A Multisite Neurobehavioral Assessment of FASD Sarah Mattson, PI CIFASD Mid-Year Progress Meeting Joint Meeting January 5-7, 2010

Progress 2009 Continued to collect data across 6 sites Work with Informatics to finalize neurobehavioral and demographics input and upload tools; entered data into central repository Published paper in Alcohol special issue on CIFASD clinical methods (Jan 2010) Submitted paper on defining neurobehavioral profile in FASD Identified 2010 goals and begun new data analysis

Progress to Date: All Measures Site NB I NB II Goal (II) Dysm* 3-D DNA MRI SDSU 118 108 150 163 110 82 38 UCLA -- 48 75-95 46 45 Emory 61 100-150 50 34 UNM 31 280 5 Plains 83 36 240 UCT 113 67 33 Total 314 350 1085-1155 432 268 162 112 Goals don’t reflect the 10% budget cut in all years. UCT dysmorphology is different than reported b/c of duplicates. SDSU scheduled: MRI: 3 NP: 7 *Dysmorphology includes Phase I data for some sites

Data in Central Repository Site Children Tested CR: Neuro CR: Dem-Static CR: Dem-Dynamic SDSU 108 107 184* 97 UCLA 48 33 Emory 61 34 43 42 UNM 31 1 Plains 36 4 UCT 67 62 Total 350 256 207 123 correct

Describe rationale for SCT/DBD

Profile Methods Data were collected using a broad neurobehavioral protocol from two sites of a multisite study of FASD Subjects were children with heavy prenatal alcohol exposure and children with unexposed controls The alcohol-exposed group included children with and with out fetal alcohol syndrome (FAS) From nearly 800 neuropsychological and behavioral variables, 22 variables were selected for analysis based on their ability to distinguish children with heavy prenatal alcohol exposure from nonexposed controls Data were analyzed using latent profile analysis (LPA) LPA allows us to classify subjects based on the observed variables (in this case neurobehavioral data). The hope is that the resulting classes relates to subject group, i.e., that FAS subjects are one class and controls are in the other.

Profile Results A 2-class model best fit the data Profiles were best distinguished using measures of executive function and spatial processing Using the profile, subjects with FAS were distinguished from nonexposed controls without FAS with 92% overall accuracy (87.8% of FAS cases and 95.7% of controls) The analysis was repeated with children with heavy prenatal alcohol exposure but without FAS and non-exposed controls Overall accuracy was 84.7% (68.4% of alcohol-exposed cases and 95% of controls) In both analyses, the profile based on neuropsychological variables was more successful at distinguishing the groups than was IQ alone

Profile Results

Plans for 2010 Goal 1: Establish a definition of the “middle group” of alcohol-exposed children: those with heavy alcohol exposure but without FAS Compare exposed subjects (without FAS) to controls using neurobehavior, facial measures, brain measures from Phase II Determine the smallest set of variables that can be used to distinguish the two groups Cross-validate the model using Phase I data Goal 2: Determine the relationships between measures of face and measures of neurobehavior

Behavior-Dysmorphology Correlations Methods Subjects were 137 alcohol-exposed and control subjects from Phase I Identified 9 best neurobehavioral variables based on FASD literature and neuropsychological literature Used the first PC based on these 9 variables, which accounted for most of the variability Correlated neurobehavioral composite score (PC) with 37 dysmorphology variables

Neurobehavioral Variables Included CANTAB SS Length CANTAB SWM Total Errors VDRL Number of Reversals PPT Total Score (Maximally Constrained) PPT Total Cards Correct (Maximally Constrained) DKEFS Verbal Fluency Correct Letter DKEFS Verbal Fluency Correct Switch DKEFS Verbal Fluency Correct Switch Accuracy DKEFS Trail Making Switch

Dysmorphology Variables Correlated (p<.05) with NB Composite Score Mandibular arc, maxillary arc Clinodactyly 5th fingers, camptodactyly Ptosis, strabismus, epicanthal folds PFL, PFL<10%, PFL% Other altered palmar creases Vermillion border: lipometer code; lipometer code vermillion border; thin vermillion border Smooth philtrum OFC, OFC<10%, OFC%, Railroad track configuration of ears Height, Weight 17 variables were correlated at <.01 4 at p<.05 (italics) Although height and weight were correlated, % was not. Some are repetitive, we could use this list to reduce the number of variables we use in future analyses

SCT items are related to inattention, but load separately.

Results FASSTATUS/Group N Age % Male SCT Total DBR-Any ADHD Dx Yes 23 12.06 57% 1.10 96% Deferred 39 11.63 36% 0.98 90% No 57 12.42 47% 0.60 ADHD 25 12.23 60% 1.21 92% No/Deferred include both exposed and control. PMP N = 45 SMS N = 74 SMS ADHD N = 25 Of the “no” group, 18/57 were in the exposed group. Of the “deferred” group, 23/39 were in the exposed group.

Sluggish Cognitive Tempo

SCT Data: Site Comparison

SCT Score Distinguishes Exposed vs. Controls SCT significantly different between children recruited for FASD and CON (Nonexposed) groups.

DBD: Externalizing Diagnoses ADHD 3-5% of school-age children ODD 1-6% CD 1-4% Inattentive is typically the most common subtype. Reference for surgeon general’s report: U.S. Department of Health and Human Services. Mental Health: A Report of the Surgeon General—Executive Summary. Rockville, MD: U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Mental Health Services, National Institutes of Health, National Institute of Mental Health, 1999.

DBD: Externalizing Diagnoses by Site FAS STATUS ADHD-C ADHD-I ADHD-H/I ODD CD SMS Yes 2 (18%) 5 (45%) 1 (9%) Deferred 6 (25%) 12 (50%) 7 (29%) 4 (17%) No 6 (15%) 12 (31%) 7 (18%) 10 (26%) 3 (8%) ADHD 4 (16%) 14 (56%) 5 (20%) 6 (24%) 0 (0%) PMP 3 (25%) 4 (33%) 2 (17%) 1 (8%) 2 (13%) 5 (33%) 3 (20%) 1 (7%) 1 (5%) Inattentive is typically the most common subtype.