Though progress is being made, our brains remain organs of many mysteries. Among these are the exact workings of neurons, with some 86 billion of them in the human brain. Neurons are interconnected in complicated, labyrinthine networks across which they exchange information in the form of electrical signals. We know that signals exit an individual neuron through a fiber called an axon, and also that signals are received by each neuron through input fibers called dendrites.
Understanding the electrical capabilities of dendrites in particular — which, after all, may be receiving signals from countless other neurons at any given moment — is fundamental to deciphering neurons’ communication. It may surprise you to learn, though, that much of everything we assume about human neurons is based on observations made of rodent dendrites — there’s just not a lot of fresh, still-functional human brain tissue available for thorough examination.
For a new study published January 3 in the journal Science, however, scientists got a rare chance to explore some neurons from the outer layer of human brains, and they discovered startling dendrite behaviors that may be unique to humans, and may even help explain how our billions of neurons process the massive amount of information they exchange.
Electrical signals weaken with distance, and that poses a riddle to those seeking to understand the human brain: Human dendrites are known to be about twice as long as rodent dendrites, which means that a signal traversing a human dendrite could be much weaker arriving at its destination than one traveling a rodent’s much shorter dendrite. Says paper co-author biologist Matthew Larkum of Humboldt University in Berlin speaking to LiveScience, «If there was no change in the electrical properties between rodents and people, then that would mean that, in the humans, the same synaptic inputs would be quite a bit less powerful.» Chalk up another strike against the value of animal-based human research. The only way this would not be true is if the signals being exchanged in our brains are not the same as those in a rodent. This is exactly what the study’s authors found.
The researchers worked with brain tissue sliced for therapeutic reasons from the brains of tumor and epilepsy patients. Neurons were resected from the disproportionately thick layers 2 and 3 of the cerebral cortex, a feature special to humans. In these layers reside incredibly dense neuronal networks.
Without blood-borne oxygen, though, such cells only last only for about two days, so Larkum’s lab had no choice but to work around the clock during that period to get the most information from the samples. «You get the tissue very infrequently, so you’ve just got to work with what’s in front of you,» says Larkum. The team made holes in dendrites into which they could insert glass pipettes. Through these, they sent ions to stimulate the dendrites, allowing the scientists to observe their electrical behavior.
In rodents, two type of electrical spikes have been observed in dendrites: a short, one-millisecond spike with the introduction of sodium, and spikes that last 50- to 100-times longer in response to calcium.
In the human dendrites, one type of behavior was observed: super-short spikes occurring in rapid succession, one after the other. This suggests to the researchers that human neurons are «distinctly more excitable » than rodent neurons, allowing them to successfully traverse our longer dendrites.
In addition, the human neuronal spikes — though they behaved somewhat like rodent spikes prompted by the introduction of sodium — were found to be generated by calcium, essentially the opposite of rodents.
The study also reports a second major finding. Looking to better understand how the brain utilizes these spikes, the team programmed computer models based on their findings. (The brains slices they’d examined could not, of course, be put back together and switched on somehow.)
The scientists constructed virtual neuronal networks, each of whose neurons could could be stimulated at thousands of points along its dendrites, to see how each handled so many input signals. Previous, non-human, research has suggested that neurons add these inputs together, holding onto them until the number of excitatory input signals exceeds the number of inhibitory signals, at which point the neuron fires the sum of them from its axon out into the network.
However, this isn’t what Larkum’s team observed in their model. Neurons’ output was inverse to their inputs: The more excitatory signals they received, the less likely they were to fire off. Each had a seeming «sweet spot» when it came to input strength.
What the researchers believe is going on is that dendrites and neurons may be smarter than previously suspected, processing input information as it arrives. Mayank Mehta of UC Los Angeles, who’s not involved in the research, tells LiveScience, «It doesn’t look that the cell is just adding things up — it’s also throwing things away.» This could mean each neuron is assessing the value of each signal to the network and discarding «noise.» It may also be that different neurons are optimized for different signals and thus tasks.
Much in the way that octopuses distribute decision-making across a decentralized nervous system, the implication of the new research is that, at least in humans, it’s not just the neuronal network that’s smart, it’s all of the individual neurons it contains. This would constitute exactly the kind of computational super-charging one would hope to find somewhere in the amazing human brain.
The dendritic arms of some human neurons can perform logic operations that once seemed to require whole neural networks.
The information-processing capabilities of the brain are often reported to reside in the trillions of connections that wire its neurons together. But over the past few decades, mounting research has quietly shifted some of the attention to individual neurons, which seem to shoulder much more computational responsibility than once seemed imaginable.
The latest in a long line of evidence comes from scientists’ discovery of a new type of electrical signal in the upper layers of the human cortex. Laboratory and modeling studies have already shown that tiny compartments in the dendritic arms of cortical neurons can each perform complicated operations in mathematical logic. But now it seems that individual dendritic compartments can also perform a particular computation — “exclusive OR” — that mathematical theorists had previously categorized as unsolvable by single-neuron systems.
“I believe that we’re just scratching the surface of what these neurons are really doing,” said Albert Gidon, a postdoctoral fellow at Humboldt University of Berlin and the first author of the paper that presented these findings in Science earlier this month.
The discovery marks a growing need for studies of the nervous system to consider the implications of individual neurons as extensive information processors. “Brains may be far more complicated than we think,” said Konrad Kording, a computational neuroscientist at the University of Pennsylvania, who did not participate in the recent work. It may also prompt some computer scientists to reappraise strategies for artificial neural networks, which have traditionally been built based on a view of neurons as simple, unintelligent switches.
The Limitations of Dumb Neurons
In the 1940s and ’50s, a picture began to dominate neuroscience: that of the “dumb” neuron, a simple integrator, a point in a network that merely summed up its inputs. Branched extensions of the cell, called dendrites, would receive thousands of signals from neighboring neurons — some excitatory, some inhibitory. In the body of the neuron, all those signals would be weighted and tallied, and if the total exceeded some threshold, the neuron fired a series of electrical pulses (action potentials) that directed the stimulation of adjacent neurons.
At around the same time, researchers realized that a single neuron could also function as a logic gate, akin to those in digital circuits (although it still isn’t clear how much the brain really computes this way when processing information). A neuron was effectively an AND gate, for instance, if it fired only after receiving some sufficient number of inputs.
Networks of neurons could therefore theoretically perform any computation. Still, this model of the neuron was limited. Not only were its guiding computational metaphors simplistic, but for decades, scientists lacked the experimental tools to record from the various components of a single nerve cell. “That’s essentially the neuron being collapsed into a point in space,” said Bartlett Mel, a computational neuroscientist at the University of Southern California. “It didn’t have any internal articulation of activity.” The model ignored the fact that the thousands of inputs flowing into a given neuron landed in different locations along its various dendrites. It ignored the idea (eventually confirmed) that individual dendrites might function differently from one another. And it ignored the possibility that computations might be performed by other internal structures.
This compartmentalization of signals meant that separate dendrites could be processing information independently of one another. “This was at odds with the point-neuron hypothesis, in which a neuron simply added everything up regardless of location,” Mel said.
That prompted Koch and other neuroscientists, including Gordon Shepherd at the Yale School of Medicine, to model how the structure of dendrites could in principle allow neurons to act not as simple logic gates, but as complex, multi-unit processing systems. They simulated how dendritic trees could host numerous logic operations, through a series of complex hypothetical mechanisms.
Later, Mel and several colleagues looked more closely at how the cell might be managing multiple inputs within its individual dendrites. What they found surprised them: The dendrites generated local spikes, had their own nonlinear input-output curves and had their own activation thresholds, distinct from those of the neuron as a whole. The dendrites themselves could act as AND gates, or as a host of other computing devices.
Mel, along with his former graduate student Yiota Poirazi (now a computational neuroscientist at the Institute of Molecular Biology and Biotechnology in Greece), realized that this meant that they could conceive of a single neuron as a two-layer network. The dendrites would serve as nonlinear computing subunits, collecting inputs and spitting out intermediate outputs. Those signals would then get combined in the cell body, which would determine how the neuron as a whole would respond.
Whether the activity at the dendritic level actually influenced the neuron’s firing and the activity of neighboring neurons was still unclear. But regardless, that local processing might prepare or condition the system to respond differently to future inputs or help wire it in new ways, according to Shepherd.
Whatever the case, “the trend then was, ‘OK, be careful, the neuron might be more powerful than you thought,’” Mel said.
Shepherd agreed. “Much of the power of the processing that takes place in the cortex is actually subthreshold,” he said. “A single-neuron system can be more than just one integrative system. It can be two layers, or even more.” In theory, almost any imaginable computation might be performed by one neuron with enough dendrites, each capable of performing its own nonlinear operation.
In the recent Science paper, the researchers took this idea one step further: They suggested that a single dendritic compartment might be able to perform these complex computations all on its own.
Unexpected Spikes and Old Obstacles
Matthew Larkum, a neuroscientist at Humboldt, and his team started looking at dendrites with a different question in mind. Because dendritic activity had been studied primarily in rodents, the researchers wanted to investigate how electrical signaling might be different in human neurons, which have much longer dendrites. They obtained slices of brain tissue from layers 2 and 3 of the human cortex, which contain particularly large neurons with many dendrites. When they stimulated those dendrites with an electrical current, they noticed something strange.
They saw unexpected, repeated spiking — and those spikes seemed completely unlike other known kinds of neural signaling. They were particularly rapid and brief, like action potentials, and arose from fluxes of calcium ions. This was noteworthy because conventional action potentials are usually caused by sodium and potassium ions. And while calcium-induced signaling had been previously observed in rodent dendrites, those spikes tended to last much longer.
Stranger still, feeding more electrical stimulation into the dendrites lowered the intensity of the neuron’s firing instead of increasing it. “Suddenly, we stimulate more and we get less,” Gidon said. “That caught our eye.”
To figure out what the new kind of spiking might be doing, the scientists teamed up with Poirazi and a researcher in her lab in Greece, Athanasia Papoutsi, who jointly created a model to reflect the neurons’ behavior.
The model found that the dendrite spiked in response to two separate inputs — but failed to do so when those inputs were combined. This was equivalent to a nonlinear computation known as exclusive OR (or XOR), which yields a binary output of 1 if one (but only one) of the inputs is 1.
This finding immediately struck a chord with the computer science community. XOR functions were for many years deemed impossible in single neurons: In their 1969 book Perceptrons, the computer scientists Marvin Minsky and Seymour Papert offered a proof that single-layer artificial networks could not perform XOR. That conclusion was so devastating that many computer scientists blamed it for the doldrums that neural network research fell into until the 1980s.
Neural network researchers did eventually find ways of dodging the obstacle that Minsky and Papert identified, and neuroscientists found examples of those solutions in nature. For example, Poirazi already knew XOR was possible in a single neuron: Just two dendrites together could achieve it. But in these new experiments, she and her colleagues were offering a plausible biophysical mechanism to facilitate it — in a single dendrite.
“For me, it’s another degree of flexibility that the system has,” Poirazi said. “It just shows you that this system has many different ways of computing.” Still, she points out that if a single neuron could already solve this kind of problem, “why would the system go to all the trouble to come up with more complicated units inside the neuron?”
Processors Within Processors
Certainly not all neurons are like that. According to Gidon, there are plenty of smaller, point-like neurons in other parts of the brain. Presumably, then, this neural complexity exists for a reason. So why do single compartments within a neuron need the capacity to do what the entire neuron, or a small network of neurons, can do just fine? The obvious possibility is that a neuron behaving like a multilayered network has much more processing power and can therefore learn or store more. “Maybe you have a deep network within a single neuron,” Poirazi said. “And that’s much more powerful in terms of learning difficult problems, in terms of cognition.”
Perhaps, Kording added, “a single neuron may be able to compute truly complex functions. For example, it might, by itself, be able to recognize an object.” Having such powerful individual neurons, according to Poirazi, might also help the brain conserve energy.
Larkum’s group plans to search for similar signals in the dendrites of rodents and other animals, to determine whether this computational ability is unique to humans. They also want to move beyond the scope of their model to associate the neural activity they observed with actual behavior. Meanwhile, Poirazi now hopes to compare the computations in these dendrites to what happens in a network of neurons, to suss out any advantages the former might have. This will include testing for other types of logic operations and exploring how those operations might contribute to learning or memory. “Until we map this out, we can’t really tell how powerful this discovery is,” Poirazi said.
Though there’s still much work to be done, the researchers believe these findings mark a need to rethink how they model the brain and its broader functions. Focusing on the connectivity of different neurons and brain regions won’t be enough.
The new results also seem poised to influence questions in the machine learning and artificial intelligence fields. Artificial neural networks rely on the point model, treating neurons as nodes that tally inputs and pass the sum through an activity function. “Very few people have taken seriously the notion that a single neuron could be a complex computational device,” said Gary Marcus, a cognitive scientist at New York University and an outspoken skeptic of some claims made for deep learning.
Although the Science paper is but one finding in an extensive history of work that demonstrates this idea, he added, computer scientists might be more responsive to it because it frames the issue in terms of the XOR problem that dogged neural network research for so long. “It’s saying, we really need to think about this,” Marcus said. “The whole game — to come up with how you get smart cognition out of dumb neurons — might be wrong.”
“This is a super clean demonstration of that,” he added. “It’s going to speak above the noise.”
Few creatures have captured the attention of both the general public and scientists as thoroughly as a peculiar-looking salamander known as the axolotl. Native only to Lake Xochimilco, south of Mexico City, axolotls are less and less frequently found in the wild. However, they are relatively abundant in captivity, with pet enthusiasts raising them due to their alien features, such as the striking, fringy crown they wear on their heads. Researchers also keep a large supply of axolotl in captivity due to the many unique properties that make them attractive subjects of study.
Perhaps the most notable and potentially useful of these characteristics is the axolotl’s uncanny ability to regenerate. Unlike humans and other animals, axolotls don’t heal large wounds with the fibrous tissue that composes scars. Instead, they simply regrow their injured part.
«It regenerates almost anything after almost any injury that doesn’t kill it,» said Yale researcher Parker Flowers in a statement. This capability is remarkably robust, even for salamanders. Where regular salamanders are known to regrow lost limbs, axolotls have been observed regenerating ovaries, lung tissues, eyes, and even parts of the brain and spinal cord.
Obviously, figuring out how these alien-looking salamanders manage this magic trick is of great interest to researchers. Doing so could reveal a method for providing humans with a similar regenerative capability. But identifying the genes involved in this process has been tricky — the axolotl has a genome 10 times larger than that of a human’s, making it the largest animal genome sequenced to date.
Fortunately, Flowers and colleagues recently discovered a means of more easily navigating this massive genome and, in the process, identified two genes involved in the axolotl’s remarkable regenerative capacity.
A new role for two genes
We’ve understood the basic process of regeneration in axolotls for a while now. After a limb is severed, for instance, blood cells clot at the site, and skin cells start to divide and cover the exposed wound. Then, nearby cells begin to travel to the site and congregate in a blob called the blastema. The blastema then begins to differentiate into the cells needed to grow the relevant body part and grow outward according to the appropriate limb structure, resulting in a new limb identical to its severed predecessor.
But identifying which genes code for this process and what mechanisms guide its actions is less clear. Building off of previous work using CRISPR/Cas9, Flowers and colleagues were able to imprint regenerated cells with a kind of genetic barcode that enabled them to trace the cells back to their governing genes. In this way, they were able to identify and track 25 genes suspected to be involved in the regeneration process. From these 25, they identified two genes related to the axolotls’ tail regeneration; specifically, the catalase and fetub genes.
Although the researchers stressed that many more genes were likely driving this complicated process, the finding does have important implications for human beings — namely that humans also possess similar genes to the two identified in this study. Despite sharing similar genes, the same gene can do very different work across species and within a single animal. The human equivalent gene FETUB, for example, produces proteins that regulate bone resorption, regulates insulin and hepatocyte growth factor receptors, responds to inflammation, and more. In the axolotl, it appears that regulating the regenerative process is another duty.
Since humans possess the same genes that enable axolotls to regenerate, researchers are optimistic that one day we will be able to speed up wound healing or even to completely replicate the axolotl’s incredible ability to regenerate organs and limbs. With continued research such as this, it’s only a matter of time until this strange salamander gives ups its secrets.
A study has found that adolescents who frequently use cannabis may experience a decline in Intelligence Quotient (IQ) over time. The findings of the research provide further insight into the harmful neurological and cognitive effects of frequent cannabis use on young people.
The paper, led by researchers at RCSI University of Medicine and Health Sciences, is published in Psychological Medicine.
The results revealed that there were declines of approximately 2 IQ points over time in those who use cannabis frequently compared to those who didn’t use cannabis. Further analysis suggested that this decline in IQ points was primarily related to reduction in verbal IQ.
The research involved systematic review and statistical analysis on seven longitudinal studies involving 808 young people who used cannabis at least weekly for a minimum of 6 months and 5308 young people who did not use cannabis. In order to be included in the analysis each study had to have a baseline IQ score prior to starting cannabis use and another IQ score at follow-up. The young people were followed up until age 18 on average although one study followed the young people until age 38.
«Previous research tells us that young people who use cannabis frequently have worse outcomes in life than their peers and are at increased risk for serious mental illnesses like schizophrenia. Loss of IQ points early in life could have significant effects on performance in school and college and later employment prospects,» commented senior author on the paper Professor Mary Cannon, Professor of Psychiatric Epidemiology and Youth Mental Health, RCSI.
«Cannabis use during youth is of great concern as the developing brain may be particularly susceptible to harm during this period. The findings of this study help us to further understand this important public health issue,» said Dr Emmet Power, Clinical Research Fellow at RCSI and first author on the study.
The study was carried out by researchers from the Department of Psychiatry, RCSI and Beaumont Hospital, Dublin (Prof Mary Cannon, Dr Emmet Power, Sophie Sabherwal, Dr Colm Healy, Dr Aisling O’Neill and Professor David Cotter).
The research was funded by a YouLead Collaborative Doctoral Award from the Health Research Board (Ireland) and a European Research Council Consolidator Award.
James Heckman já era vencedor do Nobel de Economia quando começou a se dedicar ao assunto pelo qual passaria a ser realmente conhecido: a primeira infância (de 0 a 5 anos de idade), sua relação com a desigualdade social e o potencial que há nessa fase da vida para mudanças que possam tirar pessoas da pobreza.
O glioblastoma é o tipo de tumor primário mais comum no cérebro, altamente agressivo e maligno. Pacientes com esse tipo de câncer geralmente são submetidos a ressecção seguida de radioterapia e quimioterapia. Apesar do tratamento, a sobrevida é de 12 a 18 meses a partir da data do diagnóstico. Novos tratamentos têm sido desenvolvidos e um deles tem se mostrado promissor. O estudo da referência aborda uma técnica que envolve a aplicação de ultrassom focal na região tumoral através do crânio intacto associada à aplicação de uma substância que sensibiliza as células para os efeitos prejudiciais do som. A terapia sonodinâmica representa uma grande promessa para o tratamento de cânceres que se espalharam para áreas sensíveis do corpo (metástases) e, em particular, do cérebro. 📑♒🧠Este tema será abordado no módulo Tratamento & Reabilitação do Cérebro da @mybrainuniversity
Referência: Sheehan, K., Sheehan, D., Sulaiman, M. et al. Investigation of the tumoricidal effects of sonodynamic therapy in malignant glioblastoma brain tumors. J Neurooncol 148, 9-16 (2020). doi.org/10.1007/s11060-020-03504-w (imagem autoral)
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The brain continues to surprise us with its magnificent complexity. Groundbreaking research that combines neuroscience with math tells us that our brain creates neural structures with up to 11 dimensions when it processes information. By «dimensions,» they mean abstract mathematical spaces, not other physical realms. Still, the researchers «found a world that we had never imagined,» said Henry Markram, director of the Blue Brain Project, which made the discovery.