computational model mental illness

Three-quarters of lifetime mental illness occurs by the age of 24, but relatively little is known about how to robustly identify youth at risk to target intervention efforts known to improve outcomes. Barriers to knowledge have included obtaining robust predictions while simultaneously analyzing large numbers of different types of candidate predictors. In a new, large, transdiagnostic youth sample and multidomain high-dimension data, we used 160 candidate predictors encompassing neural, prenatal, developmental, physiologic, sociocultural, environmental, emotional and cognitive features and leveraged three different machine learning algorithms optimized with a novel artificial intelligence meta-learning technique to predict individual cases of anxiety, depression, attention deficit, disruptive behaviors and post-traumatic stress...

Intensive care medicine is complex and resource-demanding. A critical and common challenge lies in inferring the underlying physiological state of a patient from partially observed data. Specifically for the cardiovascular system, clinicians use observables such as heart rate, arterial and venous blood pressures, as well as findings from the physical examination and ancillary tests to formulate a mental model and estimate hidden variables such as cardiac output, vascular resistance, filling pressures and volumes, and autonomic tone...

The non-implantation bi-directional brain-computer interface (BCI) is a neural interface technology that enables direct two-way communication between the brain and the external world by both "reading" neural signals and "writing" stimulation patterns to the brain. This technology has vast potential applications, such as improving the quality of life for individuals with neurological and mental illnesses and even expanding the boundaries of human capabilities. Nonetheless, non-implantation bi-directional BCIs face challenges in generating real-time feedback and achieving compatibility between stimulation and recording...

Dementia is a significant cause of elderly disability and Alzheimer's disease (AD) is the most prevalent form of dementia. As an early stage of AD, the mechanism related to mild cognitive impairment (MCI) and heavy metals is still unclear. This study utilized a cross-sectional design and enrolled 514 older adults in Bejing, China. Cognitive function was assessed by the Mini-Mental State Examination (MMSE) and fourteen blood metals were measured by inductively coupled plasma mass spectrometry (ICP-MS). In the adjusted single-metal models, we observed that copper [Cu, β (95% CI): 3...

Progression to psychosis has been associated with increased cortical thinning in the frontal, temporal and parietal lobes in individuals at clinical high risk for the disorder (CHR-P). The timing and spatial extent of these changes are thought to be influenced by age. However, most evidence so far stems from adult samples. Longitudinal studies are essential to understanding the neuroanatomical changes associated to transition to psychosis during adolescence, and their relationship with age. We conducted a longitudinal, multisite study including adolescents at CHR-P and healthy controls (HC), aged 10-17 years...

This invited article ad memoriam of Bruce McEwen discusses emerging epigenetic mechanisms underlying the long and winding road from adverse childhood experiences to adult physiology and brain functions. The conceptual framework that we pursue suggest multidimensional biological pathways for the rapid regulation of neuroplasticity that utilize rapid non-genomic mechanisms of epigenetic programming of gene expression and modulation of metabolic function via mitochondrial metabolism. The current article also highlights how applying computational tools can foster the translation of basic neuroscience discoveries for the development of novel treatment models for mental illnesses, such as depression to slow the clinical manifestation of Alzheimer's disease...

Serotonin is implicated in the valuation of aversive costs, such as delay or physical effort. However, its role in governing sensitivity to cognitive effort, for example deliberation costs during information gathering, is unclear. We show that treatment with a serotonergic antidepressant in healthy human individuals of either sex enhances a willingness to gather information when trying to maximize reward. Using computational modelling, we show this arises from a diminished sensitivity to subjective deliberation costs during the sampling process...

BACKGROUND: Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care. METHODS: We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus...

BACKGROUND: One in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients. METHODS: Historical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard...

Subjective experience is experience in time. Unfolding in a continuous river of moments, our experience, however, consists not only in the changing phenomenological content per se but, further, in additional retrodiction and prospection of the moments that immediately preceded and followed it. It is in this way that William James's 'specious present' presents itself as extending between the past and future. While the phenomenology of temporality always happens, in normal waking states, to someone , and the notions of self-representation and temporal experience have continuously been associated with each other, there has not yet been an explicit account of their relationship...

Brain functional networks identified from resting functional magnetic resonance imaging (fMRI) data have the potential to reveal biomarkers for brain disorders, but studies of complex mental illnesses such as schizophrenia (SZ) often yield mixed results across replication studies. This is likely due in part to the complexity of the disorder, the short data acquisition time, and the limited ability of the approaches for brain imaging data mining. Therefore, the use of analytic approaches which can both capture individual variability while offering comparability across analyses is highly preferred...

BACKGROUND: Occupancy rates within skilled nursing facilities (SNFs) in the United States have reached a record low. Understanding drivers of occupancy, including admission decisions, is critical for assessing the recovery of the long-term care sector as a whole. We provide the first comprehensive analysis of financial, clinical, and operational factors that impact whether a patient referral to an SNF is accepted or denied, using a large health informatics database. OBJECTIVE: Our key objectives were to describe the distribution of referrals sent to SNFs in terms of key referral- and facility-level features; analyze key financial, clinical, and operational variables and their relationship to admission decisions; and identify the key potential reasons behind referral decisions in the context of learning health systems...

BACKGROUND: Several studies have reported on the feasibility of electronic (e-)monitoring using computers or smartphones in patients with mental disorders, including bipolar disorder (BD). While studies on e-monitoring have examined the role of demographic factors, such as age, gender, or socioeconomic status and use of health apps, to our knowledge, no study has examined clinical characteristics that might impact adherence with e-monitoring in patients with BD. We analyzed adherence to e-monitoring in patients with BD who participated in an ongoing e-monitoring study and evaluated whether demographic and clinical factors would predict adherence...

Our study is guided by Beck's cognitive stress-vulnerability model of depression. We examined the associations between perceived everyday discrimination (PED) and TNF-⍺, an inflammatory biomarker associated with risk for severe illness, through the negative cognitive triad (NCT; negative thoughts about the self, world, and future) and depressive symptoms in adolescents. We utilized a sample of 99 adolescents (36.4% female; ages 13-16, M = 14.10, SD = 0.52) in our cross-sectional study...

Coronavirus disease (COVID-19) represents one of the greatest challenges to public health in modern history. As the disease continues to spread globally, medical and allied healthcare professionals have become one of the most affected sectors. Stress and anxiety are indirect effects of the COVID-19 pandemic. Therefore, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. Here, we propose a computer-based method to better understand stress in healthcare workers facing COVID-19 at the beginning of the pandemic...

PURPOSE: Suicide is a global concern, especially among young people. Suicide prediction models have the potential to make it easier to identify patients who are at a high risk of suicide, but they have very little predictive power when there is a positive value for suicide mortality. Therefore, the aim of the study is to uncover potential risk factors associated with suicide by self-poisoning and further to provide a trustworthy nomogram to predict self-poisoning suicide among poisoned patients...

Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia...

UNLABELLED: Few studies and almost exclusively from the USA have recently investigated mobile phone and computer use among users of psychiatric services, which is of high relevance regarding the increasing development of digital health applications and services. OBJECTIVE, DESIGN AND SETTING: In a cross-sectional patient survey, we examined (a) rates and purposes of mobile phone, computer, internet and social media use, and (b) the role of social and clinical predictors on rates of utilisation among psychiatric inpatients in Berlin, Germany...

Depression, a common mental illness worldwide, needs to be diagnosed and cured at an early stage. To assist clinical diagnosis, an EEG-based deep learning frame, which is named the gated temporal-separable attention network (GTSAN), is proposed in this paper for depression recognition. GTSAN model extracts discriminative information from EEG recordings in two ways. On the one hand, the gated recurrent unit (GRU) is used in the GTSAN model to capture the EEG historical information to form the features. On the other hand, the model digs the multilevel information by using an improved version of temporal convolutional network (TCN), called temporal-separable convolution network (TSCN), which applies causal convolution and dilated convolution to extract features from fine to coarse scales...

Eating disorders are severe mental illnesses characterized by the hallmark behaviors of binge eating, restriction, and purging. These disordered eating behaviors carry extreme impairment and medical complications, regardless of eating disorder diagnosis. Despite the importance of these disordered behaviors to every eating disorder diagnosis, our current models are not able to accurately predict behavior occurrence. The current study utilized machine learning to develop longitudinal predictive models of binge eating, purging, and restriction in an eating disorder sample (N = 60) using real-time intensive longitudinal data...