Neurovascular Reactivity in the Aging Mouse Brain Assessed by Laser Speckle Contrast Imaging and 2-Photon Microscopy: Quantification by an Investigator-Independent Analysis Tool

The brain has a high energy demand but little to no energy stores. Therefore, proper brain function relies on the delivery of glucose and oxygen by the cerebral vasculature. The regulation of cerebral blood flow (CBF) occurs at the level of the cerebral capillaries and is driven by a fast and efficient crosstalk between neurons and vessels, a process termed neurovascular coupling (NVC). Experimentally NVC is mainly triggered by sensory stimulation and assessed by measuring either CBF by laser Doppler fluxmetry, laser speckle contrast imaging (LSCI), intrinsic optical imaging, BOLD fMRI, near infrared spectroscopy (NIRS) or functional ultrasound imaging (fUS). Since these techniques have relatively low spatial resolution, diameters of cerebral vessels are mainly assessed by 2-photon microscopy (2-PM). Results of studies on NVC rely on stable animal physiology, high-quality data acquisition, and unbiased data analysis, criteria, which are not easy to achieve. In the current study, we assessed NVC using two different imaging modalities, i.e., LSCI and 2-PM, and analyzed our data using an investigator-independent Matlab-based analysis tool, after manually defining the area of analysis in LSCI and vessels to measure in 2-PM. By investigating NVC in 6–8 weeks, 1-, and 2-year-old mice, we found that NVC was maximal in 1-year old mice and was significantly reduced in aged mice. These findings suggest that NVC is differently affected during the aging process. Most interestingly, specifically pial arterioles, seem to be distinctly affected by the aging. The main finding of our study is that the automated analysis tool works very efficiently in terms of time and accuracy. In fact, the tool reduces the analysis time of one animal from approximately 23 h to about 2 s while basically making no mistakes. In summary, we developed an experimental workflow, which allows us to reliably measure NVC with high spatial and temporal resolution in young and aged mice and to analyze these data in an investigator-independent manner

Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia

Background: White matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research. Methods: We used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS). Results: Across tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions. Conclusion: To conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia

Methyl methacrylate and respiratory sensitization: A Critical review

Methyl methacrylate (MMA) is a respiratory irritant and dermal sensitizer that has been associated with occupational asthma in a small number of case reports. Those reports have raised concern that it might be a respiratory sensitizer. To better understand that possibility, we reviewed the in silico, in chemico, in vitro, and in vivo toxicology literature, and also epidemiologic and occupational medicine reports related to the respiratory effects of MMA. Numerous in silico and in chemico studies indicate that MMA is unlikely to be a respiratory sensitizer. The few in vitro studies suggest that MMA has generally weak effects. In vivo studies have documented contact skin sensitization, nonspecific cytotoxicity, and weakly positive responses on local lymph node assay; guinea pig and mouse inhalation sensitization tests have not been performed. Cohort and cross-sectional worker studies reported irritation of eyes, nose, and upper respiratory tract associated with short-term peaks exposures, but little evidence for respiratory sensitization or asthma. Nineteen case reports described asthma, laryngitis, or hypersensitivity pneumonitis in MMA-exposed workers; however, exposures were either not well described or involved mixtures containing more reactive respiratory sensitizers and irritants.The weight of evidence, both experimental and observational, argues that MMA is not a respiratory sensitizer

A study on loss functions and decision thresholds for the segmentation of multiple sclerosis lesions on spinal cord MRI

Multiple sclerosis (MS) patients often present hyper-intense T2-w lesions in the spinal cord. The severe imbalance between background and lesion classes poses a major challenge to Deep Learning segmentation approaches, requiring for ad hoc strategies. Careful selection of the loss function and adjustment of the conventional 0.5-thresholding may help mitigating this issue. Our results show the performance advantages of loss functions based on the Tversky Index and the benefits of threshold tuning over more standard settings and the state-of-the-art model for MS lesion segmentation on spinal cord MRI

Social problems and brain structure trajectories following pediatric mild traumatic brain injury

International audienc

Social problems and brain structure trajectories following pediatric mild traumatic brain injury

International audienc

Social problems and brain structure trajectories following pediatric mild traumatic brain injury

International audienc