Spatial correlation maps of the hippocampus with cerebrospinal fluid biomarkers and cognition in Alzheimer's disease: A longitudinal study.

This study is an observational study that takes the existing longitudinal data from Alzheimer's disease Neuroimaging Initiative to examine the spatial correlation map of hippocampal subfield atrophy with CSF biomarkers and cognitive decline in the course of AD. This study included 421 healthy controls (HC), 557 patients of stable mild cognitive impairment (s-MCI), 304 Alzheimer's Disease (AD) patients, and 241 subjects who converted to be AD from MCI (c-MCI), and 6,525 MRI scans in a period from 2004 to 2019. Our findings revealed that all the hippocampal subfields showed their accelerated atrophy rate from cognitively normal aging to stable MCI and AD. The presubiculum, dentate gyrus, and fimbria showed greater atrophy beyond the whole hippocampus in the HC, s-MCI, and AD groups and corresponded to a greater decline of memory and attention in the s-MCI group. Moreover, the higher atrophy rates of the subiculum and CA2/3, CA4 were also associated with a greater decline in attention in the s-MCI group. Interestingly, patients with c-MCI showed that the presubiculum atrophy was associated with CSF tau levels and corresponded to the onset age of AD and a decline in attention in patients with c-MCI. These spatial correlation findings of the hippocampus suggested that the hippocampal subfields may not be equally impacted by normal aging, MCI, and AD, and their atrophy was selectively associated with declines in specific cognitive domains. The presubiculum atrophy was highlighted as a surrogate marker for the AD prognosis along with tau pathology and attention decline.

Pathways link environmental and genetic factors with structural brain networks and psychopathology in youth.

Adolescence is a period of significant brain development and maturation, and it is a time when many mental health problems first emerge. This study aimed to explore a comprehensive map that describes possible pathways from genetic and environmental risks to structural brain organization and psychopathology in adolescents. We included 32 environmental items on developmental adversity, maternal substance use, parental psychopathology, socioeconomic status (SES), school and family environment; 10 child psychopathological scales; polygenic risk scores (PRS) for 10 psychiatric disorders, total problems, and cognitive ability; and structural brain networks in the Adolescent Brain Cognitive Development study (ABCD, n = 9168). Structural equation modeling found two pathways linking SES, brain, and psychopathology. Lower SES was found to be associated with lower structural connectivity in the posterior default mode network and greater salience structural connectivity, and with more severe psychosis and internalizing in youth (p < 0.001). Prematurity and birth weight were associated with early-developed sensorimotor and subcortical networks (p < 0.001). Increased parental psychopathology, decreased SES and school engagement was related to elevated family conflict, psychosis, and externalizing behaviors in youth (p < 0.001). Increased maternal substance use predicted increased developmental adversity, internalizing, and psychosis (p < 0.001). But, polygenic risks for psychiatric disorders had moderate effects on brain structural connectivity and psychopathology in youth. These findings suggest that a range of genetic and environmental factors can influence brain structural organization and psychopathology during adolescence, and that addressing these risk factors may be important for promoting positive mental health outcomes in young people.

Metabolism-related brain morphology accelerates aging and predicts neurodegenerative diseases and stroke: a UK Biobank study.

Metabolic syndrome (MetS) is characterized by a constellation of metabolic risk factors, including obesity, hypertriglyceridemia, low high-density lipoprotein (HDL) levels, hypertension, and hyperglycemia, and is associated with stroke and neurodegenerative diseases. This study capitalized on brain structural images and clinical data from the UK Biobank and explored the associations of brain morphology with MetS and brain aging due to MetS. Cortical surface area, thickness, and subcortical volumes were assessed using FreeSurfer. Linear regression was used to examine associations of brain morphology with five MetS components and the MetS severity in a metabolic aging group (N = 23,676, age 62.8 ± 7.5 years). Partial least squares (PLS) were employed to predict brain age using MetS-associated brain morphology. The five MetS components and MetS severity were associated with increased cortical surface area and decreased thickness, particularly in the frontal, temporal, and sensorimotor cortex, and reduced volumes in the basal ganglia. Obesity best explained the variation of brain morphology. Moreover, participants with the most severe MetS had brain age 1-year older than those without MetS. Brain age in patients with stroke (N = 1042), dementia (N = 83), Parkinson's (N = 107), and multiple sclerosis (N = 235) was greater than that in the metabolic aging group. The obesity-related brain morphology had the leading discriminative power. Therefore, the MetS-related brain morphological model can be used for risk assessment of stroke and neurodegenerative diseases. Our findings suggested that prioritizing adjusting obesity among the five metabolic components may be more helpful for improving brain health in aging populations.

Monte Carlo Ensemble Neural Network for the diagnosis of Alzheimer's disease.

Convolutional neural networks (CNNs) have been increasingly used in the computer-aided diagnosis of Alzheimer's Disease (AD). This study takes the advantage of the 2D-slice CNN fast computation and ensemble approaches to develop a Monte Carlo Ensemble Neural Network (MCENN) by introducing Monte Carlo sampling and an ensemble neural network in the integration with ResNet50. Our goals are to improve the 2D-slice CNN performance and to design the MCENN model insensitive to image resolution. Unlike traditional ensemble approaches with multiple base learners, our MCENN model incorporates one neural network learner and generates a large number of possible classification decisions via Monte Carlo sampling of feature importance within the combined slices. This can overcome the main weakness of the lack of 3D brain anatomical information in 2D-slice CNNs and develop a neural network to learn the 3D relevance of the features across multiple slices. Brain images from Alzheimer's Disease Neuroimaging Initiative (ADNI, 7199 scans), the Open Access Series of Imaging Studies-3 (OASIS-3, 1992 scans), and a clinical sample (239 scans) are used to evaluate the performance of the MCENN model for the classification of cognitively normal (CN), patients with mild cognitive impairment (MCI) and AD. Our MCENN with a small number of slices and minimal image processing (rigid transformation, intensity normalization, skull stripping) achieves the AD classification accuracy of 90%, better than existing 2D-slice CNNs (accuracy: 63%∼84%) and 3D CNNs (accuracy: 74%∼88%). Furthermore, the MCENN is robust to be trained in the ADNI dataset and applied to the OASIS-3 dataset and the clinical sample. Our experiments show that the AD classification accuracy of the MCENN model is comparable when using high- and low-resolution brain images, suggesting the insensitivity of the MCENN to image resolution. Hence, the MCENN does not require high-resolution 3D brain structural images and comprehensive image processing, which supports its potential use in a clinical setting.

Predicting diagnosis 4 years prior to Alzheimer's disease incident.

This study employed a deep learning longitudinal model, graph convolutional and recurrent neural network (graph-CNN-RNN), on a series of brain structural MRI scans for AD prognosis. It characterized whole-brain morphology via incorporating longitudinal cortical and subcortical morphology and defined a probabilistic risk for the prediction of AD as a function of age prior to clinical diagnosis. The graph-CNN-RNN model was trained on half of the Alzheimer's Disease Neuroimaging Initiative dataset (ADNI, n = 1559) and validated on the other half of the ADNI dataset and the Open Access Series of Imaging Studies-3 (OASIS-3, n = 930). Our findings demonstrated that the graph-CNN-RNN can reliably and robustly diagnose AD at the accuracy rate of 85% and above across all the time points for both datasets. The graph-CNN-RNN predicted the AD conversion from 0 to 4 years before the AD onset at ∼80% of accuracy. The AD probabilistic risk was associated with clinical traits, cognition, and amyloid burden assessed using [18F]-Florbetapir (AV45) positron emission tomography (PET) across all the time points. The graph-CNN-RNN provided the quantitative trajectory of brain morphology from prognosis to overt stages of AD. Such a deep learning tool and the AD probabilistic risk have great potential in clinical applications for AD prognosis.

Evading strength-corrosion tradeoff in Mg alloys via dense ultrafine twins.

Conventional ultrafine-grains can generate high strength in Mg alloys, but significant tradeoff of corrosion resistance due to inclusion of a large number of non-equilibrium grain boundaries. Herein, an ultrafine-grain structure consisting of dense ultrafine twins is prepared, yielding a high strength up to 469 MPa and decreasing the corrosion rate by one order of magnitude. Generally, the formation of dense ultrafine twins in Mg alloys is rather difficult, but a carefully designed multi-directional compression treatment effectively stimulates twinning nucleation within twins and refines grain size down to 300 nm after 12-passes compressions. Grain-refinement by low-energy twins not only circumvents the detrimental effects of non-equilibrium grain boundaries on corrosion resistance, but also alters both the morphology and distribution of precipitates. Consequently, micro-galvanic corrosion tendency decreases, and severe localized corrosion is suppressed completely. This technique has a high commercial viability as it can be readily implemented in industrial production.

Fast splitting algorithm for multiframe total variation blind video deconvolution.

We consider the recovery of degraded videos without complete knowledge about the degradation. A spatially shift-invariant but temporally shift-varying video formation model is used. This leads to a simple multiframe degradation model that relates each original video frame with multiple observed frames and point spread functions (PSFs). We propose a variational method that simultaneously reconstructs each video frame and the associated PSFs from the corresponding observed frames. Total variation (TV) regularization is used on both the video frames and the PSFs to further reduce the ill-posedness and to better preserve edges. In order to make TV minimization practical for video sequences, we propose an efficient splitting method that generalizes some recent fast single-image TV minimization methods to the multiframe case. Both synthetic and real videos are used to show the performance of the proposed method.

Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations.

Combining machine learning with neuroimaging data has a great potential for early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, it remains unclear how well the classifiers built on one population can predict MCI/AD diagnosis of other populations. This study aimed to employ a spectral graph convolutional neural network (graph-CNN), that incorporated cortical thickness and geometry, to identify MCI and AD based on 3089 T1-weighted MRI data of the ADNI-2 cohort, and to evaluate its feasibility to predict AD in the ADNI-1 cohort (n = 3602) and an Asian cohort (n = 347). For the ADNI-2 cohort, the graph-CNN showed classification accuracy of controls (CN) vs. AD at 85.8% and early MCI (EMCI) vs. AD at 79.2%, followed by CN vs. late MCI (LMCI) (69.3%), LMCI vs. AD (65.2%), EMCI vs. LMCI (60.9%), and CN vs. EMCI (51.8%). We demonstrated the robustness of the graph-CNN among the existing deep learning approaches, such as Euclidean-domain-based multilayer network and 1D CNN on cortical thickness, and 2D and 3D CNNs on T1-weighted MR images of the ADNI-2 cohort. The graph-CNN also achieved the prediction on the conversion of EMCI to AD at 75% and that of LMCI to AD at 92%. The find-tuned graph-CNN further provided a promising CN vs. AD classification accuracy of 89.4% on the ADNI-1 cohort and >90% on the Asian cohort. Our study demonstrated the feasibility to transfer AD/MCI classifiers learned from one population to the other. Notably, incorporating cortical geometry in CNN has the potential to improve classification performance.

Evolution of the degradation mechanism of pure zinc stent in the one-year study of rabbit abdominal aorta model.

In the present study, pure zinc stents were implanted into the abdominal aorta of rabbits for 12 months. Multiscale analysis including micro-CT, scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM) and histological stainings was performed to reveal the fundamental degradation mechanism of the pure zinc stent and its biocompatibility. The pure zinc stent was able to maintain mechanical integrity for 6 months and degraded 41.75 ± 29.72% of stent volume after 12 months implantation. No severe inflammation, platelet aggregation, thrombosis formation or obvious intimal hyperplasia was observed at all time points after implantation. The degradation of the zinc stent played a beneficial role in the artery remodeling and healing process. The evolution of the degradation mechanism of pure zinc stents with time was revealed as follows: Before endothelialization, dynamic blood flow dominated the degradation of pure zinc stent, creating a uniform corrosion mode; After endothelialization, the degradation of pure zinc stent depended on the diffusion of water molecules, hydrophilic solutes and ions which led to localized corrosion. Zinc phosphate generated in blood flow transformed into zinc oxide and small amounts of calcium phosphate during the conversion of degradation microenvironment. The favorable physiological degradation behavior makes zinc a promising candidate for future stent applications.

Framelet-based blind motion deblurring from a single image.

How to recover a clear image from a single motion-blurred image has long been a challenging open problem in digital imaging. In this paper, we focus on how to recover a motion-blurred image due to camera shake. A regularization-based approach is proposed to remove motion blurring from the image by regularizing the sparsity of both the original image and the motion-blur kernel under tight wavelet frame systems. Furthermore, an adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problem. The experiments on both synthesized images and real images show that our algorithm can effectively remove complex motion blurring from natural images without requiring any prior information of the motion-blur kernel.

A variational model for segmentation of overlapping objects with additive intensity value.

We propose a variant of the Mumford-Shah model for the segmentation of a pair of overlapping objects with additive intensity value. Unlike standard segmentation models, it does not only determine distinct objects in the image, but also recover the possibly multiple membership of the pixels. To accomplish this, some a priori knowledge about the smoothness of the object boundary is integrated into the model. Additivity is imposed through a soft constraint which allows the user to control the degree of additivity and is more robust than the hard constraint. We also show analytically that the additivity parameter can be chosen to achieve some stability conditions. To solve the optimization problem involving geometric quantities efficiently, we apply a multiphase level set method. Segmentation results on synthetic and real images validate the good performance of our model, and demonstrate the model's applicability to images with multiple channels and multiple objects.