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Ablation of oligodendrogenesis in adult mice alters brain microstructure and activity independently of behavioral deficits
AbstractOligodendrocytes continue to differentiate from their precursor cells even in adulthood, a process that can be modulated by neuronal activity and experience. Previous work has indicated that conditional ablation of oligodendrogenesis in adult mice leads to learning and memory deficits in a range of behavioral tasks. The current study replicated and re‐evaluated evidence for a role of oligodendrogenesis in motor learning, using a complex running wheel task. Further, we found that ablating oligodendrogenesis alters brain microstructure (ex vivo MRI) and brain activity (in vivo EEG) independent of experience with the task. This suggests a role for adult oligodendrocyte formation in the maintenance of brain function and indicates that task‐independent changes due to oligodendrogenesis ablation need to be considered when interpreting learning and memory deficits in this model.
Corrigendum: Motor learning in developmental coordination disorder: behavioral and neuroimaging study.
[This corrects the article DOI: 10.3389/fnins.2023.1187790.].
White Matter
Diffusion weighted imaging has further pushed the boundaries of neuroscience by allowing us to peer farther into the white matter microstructure of the living human brain. By doing so, it has provided answers to fundamental neuroscientific questions, launching a new field of research that had been largely inaccessible. We will briefly summarize key questions, that have historically been raised in neuroscience, concerning the brain's white matter. We will then expand on the benefits of diffusion weighted imaging and its contribution to the fields of brain anatomy, functional models and plasticity. In doing so, this article will highlight the invaluable contribution of diffusion weighted imaging in neuroscience, present its limitations and put forth new challenges for the future generations who may wish to exploit this powerful technology to gain novel insights.
Performance evaluation of computerized antepartum fetal heart rate monitoring: Dawes-Redman algorithm at term.
OBJECTIVES: To assess the effectiveness of the Dawes-Redman algorithm in identifying fetal wellbeing at term by analyzing 30 years of retrospective clinical data, comparing normal and adverse pregnancy outcomes, evaluating key metrics and testing its performance when used 0-48 h before delivery. METHODS: Antepartum fetal heart rate (FHR) traces from term singleton pregnancies at 37 + 0 to 41 + 6 weeks' gestation obtained between 1991 and 2024 were extracted from the Oxford University Hospitals database. Traces with > 30% of their signal information missing or with incomplete Dawes-Redman analyses were excluded. Only traces performed within 48 h prior to delivery were considered. A cohort of pregnancies with subsequent normal pregnancy outcome (NPO) was established using rigorous inclusion and exclusion criteria. Another cohort of pregnancies with adverse pregnancy outcome (APO) was developed if the neonate experienced at least one of seven APOs after delivery. Propensity score matching (PSM) facilitated a balanced comparison between NPO and APO cohorts using six factors: gestational age at FHR monitoring, fetal sex, maternal body mass index at presentation, maternal age at delivery, parity and time interval between FHR trace and delivery. FHR traces were categorized as either 'criteria met' (indicating fetal wellbeing) or 'criteria not met' (indicating a need for further evaluation) according to the Dawes-Redman algorithm, which informed the evaluation of predictive performance metrics. Performance was assessed using accuracy, sensitivity, specificity, positive predictive value, and negative predictive value (NPV) adjusted for various population risk prevalences of APO. RESULTS: A balanced dataset of 3316 antepartum FHR traces was developed with PSM (standardized mean difference
Aspirin after completion of standard adjuvant therapy for colorectal cancer (ASCOLT): an international, multicentre, phase 3, randomised, double-blind, placebo-controlled trial.
BACKGROUND: Aspirin is a simple, globally available medication that has been shown to reduce the incidence of colorectal cancer. We aimed to evaluate the safety and efficacy of aspirin in the secondary prevention of colorectal cancer. METHODS: This phase 3, randomised, double-blind, placebo-controlled trial was conducted at 66 centres across 11 countries and territories (ten in Asia-Pacific; one in the Middle East). The trial included patients aged 18 years and older with Dukes' C or high-risk Dukes' B colon cancer or Dukes' B or C rectal cancer who had undergone resection and had completed standard adjuvant therapy (at least 3 months of chemotherapy). Patients with contraindications to aspirin, familial syndromes of colorectal cancer, recent other cancers, and clinically significant history of cardiovascular disease or stroke were excluded. Patients were randomly assigned (1:1) to aspirin 200 mg daily or placebo for 3 years, and were followed up for 5 years. Randomisation was stratified by study centre, tumour site and stage, and inclusion of oxaliplatin in adjuvant chemotherapy. The patients, study team, and sponsor were masked to treatment assignment. The primary endpoint was disease-free survival. The primary analysis used a stratified Cox model in those commencing study treatment (modified intention-to-treat population), analysing all events to March 31, 2023. Safety was analysed in the same population. This trial is registered at ClinicalTrials.gov (NCT00565708). The primary analysis has been completed, but translational studies of putative aspirin sensitivity biomarkers are ongoing. FINDINGS: Between Feb 25, 2009, and June 30, 2021, 1587 patients underwent randomisation, of whom 1550 were included in the modified intention-to-treat analysis: 791 (51%) in the aspirin group and 759 (49%) in the placebo group. Of these patients, the median age was 57 years (IQR 48-65); 897 (58%) were male and 653 (42%) female; 271 (17%) had Dukes' B colon cancer, 770 (50%) Dukes' C colon cancer, and 509 (33%) rectal cancer. Median follow-up at data cutoff was 59·2 months (IQR 36·7-60·0). 5-year disease-free survival was 77·0% (95% CI 73·6-80·0) in the aspirin group and 74·8% (71·3-77·9) in the placebo group (hazard ratio of 0·91 [95% CI 0·73-1·13]; p=0·38). Any-grade adverse events were reported in 390 (49%) of 791 patients in the aspirin group versus 386 (51%) of 759 in the placebo group. Serious adverse events were reported in 95 (12%) patients in the aspirin group versus 107 (14%) in the placebo group. There were no treatment-related deaths in either group. Among adverse events of special interest, there were no cases of acute myocardial infarction in the aspirin group versus two in the placebo group; no ischaemic cerebrovascular events in the aspirin group versus two in the placebo group; and three major gastrointestinal bleeds in the aspirin group versus one in the placebo group. INTERPRETATION: In patients with colorectal cancer, aspirin 200 mg daily for 3 years after completion of standard adjuvant therapy was well tolerated but did not significantly improve disease-free survival. FUNDING: SingHealth Foundation, National Medical Research Council Singapore, National Cancer Centre Research Fund, Rising Tide Foundation, Lee Foundation, Lee Kim Tah Foundation, Duke-NUS Khoo Bridge Funding Award, Terry-Fox Run, Silent Foundation, Cancer Australia, Bowel Cancer Australia, and Cancer Council NSW.
Is Your Style Transfer Doing Anything Useful? An Investigation into Hippocampus Segmentation and the Role of Preprocessing
Brain atrophy assessment in MRI, particularly of the hippocampus, is commonly used to support diagnosis and monitoring of dementia. Consequently, there is a demand for accurate automated hippocampus quantification. Most existing segmentation methods have been developed and validated on research datasets and, therefore, may not be appropriate for clinical MR images and populations, leading to potential gaps between dementia research and clinical practice. In this study, we investigated the performance of segmentation models trained on research data that were style-transferred to resemble clinical scans. Our results highlighted the importance of intensity normalisation methods in MRI segmentation, and their relation to domain shift and style-transfer. We found that whilst normalising intensity based on min and max values, commonly used in generative MR harmonisation methods, may create a need for style transfer, Z-score normalisation effectively maintains style consistency, and optimises performance. Moreover, we show for our datasets spatial augmentations are more beneficial than style harmonisation. Thus, emphasising robust normalisation techniques and spatial augmentation significantly improves MRI hippocampus segmentation.