1. The Relationship between
Cognitive Deficits and Academic
Achievement for Children with a
Fetal Alcohol Spectrum Disorder
Lindsey Jackson
Project Mentor: Dr. Rachel Tangen, Ph.D., Neuropsychologist
Division of Developmental/Behavioral Pediatrics and Psychology

2. Outline
• About FASD
• Diagnosis
• Previous Research
• My Project
• Future Directions

3. What is FASD?

4. About FASD
• Describes a range of effects including
physical, mental, behavioral, and learning
disabilities
• Caused by maternal alcohol consumption
• Is not a diagnosis, only an umbrella term
(Benz et. al., 2009)

5. 1 in 10

6. 5 in 100

8. How is FASD
diagnosed?

9. The 4-Digit Diagnostic Code
• Growth Deficiency
• FAS Facial Phenotype
• CNS Abnormalities
• Prenatal Alcohol Exposure
(Astley & Clarren, 2000)

10. FAS Facial Phenotype
(Douglas & Mutsvangwa, 2010)

11. Epicanthal
folds
Smooth
philtrumThin upper lip
Flat
midface
Low nasal
bridge
Short nose
Short palpebral
fissure length
FAS Facial Phenotype

12. Structural CNS Damage
(Riley & McGee,
2005)

13. (Riley & McGee, 2005)

14. Neuropsychological Profile
• IQ
• Memory
• Attention
• Language
• Motor skills
• Executive functioning
• Visual-spatial abilities
(Kodituwakku, 2009)

15. Diagnostic Categories
1. Fetal Alcohol
Syndrome (FAS)
2. Partial Fetal Alcohol
Syndrome (pFAS)
3. Static
Encephalopathy
(SE)
4. Neurobehavioral
Disorder (ND)
(Astley, 2010)

16. Diagnostic Categories
(Astley, 2010)

17. Previous Research

18. Academic Achievement
• Limited research that looks comprehensively at reading, math, and writing in
children across different FASD diagnosis.
• In available research:
• Limited studies looking at math and reading abilities in the same study
• Compared to other children with special needs, children
with FASD had more trouble with math (Howell et. al., 2006)
• Compared to alcohol-exposed children not diagnosed with FASD, more likely
to fall in clinical range for math abilities (Nash et. al., 2013)
• Australian study using standardized test scores showed poor performance
on a screening of reading, writing, and spelling
• Academic achievement at age 10 showed overall poor academic
performance, especially in letter recognition and reading comprehension
(Goldschmidt et. al., 2004; O’Leary et. al., 2013)

19. Cognitive Processes
Attention, executive function, and memory:
• Visual-spatial working memory predicted math abilities
• Verbal and visual-spatial short term memory predicted reading performance
• Executive functioning skills predicted general
learning
Working memory and inhibition:
• Working memory related to literacy
• Inhibition related to general academic performance
(Bull et. al., 2008; Rasmussen & Bisanz, 2010; St Clair-Thompson & Gathercole, 2006)

20. My Project

21. Research Questions
• How does academic performance vary based on
FASD diagnosis?
• Do children with FASD have greater difficulties with
math or reading?
• Which cognitive processes are the best indicators
of math and reading performance for children with
FASD?

22. Hypotheses
• Between Subject Hypotheses:
 Children with FAS and pFAS will have lower math, reading, and writing
scores compared to children with Static Encephalopathy and
Neurobehavioral Disorder.
 Children with Static Encephalopathy will have lower math, reading,
and writing scores compared to children with Neurobehavioral
Disorder.
• Within Subject Hypothesis:
 Math performance scores for children with FASD will be lower than
reading performance scores for children with FASD.
• Multiple Regression Hypotheses:
 Working memory, verbal memory, visual memory, sustained attention,
inhibition, and FSIQ will be predictors of reading and math skills in
children with FASD.
 Working memory skills will be the greatest predictor of both math and
reading skills with FASD.

23. Methods
Standardized Measures of Cognitive and Academic Achievement
(Standard Score = 100; Standard Deviation = 15)
• Academic achievement- Woodcock-Johnson III and Woodcock-
Johnson IV
• Memory- Children’s Memory Scale (CMS)
• Attention and Disinhibition - Conners’ Continuous Performance Test-
Second Edition
• Working Memory – Wechsler Intelligence Scale for Children
• FSIQ- Wechsler Intelligence Scale for Children IV or Wechsler
Intelligence Scale for Children V (WISC-IV or WISC-V)

24. Participant Information
FAS/pFAS
n=12 (20%)
SE
n=26 (44%)
ND
n=21 (36%)
Total
n=59
Mean Age in Months 113.750 134.769 120.619 125
% Female 50% 38% 48% 43%
Ethnicity (% Caucasian) 67% 62% 43% 56%
Full-Scale IQ 77.833 77.885 93.714 83.65
% Adopted 75% 58% 76% 70%

25. Analyses
• A series of one way ANOVAs with post hoc Bonferroni correction were
conducted to assess FASD diagnosis group differences for reading,
math, and writing subtests.
• Within subject repeated measures ANOVAs were conducted to assess
within subject differences on selected comparisons including: Brief
Math and Brief Reading, letter/word recognition and passage
comprehension, and calculations and applied problems.
• Multiple regression analyses were conducted to determine a predicative
relationship between cognitive measures (WMI, visual immediate
memory, verbal immediate memory, omission, commission, and FSIQ)
and academic performance in math and reading.

26. Results
At a p<.05 level, there were significant differences between groups 2 and 3 on all academic measures.
Results were not significant for all academic measures between groups 1 and 2. There were significant
differences between groups 1 and 3 for several measurements: LW p=.033, Calc p=.002, and WS
p=.031.

27. Percent of Children with Significant
Reading and Math Deficits
FAS/pFAS
n=12
SE
n=26
ND
n=21
FASD Total
Brief Reading <85 75% 46% 19% 32%
Brief Reading <70 33% 25% 0% 16%
Brief Math <85 75% 88% 19% 43%
Brief Math <70 33% 46% 10% 23%
* 12% of sample has IQ <70

28. There were significant differences between Brief
Reading scores and Brief Math scores at a p<.05 level
[F(1,55)=8.205, p=.006]
Reading Scores vs. Math Scores

29. There were significant differences
between Letter/Word Recognition and
Passage Comprehension at a p<.05 level
[F(1,57)=25.444, p=.000]
There were significant differences
between Calculations and Applied
Problems at a p<.05 level
[F(1,54)=11.195, p=.001]
Reading Subtests Math Subtests

30. Applied Problems R2 F DF P
.578 8.206 (6,36) .000
Predictors β t p r
Working Memory -.098 -.546 .589 -.091
Visual Immediate Memory -.028 -.218 .829 -.036
Verbal Immediate Memory .058 .425 .673 .071
Omission -.084 -.716 .479 -.118
Commission .048 .386 .702 .064
FSIQ .780 4.000 .000 .555
Calculations R2 F DF P
.679 13.049 (6,37) .000
Predictors β t p r
Working Memory .297 1.922 .062 .301
Visual Immediate Memory -.052 -.484 .631 -.079
Verbal Immediate Memory .184 1.580 .123 .251
Omission -.009 -.087 .931 -.014
Commission .147 1.369 .179 .220
FSIQ .436 2.607 .013 .394

31. Letter/Word Recognition R2 F DF P
.662 12.418 (6,38) .000
Predictors β t p r
Working Memory .609 3.919 .000 .536
Visual Immediate Memory .101 .918 .364 .147
Verbal Immediate Memory .259 2.193 .035 .335
Omission .111 1.092 .282 .174
Commission .175 1.618 .114 .254
FSIQ -.008 -.050 .960 -.008
Passage Comprehension R2 F DF P
.738 17.805 (6,38) .000
Predictors β t p r
Working Memory .586 4.274 .000 .570
Visual Immediate Memory .057 .592 .558 .096
Verbal Immediate Memory .199 1.916 .063 .297
Omission .188 2.091 .043 .321
Commission .226 2.365 .023 .358
FSIQ .130 .878 .385 .141

32. Discussion
• Academic performance varies based on FASD
diagnosis.
 Children with FASD do not need to have full FAS to have
significant academic deficits.
 Children with Static Encephalopathy and Neurobehavioral
Disorder have different profiles. It may be problematic to
group them under one label (Alcohol Related
Neurodevelopmental Disorder).
• Children with FASD have a greater percentage of
children that fall below expected levels in reading and
math than the population norms. A greater number of
children would be considered to have learning
disabilities.

33. Discussion
• Overall, math performance is worse than reading performance for
children with FASD, although both are below normative standards.
Word reading and applied math (with visual cues) were better preserved
than reading comprehension and calculation which may require greater
reasoning skills.
• IQ is the best predictor of math achievement for both calculations and
applied problems. In contrast, IQ is not a significant predictor for reading
skills. Working memory, attention, and inhibition are the best predictors
of reading comprehension ability. Working memory and verbal
immediate memory are the best predictors of letter/word recognition
• Results provide better understanding of academic performance for
children with FASD to influence the interventions developed and used
for these children.

34. Limitations
• Clinical research
 No control group
 Variation in testing
• Small sample size
• Limited knowledge of maternal alcohol
consumption

35. Future Research
• Longitudinal study looking at academic growth
• Partnering with research facilities in other places to
include other demographics in the study

36. References
Astley, S. J. (2010). Profile of the first 1,400 patients receiving diagnostic evaluations for fetal alcohol spectrum disorder at the Washington State Fetal
Alcohol Syndrome Diagnostic & Prevention Network. Can J Clin Pharmacol,17(1), e132-e164.
Astley, S. J., & Clarren, S. K. (2000). Diagnosing the full spectrum of fetal alcohol-exposed individuals: introducing the 4-digit diagnostic code. Alcohol
and alcoholism, 35(4), 400-410.
Benz, J., Rasmussen, C., & Andrew, G. (2009). Diagnosing fetal alcohol spectrum disorder: History, challenges and future directions. Paediatrics &
child health, 14(4), 231.
Bull, R., Espy, K. A., & Wiebe, S. A. (2008). Short-term memory, working memory, and executive functioning in preschoolers: Longitudinal predictors of
mathematical achievement at age 7 years. Developmental neuropsychology, 33(3), 205-228.
Douglas, T. S., & Mutsvangwa, T. E. (2010). A review of facial image analysis for delineation of the facial phenotype associated with fetal alcohol
syndrome. American Journal of Medical Genetics Part A, 152(2), 528-536.
Goldschmidt, L., Richardson, G. A., Cornelius, M. D., & Day, N. L. (2004). Prenatal marijuana and alcohol exposure and academic achievement at age
10. Neurotoxicology and teratology, 26(4), 521-532.
Howell, K. K., Lynch, M. E., Platzman, K. A., Smith, G. H., & Coles, C. D. (2006). Prenatal alcohol exposure and ability, academic achievement, and
school functioning in adolescence: a longitudinal follow-up. Journal of Pediatric Psychology, 31(1), 116-126.
Kodituwakku, P. W. (2009). Neurocognitive Profile In Children With Fetal Alcohol Spectrum Disorders. Developmental Disabilities Research
Reviews,15(3), 218–224.
O’Leary, C. M., Taylor, C., Zubrick, S. R., Kurinczuk, J. J., & Bower, C. (2013). Prenatal alcohol exposure and educational achievement in children
aged 8–9 years. Pediatrics, peds-2012.
Rasmussen, C., & Bisanz, J. (2010). The relation between mathematics and working memory in young children with fetal alcohol spectrum disorders.
The Journal of Special Education.
Riley, E. P., & McGee, C. L. (2005). Fetal alcohol spectrum disorders: an overview with emphasis on changes in brain and behavior. Experimental
biology and medicine, 230(6), 357-365.
St Clair-Thompson, H. L., & Gathercole, S. E. (2006). Executive functions and achievements in school: Shifting, updating, inhibition, and working
memory. The quarterly journal of experimental psychology, 59(4), 745-759.

37. Thank you!
• Dr. Rachel Tangen
• Dr. Mitchell Drum
• Connie May
• SURP

38. Questions?