Tumor region associated with specific processing speed outcomes

Abstract

Objective:

Processing speed (PS) is a vulnerable cognitive skill in pediatric cancer survivors as a consequence of treatments and, less consistently, tumor region. Studies conventionally examine graphomotor PS; emerging research suggests other aspects of PS may be impacted. This study examined types of PS in pediatric brain tumor survivors to determine which aspects are impaired. Given discordance across studies, we additionally investigated the relationship between brain region and PS.

Methods:

The sample consisted of 167 pediatric brain tumor patients (100 supratentorial). PS (oral naming, semantic fluency, phonemic fluency, motor speed, graphomotor speed, visual scanning) was gathered via clinical neuropsychological assessment. To examine PS by region, infratentorial and supratentorial groups were matched on age at diagnosis and neuropsychological assessment, and time since diagnosis.

Results:

The whole sample performed below normative means on measures of oral naming (p < .001), phonemic fluency (p < .001), motor speed (p = .03), visual scanning (p < .001), and graphomotor speed (p < .001). Only oral naming differed by region (p = .03), with infratentorial tumors associated with slower performance. After controlling for known medical and demographic risk factors, brain region remained a significant predictor of performance (p = .04). Among the whole sample, greater than expected proportions of patients with impairment (i.e., >1 standard deviation below the normative mean) were seen across all PS measures. Infratentorial tumors had higher rates of impairments across all PS measures except phonemic fluency.

Conclusions:

Results indicate pediatric brain tumor survivors demonstrate weaknesses in multiple aspects of PS, suggesting impairments are not secondary to peripheral motor slowing alone. Additionally, tumor region may predict some but not all neuropsychological outcomes in this population.

1 |. INTRODUCTION

Processing speed (PS) is one of the core cognitive skills that underlies higher-order abilities such as working memory and fluid reasoning (Schatz et al., 2000). It refers to the speed with which an individual is able to accurately complete cognitive operations. The development of PS is facilitated, at least in part, by the myelination of neuronal white matter; white matter maturation is associated with improved speed of information processing in healthy children.^1–5

PS has been found to be a particularly vulnerable cognitive skill in pediatric cancer survivors as a consequence of specific treatments. Neuroimaging research has shown that cancer treatments such as cranial radiation and intrathecal chemotherapy disrupt white matter integrity, resulting in diffuse and multifocal white matter abnormalities, cerebral atrophy, and white matter volume loss, likely secondary to alterations in brain microstructure, damage to oligodendrocytes, and glial apoptosis.^2,6,7

PS can be examined via a variety of cognitive tasks including motoric (i.e., psychomotor speed) and oral (i.e., speeded naming, oral fluency) measures. Researchers have primarily examined graphomotor PS in pediatric brain tumor survivors using the Wechsler Coding and Symbol Search subtests, despite acknowledgment that these common neuropsychological measures may lack the specificity to delineate cognitive processes that are affected by treatment.⁸ In addition, these traditional graphomotor PS measures may be confounded by fine motor dysfunction,⁹ which is not uncommon following specific cancer treatments such as vincristine-associated peripheral neuropathy or cerebellar involvement. In these cases, it is difficult to ascertain whether slowed performance is due to PS impairments or fine motor deficits. On Wechsler graphomotor-based PS subtests, pediatric brain tumor patients have consistently demonstrated weaknesses relative to normative means and healthy controls.¹⁰ Fewer studies have examined other measures of PS, though results have suggested weaknesses in these domains as well. Semmel et al.⁹ found oral PS to mediate the relationship between neurological risk and adaptive functioning in adult brain tumor survivors. King et al. (2019) found oral PS negatively impacted attention and working memory in pediatric brain tumor survivors, though all three cognitive skills made a unique contribution to IQ and academic achievement. Of note, both studies only examined PS via oral tasks so as to eliminate potential confounds of fine motor skills that are required by graphomotor speeded tasks, skills that also may be impacted by cancer and/or treatment. To our knowledge, no study has examined multiple measures of PS at one time to determine their relative sensitivity in identifying deficits among survivors of a pediatric brain tumor. In addition, further research is needed to improve specification of PS impairment in this clinical population.

As noted above, cancer treatment (e.g., neurosurgery, intrathecal chemotherapy, cranial radiation) disrupts white matter integrity, and is a well-documented risk factor for neurocognitive deficits. One means of examining cancer treatment burden and associated complications is through the Neurological Predictor Scale (NPS; please see Ref. ¹¹ for Scale). This composite score quantifies the cumulative risk factors of tumor and treatment-related conditions by considering multiple and combined treatment modalities (e.g., surgery, chemotherapy, radiation), extent of cranial radiation therapy (e.g., focal, craniospinal), and neurological sequelae (e.g., endocrinopathies, hydrocephalus, seizures). The NPS has been found to be a good metric of treatment burden over and above individual treatment and tumor-related variables.¹² Other risk factors for neurocognitive sequelae include younger age at diagnosis and longer time since treatment, given the vulnerability of the young brain to white matter damage and the emergence of late effects across time.¹³ In contrast to these well-documented risk factors, brain tumor region has not consistently been found to predict neuropsychological functioning, with some studies showing worse cognitive functioning for survivors of supratentorial tumors^14–17 or infratentorial tumors,^18–21 and still others failing to detect any differences by brain region.^22,23 Discordance may reflect the focus of each of these studies on different cognitive abilities, enrollment of patients of different ages, or sociodemographic variables that may influence neurocognitive performance.

Once thought to support only motor function, the cerebellum has since been found to play a contributory role in many non-motor, cognitive skills including memory, executive function, and behavior regulation.²⁴ Within the domain of PS, studies in children with attention deficit hyperactivity disorder (ADHD) and/or dyslexia have shown the cerebellum to contribute to rapid naming/speeded oral naming, with cerebellar damage resulting in impaired oral automaticity.^25,26 Within pediatric brain tumor samples, abnormalities in cerebrocerebellar connections via the cerebello-thalamo-cortical pathway have been implicated in task efficiency²⁷ and working memory.¹³ Thus, the cerebellum may be an important neural structure associated with PS, and survivors of infratentorial tumors may be at greater risk for slowed output speed.

Given the vulnerability of PS to cancer treatment, this study aimed to examine different methods of assessing PS in pediatric brain tumor survivors to shed light on which aspects are selectively impaired in this population. We hypothesized that brain tumor patients as a whole would demonstrate normative weaknesses across all PS measures. Second, given discordance across studies in the role of tumor region in cognitive outcomes, we aimed to investigate the relationship between brain tumor region and multiple PS measures. We hypothesized that patients with infratentorial tumors would show greater motor and oral PS deficits relative to patients with supratentorial tumors given the role of the cerebellum in motor and cognitive function.

2 |. METHODS

2.1 |. Participants

The study was approved by the hospital Institutional Review Board. Patient data were gathered from retrospective record review of all children referred for clinical neuropsychological testing and diagnosed with a brain tumor before 18 years of age. Inclusion criteria included patient age of 6 years or older at time of assessment and completion of at least one measure of motor or oral PS (detailed below). Patients were not excluded from analyses based upon treatment history (e.g., recurrence, death) or comorbid diagnoses (e.g., autism spectrum disorder, ADHD, intellectual disability). Given that these data were acquired from clinical assessments, not all participants completed all measures of PS. This was based upon the clinical judgment of the neuropsychologist.

2.2 |. Measures

2.2.1 |. Medical and demographics

Age at diagnosis was calculated based on the date of clinical presentation, and age at assessment was based on the date of the child’s most recent neuropsychological assessment. Time since diagnosis was calculated as the difference between these two time points. Medical data including treatment history, pathology, and tumor region were extracted from the medical record. Supratentorial tumors included tumors in the suprasellar region. Score on the NPS¹¹ was calculated for each participant based upon treatment history at time of neuropsychological evaluation. Demographic information (e.g., race, ethnicity, sex, maternal education) was collected as part of a comprehensive clinical neuropsychological assessment completed by a parent or caregiver. In order to examine differences in PS by brain tumor region, infratentorial and supratentorial groups were matched on age at diagnosis, age at assessment, and time since diagnosis.

2.2.2 |. PS outcomes

PS was examined across multiple measures. As noted above, given the clinical nature of these assessments, different measures assessing the same/similar constructs were administered and not all participants were administered all measures. Measures assessing similar constructs (i.e., semantic fluency) were thus combined into composite scores, each with a mean of 10 and standard deviation of 3. This approach of compositing similar constructs has been used in prior studies.²⁸

Speeded oral naming

Speeded oral naming was assessed using either the Color Naming subtest from the Rapid Automatized Naming subtest (RAN),²⁹ Rapid Color Naming subtest from the Comprehensive Test of Phonological Processing, Second Edition (CTOPP-2),³⁰ or Trial 1 from the Delis Kaplan Executive Function System Color-Word Interference subtest (DKEFS).³¹ A composite score of speeded oral naming was calculated based upon these three subtests, all of which asked the examinee to quickly name colors presented in an array. The RAN/Colors subtest is strongly correlated with the CTOPP Color Naming subtest (.87).³⁰ Reliability coefficients for the RAN, CTOPP-2, and DKEFS subtests range from .76 to .92.^29–31 Correlations between these measures are not presented by the publisher.

Semantic fluency

Performance on the Developmental Neuropsychological Assessment, Second Edition Word Generation: Semantic (NEPSY-2³²) and DKEFS Verbal Fluency: Category Fluency³¹ subtests were composited as a measure of verbal semantic fluency. For each of these subtests, participants were asked to spontaneously generate as many words related to categorical prompts (e.g., animals) as possible in 60 seconds. Reliability coefficients for the NEPSY-II and DKEFS subtests are .74 and .79, respectively.³¹ These semantic fluency measures are moderately correlated (r = .59).³²

Phonemic fluency

Verbal phonemic fluency was measured as a composite score of either the NEPSY-2 Word Generation: Initial Letter or DKEFS Verbal Fluency: Letter Fluency subtests. For these tasks, participants were asked to spontaneously produce as many words belonging to a specific letter as possible in 60 seconds. Reliability coefficients for the NEPSY-II and DKEFS subtests are .74 and .80, respectively.³¹ These phonemic fluency measures are moderately correlated (r = .71).³²

Simple motor speed

Simple motor speed was examined via the NEPSY-2 Visual Motor Precision subtest or DKEFS Trail Making Test Condition 5. The time-to-completion score on either of these two measures was compiled into a composite score. Both tasks are timed paper-and-pencil tasks in which the participant rapidly traces a path on paper; accuracy is not taken into account in the time score. Reliability coefficients for the NEPSY-II and DKEFS subtests are .75 and .77, respectively.³¹ Correlations between these two measures are not presented by the publisher.

Complex graphomotor speed

Complex graphomotor PS was examined via the Coding subtest from the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV),³³ Fifth Edition (WISC-V),³⁴ or Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV).³⁵ For this task, participants were presented with a “key” or “legend” in which the numbers 1–9 are each paired with a different symbol. The participant was required to use this key to write in the appropriate symbols for a list of numbers between 1 and 9. The scaled score is a combination of speed and accuracy. Reliability coefficients for the WISC-IV, WISC-V, and WAIS-IV Coding subtest are .85, .83, and .86, respectively.³⁴ Correlation coefficients for the WISC-IV, WISC-V, and WAIS-IV Coding subtest are moderate, and range from .70 to .74.³⁴

Visual scanning speed

Visual scanning speed was examined via the Symbol Search subtest from the WISC-IV/-V or WAIS-IV. For this task, participants looked at target symbols and then identified a matching target from among an array. The scaled score is a combination of speed and accuracy. Reliability coefficients for the WISC-IV, WISC-V, and WAIS-IV Coding subtest are .79, .81, and .81, respectively.³⁴ Correlational coefficients for the WISC-IV, WISC-V, and WAIS-IV Symbol Search subtest range from .57 to .63.³⁴

2.3 |. Statistical analyses

Descriptive statistics characterized the medical, demographic, and neuropsychological features of the sample. Pairwise deletion was used to account for missing data. One-sample t-tests were used to determine how the participants compared with the normative mean. Frequency of the number of participants with impaired performance was calculated for each PS outcome measure, with impairment defined as below the 16th percentile (i.e., ≥1 standard deviation below the mean). Independent samples t-tests compared percentage of impaired participants to percentage impaired in the normal distribution by each PS measure. Independent samples t-tests or chi square tests compared medical and demographic variables by tumor region (supratentorial versus infratentorial). To further examine the role of brain tumor region in PS outcomes, PS measures that differed by tumor region were entered into separate linear regression models. We utilized a stepwise approach, first entering well-documented risk factors including treatment type/complexity (NPS), age at diagnosis, and time since treatment before entering the tumor region.

3 |. RESULTS

The sample consisted of a total of 167 patients treated for a pediatric brain tumor (100 supratentorial). Participants with a history of an infratentorial tumor had significantly higher NPS scores (p = .01), suggestive of greater tumor-related treatments (e.g., greater number of surgeries, history of chemotherapy and/or cranial radiation therapy). Please see Table 1 and Supplemental Table 1 for sample characteristics.

TABLE 1.

Demographic and treatment exposures of the sample

	Supratentorial M (SD) n = 100	Infratentorial M (SD) n = 67	p c	Total sample M (SD) n = 167
Age at diagnosis	8.70 (4.89)	7.97 (4.27)	.31	8.41 (4.65)
Age at assessment	14.59 (3.85)	13.44 (4.75)	.10	14.13 (4.26)
Time since diagnosis	5.88 (4.53)	5.41 (4.27)	.51	5.69 (4.42)
Surgery (yes)	85 (85.00%)	63 (94.03%)	.07	148 (88.62%)
Chemotherapy (yes)	42 (42.00%)	40 (59.70%)	.03	82 (49.10%)
CRTa (yes)	39 (39.00%)	36 (53.73%)	.06	72 (43.11%)
Focal	20 (20.00)	4 (5.97%)	.01	24 (14.37%)
Whole brain	19 (19.00)	32 (47.76%)	<.001	51 (30.54%)
NPSb	4.73 (2.09)	5.79 (2.82)	.01	5.16 (2.45)
Sex (male)	50 (50.00%)	36 (53.73%)	.64	86 (51.50%)
Race			.42
Caucasian	64 (64.00%)	41 (61.19)		105 (62.87)
Black	17 (17.00%)	9 (13.43)		26 (15.57)
Asian	2 (2.00%)	3 (4.48)		5 (2.99)
Hispanic	5 (5.00%)	1 (1.49)		6 (3.59)
Multi-racial	4 (4.00%)	2 (2.99)		6 (3.59)
Native Hawaiian/Pacific Islander	1 (1.00%)	0 (0.00)		1 (0.60)
Other/Unknown	7 (7.00%)	11 (16.42)		18 (10.78)
Tumor grade			<.001
Low grade	82 (82.00%)	36 (53.73%)		118 (70.66%)
High grade	18 (18.00%)	31 (46.27%)		49 (29.34%)
Treatment complications
Endocrinopathy	40 (40.00%)	19 (28.36%)	.12	59 (35.33%)
Hydrocephalus	40 (40.00%)	47 (70.15%)	<.001	87 (52.10%)
Seizures	35 (35.00%)	7 (10.45%)	<.001	42 (25.15%)
Ototoxicity	5 (5.00%)	16 (23.88%)	<.001	21 (12.57%)
Relapse (yes)	9 (9.00%)	11 (16.42%)	.15	20 (11.98%)
Mutism (yes)	0 (0%)	10 (14.93%)	<.001	10 (5.99%)
Intellectual disability (yes)	3 (3.00%)	3 (4.48%)	.61	6 (3.59%)
Autism spectrum disorder (yes)	2 (2.00%)	1 (1.49%)	.81	3 (1.78%)

^aCranial radiation therapy.

^bNeurological Predictor Scale.

^cDifference between supratentorial and infratentorial values.

3.1 |. Whole sample

Table 2 shows PS outcomes for the brain tumor sample as a whole as well as by tumor region. Clinically, as a whole, the sample demonstrated average oral naming, phonemic fluency, semantic fluency, and simple motor speed, with low average performance on measures of graphomotor speed and visual scanning. The sample as a whole performed significantly below the normative mean on measures of oral naming (p < .001), phonemic fluency (p < .001), simple motor speed (p = .03), visual scanning (p < .001), and complex graphomotor speed (p < .001). Furthermore, greater than expected proportions of participants with impairment were seen across all PS measures (Table 3).

TABLE 2.

Processing speed outcomes by tumor location

	Supratentorial M (SD), range	Median [IQR]	n b	Infratentorial M (SD), range	Median [IQR]	N a	p b	Total sample M (SD), range	Median [IQR]	p c
Oral naming1	9.26 (2.69), 1–15	9.7 [8–11]	61	7.97 (3.36), 1–14	8.6 [6–10]	45	.04	8.74 (3.08), 1–15	9 [7–11]	<.001
Phonemic fluency2	8.94 (3.73), 1–19	9 [6–12]	89	8.73 (3.31), 1–19	9 [7–11]	62	.18	8.85 (3.55), 1–19	9 [6–11]	<.001
Semantic fluency3	9.43 (3.48), 1–16	10 [7–12]	89	9.84 (3.99), 1–19	9 [7–13]	64	.12	9.60 (3.69), 1–19	9 [7–12]	.184
Simple motor speed4	9.35 (3.11), 1–13	10 [8–11]	69	9.15 (3.43), 1–13	10 [7–12]	50	.32	9.31 (3.23), 1–13	10 [8–12]	.021
Graphomotor speed5	7.33 (3.23), 1–14	7 [5–12]	85	6.77 (3.29), 1–15	7 [4–9]	65	.20	7.09 (3.26), 1–15	7 [4–10]	<.001
Visual scanning6	7.89 (2.95), 1–18	8 [6–10]	80	7.68 (3.51), 1–15	8 [5–10]	62	.08	7.80 (3.20), 1–18	8 [6–10]	<.001

^aSample size for each measure.

^bp Value for difference between infratentorial and supratentorial groups.

^cp Value for difference between total sample and normative means (M = 10, SD = 3). M, mean; SD, standard deviation; IQR, interquartile range 25th–75th percentile.

¹Composite score from the Color Naming subtest from the Rapid Automatized Naming subtest (RAN),²⁹ Rapid Color Naming subtest from the Comprehensive Test of Phonological Processing, Second Edition (CTOPP-2),³⁰ or Trial 1 from the Delis Kaplan Executive Function System Color-Word Interference subtest (DKEFS).³¹

²Composite score from the NEPSY-2³² or DKEFS Verbal Fluency: Letter Fluency subtests.³¹

³Composite score from the NEPSY-II Word Generation: Semantic (NEPSY-2³²; or DKEFS Verbal Fluency: Category Fluency subtests.³¹

⁴Coding subtest from the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV),³³ Fifth Edition (WISC-V),³⁴ or Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV).³⁵

⁵Composite score from the NEPSY-2 Visual Motor Precision subtest³² or DKEFS Trail Making Test Condition 5.³¹

Symbol Search subtest from the WISC-IV/-V)³³ or WAIS-IV.³⁵

TABLE 3.

Processing speed impairment by tumor location

	Supratentorial impairmenta	Infratentorial impairmenta	Whole sample impairmenta	X 2 b
Oral naming1	9/61 (14.75%)	12/45 (26.67%)	21/106 (19.81%)	<.001
Phonemic fluency2	24/89 (26.97%)	14/62 (22.58%)	38/151 (25.17%)	<.001
Semantic fluency3	17/89 (19.10%)	14/64 (21.88%)	31/153 (20.26%)	<.001
Simple motor speed4	11/69 (15.94%)	10/50 (20.00%)	21/119 (17.65%)	<.001
Graphomotor speed5	30/85 (35.29%)	29/65 (44.62%)	59/150 (39.33%)	<.001
Visual scanning6	25/80 (31.25%)	22/62 (35.48%)	47/142 (33.10%)	<.001

^aPercentage falling below the 16th percentile based on a normal distribution.

^bChi square test comparing differences between whole sample impairment and normative distribution.

¹Composite score from the Color Naming subtest from the Rapid Automatized Naming subtest (RAN),²⁹ Rapid Color Naming subtest from the Comprehensive Test of Phonological Processing, Second Edition (CTOPP-2),³⁰ or Trial 1 from the Delis Kaplan Executive Function System Color-Word Interference subtest (DKEFS).³¹

²Composite score from the NEPSY-2³² or DKEFS Verbal Fluency: Letter Fluency subtests.³¹

³Composite score from the NEPSY-II Word Generation: Semantic (NEPSY-2³²) or DKEFS Verbal Fluency: Category Fluency subtests.³¹

⁴Coding subtest from the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV),³³ Fifth Edition (WISC-V),³⁴ or Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV).³⁵

⁵Composite score from the NEPSY-2 Visual Motor Precision subtest³² or DKEFS Trail Making Test Condition 5.³¹

Symbol Search subtest from the WISC-IV/-V^33,34 or WAIS-IV.³⁵

3.2 |. Tumor region

Infratentorial tumors had greater proportion of impairment across all PS measures, with the exception of phonemic fluency. Oral naming was the only PS measure that differed by brain tumor region (p = .04), with participants with infratentorial tumors demonstrating slower oral naming speed that fell in the low average range (Table 2). After controlling for known medical and demographic risk factors (NPS, age at diagnosis, time since diagnosis), brain tumor region remained a significant predictor of performance (p = .04) (Table 4).

TABLE 4.

Stepwise linear regression model for oral naming

Measure	Predictor	β	t	p	R	R 2
Oral naminga					.29	.09
	Constant	10.558	9.227	<.001
	NPSb	−.047	−.372	.71
	Age at diagnosis	−.021	−.288	.77
	Time since diagnosis	−.162	−1.917	.06
	Tumor location	−1.239	−2.039	.04

^aComposite score from the Color Naming subtest from the Rapid Automatized Naming subtest (RAN),²⁹ Rapid Color Naming subtest from the Comprehensive Test of Phonological Processing, Second Edition (CTOPP-2),³⁰ or Trial 1 from the Delis Kaplan Executive Function System Color-Word Interference subtest (DKEFS).³¹

^bNeurological Predictor Scale.

4 |. DISCUSSION

This study aimed to delineate a variety of PS outcomes in patients with pediatric brain tumors referred for a clinical neuropsychological evaluation as well as examine the role of tumor region on these speeded outcomes. As a whole, the clinical sample demonstrated statistically significant weaknesses in many aspects of PS relative to normative means, including oral naming, phonemic fluency, simple motor speed, speeded visual scanning, and complex graphomotor speed. Visual scanning speed and graphomotor speed were also clinically significant, with scores in the low average range. While only these two subtests showed clinically significant deficits, a greater than expected percentage of the sample showed impairment on each of the timed outcome measures (below the 16th percentile). Results suggest that all measures of PS, with the exception of verbal fluency, are vulnerable in brain tumor survivors and should be examined as part of a clinical neuropsychological assessment.

Interestingly, phonemic but not semantic fluency was an area of weakness regardless of brain tumor region. This may implicate specific neuroanatomic correlates. Phonemic skills are thought to be mediated by the frontal cortex, primarily the prefrontal cortex, whereas semantic fluency skills show anatomical correlates in the temporal cortex.³⁶ Additionally, the prefrontal cortex is associated with specific cognitive skills, namely attention, working memory, and shifting, which are all vulnerable skills in brain tumor populations. Indeed, studies in survivors of brain tumors have shown that the frontal lobe may be particularly vulnerable to cancer treatment, with patients showing a greater reduction in fractional anisotropy (a measure of white matter integrity) in frontal lobe white matter relative to that in the parietal lobe despite exposures to equivalent doses of radiation.^2,37

When examining PS outcomes by brain region, only speeded oral naming was significantly poorer in participants with a history of infratentorial tumors compared to those with supratentorial tumors. There are multiple potential mechanisms that may explain this finding. First, oral naming may be particularly sensitive to cancer treatments such as chemotherapy and radiation therapy, which were more frequent in the infratentorial group. The infratentorial group also reflected greater treatment burden (e.g., more surgeries, secondary complications of treatment, greater radiation demands as measured by the NPS). For example, hydrocephalus requiring shunting has been found to result in white matter damage.^38–40 Intrathecal chemotherapies and radiation therapy also affect white matter tracts, potentially secondary to alterations of the microvasculature, damage to myelin, and white matter abnormalities/hyperintensities.² However, even after controlling for these medical variables, brain region remained a significant predictor of oral naming. The posterior inferior frontal cortex as well as cerebellum have been implicated in oral naming.^25,26,41 Given that the cerebellum is part of a neural network with reciprocal connectivity with the frontal cortex via cerebrocerebellar white matter circuitry,⁴² it is possible that damage to these white matter tracts results in oral naming deficits in individuals with infratentorial tumors.

More broadly, the finding of slowed oral naming in this population lends support to the findings of McGrath and colleagues (2010) who found that both oral naming speed and motor PS accounted for the majority of the variance in dyslexia and ADHD. Their study highlighted the overlap in cognitive deficits present in both pathologies, which may explain the high rates of comorbidity (25–40%). Indeed, reading speed/fluency is a key deficit in dyslexia. ADHD is a common comorbid diagnosis in pediatric cancer survivors given the emergence of inattention and slowed PS that emerges following cancer treatment.²⁸

While this study offers novel findings, there are several limitations that warrant discussion. First, data were gathered from clinical neuropsychological evaluations. While some participants were referred based on international guidelines for standardized assessment of neurocognition following cancer treatment,^43,44 it is also reasonable to expect that some of the participants may have been referred for neuropsychological evaluation due to clinical suspicion of impairment. Future studies should examine PS in all childhood cancer survivors rather than just those seen for clinical neuropsychological evaluation to reduce referral/sample bias. Additionally, given the clinical nature of the data, different measures were used to examine similar cognitive constructs at the discretion of the neuropsychologist. While we would anticipate different measures of PS constructs to be highly correlated, the various normative samples employed across tests differed by age range (Supplemental Table 2). In addition, while we did not exclude those individuals who developed posterior fossa syndrome/cerebellar mutism, we also were unable to identify who these patients were given the lack of diagnostic criteria/assessment measures needed to make this diagnosis.⁴⁵ This is important to acknowledge as a limitation to research examining outcomes in childhood brain tumor survivors given that mutism has been shown to result in enduring slowed PS⁴⁶ and oral naming.⁴⁷ While we did not identify those whose developed posterior fossa syndrome, the infratentorial tumor groups’ overall deficits may be reflective of a larger proportion of patients with treatment related complications (i.e., posterior fossa syndrome/cerebellar mutism) as opposed to the impact of the tumor region alone. Oral naming may be particularly sensitive to cerebellar involvement and additional research is needed to better understand the mechanisms of injury that result in impairment in oral automaticity. This is particularly important for children, as slowed speeded oral naming may negatively impact how quickly they can participate in the classroom environment and is a key deficit in dyslexia. Lastly, PS outcomes were extracted from the most recent neuropsychological evaluation. This timepoint was chosen based upon evidence that deficits emerge over time, with longer time since diagnosis serving as a predictor of worse neuropsychological outcomes.⁴³ Future studies should assess different aspects of PS longitudinally to better determine if motor and oral measures are affected at the same rate. Similarly, more information is needed to support better understanding of the temporal emergence of PS weaknesses, and whether this follows a linear, negative slope or a more fluctuating pattern across time.

There are important clinical implications from these findings. Pediatric neuropsychologists typically recommend accommodations to support slowed graphomotor PS in childhood cancer patients. This includes access to a scribe, copies of class notes, and reduced writing demands or extended time to complete writing tasks. This study, however, suggests that recommendations should also address slowed oral PS, such as extended time to produce verbal responses, verbal cueing to support verbal fluency, and speech-language therapy to address oral naming deficits.

Overall, these results suggest that pediatric brain tumor survivors demonstrate weaknesses in multiple aspects of PS, suggesting impairments are not secondary to peripheral motor slowing, but rather global deficits in this cognitive domain that may increase risk for difficulty with acquisition of other key cognitive skills over time. Thus, clinical neuropsychological assessment should utilize multiple means of examining PS in this population, including oral naming, verbal fluency, visual scanning, and motor speed. Likewise, research/study protocols, which have traditionally utilized only the PS index from Wechsler measures should consider a more systematic assessment of PS outcomes. Given that PS has been found to predict cognitive outcomes in pediatric cancer,⁹ weaknesses across multiple areas of PS contribute to our understanding of neuropsychological functioning in this population. Furthermore, interventions or accommodations that address slowed PS will be important for pediatric brain tumor patients.

Abbreviations:

ADHD

attention deficit hyperactivity disorger

CTOPP-2

Comprehensive Test of Phonological Processing, Second Edition

DKEFS

Delis Kaplan Executive Function System

NEPSY-II

Developmental Neuropsychological Assessment, Second Edition

NPS

Neurological Predictor Scale

PS

processing speed

RAN

Rapid Automatized Naming

WAIS-IV

Wechsler Adult Intelligence Scale, Fourth Edition

WISC-IV

Wechsler Intelligence Scale for Children, Fourth Edition

WISC-V

Wechsler Intelligence Scale for Children, Fifth Edition