Back

Personalized Virtual Reality Future Selves Elicit Introspective Brain Activation in Early Substance Use Disorder Recovery

Oberlin, B. G.; Dzemidzic, M.; Shen, Y. I.; Nelson, A. J.

2026-01-24 addiction medicine
10.64898/2026.01.23.26344667 medRxiv
Show abstract

Substance use disorder (SUD) recovery typically requires transformative change and prioritizing long-term healthy goals. Unfortunately, successful recovery is threatened by relapse rates that often exceed 50% in the first year. We previously reported on an experiential virtual reality (VR) SUD recovery intervention using personalized future self-avatars that produced emotional engagement and positive behavioral change, ie, stronger connection with the future self and future rewards and reduced craving. Here, we used fMRI to identify brain engagement to a future self experience with divergent futures. Twenty adults (14 male, 33 years old) in early SUD recovery (<1 year) interacted with age-progressed versions of themselves in two different VR future realities: an SUD Future Self and a Recovery Future Self. Vivid lifelike visual and audio animation was augmented with a personalized narrative concerning future drug use and recovery. MRI immediately followed. Participants viewed videos of their future selves in the virtual environment and were directed to contemplate what they were seeing. Viewing and contemplating the future selves elicited activation in midline default mode regions (posterior cingulate and ventromedial prefrontal cortices), visual regions including the occipital and fusiform face areas, and left middle frontal gyrus. The Recovery Future Self produced significant left occipital face area activation compared with the SUD Future Self. Midline default mode activation correlated with VR-induced increases in delayed reward preference, and also with greater trait perseverance. Using digital selves as therapeutic agents reveals an entirely novel set of possible interventions and opens exciting new frontiers in behavior change methodology. Future studies targeting decision-making and future behavior could be informed by evaluating increased midline default mode engagement, with uniquely self-focused mechanisms signaled by executive network and face area coactivation. New hope for treatment-resistant mental health conditions is offered by the nearly limitless range of therapeutic experiences enabled by immersive digital therapeutics. Plain Language SummaryHigh relapse rates in early recovery remains a serious challenge. To promote better outcomes, our team recently developed a virtual reality experience where people interacted with future versions of themselves. We used magnetic resonance imaging (MRI) to understand how the brain activated to this experience, and what brain responses were linked to positive outcomes. We worked with 20 adults in early recovery. Each person used virtual reality to interact with two different future selves: one who had returned to substance use, and one who had stayed in recovery. These digital future selves looked and sounded like the participants and were paired with a personalized story about future drug use and recovery. Right after the virtual reality session, participants brains were scanned while they watched videos of these future selves and were asked to think about what they were seeing. When people viewed and reflected on their future selves, brain areas involved in self-reflection and imagining the future became more active, along with regions that process faces. The future selves triggered brain activation in "self-focused" brain networks and in face-processing regions. Activity in key "self-focused" brain regions was linked to choosing larger, delayed rewards over smaller, immediate ones, and to lower impulsivity. These findings suggest that lifelike digital versions of peoples future selves engage brain systems that support thinking ahead, persistence, and valuing long-term outcomes. This creates a promising new avenue for immersive digital therapeutic experiences to encourage lasting behavior change in early recovery from substance use disorder.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Frontiers in Psychiatry
83 papers in training set
Top 0.1%
33.7%
2
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
62 papers in training set
Top 0.1%
8.6%
3
JAMA Network Open
127 papers in training set
Top 0.8%
3.9%
4
Drug and Alcohol Dependence
37 papers in training set
Top 0.3%
3.7%
5
Neuropsychopharmacology
134 papers in training set
Top 1.0%
3.3%
50% of probability mass above
6
Scientific Reports
3102 papers in training set
Top 47%
2.4%
7
Brain Stimulation
112 papers in training set
Top 0.7%
2.1%
8
Human Brain Mapping
295 papers in training set
Top 2%
2.1%
9
British Journal of Pharmacology
34 papers in training set
Top 0.1%
2.1%
10
Nature Mental Health
18 papers in training set
Top 0.1%
2.1%
11
Brain Sciences
52 papers in training set
Top 0.5%
1.9%
12
Translational Psychiatry
219 papers in training set
Top 2%
1.9%
13
American Journal of Psychiatry
20 papers in training set
Top 0.1%
1.7%
14
Imaging Neuroscience
242 papers in training set
Top 2%
1.7%
15
Journal of Psychopharmacology
14 papers in training set
Top 0.2%
1.7%
16
Biological Psychiatry
119 papers in training set
Top 2%
1.7%
17
Progress in Neuro-Psychopharmacology and Biological Psychiatry
36 papers in training set
Top 0.5%
1.5%
18
PLOS Digital Health
91 papers in training set
Top 2%
1.5%
19
npj Digital Medicine
97 papers in training set
Top 2%
1.5%
20
Addiction Biology
47 papers in training set
Top 0.6%
1.3%
21
Molecular Psychiatry
242 papers in training set
Top 3%
1.0%
22
PLOS ONE
4510 papers in training set
Top 62%
1.0%
23
Cells
232 papers in training set
Top 5%
0.9%
24
Biological Psychiatry Global Open Science
54 papers in training set
Top 1%
0.9%
25
NeuroImage
813 papers in training set
Top 5%
0.8%
26
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 44%
0.8%
27
The Journal of Neuroscience
928 papers in training set
Top 8%
0.7%
28
Journal of Affective Disorders
81 papers in training set
Top 2%
0.7%
29
Computational Psychiatry
12 papers in training set
Top 0.1%
0.7%
30
International Journal of Drug Policy
11 papers in training set
Top 0.4%
0.7%