Brain Magnetic Resonance Imaging Data Research Articles

Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks.

Deep learning models for semantic segmentation are able to learn powerful representations for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible topologies. Image registration models on the other hand are able to warp known topologies to target images as a means of segmentation, but typically require large amounts of training data, and have not widely been benchmarked against pixel-wise segmentation models. We propose the Atlas Image-and-Spatial Transformer Network (Atlas-ISTN), a framework that jointly learns segmentation and registration on 2D and 3D image data, and constructs a population-derived atlas in the process. Atlas-ISTN learns to segment multiple structures of interest and to register the constructed atlas labelmap to an intermediate pixel-wise segmentation. Additionally, Atlas-ISTN allows for test time refinement of the model's parameters to optimize the alignment of the atlas labelmap to an intermediate pixel-wise segmentation. This process both mitigates for noise in the target image that can result in spurious pixel-wise predictions, as well as improves upon the one-pass prediction of the model. Benefits of the Atlas-ISTN framework are demonstrated qualitatively and quantitatively on 2D synthetic data and 3D cardiac computed tomography and brain magnetic resonance image data, out-performing both segmentation and registration baseline models. Atlas-ISTN also provides inter-subject correspondence of the structures of interest.

Backdoor Attacks Against Transfer Learning With Pre-Trained Deep Learning Models

Transfer learning provides an effective solution for feasibly and fast customize accurate \textit{Student} models, by transferring the learned knowledge of pre-trained \textit{Teacher} models over large datasets via fine-tuning. Many pre-trained Teacher models used in transfer learning are publicly available and maintained by public platforms, increasing their vulnerability to backdoor attacks. In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: \textit{pruning-based}, \textit{retraining-based} and \textit{input pre-processing-based defenses}. Specifically, (A) ranking-based selection mechanism to speed up the backdoor trigger generation and perturbation process while defeating \textit{pruning-based} and/or \textit{retraining-based defenses}. (B) autoencoder-powered trigger generation is proposed to produce a robust trigger that can defeat the \textit{input pre-processing-based defense}, while guaranteeing that selected neuron(s) can be significantly activated. (C) defense-aware retraining to generate the manipulated model using reverse-engineered model inputs. We launch effective misclassification attacks on Student models over real-world images, brain Magnetic Resonance Imaging (MRI) data and Electrocardiography (ECG) learning systems. The experiments reveal that our enhanced attack can maintain the $98.4\%$ and $97.2\%$ classification accuracy as the genuine model on clean image and time series inputs respectively while improving $27.9\%-100\%$ and $27.1\%-56.1\%$ attack success rate on trojaned image and time series inputs respectively in the presence of pruning-based and/or retraining-based defenses.

Association of a wide range of individual chronic diseases and their multimorbidity with brain volumes in the UK Biobank: A cross-sectional study

SummaryBackgroundLittle is known regarding associations of conventional and emerging diseases and their multimorbidity with brain volumes.MethodsThis cross-sectional study included 36,647 European ancestry individuals aged 44–81 years with brain magnetic resonance imaging data from UK Biobank. Brain volumes were measured between 02 May 2014 and 31 October 2019. General linear regression models were used to associate 57 individual major diseases with brain volumes. Latent class analysis was used to identify multimorbidity patterns. A multimorbidity score for brain volumes was computed based on the estimates for individual groups of diseases.FindingsOut of 57 major diseases, 16 were associated with smaller volumes of total brain, 14 with smaller volumes of grey matter, and six with smaller hippocampus volumes, and four major diseases were associated with higher white matter hyperintensity (WMH) load after adjustment for all other diseases. The leading contributors to the variance of total brain volume were hypertension (R2=0·0229), dyslipidemia (0·0190), cataract (0·0176), coronary heart disease (0·0107), and diabetes (0·0077). We identified six major multimorbidity patterns and multimorbidity patterns of cardiometabolic disorders (CMD), and CMD-multiple disorders, and metabolic disorders were independently associated with smaller volumes of total brain (β (95% CI): -6·6 (-8·9, -4·3) ml, -7·3 (-10·4, -4·1) ml, and -10·4 (-13·5, -7·3) ml, respectively), grey matter (-7·1 (-8·5, -5·7) ml, -9·0 (-10·9, -7·1) ml, and -11·8 (-13·6, -9·9) ml, respectively), and higher WMH load (0·23 (0·19, 0·27), 0·25 (0·19, 0·30), and 0·33 (0·27, 0·39), respectively) after adjustment for geographic, socioeconomic, and lifestyle factors (all P-values<0·0001). The percentage of the variance of total brain volume explained by multimorbidity patterns, multimorbidity defined by the number of diseases, and multimorbidity score was 1·2%, 3·1%, and 7·2%, respectively. Associations between CMD-multiple disorders pattern, and metabolic disorders pattern and volumes of total brain, grey matter, and WMH were stronger in men than in women. Associations between multimorbidity and brain volumes were stronger in younger than in older individuals.InterpretationBesides conventional diseases, we found an association between numerous emerging diseases and smaller brain volumes. CMD-related multimorbidity patterns are associated with smaller brain volumes. Men or younger adults with multimorbidity are more in need of care for promoting brain health. These findings are from an association study and will need confirmation.FundingThe Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075), Science and Technology Program of Guangzhou, China (202,002,020,049).

Construction of a confounder-free clinical MRI dataset in the Mass General Brigham system for classification of Alzheimer's disease

Deep learning has the potential to standardize and automate diagnostics for complex medical imaging data, but real-world clinical images are plagued by a high degree of heterogeneity and confounding factors that may introduce imbalances and biases to such processes. To address this, we developed and applied a data matching algorithm to 467,464 clinical brain magnetic resonance imaging (MRI) data from the Mass General Brigham (MGB) healthcare system for Alzheimer's disease (AD) classification. We identified 18 technical and demographic confounding factors that can be readily distinguished by MRI or have significant correlations with AD status and isolated a training set free from these confounds. We then applied an ensemble of 3D ResNet-50 deep learning models to classify brain MRIs between groups of AD, mild cognitive impairment (MCI), and healthy controls. From a confounder-free matched dataset of 287,367 MRI files, we achieved an area under the receiver operating characteristic (AUROC) of 0.82 in distinguishing healthy controls from patients with AD or MCI. We also showed that confounding factors in heterogeneous clinical data could lead to artificial gains in model performance for disease classification, which our data matching approach could correct. This approach could accelerate using deep learning models for clinical diagnosis and find broad applications in medical image analysis.

Poststroke White Matter Hyperintensities and Physical Activity: A CANVAS Study Exploratory Analysis.

White matter hyperintensities (WMHs) are associated with poststroke cognitive decline and mortality. Physical activity (PA) may decrease WMH risk by reducing vascular risk factors and promoting cerebral perfusion. However, the association between poststroke PA and WMH progression remains unclear. We examined the association between PA and WMH volume 12 months after stroke, and between PA and change in WMH volume between 3 and 12 months after stroke. We included ischemic stroke survivors from the Cognition And Neocortical Volume After Stroke cohort with available brain magnetic resonance imaging and objective PA data. Total, periventricular, and deep WMH volumes (in milliliters) were estimated with manually edited, automated segmentations (Wisconsin White Matter Hyperintensities Segmentation toolbox). Moderate-to-vigorous intensity PA (MVPA) was estimated using the SenseWear® Armband. Participants with MVPA ≥30 min·d -1 were classified as "meeting PA guidelines." We used quantile regression to estimate the associations between PA (MVPA and meeting PA guidelines) with WMH volume at 12 months and change in WMH volume between 3 and 12 months after stroke. A total of 100 participants were included (median National Institutes of Health Stroke Scale 2; interquartile range, 1-4). MVPA was not associated with WMH volume. In univariable analysis, meeting PA guidelines was associated with lower total, periventricular, and deep WMH volumes by 3.0 mL (95% confidence interval (CI), 0.5-9.7 mL), 2.8 mL (95% CI, 0.5-7.1 mL), and 0.9 mL (95% CI, 0.1-3.0 mL), respectively. However, in multivariable analysis, meeting PA guidelines was not associated with WMH volume, and older age was associated with greater WMH volume at 12 months. PA was not associated with change in WMH volume. Meeting PA guidelines was associated with lower WMH volume at 12 months in univariable analysis, but not in multivariable analysis. Age consistently predicted greater WMH volume.

The application of the distributed-order time fractional Bloch model to magnetic resonance imaging

It is now well known that the magnetic resonance imaging (MRI) signal decay deviates from the classical mono-exponential relaxation. This deviation is referred to in the literature as anomalous relaxation. The modelling of this anomalous relaxation can provide a better understanding of MRI magnetization. The purpose of this work is to investigate the utility of the distributed-order time fractional Bloch equations to describe anomalous relaxation processes in human brain MRI data. Two choices of continuous distribution weight functions, which are parameterised by their mean μ and standard deviation σ, are studied to investigate their impact on the model solution behaviour. An implicit numerical method implemented on a graded mesh is proposed to solve the model and the stability and convergence analysis are presented. We also derive semi-analytical solutions of the fully coupled Bloch equations using the Laplace transform technique to assess the accuracy of the numerical scheme. Furthermore, three different voxel models of continuous distribution weight functions, namely a single continuous probability distribution (model 1), two distinct continuous probability distributions (model 2) and a mixture of two continuous probability distributions (model 3), are applied to the in vivo human brain MRI data, and a feasible and reliable parameter estimation method based on a modified hybrid Nelder-Mead simplex search and particle swarm optimization is presented to perform the voxel-level temporal fitting of the MRI data. The application of these distributed-order time fractional Bloch models highlights the validity of the proposed models, and based on the mean square error we conclude that models 2 and 3 might be more suitable than model 1 to characterize anomalous relaxation processes in human brain MRI data.

Antiepileptic drugs in treating structural epilepsy of patients with metastatic brain cancer: a combination approach

Objective: to study the effect of perampanel on the dynamics of epileptic seizures and anxiety in patients with brain metastases and epileptic seizures during chemotherapy (CT), radiotherapy (RT), and radiochemotherapy (RCT).Material and methods. The prospective clinical study included 48 patients with lung cancer and 39 women with breast cancer who underwent removal of primary focus. After CT, RT or RCT epileptic seizures were registered. Brain metastases were revealed at brain magnetic resonance imaging data. All patients underwent general and neurological examination as well as electroencephalography. The level of anxiety was assessed by the Beck Anxiety Inventory. Рatients were prescribed perampanel by the concilium decision.Results. It was shown that administered perampanel was effective in any primary tumor location. The quantity of repeated epileptic seizures in all patients with lung and breast metastatic cancer was significantly decreased on Day 6–7 after adding perampanel to either earlier received antiepileptic drugs or as a monotherapy (p=0.0431). In the follow-up period with the median survival rate of 8–13 months, the frequency of epileptic seizures was decreased and then remained stable. Perampanel as mono- or multitherapy positively influenced on controlling anxiety disorders in 25–75% patients with brain metastases.The level of anxiety was decreased due to lowered epileptic activity.Conclusion. Based on the study results, we could recommend administration of perampanel for the treatment of metastatic brain cancers in patients with diverse primary tumors as well as both partial and generalized epileptic seizures while applying CT, RT, RCT or palliative care.

The relationship between isolated hypertension with brain volumes in UK Biobank.

BackgroundHypertension is a well‐established risk factor for cognitive impairment, brain atrophy, and dementia. However, the relationship of other types of hypertensions, such as isolated hypertension on brain health and its comparison to systolic‐diastolic hypertension (where systolic and diastolic measures are high), is still relatively unknown. Due to its increased prevalence, it is important to investigate the impact of isolated hypertension to help understand its potential impact on cognitive decline and future dementia risk. In this study, we compared a variety of global brain measures between participants with isolated hypertension to those with normal blood pressure (BP) or systolic‐diastolic hypertension using the largest cohort of healthy individuals.MethodsUsing the UK Biobank cohort, we carried out a cross‐sectional study using 29,775 participants (mean age 63 years, 53% female) with BP measurements and brain magnetic resonance imaging (MRI) data. We used linear regression models adjusted for multiple confounders to compare a variety of global, subcortical, and white matter brain measures. We compared participants with either isolated systolic or diastolic hypertension with normotensives and then with participants with systolic‐diastolic hypertension.ResultsThe results showed that participants with isolated systolic or diastolic hypertension taking BP medications had smaller gray matter but larger white matter microstructures and macrostructures compared to normotensives. Isolated systolic hypertensives had larger total gray matter and smaller white matter traits when comparing these regions with participants with systolic‐diastolic hypertension.ConclusionsThese results provide support to investigate possible preventative strategies that target isolated hypertension as well as systolic‐diastolic hypertension to maintain brain health and/or reduce dementia risk earlier in life particularly in white matter regions.

Effect of Tranexamic Acid Administration on Remote Cerebral Ischemic Lesions in Acute Spontaneous Intracerebral Hemorrhage

Hyperintense foci on diffusion-weighted imaging (DWI) that are spatially remote from the acute hematoma occur in 20% of people with acute spontaneous intracerebral hemorrhage (ICH). Tranexamic acid, a hemostatic agent that is under investigation for treating acute ICH, might increase DWI hyperintense lesions (DWIHLs). To establish whether tranexamic acid compared with placebo increased the prevalence or number of remote cerebral DWIHLs within 2 weeks of ICH onset. This prospective nested magnetic resonance imaging (MRI) substudy of a randomized clinical trial (RCT) recruited participants from the multicenter, double-blind, placebo-controlled, phase 3 RCT (Tranexamic Acid for Hyperacute Primary Intracerebral Hemorrhage [TICH-2]) from July 1, 2015, to September 30, 2017, and conducted follow-up to 90 days after participants were randomized to either the tranexamic acid or placebo group. Participants had acute spontaneous ICH and included TICH-2 participants who provided consent to undergo additional MRI scans for the MRI substudy and those who had clinical MRI data that were compatible with the brain MRI protocol of the substudy. Data analyses were performed on an intention-to-treat basis on January 20, 2020. The tranexamic acid group received 1 g in 100-mL intravenous bolus loading dose, followed by 1 g in 250-mL infusion within 8 hours of ICH onset. The placebo group received 0.9% saline within 8 hours of ICH onset. Brain MRI scans, including DWI, were performed within 2 weeks. Prevalence and number of remote DWIHLs were compared between the treatment groups using binary logistic regression adjusted for baseline covariates. A total of 219 participants (mean [SD] age, 65.1 [13.8] years; 126 men [57.5%]) who had brain MRI data were included. Of these participants, 96 (43.8%) were randomized to receive tranexamic acid and 123 (56.2%) were randomized to receive placebo. No baseline differences in demographic characteristics and clinical or imaging features were found between the groups. There was no increase for the tranexamic acid group compared with the placebo group in DWIHL prevalence (20 of 96 [20.8%] vs 28 of 123 [22.8%]; odds ratio [OR], 0.71; 95% CI, 0.33-1.53; P = .39) or mean (SD) number of DWIHLs (1.75 [1.45] vs 1.81 [1.71]; mean difference [MD], -0.08; 95% CI, -0.36 to 0.20; P = .59). In an exploratory analysis, participants who were randomized within 3 hours of ICH onset or those with chronic infarcts appeared less likely to have DWIHLs if they received tranexamic acid. Participants with probable cerebral amyloid angiopathy appeared more likely to have DWIHLs if they received tranexamic acid. This substudy of an RCT found no evidence of increased prevalence or number of remote DWIHLs after tranexamic acid treatment in acute ICH. These findings provide reassurance for ongoing and future trials that tranexamic acid for acute ICH is unlikely to induce cerebral ischemic events. isrctn.org Identifier: ISRCTN93732214.

Temporal Progression Patterns of Brain Atrophy in Corticobasal Syndrome and Progressive Supranuclear Palsy Revealed by Subtype and Stage Inference (SuStaIn).

Differentiating corticobasal degeneration presenting with corticobasal syndrome (CBD-CBS) from progressive supranuclear palsy with Richardson's syndrome (PSP-RS), particularly in early stages, is often challenging because the neurodegenerative conditions closely overlap in terms of clinical presentation and pathology. Although volumetry using brain magnetic resonance imaging (MRI) has been studied in patients with CBS and PSP-RS, studies assessing the progression of brain atrophy are limited. Therefore, we aimed to reveal the difference in the temporal progression patterns of brain atrophy between patients with CBS and those with PSP-RS purely based on cross-sectional data using Subtype and Stage Inference (SuStaIn)—a novel, unsupervised machine learning technique that integrates clustering and disease progression modeling. We applied SuStaIn to the cross-sectional regional brain volumes of 25 patients with CBS, 39 patients with typical PSP-RS, and 50 healthy controls to estimate the two disease subtypes and trajectories of CBS and PSP-RS, which have distinct atrophy patterns. The progression model and classification accuracy of CBS and PSP-RS were compared with those of previous studies to evaluate the performance of SuStaIn. SuStaIn identified distinct temporal progression patterns of brain atrophy for CBS and PSP-RS, which were largely consistent with previous evidence, with high reproducibility (99.7%) under cross-validation. We classified these diseases with high accuracy (0.875) and sensitivity (0.680 and 1.000, respectively) based on cross-sectional structural brain MRI data; the accuracy was higher than that reported in previous studies. Moreover, SuStaIn stage correctly reflected disease severity without the label of disease stage, such as disease duration. Furthermore, SuStaIn also showed the genialized performance of differentiation and reflection for CBS and PSP-RS. Thus, SuStaIn has potential for improving our understanding of disease mechanisms, accurately stratifying patients, and providing prognoses for patients with CBS and PSP-RS.

Brain regional homogeneity changes after short-term positive airway pressure treatment in patients with obstructive sleep apnea

Patients with obstructive sleep apnea (OSA) reveal functional changes in brain sites involved in autonomic, cognitive, and mood regulations. However, it is unclear whether these brain changes reverse with short-term positive airway pressure (PAP) treatment. Our aim was to examine brain functional changes in response to 3-months of PAP treatment using regional homogeneity (ReHo) measures, where increased and decreased ReHo value indicates hyper- and hypo-local neural activities, respectively, and considered as functional deficits. We collected brain magnetic resonance imaging data as well as mood, cognitive, and sleep variables from 17 treatment-naïve OSA at baseline and after 3-months of PAP treatment and 25 age- and gender-matched healthy controls. Whole-brain ReHo maps were calculated and compared between OSA and controls and OSA subjects before and after PAP treatment. At baseline, treatment-naïve OSA subjects showed higher ReHo in the bilateral thalamus, putamen, postcentral gyrus, paracentral lobule, supplementary motor area, and right insula, and lower ReHo in the frontal and parietal cortices, compared to controls. After 3-months of PAP treatment, abnormal sleep and mood scores decreased significantly to normal levels. ReHo decreased in the autonomic and somatosensory control areas, including the thalamus, putamen, postcentral gyrus, and insula, and increased in the cognitive and affective regulatory parietal regions. The normalized ReHo was correlated with improved sleep quality and reduced anxiety symptoms. These findings suggest that 3-months of PAP use can improve sleep, mood issues, and partly recover brain activities, however, longer PAP treatment may be required to fully and permanently reverse brain functional deficits.

Virtual mouse brain histology from multi-contrast MRI via deep learning.

1H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from magnetic resonance imaging (MRI) findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we show that deep convolutional neural networks, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimic target histology and enhanced sensitivity and specificity compared to conventional MRI markers. Furthermore, the relative contribution of each MRI contrast within the networks can be used to optimize multi-contrast MRI acquisition. We anticipate our method to be a starting point for translation of MRI results into easy-to-understand virtual histology for neurobiologists and provide resources for validating novel MRI techniques.

Foetal Brain Biometry Using Magnetic Resonance Imaging in an Indian PopulationA Retrospective Cohort Study

Introduction: The utility of Magnetic Resonance Imaging (MRI) has increased significantly in the assessment of foetal brain development. Foetal biometry is a part of every imaging examination and is needed to confirm that the growth is proceeding normally. Aim: To provide normal MRI reference biometric data of the foetal brain in Indian population. Materials and Methods: This was a retrospective cohort study conducted in the Department of Radiodiagnosis at Sri Ramachandra Hospital, Chennai, Tamil Nadu, India. The data was collected from the MR imaging studies done between January 2013 to December 2020. The present study was carried out on 101 foetuses with gestational age ranging from 20 to 38 gestational weeks with normal brain appearance. Following biometric parameters had been obtained: skull occipitofrontal diameter and Biparietal Diameter (BPD), Brain-Biparietal Diameter (BPD) and fronto-occipital length, Head Circumference (HC), atrial diameter, vermian height, width and area. Descriptive statistics was used to calculate the median value with Interquartile Range (IQR). A p-value of less than 0.05 was considered as significant using Spearman’s rank correlation. Results: The skull occipitofrontal diameter (r-value=0.88, p-value &lt;0.001), skull biparietal diameter (r-value=0.92, p-value &lt;0.001), brain biparietal diameter (r-value=0.95, p-value &lt;0.001), brain fronto-occipital length (r=0.94, p-value &lt;0.001), head circumference (r-value=0.92, p-value &lt;0.001), atrial diameter (r-value=0.86, p-value &lt;0.001), Vermian height (r-value=0.86, p-value &lt;0.001), Vermian width (r-value=0.84, p-value &lt;0.001) and Vermian area (r-value=0.88, p-value &lt;0.001) showed positive correlation with gestational age. Mean Head Circumference (HC) and BPD values were almost similar to the standard Indian reference mean values of ultrasound. All biometric parameters were compared with the European references. The values from present study were found to be in the lower range of the European biometric values. Conclusion: Normal biometric MRI data of the foetal brain in an Indian population from 20 to 38 weeks gestational age was presented. A significant correlation was found between gestational age and parameters. MRI BPD and HC values showed a positive correlation with ultrasound BPD and HC values.

BMAT: An open-source BIDS managing and analysis tool

Magnetic Resonance Imaging (MRI) is an established technique to study in vivo neurological disorders such as Multiple Sclerosis (MS). To avoid errors on MRI data organization and automated processing, a standard called Brain Imaging Data Structure (BIDS) has been recently proposed. The BIDS standard eases data sharing and processing within or between centers by providing guidelines for their description and organization. However, the transformation from the complex unstructured non-open file data formats coming directly from the MRI scanner to a correct BIDS structure can be cumbersome and time consuming. This hinders a wider adoption of the BIDS format across different study centers. To solve this problem and ease the day-to-day use of BIDS for the neuroimaging scientific community, we present the BIDS Managing and Analysis Tool (BMAT). The BMAT software is a complete and easy-to-use local open-source neuroimaging analysis tool with a graphical user interface (GUI) that uses the BIDS format to organize and process brain MRI data for MS imaging research studies. BMAT provides the possibility to translate data from MRI scanners to the BIDS structure, create and manage BIDS datasets as well as develop and run automated processing pipelines, and is faster than its competitor. BMAT software propose the possibility to download useful analysis apps, especially applied to MS research with lesion segmentation and processing of imaging contrasts for novel disease biomarkers such as the central vein sign and the paramagnetic rim lesions.

An Efficient Hybrid Clustering and Feature Extraction Techniques for Brain Tumor Classification

Most aggressive and common disease is Brain tumors and it leads to very short life expectancy in its highest grade. For proper treatment, such tumors needs to be identified in early stages and detecting brain tumors, medical imaging is used as an important tool. Although, for diagnosing such tumors, MRI (Magnetic Resonance Imaging) is used very often and it is assumed as a highly suitable technique. From brain magnetic resonance imaging (MRI) data, edema and tumor inference is a challenging task due to brain tumors blurred boundaries, complex structure and external factors like noise. For alleviating noise sensitivity and enhancing segmentation stability, a hybrid clustering algorithm is proposed in this research work. Certain processes like classification, feature extraction, hybrid clustering and pre-processing are included in this proposed model. For segmentation of brain tumors, proposed a morphological operation. Skull stripping and contrast enhancement are two process performed in pre-processing stage. It is possible to detect high contrast regions under contrast enhancement. In second stage, Enhanced K- means algorithm is combined with Fuzzy C- Means Clustering (FCM), where images are segmented as clusters. Algorithm’s stability can be enhanced using this clustering techniques while minimizing clustering parameter’s sensitivity. Segmented objects are converted into representations using representation and feature extraction techniques. Major attributes and features are described in a better manner using these techniques. The Fast Discrete Curvelet Transform (FDCT) is used for performing feature extraction in this technique for minimizing complexity and enhancing performance. At last, for classification, deep belief network (DBN) is used in this work. And it uses the concept of optimized DBN, for which Improved dragonfly optimisation algorithm (IDOA) is utilized. This proposed model is termed as IDOA-DBN model. When compared with other classification techniques, brain tumors can be detected effectively using proposed model.

3D U-Net Improves Automatic Brain Extraction for Isotropic Rat Brain Magnetic Resonance Imaging Data.

Brain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation, Pulse-Coupled Neural Network, SHape descriptor selected External Regions after Morphologically filtering, and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, center-of-mass distance, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community.Significant Methodological Contribution: We proposed a deep-learning-based framework to automatically identify the rodent brain boundaries in MRI. With a fully 3D convolutional network model, 3D U-Net, our proposed method demonstrated improved performance compared to current automatic brain extraction methods, as shown in several qualitative metrics (Dice, Jaccard, PPV, SEN, and Hausdorff). We trust that this tool will avoid human bias and streamline pre-processing steps during 3D high resolution rodent brain MRI data analysis. The software developed herein has been disseminated freely to the community.

White Matter Hyperintensities as a Risk Factor for Ischemic Stroke in Patients With Systemic Lupus Erythematosus.

Objective: The occurrence of ischemic stroke in patients with systemic lupus erythematosus (SLE) can cause extended periods of reduced daily activities. However, the risk factors for ischemic stroke in SLE patients are not fully elucidated. Herein, we examined the effect of white matter hyperintensities (WMH) on the occurrence of ischemic stroke in SLE patients.Methods: We analyzed the relationship between WMH burden and ischemic stroke using follow-up brain magnetic resonance imaging (MRI) data of 79 patients with SLE. Of these patients, 16 developed stroke during the observation period. WMH on MRI were classified into periventricular hyperintensities and deep white matter hyperintensities (DWMH), while the lesion extent was graded using the Fazekas scale.Results: Kaplan–Meier curves showed that ischemic stroke events were significantly associated with age at initial brain MRI of ≥40 years (p = 0.015) and history of anti-phospholipid syndrome (p = 0.030). Additionally, ischemic stroke events were significantly associated with a one grade deterioration of periventricular hyperintensities (p = 0.003) and a one grade deterioration of DWMH (p = 0.002). Multivariate analysis using the logistic regression model showed that a one grade deterioration of DWMH was an independent risk factor for ischemic stroke (hazard ratio, 6.0; 95% confidence interval, 1.3–27.4).Conclusions: Although several factors affect the occurrence of ischemic stroke, SLE patients show increased risk of ischemic stroke via development of DWMH. An observation of DWMH deterioration on follow-up brain MRI may be useful for assessing the risk of ischemic stroke in SLE patients.

Automatic Diagnosis of Alzheimer’s Disease Using Deep Learning Techniques

Neuroimaging data analysis has attracted a great deal of attention with respect to the accurate diagnosis of Alzheimer’s disease (AD). Magnetic Resonance Imaging (MRI) scanners have thus been commonly used to study AD-related brain structural variations, providing images that demonstrate both morphometric and anatomical changes in the human brain. Deep learning algorithms have already been effectively exploited in other medical image processing applications to identify features and recognise patterns for many diseases that affect the brain and other organs; this paper extends on this to describe a novel computer aided software pipeline for the classification and early diagnosis of AD. The proposed method uses two types of three-dimensional Convolutional Neural Networks (3D CNN) to facilitate brain MRI data analysis and automatic feature extraction and classification, so that pre-processing and post-processing are utilised to normalise the MRI data and facilitate pattern recognition. The experimental results show that the proposed approach achieves 97.5%, 82.5%, and 83.75% accuracy in terms of binary classification AD vs. cognitively normal (CN), CN vs. mild cognitive impairment (MCI) and MCI vs. AD, respectively, as well as 85% accuracy for multi class-classification, based on publicly available data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI).

Subcortical-cortical functional connectivity as a potential biomarker for identifying patients with functional dyspepsia.

The diagnosis of functional dyspepsia (FD) presently relies on the self-reported symptoms. This study aimed to determine the potential of functional brain network features as biomarkers for the identification of FD patients. Firstly, the functional brain Magnetic Resonance Imaging data were collected from 100 FD patients and 100 healthy subjects, and the functional brain network features were extracted by the independent component analysis. Then, a support vector machine classifier was established based on these functional brain network features to discriminate FD patients from healthy subjects. Features that contributed substantially to the classification were finally identified as the classifying features. The results demonstrated that the classifier performed pretty well in discriminating FD patients. Namely, the accuracy of classification was 0.84 ± 0.03 in cross-validation set and 0.80 ± 0.07 in independent test set, respectively. A total of 15 connections between the subcortical nucleus (the thalamus and caudate) and sensorimotor cortex, parahippocampus, orbitofrontal cortex were finally determined as the classifying features. Furthermore, the results of cross-brain atlas validation showed that these classifying features were quite robust in the identification of FD patients. In summary, the current findings suggested the potential of using machine learning method and functional brain network biomarkers to identify FD patients.

Abstract 11878: Atrial Fibrillation is Correlated With Brain Atrophy in Regions Associated With Alzheimer Disease

Introduction: Atrial fibrillation (AF) has been associated with neurocognitive impairment. The broad effects of AF on the brain, however, remain to be clarified. We investigated using data from the UK Biobank (UKB) whether an automated electrocardiography (ECG) diagnosis of AF or atrial flutter (AFL) is associated with imaging-based markers of brain atrophy, including volume, thickness, and white matter microstructure, in brain regions associated with Alzheimer Disease (AD). Methods: We studied a subset of 48,705 UKB study participants with available brain magnetic resonance imaging (MRI) and cognitive function test data. AF/AFL were determined by either automatic ECG interpretation or history by self-reported and hospital-visit derived data. Participants were classified into four groups: 1) no history or ECG detection of AF/AFL; 2) history of AF/AFL but no AF/AFL on ECG; 3) no history of AF/AFL but AF/AFL on ECG; and 4) history of AF/AFL and AF/AFL on ECG. The following brain MRI measurements were obtained: mean bilateral hippocampal volume, fornix fractional anisotropy (FA), and an AD cortical thickness signature. The AD cortical thickness signature was computed as the average of thickness values in various frontal and temporal cortices involved in AD progression. Results: A total of 17,723 participants aged 46-81 (mean age 64 years, 52.2% female) with group sizes of 17182, 307, 100, and 134 (groups 1-4 respectively) were included. Compared to participants with no AF/AFL (group 1), hippocampal volume was significantly smaller in the two groups with AF/AFL on ECG (groups 3 and 4; 97.5 and 95.8 mm 3 smaller; p&lt;0.005). No difference in the volume was found between groups 1 and 2. This pattern of group differences was similar for both fornix FA and the AD cortical thickness signature. A post-hoc analysis for the cognitive function variables showed that scores on the matrix pattern and symbol digit substitution cognitive tests were lower for participants with AF/AFL on ECG (groups 3+4) than those without AF/AFL (groups 1), each with a beta estimate of -0.16 standard deviations (p&lt;0.05). Conclusion: AF/AFL on ECG at time of brain imaging, but not AF/AFL history, is associated with brain atrophy and disrupted white matter microstructure in AD-associated regions.