The prenatal brain readiness for speech processing: A review on foetal development of auditory and primordial language networks.

Despite consolidated evidence for the prenatal ability to elaborate and respond to sounds and speech stimuli, the ontogenetic functional brain maturation of language responsiveness in the foetus is still poorly understood. Recent advances in in-vivo foetal neuroimaging have contributed to a finely detailed picture of the anatomo-functional hallmarks that define the prenatal neurodevelopment of auditory and language-related networks. Here, we first outline available evidence for the prenatal development of auditory and language-related brain structures and of their anatomical connections. Second, we focus on functional connectivity data showing the emergence of auditory and primordial language networks in the foetal brain. Third, we recapitulate functional neuroimaging studies assessing the prenatal readiness for sound processing, as a crucial prerequisite for the foetus to experientially respond to spoken language. In conclusion, we suggest that the state of the art has reached sufficient maturity to directly assess the neural mechanisms underlying the prenatal readiness for speech processing and to evaluate whether foetal neuromarkers can predict the postnatal development of language acquisition abilities and disabilities.

Predicting differential diagnosis between bipolar and unipolar depression with multiple kernel learning on multimodal structural neuroimaging.

One of the greatest challenges in providing early effective treatment in mood disorders is the early differential diagnosis between major depression (MDD) and bipolar disorder (BD). A remarkable need exists to identify reliable biomarkers for these disorders. We integrate structural neuroimaging techniques (i.e. Tract-based Spatial Statistics, TBSS, and Voxel-based morphometry) in a multiple kernel learning procedure in order to define a predictive function of BD against MDD diagnosis in a sample of 148 patients. We achieved a balanced accuracy of 73.65% with a sensitivity for BD of 74.32% and specificity for MDD of 72.97%. Mass-univariates analyses showed reduced grey matter volume in right hippocampus, amygdala, parahippocampal, fusiform gyrus, insula, rolandic and frontal operculum and cerebellum, in BD compared to MDD. Volumes in these regions and in anterior cingulate cortex were also reduced in BD compared to healthy controls (n = 74). TBSS analyses revealed widespread significant effects of diagnosis on fractional anisotropy, axial, radial, and mean diffusivity in several white matter tracts, suggesting disruption of white matter microstructure in depressed patients compared to healthy controls, with worse pattern for MDD. To best of our knowledge, this is the first study combining grey matter and diffusion tensor imaging in predicting BD and MDD diagnosis. Our results prompt brain quantitative biomarkers and multiple kernel learning as promising tool for personalized treatment in mood disorders.