SHEN ET AL discounting (22.23).In neuroi ralidated a t tool for mental health problems in children and adolescents (30)and has been demonstrated discussed (24):for example.a 2016 meta-analysis (25)re in IMAGEN to be a promising assessment for ADHD 山四少 symptoms(31-34).The hyperactivity-inattention subscale com for ve items covering three key symptom Our main goal in the pr sent study then wastotest whether (Cronbach's alpha=0.75)is at an acceptable level (35) these two pathways are linked to ADHD symptoms by shared The ADHD total score was the total score of all five items; tomical co elate Given tha score was calculated using twe nd activity-i other three items restless.”fidgetv.”and "reflective" As used in nationwide epidemiological studies (36),a hree nd class ication was established fo the sDQu medication were 、This is the cu >6,109% first study to our knowledge to assess the independent as parent-report SDQ because it is more reliable than the sociations of the identihed neuroanatomical correlates with hild self-report version,and the parent-report SDQ also gnitive d working ass op-sgin nd 5 fo D)(30.36 mal s s at hoth a 14 and findings to be an intermediate phenotype of ADHD(27).we 6 were classified into the persistent adHd oup and those with normal scores at both ages were classified into elates ted to explair the typically developed control group were Delay discounting.the monetary choice ou aire (37 an efficient and reliable measurement of delay discounting METHODS in ains 27c e items pittin Participant “g3 logiudinalatudy2ofaoescemtbrhndeaopmtape sed cohor e levels of large).Higher k coefficients in a hyperbolic discounting ewhere (28).W con sent wa reward an high participants (9524%of them male)who had completed eat age our analyses. magingdata were available were included in the analysis (Ta able 1) Working memory.Spatial working memory,as as by th Clinical cohort.AdHd-200is a multice er clinical study (29 approved by the local research ethics review boards at each task to measure participants'ability to preserve spatial in- center.A total of 233 patients nith ADHD and 267 typically formation(40)is widely used in studies of ADHD in children ope cts41[53% (he number of available were included in the analysis (see 1and Table S1in theonlinesupplement).Ofthe ADHDpatients, Intrasubject variability and stop-signal reaction time.Intra- 129 had the combined subtyp subject variability and eight the hyperacti e subtype;56 v k42 tiona missing for78 patients A full-scale IO was an in stimated hy the standard deviation of reaction time in clusion criterion (see Table S2 in the online supplement). successful go trials.Stop-signal reaction time was estimated naire (SDO ytgoresponse time.Partieipants nal reasth .September 2020 discounting (22, 23). In neuroimaging studies, a large-scale brain system beyond the frontostriatal model has also been discussed (24); for example, a 2016 meta-analysis (25) re￾ported structural abnormalities in ADHD patients in both the right basal ganglia/insula and prefrontal cortex as well as in the left occipital lobe. Ourmaingoalin the present study, then,was to testwhether these two pathways are linked to ADHD symptoms by shared and/or separable neuroanatomical correlates. Given that ADHD has been considered an extreme of a quantitative trait (26),wefirstanalyzedalarge-scale population-based sample to identify its neuroanatomical correlates, and then validated the findings using an independent clinical sample. With both medicated and never-medicated patients with ADHD in this clinical sample, we were also able to assess the effects of medication on these neuroanatomical correlates. This is the first study, to our knowledge, to assess the independent as￾sociations of the identified neuroanatomical correlates with both cognitive deficits (i.e., working memory, intrasubject variability, stop-signal reaction time) and motivational deficits (i.e., delay discounting). To demonstrate the potential of our findings to be an intermediate phenotype of ADHD (27), we further tested whether the identified neuroanatomical cor￾relates contributed to explaining ADHD symptoms 2 years later and whether these correlates were associated with ge￾netic risks for the disorder. METHODS Participants Population-based cohort. IMAGEN is a community-based longitudinal study of adolescent brain development. De￾tails on the recruitment procedure have been published elsewhere (28).Written informed consent was obtained from all participants and their legal guardians. A total of 1,963 participants (952 [49%] of them male) who had completed psychometric assessments and for whom baseline (i.e., at age 14) quality-controlled neuroimaging data were available were included in the analysis (Table 1). Clinical cohort.ADHD-200 is a multicenter clinical study (29) approved by the local research ethics review boards at each center. A total of 233 patients with ADHD and 267 typically developed control subjects (141 [53%] of themmale;mean age, 11.98 years [SD=3.04]) for whom quality-controlled MRI data were available were included in the analysis (see eMethods 1 andTable S1in the online supplement). Of theADHD patients, 129 had the combined subtype, 96 the inattentive subtype, and eight the hyperactive/impulsive subtype; 56 were medicated, 99 were never medicated, and medication information was missing for 78 patients. A full-scale IQ score .80 was an in￾clusion criterion (see Table S2 in the online supplement). Measurements ADHD. The Strengths and Difficulties Questionnaire (SDQ), administered at both baseline and follow-up in IMAGEN, is a validated assessment tool for mental health problems in children and adolescents (30) and has been demonstrated in IMAGEN to be a promising assessment for ADHD symptoms (31–34). The hyperactivity-inattention subscale is composed of five items covering three key symptom domains for ADHD; the subscale’s internal consistency (Cronbach’s alpha=0.75) is at an acceptable level (35). The ADHD total score was the total score of all five items; the inattention score was calculated using two items (“poor concentration” and “good attention”), and the hyperactivity-impulsivity score was estimated using the other three items (“restless,” “fidgety,” and “reflective”). As used in nationwide epidemiological studies (36), a three-band classification was established for the SDQ using a cut-off score of 6 (normal: scores ,6, 80%; borderline: score of 6, 10%; abnormal: scores .6, 10%). We used the parent-report SDQ because it is more reliable than the child self-report version, and the parent-report SDQ also has a stronger association with clinical assessments (re￾ported odds ratios of 32.3 and 5 for ADHD) (30, 36). Par￾ticipants who had abnormal scores at both ages 14 and 16 were classified into the persistent ADHD group, and those with normal scores at both ages were classified into the typically developed control group. Delay discounting. The Monetary Choice Questionnaire (37), an efficient and reliable measurement of delay discounting that has been validated in adolescents (38), was administered at baseline. It contains 27 dichotomous-choice items pitting a smaller immediate reward against a larger delayed reward for three levels of reward magnitude (small, medium, and large). Higher k coefficients in a hyperbolic discounting equation for each rewardlevel represent greater preference for small immediate rewards and higher impulsivity (see eMethods 2 in the online supplement). The geometric mean was calculated and logarithmically transformed to use in our analyses. Working memory. Spatial working memory, as assessed by the Cambridge Neuropsychological Testing Automated Battery (39), was measured at baseline. This self-ordered searching task to measure participants’ ability to preserve spatial in￾formation (40) is widely used in studies of ADHD in children and adolescents (41). The number of errors was used as an index of working memory. Intrasubject variability and stop-signal reaction time. Intra￾subject variability and stop-signal reaction time were obtained by behavioral data for the stop-signal functional MRI (fMRI) task (42) (N=1,846). Intrasubject variability was estimated by the standard deviation of reaction time in successful go trials. Stop-signal reaction time was estimated by subtracting the mean stop-signal latency from the mean correct go response time. Participants who had less than 50% correct hits and who had negative stop-signal reaction time were excluded. Am J Psychiatry 177:9, September 2020 ajp.psychiatryonline.org 845 SHEN ET AL