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

2. 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

3. 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 50 34
UNM 31
280 5
Plains 83 36
240
UCT
113 67 33
Total
314
350
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

4. 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

5. Describe rationale for SCT/DBD

6. 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.

7. 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

8. Profile Results

9. 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

10. 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

11. 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

12. 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

13. SCT items are related to inattention, but load separately.

14. 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.

15. Sluggish Cognitive Tempo

16. SCT Data: Site Comparison

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

19. 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.

20. 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.