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.     

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.     

Contribution of neuroimaging in the diagnosis of brain disorders: Recent findings and future applications

Brain disorders including neurological and psychiatric disorders have become a major global public health issue due to their high prevalence and burden. However, elucidating ultimate causes and improving strategies for diagnosis and treatment of these disorders remain challenging due to limited accessibility to living human brain. In addition, there has been a great need for robust biomarkers for them at a very early stage. Neuroimaging technologies have demonstrated the potential in investigating the pathophysiology and developing the biomarkers. The current review discusses the potential diagnostic applications of neuroimaging in brain disorders. We summarized major findings in recent neuroimaging meta‐analyses, which could be used as future biomarkers, in Alzheimer's disease, Parkinson's disease, central nervous system inflammation, depression, and schizophrenia. New possibilities of novel imaging techniques were also demonstrated. Finally, future directions for the imaging‐based diagnosis were suggested. In spite of promising results from preliminary studies and rapid technological advances, further studies on the reliability and validity of potential imaging biomarkers in larger patient populations and the development of new guidelines for the clinical applications would be required.     

Recent developments in frequency domain multi-axial fatigue analysis

The purpose of this paper is to discuss the recent developments in multi-axial spectral methods, used for estimating fatigue damage of multi-axial random loadings from Power Spectral Density (PSD) data. The difference between time domain and frequency domain approaches in multi-axial fatigue is first addressed, the main advantages of frequency domain approach being pointed out. The paper then critically reviews some categories of multi-axial spectral methods: approaches based on uniaxial equivalent stress (strength criteria, “equivalent von Mises stress”, multi-axial rainflow counting), critical plane criteria (Matake, Carpinteri-Spagnoli, criterion based on resolved shear stress on critical plane), stress-invariants based criteria (Crossland, Sines, “Projection-by-Projection”). The “maximum variance” method and the Minimum Circumscribed Circle/Ellipse formulations defined in the frequency domain are also discussed. The paper critically analyses also non-proportional multi-axial loadings and the role of material fatigue parameters (e.g. S/N curves for bending/torsion) in relation to specific methods. The paper concludes with general comments on advantages and possible limitations in the use of multi-axial spectral methods, with special focus on the assumption of stationarity and Gaussianity in modelling multi-axial random loadings.     

A multi‐modal,multi‐atlas‐based approach for Alzheimer detection via machine learning

The use of biomarkers for early detection of Alzheimer's disease (AD) improves the accuracy of imaging‐based prediction of AD and its prodromal stage that is mild cognitive impairment (MCI). Brain parcellation‐based computer‐aided methods for detecting AD and MCI segregate the brain in different anatomical regions and use their features to predict AD and MCI. Brain parcellation generally is carried out based on existing anatomical atlas templates, which vary in the boundaries and number of anatomical regions. This works considers dividing the brain based on different atlases and combining the features extracted from these anatomical parcellations for a more holistic and robust representation. We collected data from the ADNI database and divided brains based on two well‐known atlases: LONI Probabilistic Brain Atlas (LPBA40) and Automated Anatomical Labeling (AAL). We used baselines images of structural magnetic resonance imaging (MRI) and ¹⁸F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) to calculate average gray‐matter density and average relative cerebral metabolic rate for glucose in each region. Later, we classified AD, MCI and cognitively normal (CN) subjects using the individual features extracted from each atlas template and the combined features of both atlases. We reduced the dimensionality of individual and combined features using principal component analysis, and used support vector machines for classification. We also ranked features mostly involved in classification to determine the importance of brain regions for accurately classifying the subjects. Results demonstrated that features calculated from multiple atlases lead to improved performance compared to those extracted from one atlas only.     

Alzheimer's detection using various feature extraction approaches using a multimodal multi-class deep learning model

Alzheimer's disease is a chronic brain condition that takes a toll on memory and potential to do even the most basic tasks. With no specific solution viable at this time, it's critical to pinpoint the start of Alzheimer's disease so that necessary steps may be initiated to limit its progression. We used three distinct neuroanatomical computational methodologies namely 3D-Subject, 3D-Patches, and 3D-Slices to construct a multimodal multi-class deep learning model for three class and two class Alzheimer's classification using T1w-MRI and AV-45 PET scans obtained from ADNI database. Further, patches of various sizes were created using the patch-extraction algorithm designed with torch package leading to separate datasets of patch size 32, 40, 48, 56, 64, 72, 80, and 88. In addition, Slices were produced from images using either uniform slicing, subset slicing, or interpolation zoom approaches then joined back to form a 3D image of varying depth (8,16,24,32,40,48,56, and 64) for the Slice-based technique. Using T1w-MRI and AV45-PET scans, our multimodal multi-class Ensembled Volumetric ConvNet framework obtained 93.01% accuracy for AD versus NC versus MCI (highest accuracy achieved using multi-modalities as per our knowledge). The 3D-Subject-based neuroanatomy computation approach achieved 93.01% classification accuracy and it overruled Patch-based approach which achieved 89.55% accuracy and Slice-Based approach that achieved 89.37% accuracy. Using a 3D-Patch-based feature extraction technique, it was discovered that patches of greater size (80, 88) had accuracy over 89%, while medium-sized patches (56, 64, and 72) had accuracy ranging from 83 to 88%, and small-sized patches (32, 40, and 48) had the least accuracy ranging from 57 to 80%. From the three independent algorithms created for 3D-Slice-based neuroanatomy computational approach, the interpolation zoom technique outperformed uniform slicing and subset slicing, obtaining 89.37% accuracy over 88.35% and 82.83%, respectively. Link to GitHub code: https://github.com/ngoenka04/Alzheimer-Detection .     

Antibody‐Mimetic Peptoid Nanosheet for Label‐Free Serum‐Based Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia characterized by progressive cognitive decline. Current diagnosis of AD is based on symptoms, neuropsychological tests, and neuroimaging, and is usually evident years after the pathological process. Early assessment at the preclinical or prodromal stage is in a great demand since treatment after the onset can hardly stop or reverse the disease progress. However, early diagnosis of AD is challenging due to the lack of reliable noninvasive approaches. Here, an antibody‐mimetic self‐assembling peptoid nanosheet containing surface‐exposed Aβ42‐recognizing loops is constructed, and a label‐free sensor for the detection of AD serum is developed. The loop‐displaying peptoid nanosheet is demonstrated to have high affinity to serum Aβ42, and to be able to identify AD sera with high sensitivity. The dense distribution of molecular recognition loops on the robust peptoid nanosheet scaffold not only mimics the architecture of antibodies, but also reduces the nonspecific binding in detecting multicomponent samples. This antibody‐mimetic 2D material holds great potential toward the blood‐based diagnosis of AD, and meanwhile provides novel insights into the antibody alternative engineering and the universal application in biological and chemical sensors.     

Near‐Infrared Switchable Fullerene‐Based Synergy Therapy for Alzheimer's Disease

C₆₀ has a special dual function; it can act as both a powerful reactive oxygen species (ROS) producer under UV or visible light and an ROS scavenger in the dark. However, ROS has double‐edged effects in living systems. It is still a great challenge for biomedical application to switch and adjust the two opposite properties of C₆₀ in one system. Herein, UCNP@C₆₀‐pep (UCNP: upconversion nanoparticle, pep: Aβ‐target peptide KLVFF) is designed as a near‐infrared‐switchable nanoplatform for synergy therapy of Alzheimer's disease (AD). Under near‐infrared (NIR) light, the Aβ‐targeting hybrid nanoparticles produce ROS and result in Aβ photooxygenation, which can hinder Aβ aggregation and mitigate the attendant cytotoxicity. In the dark, UCNP@C₆₀‐pep shows protective effects against the increased oxidative stress. The ROS‐generating and ROS‐quenching abilities of UCNP@C₆₀‐pep are both beneficial for decreasing Aβ‐induced neurotoxicity and extending the longevity of the commonly used transgenic AD model Caenorhabditis elegans CL2006. Moreover, UCNP@C60‐pep can also be used for upconversion luminescence (UCL) and magnetic resonance imaging (MRI), which has benefits for “image‐guided therapy.” This study may offer a new perspective for the biological applications of C₆₀.     

Magnetic resonance brain image classification based on weighted‐type fractional Fourier transform and nonparallel support vector machine

To classify brain images into pathological or healthy is a key pre‐clinical state for patients. Manual classification is tiresome, expensive, time‐consuming, and irreproducible. In this study, we aimed to present an automatic computer‐aided system for brain‐image classification. We used 90 T2‐weighted images obtained by magnetic resonance images. First, we used weighted‐type fractional Fourier transform (WFRFT) to extract spectrums from each magnetic resonance image. Second, we used principal component analysis (PCA) to reduce spectrum features to only 26. Third, those reduced spectral features of different samples were combined and were fed into support vector machine (SVM) and its two variants: generalized eigenvalue proximal SVM and twin SVM. A 5 × 5‐fold cross‐validation results showed that this proposed “WFRFT + PCA + generalized eigenvalue proximal SVM” yielded sensitivity of 99.53%, specificity of 92.00%, precision of 99.53%, and accuracy of 99.11%, which are comparable with the proposed “WFRFT + PCA + twin SVM” and better than the proposed “WFRFT + PCA + SVM.” Besides, all three proposed methods were superior to eight state‐of‐the‐art algorithms. Thus, WFRFT is effective, and the proposed methods can be used in practical. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 317–327, 2015     

Self‐Assembled Peptide–Polyoxometalate Hybrid Nanospheres: Two in One Enhances Targeted Inhibition of Amyloid β‐Peptide Aggregation Associated with Alzheimer's Disease

Amyloid fibril formation is a critical step in Alzheimer's disease (AD) pathogenesis. Inhibition of Aβ aggregation has shown promising against AD and has been used in clinic trials. Here, a novel strategy is reported for the self‐assembly of polyoxometalate–peptide (POM@P) hybrid particles as bifunctional Aβ inhibitors. The two‐in‐one bifunctional POM@P nanoparticles show an enhanced inhibition effect on amyloid aggregation in mice cerebrospinal fluid. Incorporating a clinically used Aβ fibril‐staining dye, congo red (CR), into the hybrid colloidal spheres, the nanoparticles can also act as an effective fluorescent probe to monitor the inhibition process of POM@P via CR fluorescence change in real time. It is believed that such flexible organic–inorganic hybrid systems may prompt the design of new multifunctional materials for AD treatment.     

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.     

Alzheimer disease classification using KPCA,LDA, and multi‐kernel learning SVM

Early diagnosis of Alzheimer disease (AD) and mild cognitive impairment (MCI) is always useful. Preventive measures might have an impact on reducing AD risk factors. Structural magnetic resonance (MR) imaging, one of the vital sensitive biomarkers for cerebral atrophy in the brain, is used to extract volumetric feature by FreeSurfer and the CIVET toolbox. All of the structural magnetic resonance imaging (s‐MRI) data that we used were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) of imaging data. This novel approach is applied for the diagnosis of AD and MCI from healthy controls (HCs) combining extracted features with the MMSE (mini‐mental state examination) scores, applying a two sample t‐test to select a subset of features. The subset of features is fed to kernel principal component analysis (KPCA) module to project data onto the reduced principal component coefficients at higher dimensional kernel space to increase the linear separability. Then, the kernel PCA coefficients are projected into the more efficient linear discriminant space using linear discriminant analysis. A multi‐kernel learning support vector machine (SVM) is used on newly projected data for stratification of AD and MCI from HCs. Using this approach, we obtain 93.85% classification accuracy when detecting AD from HCs for segmented volumetric features (using FreeSurfer) with high sensitivity and specificity. When distinguishing MCI from HCs and AD using volumetric features after subcortical segmentation, the detection rate reaches 86.54% and 75.12%, respectively.     

Congo Red-Derived Carbon Dots: Simultaneously as Fluorescence Probe for Protein Aggregates,Inhibitor for Protein Aggregation,and Scavenger of Free Radicals

The pathological aggregation of some proteins is claimed to be highly related to several human diseases, such as β-amyloid 1–42 (Aβ₄₂) to Alzheimer's disease (AD), islet amyloid polypeptide, and insulin to type 2 diabetes mellitus. Therefore, it is in desperate need to develop effective methods for detection of protein aggregates and inhibition of abnormal aggregation. Herein, to construct all-in-one probe with both diagnosis and treatment potentials for protein aggregation diseases, Congo red (CR), a classical staining reagent with red fluorescence signal output for protein aggregates, is deliberately adopted to react with three different reductive carbon sources and ammonium persulfate to generate three CR-derived carbon dots (CDs). The obtained CDs exhibit the capabilities of turn-on red fluorescence imaging of protein aggregates, and/or inhibition of protein aggregation as well as scavenging of free radicals. Among them, CA-CDs, using citric acid as the reductive carbon source, demonstrate the superiority to the other two studied CDs in integrating all of these functions, and particularly exert excellent cytoprotection effect against toxic Aβ₄₂ species, possessing tremendous potential in diagnosis and treatment of AD for future study. The present study paves a new way to develop all-in-one CDs for the protein disease research.     

微惯性测量组合的计算机测试系统

介绍了一个微惯性测量组合(MIMU)的计算机测试系统,对该系统的结构,硬件组成、软件设计进行了描述,重点介绍了系统软件设计中的关键技术：RS232通讯测试技术、VC和Labwindows／CVI的混合编程技术、多线程技术、VC和Matlab的混合编程技术。该系统具有良好的通用性和可扩展性,基于高性能CPU和(CPLD、FPGA等技术的微惯性测量组合的测试过程可以大大提高工作效率,缩短研发周期。     

Driving behaviors in early stage dementia: A study using in-vehicle technology

According to the Alzheimer's Association (2011), (1) in 8 people age 65 and older, and about one-half of people age 85 and older, have Alzheimer's disease in the United States (US). There is evidence that drivers with Alzheimer's disease and related dementias are at an increased risk for unsafe driving. Recent advances in sensor, computer, and telecommunication technologies provide a method for automatically collecting detailed, objective information about the driving performance of drivers, including those with early stage dementia. The objective of this project was to use in-vehicle technology to describe a set of driving behaviors that may be common in individuals with early stage dementia (i.e., a diagnosis of memory loss) and compare these behaviors to a group of drivers without cognitive impairment. Seventeen drivers with a diagnosis of early stage dementia, who had completed a comprehensive driving assessment and were cleared to drive, participated in the study. Participants had their vehicles instrumented with a suite of sensors and a data acquisition system, and drove 1–2 months as they would under normal circumstances. Data from the in-vehicle instrumentation were reduced and analyzed, using a set of algorithms/heuristics developed by the research team. Data from the early stage dementia group were compared to similar data from an existing dataset of 26 older drivers without dementia. The early stage dementia group was found to have significantly restricted driving space relative to the comparison group. At the same time, the early stage dementia group (which had been previously cleared by an occupational therapist as safe to drive) drove as safely as the comparison group. Few safety-related behavioral errors were found for either group. Wayfinding problems were rare among both groups, but the early stage dementia group was significantly more likely to get lost.     

Amyloid‐β Aggregation with Gold Nanoparticles on Brain Lipid Bilayer

Understanding and manipulating amyloid‐β (Aβ) aggregation provide key knowledge and means for the diagnosis and cure of Alzheimer's disease (AD) and the applications of Aβ‐based aggregation systems. Here, we studied the formation of various Aβ aggregate structures with gold nanoparticles (AuNPs) and brain total lipid extract‐based supported lipid bilayer (brain SLB). The roles of AuNPs and brain SLB in forming Aβ aggregates were studied in real time, and the structural details of Aβ aggregates were monitored and analyzed with the dark‐field imaging of plasmonic AuNPs that allows for long‐term in situ imaging of Aβ aggregates with great structural details without further labeling. It was shown that the fluid brain SLB platform provides the binding sites for Aβ and drives the fast and efficient formation of Aβ aggregate structures and, importantly, large Aβ plaque structures (>15 μm in diameter), a hallmark for AD, were formed without going through fibril structures when Aβ peptides were co‐incubated with AuNPs on the brain SLB. The dark‐field scattering and circular dichroism‐correlation data suggest that AuNPs were heavily involved with Aβ aggregation on the brain SLB and less α‐helix, less β‐sheet and more random coil structures were found in large plaque‐like Aβ aggregates.     

Lung computed axial tomography image segmentation using possibilistic fuzzy C-means approach for computer aided diagnosis system

Soft computing is an associate rising field that plays a crucial half in the area of engineering and science. One of the most significant applications of soft computing is image segmentation. It focuses on an exploiting tolerance of imprecision and uncertainty. Segmentation supported soft computing remains a difficult task within the medical field. Medical images are habitually used in the segmentation process to extract the meaningful portions and to know and clarify the condition of the particular patient. In this article, we implement an efficient possibilistic fuzzy C-means (PFCM) approach to segment the lung portion in the computed tomography (CT) image and the result shows that it improves the segmentation accuracy upto 98.5012% and results are compared with existing segmenting approaches like fuzzy possibilistic C-means method, fuzzy bitplane method and so forth. Also, the PFCM approach increases the diagnostic accuracy of the computer aided diagnosis system using CT images. The radiologist may utilize this computer aided diagnosis system results as a second opinion of their diagnosed results.     

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.     

Emulsion Speed Rating Systems

Early methods for determining and expressing film speeds were empirical. Hurter and Driffield’s classic paper of 1890 described the “characteristic cuwe” and established the first rigorous criterion for film speed based on scientific principles.

Criteria used have been based on threshold, inertia, fixed density, minimum useful gradient, and fractional gradient. The various criteria and systems based on them, some of which preceded, but most of which followed from Hurter and Driffield's work, and which have been used for longer or shorter periods of time since then, are reviewed.