A developmental account of curiosity and creativity.

Ivancovsky et al.'s Novelty-Seeking Model suggests several mechanisms that might underlie developmental change in creativity and curiosity. We discuss how these implications both do and do not align with extant developmental findings, suggest two further elements that can provide a more complete developmental account, and discuss current methodological barriers to formulating an integrated developmental model of curiosity and creativity.

Shaping immunity for life: Layered development of CD8+ T cells.

Historically, the immune system was believed to develop along a linear axis of maturity from fetal life to adulthood. Now, it is clear that distinct layers of immune cells are generated from unique waves of hematopoietic progenitors during different windows of development. This model, known as the layered immune model, has provided a useful framework for understanding why distinct lineages of B cells and γδ T cells arise in succession and display unique functions in adulthood. However, the layered immune model has not been applied to CD8+ T cells, which are still often viewed as a uniform population of cells belonging to the same lineage, with functional differences between cells arising from environmental factors encountered during infection. Recent studies have challenged this idea, demonstrating that not all CD8+ T cells are created equally and that the functions of individual CD8+ T cells in adults are linked to when they were created in the host. In this review, we discuss the accumulating evidence suggesting there are distinct ontogenetic subpopulations of CD8+ T cells and propose that the layered immune model be extended to the CD8+ T cell compartment.

Revolução tecnológica e smartphone: considerações sobre a constituição do sujeito contemporâneo / Revolución tecnológica y smartphone: consideraciones sobre la constitución del sujeto contemporáneo / Technological revolution and smartphone: considerations about the constitution of the contemporary subject

RESUMO. O texto, resultante de estudos teóricos empreendidos à luz da Psicologia Histórico-Cultural entre 2016 e 2020, tem como objetivo recuperar a revolução tecnológica como um recurso para a compreensão da constituição dos sujeitos contemporâneos, partindo da máquina a vapor da Primeira Revolução Industrial, empregada nos meios de produção, até o smartphone, empregado na cotidianidade. O smartphone possui níveis tão altos de compactação, portabilidade e operacionalidade que o tornaram uma das mais avançadas tecnologias da história, revelando o elevado grau de desenvolvimento do psiquismo alcançado pelo gênero humano. Ele, mais do que outras tecnologias digitais de informação e comunicação (TDICs), tem impactado notadamente a constituição dos sujeitos contemporâneos, especialmente suas funções psicológicas cognitivas. Os resultados demonstram que recuperar dialeticamente o percurso histórico das criações tecnológicas é essencial à psicologia, permitindo ampliar o espectro de análise de como os sujeitos se constituem na atualidade. Conclui-se que a revolução microtecnológica deve ser tomada sob um viés crítico e ético, por tudo o que pode impactar nas relações entre os sujeitos e no desenvolvimento dos seus processos psíquicos.

RESUMEN. El texto, resultante de estudios teóricos realizados a la luz de la Psicología Histórico-Cultural entre 2016 y 2020, tiene como objetivo recuperar la revolución tecnológica como un recurso para comprender la constitución de los sujetos contemporáneos, a partir de la máquina de vapor de la Primera Revolución Industrial, empleada en los medios de producción, hasta el smartphone, utilizado en la vida cotidiana. El smartphone tiene niveles tan altos de compacidad, portabilidad y operabilidad que se ha convertido en una de las tecnologías más avanzadas de la historia, revelando el alto grado de desarrollo de la psique alcanzado por la humanidad. Él, más que otras tecnologías digitales de información y comunicación, ha impactado notablemente la constitución de los sujetos contemporáneos, especialmente sus funciones psicológicas cognitivas. Los resultados demuestran que recuperar dialécticamente el camino histórico de las creaciones tecnológicas es esencial para la psicología, lo que permite ampliar el espectro de análisis de cómo se constituyen los sujetos en la actualidad. Concluye que la revolución microtecnológica debe tomarse desde una perspectiva crítica y ética, para todo lo que pueda afectar las relaciones entre los sujetos y el desarrollo de sus procesos psíquicos.

ABSTRACT. The text, resulting from theoretical studies undertaken in the light of Historical-Cultural Psychology between 2016 and 2020, aims to recover the technological revolution as a resource for understanding the constitution of contemporary subjects, starting from the steam engine of the First Industrial Revolution, used in the means of production, even the smartphone, used in everyday life. The smartphone has such high levels of compactness, portability and operability that it has become one of the most advanced technologies in history, revealing the high degree of development of the psyche achieved by mankind. It, more than other digital information and communication technologies, has impacted notably on the constitution of contemporary subjects, especially on their cognitive psychological functions. The results demonstrate that recovering dialectically the historical path of technological creations is essential to Psychology, allowing to expand the spectrum of analysis of how subjects are constituted today. It concludes that the microtechnological revolution must be taken under a critical and ethical bias, due to everything that can impact on the relationships between the subjects and the development of their psychic processes.

Cognitive estimation of speed, movement and time across the lifespan.

We investigated the role of age and gender in cognitive estimation abilities. Participants completed two online tasks, where different categories of estimation were assessed (speed estimation by time and distance, and auditory estimation). Data from a total of 18886 participants (9911 females) with ages between 18 and 65 years old were gathered. Results showed variations in the estimation capacity as a function of age, with a slow but progressive decline. Estimates of duration and sound seemed to be more affected by age than estimates of speed and movement. Overall, male participants showed a better performance on both auditory and visual estimation tasks compared to females.

Digital dementia in the internet generation: excessive screen time during brain development will increase the risk of Alzheimer's disease and related dementias in adulthood.

Converging evidence from biopsychosocial research in humans and animals demonstrates that chronic sensory stimulation (via excessive screen exposure) affects brain development increasing the risk of cognitive, emotional, and behavioural disorders in adolescents and young adults. Emerging evidence suggests that some of these effects are similar to those seen in adults with symptoms of mild cognitive impairment (MCI) in the early stages of dementia, including impaired concentration, orientation, acquisition of recent memories (anterograde amnesia), recall of past memories (retrograde amnesia), social functioning, and self-care. Excessive screen time is known to alter gray matter and white volumes in the brain, increase the risk of mental disorders, and impair acquisition of memories and learning which are known risk factors for dementia. Chronic sensory overstimulation (i.e., excessive screen time) during brain development increases the risk of accelerated neurodegeneration in adulthood (i.e., amnesia, early onset dementia). This relationship is affected by several mediating/moderating factors (e.g., IQ decline, learning impairments and mental illness). We hypothesize that excessive screen exposure during critical periods of development in Generation Z will lead to mild cognitive impairments in early to middle adulthood resulting in substantially increased rates of early onset dementia in later adulthood. We predict that from 2060 to 2100, the rates of Alzheimer's disease and related dementias (ADRD) will increase significantly, far above the Centres for Disease Control (CDC) projected estimates of a two-fold increase, to upwards of a four-to-six-fold increase. The CDC estimates are based entirely on factors related to the age, sex, race and ethnicity of individuals born before 1950 who did not have access to mobile digital technology during critical periods of brain development. Compared to previous generations, the average 17-19-year-old spends approximately 6 hours a day on mobile digital devices (MDD) (smartphones, tablets, and laptop computers) whereas individuals born before 1950 at the same age spent zero. Our estimates include the documented effects of excessive screen time on individuals born after 1980, Millennials and Generation Z, who will be the majority of individuals ≥65 years old. An estimated 4-to-6-fold increase in rates of ADRD post-2060 will result in widespread societal and economic distress and the complete collapse of already overburdened healthcare systems in developed countries. Preventative measures must be set in place immediately including investments and interventions in public education, social policy, laws, and healthcare.

Accurate brain-age models for routine clinical MRI examinations.

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility. Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 'radiologically normal for age' head MRI examinations from two large UK hospitals for model training and testing (age range = 18-95 years), and demonstrate fast (< 5 s), accurate (mean absolute error [MAE] < 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE < 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy 'excessive for age'. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p < 0.0001). Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Greater male than female variability in regional brain structure across the lifespan.

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

When I'm 64: Age-Related Variability in Over 40,000 Online Cognitive Test Takers.

OBJECTIVES: Age-related differences in cognition are typically assessed by comparing groups of older to younger participants, but little is known about the continuous trajectory of cognitive changes across age, or when a shift to older adulthood occurs. We examined the pattern of mean age differences and variability on episodic memory and executive function measures over the adult life span, in a more fine-grained way than past group or life-span comparisons. METHOD: We used a sample of over 40,000 people aged 18-90 who completed psychometrically validated online tests measuring episodic memory and executive functions (the Cogniciti Brain Health Assessment). RESULTS: Cognitive performance declined gradually over adulthood, and rapidly later in life on spatial working memory, processing speed, facilitation (but not interference), associative recognition, and set shifting. Both polynomial and segmented regression fit the data well, indicating a nonlinear pattern. Segmented regression revealed a shift from gradual to rapid decline that occurred in the early 60s. Variability between people (interindividual variability or diversity) and variability within a person across tasks (intraindividual variability or dispersion) also increased gradually until the 60s, and rapidly after. Confirmatory factor analysis revealed a single general factor (of variance shared between tasks) offered a good fit for performance across tasks. DISCUSSION: Life-span cognitive performance shows a nonlinear pattern, with gradual decline over early and mid-adulthood, followed by a transition in the 60s to notably accelerated, but more variable, decline. Some people show less decline than others, and some cognitive abilities show less within-person decline than others.

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

A developmental examination of medial frontal theta dynamics and inhibitory control.

Medial frontal theta-band oscillations are a robust marker of action-outcome monitoring. In a large developmental sample (n = 432, 9-16 years), we examined whether phase and non-phase locked medial frontal theta power were related to inhibitory control among children and adolescents. Our results showed that the well-established increase in medial frontal theta power during inhibitory control was captured largely by non-phase locked dynamics, which partially mediated the positive effect of age on task performance. A person-centered approach also revealed latent classes of individuals based on their multivariate theta power dynamics (phase locked/non-phase locked, GO/NOGO). The class of individuals showing low phase locked and high non-phase locked medial frontal theta were significantly older, had better inhibitory control, scored higher on measures of general cognitive function, and were more efficient in their behavioural responses. The functional significance of phase and non-phase locked theta dynamics, and their potential changes, could have important implications for action-outcome monitoring and cognitive function in both typical and atypical development, as well as related psychopathology .

Differences in executive abilities rather than associative processes contribute to memory development.

Children's learning capabilities change while growing up. One framework that describes the cognitive and neural development of children's growing learning abilities is the two-component model. It distinguishes processes that integrate separate features into a coherent memory representation (associative component) and executive abilities, such as elaboration, evaluation, and monitoring, that support memory processing (strategic component). In an fMRI study using an object-location association paradigm, we investigated how the two components influence memory performance across development. We tested children (10-12 years, n = 31), late adolescents (18 years, n = 29), and adults (25+ years, n = 30). For studying the associative component, we also probed how the utilisation of prior knowledge (schemas) facilitates memory across age groups. Children had overall lower retrieval performance, while adolescents and adults did not differ from each other. All groups benefitted from schemas, but this effect did not differ between groups. Performance differences between groups were associated with deactivation of the dorsal medial prefrontal cortex (dmPFC), which in turn was linked to executive functioning. These patterns were stronger in adolescents and adults and seemed absent in children. Thus, the children's executive system, the strategic component, is not as mature and thus cannot facilitate memory performance in the same way as in adolescents/adults. In contrast, we did not find age-related differences in the associative component; with activity in the angular gyrus predicting memory performance systematically across groups. Overall, our results suggest that differences of executive rather than associative abilities explain memory differences between children, adolescents, and adults.

Predicting brain age during typical and atypical development based on structural and functional neuroimaging.

Exploring typical and atypical brain developmental trajectories is very important for understanding the normal pace of brain development and the mechanisms by which mental disorders deviate from normal development. A precise and sex-specific brain age prediction model is desirable for investigating the systematic deviation and individual heterogeneity of disorders associated with atypical brain development, such as autism spectrum disorders. In this study, we used partial least squares regression and the stacking algorithm to establish a sex-specific brain age prediction model based on T1-weighted structural magnetic resonance imaging and resting-state functional magnetic resonance imaging. The model showed good generalization and high robustness on four independent datasets with different ethnic information and age ranges. A predictor weights analysis showed the differences and similarities in changes in structure and function during brain development. At the group level, the brain age gap estimation for autistic patients was significantly smaller than that for healthy controls in both the ABIDE dataset and the healthy brain network dataset, which suggested that autistic patients as a whole exhibited the characteristics of delayed development. However, within the ABIDE dataset, the premature development group had significantly higher Autism Diagnostic Observation Schedule (ADOS) scores than those of the delayed development group, implying that individuals with premature development had greater severity. Using these findings, we built an accurate typical brain development trajectory and developed a method of atypical trajectory analysis that considers sex differences and individual heterogeneity. This strategy may provide valuable clues for understanding the relationship between brain development and mental disorders.

Ignore the faces: Neural characterisation of emotional inhibition from childhood to adulthood using MEG.

The ability to effectively and automatically regulate one's response to emotional information is a basic, fundamental skill for social functioning. The neural mechanisms underlying emotion regulation processing have been assessed, however few investigations have leveraged neurophysiological techniques, particularly magnetoencephalography (MEG) to determine the development of this critical ability. The current MEG study is the first to examine developmental changes in the neural mechanisms supporting automatic emotion regulation. We used an emotional go/no-go task with happy and angry faces in a single-site cohort of 97 healthy participants, 4-40 years of age. We found age-related changes as a function of emotion and condition in brain regions key to emotion regulation, including the right inferior frontal gyrus, orbitofrontal cortices and primarily right-lateralized temporal areas. Interaction effects, including an age by emotion and condition, were also found in the left angular gyrus, an area critical in emotion regulation and attention. Findings demonstrate protracted and nonlinear development, due to the adolescent group, of emotion regulation processing from child to adulthood, and highlight that age-related differences in emotion regulation are modulated by emotional face type.

Metabolic decisions in development and disease-a Keystone Symposia report.

There is an increasing appreciation for the role of metabolism in cell signaling and cell decision making. Precise metabolic control is essential in development, as evident by the disorders caused by mutations in metabolic enzymes. The metabolic profile of cells is often cell-type specific, changing as cells differentiate or during tumorigenesis. Recent evidence has shown that changes in metabolism are not merely a consequence of changes in cell state but that metabolites can serve to promote and/or inhibit these changes. Metabolites can link metabolic pathways with cell signaling pathways via several mechanisms, for example, by serving as substrates for protein post-translational modifications, by affecting enzyme activity via allosteric mechanisms, or by altering epigenetic markers. Unraveling the complex interactions governing metabolism, gene expression, and protein activity that ultimately govern a cell's fate will require new tools and interactions across disciplines. On March 24 and 25, 2021, experts in cell metabolism, developmental biology, and human disease met virtually for the Keystone eSymposium, "Metabolic Decisions in Development and Disease." The discussions explored how metabolites impact cellular and developmental decisions in a diverse range of model systems used to investigate normal development, developmental disorders, dietary effects, and cancer-mediated changes in metabolism.

Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth.

Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held-out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (pcorr  = .012) and lower functioning on the Children's Global Assessment Scale (pcorr  = .012). Higher BrainPAD values were associated with better performance on the Flanker task (pcorr  = .008). Brain age prediction was more accurate using ComBat-harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth.

Dynamic functional connectivity profile of the salience network across the life span.

The insular cortex and anterior cingulate cortex together comprise the salience or midcingulo-insular network, involved in detecting salient events and initiating control signals to mediate brain network dynamics. The extent to which functional coupling between the salience network and the rest of the brain undergoes changes due to development and aging is at present largely unexplored. Here, we examine dynamic functional connectivity (dFC) of the salience network in a large life span sample (n = 601; 6-85 years old). A sliding-window analysis and k-means clustering revealed five states of dFC formed with the salience network, characterized by either widespread asynchrony or different patterns of synchrony between the salience network and other brain regions. We determined the frequency, dwell time, total transitions, and specific state-to-state transitions for each state and subject, regressing the metrics with subjects' age to identify life span trends. A dynamic state characterized by low connectivity between the salience network and the rest of the brain had a strong positive quadratic relationship between age and both frequency and dwell time. Additional frequency, dwell time, total transitions, and state-to-state transition trends were observed with other salience network states. Our results highlight the metastable dynamics of the salience network and its role in the maturation of brain regions critical for cognition.

Moving Toward Understanding Autism: Visual-Motor Integration, Imitation, and Social Skill Development.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a behavioral phenotype characterized by impaired development of social-communicative skills and excessive repetitive and stereotyped behaviors. Despite high phenotypic heterogeneity in ASD, a meaningful subpopulation of children with ASD (∼90%) show significant general motor impairment. More focused studies on the nature of motor impairment in ASD reveal that children with ASD are particularly impaired on tasks such as ball catching and motor imitation that require efficient visual-motor integration (VMI). Motor computational approaches also provide evidence for VMI impairment showing that children with ASD form internal sensorimotor representations that bias proprioceptive over visual feedback. Impaired integration of visual information to form internal representations of others' and the external world may explain observed impairments on VMI tasks and motor imitation of others. Motor imitation is crucial for acquiring both social and motor skills, and impaired imitation skill may contribute to the observed core behavioral phenotype of ASD. The current review examines evidence supporting VMI impairment as a core feature of ASD that may contribute to both impaired motor imitation and social-communicative skill development. We propose that understanding the neurobiological mechanisms underlying VMI impairment in ASD may be key to discovery of therapeutics to address disability in children and adults with ASD.

The interplay of resting and inhibitory control-related theta-band activity depends on age.

Resting-state neural activity plays an important role for cognitive control processes. Regarding response inhibition processes, an important facet of cognitive control, especially theta-band activity has been the focus of research. Theoretical considerations suggest that the interrelation of resting and task-related theta activity is subject to maturational effects. To investigate whether the relationship between resting theta activity and task-related theta activity during a response inhibition task changes even in young age, we tested N = 166 healthy participants between 8 and 30 years of age. We found significant correlations between resting and inhibitory control-related theta activity as well as behavioral inhibition performance. Importantly, these correlations were moderated by age. The moderation analysis revealed that higher resting theta activity was associated with stronger inhibition-related theta activity in individuals above the age of ~10.7 years. The EEG beamforming analysis showed that this activity is associated with superior frontal region function (BA6). The correlation between resting and superior frontal response inhibition-related theta activity became stronger with increasing age. A similar pattern was found for response inhibition performance, albeit only evident from the age of ~19.5 years. The results suggest that with increasing age, resting theta activity becomes increasingly important for processing the alarm/surprise signals in superior frontal brain regions during inhibitory control. Possible causes for these developmental changes are discussed.