Cross validation of bi-modal health-related stress assessment

This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a "happy" and a "stress triggering" part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, high-frequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care

Staging evaluation of posttraumatic stress disorder : a machine learning study

Os transtornos de estresse relacionados a um evento traumático, como o transtorno de estresse agudo (TEA) e o transtorno de estresse pós-traumático (TEPT), são caracterizados por alta morbidade e prejuízo social significativo. No Brasil, estima-se que 80% da população já foi exposta a pelo menos um evento traumático ao longo da vida em grandes centros urbanos, como São Paulo e Rio de Janeiro; o crescente problema da violência urbana mostra-se fator importante para a gênese dos transtornos relacionados ao trauma. Devido à etiologia do TEPT ser multicausal e complexa, técnicas de Machine Learning (Aprendizado de Máquina – ML) tem sido usadas para desenvolver escores de risco, para predição diagnóstica e para definição de tratamento. Contudo, considerando sua heterogeneidade clínica e etiológica, realizar o diagnóstico e definir um tratamento adequado pode ser muitas vezes desafiador. O uso do estadiamento clínico surge como um método mais refinado de diagnóstico, procurando definir a progressão do transtorno em momentos específicos durante o continuum da enfermidade. Esta abordagem pode auxiliar em um diagnóstico mais aprimorado, conhecer melhor o prognóstico e escolher o melhor tratamento de acordo com o estágio do transtorno. Assim, o TEPT aparece como um exemplo importante de como um método de estadiamento pode trazer benefícios. O objetivo desta tese é avaliar como os aspectos pessoais, clínicos e relacionados ao trauma dos pacientes atendidos em ambulatórios especializados em trauma psíquico podem estar relacionados à predição do estadiamento clínico de TEPT usando técnicas de ML.Stress disorders related to a traumatic event, such as acute stress disorder (ASD) and posttraumatic stress disorder (PTSD), are characterized by high morbidity and significant social impairment. In Brazil, it is estimated that 80% of the population has already been exposed to at least one traumatic event throughout life in large urban centers, such as São Paulo and Rio de Janeiro; the growing problem of urban violence proves to be an important factor in the genesis of trauma-related disorders. The etiology of PTSD is multicausal and complex; techniques of Machine Learning (ML) have been used to develop PTSD risk scores, to predict its diagnosis and to choose better treatments. However, considering its clinical and etiological heterogeneity, making the diagnosis and defining an appropriate treatment can often be challenging. The use of clinical staging appears as a refined method of diagnosis, aiming to define the progression of the disorder at specific times during the continuum of the illness. This approach may provide improved diagnosis, better understand the prognosis and choose the best treatment according to the stage of the disorder. Thus, PTSD appears as an important example of how a staging method can bring benefits. The objective of this thesis is to evaluate how the personal, clinical and trauma-related aspects of patients who sought care at outpatient clinics specialized in emotional trauma can be related to the prediction of the PTSD staging using ML techniques

Exploring Emotion Recognition for VR-EBT Using Deep Learning on a Multimodal Physiological Framework

Post-Traumatic Stress Disorder is a mental health condition that affects a growing number of people. A variety of PTSD treatment methods exist, however current research indicates that virtual reality exposure-based treatment has become more prominent in its use.Yet the treatment method can be costly and time consuming for clinicians and ultimately for the healthcare system. PTSD can be delivered in a more sustainable way using virtual reality. This is accomplished by using machine learning to autonomously adapt virtual reality scene changes. The use of machine learning will also support a more efficient way of inserting positive stimuli in virtual reality scenes. Machine learning has been used in medical areas such as rare diseases, oncology, medical data classification and psychiatry. This research used a public dataset that contained physiological recordings and emotional responses. The dataset was used to train a deep neural network, and a convolutional neural network to predict an individual’s valence, arousal and dominance. The results presented indicate that the deep neural network had the highest overall mean bounded regression accuracy and the lowest computational time

Protocol for a randomized controlled trial examining multilevel prediction of response to behavioral activation and exposure-based therapy for generalized anxiety disorder.

BACKGROUND:Only 40-60% of patients with generalized anxiety disorder experience long-lasting improvement with gold standard psychosocial interventions. Identifying neurobehavioral factors that predict treatment success might provide specific targets for more individualized interventions, fostering more optimal outcomes and bringing us closer to the goal of "personalized medicine." Research suggests that reward and threat processing (approach/avoidance behavior) and cognitive control may be important for understanding anxiety and comorbid depressive disorders and may have relevance to treatment outcomes. This study was designed to determine whether approach-avoidance behaviors and associated neural responses moderate treatment response to exposure-based versus behavioral activation therapy for generalized anxiety disorder. METHODS/DESIGN:We are conducting a randomized controlled trial involving two 10-week group-based interventions: exposure-based therapy or behavioral activation therapy. These interventions focus on specific and unique aspects of threat and reward processing, respectively. Prior to and after treatment, participants are interviewed and undergo behavioral, biomarker, and neuroimaging assessments, with a focus on approach and avoidance processing and decision-making. Primary analyses will use mixed models to examine whether hypothesized approach, avoidance, and conflict arbitration behaviors and associated neural responses at baseline moderate symptom change with treatment, as assessed using the Generalized Anxiety Disorder-7 item scale. Exploratory analyses will examine additional potential treatment moderators and use data reduction and machine learning methods. DISCUSSION:This protocol provides a framework for how studies may be designed to move the field toward neuroscience-informed and personalized psychosocial treatments. The results of this trial will have implications for approach-avoidance processing in generalized anxiety disorder, relationships between levels of analysis (i.e., behavioral, neural), and predictors of behavioral therapy outcome. TRIAL REGISTRATION:The study was retrospectively registered within 21 days of first participant enrollment in accordance with FDAAA 801 with ClinicalTrials.gov, NCT02807480. Registered on June 21, 2016, before results