Neural predictors of initiating alcohol use during adolescence

American Journal of Psychiatry

Volumn 174, Issue 2, 2017, Pages 172-185

  Squeglia, Lindsay M ^a,b,c,d,e,f   Ball, Tali M ^a,b,c,d,e,f   Jacobus, Joanna ^a,b,c,d,e,f   Brumback, Ty ^a,b,c,d,e,f   McKenna, Benjamin S ^a,b,c,d,e,f   Nguyen Louie, Tam T ^a,b,c,d,e,f   Sorg, Scott F ^a,b,c,d,e,f   Paulus, Martin P ^a,b,c,d,e,f   Tapert, Susan F ^a,b,c,d,e,f  

^a MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

^b STANFORD UNIVERSITY   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d VA SAN DIEGO HEALTHCARE SYSTEM   (United States)

^e SAN DIEGO STATE UNIVERSITY   (United States)

^f LAUREATE INSTITUTE FOR BRAIN RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADOLESCENT; ADOLESCENT BEHAVIOR; ARTICLE; BOLD SIGNAL; BRAIN REGION; CHILD; CONTROLLED STUDY; CORTICAL THICKNESS (BRAIN); DEMOGRAPHY; DRINKING BEHAVIOR; EXECUTIVE FUNCTION; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GENDER; HUMAN; HUMAN EXPERIMENT; MACHINE LEARNING; MALE; NEUROIMAGING; NEUROPSYCHOLOGICAL TEST; NEUROPSYCHOLOGY; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PREDICTION; PROSPECTIVE STUDY; SEXUAL BEHAVIOR; SOCIAL STATUS; STRUCTURAL MAGNETIC RESONANCE IMAGING; UNDERAGE DRINKING; AGE; ALCOHOLISM; BRAIN; BRAIN CORTEX; BRAIN MAPPING; CONTROL; CULTURAL ANTHROPOLOGY; FOLLOW UP; PATHOPHYSIOLOGY; PREDICTIVE VALUE; PSYCHOLOGY; RISK FACTOR; SEX DIFFERENCE; SOCIOECONOMICS; UNITED STATES;

ADOLESCENT; AGE FACTORS; ALCOHOLISM; BRAIN; BRAIN MAPPING; CEREBRAL CORTEX; CULTURE; FEMALE; FOLLOW-UP STUDIES; HUMANS; INTERNAL-EXTERNAL CONTROL; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MALE; NEUROPSYCHOLOGICAL TESTS; PREDICTIVE VALUE OF TESTS; RISK FACTORS; SEX FACTORS; SOCIOECONOMIC FACTORS; UNDERAGE DRINKING; UNITED STATES;

EID: 85011623895     PISSN: 0002953X     EISSN: 15357228     Source Type: Journal    
DOI: 10.1176/appi.ajp.2016.15121587     Document Type: Article

References (49)

1. Johnston LD, O'Malley PM, Miech RA, et al: Monitoring the Future: National Survey Results on Drug Use: 1975-2014: Overview, Key Findings on Adolescent Drug Use. Ann Arbor, University of Michigan, Institute for Social Research, 2015
2. Miller JW, Naimi TS, Brewer RD, et al: Binge drinkingand associated health risk behaviors among high school students. Pediatrics 2007; 119:76-85
3. Squeglia LM, Jacobus J, Tapert SF: The effect of alcohol use on human adolescent brain structures and systems. Handb Clin Neurol 2014; 125:501-510
4. Squeglia LM, Gray KM: Alcohol and drug use and the developing brain. Curr Psychiatry Rep 2016; 18:46
5. Brown SA, McGue M, Maggs J, et al: Adevelopmental perspective on alcohol and youths 16 to 20years of age. Pediatrics 2008; 121(suppl 4): S290-S310
6. Squeglia LM, Jacobus J, Nguyen-Louie TT, et al: Inhibition during early adolescence predicts alcohol and marijuana use by late adolescence. Neuropsychology 2014; 28:782-790
7. Squeglia LM, Tapert SF, Sullivan EV, et al: Brain development in heavy-drinking adolescents. Am J Psychiatry 2015; 172:531-542
8. Squeglia LM, Pulido C, Wetherill RR, et al: Brain response to working memory over three years of adolescence: influence of initiating heavy drinking. J Stud Alcohol Drugs 2012; 73:749-760
9. Wetherill RR, Squeglia LM, Yang TT, et al: A longitudinal examination of adolescent response inhibition: neural differences before and after the initiation of heavy drinking. Psychopharmacology (Berl) 2013; 230:663-671
10. Heitzeg MM, Nigg JT, Hardee JE, et al: Left middle frontal gyrus response to inhibitory errors in children prospectively predicts early problem substance use. Drug Alcohol Depend 2014; 141:51-57
11. Whelan R, Watts R, OrrCA, et al:Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 2014; 512:185-189
12. Passos IC, Mwangi B, Kapczinski F: Big data analytics and machine learning: 2015 and beyond. Lancet Psychiatry 2016; 3:13-15
13. Patel MJ, AndreescuC, Price JC, et al: Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry 2015; 30:1056-1067
14. Lavagnino L, Amianto F, Mwangi B, et al: Identifying neuroanatomical signatures of anorexia nervosa: a multivariate machine learning approach. Psychol Med 2015; 45:2805-2812
15. Hajek T, CookeC, Kopecek M, et al: Using structuralMRIto identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study. J Psychiatry Neurosci 2015; 40:316-324
16. Sato JR, Moll J, Green S, et al:Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression. Psychiatry Res 2015; 233:289-291
17. Shouval R, Bondi O, Mishan H, et al: Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT. Bone Marrow Transplant 2014; 49:332-337
18. Pariyadath V, Stein EA, Ross TJ: Machine learning classification of resting state functional connectivity predicts smoking status. Front Hum Neurosci 2014; 8:425
19. Brodersen KH, Deserno L, Schlagenhauf F, et al: Dissecting psychiatric spectrum disorders by generative embedding. Neuroimage Clin 2013; 4:98-111
20. Breiman L: Random forests. Mach Learn 2001; 45:5-32
21. Qi Y, Bar-Joseph Z, Klein-Seetharaman J: Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins 2006; 63:490-500
22. Strobl C, Malley J, Tutz G: An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods 2009; 14:323-348
23. Ball TM, Stein MB, Ramsawh HJ, et al: Single-subject anxiety treatment outcome prediction using functional neuroimaging. Neuropsychopharmacology 2014; 39:1254-1261
24. Squeglia LM, Spadoni AD, Infante MA, et al: Initiating moderate to heavy alcohol use predicts changes in neuropsychological functioning for adolescent girls and boys. Psychol Addict Behav 2009; 23:715-722
25. Brown SA, Myers MG, Lippke L, et al: Psychometric evaluation of the Customary Drinking and Drug Use Record (CDDR): a measure of adolescent alcohol and drug involvement. J Stud Alcohol 1998; 59:427-438
26. Brown SA, Myers MG, Mott MA, et al: Correlates of success following treatment for adolescent substance abuse. Appl Prev Psychol 1994; 3:61-73
27. Hollingshead AB: Two-factor index of social position. New Haven, Conn, Yale University Press, 1965
28. Rice JP, Reich T, Bucholz KK, et al: Comparison of direct interview and family history diagnoses of alcohol dependence. Alcohol Clin Exp Res 1995; 19:1018-1023
29. Petersen AC, Crockett L, Richards M, et al: A self-report measure of pubertal status: reliability, validity, and initial norms. J Youth Adolesc 1988; 17:117-133
30. Achenbach TM, Rescorla LA: Manual for the ASEBA School-Age Forms & Profiles. Burlington, Vt, University of Vermont, Research Center for Children, Youth, & Families, 2001
31. Brown SA, Gleghorn A, SchuckitMA, et al: Conduct disorder among adolescent alcohol and drug abusers. J Stud Alcohol 1996; 57:314-324
32. Beck AT, Steer RA, Brown GK: Manual for the Beck Depression Inventory-2. San Antonio, Tex, Psychological Corporation, 1996
33. Spielberger CD, Gorsuch RL, Lushene RE: Manual for the State-Trait Anxiety Inventory. PaloAlto, Calif, Consulting Psychologists Press, 1970
34. Brown SA, Christiansen BA, Goldman MS: The Alcohol Expectancy Questionnaire: an instrument for the assessment of adolescent and adult alcohol expectancies. J Stud Alcohol 1987; 48:483-491
35. TapertSF, Pulido C, PaulusMP, et al: Level of responseto alcoholand brain response during visual working memory. J Stud Alcohol 2004; 65:692-700
36. Luck SJ, Vogel EK: The capacity of visual working memory for features and conjunctions. Nature 1997; 390:279-281
37. Dale AM, Fischl B, Sereno MI: Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 1999; 9:179-194
38. Fischl B, Sereno MI, Dale AM: Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 1999; 9:195-207
39. Desikan RS, Ségonne F, Fischl B, et al: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006; 31:968-980
40. Cox RW: AFNI: software for analysis and visualization of functional magnetic resonanceneuroimages.ComputBiomedRes 1996;29:162-173
41. Cox RW, Jesmanowicz A: Real-time 3D image registration for functional MRI. Magn Reson Med 1999; 42:1014-1018
42. CohenJD, PerlsteinWM, BraverTS, et al:Temporaldynamicsof brain activation during a working memory task. Nature 1997; 386:604-608
43. Saad ZS, Reynolds RC: SUMA. Neuroimage 2012; 62:768-773
44. Squeglia LM, McKenna BS, Jacobus J, et al: BOLD response to working memory not related to cortical thickness during early adolescence. Brain Res 2013; 1537:59-68
45. Genuer R, Poggi JM, Tuleau-Malot C: Variable selection using random forests. Pattern Recognit Lett 2010; 31:2225-2236
46. Kriegeskorte N, Simmons WK, Bellgowan PS, et al: Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 2009; 12:535-540
47. Brown SA, Brumback T, Tomlinson K, et al: The National Consortium on Alcohol and Neuro Development in Adolescence (NCANDA): a multisite study of adolescent development and substance use. J Stud Alcohol Drugs 2015; 76:895-908
48. Peeters M, Janssen T, Monshouwer K, et al: Weaknesses in executive functioning predict the initiating of adolescents' alcohol use. Dev Cogn Neurosci 2015; 16:139-146
49. Squeglia LM, Rinker DA, Bartsch H, et al: Brain volume reductions in adolescent heavy drinkers. Dev Cogn Neurosci 2014; 9:117-125