Abstract

Executive functions (EFs) are vulnerable to disruption in pediatric-onset multiple sclerosis (MS) patients. We describe the pattern and correlates of executive dysfunction in 34 adolescents with MS on neuropsychological tests and the parent version of the Behavior Rating Inventory of Executive Function (BRIEF). The adolescents with MS performed lower than age-matched controls in several areas of executive functioning, with 44% of patients being impaired on the Trail Making Test–Part B. On the BRIEF, problems in working memory and planning/organization were identified in the patient group compared with controls, particularly in patients with a younger age at disease onset. Task performance and parent-ratings of EF skills were strongly related to whole brain and regional brain volume metrics and, to a lesser extent, T2-weighted lesion volume. Working memory and attention switching are at greatest risk of impairment. Results support the inclusion of neuropsychological assessment alongside parent-report measures of EF skills in childhood-onset MS.

Introduction

Multiple sclerosis (MS) is an autoimmune disease characterized by inflammatory demyelination in the central nervous system (CNS) and grey matter atrophy (Crespy et al., 2011; Furby et al., 2008; Rovaris et al., 2006). Onset of MS in childhood and adolescence is increasingly being recognized and is estimated to represent approximately 5% of the MS population (Yeh et al., 2009). The inflammatory and neurodegenerative features associated with the disease, even in early stages (Waubant et al., 2009), can disrupt normal brain development and have serious consequences on cognitive functioning and academic performance (Amato et al., 2008; MacAllister et al., 2005; Till et al., 2011). Executive functions (EFs) are particularly vulnerable to disruption given the proclivity of the disease for diffuse neuronal networks that underlie these complex cognitive skills (Casey, Tottenham, Liston, & Durston, 2005). Systemic disruption to distributed networks encompassing both frontal and posterior associative cortices, as well as subcortical, cerebellar, and thalamic pathways, can produce disconnections of the neural circuitry that underlie EF skills that are both established and those that are yet to be acquired (Dennis, 2000; Levin, Song, Ewing-Cobbs, Chapman, & Mendelsohn, 2001).

EF is the term used to describe the supervisory and self-regulatory control system that directs a person to engage in goal-directed behavior (Anderson, 2002; Miyake et al., 2000). This umbrella term is comprised of multiple, interdependent subfunctions that, according to Anderson's model of EF (Anderson, 2002), include the ability to: (i) selectively attend to relevant information and inhibit unwanted responses (“attentional control”), (ii) efficiently process information (“information processing”), (iii) apply working memory, divide attention, and use feedback to develop alternative strategies(“cognitive flexibility”), and (iv) initiate, plan, and organize a strategy (“goal setting” or “planning”). Assessment of EFs is routinely recommended because of the importance of EF in adaptive functioning, learning, and academic achievement in children (Blair & Razza, 2007; Gathercole, Pickering, Ambridge, & Wearing, 2004).

Executive dysfunction has been commonly reported in prior studies of children and adolescents with MS, though the extent of impairment varies across tests and across studies. For example, difficulties in neuropsychological measures of cognitive flexibility, such as the Contingency Naming Test and Part B of the Trail Making Test, occur in up to 50% of children and adolescents with MS (Deery, Anderson, Jacobs, Neale, & Kornberg, 2010; MacAllister et al., 2005; Till et al., 2011). Impaired verbal fluency have also been reported in some studies of childhood-onset MS (Banwell & Anderson, 2005; Till et al., 2011), but not in others (Deery et al., 2010; MacAllister et al., 2005; Portaccio et al., 2009). Given the reliance of these measures on efficient processing, it is reasonable to propose that the observed difficulties emerge or are exacerbated as a consequence of a primary processing speed deficit that frequently characterizes MS patients (Drew, Starkey, & Isler, 2009). Children and adolescents with MS have also been shown to demonstrate deficits on nontimed measures of problem solving and concept formation, such as the Modified Card Sorting Test (Amato et al., 2008) and the Tower of London (MacAllister, 2010). Studies assessing these EF skills using the Wisconsin Card Sorting Task (Banwell & Anderson, 2005; Till et al., 2011), however, reported that the performance was within normal limits, possibly reflecting differences in specific task demands.

A drawback of conventional neuropsychological measures is their inconsistent relationship with observational measures of everyday executive functioning (Toplak, Bucciarelli, Jain, & Tannock, 2009; Trowbridge & Schutte, 2007). Recent studies in children with acquired brain injury (Long et al., 2011) show that neuropsychological measures may not detect problems in everyday executive functioning that manifest as deficits in behavioral and socioemotional EF skills. For example, difficulties with the modulation of emotions, personal and social decision-making, and inhibitory control may be evident due to the requirement for self-imposed structure and focused attention in the face of real-life distractions that are not usually present in the structured clinical setting. To date, the functional assessment of EF skills has received little attention in youth with MS, with the exception of one study that used the Behavioral Rating Inventory of Executive Functions (BRIEF) (Gioia, Isquith, Guy, & Kenworthy, 2000) in 44 children and adolescents with MS (MacAllister, 2010). Half of the patients in this study were reported by parents to have deficits in at least one aspect of everyday EF skill. Organization of materials and working memory were the EF skills that were most frequently rated as being impaired by parents, with elevations noted in approximately one-third of these individuals.

Brain magnetic resonance imaging (MRI) is used to monitor disease evolution and to provide in vivo insight into the pathogenesis of cognitive disturbances in MS. Studies in adult MS patients show moderate correlations between T2-weighted lesion load in the frontal lobe and executive dysfunction (Arnett et al., 1994; Foong et al., 1997; Foong et al., 1999; Swirsky-Sacchetti et al., 1992), in support of the prefrontal cortex in its role as the “central executive” (e.g., Stuss et al., 1999). However, other studies in adult MS patients demonstrate that frontal lobe pathology is not exclusively associated with EF impairment (Lazeron et al., 2005; Rao, Leo, Haughton, St Aubin-Faubert, & Bernardin, 1989; Sanfilipo, Benedict, Weinstock-Guttman, & Bakshi, 2006), but rather cortical and subcortical lesions that are distributed widely throughout the brain can also compromise EF skills. Moreover, because of the difficulty in detecting cortical pathology with MRI (Hulst & Geurts, 2011), the contribution of lesion location to EF is difficult to precisely define. As such, spatial analyses of lesional pathology as a predictor of functional outcome have yielded weak correlations, even when regions of interest that commonly involve high-intensity lesions, such as the periventricular region, are examined (Foong et al., 1999; Swirsky-Sacchetti et al., 1992).

The primary aim of the current study was to examine both performance-based and everyday EF skills in children and adolescents with MS. The secondary aim was to examine the neural correlates of executive dysfunction in patients with childhood-onset MS through inclusion of MRI volumetric metrics for both global and segmented brain regions. It was hypothesized that children and adolescents in the MS group would display a wide range of deficits in both performance-based and parent-rated EF skills. We anticipated that higher degree of brain lesion volume (LV) and smaller regional and global brain volume would associate with more EF problems. Given the multi-determined nature of many developing EF skills and their reliance on a distributed neuroanatomical network, we did not expect a difference between the extent of lesions visible in frontal or extra-frontal regions.

Methods

Participants

The sample consisted of 34 patients with pediatric-onset MS who were aged 10–19 years at assessment. Patients were recruited to the study if they were diagnosed with clinically definite MS prior to age 18. Patients were recruited from the [PLACE] in [CITY, COUNTRY]. All of the patients had participated in a larger study examining neurocognitive outcomes and MRI features of children and adolescents with relapsing-remitting MS. Additionally, prior to the neurocognitive evaluation, participants underwent a screening interview and were asked questions related to their medical, psychiatric, and school history. Exclusionary criteria included: (i) non-English-speaking; (ii) reported alcohol or illicit drug dependence or abuse; (iii) history of neurological condition other than MS; and (iv) prior head injury (defined as a physician-diagnosed concussion or witnessed post-traumatic loss of consciousness for >5 min in duration). Because clinically elevated symptoms of depression, anxiety, and problems of attention and hyperactivity can occur as a component of MS (Till et al., 2012), we did not exclude individuals who reported a history of a physician-diagnosed mood disorder or attention-deficit hyperactivity disorder (ADHD). Two MS patients had a learning disability, and another five had a history of clinical depression and/or anxiety. One MS patient was taking anti-depressant medication (Sertaline) at the time of assessment. No other psychiatric disorders were reported to warrant exclusion.

A comparison group of 33 age- and sex-matched healthy controls (HCs), matched on age, sex, and parental education level, was recruited from the community through various forms of local advertisement. Controls were first screened by telephone before being invited to participate to ensure they satisfied inclusion/exclusion criteria.

For MS patients meeting study criteria, the participation rate was 89.5% (34/38). For patients on a corticosteroid regimen, neuropsychological assessments and MRI analyses were performed at least 4 weeks after drug discontinuation. One patient was excluded from analyses of performance-based EF skills due to marked visual impairment that precluded completion of any visual-based measures. Hence, valid neuropsychological data were obtained for 33 of the 34 patients. None of the patients included in the study reported in interview a clinical relapse between the MRI and neurocognitive evaluation. However, the possibility of subclinical activity occurring during the MRI-cognitive evaluation interval that may not have been detected is acknowledged.

Institutional Research Ethics Board approval and written informed consent was obtained from each study participant prior to study initiation. In cases where the participant was under the age of consent, substitute consent was obtained from a guardian/parent and assent was obtained from the minor. Participants were compensated for transportation and meals, and were provided a $25 gift certificate towards a local retail store.

Measures

Clinical-demographic variables

Parent interview and medical record-based information was obtained regarding age at disease onset (defined by age at first MS attack) and at diagnosis (clinical or MRI evidence of dissemination of disease in time), total number of relapses (defined as physician confirmed presence of a new neurologic symptom or deficit persisting for more than 24 h), medications, and family history of MS. Physical disability was assessed by a neurologist (B.B.) using the Expanded Disability Status Scale (EDSS) score on the day of testing (Kurtzke, 1983). The EDSS score evaluates physical functioning in a nonlinear manner. Scores >6 infer a fixed impairment in gait requiring ambulatory aid. Parental education was measured as the average of maternal and paternal years of education, which served as an estimate of socioeconomic status (American Psychological Association, 2006).

Neuropsychological assessment

Neuropsychological assessment was conducted in a quiet room by a pediatric neuropsychologist or a trained psychometrist. In most cases, testing was completed in 1 day (∼4h in length), with breaks provided as deemed necessary by the psychometrist and/or participant. Parents completed the questionnaires and a semi-structured interview during the assessment session.

The Full-Scale IQ (mean = 100, SD = 15) from the Wechsler Abbreviated Scales of Intelligence (Wechsler, 1999) was used to estimate overall intelligence. Anxiety and depression for each participant were measured using the age-appropriate self-report form of the Behavior Assessment System for Children—second edition (BASC-2) (Reynolds & Kamphaus, 1998), a standardized questionnaire used to assess behavioral and socioemotional outcomes. T-scores on the anxiety and depression clinical scales were used. Reliability indices obtained in validation studies conducted by the test developers are high for the clinical scales (median r value of 0.85) (Reynolds & Kamphaus, 1998). Our prior work examining the use of the BASC-2 in pediatric-onset MS patients also showed strong internal consistency reliability of the items included on the anxiety and depression clinical scales (Till et al., 2012).

From a comprehensive neuropsychological battery, measures were selected on the basis of strong reliability and validity properties. The following tests were used to evaluate specific components of EF skills:

  • Attentional control: (a) Conners' Continuous Performance Test (CPT-II) (Conners, 2000). This is a 14-min computer-administered task that requires participants to press a spacebar in response to any letter except the letter “X.” The Conners' CPT utilizes a high rate of signal probability, which increases the likelihood of a commission error. A commission error is defined as a failure to inhibit a spacebar press when an “X” is presented whereas an omission error is defined as a failure to respond (i.e., symptoms of inattention). Given our prior work showing that impulse control problems are not commonly observed in pediatric-onset MS patients (Till et al., 2011), we focused our current analyses on the total number of omission errors on the CPT-II. The high consistency assessed by split-half reliability in a large normative sample for all performance measures on the CPT-II range between 0.73 and 0.95 (Conners, 2000), providing support for the psychometric soundness of this test. (b) Color-Word Interference Test from the D-KEFS (Delis, Kaplan, & Kramer, 2001). Condition 3 of this measure (DKEFS-inhib) is similar to the Stroop paradigm and requires participants to inhibit the over-learned reading response by naming the ink color of words that spell a different color. The DKEFS-inhib measure was used as an indication of response inhibition. Because an examinee may have difficulty in performing the complex task of the DKEFS-inhib condition as a result of an impairment in processing speed (or basic naming and reading), a contrast score (DKEFS-inhib contrast) was calculated to parcel out color-naming performance from the performance on the DKEFS-inhib task.

  • Information Processing: (a) Symbol Digit Modalities Test (SDMT) (Smith, 1991) (oral version). This test requires participants to orally match symbols to numbers as quickly as possible within 90 s. The total number of items correct on the SDMT was measured. Test–retest reliability for the SDMT was shown to be consistently strong (r> 0.80) in 85 adult patients with MS assessed over 5 monthly assessments (Benedict et al., 2008). (b) Color-Word Interference Test from the Delis–Kaplan Executive Function System (D-KEFS) (Delis et al., 2001). Condition 1 (color naming) requires participants to accurately name the color of ink swatches as quickly as possible. Condition 2 (word reading) requires participants to read color names printed in black-and-white text. A combined score for Conditions 1 and 2 was used as an indication of information processing speed (DKEFS-Combined Naming + Reading). (c) Verbal Fluency (VF) test from the D-KEFS (Delis et al., 2001).The participant was required to generate as many words as possible beginning with a specific letter in 1min, excluding proper nouns and the same word repeated with a different suffix. The total number of correct words generated was measured. Verbal fluency is associated with the integrity of frontal brain regions in children (Levin et al., 2001). In addition to information processing, verbal fluency can involve a range of EF abilities including initiation, verbal productivity, self-monitoring of words that have already been generated, and the ability to rapidly shift from one word to the next under restricted search and retrieval conditions. Both the Color-Word Interference and Verbal Fluency subtests of the D-KEFS are modifications of long-standing clinical tests for which validity and reliability have been demonstrated in numerous neuropsychological studies conducted over the past 50 years (Lezak, 1995).

  • Cognitive Flexibility:(a) Wisconsin Card Sorting Test (WCST) (Heaton, Chelune, & Talley, 1993). The WCST requires participants to correctly sort cards based on the color, shape, or number of items appearing on the card in response to matching rules. The participant first learns the rule using feedback from the examiner. After the participant successfully applies the principle, the examiner alters the matching rule and the participant must alter their approach. Perservative errors are defined as the number of incorrect matches made before the new matching rule was learned. Total number of perservative errors on the WCST was measured. This outcome score was shown to have good discriminant validity (i.e., ability to discriminate between patients and controls) and concurrent validity (i.e., high likelihood of being associated with cerebral pathology) in a study of 111 adults with MS (Parmenter et al., 2007). (b) Trail Making Test (TMT) (Lezak, 1995). The TMT is a timed paper-and-pencil test which consists of two parts. Part A of the TMT, a measure of visual scanning speed, requires participants to use a pencil to connect numbers on a page in sequential order. Part B of the TMT requires participants to shift their attention as they alternate between connecting numbers and letters in sequential order (1→A→2→B). Completion time for each condition was recorded. To control for psychomotor and information processing speed, a contrast measure (difference score) was computed by subtracting the TMT-A completion time from the TMT-B completion time, and then taking the square-root of the difference score, as used in previous work in patients with diffuse injury to the brain (Felmingham, Baguley, & Green, 2004). Higher difference scores are indicative of poorer outcome. Reported reliability coefficients for the TMT vary considerably, though generally are >0.60 (Lezak, 1995). The TMT is a well-established test that is highly sensitive to brain damage and is backed by a solid body of research (Spreen & Strauss, 1998). (c) Color-Word Interference Test from the D-KEFS (Delis et al., 2001). Condition four of this measure (DKEFS-inhib/switch) is similar to Condition 3 (DKEFS-inhib) with the additional requirement that participants must read the word instead of naming the ink color when they encounter a word contained in a square border. The DKEFS-inhib/switch measure provided an indication of set shifting. A contrast score (DKEFS-inhib/switch contrast) was computed by factoring out the inhibition component from the performance on the DKEFS-inhib/switch task.

Parent report of executive function behavior

Parents or guardians of children and adolescents completed the Behavioral Rating Inventory of Executive Function (BRIEF)—Parent Form (Gioia et al., 2000), which is valid for patients aged 5–18. The BRIEF-Adult Informant Report form (Roth, Isquith, & Gioia, 2005), which is based on the BRIEF—Parent Version, was administered for two individuals (one MS patient and one control) who were 19 years of age at the time of evaluation. Each version of the BRIEF demonstrates appropriate internal consistency per scale and index (BRIEF-Parent α = 0.80–0.98; BRIEF Adult Version α = 0.73–0.98) (Gioia et al., 2000). The BRIEF questionnaire is designed to provide insight into the child's executive functioning in the home and school environment. Each item on the BRIEF provides a description of poor executive functioning, such as “Thinks too much about the same topic,” and parents are instructed to indicate the frequency with which they observe the behavior on a three-point scale that ranges from never to often. Each item is associated with one of the eight clinical scales: (i) inhibit (the ability to resist a behavior at the appropriate time), (ii) shift (the ability to make transitions and change focus), (iii) emotional control (the ability to regulate emotional responses), (iv) initiate (the ability to begin a task), (v) working memory (the ability to hold information in mind during task completion and sustain attention), (vi) plan/organize (the ability to develop appropriate steps for a future-oriented task), (vii) organization of materials (the ability to organize their world and belongings), and (viii) monitor (the ability to monitor and evaluate performance). Parent ratings are suggested to better reflect behavioral EF problems than self-reported measures completed by children and adolescents due to their still-developing self-awareness or the potential for self-awareness to be adversely affected by brain insult. The clinical utility of the BRIEF is supported by reports of high reliability and validity as well as normative data that reflect youth from diverse socioeconomic, racial, and geographical backgrounds.

Scores on the BRIEF are transformed into a standard T-score (M = 50, SD = 10) with T-scores at or above 65 reflecting a clinically significant elevation, consistent with the recommendations of the BRIEF's authors (Gioia et al., 2000). Integration of several scales into two index scores indicates whether symptoms reflect dysfunction in Behavioral Regulation (derived from scores on the Inhibit, Shift, and Emotional Control subscales) or Metacognition (derived from scores on the Initiate, Working Memory, Plan/Organize, Organization of Materials, and Monitor subscales), whereas a Global Executive Composite (GEC) incorporates all eight-scale scores. The BRIEF also includes two validity scales that provide information about the quality of data obtained. The negativity scale assesses the extent to which the informant responds in an unusually negative manner and the inconsistency scale assesses the extent to which the informant answers similar questions in an inconsistent manner. Data from respondents scoring above the 98th percentile on either the Negativity or Inconsistency scale were excluded.

Magnetic Resonance Imaging

MRI scans were performed on a single 1.5 T GETwinSpeed Excite 12.0 scanner within a 3-month interval from the time of cognitive evaluation (median = 13 days). The following sequences were computer analyzed: (i) whole-brain sagittal three-dimensional (3D) T1-weighted RF-spoiled gradient echo sequence, repetition time (TR) 26 ms, echo time (TE) 10 ms, excitation pulse angle 30° with 1 × 1 × 1.5 mm3 resolution; and (ii) two-dimensional (2D) multislice proton density (PD)-/T2-weighted sequence (Fast spin echo) echo train length/Turbo factor = 8, TR, 3500 ms, TE1/TE2 (effective) 16/110 ms, 2 mm slice without gaps oriented parallel to anterior–posterior pole and covering the apex of the head to the bottom of the cerebellum. Healthy controls were imaged using the same protocol for comparison purposes.

MRI analyses were performed at the [PLACE] in [COUNTRY] by trained staff who were blinded to patient clinical information. Briefly, all images underwent quality control examination (evaluated for adequate signal-to-noise ratio, freedom from significant motion or other artifact, and consistency of the sequence parameters) and a pre-processing routine was run on all images to correct for intensity nonuniformity and to anatomically align T1- and T2-weighted images.

The following neuroimaging variables were analyzed in the study: (a) T2-weighted LV in the total brain, frontal lobe, and extra-frontal lobe; (b) normalized brain volume (NBV); (c) normalized grey matter volume (NGMV); (d) frontal lobe volume; and (e) thalamic volume (all normalized for head size). Brain volume measures were transformed into z-scores by subtracting the average brain volume obtained on age- and sex-matched healthy controls who were scanned as part of the National Institutes of Health (NIH)-funded MRI Study of Normal Brain Development (using the same T1-weighted MRI protocol as used in the current study), and then dividing by their standard deviation. Thus, the z-score value represents the number of standard deviations by which each subject's volume was above or below the average calculated for a large age- and sex-matched healthy pediatric population. The healthy control group in the current study was used to rule out any scanner-specific bias that could potentially affect the normalization of the structural MRI data. Thus, instead of comparing the MRI data obtained for the MS group with MRI data collected on the NIHPD group of healthy volunteers (scanned elsewhere), we compared the MRI data that were collected on the same scanner for our cohort of patients and healthy controls.

Images were processed using SIENAx (Smith et al., 2002). The brain and the skull were extracted using the Brain Extraction Tool (BET) (Smith, 2002). The brain was then segmented using FMRIB's Automated Segmentation Tool (FAST) into grey and white matter and cerebral spinal fluid (CSF), while also corrected for spatial intensity variations and partial volume effects (see Collins, Neelin, Peters, & Evans, 1994; Sled, Zijdenbos, & Evans, 1998). To avoid lesion misclassification of grey matter and cerebral spinal fluid, lesions were masked out using lesion labels. The methodology utilized for T2-weighted lesion brain load calculation is detailed elsewhere (Till et al., 2011). Briefly, all images were processed to remove skull and scalp (Smith, 2002) linearly register the T1-weighted image to the PD-/T2-weighted images, and normalize the intensity range (Nyul & Udupa, 1999). T2-weighted lesion labels output were processed using a locally developed automated Bayesian classifier and, if necessary, were corrected manually (Ghassemi et al., 2008; Sled et al., 1998). Supratentorial and infratentorial LV were combined as a measure of total brain LV.

The scaling factor, as determined using the external part of the skull and computed by SIENAx, is used to normalize the total and segmented brain volumes and grey matter volume for skull size. The frontal lobe was extracted using a template-matching approach called ANIMAL (Collins, Holmes, Peters, & Evans, 1995). The T1-weighted image was nonlinearly registered to a template, where the frontal lobe was manually defined, and then the label of the frontal lobe was transferred from the template to the T1-weighted image using the deformation field estimated by the registration. A model-based segmentation method was used to identify the thalamus in each T1-weighted image, as described in prior work (Aubert-Broche et al., 2011; Collins & Pruessner, 2010). The Montreal Neurological Institute (MNI) Talairach-like MNI ICBM152 stereotaxic 18.5–43.5 template was used as the standard space (Fonov et al., 2011).

Statistical Analysis

All EF outcome data were examined for non-normality with the Shapiro-Wilk test. For performance-based EF outcomes, scores were age- and sex-normed as per test manual and were then converted to z-scores, with the exception of the TMT B-A difference score, which represented a square-root transformed time score. Scores for three patients who failed to complete the TMT—Part B within a 2.5 min time limit were assigned a z-score of −5 before proceeding with the analysis to reduce skewing of the data. For the BRIEF outcomes, a higher T score represents more problems in everyday EF, whereas for the performance-based measures, a higher score represents a stronger EF skill (with the exception of the TMT B–A difference score where a higher difference score indicated worse performance). Analysis of variance (ANOVA) or the Mann–Whitney U test (if variable did not meet test assumptions) was used to compare the groups on demographics, EF outcomes, and MRI metrics. Categorical data, including the proportion of participants showing impaired scores (defined as a z-score of <1.5) were analyzed using chi-square analysis. Because groups did not differ with respect to sex, parental education, and age at testing, these variables were not covaried in our between-group analyses. Moreover, we did not covary IQ in our analyses given the high interrelation between IQ and EF and recent discussion that both IQ and EF are likely to be affected by early brain insult (Dennis et al., 2009).

The relationship between EF performance (for both the performance based and BRIEF measures) and the clinical and MRI measures was analyzed using Spearman's correlation. For these correlations, we selected only measures that were impaired in 15% or more of patients (n = 5), as the purpose of these analyses is to examine the neural substrates of EF impairment and thus we wanted to ensure that there was enough variability in the outcome measure as to correlate with the MRI measures. Because of the multiple comparisons involved, a conservative alpha of 0.01 was used to assess significance in the between-groups analyses of the EF data and the within-group correlational analyses. All statistical analyses were performed using PASW Statistics software (version 18.0).

Results

Sample Characteristics

Demographic and disease characteristics from 34 MS and 33 healthy control (HC) participants included in this study are presented in Table 1. In both groups, a large proportion was female, consistent with the higher female-to-male ratio (2.2:1) reported in the pediatric MS literature, particularly if onset is after 12 years (Ghezzi et al., 1997). There were no significant differences between the MS and HC groups in terms of sex, age, and average years of parental education. Symptoms of anxiety and depression were slightly higher in the MS group compared with the HC group, but differences were not significant. The MS group had a lower estimate of overall IQ (t (65) = −4.20, p< .001), though at the group level, the average IQ was well within normal limits (mean = 101.5; SD = 12.02). The age at assessment ranged from 11.08 to 19.58 years for the MS group and 10.83 to 19.50 years for the HC group. Eighteen patients experienced disease onset prior to age 13 (range 4.48–12.25 years) and 16 patients were 13 years or older at disease onset (range 13.08–16.75 years). MS patients varied greatly in disease duration (range 0.25–13.33 years), and number of relapses experienced (median 3; range 1–17). It is important to note that, in our sample, the disease duration was longer amongst the 18 younger-onset patients (mean = 6.26 years, SD = 2.97) when compared with the 16 patients who were older at onset (mean = 1.91 years; SD = 1.91), t(33) = 5.53, p< .001). The majority (85%) of patients exhibited minimal or no physical disability as indicated by an EDSS score <2.0.

Table 1.

Descriptive statistics for participant groups

 MS (n = 34) Control (n = 33) Statistic p-value 
Female (%) 27 (79.4%) 27 (81.8%) X2 = 0.06 0.80 
Age at study (years) 16.1 (2.12) 15.9 (2.14) t = 0.27 0.78 
Parental Education (years) 15.5 (2.16) 15.8 (1.88) t = − 0.54 0.59 
Disease duration (years) 4.2 (3.22) – – – 
EDSS score (median, range) 1.0 (0–4) – – – 
Number of relapses 3.0 (1–17) – – – 
Full-scale IQ (standard score) 101.5 (12.02) 112.2 (8.41) t = − 4.20 <0.001 
BASC-2 anxiety (T-score) 53.12 (10.22) 51.25 (10.65) t = 0.67 0.51 
BASC-2 depression (T-score) 49.36 (10.87) 45.75 (7.68) U = 325.5 0.23 
 MS (n = 34) Control (n = 33) Statistic p-value 
Female (%) 27 (79.4%) 27 (81.8%) X2 = 0.06 0.80 
Age at study (years) 16.1 (2.12) 15.9 (2.14) t = 0.27 0.78 
Parental Education (years) 15.5 (2.16) 15.8 (1.88) t = − 0.54 0.59 
Disease duration (years) 4.2 (3.22) – – – 
EDSS score (median, range) 1.0 (0–4) – – – 
Number of relapses 3.0 (1–17) – – – 
Full-scale IQ (standard score) 101.5 (12.02) 112.2 (8.41) t = − 4.20 <0.001 
BASC-2 anxiety (T-score) 53.12 (10.22) 51.25 (10.65) t = 0.67 0.51 
BASC-2 depression (T-score) 49.36 (10.87) 45.75 (7.68) U = 325.5 0.23 

Note: Standard deviations are given in parenthesis. BASC-2 = Behavioral Assessment Scale for Children—2nd edition (Self Report of Personality); EDSS = Expanded Disability Status Scale; MS = multiple sclerosis.

Neuropsychological Test Performance

Table 2 displays the group means and standard deviations for the EF tests. Significant differences were obtained on measures of information processing (SDMT: F (1, 65) = 7.87, p = .007) and attention shifting/working memory (TMT-B: F (1, 63) = 12.55, p = .001) and approached significance on the TMT B-A difference score (F (1, 63) = 5.76, p = .019) and Verbal Fluency (F(1, 62) = 7.11, p = .01). When comparing the EF scores to normative mean values (z = 0, SD = 1), mean scores for the MS group fell within the expected range on all EF measures, except for TMT-B which was below average relative to age expectations. There were no between-group differences with regard to the number of errors made on the TMT-B task, suggesting that the difference between the groups reflects the MS group's slower time to complete this task. The performance did not differ between groups on measures of attentional control (CPT-II omission errors; DKEFS-inhib measures) and cognitive flexibility (perseverative errors on the WCST; and DKEFS-inhib/switch measures).

Table 2.

Descriptive statistics for multiple sclerosis (MS) patients and healthy controls (HCs) on neuropsychological measures of executive functioning

Task MS
 
HC
 
p value Cohen's d No. (%) MS below avga No. (%) HC below avga 
N Mean (SDN Mean (SD
CPT -II omm. errors 32 −0.06 (1.21) 30 0.32 (0.48) .11 0.42 3 (9.4) 5 (16.7) 
SDMT – oral 33 0.48 (1.57) 33 1.40 (1.05) .007 0.69 3 (9.1) 0 (0) 
TMT-B 32 −1.47 (1.86) 32 0.08 (1.21) .001 0.99 14 (43.8) 3 (9.4) 
TMT B-Ab 32 5.50 (2.46) 32 4.27 (1.73) .019 0.58 8 (25.0) 3 (9.4) 
WCST perseveration errors 33 0.60 (1.14) 32 0.95 (1.20) .23 0.30 3 (9.1) 1 (3.1) 
DKEFS-speed 23 −0.04 (1.06) 20 −0.53 (0.39) .052 0.61 2 (8.7) 0 (0) 
DKEFS-inhibition 23 0.41 (1.14) 20 0.22 (0.61) .57 0.21 2 (8.7) 1 (5.0) 
DKEFS-shift 23 −0.06 (0.64) 20 0.00 (0.48) .77 0.11 3 (13.0) 1 (5.0) 
VF 30 0.21 (0.99) 33 0.90 (1.04) .010 0.68 1 (3.3) 1 (3.0) 
Task MS
 
HC
 
p value Cohen's d No. (%) MS below avga No. (%) HC below avga 
N Mean (SDN Mean (SD
CPT -II omm. errors 32 −0.06 (1.21) 30 0.32 (0.48) .11 0.42 3 (9.4) 5 (16.7) 
SDMT – oral 33 0.48 (1.57) 33 1.40 (1.05) .007 0.69 3 (9.1) 0 (0) 
TMT-B 32 −1.47 (1.86) 32 0.08 (1.21) .001 0.99 14 (43.8) 3 (9.4) 
TMT B-Ab 32 5.50 (2.46) 32 4.27 (1.73) .019 0.58 8 (25.0) 3 (9.4) 
WCST perseveration errors 33 0.60 (1.14) 32 0.95 (1.20) .23 0.30 3 (9.1) 1 (3.1) 
DKEFS-speed 23 −0.04 (1.06) 20 −0.53 (0.39) .052 0.61 2 (8.7) 0 (0) 
DKEFS-inhibition 23 0.41 (1.14) 20 0.22 (0.61) .57 0.21 2 (8.7) 1 (5.0) 
DKEFS-shift 23 −0.06 (0.64) 20 0.00 (0.48) .77 0.11 3 (13.0) 1 (5.0) 
VF 30 0.21 (0.99) 33 0.90 (1.04) .010 0.68 1 (3.3) 1 (3.0) 

Note: Mean (z-scores); standard deviations are given in parenthesis, except where indicated. Median and range reported for non-normally distributed data.

COWAT = Controlled One Word Association Test; CPT-II = Continuous Performance Test—2nd edition (Version 5); DKEFS = Delis–Kaplan Executive Function Scale; HC = healthy controls; SDMT = Symbol Digits Modalities Test; TMT-B = Trail Making Test—Part B; TMT B-A = Trail Making Test—Time difference between Part B and Part A; VF = Verbal Fluency; WCST = Wisconsin Card Sorting Test.

aBelow average performance defined as a score falling ≥1.5 SD below normative value.

bTime difference score is square-root transformed and higher score denotes worse performance; a cut-off of 6 (i.e., 36 s) was used for determining impaired performance on this measure.

A partial correlational analysis was conducted to determine whether processing speed, as measured using the SDMT, accounted for the between-group differences on the TMT-B, TMT B-A, and VF tests. After covarying for processing speed, group differences were no longer significant, although TMT-B approached significance—F(2, 63) = 5.18, p = .026. Another approach to parceling out the effects of processing speed from higher order skills is to use the contrast scores on the DKEFS test. A contrast score of 13 or greater reflects disproportionately better performance on the higher level task relative to that on the baseline measure whereas a score of 7 or lower is interpreted to reflect disproportionately worse performance on the higher level task relative to that on the baseline measure. The DKEFS-inhib contrast score (used to parcel out color naming performance from performance on the Inhibition task) showed that 8 of 23 (34%) patients (versus 2 of the 24 (8%) controls) had disproportionately better performance on the inhibition task relative to that on the baseline measure (Yates' X2 (1) = 3.45, p = .06), suggesting that an overall low score on inhibition task may be accounted for by the overall reduction in color naming speed. The second contrast score (DKEFS-inhib/switch contrast) was computed by factoring out the inhibition component from performance on the inhib/switching condition. Only 1 of 23 (4.3%) patients showed better performance on the higher level task (inhibition/switching) relative to the inhibition-only condition.

Table 2 also displays the proportion of participants showing below average performance on each EF measure (defined as a score falling ≥ 1.5 standard deviation below the normative value). A significantly higher proportion of MS patients showed below average performance on the TMT-B (n = 14, 43.8%) compared with HCs (n = 3, 9.4%), X2 (1) = 9.69, p< .01. On all other EF tests, the performance was within the average range in 80% or more of the sample.

Measure of Everyday EF

Functional executive skills were assessed by the BRIEF parent-report in 30 MS patients and 31 control participants. Questionnaires were not included in the analysis for three parents of MS patients for the following reasons: one did not return the questionnaire, one did not complete due to difficulties with reading English, and one was given the adult version of the BRIEF because the patient was 19 years of age. One parent of an HC did not return the questionnaire. All remaining questionnaires were valid. As shown in Table 3, group differences were found on the following Working Memory (Mann–Whitney U = 236.5, p = .001) and Plan/Organize (Mann–Whitney U = 282.0, p = .008) scales of the BRIEF, and approached significance on the Shift scale (Mann–Whitney U = 320, p = .04). Parent ratings were higher in the MS group on two composite scales: Metacognition Index (Mann–Whitney U = 266.0, p = .004) and GEC (Mann–Whitney U = 292.0, p = .01). With regard to the proportion in each group displaying clinically elevated scores relative to age norms, parents of children and adolescents with MS were more likely than parents of HCs to report problems on the Shift (MS: n = 7, 22.6% vs. HC: 0%; X2(1) = 7.89, p< .05) and Working Memory (MS: n = 8, 25.8% vs. HC: n = 1, 3.2%, X2(1) = 6.3, p< .05) scales (Continuity correction applied). A generalized pattern of functional executive deficits was suggested in 6 of 31 (19.4%) patients with MS on the GEC.

Table 3.

BRIEF parent-report outcomesa and proportion of MS and healthy control (HC) participants scoring in the clinically elevated range

Outcome (M= 50; SD = 10) MS (n = 31) (Meana; SDHC (n= 31) (Meana; SDp value Cohen's d No. (%) MS Clinically Elevatedb No. (%) HC Clinically Elevatedb 
Inhibit 53.07 (14.06) 47.90 (8.22) .23 0.41 7 (22.6) 2 (6.4) 
Shift 54.57 (12.86) 47.35 (7.61) .04 0.65 7 (22.6) 0 (0) 
Emotional Control 55.07 (12.90) 49.64 (9.46) .19 0.46 6 (19.4) 2 (6.4) 
Initiate 52.23 (11.22) 47.16 (9.20) .07 0.50 6 (19.4) 2 (6.4) 
Working Memory 58.43 (13.61) 48.35 (10.08) .001 0.84 8 (25.8) 1 (3.2) 
Plan/Organize 56.40 (12.97) 47.97 (9.33) .008 0.75 6 (19.4) 3 (9.7) 
Organization of Materials 54.87 (9.90) 50.10 (10.67) .08 0.49 7 (22.6) 4 (12.9) 
Monitor 53.17 (14.03) 46.29 (8.94) .06 0.55 5 (16.1) 1 (3.2) 
Behavior Regulation Index (BRI) 54.90 (14.20) 48.19 (9.00) .07 0.52 7 (22.6) 2 (6.4) 
Metacognition Index (MI) 56.40 (12.09) 48.06 (9.14) .004 0.76 4 (12.9) 3 (9.7) 
Global Executive Composite (GEC) 56.00 (13.16) 47.61 (8.93) .01 0.71 6 (19.4) 2 (6.4) 
Outcome (M= 50; SD = 10) MS (n = 31) (Meana; SDHC (n= 31) (Meana; SDp value Cohen's d No. (%) MS Clinically Elevatedb No. (%) HC Clinically Elevatedb 
Inhibit 53.07 (14.06) 47.90 (8.22) .23 0.41 7 (22.6) 2 (6.4) 
Shift 54.57 (12.86) 47.35 (7.61) .04 0.65 7 (22.6) 0 (0) 
Emotional Control 55.07 (12.90) 49.64 (9.46) .19 0.46 6 (19.4) 2 (6.4) 
Initiate 52.23 (11.22) 47.16 (9.20) .07 0.50 6 (19.4) 2 (6.4) 
Working Memory 58.43 (13.61) 48.35 (10.08) .001 0.84 8 (25.8) 1 (3.2) 
Plan/Organize 56.40 (12.97) 47.97 (9.33) .008 0.75 6 (19.4) 3 (9.7) 
Organization of Materials 54.87 (9.90) 50.10 (10.67) .08 0.49 7 (22.6) 4 (12.9) 
Monitor 53.17 (14.03) 46.29 (8.94) .06 0.55 5 (16.1) 1 (3.2) 
Behavior Regulation Index (BRI) 54.90 (14.20) 48.19 (9.00) .07 0.52 7 (22.6) 2 (6.4) 
Metacognition Index (MI) 56.40 (12.09) 48.06 (9.14) .004 0.76 4 (12.9) 3 (9.7) 
Global Executive Composite (GEC) 56.00 (13.16) 47.61 (8.93) .01 0.71 6 (19.4) 2 (6.4) 

a5% trimmed mean shown.

bClinically elevated range defined as performance falling 1.5 SD or greater above norm.

p < .01 (in bold).

Clinical Correlates of EF Outcomes for MS Patients

Clinical variables

Physical disability as assessed by the EDSS did not correlate with any of the neuropsychological measures of EF. In contrast, higher EDSS score (i.e., higher physical disability) was associated with poorer ratings on the Shift (r = .50, p = .005) and Plan/Organize (r = .47, p< .01) scales, and approached significance on the Monitoring (r = .42, p< .05), Initiation (r = .37, p< .05), and Working Memory (r = .40, p< .05) scales. Higher EDSS score was positively correlated (p< .05) with all composite scores on the BRIEF: Metacognition Index (r = .43), Behavioral Regulation Index (r = .42), and the GEC (r = .42). The total number of clinical relapses did not correlate with any of the measures.

Age at disease onset

Younger age at disease onset was not associated with any of the EF measures, with the exception of the SDMT (r = .47, p< .005) and trended towards significance on the DKEFS-inhib condition (r = .35, p = .05). With regard to outcomes on the BRIEF, younger age at disease onset predicted more difficulties on the Metacognition Index (r = − 0.46, p = .005) as well as the Working Memory (r = − 0.52, p< .005) and Organization of Materials (r = − 0.46, p = .005) scales, and approached significance on the Plan/Organize (r = − 0.40, p< .05), Shift (r = − 0.34, p< .05), and Initiation (r = − 0.39, p< .05) scales, and the GEC (r = − 0.40, p< .05). As the young disease-onset patients had a longer disease duration in our study cohort, we examined whether the relationship between age at disease onset and EF outcome depended on disease duration. Using partial correlations, the age at disease onset was no longer correlated with the EF outcome after controlling for disease duration.

MRI outcomes

Valid MRI data were available for 29 MS patients and all 33 healthy controls. Data from five MS patients were not analyzed for the following reasons: three failed quality control; one was reported to have a cyst in the temporal lobe, which precluded an accurate measure of brain volume; and one did not complete the MRI within the three month interval due to scheduling conflicts. Of the patients with valid MRI data, the majority (55%; 16 of 29) had an MRI-cognitive evaluation interval of 2 days or less, which decreases the probability of a relapse occurring in such a short time frame.

Lesion volumes

In MS patients, the median total brain T1- and T2-weighted LV was 381.50 mm3 (range 22.89–10,245.16 mm3) and 5220.82 mm3 (range 520.75–35,613.03 mm3), respectively. The median T2-weighted LV in the frontal lobe was 1,702.44 mm3 (range 7.63–19,565.01 mm3), which represents 44.4% of the total brain LV. LV in the right and left frontal lobe was strongly correlated (r = .87, p< .001) and thus the combined LV in the entire frontal lobe was used in the correlational analyses. Total brain LV was strongly correlated with LV in the frontal lobe (r = .87, p< .001) and the nonfrontal lobe (r = .95, p< .001).

Brain volumes

NBV was significantly lower in the MS group (M = 1639.31 cm3, SD = 104.50) compared with the HC group (M = 1705.82 cm3, SD = 93.51, F(1, 61) = 7.00, p = .01), representing a 3.90% mean difference in overall brain volume. MS participants also had lower NGMV (M = 897.18 cm3, SD = 64.08) and frontal lobe volume (M = 401.40 cm3, SD = 48.21) compared with measurements in the control group for NGMV (M = 937.45 cm3, SD = 67.87, F(1, 61) = 5.72, p = .02) and frontal lobe volume (M = 427.64 cm3, SD = 38.31; F(1, 61) = 5.69, p = .02), representing a mean difference of 4.30% and 6.14%, respectively. The difference in mean thalamic volume between the MS group (M = 15.42 cm3, SD = 1.95) and the HC group (M = 17.43 cm3, SD = 1.03, F(1, 60) = 24.92, p< .001) was most pronounced, reflecting a mean difference of 11.60%.

Neuroimaging Correlates of EF Outcomes for MS Patients

Spearman correlations between MRI metrics and EF scores are shown in Table 4. Frontal lobe volume and thalamic volume were most strongly correlated with the performance on the SDMT and TMT (both Part B and B-A) with r values ranging from 0.40 to 0.66 (p< .01), and to a lesser extent Verbal Fluency; NGMV was only weakly correlated with TMT B-A (r = − 0.36, p< .01), whereas the correlations with NBV did not meet the statistical threshold. The LV measurements did not predict performance on any of the neuropsychological measures. To delineate the contribution of LV in the frontal lobe on EF test scores, we repeated the analyses, controlling for nonfrontal lobe LV and did not find any significant correlations.

Table 4.

Neuroimaging correlates (Spearman r) of executive function outcomes in children and adolescent with MS

Outcomea T2-weighted LV
 
Brain volumes (z-score)
 
Total Brain Frontal lobe Non-frontal lobe NBV Frontal lobe Thalamic volume NGMV 
CPT-II omm. errors −0.233 −0.198 −0.189 0.333* 0.204 0.275* 0.206 
SDMT −0.324 −0.244 −0.331 0.390* 0.627** 0.663** 0.078 
TMT B −0.101 −0.125 −0.077 0.309* 0.569** 0.484** 0.266 
TMT B-A −0.072 −0.000 −0.088 −0.309* −0.521** −0.400** −0.364** 
WCST persev. err. 0.243 9.182 0.224 −0.098 0.097 −0.032 −0.177 
Verbal Fluency −0.332 −0.243 −0.255 0.256 9.447* 0.460* −0.103 
 BRIEF outcomesb 
Inhibit −0.030 9.033 −0.055 −0.200 −0.136 −0.139 −0.163 
Shift 0.464** 9.400* 0.438* −0.158 −0.253 −0.321 −0.117 
Emotional Control 0.226 9.183 0.213 −0.174 −0.170 −0.178 −0.283 
Initiate 0.238 9.113 0.274 −0.506** −0.303 −0.447** −0.253 
Working Memory 0.290 9.277 0.266 −0.478** −0.487** −0.439** −0.203 
Plan/Organize 0.294 9.300 0.219 −0.470** −0.508** −0.463** −0.148 
Organ. of Materials −0.050 −0.121 0.007 −0.410* −0.082 −0.113 −0.430* 
Monitor 0.144 9.185 0.119 −0.155 −0.365 −0.273 −0.072 
Outcomea T2-weighted LV
 
Brain volumes (z-score)
 
Total Brain Frontal lobe Non-frontal lobe NBV Frontal lobe Thalamic volume NGMV 
CPT-II omm. errors −0.233 −0.198 −0.189 0.333* 0.204 0.275* 0.206 
SDMT −0.324 −0.244 −0.331 0.390* 0.627** 0.663** 0.078 
TMT B −0.101 −0.125 −0.077 0.309* 0.569** 0.484** 0.266 
TMT B-A −0.072 −0.000 −0.088 −0.309* −0.521** −0.400** −0.364** 
WCST persev. err. 0.243 9.182 0.224 −0.098 0.097 −0.032 −0.177 
Verbal Fluency −0.332 −0.243 −0.255 0.256 9.447* 0.460* −0.103 
 BRIEF outcomesb 
Inhibit −0.030 9.033 −0.055 −0.200 −0.136 −0.139 −0.163 
Shift 0.464** 9.400* 0.438* −0.158 −0.253 −0.321 −0.117 
Emotional Control 0.226 9.183 0.213 −0.174 −0.170 −0.178 −0.283 
Initiate 0.238 9.113 0.274 −0.506** −0.303 −0.447** −0.253 
Working Memory 0.290 9.277 0.266 −0.478** −0.487** −0.439** −0.203 
Plan/Organize 0.294 9.300 0.219 −0.470** −0.508** −0.463** −0.148 
Organ. of Materials −0.050 −0.121 0.007 −0.410* −0.082 −0.113 −0.430* 
Monitor 0.144 9.185 0.119 −0.155 −0.365 −0.273 −0.072 

Note: CPT-II omm. = Continuous Performance Test (5th ed.)—Omission errors; NBV = Normalized brain volume; NGMV = Normalized gray matter volume; TMT-B = Trail Making Test—Part B; TMT B-A = Time difference between Part B and Part A (square-root transformed).

aOutcomes only examined if at least 5 MS patients (15%) showed below average performance relative to normative values,

bHigher score on BRIEF denotes worse performance in everyday EF.

*p< .05; **p< .01 (in bold).

Regarding everyday EF behaviors, thalamic volume was associated with three BRIEF scales: Initiate, Working Memory, and Planning/Organization (r values ranging from −0.44 to −0.46, p< .01). NBV was moderately associated with Initiate, Working Memory, and Planning/Organization (r values ranging from −0.47 to −0.51, p< .01), whereas NGMV did not meet the statistical threshold. Frontal lobe volume associated with the Working Memory (r = − 0.49, p< .01) and Planning/Organization scales (r = − 0.51, p< .01). Regarding correlations with LV, problems on the Shift scale correlated with greater LV in total brain (r = .46, p< .01) and, to a lesser extent, in the frontal lobe (r = .40, p< .05) and extra-frontal lobe (r = .44, p< .05). No other BRIEF scales correlated with LV metrics.

Neuropsychological Correlates of Everyday EF Impairment

Correlations between the performance on conventional neuropsychological EF measures and parent ratings of everyday EF skill on the BRIEF are shown in Table 5. Reduced performance on two measures of information processing (SDMT and DKEFS-Combined Naming + Reading) and the DKEFS–shift contrast measure was correlated with more reported difficulties on the BRIEF Shift scale (r values ranging from −0.32 to −0.40, p< .05). The DKEFS-shift contrast measure was also correlated with the Emotional Control scale (r = − 0.48, p< .01). Lower performances on task requiring efficient information processing (SDMT, TMT-B, and DKEFS-Combined Naming + Reading) were significantly correlated with more problems on the Working Memory scale of the BRIEF (r values ranging from −0.36 to −0.61, p< .05). A similar pattern, albeit slightly weaker correlations, was revealed with these neuropsychological tests and the Initiate scale (r values ranging from −0.34 to −0.49, p< .05). More problems on the Plan/Organize scale correlated with poorer Verbal Fluency, as well as lower performance on the DKEFS-Combined Naming + Reading and DKEFS-inhib contrast measures (r values ranging from −0.34 to −0.44, p< .05). Organization of Materials on the BRIEF did not correlate with any neuropsychological measure.

Table 5.

Correlations (Spearman r) between BRIEF subscalesand neuropsychological tests for MS patients.

BRIEF Scale CPT-II omm. err. SDMT—Oral VF TMT-B WCST- PE DKEFS- Speed DKEFS-inhib DKEFS-Shift 
Inhibit −0.375* −0.067 .148 −0.042 −0.210 −0.191 −0.114 −0.110 
Shift −0.281 −0.317* −0.255 −0.302 −0.238 −0.395* −0.130 −0.373* 
Emotional Control −0.315 −0.203 −0.123 −0.209 −0.144 −0.187 .054 −0.478* 
Initiate −0.540** −0.345* −0.160 −0.414* −0.144 −0.486* .315 −0.098 
Working Memory −0.559** −0.575** −0.287 −0.568** −0.288 −0.613** −0.338 −0.211 
Plan/Organize −0.585** −0.309 −0.340* −0.361 −0.002 −0.389* −0.442* −0.054 
Organiz. of Materials .285 −0.311 .067 −0.244 .020 −0.296 −0.172 −0.339 
Monitor −0.334 −0.271 −0.411* −0.448** −0.222 −0.464* −0.098 −0.361 
BRIEF Scale CPT-II omm. err. SDMT—Oral VF TMT-B WCST- PE DKEFS- Speed DKEFS-inhib DKEFS-Shift 
Inhibit −0.375* −0.067 .148 −0.042 −0.210 −0.191 −0.114 −0.110 
Shift −0.281 −0.317* −0.255 −0.302 −0.238 −0.395* −0.130 −0.373* 
Emotional Control −0.315 −0.203 −0.123 −0.209 −0.144 −0.187 .054 −0.478* 
Initiate −0.540** −0.345* −0.160 −0.414* −0.144 −0.486* .315 −0.098 
Working Memory −0.559** −0.575** −0.287 −0.568** −0.288 −0.613** −0.338 −0.211 
Plan/Organize −0.585** −0.309 −0.340* −0.361 −0.002 −0.389* −0.442* −0.054 
Organiz. of Materials .285 −0.311 .067 −0.244 .020 −0.296 −0.172 −0.339 
Monitor −0.334 −0.271 −0.411* −0.448** −0.222 −0.464* −0.098 −0.361 

Note: CPT-II omm. err. = Continuous Performance Test (2nd edition, Version 5)—Omission errors (scores transformed such that a high score reflects fewer errors or better performance); DKEFS = Delis–Kaplan Executive Function Scale (Speed, Inhibition, Shifting conditions); SDMT = Symbol Digits Modalities Test—oral version; TMT-B = Trail Making Test—Part B; TMT B-A = Trail Making Test—Time difference between Part B and Part A; WCST–PE = Wisconsin Card Sorting Test—Perseverative errors; VF = Verbal Fluency.

*p < .05, **p < .01 (in bold).

Discussion

The aim of this investigation was to examine executive functioning in children and adolescents with MS. We did this by first examining EFs at the group level and then at the individual level. A second aim was to explore whether executive dysfunction can be associated with brain volume reduction and lesion burden as measured by MRI. EFs were assessed across a range of domains using both neuropsychological tests and parent-reported measures of everyday EF skills.

Youth with MS performed lower than age-matched controls in several areas of executive functioning. However, in terms of impairment on the neuropsychological measures, only the TMT-B emerged as a sensitive measure with almost half (44%) of the MS sample showing performance falling below 1.5 standard deviations from age expectations. This finding is consistent with two other childhood MS studies that documented impairment on the TMT-Bin 30% to 40% of patients (Deery et al., 2010; MacAllister et al., 2005). Symbol Digit Modalities Test and Verbal Fluency were the next two EF tests that differed between the groups at the mean level, though in terms of sensitivity to impairment, fewer than 10% of patients were impaired on these measures in our sample. Patients did not differ from the comparison group with regard to attentional control (as measured by omission errors on the CPT-II and DKEFS-inhibition) and on non-timed measures of cognitive flexibility (as measured by perseverative errors on the WCST and performance on the DKEFS-shift condition). Taken together, the results suggest that youth with MS show a stronger performance when provided enough time for thinking and planning responses when compared with EF tasks that rely heavily on efficient information processing.

In terms of everyday EF skills, results suggested that pediatric-onset MS patients may experience difficulties in everyday EFs that cut across multiple functional domains as indicated by clinically elevated scores in roughly one-fifth of the sample on any one scale on the BRIEF. This broad pattern of EF deficits in patients with MS underscores the importance of assessing EF sequelae in everyday life along with conventional neuropsychological assessment. Difficulty in everyday working memory skills (i.e., holding information in mind while simultaneously manipulating it for some purpose) was particularly notable in the MS group, with differences revealed at both the group level, and in terms of the high number of individuals (8 of 31; 26%) reported by their parent as having impairment in this area. These findings are consistent with results reported in a previous study of 44 children and adolescents with MS (MacAllister, 2010) as well as in adults with MS (Chiaravalloti & DeLuca, 2003; Lima et al., 2007; Smith & Arnett, 2010) showing that working memory may be particularly vulnerable to disruption in MS.

Previous studies in adults with MS have reported that reduced processing speed may contribute to difficulties in executive functioning (Archibald & Fisk, 2000; DeLuca, Chelune, Tulsky, Lengenfelder, & Chiaravalloti, 2004; Demaree, DeLuca, Gaudino, & Diamond, 1999). In the current study, group differences on the TMT-B persisted, albeit weakly, after controlling for processing speed. This finding suggests impairment in set shifting and working memory ability independent of reduced processing speed. Difficulties in shifting and working memory were also reported on the BRIEF by parents of youth with MS, which bolsters our finding of a specific deficit in these EF skills.

Regarding MRI correlates of executive behaviors, lower volume in the frontal lobe and thalamus emerged as a robust correlate of poor performance on the TMT-B and Verbal Fluency, implicating the frontal–subcortical circuit in these “gold standard” neuropsychological measures (Houtchens et al., 2007; Seidenberg et al., 2008; Van Der Werf et al., 2001). LV, in contrast, did not associate with performance-based EF outcome, perhaps a result of the fluctuating and often transient nature of visible T2-weighted lesions. The only association between LV and outcome was revealed on the Shift scale of the BRIEF. As expected, findings did not provide support for frontal lobe functional specificity in the developing brain and suggest that pathology in posterior brain regions may also be contributing equally to the disruption of executive functioning in pediatric-onset MS, as suggested in prior studies of early brain insult (Anderson et al., 2010; Jacobs, Harvey, & Anderson, 2010; Levin, Williams, Eisenberg, High, & Guinto, 1992; Levin et al., 2001; Long et al., 2011). In other words, findings do not suggest frontal lobe pathology as the primary brain region responsible for executive dysfunction. Cerebellar and thalamic lesions, in particular, are common in pediatric-onset MS (Waubant et al., 2009) and because of the multiple connections between the frontal lobes and these structures (Behrens et al., 2003; Schmahmann & Pandya, 1997), posterior and subcortical structures should also be conceived as important components of the executive system.

In the current patient sample, EDSS values were very low, with fewer than five patients having a score of 2.0 or greater, which is indicative of mild disability. Despite this, findings showed a significant, albeit rather weak, relationship between higher EDSS score and more problems in everyday EF behaviors on the BRIEF. One possible explanation for this observed relationship is that the patients with higher EDSS scores also had longer disease durations. Indeed, EDSS score and disease duration trended toward significance (Spearman r = .27, p = .06) in our sample; thus, caution is warranted when interpreting the observed relationship with the EDSS score. Overall, the implication of these findings is that executive dysfunction may manifest much earlier than physical disability in pediatric-onset MS patients.

While not a primary focus of the study, younger age at disease onset was expected to relate with poorer EF outcome because younger age is associated with less-established EFs and less mature EF networks, particularly for skills that emerge in late childhood, such as goal setting, or skills that gradually improve throughout childhood, such as processing speed. Indeed, prior studies in childhood MS (e.g., Amato et al., 2008; Banwell and Anderson, 2005) emphasize the importance of young age of onset as a predictor of cognitive dysfunction. Results of the current study showed that younger age at disease onset was a strong predictor of problems in everyday EF skills in patients with MS, supporting an early vulnerability perspective. In other words, children may be at greater risk of failing to meet cognitive developmental milestones as they “grow into the deficit” and, as a consequence, appear less like their typically developing peers as they mature. However, performance-based EF outcomes were only weakly associated with age at disease onset, which raises questions about how child's age of disease onset may influence a parent's rating of their child's executive skills. The influence of younger age at MS onset for the current population should be interpreted with caution, however, as it may be confounded by the co-occurrence of younger age of onset and longer disease duration in our sample. Longitudinal studies that can control for disease duration are needed to increase our understanding of the emergence of EF deficits in a young-onset group.

Correlations between the neuropsychological tests purported to assess the same skill as described by a scale on the BRIEF were variable, or sometimes not even associated. For example, the performance-based measures of response inhibition (CPT-IIomission errors, DKEFS-inhib) did not correlate with the Inhibition scale on the BRIEF. These findings, along with those of previous studies (Payne, Hyman, Shores, & North, 2011; Toplak et al., 2009; Trowbridge & Schutte, 2007), have led to suggestions that some neuropsychological measures have limited ecological validity. Other neuropsychological tests, however, did correlate with multiple domains on the BRIEF. For example, Verbal Fluency performance correlated with the Planning/Organize and Monitor scales on the BRIEF, reflecting a reliance upon a wide range of EF abilities for completion of this complex test. Verbal Fluency performance involves initiation, planning, verbal productivity, monitoring words that have already been generated, and cognitive flexibility needed to shift from one word to the next.

One reason for these discrepant findings is that the contexts in which EF skills are assessed differ considerably. BRIEF outcomes are obtained through parent observations of their child's behavior in real-world environments in which situational factors, such as psychological adjustment to the disease or parental expectations of child function, may contribute to determine the outcome. In contrast, standardized cognitive tasks are administered in structured, quiet, supervised settings which can contribute to the outcome. The weak-to-modest association between BRIEF and performance-based measures of EFs may also reflect differences in the specific skill set assessed by each measure. Given these fundamental differences, a combination of both broad-band approach and more specific neuropsychological tests that represent different dimensions of EFs is most likely to yield the full clinical picture in understanding the functional impact of a disease.

One limitation in our study relates to the relatively higher IQ score in the control group, which rendered between-group comparisons on EF tasks problematic. We note that the mean differences in EF outcomes may reflect stronger EF performance by the control group as a result of higher IQ, rather than an EF deficit in the MS group. Recent discussions in the neuropsychological literature (e.g., Dennis et al., 2009) argue against the decision to covary IQ in analyses given the high interrelation between IQ and EF following insult to the developing brain. For this reason, it seems prudent to focus on the extent of impaired performance on the EF measures relative to age expectations, which we have reported in the current study. In terms of overall average IQ in the MS group, we stress that our sample of patients was recruited from a large [CITY] pediatric health-care facility that is based in a region of relatively high socioeconomic status. Indeed, parents of youth with MS had, on average, completed post-secondary education (mean of 15.5 years of education), which suggests our sample reflects a higher-than-average socioeconomic group relative to prior studies. Hence, the overall average range performances shown on all EF measures, except for the TMT-B, are commensurate with the overall average level IQ of our MS sample.

Another limitation of the study is its cross-sectional design, which restricts conclusions regarding the stability of executive dysfunction in our sample. Longitudinal studies are necessary to determine whether EF deficits reported in the current study represent a delay in skill development or a permanent deficit. For example, do the observed deficits on the TMT-B test reflect a reduced working memory capacity during development that can eventually show improvement in its capacity as the system matures?

In summary, we document executive dysfunction in patients with pediatric-onset MS, particularly related to information processing, attention shifting, and working memory. Using quantitative neuroimaging techniques, brain volume reduction, both globally and in frontal lobe and thalamic regions, was found to relate moderately to difficulties in EF, whereas T2-weighted LV was not a robust marker of executive dysfunction in pediatric-onset MS patients. Our findings support the clinical utility of the BRIEF in providing unique information alongside traditional performance-based measures of EF. Future studies incorporating serial neuroimaging and longitudinal neuropsychological assessments are needed to fully understand the development of EFs over time and how progressive neuropathology may impact EF. Functional neuroimaging techniques would also be useful in characterizing potential changes in brain activity underlying executive abilities and in understanding the potential for plasticity when the onset of the disease occurs at a young age.

Funding

This work was supported by the Canadian Institutes of Health Research (CIHR); Multiple Sclerosis Society of Canada; and the Canadian Multiple Sclerosis Scientific Research Foundation.

Conflict of Interest

None declared.

Acknowledgements

We give special thanks to the participants and their families who contributed their time to this project.

References

Amato
M. P.
Goretti
B.
Ghezzi
A.
Lori
S.
Zipoli
V.
Portaccio
E.
, et al.  . 
Cognitive and psychosocial features of childhood and juvenile MS
Neurology
 , 
2008
, vol. 
70
 
20
(pg. 
1891
-
1897
)
American Psychological Association
Report of the APA task force on socioeconomic status
2006
 
Anderson
P.
Assessment and development of executive function (EF) during childhood
Child Neuropsychology
 , 
2002
, vol. 
8
 
2
(pg. 
71
-
82
)
Anderson
V.
Spencer-Smith
M.
Coleman
L.
Anderson
P.
Williams
J.
Greenham
M.
, et al.  . 
Children's executive functions: Are they poorer after very early brain insult
Neuropsychologia
 , 
2010
, vol. 
48
 
7
(pg. 
2041
-
2050
)
Archibald
C. J.
Fisk
J. D.
Information processing efficiency in patients with multiple sclerosis
Journal of Clinical and Experimental Neuropsychology
 , 
2000
, vol. 
22
 
5
(pg. 
686
-
701
)
Arnett
P. A.
Rao
S. M.
Bernardin
L.
Grafman
J.
Yetkin
F. Z.
Lobeck
L.
Relationship between frontal lobe lesions and Wisconsin card sorting test performance in patients with multiple sclerosis
Neurology
 , 
1994
, vol. 
44
 
3
(pg. 
420
-
425
)
Aubert-Broche
B.
Fonov
V.
Ghassemi
R.
Narayanan
S.
Arnold
D. L.
Banwell
B.
, et al.  . 
Regional brain atrophy in children with multiple sclerosis
NeuroImage
 , 
2011
, vol. 
58
 
2
(pg. 
409
-
415
)
Banwell
B. L.
Anderson
P. E.
The cognitive burden of multiple sclerosis in children
Neurology
 , 
2005
, vol. 
64
 
5
(pg. 
891
-
894
)
Behrens
T. E.
Johansen-Berg
H.
Woolrich
M. W.
Smith
S. M.
Wheeler-Kingshott
C. A.
Boulby
P. A.
, et al.  . 
Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging
Nature Neuroscience
 , 
2003
, vol. 
6
 
7
(pg. 
750
-
757
)
Benedict
R.
Duquin
J.
Jurgensen
S.
Rudick
R.
Feitcher
J.
Munschauer
F.
, et al.  . 
Repeated assessment of neuropsychological deficits in multiple sclerosis using the symbol digit modalities test and the MS neuropsychological screening questionnaire
Multiple Sclerosis
 , 
2008
, vol. 
14
 
7
(pg. 
940
-
946
)
Blair
C.
Razza
R. P.
Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten
Child Development
 , 
2007
, vol. 
78
 
2
(pg. 
647
-
663
)
Casey
B. J.
Tottenham
N.
Liston
C.
Durston
S.
Imaging the developing brain: What have we learned about cognitive development?
Trends in Cognitive Science
 , 
2005
, vol. 
9
 
3
(pg. 
104
-
110
)
Chiaravalloti
N. D.
DeLuca
J.
Assessing the behavioral consequences of multiple sclerosis: An application of the frontal systems behavior scale (FrSBe)
Cognitive and Behavioral Neurology
 , 
2003
, vol. 
16
 
1
(pg. 
54
-
67
)
Collins
D. L.
Holmes
C. J.
Peters
T. M.
Evans
A. C.
Automatic 3D model-based neuro-anatomical segmentation
Human Brain Mapping
 , 
1995
, vol. 
3
 
3
(pg. 
190
-
208
)
Collins
D. L.
Neelin
P.
Peters
T. M.
Evans
A. C.
Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space
Journal of Computer Assisted Tomography
 , 
1994
, vol. 
18
 
2
(pg. 
192
-
205
)
Collins
D. L.
Pruessner
J. C.
Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion
NeuroImage
 , 
2010
, vol. 
52
 
4
(pg. 
1355
-
1366
)
Conners
C. K.
Conners’ continuous performance test (CPT II): Computer program for windows, technical guide and software manual
 , 
2000
 
Toronto Multi-Health Systems Inc.
Crespy
L.
Zaaraoui
W.
Lemaire
M.
Rico
A.
Faivre
A.
Reuter
F.
, et al.  . 
Prevalence of grey matter pathology in early multiple sclerosis assessed by magnetization transfer ratio imaging
PloS One
 , 
2011
, vol. 
6
 
9
pg. 
e24969
 
Deery
B.
Anderson
V.
Jacobs
R.
Neale
J.
Kornberg
A.
Childhood MS and ADEM: Investigation and comparison of neurocognitive features in children
Developmental Neuropsychology
 , 
2010
, vol. 
35
 
5
(pg. 
506
-
521
)
Delis
D. C.
Kaplan
E.
Kramer
J. H.
Delis–Kaplan executive function system™ (D–KEFS)
 , 
2001
San Antonio, TX
Pearson
DeLuca
J.
Chelune
G. J.
Tulsky
D. S.
Lengenfelder
J.
Chiaravalloti
N. D.
Is speed of processing or working memory the primary information processing deficit in multiple sclerosis?
Journal of Clinical and Experimental Neuropsychology
 , 
2004
, vol. 
26
 
4
(pg. 
550
-
562
)
Demaree
H. A.
DeLuca
J.
Gaudino
E. A.
Diamond
B. J.
Speed of information processing as a key deficit in multiple sclerosis: Implications for rehabilitation
Journal of Neurology, Neurosurgery, and Psychiatry
 , 
1999
, vol. 
67
 
5
(pg. 
661
-
663
)
Dennis
M.
Yeates
K. O.
Ris
M. D.
Taylor
H. G.
Childhood medical disorders and cognitive impairment: Biological risk, time, development, and reserve
Pediatric neuropsychology: Research, theory, and practice (pp. 3–22)
 , 
2000
New York: Guilford Press
Dennis
M.
Francis
D. J.
Cirino
P. T.
Schachar
R.
Barnes
M. A.
Fletcher
J. M.
Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders
Journal of the International Neuropsychological Society
 , 
2009
, vol. 
15
 
3
(pg. 
331
-
343
)
Drew
M. A.
Starkey
N. J.
Isler
R. B.
Examining the link between information processing speed and executive functioning in multiple sclerosis
Archives of Clinical Neuropsychology
 , 
2009
, vol. 
24
 
1
(pg. 
47
-
58
)
Felmingham
K. L.
Baguley
I. J.
Green
A. M.
Effects of diffuse axonal injury on speed of information processing following severe traumatic brain injury
Neuropsychology
 , 
2004
, vol. 
18
 
3
(pg. 
564
-
571
)
Fonov
V.
Evans
A. C.
Botteron
K.
Almli
C. R.
McKinstry
R. C.
Collins
D. L.
, et al.  . 
Unbiased average age-appropriate atlases for pediatric studies
NeuroImage
 , 
2011
, vol. 
54
 
1
(pg. 
313
-
327
)
Foong
J.
Rozewicz
L.
Davie
C. A.
Thompson
A. J.
Miller
D. H.
Ron
M. A.
Correlates of executive function in multiple sclerosis: The use of magnetic resonance spectroscopy as an index of focal pathology
Journal of Neuropsychiatry and Clinical Neuroscience
 , 
1999
, vol. 
11
 
1
(pg. 
45
-
50
)
Foong
J.
Rozewicz
L.
Quaghebeur
G.
Davie
C. A.
Kartsounis
L. D.
Thompson
A. J.
, et al.  . 
Executive function in multiple sclerosis. The role of frontal lobe pathology
Brain
 , 
1997
, vol. 
120
 (pg. 
15
-
26
)
Furby
J.
Hayton
T.
Anderson
V.
Altmann
D.
Brenner
R.
Chataway
J.
, et al.  . 
Magnetic resonance imaging measures of brain and spinal cord atrophy correlate with clinical impairment in secondary progressive multiple sclerosis
Multiple Sclerosis
 , 
2008
, vol. 
14
 
8
(pg. 
1068
-
1075
)
Gathercole
S. E.
Pickering
S. J.
Ambridge
B.
Wearing
H.
The structure of working memory from 4 to 15 years of age
Developmental Psychology
 , 
2004
, vol. 
40
 
2
(pg. 
177
-
190
)
Ghassemi
R.
Antel
S. B.
Narayanan
S.
Francis
S. J.
Bar-Or
A.
Sadovnick
A. D.
, et al.  . 
Lesion distribution in children with clinically isolated syndromes
Annals of Neurology
 , 
2008
, vol. 
63
 
3
(pg. 
401
-
405
)
Ghezzi
A.
Deplano
V.
Faroni
J.
Grasso
M. G.
Liguori
M.
Marrosu
G.
, et al.  . 
Multiple sclerosis in childhood: Clinical features of 149 cases
Multiple Sclerosis
 , 
1997
, vol. 
3
 
1
(pg. 
43
-
46
)
Gioia
G. A.
Isquith
P. K.
Guy
S. C.
Kenworthy
L.
Behavior rating inventory of executive function
 , 
2000
Odessa, FL: Psychological Assessment Resources
Heaton
R. K.
Chelune
G. J.
Talley
J. L.
Wisconsin card sorting test. Manual
 , 
1993
Odessa
Psychological Assessment Resources
Houtchens
M. K.
Benedict
R. H.
Killiany
R.
Sharma
J.
Jaisani
Z.
Singh
B.
, et al.  . 
Thalamic atrophy and cognition in multiple sclerosis
Neurology
 , 
2007
, vol. 
69
 
12
(pg. 
1213
-
1223
)
Hulst
H. E.
Geurts
J. J.
Gray matter imaging in multiple sclerosis: What have we learned?
BMC Neurology
 , 
2011
, vol. 
11
 pg. 
153
 
Jacobs
R.
Harvey
A. S.
Anderson
V.
Are executive skills primarily mediated by the prefrontal cortex in childhood? Examination of focal brain lesions in childhood
Cortex
 , 
2011
, vol. 
47
 
7
(pg. 
808
-
824
)
Kurtzke
J.
Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS)
Neurology
 , 
1983
, vol. 
33
 
11
(pg. 
1444
-
1452
)
Lazeron
R. H.
Boringa
J. B.
Schouten
M.
Uitdehaag
B. M.
Bergers
E.
Lindeboom
J.
, et al.  . 
Brain atrophy and lesion load as explaining parameters for cognitive impairment in multiple sclerosis
Multiple Sclerosis
 , 
2005
, vol. 
11
 
5
(pg. 
524
-
531
)
Levin
H. S.
Song
J.
Ewing-Cobbs
L.
Chapman
S. B.
Mendelsohn
D.
Word fluency in relation to severity of closed head injury, associated frontal brain lesions, and age at injury in children
Neuropsychologia
 , 
2001
, vol. 
39
 
2
(pg. 
122
-
131
)
Levin
H. S.
Williams
D. H.
Eisenberg
H. M.
High
W. M.
Jr.
Guinto
F. C.
Jr.
Serial MRI and neurobehavioural findings after mild to moderate closed head injury
Journal of Neurology, Neurosurgery and Psychiatry
 , 
1992
, vol. 
55
 
4
(pg. 
255
-
262
)
Lezak
M.
Neuropsychological assessment
 , 
1995
New York
Oxford University Press
Lima
F. S.
Simioni
S.
Bruggimann
L.
Ruffieux
C.
Dudler
J.
Felley
C.
, et al.  . 
Perceived behavioral changes in early multiple sclerosis
Behavioural Neurology
 , 
2007
, vol. 
18
 
2
(pg. 
81
-
90
)
Long
B.
Spencer-Smith
M. M.
Jacobs
R.
Mackay
M.
Leventer
R.
Barnes
C.
, et al.  . 
Executive function following child stroke: The impact of lesion location
Journal of Child Neurology
 , 
2011
, vol. 
26
 
3
(pg. 
279
-
287
)
MacAllister
W. S.
Riva
D.
Njiokiktjien
C.
Multiple sclerosis in children and adolescents: Neurocognitive disorders
Brain lesion localization and developmental functions. Basal ganglia, connecting systems, cerebellum, mirror neurons
 , 
2010
France
John Libbey Eurotext
(pg. 
81
-
88
)
MacAllister
W. S.
Belman
A. L.
Milazzo
M.
Weisbrot
D. M.
Christodoulou
C.
Scherl
W. F.
, et al.  . 
Cognitive functioning in children and adolescents with multiple sclerosis
Neurology
 , 
2005
, vol. 
64
 
8
(pg. 
1422
-
1425
)
Miyake
A.
Friedman
N. P.
Emerson
M. J.
Witzki
A. H.
Howerter
A.
Wager
T. D.
The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis
Cognitive Psychology
 , 
2000
, vol. 
41
 
1
(pg. 
49
-
100
)
Nyul
L. G.
Udupa
J. K.
On standardizing the MR image intensity scale
Magnetic Resonance in Medicine
 , 
1999
, vol. 
42
 
6
(pg. 
1072
-
1081
)
Parmenter
B. A.
Zivadinov
R.
Kerenyi
L.
Gavett
R.
Weinstock-Guttman
B.
Dwyer
M. G.
, et al.  . 
Validity of the Wisconsin card sorting and Delis–Kaplan executive function system (DKEFS) sorting tests in multiple sclerosis
Journal of Clinical and Experimental Neuropsychology
 , 
2007
, vol. 
29
 
2
(pg. 
215
-
223
)
Payne
J. M.
Hyman
S. L.
Shores
E. A.
North
K. N.
Assessment of executive function and attention in children with neurofibromatosis type 1: Relationships between cognitive measures and real-world behavior
Child Neuropsychology
 , 
2011
, vol. 
17
 
4
(pg. 
313
-
329
)
Portaccio
E.
Goretti
B.
Lori
S.
Zipoli
V.
Centorrino
S.
Ghezzi
A.
, et al.  . 
The brief neuropsychological battery for children: A screening tool for cognitive impairment in childhood and juvenile multiple sclerosis
Multiple Sclerosis
 , 
2009
, vol. 
15
 
5
(pg. 
620
-
626
)
Rao
S. M.
Leo
G. J.
Haughton
V. M.
St Aubin-Faubert
P.
Bernardin
L.
Correlation of magnetic resonance imaging with neuropsychological testing in multiple sclerosis
Neurology
 , 
1989
, vol. 
39
 
2
(pg. 
161
-
166
)
Reynolds
C. R.
Kamphaus
R. W.
Behavior assessment system for children, second edition (BASC-2)
 , 
1998
Bloomington, MN: American Guidance Services
Roth
R. M.
Isquith
P. K.
Gioia
G. A.
Behavior rating inventory of executive function—adult version (BRIEF-A)
 , 
2005
Florida
Psychological Assessment Resources, Inc
Rovaris
M.
Judica
E.
Gallo
A.
Benedetti
B.
Sormani
M. P.
Caputo
D.
, et al.  . 
Grey matter damage predicts the evolution of primary progressive multiple sclerosis at 5 years
Brain
 , 
2006
, vol. 
129
 (pg. 
2628
-
2634
)
Sanfilipo
M. P.
Benedict
R. H.
Weinstock-Guttman
B.
Bakshi
R.
Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis
Neurology
 , 
2006
, vol. 
66
 
5
(pg. 
685
-
692
)
Schmahmann
J. D.
Pandya
D. N.
Anatomic organization of the basilar pontine projections from prefrontal cortices in rhesus monkey
Journal of Neuroscience
 , 
1997
, vol. 
17
 (pg. 
438
-
458
)
Seidenberg
M.
Hermann
B.
Pulsipher
D.
Morton
J.
Parrish
J.
Geary
E.
, et al.  . 
Thalamic atrophy and cognition in unilateral temporal lobe epilepsy
Journal of the International Neuropsychological Society
 , 
2008
, vol. 
14
 
3
(pg. 
384
-
393
)
Sled
J. G.
Zijdenbos
A. P.
Evans
A. C.
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
IEEE Transactions on Medical Imaging
 , 
1998
, vol. 
17
 
1
(pg. 
87
-
97
)
Smith
A.
Symbol digit modalities test (SDMT)
 , 
1991
Los Angeles
Western Psychological Services
Smith
M. M.
Arnett
P. A.
Awareness of executive functioning deficits in multiple sclerosis: Self versus informant ratings of impairment
Journal of Clinical and Experimental Neuropsychology
 , 
2010
, vol. 
32
 
7
(pg. 
780
-
787
)
Smith
S. M.
Fast robust automated brain extraction
Human Brain Mapping
 , 
2002
, vol. 
17
 
3
(pg. 
143
-
155
)
Smith
S. M.
Zhang
Y.
Jenkinson
M.
Chen
J.
Matthews
P. M.
Federico
A.
, et al.  . 
Accurate, robust, and automated longitudinal and cross-sectional brain change analysis
NeuroImage
 , 
2002
, vol. 
17
 
1
(pg. 
479
-
489
)
Spreen
O.
Strauss
E.
A compendium of neuropsychological tests: Administration, norms, and commentary
 , 
1998
New York
Oxford University Press
Stuss
D. T.
Toth
J. P.
Franchi
D.
Alexander
M. P.
Tipper
S.
Craik
F. I.
Dissociation of attentional processes in patients with focal frontal and posterior lesions
Neuropsychologia
 , 
1999
, vol. 
37
 
9
(pg. 
1005
-
1027
)
Swirsky-Sacchetti
T.
Mitchell
D. R.
Seward
J.
Gonzales
C.
Lublin
F.
Knobler
R.
, et al.  . 
Neuropsychological and structural brain lesions in multiple sclerosis: A regional analysis
Neurology
 , 
1992
, vol. 
42
 
7
(pg. 
1291
-
1295
)
Till
C.
Ghassemi
R.
Aubert-Broche
B.
Kerbrat
A.
Collins
D. L.
Narayanan
S.
, et al.  . 
MRI correlates of cognitive impairment in childhood onset multiple sclerosis
Neuropsychology
 , 
2011
, vol. 
25
 
3
(pg. 
319
-
332
)
Till
C.
Udler
E.
Ghassemi
R.
Narayanan
S.
Arnold
D.
Banwell
B.
Factors associated with emotional and behavioral outcomes in adolescents with multiple sclerosis
Multiple Sclerosis
 , 
2012
Toplak
M. E.
Bucciarelli
S. M.
Jain
U.
Tannock
R.
Executive functions: Performance-based measures and the behavior rating inventory of executive function (BRIEF) in adolescents with attention deficit/hyperactivity disorder (ADHD)
Child Neuropsychology
 , 
2009
, vol. 
15
 
1
(pg. 
53
-
72
)
Trowbridge
B. C.
Schutte
J. W.
Some problems inherent in neuropsychological testing
American Journal of Forensic Psychology
 , 
2007
, vol. 
25
 (pg. 
5
-
34
)
Van Der Werf
Y. D.
Tisserand
D. J.
Visser
P. J.
Hofman
P. A.
Vuurman
E.
Uylings
H. B.
, et al.  . 
Thalamic volume predicts performance on tests of cognitive speed and decreases in healthy aging. A magnetic resonance imaging-based volumetric analysis
Brain Research
 , 
2001
, vol. 
11
 
3
(pg. 
377
-
385
)
Waubant
E.
Chabas
D.
Okuda
D. T.
Glenn
O.
Mowry
E.
Henry
R. G.
, et al.  . 
Difference in disease burden and activity in pediatric patients on brain magnetic resonance imaging at time of multiple sclerosis onset vs adults
Archives of Neurology
 , 
2009
, vol. 
66
 
8
(pg. 
967
-
971
)
Wechsler
D.
Wechsler abbreviated scale of intelligence (WASI)
 , 
1999
San Antonio
The Psychological Corporation
Yeh
E. A.
Chitnis
T.
Krupp
L.
Ness
J.
Chabas
D.
Kuntz
N.
, et al.  . 
Pediatric multiple sclerosis
Nature Reviews
 , 
2009
, vol. 
5
 
11
(pg. 
621
-
631
)