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This work proposes multiclass deep learning classification of Alzheimer's disease (AD) using novel texture and other associated features extracted from structural MRI. Two distinct learning models (Model 1 and 2) are presented where both include subcortical area specific feature extraction, feature selection and stacked auto-encoder (SAE) deep neural network (DNN). The models learn highly complex...
Among acoustic waves sensors, the thickness shear mode resonator presents high sensitivity for the measurement of liquid viscoelastic properties, enabling the monitoring of the shear moduli, G′ G″, by using a suitable physical model. The development of an instrumental system for detecting the tau protein involved in Alzheimer's disease is proposed which will aid in understanding the mechanisms of...
Computational visual atention models aims to emulate the Human Visual System performance in selecting relevant features for efficient visual scene processing. As a result, visual saliency maps highlights relevant visual patterns in an image, possibly associated with objects or specific concepts. In the analysis of medical images, this allows the radiologist or clinical expert to focus the attention...
In spite of the worldwide financial and research efforts made, the pathophysiological mechanism at the basis of Alzheimer's disease (AD) is still poorly understood. Previous studies using electroencephalography (EEG) have focused on the slowing of oscillatory brain rhythms, coupled with complexity reduction of the corresponding time-series and their enhanced compressibility. These analyses have been...
Using salivary beta-amyloid peptides, the results to diagnose AD are briefly introduced. The quantity of AD peptides in the saliva of normal young man (nYM, normal elderly (nE) group and AD patients is measured in the range from very low concentration (∼pg/ml) to high concentration (∼ng/ml). For about 100 persons, nYM group and AD patients are below ∼30pg/ml and ∼ng/ml, respectively. In protein sequencing,...
Statistical data analysis plays a major role in discovering structural and functional imaging phenotypes for mental disorders such as Alzheimer's disease (AD). The goal here is to identify, ideally early on, which regions in the brain show abnormal variations with a disorder. To make the method more sensitive, we rely on a multi-resolutional perspective of the given data. Since the underlying imaging...
Endoplasmic reticulum (ER) is a communication hub for several signaling and secretory pathways that regulate the cell cycle progression. Recent studies revealed that deregulation of ER stress-induced signaling pathways are involved in the patho-genesis of cancer and Alzheimers disease. Computational analysis and verification of these pathways will provide insights into the mechanism linking ER stress...
Objective: Alzheimer's disease (AD) is a severe threat to the elderly. But only a few drugs were available for AD patients. In the long history of development of traditional Chinese medicine, some herb prescriptions have been discovered to have effects in treating AD. Our study was to search the randomized controlled trials (RCTs) of Chinese medicine compound for treating AD and to summarize the rule...
Alzheimer's disease (AD) is currently the most common form of dementia affecting the elderly, and its occurrence rate is only expected to increase over the next several decades. Though there is a vast array of knowledge about individual molecules and genetics involved with the disease, there is no clear understanding of the mechanism of pathogenesis. To help better understand the disease process,...
In humans, motion information is mainly processed by the dorsal visual stream. This stream consists of two functional streams: the ventro-dorsal (v-d) and dorso-dorsal (d-d) streams. Patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) exhibit an impairment in motion perception. By using visual event-related potentials (ERPs), we have previously demonstrated that v-d function...
In this paper, we present a new metric combining regional measurements to improve image based population studies that use manifold learning techniques. These studies currently rely on a single score over the whole brain image domain. Thus, they require large amount of training data to uncover spatially complex variation in the whole brain impacted by diseases. We reduce the impact of this issue by...
A standard segmentation problem within Magnetic Resonance Imaging (MRI) is the task of labelling voxels according to their tissue type that are White Matter (WM), Gray Matter (GM), and Cerebrospinal fluid (CSF).Image segmentation provides volumetric quantification of cortical atrophy and thus helps in the diagnosis of degenerative diseases such as Epilepsy, Schizophrenia, Alzheimer's disease, Dementia...
Probabilistic atlases present prior knowledge about the spatial distribution of various structures or tissues in a population, used commonly in segmentation. We propose three methods for generating probabilistic atlases: 1) the atlases are constructed in a template space using dense non-rigid transformations and transformed to the space of unseen data, 2) as the method 1 but atlas selection is performed...
We present the first use of multi-region FDG-PET data for classification of subjects from the Alzheimer's Disease Neuroimaging Initiative. Image data were obtained from 69 healthy controls, 71 AD patients, and 147 patients with a baseline diagnosis of MCI. Anatomical segmentations were automatically generated in the native MRI-space of each subject, and the mean signal intensity per cubic millimetre...
We introduce a new representation of cortical regions via distribution functions of their features. The distribution functions are estimated non-parametrically from the data and are observed to be non Gaussian. Cortical pattern matching is enabled by using the information-based Jensen-Shannon divergence as a measure between features. Our approach explicitly avoids pairwise registrations between brains,...
We have investigated functional connectivity of the default mode network (DMN) in normal aging and Alzheimer's disease (AD) using resting state fMRI at 3T. Images from young and elderly controls, and patients with AD were processed using spatial independent component analysis to identify the DMN. Functional connectivity was quantified using integration and indices derived from graph theory. Four DMN...
In imaging genomics, there have been rapid advances in genome-wide, image-wide searches for genes that influence brain structure. Most efforts focus on univariate tests that treat each genetic variation independently, ignoring the joint effects of multiple variants. Instead, we present a gene-based method to detect the joint effect of multiple single nucleotide polymorphisms (SNPs) in 18,044 genes...
Automatic segmentation of white matter hyperintensities (WMH) from T2-Weighted and FLAIR MRI is a common task that needs to be performed in the analysis of many different diseases. A method to segment the WMH is proposed whereby a local intensity model (LIM) of normal tissue is generated. WMH are detected as outliers from this model. The LIM enables an accurate modeling of intensity variations thus...
In multi-atlas based segmentation propagation, segmentations from multiple atlases are propagated to the target image and combined to produce the segmentation result. Local weighted voting (LWV) method is a classifier fusion method which combines the propagated atlases weighted by local image similarity. We demonstrate that the segmentation accuracy using LWV improves as the number of atlases increases...
One challenge in identification of Alzheimer's disease (AD) is that the number of AD patients and healthy controls (HCs) is generally very small, thus difficult to train a powerful AD classifier. On the other hand, besides AD and HC subjects, we often have MR brain images available from other related subjects such as those with mild cognitive impairment (MCI), a prodromal stage of AD, or possibly...
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