Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis.

Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.

Mental stress recognition on the fly using neuroplasticity spiking neural networks.

Mental stress is found to be strongly connected with human cognition and wellbeing. As the complexities of human life increase, the effects of mental stress have impacted human health and cognitive performance across the globe. This highlights the need for effective non-invasive stress detection methods. In this work, we introduce a novel, artificial spiking neural network model called Online Neuroplasticity Spiking Neural Network (O-NSNN) that utilizes a repertoire of learning concepts inspired by the brain to classify mental stress using Electroencephalogram (EEG) data. These models are personalized and tested on EEG data recorded during sessions in which participants listen to different types of audio comments designed to induce acute stress. Our O-NSNN models learn on the fly producing an average accuracy of 90.76% (σ = 2.09) when classifying EEG signals of brain states associated with these audio comments. The brain-inspired nature of the individual models makes them robust and efficient and has the potential to be integrated into wearable technology. Furthermore, this article presents an exploratory analysis of trained O-NSNNs to discover links between perceived and acute mental stress. The O-NSNN algorithm proved to be better for personalized stress recognition in terms of accuracy, efficiency, and model interpretability.

Multicohort cross-sectional study of cognitive and behavioural digital biomarkers in neurodegeneration: the Living Lab Study protocol.

INTRODUCTION AND AIMS: Digital biomarkers can provide a cost-effective, objective and robust measure for neurological disease progression, changes in care needs and the effect of interventions. Motor function, physiology and behaviour can provide informative measures of neurological conditions and neurodegenerative decline. New digital technologies present an opportunity to provide remote, high-frequency monitoring of patients from within their homes. The purpose of the living lab study is to develop novel digital biomarkers of functional impairment in those living with neurodegenerative disease (NDD) and neurological conditions. METHODS AND ANALYSIS: The Living Lab study is a cross-sectional observational study of cognition and behaviour in people living with NDDs and other, non-degenerative neurological conditions. Patients (n≥25 for each patient group) with dementia, Parkinson's disease, amyotrophic lateral sclerosis, mild cognitive impairment, traumatic brain injury and stroke along with controls (n≥60) will be pragmatically recruited. Patients will carry out activities of daily living and functional assessments within the Living Lab. The Living Lab is an apartment-laboratory containing a functional kitchen, bathroom, bed and living area to provide a controlled environment to develop novel digital biomarkers. The Living Lab provides an important intermediary stage between the conventional laboratory and the home. Multiple passive environmental sensors, internet-enabled medical devices, wearables and electroencephalography (EEG) will be used to characterise functional impairments of NDDs and non-NDD conditions. We will also relate these digital technology measures to clinical and cognitive outcomes. ETHICS AND DISSEMINATION: Ethical approvals have been granted by the Imperial College Research Ethics Committee (reference number: 21IC6992). Results from the study will be disseminated at conferences and within peer-reviewed journals.

The relationships between expressed emotion, cortisol, and EEG alpha asymmetry.

Families can express high criticism, hostility and emotional over-involvement towards a person with or at risk of mental health problems. Perceiving such high expressed emotion (EE) can be a major psychological stressor for individuals, especially those at risk of mental health problems. To reveal the biological mechanisms underlying the effect of EE on health, this study investigated physiological response (salivary cortisol, frontal alpha asymmetry (FAA)) to verbal criticism and their relationship to anxiety and perceived EE. Using a repeated-measures design, healthy participants attended three testing sessions on non-consecutive days. On each day, participants listened to one of three types of auditory stimuli, namely criticism, neutral or praise, and Electroencephalography (EEG) and salivary cortisol were measured. Results showed a reduction in cortisol following criticism but there was no significant change in FAA. Post-criticism cortisol concentration negatively correlated with perceived EE after controlling for baseline mood. Our findings suggest that salivary cortisol change responds to criticism in non-clinical populations and this response might be largely driven by individual differences in the perception of criticism (e.g., arousal and relevance). Criticisms expressed by audio comments may not be explicitly perceived as an acute emotional stressor, and thus, physiological response to criticisms could be minimum.

A shortened surface electromyography recording is sufficient to facilitate home fasciculation assessment.

INTRODUCTION/AIMS: Fasciculations are an early clinical hallmark of amyotrophic lateral sclerosis (ALS), amenable to detection by high-density surface electromyography (HDSEMG). In conjunction with the Surface Potential Quantification Engine (SPiQE), HDSEMG offers improved spatial resolution for the analysis of fasciculations. This study aims to establish an optimal recording duration to enable longitudinal remote monitoring in the home. METHODS: Twenty patients with ALS and five patients with benign fasciculation syndrome (BFS) underwent serial 30 min HDSEMG recordings from biceps brachii and gastrocnemii. SPiQE was independently applied to abbreviated epochs within each 30-min recording (0-5, 0-10, 0-15, 0-20, and 0-25 min), outputting fasciculation frequency, amplitude median and amplitude interquartile range. Bland-Altman plots and intraclass correlation coefficients (ICC) were used to assess agreement with the validated 30-min recording. RESULTS: In total, 506 full recordings were included. The 5 min recordings demonstrated diverse and relatively poor agreement with the 30 min baselines across all parameters, muscles and patient groups (ICC = 0.32-0.86). The 15-min recordings provided more acceptable and stable agreement (ICC = 0.78-0.98), which did not substantially improve in longer recordings. DISCUSSION: For the detection and quantification of fasciculations in patients with ALS and BFS, HDSEMG recordings can be halved from 30 to 15 min without significantly compromising the primary outputs. Reliance on a shorter recording duration should lead to improved tolerability and repeatability among patients, facilitating longitudinal remote monitoring in patients' homes.

Neurophysiological markers of prospective memory and working memory in typical ageing and mild cognitive impairment.

OBJECTIVE: Prospective memory (PM) -the memory of delayed intentions- is impacted by age-related cognitive decline. The current event-related potential study investigates neural mechanisms underpinning typical and atypical (Mild Cognitive Impairment, MCI) age-related decline in PM. METHODS: Young adults (YA, n = 30, age = 24.7, female n = 13), healthy older adults (OA, n = 39, age = 72.87, female n = 24) and older adults with MCI (n = 27, age = 77.54, female n = 12) performed two event-based PM tasks (perceptual, conceptual) superimposed on an ongoing working memory task. Electroencephalographic data was recorded from 128 electrodes. Groups were compared for P2 (higher order perceptual processing), N300/frontal positivity (cue detection), the parietal positivity (retrieval), reorienting negativity (RON; attention shifting). RESULTS: Participants with MCI had poorer performance (ongoing working memory task, conceptual PM), lower P2 amplitudes, and delayed RON (particularly for perceptual PM) than YA and OA. MCI had lower parietal positivity relative to YA only. YA had earlier latencies for the parietal positivity than MCI and OA, and lower amplitudes for N300 (than OA) and frontal positivity (than OA and MCI). CONCLUSIONS: Impaired attention and working memory may underpin PM deficits in MCI. SIGNIFICANCE: This is the first study to document the role of RON in PM and to investigate neurophysiological mechanisms underpinning PM in MCI.

Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia.

BACKGROUND: Longitudinal neuroimaging provides spatiotemporal brain data (STBD) measurement that can be utilised to understand dynamic changes in brain structure and/or function underpinning cognitive activities. Making sense of such highly interactive information is challenging, given that the features manifest intricate temporal, causal relations between the spatially distributed neural sources in the brain. METHODS: The current paper argues for the advancement of deep learning algorithms in brain-inspired spiking neural networks (SNN), capable of modelling structural data across time (longitudinal measurement) and space (anatomical components). The paper proposes a methodology and a computational architecture based on SNN for building personalised predictive models from longitudinal brain data to accurately detect, understand, and predict the dynamics of an individual's functional brain state. The methodology includes finding clusters of similar data to each individual, data interpolation, deep learning in a 3-dimensional brain-template structured SNN model, classification and prediction of individual outcome, visualisation of structural brain changes related to the predicted outcomes, interpretation of results, and individual and group predictive marker discovery. RESULTS: To demonstrate the functionality of the proposed methodology, the paper presents experimental results on a longitudinal magnetic resonance imaging (MRI) dataset derived from 175 older adults of the internationally recognised community-based cohort Sydney Memory and Ageing Study (MAS) spanning 6 years of follow-up. SIGNIFICANCE: The models were able to accurately classify and predict 2 years ahead of cognitive decline, such as mild cognitive impairment (MCI) and dementia with 95% and 91% accuracy, respectively. The proposed methodology also offers a 3-dimensional visualisation of the MRI models reflecting the dynamic patterns of regional changes in white matter hyperintensity (WMH) and brain volume over 6 years. CONCLUSION: The method is efficient for personalised predictive modelling on a wide range of neuroimaging longitudinal data, including also demographic, genetic, and clinical data. As a case study, it resulted in finding predictive markers for MCI and dementia as dynamic brain patterns using MRI data.

Age differences in resting state EEG and their relation to eye movements and cognitive performance.

Prior research has focused on EEG differences across age or EEG differences across cognitive tasks/eye tracking. There are few studies linking age differences in EEG to age differences in behavioural performance which is necessary to establish how neuroactivity corresponds to successful and impaired ageing. Eighty-six healthy participants completed a battery of cognitive tests and eye-tracking measures. Resting state EEG (n = 75, 31 young, 44 older adults) was measured for delta, theta, alpha and beta power as well as for alpha peak frequency. Age deficits in cognition were aligned with the literature, showing working memory and inhibitory deficits along with an older adult advantage in vocabulary. Older adults showed poorer eye movement accuracy and response times, but we did not replicate literature showing a greater age deficit for antisaccades than for prosaccades. We replicated EEG literature showing lower alpha peak frequency in older adults but not literature showing lower alpha power. Older adults also showed higher beta power and less parietal alpha power asymmetry than young adults. Interaction effects showed that better prosaccade performance was related to lower beta power in young adults but not in older adults. Performance at the trail making test part B (measuring task switching and inhibition) was improved for older adults with higher resting state delta power but did not depend on delta power for young adults. It is argued that individuals with higher slow-wave resting EEG may be more resilient to age deficits in tasks that utilise cross-cortical processing.

Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture.

Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanisms underpinning these changes are uncertain. This study uses a novel brain-inspired artificial neural network to investigate the effect of mindfulness training on electroencephalographic function. Participants completed a 4-tone auditory oddball task (that included targets and physically similar distractors) at three assessment time points. In Group A (n = 10), these tasks were given immediately prior to 6-week mindfulness training, immediately after training and at a 3-week follow-up; in Group B (n = 10), these were during an intervention waitlist period (3 weeks prior to training), pre-mindfulness training and post-mindfulness training. Using a spiking neural network (SNN) model, we evaluated concurrent neural patterns generated across space and time from features of electroencephalographic data capturing the neural dynamics associated with the event-related potential (ERP). This technique capitalises on the temporal dynamics of the shifts in polarity throughout the ERP and spatially across electrodes. Findings support anteriorisation of connection weights in response to distractors relative to target stimuli. Right frontal connection weights to distractors were associated with trait mindfulness (positively) and depression (inversely). Moreover, mindfulness training was associated with an increase in connection weights to targets (bilateral frontal, left frontocentral, and temporal regions only) and distractors. SNN models were superior to other machine learning methods in the classification of brain states as a function of mindfulness training. Findings suggest SNN models can provide useful information that differentiates brain states based on distinct task demands and stimuli, as well as changes in brain states as a function of psychological intervention.

Problematic Mobile Phone Use and Addiction Across Generations: the Roles of Psychopathological Symptoms and Smartphone Use.

Contemporary technological advances have led to a significant increase in using mobile technologies. Recent research has pointed to potential problems as a consequence of mobile overuse, including addiction, financial problems, dangerous use (i.e. whilst driving) and prohibited use (i.e. use in forbidden areas). The aim of this study is to extend previous findings regarding the predictive power of psychopathological symptoms (depression, anxiety and stress), mobile phone use (i.e. calls, SMS, time spent on the phone, as well as the engagement in specific smartphone activities) across Generations X and Y on problematic mobile phone use in a sample of 273 adults. Findings revealed prohibited use and dependence were predicted by calls/day, time on the phone and using social media. Only for dependent mobile phone use (rather than prohibited), stress appeared as significant. Using social media and anxiety significantly predicted belonging to Generation Y, with calls per day predicted belonging to Generation X. This finding suggests Generation Y are more likely to use asynchronous social media-based communication, whereas Generation X engage more in synchronous communication. The findings have implications for prevention and awareness-raising efforts of possibly problematic mobile phone use for educators, parents and individuals, particularly including dependence and prohibited use.