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Investigating Links Between Prenatal Cannabis Exposure and Brain Development Using Magnetic Resonance Imaging
Priscila Dib Gonçalves1, James O Woodruff2, Maria Olivia Pozzolo Pedro3
1Department of Psychiatry, Columbia University, New York, New York.
Biological Psychiatry Global Open Science
|December 1, 2025
Summary
Prenatal cannabis exposure (PCE) may affect brain development, but current research shows inconsistent magnetic resonance imaging (MRI) findings. More rigorous studies are needed to understand PCE
Area of Science:
- Neuroscience
- Developmental Psychology
- Public Health
Background:
- Increasing cannabis use during pregnancy necessitates updated research on prenatal cannabis exposure (PCE) impacts.
- Existing reviews are outdated and lack insights from recent large longitudinal datasets.
- Understanding PCE's effects on brain development is crucial for public health guidance.
Purpose of the Study:
- To provide an updated narrative review of associations between PCE and MRI-based brain outcomes.
- To examine brain development from in utero through adolescence following PCE.
- To identify gaps and inconsistencies in current research methodologies.
Main Methods:
- Systematic literature search for studies published after 2019.
- Inclusion of studies utilizing structural MRI, diffusion-weighted imaging, and fMRI (resting-state and task-based).
- Analysis of 9 studies with varying focus on in utero, infant, and adolescent brain outcomes.
Main Results:
- Prenatal cannabis exposure (PCE) linked to brain differences in frontal, parietal, and temporal regions across developmental stages.
- Multiple MRI modalities showed associations, but no consistent trend in PCE's impact was detected.
- Limited studies integrated MRI findings with behavioral outcomes (3 out of 9).
Conclusions:
- Current literature presents inconsistent MRI findings regarding PCE's impact on brain development.
- Significant methodological variations exist in assessing exposure and controlling for confounding factors.
- Future research requires robust designs capturing exposure details and confounding variables for meta-analysis.

