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1. A Deep-Learning Approach for the Prediction of Mini-Mental State Examination Scores in a Multimodal Longitudinal Study

   https://doi.org/10.1109/CSCI51800.2020.00144  

   Morar, Ulyana; Martin, Harold; Izquierdo, Walter; Forouzannezhad, Parisa; Zarafshan, Elaheh; Curiel, Rosie E.; Roselli, Monica; Loewenstein, David; Duara, Ranjan; Unger, Elona; et al (December 2020, 2020 International Conference on Computational Science and Computational Intelligence (CSCI))

   null (Ed.)

   This study introduces a new multimodal deep regression method to predict cognitive test score in a 5-year longitudinal study on Alzheimer’s disease (AD). The proposed model takes advantage of multimodal data that includes cerebrospinal fluid (CSF) levels of tau and beta-amyloid, structural measures from magnetic resonance imaging (MRI), functional and metabolic measures from positron emission tomography (PET), and cognitive scores from neuropsychological tests (Cog), all with the aim of achieving highly accurate predictions of future Mini-Mental State Examination (MMSE) test scores up to five years after baseline biomarker collection. A novel data augmentation technique is leveraged to increase the numbers of training samples without relying on synthetic data. With the proposed method, the best and most encompassing regressor is shown to achieve better than state-of-the-art correlations of 85.07%(SD=1.59) for 6 months in the future, 87.39% (SD =1.48) for 12 months, 84.78% (SD=2.66) for 18 months, 85.13% (SD=2.19) for 24 months, 81.15% (SD=5.48) for 30 months, 81.17% (SD=4.44) for 36 months, 79.25% (SD=5.85) for 42 months, 78.98% (SD=5.79) for 48 months, 78.93%(SD=5.76) for 54 months, and 74.96% (SD=7.54) for 60 months. 

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2. Comparison of Dimensionality Reduction Methods for Multimodal Classification of Early Stages of Alzheimer’s Disease

   https://doi.org/10.1109/CSCI58124.2022.00325  

   L. O. Sawada, U. Morar (December 2022, 2022 International Conference on Computational Science and Computational Intelligence (CSCI))

   Early diagnosis of Alzheimer’s Disease (AD) is challenging due to its progressive nature. This study proposes a comprehensive comparison of four classifiers combined with different dimensionality reduction methods to discriminate normal controls (CN) from pre-mild cognitive impairment (pMCI) and early MCI (EMCI) using multimodal datasets including MRIs, PETs, SUVr, clinician amyloid visual reads, and subjects demographics. The most robust classifier for CN vs. MCI is the Mutual Information Best Percentile - Bagging Classifier combination, with 73.91% accuracy and a 4.82% standard deviation (SD). The best performance of 65.23% (11.84% SD) accuracy for CN vs. EMCI was DTC with ANOVA. In comparing CN with pMCI the best classification accuracy was ANOVA-DTC 51.06% (14.19% SD). An accuracy of 56.34% (10.67% SD) was achieved by bagging with ANOVA for multiclass classification ofCN vs. pMCI vs. EMCI. 

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3. Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment

   https://doi.org/10.1186/s12859-022-04946-x  

   Feng, Yixue; Kim, Mansu; Yao, Xiaohui; Liu, Kefei; Long, Qi; Shen, Li; for the Alzheimer’s Disease Neuroimaging Initiative (September 2022, BMC Bioinformatics)

   Abstract BackgroundIn Alzheimer’s Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. ResultsDGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. ConclusionOverall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI. 

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4. Plasma Protein Risk Scores for Mild Cognitive Impairment and Alzheimer Disease in the Framingham Heart Study

   Rehman, H; Ang, TFA; Tao, Q; Au, R; Farrer, LA; Qiu, W; Zhang, X (April 2025, Alzheimers dementia)

   INTRODUCTION: It is unclear whether aggregated plasma protein risk scores (PPRS) could be useful to predict the risks of mild cognitive impairment (MCI) and Alzheimer’s disease (AD). METHODS: The Cox proportional hazard model with the LASSO penalty was used to build the PPRS for MCI and AD in 1,515 Framingham Heart Study Generation2 with 1,128 proteins measured in plasma at exam 5 [cognitive normal (CN)=1,258, MCI=129, AD=128]. RESULTS: MCI PPRS had a hazard ratio (HR) of 6.97[5.34,9.12], with a discriminating power (C-index=82.52%). AD PPRS had an HR of 5.74[4.67,7.05] (C-index=88.15%). Both PPRSs were also significantly associated with cognitive changes, brain-atrophy, and plasma AD biomarkers. Proteins in the MCI and AD PPRSs were enriched in several pathways related to leukocyte, chemotaxis, immunity, inflammation, and cellular migration. DISCUSSION: This study suggests that PPRS serve well to predict the risk of developing MCI and AD as well as cognitive changes and AD related pathogenesis in the brain. 

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5. Stability in cognitive classification as a function of severity of impairment and ethnicity: A longitudinal analysis

   https://doi.org/10.1080/23279095.2023.2222861  

   Arruda, Fernanda; Rosselli, Mónica; Mejia Kurasz, Andrea; Loewenstein, David A.; DeKosky, Steven T.; Lang, Merike K.; Conniff, Joshua; Vélez-Uribe, Idaly; Ahne, Emily; Shihadeh, Layaly; et al (July 2023, Applied Neuropsychology: Adult)

   Objective: The interaction of ethnicity, progression of cognitive impairment, and neuroimaging biomarkers of Alzheimer’s Disease remains unclear. We investigated the stability in cognitive status classification (cognitively normal [CN] and mild cognitive impairment [MCI]) of 209 participants (124 Hispanics/Latinos and 85 European Americans). Methods: Biomarkers (structural MRI and amyloid PET scans) were compared between Hispanic/Latino and European American individuals who presented a change in cognitive diagnosis during the second or third follow-up and those who remained stable over time. Results: There were no significant differences in biomarkers between ethnic groups in any of the diagnostic categories. The frequency of CN and MCI participants who were progressors (progressed to a more severe cognitive diagnosis at follow-up) and non-progressors (either stable through follow-ups or unstable [progressed but later reverted to a diagnosis of CN]) did not significantly differ across ethnic groups. Progressors had greater atrophy in the hippocampus (HP) and entorhinal cortex (ERC) at baseline compared to unstable non-progressors (reverters) for both ethnic groups, and more significant ERC atrophy was observed among progressors of the Hispanic/Latino group. For European Americans diagnosed with MCI, there were 60% more progressors than reverters (reverted from MCI to CN), while among Hispanics/Latinos with MCI, there were 7% more reverters than progressors. Binomial logistic regressions predicting progression, including brain biomarkers, MMSE, and ethnicity, demonstrated that only MMSE was a predictor for CN participants at baseline. However, for MCI participants at baseline, HP atrophy, ERC atrophy, and MMSE predicted progression. 

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