Research on Cannabis Use and Brain Responses (RiDE-2)
This study, called RiDE-2, aims to understand how people's mood, behavior, and brains react to recreational drugs like cannabis. Researchers want to see why some individuals might respond differently to the same drug. You might receive either a placebo (a capsule with no active drug) or a capsule containing 7.5 mg of THC (the main active ingredient in cannabis). The study will track your substance use, symptoms related to cannabis or substance use disorder (SUD), and brain activity using fMRI (a type of brain scan) over two years. They are looking for 144 participants aged 18-21 who have used cannabis at least 10 times in their life but not daily.
- Study design
- This is an interventional study with 144 planned participants. It involves receiving either a placebo or THC capsule.
- What's involved
- You would have a baseline screening, drug administration visits, and follow-up appointments every 6 months for two years. You would also have yearly symptom assessments and fMRI brain scans.
- Compensation
- Not stated in the trial record.
- Follow-up
- Participants will be followed for two years after their initial drug administration.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Research Investigating Drug Effects-2 (RiDE-2)
At a glance
Conditions
Where it's being run
1 sites across 1 statesWho to contact
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What this trial measures
- Recent Substance UseBaseline screening, baseline drug administration, and every 6 months over two years of follow-up
Participants will complete the 6-month Timeline Follow-Back (TLFB) and Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Inventory (DFAQ-CU) to assess recent substance use. This measure will capture the frequency of substance use during the past 6 months, with greater values indicating more frequent substance use.
- CUD/SUD SymptomsBaseline screening and yearly over the two-year follow-up.
Participants will complete the SCID CUD diagnosis and symptom assessment at baseline screening and yearly follow-ups to evaluate the presence and severity of cannabis use disorder (CUD) and other substance use disorder (SUD) symptoms.
- Neural Reward Anticipation - fMRI response to reward anticipation (MID)First and second laboratory visits, around 90 minutes to 2 hours after drug administration.
Participants will complete the Monetary Incentive Delay (MID) task during fMRI scanning to assess neural activation related to reward anticipation. Greater activation in reward-related brain regions (e.g., striatum, prefrontal cortex) indicates greater neural sensitivity to anticipated rewards and the impact of THC on reward processing.
- Computationally-Derived Behavioral Reward Learning Rate - Learning rate (alpha parameter)First and second laboratory visits, around 90 minutes to 2 hours after drug administration.
Participants will complete the Bandit task during fMRI scanning. A behavioral measure of learning rate (alpha parameter) will be derived from computational models based on the Rescorla-Wagner (RW) model, with higher learning rates indicating greater learning and adaptation to reward contingencies.
- Computationally-Derived Behavioral Reward Sensitivity - Reward sensitivity (inverse temperature parameter)First and second laboratory visits, around 90 minutes to 2 hours after drug administration.
Participants will complete the Bandit task during fMRI scanning, and a behavioral measure of reward sensitivity will be derived from the Rescorla-Wagner (RW) model. Greater values indicate higher sensitivity to rewards and a stronger preference for rewarding outcomes.
- Neural Encoding of Reward Prediction Errors - fMRI response to reward prediction errors (Bandit Task)First and second laboratory visits, around 90 minutes to 2 hours after drug administration.
Participants will complete the Bandit task during fMRI scanning. fMRI analysis will focus on trial-wise neural encoding of prediction errors, with greater activation indicating more salient reward learning signals in response to prediction errors, reflecting how the brain processes unexpected outcomes during reward learning.