Late Mild Cognitive Impairment Research Articles

Thalamic nuclei segmentation from T1-weighted MRI: Unifying and benchmarking state-of-the-art methods

Abstract The thalamus and its constituent nuclei are critical for a broad range of cognitive, linguistic, and sensorimotor processes, and are implicated in many neurological and neurodegenerative conditions. However, the functional involvement and specificity of thalamic nuclei in human neuroimaging work is underappreciated and not well studied due, in part, to technical challenges of accurately identifying and segmenting nuclei. This challenge is further exacerbated by a lack of common nomenclature for comparing segmentation methods. Here, we use data from healthy young (Human Connectome Project, n = 100) and older healthy adults, plus those with mild cognitive impairment and Alzheimer’s disease (Alzheimer’s Disease Neuroimaging Initiative, n = 540), to benchmark four state-of-the-art thalamic segmentation methods for T1 MRI (FreeSurfer, histogram-based polynomial synthesis [HIPS]-THOMAS, synthesized contrast segmentation [SCS]-convolutional neural network [CNN], and T1-THOMAS) under a single segmentation framework. Segmentations were compared using overlap and dissimilarity metrics to the Morel stereotaxic atlas, a widely accepted thalamic atlas. We also quantified each method’s estimation of thalamic nuclear degeneration across Alzheimer’s disease progression, and how accurately early and late mild cognitive impairment, and Alzheimer’s disease could be distinguished from healthy controls. We show that the HIPS-THOMAS approach produced the most effective segmentations of individual thalamic nuclei relative to the Morel atlas, and was also most accurate in discriminating healthy controls from those with mild cognitive impairment and Alzheimer’s disease using individual nucleus volumes. This latter result was different when using whole thalamus volumes, where the SCS-CNN approach was the most accurate in classifying healthy controls. This work is the first to systematically compare the efficacy of anatomical thalamic segmentation approaches under a unified nomenclature. We also provide recommendations of which segmentation method to use for studying the functional relevance of specific thalamic nuclei, based on their overlap and dissimilarity with the Morel atlas.

Irregular word reading as a marker of semantic decline in Alzheimer’s disease: implications for premorbid intellectual ability measurement

BackgroundIrregular word reading has been used to estimate premorbid intelligence in Alzheimer’s disease (AD) dementia. However, reading models highlight the core influence of semantic abilities on irregular word reading, which shows early decline in AD. The primary objective of this study is to ascertain whether irregular word reading serves as an indicator of cognitive and semantic decline in AD, potentially discouraging its use as a marker for premorbid intellectual abilities.MethodSix hundred eighty-one healthy controls (HC), 104 subjective cognitive decline, 290 early and 589 late mild cognitive impairment (EMCI, LMCI) and 348 AD participants from the Alzheimer’s Disease Neuroimaging Initiative were included. Irregular word reading was assessed with the American National Adult Reading Test (AmNART). Multiple linear regressions were conducted predicting AmNART score using diagnostic category, general cognitive impairment and semantic tests. A generalized logistic mixed-effects model predicted correct reading using extracted psycholinguistic characteristics of each AmNART words. Deformation-based morphometry was used to assess the relationship between AmNART scores and voxel-wise brain volumes, as well as with the volume of a region of interest placed in the left anterior temporal lobe (ATL), a region implicated in semantic memory.ResultsEMCI, LMCI and AD patients made significantly more errors in reading irregular words compared to HC, and AD patients made more errors than all other groups. Across the AD continuum, as well as within each diagnostic group, irregular word reading was significantly correlated to measures of general cognitive impairment / dementia severity. Neuropsychological tests of lexicosemantics were moderately correlated to irregular word reading whilst executive functioning and episodic memory were respectively weakly and not correlated. Age of acquisition, a primarily semantic variable, had a strong effect on irregular word reading accuracy whilst none of the phonological variables significantly contributed. Neuroimaging analyses pointed to bilateral hippocampal and left ATL volume loss as the main contributors to decreased irregular word reading performances.ConclusionsWhile the AmNART may be appropriate to measure premorbid intellectual abilities in cognitively unimpaired individuals, our results suggest that it captures current semantic decline in MCI and AD patients and may therefore underestimate premorbid intelligence. On the other hand, irregular word reading tests might be clinically useful to detect semantic impairments in individuals on the AD continuum.

Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer's Disease.

Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer's disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3-5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability.

Use of 3D-CAPSNET and RNN models for 4D fMRI-based Alzheimer’s Disease Pre-detection

Predicting Alzheimer's disease (AD) at an early stage can assist more successfully prevent cognitive decline. Numerous investigations have focused on utilizing various convolutional neural network (CNN)-based techniques for automated diagnosis of AD through resting-state functional magnetic resonance imaging (rs-fMRI). Two main constraints face the methodologies presented in these studies. First, overfitting occurs due to the small size of fMRI datasets. Second, an effective modeling of the 4D information from fMRI sessions is required. In order to represent the 4D information, some studies used the deep learning techniques on functional connectivity matrices created from fMRI data, or on fMRI data as distinct 2D slices or 3D volumes. However, this results in information loss in both types of methods. In order to model the spatiotemporal (4D) information of fMRI data for AD diagnosis, a new model based on the capsule network (CapsNet) and recurrent neural network (RNN) is proposed in this study. To assess the suggested model's effectiveness, experiments were run. The findings show that the suggested model could classify AD against normal control (NC) and late mild cognitive impairment (lMCI) against early mild cognitive impairment (eMCI) with accuracy rates of 94.5% and 61.8%, respectively.

Utilizing Siamese 4D-AlzNet and Transfer Learning to Identify Stages of Alzheimer’s Disease

Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cell damage. Artificial Intelligence (AI) assists in the early identification and prediction of AD patients, determining future risks and benefits for radiologists and doctors to save time and cost. Since deep learning (DL) approaches work well with massive datasets and have recently become helpful for AD detection, there remains an area for improvement in automating detection performance. Present approaches somehow addressed the challenges of limited annotated data samples for binary classification. This contrasts with prior state-of-the-art techniques, which were constrained by their incapacity to capture abstract-level information. In this paper, we proposed a Siamese 4D-AlzNet model comprised of four parallel convolutional neural network (CNN) streams (Five CNN layer blocks) and customized transfer learning models (Frozen VGG-19, Frozen VGG-16, and customized AlexNet). Siamese 4D-AlzNet was vertically and horizontally stored, and the spatial features were passed to the final layer for classification. For experiments, T1-weighted MRI images comprised of four distinct subject classes, normal control (NC), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and AD, have been employed. Our proposed models achieved outstanding accuracy, with a remarkable 95.05% accuracy distinguishing between normal and AD subjects. The performance across remaining binary class pairs consistently exceeded 90%. We thoroughly compared our model with the latest methods using the same dataset as our reference. Our proposed model improved NC-AD and MCI-AD classification accuracy by 2% 7%.

A novel method for PET connectomics guided by fibre-tracking MRI: Application to Alzheimer's disease.

This study introduces a novel brain connectome matrix, track-weighted PET connectivity(twPC) matrix, which combines positron emission tomography (PET) and diffusion magnetic resonance imaging data to compute a PET-weighted connectome at the individual subject level. The new method is applied to characterise connectivity changes in the Alzheimer's disease (AD) continuum. The proposed twPC samples PET tracer uptake guided by the underlying white matter fibre-tracking streamline point-to-point connectivity calculated from diffusion MRI (dMRI). Using tau-PET, dMRI and T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative database, structural connectivity (SC) and twPC matrices were computed and analysed using the network-based statistic (NBS) technique to examine topological alterations in early mild cognitive impairment (MCI), late MCI and AD participants. Correlation analysis was also performed to explore the coupling between SC and twPC. The NBS analysis revealed progressive topological alterations in both SC and twPC as cognitive decline progressed along the continuum. Compared to healthy controls, networks with decreased SC were identified in late MCI and AD, and networks with increased twPC were identified in early MCI, late MCI and AD. The altered network topologies were mostly different between twPC and SC, although with several common edges largely involving the bilateral hippocampus, fusiform gyrus and entorhinal cortex. Negative correlations were observed between twPC and SC across all subject groups, although displaying an overall reduction in the strength of anti-correlation with disease progression. twPC provides a new means for analysing subject-specific PET and MRI-derived information within a hybrid connectome using established network analysis methods, providing valuable insights into the relationship between structural connections and molecular distributions. PRACTITIONER POINTS: New method is proposed to compute patient-specific PET connectome guided by MRI fibre-tracking. Track-weighted PET connectivity (twPC) matrix allows to leverage PET and structural connectivity information. twPC was applied to dementia, to characterise the PET nework abnormalities in Alzheimer's disease and mild cognitive impairment.

Polygenic effects on the risk of Alzheimer's disease in the Japanese population.

Polygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer's disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans. In this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD. The PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results. We showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.

Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model

Alzheimer’s disease (AD) is a significant neurological disorder characterized by progressive cognitive decline and memory loss. One essential task is understanding the molecular mechanisms underlying brain disorders of AD. Detecting biomarkers that contribute significantly to the classification of AD is an effective means to accomplish this essential task. However, most machine learning methods used to detect AD biomarkers require lengthy training and are unable to rapidly and effectively detect AD biomarkers. To detect biomarkers for AD accurately and efficiently, we proposed a novel approach using the Multi-Kernel Support Vector Machine (SVM) with Apriori algorithm to mine strongly associated feature sets from functional magnetic resonance imaging (fMRI) and gene expression profiles. Firstly, we downloaded the imaging data and genetic data of 121 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and transformed gene sequences into labeled sequences by encoding the four types of bases (A, T, C, and G) into distinct labels. Subsequently, we extracted the first 130 temporal sequences of brain regions and employed Pearson correlation analysis to construct “brain region gene pairs”. The integration of these data allowed us to explore the correlations between genes and brain regions. To improve classification accuracy and feature selection, we applied the Apriori algorithm to the multi-kernel SVM, dynamically building feature combinations and continuously validating classification results. By iteratively generating frequent itemsets, we obtained important brain region gene pairs. Experimental results show the effectiveness of our proposed approach. The Multi-Kernel SVM with Apriori model achieves an accuracy of 92.9%, precision of 95%, and an F1 score of 95% in classifying brain region-gene pairs within the AD–Late mild cognitive impairment (AD-LMCI) group. The amygdala, BIN1, RPN2, and IL15 associated with AD have been identified and demonstrate potential in identifying potential pathogenic factors of AD. The selected brain regions and associated genes may serve as valuable biomarkers for early AD diagnosis and better understanding of the disease’s molecular mechanisms. The integration of fMRI and gene data using the Multi-Kernel SVM–Apriori model holds great potential for advancing our knowledge of brain function and the genetic basis of neurological disorders. This approach provides a valuable tool for neuroscientists and researchers in the field of genomics and brain imaging studies.

Atrophy of the cholinergic regions advances from early to late mild cognitive impairment.

We investigated the volumetric changes in the components of the cholinergic pathway for patients with early mild cognitive impairment (EMCI) and those with late mild cognitive impairment (LMCI). The effect of patients' apolipoprotein 4 (APOE-ε4) allele status on the structural changes were analyzed. Structural magnetic resonance imaging data were collected. Patients' demographic information, plasma data, and validated global cognitive composite scores were included. Relevant features were extracted for constructing machine learning models to differentiate between EMCI (n = 312) and LMCI (n = 541) and predict patients' neurocognitive function. The data were analyzed primarily through one-way analysis of variance and two-way analysis of covariance. Considerable differences were observed in cholinergic structural changes between patients with EMCI and LMCI. Cholinergic atrophy was more prominent in the LMCI cohort than in the EMCI cohort (P < 0.05 family-wise error corrected). APOE-ε4 differentially affected cholinergic atrophy in the LMCI and EMCI cohorts. For LMCI cohort, APOE-ε4 carriers exhibited increased brain atrophy (left amygdala: P = 0.001; right amygdala: P = 0.006, and right Ch123, P = 0.032). EMCI and LCMI patients showed distinctive associations of gray matter volumes in cholinergic regions with executive (R2 = 0.063 and 0.030 for EMCI and LMCI, respectively) and language (R2 = 0.095 and 0.042 for EMCI and LMCI, respectively) function. Our data confirmed significant cholinergic atrophy differences between early and late stages of mild cognitive impairment. The impact of the APOE-ε4 allele on cholinergic atrophy varied between the LMCI and EMCI groups.

A Novel Approach Analysing the Dynamic Brain Functional Connectivity for Improved MCI Detection.

Dynamic functional connectivity (dFC) of the brain has been explored for the detection of mild cognitive impairment (MCI), preventing potential development of Alzheimer's disease. Deep learning is widely used method for dFC analysis but is unfortunately computationally expensive and unexplainable. Root mean square value (RMS) of the pairwise Pearson's correlation of the dFC is also proposed but is insufficient for accurate MCI detection. The present study aims at exploring the feasibility of several novel features for dFC analysis, and thus, reliable MCI detection. A public resting-state functional magnetic resonance imaging dataset containing healthy controls (HC), early MCI (eMCI), and late MCI (lMCI) patients was used. In addition to RMS, nine features were extracted from the pairwise Pearson's correlation of the dFC, inducing amplitude-, spectral-, entropy-, and autocorrelation-related features, and time reversibility. A Student's t-test and a least absolute shrinkage and selection operator (LASSO) regression were employed for feature dimension reduction. A SVM was then adopted for two classification objectives: HC vs. lMCI and HC vs. eMCI. Accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve were calculated as performance metrics. 6109 out of 66700 features are significantly different between HC and lMCI and 5905 between HC and eMCI. Besides, the proposed features produce excellent classification results for both tasks, outperforming most of the existing methods. This study proposes a novel and general framework for dFC analysis, providing a promising tool for the detection of many neurological brain diseases using different brain signals.

Predicting Amyloid-β Positivity in Alzheimer’s Disease: Comprehensive Analysis of Feature Selection and Machine Learning Models for Accurate Identification

To accurately identify individuals at risk of Alzheimer’s disease (AD), it is crucial to develop precise tools for predicting amyloid-β (Aβ) positivity in the brain. We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to analyze 1,377 human subjects. These participants were divided into five groups: cognitive normal (CN), subjective memory complaints (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and confirmed AD. Each group was further divided into ten subgroups based on sex, resulting in a comprehensive analysis. The dataset was used to create and evaluate the performance of 15 machine learning (ML) models. A set of 17 potential predictors was generated by combining variables from different categories, including six demographic factors (such as age), ten measurements (such as ADAS13), and APOE4 status. Through ML-based predictive modeling, several cognitive assessment measures, including ADAS13, demonstrate significant importance in multiple ML models. The highest accuracies in the 10 subgroups were 0.875, 0.892, 0.778, 0.850, 0.771, 0.739, 0.781, 0.791, 0.879, and 0.903, respectively. The collection of ML models consists of practical and valuable risk feature scores that can significantly enhance the identification of individuals who are likely to test positive for Aβ.

Serum Bile Acids Improve Prediction of Alzheimer's Progression in a Sex-Dependent Manner.

Sex disparities in serum bile acid (BA) levels and Alzheimer's disease (AD) prevalence have been established. However, the precise link between changes in serum BAs and AD development remains elusive. Here, authors quantitatively determined 33 serum BAs and 58 BA features in 4219 samples collected from 1180 participants from the Alzheimer's Disease Neuroimaging Initiative. The findings revealed that these BA features exhibited significant correlations with clinical stages, encompassing cognitively normal (CN), early and late mild cognitive impairment, and AD, as well as cognitive performance. Importantly, these associations are more pronounced in men than women. Among participants with progressive disease stages (n=660), BAs underwent early changes in men, occurring before AD. By incorporating BA features into diagnostic and predictive models, positive enhancements are achieved for all models. The area under the receiver operating characteristic curve improved from 0.78 to 0.91 for men and from 0.76 to 0.83 for women for the differentiation of CN and AD. Additionally, the key findings are validated in a subset of participants (n=578) with cerebrospinal fluid amyloid-beta and tau levels. These findings underscore the role of BAs in AD progression, offering potential improvements in the accuracy of AD prediction.

Performance Evaluation of CNN Models for Alzheimer’s Disease Detection with MRI Scans

The primary symptom of Alzheimer's disease is memory impairment, which is a neurodegenerative condition. The manifestation of these symptoms can be attributed to the impairment of the cerebral nerve responsible for cognitive functions such as learning, thinking and memory. Alzheimer’s disease is a prominent cause of mortality and lacks a definitive curve. However, appropriate medicinal interventions have demonstrated the potential to mitigate the progression and severity of the condition. This study presents the comparison of Convolutional Neural Network (CNN) models, namely ResNet50, VGG19 and VGG16 architectures, as an approach to construct an automated classification system for Alzheimer’s disease in future. The study utilises Magnetic Resonance Imaging (MRI) datasets to identify MRI datasets of individual with Alzheimer’s disease (AD), Cognitively normal (CN), mild cognitive impairment (MCI), early mild cognitive impairment (EMCI), and late mild cognitive impairment (LMCI). In the conducted experiment, the study achieved accuracy rates of 91.18% and 94.56% while utilising an epoch size of 2. The accuracy results indicate that the VGG16 model outperforms the ResNet50 model. The utilisation of automated Alzheimer’s disease classification holds potential as an auxiliary tool for healthcare professionals in determining the stage of Alzheimer’s disease hence facilitating the administration of suitable medicinal interventions.

Visualizing Braak stages with deformation‐based morphometry in super‐sampled MRI

AbstractBackgroundUnlike MRI data, histological data collected post‐mortem can’t be used to study the longitudinal formation of neurofibrillary tangles (NFT) and corresponding brain changes over time for individuals. Although structural MRI offers the possibility of non‐invasive longitudinal antemortem imaging, its resolution is a limiting factor. Using image processing and super‐sampling techniques, we examined the possibility of employing structural MRI to observe the NFT footprints in the brain.MethodData included baseline MRI scans of 1473 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset including ADNI1, 2, and GO. Voxel size was super‐sampled to 0.5×0.5×0.5mm (Manjón et al. 2010) before non‐linear registration to an ADNI‐based unbiased symmetric MRI template. The resulting deformation fields were used to compute the Jacobian determinant map for each subject as a proxy for local volume. For each subject, the sex‐ and age‐corrected z‐scored Jacobian maps were then calculated and averaged for each diagnostic group: normal subjects(CN), early MCI, late MCI, and AD. The average map for CN group was subtracted from the other average maps to show the AD‐stage differences.ResultThe Braak tau staging pattern (Braak &amp; Braak, 1995) is seen in average Jacobian maps as the disease progresses. Subjects in the MCI groups showed on average more pronounced neurodegeneration in the transentorhinal and entorhinal region (Braak Stages 1‐2), followed by degeneration in the hippocampus (Stages 3‐4). Later on, this neurodegeneration progresses toward the neocortex (Stages 5‐6). Figure 1 illustrates these results: A) the template, B)EMCI vs CN, C) LMCI vs EMCI, D) LMCI vs CN, E) AD vs CN. Red color shows an increase and blue indicates a decrease in local volume.ConclusionUsing novel image processing techniques, we were able to verify the staging of AD‐related neurofibrillary pathology progression in MRI data. This shows that structural MRI data, although limited in resolution, can still offer in‐depth insight into the brain antemortem which will in return allow the longitudinal and possible lifespan studies with an accuracy close to histological data.

Odor Identification Moderates Relationship Between Cognitive Training and Cognitive Decline

AbstractBackgroundOlfactory deficits have been shown to predict cognitive decline. Recent findings suggest that computerized cognitive training may reduce the risk of cognitive decline and dementia. However, the impact of olfaction deficits on cognitive training outcomes is unclear. In this study, we investigate how lower odor identification may moderate the relationship between treatment assignment (cognitive games versus crossword puzzles) and cognitive outcomes in older adults diagnosed with mild cognitive impairment (MCI).MethodData were drawn from the Cognitive Training and Neuroplasticity in Mild Cognitive Impairment (COG‐IT) Trial (NCT03205709). In this two‐site, single‐blinded, 78‐week trial, participants with MCI — stratified by age, severity (early/late MCI), and site — were randomly assigned to 12 weeks of intensive, home‐based, Computerized Cognitive Training (CCT) or computerized Crossword Puzzle Training (CPT), followed by six booster sessions. 106 patients received neuropsychological and olfactory assessments through the 11‐item Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS‐Cog) and the 40‐item University of Pennsylvania Smell Identification Test (UPSIT). In linear mixed‐model analyses, the effect of baseline UPSIT score on change in the ADAS‐Cog score from baseline over 78 weeks by treatment group, adjusted for age and baseline ADAS‐Cog, was assessed.ResultAmong 106 older adults (n = 50 [games]; n = 56 [crosswords]), higher baseline UPSIT scores predicted improved ADAS‐Cog performance at week 78 for all patients randomized to CPT (beta coefficient, 0.15, 95% confidence interval [CI], 0.02 to 0.27; P = 0.02). Greater baseline UPSIT score predicted improved ADAS‐Cog performance at weeks 12, 52, and 78 for early MCI patients randomized to CPT (Week 12: beta coef. 0.26, 95% CI 0.09 to 0.43; Week 52: beta coef. 0.23, 95% CI 0.05 to 0.40; Week 78: beta coef. 0.22, 95% CI 0.05 to 0.39). This was not shown in early MCI patients randomized to CCT. Higher baseline UPSIT predicted significant improvement in ADAS‐Cog for late MCI patients randomized to CCT at week 78 (beta coefficient, 0.24, 95% CI 0.05 to 0.42; P = 0.01).ConclusionOur results are consistent with literature, suggesting that impaired olfaction moderates memory decline. Our preliminary results show that individuals with less impaired olfaction may experience greater cognitive improvement following cognitive training.

PROGNOSTIC VALUE OF AT(N) CLASSIFICATION IN CSF USING CLUSTERING ANALYSIS

AbstractBackgroundThe biological definition of Alzheimer’s disease (AD) and the use of biomarkers according to the AT(N) system may improve the clinical characterization of patients and the sequence of events during the clinical course. However, some studies have raised challenges in the clinical application of AT(N) system. Unbiased, data‐driven techniques such as unsupervised machine learning may help optimize it. We aimed to optimize the classification using unsupervised machine learning algorithms (clustering analysis).MethodWe included 421 patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and 165 from our centre. We included subjective memory complaints (SMC), early mild cognitive impairment due to AD (EMCI), late MCI due to AD (LMCI) and MCI without evidence of neurodegeneration (MCI‐NN) patients according to clinical manifestations, neuropsychological test and CSF biomarkers from our center and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. We clustered the biomarkers separately and all together using the KMeans algorithm for each cohort and compared them in terms of diagnosis, AT(N) system, biomarker values distribution, and risk of progression to dementia.ResultThe optimal number of clusters for all the biomarkers together was three. These clusters differed significantly in both cohorts according to diagnosis, AT(N) categories, biomarker values distribution, and progression to dementia. We found amyloid biomarkers to be more dichotomic, while Tau biomarkers were more continuous. Intergroup comparisons revealed the following groups: 1) non‐defined or unrelated to AD subjects, with no risk of progression to dementia; 2) early stages and/or more delayed risk of conversion to dementia subjects, and 3) more severe cognitive impairment subjects within the early stages of AD and with faster progression to dementia. Findings were largely similar between the two cohorts.ConclusionThis new 3‐group classification, developed with a data‐driven approach, represents a novel perspective to assess the risk of conversion to dementia in a much more simplified way and is complementary to the AT(N) system classification

Computerized Games versus Crosswords Training in Mild Cognitive Impairment: The COG‐IT trial

AbstractBackgroundMild cognitive impairment (MCI) increases the risk of dementia. The efficacy of cognitive training in MCI is unclear.MethodIn a two‐site, single‐blinded, 78‐week trial, participants with MCI — stratified by age, severity (early/late MCI), and site — were randomly assigned to 12 weeks of intensive, home‐based, computerized training with Web‐based cognitive games or Web‐based crossword puzzles, followed by six booster sessions. In mixed‐model analyses, the primary outcome was change from baseline in the 11‐item Alzheimer’s Disease Assessment Scale‐Cognitive (ADAS‐Cog) score, adjusted for baseline. Secondary outcomes included change from baseline in neuropsychological composite score and Functional Activities Questionnaire score at 78 weeks, adjusted for baseline. Changes in hippocampal volume and cortical thickness on magnetic resonance imaging were assessed.ResultAmong 107 participants (n = 51 [games]; n = 56 [crosswords]), ADAS‐Cog11 score worsened slightly for games and improved for crosswords at week 78 (least squares [LS] means difference, 21.44; 95% CI 22.83 to 20.06; P = 0.04). From baseline to week 78, mean ADAS‐Cog score worsened for games (9.53 to 9.93) and improved for crosswords (9.59 to 8.61). The late MCI subgroup showed similar results (LS means difference, 22.45; SE, 0.89; 95% CI 24.21 to 20.70). Among secondary outcomes, the Functional Activities Questionnaire score worsened more with games than with crosswords at week 78 (LS means difference, 21.08; 95% CI 21.97 to 20.18, p = 0.02). Other secondary outcomes showed no differences. Decreases in hippocampal volume and cortical thickness were greater for games than for crosswords (LS means difference, 34.07; SE, 17.12; 95% CI 0.51 to 67.63, p &lt; .05 [hippocampal volume]; LS means difference, 0.02; SE, 0.01; 95% CI, 0.00 to 0.04, p = .02 [cortical thickness]).ConclusionHome‐based computerized training with crosswords demonstrated superior efficacy to games at 78 weeks. Common directionality was observed in the primary outcome of ADAS‐Cog (cognition) and the secondary outcomes of FAQ (function) score and changes in MRI hippocampal volume and cortical thickness (neurodegeneration). If these effects are replicated and expanded in future trials with the inclusion of a control group that does not receive cognitive training, crossword puzzles training could become a home‐based, scalable, cognitive enhancement tool for individuals with MCI.

Alterations via inter-regional connective relationships in Alzheimer's disease.

Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.

76 Baseline Frontoparietal Gray Matter Volume Predicts Executive Function Performance at 24-Months in Early and Late Mild Cognitive Impairment

Objective:To examine the relationships between baseline gray matter volumes, diagnostic status, and executive function performance at 24-month follow-up, and the relative importance of predictors of executive function in a cohort of non-demented older adults.Participants and Methods:The study sample included 147 participants from the Alzheimer’s Disease Neuroimaging Initiative (mean age = 70.6, SD = 6.4; mean education = 17 years, SD = 2.4). At baseline, 49 participants were diagnosed as cognitively normal (CN), 60 as early mild cognitive impairment (EMCI), and 38 as late mild cognitive impairment (LMCI). Magnetic resonance imaging (MRI) data were collected at baseline. A composite score of executive function and FreeSurfer-derived gray matter regions-of-interest (ROI; whole brain, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, orbitofrontal cortex, anterior cingulate cortex, superior parietal lobule, inferior parietal lobule, hippocampus) were examined. Hierarchical linear regression models were employed to assess whether brain volume predicted executive function at 24-month follow-up and interaction effects between baseline ROI volume and diagnostic status. Age, gender, education, Mini-Mental State Examination scores, and APOE-e4 allele status were included as control variables in each model. Relative importance metrics, which quantifies an individual regressor’s contribution to a multiple regression model, were computed using the Lindemen, Merenda, and Gold (lmg) method to assess the relative contribution of each variable in predicting executive function performance.Results:Across all participants, baseline gray matter ROI volume accounted for a significant amount of variance in executive function at 24-months after accounting for control variables. Specifically, anterior cingulate cortex and superior parietal lobule accounted for an additional 7% and 6% of variance in executive function at 24-months. Significant brain region X diagnostic status interaction effects were observed in executive function performance at 24-months. Relative importance metrics within each group indicated that age is the most important predictor of executive function at 24-months for CN, anterior cingulate cortex is most important for EMCI, and Mini-Mental Examination score is most important for LMCI.Conclusions:Our findings implicate frontoparietal gray matter regions as significant predictors of executive function performance at 24-months, and that this relationship is moderated by diagnostic status. Our results indicate that the value of specific variables to predict executive function performance varies based on diagnostic status. Specifically, anterior cingulate cortex was a significant predictor of executive function performance across all participants and was the most important variable in predicting performance in the earliest stage of mild cognitive impairment. These results support previous studies examining gray matter correlates of executive function and extend the literature by exploring predictors of executive function in early and late stages of mild cognitive impairment.

Resilience in Mild Cognitive Impairment (MCI): Examining the Level and the Associations of Resilience with Subjective Wellbeing and Negative Affect in Early and Late-Stage MCI.

The current study examines the relationship between the cognitive state of participants [healthy-early mild cognitive impairment (MCI)-late MCI], some subjective wellbeing factors (positive emotions, engagement, positive relationships, meaning in life, accomplishment, and negative emotions), and negative psychological outcomes (depression, anxiety, stress), as well as psychological resilience. We expected that people with advanced MCI would perceive increased negative psychological outcomes, poorer psychological resilience, and lower levels of subjective wellbeing in contrast to early MCI and healthy participants. The study involved 30 healthy, 31 early, and 28 late MCI individuals. A series of questionnaires have been applied to assess the aforementioned constructs. To examine the hypotheses of the study, path analysis (EQS program) was applied. Results showed that early MCI persons maintain the same levels of positive emotions and feelings of accomplishment with healthy peers. Late-stage patients present those feelings in a diminished form, which adversely impacts psychological resilience. Individuals with early and late MCI exhibit negative emotions and stress that impact their resilience; however, those with early MCI experience greater stress, negative emotions, depression, and anxiety. These findings may be utilized to design psychological interventions for resilience enhancement and support brain health in elderly adults who are at risk of neurodegeneration.