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Archive for the ‘Brain-Computer Interface’ Category

In the past 20 years, deep brain stimulation (DBS) has been used for over 100,000 patients with Parkinson’s disease. The success of this procedure has led investigators to try DBS for other neurologic conditions, such as Alzheimer’s disease (AD).

In 2016, Lozano et al reported on one of the largest trials for DBS in AD, the “ADvance” trial, in which they targeted the fornix, a bundle of nerve fibers in the center of the brain that is the major output tract of the hippocampus.

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This was a well-run, double-blind, randomized study. One of the nice aspects about brain stimulation trials is the ease of performing a sham stimulation arm. That is, treatment can be randomly turned either “on” and “off” for a period of time, allowing a subset of participants to serve as controls (stimulation turned “off”) for a period of time before they actually do get the stimulation (stimulation turned “on”) in case it is actually helpful.

In terms of the trial results, one of the patients (out of 42) had an implant infection. Overall, the trial did not show a significant benefit mitigating the decline in ADAS-13 or CDR-SB scores (measures of cognitive function):

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Lozano et al 2016; doi: 10.3233/JAD-160017

While this trial did not show efficacy at their sample sizes, personally I expect that DBS for early AD could work to at least alleviate symptoms, if the right circuits were targeted at the right time.

My reasoning here is that we know that a few other cognitive strategies can help slow the course of AD, including processing speed training and acetylcholinesterase inhibitors.

There are at least 4 active DBS trials for AD on clinicaltrials.gov:

It will be interesting to monitor this growing field in the coming years.

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The history of neuroscience in general, and myelination in particular, is replete with comparisons between brains and computers.

For example, the first suggested function of myelin in the 1850s as an insulator of electricity was made by analogy to electric wires, which had just recently been developed.

In today’s high performance computers (“supercomputers”), one of the big bottlenecks in computer processing speed is communication between processors and memory units.

For example, one measure of computer communication speed is traversed edges per second (TEPS). This quantifies the speed with which data can be transferred between nodes of a computer graph.

A standard measure of TEPS is Graph500, which quantifies computer performance in a breadth-first search task on a large graph, and can require up to 1.1 PB of RAM. As of June 2016, these are the known supercomputers with the most TEPS:

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I’m pointing all of this out to give some concrete context about TEPS. Here’s the link to neuroscience: as AI Impacts discussed a couple of years ago, it seems that TEPS is a good way to quantify how fast brains can operate.

The best evidence for this is the growing body of data that memory and cognition require recurrent communication loops both within and between brain regions. For example, stereotyped electrical patterns with functional correlates can be seen in hippocampal-cortical and cortical-hippocampal-cortical circuits.

Here’s my point: we know that myelin is critical for regulating the speed at which between-brain region communication occurs. So, what we have learned about the importance of communication between processors in computers suggests that the degree of myelination is probably more important to human cognition than is commonly recognized. This in turn suggests:

  1. An explanation for why human cognition appears to be better in some ways than primates: human myelination patterns are much more delayed, allowing for more plasticity in development. Personally, I expect that this explain more human-primate differences in cognition than differences in neurons (granted I’m not an expert in this field!).
  2. Part of an explanation for why de- and dys-myelinating deficits, even when they are small, can affect cognition in profound ways.

 

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In the same review I mentioned yesterday, the authors discuss the potential of combining optogenetic stimulation with electrical recording arrays.

There are many advantages to the light and opsin-based approach as compared to the electrical stimulation one, including:

1) More (dynamic) control over the size of the area in the CNS affected.

2) The ability to target specific cell types with different gene implantation techniques.

3) The light stimulation would not cause electrical interference, thus eliminating recording artifacts and allowing for continuous feedback.

4) The stimulation can be precisely controlled by varying the light intensity.

The chief drawback to the approach, and it is a big one, is that the technique would rely upon the precise genetic manipulation of human cells.

Despite being in clinical trials for 21 years, according to the U.S. Department of Energy Genome Programs, a gene therapy treatment has yet to be approved by the FDA.

Neurons do have some particular advantages for gene therapy, however, because they are terminally differentiated (i.e., they no longer divide). Plus, optogenetics has already been used successfully in at least two monkeys.

The potential of optogenetics to help treat brain disorders provides an often overlooked reason to avidly support research into gene therapy.

Reference

Gilja V, et al. 2011 Challenges and Opportunities for Next-Generation Intracortically Based Neural Prostheses. IEEE TBE. doi:  10.1109/TBME.2011.2107553.

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The use of neural prosthetics is surprisingly widespread. For example, according to the FDA, as of Dec ’10 more than 200,000 patients have received cochlear implants for hearing impairments. A summer ’11 report indicates that over 70,000 patients have been treated with deep brain stimulation (DBS) for movement disorders such as Parkinson’s.

DBS has also been used experimentally. A Mar ’10 review summarizes its effects on more than 200 patients with treatment resistant depression and/or OCD. And an Aug ’10 review refers to studies of its efficacy on around 200 patients with various forms of epilepsy.

A review from the beginning of this year, which just came to my attention, discusses some of the challenges involved in making neural prostheses robust over several decades. Three of the problems they mention are:

  • electrode encapsulation, whereby native tissue forms a glial scar and renders the electrode non-functional
  • motion of the surrounding tissue relative to the stationary electrode, due to head movements or pressure changes during respiration
  • material or engineering failures, which would presumably decrease in frequency with more widespread use

One of the other problems that the authors discuss is electrical interference, which causes artifacts and thus occasional blackout periods of the electrode’s ability to record signals. The lack of continuous recording would hinder bidirectional feedback.

It remains to be seen whether these problems in endurance and capacity can be overcome with the electrode approach, and, perhaps more realistically, over what timescale they can be addressed.

Reference

Gilja V, et al. 2011 Challenges and Opportunities for Next-Generation Intracortically Based Neural Prostheses. IEEE TBE. doi:  10.1109/TBME.2011.2107553.

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Book notes Neuroengineering

I learned a good amount from reading this collection of articles but it would have been better if the book were collected into a coherent whole instead of being so fragmented. Here are summaries of random parts of the book:

Passing electrical current through tissues can stimulate neurons to produce action potentials. In vitro neuron data indicates that action potential initiation occurs in the axon even when the electrode is placed the cell body or dendrites. The electrode is usually placed around 1 mm away from the cell body. Cathode and anode stimulation have different mechanisms but they both eventually lead the depolarization of axons and thus stimulate action potentials in some form. Passing axons near to the electrode may also be stimulated as well as local neurons–this is especially true of cathodic pulses and needs to be taken into account. Moreover, since the action potential is initiated in the axon and not the cell body, extracellular unit recordings of the cell body’s electrical potential may not accurately reflect the neuron’s action potential output.

Deep brain stimulation uses an electrode (aka, a brain pacemaker) implanted in the subthalamic nucleus to stimulate electrical activity to relieve symptoms of Parkinson’s disease, pain, and other neurological disorders. One explanation for its efficay is that neurons are functionally deafferentated by the electrical stimulation, thus limiting the propagation of tremor signals without disrupting other information pathways in the brain.

Studying DBS has yielded some principles that should be true no matter where in the brain the stimulating electrode is placed. The proximate effect of DBS will be axon and dendrite fiber excitation, and it will depend upon their ability to transmit the signal. Below frequencies of ~ 50 Hz the signal should be transduced with high fidelity, but above ~ 100 Hz axons may fail to conduct the signal properly, and synapses may not be able to recycle neurotransmitters in time. This makes sense given that that’s a lot of chemistry that needs to occur more than 100 times per second!

Another interesting avenue is brain-computer interfaces, which change electrophysiological signals (like an EEG rhythm or neuronal firing rate) into a real-world output. Current signal detection methods include EEG, scalp recordings, ECoG, field potentials measured by electrodes, and single units that measured the action potentials of individual neurons. In order to shift the time scale from 1-2 seconds to 2-400 ms, penetrating electrodes that record field potentials or individuals units must be used. Penentrating electrodes that stimulate individual neurons run into issues of glial scarring and general problems with respect to biocompatability, but bioelectrodes and maybe even carbon nanotubes should overcome these problems eventually. ECoG based systems which place electrodes directly below the skin have a 5 times greater magnitude than EEG as well as a wider frequency range. Because ECoG systems also avoid the biocompatability issues of single-unit microelectrodes, they probably have the greatest clinical potential.

Noninvasive sensors will probably required in order for BCIs to become more mainstream. Unfortunately, amplification and recording or spikes noninvasively is not yet possible. Possibly some subset of neurons could be modified and could then turn electrical signals into light of a given intensity so that an external optical sensor could detect it. One possibility way to accomplish this is fluorescent seminconductor quantum dots, but that possibility is only in a theoretical stage currently.

Another interesting technique that the book reviewed was optical nerve stimulation. This is the transient deposition of energy in the form of light leading to an action potential in neural tissue.  A pulsed low energy laser beam has been shown to elicit neural action potentials indistinguishable from conventional electrical approaches on rat sciatic nerves in vivo, with far superior spatial precision and no nerve trauma due to contact. The wavelength of the photon will determine the penetration depth of the simulation, the lazer spot size can be varied down to several micrometers by changing the optical fiber diameter, and the irradiation that the tissue will experience can be varied too.

Since no specific wavelength has been found that always causes stimulation to occur, a single chromophore could not be possible for the direct photochemical effects of the laser. Also in terms of the mechanism of the laser, nerve temperature has been found to increase linearly with laser radiant exposure. So the neuronal activation may be photothermally mediated. As light energy is converted to heat, it causes a temperature gradient in time and space that is relaxed after ~ 90 ms. The molecular mechanism of action has yet to be identified, but the idea that a heat gradient may cause the action potential is very interesting. Importantly, the amount of radiant exposure needed to stimulate neurons (0.3 – 0.4 J/cm^2) is below the energy level that will deal damage to histological tissue (0.8 – 1.0 J/cm^2), making this a clinically viable technique.

Transcranial magnetic stimulation of deep brain regions is also reviewed. This technique relies on an induced electrical field that depends on a time varying magentic field which is generated by rate of change of current (dI/dt) in a bank of capacitors. The coils need to be oriented to produce an E field tangent to the surface of the skull and in the preferred direction of the neurons or axons under consideration. This is another super cool technique. The limiting factor here is often that the intensity of the magnetic field necessary to stimulate the deepest brain regions might cause facial pain or contractions as well as a risk of epileptic seizures.

Most of the techniques reviewed in the book aren’t as new as some might make you believe, although there has been lots of progress in recent years. DARPA grants may spur research into the field, and the book notes that the governments of Japan and France are making BCI’s a national research priority as well. Generally, neuroengineering is a highly technical pursuit that has enormous implications in the long run.

Reference

Editors: DiLorenzo DJ, Bronzino JD. 2008 Neuroengineering. CRC Press, Boca Raton. Amazon Link here.

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Airan and colleagues describe the work that they have done to genetically couple optic receptors to g-proteins (like cAMP) inside neuronal molecules. After developing the coupled receptor, they first show that their technique can recruit the same amount of cAMP after 60 seconds of exposure to light as the cell emits after 5 minute exposure to 10 microM of noepinephrine. Their other coupled receptor recruits the same amount of inositol trisphosphate after 60 seconds of light exposure than the similarly compatible type of noepinpephine exposure. So basically they engineered a way to control the stimulation of individual neurons in two pathways via the differential elevation of light.

After showing that their optogenetic approach can work to stimulate mice neurons in vivo, the researchers go on to show that it can modulate behavior in a learning paradigm. By optically controlling neurons in the nucleus accumbens with a 473 nm laser diode inserted in the brain, they caused mice to show a significant preference to move into one chamber over another as compared to control mice. In order to accomplish this, they delivered a laser-diode-coupled optical fibre to the accumbens with light pulses at 10 Hz, simulating the monoaminergic input that would likely follow a strong reward. This is a powerful new method for learning and behavior research and hopefully the technique and its probable iterations (like a wireless approach) will soon become widespread.

Reference

Airan DG, et al. 2009 Temporally precise in vivo control of intracellular signalling. Nature, doi:10.1038/nature07926.

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Discover Magazine has a refreshing piece about how it is a good thing that we are delegating some of our mental brainpower to google searches. As the article explains,

Scientists have found that when test monkeys spent five minutes learning how to use a rake, some of the neurons in their hands began behaving in a new way. They began to fire in response to stimuli at the end of the rake, not on the monkey’s hand. Other neurons, in the brain, respond to things that appear to lie within arm’s reach. Training the monkeys to use the rakes caused these neurons to change—reacting to objects lying within rake’s reach rather than arm’s reach.

This is the optimistic approach to the future of brain-computer interfaces, and the preliminary evidence is fairly positive. Nevertheless, there will probably be many roadblocks ahead.

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