1. Joan Carles Soliva Vila Cognitive Neuroscience Research Unit (URNC) Dept. of Psychiatry. Autonomous University of Barcelona (UAB)

2. Introduction Attention-deficit/hyperactivity disorder (ADHD) is the most prevalent psychiatric disorder in childhood Between 8-12 % of the pediatric population Half of children with ADHD will display the disorder in adulthood The diagnosis is made according to the DSM-IV-TR

3. Introduction: DSM-IV-TR criteria Six or more symptoms of inattention have been present for at least 6 months to a point that is disruptive and inappropriate for developmental level Six or more symptoms of hyperactivity-impulsivity have been present for at least 6 months to an extent that is disruptive and inappropriate for developmental level Some symptoms that cause impairment were present before age 7 years Some impairment from the symptoms is present in two or more settings (e.g. at school/work and at home) There must be clear evidence of significant impairment in social, school, or work functioning

4. Introduction Difficulties arise when the clinician needs to distinguish ADHD from a normal developmental level of inattention, impulsivity and hyperactivity Further difficulties surface when one attempts to integrate diagnostic data culled from different informants

5. Introduction A growing number of morphometric brain magnetic resonance imaging (MRI) studies show neuroanatomical abnormalities in pediatric ADHD Our group using a ROI-based methodology and a new semi-automated caudate-nucleus segmentation procedure has replicated right caudate volume abnormalities (Trèmols et al., 2008) reported in nearly all (3 out of 4) caudate-nucleus morphometric MRI studies with an appropriate sample size (n>30)

6. Introduction In addition, we reported a significant decreased volume of the right caudate-nucleus body, with a medium effect size We hypothesize that a decreased normalized right caudate-body volume could be found in the ADHD pediatric population.

7. Objective To test this hypothesis and to determine the diagnostic accuracy of quantitative MR imaging in diagnosing pediatric ADHD

8. Methods: Participants 39 ADHD childreen diagnosed according to DSM-IV- TR 39 IQ-matched controls ADHD subjects were receiving stimulant medication (methylphenidate) and were considered by their physicians,parents, and teachers to have a positive response

9. Methods: MR imaging MRI examination with a 1.5 T system (Signa, General Electric, Milwaukee, USA) FSPGR-T1 3D axial sequence (TR=13.2 ms; TE=4.2 ms; FA=15; NEX=1; 256x256 matrix), with 2 mm partitions FSE-DP-T2 axial sequence (TR=3980 ms; TE=20/100 ms; NEX= 2; 512x512 matrix), with 5 mm sections and 2 mm gap. Two neuroradiologists, blind to the clinical diagnosis, independently read and analyzed all MR images FSPGR-T1 3D was used for the morphometric analysis

10. Methods: Caudate segmentation method The first HEAD section to be measured is the first in which the caudate nucleus can be separated from the putamen nucleus, hence, excluding the ventral striatum The last HEAD section is previous to that in which the caudate’s antero-posterior diameter is more than two- times larger than the latero-lateral diameter All the remaining dorsal sections are adscribed to the BODY of the caudate nucleus

12. Methods: Statistical analyses In order to counterbalance for different brain sizes, we normalized the rCBV using either the total bilateral caudate volume (tbCV) or the bilateral caudate body volume (bCBV) A Student t-test was conducted between the two samples, targeting both tbCV-normalized and bCBV- normalized rCBV.

13. Methods: Statistical analyses We performed a ROC analysis We divided our samples in two groups: Training group: 20 cases and 20 IQ-matched controls Test group: 19 cases and 19 IQ-matcehd controls The area under the curve (AUC) and its confidence intervals were estimated using a non-parametrical method in the training group The optimal cut-off value was selected in the training group using the sensitivity function

14. Methods: Statistical analyses To assess the inter-rater reliability of our caudate segmentation procedure the intra-class correlation coefficient (ICC) was used

15. Results The bCBV-normalized rCBV was found to be statistically different, in the two samples (t=3.16, p=0.001) with a high effect size (Δ=0.84)

16. Results NMSDd tp CI(95%) d ΔCI(95.%) Δ Control390.530.05 0.043.160.0010.02 to 0.070.840.36 to 1.32 ADHD390.490.07 bCBV-normalized rCBV

17. Results The ROC analysis yielded an AUC=0.84 (CI 0.95 : 0.69 to 0.94) and Z=5.37 (p=0.001). The optimal cut-off value (OCOV) was: OCOV≤ 0.4818 It provided a sensitivity of 60.00% (CI 0.95: 36.10% to 80.00%) and the specificity of 95.00% (CI 0.95: 75.10% to 99.20%)

18. Results Empirical ROC analysis ValueCI(95%)zp AUC0.840.69 to 0.945.370.0001 Se60.0036.10 to 80.80 Sp95.0075.10 to 99.20

19. Results Predictive values at different levels of prevalence PPPVCI(0.95) PPV NPVCI(0.95) NPV 841.048.77 to 83.4594.9592.67 to 83.45 944.199.86 to 85.1594.3091.75 to 96.10 1047.0810.94 to 85.5693.6490.82 to 95.64 1149.7412.02 to 87.7592.9889.89 to 95.17 1252.1913.10 to 88.7792.3189.96 to 94.70 5088.8952.51 to 98.3062.0752.37 to 70.90

20. Conclusions We believe that quantitative MRI imaging, employing the bCBV-normalized rCBV obtained by our segmentation procedure, may play a role as an ancillary test to assist in the diagnosis of ADHD in the pediatric population