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. Author manuscript; available in PMC: 2017 May 1.
Published in final edited form as: Neuropsychology. 2016 Jan 11;30(4):492–501. doi: 10.1037/neu0000266

Gray matter integrity within regions of the dorsolateral prefrontal cortical-subcortical network predicts executive function and fine motor dexterity in spina bifida

Ashley L Ware 1, Paulina A Kulesz 1, Victoria J Williams 1, Jenifer Juranek 2, Paul T Cirino 1, Jack M Fletcher 1
PMCID: PMC4840030  NIHMSID: NIHMS753854  PMID: 26752120

Abstract

Objectives

This study examined microstructural properties of cortical and subcortical gray matter components of the dorsolateral prefrontal (DLPFC) cortical-subcortical circuit in relation to parent rated executive function and fine motor dexterity performance in youth with spina bifida myelomeningocele (SBM). Aberrant gray matter integrity of the DLPFC, basal ganglia nuclei, and thalamus were hypothesized to differentially relate to neurobehavioral outcomes.

Methods

Forty-nine youth between 8–18 years (M = 12.2) old with SBM underwent a 3T MRI including diffusion tensor imaging. Neurobehavioral measures of parent-rated executive function and fine motor dexterity were obtained from a standardized neuropsychological evaluation. Relations among indices of gray matter microstructural integrity (mean diffusivity [MD], fractional anisotropy [FA], cortical thickness) and neurobehavior were examined using three correlational methods to enhance reliability of estimates of brain-behavior relations.

Results

In SBM, higher FA values in the caudate were associated with poorer behavioral regulation. Higher FA values in the putamen and greater DLPFC thickness were both associated with poorer fine motor dexterity.

Conclusion

Behavioral regulation and FA in the caudate related to behavioral inhibition in SBM. Similarly, associations between fine motor dexterity and indices of gray matter integrity in the putamen and DLPFC support fronto-striatal involvement in motor control in SBM. Examination of these neurobehavioral correlates revealed a pattern of attenuated behavioral impairments when gray matter structure was more similar to that of typically developing youth.

Keywords: Spina bifida myelomeningocele, DTI, executive function, fine motor dexterity, basal ganglia, thalamus, dorsolateral prefrontal circuitry

Spina bifida myelomeningocele (SBM) is associated with executive and motor dysregulation (Kelly et al., 2012; Rose & Holmbeck, 2007; Yeates et al., 2003). These deficits are related to aberrations in the central nervous system that arise from the congenital failure of neural tube closure during the first prenatal trimester (i.e., the herniated protrusion of spinal cord and meninges from the vertebrae at birth) (Sadler et al., 2000). SBM represents the most common birth defect affecting the central nervous system that is compatible with survival (Au et al., 2010; Boulet et al., 2009; Fletcher & Brei, 2010) and occurs in 2–3 of 10,000 live births (Shin et al., 2010). The Chiari II malformation of the hindbrain and resultant hydrocephalus are highly prevalent in SBM, co-occurring in approximately 90% of individuals with SBM (Juranek & Salman, 2010).

Neurobehavioral outcomes are variable across individuals with SBM (Dennis & Barnes, 2010). On average, they include relative weaknesses in higher-order cognitive processes such as executive function and attention (Burmeister et al., 2005; Dennis & Barnes, 2010). Executive functions encompass self-regulatory behaviors necessary to select and sustain goal-directed, purposeful behavior (Denckla, 1996). Successful self-regulation is dependent upon aspects of both cognitive and behavioral (motoric) control. Children with SBM have poorer scores on performance-based tests and parent ratings of behavioral inhibition, working memory, planning, and organization compared to typically developing peers (Brown et al., 2008; Burmeister et al., 2005; Hampton et al., 2011; Tuminello et al., 2012). Together, results suggest relative weaknesses in these aspects of cognitive regulation.

At the same time, relative preservation of “top down” processes involving components of behavioral regulation such as motor adaption, response control in attention, and sociability have been interpreted as relative strengths in SBM compared to motor, attentional, and spatial processing (Dennis et al., 2010). Despite weaknesses in motor functions involving motor control and dexterity, children with SBM show less impairments on tasks involving synchronization, motor learning and adaptation, and controlled eye movements (Dennis & Barnes, 2010; Dennis et al., 2004). On a continuous performance test, Brewer et al. (2003) and Swartwout et al. (2008) found relative preservation of performance after initial trials, suggesting intact response control during attention despite initial orienting deficits and slower learning in youth with SBM (Brewer et al., 2001; Swartwout et al., 2008). Similar patterns have also been reported on the Wisconsin Card Sorting task (Fletcher et al., 1996). However, evaluations of the neural correlates of cognitive and behavioral strengths and weaknesses in SBM have largely focused on posterior brain regions and relations with fronto-striatal regions remain largely unknown.

Dorsolateral prefrontal cortex (DLPFC) circuitry, a neural network between the DLPFC, basal ganglia, and thalamus, is a main circuit underlying cognitive and behavioral regulation (Alexander et al., 1986; Stewart, 2006). Neurobehavioral deficits in executive and motor domains have been attributed to structural and functional alterations of the DLPFC circuit, particularly in neurodevelopmental populations with disrupted prenatal development of the central nervous system (Fryer et al., 2012; Fryer et al., 2007; Ware, Infante, et al., 2014). While all brain structures are not typically anomalous in individuals with SBM, overall regional patterning suggests that anterior and posterior regions may be differentially affected.

In SBM, prefrontal cortices are thicker and have greater gyrification, whereas posterior cortical regions are thinner and less complex (i.e., gyrification) compared to typically developing peers (Juranek et al., 2008; Treble et al., 2013). Subcortical structures are also aberrant. Posterior and inferior brain regions are generally reduced in volume, particularly in the midbrain and cerebellum of individuals with SBM (Juranek et al., 2010). Although deep subcortical gray matter structures are considered visibly normal upon radiological examination in SBM, volumetric magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) have indicated macro- and microstructural differences in subcortical gray matter (Hasan et al., 2008; Juranek et al., 2008; Kumar et al., 2010; Ware, Juranek, et al., 2014).

Deviations in isotropy in subcortical gray matter are thought to reflect aberrant structural patterning during neuronal and glial arborization (Mukherjee et al., 2002). Ware, Juranek, et al. (2014) observed significantly higher mean diffusivity (MD) in the thalamus and caudate and significantly lower MD in the putamen as well as significantly higher fractional anisotropy (FA) in the thalamus and basal ganglia (i.e., caudate, putamen, pallidum) in youth with SBM compared to typically developing youth. Given the proximity of the thalamus and caudate to the ventricles, this altered patterning likely suggests lower cellular density and unplanned apoptosis following the mechanical effects of hydrocephalus in this population (Ware, Juranek, et al., 2014). Conversely, microstructural alterations observed in the putamen in youth with SBM suggest increased cellular density that may reflect atypical structural organization and programmed cell death that is expected in the putamen during typical adolescent development (e.g., Abe et al., 2008; Lebel et al., 2008; Mukherjee et al., 2002). However, it is unclear whether or not these microstructural abnormalities relate to functional impairment in SBM.

Limited research has investigated neurobehavioral correlates of anterior brain abnormalities in SBM, reflecting a major focus on the cerebellum and posterior cortex. Together various studies have indicated that functional impairments associated with anterior and posterior brain regions attenuate when indices of brain structure in SBM more closely resemble typically developing individuals. In the anterior regions, an early study found that significant reductions of transverse diffusivity in the caudate in individuals with SBM significantly and positively predicted intelligence in this population (Hasan et al., 2008). Intelligence and fine motor dexterity are also weakly but significantly related to measurements of prefrontal cortical thickness and complexity in SBM (Treble et al., 2013). Treble et al. (2013) found that increased cortical thickness and greater gyrification observed in the prefrontal cortex of youth and young adults with SBM relative to typically developing peers correlated negatively with both intelligence and fine motor dexterity. Similarly, Kulesz et al. (2015) found negative relations between attentional control and the anterior cingulate, indicating that the increased cortical thickness observed in youth with SBM was related to poorer control.

The current study aimed to elucidate the functional implications of altered gray matter microstructure in SBM. We examined whether executive function and fine motor dexterity were related to microstructural properties of cortical and subcortical gray matter components of the DLPFC circuit in youth with SBM. Since inaccurate estimates of structure–function associations may stem from frequently occurring outliers, especially in relatively small samples (Fletcher, Stuebing et al., 1996), robust statistical approaches were used to examine brain-behavior relations in the current study (Kulesz et al., 2015). These approaches are outlier resistant statistical methods that estimate the degree of linear relation between two variables. Robust correlations can also provide more accurate assessments of the population correlation than the Pearson correlation (for greater detail see Kulesz et al., 2015). When correlational methods are examined concurrently, they can provide more reliable or robust indications of brain-behavior relations and reduce the likelihood of Type II errors associated with stringent p-values for the estimation of multiple correlations.

Using this approach to estimation, we examined whether estimates of cortical thickness of the DLPFC and DTI indices of FA and MD for basal ganglia structures and the thalamus (as reported in Ware, Juranek, et al., 2014) were related to parent ratings of executive function and performance on a fine motor dexterity task. We expected that neurobehavioral outcomes in SBM would be differentially associated with aberrant gray matter integrity of these cortical and subcortical regions.

Hypotheses

  1. We hypothesized that thicker DLPFC in youth with SBM would relate to poorer executive function ratings and fine motor dexterity in SBM (Juranek et al., 2008).

  2. Given previously established relations between executive and behavioral dysregulation with the caudate (Alexander et al., 1986; Arsalidou et al., 2013; Fryer et al., 2012) and thalamus (Little et al., 2010; Stuss, 2011), we predicted that poorer executive function ratings, but not fine motor dexterity, would be associated with higher FA and MD values in the caudate and thalamus in youth with SBM.

  3. We expected that fine motor dexterity would be negatively associated with FA and positively associated with MD values in the pallidum in SBM since this region is implicated in motor control (Alexander et al., 1986; Arsalidou et al., 2013).

  4. Lastly, given that the literature has indicated putamen involvement in behavioral (motor) regulation and inhibition (Alexander et al., 1986; Arsalidou et al., 2013; Lehericy & Gerardin, 2002), we expected that better fine motor dexterity performance and ratings of executive function would be associated with higher MD values and lower FA values in the putamen.

Method

Participants

Participants included a retrospective cohort of children and adolescents with SBM (N = 49) who were between 8–18 years (M = 12.34, SD = 2.9) of age. The current study only included participants who had valid neurobehavioral data and for whom quantitative neuroimaging had been reported in Ware, Juranek, et al. (2014). The majority (58%) of participants underwent neurobehavioral testing and neuroimaging on the same day, with median absolute deviation between testing days of 1.0. There were some exceptions, usually because of missed appointments and unexpected metallic artifacts (i.e., braces). The participants in the current study were part of a larger study examining cognitive and neurobiological variability in SBM and related disorders like that reported in Fletcher et al. (2005). The overall cohort for the larger parent study included participants from multiple international sites. However, the current study only included participants whose MRI sequences were obtained on a 3T scanner. The study also excluded a subset of individuals in the overall Houston cohort who were unable to undergo MRI for various reasons (e.g., MRI contraindications, parent consent). Of those who underwent MRI, only individuals with usable imaging data (as determined through strict image quality criteria described below) who had been included in Ware, Juranek, et al. (2014) and who had completed neurobehavioral data (as previously described in Burmeister et al., 2005; Treble et al., 2013) were included in the current study.

To be included in the current study, participants had to be predominantly English speaking and have verbal and/or nonverbal intelligence quotient (IQ) scores above 70 on the Stanford-Binet Intelligence Scale: Fourth Edition (Thorndike et al., 1986). Participants were excluded if they had history of severe psychiatric (e.g., pervasive developmental disorder, psychosis, and severe conduct problems) difficulties, additional neurological disorder (e.g., tumor, traumatic brain injury), and any Chiari or shunt malfunction symptoms at the time of the evaluation. Participants were tested off medication (e.g., psychostimulants) unless it was medically contraindicated (i.e., anticonvulsants). This study was conducted in compliance with IRB approval at The University of Houston and The University of Texas Health Science Center at Houston.

Demographic characteristics for participants with SBM are presented in Table 1. Participants were medically stable at time of participation and all but two had been treated with a shunt near the time of birth. For the shunt-treated individuals, the number of shunt revisions ranged from 0 to 14, although the majority had less than 5 shunt revisions (71%). For participants who underwent shunt revisions, 68% resulted from obstruction only, 3% resulted from infection only, and 18% of these participants had both histories of obstruction and infection. Most participants had lumbar and sacral spinal lesions (84%), Chiari II malformation (86%), callosal hypogenesis or hypoplasia (96%), infrequent seizure history (past: 8%; current: 4%), and orthopedic deformity of the lower (33%) and upper (48%) extremities. Demographics of this sample subset were representative of the larger, overall Houston cohort that included 119 youth with SBM between the ages of 8 and 18 years. The sample included in the current study did not significantly differ from the excluded Houston cohort in age, t(117) = −0.15, p > 0.05), socioeconomic status per the Hollingshead (1975), t(117) = −1.83, p > 0.05), and number of shunt revisions, t(117) = 1.16, p > 0.05).

Table 1.

Demographic and neurobehavioral data for participants with spina bifida myelomeningocele (SBM).

Variable (n = 49)
Age at MRI (M [SD]) 12.34 (2.9)
Sex (n [%Male]) 24 (57.1)
Handedness (n [% Right]) 35 (72.9)
Ethnicity (n [% Hispanic]) 28 (58.3)
Socioeconomic status (M [SD]) 31.64 (13.2)
Full Scale IQ 85.47 (12.4)
Verbal IQ (M [SD]) 85.54 (16.0)
Nonverbal IQ (M [SD]) 92.00 (14.1)
Behavioral Regulation Index (M [SD t-score]) 56.00 (13.0)
Metacognitive Index (M [SD t-score]) 61.57 (13.6)
Fine Motor Dexterity (M [SD z-score]) −2.63 (1.5)
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Magnetic Resonance Imaging

Data Acquisition

All MRI acquisitions were acquired at the University of Texas Medical School in Houston using a research-dedicated Philips 3T scanner with SENSE (Sensitivity Encoding) technology and an 8-channel phased array head coil. After a conventional scout sequence, high-resolution T1-weighted anatomical images were acquired in the coronal plane using a 3D turbo fast echo sequence with the following parameters: voxel dimensions = .94 × .94, slice thickness = 1.5 mm, TR = 6.50–6.70 ms, TE = 3.04–3.14 ms, flip angle = 8°, DFOV = 240 mm2, matrix = 256 × 256.

Diffusion tensor images were acquired in the axial plane using a single-shot spin-echo diffusion sensitized echo-planar imaging sequence. Diffusion sensitizing gradients were applied in 21 directions (weighting: b = 1000 s/mm2) with one reference image (b = 0 s/mm2) and the following parameters: voxel dimensions = .94 × .94, slice thickness = 3 mm, TR = 6500 ms, TE = 65 ms, flip angle = 9°, DFOV = 240 mm, matrix = 256 × 256.

Image Analysis

More details regarding neuroimaging data preprocessing and processing can be found in Ware, Juranek, et al. (2014). Briefly, image segmentation and morphometric analyses were conducted in FreeSurfer v4.0.5 (www.surfer.nmr.mgh.harvard.edu; Fischl, 2012). Subsequent to skull stripping and regional segmentation, a fully automated routine within FreeSurfer determined delimiting boundaries of deep gray matter structures (i.e., caudate, putamen, pallidum, thalamus). Additionally, as part of the surface-based processing stream, the cortical ribbon was parcellated and labeled based on the Destrieux cortical annotation nomenclature (Destrieux et al., 2010). Subsequently, cortical thickness values for each individual cortical label were automatically quantified within FreeSurfer on a vertex-by-vertex basis by computing the shortest distance between the white matter boundary and pial surface (Fischl & Dale, 2000). The DLPFC label was created by merging together three labels from the Destrieux annotation file associated with each individual brain (G_frontal_middle, S_frontal_middle, and S_frontal_inferior).

The brains of individuals with SBM are dysmorphic and boundaries are not always reliably indicated by the FreeSurfer output. Therefore the output images from FreeSurfer’s segmentation and classification algorithms were visually inspected and extensively manually edited by a single rater (JJ) with substantial expertise using FreeSurfer’s software as well as its predecessor, Cardviews (Filipek et al., 1989).

For DTI data, minor head motion and eddy currents were corrected in the DTI series using the Eddy Current Correction tool included in FSL v4.1.0 (FMRIB’s Software Library, www.fmrib/ox.ac.uk/fsl) (Smith et al., 2004). Within-subject co-registration of the nondiffusion-weighted volume (b=0 s/mm2) to the T1-weighted image (processed in FreeSurfer) was performed using FSL’s Linear Image Registration Tool v5.5 (FLIRT) (detailed in Juranek et al., 2012). The resultant transformation matrix and calculated inverse were used to transform binary segmentation masks of each subcortical gray matter structure the gray matter masks (obtained from the FreeSurfer analyses) to corresponding diffusion-weighted space, which were assured for quality. Prior to transformation into diffusion space, each binary segmentation mask was eroded using 2 × 2 × 2 kernel to reduce contamination from neighboring voxels of cerebrospinal fluid and/or white matter.

The diffusion tensors were reconstructed using FSL’s DTIFIT tool within the Diffusion Toolbox and individual maps of MD and FA were overlaid with the binary mask for each gray matter structure. Quantitative measures of MD and FA were extracted from areas delineated by each gray matter mask and used in subsequent statistical analyses.

Neurobehavioral Measures

Executive function

The Behavior Inventory of Executive Function-Parent Report (BRIEF-PR) was completed by participant caregivers (i.e., biological parents and biological grandparents in rare instances) who rated items as occurring “never”, “sometimes”, or “often” (Gioia et al., 2000). The inventory provides two primary indices: (a) Behavioral Regulation and (b) Metacognition, and one composite index - the Global Executive Composite. The Behavioral Regulation Index is comprised of three subscales (Inhibit, Shift, Emotional Control) and represents behavioral self-regulation. The Metacognition Index is comprised of five subscales (Initiate, Working Memory, Plan/Organize, Organization of Materials, and Monitor), representing cognitive self-regulation skills. The validity of the BRIEF-PR and its two-factor structure has been supported in various clinical groups and in SBM (including the current cohort of youth with SBM), who show poorer ratings on Metacognition Index and Behavioral Regulation Index compared to TD peers (Burmeister et al., 2005; Mahone et al., 2002; Rose & Holmbeck, 2007; Zabel et al., 2011). All parent ratings were within the acceptable range for validity using the BRIEF-PR Inconsistency scale (M = 2.87, SD = 1.83). Scores were derived from age-normed T-scores, whereby higher T-scores indicate greater impairment.

Fine motor dexterity

Fine motor dexterity was measured using the Purdue Pegboard (Tiffin, 1968). This task requires the placement of small pins into a perforated board as quickly as possible in 30s time intervals. Given that left- and right-handed performance was strongly correlated for the group with SBM (p < .001) average age-corrected z-scores were calculated across dominant and non-dominant hands to provide an estimate of overall fine motor dexterity. For this task, higher z-scores indicated lower impairment (i.e., faster performance). The current sample of youth with SBM showed significantly deficient (i.e., slow) fine motor dexterity performance as expected (Treble et al., 2013).

Analytic Approach

Robust correlations are divided into two groups: (a) correlations that are robust to the univariate outliers and do not consider the overall structure of the data (the percentage bend correlation) and (b) correlations that are robust to the univariate and bivariate outliers and consider the overall structure of the data to deal with outlying observations occurring in a bivariate space (the skipped correlation using the Donoho–Gasko median [DGM]) (Wilcox, 2004; Wilcox, 1994).

In the current study, brain-behavior relations were examined using three correlational methods: the Pearson product moment correlation, percentage bend correlation, and skipped correlation using Donoho-Gasko median (DGM). The robust properties of the latter two correlations against univariate and/or multivariate outliers enhance the statistical conclusion validity regarding brain-behavior relations in clinical samples with frequently occurring outliers (Kulesz et al., 2015). Simultaneous utilization of the three correlational estimates along with the bootstrap procedure helped ensure that findings reflected underlying relations between variables of interest rather than the characteristics of method of estimation or chance characteristics of the current sample. Specifically, statistical inference using a single sample is typically based on computing estimates from that sample and making inferences about characteristics of unobserved populations based on those estimates and their estimated standard errors. Accuracy of estimation, especially the standard errors of estimates and the associated probability statements, depend on certain statistical assumptions and the validity of those assumptions. All of these problems and limitations of comparing estimators using a single sample of field data derived from a population with unknown characteristics can be addressed by using bootstrap procedures.

Simultaneous utilization of the three correlational estimates should be viewed as an alternative to multiple comparisons, and as such it is not typical to control the familywise error rate for the number of estimated correlations. Such approaches may be excessively conservative and run the risk of a Type II error, especially when it is difficult to assemble large samples of participants. In order to ensure that the findings were not attributed to the characteristics of the sample we used bootstrap procedures for the three different correlations. The bootstrap procedures involved sampling n observations with replacement 10,000 times for each brain-behavior relation (n was equal to 45, 46 or 49 based on the examined relation and missing data points) to obtain bootstrap samples. The three correlational estimates were computed for each single pair of variables on every bootstrap sample. Empirical distributions of the correlations were characterized in terms of means and confidence intervals based on 2.5 and 97.5 percentiles. The confidence intervals were used instead of significance test because no simple test of the hypothesis of zero correlation has yet been found for the skipped correlations (Wilcox, 2008). Increased reliability of findings is apparent for brain-behavior relations when confidence intervals based on the bootstrap distributions excluded 0 for all three correlational estimates. Furthermore, reliability of correlations were considered to be robust when significant brain-behavior relations are observed across all three correlational approaches. Statistical procedures were completed in R Version 3.0.2 (R Development Core Team, 2008) using the boot package Version 1.3-9, foreign package Version 0.8-55, MASS package Version 7.3-29, and custom written functions.

Preliminary Data Analysis

Tests for Covariates

Given age related effects on gray matter volume and microstructure values across typical adolescent development (e.g., Lebel et al., 2008), age was tested as a possible covariate using regression analyses. Each gray matter region was regressed on individual neurobehavioral measures (i.e., Behavioral Regulation Index, Metacognition Index, or fine motor dexterity), age, and their interaction. Neither age nor the interaction terms were significant (p > .05) for any of the gray matter structures and were thus trimmed from the model.

This approach was also repeated for socioeconomic status and number of shunt revisions, as both of these relate to neurobehavioral outcomes in youth with SBM (Fletcher et al., 2005; Arrington et al., under review). Results indicated that, similar to age, neither socioeconomic status, shunt revision history, nor any of the interaction terms were significant (p > .05). Thus, socioeconomic status and shunt revision history were trimmed from the final models.

Results

Brain-Behavior Relations

Table 2 presents mean values of estimates across 10,000 bootstrap samples for brain-behavior relations across the Pearson correlation, percentage bend correlation, and skipped correlation using DGM.

Table 2.

Mean values of estimates across 10,000 bootstrap samples for participants with spina bifida myelomeningocele (SBM).

Correlated Variables Pearson Correlation Percentage Bend Correlation Skipped Correlation using DGM
Behavioral Regulation Index (n = 46)
Thalamus MD −0.22 −0.20 −0.19
Pallidum MD 0.00 −0.01 −0.01
Caudate MD 0.11 0.08 0.03
Putamen MD 0.13 0.12 0.10
Thalamus FA −0.26 −0.23 −0.26
Pallidum FA −0.25 −0.27 −0.25
Caudate FA 0.36 0.31 0.35
Putamen FA −0.07 −0.05 −0.07
DLPFC Thickness 0.17 0.12 0.16
Metacognitive Index (n = 46)
Thalamus MD −0.25 −0.24 −0.21
Pallidum MD 0.07 0.00 −0.05
Caudate MD 0.05 −0.02 −0.11
Putamen MD −0.03 0.05 0.09
Thalamus FA −0.03 −0.06 −0.03
Pallidum FA −0.12 −0.16 −0.12
Caudate FA 0.14 0.14 0.14
Putamen FA −0.10 −0.14 −0.10
DLPFC Thickness 0.08 0.03 0.08
Fine Motor Dexterity (n = 45)
Thalamus MD 0.18 0.05 0.01
Pallidum MD 0.32 0.26 0.31
Caudate MD 0.04 0.08 0.15
Putamen MD 0.11 −0.08 −0.11
Thalamus FA −0.08 −0.14 −0.10
Pallidum FA −0.29 −0.21 −0.28
Caudate FA −0.26 −0.28 −0.26
Putamen FA −0.38 −0.39 −0.38
DLPFC Thickness −0.31 −0.36 −0.36
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Note. DLPFC = Dorsolateral prefrontal cortex; FA = fractional anisotropy; MD = mean diffusivity; DGM = Donoho-Gasko median; Bolded indicates statistically significant results based on 2.5 and 97.5 percentile values taken from 10,000 bootstrap samples (i.e., confidence interval excluded 0).

Executive function

As predicted, the Behavioral Regulation Index was moderately significantly and positively correlated with FA in the caudate. This finding was observed across all correlational methods, indicating that higher FA values were related to worse ratings of behavioral regulation. Contrary to initial expectations, the Behavioral Regulation Index was negatively associated with FA in the thalamus (i.e., higher FA values related to better behavioral regulation). However, this correlation was small and was only statistically significant for the Pearson and the skipped correlation using DGM estimates, but not for the percentage bend estimate. The lack of significance across all three correlational methods implies that results should be interpreted cautiously because they may be partially attributed to a method of estimation.

Unexpectedly, the Metacognition Index was also negatively related to MD in the thalamus, indicating that higher MD values were associated with better metacognition. However, the magnitude of this correlation was small, and was only significant for bootstrap samples for the Pearson correlation and not for the percentage bend correlation or the skipped correlation using the DGM.

Fine motor dexterity

There were significant, negative moderate relations between fine motor dexterity and both FA values in the putamen as well as DLPFC thickness across all three correlational estimates. As expected, slower fine motor dexterity was associated with higher FA values in the putamen and increased DLPFC thickness. As predicted, fine motor dexterity was moderately correlated with MD values in the pallidum and FA values in the caudate. Better fine motor dexterity related to higher MD in the pallidum although poorer performance was associated with higher FA in the caudate. These relations were statistically significant for the Pearson correlation and the skipped correlation using DGM, but not for the percentage bend correlation.

Discussion

The inter- and intra-individual heterogeneity of brain dysmorphologies and neurobehavioral outcomes in SBM are frequently independently documented in the literature with little integration. In addition, most studies have focused on the cerebellum and posterior brain regions because of the impact of hydrocephalus and the Chiari malformation (e.g., Dennis et al., 2010). In contrast, the present study investigated correlates of distinct subcortical and cortical gray matter integrity indices (i.e., MD, FA, and cortical thickness) of the DLPFC-subcortical network with neurobehavioral outcomes in youth with SBM. This assessment is especially interesting because the basal ganglia are visibly normal in people with SBM (Ware, Juranek, et al., 2014), although problems with executive functions and fine motor control are commonly reported. Current results clarified whether these difficulties were related to quantitative reductions in gray matter integrity observed in people with SBM.

Ware, Juranek, et al. (2014) found higher FA values in the caudate and thalamus of youth with SBM compared to typically developing peers. For behavioral regulation, current results indicated that youth with higher FA values in the caudate were rated as having worse behavioral regulation than youth with lower caudate FA values. The expected positive relation between behavioral regulation and the caudate indicated that as FA values become more anomalous compared to typically developing youth, behavioral dysregulation increased. This finding supports the relation between anomalous striatal structure and behavioral dysregulation observed in neurodevelopmental populations with disrupted prenatal development (e.g., fetal alcohol spectrum disorders) and in children with attention-deficit/hyperactivity disorder (Barkley, 1997; Fryer et al., 2012). For example, volumetric and microstructural aberrations in striatal structures have been related to higher-order cognitive function in youth with heavy prenatal alcohol exposure (Fryer et al., 2007; Lebel et al., 2011).

Alternatively, the negative relation observed between behavioral regulation and gray matter integrity in the thalamus suggested that increased FA in this structure related to better behavioral regulation scores in youth with SBM. The differential relations observed between behavioral regulation and the caudate relative to the thalamus was unexpected given that both of these brain regions showed similarly increased FA values in SBM (Ware, Juranek, et al., 2014). Susceptibility of these structures to secondary effects of hydrocephalus may result from their proximity to the ventricles and may explain the differential relations observed between FA values in these two structures and the Behavioral Regulation Index scores. For instance, apoptotic cell death resulting from periventricular erosion subsequent mechanical effects of hydrocephalus is observed in the caudate but not in the thalamus in experimental hydrocephalus (Mori et al., 2002). Thus, microstructural differences between the caudate and thalamus may result in discrete functional outcomes subsequent to hydrocephalus in SBM population. However, the associations between behavioral regulation ratings with FA values in both the caudate and thalamus support that the caudate facilitates planning and execution of behavior (reviewed in Grahn et al., 2008) and thalamic involvement in higher-order cognitive and performance monitoring processes required to complete complex goals (Herrero et al., 2002).

The putamen has been implicated in motor skill learning and performance whereas the DLPFC has been implicated in initial motor skill automaticity (Poldrack et al., 2005). Given that youth with SBM have increased FA values in the putamen and increased cortical thickness of the DLPFC, the negative relations between these brain regions and fine motor dexterity were expected. Increased gray matter integrity (as evidenced by lower FA values and thinner cortex) resulted in better fine motor dexterity for youth with SBM. Fine motor performance was similarly related to FA values in the caudate and pallidum, although these results were less reliable.

While initially involved in automatization, the DLPFC and caudate become less involved once movement is automatized, and putamen and pallidum involvement increases (Poldrack et al., 2005). Given putamen involvement in performance of motor skills and caudate involvement in unique motor learning and skill acquisition, the results suggested that disruptions resulting from SBM in both cortical and subcortical components of the DLPFC network may deleteriously affect fine motor performance by hindering initial task learning, resulting in slower, more slavish automatization. The current findings are in concordance with previous literature supporting functional involvement of the caudate and putamen in motor dexterity (e.g., Poldrack et al., 2005). Thus, fine motor difficulties in SBM could result from inefficient functioning of fronto-striatal components and/or circuitry, although the role of the prominent cerebellar anomalies must also be considered. These deficits are clearly related to poor auto mitigation of fine motor skills in SBM (Dennis et al., 2010).

The most statistically reliable results, i.e., consistent across the three correlation estimates, were found among ratings of behavioral regulation and FA values in the caudate as well as between fine motor dexterity with FA values in the putamen and thickness of the DLPFC (discussed below). Relations among fine motor dexterity with FA in the caudate and MD in the pallidum as well as between ratings of behavioral regulation and FA values in the thalamus were only observed across the Pearson correlation and skipped correlation using DGM. Similarity of findings across the Pearson correlation and skipped correlation using DGM, but not the percentage bend correlation may be attributed to the method of computing the population correlation in these estimates. The percentage bend correlation estimates the population correlation differently than the Pearson and skipped correlation using DGM, which yield similar results when data include none to few outliers. Therefore, findings where the Pearson and skipped correlation using DGM yielded similar results should be interpreted with some caution. Lastly, the statistically significant relation between ratings of metacognition and MD values in the thalamus were found for Pearson correlation, but not for percentage bend correlation or the skipped correlation using the DGM, and is considered the least reliable.

The lack of statistically significant associations among the majority or neurobehavioral outcomes and DTI metrics in the thalamus in SBM was surprising and may reflect the use of total thalamic integrity as opposed to specific regions in the current analyses. Since projections from the DLPFC are output from the basal ganglia into the ventral anterior and mediodorsal thalamic regions (Lichter & Cummings, 2001), further specification of the thalamus and/or the use of performance measures of executive function may have provided a more in-depth investigation of how thalamic integrity relates to specific behavioral impairments in SBM.

Limitations

The mechanical effects of hydrocephalus and associated shunting likely complicate the protracted impairment of brain development in SBM. In the current sample, the majority of children and adolescents with SBM had hydrocephalus and had histories of shunt surgery. Although DTI metrics were unrelated to number of shunt revisions (Ware, Juranek, et al., 2014), it is possible that this treatment disrupts surrounding brain tissue. Shunting per se is associated with poorer scores on executive functioning, IQ, and movement (Brookshire et al., 1995; Hampton et al., 2011). Therefore, neuropsychological deficits may also be moderated by complex conditions associated with SBM, particularly shunt treatment and revision history for hydrocephalus.

About one-third of children with SBM meet rating scale criteria for attention-deficit/hyperactivity disorder (Burmeister et al., 2005). However, in SBM, children who meet criteria for attention-deficit/hyperactivity disorder do not differ from those who do not meet criteria for attention-deficit/hyperactivity disorder is associated with differences in attention (Swarthout et al., 2008) and executive functions, including the BRIEF-PR (Burmeister et al., 2005). Examination of these relations on cognitive, behavioral, and neuroimaging studies have consistently revealed null results. We have hypothesized in previous work that these results occur because attention-deficit/hyperactivity disorder behavior in children with SBM is not like developmental forms of attention-deficit/hyperactivity disorder, but reflects impairment of a posterior attention system as opposed to frontal and subcortical networks (Dennis et al., 2008). Future comparisons of children with SBM and children with attention-deficit/hyperactivity disorder would be beneficial in elucidating whether this is the case.

The use of parent ratings of executive function could have limited current findings. Evaluating performance measures of behavioral and cognitive regulation could likely have strengthen current results and allowed for a more accurate clinical description of the specificity of brain regions involved in specific executive dysfunctions in SBM. While the current investigation may have benefited from such an examination, data collected as part of the larger parent study did not include performance measures of executive function. Future investigations regarding the DLPFC-subcortical network involvement in a broader number of performance based tasks of higher order cognition, particularly specific aspects of executive function, could be beneficial. However, the reliability and construct validity of the BRIEF-PR may provide more clinical utility of the current results (Gioia et al., 2000), particularly given that procedural learning deficits are shown to affect performance on tasks of higher order cognition in SBM are not likely to affect parent ratings (Fletcher et al., 1996).

Lastly, the DLPFC-subcortical network is facilitated by white matter connections among the disparate gray matter regions. While the current study did not examine white matter integrity, the findings of aberrant thickness in the DLPFC and altered gray matter integrity in subcortical gray matter structures suggest that disrupted white matter between these structures exists in SBM. Therefore, future research would benefit from a general examination of white matter integrity in SBM. Specifically, disrupted white matter integrity may contribute to the neurobehavioral deficits observed in this population generally and may explain these disruptions to a greater extent than gray matter regions in isolation. This may be particularly true for higher order executive control processes, since these behaviors are associated with efficient connections between gray matter regions in other neurodevelopmental populations (e.g., Cubillo et al., 2012). Additionally, faster motor speed relates to the integrity of putamen-thalamic connectivity in typically developing children (Barber et al., 2012).

Conclusions and Future Directions

These findings indicate that disruptions in higher-order cognition and motor functioning following SBM may be accounted by aberrant development of subcortical gray matter. However, future research would significantly benefit from an investigation into white matter connections between these regions and how it relates to neurobehavioral outcomes. Though recent findings have utilized functional neuroimaging to examine global activation patterns in individuals with SBM during attention and inhibition (Ou et al., 2013), current findings illustrate the need for greater understanding of the functional implications of brain dysmorphologies in SBM and hydrocephalus. Future investigations into the neural associations of executive and motor dysfunction, particularly in comparison to other neurodevelopmental populations such as ADHD, may illuminate mechanisms of neural plasticity of functional networks and provide a foundation for effective intervention and treatment in this neurodevelopmental disorder.

Acknowledgments

Preparation of this paper was supported in part by grant 5 P01 HD35946 awarded from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD).

Footnotes

Conflict of interests: None to report.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NICHD or the National Institutes of Health.

Contributor Information

Paulina A. Kulesz, Email: Paulina.Kulesz@times.uh.edu.

Victoria J. Williams, Email: tori85@gmail.com.

Jenifer Juranek, Email: Jenifer.Juranek@uth.tmc.edu.

Paul T. Cirino, Email: Paul.Cirino@times.uh.edu.

Jack M. Fletcher, Email: Jack.Fletcher@times.uh.edu.

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