Early Brain Research Articles

Mapping fetal brain development of 10 weeks gestational age with 9.4T postmortem MRI and histological sections.

The 10th week of gestational age (GA) is a critical period for evaluating brain development, but there is limited knowledge regarding the specific characteristics at this GA. This study is to map the brain structures at 10 weeks GA using 9.4T MRI and histological sections. Six fetal specimens with normal brain development were imaged using 9.4T MRI, followed by sectioning and staining. The three-dimensional (3D) reconstruction models and quantitative measurements were obtained with Amira software. Four zones of the laminar organization were identified in T2-weighted MRIs, while six zones were delineated in the corresponding histological sections, demonstrating a strong consistency between the two. The key structures, including the midline structures, choroid plexus, subcortical nuclei, ventricles, brainstem, and cerebellum, were clearly distinguishable with unique characteristics in both MRIs and histological sections. The 3D visualization model effectively demonstrated the shape, spatial positions, and interrelationships of the early fetal brain structures, and their qualitative measurements were obtained. The lateral ventricles and choroid plexus constituted a substantial proportion, with the lateral ventricles accounting for 43.0% of the cerebral hemisphere and the choroid plexus comprising 41.2% of the lateral ventricles. At 10 weeks GA, the fetal brain is in an early developmental stage. The integration of 9.4T T2-weighted MRIs, 3D reconstruction, and histological sections offers a comprehensive view of the early fetal brain's characteristics, providing valuable insights for clinicians and anatomists. GA=gestational age; 3D=three-dimensional; US=ultrasound; CNS=central nervous system; microfocus-CT=micro-CT.

Brain volumes in genetic syndromes associated with mTOR dysregulation: a systematic review and meta-analysis.

Dysregulation of molecular pathways associated with mechanistic target of rapamycin (mTOR) and elevated rates of neurodevelopmental disorders are implicated in the genetic syndromes neurofibromatosis type 1 (NF1), tuberous sclerosis complex (TSC), fragile X syndrome (FXS), and Noonan syndrome (NS). Given shared molecular and clinical features, understanding convergent and divergent implications of these syndromes on brain development may offer unique insights into disease mechanisms. While an increasing number of studies have examined brain volumes in these syndromes, the effects of each syndrome on global and subcortical brain volumes are unclear. Therefore, the aim of the current study was to conduct a systematic review and meta-analysis to synthesize existing literature on volumetric brain changes across TSC, FXS, NF1, and NS. Study outcomes were the effect sizes of the genetic syndromes on whole brain, gray and white matter, and subcortical volumes compared to typically developing controls. We performed a series of meta-analyses synthesizing data from 23 studies in NF1, TSC, FXS, and NS (pooled N = 1556) reporting whole brain volume, gray and white matter volumes, and volumes of subcortical structures compared to controls. Meta-analyses revealed significantly larger whole brain volume, gray and white matter volumes, and subcortical volumes in NF1 compared to controls. FXS was associated with increased whole brain, and gray and white matter volumes relative to controls, but effect sizes were smaller than those seen in NF1. In contrast, studies in NS indicated smaller whole brain and gray matter volumes, and reduced subcortical volumes compared to controls. For individuals with TSC, there were no significant differences in whole brain, gray matter, and white volumes compared to controls. Volumetric effect sizes were not moderated by age, sex, or full-scale IQ. This meta-analysis revealed that dysregulation of mTOR signaling across pre- and post-natal periods of development can result in convergent and divergent consequences for brain volume among genetic syndromes. Further research employing advanced disease modeling techniques with human pluripotent stem cell-derived in vitro models is needed to further refine our understanding of between and within syndrome variability on early brain development and identify shared molecular mechanisms for the development of pharmaceutical interventions.

The menopausal transition and multiple physiologic measures of early brain health in the Coronary Artery Risk Development in Young Adults study.

This study proposed to investigate the cross-sectional and longitudinal associations of menopausal status with physiologic brain magnetic resonance imaging measures. The sample included women from the Coronary Artery Risk Development in Young Adults study who self-reported their reproductive histories and participated in the brain magnetic resonance imaging substudies at the year 25 (n = 292) and year 30 (n = 258) follow-up examinations. Menopausal status was classified based on natural menstrual cycle regularity/cessation at both time points. Gray matter cerebrovascular reactivity (CVR) was calculated as mean percent change in blood oxygen level-dependent signals in activated voxels following a breath-hold challenge. Gray matter cerebral blood flow (CBF) was assessed using pseudo-continuous arterial spin labeling. Linear regression models were used to examine cross-sectional and longitudinal associations of menopausal status with gray matter CVR and CBF after adjustment for potential age-related covariates. Women were mean age 50 years at year 25; 37% were Black; and 46% were postmenopausal. Relative to premenopause or perimenopause, postmenopause was associated with lower gray matter CVR at year 30 cross-sectionally (1.86% vs 1.69%, P = 0.03, respectively) and longitudinally for women who were postmenopausal at both time points (-0.32% [95% CI, -0.63% to -0.02%]) after covariate adjustment. Mean CVR values were also lower for these women when compared with women who remained premenopausal or perimenopausal (1.71% compared with 2.04%, respectively). Menopausal status was unrelated to either concurrent or longitudinal gray matter CBF. These findings suggest that the ability of vessels to adapt in response to hypercapnia may be impaired during menopause, even within a relatively short time window.

Coupling of mitochondrial state with active zone plasticity in early brain aging

Neurodegenerative diseases typically emerge after an extended prodromal period, underscoring the critical importance of initiating interventions during the early stages of brain aging to enhance later resilience. Changes in presynaptic active zone proteins ("PreScale") are considered a dynamic, resilience-enhancing form of plasticity in the process of early, still reversible aging of the Drosophila brain. Aging, however, triggers significant changes not only of synapses but also mitochondria. While the two organelles are spaced in close proximity, likely reflecting a direct functional coupling in regard to ATP and Ca2+ homeostasis, the exact modes of coupling in the aging process remain to understood.We here show that genetic manipulations of mitochondrial functional status, which alters brain oxidative phosphorylation, ATP levels, or the production of reactive oxygen species (ROS), can bidirectionally regulate PreScale during early Drosophila brain aging. Conversely, genetic mimicry of PreScale resulted in decreased oxidative phosphorylation and ATP production, potentially due to reduced mitochondrial calcium (Ca2+) import.Our findings indicate the existence of a positive feedback loop where mitochondrial functional state and PreScale are reciprocally coupled to optimize protection during the early stages of brain aging.

Predicting cortical-thalamic functional connectivity using functional near-infrared spectroscopy and graph convolutional networks

Functional near-infrared spectroscopy (fNIRS) measures cortical hemodynamic changes, yet it cannot collect this information from subcortical structures, such as the thalamus, which is involved in several key functional networks. To address this drawback, we propose a machine-learning-based approach to predict cortical-thalamic functional connectivity using cortical fNIRS data. We applied graph convolutional networks (GCN) to two datasets obtained from healthy adults and neonates with early brain injuries, respectively. Each dataset contained fNIRS connectivity data as input to the predictive models, while the connectivity from functional magnetic resonance imaging (fMRI) served as training targets. GCN models performed better compared to conventional methods, such as support vector machine and feedforward fully connected artificial neural networks, on both identifying the connections as binary classification tasks, and regressing the quantified strengths of connections. We also propose the addition of inter-subject connections into the GCN kernels could improve performance and that GCN models are resilient to noise in fNIRS data. Our results show it is feasible to identify subcortical activity from cortical fNIRS recordings. The findings can potentially extend the use of fNIRS in clinical settings for brain monitoring in critically ill patients.

Unravelling age-related gait decline in cerebral palsy: insights into physiological changes and functional implications through an observational study—a French study protocol in a laboratory setting

IntroductionCerebral palsy (CP) presents a complex neurodevelopmental disorder with a spectrum of motor impairments stemming from early brain injury. Whereas CP is traditionally viewed as a non-progressive condition, emerging evidence...

Association of Self-Reported Sleep Characteristics With Neuroimaging Markers of Brain Aging Years Later in Middle-Aged Adults.

To determine the association between early midlife sleep and advanced brain aging patterns in late midlife. Using the CARDIA study, we analyzed sleep data at baseline and 5 years later, focusing on short sleep duration, bad sleep quality (SQ), difficulty initiating and maintaining sleep (DIS and DMS), early morning awakening (EMA), and daytime sleepiness. These were categorized into 0-1, 2-3, and >3 poor sleep characteristics (PSC). Brain MRIs obtained 15 years later were used to determine brain age through a machine learning approach based on age-related atrophy. This cohort study included 589 participants (mean age 40.4 ± 3.4 years, 53% women). At baseline, around 70% reported 0-1 PSC, 22% reported 2%-3%, and 8% reported >3 PSC. In multivariable linear regression analyses, participants with 2-3 or >3 PSC had 1.6-year (β = 1.61, 95% CI 0.28-2.93) and 2.6-year (β = 2.64, 95% CI 0.59-4.69) older brain age, respectively, compared with those with 0-1 PSC. Of the individual characteristics, bad SQ, DIS, DMS, and EMA were associated with greater brain age, especially when persistent over the 5-year follow-up. Poor sleep was associated with advanced brain age in midlife, highlighting the importance of investigating early sleep interventions for preserving brain health.

Cilostazol Alleviates Delayed Cerebral Ischemia After Subarachnoid Hemorrhage by Attenuating Microcirculatory Dysfunction.

Cilostazol, a phosphodiesterase 3 (PDE3) inhibitor that exerts antiplatelet effects, has therapeutic potential for delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). However, its mechanism of action remains unclear. We hypothesized that cilostazol alleviates DCI by improving cerebral microcirculatory dysfunction, which is a component of early brain injury. To test this hypothesis, we analyzed the intracerebral circulation time (iCCT) in 256 patients with aSAH from two randomized controlled trials (74 received cilostazol, 54 received pitavastatin, and 128 were controls). A minority of patients (n = 72, 28%) developed severe angiographic vasospasm (aVS) and DCI (n = 42, 16%) and had poor outcomes (n = 35, 14%). We measured iCCT as the time to peak in the ultraearly phase (baseline) and the subacute phase or at DCI onset (follow-up). The cilostazol group had shorter follow-up iCCT and larger iCCT differences than the other groups, indicating improved microcirculatory function, particularly in patients with DCI and poor outcomes. Multivariate analysis revealed that cilostazol treatment is a significant predictor of favorable outcomes, whereas DCI occurrence, a decrease in iCCT differences, and high clinical severity (Hunt & Hess grades 3-4) were associated with poor outcomes. Diminished microcirculatory dysfunction may alleviate DCI and improve outcomes among patients with aSAH following cilostazol treatment. Further research is warranted to confirm these findings and explore the dose-dependent effects of cilostazol on the microcirculatory function.

Trajectories of human brain functional connectome maturation across the birth transition.

Understanding the sequence and timing of brain functional network development at the beginning of human life is critically important from both normative and clinical perspectives. Yet, we presently lack rigorous examination of the longitudinal emergence of human brain functional networks over the birth transition. Leveraging a large, longitudinal perinatal functional magnetic resonance imaging (fMRI) data set, this study models developmental trajectories of brain functional networks spanning 25 to 55 weeks of post-conceptual gestational age (GA). The final sample includes 126 fetal scans (GA = 31.36 ± 3.83 weeks) and 58 infant scans (GA = 48.17 ± 3.73 weeks) from 140 unique subjects. In this study, we document the developmental changes of resting-state functional connectivity (RSFC) over the birth transition, evident at both network and graph levels. We observe that growth patterns are regionally specific, with some areas showing minimal RSFC changes, while others exhibit a dramatic increase at birth. Examples with birth-triggered dramatic change include RSFC within the subcortical network, within the superior frontal network, within the occipital-cerebellum joint network, as well as the cross-hemisphere RSFC between the bilateral sensorimotor networks and between the bilateral temporal network. Our graph analysis further emphasized the subcortical network as the only region of the brain exhibiting a significant increase in local efficiency around birth, while a concomitant gradual increase was found in global efficiency in sensorimotor and parietal-frontal regions throughout the fetal to neonatal period. This work unveils fundamental aspects of early brain development and lays the foundation for future work on the influence of environmental factors on this process.

Cognitive Divergence in the Womb: An Analysis of IQ and Developmental Indicators in Twin Fetuses

The study of cognitive divergence in twin fetuses offers a unique window into the interplay of genetic, environmental, and developmental factors influencing early brain development. This research investigates the prenatal origins of cognitive differences, focusing on indicators such as brain volume asymmetry, neural connectivity, and behavioral patterns. By examining these factors through advanced imaging techniques like fetal MRI and ultrasound, the study explores how cognitive divergence manifests in twin fetuses, potentially affecting postnatal IQ and developmental trajectories. Key areas of focus include the role of genetic variance, in-utero environmental disparities, and the impact of placental sharing on neural development. Additionally, the research addresses methodological challenges in assessing cognitive markers during the prenatal period and explores implications for early intervention strategies. By shedding light on the earliest stages of cognitive differentiation, this study aims to advance our understanding of developmental neuroscience and inform approaches to optimize cognitive outcomes in twin populations.

Optimizing early diagnosis by integrating multiple classifiers for predicting brain stroke and critical diseases

Machine learning has gained attention in the medical field. Continuous efforts are being made to develop robust models for early prognosis purposes. The brain is the most pivotal organ in the human body. A brain stroke is generally caused by a blockage in the brain arteries. A brain stroke is one of the primary reasons for death. Therefore, early prediction of diseases like brain stroke, heart attack can significantly help in making decisions for doctors. The research study aims to find a robust and potential technique for the early prediction of brain stroke, Alzheimer’s, heart attack, cancer, Parkinson’s and potentially reducing the incidence of severe post complications of the mentioned diseases. By considering the five datasets as input, machine learning models have been trained for the research study. Early prediction of brain stroke has been done using eight individual classifiers along with 56 other models which are designed by merging the pairs of individual models using soft and hard voting for brain stroke and eight individual classifiers have been used for early prediction of heart attack, cancer, Alzheimer and Parkinson’s. After analyzing the results of each classifier for each disease, the proposed method, which is a pair of random forest and decision tree using a hard voting method for early brain stroke prediction, achieves the highest accuracy of 99%, which is better than all classifiers. Along with accuracy, the proposed method attained a value of 98% in precision, an outstanding 100% in recall, and 99% in F1 score. XGBoost performed best for cancer, Parkinson’s, Alzeihmer’s and Bernoulli naive bayes performed best in case of Heart attack .Upon comparing the values of these performance metrics, they outshine all the other model’s values.

Paeonol Alleviates Subarachnoid Hemorrhage Injury in Rats Through Upregulation of SIRT1 and Inhibition of HMGB1/TLR4/MyD88/NF‐κB Pathway

ABSTRACTPaeonol is a principle bioactive compound separated from the root bark of Cortex Moutan and has been shown to confer various biological functions, including antineuroinflammation and neuroprotection. Inflammation, blood–brain barrier (BBB), permeability, and apoptosis are three major underlying mechanisms involved in early brain injury (EBI) postsubarachnoid hemorrhage (SAH). This study aimed to detect the roles and mechanisms of paeonol in EBI following SAH. A SAH model was established by an endovascular perforation method in Sprague‐Dawley rats. The localizations of HMGB1 and p65 were identified by immunofluorescence staining. Protein levels were measured by western blot analysis. The serum levels of HMGB1 and the levels of inflammatory cytokines in the brain cortex were evaluated by ELISA. Hematoxylin and eosin staining was conducted to detect neuronal degeneration. Brain water content and Evans blue extravasation were assessed to determine EBI. Neuronal apoptosis was examined by TUNEL. Paeonol deacetylated HMGB1 by upregulating SIRT1 level. SIRT1 inhibition attenuated the protective effects of paeonol against neurological dysfunctions, brain edema, and BBB disruption. SIRT1 inhibition rescued the paeonol‐induced inhibition in inflammatory response. The paeonol‐induced decrease in neuronal apoptosis was restored by SIRT1 inhibitor. The paeonol‐mediated deactivated TLR4/MyD88/NF‐κB pathway was activated by SIRT1 inhibitor. Paeonol alleviates the SAH injury in rats by upregulating SIRT1 to inactivate the HMGB1/TLR4/MyD88/NF‐κB pathway.

CNN-Based Cancer Image Diagnosis: Current Progress and Future Directions

Abstract. With the development of deep learning technology, CNN (Convolutional Neural Network) models have shown great value in medical image analysis, especially in diagnosing early lung cancer, breast cancer, and brain tumors. In this study, we recall and organize the application and progress of CNN models in the field of cancer and tumor diagnosis in the past five years to provide a theoretical basis and reference for related researchers. This article introduces the principles of different CNN cancer diagnostic models and compares and analyzes their results, and ultimately finds that these models have significant advantages in improving the accuracy and efficiency of cancer diagnosis, but at the same time, there are also problems such as too much reliance on large datasets, high model complexity, and poor generalization ability. In the future, we can consider optimizing the performance of CNN network models, enhancing the generalization ability of the models, and developing data enhancement techniques on this basis, so that CNN models can be better applied in the field of cancer diagnosis.

Optimal functioning after early mild traumatic brain injury: Evolution and predictors.

Early mild traumatic brain injury (mTBI or concussion sustained between 0 and 5 years old) can lead to post-concussive symptoms, behavioral changes, and cognitive difficulties. Although school-age children (6-17 years old) experience similar consequences, severe neuropsychological deficits are not common, and the majority have no persisting symptoms after one month. Thus, there may be value in focusing on what characterizes optimal functioning (or wellness) after mTBI, but this has not been explored in young children. This study documents the evolution and predictors of optimal functioning after early mTBI. Participants were 190 children aged 18 - 60 months with mTBI (n = 69), orthopedic injury (OI; n = 50), or typical development (TDC; n = 71). Optimal functioning was defined as: (1) no clinically significant behavioral problems; (2) no cognitive difficulties; (3) no persisting post-concussive symptoms; (4) average quality of life or better. Predictors related to sociodemographic, injury, child, and caregiver characteristics included number of acute symptoms, child sex, age, temperament, maternal education, parent-child attachment and interaction quality, and parenting stress. Fewer children with mTBI had optimal functioning over 6 and 18-months post-injury compared to those with OI and TDC. Higher parent-child interaction quality and lower child negative affectivity temperament independently predicted optimal functioning. Children who sustain early mTBI are less likely to exhibit optimal functioning thantheir peers in the long-term. Parent-child interaction quality could be a potential intervention target for promoting optimal function.

Cortical structural network characteristics in non-cognitive impairment end-stage renal disease.

Explore alterations in topological features of gray matter volume (GMV) and structural networks in non-cognitive impairment end-stage renal disease (Non-CI ESRD). Utilizing graph theory, we collected structural magnetic resonance imaging (sMRI) data from 38 Non-CI ESRD patients and 50 normal controls (NC). We compared, and extracted the GMV across subject groups, constructed corresponding structural covariance networks (SCNs), and investigated the alterations in SCNs feature parameters between groups. In Non-CI ESRD patients, The GMV were reduced in several brain regions, predominantly on the left side (p < 0.05, FWE correction). The small-world network characteristics of the patient group's brain networks showed a tendency toward regular. In a few densities, global network parameters, transitivity, (p < 0.05) was significantly increased in the ESRD group. Regional network measurements revealed inconsistent changes in regional efficiency across different brain areas. In the analysis of network hubs, the right temporal pole is likely a compensatory hub for Non-CI ESRD patients. The SCNs in Non-CI ESRD patients demonstrated reduced topological stability against targeted attacks. This study reveals that patients with renal failure exhibited subtle changes in brain network characteristics even before a decline in cognitive scores. These changes involve compensatory activation in certain brain regions, which enhances network transitivity to maintain the efficiency of whole-brain network information integration without significant loss. Additionally, the SCNs characteristics can serve as a neuroanatomical marker for brain alterations in Non-CI ESRD patients, offering new insights into the mechanisms of early brain injury in ESRD patients.

Logistic organ dysfunction system as an early risk stratification tool after aneurysmal subarachnoid hemorrhage

Aneurysmal subarachnoid hemorrhage (aSAH) not only causes neurological deficits but also influences extracerebral organ functions. The Logistic Organ Dysfunction System (LODS) reliably captures organ dysfunctions and predicts mortality of critically ill patients. This study investigated LODS in the setting of aSAH as a surrogate marker for early brain injury (EBI). Patients with aSAH treated between 2012 and 2020 were retrospectively analyzed. LODS was calculated within 24 h upon admission applying functional parameters for each organ system. The EBI was evaluated based on 1-persistent loss of consciousness, 2-global cerebral edema, and 3-intracranial blood burden. The outcome was assessed with the modified Rankin scale (mRS) at 3-months after ictus (mRS > 2 = unfavorable outcome). A total of 324 patients with a mean age of 55.9 years were included. Severe EBI (EBI grade ≥ 3) was found in 38% (124/324) of patients. Higher LODS score correlated with severe EBI (p < 0.0001) and poor outcome (p < 0.0001). LODS with a cutoff of 7 allowed a reliable discrimination (AUC 78%, p < 0.0001) of patients with severe from those with mild EBI. The LODS-calculation as an early risk stratification and prognostic tool reliably reflected the severity of EBI after aSAH and correlated with outcome.

Complement Molecule C3a Exacerbates Early Brain Injury After Subarachnoid Hemorrhage by Inducing Neuroinflammation Through the C3aR-ERK-P2X7-NLRP3 Inflammasome Signaling Axis.

An important aspect of the pathophysiology of early brain damage (EBI) after subarachnoid hemorrhage (SAH) is inflammasome-mediated neuroinflammation. It has been demonstrated that C3aR activation exacerbates neuronal damage in a number of neurological disorders. This study aims to explore the role of C3a in activating the NLRP3 inflammasome and exacerbating neuroinflammation after SAH. Preprocessing of RNA-seq transcriptome datasets using bioinformatics analysis, and screening of differentially expressed genes between SAH patients and healthy individuals from the GEO database. Internal carotid artery puncture was performed to establish SAH models in rats and mice. SAH grading, neurological scoring, brain water content, behavioral analysis, and assessments using ELISA, Western blot, immunofluorescence, and immunohistochemistry were conducted. An in vitro model of SAH was induced in BV-2 cells treated with heme (200μM). The mechanism of C3a in post-SAH neuroinflammation was studied by interfering with and inhibiting C3aR. Results showed that the expression of C3aR was upregulated in the GEO dataset (serum of SAH patients) and identified as a key differential gene in SAH. Further, elevated levels of C3a were found in the cerebrospinal fluid of clinically collected SAH patients. In the cerebral cortex and/or serum of SAH rats, expression of C3a, IL-1β, IL-6, TNF-α, CD11b, and Ki67 were significantly increased, while IL-10 was significantly decreased. Correlation analysis revealed that C3a showed negative correlation with IL-10 and positive correlation with IL-1β, IL-6, TNF-α, CD11b, and Ki67. After stimulation with heme, protein levels of C3a increased in BV-2 cells. Interfering with C3aR significantly reduced LDH release, IL-1β secretion, Caspase1 activation, levels of NLRP3 expression and ASC oligomerization, and ATP release after heme stimulation in BV-2. Subsequently, the addition of inhibitors of ERK1/2 phosphorylation demonstrated that C3a promotes ATP efflux by activating ERK1/2 phosphorylation, thereby activating P2X7. Further addition of JNJ-55308942 (a P2X7R antagonist) revealed that C3a activated the NLRP3 inflammasome via P2X7. Finally, administering SB290157 (a C3aR inhibitor) in vivo effectively alleviated brain edema, reduced mortality, improved Garcia score, ameliorated motor dysfunction, and suppressed inflammation and NLRP3 inflammasome activation in mice after SAH. Overall, C3a exacerbates EBI-associated NLRP3 inflammasome and neuroinflammation via the C3aR-ERK-P2X7 pathway after SAH. Inhibiting C3aR may serve as a one possible treatment approach to alleviate SAH after EBI.

From the genome's perspective: Bearing somatic retrotransposition to leverage the regulatory potential of L1 RNAs.

Transposable elements (TEs) are mobile genomic elements constituting a big fraction of eukaryotic genomes. They ignite an evolutionary arms race with host genomes, which in turn evolve strategies to restrict their activity. Despite being tightly repressed, TEs display precisely regulated expression patterns during specific stages of mammalian development, suggesting potential benefits for the host. Among TEs, the long interspersed nuclear element (LINE-1 or L1) has been found to be active in neurons. This activity prompted extensive research into its possible role in cognition. So far, no specific cause-effect relationship between L1 retrotransposition and brain functions has been conclusively identified. Nevertheless, accumulating evidence suggests that interactions between L1 RNAs and RNA/DNA binding proteins encode specific messages that cells utilize to activate or repress entire transcriptional programs. We summarize recent findings highlighting the activity of L1 RNAs at the non-coding level during early embryonic and brain development. We propose a hypothesis suggesting a mutualistic relationship between L1 mRNAs and the host cell. In this scenario, cells tolerate a certain rate of retrotransposition to leverage the regulatory effects of L1s as non-coding RNAs on potentiating their mitotic potential. In turn, L1s benefit from the cell's proliferative state to increase their chance to mobilize.

Molecular signatures of cortical expansion in the human foetal brain

The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. Recent studies have revealed a remarkable molecular diversity across the prenatal cortex but little is known about how this diversity translates into the differential rates of cortical expansion observed during gestation. We present a digital resource, μBrain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal brain. Using μBrain, we evaluate the molecular signatures of preferentially-expanded cortical regions, quantified in utero using magnetic resonance imaging. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of neocortical expansion during gestation. We identify genes, upregulated from mid-gestation, that are highly expressed in rapidly expanding neocortex and implicated in genetic disorders with cognitive sequelae. The μBrain atlas provides a tool to comprehensively map early brain development across domains, model systems and resolution scales.

Systematic assessment of early brain injury severity at admission with aneurysmal subarachnoid hemorrhage

Early brain injury (EBI) after aneurysmal subarachnoid hemorrhage (aSAH) has been increasingly recognized as a risk factor for delayed cerebral ischemia (DCI). While several clinical and radiological EBI biomarkers have been identified, no tool for systematic assessment of EBI severity has been established so far. This study aimed to develop an EBI grading system based on clinical signs and neuroimaging for estimation of EBI severity at admission. This is a retrospective observational study assessing imaging parameters (intracranial blood amount, global cerebral edema (GCE)), and clinical signs (persistent loss of consciousness [LOC]) representative for EBI. The intracranial blood amount was semi-quantitatively assessed. One point was added for GCE and LOC, respectively. All points were summed up resulting in an EBI grading ranging from 1 to 5. The estimated EBI severity was correlated with progressive GCE requiring decompressive hemicraniectomy (DHC), DCI-associated infarction, and outcome according to the modified Rankin scale (mRS) at 3-month-follow up. A consecutive cohort including 324 aSAH-patients with a mean age of 55.9 years, was analyzed. The probability of developing progressive GCE was 9% for EBI grade 1, 28% for EBI grade 2, 43% for EBI grade 3, 61% for EBI grade 4, and 89% for EBI grade 5. The EBI grading correlated significantly with the need for DHC (r = 0.25, p < 0.0001), delayed infarction (r = 0.30, p < 0.0001), and outcome (r = 0.31, p < 0.0001). An EBI grading based on clinical and imaging parameters allowed an early systematic estimation of EBI severity with a higher EBI grade associated not only with a progressive GCE but also with DCI and poor outcome.