Researchers have identified a cancer-causing mutation in the PDGFRA gene that drives cell mutation and growth when activated. The findings have implications for the treatment of a subset of glioblastoma brain cancer.
WASHINGTON (SBG) — Researchers studying cognitive deficits following traumatic brain injuries have discovered what they say is a revolutionary drug that could provide the cure for aging. The study by the University of California San Francisco has shown promising results among mice, essentially reversing age-related declines in memory. “We went on with this crazy experiment… and were able to return their cognitive function to as if they were never injured,” said Dr.
Researchers at EPFL have developed an approach to print tiny tissues that look and function almost like their full-sized counterpart. Measuring just a few centimeters across, the mini-tissues could allow scientists to study biological processes—and even test new treatment approaches—in ways that were previously not possible.
For years, mini versions of organs such as the brain, kidney and lung—known as “organoids”—have been grown from stem cells. Organoids promise to cut down on the need for animal testing and offer better models to study how human organs form and how that process goes awry in disease. However, conventional approaches to grow organoids result in stem cells assembling into micro-to millimeter-sized, hollow spheres. “That is non-physiological, because many organs, such as the intestine or the airway, are tube-shaped and much larger,” says Matthias Lütolf, a professor at EPFL’s Institute of Bioengineering, who led the study published today in Nature Materials.
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Neuroscientists find that interpreting code activates a general-purpose brain network, but not language-processing centers.
In some ways, learning to program a computer is similar to learning a new language. It requires learning new symbols and terms, which must be organized correctly to instruct the computer what to do. The computer code must also be clear enough that other programmers can read and understand it.
In spite of those similarities, MIT neuroscientists have found that reading computer code does not activate the regions of the brain that are involved in language processing. Instead, it activates a distributed network called the multiple demand network, which is also recruited for complex cognitive tasks such as solving math problems or crossword puzzles.
“People want to know what makes someone a good programmer,” Liu said. “If we know what kind of neuro mechanisms are activated when someone is programming, we might be able to find a better training program for programmers.” By mapping the brain activity of expert computer programmers while they puzzled over code, Johns Hopkins University scientists have found the neural mechanics behind this increasingly vital skill.
Though researchers have long suspected the brain mechanism for computer programming would be similar to that for math or even language, this study revealed that when seasoned coders work, most brain activity happens in the network responsible for logical reasoning, though in the left brain region, which is favored by language.
“Because there are so many ways people learn programming, everything from do-it-yourself tutorials to formal courses, it’s surprising that we find such a consistent brain activation pattern across people who code,” said lead author Yun-Fei Liu, a Ph.D. student in the university’s Neuroplasticity and Development Lab. “It’s especially surprising because we know there seems to be a crucial period that usually terminates in early adolescence for language acquisition, but many people learn to code as adults.”
Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used functional magnetic resonance imaging to investigate two candidate brain systems: the multiple demand (MD) system, typically recruited during math, logic, problem solving, and executive tasks, and the language system, typically recruited during linguistic processing. We examined MD and language system responses to code written in Python, a text-based programming language (Experiment 1) and in ScratchJr, a graphical programming language (Experiment 2); for both, we contrasted responses to code problems with responses to content-matched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments, whereas the language system responded strongly to sentence problems, but weakly or not at all to code problems. Thus, the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.
Second, we chose 2 major Appraisals with well-established roles in emotion elicitation, but interactive game paradigms could also investigate the neural basis of other appraisals (e.g., novelty, social norms). Furthermore, our study did not elucidate the precise cognitive mechanisms of particular appraisals or their neuroanatomical substrates but rather sought to dissect distinct brain networks underlying appraisals and other emotion components in order to assess any transient synchronization among them during emotion-eliciting situations. Importantly, even though different appraisals would obviously engage different brain networks, a critical assumption of the CPM is that synchronization between these networks and other components would arise through similar mechanisms as found here.
Third, our task design and event durations were chosen for fMRI settings, with blocked conditions and sufficient repetitions of similar trials. The limited temporal resolution of fMRI did not allow the investigation of faster, within-level dynamics which may be relevant to emotions. Additionally, this slow temporal resolution and our brain-based synchronization approach are insufficient to uncover fast and recurrent interactions among component networks during synchronization, as hypothesized by the CPM. Nonetheless, our computational model for the peripheral synchronization index did include recurrence as one of its parameters, allowing us refine our model-based analysis of network synchronization in ways explicitly taking recurrent effects into account (see S1 Text and Table J in S1 Table). In any case, neither the correlation of a model-based peripheral index nor an instantaneous phase synchronization approach could fully verify this hypothesis at the neuronal level using fMRI. To address these limitations, future studies might employ other paradigms with different game events or other imaging analyses and methodologies with higher temporal resolution. Higher temporal resolution may also help shed light on causality factors hypothesized by the CPM, which could not be addressed here. Finally, our study focused on the 4 nonexperiential components of emotion, with feelings measured purely retrospectively for manipulation-check purposes. This approach was motivated conceptually by the point of view that an emotion can be characterized comprehensively by the combination of its nonexperiential parts [10] and methodologically by the choice to avoid self-report biases and dual task conditions in our experimental setting. However, future work will be needed to link precise moments of component synchronization more directly to concurrent measures along relevant emotion dimensions, without task biases, as previously examined in purely behavioral research [20].
Nevertheless, by investigating emotions from a dynamic multi-componential perspective with interactive situations and model-based parameters, our study demonstrates the feasibility of a new approach to emotion research. We provide important new insights into the neural underpinnings of emotions in the human brain that support theoretical accounts of emotions as transient states emerging from embodied and action-oriented processes which govern adaptive responses to the environment. By linking transient synchronization between emotion components to specific brain hubs in basal ganglia, insula, and midline cortical areas that integrate sensorimotor, interoceptive, and self-relevant representations, respectively, our results provide a new cornerstone to bridge neuroscience with psychological and developmental frameworks in which affective functions emerge from a multilevel integration of both physical/bodily and psychological/cognitive processes [62].
This work develops PSID, a dynamic modeling method to dissociate and prioritize neural dynamics relevant to a given behavior.
Is it possible to read a person’s mind by analyzing the electric signals from the brain? The answer may be much more complex than most people think.
Purdue University researchers—working at the intersection of artificial intelligence and neuroscience—say a prominent dataset used to try to answer this question is confounded, and therefore many eye-popping findings that were based on this dataset and received high-profile recognition are false after all.
The Purdue team performed extensive tests over more than one year on the dataset, which looked at the brain activity of individuals taking part in a study where they looked at a series of images. Each individual wore a cap with dozens of electrodes while they viewed the images.
