Frontotemporal EEG as potential biomarker for early MCI: a case-control study

Neurobiological effects of music-making interventions for older adults: a systematic review

BACKGROUND

Evidence on the impact of music-making interventions on brain plasticity in older adults is limited.

AIMS

To investigate whether music-making interventions in older adults induce neurobiological changes and if such changes relate to cognitive improvements.

METHODS

A systematic search was conducted in Medline, PsycINFO, and Scopus. Inclusion criteria targeted randomised controlled trials with older adults (with and without mild cognitive impairment [MCI]), music-making interventions as exposure, and neurobiological measures as the primary outcome.

RESULTS

Six studies (555 cognitively intact older adults) met inclusion criteria-five used piano training, one used choral singing. Three studies had overlapping cohorts, and four had a high risk of bias. One study employed electroencephalography (EEG) to measure frontal and parietal activity, while five used structural MRI to assess cortical, subcortical, and white matter integrity. Methodological heterogeneity limited comparability. Findings in the piano group included increased frontal theta power during an improvisation task, greater grey matter volume in the dorsolateral prefrontal cortex and cerebellum, slower fibre density decline in the fornix and preserved grey matter volume in the right auditory cortex and hippocampus. Only one study reported a positive correlation between neurobiological changes and executive functioning improvements. No studies assessed neurobiological outcomes in MCI.

DISCUSSION

Evidence on music-making interventions and neuroplasticity in older adults remains inconclusive due to limited studies, high risk of bias, and methodological variability. While preliminary findings suggest potential neurobiological changes with music-making interventions, there is insufficient evidence to draw firm conclusions.

CONCLUSIONS

High-quality trials are needed to clarify the neurobiological impact of music-making, particularly in MCI populations.

Electroencephalographic Biomarkers for Neuropsychiatric Diseases: The State of the Art

Because of their nature, biomarkers for neuropsychiatric diseases were out of the reach of medical diagnostic technology until the past few decades. In recent years, the confluence of greater, affordable computer power with the need for more efficient diagnoses and treatments has increased interest in and the possibility of their discovery. This review will focus on the progress made over the past ten years regarding the search for electroencephalographic biomarkers for neuropsychiatric diseases. This includes algorithms and methods of analysis, machine learning, and quantitative electroencephalography as applied to neurodegenerative and neurodevelopmental diseases as well as traumatic brain injury and COVID-19. Our findings suggest that there is a need for consensus among quantitative electroencephalography researchers on the classification of biomarkers that most suit this field; that there is a slight disconnection between the development of increasingly sophisticated methods of analysis and what they will actually be of use for in the clinical setting; and finally, that diagnostic biomarkers are the most favored type in the field with a few caveats. The main goal of this state-of-the-art review is to provide the reader with a general panorama of the state of the art in this field.

From Gut Microbiota to Brain Waves: The Potential of the Microbiome and EEG as Biomarkers for Cognitive Impairment

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder and a leading cause of dementia. Aging is a significant risk factor for AD, emphasizing the importance of early detection since symptoms cannot be reversed once the advanced stage is reached. Currently, there is no established method for early AD diagnosis. However, emerging evidence suggests that the microbiome has an impact on cognitive function. The gut microbiome and the brain communicate bidirectionally through the gut-brain axis, with systemic inflammation identified as a key connection that may contribute to AD. Gut dysbiosis is more prevalent in individuals with AD compared to their cognitively healthy counterparts, leading to increased gut permeability and subsequent systemic inflammation, potentially causing neuroinflammation. Detecting brain activity traditionally involves invasive and expensive methods, but electroencephalography (EEG) poses as a non-invasive alternative. EEG measures brain activity and multiple studies indicate distinct patterns in individuals with AD. Furthermore, EEG patterns in individuals with mild cognitive impairment differ from those in the advanced stage of AD, suggesting its potential as a method for early indication of AD. This review aims to consolidate existing knowledge on the microbiome and EEG as potential biomarkers for early-stage AD, highlighting the current state of research and suggesting avenues for further investigation.

Positive Effect of Super-Resolved Structural Magnetic Resonance Imaging for Mild Cognitive Impairment Detection

This paper presents a novel approach to improving the detection of mild cognitive impairment (MCI) through the use of super-resolved structural magnetic resonance imaging (MRI) and optimized deep learning models. The study introduces enhancements to the perceptual quality of super-resolved 2D structural MRI images using advanced loss functions, modifications to the upscaler part of the generator, and experiments with various discriminators within a generative adversarial training setting. It empirically demonstrates the effectiveness of super-resolution in the MCI detection task, showcasing performance improvements across different state-of-the-art classification models. The paper also addresses the challenge of accurately capturing perceptual image quality, particularly when images contain checkerboard artifacts, and proposes a methodology that incorporates hyperparameter optimization through a Pareto optimal Markov blanket (POMB). This approach systematically explores the hyperparameter space, focusing on reducing overfitting and enhancing model generalizability. The research findings contribute to the field by demonstrating that super-resolution can significantly improve the quality of MRI images for MCI detection, highlighting the importance of choosing an adequate discriminator and the potential of super-resolution as a preprocessing step to boost classification model performance.

A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties

Alzheimer's Disease (AD) accounts for the majority of dementia, and Mild Cognitive Impairment (MCI) is the early stage of AD. Early and accurate diagnosis of dementia plays a vital role in more targeted treatments and effectively halting disease progression. However, the clinical diagnosis of dementia requires various examinations, which are expensive and require a high level of expertise from the doctor. In this paper, we proposed a classification method based on multi-modal data including Electroencephalogram (EEG), eye tracking and behavioral data for early diagnosis of AD and MCI. Paradigms with various task difficulties were used to identify different severity of dementia: eye movement task and resting-state EEG tasks were used to detect AD, while eye movement task and delayed match-to-sample task were used to detect MCI. Besides, the effects of different features were compared and suitable EEG channels were selected for the detection. Furthermore, we proposed a data augmentation method to enlarge the dataset, designed an extra ERPNet feature extract layer to extract multi-modal features and used domain-adversarial neural network to improve the performance of MCI diagnosis. We achieved an average accuracy of 88.81% for MCI diagnosis and 100% for AD diagnosis. The results of this paper suggest that our classification method can provide a feasible and affordable way to diagnose dementia.

Effects of Robot-Assisted Activity Using a Communication Robot on Neurological Activity in Older Adults with and without Cognitive Decline

Robot-assisted activity (RAA) using a communication robot (RAA-CR) has been proposed as a tool for alleviating behavioral and psychological symptoms accompanying dementia (BPSD) in patients with cognitive decline. This study aimed to clarify the effects of differences in cognitive function among older adults on changes in active brain areas induced by RAA-CR. Twenty-nine older adults were divided into a cognitive decline group (n = 11) and a control group (n = 18). The participants individually received a 5-minute RAA session, and their resting EEG activity was measured before and after the session. Brain spatial analysis was performed on recorded EEG data using standardized low-resolution brain electromagnetic tomography. In addition, statistical comparisons of neural activity in the brain were made before and after RAA-CR and between the cognitively impaired and control groups. These results suggest that RAA-CR stimulates neural activity in the region centered on the posterior cingulate gyrus and precuneus in cognitively healthy older adults but does not significantly alter brain neural activity in cognitively impaired older adults. Therefore, modifications to the implementation methods may be necessary to effectively implement RAA-CR in cognitively impaired individuals.