Self-Regulation of Amygdala Activation Using Real-Time fMRI Neurofeedback

Real-time functional magnetic resonance imaging (rtfMRI) with neurofeedback allows investigation of human brain neuroplastic changes that arise as subjects learn to modulate neurophysiological function using real-time feedback regarding their own hemodynamic responses to stimuli. We investigated the feasibility of training healthy humans to self-regulate the hemodynamic activity of the amygdala, which plays major roles in emotional processing. Participants in the experimental group were provided with ongoing information about the blood oxygen level dependent (BOLD) activity in the left amygdala (LA) and were instructed to raise the BOLD rtfMRI signal by contemplating positive autobiographical memories. A control group was assigned the same task but was instead provided with sham feedback from the left horizontal segment of the intraparietal sulcus (HIPS) region. In the LA, we found a significant BOLD signal increase due to rtfMRI neurofeedback training in the experimental group versus the control group. This effect persisted during the Transfer run without neurofeedback. For the individual subjects in the experimental group the training effect on the LA BOLD activity correlated inversely with scores on the Difficulty Identifying Feelings subscale of the Toronto Alexithymia Scale. The whole brain data analysis revealed significant differences for Happy Memories versus Rest condition between the experimental and control groups. Functional connectivity analysis of the amygdala network revealed significant widespread correlations in a fronto-temporo-limbic network. Additionally, we identified six regions — right medial frontal polar cortex, bilateral dorsomedial prefrontal cortex, left anterior cingulate cortex, and bilateral superior frontal gyrus — where the functional connectivity with the LA increased significantly across the rtfMRI neurofeedback runs and the Transfer run. The findings demonstrate that healthy subjects can learn to regulate their amygdala activation using rtfMRI neurofeedback, suggesting possible applications of rtfMRI neurofeedback training in the treatment of patients with neuropsychiatric disorders

The Australian Racism, Acceptance, and Cultural-Ethnocentrism Scale (RACES): item response theory findings

BACKGROUND: Racism and associated discrimination are pervasive and persistent challenges with multiple cumulative deleterious effects contributing to inequities in various health outcomes. Globally, research over the past decade has shown consistent associations between racism and negative health concerns. Such research confirms that race endures as one of the strongest predictors of poor health. Due to the lack of validated Australian measures of racist attitudes, RACES (Racism, Acceptance, and Cultural-Ethnocentrism Scale) was developed. METHODS: Here, we examine RACES’ psychometric properties, including the latent structure, utilising Item Response Theory (IRT). Unidimensional and Multidimensional Rating Scale Model (RSM) Rasch analyses were utilised with 296 Victorian primary school students and 182 adolescents and 220 adults from the Australian community. RESULTS: RACES was demonstrated to be a robust 24-item three-dimensional scale of Accepting Attitudes (12 items), Racist Attitudes (8 items), and Ethnocentric Attitudes (4 items). RSM Rasch analyses provide strong support for the instrument as a robust measure of racist attitudes in the Australian context, and for the overall factorial and construct validity of RACES across primary school children, adolescents, and adults. CONCLUSIONS: RACES provides a reliable and valid measure that can be utilised across the lifespan to evaluate attitudes towards all racial, ethnic, cultural, and religious groups. A core function of RACES is to assess the effectiveness of interventions to reduce community levels of racism and in turn inequities in health outcomes within Australia. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12939-016-0338-4) contains supplementary material, which is available to authorized users

A Multi-stage Theory of Neurofeedback Learning

Neurofeedback is a training paradigm through which trainees learn to voluntarily influence their brain dynamics. Recent years have seen an exponential increase in research interest into this ability. How neurofeedback learning works is still unclear, but progress is being made by applying models from computational neuroscience to the neurofeedback paradigm. In this chapter, I will present a multi-stage theory of neurofeedback learning, which involves three stages, involving different neural networks. In stage 1, the system discovers the appropriate goal representation for increasing the frequency of positive feedback. This stage operates at a within-session timescale and is driven by reward-based learning, which updates fronto-striatal connections. Stage 2 operates on a timescale that covers multiple training sessions and is sensitive to consolidation processes. This stage involves updating striatal-thalamic and thalamo-cortical connections. Finally, after stages 1 and 2 have started, stage 3 may be triggered by the awareness of the statistical covariation between interoceptive and external feedback signals. When this awareness emerges, neurofeedback learning may speed up and its effect be maintained well after the conclusion of the training period. Research guided by this framework is described that consist of quantitative, qualitative, and computational methodologies. At present, the findings suggest that the framework is able to provide novel insights into the nature of neurofeedback learning and provides a roadmap for developing instructions that are designed to facilitate the likelihood of learning success