Conversion between the Rowland Universal Dementia Assessment Scale and Mini-Mental State Examination test scores in majority and minority populations.

INTRODUCTION: Despite the Rowland Universal Dementia Assessment Scale (RUDAS) having significant advantages as a cognitive screening tool, particularly for minority populations, the Mini-Mental State Examination (MMSE) test is the most widely used test for cognitive screening in Alzheimer's disease (AD). This study aimed to develop a conversion table to predict MMSE scores from observed RUDAS scores, allowing an easy-to-use method to compare both screening tests. METHODS: The equipercentile equating method was used to develop the conversion table using a training sample consisting of cognitively intact participants and individuals with early-stage AD. The resulting conversion table was validated in two samples, comprising participants from majority and minority populations assessed in Spanish. RESULTS: The conversion table demonstrated excellent reliability with intraclass correlation coefficients of.92 in both validation samples. CONCLUSION: This study provides a conversion table between RUDAS and MMSE scores, improving the comparability of these cognitive screening tools for use in clinical and research purposes.

Transcranial direct current stimulation and neuronal functional connectivity in MCI: role of individual factors associated to AD.

Background: Alzheimer's disease (AD) encompasses a spectrum that may progress from mild cognitive impairment (MCI) to full dementia, characterized by amyloid-beta and tau accumulation. Transcranial direct current stimulation (tDCS) is being investigated as a therapeutic option, but its efficacy in relation to individual genetic and biological risk factors remains underexplored. Objective: To evaluate the effects of a two-week anodal tDCS regimen on the left dorsolateral prefrontal cortex, focusing on functional connectivity changes in neural networks in MCI patients resulting from various possible underlying disorders, considering individual factors associated to AD such as amyloid-beta deposition, APOE ϵ4 allele, BDNF Val66Met polymorphism, and sex. Methods: In a single-arm prospective study, 63 patients with MCI, including both amyloid-PET positive and negative cases, received 10 sessions of tDCS. We assessed intra- and inter-network functional connectivity (FC) using fMRI and analyzed interactions between tDCS effects and individual factors associated to AD. Results: tDCS significantly enhanced intra-network FC within the Salience Network (SN) and inter-network FC between the Central Executive Network and SN, predominantly in APOE ϵ4 carriers. We also observed significant sex*tDCS interactions that benefited inter-network FC among females. Furthermore, the effects of multiple modifiers, particularly the interaction of the BDNF Val66Met polymorphism and sex, were evident, as demonstrated by increased intra-network FC of the SN in female Met non-carriers. Lastly, the effects of tDCS on FC did not differ between the group of 26 MCI patients with cerebral amyloid-beta deposition detected by flutemetamol PET and the group of 37 MCI patients without cerebral amyloid-beta deposition. Conclusions: The study highlights the importance of precision medicine in tDCS applications for MCI, suggesting that individual genetic and biological profiles significantly influence therapeutic outcomes. Tailoring interventions based on these profiles may optimize treatment efficacy in early stages of AD.

Development of a diagnostic model for detecting mild cognitive impairment in young and middle-aged patients with obstructive sleep apnea: a prospective observational study.

Objectives: Obstructive sleep apnea (OSA) is a common sleep-disordered breathing condition linked to the accelerated onset of mild cognitive impairment (MCI). However, the prevalence of undiagnosed MCI among OSA patients is high and attributable to the complexity and specialized nature of MCI diagnosis. Timely identification and intervention for MCI can potentially prevent or delay the onset of dementia. This study aimed to develop screening models for MCI in OSA patients that will be suitable for healthcare professionals in diverse settings and can be effectively utilized without specialized neurological training. Methods: A prospective observational study was conducted at a specialized sleep medicine center from April 2021 to September 2022. Three hundred and fifty consecutive patients (age: 18-60 years) suspected OSA, underwent the Montreal Cognitive Assessment (MoCA) and polysomnography overnight. Demographic and clinical data, including polysomnographic sleep parameters and additional cognitive function assessments were collected from OSA patients. The data were divided into training (70%) and validation (30%) sets, and predictors of MCI were identified using univariate and multivariate logistic regression analyses. Models were evaluated for predictive accuracy and calibration, with nomograms for application. Results: Two hundred and thirty-three patients with newly diagnosed OSA were enrolled. The proportion of patients with MCI was 38.2%. Three diagnostic models, each with an accompanying nomogram, were developed. Model 1 utilized body mass index (BMI) and years of education as predictors. Model 2 incorporated N1 and the score of backward task of the digital span test (DST_B) into the base of Model 1. Model 3 expanded upon Model 1 by including the total score of digital span test (DST). Each of these models exhibited robust discriminatory power and calibration. The C-statistics for Model 1, 2, and 3 were 0.803 [95% confidence interval (CI): 0.735-0.872], 0.849 (95% CI: 0.788-0.910), and 0.83 (95% CI: 0.763-0.896), respectively. Conclusion: Three straightforward diagnostic models, each requiring only two to four easily accessible parameters, were developed that demonstrated high efficacy. These models offer a convenient diagnostic tool for healthcare professionals in diverse healthcare settings, facilitating timely and necessary further evaluation and intervention for OSA patients at an increased risk of MCI.

Correlation of Cognitive Status and Atrophy Score in Alzheimer's Disease Among the Bangladeshi Population.

BACKGROUND: Alzheimer's disease (AD) patients suffer from cognitive dysfunction. This study assessed the structural magnetic resonance imaging (MRI) scoring among Alzheimer's patients (age ≥18 years) to correlate with dementia severity according to mini-mental state exam (MMSE) scores. METHODS: This cross-sectional study evaluated Bangladeshi adult AD patients from January 2018 to December 2022 who attended with subjective memory complaints and fulfilled the diagnostic and statistical manual of mental disorders criteria (DSM 5) for diagnosing dementia. The medial temporal lobe atrophy (MTA) and Koedam's score of the atrophy were measured utilising the 1.5 and 3 Tesla Magnetom symphony MRI systems. RESULTS: Of the 62 patients enrolled, the majority (39 cases; 62.9%) were aged over 60 years. Males were more predominant than females, with a male-to-female ratio of 2.6:1, and the moderate MMSE group consisted of 35.6% males and 64.7% females (P = 0.01). Further, MTA score severity is paradoxically associated with the MMSE score (P = 0.005). Additionally, we found a statistically significant negative correlation between the severity of the MMSE and only MTA scores (r = -0.350; 95% CI -0.551 to -0.110; P = 0.005). CONCLUSION: Structural magnetic resonance imaging among Alzheimer's patients is significantly correlated with the severity of dementia as per mini-mental state exam scores.

Frontiers and hotspots evolution in mild cognitive impairment: a bibliometric analysis of from 2013 to 2023.

Background: Mild cognitive impairment is a heterogeneous syndrome. The heterogeneity of the syndrome and the absence of consensus limited the advancement of MCI. The purpose of our research is to create a visual framework of the last decade, highlight the hotspots of current research, and forecast the most fruitful avenues for future MCI research. Methods: We collected all the MCI-related literature published between 1 January 2013, and 24 April 2023, on the "Web of Science." The visual graph was created by the CiteSpace and VOSviewer. The current research hotspots and future research directions are summarized through the analysis of keywords and co-cited literature. Results: There are 6,075 articles were included in the final analysis. The number of publications shows an upward trend, especially after 2018. The United States and the University of California System are the most prolific countries and institutions, respectively. Petersen is the author who ranks first in terms of publication volume and influence. Journal of Alzheimer's Disease was the most productive journal. "neuroimaging," "fluid markers," and "predictors" are the focus of current research, and "machine learning," "electroencephalogram," "deep learning," and "blood biomarkers" are potential research directions in the future. Conclusion: The cognition of MCI has been continuously evolved and renewed by multiple countries' joint efforts in the past decade. Hotspots for current research are on diagnostic biomarkers, such as fluid markers, neuroimaging, and so on. Future hotspots might be focused on the best prognostic and diagnostic models generated by machine learning and large-scale screening tools such as EEG and blood biomarkers.

User Experience of a (Semi-) Automated Cognitive Phone-Based Assessment Within a Memory Clinic Population.

OBJECTIVE: We examined the user experience in different modalities (face-to-face, semi-automated phone-based, and fully automated phone-based) of cognitive testing in people with subjective cognitive decline and mild cognitive impairment. METHOD: A total of 67 participants from the memory clinic of the Maastricht University Medical Center+ participated in the study. The study consisted of cognitive tests in different modalities, namely, face-to-face, semi-automated phone-based guided by a researcher, and fully automated phone-based without the involvement of a researcher. After each assessment, a user experience questionnaire was administered, including questions about, for example, satisfaction, simplicity, and missing personal contact, on a seven-point Likert scale. Non-parametric tests were used to compare user experiences across different modalities. RESULTS: In all modalities, user experiences were rated above average. The face-to-face ratings were comparable to the ratings of the semi-automated phone-based assessment, except for the satisfaction and recommendation items, which were rated higher for the face-to-face assessment. The face-to-face assessment was preferred above the fully automated phone-based assessment on all items. In general, the semi- and fully automated phone-based assessments were comparable (simplicity, conceivability, quality of sound, visiting the hospital, and missing personal contact), while on all the other items, the semi-automated phone-based assessment was preferred. CONCLUSIONS: User experience was rated high within all modalities. Simplicity, conceivability, comfortability, and participation scores were comparable in the semi-automated phone-based and face-to-face assessment. Based on these findings and earlier research on validation of the semi-automated phone-based assessment, the semi-automated assessment could be useful for screening for clinical trials, and after more research, in clinical practice.

The application value of Rs-fMRI-based machine learning models for differentiating mild cognitive impairment from Alzheimer's disease: a systematic review and meta-analysis.

BACKGROUND: Various machine learning (ML) models based on resting-state functional MRI (Rs-fMRI) have been developed to facilitate differential diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the diagnostic accuracy of such models remains understudied. Therefore, we conducted this systematic review and meta-analysis to explore the diagnostic accuracy of Rs-fMRI-based radiomics in differentiating MCI from AD. METHODS: PubMed, Embase, Cochrane, and Web of Science were searched from inception up to February 8, 2024, to identify relevant studies. Meta-analysis was conducted using a bivariate mixed-effects model, and sub-group analyses were carried out by the types of ML tasks (binary classification and multi-class classification tasks). FINDINGS: In total, 23 studies, comprising 5,554 participants were enrolled in the study. In the binary classification tasks (twenty studies), the diagnostic accuracy of the ML model for AD was 0.99 (95%CI: 0.34 ~ 1.00), with a sensitivity of 0.94 (95%CI: 0.89 ~ 0.97) and a specificity of 0.98 (95%CI: 0.95 ~ 1.00). In the multi-class classification tasks (six studies), the diagnostic accuracy of the ML model was 0.98 (95%CI: 0.98 ~ 0.99) for NC, 0.96 (95%CI: 0.96 ~ 0.96) for early mild cognitive impairment (EMCI), 0.97 (95%CI: 0.96 ~ 0.97) for late mild cognitive impairment (LMCI), and 0.95 (95%CI: 0.95 ~ 0.95) for AD. CONCLUSIONS: The Rs-fMRI-based ML model can be adapted to multi-class classification tasks. Therefore, multi-center studies with large samples are needed to develop intelligent application tools to promote the development of intelligent ML models for disease diagnosis.

Feasibility and acceptability of a group music creativity intervention for adults with varying cognitive function.

Maintaining healthy cognitive functioning and delaying cognitive decline in cognitively intact and cognitive impaired adults are major research initiatives for addressing dementia disease burden. Music interventions are promising, non-pharmaceutical treatment options for preserving cognitive function and psychological health in older adults with varying levels of cognitive function. While passive, music interventions have attracted considerable attention in the abnormal cognitive aging literature, active, music interventions such as music creativity are less well-studied. Among 58 older adults with different levels of cognitive function (cognitively healthy to mild cognitive impairment), we examined the feasibility and acceptability of Project CHROMA, a Stage 1 clinical trial developed to assess the effects of a novel, music creativity curriculum on various health outcomes. Music intervention participation (93%), overall study retention (78%), and intervention satisfaction (100%) rates were comparable to other similarly designed clinical trials. Exploratory analyses using mixed-level modeling tested the efficacy of the intervention on cognitive and psychological outcomes. Compared to those in the control condition, participants in the music condition showed some improvements in cognitive functioning and socioemotional well-being. Findings suggest that a 6-week music creativity clinical trial with several multi-modal health assessments can be feasibly implemented within a sample of varying cognitive ability.

MC-ViViT: Multi-branch Classifier-ViViT to Detect Mild Cognitive Impairment in Older Adults Using Facial Videos.

Deep machine learning models including Convolutional Neural Networks (CNN) have been successful in the detection of Mild Cognitive Impairment (MCI) using medical images, questionnaires, and videos. This paper proposes a novel Multi-branch Classifier-Video Vision Transformer (MC-ViViT) model to distinguish MCI from those with normal cognition by analyzing facial features. The data comes from the I-CONECT, a behavioral intervention trial aimed at improving cognitive function by providing frequent video chats. MC-ViViT extracts spatiotemporal features of videos in one branch and augments representations by the MC module. The I-CONECT dataset is challenging as the dataset is imbalanced containing Hard-Easy and Positive-Negative samples, which impedes the performance of MC-ViViT. We propose a loss function for Hard-Easy and Positive-Negative Samples (HP Loss) by combining Focal loss and AD-CORRE loss to address the imbalanced problem. Our experimental results on the I-CONECT dataset show the great potential of MC-ViViT in predicting MCI with a high accuracy of 90.63% accuracy on some of the interview videos.

Early Prediction of Progression to Alzheimer's Disease using Multi-Modality Neuroimages by a Novel Ordinal Learning Model ADPacer.

Machine learning has shown great promise for integrating multi-modality neuroimaging datasets to predict the risk of progression/conversion to Alzheimer's Disease (AD) for individuals with Mild Cognitive Impairment (MCI). Most existing work aims to classify MCI patients into converters versus non-converters using a pre-defined timeframe. The limitation is a lack of granularity in differentiating MCI patients who convert at different paces. Progression pace prediction has important clinical values, which allow from more personalized interventional strategies, better preparation of patients and their caregivers, and facilitation of patient selection in clinical trials. We proposed a novel ADPacer model which formulated the pace prediction into an ordinal learning problem with a unique capability of leveraging training samples with label ambiguity to augment the training set. This capability differentiates ADPacer from existing ordinal learning algorithms. We applied ADPacer to MCI patient cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL), and demonstrated the superior performance of ADPacer compared to existing ordinal learning algorithms. We also integrated the SHapley Additive exPlanations (SHAP) method with ADPacer to assess the contributions from different modalities to the model prediction. The findings are consistent with the AD literature.

Path Integration Detects Prodromal Alzheimer's Disease and Predicts Cognitive Decline.

Background: The entorhinal cortex is the very earliest involvement of Alzheimer's disease (AD). Grid cells in the medial entorhinal cortex form part of the spatial navigation system. Objective: We aimed to determine whether path integration performance can be used to detect patients with mild cognitive impairment (MCI) at high risk of developing AD, and whether it can predict cognitive decline. Methods: Path integration performance was assessed in 71 patients with early MCI (EMCI) and late MCI (LMCI) using a recently developed 3D virtual reality navigation task. Patients with LMCI were further divided into those displaying characteristic brain imaging features of AD, including medial temporal lobe atrophy on magnetic resonance imaging and posterior hypoperfusion on single-photon emission tomography (LMCI+), and those not displaying such features (LMCI-). Results: Path integration performance was significantly lower in patients with LMCI+than in those with EMCI and LMCI-. A significantly lower performance was observed in patients who showed progression of MCI during 12 months, than in those with stable MCI. Path integration performance distinguished patients with progressive MCI from those with stable MCI, with a high classification accuracy (a sensitivity of 0.88 and a specificity of 0.70). Conclusions: Our results suggest that the 3D virtual reality navigation task detects prodromal AD patients and predicts cognitive decline after 12 months. Our navigation task, which is simple, short (12-15 minutes), noninvasive, and inexpensive, may be a screening tool for therapeutic choice of disease-modifiers in individuals with prodromal AD.

Enhancing Alzheimer's Disease Diagnosis and Care by Focusing on Plasma Biomarkers for Identifying Mild Cognitive Impairment.

Biomarkers that accurately identify mild cognitive impairment (MCI) are of greater importance for Alzheimer's disease (AD) management and treatment. On the other hand, blood-based biomarkers are not only more practical but also less invasive than the common cerebrospinal fluid biomarkers. In their report in the Journal of Alzheimer's Disease, Wang and collaborators identified 67 upregulated and 220 downregulated long noncoding RNAs (lncRNAs). They further demonstrated that 4 of these lncRNAs could discriminate MCI from cognitively healthy individuals. Apart from their significance as potential biomarkers for MCI diagnosis, these lncRNAs can offer additional information on the cellular mechanisms of AD pathology.

Limbic Network Derangement Mediates Unawareness of Apathy in Mild Cognitive Impairment due to Alzheimer's Disease: Clues from [18F]FDG PET Voxel-Wise Analysis.

Background: Discrepancy between caregiver and patient assessments of apathy in mild cognitive impairment (MCI) is considered an index of apathy unawareness, independently predicting progression to AD dementia. However, its neural underpinning are uninvestigated. Objective: To explore the [18F]FDG PET-based metabolic correlates of apathy unawareness measured through the discrepancy between caregiver and patient self-report, in patients diagnosed with MCI. Methods: We retrospectively studied 28 patients with an intermediate or high likelihood of MCI-AD, progressed to dementia over an average of two years, whose degree of apathy was evaluated by means of the Apathy Evaluation Scale (AES) for both patients (PT-AES) and caregivers (CG-AES). Voxel-based analysis at baseline was used to obtain distinct volumes of interest (VOIs) correlated with PT-AES, CG-AES, or their absolute difference (DISCR-AES). The resulting DISCR-AES VOI count densities were used as covariates in an inter-regional correlation analysis (IRCA) in MCI-AD patients and a group of matched healthy controls (HC). Results: DISCR-AES negatively correlated with metabolism in bilateral parahippocampal gyrus, posterior cingulate cortex, and thalamus, PT-AES score with frontal and anterior cingulate areas, while there was no significant correlation between CG-AES and brain metabolism. IRCA revealed that MCI-AD patients exhibited reduced metabolic/functional correlations of the DISCR-AES VOI with the right cingulate gyrus and its anterior projections compared to HC. Conclusions: Apathy unawareness entails early disruption of the limbic circuitry rather than the classical frontal-subcortical pathways typically associated with apathy. This reaffirms apathy unawareness as an early and independent measure in MCI-AD, marked by distinct pathophysiological alterations.

Storyteller in ADNI4: Application of an early Alzheimer's disease screening tool using brief, remote, and speech-based testing.

INTRODUCTION: Speech-based testing shows promise for sensitive and scalable objective screening for Alzheimer's disease (AD), but research to date offers limited evidence of generalizability. METHODS: Data were taken from the AMYPRED (Amyloid Prediction in Early Stage Alzheimer's Disease from Acoustic and Linguistic Patterns of Speech) studies (N = 101, N = 46 mild cognitive impairment [MCI]) and Alzheimer's Disease Neuroimaging Initiative 4 (ADNI4) remote digital (N = 426, N = 58 self-reported MCI, mild AD or dementia) and in-clinic (N = 57, N = 13 MCI) cohorts, in which participants provided audio-recorded responses to automated remote story recall tasks in the Storyteller test battery. Text similarity, lexical, temporal, and acoustic speech feature sets were extracted. Models predicting early AD were developed in AMYPRED and tested out of sample in the demographically more diverse cohorts in ADNI4 (> 33% from historically underrepresented populations). RESULTS: Speech models generalized well to unseen data in ADNI4 remote and in-clinic cohorts. The best-performing models evaluated text-based metrics (text similarity, lexical features: area under the curve 0.71-0.84 across cohorts). DISCUSSION: Speech-based predictions of early AD from Storyteller generalize across diverse samples. HIGHLIGHTS: The Storyteller speech-based test is an objective digital prescreener for Alzheimer's Disease Neuroimaging Initiative 4 (ADNI4). Speech-based models predictive of Alzheimer's disease (AD) were developed in the AMYPRED (Amyloid Prediction in Early Stage Alzheimer's Disease from Acoustic and Linguistic Patterns of Speech) sample (N = 101). Models were tested out of sample in ADNI4 in-clinic (N = 57) and remote (N = 426) cohorts. Models showed good generalization out of sample. Models evaluating text matching and lexical features were most predictive of early AD.

Personalizing progressive changes to brain structure in Alzheimer's disease using normative modeling.

INTRODUCTION: Neuroanatomical normative modeling captures individual variability in Alzheimer's disease (AD). Here we used normative modeling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD. METHODS: Cortical and subcortical normative models were generated using healthy controls (n ≈ 58k). These models were used to calculate regional z scores in 3233 T1-weighted magnetic resonance imaging time-series scans from 1181 participants. Regions with z scores < -1.96 were classified as outliers mapped on the brain and summarized by total outlier count (tOC). RESULTS: tOC increased in AD and in people with MCI who converted to AD and also correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of progression from MCI to AD. Brain outlier maps identified the hippocampus as having the highest rate of change. DISCUSSION: Individual patients' atrophy rates can be tracked by using regional outlier maps and tOC. HIGHLIGHTS: Neuroanatomical normative modeling was applied to serial Alzheimer's disease (AD) magnetic resonance imaging (MRI) data for the first time. Deviation from the norm (outliers) of cortical thickness or brain volume was computed in 3233 scans. The number of brain-structure outliers increased over time in people with AD. Patterns of change in outliers varied markedly between individual patients with AD. People with mild cognitive impairment whose outliers increased over time had a higher risk of progression from AD.

Impact of Apolipoprotein E ε4 in Alzheimer's Disease: A Meta-Analysis of Voxel-Based Morphometry Studies.

BACKGROUND AND PURPOSE: Alzheimer's disease (AD) is the most-prevalent form of dementia and imposes substantial burdens at the personal and societal levels. The apolipoprotein E (APOE) ε4 allele is a genetic factor known to increase AD risk and exacerbate brain atrophy and its symptoms. We aimed to provide a comprehensive review of the impacts of APOE ε4 on brain atrophy in AD as well as in mild cognitive impairment (MCI) as a transitional stage of AD. METHODS: We performed a coordinate-based meta-analysis of voxel-based morphometry studies to compare gray-matter atrophy patterns between carriers and noncarriers of APOE ε4. We obtained coordinate-based structural magnetic resonance imaging data from 1,135 individuals who met our inclusion criteria among 12 studies reported in PubMed and Google Scholar. RESULTS: We found that atrophy of the hippocampus and parahippocampus was significantly greater in APOE ε4 carriers than in noncarriers, especially among those with AD and MCI, while there was no significant atrophy in these regions in healthy controls who were also carriers. CONCLUSIONS: The present meta-analysis has highlighted the significant link between the APOE ε4 allele and hippocampal atrophy in both AD and MCI, which emphasizes the critical influence of the allele on neurodegeneration, especially in the hippocampus. These findings improve the understanding of AD pathology, potentially facilitating progress in early detection, targeted interventions, and personalized care strategies for individuals at risk of AD who carry the APOE ε4 allele.

Predicting the Progression of Mild Cognitive Impairment to Alzheimer's Dementia Using Recurrent Neural Networks With a Series of Neuropsychological Tests.

BACKGROUND AND PURPOSE: The prevalence of Alzheimer's dementia (AD) is increasing as populations age, causing immense suffering for patients, families, and communities. Unfortunately, no treatments for this neurodegenerative disease have been established. Predicting AD is therefore becoming more important, because early diagnosis is the best way to prevent its onset and delay its progression. METHODS: Mild cognitive impairment (MCI) is the stage between normal cognition and AD, with large variations in its progression. The disease can be effectively managed by accurately predicting the probability of MCI progressing to AD over several years. In this study we used the Alzheimer's Disease Neuroimaging Initiative dataset to predict the progression of MCI to AD over a 3-year period from baseline. We developed and compared various recurrent neural network (RNN) models to determine the predictive effectiveness of four neuropsychological (NP) tests and magnetic resonance imaging (MRI) data at baseline. RESULTS: The experimental results confirmed that the Preclinical Alzheimer's Cognitive Composite score was the most effective of the four NP tests, and that the prediction performance of the NP tests improved over time. Moreover, the gated recurrent unit model exhibited the best performance among the prediction models, with an average area under the receiver operating characteristic curve of 0.916. CONCLUSIONS: Timely prediction of progression from MCI to AD can be achieved using a series of NP test results and an RNN, both with and without using the baseline MRI data.

Dual attention based fusion network for MCI Conversion Prediction.

Alzheimer's disease (AD) severely impacts the lives of many patients and their families. Predicting the progression of the disease from the early stage of mild cognitive impairment (MCI) is of substantial value for treatment, medical research and clinical trials. In this paper, we propose a novel dual attention network to classify progressive MCI (pMCI) and stable MCI (sMCI) using both magnetic resonance imaging (MRI) and neurocognitive metadata. A 3D CNN ShuffleNet V2 model is used as the network backbone to extract MRI image features. Then, neurocognitive metadata is used to guide the spatial attention mechanism to steer the model to focus attention on the most discriminative regions of the brain. In contrast to traditional fusion methods, we propose a ViT based self attention fusion mechanism to fuse the neurocognitive metadata with the 3D CNN feature maps. The experimental results show that our proposed model achieves an accuracy, AUC, and sensitivity of 81.34%, 0.874, and 0.85 respectively using 5-fold cross validation evaluation. A comprehensive experimental study shows our proposed approach significantly outperforms all previous methods for MCI progression classification. In addition, an ablation study shows both fusion methods contribute to the high final performance.

A meta-analysis of the consequences of cognitive training on the cognitive function of aged mild cognitive impairment patients.

Cognitive training has gained popularity as a means to aid older adults with mild cognitive impairment (MCI), a transitional phase between normal ageing and Alzheimer's disease (AD). MCI represents a critical and potentially reversible state that can either improve or progress to full-blown dementia. This study aims to evaluate the impact of cognitive training on cognitive function in aged patients with MCI. PubMed, Embase, Medline, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang databases were systematically retrieved from inception until May 2024. We rigorously applied the risk-of-bias methodology recommended by the Cochrane Handbook to assess the quality of the included studies. After two rounds of screening and removing duplicates, a total of 2685 articles were initially identified, from which 28 met the inclusion criteria. The meta-analysis included 28 randomised controlled trials with 1960 participants. In this meta-analysis, Review Manager 5.4 was used for statistical analysis. Findings revealed that cognitive training significantly improved the global cognitive function in aged MCI patients, as evidenced by the results of the Montreal Cognitive Assessment (standard mean difference (SMD) = 3.26; 95% CI, 2.69-3.82; P < 0.00001) and Mini-Mental State Examination (SMD = 2.27; 95% CI, 1.52-3.01; P < 0.00001). The beneficial effects of cognitive training interventions were consistent regardless of duration, including periods of 2 months or less (SMD = 1.94; 95% CI, 1.25-2.63; P < 0.00001), 2 to 6 months (SMD = 2.53; 95% CI, 1.52-3.53; P < 0.00001), and over 6 months (SMD = 4.12; 95% CI, 0.97-7.27; P = 0.01). The analysis indicates that cognitive training significantly benefits overall cognitive function, delayed memory, orientation, attention, and language skills in aged patients with MCI. Furthermore, cognitive training interventions are effective in enhancing cognitive function, irrespective of their duration.

Brain network dynamics in patients with single- and multiple-domain amnestic mild cognitive impairment.

INTRODUCTION: Brain network dynamics have been extensively explored in patients with amnestic mild cognitive impairment (aMCI); however, differences in single- and multiple-domain aMCI (SD-aMCI and MD-aMCI) remain unclear. METHODS: Using multicenter datasets, coactivation patterns (CAPs) were constructed and compared among normal control (NC), SD-aMCI, MD-aMCI, and Alzheimer's disease (AD) patients based on individual high-order cognitive network (HOCN) and primary sensory network (PSN) parcellations. Correlations between spatiotemporal characteristics and neuropsychological scores were analyzed. RESULTS: Compared to NC, SD-aMCI showed temporal alterations in HOCN-dominant CAPs, while MD-aMCI showed alterations in PSN-dominant CAPs. In addition, transitions from SD-aMCI to AD may involve PSN, while MD-aMCI to AD involves both PSN and HOCN. Results were generally consistent across datasets from Chinese and White populations. DISCUSSION: The HOCN and PSN are distinctively involved in aMCI subtypes and in the transformation between aMCI subtypes and AD, highlighting the necessity of aMCI subtype classification in AD studies. HIGHLIGHTS: Individual functional network parcellations and coactivation pattern (CAP) analysis were performed to characterize spatiotemporal differences between single- and multiple-domain amnestic mild cognitive impairment (SD-aMCI and MD-aMCI), and between distinct aMCI subtypes and Alzheimer's disease (AD). The analysis of multicenter datasets converged on four pairs of recurrent CAPs, including primary sensory networks (PSN)-dominant CAPs, high-order cognitive networks (HOCN)-dominant CAPs, and PSN-HOCN-interacting CAPs. The HOCN and PSN are distinctively involved in aMCI subtypes and in the transformation between distinct aMCI subtypes and AD.