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Prolonged amygdala response and a functional disconnection between ventral and dorsal mPFC regions may be part of the neural mechanisms underlying emotional dysregulation in BPD patients.

Recent studies have begun to carve out a specific role for the rostral part of the dorsal medial prefrontal cortex (dmPFC) and adjacent dorsal anterior cingulate cortex (dACC) in fear/anxiety. Within a novel general framework of dorsal mPFC/ACC areas subserving the appraisal of threat and concomitant expression of fear responses and ventral mPFC/ACC areas subserving fear regulation, the rostral dmPFC/dACC has been proposed to specifically mediate the conscious, negative appraisal of threat situations including, as an extreme variant, catastrophizing. An alternative explanation that has not been conclusively ruled out yet is that the area is involved in fear learning. We tested two different fear expression paradigms in separate fMRI studies (study 1: instructed fear, study 2: testing of Pavlovian conditioned fear) with independent groups of healthy adult subjects. In both paradigms the absence of reinforcement precluded conditioning. We demonstrate significant BOLD activation of an identical rostral dmPFC/dACC area. In the Pavlovian paradigm (study 2), the area only activated robustly once prior conditioning had finished. Thus, our data argue against a role of the area in fear learning. We further replicate a repeated observation of a dissociation between peripheral-physiological fear responding and rostral dmPFC/dACC activation, strongly suggesting the area does not directly generate fear responses but rather contributes to appraisal processes. Although we succeeded in preventing extinction of conditioned responding in either paradigm, the data do not allow us to definitively exclude an involvement of the area in fear extinction learning. We discuss the broader implications of this finding for our understanding of mPFC/ACC function in fear and in negative emotion more generally.

PurposePhenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists.MethodsHere, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds.ResultsThe additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene.ConclusionImage analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.