Prediction Of Mild Cognitive Impairment Research Articles

A nomogram for individualized prediction of mild cognitive impairment in patients with subjective cognitive decline during physical examinations: a cross-sectional study.

To develop a nomogram for mild cognitive impairment (MCI) in patients with subjective cognitive decline (SCD) undergoing physical examinations in China. We enrolled 370 patients undergoing physical examinations at the Medical Center of the First Hospital of Jilin University, Jilin Province, China, from October 2022 to March 2023. Of the participants, 256 were placed in the SCD group, and 74 were placed in the MCI group. The population was randomly divided into a training set and a validation set at a 7:3 ratio. A least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. The performance and clinical utility of the nomogram were determined using Harrell's concordance index, calibration curves, and decision curve analysis (DCA). Cognitive reserve (CR), age, and a family history of hypertension were associated with the occurrence of MCI. The predictive nomogram showed satisfactory performance, with a concordance index of 0.755 (95% CI: 0.681-0.830) in internal verification. The Hosmer-Lemeshow test results suggested that the model exhibited good fit (p = 0.824). In addition, DCA demonstrated that the predictive nomogram had a good clinical net benefit. We developed a simple nomogram that could help secondary preventive health care workers to identify elderly individuals with SCD at high risk of MCI during physical examinations to enable early intervention.

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Mild cognitive impairment (MCI) is a cognitive disease that primarily affects elderly persons. Patients with MCI have impairments in one or more cognitive areas, such as memory, attention, language, and problem-solving. The risk of Alzheimer’s disease development is 10 times higher among individuals who meet the MCI diagnosis than in those who do not have such a diagnosis. Identifying the primary neurophysiological variations between those who are suffering from cognitive impairment and those who are ageing normally may provide helpful techniques to assess the effectiveness of therapies. Event-related Potentials (ERPs) are utilized to investigate the processing of sensory, cognitive, and motor information in the brain. ERPs enable excellent temporal resolution of underlying brain activity. ERP data is complex due to the temporal variation that occurs in the time domain. It is actually a type of electroencephalography (EEG) signal that is time-locked to a specific event or behavior. To remove artifacts from the data, this work utilizes Independent component analysis, finite impulse response filter, and fast Fourier transformation as preprocessing techniques. The bidirectional long short-term memory network is utilized to retain the spatial relationships between the ERP data while learning changes in temporal information for a long time. This network performed well both in modeling and information extraction from the signals. To validate the model performance, the proposed framework is tested on two benchmark datasets. The proposed framework achieved a state-of-the-art accuracy of 96.03% on the SJTU Emotion EEG Dataset dataset and 97.31% on the Chung–Ang University Hospital EEG dataset for the classification tasks.

Development of Malaysian-MIND diet scores for prediction of mild cognitive impairment among older adults in Malaysia.

Mild Cognitive impairment (MCI) is a pre-demented state in the elderly populace. The Mediterranean & Dietary Approaches to Stop Hypertension (DASH) Intervention for Neurodegenerative Delay (MIND) diet has shown promise in reducing the risk of MCI and Alzheimer's disease in older people. Notably, the existing MIND diet is not adapted to the specific needs of older adults in Malaysia, considering distinct food cultures and availability. Consequently, this study aimed to develop the Malaysian version of the MIND diet (MY-MINDD) scores and investigate their association with MCI in the older adult populace of Malaysia. A comprehensive pooled data analysis was conducted on combined data from 810 participants sourced from the longitudinal Long-Term Research Grant Scheme-Towards Useful Aging (LRGS-TUA) and Fundamental Research Grant Scheme (FRGS) studies. The MY-MINDD scores were developed by incorporating existing MIND diet food groups, their corresponding scoring mechanisms, and consideration of common Malaysian foods which are proven to be beneficial and detrimental to cognitive function. To substantiate the MY-MINDD scoring system, its association with MCI was evaluated using a series of validated neuropsychological test batteries. MY-MINDD consists of seven food groups promote brain health and four food groups exert negative cognitive outcomes. The study participants had an average age of 67.9 ± 4.7 years. The collective MY-MINDD score for all participants was 6.4 ± 0.1 (out of a maximum 11 points), revealing a lower score in individuals with MCI at 6.0 ± 1.7 compared to those without MCI at 6.6 ± 1.6 (p < 0.001). According to hierarchical multivariate binary logistic regression analysis, being in the highest tertile of MY-MINDD score was linked to reduced odds of MCI (odds ratio (OR) = 0.43, 95% confidence interval (CI): 0.26-0.72, p < 0.001) in the fully adjusted model in comparison to the lowest tertile. The development of the MY-MINDD scores for Malaysian older population revealed that a stronger adherence to this diet is linked to a reduced risk of MCI. Further substantiation of the MY-MINDD scores using more objective measures, such as neuroimaging approaches and other neuropsychological batteries, is necessary.

A new nomogram model for the individualized prediction of mild cognitive impairment in elderly patients with type 2 diabetes mellitus.

A high risk of developing mild cognitive impairment (MCI) is faced by elderly patients with type 2 diabetes mellitus (T2DM). In this study, independent risk factors for MCI in elderly patients with T2DM were investigated, and an individualized nomogram model was developed. In this study, clinical data of elderly patients with T2DM admitted to the endocrine ward of the hospital from November 2021 to March 2023 were collected to evaluate cognitive function using the Montreal Cognitive Assessment scale. To screen the independent risk factors for MCI in elderly patients with T2DM, a logistic multifactorial regression model was employed. In addition, a nomogram to detect MCI was developed based on the findings of logistic multifactorial regression analysis. Furthermore, the accuracy of the prediction model was evaluated using calibration and receiver operating characteristic curves. Finally, decision curve analysis was used to evaluate the clinical utility of the nomogram. In this study, 306 patients were included. Among them, 186 patients were identified as having MCI. The results of multivariate logistic regression analysis demonstrated that educational level, duration of diabetes, depression, glycated hemoglobin, walking speed, and sedentary duration were independently correlated with MCI, and correlation analyses showed which influencing factors were significantly correlated with cognitive function (p <0.05). The nomogram based on these factors had an area under the curve of 0.893 (95%CI:0.856-0.930)(p <0.05), and the sensitivity and specificity were 0.785 and 0.850, respectively. An adequate fit of the nomogram in the predictive value was demonstrated by the calibration plot. The nomogram developed in this study exhibits high accuracy in predicting the occurrence of cognitive dysfunction in elderly patients with T2DM, thereby offering a clinical basis for detecting MCI in patients with T2DM.

Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function.

Previous studies mainly focused on risk factors in patients with mild cognitive impairment (MCI) or dementia. The aim of the study was to provide basis for preventing MCI in cognitive normal populations. The data came from a longitudinal retrospective study involving individuals with brain magnetic resonance imaging scans, clinical visits, and cognitive assessment with interval of more than 3 years. Multiple machine-learning technologies, including random forest, support vector machine, logistic regression, eXtreme Gradient Boosting, and naïve Bayes, were used to establish a prediction model of a future risk of MCI through a combination of clinical and image variables. Among these machine learning models; eXtreme Gradient Boosting (XGB) was the best classification model. The classification accuracy of clinical variables was 65.90%, of image variables was 79.54%, of a combination of clinical and image variables was 94.32%. The best result of the combination was an accuracy of 94.32%, a precision of 96.21%, and a recall of 93.08%. XGB with a combination of clinical and image variables had a potential prospect for the risk prediction of MCI. From clinical perspective, the degree of white matter hyperintensity (WMH), especially in the frontal lobe, and the control of systolic blood pressure (SBP) were the most important risk factor for the development of MCI. The best MCI classification results came from the XGB model with a combination of both clinical and imaging variables. The degree of WMH in the frontal lobe and SBP control were the most important variables in predicting MCI.

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm.

The main objective of this study is to evaluate working memory and determine EEG biomarkers that can assist in the field of health neuroscience. Our ultimate goal is to utilize this approach to predict the early signs of mild cognitive impairment (MCI) in healthy elderly individuals, which could potentially lead to dementia. The advancements in health neuroscience research have revealed that affective reminiscence stimulation is an effective method for developing EEG-based neuro-biomarkers that can detect the signs of MCI. We use topological data analysis (TDA) on multivariate EEG data to extract features that can be used for unsupervised clustering, subsequent machine learning-based classification, and cognitive score regression. We perform EEG experiments to evaluate conscious awareness in affective reminiscent photography settings. We use EEG and interior photography to distinguish between healthy cognitive aging and MCI. Our clustering UMAP and random forest application accurately predict MCI stage and MoCA scores. Our team has successfully implemented TDA feature extraction, MCI classification, and an initial regression of MoCA scores. However, our study has certain limitations due to a small sample size of only 23 participants and an unbalanced class distribution. To enhance the accuracy and validity of our results, future research should focus on expanding the sample size, ensuring gender balance, and extending the study to a cross-cultural context.

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.

Detection of Cognitive Impairment From eSAGE Metadata Using Machine Learning.

Using the metadata collected in the digital version of the Self-Administered Gerocognitive Examination (eSAGE), we aim to improve the prediction of mild cognitive impairment (MCI) and dementia (DM) by applying machine learning methods. A total of 66 patients had a diagnosis of normal cognition (NC), MCI, or DM, and eSAGE scores and metadata were used. eSAGE scores and metadata were obtained. Each eSAGE question was scored and behavioral features (metadata) such as the time spent on each test page, drawing speed, and average stroke length were extracted for each patient. Logistic regression (LR) and gradient boosting models were trained using these features to detect cognitive impairment (CI). Performance was evaluated using 10-fold cross-validation, with accuracy, precision, recall, F1 score, and receiver operating characteristic area under the curve (AUC) score as evaluation metrics. LR with feature selection achieved an AUC of 89.51%, a recall of 87.56%, and an F1 of 85.07% using both behavioral and scoring. LR using scores and metadata also achieved an AUC of 84.00% in detecting MCI from NC, and an AUC of 98.12% in detecting DM from NC. Average stroke length was particularly useful for prediction and when combined with 4 other scoring features, LR achieved an even better AUC of 92.06% in detecting CI. The study shows that eSAGE scores and metadata are predictive of CI. eSAGE scores and metadata are predictive of CI. With machine learning methods, the metadata could be combined with scores to enable more accurate detection of CI.

Prediction of Mild Cognitive Impairment using eye movements and other easily accessible measures

AbstractBackgroundThe early identification of patients at risk of dementia and timely medical intervention can be effective in preventing disease progression and mitigate burdens and costs of health insurance. Although the current diagnostic tests for preclinical and prodromal Alzheimer’s disease (AD) using neuropsychological evaluation and biomarkers can accurately predict the progression to AD dementia, the high cost, invasive nature, and limited availability restrict their use to wealthy clinics only. Therefore, we developed a model to predict cognitive impairment based on non‐invasive information, including eye movement (EM) data.MethodEye‐tracking data was extracted from 594 subjects, 428 cognitively normal (CN) controls, and 166 patients with mild cognitive impairment (MCI) while they performed Prosaccade/Antisaccade (PS/AS) and Go/No‐go tasks. EM behavior was compared by task after stratification by cognitive status and controlling for age, sex, and education. We used logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI) from the EM metrics.ResultLogistic regression models suggested increased OR of MCI from EM metrics, including wider latency variability, fixation duration variability, a higher number of errors committed, anticipations, and omissions with 95% CI of OR between 1.213‐1.621.ConclusionEM metric changes linked with MCI are associated with deficits in attentional and executive functions. The models built from non‐invasive data allow the discrimination of patients with MCI from cognitively normal adults. In summary, our results suggest that eye tracking, in combination with demographics and brief cognitive tests, might significantly improve the diagnostic prediction of MCI.

Multi-view imputation and cross-attention network based on incomplete longitudinal and multimodal data for conversion prediction of mild cognitive impairment

Multi-view imputation and cross-attention network based on incomplete longitudinal and multimodal data for conversion prediction of mild cognitive impairment

Application of machine learning in dementia diagnosis: A systematic literature review

According to the World Health Organization forecast, over 55 million people worldwide have dementia, and about 10 million new cases are detected yearly. Early diagnosis is essential for patients to plan for the future and deal with the disease. Machine Learning algorithms allow us to solve the problems associated with early disease detection. This work attempts to identify the current relevance of the application of machine learning in dementia prediction in the scientific world and suggests open fields for future research. The literature review was conducted by combining bibliometric and content analysis of articles originating in a period of 20 years in the Scopus database. Twenty-seven thousand five hundred twenty papers were identified firstly, of which a limited number focused on machine learning in dementia diagnosis. After the exclusion process, 202 were selected, and 25 were chosen for analysis. The recent increasing interest in the past five years in the theme of machine learning in dementia shows that it is a relevant field for research with still open questions. The methods used to identify dementia or what features are used to identify or predict this disease are explored in this study. The literature review revealed that most studies used magnetic resonance imaging (MRI) and its types as the main feature, accompanied by demographic data such as age, gender, and the mini-mental state examination score (MMSE). Data are usually acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Classification of Alzheimer's disease is more prevalent than prediction of Mild Cognitive Impairment (MCI) or their combination. The authors preferred machine learning algorithms such as SVM, Ensemble methods, and CNN because of their excellent performance and results in previous studies. However, most use not one machine-learning technique but a combination of techniques. Despite achieving good results in the studies considered, there are new concepts for future investigation declared by the authors and suggestions for improvements by employing promising methods with potentially significant results.

An end-to-end multimodal 3D CNN framework with multi-level features for the prediction of mild cognitive impairment

In recent years, deep learning methods based on brain image have been used for the diagnosis of cognitive impairment-related disorders. With the development of neuroimaging techniques, multi-modality image such as structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) reflect structural and functional information of the brain respectively, and provide more techniques for the diagnosis of cognitive impairment diseases. Combining these complementary image features can lead to more accurate diagnostic assessments compared to using a single modality. Therefore, how to effectively combine multi-modality image features to realize the diagnosis of cognitive impairment disease needs to be further explored. In this work, we propose an end-to-end multimodal 3D CNN framework based on ResNet architecture, which integrates multi-level features obtained under the role of attention mechanisms to better capture subtle differences among brain images, and achieves remarkable diagnostic performance through spatial pyramid pooling strategy and effective fusion of multi-modality features. In this process, we demonstrate that the multimodal framework is more effective by means of non-shared parameters for multi-modality features learning. Moreover, the visualized attention maps show that our model can focus on important brain regions relevant to disease diagnosis. The experimental results demonstrated that our method improved the diagnostic performance in AD diagnosis and MCI conversion prediction by 6.37 % and 3.51 % compared to the single modality, and it also outperformed some recent state-of-the-art multimodal methods. Especially in AD diagnosis achieved an average accuracy of 94.61 %, which provides a more feasible technology for diagnostic assessment of patients with AD.

Machine learning prediction of mild cognitive impairment and its progression to Alzheimer's disease.

Machine learning prediction of mild cognitive impairment and its progression to Alzheimer's disease.

Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson's disease.

Parkinson's disease which is the second most prevalent neurodegenerative disorder in the United States is a serious and complex disease that may progress to mild cognitive impairment and dementia. The early detection of the mild cognitive impairment and the identification of its biomarkers is crucial to support neurologists in monitoring the progression of the disease and allow an early initiation of effective therapeutic treatments that will improve the quality of life for the patients. In this paper, we propose the first deep-learning based approaches to detect mild cognitive impairment in the sleep Electroencephalography for patients with Parkinson's disease and further identify the discriminative features of the disease. The proposed frameworks start by segmenting the sleep Electroencephalography time series into three sleep stages (i.e., two non-rapid eye movement sleep-stages and one rapid eye movement sleep stage), further transforming the segmented signals in the time-frequency domain using the continuous wavelet transform and the variational mode decomposition and finally applying novel convolutional neural networks on the time-frequency representations. The gradient-weighted class activation mapping was also used to visualize the features based on which the proposed deep-learning approaches reached an accurate prediction of mild cognitive impairment in Parkinson's disease. The proposed variational mode decomposition-based model offered a superior accuracy, sensitivity, specificity, area under curve, and quadratic weighted Kappa score, all above 99% as compared with the continuous wavelet transform-based model (that achieved a performance that is almost above 92%) in differentiating mild cognitive impairment from normal cognition in sleep Electroencephalography for patients with Parkinson's disease. In addition, the features attributed to the mild cognitive impairment in Parkinson's disease were demonstrated by changes in the middle and high frequency variational mode decomposition components across the three sleep-stages. The use of the proposed model on the time-frequency representation of the sleep Electroencephalography signals will provide a promising and precise computer-aided diagnostic tool for detecting mild cognitive impairment and hence, monitoring the progression of Parkinson's disease.

Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG.

Early discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency. Our research is based on resting-state electroencephalography (EEG) and the current dataset includes 137 consensus-diagnosed, community-dwelling Black Americans (ages 60-90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time-varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity-based score. The leave-one-out cross-validation accuracy is 91.97% and 3-fold accuracy is 91.17%. The 9 to 18 months' progression trend prediction accuracy over an availability-limited subset sample is 84.61%. The EEG-based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.

Vision transformers for the prediction of mild cognitive impairment to Alzheimer's disease progression using mid-sagittal sMRI.

Alzheimer's disease (AD) is one of the most common causes of neurodegenerative disease affecting over 50 million people worldwide. However, most AD diagnosis occurs in the moderate to late stage, which means that the optimal time for treatment has already passed. Mild cognitive impairment (MCI) is an intermediate state between cognitively normal people and AD patients. Therefore, the accurate prediction in the conversion process of MCI to AD may allow patients to start preventive intervention to slow the progression of the disease. Nowadays, neuroimaging techniques have been developed and are used to determine AD-related structural biomarkers. Deep learning approaches have rapidly become a key methodology applied to these techniques to find biomarkers. In this study, we aimed to investigate an MCI-to-AD prediction method using Vision Transformers (ViT) to structural magnetic resonance images (sMRI). The Alzheimer's Disease Neuroimaging Initiative (ADNI) database containing 598 MCI subjects was used to predict MCI subjects' progression to AD. There are three main objectives in our study: (i) to propose an MRI-based Vision Transformers approach for MCI to AD progression classification, (ii) to evaluate the performance of different ViT architectures to obtain the most advisable one, and (iii) to visualize the brain region mostly affect the prediction of deep learning approach to MCI progression. Our method achieved state-of-the-art classification performance in terms of accuracy (83.27%), specificity (85.07%), and sensitivity (81.48%) compared with a set of conventional methods. Next, we visualized the brain regions that mostly contribute to the prediction of MCI progression for interpretability of the proposed model. The discriminative pathological locations include the thalamus, medial frontal, and occipital-corroborating the reliability of our model. In conclusion, our methods provide an effective and accurate technique for the prediction of MCI conversion to AD. The results obtained in this study outperform previous reports using the ADNI collection, and it suggests that sMRI-based ViT could be efficiently applied with a considerable potential benefit for AD patient management. The brain regions mostly contributing to prediction, in conjunction with the identified anatomical features, will support the building of a robust solution for other neurodegenerative diseases in future.

Earlier Detection of Alzheimer’s Disease Using 3D-Convolutional Neural Networks

The prediction of mild cognitive impairment or Alzheimer’s disease (AD) has gained the attention of huge researchers as the disease occurrence is increasing, and there is a need for earlier prediction. Regrettably, due to the high-dimensionality nature of neural data and the least available samples, modelling an efficient computer diagnostic system is highly solicited. Learning approaches, specifically deep learning approaches, are essential in disease prediction. Deep Learning (DL) approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging. A novel 3D-Convolutional Neural Network (3D-CNN) architecture is proposed to predict AD with Magnetic resonance imaging (MRI) data. The proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary classification. The proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative (ADNI) data. The outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general approaches. The predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive. Keras’ experimentation is carried out, and the model’s superiority is compared with various advanced approaches for multi-level classification. The proposed model gives better prediction accuracy, precision, recall, and F-measure than other systems like Long Short Term Memory- Recurrent Neural Networks (LSTM-RNN), Stacked Autoencoder with Deep Neural Networks (SAE-DNN), Deep Convolutional Neural Networks (D-CNN), Two Dimensional Convolutional Neural Networks (2D-CNN), Inception-V4, ResNet, and Two Dimensional Convolutional Neural Networks (3D-CNN).

A cross-attention based image fusion Network for prediction of mild cognitive impairment

Mild cognitive impairment (MCI) is the intermediate stage in the progression of Alzheimer’s disease, where patients exhibit cognitive decline, decreasing ability to perform complex daily tasks and other symptoms that can be mistaken for normal aging, thus missing the optimal time for treatment and leading to further deterioration of the patient’s condition. Structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) are commonly used to examine patients in clinical practice. Currently, the fusion of sMRI and PET based on deep learning are simply extracting features individually and then fusing the extracted features, ignoring the correlation between features of two different modalities. Therefore, this paper proposes an image fusion method based on cross-attention mechanism, which makes it possible to focus not only on the features of its own view but also on the features of the other view during feature extraction to achieve dual-attempt cross-learning. The experimental results from the Alzheimer’s Disease Neuroimaging Initiative dataset show that the images fused based on the method proposed in this paper outperform the original method in three evaluation indexes: peak signal-to-noise ratio, structural similarity and classification accuracy.

Retinal Imaging Techniques Based on Machine Learning Models in Recognition and Prediction of Mild Cognitive Impairment.

Background and PurposeMild Cognitive Impairment (MCI) is thought to be the signal of many progressive diseases but is easily ignored. Therefore, a simple and easy screening method for recognizing and predicting MCI is urgently needed. The study aimed to establish machine learning models of retinal vascular features to categorize and predict MCI.Patients and MethodsSubjects enrolled underwent cognitive function assessment and were divided into a normal group, an MCI group, and a dementia group, and fundus photography was performed. MATLAB 2019b was used for fundus image preprocessing and vascular segmentation. Via the Green channel, adaptive histogram equalization (AHE), image binarization, and median filtering, we obtained the original and segmentation retinal vessel images. Afterwards, the histogram of oriented gradient (HOG) was used for image feature extraction. Support vector machine (SVM) and extreme learning machine (ELM) were selected for training models in the fundus original images and fundus vascular segmentation images, respectively. Among the three cognitive groups, sensitivity, specificity, the receiver operating characteristic (ROC) curves, and the area under the curve (AUC) were used to evaluate and compare the predictive performance of the two models in the fundus original and vascular segmentation images, respectively.ResultsA total of 86 eligible subjects were enrolled in the study. After a clinical cognitive assessment, the participants were divided into the normal group (N = 38), the MCI group (N = 26), and the dementia group (N = 22). A total of 332 qualified fundus images were adopted after screening. Comparing the models among the three groups showed that the SVM model had more advantages than the ELM model in the fundus original images and vascular segmentation images. Meanwhile, we found that the original images performed better than the segmentation images in the same prediction model. Among the three groups, the SVM model of the fundus original images had the best performance.ConclusionThe establishment of a predictive model based on vascular-related feature extraction from fundus images has high recognition and prediction abilities for cognitive function and can be used as a screening method for MCI.Clinical Trial RegistrationChiCTR.org.cn (ChiCTR1900027404), Registered on Nov 12, 2019.

501 - Prediction of Mild Cognitive Impairment (MCI) progression to Alzheimer Disease (AD) or Dementia with Lewy Bodies (DLB): Is this possible neuropsychologically?

Objective:Aim of the present review study was to describe and compare the neurocognitive features of MCI which could predict its progression to DLB vs AD.Background:Progression of MCI to AD or DLB is a relatively recent field of study with emphasis on the clinical or neuropsychological features of MCI which could potentially predict its progression to specific types of dementia.Methods:A literature review in the Pubmed database has been made, after the year 2005, using the key- words: neuropsychological assessment; MCI; AD; DLB; progression to dementia. Seventeen relevant articles have been found.Results:Data from most studies supports that, in MCI, impairment in executive, attentional and visuospatial functions, as well as letter fluency and fluctuating concentration are mainly related to progression to DLB. In contrast, prominent episodic and recognition memory deficits are mostly found in MCI which progresses in AD. Furthermore, non-amnestic MCI has been related most often to progression in DLB, whereas the amnestic type to AD, although memory loss may not necessarily predict the development of AD. Nevertheless, fewer studies suggest that MCI-DLB is related to cognitive profile similar to that of MCI-AD, while cognitive scoring alone does not accurately predict MCI-DLB vs MCI-AD. Interestingly, quantitative electroencephalogram has been found to help in predicting the progression of MCI to DLB, while preservation of hippocampal volume is associated with increased risk of DLB vs AD, especially in non-amnestic MCI. Moreover, specific patterns on neuroimaging MCI may predict progression to AD in contrast to DLB.Conclusions:Predicting the progression of MCI to AD or DLB based on neuropsychological profiles is challenging and useful for early therapeutic interventions. More studies are needed, since there are some conflicting findings and, at present, the combination of clinical symptoms with neurocognitive assessment and neuroimaging is the ideal method for the prediction of MCI progression to various types of dementia.