Measuring deep-brain neurons’ electrical signals at high speed with light instead of electrodes

MIT researchers have developed a light-sensitive protein that can be embedded into neuron membranes, where it emits a fluorescent signal that indicates how much voltage a particular cell is experiencing. This could allow scientists to more effectively study how neurons behave, millisecond by millisecond, as the brain performs a particular function. (credit: Courtesy of the researchers)

Researchers at MIT have developed a new approach to measure electrical activity deep in the brain: using light — an easier, faster, and more informative method than inserting electrodes.

They’ve developed a new light-sensitive protein that can be embedded into neuron membranes, where it emits a fluorescent signal that indicates how much voltage a particular cell is experiencing. This could allow scientists to study how neurons behave, millisecond by millisecond, as the brain performs a particular function.

Better than electrodes. “If you put an electrode in the brain, it’s like trying to understand a phone conversation by hearing only one person talk,” says Edward Boyden*, Ph.D., an associate professor of biological engineering and brain and cognitive sciences at MIT and a pioneer in optogenetics (a technique that allows scientists to control neurons’ electrical activity with light by engineering them to express light-sensitive proteins). Boyden is the senior author of the study, which appears in the Feb. 26 issue of Nature Chemical Biology.

“Now we can record the neural activity of many cells in a neural circuit and hear them as they talk to each other,” he says. The new method is also more effective than current optogenetics methods, which also use light-sensitive proteins to silence or stimulate neuron activity.

“Imaging of neuronal activity using voltage sensors opens up the exciting possibility for simultaneous recordings of large populations of neurons with single-cell single-spike resolution in vivo,” the researchers report in the paper.

Robot-controlled protein evolution. For the past two decades, Boyden and other scientists have sought a way to monitor electrical activity in the brain through optogenetic imaging, instead of recording with electrodes. But fluorescent molecules used for this kind of imaging have been limited in their speed of response, sensitivity to changes in voltage, and resistance to photobleaching (fading caused by exposure to light).

Instead, Boyden and his colleagues built a robot to screen millions of proteins. They generated the appropriate proteins for the traits they wanted by using a process called “directed protein evolution.” To demonstrate the power of this approach, they then narrowed down the evolved protein versions to a top performer, which they called “Archon1.” After the Archon1 gene is delivered into a cell, the expressed Archon1 protein embeds itself into the cell membrane — the ideal place for accurate measurement of a cell’s electrical activity.

Using light to measure neuron voltages. When the Archon1 cells are then exposed to a certain wavelength of reddish-orange light, the protein emits a longer wavelength of red light, and the brightness of that red light corresponds to the voltage (in millivolts) of that cell at a given moment in time. The researchers were able to use this method to measure electrical activity in mouse brain-tissue slices, in transparent zebrafish larvae, and in the transparent worm C. elegans (being transparent makes it easy to expose these organisms to light and to image the resulting fluorescence).

The researchers are now working on using this technology to measure brain activity in live mice as they perform various tasks, which Boyden believes should allow for mapping neural circuits and discovering how the circuits produce specific behaviors. “We will be able to watch a neural computation happen,” he says. “Over the next five years or so, we’re going to try to solve some small brain circuits completely. Such results might take a step toward understanding what a thought or a feeling actually is.”

The researchers also showed that Archon1 can be used in conjunction with current optogenetics methods. In experiments with C. elegans, the researchers demonstrated that they could stimulate one neuron using blue light and then use Archon1 to measure the resulting effect in neurons that receive input from that cell.

Detecting electrical activity at millisecond-speed. Harvard professor Alan Cohen, who developed the predecessor to Archon1, says the new protein brings scientists closer to the goal of imaging electrical activity in live brains at a millisecond timescale (1,000 measurements per second).

“Traditionally, it has been excruciatingly labor-intensive to engineer fluorescent voltage indicators, because each mutant had to be cloned individually and then tested through a slow, manual patch-clamp electrophysiology measurement,” says Cohen, who was not involved in this study.  “The Boyden lab developed a very clever high-throughput screening approach to this problem. Their new reporter looks really great in fish and worms and in brain slices. I’m eager to try it in my lab.”

The research was funded by the HHMI-Simons Faculty Scholars Program, the IET Harvey Prize, the MIT Media Lab, the New York Stem Cell Foundation Robertson Award, the Open Philanthropy Project, John Doerr, the Human Frontier Science Program, the Department of Defense, the National Science Foundation, and the National Institutes of Health, including an NIH Director’s Pioneer Award.

* Boyden is also a member of MIT’s Media Lab, McGovern Institute for Brain Research, and Koch Institute for Integrative Cancer Research, and an HHMI-Simons Faculty Scholar.

** The researchers made 1.5 million mutated versions of a light-sensitive protein called QuasAr2 (previously engineered by Adam Cohen’s lab at Harvard University and based on the molecule Arch, which the Boyden lab reported in 2010). The researchers put each of those genes into mammalian cells (one mutant per cell), then grew the cells in lab dishes and used an automated microscope to take pictures of the cells. The robot was able to identify cells with proteins that met the criteria the researchers were looking for, the most important being the protein’s location within the cell and its brightness. The research team then selected five of the best candidates and did another round of mutation, generating 8 million new candidates. The robot picked out the seven best of these, which the researchers then narrowed down to Archon1.


Abstract of A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters

We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created a genetically encoded fluorescent voltage indicator, simultaneously optimizing its brightness and membrane localization using our microscopy-guided cell-picking strategy. We produced the high-performance opsin-based fluorescent voltage reporter Archon1 and demonstrated its utility by imaging spiking and millivolt-scale subthreshold and synaptic activity in acute mouse brain slices and in larval zebrafish in vivo. We also measured postsynaptic responses downstream of optogenetically controlled neurons in C. elegans.

Low-cost EEG can now be used to reconstruct images of what you see

(left:) Test image displayed on computer monitor. (right:) Image captured by EEG and decoded. (credit: Dan Nemrodov et al./eNeuro)

A new technique developed by University of Toronto Scarborough neuroscientists has, for the first time, used EEG detection of brain activity in reconstructing images of what people perceive.

The new technique “could provide a means of communication for people who are unable to verbally communicate,” said Dan Nemrodov, Ph.D., a postdoctoral fellow in Assistant Professor Adrian Nestor’s lab at U of T Scarborough. “It could also have forensic uses for law enforcement in gathering eyewitness information on potential suspects, rather than relying on verbal descriptions provided to a sketch artist.”

(left:) EEG electrodes used in the study (photo credit: Ken Jones). (right in red:) The area where the images were detected, the occipital lobe, is the visual processing center of the mammalian brain, containing most of the anatomical region of the visual cortex. (credit: CC/Wikipedia)

For the study, test subjects were shown images of faces while their brain activity was detected by EEG (electroencephalogram) electrodes over the occipital lobe, the visual processing center of the brain. The data was then processed by the researchers, using a technique based on machine learning algorithms that allowed for digitally recreating the image that was in the subject’s mind.

More practical than fMRI for reconstructing brain images

This new technique was pioneered by Nestor, who successfully reconstructed facial images from functional magnetic resonance imaging (fMRI) data in the past.

According to Nemrodov, techniques like fMRI — which measures brain activity by detecting changes in blood flow — can grab finer details of what’s going on in specific areas of the brain, but EEG has greater practical potential given that it’s more common, portable, and inexpensive by comparison.

While fMRI captures activity at the time scale of seconds, EEG captures activity at the millisecond scale, he says. “So we can see, with very fine detail, how the percept of a face develops in our brain using EEG.” The researchers found that it takes the brain about 120 milliseconds (0.12 seconds) to form a good representation of a face we see, but the important time period for recording starts around 200 milliseconds, Nemrodov says. That’s followed by machine-learning processing to decode the image.*

This study provides validation that EEG has potential for this type of image reconstruction, notes Nemrodov, something many researchers doubted was possible, given its apparent limitations.

Clinical and forensic uses

“The fact we can reconstruct what someone experiences visually based on their brain activity opens up a lot of possibilities,” says Nestor. “It unveils the subjective content of our mind and it provides a way to access, explore, and share the content of our perception, memory, and imagination.”

Work is now underway in Nestor’s lab to test how EEG could be used to reconstruct images from a wider range of objects beyond faces — even to show “what people remember or imagine, or what they want to express,” says Nestor. (A new creative tool?)

The research, which is published (open-access) in the journal eNeuro, was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) and by a Connaught New Researcher Award.

* “After we obtain event-related potentials (ERPs) [the measured brain response from a visual sensory event, in this case] — we use a support vector machine (SVM) algorithm to compute pairwise classifications of the visual image identities,” Nemrodov explained to KurzweilAI. “Based on the resulting dissimilarity matrix, we build a face space from which we estimate in a pixel-wise manner the appearance of every individual left-out (to avoid circularity) face. We do it by a linear combination of the classification images plus the origin of the face space.” The method is based on a former study: Nestor, A., Plaut, D. C., & Behrmann, M. (2016). Feature-based face representations and image reconstruction from behavioral and neural data. Proceedings of the National Academy of Sciences. 25 113: 416-421.


University of Toronto Scarborough | Do you see what I see? Harnessing brain waves can help reconstruct mental images


Nature Video | Reading minds


Abstract of The Neural Dynamics of Facial Identity Processing: insights from EEG-Based Pattern Analysis and Image Reconstruction

Uncovering the neural dynamics of facial identity processing along with its representational basis outlines a major endeavor in the study of visual processing. To this end, here we record human electroencephalography (EEG) data associated with viewing face stimuli; then, we exploit spatiotemporal EEG information to determine the neural correlates of facial identity representations and to reconstruct the appearance of the corresponding stimuli. Our findings indicate that multiple temporal intervals support: facial identity classification, face space estimation, visual feature extraction and image reconstruction. In particular, we note that both classification and reconstruction accuracy peak in the proximity of the N170 component. Further, aggregate data from a larger interval (50-650 ms after stimulus onset) support robust reconstruction results, consistent with the availability of distinct visual information over time. Thus, theoretically, our findings shed light on the time course of face processing while, methodologically, they demonstrate the feasibility of EEG-based image reconstruction.

Neuroscientists reverse Alzheimer’s disease in mice

The brain of a 10-month-old mouse with Alzheimer’s disease (left) is full of amyloid plaques (red). These hallmarks of Alzheimer’s disease are reversed in animals that have gradually lost the BACE1 enzyme (right). (credit: Hu et al., 2018)

Researchers from the Cleveland Clinic Lerner Research Institute have completely reversed the formation of amyloid plaques in the brains of mice with Alzheimer’s disease by gradually depleting an enzyme called BACE1. The procedure also improved the animals’ cognitive function.

The study, published February 14 in the Journal of Experimental Medicine, raises hopes that drugs targeting this enzyme will be able to successfully treat Alzheimer’s disease in humans.


Background: Serious side effects

One of the earliest events in Alzheimer’s disease is an abnormal buildup of beta-amyloid peptide, which can form large, amyloid plaques in the brain and disrupt the function of neuronal synapses. The BACE1 (aka beta-secretase) enzyme helps produce beta-amyloid peptide by cleaving (splitting) amyloid precursor protein (APP). So drugs that inhibit BACE1 are being developed as potential Alzheimer’s disease treatments. But that’s a problem because BACE1 also controls many important neural processes; accidental cleaving of other proteins instead of APP could lead these drugs to have serious side effects. For example, mice completely lacking BACE1 suffer severe neurodevelopmental defects.


A genetic-engineering solution

To deal with the serious side effects, the researchers generated mice that gradually lose the BACE1 enzyme as they grow older. These mice developed normally and appeared to remain perfectly healthy over time. The researchers then bred these rodents with mice that start to develop amyloid plaques and Alzheimer’s disease when they are 75 days old.

The resulting offspring’s BACE1 levels were approximately 50% lower than normal (but also formed plaques at this age). However, as these mice continued to age and lose BACE1 activity, there were lower beta-amyloid peptide levels and the plaques began to disappear. At 10 months old, the mice had no plaques in their brains. Loss of BACE1 also improved the learning and memory of mice with Alzheimer’s disease.

“To our knowledge, this is the first observation of such a dramatic reversal of amyloid deposition in any study of Alzheimer’s disease mouse models,” says senior author Riqiang Yan, who will become chair of the department of neuroscience at the University of Connecticut this spring.

Decreasing BACE1 activity also reversed other hallmarks of Alzheimer’s disease, such as activation of microglial cells and the formation of abnormal neuronal processes.

However, the researchers also found that depletion of BACE1 only partially restored synaptic function, suggesting that BACE1 may be required for optimal synaptic activity and cognition.

“Our study provides genetic evidence that preformed amyloid deposition can be completely reversed after sequential and increased deletion of BACE1 in the adult,” says  Yan. “Our data show that BACE1 inhibitors have the potential to treat Alzheimer’s disease patients without unwanted toxicity. Future studies should develop strategies to minimize the synaptic impairments arising from significant inhibition of BACE1 to achieve maximal and optimal benefits for Alzheimer’s patients.”


Abstract of BACE1 deletion in the adult mouse reverses preformed amyloid deposition and improves cognitive functions

BACE1 initiates the generation of the β-amyloid peptide, which likely causes Alzheimer’s disease (AD) when accumulated abnormally. BACE1 inhibitory drugs are currently being developed to treat AD patients. To mimic BACE1 inhibition in adults, we generated BACE1 conditional knockout (BACE1fl/fl) mice and bred BACE1fl/fl mice with ubiquitin-CreER mice to induce deletion of BACE1 after passing early developmental stages. Strikingly, sequential and increased deletion of BACE1 in an adult AD mouse model (5xFAD) was capable of completely reversing amyloid deposition. This reversal in amyloid deposition also resulted in significant improvement in gliosis and neuritic dystrophy. Moreover, synaptic functions, as determined by long-term potentiation and contextual fear conditioning experiments, were significantly improved, correlating with the reversal of amyloid plaques. Our results demonstrate that sustained and increasing BACE1 inhibition in adults can reverse amyloid deposition in an AD mouse model, and this observation will help to provide guidance for the proper use of BACE1 inhibitors in human patients.

Are you a cyborg?

Bioprinting a brain

Cryogenic 3D-printing soft hydrogels. Top: the bioprinting process. Bottom: SEM image of general microstructure (scale bar: 100 µm). (credit: Z. Tan/Scientific Reports)

A new bioprinting technique combines cryogenics (freezing) and 3D printing to create geometrical structures that are as soft (and complex) as the most delicate body tissues — mimicking the mechanical properties of organs such as the brain and lungs.

The idea: “Seed” porous scaffolds that can act as a template for tissue regeneration (from neuronal cells, for example), where damaged tissues are encouraged to regrow — allowing the body to heal without tissue rejection or other problems. Using “pluripotent” stem cells that can change into different types of cells is also a possibility.

Smoothy. Solid carbon dioxide (dry ice) in an isopropanol bath is used to rapidly cool hydrogel ink (a rapid liquid-to-solid phase change) as it’s extruded, yogurt-smoothy-style. Once thawed, the gel is as soft as body tissues, but doesn’t collapse under its own weight — a previous problem.

Current structures produced with this technique are “organoids” a few centimeters in size. But the researchers hope to create replicas of actual body parts with complex geometrical structures — even whole organs. That could allow scientists to carry out experiments not possible on live subjects, or for use in medical training, replacing animal bodies for surgical training and simulations. Then on to mechanobiology and tissue engineering.

Source: Imperial College London, Scientific Reports (open-access).

How to generate electricity with your body

Bending a finger generates electricity in this prototype device. (credit: Guofeng Song et al./Nano Energy)

A new triboelectric nanogenerator (TENG) design, using a gold tab attached to your skin, will convert mechanical energy into electrical energy for future wearables and self-powered electronics. Just bend your finger or take a step.

Triboelectric charging occurs when certain materials become electrically charged after coming into contact with a different material. In this new design by University of Buffalo and Chinese scientists, when a stretched layer of gold is released, it crumples, creating what looks like a miniature mountain range. An applied force leads to friction between the gold layers and an interior PDMS layer, causing electrons to flow between the gold layers.

More power to you. Previous TENG designs have been difficult to manufacture (requiring complex lithography) or too expensive. The new 1.5-centimeters-long prototype generates a maximum of 124 volts but at only 10 microamps. It has a power density of 0.22 millwatts per square centimeter. The team plans larger pieces of gold to deliver more electricity and a portable battery.

Source: Nano Energy. Support: U.S. National Science Foundation, the National Basic Research Program of China, National Natural Science Foundation of China, Beijing Science and Technology Projects, Key Research Projects of the Frontier Science of the Chinese Academy of Sciences ,and National Key Research and Development Plan.

This artificial electrical eel may power your implants

How the eel’s electrical organs generate electricity by moving sodium (Na) and potassium (K) ions across a selective membrane. (credit: Caitlin Monney)

Taking it a giant (and a bit scary) step further, an artificial electric organ, inspired by the electric eel, could one day power your implanted implantable sensors, prosthetic devices, medication dispensers, augmented-reality contact lenses, and countless other gadgets. Unlike typical toxic batteries that need to be recharged, these systems are soft, flexible, transparent, and potentially biocompatible.

Doubles as a defibrillator? The system mimicks eels’ electrical organs, which use thousands of alternating compartments with excess potassium or sodium ions, separated by selective membranes. To create a jolt of electricity (600 volts at 1 ampere), an eel’s membranes allow the ions to flow together. The researchers built a similar system, but using sodium and chloride ions dissolved in a water-based hydrogel. It generates more than 100 volts, but at safe low current — just enough to power a small medical device like a pacemaker.

The researchers say the technology could also lead to using naturally occurring processes inside the body to generate electricity, a truly radical step.

Source: Nature, University of Fribourg, University of Michigan, University of California-San Diego. Funding: Air Force Office of Scientific Research, National Institutes of Health.

E-skin for Terminator wannabes

A section of “e-skin” (credit: Jianliang Xiao / University of Colorado Boulder)

A new type of thin, self-healing, translucent “electronic skin” (“e-skin,” which mimicks the properties of natural skin) has applications ranging from robotics and prosthetic development to better biomedical devices and human-computer interfaces.

Ready for a Terminator-style robot baby nurse? What makes this e-skin different and interesting is its embedded sensors, which can measure pressure, temperature, humidity and air flow. That makes it sensitive enough to let a robot take care of a baby, the University of Colorado mechanical engineers and chemists assure us. The skin is also rapidly self-healing (by reheating), as in The Terminator, using a mix of three commercially available compounds in ethanol.

The secret ingredient: A novel network polymer known as polyimine, which is fully recyclable at room temperature. Laced with silver nanoparticles, it can provide better mechanical strength, chemical stability and electrical conductivity. It’s also malleable, so by applying moderate heat and pressure, it can be easily conformed to complex, curved surfaces like human arms and robotic hands.

Source: University of Colorado, Science Advances (open-access). Funded in part by the National Science Foundation.

Altered Carbon

Vertebral cortical stack (credit: Netflix)

Altered Carbon takes place in the 25th century, when humankind has spread throughout the galaxy. After 250 years in cryonic suspension, a prisoner returns to life in a new body with one chance to win his freedom: by solving a mind-bending murder.

Resleeve your stack. Human consciousness can be digitized and downloaded into different bodies. A person’s memories have been encapsulated into “cortical stack” storage devices surgically inserted into the vertebrae at the back of the neck. Disposable physical bodies called “sleeves” can accept any stack.

But only the wealthy can acquire replacement bodies on a continual basis. The long-lived are called Meths, as in the Biblical figure Methuselah. The uber rich are also able to keep copies of their minds in remote storage, which they back up regularly, ensuring that even if their stack is destroyed, the stack can be resleeved (except for periods of time not backed up — as in the hack-murder).

Source: Netflix. Premiered on February 2, 2018. Based on the 2002 novel of the same title by Richard K. Morgan.

 

 

 

 

 

How to shine light deeper into the brain

Near-infrared (NIR) light can easily pass through brain tissue with minimal scattering, allowing it to reach deep structures. There, up-conversion nanoparticles (UCNPs; blue) previously inserted in the tissue can absorb this light to generate shorter-wavelength blue-green light that can activate nearby neurons. (credit: RIKEN)

An international team of researchers has developed a way to shine light at new depths in the brain. It may lead to development of new, non-invasive clinical treatments for neurological disorders and new research tools.

The new method extends the depth that optogenetics — a method for stimulating neurons with light — can reach. With optogenetics, blue-green light is used to turn on “light-gated ion channels” in neurons to stimulate neural activity. But blue-green light is heavily scattered by tissue. That limits how deep the light can reach and currently requires insertion of invasive optical fibers.

The researchers took a new approach to brain stimulation, as they reported in Science on February 9.

  1. They used longer-wavelength (650 to 1350nm) near-infrared (NIR) light, which can penetrate deeper into the brain (via the skull) of mice.
  2. The NIR light illuminated “upconversion nanoparticles” (UCNPs), which absorbed the near-infrared laser light and glowed blue-green in formerly inaccessible (deep) targeted neural areas.*
  3. The blue-green light then triggered (via chromophores, light-responsive molecules) ion channels in the neurons to turn on memory cells in the hippocampus and other areas. These included the medial septum, where nanoparticle-emitted light contributed to synchronizing neurons in a brain wave called the theta cycle.**

Non-invasive activation of neurons in the VTA, a reward center of the mouse brain. The blue-light sensitive ChR2 chromophores (green) were expressed (from an injection) on both sides of the VTA. But upconversion nanoparticles (blue) were only injected on the right. So when near-IR light was applied to both sides, it only activated the expression of the activity-induced chromophore cFos gene (red) on the side with the nanoparticles. (credit: RIKEN)

This study was a collaboration between scientists at the RIKEN Brain Science Institute, the National University of Singapore, the University of Tokyo, Johns Hopkins University, and Keio University.

Non-invasive light therapy

“Nanoparticles effectively extend the reach of our lasers, enabling ‘remote’ delivery of light and potentially leading to non-invasive therapies,” says Thomas McHugh, research group leader at the RIKEN Brain Science Institute in Japan. In addition to activating neurons, UCNPs can also be used for inhibition. In this study, UCNPs were able to quell experimental seizures in mice by emitting yellow light to silence hyperexcitable neurons.

Schematic showing near-infrared radiation (NIR) being absorbed by upconversion nanoparticles (UCNPs) and re-radiated as shorter-wavelength (peaking at 450 and 475 nm) blue light that triggers a previously injected chromophore (a light emitting molecule expressed by neurons) — in this case, channelrhodopsin-2 (ChR2). In one experiment, the chromophore triggered a calcium ion channel in neurons in the ventral tegmental area (VTA) of the mouse brain (a region located ~4.2 mm below the skull), causing stimulation of neurons. (credit: Shuo Chen et al./Science)

While current deep brain stimulation is effective in alleviating specific neurological symptoms, it lacks cell-type specificity and requires permanently implanted electrodes, the researchers note.

The nanoparticles described in this study are compatible with the various light-activated channels currently in use in the optogenetics field and can be employed for neural activation or inhibition in many deep brain structures. “The nanoparticles appear to be quite stable and biocompatible, making them viable for long-term use. Plus, the low dispersion means we can target neurons very specifically,” says McHugh.

However, “a number of challenges must be overcome before this technique can be used in patients,” say Neus Feliu et al. in “Toward an optically controlled brain, Science  09 Feb 2018. “Specifically, neurons have to be transfected with light-gated ion channels … a substantial challenge [and] … placed close to the target neurons. … Neuronal networks undergo continuous changes [so] the stimulation pattern and placement of [nanoparticles] may have to be adjusted over time. … Potent upconverting NPs are also needed … [which] may change properties over time, such as structural degradation and loss of functional properties. … Long-term toxicity studies also need to be carried out.”

* “The lanthanide-doped up-conversion nanoparticles (UCNPs) were capable of converting low-energy incident NIR photons into high-energy visible emission with an efficiency orders of magnitude greater than that of multiphoton processes. … The core-shell UCNPs exhibited a characteristic up-conversion emission spectrum peaking at 450 and 475 nm upon excitation at 980 nm. Upon transcranial delivery of 980-nm CW laser pulses at a peak power of 2.0 W (25-ms pulses at 20 Hz over 1 s), an upconverted emission with a power density of ~0.063 mW/mm2 was detected. The conversion yield of NIR to blue light was ~2.5%. NIR pulses delivered across a wide range of laser energies to living tissue result in little photochemical or thermal damage.” — Shuo Chen et al./Science

** “Memory recall in mice also persisted in tests two weeks later. This indicates that the UCNPs remained at the injection site, which was confirmed through microscopy of the brains.” — Shuo Chen et al./Science

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Abstract of Near-infrared deep brain stimulation via upconversion nanoparticle–mediated optogenetics

Optogenetics has revolutionized the experimental interrogation of neural circuits and holds promise for the treatment of neurological disorders. It is limited, however, because visible light cannot penetrate deep inside brain tissue. Upconversion nanoparticles (UCNPs) absorb tissue-penetrating near-infrared (NIR) light and emit wavelength-specific visible light. Here, we demonstrate that molecularly tailored UCNPs can serve as optogenetic actuators of transcranial NIR light to stimulate deep brain neurons. Transcranial NIR UCNP-mediated optogenetics evoked dopamine release from genetically tagged neurons in the ventral tegmental area, induced brain oscillations through activation of inhibitory neurons in the medial septum, silenced seizure by inhibition of hippocampal excitatory cells, and triggered memory recall. UCNP technology will enable less-invasive optical neuronal activity manipulation with the potential for remote therapy.

Superconducting ‘synapse’ could enable powerful future neuromorphic supercomputers

NIST’s artificial synapse, designed for neuromorphic computing, mimics the operation of switch between two neurons. One artificial synapse is located at the center of each X. This chip is 1 square centimeter in size. (The thick black vertical lines are electrical probes used for testing.) (credit: NIST)

A superconducting “synapse” that “learns” like a biological system, operating like the human brain, has been built by researchers at the National Institute of Standards and Technology (NIST).

The NIST switch, described in an open-access paper in Science Advances, provides a missing link for neuromorphic (brain-like) computers, according to the researchers. Such “non-von Neumann architecture” future computers could significantly speed up analysis and decision-making for applications such as self-driving cars and cancer diagnosis.

The research is supported by the Intelligence Advanced Research Projects Activity (IARPA) Cryogenic Computing Complexity Program, which was launched in 2014 with the goal of paving the way to “a new generation of superconducting supercomputer development beyond the exascale.”*

A synapse is a connection or switch between two neurons, controlling transmission of signals. (credit: NIST)

NIST’s artificial synapse is a metallic cylinder 10 micrometers in diameter — about 10 times larger than a biological synapse. It simulates a real synapse by processing incoming electrical spikes (pulsed current from a neuron) and customizing spiking output signals. The more firing between cells (or processors), the stronger the connection. That process enables both biological and artificial synapses to maintain old circuits and create new ones.

Dramatically faster, lower-energy-required, compared to human synapses

But the NIST synapse has two unique features that the researchers say are superior to human synapses and to other artificial synapses:

  • Operating at 100 GHz, it can fire at a rate that is much faster than the human brain — 1 billion times per second, compared to a brain cell’s rate of about 50 times per second.
  • It uses only about one ten-thousandth as much energy as a human synapse. The spiking energy is less than 1 attojoule** — roughly equivalent to the miniscule chemical energy bonding two atoms in a molecule — compared to the roughly 10 femtojoules (10,000 attojoules) per synaptic event in the human brain. Current neuromorphic platforms are orders of magnitude less efficient than the human brain. “We don’t know of any other artificial synapse that uses less energy,” NIST physicist Mike Schneider said.

Superconducting devices mimicking brain cells and transmission lines have been developed, but until now, efficient synapses — a crucial piece — have been missing. The new Josephson junction-based artificial synapse would be used in neuromorphic computers made of superconducting components (which can transmit electricity without resistance), so they would be more efficient than designs based on semiconductors or software. Data would be transmitted, processed, and stored in units of magnetic flux.

The brain is especially powerful for tasks like image recognition because it processes data both in sequence and simultaneously and it stores memories in synapses all over the system. A conventional computer processes data only in sequence and stores memory in a separate unit.

The new NIST artificial synapses combine small size, superfast spiking signals, and low energy needs, and could be stacked into dense 3D circuits for creating large systems. They could provide a unique route to a far more complex and energy-efficient neuromorphic system than has been demonstrated with other technologies, according to the researchers.

Nature News does raise some concerns about the research, quoting neuromorphic-technology experts: “Millions of synapses would be necessary before a system based on the technology could be used for complex computing; it remains to be seen whether it will be possible to scale it to this level. … The synapses can only operate at temperatures close to absolute zero, and need to be cooled with liquid helium. That this might make the chips impractical for use in small devices, although a large data centre might be able to maintain them. … We don’t yet understand enough about the key properties of the [biological] synapse to know how to use them effectively.”


Inside a superconducting synapse 

The NIST synapse is a customized Josephson junction***, long used in NIST voltage standards. These junctions are a sandwich of superconducting materials with an insulator as a filling. When an electrical current through the junction exceeds a level called the critical current, voltage spikes are produced.

Illustration showing the basic operation of NIST’s artificial synapse, based on a Josephson junction. Very weak electrical current pulses are used to control the number of nanoclusters (green) pointing in the same direction. Shown here: a “magnetically disordered state” (left) vs. “magnetically ordered state” (right). (credit: NIST)

Each artificial synapse uses standard niobium electrodes but has a unique filling made of nanoscale clusters (“nanoclusters”) of manganese in a silicon matrix. The nanoclusters — about 20,000 per square micrometer — act like tiny bar magnets with “spins” that can be oriented either randomly or in a coordinated manner. The number of nanoclusters pointing in the same direction can be controlled, which affects the superconducting properties of the junction.

Diagram of circuit used in the simulation. The blue and red areas represent pre- and post-synapse neurons, respectively. The X symbol represents the Josephson junction. (credit: Michael L. Schneider et al./Science Advances)

The synapse rests in a superconducting state, except when it’s activated by incoming current and starts producing voltage spikes. Researchers apply current pulses in a magnetic field to boost the magnetic ordering — that is, the number of nanoclusters pointing in the same direction.

This magnetic effect progressively reduces the critical current level, making it easier to create a normal conductor and produce voltage spikes. The critical current is the lowest when all the nanoclusters are aligned. The process is also reversible: Pulses are applied without a magnetic field to reduce the magnetic ordering and raise the critical current. This design, in which different inputs alter the spin alignment and resulting output signals, is similar to how the brain operates.

Synapse behavior can also be tuned by changing how the device is made and its operating temperature. By making the nanoclusters smaller, researchers can reduce the pulse energy needed to raise or lower the magnetic order of the device. Raising the operating temperature slightly from minus 271.15 degrees C (minus 456.07 degrees F) to minus 269.15 degrees C (minus 452.47 degrees F), for example, results in more and higher voltage spikes.


* Future exascale supercomputers would run at 1018 exaflops (“flops” = floating point operations per second) or more. The current fastest supercomputer — the Sunway TaihuLight — operates at about 0.1 exaflops; zettascale computers, the next step beyond exascale, would run 10,000 times faster than that.

** An attojoule is 10-18 joule, a unit of energy, and is one-thousandth of a femtojoule.

*** The Josephson effect is the phenomenon of supercurrent — i.e., a current that flows indefinitely long without any voltage applied — across a device known as a Josephson junction, which consists of two superconductors coupled by a weak link. — Wikipedia


Abstract of Ultralow power artificial synapses using nanotextured magnetic Josephson junctions

Neuromorphic computing promises to markedly improve the efficiency of certain computational tasks, such as perception and decision-making. Although software and specialized hardware implementations of neural networks have made tremendous accomplishments, both implementations are still many orders of magnitude less energy efficient than the human brain. We demonstrate a new form of artificial synapse based on dynamically reconfigurable superconducting Josephson junctions with magnetic nanoclusters in the barrier. The spiking energy per pulse varies with the magnetic configuration, but in our demonstration devices, the spiking energy is always less than 1 aJ. This compares very favorably with the roughly 10 fJ per synaptic event in the human brain. Each artificial synapse is composed of a Si barrier containing Mn nanoclusters with superconducting Nb electrodes. The critical current of each synapse junction, which is analogous to the synaptic weight, can be tuned using input voltage spikes that change the spin alignment of Mn nanoclusters. We demonstrate synaptic weight training with electrical pulses as small as 3 aJ. Further, the Josephson plasma frequencies of the devices, which determine the dynamical time scales, all exceed 100 GHz. These new artificial synapses provide a significant step toward a neuromorphic platform that is faster, more energy-efficient, and thus can attain far greater complexity than has been demonstrated with other technologies.

MIT nanosystem delivers precise amounts of drugs directly to a tiny spot in the brain

MIT’s miniaturized system can deliver multiple drugs to precise locations in the brain, also monitor and control neural activity (credit: MIT)

MIT researchers have developed a miniaturized system that can deliver tiny quantities of medicine to targeted brain regions as small as 1 cubic millimeter, with precise control over how much drug is given. The goal is to treat diseases that affect specific brain circuits without interfering with the normal functions of the rest of the brain.*

“We believe this tiny microfabricated device could have tremendous impact in understanding brain diseases, as well as providing new ways of delivering biopharmaceuticals and performing biosensing in the brain,” says Robert Langer, the David H. Koch Institute Professor at MIT and one of the senior authors of an open-access paper that appears in the Jan. 24 issue of Science Translational Medicine.**

Miniaturized neural drug delivery system (MiNDS). Top: Miniaturized delivery needle with multiple fluidic channels for delivering different drugs. Bottom: scanning electron microscope image of cannula tip for delivering a drug or optogenetic light (to stimulate neurons) and a tungsten electrode (yellow dotted area — magnified view in inset) for detecting neural activity. (credit: Dagdeviren et al., Sci. Transl. Med., adapted by KurzweilAI)

The researchers used state-of-the-art microfabrication techniques to construct cannulas (thin tubes) with diameters of about 30 micrometers (width of a fine human hair) and lengths up to 10 centimeters. These cannulas are contained within a stainless steel needle with a diameter of about 150 micrometers. Inside the cannulas are small pumps that can deliver tiny doses (hundreds of nanoliters***) deep into the brains of rats — with very precise control over how much drug is given and where it goes.

In one experiment, they delivered a drug called muscimol to a rat brain region called the substantia nigra, which is located deep within the brain and helps to control movement. Previous studies have shown that muscimol induces symptoms similar to those seen in Parkinson’s disease. The researchers were able to stimulate the rats to continually turn in a clockwise direction. They also could also halt the Parkinsonian behavior by delivering a dose of saline through a different channel to wash the drug away.

“Since the device can be customizable, in the future we can have different channels for different chemicals, or for light, to target tumors or neurological disorders such as Parkinson’s disease or Alzheimer’s,” says Canan Dagdeviren, the LG Electronics Career Development Assistant Professor of Media Arts and Sciences and the lead author of the paper.

This device could also make it easier to deliver potential new treatments for behavioral neurological disorders such as addiction or obsessive compulsive disorder. (These may be caused by specific disruptions in how different parts of the brain communicate with each other.)

Measuring drug response

The researchers also showed that they could incorporate an electrode into the tip of the cannula, which can be used to monitor how neurons’ electrical activity changes after drug treatment. They are now working on adapting the device so it can also be used to measure chemical or mechanical changes that occur in the brain following drug treatment.

The cannulas can be fabricated in nearly any length or thickness, making it possible to adapt them for use in brains of different sizes, including the human brain, the researchers say.

“This study provides proof-of-concept experiments, in large animal models, that a small, miniaturized device can be safely implanted in the brain and provide miniaturized control of the electrical activity and function of single neurons or small groups of neurons. The impact of this could be significant in focal diseases of the brain, such as Parkinson’s disease,” says Antonio Chiocca, neurosurgeon-in-chief and chairman of the Department of Neurosurgery at Brigham and Women’s Hospital, who was not involved in the research.

The research was funded by the National Institutes of Health and the National Institute of Biomedical Imaging and Bioengineering.

* To treat brain disorders, drugs (such as l-dopa, a dopamine precursor used to treat Parkinson’s disease, and Prozac, used to boost serotonin levels in patients with depression) often interact with brain chemicals called neurotransmitters (or the cell receptors interact with neurotransmitters) — creating side effects throughout the brain.

** Michael Cima, the David H. Koch Professor of Engineering in the Department of Materials Science and Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research, is also a senior author of the paper.

*** It would take one billion nanoliter drops to fill 4 cups.


Abstract of Miniaturized neural system for chronic, local intracerebral drug delivery

Recent advances in medications for neurodegenerative disorders are expanding opportunities for improving the debilitating symptoms suffered by patients. Existing pharmacologic treatments, however, often rely on systemic drug administration, which result in broad drug distribution and consequent increased risk for toxicity. Given that many key neural circuitries have sub–cubic millimeter volumes and cell-specific characteristics, small-volume drug administration into affected brain areas with minimal diffusion and leakage is essential. We report the development of an implantable, remotely controllable, miniaturized neural drug delivery system permitting dynamic adjustment of therapy with pinpoint spatial accuracy. We demonstrate that this device can chemically modulate local neuronal activity in small (rodent) and large (nonhuman primate) animal models, while simultaneously allowing the recording of neural activity to enable feedback control.

An artificial synapse for future miniaturized portable ‘brain-on-a-chip’ devices

Biological synapse structure (credit: Thomas Splettstoesser/CC)

MIT engineers have designed a new artificial synapse made from silicon germanium that can precisely control the strength of an electric current flowing across it.

In simulations, the researchers found that the chip and its synapses could be used to recognize samples of handwriting with 95 percent accuracy. The engineers say the new design, published today (Jan. 22) in the journal Nature Materials, is a major step toward building portable, low-power neuromorphic chips for use in pattern recognition and other machine-learning tasks.

Controlling the flow of ions: the challenge

Researchers in the emerging field of “neuromorphic computing” have attempted to design computer chips that work like the human brain. The idea is to apply a voltage across layers that would cause ions (electrically charged atoms) to move in a switching medium (synapse-like space) to create conductive filaments in a manner that’s similar to how the “weight” (connection strength) of a synapse changes.

There are more than 100 trillion synapses (in a typical human brain) that mediate neuron signaling in the brain, strengthening some neural connections while pruning (weakening) others — a process that enables the brain to recognize patterns, remember facts, and carry out other learning tasks, all at lightning speeds.

Instead of carrying out computations based on binary, on/off signaling, like current digital chips, the elements of a “brain on a chip” would work in an analog fashion, exchanging a gradient of signals, or “weights” — much like neurons that activate in various ways (depending on the type and number of ions that flow across a synapse).

But it’s been difficult to control the flow of ions in existing synapse designs. These have multiple paths that make it difficult to predict where ions will make it through, according to research team leader Jeehwan Kim, PhD, an assistant professor in the departments of Mechanical Engineering and Materials Science and Engineering, a principal investigator in MIT’s Research Laboratory of Electronics and Microsystems Technology Laboratories.

“Once you apply some voltage to represent some data with your artificial neuron, you have to erase and be able to write it again in the exact same way,” Kim says. “But in an amorphous solid, when you write again, the ions go in different directions because there are lots of defects. This stream is changing, and it’s hard to control. That’s the biggest problem — nonuniformity of the artificial synapse.”

Epitaxial random access memory (epiRAM)

(Left) Cross-sectional transmission electron microscope image of 60 nm silicon-germanium (SiGe) crystal grown on a silicon substrate (diagonal white lines represent candidate dislocations). Scale bar: 25 nm. (Right) Cross-sectional scanning electron microscope image of an epiRAM device with titanium (Ti)–gold (Au) and silver (Ag)–palladium (Pd) layers. Scale bar: 100 nm. (credit: Shinhyun Choi et al./Nature Materials)

So instead of using amorphous materials as an artificial synapse, Kim and his colleagues created an new “epitaxial random access memory” (epiRAM) design.

They started with a wafer of silicon. They then grew a similar pattern of silicon germanium — a material used commonly in transistors — on top of the silicon wafer. Silicon germanium’s lattice is slightly larger than that of silicon, and Kim found that together, the two perfectly mismatched materials could form a funnel-like dislocation, creating a single path through which ions can predictably flow.*

This is the most uniform device we could achieve, which is the key to demonstrating artificial neural networks,” Kim says.

Testing the ability to recognize samples of handwriting

As a test, Kim and his team explored how the epiRAM device would perform if it were to carry out an actual learning task: recognizing samples of handwriting — which researchers consider to be a practical test for neuromorphic chips. Such chips would consist of artificial “neurons” connected to other “neurons” via filament-based artificial “synapses.”

Image-recognition simulation. (Left) A 3-layer multilayer-perception neural network with black and white input signal for each layer in algorithm level. The inner product (summation) of input neuron signal vector and first synapse array vector is transferred after activation and binarization as input vectors of second synapse arrays. (Right) Circuit block diagram of hardware implementation showing a synapse layer composed of epiRAM crossbar arrays and the peripheral circuit. (credit: Shinhyun Choi et al./Nature Materials)

They ran a computer simulation of an artificial neural network consisting of three sheets of neural layers connected via two layers of artificial synapses, based on measurements from their actual neuromorphic chip. They fed into their simulation tens of thousands of samples from the MNIST handwritten recognition dataset**, commonly used by neuromorphic designers.

They found that their neural network device recognized handwritten samples 95.1 percent of the time — close to the 97 percent accuracy of existing software algorithms running on large computers.

A chip to replace a supercomputer

The team is now in the process of fabricating a real working neuromorphic chip that can carry out handwriting-recognition tasks. Looking beyond handwriting, Kim says the team’s artificial synapse design will enable much smaller, portable neural network devices that can perform complex computations that are currently only possible with large supercomputers.

“Ultimately, we want a chip as big as a fingernail to replace one big supercomputer,” Kim says. “This opens a stepping stone to produce real artificial intelligence hardware.”

This research was supported in part by the National Science Foundation. Co-authors included researchers at Arizona State University.

* They applied voltage to each synapse and found that all synapses exhibited about the same current, or flow of ions, with about a 4 percent variation between synapses — a much more uniform performance compared with synapses made from amorphous material. They also tested a single synapse over multiple trials, applying the same voltage over 700 cycles, and found the synapse exhibited the same current, with just 1 percent variation from cycle to cycle.

** The MNIST (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems and for training and testing in the field of machine learning. It contains 60,000 training images and 10,000 testing images. 


Abstract of SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations

Although several types of architecture combining memory cells and transistors have been used to demonstrate artificial synaptic arrays, they usually present limited scalability and high power consumption. Transistor-free analog switching devices may overcome these limitations, yet the typical switching process they rely on—formation of filaments in an amorphous medium—is not easily controlled and hence hampers the spatial and temporal reproducibility of the performance. Here, we demonstrate analog resistive switching devices that possess desired characteristics for neuromorphic computing networks with minimal performance variations using a single-crystalline SiGe layer epitaxially grown on Si as a switching medium. Such epitaxial random access memories utilize threading dislocations in SiGe to confine metal filaments in a defined, one-dimensional channel. This confinement results in drastically enhanced switching uniformity and long retention/high endurance with a high analog on/off ratio. Simulations using the MNIST handwritten recognition data set prove that epitaxial random access memories can operate with an online learning accuracy of 95.1%.

Tracking a thought’s fleeting trip through the brain


Repeating a word: as the brain receives (yellow), interpretes (red), and responds (blue) within a second, the prefrontal cortex (red) coordinates all areas of the brain involved. (video credit: Avgusta Shestyuk/UC Berkeley).

Recording the electrical activity of neurons directly from the surface of the brain, using electrocorticograhy (ECoG)*, neuroscientists were able to track the flow of thought across the brain in real time for the first time. They showed clearly how the prefrontal cortex at the front of the brain coordinates activity to help us act in response to a perception.

Here’s what they found.

For a simple task, such as repeating a word seen or heard:

The visual and auditory cortices react first to perceive the word. The prefrontal cortex then kicks in to interpret the meaning, followed by activation of the motor cortex (preparing for a response). During the half-second between stimulus and response, the prefrontal cortex remains active to coordinate all the other brain areas.

For a particularly hard task, like determining the antonym of a word:

During the time the brain takes several seconds to respond, the prefrontal cortex recruits other areas of the brain — probably including memory networks (not tracked). The prefrontal cortex then hands off to the motor cortex to generate a spoken response.

In both cases, the brain begins to prepare the motor areas to respond very early (during initial stimulus presentation) — suggesting that we get ready to respond even before we know what the response will be.

“This might explain why people sometimes say things before they think,” said Avgusta Shestyuk, a senior researcher in UC Berkeley’s Helen Wills Neuroscience Institute and lead author of a paper reporting the results in the current issue of Nature Human Behavior.


For a more difficult task, like saying a word that is the opposite of another word, people’s brains required 2–3 seconds to detect (yellow), interpret and search for an answer (red), and respond (blue) — with sustained prefrontal lobe activity (red) coordinating all areas of the brain involved. (video credit: Avgusta Shestyuk/UC Berkeley).

The research backs up what neuroscientists have pieced together over the past decades from studies in monkeys and humans.

“These very selective studies have found that the frontal cortex is the orchestrator, linking things together for a final output,” said co-author Robert Knight, a UC Berkeley professor of psychology and neuroscience and a professor of neurology and neurosurgery at UCSF. “Here we have eight different experiments, some where the patients have to talk and others where they have to push a button, where some are visual and others auditory, and all found a universal signature of activity centered in the prefrontal lobe that links perception and action. It’s the glue of cognition.”

Researchers at Johns Hopkins University, California Pacific Medical Center, and Stanford University were also involved. The work was supported by the National Science Foundation, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke.

* Other neuroscientists have used functional magnetic resonance imaging (fMRI) and electroencephelography (EEG) to record activity in the thinking brain. The UC Berkeley scientists instead employed a much more precise technique, electrocorticograhy (ECoG), which records from several hundred electrodes placed on the brain surface and detects activity in the thin outer region, the cortex, where thinking occurs. ECoG provides better time resolution than fMRI and better spatial resolution than EEG, but requires access to epilepsy patients undergoing highly invasive surgery involving opening the skull to pinpoint the location of seizures. The new study employed 16 epilepsy patients who agreed to participate in experiments while undergoing epilepsy surgery at UC San Francisco and California Pacific Medical Center in San Francisco, Stanford University in Palo Alto and Johns Hopkins University in Baltimore. Once the electrodes were placed on the brains of each patient, the researchers conducted a series of eight tasks that included visual and auditory stimuli. The tasks ranged from simple, such as repeating a word or identifying the gender of a face or a voice, to complex, such as determining a facial emotion, uttering the antonym of a word, or assessing whether an adjective describes the patient’s personality.


Abstract of Persistent neuronal activity in human prefrontal cortex links perception and action

How do humans flexibly respond to changing environmental demands on a subsecond temporal scale? Extensive research has highlighted the key role of the prefrontal cortex in flexible decision-making and adaptive behaviour, yet the core mechanisms that translate sensory information into behaviour remain undefined. Using direct human cortical recordings, we investigated the temporal and spatial evolution of neuronal activity (indexed by the broadband gamma signal) in 16 participants while they performed a broad range of self-paced cognitive tasks. Here we describe a robust domain- and modality-independent pattern of persistent stimulus-to-response neural activation that encodes stimulus features and predicts motor output on a trial-by-trial basis with near-perfect accuracy. Observed across a distributed network of brain areas, this persistent neural activation is centred in the prefrontal cortex and is required for successful response implementation, providing a functional substrate for domain-general transformation of perception into action, critical for flexible behaviour.

Scientists map mammalian neural microcircuits in precise detail

Nanoengineered electroporation microelectrodes (NEMs) allow for improved current distribution and electroporation effectiveness by reducing peak voltage regions (to avoid damaging tissue). (left) Cross-section of NEM model, illustrating the total effective electroporation volume and its distribution of the voltage around the pipette tip, at a safe current of 50 microamperes. (Scale bar = 5 micrometers.) (right) A five-hole NEM after successful insertion into brain tissue, imaged with high-resolution focused ion beam (FIB). (Scale bar = 2 micrometers) (credit: D. Schwartz et al./Nature Communications)

Neuroscientists at the Francis Crick Institute have developed a new technique to map electrical microcircuits* in the brain at far more detail than existing techniques*, which are limited to tiny sections of the brain (or remain confined to simpler model organisms, like zebrafish).

In the brain, groups of neurons that connect up in microcircuits help us process information about things we see, smell and taste. Knowing how many neurons and other types of cells make up these microcircuits would give scientists a deeper understanding of how the brain computes complex information.

Nanoengineered microelectrodes

The researchers developed a new design called “nanoengineered electroporation** microelectrodes” (NEMs). They were able to use an NEM to map out all 250 cells that make up a specific microcircuit in a part of a mouse brain that processes smell (known as the “olfactory bulb glomerulus”) in a horizontal slice of the olfactory bulb — something never before achieved.

To do that, the team created a series of tiny pores (holes) near the end of a micropipette using nano-engineering tools. The new design distributes the electrical current uniformly over a wider area (up to a radius of about 50 micrometers — the size of a typical neural microcircuit), with minimal cell damage.

The researchers tested the NEM technique with a specific microcircuit, the olfactory bulb glomerulus (which detects smells). They were able to identify detailed, long-range, complex anatomical features (scale bar = 100 micrometers). (White arrows identify parallel staining of vascular structures.) (credit: D. Schwartz et al./Nature Communications)

Seeing 100% of the cells in a brain microcircuit for the first time

Unlike current methods, the team was able to stain up to 100% of the cells in the microcircuit they were investigating, according to Andreas Schaefer, who led the research, which was published in open-access Nature Communications today (Jan. 12, 2018).

“As the brain is made up of repeating units, we can learn a lot about how the brain works as a computational machine by studying it at this [microscopic] level,” he said. “Now that we have a tool of mapping these tiny units, we can start to interfere with specific cell types to see how they directly control behavior and sensory processing.”

The work was conducted in collaboration with researchers at the Max-Planck-Institute for Medical Research in Heidelberg, Heidelberg University, Heidelberg University Hospital, University College London, the MRC National Institute for Medical Research, and Columbia University Medical Center.

* Scientists currently use color-tagged viruses or charged dyes with applied electroporation current to stain brain cells. These methods, using a glass capillary with a single hole, are limited to low current (higher current could damage tissue), so they can only allow for identifying a limited area of a microcircuit.

** Electroporation is a microbiology technique that applies an electrical field to cells to increase the permeability (ease of penetration) of the cell membrane, allowing (in this case) fluorophores (fluorescent, or glowing dyes) to penetrate into the cells to label (identify parts of) the neural microcircuits (including the “inputs” and “outputs”) under a microscope.


Abstract of Architecture of a mammalian glomerular domain revealed by novel volume electroporation using nanoengineered microelectrodes

Dense microcircuit reconstruction techniques have begun to provide ultrafine insight into the architecture of small-scale networks. However, identifying the totality of cells belonging to such neuronal modules, the “inputs” and “outputs,” remains a major challenge. Here, we present the development of nanoengineered electroporation microelectrodes (NEMs) for comprehensive manipulation of a substantial volume of neuronal tissue. Combining finite element modeling and focused ion beam milling, NEMs permit substantially higher stimulation intensities compared to conventional glass capillaries, allowing for larger volumes configurable to the geometry of the target circuit. We apply NEMs to achieve near-complete labeling of the neuronal network associated with a genetically identified olfactory glomerulus. This allows us to detect sparse higher-order features of the wiring architecture that are inaccessible to statistical labeling approaches. Thus, NEM labeling provides crucial complementary information to dense circuit reconstruction techniques. Relying solely on targeting an electrode to the region of interest and passive biophysical properties largely common across cell types, this can easily be employed anywhere in the CNS.