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Mapping White Matter Microstructure Before and After Adolescent Cannabis Use, Using FBA and TBSS

Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM

Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM

Poster No:

1293

Submission Type:

Abstract Submission

Authors:

Emily Robinson¹, Arush Honnedevasthana Arun¹, Govinda Poudel²^,3, Adam Clemente¹, Herve Lemaitre⁴^,5, Gunter Schumann⁶^,7, John Gleeson¹, H Valerie Curran⁸, Valentina Lorenzetti¹

Institutions:

¹Healthy Brain and Mind Research Centre, Australian Catholic University, Melbourne, Victoria, Australia, ²Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia, ³Braincast Neurotechnologies, Melbourne, Victoria, Australia, ⁴NeuroSpin, CEA, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France, ⁵Institut des Maladies Neurodégénératives, Université de Bordeaux, Bordeaux, France, ⁶Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ⁷Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin, Berlin, Germany, ⁸Clinical Psychopharmacology Unit, University College London, London, United Kingdom

First Author:

Emily Robinson
Healthy Brain and Mind Research Centre, Australian Catholic University
Melbourne, Victoria, Australia

Co-Author(s):

Arush Honnedevasthana Arun
Healthy Brain and Mind Research Centre, Australian Catholic University
Melbourne, Victoria, Australia

Govinda Poudel
Mary MacKillop Institute for Health Research, Australian Catholic University|Braincast Neurotechnologies
Melbourne, Victoria, Australia|Melbourne, Victoria, Australia

Adam Clemente
Healthy Brain and Mind Research Centre, Australian Catholic University
Melbourne, Victoria, Australia

Herve Lemaitre
NeuroSpin, CEA, Université Paris-Saclay|Institut des Maladies Neurodégénératives, Université de Bordeaux
F-91191, Gif-sur-Yvette, France|Bordeaux, France

Gunter Schumann
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin
Shanghai, China|Berlin, Germany

John Gleeson
Healthy Brain and Mind Research Centre, Australian Catholic University
Melbourne, Victoria, Australia

H Valerie Curran
Clinical Psychopharmacology Unit, University College London
London, United Kingdom

Valentina Lorenzetti
Healthy Brain and Mind Research Centre, Australian Catholic University
Melbourne, Victoria, Australia

Introduction:

Cannabis exposure may change the endocannabinoid system, which is involved in adolescent neuro-maturational processes including synaptic pruning and white matter development (Lubman et al., 2015). Thus, adolescent cannabis use may alter white matter microstructure. Emerging evidence comes from cross-sectional studies using diffusion tensor imaging (DTI) analysis (Robinson et al., 2023). This evidence shows primarily white-matter fractional anisotropy (FA) differences in the corpus callosum and superior longitudinal fasciculus (SLF) in adolescent cannabis users compared to non-using controls. However, it is unclear if these differences predate cannabis use onset or are evident when measured by more biologically specific techniques that overcome limitations of DTI (e.g., Fixel Based Analysis [FBA]) (Raffelt et al., 2015, 2017). Accordingly, white-matter microstructural differences predating and following adolescent cannabis use were examined in this study for the first time, using both DTI and FBA techniques.

Methods:

We selected 27 cannabis users (mean use 7.8 days past/month) and 28 controls at age 19, from the longitudinal IMAGEN consortium dataset, a large sample of community adolescents (Schumann et al., 2010), who were also cannabis naïve at age 14. Groups were matched at age 14 on sex, age, site, general ability index, pubertal development, impulsivity, and alcohol/tobacco use. Diffusion-weighted images were acquired with 3T MRI-scanners from all subjects (32 diffusion encoding directions, with b-value=1300 s/mm2). Whole brain voxelwise statistical analyses were carried out using Tract Based Spatial Statistics (TBSS) (Smith et al., 2006). Differences between cannabis users and controls were investigated for (i) average FA across time, and (ii) FA change over time. For the FBA, the effects of (i) group-by-time, and (ii) group, on Fibre Density (FD), Fibre Cross-Section (FC), and Fibre Density Cross-Section (FDC) were also examined between cannabis users and controls, with both whole brain, and region of interest (ROI) analyses, which were carried out within the SLF and CC.

Results:

There were no significant group differences, before or after cannabis use onset, using whole brain TBSS and whole brain FBA. ROI based FBA showed significant group-by-time effects on FC in the SLF and the CC (Fig. 1a & 1b), which post-hoc tests indicated were driven by baseline differences. However, these results did not survive accounting for covariates (e.g., alcohol/nicotine dependence, and sensation seeking). Prospective cannabis users, compared to controls aged 14, also showed higher FDC and FD in the SLF (Fig. 1c & 1d), the latter only significant after accounting for covariates, and significantly correlated with alcohol use days/past month at age 19.

Conclusions:

FBA but not TBSS may be sensitive to the subtle effect of cannabis on the adolescent brain. We detected subtle white matter differences predating cannabis use onset using FBA, which may represent a neuromaturational vulnerability for future substance use. Replication studies are required to confirm the impact of varying levels of cannabis use in vulnerable and diverse samples of adolescents who experience comorbid substance use, in larger samples, and with multiple higher b-values suited to FBA analysis (Tournier et al., 2004). In doing so, we may capture subtle cannabis-related changes during neurodevelopment, particularly in youth most vulnerable to potential cannabis-related harms.

Lifespan Development:

Early life, Adolescence, Aging ²

Modeling and Analysis Methods:

Diffusion MRI Modeling and Analysis ¹

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

White Matter Anatomy, Fiber Pathways and Connectivity

Novel Imaging Acquisition Methods:

Diffusion MRI

Keywords:

Addictions

White Matter

WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC

Other - Adolescence; Cannabis Use; Diffusion MRI; IMAGEN Consortium

^1|2Indicates the priority used for review

Abstract Information

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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

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Please indicate which methods were used in your research:

Diffusion MRI

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Provide references using APA citation style.

1. Lubman, D. I., Cheetham, A., & Yucel, M. (2015). Cannabis and adolescent brain development. Pharmacology & Therapeutics, 148, 1-16. https://doi.org/10.1016/j.pharmthera.2014.11.009
2. Raffelt, D. A., Smith, R. E., Ridgway, G. R., Tournier, J. D., Vaughan, D. N., Rose, S., Henderson, R., & Connelly, A. (2015). Connectivity-based fixel enhancement: Wholebrain statistical analysis of diffusion MRI measures in the presence of crossing fibres. NeuroImage, 117, 40-55. https://doi.org/10.1016/j.neuroimage.2015.05.039
3. Raffelt, D. A., Tournier, J. D., Smith, R. E., Vaughan, D. N., Jackson, G., Ridgway, G. R., & Connelly, A. (2017). Investigating white matter fibre density and morphology using fixel-based analysis. NeuroImage, 144, 58-73. https://doi.org/https://doi.org/10.1016/j.neuroimage.2016.09.029
4. Robinson, E. A., Gleeson, J., Arun, A. H., Clemente, A., Gaillard, A., Rossetti, M. G., Brambilla, P., Bellani, M., Crisanti, C., Curran, H. V., & Lorenzetti, V. (2023). Measuring white matter microstructure in 1,457 cannabis users and 1,441 controls: A systematic review of diffusion-weighted MRI studies. Frontiers in Neuroimaging, 2. https://doi.org/10.3389/fnimg.2023.1129587
5. Schumann, G., Loth, E., Banaschewski, T., Barbot, A., Barker, G., Büchel, C., Conrod, P. J., Dalley, J. W., Flor, H., Gallinat, J., Garavan, H., Heinz, A., Itterman, B., Lathrop, M., Mallik, C., Mann, K., Martinot, J. L., Paus, T., Poline, J. B., Robbins, T. W., Rietschel, M., Reed, L., Smolka, M., Spanagel, R., Speiser, C., Stephens, D. N., Str.hle, A., Struve, M., & the IMAGEN Consortium. (2010). The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Molecular Psychiatry, 15(12), 1128-1139. https://doi.org/10.1038/mp.2010.4
6. Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E., Ciccarelli, O., Cader, M. Z., Matthews, P. M., & Behrens, T. E. J. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage, 31(4), 1487-1505. https://doi.org/https://doi.org/10.1016/j.neuroimage.2006.02.024
7. Tournier, J. D., Calamante, F., Gadian, D. G., & Connelly, A. (2004b). Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage, 23(3), 1176-1185. https://doi.org/https://doi.org/10.1016/j.neuroimage.2004.07.037

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