Early Detection of Alzheimer's Disease with Blood Plasma Proteins using Support Vector Machines

The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities, we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease, suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers

Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration.

OBJECTIVE: To quantify the regional and global cerebral atrophy rates and assess acceleration rates in healthy controls, subjects with mild cognitive impairment (MCI), and subjects with mild Alzheimer disease (AD). METHODS: Using 0-, 6-, 12-, 18-, 24-, and 36-month MRI scans of controls and subjects with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we calculated volume change of whole brain, hippocampus, and ventricles between all pairs of scans using the boundary shift integral. RESULTS: We found no evidence of acceleration in whole-brain atrophy rates in any group. There was evidence that hippocampal atrophy rates in MCI subjects accelerate by 0.22%/year2 on average (p = 0.037). There was evidence of acceleration in rates of ventricular enlargement in subjects with MCI (p = 0.001) and AD (p < 0.001), with rates estimated to increase by 0.27 mL/year2 (95% confidence interval 0.12, 0.43) and 0.88 mL/year2 (95% confidence interval 0.47, 1.29), respectively. A post hoc analysis suggested that the acceleration of hippocampal loss in MCI subjects was mainly driven by the MCI subjects that were observed to progress to clinical AD within 3 years of baseline, with this group showing hippocampal atrophy rate acceleration of 0.50%/year2 (p = 0.003). CONCLUSIONS: The small acceleration rates suggest a long period of transition to the pathologic losses seen in clinical AD. The acceleration in hippocampal atrophy rates in MCI subjects in the ADNI seems to be driven by those MCI subjects who concurrently progressed to a clinical diagnosis of AD

Survey on Early Detection of Alzhiemer’s Disease Using Capsule Neural Network

Alzheimer's disease (AD) is an disorder which is irreversible of the brain related to memory loss, mostly found in the old and aged population. Alzheimer's dementia results from the degeneration or loss of brain cells. The brain-imaging technologies most often used to diagnose AD is Magnetic resonance imaging (MRI). MRI or structural magnetic resonance is a very popular and actual technique used to diagnose AD.&nbsp;An&nbsp;MRI&nbsp;uses magnets and powerful radio waves to create a complete view of your brain. To actually detect the presence of Alzheimer’s, the MRI should me studied carefullyImplementation of CBIR Content Based Image Retrival which is a revolutionary computer aided diagnosis technique will create new abilities in MRI Magnetic resonance imaging in related image retrieval and training for recognition of development of AD in early stage

Data fusion of complementary information from parietal and occipital event related potentials for early diagnosis of Alzheimer\u27s disease

The number of the elderly population affected by Alzheimer\u27s disease is rapidly rising. The need to find an accurate, inexpensive, and non-intrusive procedure that can be made available to community healthcare providers for the early diagnosis of Alzheimer\u27s disease is becoming an increasingly urgent public health concern. Several recent studies have looked at analyzing electroencephalogram signals through the use of many signal processing techniques. While their methods show great promise, the final outcome of these studies has been largely inconclusive. The inherent difficulty of the problem may be the cause of this outcome, but most likely it is due to the inefficient use of the available information, as many of these studies have used only a single EEG source for the analysis. In this contribution, data from the event related potentials of 19 available electrodes of the EEG are analyzed. These signals are decomposed into different frequency bands using multiresolution wavelet analysis. Two data fusion approaches are then investigated: i.) concatenating features before presenting them to a classification algorithm with the expectation of creating a more informative feature space, and ii.) generating multiple classifiers each trained with a different combination of features obtained from various stimuli, electrode, and frequency bands. The classifiers are then combined through the weighted majority vote, product and sum rule combination schemes. The results indicate that a correct diagnosis performance of over 80% can be obtained by combining data primarily from parietal and occipital lobe electrodes. The performance significantly exceeds that reported from community clinic physicians, despite their access to the outcomes of longitudinal monitoring of the patients

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Investigating data mining techniques for extracting information from Alzheimer\u27s disease data

Data mining techniques have been used widely in many areas such as business, science, engineering and more recently in clinical medicine. These techniques allow an enormous amount of high dimensional data to be analysed for extraction of interesting information as well as the construction of models for prediction. One of the foci in health related research is Alzheimer\u27s disease which is currently a non-curable disease where diagnosis can only be confirmed after death via an autopsy. Using multi-dimensional data and the applications of data mining techniques, researchers hope to find biomarkers that will diagnose Alzheimer\u27s disease as early as possible. The primary purpose of this research project is to investigate the application of data mining techniques for finding interesting biomarkers from a set of Alzheimer\u27s disease related data. The findings from this project will help to analyse the data more effectively and contribute to methods of providing earlier diagnosis of the disease