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Remote computational phenotyping of behavioral and affective dynamics in major depression

重度抑郁症行为和情感动态的远程计算表型

基本信息

批准号：
10248549
负责人：
Robb Brooks Rutledge
金额：
$ 81.54万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10248549
关键词：
Affect Affective Amygdaloid structure Anhedonia Anterior Behavior Behavioral Behavioral Assay Behavioral Model Biological Assay Brain Cellular Phone Clinical Communities Complement Computer Models Corpus striatum structure Data Data Collection Decision Making Dimensions Disease Environment Functional Magnetic Resonance Imaging Heterogeneity Individual Intervention Learning Life Link MRI Scans Magnetic Resonance Imaging Major Depressive Disorder Maps Measures Mental Depression Mental disorders Modeling Moods Neural Network Simulation Patient Self-Report Patients Phenotype Policies Positive Valence Prevalence Process Psychological reinforcement Reporting Research Domain Criteria Rest Rewards Risk Sampling Statistical Models Symptoms System Testing Time base behavioral phenotyping common symptom cost depressive symptoms effective therapy experience improved long short term memory neural circuit neuroimaging recruit recurrent neural network simulation sleep quality success theories

项目摘要

PROJECT SUMMARY / ABSTRACT Major depression is a highly debilitating disorder affecting over 300 million people worldwide. Treatment assignment can involve a lengthy trial-and-error process complicated by symptom heterogeneity. The Research Domain Criteria (RDoC) matrix provides a framework for investigating psychiatric disorders that integrates across multiple levels of analysis. Depressive symptoms are closely linked to the RDoC Positive Valence Systems (PVS) domain, but it is unknown how PVS constructs relate to common depressive symptoms including low mood, anhedonia, and apathy. Computational probes of behavioral and affective dynamics show great promise as a means of ‘computationally phenotyping’ individuals and providing a way to validate PVS constructs in relation to symptom heterogeneity. The ubiquity of smartphones makes them an ideal platform for remote testing. We propose to collect longitudinal data using smartphones for three ‘gamified’ tasks that measure risky decision making, probabilistic reinforcement learning, and reward-effort trade-offs and concurrent fluctuations in affective state. We will establish the reliability of remotely collected computational assays of behavioral and affective dynamics for understanding heterogeneity in depressive symptoms. We will first test a community sample (n=200) both in the lab and remotely by smartphone to verify that behavioral and affective computational parameters have the same relationship to depressive symptoms (low mood, anhedonia, and apathy) in both environments (Aim 1). We will then recruit a large sample of patients with moderate depressive symptoms (n=400) and test them remotely using smartphones for up to 12 months (Aim 2). We will test whether behavioral and affective computational parameters are related to changes in depressive symptoms over time. We will also use data-driven recurrent neural network approaches to identify additional features of our data related to depressive symptoms. Finally, we will collect MRI scans and in-lab data in a subsample of patients (n=200) from Aim 2 and ask whether reward sensitivity and reward prediction error, features of all three tasks, map onto consistent neural circuitry and depressive symptoms (Aim 3). We will test for a mapping between depression subtypes defined by brain network connectivity, behavioral and affective computational parameters, and depressive symptoms. Using computational models, we can bridge between levels of circuits, behavior, and self-report, and test for a mapping onto heterogeneity in symptoms, enhancing our understanding of RDoC constructs and paving the way for more effective and timely interventions to treat depression.

项目摘要/摘要