Imagined Musical Scale Relationships Decoded from Auditory Cortex.

Notes in a musical scale convey different levels of stability or incompleteness, forming what is known as a tonal hierarchy. Levels of stability conveyed by these scale degrees are partly responsible for generating expectations as a melody proceeds, for emotions deriving from fulfillment (or not) of those expectations, and for judgments of overall melodic well-formedness. These functions can be extracted even during imagined music. We investigated whether patterns of neural activity in fMRI could be used to identify heard and imagined notes, and if patterns associated with heard notes could identify notes that were merely imagined. We presented trained musicians with the beginning of a scale (key and timbre were varied). The next note in the scale was either heard or imagined. A probe tone task assessed sensitivity to the tonal hierarchy, and state and trait measures of imagery were included as predictors. Multivoxel classification yielded above-chance results in primary auditory cortex (Heschl's gyrus) for heard scale-degree decoding. Imagined scale-degree decoding was successful in multiple cortical regions spanning bilateral superior temporal, inferior parietal, precentral, and inferior frontal areas. The right superior temporal gyrus yielded successful cross-decoding of heard-to-imagined scale-degree, indicating a shared pathway between tonal-hierarchy perception and imagery. Decoding in right and left superior temporal gyrus and right inferior frontal gyrus was more successful in people with more differentiated tonal hierarchies and in left inferior frontal gyrus among people with higher self-reported auditory imagery vividness, providing a link between behavioral traits and success of neural decoding. These results point to the neural specificity of imagined auditory experiences-even of such functional knowledge-but also document informative individual differences in the precision of that neural response.

Breath can discriminate tuberculosis from other lower respiratory illness in children.

Pediatric tuberculosis (TB) remains a global health crisis. Despite progress, pediatric patients remain difficult to diagnose, with approximately half of all childhood TB patients lacking bacterial confirmation. In this pilot study (n = 31), we identify a 4-compound breathprint and subsequent machine learning model that accurately classifies children with confirmed TB (n = 10) from children with another lower respiratory tract infection (LRTI) (n = 10) with a sensitivity of 80% and specificity of 100% observed across cross validation folds. Importantly, we demonstrate that the breathprint identified an additional nine of eleven patients who had unconfirmed clinical TB and whose symptoms improved while treated for TB. While more work is necessary to validate the utility of using patient breath to diagnose pediatric TB, it shows promise as a triage instrument or paired as part of an aggregate diagnostic scheme.