MÓZGOWE PODŁOŻE ROZWOJU MOWY W AUTYZMIE

Although in a majority of cases, autistic children face difficulties communicating verbally, the valid diagnostic classifi cations do not identify them as the main symptoms of the disorder. The adoption of such a position has been supported by results of (mainly behavioural) research, which imply that language and speech development in the autism spectrum disorder (ASD) is extremely variable and individually diversifi ed and the observed delay of its development is not unique to autism. On the other hand, the research conducted by means of neuroimaging methods shows that an atypical structure and activity of Broca’s and Wernicke’s areas, which are important for language processes, exist in the ASD. A weak structural and functional connectivity in the arcuate fasciculus, which connects these structures, has also been discovered. It is assumed that the changes arise from neurodevelopmental irregularities occurring at an early stage of foetal life and their causes are probably genetic. This study characterises speech development disorders and atypical brain development in autism referring to results of both behavioural and neuroimaging research.

Statistical Properties of Musical Creativity: Roles of Hierarchy and Uncertainty in Statistical Learning

Creativity is part of human nature and is commonly understood as a phenomenon whereby something original and worthwhile is formed. Owing to this ability, humans can produce innovative information that often facilitates growth in our society. Creativity also contributes to esthetic and artistic productions, such as music and art. However, the mechanism by which creativity emerges in the brain remains debatable. Recently, a growing body of evidence has suggested that statistical learning contributes to creativity. Statistical learning is an innate and implicit function of the human brain and is considered essential for brain development. Through statistical learning, humans can produce and comprehend structured information, such as music. It is thought that creativity is linked to acquired knowledge, but so-called “eureka” moments often occur unexpectedly under subconscious conditions, without the intention to use the acquired knowledge. Given that a creative moment is intrinsically implicit, we postulate that some types of creativity can be linked to implicit statistical knowledge in the brain. This article reviews neural and computational studies on how creativity emerges within the framework of statistical learning in the brain (i.e., statistical creativity). Here, we propose a hierarchical model of statistical learning: statistically chunking into a unit (hereafter and shallow statistical learning) and combining several units (hereafter and deep statistical learning). We suggest that deep statistical learning contributes dominantly to statistical creativity in music. Furthermore, the temporal dynamics of perceptual uncertainty can be another potential causal factor in statistical creativity. Considering that statistical learning is fundamental to brain development, we also discuss how typical versus atypical brain development modulates hierarchical statistical learning and statistical creativity. We believe that this review will shed light on the key roles of statistical learning in musical creativity and facilitate further investigation of how creativity emerges in the brain.

DNA methylation and brain dysmaturation in preterm infants

Preterm birth is associated with dysconnectivity of structural brain networks and is a leading cause of neurocognitive impairment in childhood. Variation in DNA methylation (DNAm) is associated with early exposure to extrauterine life but there has been little research exploring its relationship with brain development. Using genome-wide DNA methylation data from saliva of 258 neonates, we investigated the impact of gestational age on the methylome and performed functional analysis to identify enriched gene sets from probes that contributed to differentially methylated probes (DMPs) or regions (DMRs). We tested the hypothesis that variation in DNAm could underpin the association between preterm birth and atypical brain development by linking DMPs with measures of white matter connectivity derived from diffusion MRI metrics: peak width of skeletonised mean diffusivity (PSMD), fractional anisotropy (PSFA) and neurite density index (PSNDI). Gestational age at birth was associated with widespread differential methylation, with genome-wide significant associations observed for 8,870 CpG probes (p<3.6x10-8) and 1,767 differentially methylated regions. Functional analysis identified 14 enriched gene ontology terms pertaining to cell-cell contacts and cell-extracellular matrix contacts. Principal component analysis of probes with genome-wide significance revealed a first principal component (PC1) that explained 23.5% of variance in DNAm, and this was negatively associated with gestational age at birth. PC1 was associated with PSMD (β=0.349, p=8.37x10-10) and PSNDI (β=0.364, p=4.15x10-5), but not with PSFA (β=-0.035, p=0.510); these relationships mirrored the imaging metrics associations with gestational age at birth. Gestational age at birth has a profound and widely distributed effect on the neonatal saliva methylome. Enriched gene ontology terms related to cell-cell contacts reveal pathways that could mediate the effect of early life environmental exposures on development. Finally, associations between differential DNAm and image markers of white matter tract microstructure suggest that variation in DNAm may provide a link between preterm birth and the dysconnectivity of developing brain networks that characterises atypical brain development in preterm infants.

Hierarchical Complexity of the Macro-Scale Neonatal Brain

The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.

Biomarkers in Pediatric Magnetoencephalography

The use of magnetoencephalography (MEG) to understand alterations in brain development in children has increased rapidly over the past two decades. Investigators have argued that MEG is an ideal neuroimaging tool for children because the technology is quiet and it provides high-density sensor systems. This participant-friendly technology has led to exploration of the use of MEG to identify biomarkers for atypical brain development to facilitate early diagnosis and intervention. Prior studies provide evidence that MEG is sensitive to a number of pediatric clinical disorders demonstrated through significant differences (e.g., latency, amplitude, spectral power) in children with autism spectrum disorder, children born prematurely, and children with fetal alcohol spectrum disorder, to name a few. At the same time, differences in age range, stimulus parameters, and study population characteristics contribute to variability in results across independent laboratories. While the current studies provide strong evidence for the sensitivity of MEG to identify brain abnormalities in children, replication studies are needed to validate biomarkers of atypical brain development to identify children at risk for atypical brain development. Additional studies are also needed to understand the dynamic changes in these brain markers across the age spectrum. Finally, future directions include gaining a broader understanding of typical and atypical brain development to identify neural targets for intervention.

Cortical recycling in high-level visual cortex during childhood development

Human ventral temporal cortex (VTC) contains category-selective regions that respond preferentially to ecologically-relevant categories such as faces, bodies, places, and words, which are causally involved in the perception of these categories. How do these regions develop during childhood? We used functional MRI to measure longitudinal development of category-selectivity in school-age children over 1 to 5 years. We discovered that from young childhood to the teens, face- and word-selective regions in VTC expand and become more category-selective, but limb-selective regions shrink and lose their preference for limbs. Critically, as a child develops, increases in face- and word-selectivity are directly linked to decreases in limb-selectivity, revealing that during childhood limb-selectivity in VTC is repurposed into word- and face-selectivity. These data provide evidence for cortical recycling during childhood development. This has important implications for understanding typical as well as atypical brain development and necessitates a rethinking of how cortical function develops during childhood.

Hierarchical complexity of the macro-scale neonatal brain

The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period; assess correspondences with hierarchical complexity in adulthood; and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers, and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.