Taking the subjectivity out of pain measurement… Using holographic manipulations to understand optogenetic thresholds… Neurons to networks…Studying how parents perceive autism in their at-risk children…
Learned lick behavior may tell scientists more about brain-driven pain processes than reflexes
Pain is subjective — each person will likely rank the same amount of pain differently. This subjectivity makes it hard for researchers to measure pain in an objective way. To solve this problem, researchers designed a self-report pain detection assay in rodents. Specifically, mice were trained to provide ‘lick reports’ in response to a painful stimulus. Taking advantage of mice’s proclivity to learn to lick, Dr. David Borton’s research group at Brown University studies the neural dynamics underlying pain processing. In a recent paper, these researchers demonstrated that a novel transgenic mouse system can be used to study pain objectively. Specifically, they created a transgenic mouse that expressed a combination of channel-rhodopsin2 (ChR2) and a heat-sensitive cation channel, transient receptor potential vanilloid family member 1 (TRPV1). Because ChR2 is an excitatory light-sensitive opsin, the researchers were able to selectively turn on the TRPV1-containing neurons using optogenetic technology. In tandem with this transgenic approach, these mice were trained in an observer-independent, lick-based detection task using operant conditioning. Optogenetic stimulation was applied in the periphery to the heat-sensitive cation channels expressing ChR2 in the hind paw. This allowed Dr. Borton and his team to train the rats to lick when objective pain was felt in the hind paw. These objective responses to pain by the mice were rewarded with water, whereas reflexive responses to tactile stimulation (‘catch’ responses) were not rewarded. This unique model enables behavioral response quantification at sub-second timescales with millisecond precision. Importantly, it also requires conscious decision-making. This novel self-report model is an objective measure of pain, rather than a subjective one. By using this new model, scientists can more precisely study how higher-order brain circuits give rise to pain, which may one day inform studies of pain and therapies in humans.
Although optogenetic techniques remain popular, scientists are still optimizing this approach for a wide variety of tasks and purposes. Newer holographic optogenetic techniques allow researchers to create three-dimensional patterns of light. The overarching goal of these efforts is to create more realistic reconstructions of natural neural activity during optogenetic manipulations. One such lab on this frontier of optogenetic technology is the Neural Interface Engineering Laboratory led by Dr. Shy Shoham at New York University. These researchers have optimized an all-optical two-photon calcium imaging and holographic optogenetic system capable of stimulating greater than 30 neurons simultaneously in a three-dimensional space within the olfactory bulb of awake-behaving mice. This method can achieve single-spike resolution with millisecond precision. To demonstrate this new system, Dr. Shoham and his team targeted excitatory mitral cells (MCs) within the olfactory bulb. Using a spatial light modulator to holographically generate soma-covering light patches, they selectively targeted the cells using two-photon optogenetic stimulation. They were particularly interested in what threshold of excitation was needed in order to produce a behavioral effect in mice. Using this methodology, they systematically omitted neurons within the targeted area in order to determine this threshold. Although there were individual differences in the mice with different degrees of sensitivity, remarkably the threshold performance varied from approximately eight to 20 neurons active at the same time. The results of this study also demonstrate that synchronous spiking, and not latency to inhale an odor, of MCs is an essential feature for the detection of sparse neural signals. Altogether, this study provides critical evidence that the number of simultaneously active neurons necessary for a behavior to occur is an order of magnitude lower (less than 30 versus greater than 300) than previously thought. Research such as this may provide key insights into how neuronal populations realistically control behavioral output.
How do we get from neural activity to networks? What is the current conceptualization of these representations? At the University of Pennsylvania, one group is particularly interested in biological, physical, and social systems by using and developing tools from network science and complex systems theory. This group is the Complex Systems Lab of Dr. Danielle Bassett. Together with her graduate student Harang Ju, Dr. Bassett recently published a new review article that explores how patterns of activity evolve from one representation to another, thus forming dynamic representations on the underlying neural network. Ju and Dr. Bassett start their review with a discussion about neural representations. One question they tackle is: How does a population of neurons represent variables by activating in a specific pattern in response to a particular stimulus? Further, they ask: How do neurons (or larger neural units) form, change, and transmit representations? Next, they move on to considering network models in order to better understand how neural representations evolve over time. Overall, they propose a holistic, theoretical perspective that could help to shape future neuroscience research that focuses on neural information processing. This could help frame recent developments in systems neuroscience in a way that moves the field toward a better understanding of the dynamic nature of neural representations. To learn more about this fascinating review and what Dr. Bassett’s student, Harang Ju, has to say about this work, check out this news release from Medical Xpress.
The National Institutes of Health BRAIN Initiative funds many different types of investigators – engineers, biologists, chemists, computer scientists, psychologists, ethicists, and more – including those who are in their postdoctoral career stage. Dr. Katherine MacDuffie is one such researcher who is supported by a postdoctoral fellowship in neuroethics through the BRAIN Initiative. In her recent, first-author paper, Dr. MacDuffie and other members of the Infant Brain Imaging Study (IBIS) Network examined how parents perceive and respond to the risk of autism spectrum disorder (ASD) in their high-risk infants. High-risk in this study was defined as an immediate family history of ASD, or, in other words, having an older sibling with a diagnosis on the spectrum. This longitudinal study enrolled high-risk infants ranging from 3- or 6-months to 24-months of age. During the study, parent interviews were conducted to investigate beliefs about early autism risk and associations with parenting stress, coping, and family functioning. This exploratory study found, through semi-structured interviews, that parents tended to overestimate the recurrence of ASD within their family and that perceptions were influenced by comparisons of the at-risk infant with another child diagnosed with ASD (wherein similarities between the two children were associated with more worry and differences were associated with less worry). Parents also reported many negative emotions (e.g., worry, fear, and sadness) during this uncertain time before a diagnosis can be made. Parents also altered their behavior in response to the perception of ASD risk. Some of these behavioral changes included aspects of their own reproductive healthcare decisions. Nevertheless, parents were able to describe the cognitive strategies that they use to cope with uncertainty, such as trying to mentally reframe the perceived risk. Altogether, this study suggests that some parents of children with ASD are aware of an increased risk to later born children. Moreover, they are motivated to engage in intensive monitoring in order to detect ASD symptoms earlier in their infant. Importantly, this study informs the development of rudimentary predictive tests of ASD and reveals how parents’ perceptions change over time in the context of ASD and their children.