Alzheimer's Disease Neuroimaging Initiative Dataset Research Articles

Optimizing gene selection for Alzheimer’s disease classification: A Bayesian approach to filter and embedded techniques

Alzheimer’s disease (AD) classification, which is crucial for identifying AD-associated genes, relies heavily on effective feature selection (FS) to tackle the curse of dimensionality. Traditional methods like filter, wrapper, and embedded techniques have their drawbacks, including ignoring feature independence, sensitivity to classifier choices, and high computational costs. Hybrid approaches combining these methods seek to harness their collective strengths but face challenges, particularly in selecting the optimal number of features from each method. This selection is typically manual or requires time-intensive k-fold cross-validation (KFCV), significantly increasing computational demands and complicating the process with the need for extensive parameter optimization across families, thereby escalating the complexity and resource requirements of model development. To overcome these challenges, this work proposes a framework for optimal FS and classification in AD using a combination of filter and embedded techniques, enhanced with hyperparameter tuning. Firstly, gene expression data (GED) from the AD Neuroimaging Initiative (ADNI) is preprocessed. Then, Chi-square filter selection is applied to decrease correlated features. Next, Logistic Regression with ElasticNet penalty (LREN) is employed to further refine the feature set. Finally, Bayesian Optimization (BO) is introduced to automatically determine the optimal number of features (k for Chi-square and max_features for LREN), iteratively evaluating different combinations to find the set that maximizes model accuracy. The selection process is used primarily in the function of BO to tune automatically the (k and max_features while minimizing the number of features and maximizing the accuracy of the Support Vector Machine (SVM). The SVM was used as a classifier to overcome the problem of embedded selection sensitivity to the model of selection. The tuned features are then used to select relevant features from the ADNI dataset and fitted to different models. We evaluated the performance of five classifiers—logistic regression (LR), SVM, Ridge Classifier (RC), stochastic gradient descent classifier (SGD), and Gaussian Naïve Bayes(GNB) across various metrics. Among these, SVM achieved 100% performance in all metrics. This approach significantly reduced the FS time and the number of initial features to 0.6% and 0.02%, respectively. Notably, it identified 6 out of 20 selected features as directly AD-related. Comparative analysis reveals that the proposed method outperforms existing approaches on the ADNI dataset and other datasets. Statistical tests were conducted to assess the significance of the results compared to other methods, confirming a significant improvement and underscoring the effectiveness of the proposed framework in optimal FS and biological relevance confirmation.

A Data-Driven Cognitive Composite Sensitive to Amyloid-β for Preclinical Alzheimer's Disease.

Integrating scores from multiple cognitive tests into a single cognitive composite has been shown to improve sensitivity to detect AD-related cognitive impairment. However, existing composites have little sensitivity to amyloid-β status (Aβ +/-) in preclinical AD. Evaluate whether a data-driven approach for deriving cognitive composites can improve the sensitivity to detect Aβ status among cognitively unimpaired (CU) individuals compared to existing cognitive composites. Based on the data from the Anti-Amyloid Treatment in the Asymptomatic Alzheimer's Disease (A4) study, a novel composite, the Data-driven Preclinical Alzheimer's Cognitive Composite (D-PACC), was developed based on test scores and response durations selected using a machine learning algorithm from the Cogstate Brief Battery (CBB). The D-PACC was then compared with conventional composites in the follow-up A4 visits and in individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The D-PACC showed a comparable or significantly higher ability to discriminate Aβ status [median Cohen's d = 0.172] than existing composites at the A4 baseline visit, with similar results at the second visit. The D-PACC demonstrated the most consistent sensitivity to Aβ status in both A4 and ADNI datasets. The D-PACC showed similar or improved sensitivity when screening for Aβ+ in CU populations compared to existing composites but with higher consistency across studies.

Multimodal Neuroimaging Fusion for Alzheimer's Disease: An Image Colorization Approach With Mobile Vision Transformer

ABSTRACTMultimodal neuroimaging, combining data from different sources, has shown promise in the classification of the Alzheimer's disease (AD) stage. Existing multimodal neuroimaging fusion methods exhibit certain limitations, which require advancements to enhance their objective performance, sensitivity, and specificity for AD classification. This study uses the use of a Pareto‐optimal cosine color map to enhance classification performance and visual clarity of fused images. A mobile vision transformer (ViT) model, incorporating the swish activation function, is introduced for effective feature extraction and classification. Fused images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Whole Brain Atlas (AANLIB), and Open Access Series of Imaging Studies (OASIS) datasets, obtained through optimized transposed convolution, are utilized for model training, while evaluation is achieved using images that have not been fused from the same databases. The proposed model demonstrates high accuracy in AD classification across different datasets, achieving 98.76% accuracy for Early Mild Cognitive Impairment (EMCI) versus LMCI, 98.65% for Late Mild Cognitive Impairment (LMCI) versus AD, 98.60% for EMCI versus AD, and 99.25% for AD versus Cognitive Normal (CN) in the ADNI dataset. Similarly, on OASIS and AANLIB, the precision of the AD versus CN classification is 99.50% and 96.00%, respectively. Evaluation metrics showcase the model's precision, recall, and F1 score for various binary classifications, emphasizing its robust performance.

Diffusion model enables quantitative CBF analysis of Alzheimer's Disease.

Cerebral blood flow (CBF) measured by arterial spin labeling (ASL) is a promising biomarker for Alzheimer's Disease (AD). ASL data from multiple vendors were included in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. However, the M0 images were missing in Siemens ASL data, prohibiting CBF quantification. Here, we utilized a generative diffusion model to impute the missing M0 and validated generated CBF data with acquired data from GE. A conditional latent diffusion model was trained to generate the M0 image and validate it on an in-house dataset (N=55) based on image similarity metrics, accuracy of CBF quantification, and consistency with the physical model. This model was then applied to the ADNI dataset (Siemens: N=211) to impute the missing M0 for CBF calculation. We further compared the imputed data (Siemens) and acquired data (GE) regarding regional CBF differences by AD stages, their classification accuracy for AD prediction, and CBF trajectory slopes estimated by a mixed effect model. The trained diffusion model generated the M0 image with high fidelity (Structural similarity index, SSIM=0.924±0.019; peak signal-to-noise ratio, PSNR=33.348±1.831) and caused minimal bias in CBF values (mean difference in whole brain is 1.07±2.12ml/100g/min). Both generated and acquired CBF data showed similar differentiation patterns by AD stages, similar classification performance, and decreasing slopes with AD progression in specific AD-related regions. Generated CBF data also improved accuracy in classifying AD stages compared to qualitative perfusion data. This study shows the potential of diffusion models for imputing missing modalities for large-scale studies of CBF variation with AD.

Brain magnetic resonance imaging image classification for Alzheimer's disease and its hardware acceleration

<span lang="EN-US">Alzheimer's is a progressive neurodegenerative disorder and is considered the sixth leading cause of death after cancer and heart attack. Early detection and diagnosis provide individuals to go through a wider variety of clinical trials and get multiple medical benefits. Research on the application of deep learning and machine learning to the early detection of Alzheimer's disease has recently gained considerable attention. In this paper, we propose a deep learning classification framework to classify the individual with different progression stages of Alzheimer's disease such as mild cognitive impairment (MCI) and cognitive normal (CN). The dataset from Alzheimer’s disease neuroimaging initiative (ADNI) is considered in this paper which is a multisite having collection of Neuroimaging data for researchers. Structural magnetic resonance imaging (MRI) images are considered from the ADNI data set and feature extraction is done using a 2D discrete wavelet transform. 97% of data reduction is achieved during data pre-processing. The algorithm is trained and validated. The algorithm is accelerated in Nvidia Tx2 graphics processing unit (GPU) to get the better throughput. The result shows our algorithm outperforms the other deep learning algorithms with 91.56% accuracy. </span>

Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons with and without Significant Amyloid Plaques.

Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer's disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques. To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio. We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer's Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score. Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model's superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors. Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer's disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.

Dual Attention Aware Octave Convolution Network for Early-Stage Alzheimer's Disease Detection

Some of the most fundamental human capabilities, including thought, speech, and movement, may be lost due to brain illnesses. The most prevalent form of dementia, Alzheimer's disease (AD), is caused by a steady decline in brain function and is now incurable. Despite the challenges associated with making a conclusive diagnosis of AD, the field has generally shifted toward making diagnoses justified by patient records and neurological analysis, such as MRI. Reports of studies utilizing machine learning for AD identification have increased in recent years. In this publication, we report the results of our most recent research. It details a deep learning-based, 3D brain MRI-based method for automated AD detection. As a result, deep learning models have become increasingly popular in recent years for analyzing medical images. To aid in detecting Alzheimer's disease at an initial phase, we suggest a dual attention-aware Octave convolution-based deep learning network (DACN). The three main parts of DACN are as follows: First, we use Patch Convolutional Neural Network (PCNN) to identify discriminative features within each MRI patch while simultaneously boosting the features of abnormally altered micro-structures in the brain; second, we use an Octave convolution to minimize the spatial redundancy and widen the field of perception of the brain's structure; and third, we use a dual attention aware convolution classifier to dissect the resulting depiction further. An outstanding test accuracy of 99.87% is reached for categorizing dementia phases by employing the suggested method in experiments on a publically available ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset. The proposed model was more effective, efficient, and reliable than the state-of-the-art models through our comparisons.

An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation.

Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmostimportance. Our aim is to propose and validate a HS segmentation model that is both training-free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state-of-the-art techniques. The segmented HS can serve as a biomarker for studying the progression ofAD. We utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwudatasets. The median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter-group differences revealed that, except for the right rostral region in the ADNI dataset, the p-values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA,respectively. Our framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas-based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression ofAD.

Multi-modal imaging genetics data fusion by deep auto-encoder and self-representation network for Alzheimer's disease diagnosis and biomarkers extraction

Alzheimer's disease (AD) is an incurable neurodegenerative disease, so it is important to intervene in the early stage of the disease. Brain imaging genetics is an effective technique to identify AD-related biomarkers, which can early diagnosis of AD patients once they are clinically verified. With the development of medical imaging and gene sequencing techniques, the association analysis between multi-modal imaging data and genetic data has garnered increasing attention. However, current imaging genetics studies have problem with non-intuitive data fusion. Meanwhile, the characteristics of multi-modal imaging genetics data are high-dimensional, non-linearity, and fewer subjects, so it is necessary to select effective features. In this paper, a multi-modal data fusion framework by deep auto-encoder and self-representation (MFASN) was proposed for early diagnosis of AD. First, a multi-modality brain network was constructed by combining information from the resting-state functional magnetic resonance imaging (fMRI) data and structural magnetic resonance imaging (sMRI) data. Then, we utilized the deep auto-encoder to achieve non-linear transformations and select the informative features. A sparse self-representation module was employed to capture the multi-subspaces structure of the latent representation. At last, a multi-task structured sparse association model was developed to fully mine correlations between the genetic data and multi-modal brain network features. Experiments on AD neuroimaging initiative datasets proved the superiority of the proposed method, while discovering discriminative biomarkers were strongly associated with AD.

BIGFormer: A Graph Transformer with Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data.

Alzheimer's disease (AD) is a highly inheritable neurological disorder, and brain imaging genetics (BIG) has become a rapidly advancing field for comprehensive understanding its pathogenesis. However, most of the existing approaches underestimate the complexity of the interactions among factors that cause AD. To take full appreciate of these complexity interactions, we propose BIGFormer, a graph Transformer with local structural awareness, for AD diagnosis and identification of pathogenic mechanisms. Specifically, the factors interaction graph is constructed with lesion brain regions and risk genes as nodes, where the connection between nodes intuitively represents the interaction between nodes. After that, a perception with local structure awareness is built to extract local structure around nodes, which is then injected into node representation. Then, the global reliance inference component assembles the local structure into higher-order structure, and multi-level interaction structures are jointly aggregated into a classification projection head for disease state prediction. Experimental results show that BIGFormer demonstrated superiority in four classification tasks on the AD neuroimaging initiative dataset and proved to identify biomarkers closely intimately related to AD.

Hybrid D-OCapNet: Automated Multi-Class Alzheimer’s Disease Classification in Brain MRI Using Hybrid Dense Optimal Capsule Network

Efficient detection of Alzheimer’s disease (AD) is challenging in medical image processing. Different methodologies are proposed for detecting AD at earlier stages, but certain demerits emerge, like less robust, less convergent, time-consuming, and lead over maximized losses. Hence the proposed research paper develops an efficient and automated deep learning-based AD detection using MRI image data. The initial step in AD classification is data acquisition which focuses on collecting brain MRI images from Alzheimer’s disease Neuroimaging Initiative (ADNI) Extracted Axial dataset and OASIS dataset. The gathered data are pre-processed through an Improved Median Filter (IMF), normalization is performed using the [0, 1] rescaling method, and skull stripping is done using Morphological Thresholding (MoT). The pre-processed images are fed into Multiview Fuzzy Clustering (MvFC) algorithm to segment the brain tissues as Gray Matter (GM), Cerebrospinal Fluid (CSF) and White Matter (WM) effectively. The process of Deep Feature Extraction, Multi-class Classification and loss optimization is performed using the Hybrid Dense Optimal Capsule Network (Hybrid D-OCapNet). The loss evaluated in the proposed Hybrid D-OCapNet model is optimized using the Modified Bald Eagle Search (M-BES) optimization algorithm. The simulation outcomes of training, testing and validation in AD classification are analyzed using MATLAB. The overall accuracy in classifying AD is 99.32%, sensitivity is 98.42%, specificity is 98.90%, precision is 98.93%, and F1 score is 98.44 for the ADNI dataset. The accuracy of 98.97%, the sensitivity of 98.31% and the F1 score of 98.39% are obtained for the OASIS dataset.

EAMNet: an Alzheimer’s disease prediction model based on representation learning

Objective. Brain 18F-FDG PET images indicate brain lesions’ metabolic status and offer the predictive potential for Alzheimer’s disease (AD). However, the complexity of extracting relevant lesion features and dealing with extraneous information in PET images poses challenges for accurate prediction. Approach. To address these issues, we propose an innovative solution called the efficient adaptive multiscale network (EAMNet) for predicting potential patient populations using positron emission tomography (PET) image slices, enabling effective intervention and treatment. Firstly, we introduce an efficient convolutional strategy to enhance the receptive field of PET images during the feature learning process, avoiding excessive extraction of fine tissue features by deep-level networks while reducing the model’s computational complexity. Secondly, we construct a channel attention module that enables the prediction model to adaptively allocate weights between different channels, compensating for the spatial noise in PET images’ impact on classification. Finally, we use skip connections to merge features from different-scale lesion information. Through visual analysis, the network constructed in this article aligns with the regions of interest of clinical doctors. Main results. Through visualization analysis, our network aligns with regions of interest identified by clinical doctors. Experimental evaluations conducted on the ADNI (Alzheimer’s Disease Neuroimaging Initiative) dataset demonstrate the outstanding classification performance of our proposed method. The accuracy rates for AD versus NC (Normal Controls), AD versus MCI (Mild Cognitive Impairment), MCI versus NC, and AD versus MCI versus NC classifications achieve 97.66%, 96.32%, 95.23%, and 95.68%, respectively. Significance. The proposed method surpasses advanced algorithms in the field, providing a hopeful advancement in accurately predicting and classifying Alzheimer’s Disease using 18F-FDG PET images. The source code has been uploaded to https://github.com/Haoliang-D-AHU/EAMNet/tree/master.

A New Deep Learning Model based on Neuroimaging for Predicting Alzheimer's Disease

Background: The psychological aspects of the brain in Alzheimer's disease (AD) are significantly affected. These alterations in brain anatomy take place due to a variety of reasons, including the shrinking of grey and white matter in the brain. Magnetic resonance imaging (MRI) scans can be used to measure it, and these scans offer a chance for early identification of AD utilizing classification methods, like convolutional neural network (CNN). The majority of AD-related tests are now constrained by the test measures. It is, thus, crucial to find an affordable method for image categorization using minimal information. Because of developments in machine learning and medical imaging, the field of computerized health care has evolved rapidly. Recent developments in deep learning, in particular, herald a new era of clinical decision-making that is heavily reliant on multimedia systems. Methods: In the proposed work, we have investigated various CNN-based transfer-learning strategies for predicting AD using MRI scans of the brain's structural organization. According to an analysis of the data, the suggested model makes use of a number of sites related to Alzheimer's disease. In order to interpret structural brain pictures in both 2D and 3D, the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset includes straightforward CNN designs based on 2D and 3D convolutions. Results: According to these results, deep neural networks may be able to automatically learn which imaging biomarkers are indicative of Alzheimer's disease and exploit them for precise early disease detection. The proposed techniques have been found to achieve an accuracy of 93.24%. Conclusion: This research aimed to classify Alzheimer's disease (AD) using transfer learning. We have used strict pre-processing steps on raw MRI data from the ADNI dataset and used the AlexNet, i.e., Alzheimer's disease has been categorized using pre-processed data and the CNN classifier.

An effective feature selection using improved marine predators algorithm for Alzheimer’s disease classification

<span lang="EN-US">Alzheimer’s disease (AD) is an irremediable neurodegenerative illness developed by the fast deterioration of brain cells. AD is mostly common in elder people and it extremely disturbs the physical and mental health of patients, therefore early detection is essential to prevent AD development. However, the precise detection of AD and mild cognitive impairment (MCI) is difficult during classification. In this paper, the Residual network i.e., ResNet-18 is used for extracting the features, and the proposed improved marine predators algorithm (IMPA) is developed for choosing the optimum features to perform an effective classification of AD. The multi-verse optimizer (MVO) used in the IMPA helps to balance exploration and exploitation, which leads to the selection of optimal relevant features. Further, the classification of AD is accomplished using the multiclass support vector machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and Alzheimer disease neuroimaging initiative (ADNI) datasets are used to evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM method is analyzed using accuracy, sensitivity, specificity, positive predictive value (PPV) and matthews correlation coefficient (MCC). The existing methods such as the <a name="_Hlk134426295"></a>deep learning-based segmenting method using SegNet (DLSS), mish activation function (MAF) with spatial transformer network (STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more when compared to the DLSS and MAF-STN.</span>

An ontology-based approach for modelling and querying Alzheimer’s disease data

BackgroundThe recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being “standardized” so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer’s Disease (AD) in whose context specialized data collections such as the one by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) are maintained.MethodsOntologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner.ResultsWe developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer’s disease.ConclusionsThe proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources.

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer’s disease

BackgroundDue to the class imbalance issue faced when Alzheimer’s disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD.MethodsWe obtained patient data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer’s Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset.ResultsCompared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL, mPACCdigit, RAVLT_immediate, and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset.ConclusionsThe proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.

SUVR quantification using attention-based 3D CNN with longitudinal Florbetapir PET images in Alzheimer’s disease

PurposeFlorbetapir PET images provide valuable information about the amount of amyloid deposition in the brain due to neurodegenerative diseases, which helps in the prognosis of patients. The purpose of this study is to develop a system that helps in the automatic amyloid quantification of the standard uptake value ratio so that drug treatments could be effectively determined. Methods2647 Florbetapir PET images obtained from multiple centres of Alzheimer Disease Neuroimaging Initiative (ADNI) and an external dataset of 1413 scans from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s study are used to design and test the proposed 3D CNN attention-based model to quantify the amyloid deposits. Only 80% of scans from the ADNI dataset are used to train the model. The remaining 20% of scans from ADNI and the external dataset are used for testing the trained model. ResultsThe proposed model achieves a root mean square error of 0.0362 and a mean absolute error of 0.026 on separate hold-out test data from ADNI and a root mean square error of 0.058 and a mean absolute error of 0.044 Anti-Amyloid Treatment in Asymptomatic Alzheimer’s study dataset. A graphical user interface is developed for the proposed model which will display a slice of the Florbetapir PET volume and its predicted standard uptake value ratio. Conclusion3D CNN architecture with both spatial and channel attention provided better results when compared to models without attention. The proposed model proves to be an efficient tool in the automatic amyloid standard uptake value ratio quantification.

Federated Deep Learning for Detecting Alzheimer’s Disease in Multi‐Cohort Brain MRI

AbstractBackgroundDeep learning shows promise in detecting Alzheimer’s Disease (AD) based on brain MRI1. Models trained on limited samples may fail when applied to new datasets (other cohorts, scanners), but privacy concerns make it difficult to centralize large amounts of data2. To address this, we developed a general federated learning architecture3, which we applied to train a 3D convolutional neural network (CNN) to detect AD from T1‐weighted brain MRI data from the three independently collected phases of the Alzheimer’s Disease Neuroimaging Initiative (ADNI).MethodWe trained a 3D‐CNN over a federated learning environment of 3 sites corresponding to each ADNI phases (ADNI1, ADNI2/GO, ADNI3). No subject data are shared across sites, only model parameters, thus satisfying privacy/regulatory requirements. Each site trains the model on its own local dataset for several epochs and shares only the locally‐trained parameters with a central server. The server aggregates the parameters and computes the global model, which is sent back to each learner, and the process repeats.We trained each learner for 4 local epochs in between federation rounds for a total number of 25 rounds. We divided each ADNI dataset into training, validation and test sets with distribution 70/20/10% (Figure 1). We also compared the performance of training our CNN models with random initialization versus using pre‐trained weights from a UK Biobank MRI sex classification task.ResultTable 2 shows the accuracy, precision, recall and F1 scores for our federated CNN, compared to a centralized model that was trained over all ADNI data. All models achieved excellent performance for classifying AD (96‐98%). Overall, the federated model with pretrained weights performed best. Pre‐training boosted accuracy for the federated, but not the centralized, case.ConclusionFederated learning achieves high performance in AD prediction from MRI and promises new applications in neuroimaging.[1] Lu et al. A Practical Alzheimer Disease Classifier via Brain Imaging‐Based Deep Learning on 85,721 Samples. bioRxiv 2020_08_18_256594. 2021[2] Ming et al. COINSTAC: Decentralizing the future of brain imaging analysis. F1000Research 6:1512, 2017.[3] Stripelis et al. Scaling Neuroscience Research using Federated Learning. ISBI 2021.

Early Diagnosis of Alzheimer's Disease using Convolutional Neural Network-based MRI

Alzheimer's disease (AD) is a neurodegenerative ailment that causes cognitive deterioration due to changes in brain structure. Individuals usually see diagnostic symptoms after irreversible brain damage has occurred. In order to slow the course of the illness and enhance the quality of life for AD patients, early diagnosis is crucial. Recent advances in machine learning and scanning have made the use of these methods to detect AD in its earliest stages possible. This article uses deep learning using CNN methods to extract picture characteristics from ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets to improve Alzheimer's disease diagnosis techniques. This descriptor will be used in conjunction with the CNN to categorize the illness and add new characteristics that are more accurate, quicker, and stable than the current features. In this process, an Alzheimer's detection System will be implemented to mitigate the adverse effects of data imbalance on recognition performance, and an integrated multi-depth architectural technology will be introduced to boost recognition quality. Using the suggested model of the convolution neural network (CNN) technique, classification accuracy results were obtained above 97%.

The Application of Intelligent Data Models for Dementia Classification

Background and Objective: Dementia is a broad term for a complex range of conditions that affect the brain, such as Alzheimer’s disease (AD). Dementia affects a lot of people in the elderly community, hence there is a huge demand to better understand this condition by using cost effective and quick methods, such as neuropsychological tests, since pathological assessments are invasive and demand expensive resources. One of the promising initiatives that deals with dementia and Mild Cognitive Impairment (MCI) is the Alzheimer’s Disease Neuroimaging Initiative (ADNI), which includes cognitive tests, such as Clinical Dementia Rating (CDR) scores. The aim of this research is to investigate non-invasive dementia indicators, such as cognitive features, that are typically diagnosed by clinical assessment within ADNI’s data to understand their effect on dementia. Methods: To achieve the aim, machine learning techniques have been utilized to classify patients into Cognitively Normal (CN), MCI, or having dementia, based on the sum of CDR scores (CDR-SB) besides demographic variables. Particularly, the performance of Support Vector Machine (SVM), K-nearest neighbors (KNN), Decision Trees (C4.5), Probabilistic Naïve Bayes (NB), and Rule Induction (RIPPER) is measured with respect to different evaluation measures, including specificity, sensitivity, and harmonic mean (F-measure), among others, on a large number of cases and controls from the ADNI dataset. Results: The results indicate competitive performance when classifying subjects from the baseline selected variables using machine learning technology. Though we observed fairly good results across all machine learning algorithms utilized, there was still variation in the performance ability, indicating that some algorithms, such as NB and C4.5, are better suited to the task of classifying dementia status based on our baseline data. Conclusions: Using cognitive tests, such as CDR-SB scores, with demographic attributes to pinpoint to dementia using machine learning can be seen a less invasive approach that could be good for clinical use to aid in the diagnosis of dementia. This study gives an indication that a comprehensive assessment tool, such as CDR, may be adequate in assessing and assigning a dementia class to patients, upon their visit, in order to speed further clinical procedures.