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NT/ A sweet solution to hard brain implants

Neuroscience biweekly vol. 32, 29th April — 14th May


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Mechanically Matched Silicone Brain Implants Reduce Brain Foreign Body Response

by Edward N. Zhang, Jean‐Pierre Clément, Alia Alameri, Andy Ng, Timothy E. Kennedy, David Juncker in Advanced Materials Technologies

Brain implants are used to treat neurological dysfunction, and their use for enhancing cognitive abilities is a promising field of research. Implants can be used to monitor brain activity or stimulate parts of the brain using electrical pulses. In epilepsy, for example, brain implants can determine where in the brain seizures are happening.

Over time, implants trigger a foreign body response, creating inflammation and scar tissue around the implant that reduces their effectiveness.

The problem is that traditional implants are much more rigid than brain tissue, which has a softness comparable to pudding. Stress between the implant and the tissue caused by constant movement of the brain with respect to the implant signals the body to treat the implant as a foreign object. This interaction between the implant and the brain is similar to a knife cutting into a piece of pudding. An implant as soft as brain tissue would be ideal, but such soft implants would be difficult to manufacture and implant on the microscale.

A team of researchers from The Neuro (Montreal Neurological Institute-Hospital) and McGill’s Department of Biomedical Engineering found a solution using silicone and sugar.

By using silicone polymers, widely known for their medical applications, the scientists were able to make the softest brain implant to date with the thickness of a thin sewing thread (~0.2 mm), and the consistency of soft pudding — as soft as the brain itself. They were then able to implant it into the brain using a trick from the cookbook.

They adopted classical cooking techniques of sugar melting, caramelizing and molding both for making the implant, as well as for encapsulating it into a needle made of hardened sugar.

When surgically inserted into the brain of an anesthetized rat, the sugar needle carried the implant to the right location, and dissolved within seconds, leaving the delicate implant in place. Sugar is non-toxic and is naturally metabolized by the brain. Examining brain tissue three and nine weeks after implantation, the team found higher neuronal density and lower foreign body response compared to traditional implants.

While more research is needed to develop electrically active, soft implants, and to prove the safety and effectiveness of the technique in humans, one day it could be used to unlock the potential of brain implants in treating neurological disease and dysfunction.

“The implants we created are so soft that the body doesn’t see it as a big threat, allowing them to interact with the brain with less interference,” says Edward Zhang, the study’s first author. “I am excited about the future of brain implant technology and believe our work helps pave the path for a new generation of soft implants that could make brain implants a more viable medical treatment.”

“By reducing the brains inflammatory response, our new, very soft implants are a good thing for the brain and a good thing for the long-term function of an implant,” says Tim Kennedy, a researcher at The Neuro and the study’s co-senior author. “The miniature sugar needle devised by Zhang is a sweet solution to placing the super-soft implant into equally soft brain tissue.”

“Biomedical engineering research is about making the impossible, possible,” says David Juncker, a professor of biomedical engineering at McGill and the study’s co-senior author. “Here we set out to make an implant as soft as the brain and implant it into the brain, which was a major challenge. We are excited about the results, and the possibility it opens up for long lasting, well-tolerated brain implants”

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

by Xudong Ji, Bryan D. Paulsen, Gary K. K. Chik, Ruiheng Wu, Yuyang Yin, Paddy K. L. Chan, Jonathan Rivnay in Nature Communications

Researchers have developed a brain-like computing device that is capable of learning by association.

Similar to how famed physiologist Ivan Pavlov conditioned dogs to associate a bell with food, researchers at Northwestern University and the University of Hong Kong successfully conditioned their circuit to associate light with pressure.

The device’s secret lies within its novel organic, electrochemical “synaptic transistors,” which simultaneously process and store information just like the human brain. The researchers demonstrated that the transistor can mimic the short-term and long-term plasticity of synapses in the human brain, building on memories to learn over time.

With its brain-like ability, the novel transistor and circuit could potentially overcome the limitations of traditional computing, including their energy-sapping hardware and limited ability to perform multiple tasks at the same time. The brain-like device also has higher fault tolerance, continuing to operate smoothly even when some components fail.

“Although the modern computer is outstanding, the human brain can easily outperform it in some complex and unstructured tasks, such as pattern recognition, motor control and multisensory integration,” said Northwestern’s Jonathan Rivnay, a senior author of the study. “This is thanks to the plasticity of the synapse, which is the basic building block of the brain’s computational power. These synapses enable the brain to work in a highly parallel, fault tolerant and energy-efficient manner. In our work, we demonstrate an organic, plastic transistor that mimics key functions of a biological synapse.”

Rivnay is an assistant professor of biomedical engineering at Northwestern’s McCormick School of Engineering. He co-led the study with Paddy Chan, an associate professor of mechanical engineering at the University of Hong Kong. Xudong Ji, a postdoctoral researcher in Rivnay’s group, is the paper’s first author.

Problems with conventional computing

Conventional, digital computing systems have separate processing and storage units, causing data-intensive tasks to consume large amounts of energy. Inspired by the combined computing and storage process in the human brain, researchers, in recent years, have sought to develop computers that operate more like the human brain, with arrays of devices that function like a network of neurons.

“The way our current computer systems work is that memory and logic are physically separated,” Ji said. “You perform computation and send that information to a memory unit. Then every time you want to retrieve that information, you have to recall it. If we can bring those two separate functions together, we can save space and save on energy costs.”

Currently, the memory resistor, or “memristor,” is the most well-developed technology that can perform combined processing and memory function, but memristors suffer from energy-costly switching and less biocompatibility. These drawbacks led researchers to the synaptic transistor — especially the organic electrochemical synaptic transistor, which operates with low voltages, continuously tunable memory and high compatibility for biological applications. Still, challenges exist.

“Even high-performing organic electrochemical synaptic transistors require the write operation to be decoupled from the read operation,” Rivnay said. “So if you want to retain memory, you have to disconnect it from the write process, which can further complicate integration into circuits or systems.”

How the synaptic transistor works

To overcome these challenges, the Northwestern and University of Hong Kong team optimized a conductive, plastic material within the organic, electrochemical transistor that can trap ions. In the brain, a synapse is a structure through which a neuron can transmit signals to another neuron, using small molecules called neurotransmitters. In the synaptic transistor, ions behave similarly to neurotransmitters, sending signals between terminals to form an artificial synapse. By retaining stored data from trapped ions, the transistor remembers previous activities, developing long-term plasticity.

The researchers demonstrated their device’s synaptic behavior by connecting single synaptic transistors into a neuromorphic circuit to simulate associative learning. They integrated pressure and light sensors into the circuit and trained the circuit to associate the two unrelated physical inputs (pressure and light) with one another.

Perhaps the most famous example of associative learning is Pavlov’s dog, which naturally drooled when it encountered food. After conditioning the dog to associate a bell ring with food, the dog also began drooling when it heard the sound of a bell. For the neuromorphic circuit, the researchers activated a voltage by applying pressure with a finger press. To condition the circuit to associate light with pressure, the researchers first applied pulsed light from an LED lightbulb and then immediately applied pressure. In this scenario, the pressure is the food and the light is the bell. The device’s corresponding sensors detected both inputs.

After one training cycle, the circuit made an initial connection between light and pressure. After five training cycles, the circuit significantly associated light with pressure. Light, alone, was able to trigger a signal, or “unconditioned response.”

Future applications

Because the synaptic circuit is made of soft polymers, like a plastic, it can be readily fabricated on flexible sheets and easily integrated into soft, wearable electronics, smart robotics and implantable devices that directly interface with living tissue and even the brain.

“While our application is a proof of concept, our proposed circuit can be further extended to include more sensory inputs and integrated with other electronics to enable on-site, low-power computation,” Rivnay said. “Because it is compatible with biological environments, the device can directly interface with living tissue, which is critical for next-generation bioelectronics.”

a Analogy of synapse and OECT and the chemical structure of PEDOT+, Tos−, and PTHF. b Transfer characteristics of the PEDOT:Tos-based OECT and the P-80% PTHF-based OECT (W=500μm,L=10μm). c Memory level with respect to channel composition of OECT and programmed gate voltage. d Reversible conductance change of P-80% PTHF-based OECT in response to a pulse train with different polarity.

Anterior insula regulates brain network transitions that gate conscious access

by Zirui Huang, Vijay Tarnal, Phillip E. Vlisides, Ellen L. Janke, Amy M. McKinney, Paul Picton, George A. Mashour, Anthony G. Hudetz in Cell Reports

During our waking hours, the brain is receiving a near-constant influx of sensory signals of various strengths. For decades, scientists have wondered why some signals rise to the light of conscious awareness while other signals of a similar strength remain in the dark shadows of unconsciousness. What controls the gate that separates the shadows and the light?

In a new study from the Department of Anesthesiology and Center for Consciousness Science at Michigan Medicine, researchers identify a key area in the cortex that appears to be the gate of conscious awareness.

“Information processing in the brain has two dimensions: sensory processing of the environment without awareness and the type that occurs when a stimulus reaches a certain level of importance and enters conscious awareness,” explains Zirui Huang, Ph.D., research investigator in the Department of Anesthesiology.

Huang, along with lead researcher Anthony Hudetz, Ph.D. and their team, attempted to confirm that this switch occurs in a part of the brain called the anterior insular cortex, acting as a type of gate between low level sensory information and higher level awareness.

For the experiments, participants were put inside of a fMRI machine and administered the anesthetic drug propofol to control their level of consciousness. They were then asked to imagine themselves playing tennis, walking down a path or squeezing their hand, as well as asked to perform a motor activity (squeeze a rubber ball) as they gradually lost consciousness and regained it again after the propofol was stopped.

Previous research has shown that mental imagery produces brain activity similar to actually performing the activity. When participants imagine themselves playing tennis, the part of the brain responsible for controlling movement lights up. Other regions of the brain become deactivated when performing tasks, as mental attention is focused on the activity.

As the study participants began to lose consciousness, deactivations happened less frequently. When they completely lost consciousness, their corresponding brain areas also showed no activation in response to mental imagery tasks. As they regained some consciousness, they regained some activity related to mental imagery and with full consciousness shortly thereafter, their brain showed normal activation patterns.

Looking for the correlation across these states of consciousness revealed activation of the anterior insular cortex played a role in the successful switch between these activations and deactivations.

“A sensory stimulus will normally activate the anterior insular cortex,” says Hudetz. “But when you lose consciousness, the anterior insular cortex is deactivated and network shifts in the brain that support consciousness are disrupted.” The anterior insular cortex, he explains, might act as a filter that allows only the most important information to enter conscious awareness.

They sought to confirm their hypothesis with another classic psychological experiment, wherein a face is briefly flashed on a screen for a barely perceptible three hundredths of a second. The face image is followed by a noisy high contrast image designed to interrupt conscious processing of the face image. Participants were then asked whether they saw a face or not. Whether the face was consciously accessed was predicted by activation in the anterior insular cortex.

“Anterior insular cortex has continuously fluctuating activity,” says Huang. “Whether you can detect a stimulus depends upon the state of the anterior insula when the information arrives in your brain: if the insula’s activity is high at the point of stimulus, you will see the image. Based on evidence from these two experiments, we conclude that the anterior insular cortex could be a gate for conscious awareness.”

Neuroimaging of depression with diffuse optical tomography during repetitive transcranial magnetic stimulation

by Shixie Jiang, Jingyu Huang, Hao Yang, Ryan Wagoner, F. Andrew Kozel, Glenn Currier, Huabei Jiang in Scientific Reports

Repetitive transcranial magnetic stimulation, or rTMS, was FDA approved in 2008 as a safe and effective noninvasive treatment for severe depression resistant to antidepressant medications. A small coil positioned near the scalp generates repetitive, pulsed magnetic waves that pass through the skull and stimulate brain cells to relieve symptoms of depression. The procedure has few side effects and is typically prescribed as an alternative or supplemental therapy when multiple antidepressant medications and/or psychotherapy do not work.

Despite increased use of rTMS in psychiatry, the rates at which patients respond to therapy and experience remission of often-disabling symptoms have been modest at best.

Now, for the first time, a team of University of South Florida psychiatrists and biomedical engineers applied an emerging functional neuroimaging technology, known as diffuse optical tomography (DOT), to better understand how rTMS works so they can begin to improve the technique’s effectiveness in treating depression. DOT uses near-infrared light waves and sophisticated algorithms (computer instructions) to produce three-dimensional images of soft tissue, including brain tissue.

Comparing depressed and healthy individuals, the USF researchers demonstrated that this newer optical imaging technique can safely and reliably measure changes in brain activity induced during rTMS in a targeted region of the brain implicated in mood regulation. Their findings were published April 1 in the Nature journal Scientific Reports.

“This study is a good example of how collaboration between disciplines can advance our overall understanding of how a treatment like TMS works,” said study lead author Shixie Jiang, MD, a third-year psychiatry resident at the USF Health Morsani College of Medicine. “We want to use what we learned from the application of the diffuse optical tomography device to optimize TMS, so that the treatments become more personalized and lead to more remission of depression.”

DOT has been used clinically for imaging epilepsy, breast cancer, and osteoarthritis and to visualize activation of cortical brain regions, but the USF team is the first to introduce the technology to psychiatry to study brain stimulation with TMS.

“Diffuse optical tomography is really the only modality that can image brain function at the same time that TMS is administered,” said study principal investigator Huabei Jiang, PhD, a professor in the Department of Medical Engineering and father of Shixie Jiang. The DOT imaging system used for USF’s collaborative study was custom built in his laboratory at the USF College of Engineering.

The researchers point to three main reasons why TMS likely has not lived up to its full potential in treating major depression: nonoptimized brain stimulation targeting; unclear treatment parameters (i.e., rTMS dose, magnetic pulse patterns and frequencies, rest periods between stimulation intervals), and incomplete knowledge of how nerve cells in the brain respond physiologically to the procedure.

Portable, less expensive, and less confining than some other neuroimaging equipment like MRIs, DOT still renders relatively high-resolution, localized 3D images. More importantly, Dr. Huabei Jiang said, DOT can be used during TMS without interfering with treatment’s magnetic pulses and without compromising the images and other data generated.

DOT relies on the fact that higher levels of oxygenated blood correlate with more brain activity and increased cerebral blood flow, and lower levels indicate less activity and blood flow. Certain neuroimaging studies have also revealed that depressed people display abnormally low brain activity in the prefrontal cortex, a brain region associated with emotional responses and mood regulation.

By measuring changes in near-infrared light, DOT detects changes in brain activity and, secondarily, changes in blood volume (flow) that might be triggering activation in the prefrontal cortex. In particular, the device can monitor altered levels of oxygenated, deoxygenated, and total hemoglobin, a protein in red blood cells carrying oxygen to tissues.

The USF study analyzed data collected from 13 adults (7 depressed and 6 healthy controls) who underwent DOT imaging simultaneously with rTMS at the USF Health outpatient psychiatry clinic. Applying the standard rTMS protocol, the treatment was aimed at the brain’s left dorsolateral prefrontal cortex — the region most targeted for depression.

The researchers found that the depressed patients had significantly less brain activation in response to rTMS than the healthy study participants. Furthermore, peak brain activation took longer to reach in the depressed group, compared to the healthy control group.

This delayed, less robust activation suggests that rTMS as currently administered under FDA guidelines may not be adequate for some patients with severe depression, Dr. Shixie Jiang said. The dose and timing of treatment may need to be adjusted for patients who exhibit weakened responses to brain stimulation at baseline (initial treatment), he added.

Larger clinical trials are needed to validate the USF preliminary study results, as well as to develop ideal treatment parameters and identify other dysfunctional regions in the depression-affected brain that may benefit from targeted stimulation.

“More work is needed,” Dr. Shixie Jiang said, “but advances in neuroimaging with new approaches like diffuse optical tomography hold great promise for helping us improve rTMS and depression outcomes.”

Custom DOT head interface. (A) Two layer design with an inner and outer layer superimposed upon a modified electroencephalogram cap; (B) top-down schematic depicting the location of the TMS coil (light brown figure of eight symbol) over the left dorsolateral prefrontal cortex and the DOT cap over the right hemisphere (centered upon the R DLPFC); (C )photographs of the interface connected to a human subject; the probe comprised of 48

Diversity amongst human cortical pyramidal neurons revealed via their sag currents and frequency preferences

by Homeira Moradi Chameh, Scott Rich, Lihua Wang, Fu-Der Chen, Liang Zhang, Peter L. Carlen, Shreejoy J. Tripathy, Taufik A. Valiante in Nature Communications

Scientists at the Krembil Brain Institute, part of University Health Network (UHN), in collaboration with colleagues at the Centre for Addiction and Mental Health (CAMH), have used precious and rare access to live human cortical tissue to identify functionally important features that make human neurons unique.

This experimental work is among the first of its kind on live human neurons and one of the largest studies of the diversity of human cortical pyramidal cells to date.

“The goal of this study was to understand what makes human brain cells ‘human,’ and how human neuron circuitry functions as it does,” says Dr. Taufik Valiante, neurosurgeon, scientist at the Krembil Brain Institute at UHN and co-senior author on the paper.

“In our study, we wanted to understand how human pyramidal cells, the major class of neurons in the neocortex, differ between the upper and bottom layers of the neocortex,” says Dr. Shreejoy Tripathy, a scientist with the Krembil Centre for Neuroinformatics at CAMH and co-senior author on this study.

“In particular, we wanted to understand how electrical features of these neurons might support different aspects of cross-layer communication and the generation of brain rhythms, which are known to be disrupted in brain diseases like epilepsy.”

With consent, the team used brain tissue immediately after it had been removed during routine surgery from the brains of patients with epilepsy and tumours. Using state-of-the-art techniques, the team was then able to characterize properties of individual cells within slices of this tissue, including visualizations of their detailed morphologies.

“Little is known about the shapes and electrical properties of living adult human neurons because of the rarity of obtaining living human brain tissue, as there are few opportunities other than epilepsy surgery to obtain such recordings,” says Dr. Valiante.

To keep the resected tissue alive, it is immediately transferred into the modified cerebrospinal fluid in the operating room then taken directly into the laboratory where it is prepared for experimental characterization.

It is rare to study human tissue because accessing human tissue for scientific inquiries requires a tight-knit multidisciplinary community, including patients willing to participate in the studies, ethicists ensuring patient rights and safety, neurosurgeons collecting and delivering samples, and neuroscientists with necessary research facilities to study these tissues.

After initial analysis, members of the Krembil Centre for Neuroinformatics used further large-scale data analysis to identify the properties that distinguished neurons in this cohort from each other. These properties were then compared to those from other centres doing similar work with human brain tissue samples, including the Allen Institute for Brain Sciences in Seattle, Washington.

Noted in the team’s findings:

  • A massive amount of diversity among human neocortical pyramidal cells
  • Distinct electrophysiological features between neurons located at different layers in the human neocortex
  • Specific features of deeper layer neurons enabling them to support aspects of across-layer communication and the generation of functionally important brain rhythms

The teams also found notable and unexpected differences between their findings and similar experiments in pre-clinical models, which Dr. Tripathy believes is likely reflective of the massive expansion of the human neocortex over mammalian and primate evolution.

“These results showcase the notable diversity of human cortical pyramidal neurons, differences between similarly classified human and pre-clinical neurons, and a plausible hypothesis for the generation of human cortical theta rhythms driven by deep layer neurons,” says Dr. Homeira Moradi Chameh, a scientific associate in Dr. Valiante’s laboratory at Krembil Brain Institute and lead author on the study.

In total, the team was able to characterize over 200 neurons from 61 patients, reflecting the largest dataset of its kind to-date and encapsulating almost a decade’s worth of painstaking work at UHN and the Krembil Brain Institute.

“This unique data set will allow us to build computational models of the distinctly human brain, which will be invaluable for the study of distinctly human neuropathologies,” says Dr. Scott Rich, a postdoctoral research fellow in Dr. Valiante’s laboratory at the Krembil Brain Institute and co-author on this work.

“For instance, the cellular properties driving many of the unique features identified in these neurons are known to be altered in certain types of epilepsy. By implementing these features in computational models, we can study how these alterations affect dynamics at the various spatial scales of the human brain related to epilepsy, and facilitate the translation of these ‘basic science’ findings back to the clinic and potentially into motivations for new avenues in epilepsy research.”

“This effort was only possible because of the very large and active epilepsy program at the Krembil Brain Institute at UHN, one of the largest programs of its kind in the world and the largest program of its kind in Canada,” says Dr. Valiante.

a Example 3D reconstructions (top) and voltage traces (bottom) for L2&3, L3c, and L5 pyramidal cells following hyperpolarizing and depolarizing current injection. Cortical layer and relative position from pial surface are annotated for each reconstructed cell. Asterisk in one branch of apical dendrite in cell g with truncation (dendrite morphologies were otherwise not visibly truncated). b–d Resting membrane potentials (p = 0.007) (b), input resistances (p = 0.111) ( C), and membrane time constants (p < 0.0001) (d) for pyramidal cells in L2&3, L3c, and L5. Error bars in b–d denote mean and standard deviations (SD). One-way ANOVA post hoc with Dunn’s multiple comparison test were used for statistical comparison. L2&3 (n = 56), L3c (n = 15), and L5 (n = 105). ** denotes p = 0.007 and *** denotes p < 0.001.

GABA from vasopressin neurons regulates the time at which suprachiasmatic nucleus molecular clocks enable circadian behavior

by Takashi Maejima, Yusuke Tsuno, Shota Miyazaki, Yousuke Tsuneoka, Emi Hasegawa, Md Tarikul Islam, Ryosuke Enoki, Takahiro J. Nakamura, Michihiro Mieda in Proceedings of the National Academy of Sciences

Researchers at Kanazawa University examined a subset of GABA neurons in the circadian rhythm control center within the hypothalamus of the brain. They eliminated GABA signaling of vasopressin-producing neurons only in mice and found that it impaired circadian behavior. Specifically, time spent being active increased every day. Analysis showed a timing mismatch between the center’s molecular clock and the behavior. Thus, GABA signaling is required to make sure the timing remains in sync.

Our bodies and behaviors often seem to have rhythms of their own. Why do we go to the bathroom at the same time every day? Why do we feel off if we can’t go to sleep at the right time? Circadian rhythms are a behind-the-scenes force that shape many of our behaviors and our health. Michihiro Mieda and his team at Kanazawa University in Japan are researching how the brain’s circadian rhythm control center regulates behavior.

Termed the superchiasmatic nucleus, or SCN, the control center contains many types of neurons that transmit signals using the molecule GABA, but little is known about how each type contributes to our bodily rhythms. In their newest study, the researchers focused on GABA neurons that produce arginine vasopressin, a hormone that regulates kidney function and blood pressure in the body, and which the team recently showed is also involved in regulating the period of rhythms produced by the SCN in the brain.

To examine the function of these neurons, and only these neurons, the researchers first created mice in which a gene needed for GABA signaling between neurons was deleted only in vasopressin-producing SCN neurons. “We removed a gene that codes for a protein that allows GABA to be packaged before it is sent to other neurons,” explains Mieda. “Without packaging, none of the vasopressin neurons could send out any GABA signals.”

This means that these neurons could no longer communicate with the rest of the rhythm control center using GABA. On the surface, the results were simple. The mice showed longer periods of activity, beginning activity earlier and ending activity later than control mice. So, lack of the packaging gene in the neurons disrupted the molecular clock signal, right? In fact, the reality was not so simple. Closer examination showed that the molecular clock progresses correctly. So, what was happening?

The researchers used calcium imaging to examine the clock rhythms within the vasopressin neurons. They found that while the rhythm of activity matched the timing of behavior in control mice, this relationship was disturbed in the mice whose GABA transmission from the vasopressin neurons was missing. In contrast, the rhythm of SCN output, i.e. SCN neuronal electrical activity, in the modified mice had the same irregular rhythm as their behavior. “Our study shows that GABA signaling from vasopressin neurons in the suprachiasmatic nucleus help fix behavioral timing within the constraints of the molecular clock,” says Mieda.

AVP neuron-specific deletion of Vgat reduces the frequency of miniature GABAergic synaptic currents in both AVP neurons and non-AVP neurons during the daytime. (A) Vgat expression in the SCN of Avp-Vgat−/− mice was drastically reduced specifically in AVP neurons. In situ hybridization chain reaction was performed to detect Vgat mRNA (green dots) on coronal brain sections prepared from control (Upper) and Avp-Vgat−/− mice (Lower) crossed with Rosa26-LSL-tdTomato reporter mice. AVP neurons were identified as tdTomato(+) cells. The locations of the magnified images are indicated by white rectangles in the low-power images. (Scale bars: 100 µm and 20 µm in the left and magnified images, respectively.) (B) Amplitude–frequency histograms showing that the mGPSC frequency in AVP neurons from Avp-Vgat−/− mice (red line) was significantly reduced in 10- to 30-pA amplitude bins compared to control mice (blue line) during the daytime (Left, ZT2 to ZT10, n = 29 and 30 from 5 Avp-Vgat−/− and 6 control mice, respectively) but not during the nighttime (Right, ZT14 to ZT22, n = 28 and 26 from 5 Avp-Vgat−/− and 5 control mice, respectively). ( C) The histograms show significant reduction of the mGPSC frequency in non-AVP neurons from Avp-Vgat−/− mice across multiple amplitude bins during the daytime specifically (daytime, n = 18 and 17 from 4 Avp-Vgat−/− and 4 control mice, respectively; nighttime, n = 21 and 16 from 4 Avp-Vgat−/− and 2 control mice, respectively). (Inset) Samples of mGPSCs recorded in control (blue line) and Avp-Vgat−/− mice (red line) in each condition. (Scale bars: 0.5 s and 40 pA.) Values are mean ± SEM; *P < 0.05, **P < 0.01, ***P < 0.001 by two-way repeated-measures ANOVA followed by post hoc pairwise comparisons.

Comparative analysis reveals distinctive epigenetic features of the human cerebellum

by Elaine E. Guevara, William D. Hopkins, Patrick R. Hof, John J. Ely, Brenda J. Bradley, Chet C. Sherwood in PLOS Genetics

The cerebellum — a part of the brain once recognized mainly for its role in coordinating movement — underwent evolutionary changes that may have contributed to human culture, language and tool use. This new finding appears in a study by Elaine Guevara of Duke University and colleagues, published May 6th in the journal PLOS Genetics.

Scientists studying how humans evolved their remarkable capacity to think and learn have frequently focused on the prefrontal cortex, a part of the brain vital for executive functions, like moral reasoning and decision making. But recently, the cerebellum has begun receiving more attention for its role in human cognition. Guevara and her team investigated the evolution of the cerebellum and the prefrontal cortex by looking for molecular differences between humans, chimpanzees, and rhesus macaque monkeys. Specifically, they examined genomes from the two types of brain tissue in the three species to find epigenetic differences. These are modifications that do not change the DNA sequence but can affect which genes are turned on and off and can be inherited by future generations.

Compared to chimpanzees and rhesus macaques, humans showed greater epigenetic differences in the cerebellum than the prefrontal cortex, highlighting the importance of the cerebellum in human brain evolution. The epigenetic differences were especially apparent on genes involved in brain development, brain inflammation, fat metabolism and synaptic plasticity — the strengthening or weakening of connections between neurons depending on how often they are used.

The epigenetic differences identified in the new study are relevant for understanding how the human brain functions and its ability to adapt and make new connections. These epigenetic differences may also be involved in aging and disease. Previous studies have shown that epigenetic differences between humans and chimpanzees in the prefrontal cortex are associated with genes involved in psychiatric conditions and neurodegeneration. Overall, the new study affirms the importance of including the cerebellum when studying how the human brain evolved.

Guevara adds, “Our results support an important role for the cerebellum in human brain evolution and suggest that previously identified epigenetic features distinguishing the human neocortex are not unique to the neocortex.”

Species relationships, brain structures, and sample clustering. A = Phylogenetic tree of species included in this study from, B = brain structures included in this study depicted on a chimpanzee brain illustration modified from, C and D = hierarchical clustering of samples from the dorsolateral prefrontal cortex ( C) and cerebellum (D) based on genome-wide methylation after filtering and normalizing using correlations between samples as distance. DLPFC = dorsolateral prefrontal cortex.

Task-Dependent Functional and Effective Connectivity during Conceptual Processing

by Philipp Kuhnke, Markus Kiefer, Gesa Hartwigsen in Cerebral Cortex

To understand the world, we arrange individual objects, people, and events into different categories or concepts. Concepts such as ‘the telephone’ consist primarily of visible features, i.e. shape and color, and sounds, such as ringing. In addition, there are actions, i.e. how we use a telephone.

However, the concept of telephone does not only arise in the brain when we have a telephone in front of us. It also appears when the term is merely mentioned. If we read the word “telephone,” our brain also calls up the concept of telephone. The same regions in the brain are activated that would be activated if we actually saw, heard, or used a telephone. The brain thus seems to simulate the characteristics of a telephone when its name alone is mentioned.

Until now, however, it was unclear, depending on the situation, whether the entire concept of a telephone is called up or only individual features such as sounds or actions and whether only the brain areas that process the respective feature become active. So, when we think of a telephone, do we always think of all its features or only the part that is needed at the moment? Do we retrieve our sound knowledge when a phone rings, but our action knowledge when we use it?

Researchers at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig have now found the answer: It depends on the situation. If, for example, the study participants thought of the sounds associated with the word “telephone,” the corresponding auditory areas in the cerebral cortex were activated, which are also activated during actual hearing. When thinking about using a telephone, the somatomotor areas that underlie the involved movements came into action.

In addition to these sensory-dependent, so-called modality-specific areas, it was found that there are areas that process both sounds and actions together. One of these so-called multimodal areas is the left inferior parietal lobule (IPL). It became active when both features were requested.

The researchers also found out that, in addition to characteristics based on sensory impressions and actions, there must be other criteria by which we understand and classify terms. This became apparent when the participants were only asked to distinguish between real and invented words. Here, a region that was not active for actions or sounds kicked in: the so-called anterior temporal lobe (ATL). The ATL therefore seems to process concepts abstractly or “amodally,” completely detached from sensory impressions.

From these findings, the scientists finally developed a hierarchical model to reflect how conceptual knowledge is represented in the human brain. According to this model, information is passed on from one hierarchical level to the next and at the same time becomes more abstract with each step. On the lowest level, therefore, are the modality-specific areas that process individual sensory impressions or actions. These transmit their information to the multimodal regions such as the IPL, which process several linked perceptions simultaneously, such as sounds and actions. The amodal ATL, which represents features detached from sensory impressions, operates at the highest level. The more abstract a feature, the higher the level at which it is processed and the further it is removed from actual sensory impressions.

“We thus show that our concepts of things, people, and events are composed, on the one hand, of the sensory impressions and actions associated with them and, on the other hand, of abstract symbol-like features,” explains Philipp Kuhnke, lead author of the study. “Which features are activated depends strongly on the respective situation or task” added Kuhnke.

In a follow-up study in Cerebral Cortex, the researchers also found that modality-specific and multimodal regions work together in a situation-dependent manner when we retrieve conceptual features. The multimodal IPL interacted with auditory areas when retrieving sounds, and with somatomotor areas when retrieving actions. This showed that the interaction between modality-specific and multimodal regions determined the behavior of the study participants. The more these regions worked together, the more strongly the participants associated words with actions and sounds.

The scientists investigated these correlations with the help of various word tasks that the participants solved while lying in a functional magnetic resonance imaging (fMRI) scanner. Here, they had to decide whether they strongly associated the named object with sounds or actions. The researchers showed them words from four categories: 1) objects associated with sounds and actions, such as “guitar,” 2) objects associated with sounds but not with actions, such as “propeller,” 3) objects not associated with sounds but with actions, such as “napkin,” and 4) objects associated neither with sounds nor with actions, such as “satellite.”

Experimental procedure. In two fMRI sessions, participants performed a lexical decision task (A), and sound and action judgment tasks (B). Trials for the four word types high sound–high action (dark blue), high sound–low action (light blue), low sound–high action (dark red), and low sound–low action (light red) were presented in random order within six blocks (64 trials each). Blocks were separated by 20-s rest periods (blue-striped bars). Sound and action judgment tasks were performed in mini-blocks of 16 trials, separated by 12-s rest periods (orange-striped bars). On each trial, a word was shown for 1 s, followed by an intertrial interval (fixation cross) of 2.5–7 s. Participants responded via button press.

Neuronal complexity is attenuated in preclinical models of migraine and restored by HDAC6 inhibition

by Zachariah Bertels, Harinder Singh, Isaac Dripps, Kendra Siegersma, Alycia F Tipton, Wiktor D Witkowski, Zoie Sheets, Pal Shah, Catherine Conway, Elizaveta Mangutov, Mei Ao, Valentina Petukhova, Bhargava Karumudi, Pavel A Petukhov, Serapio M Baca, Mark M Rasenick, Amynah A Pradhan in eLife

By discovering a potential new cellular mechanism for migraines, researchers may have also found a new way to treat chronic migraine.

Amynah Pradhan, associate professor of psychiatry at the University of Illinois Chicago, is the senior author of the study, whose goal was to identify a new mechanism of chronic migraine, and propose a cellular pathway for migraine therapies.

Pradhan, whose research focus is on the neurobiology of pain and headache, explained that the dynamic process of routing and rerouting connections among nerve cells, called neural plasticity, is critical to both the causes and cures for disorders of the central nervous system such as depression, chronic pain, and addiction.

The structure of the cell is maintained by its cytoskeleton which is made up of the protein, tubulin. Tubulin is in a constant state of flux, waxing and waning to change the size and shape of the cell. This dynamic property of the cell allows the nervous system to change in response to its environment.

Tubulin is modified in the body through a chemical process called acetylation. When tubulin is acetylated it encourages flexible, stable cytoskeleton; while tubulin deacetylation — induced by histone deacetylase 6, or HDAC6, promotes cytoskeletal instability.

Studies in mice models show that decreased neuronal complexity may be a feature, or mechanism, of chronic migraine, Pradhan said. When HDAC6 is inhibited, tubulin acetylation and cytoskeletal flexibility is restored. Additionally, HDAC6 reversed the cellular correlates of migraine and relieved migraine-associated pain, according to the study.

“This work suggests that the chronic migraine state may be characterized by decreased neuronal complexity, and that restoration of this complexity could be a hallmark of anti-migraine treatments. This work also forms the basis for development of HDAC6 inhibitors as a novel therapeutic strategy for migraine,” the researchers report.

Pradhan said this research reveals a way to possibly reset the brain toward its pre-chronic migraine state.

“Blocking HDAC6 would allow neurons to restore its flexibility so the brain would be more receptive to other types of treatment. In this model we are saying, maybe chronic migraine sufferers have decreased neuronal flexibility. If we can restore that complexity maybe we could get them out of that cycle,” she said.

Once out of the cycle of decreased neuronal complexity, the brain may become more responsive to pain management therapies, Pradhan said. HDAC6 inhibitors are currently in development for cancer, and HDCA6 as a target has been identified for other types of pain.

“It opens up the possibility of something we should be looking at on a broader scale,” she said. “Are these changes maybe a hallmark of all sorts of chronic pain states?”

Migraine is a common brain disorder that is estimated to affect 14% of the world population. Current U.S. cost estimates for migraine are as high as $40 billion annually. One particularly debilitating subset of migraine patients are those with chronic migraine, defined as having more than 15 headache days a month. Migraine therapies are often only partially effective or poorly tolerated, creating a need for more diverse drug therapies.

Synergistic effect of sleep depth and seizures correlates with postictal heart rate

by Andrew C. Schomer, Morgan Lynch, Stephanie Lowenhaupt, Juliana Leonardo, Valentina Baljak, Matthew Clark, Jaideep Kapur, Mark Quigg in Epilepsia

New research from the University of Virginia School of Medicine reveals why sleep can put people with epilepsy at increased risk of sudden death.

Both sleep and seizures work together to slow the heart rate, the researchers found. Seizures also disrupt the body’s natural regulation of sleep-related changes. Together, in some instances, this can prove deadly, causing Sudden Unexpected Death in Epilepsy, or SUDEP.

“We have been trying to better understand the cardiac changes around the time of a seizure in patients with epilepsy. When we looked at the heart rates for patients with epilepsy admitted to the hospital, many of them develop tachycardia [a fast heart rate] following a seizure, but a subset of patients have a decreased heart rate. This decline was more pronounced when the patients were asleep,” said Andrew Schomer, MD, of UVA’s Department of Neurology and the UVA Brain Institute. “The mechanism of SUDEP, or Sudden Unexpected Death in Epilepsy, is still not fully understood. We know there is an increased risk during sleep and if seizures are poorly controlled. Hopefully with further study we can try to identify individuals who are at an increased risk and work to prevent this devastating outcome.”

Understanding SUDEP in Sleep

Doctors have been unsure how seizures in sleep can cause death, such as was the case with young Disney Channel star Cameron Boyce in 2019. He died of SUDEP while sleeping at age 20. (While SUDEP can occur when patients with epilepsy are awake, the majority of cases occur during sleep.)

To better understand the effect of sleep seizures, UVA researchers led by Schomer and Mark Quigg, MD, MSc, monitored the brain and heart activity of people with epilepsy as they slept. The patients were admitted to the UVA Epilepsy Monitoring Unit between February 2018 and August 2019, and all were 17 or older.

In total, the researchers evaluated 101 sleep seizures in 41 patients, with a median age of 40.5. The participants were, on average, diagnosed more than 20 years previously.

The researchers monitored how deeply the patients were sleeping when the seizures occurred. Some seizures caused heart rates to increase. But the greater sleep depth prior to a seizure, the slower the patient’s heart rate was likely to become, the scientists found.

The results suggest that seizures during sleep are more likely to lead to dangerously slow heart rate. The effect of the seizure is secondary to the natural slowing of the heart rate during sleep, the researchers believe, but the two together can, in some instances, prove deadly.

More study is needed to better understand the variables involved and to better determine what is occurring in individual patients, the researchers say. But the findings represent an important advance in the effort to prevent SUDEP during sleep.

“People with poorly controlled seizures have the greatest risk of SUDEP, and seizures during sleep may hold the higher risk,” said Quigg, of UVA’s Department of Neurology and the UVA Brain Institute. “Our findings can direct further research to determine how the heart’s and lung’s control systems fail during sleep-related seizures in order to help prevent SUDEP.”

Association of Local Variation in Neighborhood Disadvantage in Metropolitan Areas With Youth Neurocognition and Brain Structure

by Daniel A. Hackman, Dora Cserbik, Jiu-Chiuan Chen, Kiros Berhane, Bita Minaravesh, Rob McConnell, Megan M. Herting in JAMA Pediatrics

A new USC study suggests that certain neighborhoods — particularly those characterized by poverty and unemployment — may pose an environmental risk to the developing brains of children, impacting neurocognitive performance and even brain size.

These findings highlight the importance of neighborhood environments for child and adolescent brain development, the researchers said, and suggest that policies, programs and investments that help improve local neighborhood conditions and empower communities could support children’s neurodevelopment and long-term health.

“This is the first large, national study of neurodevelopment to determine that the role of neighborhood disadvantage is similar across all regions of the country, and we found that what mattered most were the local differences in neighborhood disadvantage within each city, rather than how cities differ from each other overall,” said lead author Daniel Hackman, assistant professor at the USC Suzanne Dworak-Peck School of Social Work.

Researchers from the USC Suzanne Dworak-Peck School of Social Work and the Keck School of Medicine of USC used data from the Adolescent Brain and Cognitive Development (ABCD) Study, collected from October 2016–2018. The ABCD Study is the largest long-term study of brain development and child health in the United States.

Neighborhood disadvantage, after accounting for family socioeconomic status and perceptions of neighborhood safety, showed associations with multiple aspects of neurocognition and smaller total cortical surface area, particularly in the frontal, parietal and temporal lobes.

“Our findings aren’t specific to the child’s home life, as we adjusted for socioeconomic factors at each child’s home. But the research suggests neighborhoods may have different levels of social and educational resources and opportunities that can impact a child’s neurodevelopment,” said senior author Megan Herting, assistant professor at the department of preventive medicine at the Keck School of Medicine at USC.

In addition, the researchers said, disadvantaged neighborhoods may lack quality health services, access to nutritional foods, and well-maintained parks and rec facilities; they may also expose residents to more pollutants or social stressors.

“This research is important as it not only highlights that neighborhoods matter, but it also suggests that promoting neighborhood equity based on the unique local conditions within cities may improve short and long-term health and development of children and adolescents,” said Hackman.


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