The science behind many anti-depressant medications appears to be backwards, say the authors of a paper that challenges the prevailing ideas about the nature of depression and some of the world’s most commonly prescribed medications.
A key component of survival is learning to associate rewarding outcomes with specific actions, such as searching for food or avoiding predators. Actions are represented in the cortex—the brain's outer layer of neural tissue—and rewarding outcomes activate neurons that release a brain chemical called dopamine. These neuronal signals are sent to the striatum—the input station for a collection of brain structures called the basal ganglia, which play an important role in action selection. Collectively, this evidence suggests that dopamine signals change the strength of connections between cortical and striatal neurons, thereby determining which action is appropriate for a specific set of environmental circumstances. But until now, no model had integrated these strands of evidence to test this widely held hypothesis.
In a study published this week in PLOS Biology, University of Sheffield researchers Kevin Gurney and Peter Redgrave teamed up with Mark Humphries of the University of Manchester to build a computational model that shows how the brain's internal signal for outcome changes the strength of neuronal connections, leading to the selection of rewarded actions and the suppression of unrewarded actions. By bridging the gap between the intricate subtleties of individual neuronal connections and the behavior of the whole animal, the model reveals how several brain signals work together to shape the input from the cortex to the basal ganglia at the interface between actions and their outcomes.
The researchers developed a network model of the whole basal ganglia based on previous electrophysiological studies that investigated the activity of two types of dopamine-responsive cells called D1 and D2 striatal medium spiny neurons. In addition to this action selection model, they developed an independent plasticity model by incorporating experimental data from a previous study to show how the strength of neuronal connections, called synapses, is affected by three factors: the timing of neuronal activity, the type of medium spiny neuron, and dopamine level. Then they linked the two models, testing whether plasticity rules at single synapses between cortical and striatal neurons could give rise to the predicted changes in the activity of the two types of medium spiny neurons, resulting in successful learning of the association between actions and outcomes.
Studies of motor control have almost universally examined firing rates to investigate how the brain shapes behavior. In principle, however, neurons could encode information through the precise temporal patterning of their spike trains as well as (or instead of) through their firing rates. Although the importance of spike timing has been demonstrated in sensory systems, it is largely unknown whether timing differences in motor areas could affect behavior. We tested the hypothesis that significant information about trial-by-trial variations in behavior is represented by spike timing in the songbird vocal motor system. We found that neurons in motor cortex convey information via spike timing far more often than via spike rate and that the amount of information conveyed at the millisecond timescale greatly exceeds the information available from spike counts. These results demonstrate that information can be represented by spike timing in motor circuits and suggest that timing variations evoke differences in behavior.
Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.
Is it possible to rapidly increase (or decrease) the amount of information the brain can store? A new international study led by the Research Institute of the McGill University Health Centre (RI-MUHC) suggests is may be. Their research has identified a molecule that improves brain function and memory recall is improved. Published in the latest issue of Cell Reports, the study has implications for neurodevelopmental and neurodegenerative diseases, such as autism spectral disorders and Alzheimer’s disease.
“Our findings show that the brain has a key protein called FXR1P (Fragile X Related Protein 1) that limits the production of molecules necessary for memory formation,” says RI-MUHC neuroscientist Keith Murai, the study’s senior author and Associate Professor in the Department of Neurology and Neurosurgery at McGill University. “When this brake-protein is suppressed, the brain is able to store more information.”
Murai and his colleagues used a mouse model to study how changes in brain cell connections produce new memories. When FXR1P was selectively removed from certain parts of the brain, new molecules were produced. They strengthened connections between brain cells, which correlated with improved memory and recall in the mice.
“The role of FXR1P was a surprising result,” says Dr. Murai. “Previous to our work, no-one had identified a role for this regulator in the brain. Our findings have provided fundamental knowledge about how the brain processes information. We’ve identified a new pathway that directly regulates how information is handled and this could have relevance for understanding and treating brain diseases.”
“Future research in this area could be very interesting,” he adds. “If we can identify compounds that control the braking potential of FXR1P, we may be able to alter the amount of brain activity or plasticity. For example, in autism, one may want to decrease certain brain activity and in Alzheimer’s disease, we may want to enhance the activity. By manipulating FXR1P, we may eventually be able to adjust memory formation and retrieval, thus improving the quality of life of people suffering from brain diseases.”
The brain's plasticity and its adaptability to new situations do not function the way researchers previously thought, according to a new study. Earlier theories are based on laboratory animals, but now researchers have studied the human brain, and reached some new conclusions.
Being shown pictures of others being loved and cared for reduces the brain's response to threat, new research has found. The study discovered that when individuals are briefly presented pictures of others receiving emotional support and affection, the brain's threat monitor, the amygdala, subsequently does not respond to images showing threatening facial expressions or words. This occurred even if the person was not paying attention to the content of the first pictures.
Researchers have successfully replicated a direct brain-to-brain connection between pairs of people as part of a scientific study following the team's initial demonstration a year ago. In the newly published study, which involved six people, researchers were able to transmit the signals from one person's brain over the Internet and use these signals to control the hand motions of another person within a split second of sending that signal.
Traditionally, fMRI data are analyzed using statistical parametric mapping approaches. Regardless of the precise thresholding procedure, these approaches ultimately divide the brain in regions that do or do not differ significantly across experimental conditions. This binary classification scheme fosters the so-called imager's fallacy, where researchers prematurely conclude that region A is selectively involved in a certain cognitive task because activity in that region reaches statistical significance and activity in region B does not. For such a conclusion to be statistically valid, however, a test on the differences in activation across these two regions is required. Here we propose a simple GLM-based method that defines an “in-between” category of brain regions that are neither significantly active nor inactive, but rather “in limbo”. For regions that are in limbo, the activation pattern is inconclusive: it does not differ significantly from baseline, but neither does it differ significantly from regions that do show significant changes from baseline. This pattern indicates that measurement was insufficiently precise. By directly testing differences in activation, our procedure helps reduce the impact of the imager's fallacy. The method is illustrated using concrete examples.
The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity.
A central feature of theories of spatial navigation involves the representation of spatial relationships between objects in complex environments. The parietal cortex has long been linked to the processing of spatial visual information and recent evidence from single unit recording in rodents suggests a role for this region in encoding egocentric and world-centered frames. The rat parietal cortex can be subdivided into up to four distinct rostral-caudal and medial-lateral regions, which includes a zone previously characterized as secondary visual cortex. At present, very little is known regarding the relative connectivity of these parietal subdivisions. Thus, we set out to map the connectivity of the entire anterior-posterior and medial-lateral span of this region. To do this we used anterograde and retrograde tracers in conjunction with open source neuronal segmentation and tracer detection tools to generate whole brain connectivity maps of parietal inputs and outputs. Our present results show that inputs to the parietal cortex varied significantly along the medial-lateral, but not the rostral-caudal axis. Specifically, retrosplenial connectivity is greater medially, but connectivity with visual cortex, though generally sparse, is more significant laterally. Finally, based on connection density, the connectivity between parietal cortex and hippocampus is indirect and likely achieved largely via dysgranular retrosplenial cortex. Thus, similar to primates, the parietal cortex of rats exhibits a difference in connectivity along the medial-lateral axis, which may represent functionally distinct areas.
The U.S., Europe and Asia have launched big brain research projects. What impact will they have? Scientists integral to three projects share their insights ahead of a special session hosted by the Society for Neuroscience.
Researchers have shown how a single neuron can perform multiple functions in a model organism, illuminating for the first time this fundamental biological mechanism and shedding light on the human brain.
Despite great advances in understanding how the human brain works, psychiatric conditions, neurodegenerative disorders, and brain injuries are on the rise. Progress in the development of new diagnostic and treatment approaches appears to have stalled. Experts look at the challenges associated with 'translational neuroscience,' or efforts to bring advances in the lab to the patients who need them.
Donald J Bolger's insight:
Review of the issues with translational neuroscience.
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