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. Author manuscript; available in PMC: 2010 Mar 1.
Published in final edited form as: Alcohol Clin Exp Res. 2008 Nov 11;33(3):400–407. doi: 10.1111/j.1530-0277.2008.00849.x

Central and Peripheral Timing Variability in Children with Heavy Prenatal Alcohol Exposure

Roger W Simmons 1, Susan S Levy 1, Edward P Riley 2, Naju M Madra 2, Sarah N Mattson 2
PMCID: PMC2659014  NIHMSID: NIHMS84604  PMID: 19053974

Abstract

Background

The study examined whether prenatal alcohol exposure is associated with increased motor timing variability when the timing response is partitioned into central clock variability, which indexes information processing at the central nervous system (CNS) level and motor delay variability, which reflects timing processes at the level of the peripheral nervous system (PNS).

Methods

Eighteen children with histories of prenatal alcohol exposure and 22 control children were assigned to young (7–11 years) or older (12–17 years) groups. Children tapped a single response key with the index finger in synchrony with a series of externally generated tones (the paced phase). At the conclusion of these tones, children continued tapping (the continuation phase) while attempting to maintain the same rate of tapping imposed by the paced phase. Two blocks of tapping were completed with inter-tone-intervals set at either 400 or 900 ms. Inter-response interval, central clock variability, and motor delay variability produced during the continuation phase were the dependent variables.

Results

Mean inter-response interval for the four groups did not differ for either time interval. Central clock variability produced by the young alcohol-exposed group was significantly greater than the two older groups for the 400 ms interval and all other groups for the 900 ms interval. Motor delay variability produced by the young alcohol-exposed group was significantly greater than the other three groups for both time intervals. Central and motor delay variability in children with and without alcohol exposure was directly related to the duration of the interval to be reproduced.

Conclusions

Central and peripheral timing variability was significantly greater for the young alcohol-exposed children. This atypical timing may be related to the teratogenic effects of alcohol, although the negative effects are limited to younger alcohol-exposed children since there were no differences in central and peripheral timing variability between the older alcohol-exposed children and controls.

Keywords: Fetal alcohol spectrum disorders, Timing variability

INTRODUCTION

Fetal alcohol spectrum disorders (FASDs) is an umbrella term used to describe a range of neurobehavioral deficits resulting from prenatal alcohol exposure (O’Malley and Nanson, 2002). At the extreme end of the continuum are children with fetal alcohol syndrome (FAS) as defined by a set of characteristics including central nervous system (CNS) damage or dysfunction, pre- and post-natal growth retardation, and distinct facial anomalies (Jones and Smith, 1973). At other points on the continuum are children with less severe effects related to prenatal exposure to alcohol. These children do not have the defining characteristics of FAS but do share many of the same neurobehavioral deficits as FAS children, including intellectual functioning (Steinhausen and Spohr, 1998), delayed psychosocial (Roebuck et al., 1999) and language development (Church et al., 1997), attention deficits (Connor et al., 1999), poor verbal and non-verbal learning (Mattson et al., 1996), limited recall and spatial memory (Mattson and Riley, 1999), and motor skill dysfunction (Wass et al., 2002).

Dysfunctional behavior of children with FASDs (with or without FAS) has been linked to alcohol related trauma to the cerebrum (Samson, 1986), cerebellum (Archibald et al., 2001; Mattson and Riley, 1996), corpus callosum (Riley et al., 1995) and basal ganglia (Mattson et al., 1996), all of which are involved in regulating motor timing (Chen, et al., 2001; Harrington et al., 1998). The teratogenic effects of prenatal alcohol exposure also extend to the peripheral nervous system (PNS) in the form of atypical muscle development (David and Subramaniam, 2005), reduced or delayed peripheral nerve myelination (Zoeller et al., 1994), and slowed nerve conduction velocity (Avaria et al., 2004). Therefore, with the CNS and PNS negatively impacted by prenatal alcohol exposure, it is reasonable to expect that key motor parameters, such as motor timing accuracy and variability, will be significantly compromised.

Identifying the source(s) of motor timing variability has been facilitated by using theoretical formulations that partition total timing variability into central (i.e., CNS) and motor (i.e., PNS) components. For example, Wing and Kristofferson (1973) have proposed a model in which total timing variability is attributed to variation arising from a central internal clock mechanism that periodically emits pulses that define successive inter-response-intervals (IRIs), and peripheral timing variability stemming from motor delays in the effector system executing each centrally derived pulse. A schematic representation of the timing events inherent to the model is presented in Figure 1, where the interval between central clock pulses is designated as C, the motor delay periods as MD, and the time between successive motor responses as IRI. The Wing-Kristofferson model is formally stated as,

Tvar=Cvar+2(MDvar)

where, Tvar equals total timing variability, Cvar is central clock variability, and MDvar is motor delay variability. The motor delay period is doubled since implementation of the centrally generated pulse occurs at points that define the beginning and end of each IRI. Furthermore, the covariance of adjacent IRIs is assumed to be negatively correlated due to the two tiered structure of the model, while correlations for IRIs greater than lag-1 are expected to be in the low to zero range.

Figure 1.

Figure 1

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Schematic diagram of Wing and Kristofferson’s two-stage model of motor timing variability

The Wing and Kristofferson model has been frequently applied to repetitive movements generated within the context of a continuation paradigm. In this paradigm, participants tap a response key in synchrony with a series of externally generated tones (i.e., the paced phase). After a specified period of time, the tones cease but the participant continues tapping (i.e., the continuation phase) at the same tempo imposed during the paced phase. As indicated by the Wing-Kristofferson model, increased timing variability produced by asymptomatic individuals during unimanual (Semjen et al., 2000) and bimanual movements (Turvey et al., 1986) is primarily the result of significantly increased central variability. This increase in central timing has been identified in individuals with Parkinson’s disease (Pastor et al., 1992), congenital hypothyroidism (Kooistra et al., 1997), Alzheimer’s disease (Duchek et al., 1994), cortical stroke and cerebellar disease (Ivry and Keele, 1989), together with some reports of increased motor delay variability especially for syndromes in which the cerebellum (Ivry et al., 1988) and basal ganglia (O’Boyle et al., 1996; Pastor et al., 1992) are implicated.

Assessment of prenatal alcohol exposure in infants and very young children has consistently revealed developmental delays in fine (Kalberg et al., 2006) and gross (Kyllerman, et al., 1985) motor skills, with some data indicating that alcohol related motor problems persist into childhood but not adolescence (Simmons et al., 2002; Simmons et al., 2006). Successful performance of motor skills is dependent on the cognitive processes of executive function, which align with the emergence of Piaget’s formal operations stage of development around the 12th year (Emick and Welsh, 2005). Additionally, the motor control literature indicates that beyond 11 to 13 years of age performance of motor skills is quantitatively and qualitatively distinct from that of younger children (Branta, et al., 1984; Eliasson, et al., 2006; Sadagopan and Smith, 2008. Therefore, one aim of the study was to examine the differential effects of prenatal alcohol exposure on motor timing variability as a function of age, with the demarcation point between the young and older groups being the 11th to 12 year. Specifically, the age groups were: young non-exposed controls (YNC; 7–11 years), older non-exposed controls (ONC; 12–17 years), young ALC (YALC; 7–11 years) and older ALC (OALC; 12–17 years). Based on the notion of a developmental delay, we predicted that YALC children would produce significantly greater clock and motor delay variability than the YNC children, whereas, there would be no difference in the performance of the two older groups.

The extant motor control literature indicates that central clock and motor delay variability in both non-clinical (Doumas and Wing, 2007) and clinical (Hinton et al., 2007) populations is directly related to the length of the time interval being reproduced. Therefore, a second aim of the study was to confirm this outcome for children with and without prenatal alcohol exposure. Additionally, based on the expectation that motor skills of young children with FASD are susceptible to developmental delay it is predicted that relative to the young control children timing variability for the young alcohol exposed children will be disproportionately increased as IRI increases. The timing variability of the two older groups is predicted to be the same regardless of length of time interval.

METHODS

Participants

Alcohol-Exposed Sample

Eighteen alcohol-exposed participants were recruited from a pool of children registered with the Center of Behavioral Teratology (CBT) at San Diego State University. The children were between the ages of 7 and 17 years, inclusively, with Full-Scale IQ (FSIQ) scores above 60 and confirmed histories of heavy prenatal alcohol exposure. Levels of maternal alcohol consumption were retrospectively established through the use of maternal report, social service records, or medical records. The CBT is very conservative in establishing alcohol exposure and requires concrete evidence (e.g., direct report of the parent or close relative or medical records) of such exposure. Reports of maternal alcohol consumption ranged from 3–4 glasses of wine a day, to “drunk day and night,” drinking a fifth of vodka a day, or a case of beer per day. Children with limited information on prenatal alcohol exposure were not eligible to participate. For all children, information gathered from birth included age, gender, handedness, family socioeconomic status (SES), race/ethnicity, psychiatric and medical histories of the family and child, maternal and paternal occupations and levels of education, and social behavioral traits of children in and out of the school environment. Additionally, children completed a comprehensive neuropsychological battery of 11 tests including assessment of Performance IQ (PIQ), Verbal IQ (VIQ), and Full scale IQ (FIQ), The average time between IQ assessment and participation in the experiment was 2.2 years (S.D. ± 2.0). Prenatal exposure to other substances was determined as part of the screening process. None of these factors were related to the observed patterns in motor timing and were not included in the final statistical analyses.

Alcohol-exposed participants were also screened for FAS by a pediatric dysmorphologist (Kenneth Lyons Jones, MD) using established criteria of specific facial anomalies (e.g., short palpebral fissures, smooth philtrum, and thin vermillion), pre- and post-natal growth deficiency (10th percentile or less for age group) and CNS dysfunction (Jones & Smith, 1973). Seven children received a diagnosis of FAS by the dysmorphologist and were assigned to a FAS group. An additional eleven children who did not meet criteria for a diagnosis of FAS but who had experienced heavy in utero exposure to alcohol were assigned to the prenatal exposure to alcohol (PEA) group. Although children in the PEA group do not have the physical features of FAS, they do experience the functional and/or mental problems linked to heavy prenatal alcohol exposure, including behavioral or cognitive deficits (Mattson and Riley, 1998). In the current experiment, FAS and PEA groups were independently compared for each dependent variable. For all comparisons FAS and PEA children were found to be comparable for the variables of interest and were combined into a single alcohol-exposed group designated as the ALC group. The final sample size of ALC was based on statistical power requirements using the Power Analysis and Sample Size (PASS) software program and eligibility of the children in meeting the inclusionary and exclusionary criteria.

Non-exposed Control Sample

A non-exposed control group (NC) of 22 children between the ages of 7–17 years were also recruited from the CBT pool of registered participants. Control children were evaluated for exposure to maternal alcohol use and other teratogenic agents using the same comprehensive neuropsychological battery used to assess children with FASDs. An average of 2.7 years (SD ± 2.3) occurred between IQ assessment and completion of the task for the NC group. As in the ALC group, screening was completed through a telephone interview and parent questionnaire that included information concerning alcohol consumption during pregnancy, and examination by the dysmorphologist. Potential control participants were excluded for the following reasons: prenatal exposure to alcohol or other known teratogens, closed head injury with significant loss of consciousness, or a FSIQ score below 60. None of the control participants had any known neurological problems. The control group was closely matched to the alcohol-exposed children on age, gender, handedness, socioeconomic status (measured using the Hollingshead Four Factor Index, 1975) and race.

Apparatus and Methods

The apparatus consisted of a Moart response unit (Lafayette Instrument Co, Lafayette IN, USA) measuring 5 ×10 × 2.5 cm mounted on a table. A single circular, touch sensitive response key (2cm diameter) was positioned in the center and flush with the top surface of the response unit. Finger contact with the response key produced a shift in a 5-volt analog signal that was recorded on line at 2000 samples per second using a PC fitted with a 16-bit analog-to-digital acquisition board (National Instruments, Austin, Texas, USA). Data were stored in spreadsheet form for follow-up analysis. The experimental protocol was controlled by the PC using customized software based on LabView programming (National Instruments, Austin, Texas, USA).

The center of the response key was positioned 19 cm in front of the participant. Two computer wrist pads were placed together at the front edge of the response unit with the top surface of the wrist pads and response key at the same height. The participant rested their hand on the wrist pads in a neutral anatomical position that allowed the child to comfortably tap the response key with an extended index finger.

Prior to testing, the legal guardian of the participant provided informed consent and the child gave assent in accordance with Institutional Review Board requirements. Participants sat in a height-adjusted chair facing the apparatus and were positioned with the center of the right shoulder joint (left shoulder for left handed children) aligned with the center of the response key. A verbal explanation of the task was provided together with a demonstration of the tapping response. A computer generated tone (2 seconds in duration) signaled the beginning of the trial. This ‘go’ signal was followed by 17 additional tones (60 ms in duration) presented at an inter-tone rate of either 400 ms (2.5Hz) or 900 ms (1.1 Hz) intervals. Participants were required to tap the response key with the index finger of the dominant hand in synchrony with the computer-generated tones. Following the seventeenth tone, the child continued tapping at the practiced rate for an identical time period until another single, long (2 seconds) tone indicated the end of the trial. The presence and absence of the tones comprised the paced and continuation phases of each trial, respectively.

Each subject completed a practice trial followed by six test trials for each time condition (400 or 900 ms). The inter-trial-interval was approximately 15 seconds and the presentation order of the time conditions was counterbalanced across participants to negate practice effects. No feedback was provided concerning timing accuracy during the continuation phase. Testing required approximately 20 minutes and at the conclusion of testing each participant was provided a small monetary award.

The unimanual tapping test and another test (investigating speed-accuracy tradeoff phenomena) associated with a second research project were completed in a single test session. The second test did not interfere with performance of the single tapping task reported here. The order of completing these two tests was counterbalanced across participants and the inter-test interval was approximately 5 minutes.

Statistical Analysis

Demographic information relating to participant age, FSIQ, VIQ, PIQ and SES were independently assessed using one-way analysis of variance (ANOVA) tests. Raw data for the continuation phase of each trial were separately inspected for any outlier IRIs exceeding 50 percent of the criterion time (i.e., 400ms - acceptable range of 200 to 600 ms or 900 ms - acceptable range of 450 to 1350 ms) (Ivry and Hazeltine, 1999). All IRIs were within the acceptable range for each time condition.

The dependent variables of average IRIs for the continuation phase for the 400 and 900 ms conditions were examined using one-way ANOVAs to assess timing differences in the four groups. Raw scores for each tapping series of the continuation phase were examined for central clock and motor delay variability according to modeling procedures described by Wing and Kristofferson. (1973). Separate 4(group) × 2(time condition) ANOVAs with repeated measures on the second factor were completed to assess the dependent variables of clock variability and motor delay variability. The between-subjects factor was group comprised of YALC, OALC, YNC and ONC groups. The repeated measures factor was time condition consisting of 400 and 900 ms tapping intervals. Interactions revealed by the main analysis were examined post hoc using one-way ANOVAs, comparing the groups at each time condition separately. The same post hoc procedures were adopted to examine clock variability and motor delay variability. Assumptions of sphericity and homogeneity of variance were examined using Mauchly’s and Levene’s tests, respectively. Any violations of sphericity prompted the use of the Geisser-Greenhouse conservative estimate of degrees of freedom and violations of homogeneity of variance invoked post hoc comparisons using Games-Howell procedures. Alpha was set to 0.05 for all tests of significance. Data were analyzed using SPSS version 14.0.

RESULTS

Demographic Information

Demographic information for the four groups is presented in Table 1. There was a significant main effect of group on age [F(3,39) = 43.9, p < 0.001] between the young (M = 9.6 ± SE 1.3 years) and the older (M = 14.5 ± SE 1.7 years) children. There were no age differences between the two young groups (YALC M = 8.5 ± SE .39 years versus YNC M = 10.1 ± SE .36 years), or the two older groups (OALC M = 14.9 ± SE .52 years versus ONC M = 14.1 ± SE .47 years). No significant differences in SES (p > 0.05) were found among groups.

Table 1.

Demographic information by group.

YALC OALC YNC ONC
Gender (M:F) 4:4 4:6 4:6 7:5
Age M (SD) 8.5(1.1) 14.9(1.6) 10.1(1.1) 14.1(1.6)
FSIQ1 M (SD) 92.0(14.6) 82.6(10.9) 107.5 (9.1) 101.2(13.1)
    Range 70–116 61–95 93–122 73–117
VIQ1 M (SD) 92.8(17.9) 83.7(12.4) 112.3 (10.5) 109(15.7)
    Range 65–110 56–101 98–127 80–132
PIQ1 M (SD) 93.2(16.9) 84.5(13.3) 101.6(9.5) 91.9(11.7)
    Range 71–121 69–106 87–116 70–112
Hollingshead Score2 45.8 (12.9) 49.1(12.3) 49.9.0(7.6) 47.4(15.1)
Hand Dominance (L:R) 3:5 2:8 1:9 0:12
Ethnicity n (%)
  Asian 1 (12.5) 0 (-) 0 (-) 0 (-)
  African American 3 (37.5) 2 (20.0) 3 (30.0) 3 (25.0)
  Caucasian 4 (50.0) 8 (80.0) 6 (60.0) 8 (66.6)
  Native American 0 (-) 0 (-) 1 (10.0) 1 (8.3)
  Hispanic descent 3 (37.5) 1 (20.0) 3 (30.0) 2 (16.6)
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1

Intelligence scores were derived from either the Wechsler Preschool and Primary Scales of Intelligence Revised or the Wechsler Intelligence Scale for children-III depending on the child’s age at the study entry

2

Socioeconnomic status was estimated using the Hollingshead Four Factor Index of Social Status (Hollingshead 1975, unpublished data)

Analyses of group differences in FSIQ, PIQ, and VIQ revealed significant effects for FSIQ [F(3,39) = 8.3, p < 0.001], PIQ [F(3,39) = 2.9, p < 0.04] and VIQ [F(3,39) = 9.4, p < 0.001]. Post hoc tests indicated the OALC group had significantly lower FSIQ scores than the YNC (p < 0.001) and ONC (p < 0.005) groups. The OALC group also had significantly lower PIQ scores than the YNC group (p < 0.031) and significantly lower VIQ scores than either the YNC (p < 0.001) or ONC (p <0.001) groups.

Mean inter-response-interval (IRI)

Analysis of the mean IRIs revealed no significant differences between the four groups for either the 400 [F(3,39) = 1.69, p > 0.05] or 900 ms [F(3,39) = 2.11, p > 0.05] time conditions. The mean values (SE) for the YNC, ONC, YALC and OALC groups at the 400 ms level were 428.1 (10.6), 450.1 (5.5), 429.8 (8.4), and 435.6 (11.3) ms, respectively. At the 900 ms level the mean values were 874.6 (17.6), 908.0 (10.4), 862.3 (32.2), and 924.6 (13.5) ms, respectively.

Central Clock variability

Examination of clock variability revealed significant main effects for group [F(3,36) = 12.65, p < 0.001, η2 = 0.51] and time condition [F(1,36) = 86.3, p < 0.001, η2 = 0.72], as well as a significant group × time condition interaction [F(3,36) = 5.7, p < 0.003, η2 = 0.32]. Data illustrating the group mean clock variability values for the 400 and 900 ms conditions are presented in Fig. 2. Post hoc tests indicated clock variability increased for all groups as time interval increased, particularly for the YALC group.

Figure 2.

Figure 2

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Clock variability for alcohol-exposed children and non-exposed controls as a function of age for two time conditions

To further examine the group × time interaction, group differences in clock variability were analyzed separately for the two time conditions using one-way ANOVAs. Significant group differences were revealed at the 400 ms level [F(3,36) = 5.11, p < .005, η2 = 0.30] , and the 900 ms level [F(3,36) = 11.43, p < .001, η2 = 0.49]. At the 400 ms level, the YALC group produced significantly greater clock variability (M = 33.2 ms, ± SE 4.6) than the OALC group (p < .02, M = 19.4 ms, ± SE 4.5) and ONC group (p < .02, M = 20.2 ms, ± SE 1.6) but not the YNC group (p > 0.05, M = 26.2 ms, ± SE 3.2). Furthermore, there were no significant differences (p > 0.05) between the YNC group and the two older groups. Confidence intervals confirmed the latter result as all computed CIs spanned zero (95% CIΔ range −18.22 to 16.17) (Seaman and Serlin, 1998).

At the 900 ms level, post hoc comparisons revealed the YALC group (M = 78.4 ms, ± SE 9.6) had significantly greater clock variability than the YNC (p < .002, M = 44.2 ms, ± SE 5.4), ONC (p < .001, M = 35.2 ms, ± SE 3.3) and OALC (p < .001, M = 41.9 ms, ± SE 3.4) groups. There were no differences in clock variability for other group comparisons (p > 0.05), as confirmed by the computed CIs spanning zero (95% CIΔ range −29.28 to 13.59).

Motor delay variability

Mean group data for motor delay variability for the two time conditions are presented in Fig. 3. Data analysis indicated significant main effects for time condition, [F(1,36) = 65.5, p < 0.001, η2 = 0.65], and group [F(3,36) = 13.2, p < 0.001, η2 = 0.52]. The time condition × group interaction was not significant, [F(3,36) = 0.29, p > 0.05, η2 = 0.02]. Post hoc Bonferroni comparisons revealed that the four groups produced significantly greater (p < 0.05) motor delay variability at the 900 ms time condition than at the 400 ms level. Post hoc Bonferroni comparisons of the group main effect revealed the YALC group had significantly greater motor delay variability (M = 32.5 ms, SE = 2.0) than the YNC group (p < .05, M = 21.7 ms, ± SE 1.8), the ONC (p < .001, M = 17.5 ms, ± SE 1.6) and the OALC groups (p < .001, M = 17.8 ms, ± SE 1.8). There were no differences in motor delay variability between the other three groups (p > 0.05), a result that was confirmed by all computed confidence intervals spanning zero (95% CIΔ −16.75 to .95).

Figure 3.

Figure 3

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Motor delay variability for alcohol-exposed children and non-exposed controls as a function of age for two time conditions

DISCUSSION

We predicted that the teratogenic effects of prenatal alcohol exposure on the CNS would result in significantly greater central clock variability for young children. This prediction was confirmed by the data. For the 900 ms time condition, the two older groups and the young control group produced levels of clock variability that were significantly less than the variability associated with the young alcohol-exposed group. At the 400 ms time condition, the young alcohol-exposed children again experienced significantly greater clock variability than the two older groups. However, the difference in clock variability for the two young groups was not statistically reliable even though the young controls and two older groups produced comparable clock variability values.

The finding of greater clock variability at the long time interval for the young alcohol-exposed children is consistent with previous work revealing deficits in central information processing for young, but not older, children with histories of alcohol exposure (Simmons et al., 2002; Simmons et al., 2006). Previous studies have reported developmental motor delays in infants and very young children, and our results indicate that early motor timing deficits extend into childhood but are not evident during adolescence.

Our prediction of increased motor delay variability for young alcohol-exposed children was also confirmed. This group produced significantly greater motor delay variability than the three other groups at both time intervals. The Wing and Kristofferson model predicts increased motor delay variability in the presence of syndromes that compromise the CNS such as medial cerebellar disease (Ivry, et al., 1988) and non-medicated Parkinson’s disease (O’Boyle et al., 1996). In this regard, our results are consistent with this predication for motor delay variability with increased PNS variability being attributable to alcohol exposure and not age since the young control children and two older groups produced comparable and significantly lower motor delay variability than the young alcohol-exposed children. Presumably, the fact that the older children with FASDs performed at control group levels is a result of maturational processes in which the adverse affects of alcohol exposure on the PNS diminish with time (Phillips, 1989).

The outcomes of the study should be considered in terms of other factors that could potentially contribute to the observed results. For example, while the mean age of the two younger groups are statistically comparable the alcohol-exposed children were 1.5 years younger than the controls, and this age difference may have been sufficient to affect overall performance. However, in previous work we have reported deficits in reaction time, coincident-anticipation timing and velocity timing (Simmons et al., 2002; Wass et al., 2002) for young children with FASDs who differed in age from control children by as little as a few months. These results indicate that timing deficits are related to alcohol exposure and not age.

It should also be noted that children with low PIQ and relatively high VIQ values can experience both fine and gross motor problems. Analysis of the demographic information revealed this difference in IQ scores for the older control children that could explain the similarity of central clock variability observed for the two older groups. With PIQ values for the older control children in the normal range, clock variability may have been significantly reduced leading to a separation in performance between the older alcohol and non-alcohol exposed children. Since the difference between VIQ and PIQ values manifested by the control group was not anticipated these factors were not controlled through a prior matching. An alternative would be to statistically control the PIQ variable, thereby adjusting the variability data to account for PIQ differences. We chose not to adopt this procedure because greater ecological validity is conferred on the results when the children are tested with a motor and neuropsychological profile that replicates how they function on an everyday basis. Our older control group has a lower average PIQ than would be excepted and this may limit the generalizability of the results, but this limitation is restricted to the older children and does not affect the significant clock and motor delay deficits associated with the young children with FASDs.

The significantly larger central and peripheral variability observed for the young children with FASDs could also be explained by these children producing longer estimates of the criterion time conditions (i.e., 400 and 900 ms) than the other groups. However, analysis of these data revealed that the mean inter-tap intervals for the four groups were equivalent for each time condition, which eliminates the time reproduction effect as a contributing factor to the results.

While our results indicate reduced central clock and motor delay variability with age in alcohol exposed children what remains unclear is how these behavioral outcomes relate to the neurological evidence that alcohol induced damage to the CNS persists into adolescence (O’Hare et al., 2005; Riley et al., 2004; Sowell et al., 2002). One possibility is that as children become motorically experienced with time they develop compensatory mechanisms that facilitate a reduction of timing variability to control levels. Compensatory systems have been documented for temporal (Hudson et al., 2008) and force parameters (Latash et al., 2002) and have been directly linked to neural plasticity at the spinal cord (Curt et al., 2008), cortex (Adkins et al., 2006; Helmich et al., 2007; Nishimura et al., 2007) and cerebellar levels (Collin et al., 2004). It is also known that motor training in alcohol-exposed rats ameliorates motor deficits, with experienced related plasticity occurring in the cerebellum (Klintsova, et al., 2000; Klintsova et al., 2007). Whether similar changes in neural plasticity occur in alcohol-exposed children as a result of practice needs to be confirmed with longitudinal neuroimaging studies.

We also predicted increased central and motor delay variability for all groups at the longer time interval condition, with specific interest in whether the young alcohol exposed children were disproportionately more variable with increased time interval. The data support this prediction. All groups produced significantly greater clock variability at the longer time interval, with the young alcohol exposed group being particularly sensitive to the central demands of maintaining regular tapping responses as the interval to be reproduced increased. At the short time interval, the young alcohol exposed group produced clock variability that was 21 percent larger than the young controls, whereas at the long time interval the percentage difference between the two groups increased to 81 percent. Similar comparisons between the two older groups revealed zero and 6 percent differences for the short and long time intervals, respectively.

With regard to motor variability, the performance of the young alcohol exposed group was less affected by changes in duration of the time interval. At the 400 ms level the alcohol children produced motor delay variability that was 35 percent greater than the young controls, whereas at the 900 ms level this difference was reduced to 3 percent. For the older groups the percentage differences were 17 and less than 1 percent for the short and long intervals, respectively. Collectively, the data reveal that the young alcohol exposed children are particularly challenged when processing central clock information during the long time interval, but motor delay variability is not differentially affected. The clinical implications of these results indicate that when designing rehabilitation exercises for young children with FASDs therapists should not only expect less temporal consistency as the time intervals of the exercise increase, but they should provide exercises that promote information processing at the central clock level with the aim of ameliorating the observed timing deficits.

In summary, the study revealed that prenatal alcohol exposure interrupts central time keeping operations in young children resulting in increased central clock variability, particularly at longer time intervals. Additionally, peripheral timing processes in the form of motor delay variability are also significantly increased in young FASD children. Central and motor delay variability in children with and without alcohol exposure was directly related to the duration of the interval to be reproduced. The dysfunctional central clock timing is consistent with alcohol-induced damage to the basal ganglia, cerebellum, cortex and corpus callosum, while increased motor delay variability likely reflects alcohol induced damage to the PNS.

Acknowledgments

This manuscript was supported in part by grants AA10417 and AA07456 awarded by the National Institute on Alcohol Abuse and Alcoholism.

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