Center for Translational Neuroscience Launches!

We are proud to announce the formation of the new Center for Translational Neuroscience within the Prevention Science Institute at the UO. Check out the mission statement:

The mission of the CTN is to translate knowledge from basic neuroscience and apply it to improve well-being, promote resilience, and mitigate the effects of early adverse experiences. Activities at the CTN address scientific issues of social importance with the goal of informing policy and practice. Core focal areas include, but are not limited to, poverty and maltreatment and their effects on addiction and mental health disorders. CTN takes a lifespan development approach to understand how specific brain and biological systems are affected by genetic and environmental influences. The knowledge obtained from this work is deployed in the development of evidence-based practices and science-informed public policy. An emphasis is placed on using experimental designs to help understand mechanisms and individual differences in response to interventions. The CTN employs successive iterations of neuroscience research, theory building, and practice to drive the scientific knowledge base forward. The CTN interfaces with other Centers and departments at the UO, including the Lewis Center for Neuroimaging, in the fulfillment of its mission. Relevant to the CTN mission is a focus on training at the undergraduate, graduate, and postdoctoral levels in neuroscience-informed approaches to prevention and intervention. Researchers and staff at the CTN embrace a collaborative, multidisciplinary, team-based approach in all of their activities. Like its parent Prevention Science Institute, the CTN is a multi-disciplinary center that includes faculty from several colleges and units on campus, including the Department of Psychology and the College of Education.

What is translational neuroscience? Here are 10 Principles of the field:

  1. Identify environmentally malleable neurobiological functions, circuits, and systems that underlie behavioral risk and resilience.
  2. Focus on central processes (e.g., executive function, reward) broadly implicated in well-being and maladjustment.
  3. Emphasize developmental processes, sensitive periods, and inflection points that may inform intervention strategies.
  4. Use intervention trials to test theories that connect causes to outcomes via underlying neurobiological mediators.
  5. Include rigorous measurement of contextual factors (e.g., poverty, neighborhood, culture) that confer risk or resiliency.
  6. Apply precision interventions that are (a) tailored to individuals based on biobehavioral characteristics, and (b) grounded in robust neuroscience.
  7. Prioritize those neurobiological models that parsimoniously add explanatory power to behavioral theories and evidence.
  8. Emphasize on applications of neuroscience to interpersonal processes such as co-regulation.
  9. Elucidate linkages between physical health and mental health (e.g., immune system, inflammation, neuroendocrine system, autonomic nervous system, and gut microbiome).
  10. Select neurobiological methodologies and measures based on their suitability to the hypotheses being tested.
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See our work at SANS in New York!

Elliot and other SANlabbers will be presenting new work at the Social and Affective Neuroscience Society (SANS) conference, April 28-30 in New York!

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Interview with Elliot on the neuroscience of self-control

Check out Elliot’s interview about self-control and the role of neuroscience in understanding it on BlackBoxPhD!

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See our work at APS

SAN Lab grad student Lauren Kahn will be presenting her work on incentivizing self-control at the Association for Psychological Science on Sunday. Hope to see you there!

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Do we overeat because of poor self-control?

See Elliot’s latest blog post on The Motivated Brain over at Psychology Today:

…Self-control is a resource, but a renewable, psychological one. We’ve known for a long time that goals that are motivated from within—for reasons that are personally important to us—are more likely to succeed than those that are motivated from without. … Succeeding at self-control is more about the desire rather than the technical skill to do so. We know how to stop eating in a literal sense; we just don’t know how to think about overeating in a way that motivates us to stop.

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Sociality as a natural mechanism of public goods provision

Peoples economic decisions are nearly always embedded in a social context. To what extent does that context influence their decisions, if at all? Social factors such as group memberships and affiliative motives have powerful effects on a range of behaviors. These factors carry substantial decision utility for people, but this social utilityis rarely included in formal models of economic behavior. Indeed, classical economic theory has been strongly challenged by findings that economic players often do not reason in terms of pure utility-maximization. The critical breakdown point of economic models is in explaining behaviors that are altruistic or at least non-selfish.

In this paper, we marry some of the rich theoretical models of social behavior taken from social psychology with decision modeling techniques from behavioral economics. Specifically, we used a classic minimal groupparadigm from social psychology to induce a sense of social connectedness in our experimental subjects that we call “sociality”. We then used economic choice tasks to measure the amount of utility imparted by sociality to decisions that favor group well-being but otherwise have little to no economic utility. In other words, we looked at the effect of brief “icebreaker” interactions with strangers on subsequent behavior in economic exchange games with those people.

We found that even these brief social interactions dramatically altered how people treated with each other. First, people were far more likely to cooperate, rather than compete, with others from their socialized group. This result held even when the interactions were anonymous save the knowledge that the partner was known to be in (or out) of the socialized group. Second, this general preference for fairness and cooperation persisted throughout the sequence of economic interactions to a greater degree than it did when people interacted with strangers or computer opponents. And third, socialization increased people’s tolerance for defection from others within the group; people were willing to give others in their group the “benefit of the doubt”, continuing to treat them fairly even when they occasionally took advantage. Together, the increase and maintenance in cooperation and the greater tolerance of occasional bad behavior shifted the group norm from one to mistrust and competition to one of trust and cooperation.

The implications of this work are at once pedestrian and profound. On the one hand, it makes sense that people would act more fairly to people they know than people they don’t, even if that tendency had not been demonstrated in an economic context until now. On the other hand, we were surprised at how much our light-touch sociality induction–basically a 5 minute icebreaker–had an enduring effect on behaviors that had direct economic implications for our participants. They were literally willing to forego money to maintain a norm of fairness within their group. For “social animals” like us, sometimes getting to know someone is all it takes to foster a sense of group identity, which in turn increases the kinds of prosocial feelings and behaviors that we all would like to see more often.

Citation info: Berkman, E.T., Lukinova, E., Menshikov, I., & Myagkov, M. (2015). Sociality as a natural mechanism of public goods provision. PLoS ONE, 10(3), e0119685. doi:10.1371/journal.pone.0119685 [NOTE: Author order determined alphabetically by last name.] [pdf] [PLoS]

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Predictive validity of three eating measures

The following is a guest post by Nicole Giuliani.

Food researchers (such as myself) study topics such as how often people crave their favorite foods or how well they are able to control those desires. We use a variety of laboratory measurement instruments as a proxy for how food craving and control work in the real world. However, it is not yet known how well many of those instruments—including the most common ones—predict actual food consumption out in the real world. Furthermore, we do not know how well the measurements we take in the laboratory predict real world eating for people who are on a diet versus people who are eating without restriction. Three common types of measurements are: people’s reports of how much they generally tend to desire certain foods and to regulate those desires (which we call “stable” tendencies to desire and regulate desire); people’s reports of the desirability of those foods at the moment that they view them or try to control their desire for them (“momentary”), and how people’s brain activity changes when they look at pictures of their desired foods or try to control their desire for those foods (“neural”).

My colleagues Elliot Berkman, Traci Mann, Janet Tomiyama and I recently published a study in the journal Psychosomatic Medicine in which we directly compared how well each of these three types of laboratory measurements predicted real world consumption of a favored tasty but unhealthy food. We brought a group of healthy people into the lab, asked them what their favorite unhealthy food was, and then gathered stable, momentary, and neural measurements of their craving for that food and their ability and tendency to regulate that craving. At the very end of the lab session, we split our participants into two random groups: one in which participants would simply record how much of their favorite unhealthy food they ate each day across the following two weeks (“monitor” group), and another in which participants tried not to eat their food and also recorded how much of it they ate (“restrict” group).

Graphs representing the relationship between Stable, Momentary, and Neural Reactivity (a) and Regulation (b) and Predicted Total Target Food Servings Consumed. Participants instructed to restrict target food consumption are shown in gray, participants instructed to simply monitor consumption (control) are shown in black. The x-axis scales represent the degree of reactivity (top row) or regulation (bottom row) in standard units, and reflect a composite of the measures based on independent components analyses as described in the text; y-axis scales are the predicted total number of target food servings consumed across the two-week sampling period. * ME p < 0.05; ^ interaction p < 0.05

Graphs representing the relationship between Stable, Momentary, and Neural Reactivity (a) and Regulation (b) and Predicted Total Target Food Servings Consumed. Participants instructed to restrict target food consumption are shown in gray, participants instructed to simply monitor consumption (control) are shown in black. The x-axis scales represent the degree of reactivity (top row) or regulation (bottom row) in standard units, and reflect a composite of the measures based on independent components analyses as described in the text; y-axis scales are the predicted total number of target food servings consumed across the two-week sampling period. * ME p < 0.05; ^ interaction p < 0.05

This experiment revealed some interesting differences in how well the instruments predicted actual food consumption in the two groups. First, higher scores on the momentary and neural measurements of food craving predicted more food consumption only for the monitor group, and had no relationship with eating in the restriction group. In other words, people who said they craved foods when exposed to them (and whose brains reacted strongly to those foods) generally ate more, but only when they weren’t trying to diet. Second, greater scores on the stable and momentary measurements of food regulation predicted less eating in the monitor group, whereas greater scores on the stable and neural measurements of food regulation predicted more consumption for the restriction group. That is, people who say they are “good regulators” do indeed eat less—but only when they aren’t trying to. When they try to diet, they actually eat more! This set of results is important because it suggests that food researchers should chose their measurements carefully, in part based on whether or not their participants will be restricting their food intake.

Citation info: Giuliani, N.R., Tomiyama, A.J., Mann, T., & Berkman, E.T.  (in press). Prediction of daily food intake as a function of measurement modality and restriction status. Psychosomatic Medicine.

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New grant from the National Institute on Aging for inhibitory control training

Our latest translational neuroscience project tests whether a computerized training program can reverse the effects that early adverse experiences have on inhibitory control. The project is funded by the National Institute on Aging and will last two years.

Early adversity (EA) in humans is a major contributing factor to a range of deleterious physical and mental health outcomes extending through adulthood such as depression and anxiety, obesity and heart disease, and premature death. In addition to detracting significantly from individual well-being and quality of life, these conditios also consume considerable resources from federal, state, and community organizations. The mechanisms through which EA exerts its effects on these outcomes are increasingly well understood, and include neurocognitive pathways related to executive function. An intervention that can successfully target, engage with, and alter the functioning of one or more of these mechanisms would be a promising way of mitigating the impact of EA on deleterious outcomes later in life. The proposed research focuses on one such pathway-deficits in inhibitory control (IC)-and tests the feasibility and efficacy of an intervention to increase functioning in that pathway in a sample of individuals who experienced EA. The intervention is grounded in a neurally informed model of change that specifies deficits in IC as an underlying causal factor common to several health-risking behaviors (HRBs). These IC deficits emerge during development as a result of a range of EA, and, critically, can be remediated in mid-life through targeted intervention. Research from our laboratory has validated an intervention that can increase IC performance and alter its underlying neural systems in young adults (Berkman, Kahn, & Merchant, 2014). The next step in this program of research, proposed here, is to test the efficacy of that intervention in a sample of mid-life individuals who have experienced EA and the extent to which our intervention generalizes to HRBs that are prevalent in that sample. The first Aim is to test whether the intervention alters the IC system in tasks both similar to and dissimilar from the training task in terms of both behavioral performance and neural functioning. The second Aim is to test whether alterations in the functioning of the underlying neural systems mediate the effect of the intervention on performance and The two Aims will be accomplished within the context of a single RCT with two arms (IC training vs. active control) and pre-post measurements of IC performance, IC neural systems, and HRBs. All participants (N = 110) come to the lab for an initial assessment of behavioral / neural measures of IC and HRBs, among other measures. Then, participants are randomly assigned to receive a Person-Centered Inhibitory Control (PeCIC) training or active control training, every other day for 3-4 weeks. The PeCIC systematically pairs IC engagement with alcohol, tobacco, and/or energy-dense food cues, depending on each participant’s reports of disinhibited behavior in those domains. The active control task uses personalized cues and response time tasks but does not involve IC. Finally, participants return to the laboratory for an endpoint assessment where all baseline measures are repeated. The two Aims will be robustly tested in a series of analyses comparing the behavioral and neural change from pre- to post-intervention between the groups disinhibition-related HRBs.

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New grant from the National Cancer Institute to study food craving regulation

The lab has received funding from the National Cancer Institute (NCI) for a project entitled, “Reducing craving for cancer-promoting foods via cognitive self-regulation”. You can read more about the grant here.

The grant will last for two years beginning this fall.

Eating energy-dense foods when one is not hungry is a contributor to overweight and obesity, which are risk factors for a range of cancers. Excessive eating of a subset of these foods, such as red meat or foods with a high glycemic index, is an additional risk factor for cancer, separate from overweight and obesity. We refer to foods that are linked to cancer through either or both routes as cancer-promoting foods. The goal of this project is to reduce cancer risk by improving cognitive self-regulation of cravings for cancer-promoting foods. We focus on craving of cancer-promoting foods as one proximal determinant of their consumption. Craving consists of a subjective sense of wanting to eat a food, a motivation to seek out the food, and recurrent or intrusive thoughts related to the food. Considerable research shows that craving is a strong predictor of eating, even in the absence of hunger. Thus, enhancing a simple, low-cost and easily disseminated tool to reduce craving for cancer-promoting foods would advance cancer prevention and related research. Studies from affective science and social neuroscience have identified cognitive self-regulation strategies that are effective in reducing craving and their associated neural systems. This work has focused mostly on craving for other appetitive stimuli (e.g., drug cues), and has only begun to study regulation of food craving. Recent results from our laboratory validated four strategies that are effective in reducing cravings for energy-dense foods. This work relies upon self-reports of craving, which provide an empirical starting point but do not demonstrate the validity of the strategies on their own. Thus, the goals of the proposed project are to provide additional support for the effectiveness of cognitive self-regulation of food cravings using other measures beyond self-report, and to validate a theoretically grounded means to further increase the efficacy of those strategies-strategy choice. These goals will be accomplished in the context of a single study with two sessions. First, participants will be randomly assigned to choose their regulation strategy or to have one selected for them. In Session 1, their self-reported cravings and neural responses will be recorded while they alternately view images of energy- dense foods and regulate their responses to those foods. These data will be used to examine the effects of food regulation on neural activation and self-reports of craving, and to compare the effect of strategy choice on those measures. In Session 2, participants will visit our behavioral laboratory three days following Session 1 for a session in which their actual intake of energy-dense food will be measured in a naturalistic and unobtrusive manner. The difference in energy-dense food intake between the two groups will provide a behavioral measure of the efficacy of strategy choice on eating. Also, brain activity during food cue reactivity and regulation from Session 1 will be used to predict intake during Session 2. Psychological theory and previous neuroscience data suggest that neural activity, particularly in the medial prefrontal cortex, might explain variance in energy-dense food intake above and beyond self-report, and might mediate the effect of strategy choice on intake.

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When does feedback increase or decrease motivation?

See Jordan Miller-Ziegler‘s thoughts on this question over at the lab blog on Psychology Today, The Motivated Brain.

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