Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking
Publication: American Journal of Psychiatry
Abstract
Objective:
The authors sought evidence for altered adolescent brain growth trajectory associated with moderate and heavy alcohol use in a large national, multisite, prospective study of adolescents before and after initiation of appreciable alcohol use.
Method:
This study examined 483 adolescents (ages 12–21) before initiation of drinking and 1 and 2 years later. At the 2-year assessment, 356 participants continued to meet the study’s no/low alcohol consumption entry criteria, 65 had initiated moderate drinking, and 62 had initiated heavy drinking. MRI was used to quantify regional cortical and white matter volumes. Percent change per year (slopes) in adolescents who continued to meet no/low criteria served as developmental control trajectories against which to compare those who initiated moderate or heavy drinking.
Results:
In no/low drinkers, gray matter volume declined throughout adolescence and slowed in many regions in later adolescence. Complementing gray matter declines, white matter regions grew at faster rates at younger ages and slowed toward young adulthood. Youths who initiated heavy drinking exhibited an accelerated frontal cortical gray matter trajectory, divergent from the norm. Although significant effects on trajectories were not observed in moderate drinkers, their intermediate position between no/low and heavy drinkers suggests a dose effect. Neither marijuana co-use nor baseline volumes contributed significantly to the alcohol effect.
Conclusions:
Initiation of drinking during adolescence, with or without marijuana co-use, disordered normal brain growth trajectories. Factors possibly contributing to abnormal cortical volume trajectories include peak consumption in the past year and family history of alcoholism.
Normal brain maturation, as determined in cross-sectional studies (e.g., 1–3) with longitudinal confirmation (e.g., 4–10), is characterized as increasing in cortical gray matter volume through the first decade, followed by continuous decline thereafter. Concurrently with the gray matter decline, supratentorial white matter volume continues growing throughout adolescence, with a slowing of the growth trajectory in the third decade (e.g., 11). Although there are local regional differences in rates of change (5, 11–14), these complementary tissue changes define the nature of brain structural maturation. These significant and predictable changes in normal neurodevelopment have led to the speculation that the evolution of the adolescent brain is especially vulnerable to environmental insult. Given that adolescence is also a time of risk-taking and experimentation with “adult behaviors,” such as alcohol drinking, a likely prediction is that excesses of such potentially deleterious agents may result in accelerated gray matter loss, attenuated white matter growth, or both (15, 16).
Abnormal growth patterns were observed in two recent longitudinal studies of youths who initiated and continued heavy drinking. The first study examined 55 youths, ages 14–19 at baseline, none of whom had ever consumed any alcohol or drugs (15). At that study’s 2-year follow-up, structural MRI revealed “greater than expected decreased cortical thickness in the right middle frontal gyrus” and “blunted development” in some regional white matter volumes in the 30 youths who initiated “regular” but also “subclinical“ alcohol use (1 to 10 drinks per occasion several times weekly) compared with the 25 youths who remained alcohol and drug free. It is notable, however, that the youths who refrained from drinking showed increasing rather than the typically observed decreasing cortical gray matter thickness, which has implications for interpreting the relative decreasing thickness reported in the drinking group. The second, larger study examined youths over 1- to 8-year intervals and found accelerated gray matter volume reductions in the lateral frontal and temporal cortices and attenuated white matter volume growth of the corpus callosum and pons in 75 heavy drinkers relative to 59 light or nondrinking youths, a pattern common to both sexes (16). Moderate drinkers were excluded from these reports, however, leaving unaddressed the question of whether highly prevalent, moderate drinking levels (17) could interfere with normal developmental trajectories.
To address this and related questions, we conducted a longitudinal analysis of structural MRI data collected at baseline (18) and 1 and 2 years later in 483 youths participating in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study (19). At baseline, all participants met the study entry criteria for no or low drinking and drug use. As anticipated, however, a proportion of these youths (N=127) initiated drinking to levels that exceeded, to varying degrees, the study entry criteria, thereby enabling pursuit of a naturalistic study on the effects of drinking on the adolescent brain. Accordingly, our study had two primary aims: 1) to establish normal growth trajectories, which would be predicted to show declines in regional gray matter volume concurrent with growth of white matter volumes in the youths who continued to meet the no or low age-dependent criteria (“no/low” participants); and 2) to test whether the developmental trajectories of youths who transitioned to exceeding the initial alcohol consumption criteria would conform to patterns identified in previous work in heavy drinking youths showing accelerated gray matter declines with attenuated white matter growth. In light of previous reports, expected group differences would be greatest in frontal and temporal cortices and detectable in white matter volumes (15, 16). We speculated that the trajectories of moderate drinkers would show accelerated volume loss at a level intermediate between normal decline and accelerated decline of the heavy drinkers. Consideration of these different drinking levels enabled testing for a dose effect based on reported quantities of drinking and seeking correlations between consumption quantities and cortical volume slopes showing group differences by drinking levels. Exploratory analyses examined potential differences related to family history of alcoholism (e.g., 20, 21) and compounding effects of marijuana and alcohol co-use on brain volume trajectories.
Method
Participants
The participants were 483 adolescents (ages 12–21 at study entry) who had usable baseline and 2-year follow-up data. Of these, 457 had 1-year and 2-year follow-up data, and 26 had 2-year follow-up data, having missed their 1-year visit. At baseline, these 483 youths met our double alcohol inclusion criteria, described in the next section. These youths were drawn from the 674 no/low alcohol-consuming adolescents tested at baseline and recruited across the five NCANDA sites: University of California at San Diego, SRI International, Duke University Medical Center, University of Pittsburgh Medical Center, and Oregon Health and Science University (19). The study followed an accelerated longitudinal design, which specified a three-age-band recruitment strategy at baseline with annual follow-up examinations: 12–14.9 years, 15–17.9 years, and 18–21.9 years. The institutional review boards of each site approved the study (see the data supplement that accompanies the online edition of this article).
Alcohol history determination.
Participants completed the Customary Drinking and Drug Use Record (22) to characterize past and current alcohol and substance use (see the data supplement ). At entry, all participants selected for this analysis met two sets of drinking criteria. The first set was the initial NCANDA inclusion criteria for “no/low” drinking in a lifetime. These were based on National Institute on Alcohol Abuse and Alcoholism guidelines for risky drinking and were determined in terms of maximum drinking days and maximum drinks on an occasion as follows: maximum drinking days for male and female participants varied by age, where the maximum drinking days was five for those 12–15.9 years old, 11 for those 16–16.9 years old, 23 for those 17–17.9 years old, and 51 for those age 18 and older. The maximum allowable drinks per occasion was three for female participants at any age but varied by age for male participants, for whom the maximum drinks per occasion was three for those 12–13.9 years old, four for those 14–19.9 years old, and five for those age 20 and older.
In the second criterion set, heavy, moderate, and no/low drinkers were categorized using the modified Cahalan et al. inventory (23), comprising quantity (average and maximum consumption) and frequency combinations to classify drinking levels based on past-year patterns (details are provided in the online data supplement ). At the 2-year follow-up, after applying MRI exclusion criteria (see below), the final data set comprised 356 youths who remained in the no/low double criterion group and 127 youths who transitioned from the no/low group to one of two drinking groups: 65 moderate drinkers and 62 heavy drinkers (Table 1).
| Longitudinal | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Baseline | Transitioned to Drinking | ||||||||||
| Characteristic | No/Low Drinking (N=483) | Maintained No/Low Drinking (N=356) | Moderate Drinking (N=65) | Heavy Drinking (N=62) | Post Hoc Differencesa | ||||||
| Mean | SD | Mean | SD | Mean | SD | Mean | SD | F | p | ||
| Age at baseline (years) | 15.55 | 2.25 | 15.08 | 2.19 | 16.69 | 1.95 | 17.07 | 1.75 | 34.47 | <0.001 | N<M=H |
| Maleb | 15.43 | 2.15 | 15.03 | 2.14 | 16.04 | 1.85 | 16.97 | 1.56 | 15.14 | <0.001 | N<M=H |
| Femaleb | 15.67 | 2.34 | 15.13 | 2.24 | 17.07 | 1.92 | 17.21 | 2.03 | 20.79 | <0.001 | N<M=H |
| Socioeconomic statusc | 16.71 | 2.46 | 16.63 | 2.53 | 16.98 | 2.04 | 16.85 | 2.45 | 0.70 | 0.496 | n.s. |
| Internalizing symptoms (T score) | 44.91 | 9.45 | 44.97 | 9.54 | 44.80 | 8.98 | 44.66 | 9.58 | 0.03 | 0.967 | n.s. |
| Externalizing symptoms (T score) | 43.38 | 8.18 | 42.92 | 8.34 | 44.26 | 7.30 | 45.08 | 7.93 | 2.28 | 0.103 | n.s. |
| Lifetime drinking days | 0.6 | 1.81 | 14.83 | 15.07 | 42.79 | 42.73 | 179.62 | <0.001 | N<M<H | ||
| Lifetime drinks | 0.7 | 2.21 | 35.77 | 33.69 | 157.39 | 144.02 | 227.74 | <0.001 | N<M<H | ||
| Lifetime binges | 0.0 | 0.00 | 3.71 | 5.12 | 15.81 | 18.88 | 136.32 | <0.001 | N<M<H | ||
| Maximum drinks/episode, past year | 0.2 | 0.6 | 5.2 | 2.8 | 8.4 | 3.6 | 734.29 | <0.001 | N<M<H | ||
| Mean | Median | Mean | Median | Mean | Median | ||||||
| Lifetime marijuana use days | 1.15 | 0 | 25.78 | 2 | 70.34 | 10 | |||||
| N | % | N | % | N | % | N | % | χ2 | p | ||
| >50 lifetime uses of marijuana | 1 | 0.3 | 10 | 15.4 | 17 | 27.4 | 83.86 | <0.001 | N<M<H | ||
| Family history of alcoholism | 41 | 8.5 | 27 | 7.6 | 5 | 7.7 | 9 | 14.5 | 3.33 | 0.189 | n.s. |
| Self-declared ethnicity | |||||||||||
| Caucasian | 355 | 73.5 | 248 | 69.7 | 54 | 83.1 | 53 | 85.5 | 10.31 | 0.036 | N v. M v. Hd |
| African American | 70 | 14.5 | 60 | 16.9 | 4 | 6.2 | 6 | 9.7 | 5.45 | 0.066 | N v. M |
| Asian | 51 | 10.6 | 41 | 11.5 | 7 | 10.8 | 3 | 4.8 | 5.69 | 0.058 | N v. H |
| Other | 7 | 1.4 | 7 | 2.0 | 0 | 0.0 | 0 | 0.0 | 1.94 | 0.379 | M v. H |
| Sitee | 5.63 | 0.689 | n.s. | ||||||||
| University of Pittsburgh | 60 | 12.4 | 45 | 12.6 | 6 | 9.2 | 9 | 14.5 | |||
| SRI International | 81 | 16.8 | 57 | 16.0 | 14 | 21.5 | 10 | 16.1 | |||
| Duke University | 100 | 20.7 | 81 | 22.8 | 10 | 15.4 | 9 | 14.5 | |||
| Oregon Health and Science University | 104 | 21.5 | 75 | 21.1 | 16 | 24.6 | 13 | 21.0 | |||
| University of California, San Diego | 138 | 28.6 | 98 | 27.5 | 19 | 29.2 | 21 | 33.9 | |||
a
N=no/low drinking; M=moderate drinking; H=heavy drinking.
b
Ns for male and female participants, respectively, were 241 and 242 at baseline, 180 and 176 in the no/low drinking group, 24 and 41 in the moderate-drinking group, and 37 and 25 in the heavy drinking group.
c
Based on highest parental education level.
d
Chi-square test for two-group or three-group differences in distributions of Caucasian, African American, and Asian ethnicities.
e
The University of Pittsburgh and Oregon Health and Science University sites used Siemens scanners, and the other three sites used GE scanners.
Demographic characteristics.
As described previously (18, 24), participants were characterized by age, sex, self-identified ethnicity, and socioeconomic status, which was determined as the highest level of education achieved by either parent (25) (Table 1).
To examine potential additive effects of alcohol and marijuana consumption on developmental trajectories, a marijuana-use criterion applied at the 2-year follow-up determined that more than 50 lifetime uses served as a dichotomous variable of marijuana use in the 127 heavy and moderate alcohol users. This produced 27 alcohol plus marijuana users (17 heavy and 10 moderate drinkers) and 100 non-marijuana-using alcohol users. A stepwise difference in use occasions emerged between drinking groups: no/low drinkers < moderate drinkers (t=3.42, df=58.7, p=0.001), moderate drinkers < heavy drinkers (t=2.52, df=76.2, p=0.014) (Table 1). All participants also submitted samples for a 14-panel urine toxicology screen (for details, see the data supplement ).
MRI Acquisition
The longitudinal data for the present analysis comprised MRI data from 483 of the 674 participants who were no/low drinkers at baseline (18). To be included, each participant had to meet the double alcohol inclusion criteria, have adequate quality imaging data and meet FreeSurfer (26, 27) signal-to-noise criteria, and have MRI and Customary Drinking and Drug Use Record data available for the 2-year follow-up (in most cases, they also had data available for the 1-year follow-up). Details are provided in the data supplement .
Statistical Analysis
Normal developmental brain structural trajectories.
Developmental effects were determined by quantifying regional brain volume trajectories in the participants who remained in the no/low drinking group. Accordingly, we computed the slope of six major bilateral lobar gray matter regions of interest (28) and the three white matter regions of interest as change in native values per year divided by the native value at baseline and multiplied by 100 to yield a slope expressed as percent change per year, thus placing all regions of interest on the same scale. The effects of site, ethnicity, and supratentorial volume were removed from the slopes by regression analysis. Effects due to MRI scanner manufacturer differences were removed by controlling for site (18). Effects of age, sex, and their interaction were tested with general linear models. The extent to which gray matter slopes were negative and white matter slopes positive was tested with one-sample t tests against the population 0 (Figure 1; also see the online data supplement ).
FIGURE 1. Frontal Gray Matter and Central White Matter Volumes at Baseline and at 1- and 2-Year Follow-Ups in 356 Adolescents Who Remained No/Low Drinkersa
a The plots connect baseline, 1-year, and 2-year follow-up values, plotted as a function of baseline age for frontal gray matter volume and central white matter volume; the linear mixed-effects model (lmer) fits with one and two standard deviations, separately computed for boys and girls, are also plotted. Note that the frontal lobe values decrease fairly linearly, whereas the growth in the white matter volume slows across age. For both regions, boys had larger volumes than girls. The plots on the right present the same data as those on the left but are adjusted for variation in supratentorial volume, which attenuated sex differences in regional volumes.
Effect of alcohol drinking on developmental trajectories.
The analysis tested group differences separately between the 356 participants who remained in the no/low drinking group and the 127 who transitioned to drinking by dividing the transition group into moderate (N=65) and heavy (N=62) drinkers based on Cahalan et al. (23) scores. The effects of alcohol consumption on each region of interest were tested with a series of general linear models of slope as a function of alcohol use group plus age, sex, site, ethnicity, and supratentorial volume as well as an extended model testing for group-by-age and group-by-sex interactions. For display and effect size calculations (Cohen’s d) of the differences between the no/low group and the two drinking transition groups across age, the effects of sex, site, ethnicity, and supratentorial volume were removed from the native values by regression analysis prior to application of a linear mixed-effects model of the regressed values as a function of age and age-squared. Analyses were tested unidirectionally, where steeper cortical volume declines and attenuated white matter volume growth were expected in the drinking youths; thus, using a one-tailed test with 10 region-of-interest comparisons, results were considered statistically significant when p was ≤0.01.
These analyses were repeated for the 34 individual Desikan-Killiany cortical region-of-interest FreeSurfer parcellations bilaterally (28) for finer-grained examination. Exploratory correlational analyses used Pearson tests and Spearman rank-order confirmatory tests of slopes corrected for age, sex, site, ethnicity, and supratentorial volume as functions of drinking variables at the 2-year MRI follow-up (lifetime drinking days, number of drinks, number of binges, and maximum number of drinks per occasion in the past year).
Results
Slopes of each region of interest and tissue type were expressed as percent change per year for each individual. Trajectories of the no/low drinking group are presented first and provide the context for identifying where the drinking youths deviated from the normal developmental change patterns.
Trajectories of Normal Brain Structural Maturation
On average, all six regional neocortical and cortical gray matter regions exhibited negative slopes when tested against 0 (t values ranged from −11.466 to −27.158, p<10−8 to 10−87) (Table 2). All regions of interest showed monotonic decelerating trajectories with older age (Figure 2; Table 2). The annual rate of decline was greater for the female than the male participants, but no age-by-sex interaction was significant (Table 2).
| Region of Interest | t | p |
|---|---|---|
| Gray matter | ||
| Frontal | ||
| Age | 2.610 | 0.009 |
| Sex | –2.494 | 0.013 |
| Age by sex | 1.356 | 0.176 |
| t test | –19.415 | <0.001 |
| Temporal | ||
| Age | 2.438 | 0.015 |
| Sex | –2.691 | 0.008 |
| Age by sex | 1.728 | 0.085 |
| t test | –27.158 | <0.001 |
| Parietal | ||
| Age | 6.251 | <0.001 |
| Sex | –1.873 | 0.062 |
| Age by sex | 0.759 | 0.448 |
| t test | –25.189 | <0.001 |
| Occipital | ||
| Age | 3.087 | 0.002 |
| Sex | –2.051 | 0.041 |
| Age by sex | 1.566 | 0.118 |
| t test | –19.451 | <0.001 |
| Cingulate | ||
| Age | 4.357 | <0.001 |
| Sex | –2.297 | 0.022 |
| Age by sex | 0.630 | 0.529 |
| t test | –20.731 | <0.001 |
| Insular | ||
| Age | 3.043 | 0.003 |
| Sex | –1.984 | 0.048 |
| Age by sex | –0.744 | 0.456 |
| t test | –11.466 | <0.001 |
| Total cortex | ||
| Age | 3.978 | <0.001 |
| Sex | –2.532 | 0.012 |
| Age by sex | 1.343 | 0.180 |
| t test | –24.496 | <0.001 |
| White matter | ||
| Central white matter | ||
| Age | –13.617 | <0.001 |
| Sex | –4.049 | <0.001 |
| Age by sex | 2.586 | 0.010 |
| t test | 44.134 | <0.001 |
| Pons | ||
| Age | –8.488 | <0.001 |
| Sex | –4.497 | <0.001 |
| Age by sex | 2.660 | 0.008 |
| t test | 30.656 | <0.001 |
| Corpus callosum | ||
| Age | –8.173 | <0.001 |
| Sex | –3.662 | <0.001 |
| Age by sex | 2.801 | 0.005 |
| t test | 36.712 | <0.001 |
a
All general linear model analyses include age, sex, site, ethnicity, and supratentorial volume.
FIGURE 2. Slopes of Regional Gray Matter and White Matter Volumes Over Time for 356 Adolescents Who Remained No/Low Drinkersa
a In the scatterplots, slope over age linear regression lines are plotted separately for each sex. The bar graphs below each scatterplot indicate the average slope for each of the three initial recruitment age groups.
In contrast with cortical regions, the three white matter volumes exhibited growth (t values ranged from 30.656 to 44.134, p<10−100) (Table 2). This growth slowed with advancing age (t values for simple age effects ranged from −8.173 to −13.617, p<0.001) (Table 2). Significant age-by-sex interactions for all three white matter regions of interest indicated more rapid declines in growth across age in the boys than the girls (Figure 2; Table 2).
Trajectory Deviations From Normal Developmental Patterns Observed in Drinkers
All analyses seeking group differences controlled for age, sex, site, ethnicity, and supratentorial volume.
Paired analyses: 65 moderate versus 356 no/low and 62 heavy versus 356 no/low group differences.
Significant group effects were limited to the 62 heavy versus the 356 no/low drinker comparisons (Table 3). Volumes of frontal, cingulate, and total gray matter declined more rapidly and central white matter expanded more slowly in the heavy drinkers than in the no/low drinking group (Figures 3 and 4); these differences were significant with Bonferroni adjustment for the frontal region. Although the moderate-drinking group generally showed the heavy-drinking pattern of steeper-than-expected slopes, none of those group differences was significant (Table 3; Figure 4).
| Region of Interest | t | p | Cohen’s d |
|---|---|---|---|
| Gray matter volume | |||
| Frontal | |||
| Moderate versus no/low drinkers | –1.049 | 0.295 | –0.151 |
| Heavy versus no/low drinkers | –2.626 | 0.009b | –0.394 |
| Temporal | |||
| Moderate versus no/low drinkers | –1.294 | 0.196 | –0.186 |
| Heavy versus no/low drinkers | –0.810 | 0.419 | –0.121 |
| Parietal | |||
| Moderate versus no/low drinkers | –1.140 | 0.255 | –0.164 |
| Heavy versus no/low drinkers | –1.837 | 0.067 | –0.275 |
| Occipital | |||
| Moderate versus no/low drinkers | –0.151 | 0.880 | –0.022 |
| Heavy versus no/low drinkers | –1.106 | 0.270 | –0.166 |
| Cingulate | |||
| Moderate versus no/low drinkers | –0.540 | 0.590 | –0.078 |
| Heavy versus no/low drinkers | –2.320 | 0.021 | –0.348 |
| Insular | |||
| Moderate versus no/low drinkers | –0.387 | 0.699 | –0.056 |
| Heavy versus no/low drinkers | –0.781 | 0.435 | –0.117 |
| Total cortex | |||
| Moderate versus no/low drinkers | –1.098 | 0.273 | –0.158 |
| Heavy versus no/low drinkers | –2.046 | 0.041 | –0.307 |
| White matter volume | |||
| Central white matter | |||
| Moderate versus no/low drinkers | –0.414 | 0.679 | –0.060 |
| Heavy versus no/low drinkers | –2.123 | 0.034 | –0.318 |
| Pons | |||
| Moderate versus no/low drinkers | –1.127 | 0.260 | –0.162 |
| Heavy versus no/low drinkers | –1.797 | 0.073 | –0.269 |
| Corpus callosum | |||
| Moderate versus no/low drinkers | –0.053 | 0.958 | –0.008 |
| Heavy versus no/low drinkers | –1.446 | 0.149 | –0.217 |
a
The general linear model controlled for age, sex, site, ethnicity, and supratentorial volume. Ns were 356 for no/low drinkers, 65 for moderate drinkers, and 62 for heavy drinkers. Differences were not significant between moderate and no/low drinkers.
b
Bonferroni-corrected, 1-tailed.
FIGURE 3. Brain Regions Where Heavy Drinking Adolescents (N=62) Have Significantly Steeper Reductions in Gray Matter Volume Than No/Low Drinking Adolescents (N=356)a
a The plots connect values at baseline and 1- and 2-year follow-ups for 356 adolescents who continued to meet no/low criteria and heavy drinkers as a function of baseline age; the linear mixed-effects model (lmer) fits with one and two standard deviations, computed separately for no/low and heavy drinkers, are also plotted.
FIGURE 4. Rate of Change (Mean Slope) for Each Regional Brain Volume Measured in Heavy, Moderate, and Continuing No/Low Drinking Adolescentsa
aError bars indicate 95% confidence intervals.
Because the heavy drinkers were in the older age range, we conducted a follow-up test on a subset of 62 controls matched to the heavy drinkers on age, sex, and ethnicity. Frontal gray matter volume continued to show a steeper decline in the heavy compared with the no/low drinking group (t=−2.004, p=0.047), although the p value did not meet the Bonferroni-corrected threshold for significance. Additional general linear models entered family history status and socioeconomic status to seek group differences by region and found none.
Having identified accelerated regional volume declines in several lobar regions of the cortex in heavy drinkers, we expanded our analysis to seek potential local effects in group differences in 34 more finely parcellated cortical regions. This analysis identified 12 cortical regions showing faster volume declines in the heavy drinkers than in the no/low drinkers, three of which met the false discovery rate corrected p threshold of p≤0.025: caudal middle frontal, superior frontal, and posterior cingulate cortices (Figure 5; see also Table S1 in the online data supplement ).
FIGURE 5. Brain Regions Where Heavy Drinking Adolescents Have Significantly Steeper Reductions in Gray Matter Volume Than No/Low Drinking Adolescentsa
a The figure shows lateral and medial views of the left hemisphere, with color scale showing regions where heavy drinkers have significantly (p≤0.05) steeper reductions in gray matter volume than no/low drinkers. Regions showing faster declines in cortical volumes in the heavy drinkers relative to the no/low drinkers, displayed in bright orange, are false discovery rate corrected (p<0.025).
Exploratory tests yielded four correlations meeting our double criteria based on parametric and nonparametric analyses, where faster tissue loss correlated with more maximum drinks per occasion reported in the past year (Figure 6).
Test of baseline group differences.
We tested whether the drinking youths had smaller regional brain volumes at baseline (29, 30). Analysis using a general linear model accounting for age, sex, site, ethnicity, and supratentorial volume yielded no significant differences (p≤0.05) between baseline volumes of any regions of interest for either the heavy or the moderate-drinking groups compared with the no/low group. Examination of baseline volumes of the 34 regions of interest identified one region as smaller (parahippocampal, p=0.046) and one as larger (middle temporal, p=0.045), but no group differences for the isthmus cingulate and pars triangularis (as were found in reference 29); none of these differences was significant with correction for multiple comparisons.
Family history of alcoholism.
To examine the contribution of a family history of alcoholism to the observed cortical volume results, a positive family history was defined as having at least one first-degree relative with a history of alcohol use disorder. For each participant group (heavy, moderate, and no/low drinkers), the slopes of positive versus negative family history were compared with nonparametric (Mann-Whitney-Wilcoxon) tests. There were no significant differences based on family history within the no/low or moderate-drinking groups, but within the heavy drinking group, the 21 participants with a positive family history had steeper slopes than the 41 who had a negative family history for parietal (W=231, p=0.003), occipital (W=255, p=0.008), and total gray matter volumes (W=274, p=0.019). Frontal (W=304, p=0.061) and cingulate (W=303, p=0.058) gray matter volumes showed the same pattern but fell short of statistical significance.
Marijuana and alcohol co-use.
Slopes of the 10 moderate plus 17 heavy drinkers with >50 marijuana occasions were compared with slopes of the remaining 100 drinkers who did not meet this marijuana criterion. Analysis of the primary regions of interest yielded one group difference in the direction opposite to expectations: insular gray matter volume slopes were less negative in the alcohol and marijuana co-use group than in the alcohol-only group (t=2.551, p=0.014).
Discussion
This longitudinal study revealed tissue-specific neuromaturational age-linked rates and patterns of regional volume regression and expansion over 2 years that occur in normally developing adolescents. For cortical gray matter, the general pattern followed a steeper trajectory of volume decline in early adolescence followed by a deceleration in this rate as youths approached young adulthood. Trajectories for both sexes were relatively constant (monotonic), although they were steeper in girls than boys. The parietal cortex exhibited the greatest change per year, with an average decline of about 2.5% per year in the youngest boys and about 3% per year in the youngest girls. Complementing the gray matter volume decline was evidence for growth of white matter volume, which grew at faster rates in the younger ages, especially in boys, and slowed in later adolescence and young adulthood. For example, central white matter volume expanded by about 1.5% annually in the younger adolescents but annual increases fell below 0.75% in the older adolescents. This dynamic growth pattern formed the context for addressing whether and how initiation of moderate or heavy drinking altered components of these developmental trajectories.
In search of evidence for a dose effect of alcohol consumed on volume trajectories, we combined two sets of quantitative criteria based on drinking patterns over the past year to distinguish moderate from heavy drinkers. The heavy drinking initiators showed significantly steeper slopes than the no/low drinkers. In no case did the volume trajectories of the moderate drinkers differ statistically from the no/low drinkers, even though their arithmetic means were intermediate between the no/low and heavy drinking groups for frontal cortical slopes. Modest support for a dose effect derives from correlations between steeper regional parietal slopes, indicative of faster volume loss, and the maximum number of drinks consumed per occasion in the past year. One speculative interpretation of this apparent acceleration of the pruning trend notable in young adolescent drinkers is an overexuberance of the typical synaptic refinements, suggesting an alteration of progression into the later stages of neurodevelopment.
Although the heavy drinking youths met strict drinking criteria that significantly exceeded drinking reported in the no/low drinkers, we determined from published data that, on average, our heavy drinkers consumed alcohol at lower levels and less frequently than youths in two other longitudinal MRI studies of heavy drinking initiation. We estimated that the number of days drinking per year was 47 in the Luciana et al. study (15), 115 in the Squeglia et al. study (16), and 43 for the heavy drinkers but only 15 for the moderate drinkers in the present study. Also estimated were the number of drinks consumed per year: 268 in the Luciana et al. study and 157 for the heavy drinkers and 36 for the moderate drinkers in the present study. Taken together, this study and the two earlier longitudinal studies of more heavily drinking youths reported greater than normal age-related cortical thinning or volume reduction in the frontal and cingulate cortices in two studies (the Luciana et al. study and the present study) and in the frontal cortex in all three studies. Developmentally, the frontal cortex is the last region to mature, and it may retain the plasticity for modification from environmental factors throughout late adolescence and into early adulthood.
The more finely grained parcellation analysis of cortex revealed substantially faster declines in the heavy drinkers than in the no/low drinkers in a constellation of superior and caudal middle frontal and posterior cingulate cortical volumes. The posterior cingulate cortex has been found to have the highest cerebral blood perfusion to acute alcohol infusion, as measured with arterial spin labeling MRI (31). Given that this region has been observed as affected in heavy drinking youths (15, 16), it is tempting to speculate that there is a relationship between repeated acute regional perfusion experiences with high doses of alcohol and ultimate effects on local tissue volumes.
To the extent that alcohol history documented retrospectively in interviews reflects accurate assessment of consumption, quantity and frequency metrics of alcohol consumption provide estimates of amount consumed. Exploration of available drinking variables (lifetime drinking days, lifetime drinks, lifetime binges, and maximum drinks per occasion in the past year) identified greater maximum drinks as a correlate of accelerating brain structural change slopes, but only in the total and regional parietal volumes. These relationships present novel support for an alcohol dose effect on volume declines in recently initiated adolescent drinkers. The stepwise steeper slopes in no/low to moderate to heavy drinking youths indicating volume declines also suggest a dose-response effect, with the moderate drinkers falling (statistically nonsignificantly) between the other drinking groups, a pattern most apparent for frontal and total gray matter volumes (see Figure 4). Other studies, too, have found maximum drinks per occasion to be useful in understanding drinking patterns and their consequences. For example, a trajectory analysis of young adults’ alcohol consumption in the Collaborative Study on the Genetics of Alcoholism revealed the utility of maximum drinks per occasion in predicting drinking patterns (32). An escalation of maximum drinks has been found to be predictive of greater impulsivity/compulsivity scores and disturbance of fronto-parietal control mechanisms (33).
The developmental trajectories of the no/low drinking participants are consistent with the general literature. The accelerated longitudinal study design and geographically widespread multisite recruitment provided some assurance of broad age sampling over just a few years and a measure of generalizability. Nonetheless, even this controlled, longitudinal study design has limitations. The results remain limited in age sampling, demographic representation (especially with respect to family history of alcoholism), and finely grained quantification of alcohol consumption. Despite the prospective nature of the study, factors unmeasured at baseline could present pre-existing conditions underlying the ostensibly alcohol-related effects on brain structure. Indeed, having a positive family history of alcoholism may have compounded the abnormal slope accelerations observed in the youths who transitioned into heavy drinking.
An earlier study reported smaller brain volumes at baseline in youths who initiated drinking at hazardous levels compared with low drinking control subjects (29), whereas we found no volume difference at baseline between our no/low and heavy drinkers. The former study had smaller samples, recruited participants at high risk for substance use disorders, had an underrepresentation of female participants, and had a higher proportion of drinkers with a positive family history than our NCANDA sample. Recognizing these sampling differences, we believe that the lack of regional differences at baseline in the NCANDA sample is a strength, supporting the possibility that the greater decline in volumes across age in the drinkers was attributable to drinking rather than to pre-existing conditions.
In conclusion, these results provide evidence that initiation of heavy alcohol drinking during adolescence can disrupt normal, differential growth trajectories of specific cortical gray matter regions. Although significant effects on brain volumetric trajectories were not observed in moderate drinkers, this group’s intermediate position between no/low and heavy drinkers on many measures suggests a dose or threshold effect. A factor potentially contributing to the abnormally smaller cortical volumes, notably in frontal regions, may be alcohol-related acceleration of normal pruning mechanisms (34) in regions with high cerebral blood perfusion during acute alcohol use (31, 35). The question of whether reduction in drinking restores the normal trajectories or acceleration in alcohol consumption to dependence levels results in further acceleration of gray matter volume declines and attenuation of white matter growth awaits continued longitudinal study of these NCANDA participants.
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History
Received: 27 April 2017
Revision received: 21 June 2017
Revision received: 27 July 2017
Accepted: 7 August 2017
Published online: 31 October 2017
Published in print: April 01, 2018
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Competing Interests
Dr. Baker has received research funding from Fitbit, International Flavors and Fragrances, and Ebb Therapeutics. The other authors report no financial relationships with commercial interests.
Funding Information
Moldow Women's Hope and Healing Fund
National Institute on Alcohol Abuse and Alcoholism10.13039/100000027: AA021681, AA021690, AA021691, AA021692, AA021695, AA021696, AA021697, K05 AA017168
Supported by the National Institute on Alcohol Abuse and Alcoholism with co-funding from NIDA, NIHM, and the National Institute of Child Health and Human Development (NCANDA grants AA021697 to Drs. Pfefferbaum and Pohl, AA021695 to Drs. Brown and Tapert, AA021692 to Drs. Tapert and Brown, AA021696 to Drs. Colrain and Baker, AA021681 to Dr. De Bellis, AA021690 to Dr. Clark, AA021691 to Dr. Nagel, and K05 AA017168 and the Moldow Women’s Hope and Healing Fund to Dr. Sullivan).Metrics & Citations
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