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«Longitudinal Change in Cognition and White Matter Integrity in Young Adult Cannabis Users A DISSERTATION SUBMITTED TO THE FACULTY OF UNIVERSITY OF ...»

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Longitudinal Change in Cognition and White Matter Integrity in Young Adult

Cannabis Users

A DISSERTATION

SUBMITTED TO THE FACULTY OF

UNIVERSITY OF MINNESOTA

BY

Mary P. Becker

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Adviser: Monica Luciana, Ph.D.

September 2015 © Mary P. Becker 2015 i Acknowledgements I‟d like to extend my sincere thanks to my research advisor and mentor, Monica Luciana, for her support and guidance throughout graduate school and this research project. She has been instrumental in my development as an independent researcher. I would also like to thank Paul Collins for his assistance with the technical descriptions included in Study 2, as well as for his advice, feedback, and valuable discussions regarding interpretation.

Thank you to all of the past and present members of the Luciana Lab for their contributions to data collection and management. Thank you also to my friend and colleague Snežana Urošević, for her encouragement and our valuable discussions about research methods.

This work was supported by the National Institute on Drug Abuse under Grant R01DA017843 awarded to M. Luciana, by the National Institute on Alcohol Abuse and Alcoholism under Grant R01AA020033 awarded to M. Luciana, by BTRC P41 EB015894 and P30 NS076408 grants awarded to the University of Minnesota‟s Center for Magnetic Resonance Research, by the University of Minnesota‟s Center for Neurobehavioral Development Seed Grant awarded to M. (Petrosko) Becker, and by the Minnesota Supercomputing Institute.

Finally, thank you to the research participants who volunteered their time and made this project possible.

ii Dedication This is dedicated to my husband, Robert Becker, who provided unfailing support and encouragement at all points along this journey. Please let your brother know that I finally finished that paper.

iii Abstract Cross-sectional research indicates that cannabis use is associated with cognitive and neuroanatomical damage, particularly when used regularly during development. The timing of use-related impacts on cognition and brain structure remains unclear. This dissertation includes two studies to characterize the longitudinal (1) neurocognitive profile and (2) white matter microstructure of young adult cannabis users who initiated use during adolescence. Cannabis users were assessed on a comprehensive neurocognitive battery and Diffusion Tensor Imaging (DTI) protocol at baseline and at a 2-year follow-up. In Study 1, cannabis users had stable deficits in verbal learning and memory as well as planning ability, and a stable relative strength in processing speed at baseline and follow-up. Deficits in spatial working memory and motivated decisionmaking observed at baseline recovered to control-level performance at follow-up.

Heavier and earlier use of cannabis during adolescence was associated with decline in verbal learning and memory performance over time. In Study 2, change in white matter microstructure between time points was observed. Cannabis users exhibited reduced white matter microstructure organization in the central and parietal regions of the superior longitudinal fasciculus, left superior frontal gyrus, corticospinal tract, right anterior thalamic radiation, and in the posterior cingulum; cannabis users demonstrated increased white matter microstructure in the left anterior corpus callosum and left thalamic white matter. The findings suggest that continued heavy cannabis use during adolescence and young adulthood disrupts ongoing development of white matter

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List of Figures

General Introduction

Study 1: Longitudinal Neurocognitive Profile of Young Adult Cannabis Users.. 6 1.1 Hypotheses

1.2 Methods

1.2.1 Sample

1.2.2 Procedure

1.2.3 Measures

1.2.3.1 Neurocognitive Battery

1.2.3.2 Substance Use

1.2.4 Statistical Approach

1.3 Results

1.3.1 Baseline

1.3.1.1 Sample Characteristics

1.3.1.2 Substance Use Characteristics

1.3.1.3 Baseline Group Differences in Neurocognitive Performance

1.3.1.4 Baseline Neurocognitive Performance and Substance Use

1.3.1.5 Baseline Demographic Characteristics and Cognitive Performance in Follow-up sample

1.3.2 Follow-up

1.3.2.1 Sample Characteristics

1.3.2.2 Substance Use Characteristics

1.3.2.3 Group Differences in Neurocognitive Performance at Follow-Up

1.3.2.4 Hierarchical Regression within Cannabis Users

1.4 Discussion

vi 2 Study 2: Longitudinal Changes in White Matter Microstructure in Young Adult Cannabis Users

2.1 Hypotheses

2.2 Methods

2.2.1 Sample

2.2.2 Procedure

2.2.3 Measures

2.2.3.1 Neurocognitive Battery

2.2.3.2 Substance Use

2.2.4 MRI Data Acquisition and Processing

2.2.5 Statistical Approach

2.3 Results

2.3.1 Sample Characteristics

2.3.2 Substance Use Characteristics

2.3.3 Baseline Differences in White Matter Microstructure Between Groups............ 101 2.3.4 Baseline White Matter Microstructure Behavioral Correlates





2.3.5 Follow-up Differences in White Matter Microstructure Between Groups........ 105 2.3.6 Follow-up White Matter Microstructure Behavioral Correlates

2.4 Discussion

3 General Conclusions and Future Directions

4 Tables

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Table 1. Study 1 Demographic and substance use characteristics of cannabis users and controls at baseline and follow-up

Table 2. DSM-IV-TR Diagnoses at study initiation and follow-up, as assessed by the K-SADS-PL.

.......... 131 Table 3. Substance Use Disorder Symptoms baseline and follow-up

Table 4. Lifetime other drug usage in cannabis users and controls at baseline and follow-up.

................. 134 Table 5. Iowa Gabling Task (IGT) deck contingencies.

Table 6. Baseline neuropsychological cognitive composite scores.

Table 7. Baseline neuropsychological battery scores.

Table 8. Partial correlations between baseline cognitive performance and substance use variables in cannabis users (n = 36).

Table 9. Baseline demographic and substance use characteristics between participants who returned for follow-up assessment and those who dropped out of the study at follow-up, separated by controls and cannabis users

Table 10. Baseline and follow-up neuropsychological cognitive composite scores for participants who completed the follow-up assessment (controls n = 29) and (cannabis users n = 26).

Table 11. Baseline and Follow-up neuropsychological battery scores for participants who completed the follow-up assessment (controls n = 29) and (cannabis users n = 26).

Table 12. Partial correlations between follow-up cognitive performance and cannabis use measures in cognitive measures in cannabis users (n = 26).

Table 13. Hierarchical multiple regression analyses: Cannabis use at baseline assessment predicting follow-up RAVLT Trial 1-5 performance within cannabis users.

Table 14. Hierarchical multiple regression analyses: Age of cannabis use initiation predicting follow-up forward digit span performance within cannabis users.

Table 15. Hierarchical multiple regression analyses: Age of cannabis use initiation predicting learning and memory performance within cannabis users.

Table 16. Hierarchical multiple regression analyses: Age of cannabis use initiation predicting Iowa Gambling Task performance within cannabis users.

Table 17. Study 2: Demographic and substance use characteristics of cannabis users and controls at baseline and follow-up.

Table 18. DSM-IV-TR Diagnoses at baseline and follow-up, as assessed by the K-SADS-PL.

.................. 156

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Table 21. Partial correlations between cognitive and substance use variables and mean FA in cannabis users controls baseline cluster.

Table 22. Analysis of 2-year change in fractional anisotropy and radial diffusivity.

–  –  –

Figure 1. Learning and memory composite score change between baseline and follow-up.

Figure 2. RAVLT performance across trials at baseline and follow-up for cannabis users and controls.

..... 53 Figure 3. RAVLT Trial 1 words recalled change between baseline and follow-up.

Figure 4. DRT reaction time on 500 ms 8,000 ms delay condition change between baseline and follow-up.

Figure 5. Average moves to complete 3-move trials change between baseline and follow-up.

Figure 6. Change between baseline and follow-up on first move initiation time for 4-move problems.

....... 58 Figure 7. IGT good minus bad deck choices across blocks at baseline and follow-up

Figure 8. IGT Deck selections at baseline and follow-up for cannabis users and controls.

Figure 9. Scatterplot of follow-up RAVLT Trial 1-5 performance by baseline total hits in the past year.

... 65 Figure 10. Scatterplot of follow-up Digit Span Forward performance by age of cannabis use onset........... 67 Figure 11. RAVLT scatterplots of follow-up performance by age of cannabis use onset.

Figure 12. Scatterplot of follow-up IGT deck 2 choices by age of cannabis use onset.

Figure 13. Cannabis user Control FA, Control Cannabis user RD.

Voxelwise analysis of baseline group difference and 2-year change in fractional anisotropy and radial diffusivity.

Figure 14. Control Cannabis user FA, Cannabis user Control RD.

Voxelwise analysis of 2-year change in fractional anisotropy and radial diffusivity.

Figure 15. Scatterplot of baseline total # of drinks in the past year by mean FA in cannabis users controls baseline cluster in the right genu and forceps minor of the CC.

Figure 16. Scatterplot of baseline IGT disadvantageous deck2 choices by mean FA in cannabis userscontrols baseline cluster in the right genu and forceps minor of the CC.

Figure 17. Scatterplot of total number of hits at follow-up, controlling for baseline hits, by mean FA-change in controls cannabis users cluster in the left SLF/CC forceps major.

Figure 18. Scatterplot of total number of hits at follow-up, controlling for baseline hits, by mean FA-change

–  –  –

by mean FA-change in controls cannabis users cluster in the right ATR.

Figure 20. Scatterplot of IGT total good choices, controlling for baseline total good choices, by mean FAchange in controls cannabis users cluster in the left SFG white matter.

Figure 21. Scatterplot of IGT advantageous deck 4 choices, controlling for baseline deck 4 choices, by mean FA-change in controls cannabis users cluster in the left SFG white matter.

Figure 22. Scatterplot of IGT disadvantageous deck choices, controlling for baseline total good choices, by

–  –  –

Cannabis is experiencing its moment in the spotlight in the United States. At the time of this writing, 23 states and the District of Columbia have legalized cannabis use for medical use, and 4 states have legalized cannabis for both medical and recreational use. Political, social, and legal debates about cannabis‟s legal status continue (Volkow, Baler, Compton, & Weiss, 2014), and ongoing changes to its legal status across the United States are likely.

In the context of its uncertain and changing legal status, cannabis has consistently been the most commonly used “illicit” substance in the United States (Substance Abuse and Mental Health Services Administration, 2014). Among high school juniors and seniors reporting no problems associated with substance use, 14% of students said they had used cannabis during their lifetime (Falck, Nahhas, Li, & Carlson, 2012). Among 12th grade students, 6.5% report daily cannabis use (Johnston et al., 2013).

Along with the high prevalence of cannabis use, adolescents and young adults report decreased perceived risk and disapproval of cannabis use (Johnston, Bachman, & Schulenberg, 2012; Johnston et al., 2013; Substance Abuse and Mental Health Services Administration, 2014). Historically, attitude change toward cannabis use has coincided with increased prevalence of use (Johnston et al., 2012). Higher levels of cannabis use in adolescents and young adults are associated with individuals‟ approval of cannabis use as well as the perception of approval among one‟s peers and parents (LaBrie, Hummer, & Lac, 2011; Wu, Swartz, Brady, & Hoyle, 2015). Medical and recreational cannabis laws are potential factors causing decreased perception of risk, which contributes to an increase in cannabis use. Decreased perception of risk (Schuermeyer et al., 2014; Wall et al., 2011) and increased cannabis use (Cerdá, Wall, Keyes, Galea, & Hasin, 2012;

Harper, Strumpf, & Kaufman, 2012) have been found in states with medical cannabis laws. However, establishing a causal link between newly enacted laws and shifts in perception and use requires further research (Wall et al., 2012).



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