ADNI Dataset Research Articles

A feature-aware multimodal framework with auto-fusion for Alzheimer’s disease diagnosis

Alzheimer’s disease (AD), one of the most common dementias, has about 4.6 million new cases yearly worldwide. Due to the significant amount of suspected AD patients, early screening for the disease has become particularly important. There are diversified types of AD diagnosis data, such as cognitive tests, images, and risk factors, many prior investigations have primarily concentrated on integrating only high-dimensional features and simple fusion concatenation, resulting in less-than-optimal outcomes for AD diagnosis. Therefore, We propose an enhanced multimodal AD diagnostic framework comprising a feature-aware module and an automatic model fusion strategy (AMFS). To preserve the correlation and significance features within a low-dimensional space, the feature-aware module employs a low-dimensional SHapley Additive exPlanation (SHAP) boosting feature selection as the initial step, following this analysis, diverse tiers of low-dimensional features are extracted from patients’ biological data. Besides, in the high-dimensional stage, the feature-aware module integrates cross-modal attention mechanisms to capture subtle relationships among different cognitive domains, neuroimaging modalities, and risk factors. Subsequently, we integrate the aforementioned feature-aware module with graph convolutional networks (GCN) to address heterogeneous data in multimodal AD, while also possessing the capability to perceive relationships between different modalities. Lastly, our proposed AMFS autonomously learns optimal parameters for aligning two sub-models. The validation tests using two ADNI datasets show the high accuracies of 95.9% and 91.9% respectively, in AD diagnosis. The methods efficiently select features from multimodal AD data, optimizing model fusion for potential clinical assistance in diagnostics.

Brain age gap estimation using attention-based ResNet method for Alzheimer’s disease detection

This study investigates the correlation between brain age and chronological age in healthy individuals using brain MRI images, aiming to identify potential biomarkers for neurodegenerative diseases like Alzheimer's. To achieve this, a novel attention-based ResNet method, 3D-Attention-Resent-SVR, is proposed to accurately estimate brain age and distinguish between Cognitively Normal (CN) and Alzheimer’s disease (AD) individuals by computing the brain age gap (BAG). Unlike conventional methods, which often rely on single datasets, our approach addresses potential biases by employing four datasets for training and testing. The results, based on a combined dataset from four public sources comprising 3844 data points, demonstrate the model's efficacy with a mean absolute error (MAE) of 2.05 for brain age gap estimation. Moreover, the model's generalizability is showcased by training on three datasets and testing on a separate one, yielding a remarkable MAE of 2.4. Furthermore, leveraging BAG as the sole biomarker, our method achieves an accuracy of 92% and an AUC of 0.87 in Alzheimer's disease detection on the ADNI dataset. These findings underscore the potential of our approach in assisting with early detection and disease monitoring, emphasizing the strong correlation between BAG and AD.

ANALYZE-AD: A comparative analysis of novel AI approaches for early Alzheimer’s detection

Alzheimer’s disease, characterized by progressive and irreversible deterioration of cognitive functions, represents a significant health concern, particularly among older adults, as it stands as the foremost cause of dementia. Despite its debilitating nature, early detection of Alzheimer’s disease holds considerable advantages for affected individuals. This study investigates machine-learning methodologies for the early diagnosis of Alzheimer’s disease, utilizing datasets sourced from OASIS and ADNI. The initial classification methods consist of a 5-class ADNI classification and a 3-class OASIS classification. Three unique methodologies encompass binary-class inter-dataset models, which involve training on a single dataset and subsequently testing on another dataset for both ADNI and OASIS datasets. Additionally, a hybrid dataset model is also considered. The proposed methodology entails the concatenation of both datasets, followed by shuffling and subsequently conducting training and testing on the amalgamated dataset. The findings demonstrate impressive levels of accuracy, as Light Gradient Boosting Machine (LGBM) achieved a 99.63% accuracy rate for 5-class ADNI classification and a 95.75% accuracy rate by Multilayer Perceptron (MLP) for 3-class OASIS classification, both when hyperparameter tweaking was implemented. The K-nearest neighbor algorithm demonstrated exceptional performance, achieving an accuracy of 87.50% in ADNI-OASIS (2 Class) when utilizing the Select K Best method. The Gaussian Naive Bayes algorithm demonstrated exceptional performance in the OASIS-ADNI approach, attaining an accuracy of 77.97% using Chi-squared feature selection. The accuracy achieved by the Hybrid method, which utilized LGBM with hyperparameter optimization, was 99.21%. Furthermore, the utilization of Explainable AI approaches, particularly Lime, was implemented in order to augment the interpretability of the model.

MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention.

The integration of structural magnetic resonance imaging (sMRI) and deep learning techniques is one of the important research directions for the automatic diagnosis of Alzheimer's disease (AD). Despite the satisfactory performance achieved by existing voxel-based models based on convolutional neural networks (CNNs), such models only handle AD-related brain atrophy at a single spatial scale and lack spatial localization of abnormal brain regions based on model interpretability. To address the above limitations, we propose a traceable interpretability model for AD recognition based on multi-patch attention (MAD-Former). MAD-Former consists of two parts: recognition and interpretability. In the recognition part, we design a 3D brain feature extraction network to extract local features, followed by constructing a dual-branch attention structure with different patch sizes to achieve global feature extraction, forming a multi-scale spatial feature extraction framework. Meanwhile, we propose an important attention similarity position loss function to assist in model decision-making. The interpretability part proposes a traceable method that can obtain a 3D ROI space through attention-based selection and receptive field tracing. This space encompasses key brain tissues that influence model decisions. Experimental results reveal the significant role of brain tissues such as the Fusiform Gyrus (FuG) in AD recognition. MAD-Former achieves outstanding performance in different tasks on ADNI and OASIS datasets, demonstrating reliable model interpretability.

Spatio-Temporal Graph Hubness Propagation Model for Dynamic Brain Network Classification.

Dynamic brain network has the advantage over static brain network in characterizing the variation pattern of functional brain connectivity, and it has attracted increasing attention in brain disease diagnosis. However, most of the existing dynamic brain networks analysis methods rely on extracting features from independent brain networks divided by sliding windows, making them hard to reveal the high-order dynamic evolution laws of functional brain networks. Additionally, they cannot effectively extract the spatio-temporal topology features in dynamic brain networks. In this paper, we propose to use optimal transport (OT) theory to capture the topology evolution of the dynamic brain networks, and develop a multi-channel spatio-temporal graph convolutional network that collaboratively extracts the temporal and spatial features from the evolution networks. Specifically, we first adaptively evaluate the graph hubness of brain regions in the brain network of each time window, which comprehensively models information transmission among multiple brain regions. Second, the hubness propagation information across adjacent time windows is captured by optimal transport, describing high-order topology evolution of dynamic brain networks. Moreover, we develop a spatio-temporal graph convolutional network with attention mechanism to collaboratively extract the intrinsic temporal and spatial topology information from the above networks. Finally, the multi-layer perceptron is adopted for classifying the dynamic brain network. The extensive experiment on the collected epilepsy dataset and the public ADNI dataset show that our proposed method not only outperforms several state-of-the-art methods in brain disease diagnosis, but also reveals the key dynamic alterations of brain connectivities between patients and healthy controls.

TauFlowNet: Revealing latent propagation mechanism of tau aggregates using deep neural transport equations

Mounting evidence shows that Alzheimer's disease (AD) is characterized by the propagation of tau aggregates throughout the brain in a prion-like manner. Since current pathology imaging technologies only provide a spatial mapping of tau accumulation, computational modeling becomes indispensable in analyzing the spatiotemporal propagation patterns of widespread tau aggregates from the longitudinal data. However, current state-of-the-art works focus on the longitudinal change of focal patterns, lacking a system-level understanding of the tau propagation mechanism that can explain and forecast the cascade of tau accumulation. To address this limitation, we conceptualize that the intercellular spreading of tau pathology forms a dynamic system where each node (brain region) is ubiquitously wired with other nodes while interacting with the build-up of pathological burdens. In this context, we formulate the biological process of tau spreading in a principled potential energy transport model (constrained by brain network topology), which allows us to develop an explainable neural network for uncovering the spatiotemporal dynamics of tau propagation from the longitudinal tau-PET scans. Specifically, we first translate the transport equation into a GNN (graph neural network) backbone, where the spreading flows are essentially driven by the potential energy of tau accumulation at each node. Conventional GNNs employ a l2-norm graph smoothness prior, resulting in nearly equal potential energies across nodes, leading to vanishing flows. Following this clue, we introduce the total variation (TV) into the graph transport model, where the nature of system's Euler-Lagrange equations is to maximize the spreading flow while minimizing the overall potential energy. On top of this min-max optimization scenario, we design a generative adversarial network (GAN-like) to characterize the TV-based spreading flow of tau aggregates, coined TauFlowNet. We evaluate our TauFlowNet on ADNI and OASIS datasets in terms of the prediction accuracy of future tau accumulation and explore the propagation mechanism of tau aggregates as the disease progresses. Compared to the current counterpart methods, our physics-informed deep model yields more accurate and interpretable results, demonstrating great potential in discovering novel neurobiological mechanisms through the lens of machine learning.

Distributional stochastic Planner‐Actor‐Critic for deformable image registration

AbstractImage registration is a fundamental task in medical image analysis, and large deformation registration poses significant challenges due to substantial appearance differences between the fixed image and the moving image. With the advancement of artificial intelligence, reinforcement learning has been gradually incorporated into image registration tasks. Notably, Ziwei Luo, Jing Hu, and other researchers introduced the first reinforcement learning‐based large deformation registration model called Stochastic Planner‐Actor‐Critic (SPAC), achieving superior performance in large deformation registration compared with advanced deep learning registration methods, which is the only large deformation registration reinforcement learning model. However, the existing reinforcement learning model for large deformation registration do not consider the impact of Q‐value prediction accuracy on the overall task results. This study aims to investigate brain large deformation registration under the influence of Alzheimer's disease. To further improve the accuracy of brain large deformation registration, this paper combines the proven advanced Q‐value distribution prediction in reinforcement learning with the large deformation registration model SPAC, designing a distributional SPAC model (DSPAC). Subsequent experiments on the ADNI dataset demonstrate the effectiveness of this model.

The Use of Generative Adversarial Network and Graph Convolution Network for Neuroimaging-Based Diagnostic Classification.

Functional connectivity (FC) obtained from resting-state functional magnetic resonance imaging has been integrated with machine learning algorithms to deliver consistent and reliable brain disease classification outcomes. However, in classical learning procedures, custom-built specialized feature selection techniques are typically used to filter out uninformative features from FC patterns to generalize efficiently on the datasets. The ability of convolutional neural networks (CNN) and other deep learning models to extract informative features from data with grid structure (such as images) has led to the surge in popularity of these techniques. However, the designs of many existing CNN models still fail to exploit the relationships between entities of graph-structure data (such as networks). Therefore, graph convolution network (GCN) has been suggested as a means for uncovering the intricate structure of brain network data, which has the potential to substantially improve classification accuracy. Furthermore, overfitting in classifiers can be largely attributed to the limited number of available training samples. Recently, the generative adversarial network (GAN) has been widely used in the medical field for its generative aspect that can generate synthesis images to cope with the problems of data scarcity and patient privacy. In our previous work, GCN and GAN have been designed to investigate FC patterns to perform diagnosis tasks, and their effectiveness has been tested on the ABIDE-I dataset. In this paper, the models will be further applied to FC data derived from more public datasets (ADHD, ABIDE-II, and ADNI) and our in-house dataset (PTSD) to justify their generalization on all types of data. The results of a number of experiments show the powerful characteristic of GAN to mimic FC data to achieve high performance in disease prediction. When employing GAN for data augmentation, the diagnostic accuracy across ADHD-200, ABIDE-II, and ADNI datasets surpasses that of other machine learning models, including results achieved with BrainNetCNN. Specifically, in ADHD, the accuracy increased from 67.74% to 73.96% with GAN, in ABIDE-II from 70.36% to 77.40%, and in ADNI, reaching 52.84% and 88.56% for multiclass and binary classification, respectively. GCN also obtains decent results, with the best accuracy in ADHD datasets at 71.38% for multinomial and 75% for binary classification, respectively, and the second-best accuracy in the ABIDE-II dataset (72.28% and 75.16%, respectively). Both GAN and GCN achieved the highest accuracy for the PTSD dataset, reaching 97.76%. However, there are still some limitations that can be improved. Both methods have many opportunities for the prediction and diagnosis of diseases.

A coupled-GAN architecture to fuse MRI and PET image features for multi-stage classification of Alzheimer’s disease

Alzheimer’s disease (AD) is a degenerative neurological ailment that begins with memory loss and ultimately leads to a total loss of mental capacity. Researchers are interested in using magnetic resonance imaging (MRI) and positron emission tomography (PET) to find people with mild cognitive impairment (MCI), which is a stage before Alzheimer’s disease (AD). Significant hippocampal loss and temporal lobe atrophy characterize the transition from MCI to AD, which can be visualized using T1-W structural MRI. PET visualizes brain glucose metabolism, which indicates neuronal activity, making it a viable neuroimaging method for AD diagnosis. The extraction and fusion of structural and metabolite information about brain alterations contained in multimodal data is crucial for achieving an appropriate classification result. Therefore, in this work a new end-to-end coupled-GAN (CGAN) architecture is introduced. The proposed CGANC network consists of two sub-models: a CGAN for extraction of fused features from multimodal data, and a CNN classifier to classify these features. The proposed CGAN model is trained to encode MRI and PET images into a shared latent space. The fused features are extracted from this shared latent space and then are classified according to particular stage of AD. In order to test the effectiveness of the suggested approach, experiments are done on the publicly available ADNI dataset and compared with state-of-the-art methods. The proposed method’s source code will be made freely available at https://github.com/ChandrajitChoudhury/CGAN-AD.

Multiple phenotype association tests based on sliced inverse regression

BackgroundJoint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in dimensionality has become a very challenging problem in the widespread use of joint analysis. To handle the excessiveness of variables, we consider the sliced inverse regression (SIR) method. Specifically, we propose a novel SIR-based association test that is robust and powerful in testing the association between multiple predictors and multiple outcomes.ResultsWe conduct simulation studies in both low- and high-dimensional settings with various numbers of Single-Nucleotide Polymorphisms and consider the correlation structure of traits. Simulation results show that the proposed method outperforms the existing methods. We also successfully apply our method to the genetic association study of ADNI dataset. Both the simulation studies and real data analysis show that the SIR-based association test is valid and achieves a higher efficiency compared with its competitors.ConclusionSeveral scenarios with low- and high-dimensional responses and genotypes are considered in this paper. Our SIR-based method controls the estimated type I error at the pre-specified level α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document}.

Ordinal Pattern Tree: A New Representation Method for Brain Network Analysis.

Brain networks, describing the functional or structural interactions of brain with graph theory, have been widely used for brain imaging analysis. Currently, several network representation methods have been developed for describing and analyzing brain networks. However, most of these methods ignored the valuable weighted information of the edges in brain networks. In this paper, we propose a new representation method (i.e., ordinal pattern tree) for brain network analysis. Compared with the existing network representation methods, the proposed ordinal pattern tree (OPT) can not only leverage the weighted information of the edges but also express the hierarchical relationships of nodes in brain networks. On OPT, nodes are connected by ordinal edges which are constructed by using the ordinal pattern relationships of weighted edges. We represent brain networks as OPTs and further develop a new graph kernel called optimal transport (OT) based ordinal pattern tree (OT-OPT) kernel to measure the similarity between paired brain networks. In OT-OPT kernel, the OT distances are used to calculate the transport costs between the nodes on the OPTs. Based on these OT distances, we use exponential function to calculate OT-OPT kernel which is proved to be positive definite. To evaluate the effectiveness of the proposed method, we perform classification and regression experiments on ADHD-200, ABIDE and ADNI datasets. The experimental results demonstrate that our proposed method outperforms the state-of-the-art graph methods in the classification and regression tasks.

Early Detection of Alzheimers Disease through Deep Learning in MRI Scans

Abstract: Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory impairment, leading to dementia and irreversible decline in cognitive abilities. This disease causes the person to suffer from memory loss, unusual behaviour, and language problems. The initial symptoms, such as episodic memory impairment and the navigational problem of the patient, are typical variants. In the proposed system we are using The Deep Learning Models as Modified CNN along with MobileNet, and VGG16 algorithms and classify the images as either Mild Demented or Moderate Demented or Non-Demented or Very Mild Demented. In this study we are using Augmented ADNI dataset which consists of 40000 images. This can be utilized in the future to classify the types of different classifications easily which is easy to find out the infections in initial stages and can be cured in the initial stages only.

Interpretable medical deep framework by logits-constraint attention guiding graph-based multi-scale fusion for Alzheimer’s disease analysis

Deep learning using structural MRI has been widely applied to early diagnosis study of Alzheimer’s disease. Among existing methods, attention-based 3D subject-level methods can not only provide diagnosis results but also interpret the significant brain regions, thereby attracting considerable attention. However, the performance of previous attention-based methods might be still restricted by: (i) the gap between attention scores and semantic significant regions; (ii) using only single-scale features or simply fusing multi-scale information by addition or concatenation for classification decision-making. To overcome these two issues, we propose an innovative dual-branch model called LA-GMF, which consists of two major modules: logits-constraint attention (LA) and graph-based multi-scale fusion (GMF). The LA module is designed to guide the model to focus on key areas to enhance the diagnostic performance of local lesions, by reducing the inconsistency between attention scores and class prediction probabilities. Meanwhile, by combining the graph neural network and the self-attention mechanism, the GMF module not only introduces the interaction between patches, but also explores the correlation and complementarity between features at different scales, thereby extracting feature representations more comprehensively. Experiments on the popular ADNI and AIBL datasets validate the potential of our model in boosting early AD diagnosis accuracy. Additionally, our interpretation experiments demonstrate the superior interpretability performance of the proposed method over recent state-of-the-art attention-based methods. Our source codes are released at: https://github.com/nollexu/LA-GMF.

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer’s disease in patients with mild cognitive symptoms

BackgroundPredicting future Alzheimer’s disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions.MethodsA cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: (1) clinical data only, including demographics, cognitive tests and APOE ε4 status, (2) clinical data plus hippocampal volume, (3) clinical data plus all regional MRI gray matter volumes (N = 68) extracted using FreeSurfer software, (4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. A double cross-validation scheme, with five test folds and for each of those ten validation folds, was used. External evaluation was performed on part of the ADNI dataset, including 108 patients. Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant.ResultsIn the BioFINDER cohort, 109 patients (33%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC) = 0.85 and four-year cognitive decline was R2 = 0.14. The performance was improved for both outcomes when adding hippocampal volume (AUC = 0.86, R2 = 0.16). Adding FreeSurfer brain regions improved prediction of four-year cognitive decline but not progression to AD (AUC = 0.83, R2 = 0.17), while the DL model worsened the performance for both outcomes (AUC = 0.84, R2 = 0.08). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included. In the external evaluation cohort from ADNI, 23 patients (21%) progressed to AD dementia. The results for predicted progression to AD dementia were similar to the results for the BioFINDER test data, while the performance for the cognitive decline was deteriorated.ConclusionsThe DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.

A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.

Alzheimer's disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeutics for Alzheimer's disease, but most clinical trials have failed to show significant treatment effects on slowing or halting cognitive decline. Among several challenges in such trials, one recently noticed but unsolved is biased allocation of fast and slow cognitive decliners to treatment and placebo groups during randomization caused by the large individual variation in the speed of cognitive decline. This allocation bias directly results in either over- or underestimation of the treatment effect from the outcome of the trial. In this study, we propose a stratified randomization method using the degree of cognitive decline predicted by an artificial intelligence model as a stratification index to suppress the allocation bias in randomization and evaluate its effectiveness by simulation using ADNI data set.

A patch distribution-based active learning method for multiple instance Alzheimer's disease diagnosis

Medical data, particularly the complex brain imaging structures, acquisition presents significant difficulties and high diagnostic expenses, resulting in a scarcity of the trainable samples in the real-world scenarios. To overcome this limitation, we present an active learning-based sampling strategy that selects the most informative samples from the unlabeled candidate sample pool for expert annotation, leading to high classification performance with a reduced number of training samples. This study adopts a patch-level perspective and introduces a multi-instance learning framework for Alzheimer's Disease diagnosis. Initially, a patch pre-selection module is designed to identify pathology-prone regions while excluding background areas and irrelevant information. Subsequently, an inner-patch local attention mechanism block and an outer-patch global attention mechanism block are developed to enhance the extraction of discriminative local and global information by the network model. Finally, an active learning sampling strategy is devised to minimize the costs associated with data acquisition and expert annotation in medical domain. The effectiveness of the proposed network framework and active learning strategy was validated through four sets of control experiments on the ADNI dataset.

Enhancing healthcare recommendation: transfer learning in deep convolutional neural networks for Alzheimer disease detection.

Neurodegenerative disorders such as Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) significantly impact brain function and cognition. Advanced neuroimaging techniques, particularly Magnetic Resonance Imaging (MRI), play a crucial role in diagnosing these conditions by detecting structural abnormalities. This study leverages the ADNI and OASIS datasets, renowned for their extensive MRI data, to develop effective models for detecting AD and MCI. The research conducted three sets of tests, comparing multiple groups: multi-class classification (AD vs. Cognitively Normal (CN) vs. MCI), binary classification (AD vs. CN, and MCI vs. CN), to evaluate the performance of models trained on ADNI and OASIS datasets. Key preprocessing techniques such as Gaussian filtering, contrast enhancement, and resizing were applied to both datasets. Additionally, skull stripping using U-Net was utilized to extract features by removing the skull. Several prominent deep learning architectures including DenseNet-201, EfficientNet-B0, ResNet-50, ResNet-101, and ResNet-152 were investigated to identify subtle patterns associated with AD and MCI. Transfer learning techniques were employed to enhance model performance, leveraging pre-trained datasets for improved Alzheimer's MCI detection. ResNet-101 exhibited superior performance compared to other models, achieving 98.21% accuracy on the ADNI dataset and 97.45% accuracy on the OASIS dataset in multi-class classification tasks encompassing AD, CN, and MCI. It also performed well in binary classification tasks distinguishing AD from CN. ResNet-152 excelled particularly in binary classification between MCI and CN on the OASIS dataset. These findings underscore the utility of deep learning models in accurately identifying and distinguishing neurodegenerative diseases, showcasing their potential for enhancing clinical diagnosis and treatment monitoring.

Predictive modeling of Alzheimer's disease progression: Integrating temporal clinical factors and outcomes in time series forecasting

Alzheimer's disease is a complex neurodegenerative disorder that profoundly impacts millions of individuals worldwide, presenting significant challenges in both diagnosis and treatment. Recent advances in deep learning-based methods have shown promising potential for predicting disease progression using multimodal data. However, the majority of studies in this domain have predominantly focused on cross-sectional data, neglecting the crucial temporal dimension of the disease's progression. In this study, we propose a novel approach to predict the progression of Alzheimer's disease by leveraging a multimodal time-series forecasting system based on graph representation learning. Our approach incorporates a Temporal Graph Network encoder, employing k-nearest neighbors and Cumulative Bayesian Ridge with high correlation imputation to generate graph node embeddings at each time step. Furthermore, we employ an Encoder-Decoder architecture, where a Graph Attention Network translates a dynamic graph into node embeddings, and a decoder estimates future edge probabilities. When utilizing all available patient features in the ADNI dataset, our proposed method achieved an Area Under the Curve (AUC) of 0.8090 for dynamic edge prediction. Furthermore, for neuroimaging data, the AUC improved significantly to 0.8807.

Resilience to AD pathology in Top Cognitive Performers.

Successful cognitive aging is often thought to result from resistance to the accumulation of pathology, resilience to the effects of pathological accumulation, or some combination of the two. While evidence for resilience has been found in typical aging populations, the oldest-old provide us with a unique window into the role of pathological accumulation in impacting cognition. Here, we aimed to assess group differences in measures of amyloid and tau across older age groups using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI age: 60-89) and The 90+ Study (age: 90-101). Additionally, using the ADNI dataset, we performed exploratory analyses of regional cingulate AV-45 SUVRs to assess if amyloid load in particular areas was associated with Top Cognitive Performance (TCP). Consistent with the literature, results showed no group differences in amyloid SUVRs both regionally and in the whole cortex. For tau with AV-1451, we also observed no differences in Braak composite SUVRs. Interestingly, these relationships persisted in the oldest-old. This indicates that Top Cognitive Performance throughout aging does not reflect resistance to amyloid and tau burden, but that other mechanisms may be associated with protection against amyloid and tau related neurodegeneration.

Dominating Alzheimer's disease diagnosis with deep learning on sMRI and DTI-MD.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has become one of the major health concerns for the elderly. Computer-aided AD diagnosis can assist doctors in quickly and accurately determining patients' severity and affected regions. In this paper, we propose a method called MADNet for computer-aided AD diagnosis using multimodal datasets. The method selects ResNet-10 as the backbone network, with dual-branch parallel extraction of discriminative features for AD classification. It incorporates long-range dependencies modeling using attention scores in the decision-making layer and fuses the features based on their importance across modalities. To validate the effectiveness of our proposed multimodal classification method, we construct a multimodal dataset based on the publicly available ADNI dataset and a collected XWNI dataset, which includes examples of AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN). On this dataset, we conduct binary classification experiments of AD vs. CN and MCI vs. CN, and demonstrate that our proposed method outperforms other traditional single-modal deep learning models. Furthermore, this conclusion also confirms the necessity of using multimodal sMRI and DTI data for computer-aided AD diagnosis, as these two modalities complement and convey information to each other. We visualize the feature maps extracted by MADNet using Grad-CAM, generating heatmaps that guide doctors' attention to important regions in patients' sMRI, which play a crucial role in the development of AD, establishing trust between human experts and machine learning models. We propose a simple yet effective multimodal deep convolutional neural network model MADNet that outperforms traditional deep learning methods that use a single-modality dataset for AD diagnosis.