Researchers 3D-print functional human brain tissue

A team of University of Wisconsin-Madison scientists has developed the first 3D-printed brain tissue that can grow and function like typical brain tissue.

It's an achievement with important implications for scientists studying the brain and working on treatments for a broad range of neurological and neurodevelopmental disorders, such as Alzheimer's and Parkinson's disease.

"This could be a hugely powerful model to help us understand how brain cells and parts of the brain communicate in humans," says Su-Chun Zhang, professor of neuroscience and neurology at UW-Madison's Waisman Center.

"It could change the way we look at stem cell biology, neuroscience, and the pathogenesis of many neurological and psychiatric disorders."

Printing methods have limited the success of previous attempts to print brain tissue, according to Zhang and Yuanwei Yan, a scientist in Zhang's lab.

The group behind the new 3D-printing process described their method today in the journal Cell Stem Cell.

Instead of using the traditional 3D-printing approach, stacking layers vertically, the researchers went horizontally.

They situated brain cells, neurons grown from induced pluripotent stem cells, in a softer "bio-ink" gel than previous attempts had employed.

"The tissue still has enough structure to hold together but it is soft enough to allow the neurons to grow into each other and start talking to each other," Zhang says.

The cells are laid next to each other like pencils laid next to each other on a tabletop.

"Our tissue stays relatively thin and this makes it easy for the neurons to get enough oxygen and enough nutrients from the growth media," Yan says.

The results speak for themselves -- which is to say, the cells can speak to each other.

The printed cells reach through the medium to form connections inside each printed layer as well as across layers, forming networks comparable to human brains.

The neurons communicate, send signals, interact with each other through neurotransmitters, and even form proper networks with support cells that were added to the printed tissue.

"We printed the cerebral cortex and the striatum and what we found was quite striking," Zhang says.

"Even when we printed different cells belonging to different parts of the brain, they were still able to talk to each other in a very special and specific way."

The printing technique offers precision -- control over the types and arrangement of cells -- not found in brain organoids, miniature organs used to study brains.

The organoids grow with less organization and control.

"Our lab is very special in that we are able to produce pretty much any type of neurons at any time. Then we can piece them together at almost any time and in whatever way we like," Zhang says.

"Because we can print the tissue by design, we can have a defined system to look at how our human brain network operates. We can look very specifically at how the nerve cells talk to each other under certain conditions because we can print exactly what we want."

That specificity provides flexibility. The printed brain tissue could be used to study signaling between cells in Down syndrome, interactions between healthy tissue and neighboring tissue affected by Alzheimer's, testing new drug candidates, or even watching the brain grow.

"In the past, we have often looked at one thing at a time, which means we often miss some critical components. Our brain operates in networks. We want to print brain tissue this way because cells do not operate by themselves. They talk to each other. This is how our brain works and it has to be studied all together like this to truly understand it," Zhang says.

"Our brain tissue could be used to study almost every major aspect of what many people at the Waisman Center are working on. It can be used to look at the molecular mechanisms underlying brain development, human development, developmental disabilities, neurodegenerative disorders, and more."

The new printing technique should also be accessible to many labs.

It does not require special bio-printing equipment or culturing methods to keep the tissue healthy, and can be studied in depth with microscopes, standard imaging techniques and electrodes already common in the field.

The researchers would like to explore the potential of specialization, though, further improving their bio-ink and refining their equipment to allow for specific orientations of cells within their printed tissue..

"Right now, our printer is a benchtop commercialized one," Yan says.

Read more at Science Daily

Brain recordings capture musicality of speech -- with help from Pink Floyd

As the chords of Pink Floyd's "Another Brick in the Wall, Part 1," filled the surgery suite, neuroscientists at Albany Medical Center diligently recorded the activity of electrodes placed on the brains of patients undergoing epilepsy surgery.

The goal? To capture the electrical activity of brain regions tuned to attributes of the music -- tone, rhythm, harmony and words -- to see if they could reconstruct what the patient was hearing.

More than a decade later, after detailed analysis of data from 29 such patients by neuroscientists at the University of California, Berkeley, the answer is clearly yes.

The phrase "All in all it was just a brick in the wall" comes through recognizably in the reconstructed song, its rhythms intact, and the words muddy, but decipherable. This is the first time researchers have reconstructed a recognizable song from brain recordings.

The reconstruction shows the feasibility of recording and translating brain waves to capture the musical elements of speech, as well as the syllables. In humans, these musical elements, called prosody -- rhythm, stress, accent and intonation -- carry meaning that the words alone do not convey.

Because these intracranial electroencephalography (iEEG) recordings can be made only from the surface of the brain -- as close as you can get to the auditory centers -- no one will be eavesdropping on the songs in your head anytime soon.

But for people who have trouble communicating, whether because of stroke or paralysis, such recordings from electrodes on the brain surface could help reproduce the musicality of speech that's missing from today's robot-like reconstructions.

"It's a wonderful result," said Robert Knight, a neurologist and UC Berkeley professor of psychology in the Helen Wills Neuroscience Institute who conducted the study with postdoctoral fellow Ludovic Bellier. "One of the things for me about music is it has prosody and emotional content. As this whole field of brain machine interfaces progresses, this gives you a way to add musicality to future brain implants for people who need it, someone who's got ALS or some other disabling neurological or developmental disorder compromising speech output. It gives you an ability to decode not only the linguistic content, but some of the prosodic content of speech, some of the affect. I think that's what we've really begun to crack the code on."

As brain recording techniques improve, it may be possible someday to make such recordings without opening the brain, perhaps using sensitive electrodes attached to the scalp. Currently, scalp EEG can measure brain activity to detect an individual letter from a stream of letters, but the approach takes at least 20 seconds to identify a single letter, making communication effortful and difficult, Knight said.

"Noninvasive techniques are just not accurate enough today. Let's hope, for patients, that in the future we could, from just electrodes placed outside on the skull, read activity from deeper regions of the brain with a good signal quality. But we are far from there," Bellier said.

Bellier, Knight and their colleagues reported the results today in the journal PLOS Biology, noting that they have added "another brick in the wall of our understanding of music processing in the human brain."

Reading your mind? Not yet.

The brain machine interfaces used today to help people communicate when they're unable to speak can decode words, but the sentences produced have a robotic quality akin to how the late Stephen Hawking sounded when he used a speech-generating device.

"Right now, the technology is more like a keyboard for the mind," Bellier said. "You can't read your thoughts from a keyboard. You need to push the buttons. And it makes kind of a robotic voice; for sure there's less of what I call expressive freedom."

Bellier should know. He has played music since childhood -- drums, classical guitar, piano and bass, at one point performing in a heavy metal band. When Knight asked him to work on the musicality of speech, Bellier said, "You bet I was excited when I got the proposal."

In 2012, Knight, postdoctoral fellow Brian Pasley and their colleagues were the first to reconstruct the words a person was hearing from recordings of brain activity alone.

More recently, other researchers have taken Knight's work much further. Eddie Chang, a UC San Francisco neurosurgeon and senior co-author of the 2012 paper, has recorded signals from the motor area of the brain associated with jaw, lip and tongue movements to reconstruct the speech intended by a paralyzed patient, with the words displayed on a computer screen.

That work, reported in 2021, employed artificial intelligence to interpret the brain recordings from a patient trying to vocalize a sentence based on a set of 50 words.

While Chang's technique is proving successful, the new study suggests that recording from the auditory regions of the brain, where all aspects of sound are processed, can capture other aspects of speech that are important in human communication.

"Decoding from the auditory cortices, which are closer to the acoustics of the sounds, as opposed to the motor cortex, which is closer to the movements that are done to generate the acoustics of speech, is super promising," Bellier added. "It will give a little color to what's decoded."

For the new study, Bellier reanalyzed brain recordings obtained in 2012 and 2013 as patients were played an approximately 3-minute segment of the Pink Floyd song, which is from the 1979 album The Wall. He hoped to go beyond previous studies, which had tested whether decoding models could identify different musical pieces and genres, to actually reconstruct music phrases through regression-based decoding models.

Bellier emphasized that the study, which used artificial intelligence to decode brain activity and then encode a reproduction, did not merely create a black box to synthesize speech. He and his colleagues were also able to pinpoint new areas of the brain involved in detecting rhythm, such as a thrumming guitar, and discovered that some portions of the auditory cortex -- in the superior temporal gyrus, located just behind and above the ear -- respond at the onset of a voice or a synthesizer, while other areas respond to sustained vocals.

The researchers also confirmed that the right side of the brain is more attuned to music than the left side.

"Language is more left brain. Music is more distributed, with a bias toward right," Knight said.

"It wasn't clear it would be the same with musical stimuli," Bellier said. "So here we confirm that that's not just a speech-specific thing, but that's it's more fundamental to the auditory system and the way it processes both speech and music."

Knight is embarking on new research to understand the brain circuits that allow some people with aphasia due to stroke or brain damage to communicate by singing when they cannot otherwise find the words to express themselves.

Read more at Science Daily

A neuro-chip to manage brain disorders

Mahsa Shoaran of the Integrated Neurotechnologies Laboratory in the School of Engineering collaborated with Stéphanie Lacour in the Laboratory for Soft Bioelectronic Interfaces to develop NeuralTree: a closed-loop neuromodulation system-on-chip that can detect and alleviate disease symptoms. Thanks to a 256-channel high-resolution sensing array and an energy-efficient machine learning processor, the system can extract and classify a broad set of biomarkers from real patient data and animal models of disease in-vivo, leading to a high degree of accuracy in symptom prediction.

"NeuralTree benefits from the accuracy of a neural network and the hardware efficiency of a decision tree algorithm," Shoaran says. "It's the first time we've been able to integrate such a complex, yet energy-efficient neural interface for binary classification tasks, such as seizure or tremor detection, as well as multi-class tasks such as finger movement classification for neuroprosthetic applications."

Their results were presented at the 2022 IEEE International Solid-State Circuits Conference and published in the IEEE Journal of Solid-State Circuits, the flagship journal of the integrated circuits community.

Efficiency, scalability, and versatility

NeuralTree functions by extracting neural biomarkers -- patterns of electrical signals known to be associated with certain neurological disorders -- from brain waves. It then classifies the signals and indicates whether they herald an impending epileptic seizure or Parkinsonian tremor, for example. If a symptom is detected, a neurostimulator -- also located on the chip -- is activated, sending an electrical pulse to block it.

Shoaran explains that NeuralTree's unique design gives the system an unprecedented degree of efficiency and versatility compared to the state-of-the-art. The chip boasts 256 input channels, compared to 32 for previous machine-learning-embedded devices, allowing more high-resolution data to be processed on the implant. The chip's area-efficient design means that it is also extremely small (3.48mm2), giving it great potential for scalability to more channels. The integration of an 'energy-aware' learning algorithm -- which penalizes features that consume a lot of power -- also makes NeuralTree highly energy efficient.

In addition to these advantages, the system can detect a broader range of symptoms than other devices, which until now have focused primarily on epileptic seizure detection. The chip's machine learning algorithm was trained on datasets from both epilepsy and Parkinson's disease patients, and accurately classified pre-recorded neural signals from both categories.

"To the best of our knowledge, this is the first demonstration of Parkinsonian tremor detection with an on-chip classifier," Shoaran says.

Self-updating algorithms

Shoaran is passionate about making neural interfaces more intelligent to enable more effective disease control, and she is already looking ahead to further innovations.

"Eventually, we can use neural interfaces for many different disorders, and we need algorithmic ideas and advances in chip design to make this happen. This work is very interdisciplinary, and so it also requires collaborating with labs like the Laboratory for Soft Bioelectronic Interfaces, which can develop state-of-the-art neural electrodes, or labs with access to high-quality patient data."

As a next step, she is interested in enabling on-chip algorithmic updates to keep up with the evolution of neural signals.

"Neural signals change, and so over time the performance of a neural interface will decline. We are always trying to make algorithms more accurate and reliable, and one way to do that would be to enable on-chip updates, or algorithms that can update themselves."

Read more at Science Daily