computational model psychiatry

INTRODUCTION: Repetitive transcranial magnetic stimulation (rTMS) is a widely used therapeutic tool in neurology and psychiatry, but its cellular and molecular mechanisms are not fully understood. Standardizing stimulus parameters, specifically electric field strength, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitates the translation of findings into clinical practice. However, the impact of biophysical properties inherent to the stimulated neurons and networks on the outcome of rTMS protocols remains not well understood...

BACKGROUND: Predicting cognitive decline in those already Aβ positive or Tau positive among the aging population poses clinical challenges. In Alzheimer's disease (AD) research, intra-default mode network (DMN) connections play a pivotal role in diagnosis. This paper proposes metabolic connectivity within the DMN as a supplementary biomarker to the AT(N) framework. METHODS: Extracting data from 1292 subjects in the Alzheimer's Disease Neuroimaging Initiative, we collected paired T1-weighted structural MRI and 18 F-labeled-fluorodeoxyglucose positron emission computed tomography (PET) scans...

Mental Status Assessment (MSA) holds significant importance in psychiatry. In recent years, several studies have leveraged Electroencephalogram (EEG) technology to gauge an individual's mental state or level of depression. This study introduces a novel multi-tier ensemble learning approach to integrate multiple EEG bands for conducting mental state or depression assessments. Initially, the EEG signal is divided into eight sub-bands, and then a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) model is trained for each band...

IMPORTANCE: Adolescent media use is thought to influence mental health, but whether it is associated with psychotic experiences (PEs) is unclear. OBJECTIVE: To examine longitudinal trajectories of adolescent media use and their associations with PEs at 23 years of age. DESIGN, SETTING, AND PARTICIPANTS: This cohort study included participants from the Québec Longitudinal Study of Child Development (1998-2021): children who were born in Québec, Canada, and followed up annually or biennially from ages 5 months through 23 years...

An interesting recent development in emotion research and clinical psychology is the discovery that affective states can be modeled as a network of temporally interacting moods or emotions. Additionally, external factors like stressors or treatments can influence the mood network by amplifying or dampening the activation of specific moods. Researchers have turned to multilevel autoregressive models to fit these affective networks using intensive longitudinal data gathered through ecological momentary assessment...

We investigate likelihood ratio models motivated by digital forensics problems involving time-stamped user-generated event data from a device or account. Of specific interest are scenarios where the data may have been generated by a single individual (the device/account owner) or by two different individuals (the device/account owner and someone else), such as instances in which an account was hacked or a device was stolen before being associated with a crime. Existing likelihood ratio methods in this context require that a precise time is specified at which the device or account is purported to have changed hands (the changepoint)-this is the known changepoint likelihood ratio model...

DNA mixture deconvolution in the forensic DNA community has been addressed in a variety of ways. "Front-end" methods that separate the cellular components of mixtures can provide a significant benefit over computational methods as there is no need to rely on models with inherent uncertainty to generate conclusions. Historically, cell separation methods have been investigated but have been largely ineffective due to high cost, unreliability, and the lack of proper instrumentation. However, the last decade has given rise to more innovative technology that can target and recover cells more effectively...

The vertebrate basal ganglia play an important role in action selection-the resolution of conflicts between alternative motor programs. The effective operation of basal ganglia circuitry is also known to rely on appropriate levels of the neurotransmitter dopamine. We investigated reducing or increasing the tonic level of simulated dopamine in a prior model of the basal ganglia integrated into a robot control architecture engaged in a foraging task inspired by animal behaviour. The main findings were that progressive reductions in the levels of simulated dopamine caused slowed behaviour and, at low levels, an inability to initiate movement...

AIMS AND METHOD: Selection into core psychiatry training in the UK uses a computer-delivered Multi-Specialty Recruitment Assessment (MSRA; a situational judgement and clinical problem-solving test) and, previously, a face-to-face Selection Centre. The Selection Centre assessments were suspended during the COVID-19 pandemic. We aimed to evaluate the validity of this selection process using data on 3510 psychiatry applicants. We modelled the ability of the selection scores to predict subsequent performance in the Clinical Assessment of Skills and Competencies (CASC)...

Data on the chronicity of mental disorder in children with chronic physical illness (CPI) are limited. We examined the prevalence and predictors of homotypic and heterotypic continuity of mental disorder in children with CPI. A sample of 263 children aged 2-16 years with physician-diagnosed CPI were recruited from outpatient clinics (e.g., dermatology, respiratory) at a Canadian pediatric academic hospital and followed for 24 months. Parent and child-reported mental disorders (mood, anxiety, behavioral, attention-deficit hyperactivity disorder [ADHD]) were assessed using the Mini International Neuropsychiatric Interview for Children and Adolescents at baseline, 6, 12, and 24 months...

BACKGROUND: Examining the daily experiences of older adults with depression facilitates the development and application of personalized effective treatments for them. In previous clinical research on depression, traditional mean-based approaches have mainly been employed. However, the within-person residual variance as a random effect provides greater insight into the heterogeneity of daily experiences among geriatric samples. OBJECTIVE: This study aimed to examine the relationship between depression and daily vitality in older adults...

OBJECTIVES: Digital health solutions have the potential to improve maternal mental health care. The objective of this study is to determine if utilization of a digital health platform, Maven, is associated with improved management of mental health among peripartum people with a history of mental health disorders and determine which components of utilization associate with maternal mental health outcomes. METHODS: Participants in this retrospective cohort analysis ( n  = 1561) accessed Maven as an employer-sponsored health benefit and enrolled during their pregnancy and delivered from January 2020 through September 2022...

Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites...

BACKGROUND: Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature. OBJECTIVE: Our study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified...

There is a growing focus on the computational aspects of psychiatric disorders in humans. This idea also is gaining traction in nonhuman animal studies. Commenting on a new comprehensive overview of the benefits of applying this approach in translational research by Neville et al. (Cognitive Affective & Behavioral Neuroscience 1-14, 2024), we discuss the implications for translational model validity within this framework. We argue that thinking computationally in translational psychiatry calls for a change in the way that we evaluate animal models of human psychiatric processes, with a shift in focus towards symptom-producing computations rather than the symptoms themselves...

The Deep Brain Stimulation (DBS) Think Tank XI was held on August 9-11, 2023 in Gainesville, Florida with the theme of "Pushing the Forefront of Neuromodulation". The keynote speaker was Dr. Nico Dosenbach from Washington University in St. Louis, Missouri. He presented his research recently published in Nature inn a collaboration with Dr. Evan Gordon to identify and characterize the somato-cognitive action network (SCAN), which has redefined the motor homunculus and has led to new hypotheses about the integrative networks underpinning therapeutic DBS...

Disrupted language in psychotic disorders, such as schizophrenia, can manifest as false contents and formal deviations, often described as thought disorder. These features play a critical role in the social dysfunction associated with psychosis, but we continue to lack insights regarding how and why these symptoms develop. Natural language generation (NLG) is a field of computer science that focuses on generating human-like language for various applications. The theory that psychosis is related to the evolution of language in humans suggests that NLG systems that are sufficiently evolved to generate human-like language may also exhibit psychosis-like features...

Clinical assessment procedures encounter challenges in terms of objectivity because they rely on subjective data. Computational psychiatry proposes overcoming this limitation by introducing biosignal-based assessments able to detect clinical biomarkers, while virtual reality (VR) can offer ecological settings for measurement. Autism spectrum disorder (ASD) is a neurodevelopmental disorder where many biosignals have been tested to improve assessment procedures. However, in ASD research there is a lack of studies systematically comparing biosignals for the automatic classification of ASD when recorded simultaneously in ecological settings, and comparisons among previous studies are challenging due to methodological inconsistencies...

Repetitive transcranial magnetic stimulation (rTMS) holds promise for treating psychiatric disorders; however, the variability in treatment efficacy among individuals underscores the need for further improvement. Growing evidence has shown that TMS induces a broad network modulatory effect, and its effectiveness may rely on accurate modulation of the pathological network specific to each disorder. Therefore, determining the optimal TMS coil setting that will engage the functional pathway delivering the stimulation is crucial...

INTRODUCTION: We examined changes in large-scale functional connectivity and temporal dynamics and their underlying mechanisms in schizophrenia (ScZ) through measurements of resting-state functional magnetic resonance imaging (rs-fMRI) data and computational modelling. METHODS: The rs-fMRI measurements from patients with chronic ScZ (n=38) and matched healthy controls (n=43), were obtained through the public schizConnect repository. Computational models were constructed based on diffusion-weighted MRI scans and fit to the experimental rs-fMRI data...