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临床试验/NCT07665216
NCT07665216招募中不适用

Effects of a Chatbot-Based Intervention on Resting-State Functional Connectivity and Spontaneous Neural Activity in Individuals With Anhedonia

University of Electronic Science and Technology of China1 个研究点 分布在 1 个国家目标入组 80 人开始时间: 2026年6月20日最近更新:
适应症
干预措施

试验速览

阶段
不适用
状态
招募中
发起方
入组人数
80
试验地点
1
主要终点
Intervention-Related Changes in Resting-State Functional Connectivity After the Intervention

研究概览

简要总结

The main aim of the present study is to investigate the effects of a Motivational Interviewing-based artificial intelligence chatbot on resting-state brain function in college students with elevated anhedonia and depressive symptoms. The study will use a randomized active-control intervention design with pre- and post-intervention resting-state functional magnetic resonance imaging assessments.

详细描述

Anhedonia represents a core characteristic of depression and is characterized by reduced experience of pleasure. It is closely related to decreased motivation, altered reward processing, and alterations in intrinsic brain network function. Resting-state fMRI provides a way to examine intrinsic brain activity and functional connectivity without requiring participants to perform a specific task. This is important because changes associated with anhedonia may not only appear during reward-related tasks, but may also be reflected in spontaneous brain network organization.

研究设计

研究类型
Interventional
分配方式
Randomized
干预模型
Parallel
主要目的
Basic Science
盲法
Double (Participant, Investigator)

入排标准

年龄范围
18 Years 至 40 Years(Adult)
性别
All
接受健康志愿者
是

入选标准

  • •18-40 years
  • •Right-handed
  • •Normal or corrected normal visual acuity
  • •Participants must show elevated anhedonia and depressive symptoms at screening, defined as a total score of 22 or higher on the Snaith-Hamilton Pleasure Scale and a score of 14 or higher on the Beck Depression Inventory

排除标准

  • •History of major central nervous system disorders, such as epilepsy, traumatic brain injury, stroke, or brain tumors.
  • •History of severe mental illness, including schizophrenia spectrum disorders, bipolar disorder, or other psychotic disorders.
  • •History of substance or alcohol use disorder or substance or alcohol misuse within the past 12 months that may affect study participation or outcome assessment.
  • •Individuals currently at high risk of suicide, severe self-harm, or experiencing an acute psychiatric crisis.
  • •Individuals who are currently using psychiatric medications or have undergone psychotherapy within the past 4 weeks that may significantly affect mood, motivation, or reward processing.
  • •Severe vision or hearing impairments that cannot be corrected and would interfere with task performance.
  • •Contraindications to MRI scanning, including metallic implants, pacemakers, severe claustrophobia, or other conditions incompatible with MRI.
  • •Pregnancy or breastfeeding.

研究组 & 干预措施

Motivational Interviewing-based AI chatbot group

Experimental

Motivational Interviewing-based AI chatbot intervention

干预措施: MI Chatbot Interaction (Behavioral)

Active control chatbot group

Active Comparator

Active control nature-story chatbot intervention

干预措施: Active Control Chatbot Interaction (Behavioral)

结局指标

主要结局

Intervention-Related Changes in Resting-State Functional Connectivity After the Intervention

时间窗: Baseline before the first chatbot interaction and Week 1 after completion of the chatbot intervention.

Resting-state fMRI will be used to examine functional connectivity among predefined regions involved in reward, social, and self-referential processing. Connectivity will be estimated from Pearson correlations between regional BOLD time series and then converted using the Fisher z transformation. Changes from baseline to post-intervention will be reported as dimensionless Fisher z values.

次要结局

  • Intervention-Related Changes in Spontaneous Activity After the Intervention(Baseline before the first chatbot interaction and Week 1 after completion of the chatbot intervention.)

研究者

发起方
University of Electronic Science and Technology of China
申办方类型
Other
责任方
Principal Investigator
主要研究者

Benjamin Becker

Professor

University of Electronic Science and Technology of China

研究点 (1)

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