Alzheimer's Disease Classification Based on Multi-feature Fusion

Differences between Alzheimer's disease and mild cognitive impairment using brain networks from magnetic resonance texture analysis

Several studies have aimed at identifying biomarkers in the initial phases of Alzheimer's disease (AD). Conversely, texture features, such as those from gray-level co-occurrence matrices (GLCMs), have highlighted important information from several types of medical images. More recently, texture-based brain networks have been shown to provide useful information in characterizing healthy individuals. However, no studies have yet explored the use of this type of network in the context of AD. This work aimed to employ texture brain networks to investigate the distinction between groups of patients with amnestic mild cognitive impairment (aMCI) and mild dementia due to AD, and a group of healthy subjects. Magnetic resonance (MR) images from the three groups acquired at two instances were used. Images were segmented and GLCM texture parameters were calculated for each region. Structural brain networks were generated using regions as nodes and the similarity among texture parameters as links, and graph theory was used to compute five network measures. An ANCOVA was performed for each network measure to assess statistical differences between groups. The thalamus showed significant differences between aMCI and AD patients for four network measures for the right hemisphere and one network measure for the left hemisphere. There were also significant differences between controls and AD patients for the left hippocampus, right superior parietal lobule, and right thalamus-one network measure each. These findings represent changes in the texture of these regions which can be associated with the cortical volume and thickness atrophies reported in the literature for AD. The texture networks showed potential to differentiate between aMCI and AD patients, as well as between controls and AD patients, offering a new tool to help understand these conditions and eventually aid early intervention and personalized treatment, thereby improving patient outcomes and advancing AD research.

Fine-Grained and Multiple Classification for Alzheimer's Disease With Wavelet Convolution Unit Network

In this article, we propose a novel wavelet convolution unit for the image-oriented neural network to integrate wavelet analysis with a vanilla convolution operator to extract deep abstract features more efficiently. On one hand, in order to acquire non-local receptive fields and avoid information loss, we define a new convolution operation by composing a traditional convolution function and approximate and detailed representations after single-scale wavelet decomposition of source images. On the other hand, multi-scale wavelet decomposition is introduced to obtain more comprehensive multi-scale feature information. Then, we fuse all these cross-scale features to improve the problem of inaccurate localization of singular points. Given the novel wavelet convolution unit, we further design a network based on it for fine-grained Alzheimer's disease classifications (i.e., Alzheimer's disease, Normal controls, early mild cognitive impairment, late mild cognitive impairment). Up to now, only a few methods have studied one or several fine-grained classifications, and even fewer methods can achieve both fine-grained and multi-class classifications. We adopt the novel network and diffuse tensor images to achieve fine-grained classifications, which achieved state-of-the-art accuracy for all eight kinds of fine-grained classifications, up to 97.30%, 95.78%, 95.00%, 94.00%, 97.89%, 95.71%, 95.07%, 93.79%. In order to build a reference standard for Alzheimer's disease classifications, we actually implemented all twelve coarse-grained and fine-grained classifications. The results show that the proposed method achieves solidly high accuracy for them. Its classification ability greatly exceeds any kind of existing Alzheimer's disease classification method.

Identification of major depression patients using machine learning models based on heart rate variability during sleep stages for pre-hospital screening

With the COVID-19 pandemic causing challenges in hospital admissions globally, the role of home health monitoring in aiding the diagnosis of mental health disorders has become increasingly important. This paper proposes an interpretable machine learning solution to optimise initial screening for major depressive disorder (MDD) in both male and female patients. The data is from the Stanford Technical Analysis and Sleep Genome Study (STAGES). We analyzed 5-min short-term electrocardiogram (ECG) signals during nighttime sleep stages of 40 MDD patients and 40 healthy controls, with a 1:1 gender ratio. After preprocessing, we calculated the time-frequency parameters of heart rate variability (HRV) based on the ECG signals and used common machine learning algorithms for classification, along with feature importance analysis for global decision analysis. Ultimately, the Bayesian optimised extremely randomized trees classifier (BO-ERTC) showed the best performance on this dataset (accuracy 86.32%, specificity 86.49%, sensitivity 85.85%, F1-score 0.86). By using feature importance analysis on the cases confirmed by BO-ERTC, we found that gender is one of the most important factors affecting the prediction of the model, which should not be overlooked in our assisted diagnosis. This method can be embedded in portable ECG monitoring systems and is consistent with the literature results.

An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI

Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients' lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widely to predict MCI. This study proposes optimized deep learning models for differentiating between MCI and normal control samples. In previous studies, the hippocampus region located in the brain is used extensively to diagnose MCI. The entorhinal cortex is a promising area for diagnosing MCI since severe atrophy is observed when diagnosing the disease before the shrinkage of the hippocampus. Due to the small size of the entorhinal cortex area relative to the hippocampus, limited research has been conducted on the entorhinal cortex brain region for predicting MCI. This study involves the construction of a dataset containing only the entorhinal cortex area to implement the classification system. To extract the features of the entorhinal cortex area, three different neural network architectures are optimized independently: VGG16, Inception-V3, and ResNet50. The best outcomes were achieved utilizing the convolution neural network classifier and the Inception-V3 architecture for feature extraction, with accuracy, sensitivity, specificity, and area under the curve scores of 70%, 90%, 54%, and 69%, respectively. Furthermore, the model has an acceptable balance between precision and recall, achieving an F1 score of 73%. The results of this study validate the effectiveness of our approach in predicting MCI and may contribute to diagnosing MCI through MRI.

Advanced Diagnostic Technique for Alzheimer's Disease using MRI Top-Ranked Volume and Surface-based Features

Background

Alzheimer's disease (AD) is the most dominant type of dementia that has not been treated completely yet. Few Alzheimer's patients are correctly diagnosed on time. Therefore, diagnostic tools are needed for better and more efficient diagnoses.

Objective

This study aimed to develop an efficient automated method to differentiate Alzheimer's patients from normal elderly and present the essential features with accurate Alzheimer's diagnosis.

Material and Methods

In this analytical study, 154 Magnetic Resonance Imaging (MRI) scans were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, preprocessed, and normalized by the head size for extracting features (volume, cortical thickness, Sulci depth, and Gyrification Index Features (GIF). Relief-F algorithm, t-test, and one way-ANOVA were used for feature ranking to obtain the most effective features representing the AD for the classification process. Finally, in the classification step, four classifiers were used with 10 folds cross-validation as follows: Gaussian Support Vector Machine (GSVM), Linear Support Vector Machine (LSVM), Weighted K-Nearest Neighbors (W-KNN), and Decision Tree algorithm.

Results

The LSVM classifier and W-KNN produce a testing accuracy of 100% with only seven features. Additionally, GSVM and decision tree produce a testing accuracy of 97.83% and 93.48%, respectively.

Conclusion

The proposed system represents an automatic and highly accurate AD detection with a few reliable and effective features and minimum time.

Multimodal neuroimage data fusion based on multikernel learning in personalized medicine

Neuroimaging has been widely used as a diagnostic technique for brain diseases. With the development of artificial intelligence, neuroimaging analysis using intelligent algorithms can capture more image feature patterns than artificial experience-based diagnosis. However, using only single neuroimaging techniques, e.g., magnetic resonance imaging, may omit some significant patterns that may have high relevance to the clinical target. Therefore, so far, combining different types of neuroimaging techniques that provide multimodal data for joint diagnosis has received extensive attention and research in the area of personalized medicine. In this study, based on the regularized label relaxation linear regression model, we propose a multikernel version for multimodal data fusion. The proposed method inherits the merits of the regularized label relaxation linear regression model and also has its own superiority. It can explore complementary patterns across different modal data and pay more attention to the modal data that have more significant patterns. In the experimental study, the proposed method is evaluated in the scenario of Alzheimer’s disease diagnosis. The promising performance indicates that the performance of multimodality fusion via multikernel learning is better than that of single modality. Moreover, the decreased square difference between training and testing performance indicates that overfitting is reduced and hence the generalization ability is improved.

Sleep EEG-Based Approach to Detect Mild Cognitive Impairment

Mild Cognitive Impairment (MCI) is an early stage of dementia, which may lead to Alzheimer's disease (AD) in older adults. Therefore, early detection of MCI and implementation of treatment and intervention can effectively slow down or even inhibit the progression of the disease, thus minimizing the risk of AD. Currently, we know that published work relies on an analysis of awake EEG recordings. However, recent studies have suggested that changes in the structure of sleep may lead to cognitive decline. In this work, we propose a sleep EEG-based method for MCI detection, extracting specific features of sleep to characterize neuroregulatory deficit emergent with MCI. This study analyzed the EEGs of 40 subjects (20 MCI, 20 HC) with the developed algorithm. We extracted sleep slow waves and spindles features, combined with spectral and complexity features from sleep EEG, and used the SVM classifier and GRU network to identify MCI. In addition, the classification results of different feature sets (including with sleep features from sleep EEG and without sleep features from awake EEG) and different classification methods were evaluated. Finally, the MCI classification accuracy of the GRU network based on features extracted from sleep EEG was the highest, reaching 93.46%. Experimental results show that compared with the awake EEG, sleep EEG can provide more useful information to distinguish between MCI and HC. This method can not only improve the classification performance but also facilitate the early intervention of AD.

Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease

The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD.

Differential Diagnosis of Behavioral Variant and Semantic Variant of Frontotemporal Dementia Using Visual Rating Scales

BACKGROUND

Frontotemporal dementia (FTD) represents the second most frequent early onset of dementia in people younger than 65 years. The main syndromes encompassed by the term FTD are behavioral variant of Frontotemporal dementia (bvFTD), non-fluent variant primary progressive aphasia (nfvPPA) and semantic variant (SD).

AIMS

To assess the bvFTD and SD, which represent the most common subtypes of FTD, using visual rating scales.

METHODS

Brain MRI exams of 77 patients either with bvFTD (n=43) or SD (n=34) were evaluated. The rating scales used were: Global cortical atrophy (GCA), Fazekas Scale: periventricular (PV) and white matter (WM) changes, Koedam rating scale and visual scales regarding specific cortical regions: dorsofrontal (DF), orbitofrontal (OF), anterior cingulate (AC), basal ganglia (BG), anterior- temporal (AT), insula, lateral-temporal (LT), entorhinal (ERC), perirhinal (PRC), anterior fusiform( AF), anterior hippocampus (AHIP) and posterior hippocampus (PHIP). Both Left (L) and Right (R) hemispheres were evaluated.

RESULTS

R-OF (p=0.059), L-OF (p<0.0005), L-AT (p=0.047) and L-AHIP (p=0.007) have a statistically significant effect on the variable occurrence of SD compared to bvFTD. The indicators with the highest value of the area under the curve (AUC) were R-AC (0.829), L-OF (0.808), L-AC (0.791) and L-AF (0.778). Highest sensitivity was achieved by R-OF (97%) and L-AF (75%). Highest specificity was achieved by L-OF (95%), L-AT (91%) followed by R-AC (84%). Best combination of sensitivity and specificity was achieved by L-AF (74%-79%), L-OF (56%-95%) and R-OF (97%-42%). Best combination of PPV and NPV was achieved by L-OF (90%-73%), LAT (83%-72%) and R-AC (77%-77%).

CONCLUSION

Visual rating scales can be a practical diagnostic tool in the characterization of patterns of atrophy in FTLD and may be used as an alternative to highly technical methods of quantification.

One-year Follow-up Study of Hippocampal Subfield Atrophy in Alzheimer's Disease and Normal Aging

BACKGROUND

In this study, we investigated the effect of hippocampal subfield atrophy on the development of Alzheimer's disease (AD) by analyzing baseline magnetic resonance images (MRI) and images collected over a one-year follow-up period. Previous studies have suggested that morphological changes to the hippocampus are involved in both normal ageing and the development of AD. The volume of the hippocampus is an authentic imaging biomarker for AD. However, the diverse relationship of anatomical and complex functional connectivity between different subfields implies that neurodegenerative disease could lead to differences between the atrophy rates of subfields. Therefore, morphometric measurements at subfield-level could provide stronger biomarkers.

METHODS

Hippocampal subfield atrophies are measured using MRI scans, taken at multiple time points, and shape-based normalization to a Montreal neurological institute (MNI) ICBM 152 nonlinear atlas. Ninety subjects were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI), and divided equally into Healthy Controls (HC), AD, and mild cognitive impairment (MCI) groups. These subjects underwent serial MRI studies at three time-points: baseline, 6 months and 12 months.

RESULTS

We analyzed the subfield-level hippocampal morphometric effects of normal ageing and AD based on radial distance mapping and volume measurements. We identified a general trend and observed the largest hippocampal subfield atrophies in the AD group. Atrophy of the bilateral CA1, CA2- CA4 and subiculum subfields was higher in the case of AD than in MCI and HC. We observed the highest rate of reduction in the total volume of the hippocampus, especially in the CA1 and subiculum regions, in the case of MCI.

CONCLUSION

Our findings show that hippocampal subfield atrophy varies among the three study groups.

Convolutional Neural Network-based MR Image Analysis for Alzheimer’s Disease Classification

Background:

In this study, we used a convolutional neural network (CNN) to classify Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonance (MR) images of the brain.

Materials and Methods:

The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR images were matched to the International Consortium for Brain Mapping template (ICBM) using 3D-Slicer software. Using prior knowledge and anatomical annotation label information, the hippocampal region was automatically extracted from the brain MR images.

Results:

The area of the hippocampus in each image was preprocessed using local entropy minimization with a bi-cubic spline model (LEMS) by an inhomogeneity intensity correction method. To train the CNN model, we separated the dataset into three groups, namely AD/NC, AD/MCI, and MCI/NC. The prediction model achieved an accuracy of 92.3% for AD/NC, 85.6% for AD/MCI, and 78.1% for MCI/NC.

Conclusion:

The results of this study were compared to those of previous studies, and summarized and analyzed to facilitate more flexible analyses based on additional experiments. The classification accuracy obtained by the proposed method is highly accurate. These findings suggest that this approach is efficient and may be a promising strategy to obtain good AD, MCI and NC classification performance using small patch images of hippocampus instead of whole slide images.