Art therapy is associated with a reduction in restrictive practices on an inpatient child and adolescent mental health unit.

BACKGROUND: The elimination of restrictive practices, such as seclusion and restraint, is a major aim of mental health services globally. The role of art therapy, a predominantly non-verbal mode of creative expression, is under-explored in this context. This research aimed to determine whether art therapy service provision was associated with a reduction in restrictive practices on an acute inpatient child and adolescent mental health services (CAMHS) unit. METHODS: The rate (events per 1,000 occupied bed days), frequency (percent of admitted care episodes with incident), duration, and number of incidents of restrictive practices occurring between July 2015 and December 2021 were analysed relative to art therapy service provision. The rate, frequency and number of incidents of intramuscular injected (IM) sedation, oral PRN (as-needed medication) use, and absconding incidents occurring in conjunction with an episode of seclusion or restraint were also analysed. RESULTS: The rate, frequency, duration, and total number of incidents of seclusion, the frequency and total number of incidents of physical restraint, and the rate, frequency and total number of incidents of IM sedation showed a statistically significant reduction during phases of art therapy service provision. CONCLUSIONS: Art therapy service provision is associated with a reduction in restrictive practices in inpatient CAMHS.

Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease.

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

Tamoxifen offers long-term neuroprotection after hippocampal silent infarct in male rats.

Silent infarcts (SI) are a cerebral small vessel disease characterized by small subcortical infarcts. These occur in the absence of typical ischemia symptoms but are linked to cognitive decline and dementia. While there are no approved treatments for SI, recent results from our laboratory suggest that tamoxifen, a selective estrogen receptor modulator, is a viable candidate. In the present study, we induced SI in the dorsal hippocampal CA1 region of rats and assessed the effects of systemic administration of tamoxifen (5 mg/kg, twice) 21 days after injury on cognitive and pathophysiological measures, including cell loss, apoptosis, gliosis and estrogen receptors (ERs). We found that tamoxifen protected against the SI-induced cognitive dysfunction on the hippocampal-dependent, place recognition task, cell and ER loss, and increased apoptosis and gliosis in the CA1. Exploratory data analyses using a scatterplot matrix and principal component analysis indicated that SI-tamoxifen rats were indistinguishable from sham controls while they differed from SI rats, who were characterized by enhanced cell loss, apoptosis and gliosis, lower ERs, and recognition memory deficit. Supervised machine learning using support vector machine (SVM) determined predictors of progression from the early ischemic state to the dementia-like state. It showed that caspase-3 and ERα in the CA1 and exploration proportion were reliable and accurate predictors of this progression. Importantly, tamoxifen ameliorated SI-induced effects on all three of these variables, providing further evidence for its viability as a candidate treatment for SI and prevention of associated dementia.

The selective estrogen receptor modulator tamoxifen protects against subtle cognitive decline and early markers of injury 24 h after hippocampal silent infarct in male Sprague-Dawley rats.

Silent infarcts (SI) are subcortical cerebral infarcts occurring in the absence of typical ischemia symptoms and are linked to cognitive decline and dementia development. There are no approved treatments for SI. One potential treatment is tamoxifen, a selective estrogen receptor modulator. It is critical to establish whether treatments effectively target the early consequences of SI to avoid progression to complete injury. We induced SI in the dorsal hippocampal CA1 of rats and assessed whether tamoxifen is protective 24 h later against cognitive deficits and injury responses including gliosis, apoptosis, inflammation and changes in estrogen receptors (ERs). SI led to subtle cognitive impairment on the object place task, an effect ameliorated by tamoxifen administration. SI did not lead to detectable hippocampal cell loss but increased apoptosis, astrogliosis, microgliosis and inflammation. Tamoxifen protected against the effects of SI on all measures except microgliosis. SI increased ERα and decreased ERß in the hippocampus, which were mitigated by tamoxifen. Exploratory data analyses using scatterplot matrices and principal component analysis indicated that SI rats given tamoxifen were indistinguishable from controls. Further, SI rats were significantly different from all other groups, an effect associated with low levels of ERα and increased apoptosis, gliosis, inflammation, ERß, and time spent with the unmoved object. The results demonstrate that tamoxifen is protective against the early cellular and cognitive consequences of hippocampal SI 24 h after injury. Tamoxifen mitigates apoptosis, gliosis, and inflammation and normalization of ER levels in the CA1, leading to improved cognitive outcomes after hippocampal SI.

Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.

INTRODUCTION: Proteome changes associated with APOE4 variant carriage that are independent of Alzheimer's disease (AD) pathology and diagnosis are unknown. This study investigated APOE4 proteome changes in people with AD, mild cognitive impairment, and no impairment. METHODS: Clinical, APOE genotype, and cerebrospinal fluid (CSF) proteome and AD biomarker data was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Proteome profiling was done using supervised machine learning. RESULTS: We found an APOE4-specific proteome signature that was independent of cognitive diagnosis and AD pathological biomarkers, and increased risk of progression to cognitive impairment. Proteins were enriched in brain regions including the caudate and cortex and cells including endothelial cells, oligodendrocytes, and astrocytes. Enriched peripheral immune cells included T cells, macrophages, and B cells. DISCUSSION: APOE4 carriers have a unique CSF proteome signature associated with a strong brain and peripheral immune and inflammatory phenotype that likely underlies APOE4 carriers' vulnerability to cognitive decline and AD.

Artificial intelligence-driven meta-analysis of brain gene expression identifies novel gene candidates and a role for mitochondria in Alzheimer's disease.

Alzheimer's disease (AD) is the most common form of dementia. There is no treatment and AD models have focused on a small subset of genes identified in familial AD. Microarray studies have identified thousands of dysregulated genes in the brains of patients with AD yet identifying the best gene candidates to both model and treat AD remains a challenge. We performed a meta-analysis of microarray data from the frontal cortex (n = 697) and cerebellum (n = 230) of AD patients and healthy controls. A two-stage artificial intelligence approach, with both unsupervised and supervised machine learning, combined with a functional network analysis was used to identify functionally connected and biologically relevant novel gene candidates in AD. We found that in the frontal cortex, genes involved in mitochondrial energy, ATP, and oxidative phosphorylation, were the most significant dysregulated genes. In the cerebellum, dysregulated genes were involved in mitochondrial cellular biosynthesis (mitochondrial ribosomes). Although there was little overlap between dysregulated genes between the frontal cortex and cerebellum, machine learning models comprised of this overlap. A further functional network analysis of these genes identified that two downregulated genes, ATP5L and ATP5H, which both encode subunits of ATP synthase (mitochondrial complex V) may play a role in AD. Combined, our results suggest that mitochondrial dysfunction, particularly a deficit in energy homeostasis, may play an important role in AD.

The neuroprotective effects of estrogen and estrogenic compounds in spinal cord injury.

Spinal cord injury (SCI) occurs when the spinal cord is damaged from either a traumatic event or disease. SCI is characterised by multiple injury phases that affect the transmission of sensory and motor signals and lead to temporary or long-term functional deficits. There are few treatments for SCI. Estrogens and estrogenic compounds, however, may effectively mitigate the effects of SCI and therefore represent viable treatment options. This review systematically examines the pre-clinical literature on estrogen and estrogenic compound neuroprotection after SCI. Several estrogens were examined by the included studies: estrogen, estradiol benzoate, Premarin, isopsoralen, genistein, and selective estrogen receptor modulators. Across these pharmacotherapies, we find significant evidence that estrogens indeed offer protection against myriad pathophysiological effects of SCI and lead to improvements in functional outcomes, including locomotion. A STRING functional network analysis of proteins modulated by estrogen after SCI demonstrated that estrogen simultaneously upregulates known neuroprotective pathways, such as HIF-1, and downregulates pro-inflammatory pathways, including IL-17. These findings highlight the strong therapeutic potential of estrogen and estrogenic compounds after SCI.

Measuring psychiatric symptoms online: A systematic review of the use of inventories on Amazon Mechanical Turk (mTurk).

Symptom measurement in psychiatric research increasingly uses digitized self-report inventories and is turning to crowdsourcing platforms for recruitment, e.g., Amazon Mechanical Turk (mTurk). The impact of digitizing pencil-and-paper inventories on the psychometric properties is underexplored in mental health research. Against this background, numerous studies report high prevalence estimates of psychiatric symptoms in mTurk samples. Here we develop a framework to evaluate the online implementation of psychiatric symptom inventories relative to two domains, that is, the adherence to (i) validated scoring and (ii) standardized administration. We apply this new framework to the online use of the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Alcohol Use Disorder Identification Test (AUDIT). Our systematic review of the literature identified 36 implementations of these three inventories on mTurk across 27 publications. We also evaluated methodological approaches to enhance data quality, e.g., the use of bot detection and attention check items. Of the 36 implementations, 23 reported the applied diagnostic scoring criteria and only 18 reported the specified symptom timeframe. None of the 36 implementations reported adaptations made in their digitization of the inventories. While recent reports attribute higher rates of mood, anxiety, and alcohol use disorders on mTurk to data quality, our findings indicate that this inflation may also relate to the assessment methods. We provide recommendations to enhance both data quality and fidelity to validated administration and scoring methods.

Machine learning identifies a COVID-19-specific phenotype in university students using a mental health app.

Background: Advances in smartphone technology have allowed people to access mental healthcare via digital apps from wherever and whenever they choose. University students experience a high burden of mental health concerns. Although these apps improve mental health symptoms, user engagement has remained low. Studies have shown that users can be subgrouped based on unique characteristics that just-in-time adaptive interventions (JITAIs) can use to improve engagement. To date, however, no studies have examined the effect of the COVID-19 pandemic on these subgroups. Objective: Here, we sought to examine user subgroup characteristics across three COVID-19-specific timepoints: during lockdown, immediately following lockdown, and three months after lockdown ended. Methods: To do this, we used a two-step machine learning approach combining unsupervised and supervised machine learning. Results: We demonstrate that there are three unique subgroups of university students who access mental health apps. Two of these, with either higher or lower mental well-being, were defined by characteristics that were stable across COVID-19 timepoints. The third, situational well-being, had characteristics that were timepoint-dependent, suggesting that they are highly influenced by traumatic stressors and stressful situations. This subgroup also showed feelings and behaviours consistent with burnout. Conclusions: Overall, our findings clearly suggest that user subgroups are unique: they have different characteristics and therefore likely have different mental healthcare goals. Our findings also highlight the importance of including questions and additional interventions targeting traumatic stress(ors), reason(s) for use, and burnout in JITAI-style mental health apps to improve engagement.

Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: the Vibe Up study.

INTRODUCTION: Meta-analytical evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI)-driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multiarm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity. METHODS AND ANALYSIS: The Vibe Up study is a pragmatically oriented, decentralised AI-adaptive group sequential randomised controlled trial comparing the effectiveness of one of three brief, 2-week digital self-guided interventions (mindfulness, physical activity or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (Depression, Anxiety and Stress Scale, 21-item version, DASS-21 total score) from preintervention to postintervention. Secondary outcomes include change in physical activity, sleep quality and mindfulness from preintervention to postintervention. Planned contrasts will compare the four groups (ie, the three intervention and control) using self-reported psychological distress at prespecified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions. ETHICS AND DISSEMINATION: Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). TRIAL REGISTRATION NUMBER: ACTRN12621001223820.