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Are there effective emotion regulation strategies that do not depend on top-down cognitive control? 

“Let it be: Mindful-acceptance down-regulates pain and negative emotion”

Behavioral studies have shown that mindfulness- or acceptance-based treatments ameliorate depression, anxiety, addiction, and chronic pain; improve functionality and quality of life in cancer and other conditions. Brain imaging studies have examined individuals who were trained or regularly engage in mindfulness meditation. While promising, such studies do not directly address the use of mindful acceptance as an emotion regulation strategy in individuals who do not practice meditation, and findings could depend on cumulative effects of training or characteristics of individuals who seek it. We addressed this using functional magnetic resonance imaging (fMRI) and adapting a well-established emotion regulation task to assess the effects of mindful acceptance on affective and neural responses in meditation-naïve adults. Identifying and understanding the mechanisms supporting such strategies could lead to improved treatments for emotionally vulnerable populations.

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A systematic approach to building machine learning models for neuroimaging

“Interpreting machine learning models in neuroimaging: Towards a unified framework”

Machine Learning (ML) has rapidly increased in popularity in both basic and translational research. The use of ML in neuroimaging experiments has provided new answers to many enduring research questions. However, these models are complex and often hard to interpret, making it difficult to evaluate their neuroscientific validity and contribution to understanding the brain. Therefore, there is a pressing need for methods to help interpret and explain the model decisions and provide neuroscientific validation for neuroimaging ML models. In this protocol, we introduce a unified framework that consists of model-, feature- and biology-level assessments to provide complementary results that support the understanding of how and why a model works. We first propose a unified framework for interpreting ML models in neuroimaging based on model-level, feature-level, and neurobiology-level assessments. Then, we provide a workflow that illustrates how this framework can be employed to predictive models, along with practical examples of analyses for each level of assessment with a sample fMRI dataset (available for download at https://github.com/cocoanlab/interpret_ml_neuroimaging). Continue reading “A systematic approach to building machine learning models for neuroimaging”

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Socially conveyed expectations vs. learned expectations and their underlying neural systems

“Different brain networks mediate the effects of social and conditioned expectations on pain”

Beliefs and expectations shape human experience and behavior in many important ways. Expectations could be based on what we have learned from our own prior experience, via classical conditioning or other forms of associative learning or they can stem from secondary sources, such as what others tell us about their experiences. However, it remains unclear whether similar or different neural systems mediate direct experience-driven and vicarious influences. In this study, we used fMRI to dissociate the brain mediators of social influence and associative learning effects on pain and observed that social information and conditioned stimuli each had significant effects on pain ratings, and both effects were mediated by self-reported expectations. Yet, these effects were mediated by largely separable brain activity patterns, involving different large-scale functional networks. These results show that learned versus socially instructed expectations modulate pain via partially different mechanisms—a distinction that should be accounted for by theories of predictive coding and related top-down influences.

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Can providers’ expectations affect patients’ treatment outcomes?

Source: Daily Sun

“Socially transmitted placebo effects” 

Studies of placebo effects have demonstrated that manipulations of the interpersonal and physical treatment context can, in some cases, produce substantial effects on symptoms and behaviour and associated brain processes. Despite the robustness of these interpersonal-expectancy effects, there has been surprisingly little research demonstrating a causal link between providers’ expectations and patients’ treatment outcomes. In this study, we systematically manipulated providers’ expectations in a simulated clinical interaction involving administration of thermal pain and found that patients’ subjective experiences of pain were directly modulated by providers’ expectations of treatment success, as reflected in the patients’ subjective ratings, skin conductance responses and facial expression behaviours. Our study suggests that providers’ expectations about the efficacy of a treatment can substantially affect patients’ treatment outcomes via implicit social cues. This finding has important implications for virtually all clinical interactions between patients and providers and highlights the importance of explicit training in bedside manner when delivering information and interventions.

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A new chapter for the CANlab!

The CANlab has moved to Dartmouth College! A contingent of the CANlab still resides in and is conducting research at CU Boulder, but most of our current and future research will take place in Hanover, NH.

We are hiring! From research assistants to lab managers, to postdocs. Please reach out for more information.

Sign up for The Mind Research Network’s fMRI Image Acquisition and Analyses Course! Next course being held on October 24-26, 2019 at the TReNDS Center at Georgia State University. 

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