Advances in Neurodevelopmental Disorders

, Volume 1, Issue 4, pp 271–282 | Cite as

Unpacking the Heterogeneity of Cognitive Functioning in Children and Adolescents with Fetal Alcohol Spectrum Disorder: Determining the Role of Moderators and Strengths

  • Kaitlyn McLachlan
  • Angelina Paolozza
  • Katrina Kully-Martens
  • Elodie Portales-Casamar
  • Paul Pavlidis
  • Gail Andrew
  • Ana Hanlon-Dearman
  • Christine Loock
  • Audrey McFarlane
  • Sarah M. Nikkel
  • Jacqueline Pei
  • Tim F. Oberlander
  • Dawa Samdup
  • James N. Reynolds
  • Carmen Rasmussen
ORIGINAL PAPER

Abstract

Children and adolescents with fetal alcohol spectrum disorder (FASD) experience significant impairments in cognitive functioning, though substantial within-group heterogeneity is often observed. The purpose of this study was to characterize the cognitive profile of children and adolescents with FASD with a special focus on examining moderators of functioning and cognitive strengths. Children and adolescents with FASD (n = 87) and controls (n = 110), ages 5 to 18 years completed a cognitive test battery. MANOVA was used to evaluate between-group cognitive differences, as well as the role of age and gender as potential moderators. Relative strengths were evaluated using both within-subject and between-group methods. Participants with FASD were found to show significant impairment on all cognitive tasks relative to controls, with substantial deficits evident on a measure of mathematical skill. Though neither age nor gender emerged as moderators, significant three-way interactions between age, gender, and group were evident on measures of executive functioning (inhibition), verbal memory, and word identification. Tasks measuring higher-order complex attention and visuospatial processing emerged as possible relative strengths in the FASD group. Children and adolescents with FASD had significant cognitive impairment across multiple domains confirming high need for interventions. Differences in the cognitive functioning for boys and girls with FASD at different developmental periods, along with relative strengths, may serve to inform interventions and future longitudinal research.

Keywords

Fetal alcohol spectrum disorder Prenatal alcohol exposure Cognitive functioning Moderators Relative strengths 

A well-documented range of adverse effects is associated with prenatal alcohol exposure (PAE), including impaired neurobehavioral functioning, facial dysmorphology, and growth deficiency (Astley and Clarren 1999; Mattson et al. 2011; Mattson et al. 2013). Fetal alcohol spectrum disorder (FASD) comprises the range of alcohol-related conditions resulting from PAE. While diagnostic terminologies vary across clinical guidelines, those identified in the current study and elsewhere in North America include fetal alcohol syndrome (FAS), partial fetal alcohol syndrome (pFAS), and alcohol-related neurodevelopmental disorder (ARND) (Chudley et al. 2005; Hoyme et al. 2005). Children with PAE are affected to varying degrees, resulting in a heterogeneous clinical population. Only a small proportion (approximately 10%) presents with the full physical and neurobehavioral profile of deficits that comprise FAS and pFAS (Astley 2010). The majority of children with PAE present with moderate or severe neurobehavioral impairments, and a small proportion (approximately 10%) show normal cognitive development (Astley 2010). As a result of the phenotypic heterogeneity seen among children with FASD, assessment and diagnosis in cases where they do not present with sentinel PAE-related physical features can be challenging. Many aspects of the clinical profile of children with PAE have been studied in order to improve the reliability of early assessment and diagnosis, particularly among children who present without physical features. However, there is still much to understand about possible contributing factors underlying the cognitive and behavioral functioning seen in children with FASD.

FASD remains the leading preventable neurodevelopmental disorder in North America, with ever growing prevalence estimates (e.g., 2 to 5%; May et al. 2009) and high economic and social costs (Popova et al. 2011). Early assessment, diagnosis, and intervention for children with PAE are critical factors in preventing adverse outcomes associated with FASD (Streissguth et al. 2004; McLachlan et al. 2016). However, the neuropsychological assessment of FASD can be a subjective and clinically challenging task (Astley 2004; Chudley et al. 2005). Children and adolescents with FASD present with a wide range and severity of cognitive deficits, including overall intellectual ability, attention, processing speed, executive functioning, language, visuospatial processing, learning and memory, and academic achievement (for reviews, see Kodituwakku 2009; Mattson et al. 2011). Some areas appear to be more often affected, including executive functioning, attention, adaptive functioning, and mathematics (Mattson et al. 2011; Rasmussen and Bisanz 2010). Significant variability is typically observed across cognitive indicators in studies of children with FASD, and between-group comparisons with other clinical populations show a great deal of overlap in functioning (Mattson et al. 2013; Ware et al. 2014).

Efforts to understand the variability in expression of cognitive deficits across children with PAE is key because it is possible that certain clusters or subgroups may emerge and guide clinical judgments during assessment and in intervention planning. Certainly, establishing a “typical” cognitive profile for children with FASD would facilitate the complex job of assessing neuropsychological functioning in children following PAE, particularly among those who present without pronounced physical features. In light of the complex host of factors that influence the teratogenic effects of PAE in utero (e.g., dose, timing, maternal factors, and genetic suceptibility; May et al. 2013; Ramsay 2010; Smith et al. 2014), as well as the range of environmental exposures that may further shape a child’s cognitive development, it is unlikely that a single neurobehavioral “phenotype” of FASD exists. However, refining the profile of neuropsychological problems that are typically observed in children with PAE may greatly inform the reliability of clinical assessment and confidence of clinicians tasked with rendering diagnoses on the spectrum (Kodituwakku 2009; Koren et al. 2014; Mattson et al. 2013; Nash et al. 2013).

Studies comparing the cognitive performance of children following PAE, including those with and without the physical features associated with FASD, have yielded interesting results. In the largest profile study conducted to date Astley (2010) found a linear trend of functioning, such that those diagnosed with neurodevelopmental disorder/alcohol-exposed (ND/AE) showed the best performance, followed by the static encephalopathy/alcohol-exposed (SE/AE) group, with the most pronounced deficits observed in the FAS/pFAS group. Chasnoff et al. (2010) also found that children with FAS were significantly more impaired on measures of intelligence, executive functioning, and memory compared with those with pFAS and ARND. Similarly, Nash et al. (2013) found that children with FASD were more impaired on tasks evaluating verbal reasoning, memory, language, and mathematics relative to children with PAE who did not receive an FASD diagnosis. Alternatively, other studies have shown few or no statistical differences in cognitive functioning between subgroups on the diagnostic spectrum (Green et al. 2009a; Mattson et al. 1997, 1998; Quattlebaum and O'Connor 2012). These inconsistencies may be the by-product of methodological differences, varying FASD diagnostic systems and classifications, and/or limited sample sizes that fail to accurately differentiate the broad range of individuals who do not present with the physical features of FASD (ARND). Clearly, more research is required.

Though a number of important strides have been made toward better understanding the cognitive characteristics of children with FASD, critical gaps in distinguishing phenotypic differences within this population remain and warrant further investigation. Considering possible moderators of functioning that are distinct from the PAE itself (e.g., dose, timing) may highlight both the possibility of higher-risk groups on the diagnostic continuum, and also identify critical sub-populations and developmentally sensitive time points at which interventions may be most needed and effective. A limited number of studies have assessed possible moderators, such as age, and gender, which could partially account for variability in cognitive functioning. For instance, Astley’s (2010) profile study indicated that the 9.3% of their sample who had PAE but no CNS abnormality were significantly more likely to be female. This unaffected group was also significantly younger (46.2% were under 4 years of age), though it is likely that a subset of these children were too young to assess using appropriate neuropsychological measures and rule out higher-level functioning that tends to emerge later in development. Other studies have shown age-related differences in cognitive functioning in children with FASD (Kully-Martens et al. 2013; Rasmussen and Bisanz 2009a). Recent work has identified gender differences in the profile of mental health problems of children and adolescents with PAE, with conflicting findings (Niclasen et al. 2013; Sayal et al. 2007) and animal research has shown sexually dimorphic effects following PAE on many aspects of cognitive functioning (Hellemans et al. 2010; Kelly et al. 2009; Zimmerberg et al. 1991).

Recently, Panczakiewicz et al. (2016) assessed the effects of age and sex on neuropsychological and behavioral function in children with PAE and controls. They evaluated children ages 5 to 7 and ages 10 to 16 on a battery of neuropsychological tasks and questionnaires. They found a main effect of sex, with males demonstrating stronger language and visual spatial scores and fewer somatic complaints compared to females, and a main effect for age, with older children performing better than younger children on measures of language, communication, and socialization. However, they found no significant two-way or three-way effects, indicating no evidence for sexually dimorphic effects between children with PAE and controls. The authors suggested the need for replication in a sample comprising children across the developmental age span, as 8–9-year-olds were missing from analyses, and their older child cohort spanned a broad developmental period.

Because clinicians are typically tasked with identifying deficits, and researchers aim to identify the adverse effects of PAE, studies of FASD are too often deficit-focused and ignore the importance of possible relative cognitive strengths. Not only is the identification of cognitive strengths important in the development of appropriately tailored interventions, building on individual strengths offers a positive opportunity for engaging children with FASD in challenging treatment paradigms (Coles et al. 2007; Kalberg and Buckley 2007; Padgett et al. 2006).

We sought to evaluate the cognitive profile of children and adolescents with FASD relative to an age-matched control group of typically developing children in a multi-site Canadian cohort. We planned to replicate and extend previous research examining age and gender differences in neurocognitive functioning across a wide age span of children and adolescents with FASD. Canadian research of this nature is critical because the diagnostic guidelines used by clinicians vary by jurisdiction, and there are international differences in access to health care services, and socioeconomic and cultural composition. We expected that children with FASD would have significant impairment across cognitive domains relative to typically developing controls, with differences most pronounced in complex executive functioning and mathematics. We also examined potential moderators of cognitive functioning including age and gender effects. Finally, we sought to identify relative strengths among children and adolescents with FASD in order to inform future service delivery and identify opportunities to capitalize on abilities.

Method

Participants

Participants included 197 children and adolescents ages 5 to 18 (M = 11.27, SD = 3.43, 52.7% female) from a large multi-site Canadian FASD cohort (Reynolds et al. 2011) including a group with FASD (n = 87) and typically developing children without PAE (n = 110) (Table 1). After controlling for multiple comparisons, the FASD and control groups were comparable in age, gender, and SES (calculated using Hollingshead’s four-factor index of social status; Hollingshead 1975). Consistent with previous research, children in the FASD group had significantly more variable caregiving situations, whereas all of the typically developing children were living with a biological parent. The majority of children with FASD were diagnosed with ARND, though a sizeable number had some degree of the physical features seen in FAS and pFAS (31%), which is a rate higher than typically observed in other studies (Astley 2010).
Table 1

Sample characteristics by group

 

FASD (n = 87)

Controls (n = 110)

Analyses

Effect size

   

t (χ2)

p

d (Φ)

Age (M, SD)

11.95 (3.38)

10.75 (3.39)

2.49

.01

.36

Gender (n, % male)

46 (52.9)

47 (42.7)

(2.01)

.16

(.20)

SES (M, SD)

43.78 (12.90)

41.98 (12.85)

.96

.34

.14

Caregiver (n, %)

 Biological parent(s)

11 (12.6)

110 (100.0)

(156.45)

<.001a

(.89)

 Adopted

41 (47.1)

0 (0.0)

 Foster and other

35 (40.2)

0 (0.0)

Diagnosis (n, %)

 FAS

9 (10.3)

   

 pFAS

18 (20.7)

 ARND

60 (69.0)

FASD fetal alcohol spectrum disorder, PAE prenatal alcohol exposure, M mean, SD standard deviation, SES socioeconomic status, FAS fetal alcohol syndrome, pFAS partial fetal alcohol syndrome, ARND alcohol-related neurodevelopmental disorder

aMeets Benjamini-Hochberg (1995, 2000) false discovery rate criterion

Participants with FASD were recruited from six clinical sites (Vancouver, BC; Edmonton, AB; Cold Lake, AB; Winnipeg, MB; Ottawa, ON; and Kingston, ON). Most children were assessed by an interdisciplinary team following the Canadian diagnostic guidelines (Chudley et al. 2005), though some were diagnosed by a single physician. Diagnostic information was confirmed via referring clinics, and four-digit codes following the University of Washington FAS DPN 4-Digit Code system (Astley 2004; Astley and Clarren 2000) were available for most participants (n = 83, 95.4%). PAE was confirmed for all children with FASD. Typically developing children were recruited from the same geographic regions from a range of community sites (e.g., community centers, schools, hospitals, web-based advertising) and were excluded if they reported any level of PAE, or had a neurological, genetic, or psychiatric disorder.

Procedure

All study procedures were approved by Research Ethics Boards at the University of British Columbia, University of Alberta, University of Manitoba, Queen’s University, and the Children’s Hospital of Eastern Ontario, and adhered to governing ethical guidelines. Informed consent was obtained from legal guardians, and assent obtained from all children. Participants completed a standardized battery of cognitive measures selected to evaluate key areas commonly affected in children with FASD, including executive functioning, attention, working memory, memory, visuospatial processing, mathematical ability, and word identification. Study data were collected and managed using REDCap electronic data capture tools hosted by the NeuroDevNet Neuroinformatics Core (Harris et al. 2009).

Measures

NEPSY-II

The NEPSY-II (Korkman et al. 2007) is a standardized neuropsychological test battery for children ages 3 to 16. Seven subtests were administered. Animal sorting (AS, ages 7–16) assesses basic concept formation and the ability to transfer concepts in a card-sorting paradigm. Auditory Attention (AA, ages 5–16) assesses selective and sustained attention. Response Set (RS, ages 7–16) assesses the ability to shift and maintain information while inhibiting previously learned responses. Inhibition (IN, ages 5–16) measures the ability to inhibit automatic responses in favor of novel responses, and the ability to switch between response types (Naming, IN-N, Inhibition, IN-I, and Switching, IN-S). Memory for Names (MN, ages 5–16) assesses short-term verbal learning (children’s names), and Memory for Names Delayed (MND) assesses long-term retention of verbal information. Arrows (AR, ages 5–16) is a measure of visuospatial processing via a line orientation task. Age-referenced scaled scores (M = 10, SD = 3) were analyzed for MN, MND, and AR. Combined scaled scores, which typically combine accuracy, error, and completion time, were analyzed for AS, AA, RS, IN-N, IN-I, and IN-S.

Working Memory Test Battery for Children (WMTB-C)

The WMTB-C is a standardized test battery designed to assess working memory in children ages 5 to 15 (Pickering & Gathercole 2001). Two subtests were administered: Digit Recall (DR), which measures verbal/phonological working memory and Block Recall (BR), which assesses visuospatial working memory. Age-referenced scaled scores (M = 100, SD = 15) were analyzed.

Woodcock Johnson III Tests of Achievement (WJ-III ACH)

The WJ-III ACH assesses academic achievement in children and adolescents ages 5 to 18 (Woodcock et al. 2001). Participants completed Quantitative Concepts (QC), which measures quantitative reasoning and mathematics knowledge, scored using age-referenced standard scores (M = 100, SD = 15).

Woodcock Reading Mastery Tests—Revised (WRMT—R)

The WRMT—R provides a comprehensive individual assessment of reading ability across the lifespan (Woodcock 1998). The Word Identification (WID) subtest was administered as a measure of reading ability and analyzed using age-referenced standard scores (M = 100, SD = 15).

Participants completed subtests appropriate for their age. Specifically, 16-year-olds (n = 14) were scored using the oldest available reference group on the WMTB-C (15:11), 17-year-olds (n = 7) were scored using the oldest available reference group on the NEPSY-II (16:11), but did not complete the WMTB-C, and 18-year-olds did not complete the NEPSY-II or the WMTB-C (n = 9). Also, RS and AS from the NEPSY-II was not administered to 5-year old participants as the minimum age of administration for those subtests is seven (n = 7). All children completed the WJ-III ACH and WRMT-R.

Data Analyses

Participant demographics are first described and then compared using t test and chi-square analyses for nonparametric data. Scaled and standard cognitive test scores were transformed to z scores to facilitate interpretation across different measurement scales. A multivariate analysis of variance (MANOVA) was conducted to examine the effect of group (FASD vs. controls) on the 13 cognitive test scores, with age (trichotomized into ages 5 to 9, 11 to 14, and 15 to 18) and gender considered as potential moderators of task performance between groups. Moderation effects were considered present if the interaction between group and each moderator was significant at the multivariate level. MANOVA was also used to compare cognitive task performance among the FASD subgroups, including pFAS and FAS, and ARND. Relative cognitive strengths were evaluated using both within-group and between-group approaches. In the FASD group, z scores for each cognitive test were compared with the lowest score in each groups’ profile (QC) using within-subject t tests. Performance on each cognitive task between the FASD and control groups was also compared by evaluating the mean difference score between groups. Multiple comparisons across analyses were corrected with Benjamini-Hochberg false discovery rate (FDR) correction for multiple comparisons (α = .05, Benjamini and Hochberg 1995, 2000). Effect sizes for t tests (Cohen’s d), chi-square (phi, ϕ), and F tests (partial eta squared, ηp2) are reported to indicate the size of statistically significant differences. Cohen’s d values range from .2 (small) to .5 (medium) to .8 and above (large), phi values range from .1 (small) to .3 (medium) to .5 and above (large), and partial eta squared values range from .02 (small), to .13 (medium) to.26 and above (large) (Cohen 1988). Analyses were conducted using IBM SPSS Statistics 22 for Mac.

Results

Cognitive Test Performance for the FASD and Comparison Groups

Scaled, standard, and transformed z scores for both the FASD and comparison groups are presented in Table 2 and Fig. 1. Results from a MANOVA (Table 3) revealed a significant main effect for group at the multivariate level indicating that children and adolescents with FASD had significantly lower scores across combined cognitive tasks compared with controls. A significant main effect for group was also observed across all cognitive scores at the univariate level, with the lowest performance evident on a task of mathematical skill (QC). A main effect for age was found at the multivariate level across cognitive tasks for the combined FASD and control groups, though examination of the effect of age on significant tests identified at the univariate level, namely of verbal working memory (DR), visuospatial skill (AR), and word identification (WID), rendered interpretation of this finding difficult. On DR and WID, children ages 5 to 9 had significantly higher normative scores relative to 10- to 14-year-olds (p = .02 for DR and p = .002 for WID), and 15- to 18-year-olds on WID only (p = .001). In contrast, younger children (ages 5 to 9), had lower normative scores relative to the two older ages groups on a visuospatial ability measure (AR), but this trend was not significant. There was no significant main effect for gender at the multivariate level, though at the univariate level, a main effect for gender was found on a measure of verbal immediate memory (MN), indicating that across the FASD and control groups, girls outperformed boys on this task.
Table 2

Mean scaled and standard cognitive test scores by group

Cognitive

Subtests

FASD

Control

Domains

 

M (SD)

M (SD)

Executive function

AS

6.70 (2.58)

9.67 (3.18)

IN-N

6.72 (3.81)

9.63 (3.33)

IN-I

6.16 (3.07)

10.10 (3.54)

IN-S

6.79 (2.87)

10.41 (2.85)

Attention

AA

7.69 (3.59)

10.87 (2.71)

RS

9.64 (3.12)

11.33 (2.79)

Memory

MN

6.82 (2.82)

9.61 (2.72)

MND

6.97 (3.34)

9.58 (2.54)

Working memory

DR

83.59 (12.59)

100.82 (16.53)

BR

86.61 (16.93)

101.51 (16.59)

Visuospatial ability

AR

8.72 (2.93)

10.43 (2.46)

Numerical ability

QC

78.40 (17.88)

105.48 (12.61)

Word identification

WID

89.82 (15.67)

105.42 (12.63)

Scores presented are raw (untransformed) scaled scores for each cognitive measure. Normative means for AS, AA, RS, IN-N, IN-I, IN-S, MN, MND, and AR are 10, SD = 3, and normative means for DR, BR, QC, and WID are 100, SD = 15. Sample sizes vary by cognitive test due to different normative age ranges on each measure

FASD fetal alcohol spectrum disorder, AS Animal sorting, AA Auditory Attention, RS Response Set, IN-N Inhibition-Naming, IN-I Inhibition-Inhibition, IN-S Inhibition-Switching, MN Memory for Names, MND Memory for Names Delayed, AR Arrows, DR Digit Recall, BR Block Recall, QC Quantitative Concepts

Fig. 1

Cognitive profile of children with FASD significantly lower than controls. Sample sizes vary by cognitive test due to different normative age ranges on each measure. AS Animal sorting, AA Auditory Attention, RS Response Set, IN-N Inhibition-Naming, IN-I Inhibition-Inhibition, IN-S Inhibition-Switching, MN Memory for Names, MND Memory for Names Delayed, AR Arrows, DR Digit Recall, BR Block Recall, QC Quantitative Concepts

Table 3

Multivariate analysis of moderators of group predicting cognitive test performance

  

Moderators

 

Group

Age

Age by group

Gender

Gender by group

Age by gender

Age by gender by group

 

F

ηp2

F

ηp2

F

ηp2

F

ηp2

F

ηp2

F

ηp2

F

ηp2

Multivariate

13.76***

.59

2.87***

.23

0.99

.09

1.43

.23

1.23

.12

1.34

.12

1.64*

.15

Univariate

 AS

24.10***

.15

0.25

.004

0.28

.004

0.07

<.001

1.69

.01

1.24

.02

0.06

.001

 IN-N

16.78**

.09

2.78

.04

0.56

.01

1.94

.01

0.11

.001

3.29*

.05

2.76

.04

 IN-I

31.19***

.15

0.35

.01

0.49

.01

0.18

.001

0.54

.004

0.08

.001

1.62

.02

 IN-S

40.62***

.24

0.17

.002

0.30

.004

0.03

<.001

0.06

<.001

0.77

.01

1.29

.02

 AA

20.63***

.13

0.36

.005

0.49

.01

0.56

.004

1.87

.014

0.52

.01

0.56

.01

 RS

7.83**

.06

2.13

.03

0.57

.01

1.13

.01

3.69

.03

1.85

.03

0.62

.01

 MN

48.25***

.26

1.58

.02

2.99

.04

5.47*

.04

0.80

.01

0.60

.01

8.09***

.12

 MND

32.38***

.19

2.68

.04

0.09

.001

0.13

.001

0.04

<.001

0.20

.003

1.62

.02

 DR

38.16***

.22

5.45**

.08

1.03

.02

0.56

.004

0.49

.004

1.61

.02

0.36

.01

 BR

18.99**

.12

0.69

.01

0.17

.003

0.61

.005

0.24

.002

0.87

.01

3.83*

.05

 AR

19.46***

.13

4.02*

.06

0.24

.004

2.55

.02

1.45

.01

0.92

.01

0.43

.01

 QC

98.15***

.42

2.46

.04

1.09

.02

1.24

.009

0.14

.001

0.24

.01

0.35

.01

 WID

60.95***

.31

9.54***

.12

1.06

.02

0.01

<.001

1.83

.01

0.43

.01

0.84

.01

Multivariate df for group, gender = 13, 123; multivariate df for age = 26, 248. Univariate df for group, gender = 1, 135; univariate df for age = 2, 135. Multivariate F ratios were generated from Wilks’ lambda statistic. N = 147

FASD fetal alcohol spectrum disorder, AS Animal sorting, AA Auditory Attention, RS Response Set, IN-N Inhibition-Naming, IN-I Inhibition-Inhibition, IN-S Inhibition-Switching, MN Memory for Names, MND Memory for Names Delayed, AR Arrows, DR Digit Recall, BR Block Recall, QC Quantitative Concepts, ηp2 = partial eta-squared

*p < .05, **p < .01, ***p < .001

Moderators of Cognitive Test Performance

MANOVA results showed that neither the interaction between age and group, nor gender and group were significant, suggesting that neither variable independently moderated observed group differences across cognitive tasks. However, a significant three-way interaction between age, gender, and group was found at the multivariate level, suggesting performance differences among younger and older girls and boys, with differential patterns in the FASD and control groups, across the combined test battery. Univariate models were significant for tests of inhibition (IN-N, IN-I), verbal immediate memory (MN), and visual working memory (BR) (Fig. 2). Posthoc evaluation of these findings revealed a trend toward gender differences in the FASD group that varied by age, though none were significant following the FDR correction. In the FASD group, girls ages 5 to 9 outperformed boys on all four tasks, then underperformed boys in the 10 to 14 age group, and again outperformed boys on all measures in the 15 to 18 group, with the exception of visual working memory (BR). The same trend was not observed among controls, and instead, at a cross-sectional level, performance among boys tended to be lower than girls only among 10- to 14-year-olds, and otherwise better than girls in younger children and adolescents.
Fig. 2

Age by gender by group interactions on cognitive tests. Sample sizes vary by cognitive test due to different normative age ranges on each measure

Cognitive Test Performance Within the FASD Group

A second MANOVA was run comparing performance on cognitive tests between participants with the physical features of FASD (FAS and pFAS) to those without (ARND). Main effects for type of FASD were not significant at the multivariate level, F (13, 49) = 1.45, p = .17, ηp2 = .28). At the univariate level, a significant main effect for FASD type was found on a task of short-term verbal memory (MN), F (1, 61) = 5.42, p = .02, ηp2 = .08, with post hoc analyses indicating that children and adolescents in the FAS/pFAS subgroup had lower scores on this task (M = 5.68, SD = 2.56) compared with children with ARND, M = 7.35, SD = 2.80), t = −2.53 p = .01, d = .58.

Relative Cognitive Strengths

We evaluated relative cognitive strengths in children with FASD in two ways, including a within-group profile analysis for children in the FASD group, and a between-group comparison of children with FASD to controls. In the first approach, we selected the lowest cognitive score in the FASD group’s profile (QC) as a “floor” reference point against which we compared all other cognitive scores, using z scores to facilitate comparisons across tests with different scales of measurement. A difference score was then calculated for each remaining cognitive task (QC minus each cognitive score to assess the magnitude of the difference in scores, and paired t tests were performed to evaluate the statistical significance of each difference score. This approach was thought to parallel the process of within-profile analysis sometimes used to evaluate individual strengths during a cognitive assessment in clinical practice. Second, we also referred to findings from the overall MANOVA comparing the effect of group on each cognitive score between the FASD and comparison groups (Table 3), using z scores (Table 4) to facilitate interpretation. Compared with their scores on QC, the FASD group showed relatively better functioning (one or more standard deviations above QC) on tasks of higher-order complex attention (RS) and basic visuospatial processing (AR). Though the FASD group still performed significantly worse than the controls on both tasks, their scores approximated normative means, and scores on both measures were much closer to those of the controls, on average, compared with other tasks.
Table 4

Relative strengths in participants with FASD

Cognitive domains

 

Compared with controlsa

Within FASD (Compared with QC score)

Subtests

Mean diff

Mean diffb

t

p

d

Executive function

AS

−.99

.41

−3.05

.003c

.36

 

IN-N

−1.38

.32

−2.20

.03c

.25

 

IN-I

−1.72

.12

−0.91

.37

.10

 

IN-S

−.96

.36

−2.52

.01c

.31

Attention

AA

−.78

.73

−4.40

<.001c

.50

 

RS

−.25

1.43

−8.74

<.001c

1.03

Memory

MN

−.94

.38

−2.34

.02c

.26

 

MND

−.89

.42

−2.33

.02c

.27

Working memory

DR

−1.13

.32

−2.45

.02c

.28

 

BR

−1.04

.50

−3.58

.001c

.38

Visuospatial ability

AR

−.57

1.03

−7.04

<.001c

.81

Numerical ability

QC

−1.74

Word identification

WID

−1.00

.77

−7.22

<.001c

.79

Scores are transformed and presented as z scores to facilitate comparison across cognitive measures. Sample sizes vary by cognitive test due to different normative age ranges on each measure

FASD fetal alcohol spectrum disorder, AS Animal sorting, AA Auditory Attention, RS Response Set, IN-N Inhibition-Naming, IN-I Inhibition-Inhibition, IN-S Inhibition-Switching, MN Memory for Names, MND Memory for Names Delayed; AR Arrows, DR Digit Recall, BR Block Recall, QC Quantitative Concepts

aStatistical significance of FASD vs. control group scores on cognitive tests are presented in Table 3 under main effects for group

bMean difference scores vary for within FASD group comparisons as the mean for QC includes only the same number of participants who completed both QC and the subtest against which QC is being compared (not all participants completed all subtests of each measure)

cMeets Benjamini-Hochberg (1995, 2000) false discovery rate criterion

Discussion

We explored the cognitive profile of children and adolescents with FASD relative to typically developing children, assessed the role of age and gender as possible moderators of cognitive functioning between the two groups, and evaluated relative cognitive strengths in a multi-site Canadian sample from the NeuroDevNet FASD study cohort. Consistent with research documenting wide-ranging and pronounced impairments in cognitive functioning in FASD (Kodituwakku 2007, 2009; Mattson et al. 2011) the current sample also had significantly impaired functioning in all areas assessed, including executive functioning, attention, memory, working memory, visuospatial processing, mathematical skill, and word decoding. However, significant within-group variability was observed, underscoring the substantial heterogeneity observed in children and adolescents with FASD.

At a profile level, children with FASD had particularly impaired performance in several areas relative to typically developing controls that are consistent with previous research, including mathematical skill, inhibitory function, sustained attention, and working memory (Astley 2010; Mattson et al. 2013; Mattson et al. 2010; Nash et al. 2013). These domains may be especially critical in assessment and intervention planning for children and adolescents with FASD. Mathematical skill was especially impaired among children with FASD, which is again consistent with previous research (Rasmussen and Bisanz 2009b). Briefly screening for math skill deficits may complement existing primary FASD screening measures, (e.g., neurobehavioral screening tool, Nash et al. 2009), as well as new screening approaches (e.g., eye tracking technology, Tseng et al. 2013) to identify children at risk of FASD in general settings such as schools.

Though the field has yielded mixed results, our findings are consistent with studies that have demonstrated comparable levels of cognitive deficits between children with the physical features of FASD (e.g., FAS and pFAS) and those without physical features (e.g., ARND), suggesting that the degree of deficits observed following PAE may occur independently from any physical effects (Green et al. 2009b; Mattson et al. 1997, 1998; Quattlebaum and O’Connor 2012). Alternatively, Astley’s (2010) large scale cohort in Washington found a linear trend differentiating cognitive functioning among individuals with ND/AE, SE/AE, and FAS/pFAS, and Chasnoff et al. (2010) similarly found differences in functioning between children with FAS compared with those with pFAS and ARND. Different diagnostic approaches and classifications may impact the interpretation of within-FASD comparisons in cognitive functioning, underscoring the need for further research in this area across methodologies and diagnostic classification systems. This issue may be exacerbated by international differences in the approach to FASD assessment, differences in diagnostic criteria across systems used by multidisciplinary teams, and variations in the specific tools used (e.g., neuropsychological test use differences and differences in normative samples). This is an issue that warrants further consideration in future studies assessing these issues.

Moderators of Cognitive Test Performance

At a cross-sectional level, neither age nor gender served as significant independent moderators of cognitive differences between children and adolescents with FASD and controls. However, trends were identified suggesting possible age-mediated gender differences among children with FASD, in particular boys, on cognitive tasks including inhibition, verbal immediate memory, and visual working memory. One possible interpretation for these findings, extended with caution prior to replication, is that these differences may be tied to pubertal or environmental experiences that differ by gender, and may interact with PAE, leading to a possible period of cognitive lag or vulnerability during the developmental period during which puberty typically occurs. Previous studies have demonstrated age-related differences in cognitive functioning in FASD (Kully-Martens et al. 2013; Rasmussen and Bisanz 2009a), and gender differences in the cognitive and mental health functioning of children and adolescents with PAE have also been observed (Niclasen et al. 2013; Sayal et al. 2007; Zimmerberg et al. 1991). However, longitudinal investigations of gender differences in PAE-related outcomes, particularly pre- and post-puberty, remain to be undertaken. Our findings also differ from those of Panczakiewicz et al. (2016), who did not find evidence of age- or gender-related differences by group (PAE vs. controls) in their large sample of children and adolescents (n = 407). Several key differences between our samples could explain the discrepancy, including an overrepresentation of children with FAS relative to our sample, a wider age span of older children and adolescents, and children at the 8–9-year-old developmental stage missing from analyses. Indeed, our findings suggest the need to assess potential sexually dimorphic effects on cognitive functioning in children with FASD across multiple age periods, with trends shifting across age groups, at a cohort-level. Animal models have similarly established the sexually dimorphic effects of PAE on many aspects of cognitive functioning (Hellemans et al. 2010; Kelly et al. 2009; Zimmerberg et al. 1991). Though some studies in the general population suggest cognitive efficiency may decrease at the onset of puberty more globally (Blakemore et al. 2010; McGivern et al. 2002), further longitudinal investigation of these differences among children with PAE is warranted. Importantly, these findings suggest that the clinical needs and supports required by girls and boys may differ, and these needs may evolve at differing periods in development following PAE.

Relative Cognitive Strengths

Children with FASD showed relative strengths on measures of attention and response inhibition (RS) and visuospatial processing (AR). Interestingly, deficits in visuospatial processing (using other measures) have previously been identified as particularly affected by PAE (Korkman et al. 2003; Mattson et al. 1998; Pei et al. 2011), even after accounting for IQ (Quattlebaum and O’Connor 2012). However, one study found children with FASD performed similarly to IQ-matched controls on a visuospatial processing task (Vaurio et al. 2011), and another found no significant group differences in this area using the same NEPSY-II subtest (Rasmussen et al. 2013). These discrepancies may be explained by heterogeneity and difficulty of tasks used to assess this area (ours involved a single, simple task of line orientation versus other tasks that require complex visual integration of information). Future research is needed to identify the clinical and research implications of this finding. Similarly, the FASD group fared relatively well on a task of divided attention and inhibition, whereas research typically finds this area to be compromised following PAE relative to controls (Burden et al. 2009; Vaurio et al. 2008). A balanced discussion of FASD-related deficits as well as strengths is an important avenue to pursue in future profile-related research, particularly as assessment is optimized when it clearly directs intervention efforts, and a focus on strength-based interventions is crucial in this population.

Limitations and Future Directions

Despite this being a large multi-site study, some limitations should be noted. Our control group provided an important benchmark of “typical development” against which we could contrast the cognitive profile of children with FASD. However, even though there were no significant SES differences between the FASD and control group, our FASD group also likely experienced significant pre- and post-natal adversity in addition to PAE, and in particular, a range of caregiving situations compared with children in our control group. Inclusion of a more comparable control group in future research would augment our understanding of the role of early life adversity in cognitive development following PAE. Though we did evaluate the role of SES, a common marker for environmental adversity, we did not capture or control for the full range of environmental adversities in our FASD group. It is well documented that children with FASD experience high rates of environmental adversity, in addition to other prenatal insults, all of which may impact cognitive development and functioning. Thus, it is impossible to rule out their relative impact on the profile or within-group heterogeneity outlined for children with FASD in the current study. Indeed, the field as a whole is encouraged to better study and account for the relative impact of these experiences in children with PAE as environmental experiences may shape the trajectories of clinical need and functional outcomes in this population.

We specifically opted to use a short test battery tapping domains previously demonstrated to be sensitive to the impact of PAE and thus it may not capture the “complete” FASD neurobehavioral profile. Future research addressing other domains (e.g., higher-order language and communication skills) would complement this growing body of work, particularly because these areas may be more readily assessed at an earlier point in development compared with other cognitive domains necessitating structured neuropsychological testing. In addition, longitudinal studies of the cognitive development in children and adolescents that includes measures of pubertal attainment should be undertaken to better assess the role of these potential moderators. Results from this study are limited by the cross-sectional design and lack of information about pubertal attainment, but nevertheless suggest important avenues for future study.

This study represents an important step toward replicating and extending international findings to the Canadian context with respect to the cognitive functioning of children with FASD, with innovative findings on relative cognitive strengths. Findings also underscore the importance of beginning to understand the substantial heterogeneity seen among children following PAE. Children and adolescents evaluated in this study had significant cognitive deficits that likely impact many aspects of their daily functioning. The need for early, individualized assessment, as well as appropriate interventions and supports for children with FASD is critical in order to help them reach their full potential. Our findings suggest that various individual and clinical factors may differentially relate to cognitive strengths and weaknesses, signaling the importance of individualized consideration in clinical settings.

Notes

Acknowledgements

Authors had full access to data, are responsible for its integrity, and accuracy of analysis. Support from our clinical partners at data collection is gratefully acknowledged, including the Asante Centre, the Glenrose Rehabilitation Hospital Program, Lakeland Centre for FASD, Kingston’s Hotel Dieu Hospital Child Development Centre, and the Ottawa Children’s Hospital of Eastern Ontario. Importantly, this work would not be possible without the dedication of participating families, to whom we extend our most sincere appreciation.

Funding Information

This work was supported by funding from NeuroDevNet (EPC, PP, KM), Women and Children’s Health Research Institute (KM) and the Sunnyhill Health Centre for Children Foundation (TFO).

Author Contributions

KM participated in the execution of the study, conducted data analysis, and led the manuscript preparation. AP participated in study execution and assisted with data analysis and manuscript development. KKM participated in study execution and assisted with manuscript development. EPC assisted with data analysis and preparation of the manuscript. PP assisted with data analysis and preparation of the manuscript. GA assisted with the study design and editing of the manuscript. AHD assisted with study execution and editing of the manuscript. CL assisted with study execution and editing of the manuscript. AM assisted with study execution and editing of the manuscript. SN assisted with study execution and editing of the manuscript. JP participated in the study design, assisted with study execution, assisted in the interpretation of data, and assisted in editing the manuscript. TFO assisted in the execution of the study and editing of the manuscript. DS assisted with study execution and editing of the manuscript. JRN co-led the study design for the overall NeuroDevNet FASD Research Program, participated in the execution of the study, and assisted in the writing and editing of the manuscript. CR co-led the study design for the overall NeuroDevNet FASD Research Program, participated in the execution of the study, and assisted in data analysis, and manuscript development and revision.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

All study procedures were approved by Research Ethics Boards at the University of British Columbia, University of Alberta, University of Manitoba, Queen’s University, and the Children’s Hospital of Eastern Ontario, and adhered to governing ethical guidelines. Informed consent was obtained from legal guardians, and assent obtained from all children.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kaitlyn McLachlan
    • 1
  • Angelina Paolozza
    • 2
  • Katrina Kully-Martens
    • 3
  • Elodie Portales-Casamar
    • 4
    • 5
  • Paul Pavlidis
    • 6
    • 7
  • Gail Andrew
    • 8
    • 9
  • Ana Hanlon-Dearman
    • 10
  • Christine Loock
    • 4
  • Audrey McFarlane
    • 11
  • Sarah M. Nikkel
    • 12
  • Jacqueline Pei
    • 3
  • Tim F. Oberlander
    • 4
  • Dawa Samdup
    • 2
  • James N. Reynolds
    • 2
  • Carmen Rasmussen
    • 8
  1. 1.Department of PsychologyUniversity of GuelphGuelphCanada
  2. 2.Centre for Neuroscience StudiesQueens UniversityKingstonCanada
  3. 3.Department of Educational PsychologyUniversity of AlbertaEdmontonCanada
  4. 4.Department of PediatricsUniversity of British ColumbiaVancouverCanada
  5. 5.B.C. Children’s Hospital Research InstituteVancouverCanada
  6. 6.Department of PsychiatryUniversity of British ColumbiaVancouverCanada
  7. 7.Centre for High-Throughput BiologyUniversity of British ColumbiaVancouverCanada
  8. 8.Department of PediatricsUniversity of AlbertaEdmontonCanada
  9. 9.Glenrose Rehabilitation HospitalEdmontonCanada
  10. 10.Manitoba FASD CentreWinnipegCanada
  11. 11.Lakeland Centre for FASDCold LakeCanada
  12. 12.Department of Medical GeneticsUniversity of British ColumbiaVancouverCanada

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