A design for life: Predicting cognitive performance from lifestyle choices.

Maintaining cognitive capacity through adulthood has been the target of many recent studies that have examined the influence of lifestyle choices such as exercise, diet, and sleeping habits. Many of these studies have focused on a single factor (e.g., diet) and its effect on cognitive abilities; however, humans make numerous lifestyle choices every single day, many of which interact and influence each other. Here, we investigated whether combinations of lifestyle choices can predict better or worse cognitive performance in the general population, and whether optimal combinations of choices existed depending on the cognitive domain. Specifically, we examined 20 self-reported lifestyle choices, such as playing video games, drinking alcohol, and amount of exercise taken, in a sample of almost 10,000 participants. All participants also completed 12 cognitive tests that have been shown to generate three composite cognitive domain scores pertaining to short-term memory, verbal abilities, and reasoning. Using recursive feature elimination and random forest regression, we were able to explain 9% of the variance in short-term memory scores, 8% of the variance in reasoning scores, and 7% of the variance in verbal ability scores. While the regression model provided predictive power in all three domains, these levels indicate that even when considering a large number of lifestyle choices, there remains a considerable degree of variability in predicting short-term memory, reasoning and verbal abilities. Thus, while some modifiable lifestyle factors may have an impact on cognitive capacity, there likely exists no single optimal design for life.

T2* Mapping of Placental Oxygenation to Estimate Fetal Cortical and Subcortical Maturation.

This cohort study investigates the association between T2* mapping of placental oxygenation and cortical and subcortical fetal brain volumes in typically developing fetuses scanned longitudinally in the third trimester.

Identifying cognitive profiles in children with neurodevelopmental disorders using online cognitive testing.

Children with neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD) tend to exhibit similar deficits in attention and memory ability. Early screening of cognitive deficits in children with NDDs, particularly in preschool children, is fundamental to improving cognitive and academic outcomes. In order to determine cognitive profiles in children with ASD and ADHD, we developed accessible audiovisual instructions for an online battery of 13 cognitive tests. Children ages 4-16 who were diagnosed with ADHD (n = 83), or ASD (n = 37), or who were typically developing children (TD) (n = 86) were recruited. Data were analyzed using a stepwise Discriminant Analysis to determine which cognitive tasks were the strongest discriminators between the diagnostic groups. Results revealed four tasks reflective of working memory, reasoning, and attentional processes, which correctly classified approximately 53-60% of each group. The ADHD group had lower scores on attentional tasks compared to TD, while ASD group had lower scores on reasoning tasks compared to the TD children, and made more attempts across all four tasks. The results from this study stress the need for cognitive screening assessments that include domain-specific items to improve the characterization of executive function deficits and promote academic achievement in all children with NDDs.

Commonly diagnosed Neurodevelopmental disorders (NDDs) include autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD). Children with NDDs often experience a wide range of cognitive difficulties which can seriously impact their academic, emotional and behavioural outcomes at school. In this study, we used online cognitive tests that were developed for adults. These 'gamified' tasks assess a number of cognitive abilities including working memory, attention, verbal skills, and reasoning. We developed audiovisual instructions to make these tasks more suitable to children with and without NDDs. These tasks were then used in an online sample of children with ASD, ADHD, and typically developing children. We wanted to see how each group of children performed on the tasks, to assess their relative cognitive strengths and difficulties. We found that the tasks could successfully categorize each group of children based on their task performance. The ADHD group had lower scores on attentional tasks compared to TD children. The ASD group had lower scores on reasoning compared to TD children. The cognitive task battery may eventually be used to help identify cognitive difficulties and improve outcomes in children with NDDs.

Sex- and age-based differences in fetal and early childhood hippocampus maturation: a cross-sectional and longitudinal analysis.

The hippocampus, essential for cognitive and affective processes, develops exponentially with differential trajectories seen in girls and boys, yet less is known about its development during early fetal life until early childhood. In a cross-sectional and longitudinal study, we examined the sex-, age-, and laterality-related developmental trajectories of hippocampal volumes in fetuses, infants, and toddlers associated with age. Third trimester fetuses (27-38 weeks' gestational age), newborns (0-4 weeks' postnatal age), infants (5-50 weeks' postnatal age), and toddlers (2-3 years postnatal age) were scanned with magnetic resonance imaging. A total of 133 datasets (62 female, postmenstrual age [weeks] M = 69.38, SD = 51.39, range = 27.6-195.3) were processed using semiautomatic segmentation methods. Hippocampal volumes increased exponentially during the third trimester and the first year of life, beginning to slow at approximately 2 years. Overall, boys had larger hippocampal volumes than girls. Lateralization differences were evident, with left hippocampal growth beginning to plateau sooner than the right. This period of rapid growth from the third trimester, continuing through the first year of life, may support the development of cognitive and affective function during this period.

Subcortical brain volumes in neonatal hypoxic-ischemic encephalopathy.

BACKGROUND: Despite treatment with therapeutic hypothermia, hypoxic-ischemic encephalopathy (HIE) is associated with adverse developmental outcomes, suggesting the involvement of subcortical structures including the thalamus and basal ganglia, which may be vulnerable to perinatal asphyxia, particularly during the acute period. The aims were: (1) to examine subcortical macrostructure in neonates with HIE compared to age- and sex-matched healthy neonates within the first week of life; (2) to determine whether subcortical brain volumes are associated with HIE severity. METHODS: Neonates (n = 56; HIE: n = 28; Healthy newborns from the Developing Human Connectome Project: n = 28) were scanned with MRI within the first week of life. Subcortical volumes were automatically extracted from T1-weighted images. General linear models assessed between-group differences in subcortical volumes, adjusting for sex, gestational age, postmenstrual age, and total cerebral volumes. Within-group analyses evaluated the association between subcortical volumes and HIE severity. RESULTS: Neonates with HIE had smaller bilateral thalamic, basal ganglia and right hippocampal and cerebellar volumes compared to controls (all, p < 0.02). Within the HIE group, mild HIE severity was associated with smaller volumes of the left and right basal ganglia (both, p < 0.007) and the left hippocampus and thalamus (both, p < 0.04). CONCLUSIONS: Findings suggest that, despite advances in neonatal care, HIE is associated with significant alterations in subcortical brain macrostructure. IMPACT: Compared to their healthy counterparts, infants with HIE demonstrate significant alterations in subcortical brain macrostructure on MRI acquired as early as 4 days after birth. Smaller subcortical volumes impacting sensory and motor regions, including the thalamus, basal ganglia, and cerebellum, were seen in infants with HIE. Mild and moderate HIE were associated with smaller subcortical volumes.

Default mode network functional connectivity strength in utero and the association with fetal subcortical development.

The default mode network is essential for higher-order cognitive processes and is composed of an extensive network of functional and structural connections. Early in fetal life, the default mode network shows strong connectivity with other functional networks; however, the association with structural development is not well understood. In this study, resting-state functional magnetic resonance imaging and anatomical images were acquired in 30 pregnant women with singleton pregnancies. Participants completed 1 or 2 MR imaging sessions, on average 3 weeks apart (43 data sets), between 28- and 39-weeks postconceptional ages. Subcortical volumes were automatically segmented. Activation time courses from resting-state functional magnetic resonance imaging were extracted from the default mode network, medial temporal lobe network, and thalamocortical network. Generalized estimating equations were used to examine the association between functional connectivity strength between default mode network-medial temporal lobe, default mode network-thalamocortical network, and subcortical volumes, respectively. Increased functional connectivity strength in the default mode network-medial temporal lobe network was associated with smaller right hippocampal, left thalamic, and right caudate nucleus volumes, but larger volumes of the left caudate. Increased functional connectivity strength in the default mode network-thalamocortical network was associated with smaller left thalamic volumes. The strong associations seen among the default mode network functional connectivity networks and regionally specific subcortical volume development indicate the emergence of short-range connectivity in the third trimester.

Hippocampus long-axis specialization throughout development: A meta-analysis.

The human adult hippocampus can be subdivided into the head, or anterior hippocampus and its body and tail, or posterior hippocampus, and a wealth of functional differences along the longitudinal axis have been reported. One line of literature emphasizes specialization for different aspects of cognition, whereas another emphasizes the unique role of the anterior hippocampus in emotional processing. While some research suggests that functional differences in memory between the anterior and posterior hippocampus appear early in development, it remains unclear whether this is also the case for functional differences in emotion processing. The goal of this meta-analysis was to determine whether the long-axis functional specialization observed in adults is present earlier in development. Using a quantitative meta-analysis, long-axis functional specialization was assessed using the data from 26 functional magnetic resonance imaging studies, which included 39 contrasts and 804 participants ranging in age from 4 to 21 years. Results indicated that emotion was more strongly localized to the anterior hippocampus, with memory being more strongly localized to the posterior hippocampus, demonstrating long-axis specialization with regard to memory and emotion in children similar to that seen in adults. An additional analysis of laterality indicated that while memory was left dominant, emotion was processed bilaterally.

Funcmasker-flex: An Automated BIDS-App for Brain Segmentation of Human Fetal Functional MRI data.

Fetal functional magnetic resonance imaging (fMRI) offers critical insight into the developing brain and could aid in predicting developmental outcomes. As the fetal brain is surrounded by heterogeneous tissue, it is not possible to use adult- or child-based segmentation toolboxes. Manually-segmented masks can be used to extract the fetal brain; however, this comes at significant time costs. Here, we present a new BIDS App for masking fetal fMRI, funcmasker-flex, that overcomes these issues with a robust 3D convolutional neural network (U-net) architecture implemented in an extensible and transparent Snakemake workflow. Open-access fetal fMRI data with manual brain masks from 159 fetuses (1103 total volumes) were used for training and testing the U-net model. We also tested generalizability of the model using 82 locally acquired functional scans from 19 fetuses, which included over 2300 manually segmented volumes. Dice metrics were used to compare performance of funcmasker-flex to the ground truth manually segmented volumes, and segmentations were consistently robust (all Dice metrics ≥ 0.74). The tool is freely available and can be applied to any BIDS dataset containing fetal bold sequences. Funcmasker-flex reduces the need for manual segmentation, even when applied to novel fetal functional datasets, resulting in significant time-cost savings for performing fetal fMRI analysis.

A radiofrequency coil for infants and toddlers.

Infants and toddlers are a challenging population upon which to perform magnetic resonance imaging (MRI) of the brain, both in research and clinical settings. Because of the large range in head size during the early years of development, paediatric neuro-MRI requires a radiofrequency (RF) coil, or set of coils, that is tailored to head size to provide the highest image quality. Mitigating techniques must also be employed to reduce and correct for subject motion. This manuscript describes an RF coil with a tailored mechanical-electrical design that can adapt to the head size of 3-month-old infants to 3-year-old toddlers. The RF coil was designed with tight-fitting coil elements to improve the signal-to-noise ratio (SNR) in comparison with commercially available adult head coils, while simultaneously aiding in immobilization. The coil was designed without visual obstruction to facilitate an unimpeded view of the child's face and the potential application of camera or motion-tracking systems. Despite the lack of elements over the face, the paediatric coil produced higher SNR over most of the brain compared with adult coils, including more than twofold in the periphery. Acceleration rates of fourfold in each Cartesian direction could be achieved. High SNR allowed for short acquisition times through accelerated imaging protocols and reduced the probability of motion during a scan. Modification of the acquisition protocol, with immobilization of the head through the adjustable coil geometry, and subsequently being combined with a motion-tracking system, provides a compelling platform for scanning paediatric populations without sedation and with improved image quality.

Postnatal maternal distress, infant subcortical brain macrostructure and emotional regulation.

BACKGROUND: Maternal distress is associated with an increased risk for adverse emotional development in infants, including difficulties with emotion regulation. Prenatal maternal distress has been associated with alterations in infant brain development. However, less is known about these associations with postnatal maternal distress, despite this being an important modifiable risk factor that can promote healthy brain development and emotional outcomes in infants. METHODS & RESULTS: Infants underwent magnetic resonance imaging (MRI) and mothers completed standardized questionnaires concerning their levels of perceived distress 2-5 months postpartum. Infant emotion regulation was assessed at 8-11 months via maternal report. When examining the associations between maternal distress and infant macrostructure, maternal anxiety was associated with infant right pallidum volumes. Increased display of negative emotions at 8-11 months of age was associated with smaller hippocampal volumes and this association was stronger in girls than boys. CONCLUSION: Findings suggest that postnatal maternal distress may be associated with early infant brain development and emphasize the importance of maternal mental health, supporting previous work. Furthermore, macrostructural properties of infant subcortical structures may be further investigated as potential biomarkers to identify infants at risk of adverse emotional outcomes.

Semi-automatic segmentation of the fetal brain from magnetic resonance imaging.

Background: Volumetric measurements of fetal brain maturation in the third trimester of pregnancy are key predictors of developmental outcomes. Improved understanding of fetal brain development trajectories may aid in identifying and clinically managing at-risk fetuses. Currently, fetal brain structures in magnetic resonance images (MRI) are often manually segmented, which requires both time and expertise. To facilitate the targeting and measurement of brain structures in the fetus, we compared the results of five segmentation methods applied to fetal brain MRI data to gold-standard manual tracings. Methods: Adult women with singleton pregnancies (n = 21), of whom five were scanned twice, approximately 3 weeks apart, were recruited [26 total datasets, median gestational age (GA) = 34.8, IQR = 30.9-36.6]. T2-weighted single-shot fast spin echo images of the fetal brain were acquired on 1.5T and 3T MRI scanners. Images were first combined into a single 3D anatomical volume. Next, a trained tracer manually segmented the thalamus, cerebellum, and total cerebral volumes. The manual segmentations were compared with five automatic methods of segmentation available within Advanced Normalization Tools (ANTs) and FMRIB's Linear Image Registration Tool (FLIRT) toolboxes. The manual and automatic labels were compared using Dice similarity coefficients (DSCs). The DSC values were compared using Friedman's test for repeated measures. Results: Comparing cerebellum and thalamus masks against the manually segmented masks, the median DSC values for ANTs and FLIRT were 0.72 [interquartile range (IQR) = 0.6-0.8] and 0.54 (IQR = 0.4-0.6), respectively. A Friedman's test indicated that the ANTs registration methods, primarily nonlinear methods, performed better than FLIRT (p < 0.001). Conclusion: Deformable registration methods provided the most accurate results relative to manual segmentation. Overall, this semi-automatic subcortical segmentation method provides reliable performance to segment subcortical volumes in fetal MR images. This method reduces the costs of manual segmentation, facilitating the measurement of typical and atypical fetal brain development.

Machine learning-based predictive modeling of resilience to stressors in pregnant women during COVID-19: A prospective cohort study.

During the COVID-19 pandemic, pregnant women have been at high risk for psychological distress. Lifestyle factors may be modifiable elements to help reduce and promote resilience to prenatal stress. We used Machine-Learning (ML) algorithms applied to questionnaire data obtained from an international cohort of 804 pregnant women to determine whether physical activity and diet were resilience factors against prenatal stress, and whether stress levels were in turn predictive of sleep classes. A support vector machine accurately classified perceived stress levels in pregnant women based on physical activity behaviours and dietary behaviours. In turn, we classified hours of sleep based on perceived stress levels. This research adds to a developing consensus concerning physical activity and diet, and the association with prenatal stress and sleep in pregnant women. Predictive modeling using ML approaches may be used as a screening tool and to promote positive health behaviours for pregnant women.

Prenatal and postpartum maternal mental health and neonatal motor outcomes during the COVID-19 pandemic.

Background: Rates of prenatal and postpartum stress and depression in pregnant individuals have increased during the COVID-19 pandemic. Perinatal maternal mental health has been linked to worse motor development in offspring, with motor deficits appearing in infancy and early childhood. We aimed to evaluate the relationship between prenatal and postpartum stress and depression and motor outcome in infants born during the COVID-19 pandemic. Methods: One hundred and seventeen participants completed an online prospective survey study at two timepoints: during pregnancy and within 2 months postpartum. Depression was self-reported using the Edinburgh Perinatal/Postpartum Depression Scale (EPDS), and stress via the Perceived Stress Scale (PSS). Mothers reported total infant motor ability (fine and gross) using the interRAI 0-3 Developmental Domains questionnaire. Results: Prenatal (EPDS median=10.0, interquartile range[IQR]=6.0 - 14.0, B=-0.035, 95%CI=-0.062 to -0.007, p = 0.014) and postpartum maternal depression outcomes (median=7, IQR=4-12, B=-0.037, 95%CI= -0.066 to -0.008, p = 0.012) were significantlynegatively associated with total infant motor ability. Neither pregnancy nor postpartum perceived stress was associated with infant motor function. A cluster analysis revealed that preterm and low-birth weight infants whose mothers reported elevated depressive symptoms during pregnancy and in the postpartum period had the poorest motor outcomes. Conclusions: Prenatal and postpartum depression, but not stress, was associated with early infant motor abilities. Preterm and low-birth weight infants whose mothers reported elevated depressive symptoms maybe at-risk of experiencing poor motor outcomes. These results highlight the importance of identifying pre- and postnatal maternal mental health issues, especially during the ongoing COVID-19 pandemic.

Depression in pregnant women with and without COVID-19.

Evidence suggests that pregnant women who test positive for COVID-19 may develop more severe illness than non-pregnant women and may be at greater risk for psychological distress. The relationship between COVID-19 status (positive, negative, never tested) and symptoms of depression was examined in a survey study (May to September 2020) of pregnant women (n = 869). Pregnant women who reported testing positive for COVID-19 were significantly more likely to report depressive symptoms compared with women who tested negative (P = 0.027) and women who were never tested (P = 0.005). Findings indicate that pregnant women who test positive for COVID-19 should be screened and monitored for depressive symptoms.

Individual differences in representational similarity of first and second languages in the bilingual brain.

Current theories of bilingualism disagree on the extent to which separate brain regions are used to maintain or process one's first and second language. The present study took a novel multivariate approach to address this question. We examined whether bilinguals maintain distinct neural representations of two languages; specifically, we tested whether brain areas that are involved in processing word meaning in either language are reliably representing each language differently, and whether language representation is influenced by individual differences in proficiency level and age of acquisition (AoA) of L2. Thirty-one English-Mandarin bilingual adults performed a picture-word matching task in both languages. We then used representational similarity analysis to examine which brain regions reliably showed different patterns of activity for each language. We found that both proficiency and AoA predicted dissimilarity between language representations in several brain areas within the language network as well as several regions of the ventral visual pathway, demonstrating that top-down language knowledge and individual language experience shapes concept representation in this processing stream. The results support the model of an integrated language system in bilinguals, along with a novel description of how representations for each language change with proficiency level and L2 AoA.

Cognition across the Lifespan: Investigating Age, Sex, and Other Sociodemographic Influences.

Maintaining cognitive health across the lifespan has been the focus of a multi-billion-dollar industry. In order to guide treatment and interventions, a clear understanding of the way that proficiency in different cognitive domains develops and declines in both sexes across the lifespan is necessary. Additionally, there are sex differences in a range of other factors, including psychiatric illnesses such as anxiety, depression, and substance use, that are also known to affect cognition, although the scale of this interaction is unknown. Our objective was to assess differences in cognitive function across the lifespan in men and women in a large, representative sample. Leveraging online cognitive testing, a sample of 9451 men and 9451 women ranging in age from 12 to 69 (M = 28.21) matched on socio-demographic factors were studied. Segmented regression was used to model three cognitive domains-working memory, verbal abilities, and reasoning. Sex differences in all three domains were minimal; however, after broadening the sample in terms of socio-demographic factors, sex differences appeared. These results suggest that cognition across the lifespan differs for men and women, but is greatly influenced by environmental factors. We discuss these findings within a framework that describes sex differences in cognition as likely guided by a complex interplay between biology and environment.

Emotion regulation in emerging adults with major depressive disorder and frequent cannabis use.

In people with mental health issues, approximately 20% have co-occurring substance use, often involving cannabis. Although emotion regulation can be affected both by major depressive disorder (MDD) and by cannabis use, the relationship among all three factors is unknown. In this study, we used fMRI to evaluate the effect that cannabis use and MDD have on brain activation during an emotion regulation task. Differences were assessed in 74 emerging adults aged 16-23 with and without MDD who either used or did not use cannabis. Severity of depressive symptoms, emotion regulation style, and age of cannabis use onset were also measured. Both MDD and cannabis use interacted with the emotion regulation task in the left temporal lobe, however the location of the interaction differed for each factor. Specifically, MDD showed an interaction with emotion regulation in the middle temporal gyrus, whereas cannabis use showed an interaction in the superior temporal gyrus. Emotion regulation style predicted activity in the right superior frontal gyrus, however, this did not interact with MDD or cannabis use. Severity of depressive symptoms interacted with the emotion regulation task in the left middle temporal gyrus. The results highlight the influence of cannabis use and MDD on emotion regulation processing, suggesting that both may have a broader impact on the brain than previously thought.

Brain training habits are not associated with generalized benefits to cognition: An online study of over 1000 "brain trainers".

The foundational tenet of brain training is that general cognitive functioning can be enhanced by completing computerized games, a notion that is both intuitive and appealing. Moreover, there is strong incentive to improve our cognitive abilities, so much so that it has driven a billion-dollar industry. However, whether brain training can really produce these desired outcomes continues to be debated. This is, in part, because the literature is replete with studies that use ill-defined criteria for establishing transferable improvements to cognition, often using single training and outcome measures with small samples. To overcome these limitations, we conducted a large-scale online study to examine whether practices and beliefs about brain training are associated with better cognition. We recruited a diverse sample of over 1000 participants, who had been using an assortment of brain training programs for up to 5 years. Cognition was assessed using multiple tests that measure attention, reasoning, working memory and planning. We found no association between any measure of cognitive functioning and whether participants were currently "brain training" or not, even for the most committed brain trainers. Duration of brain training also showed no relationship with any cognitive performance measure. This result was the same regardless of participant age, which brain training program they used, or whether they expected brain training to work. Our results pose a significant challenge for "brain training" programs that purport to improve general cognitive functioning among the general population. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The Emergence of Integrated Information, Complexity, and 'Consciousness' at Criticality.

Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte-Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model.

Consciousness and the Dimensionality of DOC Patients via the Generalized Ising Model.

The data from patients with severe brain injuries show complex brain functions. Due to the difficulties associated with these complex data, computational modeling is an especially useful tool to examine the structure-function relationship in these populations. By using computational modeling for patients with a disorder of consciousness (DoC), not only we can understand the changes of information transfer, but we also can test changes to different states of consciousness by hypothetically changing the anatomical structure. The generalized Ising model (GIM), which specializes in using structural connectivity to simulate functional connectivity, has been proven to effectively capture the relationship between anatomical structures and the spontaneous fluctuations of healthy controls (HCs). In the present study we implemented the GIM in 25 HCs as well as in 13 DoC patients diagnosed at three different states of consciousness. Simulated data were analyzed and the criticality and dimensionality were calculated for both groups; together, those values capture the level of information transfer in the brain. Ratifying previous studies, criticality was observed in simulations of HCs. We were also able to observe criticality for DoC patients, concluding that the GIM is generalizable for DoC patients. Furthermore, dimensionality increased for the DoC group as compared to healthy controls, and could distinguish different diagnostic groups of DoC patients.