A multimodal investigation of cerebellar integrity associated with high-risk cannabis use.
Julia R SweigertKathleen PagulayanGabriella GrecoMatthew BlakeMary LarimerNatalia M KleinhansPublished in: Addiction biology (2019)
With legalization efforts across the United States, cannabis use is becoming increasingly mainstream. Various studies have documented the effects of acute and chronic cannabis use on brain structure and cognitive performance, including within the frontal executive control network, but little attention has been given to the effects on the cerebellum. Recent evidence increasingly points to the role of the cerebellum in various nonmotor networks, and the cerebellum's expression of cannabinoid receptors may pose particular vulnerabilities to the consequences of cannabis use. Using a combined approach of resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), the present study aims to assess how cannabis use relates to the cerebellum's intrinsic functional connectivity and underlying white matter structure and whether these properties are associated with craving or severity of cannabis use. Resting-state fMRI and DTI data, as well as self-reports of substance use history, were analyzed from a sample of 26 adults at risk for cannabis use disorder (CUD) and an age- and sex-matched comparison group of 25 cannabis-naïve adults (control). Results demonstrated that individuals at risk for a CUD showed key differences in cerebellar functional connectivity, with specific impacts on the dorsal attention and default mode networks. In addition, group differences in white matter were localized to the middle cerebellar peduncle (MCP), with a relationship between lower MCP diffusivity and higher levels of self-reported craving. These findings lend further support to the cerebellum's role in key cognitive networks and potential consequences for substance use disorders.
Keyphrases
- resting state
- functional connectivity
- white matter
- working memory
- magnetic resonance imaging
- multiple sclerosis
- poor prognosis
- spinal cord
- liver failure
- long non coding rna
- respiratory failure
- computed tomography
- spinal cord injury
- hepatitis b virus
- neuropathic pain
- drug induced
- intensive care unit
- deep learning
- risk assessment
- emergency department
- adverse drug
- brain injury
- quality improvement
- data analysis
- acute respiratory distress syndrome
- aortic dissection
- subarachnoid hemorrhage