Purpose of review Breathlessness debilitates countless people with a wide range of common diseases. For some people, the experience of breathlessness is poorly explained by the findings of medical tests. This disparity complicates diagnostic and treatment options and means that disease-modifying treatments do not always have the expected effect upon symptoms. These observations suggest that brain processing of respiratory perceptions may be somewhat independent of disease processes. This may help to explain the dissonance observed in some patients between physical disease markers and the lived experience of breathlessness. Recent findings A body of breathlessness research using functional neuroimaging has identified a relatively consistent set of brain areas that are associated with breathlessness. These areas include the insula, cingulate and sensory cortices, the amygdala and the periaqueductal gray matter. We interpret these findings in the context of new theories of perception that emphasize the importance of distributed brain networks. Within this framework, these perceptual networks function by checking an internal model (a set of expectations) against peripheral sensory inputs, instead of the brain acting as a passive signal transducer. Furthermore, other factors beyond the physiology of breathlessness can influence the system. Summary A person's expectations and mood are major contributors to the function of the brain networks that generate perceptions of breathlessness. Breathlessness, therefore, arises from inferences made by the brain's integration of both expectations and sensory inputs. By better understanding individual differences across these contributing perceptual factors, we will be better poised to develop targeted and individualized treatments for breathlessness that could complement disease-modifying therapies.

AbstractThe study of the brain’s processing of sensory inputs from within the body (‘interoception’) has been gaining rapid popularity in neuroscience, where interoceptive disturbances have been postulated to exist across a wide range of chronic physiological and psychological conditions. Here we present a task and analysis procedure to quantify specific dimensions of breathing-related interoception, including interoceptive sensitivity (accuracy), decision bias, metacognitive bias, and metacognitive performance. We describe a task that is tailored to methods for assessing respiratory interoceptive accuracy and metacognition, and pair this with an established hierarchical statistical model of metacognition (HMeta-d) to overcome significant challenges associated with the low trial numbers often present in interoceptive experiments. Two major new developments have been incorporated into this task analysis by pairing: (i) an adaptive algorithm to maintain task performance at 70-75% accuracy, and (ii) an extended metacognitive model developed to hierarchically estimate multiple regression parameters linking metacognitive performance to relevant (e.g. clinical) variables. We demonstrate the utility of both developments, using both simulated and empirical data from three separate studies. This methodology represents an important step towards accurately quantifying interoceptive dimensions from a simple experimental procedure that is compatible with the practical constraints in clinical settings.

No abstract

Stuttering is a disorder in which the smooth flow of speech is interrupted. People who stutter show structural and functional abnormalities in the speech and motor system. It is unclear whether functional differences reflect general traits of the disorder or are specifically related to the dysfluent speech state. We used a hierarchical approach to separate state and trait effects within stuttering. We collected sparse‐sampled functional MRI during two overt speech tasks (sentence reading and picture description) in 17 people who stutter and 16 fluent controls. Separate analyses identified indicators of: (1) general traits of people who stutter; (2) frequency of dysfluent speech states in subgroups of people who stutter; and (3) the differences between fluent and dysfluent states in people who stutter. We found that reduced activation of left auditory cortex, inferior frontal cortex bilaterally, and medial cerebellum were general traits that distinguished fluent speech in people who stutter from that of controls. The stuttering subgroup with higher frequency of dysfluent states during scanning (n = 9) had reduced activation in the right subcortical grey matter, left temporo‐occipital cortex, the cingulate cortex, and medial parieto‐occipital cortex relative to the subgroup who were more fluent (n = 8). Finally, during dysfluent states relative to fluent ones, there was greater activation of inferior frontal and premotor cortex extending into the frontal operculum, bilaterally. The above differences were seen across both tasks. Subcortical state effects differed according to the task. Overall, our data emphasise the independence of trait and state effects in stuttering.

No abstract