Query: Imaging techniques for early diagnosis of Alzheimer's disease  review

title: Multiplex Networks for Early Diagnosis of Alzheimer's Disease
abstract: Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI) are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD) Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra and inter subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex based score segregates (i) normal controls (NC) from AD patients with a 0.86+ 0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84+ 0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm3 "patches" without any a priori segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies.

title: The amyloid hypothesis of Alzheimer's disease at 25 years
abstract: Despite continuing debate about the amyloid b protein (or Ab hypothesis, new lines of evidence from laboratories and clinics worldwide support the concept that an imbalance between production and clearance of Ab42 and related Ab peptides is a very early, often initiating factor in Alzheimer's disease (AD) Confirmation that presenilin is the catalytic site of g secretase has provided a linchpin: all dominant mutations causing early onset AD occur either in the substrate (amyloid precursor protein, APP) or the protease (presenilin) of the reaction that generates Ab. Duplication of the wild type APP gene in Down's syndrome leads to Ab deposits in the teens, followed by microgliosis, astrocytosis, and neurofibrillary tangles typical of AD. Apolipoprotein E4, which predisposes to AD in 40% of cases, has been found to impair Ab clearance from the brain. Soluble oligomers of Ab42 isolated from AD patients' brains can decrease synapse number, inhibit long term potentiation, and enhance long term synaptic depression in rodent hippocampus, and injecting them into healthy rats impairs memory. The human oligomers also induce hyperphosphorylation of tau at AD relevant epitopes and cause neuritic dystrophy in cultured neurons. Crossing human APP with human tau transgenic mice enhances tau positive neurotoxicity. In humans, new studies show that low cerebrospinal fluid (CSF) Ab42 and amyloid PET positivity precede other AD manifestations by many years. Most importantly, recent trials of three different Ab antibodies (solanezumab, crenezumab, and aducanumab) have suggested a slowing of cognitive decline in post hoc analyses of mild AD subjects. Although many factors contribute to AD pathogenesis, Ab dyshomeostasis has emerged as the most extensively validated and compelling therapeutic target.

title: Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach
abstract: Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR) related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI) not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1 weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD related pre clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20 fold cross validation) Our data encourage the application of computer based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

title: Classification of Alzheimer's and MCI Patients from Semantically Parcelled PET Images: A Comparison between AV45 and FDG PET
abstract: Early identification of dementia in the early or late stages of mild cognitive impairment (MCI) is crucial for a timely diagnosis and slowing down the progression of Alzheimer's disease (AD) Positron emission tomography (PET) is considered a highly powerful diagnostic biomarker, but few approaches investigated the efficacy of focusing on localized PET active areas for classification purposes. In this work, we propose a pipeline using learned features from semantically labelled PET images to perform group classification. A deformable multimodal PET MRI registration method is employed to fuse an annotated MNI template to each patient specific PET scan, generating a fully labelled volume from which 10 common regions of interest used for AD diagnosis are extracted. The method was evaluated on 660 subjects from the ADNI database, yielding a classification accuracy of 91.2% for AD versus NC when using random forests combining features from cross sectional and follow up exams. A considerable improvement in the early versus late MCI classification accuracy was achieved using FDG PET compared to the AV 45 compound, yielding a 72.5% rate. The pipeline demonstrates the potential of exploiting longitudinal multiregion PET features to improve cognitive assessment.

title: Medical imaging diagnosis of early Alzheimer's disease.
abstract: Alzheimer's disease (AD) is one of the most common neurodegenerative diseases that influences the central nervous system, often leading to dire consequences for quality of life. The disease goes through some stages mainly divided into early, moderate, and severe. Among them, the early stage is the most important as medical intervention has the potential to alter the natural progression of the condition. In practice, the early diagnosis is a challenge since the neurodegenerative changes can precede the onset of clinical symptoms by 10 15 years. This factor along with other known and unknown ones, hinder the ability for the early diagnosis and treatment of AD. Numerous research efforts have been proposed to address the complex characteristics of AD exploiting various tests including brain imaging that is massively utilized due to its powerful features. This paper aims to highlight our present knowledge on the clinical and computer based attempts at early diagnosis of AD. We concluded that the door is still open for further research especially with the rapid advances in scanning and computer based technologies.

title: Analysis of MRI and OCT Images for the Early Diagnosis of Alzheimer's Disease Using Wavelet Networks
abstract: Alzheimer disease (AD) a progressive neurodegenerative disorder, is the most common cause of dementia in the old age population. Previous clinical and histological studies suggest that the neurodegenerative process, which affects the brain, may also affect the retina of AD patients. Any disease modifying treatments which are developed are most possibly to be achieving success if initiated early in the process, and this needs that we tend to develop reliable, validated and economical ways to diagnose Alzheimer's kind brain disease. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is likely that a combination will be needed. Profiling of human body parameter using computers can be utilized for the early diagnosis of Alzheimer's disease. In this paper we have focused on Magnetic Resonance Imaging (MRI) and Optical Coherence Tomography (OCT) imaging techniques for the early diagnosis of AD. For this purpose we have developed a method based on Wavelet Networks (WN) known as Fixed Grid Wavelet Networks (FGWN) for the analysis of OCT and MRI images. This method provides reliable and validated results for both OCT and MRI images.

title: A Review on the Early Diagnosis of Alzheimer's Disease (AD) through Different Tests, Techniques and Databases
abstract: Gerontology deals with the many clinical problems that are common in the elderly population, and many of these follow the orthodox pattern of clinical practice. Patients characteristically have poor insight and often attribute their early symptoms of amnesia to normal ageing. Alzheimer's disease (AD) is common form of senile dementia. There are several causes for the disease. Although our understanding of the key steps underlying neurodegeneration in Alzheimer's disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by patient. Any disease modifying treatments which are developed are most likely to be successful if initiated early in the process, and this requires that we develop reliable, validated and economical ways to diagnose Alzheimer's type pathology. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is likely that a combination will be needed. There are lot of tests and neuroimaging modalities to be performed for an effective diagnosis of the disease. Conventional clinical decision making systems are more manual in nature and ultimate conclusion in terms of exact diagnosis is remote. In this case, the use of advanced biomedical engineering technology will definitely helpful for making diagnosis. Profiling of human body parameter using computers can be utilised for the early diagnosis of Alzheimer's disease. There are several neuroimaging techniques used in clinical practice for the diagnosis of Alzheimer's type pathology. Prominent of them are Magnetic Resonance Imaging Scan (MRI) Positron Emission Tomography (PET) and Single Photon Emission CT Scanning (SPECT) Apart from above we are

title: Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer's Disease: A New Computational Approach
abstract: Alzheimer's disease (AD) is a major public health threat; however, despite decades of research, the disease mechanisms are not completely understood, and there is a significant dearth of predictive biomarkers. The availability of systems biology approaches has opened new avenues for understanding disease mechanisms at a pathway level. However, to the best of our knowledge, no prior study has characterized the nature of pathway crosstalks in AD, or examined their utility as biomarkers for diagnosis or prognosis. In this paper, we build the first computational crosstalk model of AD incorporating genetics, antecedent knowledge, and biomarkers from a national study to create a generic pathway crosstalk reference map and to characterize the nature of genetic and protein pathway crosstalks in mild cognitive impairment (MCI) subjects. We perform initial studies of the utility of incorporating these crosstalks as biomarkers for assessing the risk of MCI progression to AD dementia. Our analysis identified Single Nucleotide Polymorphism enriched pathways representing six of the seven Kyoto Encyclopedia of Genes and Genomes pathway categories. Integrating pathway crosstalks as a predictor improved the accuracy by 11.7% compared to standard clinical parameters and apolipoprotein E e4 status alone. Our findings highlight the importance of moving beyond discrete biomarkers to studying interactions among complex biological pathways.

title: Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference
abstract: The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we present a new machine learning technique Subtype and Stage Inference (SuStaIn) able to uncover data driven disease phenotypes with distinct temporal progression patterns, from widely available crosssectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal new subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes, and characterises within group heterogeneity for the first time. In Alzheimer's disease, SuStaIn uncovers three subtypes, uniquely revealing their temporal complexity. SuStaIn provides fine grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p=7.18x10 4) or temporal stage (p=3.96x10 5) SuStaIn thus offers new promise for enabling disease subtype discovery and precision medicine.

title: Classification of Alzheimer's Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG PET Images
abstract: Alzheimer's disease (AD) is an irreversible brain degenerative disorder affecting people aged older than 65 years. Currently, there is no effective cure for AD, but its progression can be delayed with some treatments. Accurate and early diagnosis of AD is vital for the patient care and development of future treatment. Fluorodeoxyglucose positrons emission tomography (FDG PET) is a functional molecular imaging modality, which proves to be powerful to help understand the anatomical and neural changes of brain related to AD. Most existing methods extract the handcrafted features from images, and then design a classifier to distinguish AD from other groups. These methods highly depends on the preprocessing of brain images, including image rigid registration and segmentation. Motivated by the success of deep learning in image classification, this paper proposes a new classification framework based on combination of 2D convolutional neural networks (CNN) and recurrent neural networks (RNNs) which learns the intra slice and inter slice features for classification after decomposition of the 3D PET image into a sequence of 2D slices. The 2D CNNs are built to capture the features of image slices while the gated recurrent unit (GRU) of RNN is cascaded to learn and integrate the inter slice features for image classification. No rigid registration and segmentation are required for PET images. Our method is evaluated on the baseline FDG PET images acquired from 339 subjects including 93 AD patients, 146 mild cognitive impairments (MCI) and 100 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an area under receiver operating characteristic curve (AUC) of 95.3% for AD vs. NC classification and 83.9% for MCI vs. NC classification, demonstrating the promising classification performance.