Stable Mild Cognitive Impairment Research Articles

Altered hierarchical gradients of intrinsic neural timescales in mild cognitive impairment and Alzheimer's disease.

Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects millions of seniors in the US. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study neurophysiology in AD and its prodromal condition, mild cognitive impairment (MCI). The intrinsic neural timescale (INT), which can be estimated through the magnitude of the autocorrelation of neural signals from rs-fMRI, is thought to quantify the duration that neural information is stored in a local circuit. Such heterogeneity of the timescales forms a basis of the brain functional hierarchy and captures an aspect of circuit dynamics relevant to excitation/inhibition balance, which is broadly relevant for cognitive functions. Given that, we applied rs-fMRI to test whether distinct changes of INT at different hierarchies are present in people with MCI, those progressing to AD (called Converter), and AD patients of both sexes. Linear mixed effect model was implemented to detect altered hierarchical gradients across populations followed by pairwise comparisons to identify regional differences. High similarities between AD and Converter were observed. Specifically, the inferior temporal, caudate, pallidum areas exhibit significant alterations in both AD and Converter. Distinct INT related pathological changes in MCI and AD were found. For AD/Converter, neural information is stored for a longer time in lower hierarchical areas, while higher levels of hierarchy seem to be preferentially impaired in MCI leading to a less pronounced hierarchical gradients. These results inform that the INT holds great potential as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis.Significance Statement We observed high similarities of intrinsic neural timescales (INT) between patients with Alzheimer's Disease (AD) and people that will later progress to AD (called Converter), deviating from cognitively normal individuals. This indicates that pathological excitation/inhibition imbalance already started before the conversion to AD. We also revealed distinct pathophysiological changes in stable mild cognitive impairment (MCI) and AD/Converter. For the AD and Converter, neural information is stored for a longer time in lower brain hierarchical areas; while higher levels of the hierarchy seem to be preferentially impaired in stable MCI. These results suggest the potential for INT as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis.

The associations of serum isoleucine with Alzheimer`s disease on assisting diagnosis, predicting conversion and assessing cognition.

Advances in blood biomarker discovery have enabled the improved diagnosis and prognosis of Alzheimer's disease (AD). Most branched-chain amino acids, except isoleucine (Ile), are correlated with both mild cognitive impairment (MCI) and AD. Therefore, this study investigated the association between serum Ile levels and MCI/AD. This study stratified 700 participants from the Alzheimer's Disease Neuroimaging Initiative database into four diagnostic groups: cognitively normal, stable MCI, progressive MCI, and AD. Analysis of covariance and chi-square analyses were used to test the demographic data. Receiver operating curve analyses were used to calculate the diagnostic accuracy of different biomarkers and were compared using MedCalc 20. Additionally, Cox proportional hazards models were used to measure the ability of serum Ile levels to predict disease conversion. Finally, a linear mixed-effects model was used to evaluate the associations between serum Ile levels and cognition, brain structure, and metabolism. Serum Ile concentration was decreased in AD and demonstrated significant diagnostic efficacy. The combination of serum Ile and cerebrospinal fluid (CSF) phosphorylated tau (P-tau) levels improved the diagnostic accuracy in AD compared to T-tau alone. Serum Ile levels significantly predicted the conversion from MCI to AD (cutoff value = 78.3 μM). Finally, the results of this study also revealed a correlation between serum Ile levels and the Alzheimer's Disease Assessment Scale cognitive subscale Q4. Serum Ile level may be a potential biomarker of AD. Ile had independent diagnostic efficacy and significantly improved the diagnostic accuracy of CSF P-tau in AD. Patients with MCI with a lower serum Ile level had a higher risk of progression to AD and a worse cognition assessment.

Shared and Specific Changes of Cortico-Striatal Functional Connectivity in Stable Mild Cognitive Impairment and Progressive Mild Cognitive Impairment.

Mild cognitive impairment (MCI), the prodromal stage of Alzheimer's disease, has two distinct subtypes: stable MCI (sMCI) and progressive MCI (pMCI). Early identification of the two subtypes has important clinical significance. We aimed to compare the cortico-striatal functional connectivity (FC) differences between the two subtypes of MCI and enhance the accuracy of differential diagnosis between sMCI and pMCI. We collected resting-state fMRI data from 31 pMCI patients, 41 sMCI patients, and 81 healthy controls. We chose six pairs of seed regions, including the ventral striatum inferior, ventral striatum superior, dorsal-caudal putamen, dorsal-rostral putamen, dorsal caudate, and ventral-rostral putamen and analyzed the differences in cortico-striatal FC among the three groups, additionally, the relationship between the altered FC within the MCI subtypes and cognitive function was examined. Compared to sMCI, the pMCI patients exhibited decreased FC between the left dorsal-rostral putamen and right middle temporal gyrus, the right dorsal caudate and right inferior temporal gyrus, and the left dorsal-rostral putamen and left superior frontal gyrus. Additionally, the altered FC between the right inferior temporal gyrus and right putamen was significantly associated with episodic memory and executive function. Our study revealed common and distinct cortico-striatal FC changes in sMCIs and pMCI across different seeds; these changes were associated with cognitive function. These findings can help us understand the underlying pathophysiological mechanisms of MCI and distinguish pMCI and sMCI in the early stage potentially.

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

BackgroundFunctional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.MethodsThis multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.ResultsThe STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.ConclusionsThis study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

Directed Functional Connectivity Changes of Triple Networks for Stable and Progressive Mild Cognitive Impairment

Mild cognitive impairment includes two distinct subtypes, namely progressive mild cognitive impairment and stable mild cognitive impairment. While alterations in extensive functional connectivity have been observed in both subtypes, limited attention has been given to directed functional connectivity. A triple network, composed of the central executive network, default mode network, and salience network, is considered to be the core cognitive network. We evaluated the alterations in directed functional connectivity within and between the triple network in progressive and stable mild cognitive impairment groups and investigated its role in predicting disease conversion. Resting-state functional magnetic resonance imaging was used to analyze directed functional connectivity within the triple networks. A correlation analysis was performed to investigate potential associations between altered directed functional connectivity within the triple networks and the neurocognitive performance of the participants. Our study revealed significant differences in directed functional connectivity within and between the triple network in the progressive and stable mild cognitive impairment groups. Altered directed functional connectivity within the triple network was involved in episodic memory and executive function. Thus, the directed functional connectivity of the triple network may be used as an imaging marker of mild cognitive impairment.

Prediction Of Mild Cognitive Impairment to Alzheimer’s Disease Conversion Via Machine Learning

Alzheimer’s Disease (AD) is a progressive neurodegenerative condition characterized by deterioration of cognitive functions. Although there does not exist a cure for AD, early diagnosis and intervention during stages of Mild Cognitive Impairment (MCI) can be incredibly beneficial in slowing its development. However, predicting the conversion from MCI to AD remains a challenging task. This paper aims to provide an overview of the current research on predicting MCI to AD conversion, with a focus on machine learning methodologies to aid feature extraction and classification. Through a literature review, we aim to offer insights into the latest state-of-the-art techniques to predict MCI to AD conversion as well as prevailing trends in this field including deep learning, transfer learning, contrastive learning, and graph neural networks. We conclude from our study that utilizing AD/HC classes for feature extraction is a promising approach for generating discriminative features for stable MCI and progressive MCI classification. In addition, employing multi-modality models is instrumental in attaining a robust validation framework for the early diagnosis of AD.

A joint autoencoder and classifier deep neural network for AD and MCI classification

AbstractIn this article, we present a new approach to distinguish progressive mild cognitively impaired (pMCI) subjects, who eventually develop Alzheimer's disease (AD) from stable MCI (sMCI) subjects whose situation does not deteriorate into AD. The proposed approach combines the discriminating capabilities of classifiers and representation learning capacities of autoencoders into a unified architecture, and is hence termed as joint autoencoder and classifier deep neural network (JACDNN). JACDNN employs a single classifier and multiple autoencoders that are trained together to perform pattern classification. The classifier in JACDNN is trained using standard approaches to distinguish between subject from different classes using the binary cross entropy loss. The autoencoders in JACDNN, regularizes individual layers in the network used for classification to learn representations useful for reconstructing a given input. The performance of JACDNN has been evaluated on several machine learning problems pertaining to dementia, namely AD versus cognitively normal (CN) subjects, AD versus sMCI, CN versus pMCI, and pMCI versus sMCI. These problems are targeted using two datasets. The first dataset consist of gray matter (GM) features of subjects and the second dataset consist of combination of GM and white matter (WM) features. It is observed that better classification results are obtained when the classifier is built on GM and WM as compared with GM features alone. Performance comparison of JACDNN with other existing approaches has been conducted for these problems. The results clearly indicate that JACDNN performs better than other existing approaches for these problems.

Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease

The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.

A deep feature fusion network with global context and cross-dimensional dependencies for classification of mild cognitive impairment from brain MRI

Background and objectives:The accurate identification of people with Mild Cognitive Impairment (MCI) who may develop Alzheimer's disease (AD) holds significant importance in facilitating timely intervention and treatment. However, current classification methods have yet to be effective due to the subtle nature of the features involved. This research aims to improve the performance of the MCI classification by enhancing the feature representation in brain MRI. MethodsWe propose an integrated model that combines Group Shuffle Depth-wise Convolution (GSDW), Global Context Network (GCN), Hybrid Multi-Focus Attention Block (HMAB), and EfficientNet-B0 architecture for MCI classification. This model extracts fine-grained features, contextual information, and long-range dependencies and aggregates low-level details with high-level context information to learn discriminative features. ResultsOur comprehensive evaluation demonstrates significant improvements in the classification performance for both progressive MCI (pMCI) and stable MCI (sMCI), surpassing existing approaches. The proposed model achieved a notable accuracy of 77.2%. ConclusionsThis study introduces a novel feature fusion technique that combines global contextual representations and cross-dimensional dependencies to enhance classification results. These findings highlight the potential of the proposed framework for early identification and intervention in individuals at risk of cognitive decline.

Prognostic Value of Gut Microbiome for Conversion from Mild Cognitive Impairment to Alzheimer's Disease Dementia within 4 Years: Results from the AlzBiom Study.

Alterations in the gut microbiome are associated with the pathogenesis of Alzheimer's disease (AD) and can be used as a diagnostic measure. However, longitudinal data of the gut microbiome and knowledge about its prognostic significance for the development and progression of AD are limited. The aim of the present study was to develop a reliable predictive model based on gut microbiome data for AD development. In this longitudinal study, we investigated the intestinal microbiome in 49 mild cognitive impairment (MCI) patients over a mean (SD) follow-up of 3.7 (0.6) years, using shotgun metagenomics. At the end of the 4-year follow-up (4yFU), 27 MCI patients converted to AD dementia and 22 MCI patients remained stable. The best taxonomic model for the discrimination of AD dementia converters from stable MCI patients included 24 genera, yielding an area under the receiver operating characteristic curve (AUROC) of 0.87 at BL, 0.92 at 1yFU and 0.95 at 4yFU. The best models with functional data were obtained via analyzing 25 GO (Gene Ontology) features with an AUROC of 0.87 at BL, 0.85 at 1yFU and 0.81 at 4yFU and 33 KO [Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog] features with an AUROC of 0.79 at BL, 0.88 at 1yFU and 0.82 at 4yFU. Using ensemble learning for these three models, including a clinical model with the four parameters of age, gender, body mass index (BMI) and Apolipoprotein E (ApoE) genotype, yielded an AUROC of 0.96 at BL, 0.96 at 1yFU and 0.97 at 4yFU. In conclusion, we identified novel and timely stable gut microbiome algorithms that accurately predict progression to AD dementia in individuals with MCI over a 4yFU period.

Lifestyle Influence on Mild Cognitive Impairment Progression: A Decision Tree Prediction Model Study.

This study assessed the influences of different lifestyle on mild cognitive impairment (MCI) progression and established a decision tree prediction model to analyse their predictive significance on MCI progression incidence. From October 2015 to February 2020,330 patients with MCI were recruited, and demographic and lifestyle information collected. They were followed up for 19.04 ± 10.227 months. Cognitive function was assessed using the Mini-Mental State Examination Scale every 6 months, and they were divided into MCI stable group and MCI progression group. The Kaplan Meier survival analysis showed an overall cohort survival rate of 33.2%; the annual conversion rate of MCI progression was 20%. Physical exercise, social engagement, high-fat diet, age, napping, and tea drinking were decision tree prediction model nodes. Hobbies were the most important factor for predicting MCI progression. The MCI progression probability rates were: with hobbies 26.829% (44 cases), without hobbies 57.831% (96 cases); for those withot hobbies, with physical exercise 43.077% (28 cases) without physical exercise 72.340% (68 cases); for those without hobbies with physical exercise and social engagement 20.000% (4 cases), without social engagement 53.333% (24 cases); for those without hobbies, physical exercises and social engagement and with nap habits 48.485% (16 cases), without nap habits 66.667% (8 cases). The decision tree prediction model AUC for predicting the MCI progression receiver operating characteristic curve was 0.737 (95% confidence interval: 0.685-0.785) (75.71% sensitivity, 71.75% specificity, P < 0.001. Hobbies, physical exercise, social engagement, napping, and drinking tea can help prevent MCI progression, while a high-fat diet may exacerbate MCI progression. In this study the rule with the lowest MCI progress probability for those who had hobbies, high-fat diet, and social engagement. And the decision tree model had good prediction efficiency.

HOPE: Hybrid-Granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease (AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtype labels, pMCI vs. stable MCI (sMCI), determined by whether or not an MCI patient will progress to AD after a long follow-up. However, prospectively acquiring MCI subtype data is time-consuming and resource-intensive; the resultant small datasets could lead to severe overfitting and difficulty in extracting discriminative information. Inspired by that various longitudinal biomarkers and cognitive measurements present an ordinal pathway on AD progression, we propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression for MCI progression prediction. First, HOPE learns an ordinal metric space that enables progression prediction by prototype comparison. Second, HOPE leverages a novel hybrid-granularity ordinal loss to learn the ordinal nature of AD via effectively integrating instance-to-instance ordinality, instance-to-class compactness, and class-to-class separation. Third, to make the prototype learning more stable, HOPE employs an exponential moving average strategy to learn the global prototypes of NC and AD dynamically. Experimental results on the internal ADNI and the external NACC datasets demonstrate the superiority of the proposed HOPE over existing state-of-the-art methods as well as its interpretability. Source code is made available at https://github.com/thibault-wch/HOPE-for-mild-cognitive-impairment.

High expression of Alzheimer’s disease‐related metabolic brain pattern is predictive of conversion to mild cognitive impairment or dementia and faster cognitive decline

AbstractBackgroundAlzheimer’s disease‐related pattern (ADRP) is a metabolic brain biomarker of Alzheimer’s disease (AD), which was previously identified and validated. Its utility for differential diagnosis is known. Here, we aimed to explore its predictive value for conversion from cognitively normal (CN) to mild cognitive impairment (MCI), from MCI to dementia and for the rate of cognitive decline.MethodsWe analysed 609 participants followed for up to 15 years and their FDG PET scans from ADNI database (CN = 133, MCI = 352, dementia due to AD = 124) and 56 participants from University Medical centre Ljubljana (MCI = 22, AD = 34). ADRP expression was calculated from pre‐processed FDG PET scans. CN and MCI participants were stratified according to their conversion status and cerebrospinal fluid biomarkers. Amyloid positivity (A+) was defined as Aβ42 &lt; 880 pg/mL and tau positivity (T+) as pTau &gt; 21.8 pg/mL. AD patients were stratified into slow (&lt; 2 points/per year decline on mini mental state examination (MMSE)) and fast (&gt; 2 points/per year decline on MMSE) progressors.ResultsA+T+ cCN had higher baseline ADRP expression than A–T– (p&lt;0.001), A+T– (p = 0.003) and A+T+ (p = 0.01) stable CN corrected for age, while the groups did not differ in gender, education or baseline MMSE scores. A+T+ cMCI had higher baseline ADRP expression than A–T– (p&lt;0.001), A+T– (p = 0.001) and A+T+ (p&lt;0.001) stable MCI, corrected for baseline age and MMSE. A+T– stable MCI had higher baseline ADRP expression than A–T– stable MCI (p = 0.02). ADRP was a predictor of conversion to dementia in survival analysis (HR = 4.5[2.4‐8.7]). Similarly, in the UMCL dataset A+T+ cMCI had higher baseline ADRP expression than A–T– stable MCI (p = 0.03). Fast progressing AD patients had higher baseline ADRP expression than slowly progressing AD patients (p&lt;0.001). Similarly, in the UMCL dataset fast progressing AD patients had higher baseline ADRP expression than slowly progressing AD patients (p = 0.02). In neither dataset, AD groups differed in age, sex or baseline MMSE scores.ConclusionADRP expression levels can be used to predict conversion from CN to MCI and from MCI to dementia in A+T+ individuals. AD patients with high ADRP expression are at an elevated risk for faster progression of cognitive decline.

High expression of Alzheimer’s disease‐related metabolic brain pattern is predictive of conversion to mild cognitive impairment or dementia and faster cognitive decline

AbstractBackgroundAlzheimer’s disease‐related pattern (ADRP) is a metabolic brain biomarker of Alzheimer’s disease (AD), which was previously identified and validated. Its utility for differential diagnosis is known. Here, we aimed to explore its predictive value for conversion from cognitively normal (CN) to mild cognitive impairment (MCI), from MCI to dementia and for the rate of cognitive decline.MethodWe analysed 609 participants followed for up to 15 years and their FDG PET scans from ADNI database (CN = 133, MCI = 352, dementia due to AD = 124) and 56 participants from University Medical centre Ljubljana (MCI = 22, AD = 34). ADRP expression was calculated from pre‐processed FDG PET scans. CN and MCI participants were stratified according to their conversion status and cerebrospinal fluid biomarkers. Amyloid positivity (A+) was defined as Aß42 &lt; 880 pg/mL and tau positivity (T+) as pTau &gt; 21.8 pg/mL. AD patients were stratified into slow (&lt; 2 points/per year decline on mini mental state examination (MMSE)) and fast (&gt; 2 points/per year decline on MMSE) progressors.ResultA+T+ cCN had higher baseline ADRP expression than A–T– (p&lt;0.001), A+T– (p = 0.003) and A+T+ (p = 0.01) stable CN corrected for age, while the groups did not differ in gender, education or baseline MMSE scores. A+T+ cMCI had higher baseline ADRP expression than A–T– (p&lt;0.001), A+T– (p = 0.001) and A+T+ (p&lt;0.001) stable MCI, corrected for baseline age and MMSE. A+T– stable MCI had higher baseline ADRP expression than A–T– stable MCI (p = 0.02). ADRP was a predictor of conversion to dementia in survival analysis (HR = 4.5[2.4‐8.7]). Similarly, in the UMCL dataset A+T+ cMCI had higher baseline ADRP expression than A–T– stable MCI (p = 0.03). Fast progressing AD patients had higher baseline ADRP expression than slowly progressing AD patients (p&lt;0.001). Similarly, in the UMCL dataset fast progressing AD patients had higher baseline ADRP expression than slowly progressing AD patients (p = 0.02). In neither dataset, AD groups differed in age, sex or baseline MMSE scores.ConclusionADRP expression levels can be used to predict conversion from CN to MCI and from MCI to dementia in A+T+ individuals. AD patients with high ADRP expression are at an elevated risk for faster progression of cognitive decline.

A comparison of machine learning based‐composite cognitive test scores to track cognitive decline in early stages of Alzheimer’s disease dementia: ADOPIC study

AbstractBackgroundClinical trials of early Alzheimer’s disease (AD) dementia use cognition as a primary outcome and therefore cognitive measures that accurately reflect disease progression and represent multiple cognitive domains are required. Current cognitive endpoints are computed by averaging standardized change from baseline scores (i.e., preclinical Alzheimer cognitive composite (PACC)). Here we compare the PACC to composite scores for which the combination of multiple performance scores has been optimized using machine learning (ML) algorithms in a large, harmonised data set, namely ADOPIC.MethodsA dataset harmonised across various cognitive scores (Table 1) was used to construct composite scores using ML‐based algorithms: a manifold learning dimension reduction technique (UMAP), principal component analysis (PCA) and Latent variable analysis (LVA). Data from ADNI (n = 1470), AIBL (n = 1105) and OASIS participants (n = 412, Table 2) with ≥3 assessments ≤5 years before clinical progression/last visit were included. Participants were classified clinically as; stable cognitively unimpaired (CU), CU progressing to mild cognitive impairment (MCI) or dementia, stable MCI, MCI progressing to dementia, or AD dementia. For UMAP, all stable participants (including dementia) were used in 4‐fold cross‐validation training sets. However, for testing UMAP and unsupervised models (PCA and LVA) all clinical groups (except for dementia) were used. The validity of ML‐based composites was challenged by modelling mean (SD) change over time in the non‐demented clinical groups, using linear mixed model (LMM) analysis. Signal‐to‐noise ratios (SNRs) were calculated (mean change/SD change) for each composite. The mean change was measured in progressives relative to stable participants of each group.ResultsEach ML‐based cognitive composite showed sensitivity to cognitive decline in the progressor groups (Figure 1 A). For the MCI progressors, PCA and UMAP composites had significantly higher SNRs than PACC (P&lt;0.01; Figure 1B), however, LVA performance was not significantly better than PACC. For the CU progressor group, SNRs for PCA and LVA did not show significant differences with PACC and UMAP performed significantly worse than PACC (P&lt;0.01).ConclusionML‐based cognitive composite score computed using PCA provides a practical solution to track cognitive decline with improved performance in tracking cognitive decline in MCI progressors compared to PACC, while being comparable in CUs.

Predicting Transitions from Mild Cognitive Impairment to Dementia within 5‐years

AbstractBackgroundEarly detection of individuals at high risk of future progression toward dementia is in great need, particularly in the era of emerging treatments for Alzheimer’s disease. Given the easier administration of neuropsychological (NP) tests over invasive and expensive biomarker acquisition, we seek to predict the future cognitive status of an individual based on digital voice recordings of NP exams.MethodWe utilized automatic speech recognition and natural language processing techniques to create an automated tool that predicts if an individual with mild cognitive impairment (MCI) will develop dementia in a fixed follow‐up window (5 years). We characterize the predictive accuracy of this approach using data from the Framingham Heart Study. First, the automated transcription of the digital voice recordings were classified into 8 main NP sub‐tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using a large language model, only the participants’ sentences were encoded into quantitative data. These data and the participants’ characteristics, such as age, sex, ApoE4 (+/‐) genotype, and education were employed to train and test a binary classifier, predicting dementia withing 5 years after a positive MCI diagnosis. We also provide a comparison between our approach and alternative models using demographics and ApoE genotype or the Mini‐Mental State Examination (MMSE) score.ResultWe evaluated the performance of the classification task on the data containing 111 stable MCI and 103 progressive MCI subjects. Our method achieved an average accuracy of 75.5% on the held‐out test data while a baseline model using age, sex, education, and ApoE has an average accuracy of 71.8%. The sensitivity and specificity of the proposed method versus the baseline model are 78.2% vs. 81.8% and 72.7% vs. 61.8%, respectively. Our approach also significantly outperforms the model based on the MMSE score by a margin of 14% in accuracy.ConclusionWe have analyzed digital voice recordings of NP exams for participants with MCI to predict their cognitive status in 5 years. The proposed approach offers a fully automated procedure with higher accuracy, providing an opportunity to develop an accessible and easy‐to‐administer screening tool that could be easily adapted to any language.

AI‐guided patient stratification for neurodegenerative disorders using unsupervised trajectory modelling

AbstractBackgroundPredicting neurodegenerative disorders early has major implications for timely clinical management and patient outcomes. Despite advances in medical technology, we still lack tools to precisely stratify patients for targeted interventions. Here, we propose an unsupervised modelling approach based on mixtures of state space models that learns from longitudinal multimodal (brain imaging, cognitive) data to stratify patients into clusters with different clinical outcomes. Our approach has the potential to support dementia prediction based on cost‐effective, non‐invasive digital measures alone.MethodWe trained (with cross‐validation, 4 components selected with elbow method) a mixture of linear Gaussian state space models on 571 trajectories (2‐4 assessments) from ADNI comprising a) state variables: neuroimaging biomarkers (temporal lobe gray matter density, β‐amyloid) and b) observations: cognitive tests (ADNI‐Mem, ADNI‐EF, MoCA, &amp; ADAS‐13) collected at regular 2‐year intervals. The model clustered individuals based on longitudinal changes in biomarkers and the relationship between biomarkers and cognitive scores (figure 1). We validated model‐derived clusters against clinical outcomes (unseen by the model) based on each individual’s final assessment.ResultOur unsupervised modelling approach effectively stratifies individuals by clinical outcome. Cluster A comprises 65% healthy controls and 33% stable MCI patients (individuals with MCI who do not convert to AD in a 3‐year period) while cluster D comprises 77% Alzheimer’s patients (table 1). Profiling these clusters using MMSE score validates our approach: individuals in cluster A maintain high memory scores over 6 years whereas individuals in cluster D decline (figure 2). Further, we find that our model maintains good stratification when tested a) with a single rather than multiple assessments and b) with cognitive data only (i.e. without biomarkers). These results enhance the clinical utility of our approach, as collecting longitudinal and biomarker data may prove challenging in clinical settings.ConclusionOur modelling approach robustly stratifies patients by clinical outcome when trained on longitudinal multimodal data and tested using cognitive scores alone. Our approach has strong potential to generalize to other non‐invasive and cost‐effective data (e.g. digital markers from wearable technologies), enhancing the translational impact of our AI‐guided tool for early and precise patient stratification in clinical practice.

Brain Age Estimation on a Dementia Cohort Using FLAIR MRI Biomarkers.

The prodromal stage of Alzheimer's disease presents an imperative intervention window. This work focuses on using brain age prediction models and biomarkers from FLAIR MR imaging to identify subjects who progress to Alzheimer's disease (converting mild cognitive impairment) or those who remain stable (stable mild cognitive impairment). A machine learning model was trained to predict the age of normal control subjects on the basis of volume, intensity, and texture features from 3239 FLAIR MRI volumes. The brain age gap estimation (BrainAGE) was computed as the difference between the predicted and true age, and it was used as a biomarker for both cross-sectional and longitudinal analyses. Differences in biomarker means, slopes, and intercepts were investigated using ANOVA and Tukey post hoc test. Correlation analysis was performed between brain age gap estimation and established Alzheimer's disease indicators. The brain age prediction model showed accurate results (mean absolute error = 2.46 years) when testing on held out normal control data. The computed BrainAGE metric showed significant differences between the stable mild cognitive impairment and converting mild cognitive impairment groups in cross-sectional and longitudinal analyses, most notably showing significant differences up to 4 years before conversion to Alzheimer's disease. A significant correlation was found between BrainAGE and previously established Alzheimer's disease conversion biomarkers. The BrainAGE metric can allow clinicians to consider a single explainable value that summarizes all the biomarkers because it considers many dimensions of disease and can determine whether the subject has normal aging patterns or if he or she is trending into a high-risk category using a single value.

Deep learning-based artificial intelligence imaging probes for Alzheimers disease

Brain medical imaging is a main diagnosis method for Alzheimers disease (AD). But the method relies on the physicians manual analysis which is subjective and time consuming. In recent years, artificial intelligence (AI) technology has been widely applied in clinical diagnosis. This thesis is about the deep learning model to be designed to realize the computer-aided diagnosis of medical images. A model of densely connected network (DenseNet) as an AI technology, automatically learns the semantic features related to AD diagnosis on the brain MRI images from ADNI data. At the same time, for solving the limited medical image samples problem, the effective transfer learning technology was applied in the experiment. The final model result achieves 90.8% accuracy, 82.2% sensitivity and 96.1% specificity on the diagnostic task of AD, and the diagnostic accuracy is better than prevailing methods. Besides 80.4% accuracy, 52.2% sensitivity, and 84.8% specificity are achieved in the task of distinguishing progressive from stable MCI patients. This method can provide more accurate diagnosis results of Alzheimers disease expected for the clinical early auxiliary diagnosis.

Deep learning based diagnosis of Alzheimer’s disease using FDG-PET images

PurposeThe aim of this study is to develop a deep neural network to diagnosis Alzheimer's disease and categorize the stages of the disease using FDG-PET scans. Fluorodeoxyglucose positron emission tomography (FDG-PET) is a highly effective diagnostic tool that accurately detects glucose metabolism in the brain of AD patients. Material and methodsIn this work, we have developed a deep neural network using FDG-PET to discriminate Alzheimer's disease subjects from stable mild cognitive impairment (sMCI), progressive mild cognitive impairment (pMCI), and cognitively normal (CN) cohorts. A total of 83 FDG-PET scans are collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 21 subjects with CN, 21 subjects with sMCI, 21 subjects with pMCI, and 20 subjects with AD. ResultsThe method has achieved remarkable accuracy rates of 99.31% for CN vs. AD, 99.88% for CN vs. MCI, 99.54% for AD vs. MCI, and 96.81% for pMCI vs. sMCI. Based on the experimental results. ConclusionThe results show that the proposed method has a significant generalisation ability as well as good performance in predicting the conversion of MCI to AD even in the absence of direct information. FDG-PET is a well-known biomarker for the identification of Alzheimer's disease using transfer learning.