The Combined Effects of Nicotine and Cannabis on Cortical Thickness Estimates in Adolescents and Emerging Adults

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Abstract

Early life substance use, including cannabis and nicotine, may result in deleterious effects on the maturation of brain tissue and gray matter cortical development. The current study employed linear regression models to investigate the main and interactive effects of past-year nicotine and cannabis use on gray matter cortical thickness estimates in 11 bilateral independent frontal cortical regions in 223 16–22-year-olds. As the frontal cortex develops throughout late adolescence and young adulthood, this period becomes crucial for studying the impact of substance use on brain structure. The distinct effects of nicotine and cannabis use status on cortical thickness were found bilaterally, as cannabis and nicotine users both had thinner cortices than non-users. Interactions between nicotine and cannabis were also observed, in which cannabis use was associated with thicker cortices for those with a history of nicotine and tobacco product (NTP) use in three left frontal regions. This study sheds light on the intricate relationship between substance use and brain structure, suggesting a potential modulation of cannabis’ impact on cortical thickness by nicotine exposure, and emphasizing the need for further longitudinal research to characterize these interactions and their implications for brain health and development.

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An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

Rahul S Desikan, Florent Ségonne, Bruce Fischl … (2006)

In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.

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Cortical surface-based analysis. I. Segmentation and surface reconstruction.

Anders Dale, Bruce Fischl, Martin I. Sereno (2000)

Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.

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Measuring the thickness of the human cerebral cortex from magnetic resonance images.

B Fischl, A M Dale (2000)

Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.

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Author and article information

Contributors

Kelly E. Courtney: (View ORCID Profile)

Natasha E. Wade: (View ORCID Profile)

Alexander Wallace: (View ORCID Profile)

Joseph Patrick Happer: (View ORCID Profile)

Journal

Journal ID (publisher-id): BSRCCS

Title: Brain Sciences

Abbreviated Title: Brain Sciences

Publisher: MDPI AG

ISSN (Electronic): 2076-3425

Publication date Created: March 2024

Publication date (Electronic): February 21 2024

Volume: 14

Issue: 3

Page: 195

Article

DOI: 10.3390/brainsci14030195

SO-VID: 132309ea-a8e5-4229-b43a-5fbc1dfcfef9

License:

https://creativecommons.org/licenses/by/4.0/

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