Typical Brain Research Articles

467. White Matter Sex Differences During Typical Brain Development Assessed With NODDI Microstructure Measures

467. White Matter Sex Differences During Typical Brain Development Assessed With NODDI Microstructure Measures

Clinical and Ultrasound Aspects of Neonatal Hypoxia Ischaemia at the Regional University Hospital of Ouahigouya

Neonatal hypoxia ischaemia (HI) is the third leading cause of neonatal death worldwide. Medical imaging plays an important role in its diagnosis and monitoring. We aim to analyze the clinical and ultrasound aspects of neonatal HI at the Ouahigouya Regional University Hospital (CHUR/OHG). This was an analytical cross-sectional study with retrospective data collection covering the period from 1 January 2017 to 30 April 2019. During the study period, neonatal HI for 20.12% of pediatric admissions, of which 33.7% underwent Transfontanellar ultrasound (7.31% of all ultrasounds). The sex ratio was 1.6. The majority (85.81%) of children were born at term. According to the Sarnat classification, mild distress predominated (66.89%), followed by moderate distress (27.03%) and severe distress (6.08%). Transfontanellar ultrasound was abnormal in 71 patients (47.97%). Typical brain lesions found were hemorrhage (27.70%), anoxic-ischaemic encephalopathy (18.24%), and periventricular leukomalacia (2.70%). The outcome was normal in 81.08% of patients, with a mortality rate of 6.08%. In view of the seriousness of neonatal brain damage, it is necessary to improve diagnostic methods (MRI) and treatment (hypothermia) to limit the sequelae.

Design of locally arranged sensor arrays in wearable OPM-MEG based on sensor volume constraints

Optically pumped magnetometers-based magnetoencephalography (OPM-MEG) has a more flexible sensor configuration. This implies that OPM-MEG can be customized as locally arranged sensor arrays (LASAs) when the number of sensors is limited. Currently, various methods are proposed to optimize LASA for accurately estimating source currents. However, the sensor volume, as a practical constraint in the construction of an OPM-MEG system, was not taken into account during the design of sensor arrays. In this study, we proposed a sensor-array design method for LASAs that considers the physical size of OPM sensors, with the array sensitivity metrics serving as the optimization objective. To illustrate the effectiveness of the proposed method, several LASAs covering typical brain functional regions were designed and tested in both simulations and practical OPM-MEG experiments. The results indicated that the designed LASAs under sensor volume constraints demonstrated reduced source localization errors and focused localization range for the target source area.

Valproic acid exposure affects social visual lateralization and asymmetric gene expression in zebrafish larvae

Cerebral asymmetry is critical for typical brain function and development; at the same time, altered brain lateralization seems to be associated with neuropsychiatric disorders. Zebrafish are increasingly emerging as model species to study brain lateralization, using asymmetric development of the habenula, a phylogenetically old brain structure associated with social and emotional processing, to investigate the relationship between brain asymmetry and social behavior. We exposed 5-h post-fertilization zebrafish embryos to valproic acid (VPA), a compound used to model the core signs of ASD in many vertebrate species, and assessed social interaction, visual lateralization and gene expression in the thalamus and the telencephalon. VPA-exposed zebrafish exhibit social deficits and a deconstruction of social visual laterality to the mirror. We also observe changes in the asymmetric expression of the epithalamic marker leftover and in the size of the dorsolateral part of the habenula in adult zebrafish. Our data indicate that VPA exposure neutralizes the animals’ visual field bias, with a complete loss of the left-eye use bias in front of their own mirror image, and alters brain asymmetric gene expression and morphology, opening new perspectives to investigate brain lateralization and its link to atypical social cognitive development.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.

The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach. We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.

Astrocyte Regulation of Synapse Formation, Maturation, and Elimination.

Astrocytes play an integral role in the development, maturation, and refinement of neuronal circuits. Astrocytes secrete proteins and lipids that instruct the formation of new synapses and induce the maturation of existing synapses. Through contact-mediated signaling, astrocytes can regulate the formation and state of synapses within their domain. Through phagocytosis, astrocytes participate in the elimination of excess synaptic connections. In this work, we will review key findings on the molecular mechanisms of astrocyte-synapse interaction with a focus on astrocyte-secreted factors, contact-mediated mechanisms, and synapse elimination. We will discuss this in the context of typical brain development and maintenance, as well as consider the consequences of dysfunction in these pathways in neurological disorders, highlighting a role for astrocytes in health and disease.

Reduced covariation between brain morphometry and local spontaneous activity in young children with ASD.

While disruptions in brain maturation in the first years of life in ASD are well documented, little is known about how the brain structure and function are related in young children with ASD compared to typically developing peers. We applied a multivariate pattern analysis to examine the covariation patterns between brain morphometry and local brain spontaneous activity in 38 toddlers and preschoolers with ASD and 31 typically developing children using T1-weighted structural MRI and resting-state fMRI data acquired during natural sleep. The results revealed significantly reduced brain structure-function correlations in ASD. The resultant brain structure and function composite indices were associated with age among typically developing children, but not among those with ASD, suggesting mistiming of typical brain maturational trajectories early in life in autism. Additionally, the brain function composite indices were associated with the overall developmental and adaptive behavior skills in the ASD group, highlighting the neurodevelopmental significance of early local brain activity in autism.

Cortical morphological networks for profiling autism spectrum disorder using tensor component analysis.

Atypical neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) can alter the cortex morphology at different levels: (i) a low-order level where cortical regions are examined individually, (ii) a high-order level where the relationship between two cortical regions is considered, and (iii) a multi-view high-order level where the relationship between regions is examined across multiple brain views. In this study, we propose to use the emerging multi-view cortical morphological network (CMN), which is derived from T1-w magnetic resonance imaging (MRI), to profile autistic and typical brains and pursue new ways of fingerprinting 'cortical morphology' at the intersection of 'network neuroscience'. Each CMN view models the pairwise morphological dissimilarity at the connection level using a specific cortical attribute (e.g., thickness). Specifically, we set out to identify the inherently most representative morphological connectivities shared across different views of the cortex in both autistic and normal control (NC) populations using tensor component analysis. We thus discover the connectional profiles of both populations shared across different CMNs of the left and right hemispheres, respectively. One of the most representative morphological cortical attributes for assessing the abnormal brain structures in patients with ASD is cortical thickness. The most representative morphological connectivities in multi-view CMN population of normal control and ASD subjects, respectively, and in both left and right hemispheres within the temporal, frontal, and insular lobes of individuals with ASD. These representative connectivities are corresponded to specific clinical features observed in individuals with ASD.

Histopathological Analysis of Brains from Dogs Infected with Canine Distemper Virus.

We describe the use of conventional histology and immunohistochemistry against canine distemper virus (CDV) to examine the brains of domestic dogs with a confirmed diagnosis of CDV infection. Histologically, to identify the main typical lesions, we used conventional H&E stain; to evaluate the progressive demyelination, we used Luxol Fast Blue stain; and to identify the presence of viral particles in these affected regions, we used immunohistochemistry against CDV. We confirm that the histopathological analysis of brains of distemper-infected dogs is a powerful tool to evaluate the typical brain lesions and could be used as an interesting natural model to continue studying the pathogenesis of canine distemper in different species and/or other morbillivirus infections, like measles.

Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition.

Major depressive disorder (MDD) is the most common psychological disease. To improve the recognition accuracy of MDD, more and more machine learning methods have been proposed to mining EEG features, i.e. typical brain functional patterns and recognition methods that are closely related to depression using resting EEG signals. Most existing methods typically utilize threshold methods to filter weak connections in the brain functional connectivity network (BFCN) and construct quantitative statistical features of brain function to measure the BFCN. However, these thresholds may excessively remove weak connections with functional relevance, which is not conducive to discovering potential hidden patterns in weak connections. In addition, statistical features cannot describe the topological structure features and information network propagation patterns of the brain's different functional regions. To solve these problems, we propose a novel MDD recognition method based on a multi-granularity graph convolution network (MGGCN). On the one hand, this method applies multiple sets of different thresholds to build a multi-granularity functional neural network, which can remove noise while fully retaining valuable weak connections. On the other hand, this method utilizes graph neural network to learn the topological structure features and brain saliency patterns of changes between brain functional regions on the multi-granularity functional neural network. Experimental results on the benchmark datasets validate the superior performance and time complexity of MGGCN. The analysis shows that as the granularity increases, the connectivity defects in the right frontal(RF) and right temporal (RT) regions, left temporal(LT) and left posterior(LP) regions increase. The brain functional connections in these regions can serve as potential biomarkers for MDD recognition.

Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images.

Knowledge of protein expression in mammalian brains at regional and cellular levels can facilitate understanding of protein functions and associated diseases. As the mouse brain is a typical mammalian brain considering cell type and structure, several studies have been conducted to analyze protein expression in mouse brains. However, labeling protein expression using biotechnology is costly and time-consuming. Therefore, automated models that can accurately recognize protein expression are needed. Here, we constructed machine learning models to automatically annotate the protein expression intensity and cellular location in different mouse brain regions from immunofluorescence images. The brain regions and sub-regions were segmented through learning image features using an autoencoder and then performing K-means clustering and registration to align with the anatomical references. The protein expression intensities for those segmented structures were computed on the basis of the statistics of the image pixels, and patch-based weakly supervised methods and multi-instance learning were used to classify the cellular locations. Results demonstrated that the models achieved high accuracy in the expression intensity estimation, and the F1 score of the cellular location prediction was 74.5%. This work established an automated pipeline for analyzing mouse brain images and provided a foundation for further study of protein expression and functions.

Chemogenetic emulation of intraneuronal oxidative stress affects synaptic plasticity

Overproduction of reactive oxygen species (ROS) and oxidative cell damage are commonly associated with most brain pathologies [1, 2]. Dysregulation of redox homeostasis in the aging brain is thought to be responsible for impaired synaptic transmission and plasticity, leading to reduced neuronal computational capacity and learning and memory deficits. Studying the contribution of oxidative stress to the development of diseases, such as age-related dementia and Alzheimer’s disease, is complex due to the lack of methods for modeling isolated oxidative damage in individual cell types [3]. We introduce a chemogenetic approach utilizing D-amino acid oxidase (DAAO) from yeast to produce hydrogen peroxide intraneuronally, which is one of the most stable ROS [4]. H2O2 generation was evaluated in primary cultured neurons and acute mouse brain slices through the utilization of a genetically encoded fluorescent biosensor, HyPer7, to validate the methodology [5]. The changes in the fluorescence signal of HyPer7 after treating neurons that expressed DAAO with D-Norvaline (D-Nva), a substrate for DAAO, confirmed the targeted production of H2O2 through chemogenetics. Using electrophysiological recordings in acute brain slices, we demonstrated that intraneuronal oxidative stress induced by chemogenetics did not affect basal synaptic transmission and the probability of neurotransmitter release from presynaptic terminals. However, it diminished long-term potentiation (LTP) at the single-cell level. Astrocytes have the ability to metabolize d-amino acids, rendering the proposed approach ineffective in vivo experiments. Consequently, in vivo testing of the tool was necessary for validation. To achieve this, an optical setup for exciting and detecting the HyPer7 signal was developed and implanted into the mouse brain via optical fibers. By using this approach, we were able to demonstrate the generation of H2O2 in DAAO-expressing neurons in vivo, upon intraperitoneal administration of D-amino acids. The results demonstrate that using a DAAO-based chemogenetic tool, along with electrophysiological recordings, clarifies numerous unanswered queries regarding the part of ROS-dependent signaling in typical brain activities and the impact of oxidative stress on the development of cognitive aging and preliminary neurodegenerative stages. The suggested method is valuable for detecting initial indicators of neuronal oxidative stress. Additionally, it can be used for evaluating probable antioxidants that can effectively combat neuronal oxidative harm.

Pushing MP2RAGE boundaries: Ultimate time-efficient parameterization combined with exhaustive T1 synthetic contrasts.

MP2RAGE parameter optimization is redefined to allow more time-efficient MR acquisitions, whereas the T1 -based synthetic imaging framework is used to obtain on-demand T1 -weighted contrasts. Our aim was to validate this concept on healthy volunteers and patients with multiple sclerosis, using plug-and-play parallel-transmission brain imaging at 7 T. A "time-efficient" MP2RAGE sequence was designed with optimized parameters including TI and TR set as small as possible. Extended phase graph formalism was used to set flip-angle values to maximize the gray-to-white-matter contrast-to-noise ratio (CNR). Several synthetic contrasts (UNI, EDGE, FGATIR, FLAWSMIN , FLAWSHCO ) were generated online based on the acquired T1 maps. Experimental validation was performed on 4 healthy volunteers at various spatial resolutions. Clinical applicability was evaluated on 6 patients with multiple sclerosis, scanned with both time-efficient and conventional MP2RAGE parameterizations. The proposed time-efficient MP2RAGE protocols reduced acquisition time by 40%, 30%, and 19% for brain imaging at (1 mm)3 , (0.80 mm)3 and (0.65 mm)3 , respectively, when compared with conventional parameterizations. They also provided all synthetic contrasts and comparable contrast-to-noise ratio on UNI images. The flexibility in parameter selection allowed us to obtain a whole-brain (0.45 mm)3 acquisition in 19 min 56 s. On patients with multiple sclerosis, a (0.67 mm)3 time-efficient acquisition enhanced cortical lesion visualization compared with a conventional (0.80 mm)3 protocol, while decreasing the scan time by 15%. The proposed optimization, associated with T1 -based synthetic contrasts, enabled substantial decrease of the acquisition time or higher spatial resolution scans for a given time budget, while generating all typical brain contrasts derived from MP2RAGE.

Estimate of fetal brain temperature using proton resonance frequency thermometry during 3 Tesla fetal magnetic resonance imaging.

T2-weighted Single Shot Fast Spin Echo (SSFSE) scans at 3 Tesla (3T) are increasingly used to image fetal pathology due to their excellent tissue contrast resolution and signal-to-noise ratio (SNR). Temperature changes that may occur in response to radio frequency (RF) pulses used for these sequences at 3T have not been studied in human fetal brains. To evaluate the safety of T2-weighted SSFSE for fetal brains at 3T, magnetic resonance (MR) thermometry was used to measure relative temperature changes in a typical clinical fetal brain MR exam. Relative temperature was estimated using sets of gradient recalled echo (GRE) images acquired before and after T2-weighted SSFSE images which lasted 27.47±8.19 minutes. Thirty-one fetuses with cardiac abnormalities, and 20 healthy controls were included in this study. Fetal brain temperature was estimated by proton resonance frequency (PRF) thermometry and compared to the estimated temperature in the gluteal muscle of the mother. Seven scans with excessive motion were excluded. Local outlier factor (LOF) was performed to remove 12 additional scans with spurious phase measurements due to motion degradation and potential field drift. Linear regression was performed to determine if temperature changes are dependent on the rate of energy deposition during the scan. For the 32 participants used in the analysis, 17 with cardiac abnormalities and 15 healthy controls, the average relative fetal temperate change was 0.19±0.73 ℃ higher than the mother, with no correlation between relative temperature change and the rate of images acquired during the scans (regression coefficient =-0.05, R-squared =0.05, P=0.22, F-statistic =1.60). The difference in the relative temperature changes between the fetal brain and mother's gluteal tissue in the healthy controls was on average 0.08 ℃ lower and found not to be statistically different (P=0.76) to the group with cardiac abnormalities. Our results indicate that the estimated relative temperature changes of the fetal brain compared to the mother's gluteal tissue from RF pulses during the course of the T2-weighted SSFSE fetal MR exam are minimal. The differences in acquired phase between these regions through the exam were found not to be statistically different. These findings support that fetal brain imaging at 3T is within FDA limits and safe.

The relationship between [18F]FDG‐PET signal and key‐players of brain glucose metabolism

AbstractBackgroundNeuronal activity is supported by activity‐dependent lactate production from astrocytes, as postulated by the astrocyte–neuron lactate shuttle (ANLS) hypothesis. Indeed, astrocytic glutamate uptake is a main trigger for brain glucose consumption. [18F]fluorodeoxyglucose (FDG)‐PET showing a hypometabolic signature has been used as an index of neurodegeneration in Alzheimer’s disease (AD). In the early stages of the disease, soluble amyloid‐beta (Aβ) has been associated with neuronal hyperactivity due to decreased astrocytic glutamate uptake. It is hypothesized that this phenomenon occurs especially in regions with high ongoing baseline activity. Over time, neurodegeneration and hypometabolism in such regions may contribute to the progress of the AD continuum. Thus, we investigated whether the [18F]FDG‐PET signal in typical hypometabolic regions in AD is associated with the expression of ANLS transporters and enzymes in the healthy brain.MethodGene expression of GLUT1, GLUT3, MCT1, MCT2, MCT4, LDHA, and LDHB (ANLS genes) from postmortem brain tissue of healthy individuals were obtained from the Allen Human Brain Atlas. [18F]FDG‐PET data from cognitively unimpaired individuals were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Pearson correlations were performed between the mean gene expression and the mean [18F]FDG‐PET SUVr across typically hypometabolic brain regions in AD (precuneus, angular and temporal gyri, and parietal and cingulate regions) and non‐vulnerable regions (uncorrected p‐value < 0.05).ResultsThe expression of ANLS genes had a stronger positive correlation with [18F]FDG‐PET SUVr across the AD hypometabolic regions than the non‐vulnerable regions (p < 0.05). Of note, the astrocytic glucose transporter GLUT1 presented a stronger correlation with [18F]FDG‐PET than the neuronal transporter GLUT3 in AD‐vulnerable regions. Figure 1 presents all correlations.ConclusionOur results show that the physiological expression of transporters and enzymes responsible for the brain glucose metabolism correlate more with the [18F]FDG‐PET signal in typical brain regions of the AD hypometabolic signature. This suggests these regions present a higher baseline activity and may be more susceptible to Aβ‐induced hyperactivation in early AD, which could lead to a bioenergetic collapse in later disease stages, as shown in the [18F]FDG‐PET of AD individuals.

2 Effects of Early Exposure to More Than One Language in the Home on Language Skills and Brain Functional Network Organization in Young Children with Autism Spectrum Disorder

Objective:Concerns that exposure to more than one language in the home might negatively impact language development in young children with autism spectrum disorder (ASD) are common among caregivers. Although research directly examining the impact of a multilingual home environment in ASD is scarce, emerging evidence shows that language outcomes might be equivalent or better in children with ASD exposed to more than one language (Romero & Uddin, 2021). However, no evidence to date exists on whether exposure to more than one language affects early brain functional development in children with ASD. The current study aims to examine the (1) cross-sectional and (2) longitudinal associations between home language environment (exposure to one v. multiple languages at home, H1l vs. H>1l) and receptive and expressive language skills in young children with and without ASD, and (3) to investigate links between home language environment and brain functional network organization.Participants and Methods:Participants included young children with ASD (n=67, mean age: 35±13 months, H>1l n=43) and typically developing (TD) children (n=39, mean age: 32±16 months, H>1l n=17) enrolled in a longitudinal study of early brain markers of autism. A subset of children with ASD for whom longitudinal behavioral data from two study visits were available (n=21, H>1l n=11) were used for exploratory analysis. Receptive language (RL) and expressive language (EL) skills were assessed by the Mullen Scales of Early Learning at each study visit. Data from 42 children with ASD (H>1l n=27) and 38 TD children (H>1l n= 15) for whom functional MRI data were acquired during natural sleep were included in functional connectivity (FC) analysis. ANCOVAs were employed to examine the effect of diagnosis, home language environment (H1l vs. H>1l) and its interaction on RL and EL skills while controlling for socioeconomic variables (i.e., maternal education level, income-to-needs ratio) and gestational age at birth. Linear mixed models were applied to explore the longitudinal effect of home language environment on RL and EL skills across two study visits in the ASD group. Lastly, FC analysis was conducted to compare functional connectivity across 7 canonical brain networks in children with and without ASD who were raised in H1l and H>1l.Results:We found significant diagnosis by home language environment interaction effect on EL skills, with children with ASD and H1l exhibiting the lowest EL skills. Longitudinal analysis identified a significant home language environment by study visit interaction effect on EL skills in children with ASD. Specifically, children with ASD and H1l showed lower EL skills at study visit 1 but equivalent EL skills at study visit 2 compared to children with ASD and H>1l. FC analysis revealed that children with ASD and H>1l displayed more typical brain network organization (similar to TD children) compared to those with H1l, specifically for FC between language, frontoparietal, and default mode networks.Conclusions:These results suggest that early exposure to more than one language in the home may be linked with better expressive language skills in young children with ASD. Results of functional connectivity analysis also suggest that exposure to more than one language may be associated with more neurotypical functional network organization, particularly involving language and high-order networks.

A normative model ofbrain responses to social scenarios reflects the maturity ofchildren andadolescents' social-emotional abilities.

The rapid brain maturation in childhood and adolescence accompanies the development of socio-emotional functioning. However, it is unclear how the maturation of the neural activity drives the development of socio-emotional functioning and individual differences. This study aimed to reflect the age dependence of inter-individual differences in brain responses to socio-emotional scenarios and to develop naturalistic imaging indicators to assess the maturity of socio-emotional ability at the individual level. Using three independent naturalistic imaging datasets containing healthy participants (n = 111, 21 and 122), we found and validated that age-modulated inter-individual concordance of brain responses to socio-emotional movies in specific brain regions. The similarity of an individual's brain response to the average response of older participants was defined as response typicality, which predicted an individual's emotion regulation strategies in adolescence and theory of mind (ToM) in childhood. Its predictive power was not superseded by age, sex, cognitive performance or executive function. We further showed that the movie's valence and arousal ratings grounded the response typicality. The findings highlight that forming typical brain response patterns may be a neural phenotype underlying the maturation of socio-emotional ability. The proposed response typicality represents a neuroimaging approach to measure individuals' maturity of cognitive reappraisal and ToM.

Magnetic Resonance Spectroscopy of Brain Metabolism in Fetuses With Congenital Heart Disease

BackgroundCongenital heart disease (CHD) remains a significant risk factor for neurologic injury because altered fetal hemodynamics may be unable to support typical brain development during critical periods of growth and maturation. ObjectivesThe primary objective was to assess differences in the cerebral biochemical profile between healthy fetuses and fetuses with complex CHD and to relate these with infant outcomes. MethodsPregnant participants underwent fetal magnetic resonance imaging with cerebral proton magnetic resonance spectroscopy acquisitions as part of a prospective observational study. Cerebral metabolites of N-acetyl aspartate, creatine, choline, myo-inositol, scyllo-inositol, lactate, and relevant ratios were quantified using LCModel. ResultsWe acquired 503 proton magnetic resonance spectroscopy images (controls = 333; CHD = 170) from 333 participants (controls = 221; CHD = 112). Mean choline levels were higher in CHD compared with controls (CHD 2.47 IU [Institutional Units] ± 0.44 and Controls 2.35 IU ± 0.45; P = 0.02), whereas N-acetyl aspartate:choline ratios were lower among CHD fetuses compared with controls (CHD 1.34 ± 0.40 IU vs controls 1.44 ± 0.48 IU; P = 0.001). Cerebral lactate was detected in all cohorts but increased in fetuses with transposition of the great arteries and single-ventricle CHD (median: 1.63 [IQR: 0.56-3.27] in transposition of the great arteries and median: 1.28 [IQR: 0-2.42] in single-ventricle CHD) compared with 2-ventricle CHD (median: 0.79 [IQR: 0-1.45]). Cerebral lactate also was associated with increased odds of death before discharge (OR: 1.75; P = 0.04). ConclusionsCHD is associated with altered cerebral metabolites in utero, particularly in the third trimester period of pregnancy, which is characterized by exponential brain growth and maturation, and is associated with survival to hospital discharge. The long-term neurodevelopmental consequences of these findings warrant further study.

The Relation Between Attention and Memory.

The relation between attention and memory has long been deemed important for understanding cognition, and it was heavily researched even in the first experimental psychology laboratory by Wilhelm Wundt and his colleagues. Since then, the importance of the relation between attention and memory has been explored in myriad subdisciplines of psychology, and we incorporate a wide range of these diverse fields. Here, we examine some of the practical consequences of this relation and summarize work with various methodologies relating attention to memory in the fields of working memory, long-term memory, individual differences, life-span development, typical brain function, and neuropsychological conditions. We point out strengths and unanswered questions for our own embedded processes view of information processing, which is used to organize a large body of evidence. Last, we briefly consider the relation of the evidence to a range of other theoretical views before drawing conclusions about the state of the field.

Update on Nonhuman Primate Models of Brain Disease and Related Research Tools.

The aging of the population is an increasingly serious issue, and many age-related illnesses are on the rise. These illnesses pose a serious threat to the health and safety of elderly individuals and create a serious economic and social burden. Despite substantial research into the pathogenesis of these diseases, their etiology and pathogenesis remain unclear. In recent decades, rodent models have been used in attempts to elucidate these disorders, but such models fail to simulate the full range of symptoms. Nonhuman primates (NHPs) are the most ideal neuroscientific models for studying the human brain and are more functionally similar to humans because of their high genetic similarities and phenotypic characteristics in comparison with humans. Here, we review the literature examining typical NHP brain disease models, focusing on NHP models of common diseases such as dementia, Parkinson's disease, and epilepsy. We also explore the application of electroencephalography (EEG), magnetic resonance imaging (MRI), and optogenetic study methods on NHPs and neural circuits associated with cognitive impairment.