SCH: Multidimensional Data Science Approach: Measuring and Characterizing Craving and Affective Profiles in Substance Use Disorders

SCH：多维数据科学方法：测量和表征药物使用障碍中的渴望和情感特征

基本信息

批准号：
10816923
负责人：
Nidal Moukaddam
金额：
$ 30万
依托单位：
RICE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10816923
关键词：
Affect Affective Alcohols Behavioral Brain Cellular Phone Characteristics Clinical Clinical assessments Cocaine Cognitive Coupled Cues Data Data Science Databases Detection Development Dimensions Emotional Evidence based intervention Functional Magnetic Resonance Imaging Impairment Intervention Knowledge Label Laboratories Life Measurement Measures Methamphetamine Methods Modality Modeling Monitor Neurons Occupational Opioid Patients Pattern Persons Pharmaceutical Preparations Phenotype Physiological Pilot Projects Psyche structure Public Health Relapse Research Running Source Stimulus Substance Use Disorder Testing Withdrawal Symptom addiction behavior measurement behavioral response biobehavior craving design digital insight intervention effect machine learning model mindfulness multidimensional data multidisciplinary multimodal data multimodality novel novel strategies pharmacologic predictive modeling prevent response sensor social substance use substance use treatment wearable sensor technology

项目摘要

Substance use, e.g. of opioid, cocaine, methamphetamine, and alcohol, has skyrocketed in the US, and has been declared a public health crisis. Substance use disorder (SUD) leads to a significant impairment in physical, mental, social, and occupational functioning, coupled with an inability to reduce or control use and the development of tolerance and withdrawal symptoms. Like any other addiction, the precursor to substance use episodes are cravings moments, and hence, they will be opportune moments to intervene if it is possible to detect them. We have the potential to characterize patients’ conditions by combining multimodal sensor measurement in the laboratory like fMRI and clinical assessment, and field measurement via wearable and ubiquitous sensing. However, there are still many challenges to be solved to leverage these multimodal data to extract clinically useful and actionable insights. The proposed research will be executed via two aims: Aim 1) Develop a multidimensional database from patients with SUD, via laboratory brain and physiological measurements and field physiological and behavioral measurements. We will use fMRI to measure ground truth of craving responses, while recording physiological signature during craving inducing or emotional cues. We will also collect physiological and behavioral responses using mobile phones and wearable sensors in the field. We will analyze the relationships among brain-biobehavioral responses and craving and affective profiles in laboratory and field settings. Aim 2) Develop robust multimodal data science methods for craving and affective profile detection and characterization: We will design 1) new approaches that positively transfer knowledge from multiple modalities to fewer modalities: we will combine data from different sensor sources while guaranteeing the model can be run with fewer sensors during testing, 2) craving and affect profile detection using regular/irregular characteristics and sparse weak labels, 3) craving and affect detection models with unlabeled data and a limited number of noisy labels, and 4) personalized craving and affect detection models with unlabeled data. We will evaluate our proposed data science approach using the data collected previously and in non-patient and patient pilot studies.

阿片类药物、可卡因、甲基苯丙胺和酒精等物质的使用在美国急剧增加已被宣布为公共卫生危机（SUD），导致严重损害。身体、心理、社交和职业功能，并且无法减少或控制使用和任何其他成瘾一样，耐受性和戒断症状的发展。物质使用事件是渴望时刻，从今以后，它们将是干预的合适时机如果能够检测到它们，我们就有可能通过结合实验室中的多模态传感器测量（例如功能磁共振成像和临床评估）以及通过可穿戴和无处不在的传感进行现场测量来表征患者的状况。利用这些多模式数据来提取临床上有用且可操作的见解。拟议的研究将通过两个目标来执行：目标 1) 通过实验室大脑和生理测量以及现场生理和行为测量，开发 SUD 患者的多维数据库，我们将使用功能磁共振成像来测量渴望反应的基本事实，同时记录渴望诱发或情绪线索期间的生理特征。我们还将在现场使用手机和可穿戴传感器收集生理和行为反应，我们将在实验室和现场环境中分析大脑生物行为反应与渴望和情感特征之间的关系。目标 2) 开发强大的多模式数据科学方法来检测渴望和情感概况和表征：我们将设计 1) 新方法，积极地将知识从多种模态转移到更少的模态：我们将结合来自不同传感器源的数据，同时保证模型在测试过程中可以使用更少的传感器运行，2) 渴望并影响轮廓检测规则/不规则特征和稀疏弱标签，3）使用未标记数据和有限数量的噪声标签的渴望和影响检测模型，以及4）使用未标记数据的个性化渴望和影响检测模型我们将评估我们提出的数据科学。方法使用之前收集的数据以及在非患者和患者试点研究中收集的数据。