Essential Nutrients and White Matter Hyperintensities: A Two-Sample Mendelian Randomization Study.

Stroke and dementia have been linked to the appearance of white matter hyperintensities (WMHs). Meanwhile, diffusion tensor imaging (DTI) might capture the microstructural change in white matter early. Specific dietary interventions may help to reduce the risk of WMHs. However, research on the relationship between specific nutrients and white matter changes is still lacking. We aimed to investigate the causal effects of essential nutrients (amino acids, fatty acids, mineral elements, and vitamins) on WMHs and DTI measures, including fraction anisotropy (FA) and mean diffusivity (MD), by a Mendelian randomization analysis. We selected single nucleotide polymorphisms (SNPs) associated with each nutrient as instrumental variables to assess the causal effects of nutrient-related exposures on WMHs, FA, and MD. The outcome was from a recently published large-scale European Genome Wide Association Studies pooled dataset, including WMHs (N = 18,381), FA (N = 17,663), and MD (N = 17,467) data. We used the inverse variance weighting (IVW) method as the primary method, and sensitivity analyses were conducted using the simple median, weighted median, and MR-Egger methods. Genetically predicted serum calcium level was positively associated with WMHs risk, with an 8.1% increase in WMHs risk per standard deviation unit increase in calcium concentration (OR = 1.081, 95% CI = 1.006-1.161, p = 0.035). The plasma linoleic acid level was negatively associated with FA (OR = 0.776, 95% CI = 0.616-0.978, p = 0.032). Our study demonstrated that genetically predicted calcium was a potential risk factor for WMHs, and linoleic acid may be negatively associated with FA, providing evidence for interventions from the perspective of gene-environment interactions.

Characteristics and Potential Neural Substrates of Encoding and Retrieval During Memory Binding in Amnestic Mild Cognitive Impairment.

BACKGROUND: Whether encoding or retrieval failure contributes to memory binding deficit in amnestic mild cognitive impairment (aMCI) has not been elucidated. Also, the potential brain structural substrates of memory binding remained undiscovered. OBJECTIVE: To investigate the characteristics and brain atrophy pattern of encoding and retrieval performance during memory binding in aMCI. METHODS: Forty-three individuals with aMCI and 37 cognitively normal controls were recruited. The Memory Binding Test (MBT) was used to measure memory binding performance. The immediate and delayed memory binding indices were computed by using the free and cued paired recall scores. Partial correlation analysis was performed to map the relationship between regional gray matter volume and memory binding performance. RESULTS: The memory binding performance in the learning and retrieval phases was worse in the aMCI group than in the control group (F = 22.33 to 52.16, all p < 0.001). The immediate and delayed memory binding index in the aMCI group was lower than that in the control group (p < 0.05). The gray matter volume of the left inferior temporal gyrus was positively correlated with memory binding test scores (r = 0.49 to 0.61, p < 0.05) as well as the immediate (r = 0.39, p < 0.05) and delayed memory binding index (r = 0.42, p < 0.05) in the aMCI group. CONCLUSION: aMCI may be primarily characterized by a deficit in encoding phase during the controlled learning process. Volumetric losses in the left inferior temporal gyrus may contribute to encoding failure.

An extensible hierarchical graph convolutional network for early Alzheimer's disease identification.

BACKGROUND AND OBJECTIVE: For early identification of Alzheimer's disease (AD) based on multi-modal magnetic resonance imaging (MRI) data, it is important to make comprehensive use of image features and non-image information to analyze the gray matter atrophy and the structural/functional connectivity abnormalities for different courses of AD. METHODS: In this study, we propose an extensible hierarchical graph convolutional network (EH-GCN) for early AD identification. Based on the extracted image features from multi-modal MRI data using the presented multi-branch residual network (ResNet), the brain regions-of-interests (ROIs) based GCN is built to extract structural and functional connectivity features between different ROIs of the brain. In order to further improve the performance of AD identification, an optimized spatial GCN is proposed as convolution operator in the population-based GCN to avoid rebuilding the graph network and take advantage of relationships between subjects. Finally, the proposed EH-GCN is built by embedding the image features and internal brain connectivity features into the spatial population-based GCN, which provides an extensible way to improve early AD identification performance by adding imaging features and non-image information from multi-modal data. RESULTS: Experiments are performed on two datasets, which illustrate the effectiveness of the extracted structural/functional connectivity features and the high computational efficiency of the proposed method. The classification accuracy of AD vs NC, AD vs MCI and MCI vs NC classification tasks reaches 88.71%, 82.71% and 79.68% respectively. The extracted connectivity features between ROIs indicate that functional abnormalities are earlier than gray matter atrophy and abnormalities of structural connections, which is consistent with the clinical manifestations. The proposed method allows for the addition of other modal image features and non-image information from multi-modal data to continuously improve the performance of clinical data analysis. CONCLUSIONS: The proposed method can help us comprehensively analyze the role of gray matter atrophy, the damage of white matter nerve fiber tracts and the degradation of functional connectivity for different courses of AD, which could be useful for further extraction of clinical biomarkers for early AD identification.

CarveMix: A simple data augmentation method for brain lesion segmentation.

Brain lesion segmentation provides a valuable tool for clinical diagnosis and research, and convolutional neural networks (CNNs) have achieved unprecedented success in the segmentation task. Data augmentation is a widely used strategy to improve the training of CNNs. In particular, data augmentation approaches that mix pairs of annotated training images have been developed. These methods are easy to implement and have achieved promising results in various image processing tasks. However, existing data augmentation approaches based on image mixing are not designed for brain lesions and may not perform well for brain lesion segmentation. Thus, the design of this type of simple data augmentation method for brain lesion segmentation is still an open problem. In this work, we propose a simple yet effective data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Like other mixing-based methods, CarveMix stochastically combines two existing annotated images (annotated for brain lesions only) to obtain new labeled samples. To make our method more suitable for brain lesion segmentation, CarveMix is lesion-aware, where the image combination is performed with a focus on the lesions and preserves the lesion information. Specifically, from one annotated image we carve a region of interest (ROI) according to the lesion location and geometry with a variable ROI size. The carved ROI then replaces the corresponding voxels in a second annotated image to synthesize new labeled images for network training, and additional harmonization steps are applied for heterogeneous data where the two annotated images can originate from different sources. Besides, we further propose to model the mass effect that is unique to whole brain tumor segmentation during image mixing. To evaluate the proposed method, experiments were performed on multiple publicly available or private datasets, and the results show that our method improves the accuracy of brain lesion segmentation. The code of the proposed method is available at https://github.com/ZhangxinruBIT/CarveMix.git.

Role of hippocampal subfields in neurodegenerative disease progression analyzed with a multi-scale attention-based network.

BACKGROUND AND OBJECTIVE: Both Alzheimer's disease (AD) and Parkinson's disease (PD) are progressive neurodegenerative diseases. Early identification is very important for the prevention and intervention of their progress. Hippocampus plays a crucial role in cognition, in which there are correlations between atrophy of Hippocampal subfields and cognitive impairment in neurodegenerative diseases. Exploring biomarkers in the prediction of early cognitive impairment in AD and PD is significant for understanding the progress of neurodegenerative diseases. METHODS: A multi-scale attention-based deep learning method is proposed to perform computer-aided diagnosis for neurodegenerative disease based on Hippocampal subfields. First, the two dimensional (2D) Hippocampal Mapping Image (HMI) is constructed and used as input of three branches of the following network. Second, the multi-scale module and attention module are integrated into the 2D residual network to improve the diversity of the extracted features and capture significance of various voxels for classification. Finally, the role of Hippocampal subfields in the progression of different neurodegenerative diseases is analyzed using the proposed method. RESULTS: Classification experiments between normal control (NC), mild cognitive impairment (MCI), AD, PD with normal cognition (PD-NC) and PD with mild cognitive impairment (PD-MCI) are carried out using the proposed method. Experimental results show that subfields subiculum, presubiculum, CA1, and molecular layer are strongly correlated with cognitive impairment in AD and MCI, subfields GC-DG and fimbria are sensitive in detecting early stage of cognitive impairment in MCI, subfields CA3, CA4, GC-DG, and CA1 show significant atrophy in PD. For exploring the role of Hippocampal subfields in PD cognitive impairment, we find that left parasubiculum, left HATA and left presubiculum could be important biomarkers for predicting conversion from PD-NC to PD-MCI. CONCLUSION: The proposed multi-scale attention-based network can effectively discover the correlation between subfields and neurodegenerative diseases. Experimental results are consistent with previous clinical studies, which will be useful for further exploring the role of Hippocampal subfields in neurodegenerative disease progression.

Deep learning-assisted classification of calcaneofibular ligament injuries in the ankle joint.

Background: The classification of calcaneofibular ligament (CFL) injuries on magnetic resonance imaging (MRI) is time-consuming and subject to substantial interreader variability. This study explores the feasibility of classifying CFL injuries using deep learning methods by comparing them with the classifications of musculoskeletal (MSK) radiologists and further examines image cropping screening and calibration methods. Methods: The imaging data of 1,074 patients who underwent ankle arthroscopy and MRI examinations in our hospital were retrospectively analyzed. According to the arthroscopic findings, patients were divided into normal (class 0, n=475); degeneration, strain, and partial tear (class 1, n=217); and complete tear (class 2, n=382) groups. All patients were divided into training, validation, and test sets at a ratio of 8:1:1. After preprocessing, the images were cropped using Mask region-based convolutional neural network (R-CNN), followed by the application of an attention algorithm for image screening and calibration and the implementation of LeNet-5 for CFL injury classification. The diagnostic effects of the axial, coronal, and combined models were compared, and the best method was selected for outgroup validation. The diagnostic results of the models in the intragroup and outgroup test sets were compared with those results of 4 MSK radiologists of different seniorities. Results: The mean average precision (mAP) of the Mask R-CNN using the attention algorithm for the left and right image cropping of axial and coronal sequences was 0.90-0.96. The accuracy of LeNet-5 for classifying classes 0-2 was 0.92, 0.93, and 0.92, respectively, for the axial sequences and 0.89, 0.92, and 0.90, respectively, for the coronal sequences. After sequence combination, the classification accuracy for classes 0-2 was 0.95, 0.97, and 0.96, respectively. The mean accuracies of the 4 MSK radiologists in classifying the intragroup test set as classes 0-2 were 0.94, 0.91, 0.86, and 0.85, all of which were significantly different from the model. The mean accuracies of the MSK radiologists in classifying the outgroup test set as classes 0-2 were 0.92, 0.91, 0.87, and 0.85, with the 2 senior MSK radiologists demonstrating similar diagnostic performance to the model and the junior MSK radiologists demonstrating worse accuracy. Conclusions: Deep learning can be used to classify CFL injuries at similar levels to those of MSK radiologists. Adding an attention algorithm after cropping is helpful for accurately cropping CFL images.

A Multi-scale Attention-based Convolutional Network for Identification of Alzheimer's Disease based on Hippocampal Subfields.

Hippocampus is an important anatomical region for Alzheimer's Disease (AD) identification. In this paper, a multi-scale attention-based convolutional network is proposed for AD identification. The two dimensional (2D) images in three different planes of hippocampal subfields are used as input of three branches of the proposed network, which achieves effective extraction of three dimensional (3D) data features while reducing the network complexity and improving the computational efficiency. The end-to-end 2D multi-scale attention-based deep learning network improves the diversity of the extracted features and captures significance of various voxels for classification, which achieves significant classification performance without handcrafted feature extraction and model stacking. Experimental results illustrate the effectiveness of the proposed method on AD identification. The proposed method will be useful for further medical analysis on hippocampal subfields of the brain for diagnosis of neurodegenerative disease.

CT-Visible Convexity Subarachnoid Hemorrhage Predicts Early Recurrence of Lobar Hemorrhage.

Background and Purpose: Convexity subarachnoid hemorrhage (cSAH) may predict an increased recurrence risk in cerebral amyloid angiopathy (CAA)-related intracerebral hemorrhage (ICH) survivors. We aimed to investigate whether cSAH detected on CT was related to early recurrence in patients with ICH related to CAA. Methods: We analyzed data from consecutive lobar ICH patients diagnosed as probable or possible CAA according to the Boston criteria using the method of cohort study. Demographic and clinical data, ICH recurrence at discharge and within 90 days were collected. The association between cSAH detected on CT and early recurrent ICH was analyzed using multivariable logistic regression. Results: A total of 197 cases (74 [66-80] years) were included. cSAH was observed on the baseline CT of 91 patients (46.2%). A total of 5.1% (10/197) and 9.5% (17/179) of patients experienced ICH recurrence within 2 weeks and 90 days, respectively. The presence of cSAH was related to recurrence within 2 weeks (OR = 5.705, 95%CI 1.070-30.412, P = 0.041) after adjusting for hypertension, previous symptomatic ICH and anticoagulant use. The presence of cSAH was related to recurrence within 90 days (OR 5.473, 95%CI 1.425-21.028, P = 0.013) after adjusting for hypertension, previous symptomatic ICH and intraventricular hemorrhage. The similar results were obtained in other models using different methods to select adjusting variables. Conclusion: In patients with lobar ICH related to CAA, 5.1% and 9.5% of them experienced ICH recurrence within 2 weeks and 90 days, respectively. CT-visible cSAH was detected in 46.2% of patients and indicates an increased risk for early recurrent ICH.

Multi-resolution 3D-HOG feature learning method for Alzheimer's Disease diagnosis.

BACKGROUND AND OBJECTIVE: Alzheimer's Disease (AD) is a progressive irreversible neurodegeneration disease and thus timely identification is critical to delay its progression. METHODS: In this work, we focus on the traditional branch to design discriminative feature extraction and selection strategies to achieve explainable AD identification. Specifically, a spatial pyramid based three-dimensional histogram of oriented gradient (3D-HOG) feature learning method is proposed. Both global and local texture changes are included in spatial pyramid 3D-HOG (SPHOG) features for comprehensive analysis. Then a modified wrapper-based feature selection algorithm is introduced to select the discriminative features for AD identification while reduce feature dimensions. RESULTS: Discriminative SPHOG histograms with various resolutions are selected, which can represent the atrophy characteristics of cerebral cortex with promising performance. As subareas corresponding to selected histograms are consistent with clinical experience, explanatory is emphasized and illustrated with Hippocampus. CONCLUSION: Experimental results illustrate the effectiveness of the proposed method on feature learning based on samples obtained from common dataset and a clinical dataset. The proposed method will be useful for further medical analysis as its explanatory on other region-of-interests (ROIs) of the brain for early diagnosis of AD.

Knowledge transfer between brain lesion segmentation tasks with increased model capacity.

Convolutional neural networks (CNNs) have become an increasingly popular tool for brain lesion segmentation in recent years due to its accuracy and efficiency. However, CNN-based brain lesion segmentation generally requires a large amount of annotated training data, which can be costly for medical imaging. In many scenarios, only a few annotations of brain lesions are available. One common strategy to address the issue of limited annotated data is to transfer knowledge from a different yet relevant source task, where training data is abundant, to the target task of interest. Typically, a model can be pretrained for the source task, and then fine-tuned with the scarce training data associated with the target task. However, classic fine-tuning tends to make small modifications to the pretrained model, which could hinder its adaptation to the target task. Fine-tuning with increased model capacity has been shown to alleviate this negative impact in image classification problems. In this work, we extend the strategy of fine-tuning with increased model capacity to the problem of brain lesion segmentation, and then develop an advanced version that is better suitable for segmentation problems. First, we propose a vanilla strategy of increasing the capacity, where, like in the classification problem, the width of the network is augmented during fine-tuning. Second, because unlike image classification, in segmentation problems each voxel is associated with a labeling result, we further develop a spatially adaptive augmentation strategy during fine-tuning. Specifically, in addition to the vanilla width augmentation, we incorporate a module that computes a spatial map of the contribution of the information given by width augmentation in the final segmentation. For demonstration, the proposed method was applied to ischemic stroke lesion segmentation, where a model pretrained for brain tumor segmentation was fine-tuned, and the experimental results indicate the benefit of our method.

Super-Resolved q-Space deep learning with uncertainty quantification.

Diffusion magnetic resonance imaging (dMRI) provides a noninvasive method for measuring brain tissue microstructure. q-Space deep learning(q-DL) methods have been developed to accurately estimate tissue microstructure from dMRI scans acquired with a reduced number of diffusion gradients. In these methods, deep networks are trained to learn the mapping directly from diffusion signals to tissue microstructure. However, the quality of tissue microstructure estimation can be limited not only by the reduced number of diffusion gradients but also by the low spatial resolution of typical dMRI acquisitions. Therefore, in this work we extend q-DL to super-resolved tissue microstructure estimation and propose super-resolvedq-DL (SR-q-DL), where deep networks are designed to map low-resolution diffusion signals undersampled in the q-space to high-resolution tissue microstructure. Specifically, we use a patch-based strategy, where a deep network takes low-resolution patches of diffusion signals as input and outputs high-resolution tissue microstructure patches. The high-resolution patches are then combined to obtain the final high-resolution tissue microstructure map. Motivated by existing q-DL methods, we integrate the sparsity of diffusion signals in the network design, which comprises two functional components. The first component computes sparse representation of diffusion signals for the low-resolution input patch, and the second component maps the low-resolution sparse representation to high-resolution tissue microstructure. The weights in the two components are learned jointly and the trained network performs end-to-end tissue microstructure estimation. In addition to SR-q-DL, we further propose probabilistic SR-q-DL, which can quantify the uncertainty of the network output as well as achieve improved estimation accuracy. In probabilistic SR-q-DL, a deep ensemble strategy is used. Specifically, the deep network for SR-q-DL is revised to produce not only tissue microstructure estimates but also the uncertainty of the estimates. Then, multiple deep networks are trained and their results are fused for the final prediction of high-resolution tissue microstructure and uncertainty quantification. The proposed method was evaluated on two independent datasets of brain dMRI scans. Results indicate that our approach outperforms competing methods in terms of estimation accuracy. In addition, uncertainty measures provided by our method correlate with estimation errors, which indicates potential application of the proposed uncertainty quantification method in brain studies.

An improved deep network for tissue microstructure estimation with uncertainty quantification.

Deep learning based methods have improved the estimation of tissue microstructure from diffusion magnetic resonance imaging (dMRI) scans acquired with a reduced number of diffusion gradients. These methods learn the mapping from diffusion signals in a voxel or patch to tissue microstructure measures. In particular, it is beneficial to exploit the sparsity of diffusion signals jointly in the spatial and angular domains, and the deep network can be designed by unfolding iterative processes that adaptively incorporate historical information for sparse reconstruction. However, the number of network parameters is huge in such a network design, which could increase the difficulty of network training and limit the estimation performance. In addition, existing deep learning based approaches to tissue microstructure estimation do not provide the important information about the uncertainty of estimates. In this work, we continue the exploration of tissue microstructure estimation using a deep network and seek to address these limitations. First, we explore the sparse representation of diffusion signals with a separable spatial-angular dictionary and design an improved deep network for tissue microstructure estimation. The procedure for updating the sparse code associated with the separable dictionary is derived and unfolded to construct the deep network. Second, with the formulation of sparse representation of diffusion signals, we propose to quantify the uncertainty of network outputs with a residual bootstrap strategy. Specifically, because of the sparsity constraint in the signal representation, we perform a Lasso bootstrap strategy for uncertainty quantification. Experiments were performed on brain dMRI scans with a reduced number of diffusion gradients, where the proposed method was applied to two representative biophysical models for describing tissue microstructure and compared with state-of-the-art methods of tissue microstructure estimation. The results show that our approach compares favorably with the competing methods in terms of estimation accuracy. In addition, the uncertainty measures provided by our method correlate with estimation errors and produce reasonable confidence intervals; these results suggest potential application of the proposed uncertainty quantification method in brain studies.

Region-of-Interest based sparse feature learning method for Alzheimer's disease identification.

BACKGROUND AND OBJECTIVE: In recent years, some clinical parameters, such as the volume of gray matter (GM) and cortical thickness, have been used as anatomical features to identify Alzheimer's disease (AD) from Healthy Controls (HC) in some feature-based machine learning methods. However, fewer image-based feature parameters have been proposed, which are equivalent to these clinical parameters, to describe the atrophy of regions-of-interest (ROIs) of the brain. In this study, we aim to extract effective image-based feature parameters to improve the diagnostic performance of AD with magnetic resonance imaging (MRI) data. METHODS: A new subspace-based sparse feature learning method is proposed, which builds a union-of-subspace representation model to realize feature extraction and disease identification. Specifically, the proposed method estimates feature dimensions reasonably, at the same time, it protects local features for the specified ROIs of the brain, and realizes image-based feature extraction and classification automatically instead of computing the volume of GM or cortical thickness preliminarily. RESULTS: Experimental results illustrate the effectiveness and robustness of the proposed method on feature extraction and classification, which are based on the sampled clinical dataset from Peking University Third Hospital of China and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The extracted image-based feature parameters describe the atrophy of ROIs of the brain well as clinical parameters but show better performance in AD identification than clinical parameters. Based on them, the important ROIs for AD identification can be identified even for correlated variables. CONCLUSION: The extracted features and the proposed identification parameters show high correlation with the volume of GM and the clinical mini-mental state examination (MMSE) score respectively. The proposed method will be useful in denoting the changes of cerebral pathology and cognitive function in AD patients.

Computerized multi-domain cognitive training reduces brain atrophy in patients with amnestic mild cognitive impairment.

The present study aimed to explore the effect of computerized multi-domain cognitive training (MDCT) on brain gray matter volume and neuropsychological performance in patients with amnestic mild cognitive impairment (amnestic MCI). Twenty-one patients with amnestic MCI participated in a computerized MDCT program. The program targeted a broad set of cognitive domains via programs focused on reasoning, memory, visuospatial, language, calculation, and attention. Seventeen Participants completed the intervention and all completed a battery of neuropsychological tests to evaluate cognitive function while 12 out of 17 underwent 3 T MRI scanning before and after the intervention to measure gray matter (GM) volume. We examined correlations between the changes in neuropsychological scores and GM volumes across participants after the intervention. After training, we observed significant increases in GM volume in the right angular gyrus (AG) and other parietal subareas near the intraparietal sulcus (p < 0.05, FWE-corrected, 10000 permutations). However, we found no significant changes in neuropsychological test scores (p > 0.05). A correlation analysis revealed positive correlations between the changes in GM volume in the right AG and scores in the immediate recall component of the Hopkins Verbal Learning Test-Revised (HVLT-R) (r = 0.64, p = 0.024) and the Brief Visuospatial Memory Test-Revised (BVMT-R) (r = 0.67, p = 0.016). Our findings indicate that a computerized MDCT program may protect patients with amnestic MCI against brain GM volume loss and has potential in preserving general cognition. Thus, our non-pharmacological intervention may slow the rate of disease progression.

Comparison and Analyzation of Different Feature Parameters for Alzheimer's disease Identification.

In this paper, we compare the performance of the derived anatomical features and the extracted feature parameters in Alzheimer's disease (AD) identification. The correlation relationship between them and clinical mini-mental state examination (MMSE) score is analyzed. Based on these feature parameters, the highly correlated combined feature vectors are built and used as variables for the presented modified elastic net (EN) classifier. Experimental results show that the extracted feature parameters can obtain similar identification performance with the cortical thickness and the volume of gray matter (GM) in AD identification. The highly correlated combined feature vectors show the best identification performance among all of feature parameters using the modified EN-based classifier.

A Volumetric and Functional Connectivity MRI Study of Brain Arginine-Vasopressin Pathways in Autistic Children.

Dysfunction of brain-derived arginine-vasopressin (AVP) systems may be involved in the etiology of autism spectrum disorder (ASD). Certain regions such as the hypothalamus, amygdala, and hippocampus are known to contain either AVP neurons or terminals and may play an important role in regulating complex social behaviors. The present study was designed to investigate the concomitant changes in autistic behaviors, circulating AVP levels, and the structure and functional connectivity (FC) of specific brain regions in autistic children compared with typically developing children (TDC) aged from 3 to 5 years. The results showed: (1) children with ASD had a significantly increased volume in the left amygdala and left hippocampus, and a significantly decreased volume in the bilateral hypothalamus compared to TDC, and these were positively correlated with plasma AVP level. (2) Autistic children had a negative FC between the left amygdala and the bilateral supramarginal gyri compared to TDC. The degree of the negative FC between amygdala and supramarginal gyrus was associated with a higher score on the clinical autism behavior checklist. (3) The degree of negative FC between left amygdala and left supramarginal gyrus was associated with a lowering of the circulating AVP concentration in boys with ASD. (4) Autistic children showed a higher FC between left hippocampus and right subcortical area compared to TDC. (5) The circulating AVP was negatively correlated with the visual and listening response score of the childhood autism rating scale. These results strongly suggest that changes in structure and FC in brain regions containing AVP may be involved in the etiology of autism.

Different post label delay cerebral blood flow measurements in patients with Alzheimer's disease using 3D arterial spin labeling.

PURPOSE: To evaluate cerebral blood flow (CBF) in patients with Alzheimer's disease (AD) using a three-dimensional pseudocontinuous arterial spin labeling (PCASL). We aimed to study the effects of different post label delay on the resulting CBF maps and to investigate the characteristics and clinical applications of brain perfusion. MATERIALS AND METHODS: Sixteen AD patients and nineteen healthy control subjects were recruited. 3D PCASL was performed using a 3.0 T MR scanner. ASL was performed twice with different post label delays (PLD). Comparisons of CBF were made between AD patients and healthy control subjects respectively with PLD of 1.5 s and PLD of 2.5 s. Relationship between the CBF values and cognition was investigated using correlation analysis. A receiver operating characteristic (ROC) curve was generated for CBF measurements in posterior cingulate region. RESULT: AD patients with PLD of 1.5 s showed lower CBF values primarily in bilateral temporal lobes, precuneus, middle and posterior cingulate gyri, left inferior parietal gyrus, left angular gyrus and left superior frontal gyrus. Lowered cerebral values were also observed in similar regions with PLD of 2.5 s, but the clusters of voxel were smaller. CBF values were associated with cognition scores in most of gyri mentioned above. CONCLUSION: Hypoperfusion areas were observed in AD patients. PLD of 1.5s was sufficient to display CBF. Considering the complicated AD pathology, multiple PLDs are strongly recommended.

Quantitative assessment and correlation analysis of cerebral microbleed distribution and leukoaraiosis in stroke outpatients.

OBJECTIVE: Cerebral microbleeds (CMBs) are bleeding events associated with cerebral small vessel disease (SVD). Strictly lobar CMBs and strictly deep CMBs are likely caused by cerebral amyloid angiopathy (CAA) and hypertensive arteriopathy, respectively. Leukoaraiosis (LA) reflects an ischaemic change in SVD, and LA severity has been correlated with CMBs. However, whether different locations (aetiologies) of CMBs correlate with LA is unknown. METHODS: Patients receiving brain MRI and tbl2*-weighted gradient-recalled echo scans in a stroke outpatient department were screened for CMBs. The MRI results of the patients with CMBs were sent to investigators for further review and were evaluated using the Microbleed Anatomical Rating Scale. Cerebral microbleed severity was graded using a numerical scale. Leukoaraiosis severity was assessed using the Fazekas scale. RESULTS: Cerebral microbleeds were observed in 14.6% of the 1289 screened patients. The CMB incidence increased with age (in years, < 50: 1.3%; 50-59: 10.7%; 60-69: 17.6% and ≥ 70: 23.6%; P = 0.000). The CMB locations were distributed as follows: 23.4% strictly lobar, 12.2% strictly deep, 6.4% strictly infratentorial and 58.0% mixed. Cerebral microbleed severity correlated with the total Fazekas scale score. The numbers of lobar, deep and infratentorial CMBs correlated with the total Fazekas scale score. The mixed CMB group displayed a significantly higher Fazekas scale score than the strictly lobar CMB group (P = 0.000). DISCUSSION: Cerebral microbleed incidence increased with age. Mixed CMB type displayed the highest incidence. The severity and number of CMBs at any location correlated with LA severity. These results suggested interactions between hypertension and CAA during LA progression.

Contrast-enhanced MR angiography: does a higher relaxivity MR contrast agent permit a reduction of the dose administered for routine vascular imaging applications?

PURPOSE: The authors prospectively compared single dose (0.1 mmol/kg bodyweight) gadobenate dimeglumine with double dose (0.2 mmol/kg bodyweight) gadopentetate dimeglumine for contrast-enhanced magnetic resonance angiography (CE-MRA) in patients with suspected or known steno-occlusive disease of the carotid, renal or peripheral vasculature using an intra-individual crossover study design. MATERIALS AND METHODS: Twenty-eight patients with suspected or known steno-occlusive disease of the carotid (n = 16), renal (n = 5) or peripheral arteries (n = 7) were randomised to receive either 0.1 mmol/kg gadobenate dimeglumine or 0.2 mmol/kg gadopentetate dimeglumine for a first CE-MRA procedure. After 3-5 days all patients underwent a second identical CE-MRA procedure with the other contrast agent. Three blinded readers assessed images for vessel anatomical delineation, disease detection/exclusion, and global preference. Diagnostic performance for detection of ≥51 % stenosis was determined for 20/28 patients who also underwent digital subtraction angiography (DSA). Non-inferiority was assessed using the Wilcoxon signed rank, McNemar and Wald tests. Quantitative (signal-to-noise and contrast-to-noise ratio) enhancement based on 3D maximum intensity projection reconstructions was compared. RESULTS: No differences were noted for any qualitative parameter. Equivalence was reported for all diagnostic preference end-points. Superiority for gadobenate dimeglumine was reported by all readers for sensitivity for disease detection (80.8-86.5 vs. 75.0-82.7 %). Quantitative enhancement was similar for single dose gadobenate dimeglumine and double dose gadopentetate dimeglumine. CONCLUSIONS: Under identical examination conditions a single 0.1 mmol/kg body weight dose of gadobenate dimeglumine can fully replace a double 0.2 mmol/kg body weight dose of gadopentetate dimeglumine for routine CE-MRA procedures.

Prognostic value of coronary artery calcium score in patients with stable angina pectoris after percutaneous coronary intervention.

OBJECTIVES: To evaluate the prognostic value of the coronary artery calcium (CAC) score in patients with stable angina pectoris (SAP) who underwent percutaneous coronary intervention (PCI). METHODS: A total of 334 consecutive patients with SAP who underwent first PCI following multi-slice computer tomography (MSCT) were enrolled from our institution between January 2007 and June 2012. The CAC score was calculated according to the standard Agatston calcium scoring algorithm. Complex PCI was defined as use of high pressure balloon, kissing balloon and/or rotablator. Procedure-related complications included dissection, occlusion, perforation, no/slow flow and emergency coronary artery bypass grafting. Main adverse cardiac events (MACE) were defined as a combined end point of death, non-fatal myocardial infarction, target lesion revascularization and rehospitalization for cardiac ischemic events. RESULTS: Patients with a CAC score > 300 (n = 145) had significantly higher PCI complexity (13.1% vs. 5.8%, P = 0.017) and rate of procedure-related complications (17.2% vs. 7.4%, P = 0.005) than patients with a CAC score ≤ 300 (n = 189). After a median follow-up of 22.5 months (4-72 months), patients with a CAC score ≤ 300 differ greatly than those patients with CAC score > 300 in cumulative non-events survival rates (88.9 vs. 79.0%, Log rank 4.577, P = 0.032). After adjusted for other factors, the risk of MACE was significantly higher [hazard ratio (HR): 4.3, 95% confidence interval (95% CI): 2.4-8.2, P = 0.038] in patients with a CAC score > 300 compared to patients with a lower CAC score. CONCLUSIONS: The CAC score is an independent predictor for MACE in SAP patients who underwent PCI and indicates complexity of PCI and procedure-related complications.