A Novel Detection Tool for Mild Cognitive Impairment Patients Based on Eye Movement and Electroencephalogram

Distinctive visual tasks for characterizing mild cognitive impairment and dementia using oculomotor behavior

Introduction

One's eye movement (in response to visual tasks) provides a unique window into the cognitive processes and higher-order cognitive functions that become adversely affected in cases with cognitive decline, such as those mild cognitive impairment (MCI) and dementia. MCI is a transitional stage between normal aging and dementia.

Methods

In the current work, we have focused on identifying visual tasks (such as horizontal and vertical Pro-saccade, Anti-saccade and Memory Guided Fixation tasks) that can differentiate individuals with MCI and dementia from their cognitively unimpaired healthy aging counterparts based on oculomotor Performance indices. In an attempt to identify the optimal combination of visual tasks that can be used to differentiate the participant groups, clustering was performed using the oculomotor Performance indices.

Results

Results of our study with a group of 60 cognitively unimpaired healthy aging individuals, a group with 60 individuals with MCI and a group with 60 individuals with dementia indicate that the horizontal and vertical Anti-saccade tasks provided the optimal combination that could differentiate individuals with MCI and dementia from their cognitively unimpaired healthy aging counterparts with clustering accuracy of ∼92% based on the saccade latencies. Also, the saccade latencies during both of these Anti-saccade tasks were found to strongly correlate with the Neuropsychological test scores.

Discussion

This suggests that the Anti-saccade tasks can hold promise in clinical practice for professionals working with individuals with MCI and dementia.

Current development of biosensing technologies towards diagnosis of mental diseases

The biosensor is an instrument that converts the concentration of biomarkers into electrical signals for detection. Biosensing technology is non-invasive, lightweight, automated, and biocompatible in nature. These features have significantly advanced medical diagnosis, particularly in the diagnosis of mental disorder in recent years. The traditional method of diagnosing mental disorders is time-intensive, expensive, and subject to individual interpretation. It involves a combination of the clinical experience by the psychiatrist and the physical symptoms and self-reported scales provided by the patient. Biosensors on the other hand can objectively and continually detect disease states by monitoring abnormal data in biomarkers. Hence, this paper reviews the application of biosensors in the detection of mental diseases, and the diagnostic methods are divided into five sub-themes of biosensors based on vision, EEG signal, EOG signal, and multi-signal. A prospective application in clinical diagnosis is also discussed.

Classification of mild cognitive impairment based on handwriting dynamics and qEEG

Subtle changes in fine motor control and quantitative electroencephalography (qEEG) in patients with mild cognitive impairment (MCI) are important in screening for early dementia in primary care populations. In this study, an automated, non-invasive and rapid detection protocol for mild cognitive impairment based on handwriting kinetics and quantitative EEG analysis was proposed, and a classification model based on a dual fusion of feature and decision layers was designed for clinical decision-marking. Seventy-nine volunteers (39 healthy elderly controls and 40 patients with mild cognitive impairment) were recruited for this study, and the handwritten data and the EEG signals were performed using a tablet and MUSE under four designed handwriting tasks. Sixty-eight features were extracted from the EEG and handwriting parameters of each test. Features selected from both models were fused using a late feature fusion strategy with a weighted voting strategy for decision making, and classification accuracy was compared using three different classifiers under handwritten features, EEG features and fused features respectively. The results show that the dual fusion model can further improve the classification accuracy, with the highest classification accuracy for the combined features and the best classification result of 96.3% using SVM with RBF kernel as the base classifier. In addition, this not only supports the greater significance of multimodal data for differentiating MCI, but also tests the feasibility of using the portable EEG headband as a measure of EEG in patients with cognitive impairment.

Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening

Background: Mild cognitive impairment (MCI) is a transitional stage between normal aging and probable Alzheimer's disease. It is of great value to screen for MCI in the community. A novel machine learning (ML) model is composed of electroencephalography (EEG), eye tracking (ET), and neuropsychological assessments. This study has been proposed to identify MCI subjects from normal controls (NC). Methods: Two cohorts were used in this study. Cohort 1 as the training and validation group, includes184 MCI patients and 152 NC subjects. Cohort 2 as an independent test group, includes 44 MCI and 48 NC individuals. EEG, ET, Neuropsychological Tests Battery (NTB), and clinical variables with age, gender, educational level, MoCA-B, and ACE-R were selected for all subjects. Receiver operating characteristic (ROC) curves were adopted to evaluate the capabilities of this tool to classify MCI from NC. The clinical model, the EEG and ET model, and the neuropsychological model were compared. Results: We found that the classification accuracy of the proposed model achieved 84.5 ± 4.43% and 88.8 ± 3.59% in Cohort 1 and Cohort 2, respectively. The area under curve (AUC) of the proposed tool achieved 0.941 (0.893-0.982) in Cohort 1 and 0.966 (0.921-0.988) in Cohort 2, respectively. Conclusions: The proposed model incorporation of EEG, ET, and neuropsychological assessments yielded excellent classification performances, suggesting its potential for future application in cognitive decline prediction.

Eye-tracking training improves the learning and memory of children with learning difficulty

Children who experience difficulty in learning at mainstream schools usually are provided with remediation classes after school to facilitate their learning. The present study aims to evaluate an innovative eye-tracking training as possible alternative remediation. Our previous findings showed that children who received eye-tracking training demonstrated improved attention and inhibitory control, and the present randomized controlled study aims to evaluate if eye-tracking training can also enhance the learning and memory of children. Fifty-three primary school students with learning difficulty (including autism spectrum disorder, attention-deficit/hyperactivity disorder, specific learning disorder, specific language impairment and borderline intellectual functioning) were recruited and randomly assigned to either the Eye-tracking Training group or the after-school remediation class. They were assessed on their learning and memory using the Hong Kong List Learning Test before and after 8-month training. Twenty weekly parallel sessions of training, 50 min per session, were provided to each group. Children who received the eye-tracking training, not those in the control group, showed a significant improvement in memory as measured by the delayed recall. In addition, the Eye-Tracking Training group showed significantly faster learning than the control group. Also, the two groups showed a significant improvement in their reading abilities. In sum, eye-tracking training may be effective training for enhancing the learning and memory of children with learning difficulties.

Automatic Classification of Subjects of the PSEN1-E280A Family at Risk of Developing Alzheimer’s Disease Using Machine Learning and Resting State Electroencephalography

Background: The study of genetic variant carriers provides an opportunity to identify neurophysiological changes in preclinical stages. Electroencephalography (EEG) is a low-cost and minimally invasive technique which, together with machine learning, provide the possibility to construct systems that classify subjects that might develop Alzheimer’s disease (AD). Objective: The aim of this paper is to evaluate the capacity of the machine learning techniques to classify healthy Non-Carriers (NonCr) from Asymptomatic Carriers (ACr) of PSEN1-E280A variant for autosomal dominant Alzheimer’s disease (ADAD), using spectral features from EEG channels and brain-related independent components (ICs) obtained using independent component analysis (ICA). Methods: EEG was recorded in 27 ACr and 33 NonCr. Statistical significance analysis was applied to spectral information from channels and group ICA (gICA), standardized low-resolution tomography (sLORETA) analysis was applied over the IC as well. Strategies for feature selection and classification like Chi-square, mutual informationm and support vector machines (SVM) were evaluated over the dataset. Results: A test accuracy up to 83% was obtained by implementing a SVM with spectral features derived from gICA. The main findings are related to theta and beta rhythms, generated in the parietal and occipital regions, like the precuneus and superior parietal lobule. Conclusion: Promising models for classification of preclinical AD due to PSEN-1-E280A variant can be trained using spectral features, and the importance of the beta band and precuneus region is highlighted in asymptomatic stages, opening up the possibility of its use as a screening methodology.

EEG Analysis with Wavelet Transform under Music Perception Stimulation

In order to improve the classification accuracy and reliability of emotional state assessment and provide support and help for music therapy, this paper proposes an EEG analysis method based on wavelet transform under the stimulation of music perception. Using the data from the multichannel standard emotion database (DEAP), α, ß, and θ rhythms are extracted in frontal (F3 and F4), temporal (T7 and T8), and central (C3 and C4) channels with wavelet transform. EMD is performed on the extracted EEG rhythm to obtain intrinsic mode function (IMF) components, and then, the average energy and amplitude difference eigenvalues of IMF components of EEG rhythm waves are further extracted, that is, each rhythm wave contains three average energy characteristics and two amplitude difference eigenvalues so as to fully extract EEG feature information. Finally, emotional state evaluation is realized based on a support vector machine classifier. The results show that the correct rate between no emotion, positive emotion, and negative emotion can reach more than 90%. Among the pairwise classification problems among the four emotions selected, the classification accuracy obtained by this EEG feature extraction method is higher than that obtained by general feature extraction methods, which can reach about 70%. Changes in EEG α wave power were closely correlated with the polarity and intensity of emotion; α wave power varied significantly between "happiness and fear," "pleasure and fear," and "fear and sadness." It has a good application prospect in both psychological and physiological research of emotional perception and practical application.

Improvement of cognitive performance by a nutraceutical formulation: Underlying mechanisms revealed by laboratory studies

Cognitive decline, decrease in neuronal function and neuronal loss that accompany normal aging and dementia are the result of multiple mechanisms, many of which involve oxidative stress. Herein, we review these various mechanisms and identify pharmacological and non-pharmacological approaches, including modification of diet, that may reduce the risk and progression of cognitive decline. The optimal degree of neuronal protection is derived by combinations of, rather than individual, compounds. Compounds that provide antioxidant protection are particularly effective at delaying or improving cognitive performance in the early stages of Mild Cognitive Impairment and Alzheimer's disease. Laboratory studies confirm alleviation of oxidative damage in brain tissue. Lifestyle modifications show a degree of efficacy and may augment pharmacological approaches. Unfortunately, oxidative damage and resultant accumulation of biomarkers of neuronal damage can precede cognitive decline by years to decades. This underscores the importance of optimization of dietary enrichment, antioxidant supplementation and other lifestyle modifications during aging even for individuals who are cognitively intact.

The effectiveness of eye tracking in the diagnosis of cognitive disorders: A systematic review and meta-analysis

Background

Eye tracking (ET) is a viable marker for the recognition of cognitive disorders. We assessed the accuracy and clinical value of ET for the diagnosis of cognitive disorders in patients.

Methods

We searched the Medline, Embase, Web of Science, Cochrane Library, and Pubmed databases from inception to March 2, 2021, as well as the reference lists of identified primary studies. We included articles written in English that investigated ET for cognitive disorder patients—Mild cognitive impairment (MCI), Alzheimer’s disease (AD), Amyotrophic lateral sclerosis (ALS), and dementia. Two independent researchers extracted the data and the characteristics of each study; We calculated pooled sensitivities and specificities. A hierarchical summary of receiver performance characteristics (HSROC) model was used to test the diagnostic accuracy of ET for cognitive impairment (CI).

Findings

11 studies met the inclusion criteria and were included in qualitative comprehensive analysis. Meta-analysis was performed on 9 trials using Neuropsychological Cognitive Testing (NCT) as the reference standard. The comprehensive sensitivity and specificity of ET for detecting cognitive disorders were 0.75 (95% CI 0.72–0.79) and 0.73 (95% CI 0.70 to 0.76), respectively. The combined positive likelihood ratio (LR+) was 2.74 (95%CI 2.32–3.24) and the negative likelihood ratio (LR−) was 0.27 (95%CI 0.18–0.42).

Conclusions

This review showed that ET technology could be used to detect the decline in CI, clinical use of ET techniques in combination with other tools to assess CI can be encouraged.

Early Diagnosis of Mild Cognitive Impairment Based on Eye Movement Parameters in an Aging Chinese Population

Background: The pathogenesis of dementia often starts several years prior to clinical onset during which the individual is asymptomatic. Existing strategies for the accurate diagnosis of early dementia are limited by high cost and the invasive nature of the procedures. Eye movement parameters associated with cognitive functions may be helpful in the early identification of dementia and in the development and evaluation of preventive and therapeutic strategies.

Objective: We aimed to assess differences in eye movement parameters between healthy elderly individuals and patients with mild cognitive impairment (MCI). Furthermore, we examined the correlations between eye movement parameters with cognitive functions and specific hemispheric region and neural structures in individuals with MCI.

Method: Eighty individuals with MCI without dementia (based on DSM-IV criteria) identified by community screening and 170 healthy controls were administered Chinese versions of MoCA and NTB, and a long (20 min) or short (5 min) version of a visual paired comparison (VPC) task. Two weeks later, 44 MCI patients and 107 healthy controls completed a retest of the VPC task, 44 MCI patients and 43 healthy controls among them administered a MRI. At the end of 1-year follow-up, a subset of 26 individuals with MCI and 57 healthy controls were administered the long version of VPC task and MoCA test again. Eye movement parameters and the relationship of eye movement parameters with cognitive functions and with changes in neural structures were compared between groups.

Results: Patients with MCI were older, had less education, and had lower scores on cognitive tests than healthy controls. After adjustment for age and level of education, patients with MCI had lower novelty preference scores on the VPC than healthy controls. Using the logistic regression model, the amount of time that subjects focused on these novel images could predict MCI patients from normal elderly with an out of sample area under the receiver operator characteristic curve of 0.62. Furthermore, the cognition score of subjects whose novelty preference score was low decreased more remarkably in 1 year. For both the patient and control groups, VPC novelty preference was significantly correlated with verbal fluency and delayed and short-term memory function. Novelty preference score was also significantly correlated with the cortical thickness of several structures in the right hemisphere.

Conclusion: Eye movement parameters are stable indicators to distinguish patients with MCI and cognitively normal subjects and are not affected by different testing versions and numbers. Additionally, the patients’ cognitive deficits and eye movement indices were correlated. Future longitudinal studies should further explore the clinical utility of eye movement parameters as early markers of MCI.