A new method to build multiplex networks using Canonical Correlation Analysis for the characterization of the Alzheimer's disease continuum

Preserved Consciousness in Alzheimer's Disease and Other Dementias: Caregiver Awareness and Communication Strategies

Alzheimer's disease is an insidious onset neurodegenerative syndrome without effective treatment or cure. It is rapidly becoming a global health crisis that is overwhelming healthcare, society, and individuals. The clinical nature of neurocognitive decline creates significant challenges in bidirectional communication between caregivers and persons with Alzheimer's disease (AD) that can negatively impact quality-of-life. This paper sought to understand how and to what extent would awareness training about the levels of consciousness in AD influence the quality-of-life interactions in the caregiver-patient dyad. A literature review of multiple databases was conducted utilizing a transdisciplinary approach. The sum of findings indicates a positive relationship between enhanced caregiver awareness and training, positive interactions, and improved QOL measures among patients and caregivers. A multidirectional relationship was found among healthcare policies, training and education resources, caregivers, and persons with AD. Specifically, the current lack of policy and inadequate training and educational resources has various detrimental effects on patients and caregivers, while improvements in training and education of caregivers yields positive outcomes in communication and QOL. Furthermore, evidence of preserved consciousness in persons with AD was demonstrated from multiple disciplines, including neurobiological, psychological, and biopsychosocial models. The literature further revealed several methods to access the preserved consciousness in persons with AD and related dementias, including sensory, emotional, and cognitive stimulations. The evidence from the literature suggests a reframed approach to our understanding and treatment of persons with AD is not only warranted, but crucial to address the needs of those affected by AD.

A Novel Approach to Learning Models on EEG Data Using Graph Theory Features—A Comparative Study

Brain connectivity is studied as a functionally connected network using statistical methods such as measuring correlation or covariance. The non-invasive neuroimaging techniques such as Electroencephalography (EEG) signals are converted to networks by transforming the signals into a Correlation Matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regression, Random Forest, Support Vector Machine, and Recurrent Neural Networks (RNN), are implemented on two different types of correlation matrices: Correlation Matrix (static connectivity) and Time-resolved Correlation Matrix (dynamic connectivity), to classify them either on their psychometric assessment or the effect of therapy. These correlation matrices are different from traditional learning techniques in the sense that they incorporate theory-based graph features into the learning models, thus providing novelty to this study. The EEG data used in this study is trail-based/event-related from five different experimental paradigms, of which can be broadly classified as working memory tasks and assessment of emotional states (depression, anxiety, and stress). The classifications based on RNN provided higher accuracy (74–88%) than the other three models (50–78%). Instead of using individual graph features, a Correlation Matrix provides an initial test of the data. When compared with the Time-resolved Correlation Matrix, it offered a 4–5% higher accuracy. The Time-resolved Correlation Matrix is better suited for dynamic studies here; it provides lower accuracy when compared to the Correlation Matrix, a static feature.

Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection

With the rapid development of modern social science and technology, the pace of life is getting faster, and brain fatigue has become a sub-health state that seriously affects the normal life of people. Electroencephalogram (EEG) signals reflect changes in the central nervous system. Using EEG signals to assess mental fatigue is a research hotspot in related fields. Most existing fatigue detection methods are time-consuming or don't achieve satisfactory results due to insufficient features extracted from EEG signals. In this paper, a 2-back task is designed to induce fatigue. The weight value of each channel under a single feature is calculated by ReliefF algorithm. The classification accuracy of each channel under the corresponding features is analyzed. The classification accuracy of each single channel is combined to perform weighted summation to obtain the weight value of each channel. The first half channels sorted in descending order based on the weight value is chosen as the common channels. Multi-features in frequency and time domains are extracted from the common channel data, and the sparse representation method is used to perform feature fusion to obtain sparse fused features. Finally, the SRDA classifier is used to detect the fatigue state. Experimental results show that the proposed methods in our work effectively reduce the number of channels for computation and also improve the mental fatigue detection accuracy.