Identifying brain regions contributing to Alzheimer's disease using self regulating particle swarm optimization

In this article, we developed an approach for detecting brain regions that contribute to Alzheimer's disease (AD) using support vector machine (SVM) classifiers and the recently developed self regulating particle swarm optimization (SRPSO) algorithm. SRPSO employs strategies inspired by the principles of learning in humans to achieve faster and better optimization results. The classifiers for distinguishing subjects into AD patients and cognitively normal (CN) individuals were built using grey matter (GM) and white matter (WM) volumetric features extracted from structural magnetic resonance (MR) images. It could be observed from results that the classifier built using both GM and WM features provided accuracy of 89.26% which is better than the performance of classifiers built using either GM or WM features only. Moreover, consideration of clinical features in addition to volumetric features improves the accuracy further to 94.63% which is better than the performance reported by recent works in literature. In order to identify the brain regions that are important for AD vs CN classification problem, we used SRPSO to extract GM and WM features that yield better classification performance. Using 50 features identified by SRPSO, an accuracy of 89.39% was obtained which is close to the accuracy based on all features. The features identified by SRPSO were mapped back to the brain to identify brain regions that exhibit degeneration in AD. In addition to identifying areas known to be involved in AD like cerebellum, hippocampus, this helped in finding newer areas that might contribute towards AD.     

3d discrete wavelet transform for computer aided diagnosis of Alzheimer's disease using t1‐weighted brain MRI

Early and antemortem diagnosis of Alzheimer's disease (AD) may help in the development of appropriate treatment and in slowing down the disease progression. In this work, a three‐phase computer aided approach is suggested for classification of AD patients and controls using T1‐weighted MRI. In the first phase, smoothed modulated gray matter (GM) probability maps are obtained from T1‐weighted MRIs. In the second phase, 3D discrete wavelet transform is applied on GM of five brain regions, which are well‐documented regions affected in AD, to construct features. In the third phase, a minimal set of relevant and nonredundant features are obtained using Fisher's discriminant ratio and minimum redundancy maximum relevance feature selection methods. To check the efficacy of the proposed approach, experiments were carried out on three datasets derived from the publicly available OASIS database, using three commonly used classifiers. The performance of the proposed approach was evaluated using three performance measures namely sensitivity, specificity and classification accuracy. Further, the proposed approach was compared with the existing state‐of‐the‐art techniques in terms of three performance measures, ROC curves, scoring and computation time. Irrespective of the datasets and the classifiers, the proposed method outperformed the existing methods. In addition, the statistical test also demonstrated that the proposed method is significantly better in comparison to the other existing methods. The appreciable performance of the proposed method supports that it will assist clinicians/researchers in the classification of AD patients and controls.     

Preventing Alzheimer's disease by means of natural selection

The amyloid cascade model for the origin of sporadic forms of Alzheimer''s disease (AD) posits that the imbalance in the production and clearance of beta-amyloid is a necessary condition for the disease. A competing theory called the entropic selection hypothesis asserts that the primary cause of sporadic AD is age-induced mitochondrial dysregulation and the following cascade of events: (i) metabolic reprogramming—the upregulation of oxidative phosphorylation in compensation for insufficient energy production in neurons, (ii) natural selection—competition between intact and reprogrammed neurons for energy substrates and (iii) propagation—the spread of the disease due to the selective advantage of neurons with upregulated metabolism. Experimental studies to evaluate the predictions of the amyloid cascade model are being continually retuned to accommodate conflicts of the predictions with empirical data. Clinical trials of treatments for AD based on anti-amyloid therapy have been unsuccessful. We contend that these anomalies and failures stem from a fundamental deficit of the amyloid hypothesis: the model derives from a nuclear-genomic perspective of sporadic AD and discounts the bioenergetic processes that characterize the progression of most age-related disorders. In this article, we review the anomalies of the amyloid model and the theoretical and empirical support for the entropic selection theory. We also discuss the new therapeutic strategies based on natural selection which the model proposes.     

Classification of mammogram images using the energy probability in frequency domain and most discriminative power coefficients

The purpose of this work is to develop a computer-aided diagnosis (CAD) system to assist radiologists in the classification of mammogram images. The CAD system is composed of three main steps. The first step is image preprocessing and segmentation with the seeded region growing algorithm applied on a localized triangular region to remove only the muscle. In the second step of the CAD system, we proposed a novel features extraction method, which consists of three stages. In the first, the discrete cosine transform (DCT) is applied on all obtained regions of interest and then only the upper left corner (ULC) of DCT coefficients is retained. Second, we have applied the energy probability to the ULCs that is used as a criterion for selecting discriminant information. At the last stage, a new Most Discriminative power coefficient algorithm has been proposed to select the most significant features. In the final step of the CAD, the support vector machines, Naive Bayes, and artificial neural network (ANN) classifiers are used to make an effective classification. The evaluation of the proposed algorithm on the mini-Mammographic Image Analysis Society database shows its efficiency over other recently proposed CAD systems in the literature, whereas an accuracy of 100% can be achieved using ANN with a small number of features.     

Alzheimer's disease: rapid and slow progression

The variability in the progression of Alzheimer''s disease (AD) across patients has made identification of disease-delaying treatments difficult. Quantitative analysis of this variability has important implications in understanding the pathophysiology of AD and identifying disease-delaying treatments. The functional assessment staging (FAST) procedure characterizes seven stages in the course of AD from normal ageing to severe dementia. The present study applied statistical methods to analyse FAST stage durations from a dataset of 648 AD patients. These methods uncovered two distinct types of disease progression, characterized by different mean progression rates. We identified two separate distributions of FAST stage progression times differing by up to 2 years in mean duration within each stage. These results further indicate that if a patient progresses rapidly through a given FAST stage, then their further progression is also likely to be rapid. These findings support the hypothesis that progression of AD can occur via two different pathophysiological mechanisms that lead to distinct average rates of decline.     

Constructing and characterizing a bioactive small molecule and microRNA association network for Alzheimer's disease

Alzheimer''s disease (AD) is an incurable neurodegenerative disorder. Much effort has been devoted to developing effective therapeutic agents. Recently, targeting microRNAs (miRNAs) with small molecules has become a novel therapy for human diseases. In this study, we present a systematic computational approach to construct a bioactive Small molecule and miRNA association Network in AD (SmiRN-AD), which is based on the gene expression signatures of bioactive small molecule perturbation and AD-related miRNA regulation. We also performed topological and functional analysis of the SmiRN-AD from multiple perspectives. At the significance level of p ≤ 0.01, 496 small molecule–miRNA associations, including 25 AD-related miRNAs and 275 small molecules, were recognized and used to construct the SmiRN-AD. The drugs that were connected with the same miRNA tended to share common drug targets (p = 1.72 × 10⁻⁴) and belong to the same therapeutic category (p = 4.22 × 10⁻⁸). The miRNAs that were linked to the same small molecule regulated more common miRNA targets (p = 6.07 × 10⁻³). Further analysis of the positive connections (quinostatin and miR-148b, amantadine and miR-15a) and the negative connections (melatonin and miR-30e-5p) indicated that our large-scale predictions afforded specific biological insights into AD pathogenesis and therapy. This study proposes a holistic strategy for deciphering the associations between small molecules and miRNAs in AD, which may be helpful for developing a novel effective miRNA-associated therapeutic strategy for AD. A comprehensive database for the SmiRN-AD and the differential expression patterns of the miRNA targets in AD is freely available at http://bioinfo.hrbmu.edu.cn/SmiRN-AD/.     

Effects of nicergoline treatment on regional cerebral blood flow in early Alzheimer's disease

Alzheimer's disease (AD) is associated with reduction of regional cerebral blood flow (rCBF) in various brain regions. Although nicergoline has been used to treat cognitive impairment in various types of dementia, the effect of nicergoline on brain perfusion in AD has not been elucidated. The aim of the study was to examine the effect of nicergoline on rCBF in patients with early AD using technetium-99m hexamethylpropylene amine oxime single-photon emission computed tomography (SPECT). For 1.7 years on average, 22 patients with early AD received nicergoline (30 mg twice daily) and acetylcholinesterase inhibitors (AChEI), whereas other 22 patients received AChEI only. All patients underwent assessment of dementia severity and SPECT scans at baseline and follow-up. We compared baseline rCBF and annual percent change of rCBF between the two groups. There were no significant differences in rCBF between the groups at baseline. Although changes in dementia severity was not statistically different, the annual percent changes of rCBF were higher in the left temporal pole (−0.12 ± 3.95 vs −3.38 ± 3.30, P = 0.002) and middle cingulate gyrus (1.91 ± 3.58 vs −1.34 ± 3.17, P = 0.002) among the nicergoline group. Our study demonstrated that nicergoline treatment combined with AChEI either better preserves or further improves cerebral perfusion in the temporal and cingulate cortices in patients with early AD compared to AChEI treatment alone. These results may indicate potential beneficial effects of nicergoline on brain functions in AD.     

An improved fast marching method and its application in Alzheimer's disease

Magnetic resonance diffusion tensor imaging (DTI) provides a noninvasive approach to characterize the fiber pathways in the human brain. Among the fiber tractography algorithms in DTI analysis, the fast marching (FM) method has been widely used in quantitatively analyzing the structural connectivity of the fibers and their changes. However, standard FM only considers the similarity and the principal direction information conveyed by two neighboring voxels. It may have poor tracking performance when image noise and fiber crossing are present. To solve this problem, we introduced an improved FM method employing a memory factor (MFFM) to better characterize the directionality of fiber propagation. Simulation showed that MFFM yields higher tracking accuracy, lower computational load, and better antinoise/crossing performance compared with standard FM. Finally, we applied MFFM to Alzheimer's disease (AD) DTI data to explore the impaired regional connectivity of fiber structure. The results augment the knowledge of the pathological alteration of white matter in AD. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 346–352, 2013     

Efficacy of an NMDA receptor antagonist for Parkinson's disease dementia: A brain perfusion SPECT study

Memantine's efficacy has been demonstrated in patients with moderate to severe Alzheimer's disease. However, memantine therapy is not typically used in treating Parkinson's disease dementia (PDD), although the deviating glutamatergic pattern evidenced in neuropathological findings suggests that N‐methyl D‐aspartate receptor antagonists in patients with Lewy‐body disorder may be effective. Therefore, we conducted this longitudinal study to confirm the efficacy of memantine for PDD by analyzing comparative changes in regional cerebral blood flow (rCBF) and neuropsychological tests before and after memantine administration in PDD. A total of 18 patients with PDD were enrolled in this study. All patients underwent technetium‐99m hexamethylpropyleneamine oxime single photon emission computed tomography and completed neuropsychological tests before and 12 months after memantine administration. This study showed that there was no significant difference in cognitive functioning before and after memantine administration. No significant difference in rCBF was observed in any regions at baseline compared to 12 months after memantine administration. Our results suggest that memantine delays the speed of deterioration in cognitive functioning and behavioral symptoms in PDD because we found no significant changes in rCBF and cognition after memantine treatment.     

Neuroimaging computer-aided diagnosis systems for Alzheimer's disease

This paper has reviewed the state-of-the-art approaches for Computer Aided Diagnosis Systems (CADS) for Alzheimer's Disease (AD) using neuroimaging. Identification of the current approaches leads to improving the efficiency of these techniques. The analysis covered 110 articles published between 2009 and January 2018. Papers were chosen according to the Newcastle-Ottawa criteria. MeSH terms were “computer aided diagnosis systems for Alzheimer's disease” and “computer aided diagnosis systems methods for diagnosis of Alzheimer's disease”. CADS algorithms have been presented with specific methods. There is no standardized approach to determine the best one. This study has tables that aimed to conclude all methods in a precise way. Among them, Statistical Parametric Mapping (SPM), Principal Component Analysis (PCA), and Support Vector Machine (SVM) were the most common, respectively. CADS for AD could become important in clinical practice in the near future. The evaluation criteria approved their efficiency as a second opinion besides the neurologist.     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

The standard reference clinical score quantifying average Parkinson''s disease (PD) symptom severity is the Unified Parkinson''s Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient''s ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient''s physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians'' estimates, p < 0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.     

Remission of refractory Crohn's disease with anti-IL-12/23 therapy in a patient undergoing hemodialysis: A case report

Crohn's disease is a relapsing chronic inflammatory condition of the intestine with increasing prevalence around the world. Biologic therapies are currently widely used and have proved safe and effective in treating moderate to severe Crohn's disease. However, contemporary bibliography contains little information about the use of these drugs in patients with end-stage renal disease undergoing hemodialysis. Here we present a case of a 47-year-old female patient with treatment-refractory Crohn's disease on hemodialysis. In this patient, treatment with the anti-IL-12/23 receptor antibody ustekinumab was effective in inducing and maintaining remission while being safe in administering throughout hemodialysis.     

Cerebrospinal fluid dynamics in the human cranial subarachnoid space: an overlooked mediator of cerebral disease. I. Computational model

Abnormal cerebrospinal fluid (CSF) flow is suspected to be a contributor to the pathogenesis of neurodegenerative diseases such as Alzheimer''s through the accumulation of toxic metabolites, and to the malfunction of intracranial pressure regulation, possibly through disruption of neuroendocrine communication. For the understanding of transport processes involved in either, knowledge of in vivo CSF dynamics is important. We present a three-dimensional, transient, subject-specific computational analysis of CSF flow in the human cranial subarachnoid space (SAS) based on in vivo magnetic resonance imaging. We observed large variations in the spatial distribution of flow velocities with a temporal peak of 5 cm s⁻¹ in the anterior SAS and less than 4 mm s⁻¹ in the superior part. This could reflect dissimilar flushing requirements of brain areas that may show differences in susceptibility to pathological CSF flow. Our methods can be used to compare the transport of metabolites and neuroendocrine substances in healthy and diseased brains.     

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.     

防治老年人阿尔茨海默症的情感关怀交互产品设计研究

目的 基于对老年阿尔茨海默症患者的调查与分析,提出运用情感关怀交互产品的设计思维和方法,设计针对老年阿尔茨海默症患者家庭陪护的情感关怀产品系统。方法 在进行文献调研和实地调研的基础上,结合交互设计在形式、内容、行为3个层次对情感交互的设计要求和侧重,进行产品设计的探讨。结论 提出了将音乐疗法与敲击游戏、脑力训练与日常就餐、专业指导与日常生活相结合的设计需求,并在实物产品和移动应用两个方面研究其情感交互的组成及创新。     

基于多感官交互的阿尔茨海默老人智能穿戴产品设计研究

目的 研究听觉、触觉与视觉多感官刺激因素介入阿尔茨海默病老人护理的反馈效果,借助智能软硬件技术增强其对音乐的感知,设计多感官交互的老人智能穿戴产品。方法 根据感知替换及通感原理,创建能够将音乐转化为振动及视觉信息的感官替代方法,并通过实验测试分别验证听–触觉和听–触–视觉两类多感官交互原型的可行性。结果 在融合听–触觉的基础上加入视觉设计,可以更好地激发老人的言语行为能力。在收集被试者使用反馈分析结论的基础上设计了一款指尖振动音乐可穿戴设备——指尖钢琴,通过提供以触觉、听觉、视觉融合的多感官体验来促进患者的主动行为。结论 多感官交互产品的设计对改善阿尔茨海默病患者的情绪和主动行为具有一定的效果,对老年患者身体健康和生活质量的提高具有积极意义。     

防治老年人阿尔茨海默症的益智关怀设计研

目的 结合对老年人阿尔茨海默症的调查研究、遵循适老设计原则,针对老年人防治该病设计一系列益智关怀产品。方法 通过调研法总结老年人生理、心理状态、致病因素以及生活习惯等,结合信息技术同时考虑易用性、安全性、趣味性等设计原则,对产品进行设计。结论 该设计从提供脑力锻炼方式、掌握阿尔茨海默症的前沿资讯、收集个人脑力健康数据几方面考虑,基于这几方面,该设计是一款带有训练游戏、实时资讯、脑力健康数据收集的智能APP,以及延伸出的适用于家里或社区活动中心的相关训练游戏实物产品。