulation exceeds 30 uC/cm^2/phase, there is a risk of tissue damage. This equates to 30ma.
  • Stainless steel electrodes are damadged by days of in vivo stimulation -- metal ions are lost.
  • STN neurons spontaneously oscillate due to leak Ca currents and C-activated K channels.
  • STN DBS seems to disrupt abnormal synchronized activity recorded in the BG-thalamocortical loops in PD. (meta-analysis of several studies).
  • STN DBS seems to reduce FR in the SNr.
  • STN excitotoxic leasion in rats causes increased impulsivity, impaired accuracy, premature responses, and more attention to food reward location in rats.
    • There is a hyperdirect pathway from the medial prefrontal cortex to the STN; breaking this decreases attention and perseverance.
    • STN HFS sometimes induces impulsive behavior in humans, with which this is consistent. (This may be sequelae from levodopa treatment).
    • STN HFS often causes weight gain in patients. But it might be because they can eat more or are more 'motivated at life'.
    • Controlled studies in rats show that STN lesion does not effect quantity consumed, either food, ehanol, or cocaine.
      • Differential effect when the reward was food vs. cocaine -- the STN may modulate the reward system based on the nature of the reward.
  • Huh: HFS of the ZI (zona incerta) has been reported to be superior to STN HFS for improving contralateral parkinsonism in PD patients.
    • Could be current diffusion into the STN, however, as lesioning this structure in rats has less effect than lesioning STN.
    • See also {1098}.
  • Chronic GPi DBS does not allow reducing L-DOPA dosage, unlike STN stimulation, but it is a good treatment for dyskinesia.
  • VIM treatment is very effective for tremor, but it does not treat the other motor symptoms of PD. Furthermore, it wears off after a few years.
    • CM/Pf seems like an even better target (Center median / parafasicular complex of the thalamus -- see {1119}.
  • DBS in the PPN (pedunculo pontine nucleus, brainstem target of the BG) at 10 HZ induces a feeling of well-being , concomitant with a modest improvement in motor function; no effect at 80 Hz.
  • Dystonia: GPi is a efficacious target for DBS.
    • Full effect takes a year (!), suggesting that the effect is through reorganization of the BG / neuroplascticity.
  • ET : lesions of the VIM, STN, or cerebellum can reduce symptoms. DBS of the VIM, STN, or ZI all have been found effective.
  • Huntington's disease involves degeneration of the projection neurons from the caudate and putamen.
    • HD affects motor, cognitive, and psychiatric functioning.
  • Drug addiction: inactivating the Nucelus accumbens (NAc) may reduce motivation to obtain the drug, but it may also reduce the motivation to do anything (apathy).
  • GPi DBS also a target for reducing chorea.
  • STN DBS may worsen treatment-resistant-depression; this seen in an animal model, and anecdotally in humans with PD.
  • OCD can be treated with DBS through the internal capsule extending toward the NAc / ventral striatum.
    • side effects include hypomania or anxiety.
    • Alas there is no satisfactory animal model of OCD, which hampers research.

____References____

[0] Gubellini P, Salin P, Kerkerian-Le Goff L, Baunez C, Deep brain stimulation in neurological diseases and experimental models: from molecule to complex behavior.Prog Neurobiol 89:1, 79-123 (2009 Sep)

{1048}
hide / / print
ref: Kim-2009.04 tags: Utah ASIC recording 2009 date: 01-15-2012 22:08 gmt revision:1 [0] [head]

PMID-19067174[0] Integrated wireless neural interface based on the Utah electrode array

  • Describes their fully integrated 100 site Utah probe.
  • "A planar power receiving coil fabricated by patterning electroplated gold films on polyimide substrates was connected to the IC by using a custom metallized ceramic spacer and SnCu reflow soldering. The SnCu soldering was also used to assemble SMD capacitors on the UEA. "

____References____

[0] Kim S, Bhandari R, Klein M, Negi S, Rieth L, Tathireddy P, Toepper M, Oppermann H, Solzbacher F, Integrated wireless neural interface based on the Utah electrode array.Biomed Microdevices 11:2, 453-66 (2009 Apr)

{911}
hide / / print
ref: Ganguly-2009.07 tags: Ganguly Carmena 2009 stable neuroprosthetic BMI control learning kinarm date: 01-14-2012 21:07 gmt revision:4 [3] [2] [1] [0] [head]

PMID-19621062 Emergence of a stable cortical map for neuroprosthetic control.

  • Question: Are the neuronal adaptations evident in BMI control stable and stored like with skilled motor learning?
    • There is mixed evidence for stationary neuron -> behavior maps in motor cortex.
      • It remains unclear if the tuning relationship for M1 neurons are stable across time; if they are not stable, rather advanced adaptive algorithms will be required.
  • A stable representation did occur.
    • Small perturbations to the size of the neuronal ensemble or to the decoder could disrupt function.
    • Compare with {291} -- opposite result?
    • A second map could be learned after primary map was consolidated.
  • Used a Kinarm + Plexon, as usual.
    • Regressed linear decoder (Wiener filter) to shoulder and elbow angle.
  • Assessed waveform stability with PCA (+ amplitude) and ISI distribution (KS test).
  • Learning occurred over the course of 19 days; after about 8 days performance reached an asymptote.
    • Brain control trajectory to target became stereotyped over the course of training.
      • Stereotyped and curved -- they propose a balance of time to reach target and effort to enforce certain firing rate profiles.
    • Performance was good even at the beginning of a day -- hence motor maps could be recalled.
  • By analyzing neuron firing wrt idealized movement to target, the relationship between neuron & movement proved to be stable.
  • Tested to see if all neurons were required for accurate control by generating an online neuron dropping curve, in which a random # of units were omitted from the decoder.
    • Removal of 3 neurons (of 10 - 15) resulted in > 50% drop in accuracy.
  • Tried a shuffled decoder as well: this too could be learned in 3-8 days.
    • Shuffling was applied by permuting the neurons-to-lags mapping. Eg. the timecourse of the lags was not changed.
  • Also tried retraining the decoder (using manual control on a new day) -- performance dropped, then rapidly recovered when the original fixed decoder was reinstated.
    • This suggests that small but significant changes in the model weights (they do not analyze what) are sufficient for preventing an established cortical map from being transformed to a reliable control signal.
  • A fair bit of effort was put into making & correcting tuning curves, which is problematic as these are mostly determined by the decoder
    • Better idea would be to analyze the variance / noise properties wrt cursor trajectory?
  • Performance was about the same for smaller (10-15) and larger (41) unit ensembles.

{971}
hide / / print
ref: Vaadia-2009.09 tags: BMI Vaadia 2009 date: 12-28-2011 20:39 gmt revision:2 [1] [0] [head]

PMID-20228862[0] Grand Challenges of Brain Computer Interfaces in the Years to Come

  • Problem 1: If you have no theory of mind, you just keep making a series of measurements.
  • EEG is like listening to lectures of millions of people ... simultaneously.
  • Single unit recordings is like probing a microchip's individual wires and trying to figure out what it's doing (this is not a good analogy, though -- brains are far more robust to part failure than a computer).
  • References Todorov 2004 PMID-15332089[1] Wolpert and Ghahramani 2000: Sensorimotor control.
  • Problem 2: noisy measurements.
    • Might be the brains problem, not us: neuronal interactions modify rapidly during sensorimotor learning. Jarosiewicz et al 2008. PMID-19047633[2]
    • Claim to have a system that learns control within 1-2 minutes or '10 seconds', coadaptively. Shpigelman 2009. bibtex:Shpigelman-2009
  • In drug resistant focal epilepsy, not only were substantial reductions in seizures reported, but also large gains in IQ and cognitive functioning were demonstrated (Kotchoubey 2001, Strehl et al 2005) after training of slow cortical potential control. Better ref: PMID-10457815[3]
  • Buch et al 2008 Demonstrated MEG BMI control. PMID-18258825[4]
  • Even with sophisticated classification solutions, EEG cannot provide much better than 2D control (Birbaumer 1990). PMID-2404287[5]
  • Ref Moritz [6]
  • Supposes that nanotechnology may ultimately find a solution -- inert nanoprobes that measure activity and transmit a compressed version.

____References____

[0] Vaadia E, Birbaumer N, Grand challenges of brain computer interfaces in the years to come.Front Neurosci 3:2, 151-4 (2009 Sep 15)
[1] Todorov E, Optimality principles in sensorimotor control.Nat Neurosci 7:9, 907-15 (2004 Sep)
[2] Jarosiewicz B, Chase SM, Fraser GW, Velliste M, Kass RE, Schwartz AB, Functional network reorganization during learning in a brain-computer interface paradigm.Proc Natl Acad Sci U S A 105:49, 19486-91 (2008 Dec 9)
[3] Thompson L, Thompson M, Neurofeedback combined with training in metacognitive strategies: effectiveness in students with ADD.Appl Psychophysiol Biofeedback 23:4, 243-63 (1998 Dec)
[4] Buch E, Weber C, Cohen LG, Braun C, Dimyan MA, Ard T, Mellinger J, Caria A, Soekadar S, Fourkas A, Birbaumer N, Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke.Stroke 39:3, 910-7 (2008 Mar)
[5] Birbaumer N, Elbert T, Canavan AG, Rockstroh B, Slow potentials of the cerebral cortex and behavior.Physiol Rev 70:1, 1-41 (1990 Jan)
[6] Moritz CT, Perlmutter SI, Fetz EE, Direct control of paralysed muscles by cortical neurons.Nature 456:7222, 639-42 (2008 Dec 4)

{922}
hide / / print
ref: Guenther-2009.12 tags: Guenther Kennedy 2009 neurotrophic electrode speech synthesize formant BMI date: 12-17-2011 02:12 gmt revision:2 [1] [0] [head]

PMID-20011034[0] A Wireless Brain-Machine Interface for Real-Time Speech Synthesis

  • Neurites grow into the glass electrode over the course of 3-4 months; the signals and neurons are henceforth stable, at least for the period prior publication (>4 years).
  • Used an FM modulator to send out the broadband neural signal; powered the implanted electronics inductively.
  • Sorted 56 spike clusters (!!)
    • quote: "We chose to err on the side of overestimating the number of clusters in our BMI since our Kalman filter decoding technique is somewhat robust to noisy inputs, whereas a stricter criterion for cluster definition might leave out information-carrying spike clusters."
    • 27 units on one wire and 29 on the other.
  • Quote: "neurons in the implanted region of left ventral premotor cortex represent intended speech sounds in terms of formant frequency trajectories, and projections from these neurons to primary motor cortex transform the intended formant trajectories into motor commands to the speech articulators."
    • Thus speech can be represented as a trajectory through formant space.
    • plus there are many simple low-load formant-based sw synthesizers
  • Used supervised methods (ridge regression), where the user was asked to imagine making vowel sounds mimicking what he heard.
    • only used the first 2 vowel formants; hence 2D task.
    • Supervised from 8 ~1-minute recording sessions.
  • 25 real-time feedback sessions over 5 months -- not much training time, why?
  • Video looks alright.

____References____

[0] Guenther FH, Brumberg JS, Wright EJ, Nieto-Castanon A, Tourville JA, Panko M, Law R, Siebert SA, Bartels JL, Andreasen DS, Ehirim P, Mao H, Kennedy PR, A wireless brain-machine interface for real-time speech synthesis.PLoS One 4:12, e8218 (2009 Dec 9)

{920}
hide / / print
ref: Arfin-2009.07 tags: ICMS birdsong wireless stimulation ARfin 2009 MIT date: 12-16-2011 04:21 gmt revision:1 [0] [head]

PMID-19386759[0] Wireless neural stimulation in freely behaving small animals.

  • Made a custom ASIC for delivering bipolar, biphasic current pulses.
  • 32 output channels.
  • Powered by small batteries
  • device in sleep state when not in use
  • controlled by inductive radio transfer with PWM modulation scheme.
  • Tested in Zebra finches, HVC: terminates song in all birds tested.
  • Impressive bit of engineering!

____References____

[0] Arfin SK, Long MA, Fee MS, Sarpeshkar R, Wireless neural stimulation in freely behaving small animals.J Neurophysiol 102:1, 598-605 (2009 Jul)