Effects of 12 weeks High Intensity Interval Training on brain plasticity in cannabis use disorder
Poster No:
479
Submission Type:
Abstract Submission
Authors:
Suzan Maleki1, Karyn Richardson1, Sam Hughes1, Edouard Kayayan1, Karen Caeyenberghs2, Rebecca Segrave1, Chao Suo3, Murat Yücel4
Institutions:
1Monash University, Melbourne, Victoria, 2Deakin University, Burwood, Victoria, 3Monash University, Melbourne, vic, 4QIMR Berghofer Medical Research Institute, Brisbane, Queensland
First Author:
Co-Author(s):
Introduction:
Each year, over 280 million people use cannabis, marking a 26% increase in the past decade and solidifying its position as the most widely consumed illicit substance globally (UNODC, 2024). CUD is associated with poor mental health, anxiety, cognitive and social impairments (Bloomfield et al., 2019). Neuroimaging studies associate CUD with structural brain changes including altered cortical thickness and white matter (WM) abnormalities in regions critical for the healthy brain and cognitive function (Lorenzetti et al., 2019). As maintaining abstinence remains challenging, developing non-abstinence-based alternative approaches are essential to mitigate these adverse effects. A wealth of evidence highlights that physical exercise, particularly aerobic exercise, can alter the structure of brain by promoting neuroplasticity (Alkadhi, 2018).
Methods:
Participants and intervention
In this clinical trial, we randomised 59 individuals with moderate to severe CUD to either a High Intensity Interval Training (HIIT) aerobic exercise or Strength and Resistance (S&R) training group. Both interventions ran for 45 minutes, 3 times per week for 12-week. The HIIT group aimed to achieve 80% of their maximum heart rate (HR Max), while the S&R group aimed to maintain their HR Max ≤ 70%.
MRI protocol
Anatomical T1 and diffusion weighted MRI images were acquired at baseline and end of the intervention at Monash Biomedical Imaging, Monash University (T1: repetition time (TR) = 2300ms, echo time (TE) = 2.07ms, 192 slices, 1mm3 isotropic, field of view = 256mm by 256mm; Diffusion sequence: 60-diffusion-encoding gradients, TR = 8800ms, TE = 110ms, voxel size = 2.5 mm3, 67 volumes (60 volumes with b=3000 s/ mm2, and 7 interleaved b0 volumes)).
Analysis and statistics
Diffusion images were denoised, corrected for head motion, eddy current distortions, and bias filed inhomogeneities using MRtrix3 and FSL software (Andersson et al., 2017; Tournier et al., 2019). Individual fractional anisotropy (FA) maps were generated, and then longitudinal changes were computed for both groups. A 5000 non-parametric permutation test was performed for each contrast to determine group differences (p<0.05). T1 images were processed using FreeSurfer Longitudinal pipeline in which PC1 (percent of change with respect to baseline) was computed for thickness changes of each hemisphere, smoothed at 10mm (Reuter et al., 2012). Longitudinal GLM was performed at significance of p<0.01). Age and gender were included as covariates for all analyses.
The group differences of the total hours over the 12 weeks of intervention spent above 80% HR Max and above the lactate threshold were computed. To evaluate changes in cannabis use, the amount consumed during the four weeks preceding both baseline and endpoint assessments were calculated using the Time Follow Back procedure.
In this clinical trial, we randomised 59 individuals with moderate to severe CUD to either a High Intensity Interval Training (HIIT) aerobic exercise or Strength and Resistance (S&R) training group. Both interventions ran for 45 minutes, 3 times per week for 12-week. The HIIT group aimed to achieve 80% of their maximum heart rate (HR Max), while the S&R group aimed to maintain their HR Max ≤ 70%.
MRI protocol
Anatomical T1 and diffusion weighted MRI images were acquired at baseline and end of the intervention at Monash Biomedical Imaging, Monash University (T1: repetition time (TR) = 2300ms, echo time (TE) = 2.07ms, 192 slices, 1mm3 isotropic, field of view = 256mm by 256mm; Diffusion sequence: 60-diffusion-encoding gradients, TR = 8800ms, TE = 110ms, voxel size = 2.5 mm3, 67 volumes (60 volumes with b=3000 s/ mm2, and 7 interleaved b0 volumes)).
Analysis and statistics
Diffusion images were denoised, corrected for head motion, eddy current distortions, and bias filed inhomogeneities using MRtrix3 and FSL software (Andersson et al., 2017; Tournier et al., 2019). Individual fractional anisotropy (FA) maps were generated, and then longitudinal changes were computed for both groups. A 5000 non-parametric permutation test was performed for each contrast to determine group differences (p<0.05). T1 images were processed using FreeSurfer Longitudinal pipeline in which PC1 (percent of change with respect to baseline) was computed for thickness changes of each hemisphere, smoothed at 10mm (Reuter et al., 2012). Longitudinal GLM was performed at significance of p<0.01). Age and gender were included as covariates for all analyses.
The group differences of the total hours over the 12 weeks of intervention spent above 80% HR Max and above the lactate threshold were computed. To evaluate changes in cannabis use, the amount consumed during the four weeks preceding both baseline and endpoint assessments were calculated using the Time Follow Back procedure.
Results:
At the end of the trail, the HIIT group showed increased FA in left uncinate fasciculate tract compared to S&R (p=0.012, corrected for multiple comparison). cortical thickness PC1 significantly increased following HIIT within right Pars-opercularis region (p=0.016). Participants in the HIIT group spent significantly more time exercising above 80% HR Max (p<0.0001) and their lactate threshold (p<0.0001), compared to S&R. Both exercise-induced brain improvements (WM integrity and morphometry) were significantly associated with each other (p=0.12), also correlated with the total time spent above 80% HR Max (p=0.027 and p=0.001), and above the lactate release threshold (p=0.061, p=0.004) respectively.
Conclusions:
This is the first longitudinal neuroimaging study using a combination of physical exercise interventions in CUD cohort. These novel findings indicate that 12 weeks HIIT aerobic without requiring abstinence, enhances brain connectivity in areas critical for emotional regulation and decision making and improves morphometry in regions involved in response inhibition control, that are often impaired in CUD.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Learning and Memory:
Neural Plasticity and Recovery of Function 2
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
White Matter Anatomy, Fiber Pathways and Connectivity
Keywords:
Addictions
Experimental Design
Physical Therapy
Plasticity
Psychiatric Disorders
Social Interactions
STRUCTURAL MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Was this research conducted in the United States?
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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Provide references using APA citation style.
Andersson, J. L., Graham, M. S., Drobnjak, I., Zhang, H., Filippini, N., & Bastiani, M. (2017). Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. Neuroimage, 152, 450-466.
Bloomfield, M. A., Hindocha, C., Green, S. F., Wall, M. B., Lees, R., Petrilli, K., Costello, H., Ogunbiyi, M. O., Bossong, M. G., & Freeman, T. P. (2019). The neuropsychopharmacology of cannabis: A review of human imaging studies. Pharmacology & therapeutics, 195, 132-161.
Lorenzetti, V., Chye, Y., Silva, P., Solowij, N., & Roberts, C. A. (2019). Does regular cannabis use affect neuroanatomy? An updated systematic review and meta-analysis of structural neuroimaging studies. Eur Arch Psychiatry Clin Neurosci, 269(1), 59-71. https://doi.org/10.1007/s00406-019-00979-1
Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B. (2012). Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage, 61(4), 1402-1418.
Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage, 202, 116137.
UNODC. (2024). United Nations Office on Drugs and Crime: World Drug Report 2024.