Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking

Pfefferbaum, Adolf; Kwon, Dong-Jin; Brumback, Ty; Thompson, Wesley K.; Cummins, Kevin; Tapert, Susan F.; Brown, Sandra A.; Colrain, Ian M.; Baker, Fiona C.; Prouty, Devin; Bellis, Michael D. De; Clark, Duncan B.; Nagel, Bonnie J.; Chu, Weiwei; Park, Sang Hyun; Pohl, Kilian M.; Sullivan, Edith V.

doi:10.1176/appi.ajp.2017.17040469

Cited by 162 publications

(155 citation statements)

References 33 publications

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“…The drinking threshold was based on National Institute on Alcohol Abuse and Alcoholism guidelines for risky drinking that varied by age and sex. As stated previously (Pfefferbaum et al, ), 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.…”

Section: Methodssupporting

confidence: 69%

Distribution of brain iron accrual in adolescence: Evidence from cross‐sectional and longitudinal analysis

Peterson

Kwon

Luna

et al. 2018

Human Brain Mapping

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To track iron accumulation and location in the brain across adolescence, we repurposed diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data acquired in 513 adolescents and validated iron estimates with quantitative susceptibility mapping (QSM) in 104 of these subjects. DTI and fMRI data were acquired longitudinally over 1 year in 245 male and 268 female, no-to-low alcohol-consuming adolescents (12-21 years at baseline) from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study.Brain region average signal values were calculated for susceptibility to nonheme iron deposition: pallidum, putamen, dentate nucleus, red nucleus, and substantia nigra. To estimate nonheme iron, the corpus callosum signal (robust to iron effects) was divided by regional signals to generate estimated R 2 (edwR 2 for DTI) and R 2 * (eR 2 * for fMRI). Longitudinal iron deposition was measured using the normalized signal change across time for each subject. Validation using baseline QSM, derived from susceptibility-weighted imaging, was performed on 46 male and 58 female participants. Normalized iron deposition estimates from DTI and fMRI correlated with age in most regions; both estimates indicated less iron in boys than girls. QSM results correlated highly with DTI and fMRI results (adjusted R 2 = 0.643 for DTI, 0.578 for fMRI). Cross-sectional and longitudinal analyses indicated an initial rapid increase in iron, notably in the putamen and red nucleus, that slowed with age. DTI and fMRI data can be repurposed for identifying regional brain iron deposition in developing adolescents as validated with high correspondence with QSM.

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Section: Methodssupporting

confidence: 69%

Distribution of brain iron accrual in adolescence: Evidence from cross‐sectional and longitudinal analysis

Peterson

Kwon

Luna

et al. 2018

Human Brain Mapping

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“…Clearly, change toward normal levels of brain tissue volumes (Durazzo et al, 2015) and white matter integrity (Pfefferbaum et al, 2014) together with improvement in neuropsychological functioning beyond simple practice effects (Durazzo and Meyerhoff, 2020;Sullivan et al, 2000) has been observed with quantitative, longitudinal assessment and provides evidence for a role for changes in drinking levels to be causative. Conversely, prospective studies of adolescents before and after initiating appreciable alcohol drinking reported accelerated gray matter volume loss predominantly in frontal cortex compared with adolescents who remained no-to-low drinkers (Pfefferbaum et al, 2018a). What the actual biological mechanisms of change are remain to be discovered and may rely on animal models (e.g., Zahr et al, 2013).…”

Section: Frontal System Insult and Recoverymentioning

confidence: 99%

The Many Levels of Relapse to Drinking: Commentary on Meyerhoff and Durazzo (ACER 2020)

Sullivan

2020

Alcoholism Clin & Exp Res

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“…Volume measures, provided by the competition organizers, were derived from the T1-weighted images as follows: the Minimal Processing pipeline [8] transformed the raw data into NIfTI format. Afterwards, the NCANDA pipeline [16] defined a brain mask by non-linearly mapping the SRI24 atlas [17] to the T1-weighted images, and it removed noise and corrected for bias-field inhomogeneities. Several skull-stripping methods were used with bias-field corrected and non-bias-field corrected images, and a majority voting of the resulting masks refined the brain masks from the previous step.…”

Section: Image Pre-processingmentioning

confidence: 99%

Predicting Intelligence Based on Cortical WM/GM Contrast, Cortical Thickness and Volumetry

Valverde

Imani

Lewis

et al. 2019

Lecture Notes in Computer Science

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We propose a four-layer fully-connected neural network (FNN) for predicting fluid intelligence scores from T1-weighted MR images for the ABCD-challenge. In addition to the volumes of brain structures, the FNN uses cortical WM/GM contrast and cortical thickness at 78 cortical regions. These last two measurements were derived from the T1-weighted MR images using cortical surfaces produced by the CIVET pipeline. The age and gender of the subjects and the scanner manufacturer are also used as features for the learning algorithm. This yielded 283 features provided to the FNN with two hidden layers of 20 and 15 nodes. The method was applied to the data from the ABCD study. Trained with a training set of 3736 subjects, the proposed method achieved a MSE of 71.596 and a correlation of 0.151 in the validation set of 415 subjects. For the final submission, the model was trained with 3568 subjects and it achieved a MSE of 94.0270 in the test set comprised of 4383 subjects.

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Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking

Abstract: 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.

Cited by 162 publications

References 33 publications

Distribution of brain iron accrual in adolescence: Evidence from cross‐sectional and longitudinal analysis

Distribution of brain iron accrual in adolescence: Evidence from cross‐sectional and longitudinal analysis

The Many Levels of Relapse to Drinking: Commentary on Meyerhoff and Durazzo (ACER 2020)

Predicting Intelligence Based on Cortical WM/GM Contrast, Cortical Thickness and Volumetry

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