Healthy
Conditions
Keywords
reward processing, fMRI, AI interaction
Brief summary
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.
Detailed description
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.
Interventions
BEHAVIORALMI Chatbot Interaction
The experimental chatbot is designed to use principles of Motivational Interviewing to support participants in exploring their personal values, motivation for change, and daily behavioral goals related to pleasure, engagement, and reward-seeking. During the intervention period, participants will interact with the chatbot regularly through brief text-based conversations. The chatbot will provide empathic, non-judgmental responses, encourage reflection on current difficulties, and help participants identify small, feasible actions that may increase daily engagement and positive experiences. It will not provide diagnosis, crisis counseling, or medical treatment.
BEHAVIORALActive Control Chatbot Interaction
Participants will interact with a chatbot matched in format and frequency of use. This chatbot will provide neutral nature-related stories or general natural history content. It will be designed to maintain participant engagement while avoiding therapeutic techniques, motivational interviewing strategies, behavioral activation guidance, or personalized mental health advice. This active control condition will help control for nonspecific effects of chatbot interaction, attention, expectancy, and digital engagement.
Sponsors
University of Electronic Science and Technology of China
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
BASIC_SCIENCE
Masking
DOUBLE (Subject, Investigator)
Intervention model description
Between-subject randomized controlled trial comparing a Motivational Interviewing-based AI chatbot intervention with an active control chatbot intervention.
Eligibility
Sex/Gender
ALL
Age
18 Years to 40 Years
Healthy volunteers
Yes
Inclusion criteria
* 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
Exclusion criteria
* 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Intervention-Related Changes in Resting-State Functional Connectivity After the Intervention | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Intervention-Related Changes in Spontaneous Activity After the Intervention | Baseline before the first chatbot interaction and Week 1 after completion of the chatbot intervention. | Indices of spontaneous neural activity in regions involved in motivation, social and self-referential processing, will be extracted from resting-state fMRI scans collected at the two assessment points. These indices will include amplitude of low-frequency fluctuations or fractional amplitude of low-frequency fluctuations, as well as regional homogeneity. Baseline and post-intervention values will then be compared to characterize pre-to-post changes. |
Countries
China
Contacts
CONTACTBenjamin Becker
Outcome results
None listed