Domain Transfer Learning for MCI Conversion Prediction

Accurate recognition of progressive mild cognitive impairment (MCI) is helpful to reduce the risk of developing Alzheimer's disease (AD). However, it is still challenging to extract effective biomarkers from multivariate brain structural magnetic resonance imaging (MRI) features to accurately differentiate the progressive MCI from stable MCI. We develop novel biomarkers by combining subspace learning methods with the information of AD as well as normal control (NC) subjects for the prediction of MCI conversion using multivariate structural MRI data. Specifically, we first learn two projection matrices to map multivariate structural MRI data into a common label subspace for AD and NC subjects, where the original data structure and the one-to-one correspondence between multiple variables are kept as much as possible. Afterwards, the multivariate structural MRI features of MCI subjects are mapped into a common subspace according to the projection matrices. We then perform the self-weighted operation and weighted fusion on the features in common subspace to extract the novel biomarkers for MCI subjects. The proposed biomarkers are tested on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results indicate that our proposed biomarkers outperform the competing biomarkers on the discrimination between progressive MCI and stable MCI. And the improvement from the proposed biomarkers is not limited to a particular classifier. Moreover, the results also confirm that the information of AD and NC subjects is conducive to predicting conversion from MCI to AD. In conclusion, we find a good representation of brain features from high-dimensional MRI data, which exhibits promising performance for predicting conversion from MCI to AD.

ASMFS: Adaptive-similarity-based multi-modality feature selection for classification of Alzheimer's disease

Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM

Prediction of Alzheimer's disease in mild cognitive impairment: A prospective study in Taiwan

Comparison of florbetapir positron emission tomography scans with cerebrospinal fluid biomarkers in healthy individuals and people with mild cognitive impairment or Alzheimer's disease dementia

Accurate estimation of cognitive scores for patients can help track the progress of neurological diseases. In this paper, we present a novel semi-supervised multimodal relevance vector regression (SM-RVR) method for predicting clinical scores of neurological diseases from multimodal imaging and biological biomarker, to help evaluate pathological stage and predict progression of diseases, e.g., Alzheimer's diseases (AD). Unlike most existing methods, we predict clinical scores from multimodal (imaging and biological) biomarkers, including MRI, FDG-PET, and CSF. Considering that the clinical scores of mild cognitive impairment (MCI) subjects are often less stable compared to those of AD and normal control (NC) subjects due to the heterogeneity of MCI, we use only the multimodal data of MCI subjects, but no corresponding clinical scores, to train a semi-supervised model for enhancing the estimation of clinical scores for AD and NC subjects. We also develop a new strategy for selecting the most informative MCI subjects. We evaluate the performance of our approach on 202 subjects with all three modalities of data (MRI, FDG-PET and CSF) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our SM-RVR method achieves a root-mean-square error (RMSE) of 1.91 and a correlation coefficient (CORR) of 0.80 for estimating the MMSE scores, and also a RMSE of 4.45 and a CORR of 0.78 for estimating the ADAS-Cog scores, demonstrating very promising performances in AD studies.

A rapid increase in the number of patients with Alzheimer's disease (AD) is expected over the next decades. Accordingly, there is a critical need for early-stage AD detection methods that can enable effective treatment strategies. In this study, we consider the ability of episodic-memory measures to predict mild cognitive impairment (MCI) to AD conversion and thus, detect early-stage AD. For our analysis, we studied 307 participants with MCI across four years using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using a binary logistic regression, we compared episodic-memory tests to each other and to prominent neuroimaging methods in MCI converter (MCI participants who developed AD) and MCI non-converter groups (MCI participants who did not develop AD). We also combined variables to test the accuracy of mixed-predictor models. Our results indicated that the best predictors of MCI to AD conversion were the following: a combined episodic-memory and neuroimaging model in year one (59.8%), the Rey Auditory Verbal Learning Test in year two (71.7%), a mixed episodic-memory predictor model in year three (77.7%) and the Logical Memory Test in year four (77.2%) of ADNI. Overall, we found that individual episodic-memory measure and mixed models performed similarly when predicting MCI to AD conversion. Comparatively, individual neuroimaging measures predicted MCI conversion worse than chance. Accordingly, our results indicate that episodic-memory tests could be instrumental in detecting early-stage AD and enabling effective treatment.

Accurate modeling of disease progression is essential for comprehending the heterogeneous neuropathologies such as Alzheimer's Disease (AD). Traditional neuroimaging analysis often confound disease effects with normal aging, complicating the differential diagnosis. Recent advancements in deep learning have catalyzed the development of disentanglement techniques in Autoencoder networks, aiming to segregate longitudinal changes attributable to aging from those due to disease-specific alterations within the latent space. However, existing longitudinal disentanglement methods usually model disease as a single axis factor which ignores the complexity and heterogeneity of Alzheimer's Disease. In response to this issue, we propose a novel Surface-based Multi-axis Disentanglement framework.This framework posits multiple disease axes within the latent space, enhancing the model's capacity to encapsulate the multifaceted nature of AD, which includes various disease trajectories. To assign axes to data trajectories without explicit ground truth labels, we implement a longitudinal contrastive loss leveraging self-supervision, thereby refining the separation of disease trajectories. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset ( ), our model demonstrates superior performance in delineating between cognitively normal (CN), mild cognitive impairment (MCI), and AD subjects,classification of stable MCI vs converting MCI and Amyloid status, compared to the single-axis model. This is further substantiated through an ablation study on the contrastive loss, underscoring the utility of our multi-axis approach in capturing the complex progression patterns of AD. The code is available at: https://github.com/jianweizhang17/MultiAxisDisentanglement.git.

P3-33 Validation du NPI-R, version réduite de l’Inventaire Neuropsychiatrique français

Graphical, voxel, and region-based analysis has become a popular approach to studying neurodegenerative disorders such as Alzheimer’s disease (AD) and its prodromal stage [mild cognitive impairment (MCI)]. These methods have been used previously for classification or discrimination of AD in subjects in a prodromal stage called stable MCI (MCIs), which does not convert to AD but remains stable over a period of time, and converting MCI (MCIc), which converts to AD, but the results reported across similar studies are often inconsistent. Furthermore, the classification accuracy for MCIs vs. MCIc is limited. In this study, we propose combining different neuroimaging modalities (sMRI, FDG-PET, AV45-PET, DTI, and rs-fMRI) with the apolipoprotein-E genotype to form a multimodal system for the discrimination of AD, and to increase the classification accuracy. Initially, we used two well-known analyses to extract features from each neuroimage for the discrimination of AD: whole-brain parcelation analysis (or region-based analysis), and voxel-wise analysis (or voxel-based morphometry). We also investigated graphical analysis (nodal and group) for all six binary classification groups (AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs). Data for a total of 129 subjects (33 AD, 30 MCIs, 31 MCIc, and 35 HCs) for each imaging modality were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) homepage. These data also include two APOE genotype data points for the subjects. Moreover, we used the 2-mm AICHA atlas with the NiftyReg registration toolbox to extract 384 brain regions from each PET (FDG and AV45) and sMRI image. For the rs-fMRI images, we used the DPARSF toolbox in MATLAB for the automatic extraction of data and the results for REHO, ALFF, and fALFF. We also used the pyClusterROI script for the automatic parcelation of each rs-fMRI image into 200 brain regions. For the DTI images, we used the FSL (Version 6.0) toolbox for the extraction of fractional anisotropy (FA) images to calculate a tract-based spatial statistic. Moreover, we used the PANDA toolbox to obtain 50 white-matter-region-parcellated FA images on the basis of the 2-mm JHU-ICBM-labeled template atlas. To integrate the different modalities and different complementary information into one form, and to optimize the classifier, we used the multiple kernel learning (MKL) framework. The obtained results indicated that our multimodal approach yields a significant improvement in accuracy over any single modality alone. The areas under the curve obtained by the proposed method were 97.78, 96.94, 95.56, 96.25, 96.67, and 96.59% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs binary classification, respectively. Our proposed multimodal method improved the classification result for MCIs vs. MCIc groups compared with the unimodal classification results. Our study found that the (left/right) precentral region was present in all six binary classification groups (this region can be considered the most significant region). Furthermore, using nodal network topology, we found that FDG, AV45-PET, and rs-fMRI were the most important neuroimages, and showed many affected regions relative to other modalities. We also compared our results with recently published results.

In this work, we propose a novel subclass-based multi-task learning method for feature selection in computer-aided Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underlying multipeak1 distribution of classes. The rationale for our approach is that it is highly likely for neuroimaging data to have multiple peaks or modes in distribution, e.g., mixture of Gaussians, due to the inter-subject variability. In this regard, we use a clustering method to discover the multipeak distributional characteristics and define subclasses based on the clustering results, in which each cluster covers a peak in the underlying multipeak distribution. Specifically, after performing clustering for each class, we encode the respective subclasses, i.e., clusters, with their unique codes. In encoding, we impose the subclasses of the same original class close to each other and those of different original classes distinct from each other. By setting the codes as new label vectors of our training samples, we formulate a multi-task learning problem in a ℓ2,1-penalized regression framework, through which we finally select features for classification. In our experimental results on the ADNI dataset, we validated the effectiveness of the proposed method by improving the classification accuracies by 1% (AD vs. Normal Control: NC), 3.25% (MCI vs. NC), 5.34% (AD vs. MCI), and 7.4% (MCI Converter: MCI-C vs. MCI Non-Converter: MCI-NC) compared to the competing single-task learning method. It is remarkable for the performance improvement in MCI-C vs. MCI-NC classification, which is the most important for early diagnosis and treatment. It is also noteworthy that with the strategy of modality-adaptive weights by means of a multi-kernel support vector machine, we maximally achieved the classification accuracies of 96.18% (AD vs. NC), 81.45% (MCI vs. NC), 73.21% (AD vs. MCI), and 74.04% (MCI-C vs. MCI-NC), respectively.

Medial temporal lobe atrophy scores translated to clinical practice: Editorial comment on ‘Influence of age, disease onset and ApoE4 on visual medial temporal lobe atrophy cut‐offs'

To assess whether different patterns of regional gray matter loss in patients with mild cognitive impairment (MCI) are associated with different risks of conversion to Alzheimer disease (AD), using MRI and voxel-based morphometry (VBM). The authors recruited 22 patients with MCI, 22 patients with probable AD, and 20 healthy subjects (HS). T1 volumes from each subject were postprocessed according to an optimized VBM protocol. All patients were clinically followed up (mean [SD] time = 28.7 [5.7] months), and patients with MCI were reclassified into two groups (converters and nonconverters to AD). When comparing patients with AD to HS, widespread areas of reduced gray matter density were found predominantly in temporal, frontal, and parietal lobes and in the insula. Comparing MCI converters and nonconverters with HS, the converters showed more widespread areas of reduced gray matter density than nonconverters, with a pattern of abnormalities similar to that seen in patients with AD. Conversely, when comparing the same groups with patients with AD, MCI nonconverters showed a pattern of gray matter density similar to that of HS. Areas of decreased gray matter density were also found in MCI converters compared with nonconverters. Different patterns of gray matter density distribution in patients with mild cognitive impairment may be associated to different rates of conversion to Alzheimer disease.

Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: An MRI study of 676 AD, MCI, and normal subjects

Previous studies have shown that hippocampal subfields may be differentially affected by Alzheimer’s disease (AD). This study used an automated analysis technique and two large cohorts to (1) investigate patterns of subfield volume loss in mild cognitive impairment (MCI) and AD, (2) determine the pattern of subfield volume loss due to age, gender, education, APOE ε4 genotype, and neuropsychological test scores, (3) compare combined subfield volumes to hippocampal volume alone at discriminating between AD and healthy controls (HC), and predicting future MCI conversion to AD at 12 months. 1,069 subjects were selected from the AddNeuroMed and Alzheimer’s disease neuroimaging initiative (ADNI) cohorts. Freesurfer was used for automated segmentation of the hippocampus and hippocampal subfields. Orthogonal partial least squares to latent structures (OPLS) was used to train models on AD and HC subjects using one cohort for training and the other for testing and the combined cohort was used to predict MCI conversion. MANCOVA and linear regression analyses showed multiple subfield volumes including Cornu Ammonis 1 (CA1), subiculum and presubiculum were atrophied in AD and MCI and were related to age, gender, education, APOE ε4 genotype, and neuropsychological test scores. For classifying AD from HC, combined subfield volumes achieved comparable classification accuracy (81.7 %) to total hippocampal (80.7 %), subiculum (81.2 %) and presubiculum (80.6 %) volume. For predicting MCI conversion to AD combined subfield volumes and presubiculum volume were more accurate (81.1 %) than total hippocampal volume. (76.7 %).