1 Adolescent Brain Development and Effects of Alcohol Use Monica Luciana, Ph.D. Professor and Chair Department of Psychology and Center for Neurobehavioral Development University of Minnesota
2 The Minnesota Adolescent Brain Development Project Longitudinal study initiated in 2004 Healthy children, adolescents and young adults (ages 9-23) recruited for initial assessment; n=203 Baseline assessment = MRI scans (volumetrics; DTI; resting state connectivity); neurobehavioral assessment; personality testing Sample re-assessed at two year intervals; just starting wave 5 End result = comprehensive biologically-based assessment of development from adolescence into young adulthood
3 Aims Characterize aspects of normative brain and behavioral development during typical adolescence Determine neuroplasticity in the context of substance use: is brain development altered as a function of alcohol or other substance use during this period?
4 Normative Changes in the Adolescent Brain Synaptic Pruning: Gray Matter Decline Myelination: White Matter volumetric increases Increased complexity of white matter s directional orientation Changes in functional connectivity, esp involving cortical control networks Changes in neurochemistry: overactivity of reward system?
5 Important to Recognize A developing brain is a malleable and receptive brain Experience impacts further brain development and subsequent behavior; neuroplasticity
6 One type of relevant experience Substance use/misuse Common but not universal
7 Percentages of Past Year Alcohol Dependence or Abuse among Adults Aged 21 or Older, by Age at First Use:
8 Alcohol as Neurotoxin Use in large quantities associated with impaired attention, memory and executive functions; neurotoxicity evident with heavy adult use, exposure during prenatal development, exposure during aging Does use in smaller amounts impact brain development in the adolescent period?
9 Will focus here on three sets of findings. I. Region-of-interest analyses: subcortical brain volumes and individual differences in reward responsivity as predictors of use and outcomes 2. Alcohol use and structural brain development 3. Behavioral changes after alcohol use initiation
10 MRI Acquisition Siemens 3T Tim Trio T1: (MPRAGE) sequence (TR = 2530 msec, TE = 3.65 msec, TI = 1100 msec, flip angle = 7, 240 slices, no gap, voxel size =1.0 x 1.0 x 1.0 mm, FOV = 256 mm). DTI: 12 noncollinear directions, axial plane, dual spin echo, single-shot, pulsed-gradient, echo-planar imaging (EPI) sequence (TR = 12.5 sec, TE = 99 msec, 64 slices, no gap, voxel size = mm, FOV = 256 mm, 2 averages, b value = 1000 sec/mm2). Freesurfer longitudinal analysis suite (v 4.5.0) Diffusion Toolbox (FDT) from the FMRIB software library; FSL s Tract-Based Spatial Statistics (TBSS); SPM8
11 I. Region-of-interest analyses: Subcortical brain volumes and individual differences in reward responsivity as predictors of use and outcomes
12 Age-related Trends in the Typical Development of Executive Function High scores = Poor performance Best Transitioning into best performance when risk-taking is highest
13 The nucleus accumbens is a key node in the brain s reward system
14 BIS/BAS Behavioral Activation: Typical Development Behavior Volume of the L Nucleus Accumbens Adolescent increases in reward sensitivity (left) are paralleled by increases in left nucleus accumbens volume (right); both show a quadratic trend with peaks in late adolescence (ages 15-19) Urosevic, S.. Collins, P.F., Muetzel, R., & Luciana, M. (2012). Longitudinal changes in adolescentbehavioral approach system and behavioral inhibition system sensitivities: associations with OFC and nucleus accumbens volumes. Developmental Psychology. advance on-line publication: doi: /a
15 Behavioral Activation: Prediction of Substance Use Initiation 1. Baseline Predictors Left Nacc Volume, Wald s 2 (1) = 4.13, p =.042, OR =.985, 95% CI = Developmental predictors Increases in BAS Total, Wald s 2 (1) = 3.88, p =.049, OR = 1.34 Increases in BAS Reward Responsiveness, Wald s 2 (1) = 4.20, p =.040, OR = 2.59 No effects for BIS (Behavioral Inhibition System; threat sensitivity) Urosevic, S., Collins, P.F., Muetzel, R., Schissel, A.M., Lim, K.O. & Luciana, M. (2014). Effects of reward sensitivity and regional brain volumes on substance use initiation in adolescence. Social, Cognitive and Affective Neuroscience.
16 2. Alcohol Use and Structural Brain Development Luciana et al., (2013) Am J. of Drug and Alcohol Abuse, 39 (6) (NOV):
17 Analytic Strategy Comparison of developmental trajectories across a two-year span for Adolescents who have never used alcohol Adolescents who transition between baseline and follow-up into a regular pattern of alcohol use Regular pattern of use = more than one drink on more than one occasion per week
18 Two-year change in cortical thickness in non-users versus alcohol use initiators. Initiators have accelerated rates of loss of cortical thickness relative to non-users in the right middle frontal gyrus. This region important for WM and inhibitory control; expected that we would see pruning across normal development Accelerated tissue loss? A more mature brain? X=24, y=26, z=34; max t= 3.54
19 Two-year change in cortical white matter extent in non-users versus alcohol use initiators 4 broad clusters; t-values= 3.62 to 5.47 Non-users show greater increases in white matter volumes over time in the right hemisphere precentral gyrus, lingual gyrus, middle temporal gyrus, and anterior cingulate cortex relative to alcohol use initiators. Alcohol use initiation associated here with a blunting of white matter development.
20 Two-year change in fractional anisotropy in non-users versus alcohol use initiators Upper row: cluster peak near dorsal caudate in left hemisphere (x=-6, y=16, z=8; t=4.51). Lower row: cluster peak in mid-temporal/polar-temporal region of inferior fronto-occipital fasciculus in right hemisphere (x=32, y=0, z=-22 t=4.32).
21 What about patterns of drinking over time?
22 Change in Cortical Thickness and alcohol use frequency Clusterwise threshold p<.05, FWE using Monte Carlo Simulation
23 Change in cortical Thickness and Maximum drinks in 24 hours
24 3. Behavioral changes after alcohol use initiation
25 Could non-use of alcohol represent the more deviant pattern? Don t think so.
26 Self-reported changes in behavioral control (MPQ) p=.06
27 Importance of self-reported control Post-initiation MPQ Control is associated with: Frequency of alcohol use : Maximum drinks in a 24-hour period : r=0.23, p=.027 r=.22, p=.03 (partial corrs controlling for age, sex, baseline Control scores)
28 Also evidence that executive functions are altered with more problematic drinking patterns
29 Summary High levels of reward sensitivity are associated with substance use initiation; baseline integrity of the ventral striatum (Nacc) is also a predictor Deviations from the normative pattern of cortical brain development are apparent after alcohol use initiation These deviations are particularly pronounced for cortical thickness as a function of high frequency of drinking and amounts consumed
30 Longitudinal Cascade Increased Reward Sensitivity (BAS) Volumetric and functional integrity of Ventral striatum Increased Risk Taking (Substance Use Initiation) Changes in Cortical thickness and WM volume, connectivity Failures of Self- Monitoring/ Decreased Control Problematic Use: Substance Use frequency Using in large amounts Binge drinking
31 Acknowledgments Research Associates Paul Collins, Ph.D. Steve Malone, Ph.D. Snezana Urosevic, Ph.D. Ryan Muetzel, M.A. (now at Erasmus University) Graduate Students Ann Schissel Mary Petrosko Becker, M.A. Elizabeth Olson, Ph.D. Dustin Wahlstrom, Ph.D. Jim Porter, Ph.D. Catalina Hooper, Ph.D. Collaborators Tonya White, M.D. (now at Erasmus University, the Netherlands) Kelvin O. Lim, M.D. (U of MN Dept of Psychiatry) Bill Iacono, Ph.D. (U of MN, Psychology) This work was supported by the U of MN s Center for Neurobehavioral Development, by NIDA (grant DA017843), and by NIAAA (AA ), by BTRC grants awarded to the Center for Magnetic Resonance Research, P41 RR008079, P41 EB015894, and 1P30 NS as well as by the Minnesota Supercomputing Institute.
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