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Introduction The mammalian cerebral cortex forms through a species-specific sequence of neural precursor cell proliferation and differentiation events (1, 2). Neuroepithelial stem cells expand through a series of symmetric proliferative [...], Brain size and cellular heterogeneity are tightly regulated by species-specific proliferation and differentiation of multipotent neural progenitor cells (NPCs). Errors in this process are among the mechanisms of primary hereditary microcephaly (MCPH), a group of disorders characterized by reduced brain size and intellectual disability. Biallelic citron rho-interacting serine/ threonine kinase (CIT) missense variants that disrupt kinase function ([CIT.sup.KI/KI]) and frameshift loss-of-function variants ([CIT.sup.FS/FS]) are the genetic basis for MCPH17; however, the function of CIT catalytic activity in brain development and NPC cytokinesis is unknown. Therefore, we created the [Cit.sup.KI/KI] mouse model and found that it did not phenocopy human microcephaly, unlike biallelic [Cit.sup.FS/FS] animals. Nevertheless, both Cit models exhibited binucleation, DNA damage, and apoptosis. To investigate human-specific mechanisms of CIT microcephaly, we generated [CIT.sup.KI/KI] and [CIT.sup.FS/FS] human forebrain organoids. We found that [CIT.sup.KI/KI] and [CIT.sup.FS/FS] organoids lost cytoarchitectural complexity, transitioning from pseudostratified to simple neuroepithelium. This change was associated with defects that disrupted the polarity of NPC cytokinesis, in addition to elevating apoptosis. Together, our results indicate that both CIT catalytic and scaffolding functions in NPC cytokinesis are critical for human corticogenesis. Species differences in corticogenesis and the dynamic 3D features of NPC mitosis underscore the utility of human forebrain organoid models for understanding human microcephaly.

Delirium is a severe postoperative complication associated with poor overall and especially neurocognitive prognosis. Altered brain mineralization is found in neurodegenerative disorders but has not been studied in postoperative delirium and postoperative cognitive decline. We hypothesized that mineralization‐related hypointensity in susceptibility‐weighted magnetic resonance imaging (SWI) is associated with postoperative delirium and cognitive decline. In an exploratory, hypothesis‐generating study, we analysed a subsample of cognitively healthy patients ≥65 years who underwent SWI before (N = 65) and 3 months after surgery (N = 33). We measured relative SWI intensities in the basal ganglia, hippocampus and posterior basal forebrain cholinergic system (pBFCS). A post hoc analysis of two pBFCS subregions (Ch4, Ch4p) was conducted. Patients were screened for delirium until the seventh postoperative day. Cognitive testing was performed before and 3 months after surgery. Fourteen patients developed delirium. After adjustment for age, sex, preoperative cognition and region volume, only pBFCS hypointensity was associated with delirium (regression coefficient [90% CI]: B = −15.3 [−31.6; −0.8]). After adjustments for surgery duration, age, sex and region volume, perioperative change in relative SWI intensities of the pBFCS was associated with cognitive decline 3 months after surgery at a trend level (B = 6.8 [−0.9; 14.1]), which was probably driven by a stronger association in subregion Ch4p (B = 9.3 [2.3; 16.2]). Brain mineralization, particularly in the cerebral cholinergic system, could be a pathomechanism in postoperative delirium and cognitive decline. Evidence from our studies is limited because of the small sample and a SWI dataset unfit for iron quantification, and the analyses presented here should be considered exploratory. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is a very aggressive and lethal primary brain cancer in adults. The multifaceted nature of GBM pathogenesis, rising from complex interactions between cells and the tumor microenvironment (TME), has posed great treatment challenges. Despite significant scientific efforts, the prognosis for GBM remains very poor, even after intensive treatment with surgery, radiation, and chemotherapy. Efficient GBM management still requires the invention of innovative treatment strategies. There is a strong necessity to complete cancer in vitro studies and in vivo studies to properly evaluate the mechanisms of tumor progression within the complex TME. In recent years, the animal models used to study GBM tumors have evolved, achieving highly invasive GBM models able to provide key information on the molecular mechanisms of GBM onset. At present, the most commonly used animal models in GBM research are represented by mammalian models, such as mouse and canine ones. However, the latter present several limitations, such as high cost and time-consuming management, making them inappropriate for large-scale anticancer drug evaluation. In recent years, the zebrafish (Danio rerio) model has emerged as a valuable tool for studying GBM. It has shown great promise in preclinical studies due to numerous advantages, such as its small size, its ability to generate a large cohort of genetically identical offspring, and its rapid development, permitting more time- and cost-effective management and high-throughput drug screening when compared to mammalian models. Moreover, due to its transparent nature in early developmental stages and genetic and anatomical similarities with humans, it allows for translatable brain cancer research and related genetic screening and drug discovery. For this reason, the aim of the present review is to highlight the potential of relevant transgenic and xenograft zebrafish models and to compare them to the traditionally used animal models in GBM research. [ABSTRACT FROM AUTHOR]

Background: There continues to be growing interest in the Science of Learning including identifying applications for findings from this work outside the laboratory to support learning. Presently, there exists a gap in our understanding of learning during healthy adulthood as well as effective ways in which that learning can be improved. Developing a more comprehensive understanding of learning during adulthood, and effective ways of improving that learning, are crucial goals given the impact of a rapidly aging global population. The main objective of the proposed systematic review is to identify and synthesize all recent cognitive and brain research investigating learning across the adult lifespan. Methods: Searches will be performed across Scopus, Web of Science, and ProQuest databases. Both published and unpublished literature will be screened for inclusion. Included articles will be limited to research in healthy adult samples reporting measures of learning-related cognition, brain structure or function and their relationship with age, or the impact of interventions to improve learning. All steps of the review will be performed by three trained reviewers. Tabular, narrative, and quantitative syntheses will be provided based on the characteristics of included studies. Discussion: Findings from the proposed review will contribute to our understanding of learning in adulthood. Additionally, this review will identify research gaps in need of further investigation and relevant findings for translation, informing the scope of future funding priorities in the Science of Learning. [ABSTRACT FROM AUTHOR]

Phase synchronization serves as an effective method for analyzing the synchronization of electroencephalogram (EEG) signals among brain regions and the dynamic changes of the brain. The purpose of this paper is to study the construction of the functional brain network (FBN) based on phase synchronization, with a special focus on neural processes related to human balance regulation. This paper designed four balance paradigms of different difficulty by blocking vision or proprioception and collected 19-channel EEG signals. Firstly, the EEG sequences are segmented by sliding windows. The phase-locking value (PLV) of core node pairs serves as the phase-screening index to extract the valid data segments, which are recombined into new EEG sequences. Subsequently, the multichannel weighted phase lag index (wPLI) is calculated based on the new EEG sequences to construct the FBN. The experimental results show that due to the randomness of the time points of body balance adjustment, the degree of phase synchronization of the datasets screened by PLV is more obvious, improving the effective information expression of the subsequent EEG data segments. The FBN topological structures of the wPLI show that the connectivity of various brain regions changes structurally as the difficulty of human balance tasks increases. The frontal lobe area is the core brain region for information integration. When vision or proprioception is obstructed, the EEG synchronization level of the corresponding occipital lobe area or central area decreases. The synchronization level of the frontal lobe area increases, which strengthens the synergistic effect among the brain regions and compensates for the imbalanced response caused by the lack of sensory information. These results show the brain regional characteristics of the process of human balance regulation under different balance paradigms, providing new insights into endogenous neural mechanisms of standing balance and methods of constructing brain networks. [ABSTRACT FROM AUTHOR]

BACKGROUND: Brain tumor is a highly destructive, aggressive, and fatal disease. The presence of brain tumors can disrupt the brain's ability to control body movements, consciousness, sensations, thoughts, speech, and memory. Brain tumors are often accompanied by symptoms like epilepsy, headaches, and sensory loss, leading to varying degrees of cognitive impairment in affected patients. OBJECTIVE: The study goal is to develop an effective method to detect and segment brain tumor with high accurancy. METHODS: This paper proposes a novel U-Net + ⁣ + network using EfficientNet as the encoder to segment brain tumors based on MRI images. We adjust the original U-Net + ⁣ + model by removing the dense skip connections between sub-networks to simplify computational complexity and improve model efficiency, while the connections of feature maps at the same resolution level are retained to bridge the semantic gap. RESULTS: The proposed segmentation model is trained and tested on Kaggle's LGG brain tumor dataset, which obtains a satisfying performance with a Dice coefficient of 0.9180. CONCLUSION: This paper conducts research on brain tumor segmentation, using the U-Net + ⁣ + network with EfficientNet as an encoder to segment brain tumors based on MRI images. We adjust the original U-Net + ⁣ + model to simplify calculations and maintains rich semantic spatial features at the same time. Multiple loss functions are compared in this study and their effectiveness are discussed. The experimental results shows the model achieves a high segmention result with Dice coefficient of 0.9180. [ABSTRACT FROM AUTHOR]

In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy‐to‐use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl. [ABSTRACT FROM AUTHOR]

In addressing the challenge of assessing healthy brain aging across diverse interventions, this study introduces the use of MRI-derived Brain Healthcare Quotients (BHQ) for comprehensive evaluation. We analyzed BHQ changes in 319 participants aged 24–69, who were allocated into dietary (collagen peptide, euglena, matcha, isohumulone, xanthophyll) and physical activity (hand massage with lavender oil, handwriting, office stretching, pink lens, clinical art) groups, alongside a control group, over a month. These interventions were specifically chosen to test the efficacy of varying health strategies on brain health, measured through BHQ indices: GM-BHQ for gray matter volume, and FA-BHQ for white matter integrity. Notably, significant improvements in FA-BHQ were observed in the collagen peptide group, with marginal increases in the hand massage and office stretching groups. These findings highlight BHQ's potential as a sensitive tool for detecting brain health changes, offering evidence that low-intensity, easily implemented interventions can have beneficial effects on brain health. Moreover, BHQ allows for the systematic evaluation of such interventions using standard statistical approaches, suggesting its value in future brain healthcare research. [ABSTRACT FROM AUTHOR]

For two decades, the field of Mind, Brain and Education (MBE) has shed light on learning mechanisms. However, the direct involvement of young learners in the design and creation of research projects, while steadily emerging, is still rare. More researchers are beginning to see the benefits of co‐construction of not only research questions but also experimental design and analysis based on young learners' ideas. Recognizing that learning is inseparable from social relationships and well‐being, the papers in this special issue respond to two main questions: (1) Why co‐production? (2) How is co‐production achieved? We hope that this special issue will invite discussions about the different domains within MBE research in which learners' input and collaboration can be valuable. We invite readers to take into consideration the diversity of methods which are represented in this special issue to engage young learners and collect their perspectives. The integration of young learners into the research process has only recently became more prevalent. In this special issue, we present five articles in which young learners shared their views on and/or engaged in Mind, Brain and Education research. These papers show that young learners' views can complement, and sometimes question, those of adult researchers. This involvement can lead to productive, well‐rounded projects incorporating both scientific methods and social values. [ABSTRACT FROM AUTHOR]

This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro‐Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi‐echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi‐echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase‐quality‐based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity‐based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting. [ABSTRACT FROM AUTHOR]

Background: 1H-MRS is increasingly used in basic and clinical research to explain brain function and alterations respectively. In psychosis research it is now one of the main tools to investigate imbalances in the glutamatergic system. Interestingly, however, the findings are extremely variable even within patients of similar disease states. One reason may be the variability in analysis strategies, despite suggestions for standardization. Therefore, our study aimed to investigate the extent to which the basis set configuration– which metabolites are included in the basis set used for analysis– would affect the spectral fit and estimated glutamate (Glu) concentrations in the anterior cingulate cortex (ACC), and whether any changes in levels of glutamate would be associated with psychotic-like experiences and autistic traits. Methods: To ensure comparability, we utilized five different exemplar basis sets, used in research, and two different analysis tools, r-based spant applying the ABfit method and Osprey using the LCModel. Results: Our findings revealed that the types of metabolites included in the basis set significantly affected the glutamate concentration. We observed that three basis sets led to more consistent results across different concentration types (i.e., absolute Glu in mol/kg, Glx (glutamate + glutamine), Glu/tCr), spectral fit and quality measurements. Interestingly, all three basis sets included phosphocreatine. Importantly, our findings also revealed that glutamate levels were differently associated with both schizotypal and autistic traits depending on basis set configuration and analysis tool, with the same three basis sets showing more consistent results. Conclusions: Our study highlights that scientific results may be significantly altered depending on the choices of metabolites included in the basis set, and with that emphasizes the importance of carefully selecting the configuration of the basis set to ensure accurate and consistent results, when using MR spectroscopy. Overall, our study points out the need for standardized analysis pipelines and reporting. [ABSTRACT FROM AUTHOR]

Background: Ketamine has emerged as one of the most promising therapies for treatment-resistant depression. However, inter-individual variability in response to ketamine is still not well understood and it is unclear how ketamine's molecular mechanisms connect to its neural and behavioral effects. Methods: We conducted a single-blind placebo-controlled study, with participants blinded to their treatment condition. 40 healthy participants received acute ketamine (initial bolus 0.23 mg/kg, continuous infusion 0.58 mg/kg/hr). We quantified resting-state functional connectivity via data-driven global brain connectivity and related it to individual ketamine-induced symptom variation and cortical gene expression targets. Results: We found that: (i) both the neural and behavioral effects of acute ketamine are multi-dimensional, reflecting robust inter-individual variability; (ii) ketamine's data-driven principal neural gradient effect matched somatostatin (SST) and parvalbumin (PVALB) cortical gene expression patterns in humans, while the mean effect did not; and (iii) behavioral data-driven individual symptom variation mapped onto distinct neural gradients of ketamine, which were resolvable at the single-subject level. Conclusions: These results highlight the importance of considering individual behavioral and neural variation in response to ketamine. They also have implications for the development of individually precise pharmacological biomarkers for treatment selection in psychiatry. Funding: This study was supported by NIH grants DP5OD012109-01 (A.A.), 1U01MH121766 (A.A.), R01MH112746 (J.D.M.), 5R01MH112189 (A.A.), 5R01MH108590 (A.A.), NIAAA grant 2P50AA012870-11 (A.A.); NSF NeuroNex grant 2015276 (J.D.M.); Brain and Behavior Research Foundation Young Investigator Award (A.A.); SFARI Pilot Award (J.D.M., A.A.); Heffter Research Institute (Grant No. 1-190420) (FXV, KHP); Swiss Neuromatrix Foundation (Grant No. 2016-0111) (FXV, KHP); Swiss National Science Foundation under the framework of Neuron Cofund (Grant No. 01EW1908) (KHP); Usona Institute (2015 - 2056) (FXV). [ABSTRACT FROM AUTHOR]

Introduction: To investigate the brain's cognitive process and perceptual holistic, we have developed a novel method that focuses on the informational attributes of stimuli. Methods: We recorded EEG signals during visual and auditory perceptual cognition experiments and conducted ERP analyses to observe specific positive and negative components occurring after 400ms during both visual and auditory perceptual processes. These ERP components represent the brain's perceptual holistic processing activities, which we have named Information-Related Potentials (IRPs). We combined IRPs with machine learning methods to decode cognitive processes in the brain. Results: Our experimental results indicate that IRPs can better characterize information processing, particularly perceptual holism. Additionally, we conducted a brain network analysis and found that visual and auditory perceptual holistic processing share consistent neural pathways. Discussion: Our efforts not only demonstrate the specificity, significance, and reliability of IRPs but also reveal their great potential for future brain mechanism research and BCI applications. [ABSTRACT FROM AUTHOR]

Conventional power-integrated wireless neural recording devices suffer from bulky, rigid batteries in head-mounted configurations, hindering the precise interpretation of the subject's natural behaviors. These power sources also pose risks of material leakage and overheating. We present the direct printing of a power-integrated wireless neural recording system that seamlessly conforms to the cranium. A quasi-solid-state Zn-ion microbattery was 3D-printed as a built-in power source geometrically synchronized to the shape of a mouse skull. Soft deep-brain neural probes, interconnections, and auxiliary electronics were also printed using liquid metals on the cranium with high resolutions. In vivo studies using mice demonstrated the reliability and biocompatibility of this wireless neural recording system, enabling the monitoring of neural activities across extensive brain regions without notable heat generation. This all-printed neural interface system revolutionizes brain research, providing bio-conformable, customizable configurations for improved data quality and naturalistic experimentation. [ABSTRACT FROM AUTHOR]

Stem cell-derived organoid technology is a powerful tool that revolutionizes the field of biomedical research and extends the scope of our understanding of human biology and diseases. Brain organoids especially open an opportunity for human brain research and modeling many human neurological diseases, which have lagged due to the inaccessibility of human brain samples and lack of similarity with other animal models. Brain organoids can be generated through various protocols and mimic whole brain or region-specific. To provide an overview of brain organoid technology, we summarize currently available protocols and list several factors to consider before choosing protocols. We also outline the limitations of current protocols and challenges that need to be solved in future investigation of brain development and pathobiology. [ABSTRACT FROM AUTHOR]

Background/Objectives: Premenstrual dysphoric disorder (PMDD) is an understudied psychiatric condition affecting reproductive-age women who experience negative mood in the luteal phase of the menstrual cycle. Cognitive functions in PMDD are not well understood as patients have been tested in the luteal phase. This may confound study results due to noted emotional interferences, as well as the potential opposing effects of the sex hormones estradiol and progesterone. In the present study, we evaluated visuospatial function in the follicular phase in women with PMDD and healthy controls, and further examined the effect of estradiol as research into the hormonal mediation of visuospatial function in reproductive-age women has produced mixed results. Methods: To this end, we analyzed estradiol concentrations using the gold standard mass spectrometry. Serum samples were collected in the early follicular and mid/late follicular subphases when estradiol is low and high, respectively, while progesterone is low and steady. We assessed visuospatial function using the classic mental rotation task. Results: Women with PMDD had a higher mental rotation total score (t = 2.17; p < 0.05). The addition of six demographic, biological, and anthropomorphic variables in a hierarchical fashion accounted for 45.3% of the total variance in the final model with diagnosis remaining statistically significant (t = 4.36; p < 0.001). Estradiol did not mediate the group difference and was not significantly associated with visuospatial function. Conclusions: The present results provide support for new research directions into the potential biological mechanisms that underlie the pathophysiology of PMDD, represented as enhanced visuospatial ability in women with PMDD in the follicular phase. We review the theory that PMDD is a disorder of the enhanced excitation-to-inhibition ratio, with a focus on findings to date from brain imaging research. [ABSTRACT FROM AUTHOR]

Organoid technologies are a potent tool for investigating human biology, modelling diseases and developing novel therapies. In this Viewpoint, experts in metabolic and endocrine research in the brain, pituitary, skeletal muscle, bone and gastrointestinal system discuss how organoids and related bioengineered systems are currently used in their field and how innovations in these technologies could transform future research. [ABSTRACT FROM AUTHOR]

In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they often sacrifice accuracy and cannot be directly relied upon. Therefore, the primary goal of this paper is to develop a neuron reconstruction tool that is both efficient and accurate. The tool aids users in reconstructing complete neurons by calculating the confidence of branches during the reconstruction process. The method models the neuron reconstruction as multiple Markov chains, and calculates the confidence of the connections between branches by simulating the reconstruction artifacts in the results. Users iteratively modify low-confidence branches to ensure precise and efficient neuron reconstruction. Experiments on both the publicly accessible BigNeuron dataset and a self-created Whole-Brain dataset demonstrate that the tool achieves high accuracy similar to manual reconstruction, while significantly reducing reconstruction time. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Brain Sciences titled "Advances in Research on Brain Health and Dementia: Prevention and Early Detection of Cognitive Decline and Dementia." The issue focuses on maintaining brain health and early detection and intervention of dementia and its precursor conditions. It includes research on various topics such as cognitive screening methods using Lego® Duplo® building blocks and odor identification tests, the use of digital technology for screening, neuroimaging studies, body fluid biomarkers for early detection, interventions for physical inactivity, and the relationship between motor signs and cognitive performance. The issue also covers other types of dementia and high-risk conditions of dementia. The authors hope that this special issue will be useful in clinical practice and provide suggestions for future research. [Extracted from the article]

In 2015, a groundswell of brain tumour patient, carer and charity activism compelled the UK Minister for Life Sciences to form a brain tumour research task and finish group. This resulted, in 2018, with the UK government pledging £20m of funding, to be paralleled with £25m from Cancer Research UK, specifically for neuro‐oncology research over the subsequent 5 years. Herein, we review if and how the adult brain tumour research landscape in the United Kingdom has changed over that time and what challenges and bottlenecks remain. We have identified seven universal brain tumour research priorities and three cross‐cutting themes, which span the research spectrum from bench to bedside and back again. We discuss the status, challenges and recommendations for each one, specific to the United Kingdom. [ABSTRACT FROM AUTHOR]

Calcium flux has been successfully verified to play an important role in the malignant proliferation and progression of brain tumors, which can serve as an important diagnosis guide. However, clinical diagnosis based on calcium information remains challenging because of the highly complex and heterogeneous features in calcium signals. Here we propose a calcium feature-based tumor diagnosis and treatment guidance platform (CA-TDT-GP) using random forest analysis framework for the efficient prediction of complex tumor behaviors for clinical therapy guidance. Multiple important features associated with brain tumor biological malignancy were screened out through comprehensive feature importance analysis. It provided useful guidance for understanding the biological process and the selection of drugs of brain tumors. Further clinical validation confirmed the accurate prediction of tumor biological characteristics by the model, with a coefficient of determination of over 0.86 in the same cohort of patients and over 0.77 for the new cohort of patients. We further verified the clinical malignant assessment by this model, which performed a 100% prediction match with diagnosed WHO grades, indicating great potential of the platform for clinical guidance. This promising model provides a new diagnostic and therapeutic tool for brain tumor research and preclinical treatment. [ABSTRACT FROM AUTHOR]

Genome editing, notably CRISPR (cluster regularly interspaced short palindromic repeats)/Cas9 (CRISPR-associated protein 9), has revolutionized genetic engineering allowing for precise targeted modifications. This technique's combination with human induced pluripotent stem cells (hiPSCs) is a particularly valuable tool in cerebral organoid (CO) research. In this study, CRISPR/Cas9-generated fluorescently labeled hiPSCs exhibited no significant morphological or growth rate differences compared with unedited controls. However, genomic aberrations during gene editing necessitate efficient genome integrity assessment methods. Optical genome mapping, a high-resolution genome-wide technique, revealed genomic alterations, including chromosomal copy number gain and losses affecting numerous genes. Despite these genomic alterations, hiPSCs retain their pluripotency and capacity to generate COs without major phenotypic changes but one edited cell line showed potential neuroectodermal differentiation impairment. Thus, this study highlights optical genome mapping in assessing genome integrity in CRISPR/Cas9-edited hiPSCs emphasizing the need for comprehensive integration of genomic and morphological analysis to ensure the robustness of hiPSC-based models in cerebral organoid research. [ABSTRACT FROM AUTHOR]

Metacognitive calibration—the capacity to accurately self-assess one's performance—forms the basis for error detection and self-monitoring and is a potential catalyst for conceptual change. Limited brain imaging research on authentic learning tasks implicates the lateral prefrontal and anterior cingulate brain regions in expert scientific reasoning. This study aimed to determine how variation in undergraduate life sciences students' metacognitive calibration relates to their brain activity when evaluating the accuracy of biological models. Fifty undergraduate students enrolled in an introductory life sciences course completed a biology model error detection task during fMRI. Students with higher metacognitive calibration recruited lateral prefrontal regions linked in prior research to expert STEM reasoning to a greater extent than those with lower metacognitive calibration. Findings suggest that metacognition relates to important individual differences in undergraduate students' use of neural resources during an authentic educational task and underscore the importance of fostering metacognitive calibration in the classroom. [ABSTRACT FROM AUTHOR]