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Adolescent brain and methamphetamine use page 1IK Lyoo et al

SUPPLEMENTARY MATERIAL

Predisposition to and effects of methamphetamine use on the adolescent brain

In Kyoon Lyoo, M.D., Ph.D., Sujung Yoon, M.D., Ph.D., Tae Suk Kim, M.D., Ph.D., Soo Mee Lim, M.D., Ph.D., Yera Choi, M.S., Jieun E. Kim, M.D., Ph.D., Jaeuk Hwang, M.D., Ph.D., Hyeonseok S. Jeong, Ph.D., Han Byul Cho, M.S., Yong An Chung, M.D., Ph.D., Perry F. Renshaw, M.D., Ph.D.

CONTENTS

SUPPLEMENTARY METHODS Participants----------------------------------------------------------------------------------------------2 Clinical assessments----------------------------------------------------------------------------------3 Cognitive assessments-------------------------------------------------------------------------------4 Magnetic resonance imaging acquisition--------------------------------------------------------4 Cortical thickness measurement and associated analyses---------------------------------5 Fractional anisotropy measurement and associated analyses-----------------------------6 Mediation analysis-------------------------------------------------------------------------------------9

SUPPLEMENTAL RESULTS Supplementary Result 1-----------------------------------------------------------------------------10 Supplementary Result 2-----------------------------------------------------------------------------10

FIGURES Supplementary Figure 1----------------------------------------------------------------------------14 Supplementary Figure 2----------------------------------------------------------------------------15 Supplementary Figure 3----------------------------------------------------------------------------16 Supplementary Figure 4----------------------------------------------------------------------------17 Supplementary Figure 5----------------------------------------------------------------------------18 Supplementary Figure 6----------------------------------------------------------------------------19

TABLES Supplementary Table 1-----------------------------------------------------------------------------20 Supplementary Table 2-----------------------------------------------------------------------------22 Supplementary Table 3-----------------------------------------------------------------------------23 Supplementary Table 4-----------------------------------------------------------------------------24

REFERENCES FOR SUPPLEMENTARY MATERIAL------------------------------------------25


SUPPLEMENTARY METHODS

ParticipantsWe enrolled 51 methamphetamine (MA) users younger than 20 years and 60 age- and

sex-matched control adolescents. Fifty-four adult MA users aged 20 years and older

and 60 age- and sex-matched control adults were also enrolled as the adult comparison

group. Detailed information including family history (FH) of drug-related problems was

obtained from participants. Among the adolescent participants, 25 MA users and 30

controls had a positive FH (FH+) of drug use, while 26 MA users and 30 controls had no

FH (FH-). Among the adult participants, 27 MA users and 30 controls were FH+, while

27 MA users and 30 controls were FH-.

While all adolescent users began taking MA before the high-school graduation

age (≤ 18 years old), all adult users started MA use after 19 years old. All participants

assigned to the MA user groups met diagnostic criteria for MA dependence using the

Structured Clinical Interview for the DSM-IV.

The presence of current Axis I diagnoses other than MA or nicotine dependence,

concurrent major neurological or medical diseases, or head trauma history with loss of

consciousness (greater than 30 minutes) were exclusion criteria. Individuals who were

seropositive for human immunodeficiency virus infection or had any contraindications to

magnetic resonance imaging (MRI) were also excluded from the study. In order to avoid

confounding effects of fetal alcohol exposure, maternal alcohol drinking during

pregnancy (three or more drinks on an occasion or more than three times per month)

was additional exclusion criterion for both FH+ and FH- participants. Control participants

were included to the study according to the same criteria, except for a diagnosis of MA

dependence.

Mean duration of regular MA use, which was calculated as the sum of the time

when MA was used more frequently than weekly, was 25.0 (standard deviation [SD] =

14.2; range, 5 to 56 months) and 83.1 months (SD = 43.1; range, 12 to 192 months) for

adolescent and adult MA users (longer in adult than in adolescent MA users, t = 9.16, P

< 0.001), respectively. Lifetime cumulative dose of MA was greater in adult than in

adolescent MA users (t = 5.90, P < 0.001). While the route of MA administration for


adolescent MA users included intravenous injection (n = 33, 64.7%), smoking (n = 16,

31.4%), and oral administration (n = 2, 3.9%), the predominant route for adult users was

intravenous administration (n = 50, 92.6%)(Fisher exact probability test, P = 0.001).

Clinical assessmentsInhibitory control was assessed using the color-word Stroop task.1 We used Stroop

interference, which is calculated by subtracting time per item of the Color task from time

per item of the Color-Word task,2 as an outcome variable. A lower score means less

susceptibility to interference and thus a higher task performance. The level of craving

symptoms was assessed using a visual analog scale (VAS) and the Amphetamine

Craving Questionnaire (ACQ). Subjective craving for MA was measured using a VAS

that ranges from 0 (no craving) to 10 (most intense craving). The ACQ was adapted

from the Cocaine Craving Questionnaire,3 which includes a 45-item self-rating

questionnaire to assess the level of craving for cocaine. In the ACQ, the Cocaine

Craving Scale was modified by substituting the word 'MA' for 'cocaine.' The ACQ

evaluates 5 dimensions of craving symptoms, including (1) desire to use, (2) intention

and planning to use, (3) anticipation of positive outcome, (4) anticipation of relief from

withdrawal or dysphoria, and (5) lack of control over use. The VAS scores were highly

correlated with the total ACQ scores in the current MA user group (r = 0.88, P < 0.001).

The withdrawal symptoms was assessed using the Amphetamine Withdrawal

Questionnaire (AWQ).4 The AWQ is a self-rating questionnaire to ask the level of

amphetamine withdrawal including 3 dimensions of symptoms; (1) hyperarousal, (2)

reversed vegetative function, and (3) anxiety. All MA users also completed the Severity

of Dependence Scale (SDS), which is 5 items to ask the overall severity of drug

dependence.5

The SDS (t = 2.04, P = 0.04) and AWQ (t = 2.58, P = 0.01) scores were higher in

adult MA users relative to adolescent MA users. However, craving severity measured by

the VAS (t = 0.92, P = 0.36) and ACQ (t = 1.51, P = 0.13) and Stroop interference (t =

1.02, P = 0.31) scores were similar in the adolescent and adult MA user groups. The

average amount of weekly alcohol consumption was greater in adult MA users than in

adolescent MA users (t = 3.62, P < 0.001).


Cognitive assessmentsNeuropsychological tests were administrated to assess the participants' cognitive

performance. Each test was classified into the specific cognitive domains according to

the criteria suggested by the previous meta-analysis on neurocognitive effects of MA

use.6 Cognitive domains included executive function, memory, learning, verbal fluency,

working memory, information processing speed, and motor skill. Executive function was

assessed with the Trail Making Test B, Stroop Test Interference, and Wisconsin Card

Sorting Test. The Rey-Osterrieth Complex Figure Test and California Verbal Learning

Test were used to assess memory function. The total score on the first five trials of the

California Verbal Learning Test was used as an indicator of learning capacity. Verbal

fluency was assessed with the Controlled Oral Word Association Test. Working memory

capacity was measured with the Digit Span and Spatial Span Tasks. As a measure of

information processing speed, the Digit Symbol Substitution Test, Stroop Test, and Trail

Making Test A were administered. Motor skill was assessed with the Grooved Pegboard

Test.

The average standardized Z score of all seven cognitive domains was used as a

measure of global cognitive function. Information on each neuropsychological test for

each of seven cognitive domains is described in Supplementary Table 3.

Magnetic resonance imaging acquisitionBrain MR images were acquired using the same 1.5-Tesla whole-body imaging system

(Signa HDx, GE Healthcare, Milwaukee, WI) at St. Paul's Hospital of the Catholic

University of Korea. Sagittal T1-weighted images were acquired using a 3-dimensional

spoiled gradient echo sequence with the following acquisition parameters: repetition

time (TR) = 24 ms, echo time (TE) = 5 ms, field of view (FOV) = 240 mm, matrix =

256x256, flip angle = 45°, number of excitation (NEX) = 2, slice thickness = 1.2 mm, no

skip. Axial T2 weighted images were acquired with the following parameters: TR =

2,817 ms, TE = 126 ms, FOV = 220 mm, matrix = 256x192, flip angle = 90°, NEX = 1,

slice thickness = 5 mm, no skip. Acquisition parameters for axial fluid-attenuated

inversion recovery axial images are as follows: TR = 8,802 ms, TE = 88 ms, inversion

time = 2,200 ms, FOV =220 mm, matrix = 256x192, FA = 90°, NEX = 1; slice


thickness=5 mm, no skip. We also acquired whole-brain diffusion weighted encoded

spin-echo echo-planar imaging images and six images without diffusion weighting (b = 0

s/m2) with the following acquisition parameters: 54 directions, b = 1000 s/m2, TR =

17,000 ms, TE = 84 ms, FOV = 220mm, matrix = 96x96, flip angle = 90°, NEX = 2, slice

thickness = 2.3 mm, no skip.

A neuroradiologist, who was blind to each individual's diagnosis or clinical

information, inspected all images to examine gross structural abnormalities and rated

for image quality. Among 225 images for T1- and diffusion-weighted images, those with

inadequate quality due to dental prosthesis or motion artifact were excluded from further

analyses (5 T1-weighted images, 2 from the adolescent MA user group, 1 from the adult

MA user group, and 2 from the adult control group; 9 diffusion-weighted images, 3 from

the adolescent MA user group, 1 from the adolescent control group, 4 from the adult MA

user group, and 1 from the adult control group).

Cortical thickness measurement and associated analysesCortical thickness, which is known to reflect the integrity of cortical cytoarchitecture was

the primary outcome variable for the measurement of MA-induced gray matter

alterations. High-resolution T1-weighted images from the adolescent group (49 MA

users and 60 controls) and the adult group (53 MA users and 58 controls) were

processed separately using the FreeSurfer tool (http://surfer.nmr.mgh.harvard.edu).7

A series of automated steps including intensity normalization, skull stripping,

segmentation of cortical white matter, subsequent tessellation of gray/white matter

boundaries, and smoothing and inflation of surface were applied to reconstruct cortical

surface and measure cortical thickness.7 Gray/white matter boundaries and surfaces

were defined with sub-millimeter precision through the deformable surface algorithm.7 At

each step through the processing stream, data from each individual were visually

inspected, manually corrected and re-inspected to ensure accuracy, by an experienced

doctoral-level rater who was blind to the participants' identity.

Atlas-based parcellation was conducted to localize cortical thickness alterations

related to MA use. Based on the recent report on the genetically based cortical surface

map,8 cerebral cortex was parcellated into 13 subregions for each hemisphere using the


composition of labeling system of the Desikan-Killiany Atlas.9 Study-specific atlas-based

parcellated gray matter regions and their abbreviations are presented in Supplementary

Figure 1. This regional distribution of cortex reflects the shared genetic influences on

cortical area expansion.8 Global mean thickness across the entire cerebrum and mean

thickness of the atlas-based parcellated regions were extracted and adjusted for age.

Age-adjusted thickness values in the adolescent and adult MA user groups were

converted to standardized Z scores using the means and standard deviations (SD) of

the corresponding FH- control groups. Independent t-tests were used to compare

standardized Z scores for mean thickness between the adolescent and adult MA user

groups.

As complementary results, a general linear model was used to examine vertex-

wise thickness differences between the MA user groups and the corresponding FH-

control groups (adolescent FH- controls vs. adolescent MA users; adult FH- controls vs.

adult MA users), adjusting for age. In addition, a vertex-wise three-way analysis of

variance (ANOVA) model was used to localize gray matter regions of a significant three-

way interaction between FH, MA use, and age group, which indicated that the

interaction between FH and MA use was greater in adolescents than in adults. A kernel

of 18 mm full width at half maximum was applied to smooth each individual's data onto

the surface tessellation. Multiple comparisons in imaging data were corrected. Z Monte-

Carlo simulation of 5,000 iterations was performed using an initial vertex-wise threshold

of P < 0.05. Clusterwise probability, which means the likelihood of forming a cluster that

size by chance, was calculated and only results that met clusterwise corrected

probability of < 0.05, were deemed statistically significant.

Fractional anisotropy measurement and associated analysesFractional anisotropy (FA), the diffusion tensor parameter reflecting white matter fiber

tract integrity, was the primary outcome variable for the measurement of MA-induced

white matter alterations. Diffusion tensor imaging (DTI) data from the adolescent group

(48 MA users and 59 controls) and the adult group (50 MA users and 59 controls) were

processed separately using the FMRIB Software Library (http://www.fmrib.ox.ac.uk/fsl).

Using the FMRIB's Diffusion Toolbox (FDT), inspected diffusion weighted images


(DWI) were registered to the averaged non-diffusion images (b = 0 s/m2) by affine

transformations in order to correct for head motion and minimize distortions due to eddy

currents. Diffusion tensors were then calculated at the level of an individual voxel to

generate FA images using the DTIFit, a part of FDT tool. Other diffusion indices, for

example, mean diffusivity (MD, λ1+λ2+λ3/3, average water molecule diffusion for all three

eigenvalues of the diffusion tensor), axial diffusivity (AD, λ1, water molecule diffusion for

the principal eigenvalues of the diffusion tensor), and radial diffusivity (RD, λ2+λ3/2,

average water molecule diffusion for the second and third eigenvalues of diffusion

tensor) were also calculated to produce diffusion maps for each index as supplementing

FA results.

Tract-based anatomical localization was conducted to examine the fiber tract

integrity alterations related to MA use. Study-specific tract-based anatomical white

matter regions and their abbreviations are presented in Supplementary Figure 1. Based

on the John Hopkins University (JHU) DTI-based white matter atlas,10 major association

tracts (SLF, ILF, IFOF, UF, and cingulum) and the corpus callosum were selected. Based

on the previous report regarding specific vulnerability of the striatal pathway to chronic

MA use,11 the white matter fibers that connect the cortices and striatum, such as tracts

of the PFC-striatum, OFC-NA, and MC-striatum, were also selected. Because these

corticostriatal tracts have not yet been determined in currently developed DTI

tractography-based white matter atlases, the group maps for these corticostriatal tracts

derived from DTI data of FH- control participants of each adolescent and adult group

were created using deterministic tractography.12

Using the Fiber Assessment by Continuous Tracking (FACT) algorithm,12 whole-

brain tractography including all white matter fibers was reconstructed from the FA maps

of each individual (FA threshold of 0.2 and angle threshold of 45°). A multiple regions of

interest approach was applied to isolate three corticostriatal tracts in each FH- control

participant. The striatal mask for those tracts was composed of the subcortical

structures including the caudate nucleus and putamen for the dorsal striatum and NA for

the ventral striatum, all of which were segmented from high-resolution T1-weighted

images using the FreeSurfer tool. For the delineation of the PFC-striatum and OFC-NA

tracts, the dorsolateral prefrontal and orbitofrontal regions were used as the PFC mask


(Supplementary Figure 6).13 For the delineation of the tract connect the MC and

striatum, the supplementary and primary motor regions were used as the MC mask

(Supplementary Figure 6).13 Regions for these cortical masks were also derived through

the cortical parcellation steps implemented in the FreeSurfer tool. The corticospinal

tracts, inter-hemispheric fibers, tracts originating or terminating in other cortical regions

were defined as relevant exclusion masks for the delineation of these corticostriatal

tracts.13 All these masks were produced in the high-resolution T1-weighted images.

They were registered to the corresponding diffusion image data by an affine

transformation. Using the Trackvis (http://trackvis.org), the corticostriatal tracts were

quantified by including fibers which were connected between each mask of interest and

excluding all fibers passing the exclusion masks. The reconstructed corticostriatal tracts

in diffusion space were then normalized to the Montreal Neurological Institute (MNI)

space. Individual binary maps of each tract in the MNI space were averaged to the

percentage overlap maps representing the probability of the sample with each

corticostriatal tract fibers reconstructed per voxel. DTI tractography-based reference

maps for each tract were defined based on these percentage overlap maps by summing

all voxels that belong to the reconstructed fibers in at least 50% of subjects

(Supplementary Figure 6).

Global FA values and mean FA values of the tract-based anatomical regions

were extracted and adjusted for age. Age-adjusted FA values in the adolescent and

adult MA user groups were converted to standardized Z scores using the means and

SD of the corresponding FH- control groups. Independent t-test was used to compare

standardized Z scores for mean FA values between the adolescent and adult MA user

groups.

As complementary results, the tract-based spatial statistics (TBSS)14 was used

to examine voxel-wise FA value differences between the MA user groups and the

corresponding FH- control groups (adolescent FH- controls vs. adolescent MA users;

adult FH- controls vs. adult MA users), adjusting for age. In addition, voxel-wise three-

way ANOVA model was used to localize white matter regions of a significant interaction

between FH, MA use, and age group, which indicated that the interaction between FH

and MA use was greater in adolescents than in adults.


For TBSS analysis, FA images of all individual participants were aligned into a

common standard space by nonlinearly registering with the FMRIB58 FA standard

template. FA maps were then averaged to make a mean FA image. This mean FA image

was narrowed to generate a mean FA skeleton which was composed of the lines of

maximum FA by applying a skeletonization algorithm. This skeleton represents the

centers of all white matter tracts derived from each individual. Skeleton threshold was

set to 0.2 or higher. Aligned FA data of the whole group was then projected onto this

skeleton to find local FA maxima by searching along perpendiculars to the

corresponding skeletal point. FA data projected onto these skeleton was employed in a

voxel-wise comparison between participants using the Randomise (a TBSS statistical

tool). Multiple comparisons in imaging data were corrected using the threshold free

cluster enhancement (TFCE) approach.15 The number of random permutations was set

to 5,000. Results of TFCE-threshold voxel clusters were deemed significant at P < 0.05.

Mediation analysis Mediation analysis was performed to test the hypothesis that thickness alterations in the

OFC cluster of a significant three-way interaction might have influenced the lifetime

cumulative dose of MA through core clinical symptoms of addiction including craving,

withdrawal, and the Stroop interference.16 The statistical significance of the mediating

effect of the variable M was calculated. The variable M mediates the relationship

between the independent variable (X) and the dependent variable (Y). All mediation

models were adjusted for age. We evaluated the significance of total effect (c) and

divided direct effect (c') and indirect effect (a*b) mediated by the presence of mediator.

The established mediation required (i) the significant total (c) and indirect effects

(across direct paths X to M and M to Y, a*b) and (ii) a reduced direct path coefficient

between X and Y by the inclusion of M into the model. In order to control the type I error,

a bootstrapping method was used to estimate the 95% confidence intervals of the

indirect effects.17 This involves the repeated extraction of samples, with replacement,

from a dataset and the estimation of the indirect effects in each resampled dataset (P <

0.05, using bootstrapping with 5,000 samples).17,18 The percent mediation was

calculated to provide an index of the strength of mediation.19


Supplementary Result 1. Results of group differences in white matter diffusion indices.

Findings regarding age-adjusted global mean diffusivity indices (RD, AD, and MD)

across the whole white matter skeleton indicated that adolescent MA users were likely

to have higher global mean RD values relative to adolescent FH- controls (t = -1.95, P =

0.06), while there were no differences in global mean AD and MD values (AD, t = 1.35,

P = 0.18; MD, t = -0.76, P = 0.45) between adolescent MA users and adolescent FH-

controls. A similar pattern of alterations related to MA use in white matter diffusion

indices was observed in the adult group. (RD, t = -2.03, P = 0.05; AD, t = -0.01, P =

0.99; MD, t = -1.38, P = 0.17).

Although the exact mechanisms underlying differential changes in diffusivity

measures are not well known, FA reductions along with increases in RD values may

indicate loss of myelin.20-22 These findings have been observed in several conditions that

are related to demyelination, such as acute multiple sclerosis23 and drug-induced

demyelination.20,24,25 Notably, prenatal exposure to MA has been related to altered

myelination in the animal model.25,26 Accordingly, the current results suggest that

demyelination or underdeveloped myelination may be one of primary pathophysiological

mechanisms underlying MA-induced white matter regional differences.

Supplementary Result 2. Repeated analyses including potential confounding factors

such as smoking history, the route of MA administration, or alcohol drinking as

additional covariates.

Analyses including smoking history as a covariate: Comparisons of standardized Z

scores for mean thickness of the OFC (t = 2.93, P = 0.004), precuneus (t = 3.18, P =

0.002), and IPC (t = 3.47, P = 0.001) showed significant differences between the

adolescent and adult MA user groups, adjusting for current smoking status. There was

no difference in standardized Z scores for global mean thickness between the

adolescent and adult MA user groups adjusting for current smoking status (t = 1.96, P =

0.05).

Standardized Z scores reflecting FA differences between MA users and FH-


controls were greater in adolescents than in adults in the PFC-striatum tract, (t = 2.49, P

= 0.01), OFC-NA tract (t = 2.76, P = 0.007), and the body of the corpus callosum (t =

2.12, P = 0.04), adjusting for current smoking status. The magnitude of differences in

global mean FA values across the whole white matter skeleton between MA users and

FH- controls was greater in adolescents than in adults adjusting for current smoking

status (t = 2.54, P = 0.01).

The relationships between lifetime cumulative dose of MA and standardized Z

scores for global mean thickness and thickness of the gray matter ROIs were examined

adjusting for current smoking status in adolescent MA users. The results remained

similar (global mean thickness, rp = -0.42, P = 0.003; OFC, rp = -0.26, P = 0.24;

precuneus, rp = -0.37, P = 0.03; IPC, rp = -0.34, P = 0.06). Lifetime cumulative dose of

MA was not associated with standardized Z scores for global mean FA values (rp = -

0.29, P = 0.05) and FA values of the white matter ROIs adjusting for current smoking

status (PFC-striatum, r = -0.14, P = 1.00; OFC-NA, r = -0.18, P = 0.68; the body of the

corpus callosum, r = -0.29, P = 0.16).

The relationships between onset age of MA and standardized Z scores for

thickness were not significant adjusting for current smoking status in adolescent MA

users (global mean thickness, rp = 0.11, P = 0.46; OFC, rp = 0.16, P = 0.83; precuneus,

rp = -0.18, P = 0.63; IPC, rp = 0.02, P = 1.00). The results from repeated analyses for the

relationships between onset age of MA and standardized Z scores for FA values in

adolescent MA users also remained unchanged adjusting for current smoking status

(global mean FA values, rp = 0.25, P = 0.08; PFC-striatum, rp = 0.35, P = 0.05; OFC-NA,

rp = 0.35, P = 0.05; the body of the corpus callosum, rp = 0.23, P = 0.37).

The three-way interaction between FH, MA use, and age group was significant

in the OFC ROI adjusting for current smoking status (F = 4.49, P = 0.04).

Analyses including the route of MA administration as a covariate: Standardized Z scores

for mean thickness were compared between the adolescent and adult MA user groups

adjusting for the route of MA administration. These repeated analyses yielded similar

results (global mean thickness, t = 0.99, P = 0.32; OFC, t = 2.24, P = 0.03; precuneus, t

= 2.44, P = 0.02; IPC, t = 2.60, P = 0.01).


We also performed the comparisons of standardized Z scores for FA values

between the adolescent and adult MA user groups adjusting for the route of MA

administration. The results from these repeated analyses remained unchanged (global

mean FA values, t = 2.51, P = 0.01; PFC-striatum, t = 3.05, P = 0.003; OFC-NA, t =

2.74, P = 0.007; the body of the corpus callosum, t = 2.05, P = 0.04).


scores for thickness were examined after adjusting for the route of MA administration in

adolescent MA users and the results remained similar (global mean thickness, rp = -

0.48, P < 0.001; OFC, rp = -0.33, P = 0.07; precuneus, rp = -0.42, P = 0.009; IPC, rp = -

0.37, P = 0.03). Repeated analyses for the relationships between lifetime cumulative

dose of MA and standardized Z scores for FA values in adolescent MA users after

adjusting for the route of MA administration also yielded similar results (global mean FA

values, rp = -0.26, P = 0.08; PFC-striatum, rp = -0.14, P = 1.00; OFC-NA, rp = -0.14, P =

1.00; the body of the corpus callosum, rp = -0.23, P = 0.36).

The relationships between onset age of MA use and standardized Z scores for

thickness (global mean thickness, rp = 0.18, P = 0.23; OFC, rp = 0.23, P = 0.34;

precuneus, rp = -0.11, P = 1.00; IPC, rp = 0.05, P = 1.00) or FA values (global mean FA

values, rp = 0.22, P = 0.14; PFC-striatum, rp = 0.37, P = 0.04; OFC-NA, rp = 0.31, P =

0.11; the body of the corpus callosum, rp = 0.17, P = 0.73) were analyzed in adolescent

MA users adjusting for the route of MA administration. The results from this repeated

analysis remained unchanged.

Analyses including alcohol drinking as a covariate: Repeated analyses for the group

differences in standardized Z scores for mean thickness remained unchanged after the

inclusion of the amount of weekly alcohol consumption as an additional covariate

(global mean thickness, t = 1.75, P = 0.08; OFC, t = 2.64, P = 0.01; precuneus, t = 3.23,

P = 0.002; IPC, t = 3.20, P = 0.002).

Results from the repeated analyses for group differences in standardized Z scores

for FA values adjusting for the amount of weekly alcohol consumption were also similar

to the main findings (global mean FA values, t = 2.33, P = 0.02; PFC-striatum, t = 2.16,

P = 0.03; OFC-NA, t = 2.67, P = 0.009; the body of the corpus callosum, t = 1.92, P =


0.06).


scores for global mean thickness and thickness of the gray matter ROIs were also

repeated after adjusting for the amount of weekly alcohol consumption in adolescents

MA users. The results remained similar (global mean thickness, rp = -0.44, P = 0.002;

OFC, rp = -0.29, P = 0.14; precuneus, rp = -0.38, P = 0.02; IPC, rp = -0.33, P = 0.07).

Likewise, no significant relationships were found between lifetime cumulative dose of

MA and standardized Z scores for global mean FA values and FA values of white matter

ROIs after adjusting the amount of weekly alcohol consumption (global mean FA values,

rp = -0.27, P = 0.06; PFC-striatum, rp = -0.15, P = 0.99; OFC-NA, rp = -0.12, P = 1.00; the

body of the corpus callosum, rp = -0.26, P = 0.24).

The relationships between onset age of MA use and standardized Z scores for

thickness (global mean thickness, rp = 0.14, P = 0.34; OFC, rp = 0.21, P = 0.49;

precuneus, rp = -0.15, P = 0.94; IPC, rp = 0.03, P = 1.00) or FA values (global mean FA

values, rp = 0.23, P = 0.12; PFC-striatum, rp = 0.35, P = 0.05; OFC-NA, rp = 0.29, P =

0.16; the body of the corpus callosum, rp = 0.19, P = 0.59) were also re-analyzed

adjusting for the amount of weekly alcohol consumption in adolescent MA users. The

results remained similar.


Supplementary Figure 1. Anatomical locations of atlas-based parcellated gray matter regions (a) and tract-based anatomical white matter regions (b).

(a) Atlas-based parcellation of gray matter was conducted according to the genetically based cortical surface map.7 The cerebral cortex was parcellated into the following subregions for each hemisphere using the composition of labeling system of the Desikan-Killiany Atlas.8 Cortical areas that consist of each subregion are as follows; 1) sensorimotor: caudal middle frontal cortex, precentral cortex, postcentral cortex, and paracentral cortex; 2) dorsolateral prefrontal (DLPFC): superior frontal cortex, rostral middle frontal cortex, pars opercularis, and pars trigangularis; 3) orbitofrontal (OFC): pars orbitalis, lateral orbitofrontal cortex, and medial orbitofrontal cortex; 4) superior parietal (SPC): superior parietal cortex; 5) inferior parietal (IPC): inferior parietal cortex and supramarginal cortex; 6) anteromedial temporal (AMTC): parahippocampal cortex, fusiform cortex, entorhinal cortex, and temporal pole; 7) superior temporal (STC): superior temporal cortex and transverse temporal cortex; 8) posterolateral temporal (PLTC): banks of superior temporal sulcus, middle temporal cortex, and inferior temporal cortex; 9) insula: insular cortex; 10) precuneus: precuneus cortex; 11) posterior cingulate (PCC): posterior cingulate cortex and isthmus of cingulate cortex; 12) anterior cingulate (ACC): rostral anterior cingulate cortex and caudal anterior cingulate cortex.

(b) Tract-based anatomical localization was conducted according to the John Hopkins University DTI-based white matter atlas9 as follows; the superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), inferior fronto-occipital fasciculus (IFOF), uncinate fasciculus (UF), cingulum, and corpus callosum. For the corticostriatal tracts (motor cortex [MC]-striatum, prefrontal cortex [PFC]-striatum, orbitofrontal cortex [OFC]-nucleus accumbens [NA]), adolescent and adult group maps derived from DTI data of FH- controls in each group were created using deterministic tractography.

Abbreviations: DTI, diffusion tensor imaging; FH-, a negative family history of drug use.


Supplementary Figure 2. t-statistical maps for clusters of significant group differences in cortical thickness.

Regions of significant group differences relative to the corresponding FH- control group *R L

Adolescent MA users The t statistic map showing regions of significant group differences between

adolescent FH- controls (n = 30) and all adolescent MA users (n = 49) is overlaid

on the standard cortical surface.

Adult MA users The t statistic map showing regions of

significant group differences between adult FH- controls (n = 30) and all adult MA

users (n = 53) is overlaid on the standard cortical surface.

t valuesThinner than the corresponding FH- control group

t = 0 t = 2.0 t > 4.0

* The t values were calculated using a general linear model after adjustment for age. There were no regions of significantly greater cortical thickness in each group than in the corresponding FH- controls. Detailed information on clusters is presented in Supplementary Table 2. Abbreviations: FH-, a negative family history of drug use; MA, methamphetamine; R, right; L, left.


Supplementary Figure 3. t-statistical maps for clusters of significant group differences in white matter FA values.

Regions of significant group differences relative to the corresponding FH- control group *y = -10 y = 0 y =10 y =20 R L y =20 y =10 y = 0 y = -10

Adolescent MA users The t statistic map showing regions of significant group differences between

adolescent FH- controls (n = 29) and all adolescent MA users (n = 48) is overlaid

on the standard MNI template.

Adult MA users

The t statistic map showing regions of significant group differences between adult

FH- controls (n = 30) and all adult MA users (n = 50) is overlaid on the standard

MNI template.

Mean skeleton of the adolescent group t values

Smaller FA values than the corresponding FH- control group t = 0 1.5 t > 3.0

* The t values were calculated using a general linear model after adjustment for age. There were no regions of significantly greater FA values in each group than in the corresponding FH- controls.Detailed information on clusters is presented in Supplementary Table 3. Abbreviations: FA, fractional anisotropy; FH-, a negative family history of drug use; MA, methamphetamine; MNI, Montreal Neurological Institute; R, right; L, left.


Supplementary Figure 4. Relationships between lifetime cumulative dose of MA and standardized Z scores for global mean thickness in adolescent and adult MA users.

Scatter plots and regression lines indicate the associations between lifetime cumulative dose of MA and standardized Z scores for global mean thickness in adolescent (r = -0.47, P = 0.001) and adult (r = -0.32, P = 0.02) MA users. The degree of the associations was greater in adolescent than in adult MA users (P for interaction = 0.01). Since there was a difference in lifetime cumulative dose of MA between adolescent and adult MA users (t = 5.90, P < 0.001), we repeated the correlation analysis within a subsample of adult users whose lifetime cumulative MA doses were less than 400g. This subsample analysis showed a correlation similar to that observed in all adult MA users (light gray background on the right column, r = -0.26, P = 0.12). *P < 0.05, **P < 0.01.Abbreviations: MA, methamphetamine.


Supplementary Figure 5. Statistical maps for a cluster of a significant three-way interaction between family history, MA use, and age group.

Detailed information on clusters is presented in Supplementary Table 4. Abbreviations: FH, family history of drug use; MA, methamphetamine; OFC, orbitofrontal cortex; R, right.


Supplementary Figure 6. Percentage overlapping maps for the corticostriatal white matter tracts.

Cortical and subcortical masks for delineation of the corticostriatal white matter tracts are overlaid on the coronal planes of the standard MNI template (Left columns). All masks were defined in high-resolution T1-weighted images of each individual using the FreeSurfer tool. Areas that consist of each cortical mask as follows; 1) PFC mask: DLPFC (rostral middle frontal cortex, pars opercularis, and pars trigangularis) and OFC (pars orbitalis, lateral orbitofrontal cortex, and medial orbitofrontal cortex); 2) MC mask: caudal middle frontal cortex, precentral cortex, and paracentral cortex; 3) OFC mask: pars orbitalis, lateral orbitofrontal cortex, and medial orbitofrontal cortex.The percentage overlapping maps for each corticostriatal white matter tract are depicted in the right columns. They were created by averaging the normalized binary tract maps in the standard MNI space, which was originally derived from diffusion tensor image data (FH- controls in each adolescent and adult group) using the deterministic tractography. Reference maps for each tract were defined by all voxels that belong to the reconstructed fibers in at least 50% of participants. Three-dimensional rendering of reconstructed tracts (red for the PFC-striatum tract, blue for the MC-striatum tract, and yellow for the OFC-NA tract) of one control participant representative of the study population is also displayed in the central columns. Abbreviations: PFC, prefrontal cortex; OFC, orbitofrontal cortex; DLPFC, dorsolateral prefrontal cortex; MC, motor cortex; NA, nucleus accumbens; MNI, Montreal Neurological Institute; FH-, a negative family history of drug use.


Supplementary Table 1. Neuropsychological measures and their domain in the present study. Neuropsychological test Dependent measure Cognitive domainTrail Making Test B Total time taken to complete B trial

Executive functionStroop Test - Interference Difference between 'time per item of color task' and 'time per item of color-word task'

Wisconsin Card Sorting Test Perseverative errors

Wisconsin Card Sorting Test Non-perseverative errors

Rey-Osterrieth Complex Figure Test Delayed recall score

MemoryRey-Osterrieth Complex Figure Test Retention score

California Verbal Learning Test Short-delay free recall score

California Verbal Learning Test Long-delay free recall scores

California Verbal Learning Test Words recalled in trials 1-5 Learning

Controlled Oral Word Association Test Total number of words generated in each category in 60 seconds Verbal fluency

Digit Span Task Total number of forward digit recall

Working memoryDigit Span Task Total number of backward digit recall

Spatial Span Task Total number of forward spatial recall

Spatial Span Task Total number of backward spatial recall

Digit Symbol Substitution Test Numbers of correctly identified digit-symbol pairs

Information processing speed

Stroop Test - C Form Total time taken to complete color task

Stroop Test - CW Form Total time taken to complete color-word task

Trail Making Test A Total time taken to complete A trial

Grooved Pegboard Test Total time taken to place pegs: dominant handsMotor skill

Grooved Pegboard Test Total time taken to place pegs: non-dominant hands


Supplementary Table 2. Detailed information on clusters of significant group differences in cortical thickness.

Regions

Corresponding regions of

atlas-based parcellation

map

Cluster size

(mm2)

Number of vertices in the cluster

Maximum t values

Talairach coordinate

x y z

Clusters showing thinner cortex in adolescent MA users relative to adolescent FH- controls *Right

1 Superior frontal gyrus (BA 9)Rostral middle frontal gyrus (BA 46)Pars opercularis (BA 44)Precentral gyrus (BA 4)

DLPFC Sensorimotor cortex

6481.7 11318 4.42 12.0 58.4 17.3

2 Insula (BA 13)Superior temporal gyrus (BA 41)

Insula cortexSTC

2206.4 4868 3.94 39.3 -12.9 -9.3

3 Fusiform gyrus (BA 37)Lingual gyrus (BA 19)

AMTCOccipital cortex

1309.4 2232 3.18 28.0 -45.4 -11.1

Left1 Lateral orbitofrontal gyrus (BA 11)

Pars orbitalis (BA 47)Insula (BA 13)

OFCInsula cortex

2531.9 5449 5.44 -42.9 26.8 -11.2

2 Caudal middle frontal gyrus (BA 6)Precentral gyrus (BA 4)

Sensorimotor cortex 1705.4 3443 4.11 -31.0 -1.3 41.2

3 Precuneus (BA 7) Precuneus cortex 1531.2 3000 4.10 -6.1 -58.2 34.74 Postcentral gyrus (BA 1,2,3)

Superior parietal gyrus (BA 7)Sensorimotor cortexSPC

1974.3 4729 3.96 -43.2 -26.7 44.5

5 Superior temporal gyrus (BA 41, 42)banks of superior temporal sulcus (BA22)

STCPLTC

1640.8 3387 3.95 -52.1 -40.1 7.2

6 Pars opercularis (BA 44)Precentral gyrus (BA 43)Postcentral gyrus (BA 43) Supramarginal gyrus (BA 40)

DLPFC Sensorimotor cortexIPC

4224.2 9536 3.73 -45.4 15.3 20.7

7 Fusiform gyrus (BA 37) Inferior temporal gyrus (BA 20)

AMTCPLTC

1713.9 2908 3.59 -38.3 -51.3 -14.3.

8 Inferior parietal gyrus (BA 39) IPC 1426.3 2389 3.11 -45.4 -72.4 15.7Clusters showing thinner cortex in adult MA users and adult FH- controls **Right

1 Precentral gyrus (BA 4)Postcentral gyrus (BA 43)

Sensorimotor cortex 4645.3 10402 5.49 31.8 -20.6 47.9

2 Superior temporal gyrus (BA 41, 42)Transverse temporal gyrus (BA 41)Middle temporal gyrus (BA 21)Banks of superior temporal sulcus (BA22)

STCPLTC

3395.7 7474 3.60 51.7 -21.6 -11.0

3 Fusiform gyrus (BA 37) Lingual gyrus (BA 19)

AMPCOccipital cortex

3992.1 5991 3.51 24.8 -76.3 -2.0


Left1 Superior temporal gyrus (BA 41, 42)

Banks of superior temporal sulcus (BA22)STC 1949.8 4070 4.91 -54.3 -6.7 -7.3

2 Precentral gyrus (BA 4)Postcentral gyrus (BA 43)

Sensorimotor cortex 1092.9 2575 3.22 -48.0 -18.0 41.3

A general linear model was used to examine the main group effects on thickness of each vertex adjusting for age. Clusterwise P values of < 0.05 (initial cluster-forming threshold at P < 0.05) based on 5,000 Monte Carlo simulations were used to correct for multiple comparisons. ** There were no regions of significantly greater cortical thickness in adolescent MA users than in adolescent FH- controls.† There were no regions of significantly greater cortical thickness in adult MA users than in adult FH- controls.

Abbreviations: MA, methamphetamine; FH-, a negative family history of drug use; BA, Brodmann area. Abbreviations for atlas-based parcellated regions of gray matter are provided in Supplementary Figure 1.


Supplementary Table 3. Detailed information on clusters of significant group differences in white matter FA values.

Corresponding cortical Area Corresponding regions of tract-based anatomical map

Number of voxels in the

clusterMaximum

t value

MNI atlas coordinates

(location of maximum t-value)

x y zClusters showing smaller FA values in adolescent MA users relative to adolescent FH- controls *

1 Cortices throughout entire cerebrum Corticostriatal tract, PFC-StriatumCorticostriatal tract, OFC-NACorticostriatal tract, MC-StriatumUFIFOFCingulumCCSLFILF

45996 6.13 27 24 -15

Clusters showing smaller FA values in adult MA users relative to adult FH- controls **Clusters of smaller FA values in all adult MA users

1 Cortices throughout entire cerebrum Corticostriatal tract, PFC-StriatumCorticostriatal tract, MC-StriatumUFIFOFSLFILF

13363 5.53 41 -30 -15

2 Frontal cortex (L) CC 1204 4.03 -17 0 393 Frontal cortex (L) Corticostriatal tract, PFC-Striatum

Corticostriatal tract, MC-Striatum338 4.11 -29 0 17

4 Frontal/ Parietal cortices (L) Corticostriatal tract, MC-Striatum 173 3.90 -20 -20 475 Frontal cortex (R) Not available 163 3.42 -11 36 426 Frontal cortex (L) Not available 121 5.50 18 20 42

Significant clusters were calculated by the tract-based spatial statistics analysis for differences in FA values between groups after adjustment for age. The threshold-free cluster enhancement approach was used to correct for multiple comparisons at P < 0.05. Minimum cluster size is greater than 50 voxels.

* There were no regions of significantly greater FA values in adolescent MA users than in adolescent FH- controls.** There were no regions of significantly greater FA values in adult MA users than in adult FH- controls. There were no regions of significant differences in FA

values between adult FH- controls and adult FH+ controls. Abbreviations: R, right; L, left; FA, fractional anisotropy; MA, methamphetamine; FH+, a positive family history of drug use; FH-, a negative family history of drug use; MNI, Montreal Neurological Institute. Abbreviations for the tract-based anatomical regions of white matter are provided in Supplementary Figure 1.


Supplementary Table 4. Detailed information on clusters of a significant three-way interaction.

Regions

Corresponding regions of

atlas-based parcellation

map

Cluster size

(mm2)

Number of vertices in the cluster

Maximum t values

Talairach coordinate

x y z

Clusters showing a significant three-way interaction between family history, MA use, and age group*Right

1 Lateral orbitofrontal gyrus (BA 11) OFC 1233.0 2035 3.82 28.8 26.6 -13.9* Three-way analysis of variance (ANOVA) model included the main effects of family history, MA use, and age group, all possible two-way interactions, and a three-way interaction. Clusterwise P values of < 0.05 (initial cluster-forming threshold at P < 0.05) based on 5,000 Monte Carlo simulations were used to correct for multiple comparisons. Abbreviations: ROI, region-of-interest; MA, methamphetamine; BA, Brodmann area; OFC, orbitofrontal cortex.


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