Dementia Care Research and Psychosocial Factors.

Background: Close to 23% of unplanned hospital admissions for people living with dementia (PLWD) are due to potentially preventable causes such as severe urinary tract infections (UTIs), falls, and respiratory problems. These affect the well-being of PLWD, cause stress to carers and increase pressure on healthcare services.

Method: We use routinely collected in-home sensory data to monitor nocturnal activity and sleep data.

Dementia Care Research and Psychosocial Factors.

Background: Changes in sleep patterns are common in Alzheimer's disease and impact the quality of life of both people living with Alzheimer's (PLWA) and their caregivers. Longitudinal recordings and assessment of night-to-night variations in sleep and physiology can improve our understanding of how sleep influences clinical outcomes and caregiver wellbeing.

Method: We collected sleep diary and contactless sleep technology data (Withings sleep analyser, WSA) in community dwelling PLWA (N = 16, Age = 72.

On the longevity and inherent hermeticity of silicon-ICs: evaluation of bare-die and PDMS-coated ICs after accelerated aging and implantation studies.

Silicon integrated circuits (ICs) are central to the next-generation miniature active neural implants, whether packaged in soft polymers for flexible bioelectronics or implanted as bare die for neural probes. These emerging applications bring the IC closer to the corrosive body environment, raising reliability concerns, particularly for chronic use. Here, we evaluate the inherent hermeticity of bare die ICs, and examine the potential of polydimethylsiloxane (PDMS), a moisture-permeable elastomer, as a standalone encapsulation material.

Simulating the psychological and neural effects of affective touch with soft robotics: an experimental study.

Human affective touch is known to be beneficial for social-emotional interactions and has a therapeutic effect. For touch initiated by robotic entities, richer affective affordance is a critical enabler to unlock its potential in social-emotional interactions and especially in care and therapeutic applications. Simulating the attributes of particular types of human affective touch to inform robotic touch design can be a beneficial step.

Assessment of sleep patterns in dementia and general population cohorts using passive in-home monitoring technologies.

Background: Nocturnal disturbances are a common symptom experienced by People Living with Dementia (PLWD), and these often present prior to diagnosis. Whilst sleep anomalies have been frequently reported, most studies have been conducted in lab environments, which are expensive, invasive and not natural sleeping environments. In this study, we investigate the use of in-home nocturnal monitoring technologies, which enable passive data collection, at low cost, in real-world environments, and without requiring a change in routine.

Utilizing graph neural networks for adverse health detection and personalized decision making in sensor-based remote monitoring for dementia care.

Background: Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, overly granular, and suffers from unreliable labeling, data drift and high variability between households. Current anomaly detection methods lack generalizability and personalization, often requiring anomaly-free training data and frequent model updates.

Methylphenidate differentially alters corticostriatal connectivity after traumatic brain injury.

Traumatic brain injury commonly impairs attention and executive function, and disrupts the large-scale brain networks that support these cognitive functions. Abnormalities of functional connectivity are seen in corticostriatal networks, which are associated with executive dysfunction and damage to neuromodulatory catecholaminergic systems caused by head injury. Methylphenidate, a stimulant medication that increases extracellular dopamine and noradrenaline, can improve cognitive function following TBI.

Experiences with home monitoring technology in older adults with traumatic brain injury: a qualitative study.

Background: Home monitoring systems utilising artificial intelligence hold promise for digitally enhanced healthcare in older adults. Their real-world use will depend on acceptability to the end user i.e.

Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study.

Background: Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs.

Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review.

Background: Markerless motion capture (MMC) uses video cameras or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia.

Objective: The primary objective of this systematic review was to investigate the application of MMC using full-body tracking, to quantify functional performance in people with dementia, mild cognitive impairment, and Parkinson disease.

Methods: A systematic search of the Embase, MEDLINE, CINAHL, and Scopus databases was conducted between November 2022 and February 2023, which yielded a total of 1595 results.

Digital endpoints in clinical trials: emerging themes from a multi-stakeholder Knowledge Exchange event.

Background: Digital technologies, such as wearable devices and smartphone applications (apps), can enable the decentralisation of clinical trials by measuring endpoints in people's chosen locations rather than in traditional clinical settings. Digital endpoints can allow high-frequency and sensitive measurements of health outcomes compared to visit-based endpoints which provide an episodic snapshot of a person's health. However, there are underexplored challenges in this emerging space that require interdisciplinary and cross-sector collaboration.

Biosynthesis of Arcyriaflavin F from Streptomyces venezuelae ATCC 10712.

Indolocarbazoles are natural products with a broad spectrum of bioactivity. A distinct feature of indolocarbazole biosynthesis is the modification of the indole and maleimide rings by regioselective tailoring enzymes. Here, we study a new indolocarbazole variant, which is encoded by the acfXODCP genes from Streptomyces venezuelae ATCC 10712.

A closed-loop auditory stimulation approach selectively modulates alpha oscillations and sleep onset dynamics in humans.

Alpha oscillations play a vital role in managing the brain's resources, inhibiting neural activity as a function of their phase and amplitude, and are changed in many brain disorders. Developing minimally invasive tools to modulate alpha activity and identifying the parameters that determine its response to exogenous modulators is essential for the implementation of focussed interventions. We introduce Alpha Closed-Loop Auditory Stimulation (αCLAS) as an EEG-based method to modulate and investigate these brain rhythms in humans with specificity and selectivity, using targeted auditory stimulation.

The PREVENT dementia programme: baseline demographic, lifestyle, imaging and cognitive data from a midlife cohort study investigating risk factors for dementia.

PREVENT is a multi-centre prospective cohort study in the UK and Ireland that aims to examine midlife risk factors for dementia and identify and describe the earliest indices of disease development. The PREVENT dementia programme is one of the original epidemiological initiatives targeting midlife as a critical window for intervention in neurodegenerative conditions. This paper provides an overview of the study protocol and presents the first summary results from the initial baseline data to describe the cohort.

From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People.

Objective: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG.

Methods And Procedures: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work.

Targeted non-invasive brain stimulation boosts attention and modulates contralesional brain networks following right hemisphere stroke.

Right hemisphere stroke patients frequently present with a combination of lateralised and non-lateralised attentional deficits characteristic of the neglect syndrome. Attentional deficits are associated with poor functional outcome and are challenging to treat, with non-lateralised deficits often persisting into the chronic stage and representing a common complaint among patients and families. In this study, we investigated the effects of non-invasive brain stimulation on non-lateralised attentional deficits in right-hemispheric stroke.

Analyzing entropy features in time-series data for pattern recognition in neurological conditions.

In the field of medical diagnosis and patient monitoring, effective pattern recognition in neurological time-series data is essential. Traditional methods predominantly based on statistical or probabilistic learning and inference often struggle with multivariate, multi-source, state-varying, and noisy data while also posing privacy risks due to excessive information collection and modeling. Furthermore, these methods often overlook critical statistical information, such as the distribution of data points and inherent uncertainties.

Alzheimer's disease heterogeneity revealed by neuroanatomical normative modeling.

Introduction: Overlooking the heterogeneity in Alzheimer's disease (AD) may lead to diagnostic delays and failures. Neuroanatomical normative modeling captures individual brain variation and may inform our understanding of individual differences in AD-related atrophy.

Methods: We applied neuroanatomical normative modeling to magnetic resonance imaging from a real-world clinical cohort with confirmed AD ( = 86).