Altered resting-state functional connectivity in emotion-processing brain regions in adults who were born very preterm.

BACKGROUND: Very preterm birth (VPT; <32 weeks of gestation) has been associated with impairments in emotion regulation, social competence and communicative skills. However, the neuroanatomical mechanisms underlying such impairments have not been systematically studied. Here we investigated the functional integrity of the amygdala connectivity network in relation to the ability to recognize emotions from facial expressions in VPT adults. METHOD: Thirty-six VPT-born adults and 38 age-matched controls were scanned at rest in a 3-T MRI scanner. Resting-state functional connectivity (rs-fc) was assessed with SPM8. A seed-based analysis focusing on three amygdalar subregions (centro-medial/latero-basal/superficial) was performed. Participants' ability to recognize emotions was assessed using dynamic stimuli of human faces expressing six emotions at different intensities with the Emotion Recognition Task (ERT). RESULTS: VPT individuals compared to controls showed reduced rs-fc between the superficial subregion of the left amygdala, and the right posterior cingulate cortex (p = 0.017) and the left precuneus (p = 0.002). The VPT group further showed elevated rs-fc between the left superficial amygdala and the superior temporal sulcus (p = 0.008). Performance on the ERT showed that the VPT group was less able than controls to recognize anger at low levels of intensity. Anger scores were significantly associated with rs-fc between the superficial amygdala and the posterior cingulate cortex in controls but not in VPT individuals. CONCLUSIONS: These findings suggest that alterations in rs-fc between the amygdala, parietal and temporal cortices could represent the mechanism linking VPT birth and deficits in emotion processing.

Molecular signatures of cortical expansion in the human fetal brain.

The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. A growing catalogue of cells in the prenatal brain has revealed remarkable molecular diversity across cortical areas.1,2 Despite this, little is known about how this translates into the patterns of differential cortical expansion observed in humans during the latter stages of gestation. Here we present a new resource, µBrain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal developing brain. Built using generative artificial intelligence, µBrain is a three-dimensional cellular-resolution digital atlas combining publicly-available serial sections of the postmortem human brain at 21 weeks gestation3 with bulk tissue microarray data, sampled across 29 cortical regions and 5 transient tissue zones.4 Using µBrain, we evaluate the molecular signatures of preferentially-expanded cortical regions during human gestation, quantified in utero using magnetic resonance imaging (MRI). We find that differences in the rates of expansion across cortical areas during gestation respect anatomical and evolutionary boundaries between cortical types5 and are founded upon extended periods of upper-layer cortical neuron migration that continue beyond mid-gestation. We identify a set of genes that are upregulated from mid-gestation and highly expressed in rapidly expanding neocortex, which are implicated in genetic disorders with cognitive sequelae. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of expansion across the neocortical sheet during the prenatal epoch. The µBrain atlas is available from: https://garedaba.github.io/micro-brain/ and provides a new tool to comprehensively map early brain development across domains, model systems and resolution scales.

What counts in estimation? The nature of the preverbal system.

It has been proposed that the development of verbal counting is supported by a more ancient preverbal system of estimation, the most widely canvassed candidates being the accumulator originally proposed by Gibbon and colleagues and the analogue magnitude system proposed by Dehaene and colleagues. The aim of this chapter is to assess the strengths and weaknesses of these models in terms of their capacity to emulate the statistical properties of verbal counting. The emphasis is put on the emergence of exact representations, autoscaling, and commensurability of noise characteristics. We also outline the modified architectures that may help improve models' power to meet these criteria. We propose that architectures considered in this chapter can be used to generate predictions for experimental testing and provide an example where we test the hypothesis whether the visual sense of number, ie, ability to discriminate numerosity without counting, entails enumeration of objects.