JSLHR
Research Article
Language Development and Brain Magnetic Resonance Imaging Characteristics in Preschool Children With Cerebral Palsy Ja Young Choi,a Yoon Seong Choi,b and Eun Sook Parka
Purpose: The purpose of this study was to investigate characteristics of language development in relation to brain magnetic resonance imaging (MRI) characteristics and the other contributing factors to language development in children with cerebral palsy (CP). Method: The study included 172 children with CP who underwent brain MRI and language assessments between 3 and 7 years of age. The MRI characteristics were categorized as normal, malformation, periventricular white matter lesion (PVWL), deep gray matter lesion, focal infarct, cortical/subcortical lesion, and others. Neurodevelopmental outcomes such as ambulatory status, manual ability, cognitive function, and accompanying impairments were assessed.
Results: Both receptive and expressive language development quotients (DQs) were significantly related to PVWL or deep gray matter lesion severity. In multivariable analysis, only cognitive function was significantly related to receptive language development, whereas ambulatory status and cognitive function were significantly associated with expressive language development. More than one third of the children had a language developmental discrepancy between receptive and expressive DQs. Children with cortical/ subcortical lesions were at high risk for this discrepancy. Conclusions: Cognitive function is a key factor for both receptive and expressive language development. In children with PVWL or deep gray matter lesion, lesion severity seems to be useful to predict language development.
T
at birth, and epilepsy (Coleman et al., 2013; Compagnone et al., 2014; Himmelmann et al., 2013; Hustad, Gorton, & Lee, 2010; Parkes et al., 2010; Vos et al., 2014; Zhang, Oskoui, & Shevell, 2015). Neuroimaging studies have improved the understanding of the neuroanatomical basis for function in CP. It has been advocated that neuroimaging should be performed in all CP cases (Himmelmann & Uvebrant, 2011). Among the neuroimaging studies, brain magnetic resonance imaging (MRI) is regarded as the most suitable tool to visualize brain lesions and to obtain insight into the outcome prediction of children with CP (Bax, Tydeman, & Flodmark, 2006; Himmelmann & Uvebrant, 2011). Exploring neuroimaging findings and relating them to functional aspects is important for the planning of intervention in children with CP (Himmelmann & Uvebrant, 2011; Iwata et al., 2012). However, studies of language developmental outcomes in relation with brain MRIs are very limited. Therefore, the aim of this study is to investigate language development in children with CP. This study also investigated the relationships between speech or language function and gross motor function, cognitive function, and brain MRI characteristics in children with CP.
he ability to communicate plays a key role in the ability of children to interact with people in their world and to have their needs met. Children with cerebral palsy (CP) frequently demonstrate communication difficulties. According to recently published populationbased studies, the communication impairment rate in children with CP is reported to be between 38% and 78% (Coleman, Weir, Ware, & Boyd, 2013; Hidecker et al., 2011; Himmelmann, Lindh, & Hidecker, 2013; Parkes, Hill, Platt, & Donnelly, 2010; Queensland Cerebral Palsy Register [QCPR], 2012). Communication skills in children with CP have been shown to be associated with gross motor function, intellectual impairments, gestational age
a
Department of Rehabilitation Medicine, Severance Hospital, Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea b Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea Correspondence to Eun Sook Park: [email protected] and [email protected] Editor: Sean Redmond Associate Editor: Steve Small Received July 6, 2016 Revision received September 21, 2016 Accepted November 21, 2016 https://doi.org/10.1044/2016_JSLHR-L-16-0281
Disclosure: The authors have declared that no competing interests existed at the time of publication.
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Participants and Methods Participants This study was conducted in a university-affiliated, tertiary-care teaching hospital. We retrospectively reviewed the medical records of children with CP between January 2006 and December 2015. Of those records, 172 children who had brain MRI entered into a picture achieving and communication system and who underwent a language assessment by speech pathologists between 3 and 7 years of age were included in this study. The characteristics of the subjects are described in Table 1. The Institutional Review Board of the hospital approved this study protocol (Approval 4-2016-0185).
Language Ability Language abilities were assessed by speech-language pathologists with the Sequenced Language Scale for Infants (SELSI) or Preschool Receptive-Expressive Language Scale (PRES). Both scales are validated in the Korean population (Kim, 2000, 2002). SELSI was used for children younger than 3 years, whereas PRES was used for preschoolers aged 3 years or older. However, SELSI was conducted in some older children whose language levels were inadequate for PRES. In this study, 76 (44.2%) children were assessed with SELSI and 96 (55.8%) were assessed with PRES. The mean age for language assessment was 53 (standard deviation ± 14, range = 36–78) months. The receptive and expressive language equivalent age was obtained through SELSI and PRES. Age equivalent was divided by chronological age to obtain developmental quotients (DQs) for receptive and expressive language. Language development was categorized into five levels based Table 1. Characteristics of the participants.
Variable Sex, n (%) Male Female Gestational age (weeks) Preterm birth (< 36 weeks), n (%) Term birth (≥ 36 weeks), n (%) GMFCS, n (%) Level I Level II Level III Level IV Level V Tone abnormality, n (%) Spastic Dyskinetic Ataxic Mixed
Participant characteristics
117 (68.0) 55 (32.0) 35.2 (23–42) 70 (40.7) 102 (59.3) 47 (27.3) 43 (25.0) 30 (17.4) 26 (15.1) 26 (15.1) 139 (80.8) 21 (12.2) 5 (2.9) 7 (4.1)
Note. Values are expressed as number of participants (percentage) or mean (range). GMFCS = Gross Motor Functional Classification System.
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on DQ (1: DQ ≥ 80, 2: 70 ≤ DQ ≤ 79, 3: 50 ≤ DQ ≤ 69, 4: 25 ≤ DQ ≤ 49, 5: DQ < 25). Articulation development was assessed by speechlanguage pathologists using the Urimal Test of Articulation and Phonology (U-TAP; Kim & Shin, 2004). The U-TAP measures the percentage of correct consonants using 23 picture cards to elicit 43 Korean consonants at word level and is standardized for use in Korean children aged 2 to 6 years. In this study, an articulation developmental problem was defined as a result less than 2 SD from the mean of agematched children.
Brain MRI Brain MRI was performed on all children using either a 1.5-T or a 3-T MRI (Achieva 1.5 T/3.0 T, Philips Medical Systems, Best, the Netherlands). Brain MRI characteristics were classified into normal, congenital malformation such as cortical dysplasia or schizencephaly, periventricular white matter lesion (PVWL), deep gray matter lesion, focal infarct, cortical/subcortical damage, and others, according to a previous study (Bax et al., 2006). According to our previous reports (Choi, Choi, Rha, & Park, 2016; Choi, Rha, & Park, 2016), the outcomes of PVWL or deep gray matter lesion varied according to severity. Therefore, PVWL patients were subgrouped into three levels: mild (hyperintensity in periventricular white matter), moderate (hyperintensity + ventricular wall irregularity), and severe (diffuse PVWL + ventricular dilatation). Deep gray matter lesion patients were grouped into hypoxic-ischemic encephalopathy (HIE) pattern or kernicterus pattern. The severity of the HIE pattern was subgrouped into focal and mild versus moderate and severe, according to previous study methods (Choi, Choi, et al., 2016). The categorical classification and severity of PVWL or deep gray matter were determined by a neuroradiologist (the second author) who was blinded to the subject’s clinical condition and neurodevelopmental status.
Neurodevelopmental Outcomes Tone abnormalities of CP were classified by spastic, dyskinetic, ataxic, and mixed type according to Surveillance of Cerebral Palsy in Europe (2000) guidelines. Neurodevelopmental outcomes were assessed with the Gross Motor Function Classification System–Expanded and Revised (GMFCS-E&R) and the Manual Ability Classification System (MACS), which are widely used for describing gross motor and upper arm function in children with CP and which have shown evidence of reliability and validity (Eliasson et al., 2006; Palisano, Rosenbaum, Bartlett, & Livingston, 2008). The GMFCS and MACS functional levels used for this study were determined based on classification at the last visit when the child was 3 years of age or older.
Neuropsychological Assessment Cognitive function was assessed using the Korean version of the Wechsler Preschool and Primary Scale of
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Intelligence, Revised Edition (K-WPPSI-R; Park, Kwak, & Park, 2002). The Korean version of the Bayley Scales of Infant Development, Second Edition (Park & Cho, 2006) was used to assess the cognitive function of children unable to complete the K-WPPSI-R. Cognitive function was categorized as normal or borderline cognitive abilities (intelligence quotient [IQ] ≥ 70 or mental developmental index [MDI] ≥ 70), mild intellectual disability (ID; 50 ≤ IQ or MDI < 70), or moderate/severe ID (IQ or MDI < 50) according to the American Psychiatric Association’s (2000) Diagnostic and Statistical Manual of Mental Disorders.
Other Accompanying Impairments By reviewing subjects’ medical records, accompanying impairments such as epilepsy, severe visual impairments representing functional blindness, or hearing impairments (HIs) requiring hearing aid and cochlear implant were assessed. Eight children had HI. The severity of HI in the better ear at diagnosis was classified using the following World Health Organization (1991) classification: mild 26 to 40 dB HI, moderate 41 to 60 dB HI, severe 61 to 80 dB HI, and profound > 80 dB HI. According to this classification, one child had mild HI, two children had moderate HI, two children had severe HI, and three children had profound HI. Two children with profound HI had undergone cochlear implantation; the other six children wore hearing aids. Five children had functional blindness in both eyes.
Statistical Analyses Statistical analysis was performed using the Statistical Package for the Social Sciences for Windows (SPSS version 23.0, IBM SPSS Incorporated, Chicago, IL). Wilcoxon signed rank test for nonparametric value or a paired t test for parametric value was used to compare the differences between receptive and expressive language DQs according to GMFCS, tone abnormality, or brain MRI characteristics. The Kruskal–Wallis test for nonparametric values or analysis of variance for parametric values was performed to compare differences in receptive and expressive language DQs among groups. Post hoc Bonferroni correction was used for all the multiple comparisons. In addition, univariable and multivariable linear regression modeling were used to identify factors significantly associated with receptive and expressive language ability including gestational age, sex, GMFCS, body part involvement, cognitive function, epilepsy history, tube feeding, visual impairment, and HI. Pearson correlation analysis was used to investigate the associations between language DQs and full-scale IQ/MDI-DQ. A p value less than .05 was considered statistically significant in all statistical tests.
Results The mean DQ in expressive language was significantly lower than the DQ in receptive language in children with
CP at every level of GMFCS. Children with spastic and dyskinetic CP had higher receptive DQs than expressive DQs. The DQs in expressive and receptive language were significantly different according to GMFCS level, MACS, and cognitive function. In addition, epilepsy, visual impairment, and tube feeding were also significantly related to expressive and receptive language DQs (see Table 2). Bonferroni-adjusted post hoc analysis revealed the significant differences in DQs between the three levels of cognitive function and also between GMFCS/MACS Level I and other GMFCS/ MACS levels, between GMFCS/ MACS Level II and IV or V, between GMFCS/MACS Level III and V, and between GMFCS/MACS Level IV and V. In addition, post hoc analysis revealed significant higher DQs in both expressive and receptive language in the children without epilepsy compared with the epilepsy groups. In addition, DQs in expressive language were statistically significantly different between types of CP. Children with ataxic CP had the highest DQs, whereas children with mixed CP had the lowest scores. Children with unilateral CP showed higher DQs in both receptive and expressive language than children with bilateral CP. The developmental levels of both receptive and expressive language are presented in Table 3. More than two thirds of the children had delayed development (DQ ≤ 70) in both receptive and expressive language. The majority of children had similar levels of receptive and expressive language development, but 61 children displayed a discrepancy between receptive and expressive language levels. Fifty children had better receptive than expressive language level. Eight children had two levels higher receptive than expressive language. Three children had three levels higher receptive than expressive language. In contrast, 11 children had better expressive than receptive language development. The children with a higher level of expressive than receptive language all had ambulatory status at GMFCS Level I to III. However, there were no significant factors relating to the presence of discrepancy on univariable analysis. Articulation problems were assessed with U-TAP in 80 children who were able to complete the test. Fifty-two (65%) children had articulation developmental problems. Among the GMFCS, MACS, types of CP, and brain MRI characteristics, only cognitive function was significantly related to presence of articulation developmental problems.
Brain MRI and Language Ability The DQs in expressive language, but not in receptive language, were significantly different between the brain MRI characteristics (see Table 4). The DQ values in both receptive and expressive language were lowest in the children with brain malformation and highest in the children with normal brain findings or focal infarct. The DQs in both receptive and expressive language were significantly different according to the severity of PVWL and deep gray matter lesion. Post hoc analysis with Bonferroni adjustment revealed significant differences in receptive and expressive DQs between PVWL I and II, between PVWL I and
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Table 2. Language ability according to functional level or associated impairment. Receptive language Variable GMFCS I II III IV V MACS I II III IV V Cognitive level Normal Mild ID Moderate-severe ID Epilepsy No previous seizure Controlled epilepsy without AED Controlled epilepsy with AED Epilepsy with AED or epileptic surgery Visual impairment No Yes Hearing impairment No Yes Tube feeding No Yes Tone abnormality Spastic Ataxic Dyskinetic Mixed Body part involvement Unilateral Bilateral
Expressive language
n
DQ
p
p
47 43 30 26 26
86.84 (65.98, 101.16) 64.10 (48.34, 91.90) 58.55 (39.61, 78.12) 47.39 (36.60, 60.19) 16.61 (10.09, 31.91)
< .001*
82.86 (64.89, 96.80) 61.11 (39.14, 79.99) 35.60 (22.62, 66.43) 35.91 (22.62, 44.84) 12.98 (7.83, 20.13)
< .001*
42 46 35 23 26
88.29 (64.57, 102.70) 76.18 (61.08, 94.82) 52.86 (41.98, 65.51) 39.02 (29.17, 51.85) 16.61 (10.09, 31.91)
< .001*
82.61 (69.42, 95.07) 71.83 (52.60, 91.15) 35.71 (22.85, 58.30) 23.53 (14.48, 38.36) 12.98 (7.83, 20.13)
< .001*
59 38 75
92.50 (78.10, 103.49) 62.66 (49.00, 68.24) 35.29 (16.98, 50.00)
< .001*
90.91 (75.60, 99.06) 55.01 (38.02, 68.04) 21.05 (12.83, 35.81)
< .001*
103 22 30 17
72.97 (47.47, 95.20) 49.60 (23.34, 81.75) 46.75 (22.19, 53.93) 47.62 (21.28, 63.42)
< .001*
68.29 (33.32, 91.96) 39.44 (19.89, 76.24) 31.59 (14.42, 45.11) 25.42 (15.69, 56.25)
< .001*
167 5
61.29 (39.15, 88.57) 14.00 (9.84, 20.73)
.009†
53.66 (23.20, 82.61) 11.76 (10.00, 14.63)
.017†
164 8
60.58 (38.31, 87.77) 49.43 (36.96, 91.48)
.824
48.69 (22.34, 79.74) 42.05 (15.94, 95.81)
.933
167 5
60.58 (38.31, 87.77) 49.43 (36.96, 91.48)
.001†
48.69 (22.34, 79.74) 42.05 (15.94, 95.81)
.003†
139 5 21 7
60.34 (39.13, 88.07) 95.28 (95.12, 100.89) 61.29 (22.92, 82.26) 39.06 (15.32, 70.46)
.080
54.76 (23.53, 81.76) 90.57 (80.49, 92.68) 34.68 (14.81, 54.55) 19.77 (12.16, 44.09)
.010*
41 131
71.05 (57.69, 88.00) 54.76 (33.77, 88.35)
.003†
70.00 (45.24, 89.55) 40.00 (19.64, 78.76)
.001†
DQ
Note. Values are expressed as median (25th, 75th percentile). DQ = developmental quotient; GMFCS = Gross Motor Functional Classification System; MACS = Manual Ability Classification System; ID = intellectual disability; AED = antiepileptic drug. *p < .05 by Kruskal–Wallis test. †p < .05 by Mann–Whitney test.
Table 3. Differences between receptive and expressive language ability. Language, n (%) DQ ≥ 80 70–79 50–69 25–49 < 25
Receptive
Expressive
52 (30.2) 13 (7.6) 40 (23.3) 37 (21.5) 30 (17.4)
45 (26.2) 15 (8.7) 27 (15.7) 36 (20.9) 49 (28.5)
Difference R = E (n = 111)
R > E (n = 50)
R < E (n = 11)
1 level = 39 2 level = 8 3 level = 3
1 level = 9 2 level = 2
Note. Values are expressed as number of participants (percentage). DQ = developmental quotient; R = receptive language; E = expressive language. R = E: within one level difference. R > E: DQ of receptive language is higher than expressive language by one level or more. R < E: DQ of receptive language is less than expressive language by one level or more.
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Table 4. Language ability according to brain MRI finding. Receptive language Finding Brain MRI Normal Malformation PVWL Deep gray matter Focal infarction Cortical/subcortical Others, cerebellar PVWL PVWL I PVWL II PVWL III PVWL Unilateral Bilateral Deep gray matter HIE pattern I/II HIE pattern III/IV Kernicterus
Expressive language
n
DQ
p
4 16 101 36 7 4 4
82.86 (52.86, 100.22) 39.53 (29.82, 55.05) 60.81 (41.18, 88.14) 63.30 (23.59, 89.04) 85.07 (73.03, 90.67) 60.90 (40.22, 77.67) 66.6 (41.06, 88.12)
.175
< .001*
DQ
77.14 (55.95, 87.14) 22.46 (14.07, 44.64) 58.90 (27.88, 80.49) 38.85 (16.51, 85.44) 89.55 (62.45, 96.67) 40.72 (28.64, 51.60) 58.55 (23.60, 91.40) 74.44 ± 29.35 52.22 ± 27.97 37.55 ± 23.60
p .044†
35 40 26
79.69 ± 27.98 57.46 ± 25.75 48.60 ± 25.45
< .001*
19 82
77.78 (65.98, 102.63) 55.74 (37.92, 83.87)
.006†
70.77 (61.41, 87.43) 49.62 (23.53, 78.85)
.012†
13 15 8
82.35 (62.50, 100.0) 22.92 (13.53, 63.25) 65.99 (44.56, 90.57)
.012†
69.23 (40.00, 88.57) 19.77 (12.04, 38.85) 38.50 (20.76, 92.28)
.426
Note. Values are expressed as median (25th, 75th percentile) or M ± SD. DQ = developmental quotient; MRI = magnetic resonance imaging; PVWL = periventricular white matter lesion; HIE = hypoxic-ischemic encephalopathy. *p < .05 by analysis of variance. †p < .05 by Kruskal–Wallis test or Mann–Whitney test.
III, and between Grade I/II and Grade III or IV of the HIE patterns of deep gray matter lesion. In addition, the DQs in both receptive and expressive language were significantly lower in bilateral PVWL compared with unilateral PVWL. Children with marked discrepancy between receptive and expressive language development (≥ two level differences) had PWVL or deep gray matter lesion and cortical/ subcortical lesions on brain MRI characteristics. Fifty percent of children with cortical/subcortical lesion had a marked discrepancy (see Figure 1). In addition, the mean DQs in expressive language were significantly lower than the DQs in receptive language in children with malformation, PVWL, or deep gray matter lesion.
Factors Relating to Language Development There were strong relationships between language DQs and IQ/MDI-DQ (r = .85 receptive language DQ, r = .87 expressive language DQ, correlation coefficient [r] as determined by the Pearson correlation test). On univariable analysis, GMFCS level (ambulatory vs. nonambulatory), body involvement (unilateral CP vs. bilateral CP), cognitive function, epilepsy history, tube feeding, visual impairment, and severity and laterality of PVWL were significantly related to receptive or expressive DQs (see Table 5). However, on multivariable analysis, only cognitive function continued to be significantly associated with receptive language development, whereas ambulatory status and cognitive function were strongly associated with expressive language development (see Table 6).
Discussion Previous studies have demonstrated common communication impairments in children with CP (Coleman et al., 2013; Himmelmann et al., 2013; Parkes et al., 2010), but little is known about language development in these children. According to a previous study (Hustad et al., 2010), 75% of children with CP at 4.5 years old had clinical speech and/ or language impairment. In addition, 85% of 2-year-old children with CP had clinical language delays relative to age expectations (Hustad, Allison, McFadd, & Riehle, 2014). Although the sample sizes of both studies were small (n = 27 and 34), the results suggest a high risk of language developmental delay in children with CP. In our study, more than two thirds of the children had language developmental delays in both receptive and expressive language ability (DQ < 80%). These findings support the importance of early speech and language assessment to counteract unfavorable language development in children with CP. Among the children tested with U-TAP, 65% had articulation developmental problems. However, a substantial number of the children who could not be tested with U-TAP are very likely to have articulation problems. Speech intelligibility has been a topic of considerable interest in children with CP. According to previous studies, production of vowels or vowel space is an important variable in speech intelligibility in preschool children with CP (DuHadway & Hustad, 2012; Lee & Hustad, 2013). A multiple speech subsystem approach, such as multiple acoustic variables reflecting the articulatory subsystem, is suggested for predicting speech intelligibility in children with CP (Lee, Hustad, & Weismer, 2014). Further studies on the relationships between articulation
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Figure 1. Discrepancy in language development according to brain magnetic resonance imaging findings. R = receptive language; E = expressive language. R = E: within one level difference of development quotient of receptive and expressive language.
Table 5. Univariable regression analysis of factors associated with language development. Receptive language Variable Gestational age < 32 vs. ≥ 32 weeks Female vs. male GMFCS IV–V vs. I–III level Bilateral vs. unilateral involvement Cognitive level (normal as reference group) Mild ID Moderate-severe ID Epilepsy history Tube feeding Visual impairment Hearing impairment PVWL severity (mild as reference group) Moderate Severe PVWL laterality (bilateral vs. unilateral lesion)
Expressive language
B (SE )
p
B (SE )
p
1.06 (5.30) −1.78 (5.19) −33.29 (4.61) −17.00 (5.52)
.842 .732 < .001* .002*
2.02 (5.48) −7.02 (5.34) −38.62 (4.58) −19.49 (5.68)
.713 .191 < .001* .001*
−28.77 (4.16) −55.91 (3.48) −24.14 (4.58) −16.58 (5.71) −38.33 (14.10) −1.25 (11.49)
< .001* < .001* < .001* .004* .007* .914
−33.06 (3.75) −62.29 (3.14) −25.45 (4.72) −14.06 (5.96) −31.54 (14.70) 3.27 (11.89)
< .001* < .001* < .001* .019* .033* .783
−22.23 (6.13) −31.09 (6.85) −20.40 (7.17)
< .001* < .001* .005*
−22.21 (6.35) −36.89 (7.10) −19.36 (7.65)
.001* < .001* .013*
Note. SE = standard error; GMFCS = Gross Motor Functional Classification System; ID = intellectual disability; PVWL = periventricular white matter lesion. *p < .05 by linear regression analysis.
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Table 6. Multivariable regression analysis of factors associated with language development. Receptive language Variable GMFCS IV–V vs. I–III level Bilateral vs. unilateral involvement Cognitive level (normal as reference group) Mild ID Moderate-severe ID Epilepsy history Tube feeding Visual impairment
Expressive language
B (SE )
p
B (SE )
p
−7.32 (4.06) 0.15 (3.92)
.073 .969
−10.62 (3.64) 0.06 (3.51)
.004* .986
−27.38 (4.06) −49.39 (4.56) −3.09 (3.56) −6.32 (4.20) −5.00 (10.35)
< .001* < .001* .386 .135 .630
−31.24 (3.74) −55.06 (4.08) −2.37 (3.19) −3.76 (3.76) 1.97 (9.27)
< .001* < .001* .458 .319 .832
Note. SE = standard error; GMFCS = Gross Motor Functional Classification System; ID = intellectual disability. *p < .05 by linear regression analysis.
developmental problems, including acoustic variables and speech intelligibility, are needed. There have been only three published studies investigating communication ability in relation to neuroimaging findings in children with CP. In two of these studies, communication impairments were determined by the ability to communicate verbally (Himmelmann & Uvebrant, 2011; Zhang et al., 2015), whereas the communication functional classification system was used to classify communication impairments in the other study (Himmelmann et al., 2013). In addition, both brain MRI and CT images were included for classifying neuroimaging findings in two of the studies (Himmelmann et al., 2013; Himmelmann & Uvebrant, 2011); only brain MRI were included in the other studies (Zhang et al., 2015). The categories classifying the neuroimaging findings and the statistical analyses used were different between studies, making it hard to directly compare the results. On the other hand, the study by Geytenbeek et al. (2015) tried to identify relations between brain abnormalities and spoken language comprehension in nonspeaking children with severe CP. In that study, the language comprehension was best in basal ganglia lesion whereas it was poorest in PVWL. In the literature, a cortical/subcortical lesion, basal ganglia lesion (Himmelmann et al., 2013; Himmelmann & Uvebrant, 2011; Zhang et al., 2015), and brain malformation (Geytenbeek et al., 2015; Himmelmann & Uvebrant, 2011; Zhang et al., 2015) were associated with nonverbal status in children with CP. The low DQ scores in children with these brain MRI characteristics in our study seem to be in line with these previous results. However, we did not find any significant differences in language development between groups of brain MRI characteristics. These findings suggest the limitation of categorical classification of brain MRI characteristics for predicting language developmental outcome. On the other hand, our study revealed wide ranges of language development, especially in children with PVWL or deep gray matter lesion. In the literature, the severity of PVWL was closely related to cognitive function (Choi, Rha, et al., 2016; Resic et al., 2008; Woodward, Clark, Bora, & Inder, 2012). Deep gray matter injury demonstrates heterogeneous patterns on brain MRI characteristics; thus, a wide range
of clinical outcomes according to involvement of deep gray matter lesion has been increasingly reported (Choi, Choi, et al., 2016; Krageloh-Mann et al., 2002; Martinez-Biarge et al., 2011; Martinez-Biarge, Diez-Sebastian, Rutherford, & Cowan, 2010). Extensive deep gray matter injury was shown to be associated with a high risk of severe cognitive impairment (Krageloh-Mann et al., 2002; Martinez-Biarge et al., 2010; Twomey, Twomey, Ryan, Murphy, & Donoghue, 2010). Because cognitive function was a key factor in language development in our study, the severity of PVWL or deep gray matter was significantly associated with language development. These findings suggest that lesion severity is more useful for predicting language development than categorization of brain MRI characteristics. Compared with PVWL or deep gray matter lesion, the number of cases with other brain MRI characteristics was too small to be subdivided into groups according to severity in our study. As a result, further delineation of language developmental outcomes in other brain MRI characteristics by severity is needed with larger number of cases. Recently published studies using more advanced techniques such as quantitative volumetric and diffusion tensor imaging measurements may be helpful for better understanding the underlying structural brain network or connectivity in children with CP (Pannek, Boyd, Fiori, Guzzetta, & Rose, 2014; Scheck, Pannek, Fiori, Boyd, & Rose, 2014). Nevertheless, the simple classification of MRI lesions is much friendlier for the clinician than rigorous and sophisticated methods such as volumetric analysis, diffusion tensor tractography, and so forth. In this context, our study is worthy of providing the general pictures of the language development of the children with CP. Interestingly, more than one third of the children had a discrepancy between receptive and expressive language development, with 7.5% of children having marked differences (≥ two level differences). This discrepancy has become a challenging issue for clinicians and/or parents dealing with language developmental problems. In our study, the children with marked discrepancy of two or more level differences had PVWL, deep gray matter lesion, or cortical/ subcortical lesion. The number of children with PVWL or deep gray matter lesion was far greater than other brain
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MRI characteristics in our study; thus, it was likely that some of the children with PVWL or deep gray matter lesion had language development discrepancy. However, despite the small sample size, the high incidence of marked discrepancy in children with cortical/subcortical lesion suggests that cortical/subcortical lesions are likely to be associated with such discrepancy. Further studies with larger sample sizes of cortical/subcortical lesion are needed to address this issue. According to previous studies, cognitive function is closely related to communication ability (Compagnone et al., 2014; Himmelmann et al., 2013; Vos et al., 2014). On the other hand, children with more severe motor deficits or more accompanying impairments are likely to have more communication impairments (Compagnone et al., 2014; Himmelmann & Uvebrant, 2011; Sigurdardottir & Vik, 2011; Zhang et al., 2015). However, the relationships between these factors have not been analyzed in prior studies. According to a previous longitudinal study, the development of expressive communication was found to be mostly related to motor disorder type, whereas the development of receptive communication was found to be most closely related to cognitive function (Vos et al., 2014). Our study results also suggest that cognitive function plays a key role in language development. Severe motor impairment can lead to more deficits in expressive language development. These findings are in line with the previous longitudinal study. The limitation of our study was its retrospective nature, with some children lost to follow-up. In general, children with good outcomes were more likely to be lost to follow-up than children with severe impairments. However, the distribution of GMFCS levels in our subjects seems to be compatible with the distributions of GMFCS levels of previous population-based studies (QCPR, 2012; Reid, Carlin, & Reddihough, 2011); thus, we believe that the lost cases did not significantly affect our results. Another limitation of our study is the distribution of brain MRI characteristics. Compared with PVWL or deep gray matter lesion, the sample sizes of patients with other brain findings were far smaller; thus, the data on language development in the groups with these small sample sizes should be cautiously interpreted. In addition, the measures for cognitive performances used in this study rely heavily on verbal abilities. Therefore, the strong relation between cognitive performance and language development should be cautiously interpreted because it might be possible from the verbal nature of the measures of cognitive performance. In addition, there are many factors affecting language developmental outcomes such as socioeconomic status, educational level of parents, parental attitude, and neonatal morbidity. Further studies are needed to delineate the outcomes with consideration of or control for such confounding factors. The strength of our study was the use of standardized tools for language developmental outcomes and cognitive function assessments and the demonstration of the relationship of language developmental outcomes with brain
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MRI characteristics. As far as we know, our study has the largest sample of PVWL on brain MRI characteristics, resulting in the identification of a relationship between language development and the severity of PVWL in children with CP.
Conclusions Our study showed a strong association between language development and cognition. In addition, severity of motor impairment was also related to expressive language development. Language developmental discrepancy was not uncommon in the children with CP, with 7.5% of children having marked discrepancy (≥ two level differences). Children with cortical/subcortical lesion were at high risk of marked language discrepancy. Wide ranges of language development in PVWL or deep gray matter lesion suggest the limited value of predicting language developmental outcome based on categorical classification of brain findings. These findings suggest that the severity of each categorical classification of brain MRI characteristics might provide useful information for language development in children with CP.
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Geytenbeek, J. J., Oostrom, K. J., Harlaar, L., Becher, J. G., Knol, D. L., Barkhof, F., . . . Vermeulen, R. J. (2015). Language comprehension in nonspeaking children with severe cerebral palsy: Neuroanatomical substrate? European Journal of Paediatric Neurology, 19, 510–520. Hidecker, M. J., Paneth, N., Rosenbaum, P. L., Kent, R. D., Lillie, J., Eulenberg, J. B., . . . Taylor, K. (2011). Developing and validating the Communication Function Classification System for individuals with cerebral palsy. Developmental Medicine and Child Neurology, 53, 704–710. Himmelmann, K., Lindh, K., & Hidecker, M. J. (2013). Communication ability in cerebral palsy: A study from the CP register of western Sweden. European Journal of Paediatric Neurology, 17, 568–574. Himmelmann, K., & Uvebrant, P. (2011). Function and neuroimaging in cerebral palsy: A population-based study. Developmental Medicine and Child Neurology, 53, 516–521. Hustad, K. C., Allison, K., McFadd, E., & Riehle, K. (2014). Speech and language development in 2-year-old children with cerebral palsy. Developmental Neurorehabilitation, 17, 167–175. Hustad, K. C., Gorton, K., & Lee, J. (2010). Classification of speech and language profiles in 4-year-old children with cerebral palsy: A prospective preliminary study. Journal of Speech Language and Hearing Research, 53, 1496–1513. Iwata, S., Nakamura, T., Hizume, E., Kihara, H., Takashima, S., Matsuishi, T., & Iwata, O. (2012). Qualitative brain MRI at term and cognitive outcomes at 9 years after very preterm birth. Pediatrics, 129, e1138–1147. Kim, Y. T. (2000). Content and reliability analyses of the Preschool Receptive-Expressive Language Scale (PRES). Communication Science and Disorders, 5, 1–25. Kim, Y. T. (2002). Content and reliability analyses of the Sequenced Language Scale for Infants (SELSI). Communication Science and Disorders, 7, 1–23. Kim, Y. T., & Shin, M. J. (2004). Urimal Test of Articulation and Phonology (U-TAP). Seoul, Korea: Hakjisa. Krageloh-Mann, I., Helber, A., Mader, I., Staudt, M., Wolff, M., Groenendaal, F., & DeVries, L. (2002). Bilateral lesions of thalamus and basal ganglia: origin and outcome. Developmental Medicine and Child Neurology, 44, 477–484. Lee, J., & Hustad, K. C. (2013). A preliminary investigation of longitudinal changes in speech production over 18 months in young children with cerebral palsy. Folia Phoniatrica et Logopaedica, 65, 32–39. Lee, J., Hustad, K. C., & Weismer, G. (2014). Predicting speech intelligibility with a multiple speech subsystems approach in children with cerebral palsy. Journal of Speech Language and Hearing Research, 57, 1666–1678. Martinez-Biarge, M., Diez-Sebastian, J., Kapellou, O., Gindner, D., Allsop, J. M., Rutherford, M. A., & Cowan, F. M. (2011). Predicting motor outcome and death in term hypoxic-ischemic encephalopathy. Neurology, 76, 2055–2061. Martinez-Biarge, M., Diez-Sebastian, J., Rutherford, M. A., & Cowan, F. M. (2010). Outcomes after central grey matter injury in term perinatal hypoxic-ischaemic encephalopathy. Early Human Development, 86, 675–682. Palisano, R. J., Rosenbaum, P., Bartlett, D., & Livingston, M. H. (2008). Content validity of the expanded and revised Gross Motor Function Classification System. Developmental Medicine and Child Neurology, 50, 744–750.
Pannek, K., Boyd, R. N., Fiori, S., Guzzetta, A., & Rose, S. E. (2014). Assessment of the structural brain network reveals altered connectivity in children with unilateral cerebral palsy due to periventricular white matter lesions. Neuroimage Clinical, 5, 84–92. Park, H. W., & Cho, B. H. (2006). Korean Bayley scales of infant development: Interpretation manual (2nd ed.). Seoul, Korea: Kidspop. Park, H. W., Kwak, G. J., & Park, K. B. (2002). Korean Wechsler Preschool and Primary Scale of Intelligence. Seoul, Korea: Special Education Publisher. Parkes, J., Hill, N., Platt, M. J., & Donnelly, C. (2010). Oromotor dysfunction and communication impairments in children with cerebral palsy: A register study. Developmental Medicine and Child Neurology, 52, 1113–1119. Queensland Cerebral Palsy Register. (2012). Report of the Queensland Cerebral Palsy Register: 1996-2005 birth years. Retrieved from http://www.qcpr.org.au/publications-folder/internalpublications-folder/qcpr-1996-2005-birth-year-report/view Reid, S. M., Carlin, J. B., & Reddihough, D. S. (2011). Using the Gross Motor Function Classification System to describe patterns of motor severity in cerebral palsy. Developmental Medicine and Child Neurology, 53, 1007–1012. Resic, B., Tomasovic, M., Kuzmanic-Samija, R., Lozic, M., Resic, J., & Solak, M. (2008). Neurodevelopmental outcome in children with periventricular leukomalacia. Collegium Antropologicum, 32, 143–147. Scheck, S. M., Pannek, K., Fiori, S., Boyd, R. N., & Rose, S. E. (2014). Quantitative comparison of cortical and deep grey matter in pathological subtypes of unilateral cerebral palsy. Developmental Medicine and Child Neurology, 56, 968–975. Surveillance of Cerebral Palsy in Europe. (2000). Surveillance of cerebral palsy in Europe: A collaboration of cerebral palsy surveys and registers. Surveillance of Cerebral Palsy in Europe (SCPE). Developmental Medicine and Child Neurology, 42, 816–824. Sigurdardottir, S., & Vik, T. (2011). Speech, expressive language, and verbal cognition of preschool children with cerebral palsy in Iceland. Developmental Medicine and Child Neurology, 53, 74–80. Twomey, E., Twomey, A., Ryan, S., Murphy, J., & Donoghue, V. B. (2010). MR imaging of term infants with hypoxicischaemic encephalopathy as a predictor of neurodevelopmental outcome and late MRI appearances. Pediatric Radiology, 40, 1526–1535. Vos, R. C., Dallmeijer, A. J., Verhoef, M., Van Schie, P. E., Voorman, J. M., Wiegerink, D. J., . . . Becher, J. G. (2014). Developmental trajectories of receptive and expressive communication in children and young adults with cerebral palsy. Developmental Medicine and Child Neurology, 56, 951–959. Woodward, L. J., Clark, C. A., Bora, S., & Inder, T. E. (2012). Neonatal white matter abnormalities an important predictor of neurocognitive outcome for very preterm children. PLoS One, 7, e51879. World Health Organization. (1991). Grades of hearing impairment. Retrieved from http://www.who.int/pbd/deafness/hearing_ impairment_grades/en/ Zhang, J. Y., Oskoui, M., & Shevell, M. (2015). A populationbased study of communication impairment in cerebral palsy. Journal of Child Neurology, 30, 277–284.
Choi et al.: Language Development and Brain MRI in CP
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Research Article
Language Development and Brain Magnetic Resonance Imaging Characteristics in Preschool Children With Cerebral Palsy Ja Young Choi,a Yoon Seong Choi,b and Eun Sook Parka
Purpose: The purpose of this study was to investigate characteristics of language development in relation to brain magnetic resonance imaging (MRI) characteristics and the other contributing factors to language development in children with cerebral palsy (CP). Method: The study included 172 children with CP who underwent brain MRI and language assessments between 3 and 7 years of age. The MRI characteristics were categorized as normal, malformation, periventricular white matter lesion (PVWL), deep gray matter lesion, focal infarct, cortical/subcortical lesion, and others. Neurodevelopmental outcomes such as ambulatory status, manual ability, cognitive function, and accompanying impairments were assessed.
Results: Both receptive and expressive language development quotients (DQs) were significantly related to PVWL or deep gray matter lesion severity. In multivariable analysis, only cognitive function was significantly related to receptive language development, whereas ambulatory status and cognitive function were significantly associated with expressive language development. More than one third of the children had a language developmental discrepancy between receptive and expressive DQs. Children with cortical/ subcortical lesions were at high risk for this discrepancy. Conclusions: Cognitive function is a key factor for both receptive and expressive language development. In children with PVWL or deep gray matter lesion, lesion severity seems to be useful to predict language development.
T
at birth, and epilepsy (Coleman et al., 2013; Compagnone et al., 2014; Himmelmann et al., 2013; Hustad, Gorton, & Lee, 2010; Parkes et al., 2010; Vos et al., 2014; Zhang, Oskoui, & Shevell, 2015). Neuroimaging studies have improved the understanding of the neuroanatomical basis for function in CP. It has been advocated that neuroimaging should be performed in all CP cases (Himmelmann & Uvebrant, 2011). Among the neuroimaging studies, brain magnetic resonance imaging (MRI) is regarded as the most suitable tool to visualize brain lesions and to obtain insight into the outcome prediction of children with CP (Bax, Tydeman, & Flodmark, 2006; Himmelmann & Uvebrant, 2011). Exploring neuroimaging findings and relating them to functional aspects is important for the planning of intervention in children with CP (Himmelmann & Uvebrant, 2011; Iwata et al., 2012). However, studies of language developmental outcomes in relation with brain MRIs are very limited. Therefore, the aim of this study is to investigate language development in children with CP. This study also investigated the relationships between speech or language function and gross motor function, cognitive function, and brain MRI characteristics in children with CP.
he ability to communicate plays a key role in the ability of children to interact with people in their world and to have their needs met. Children with cerebral palsy (CP) frequently demonstrate communication difficulties. According to recently published populationbased studies, the communication impairment rate in children with CP is reported to be between 38% and 78% (Coleman, Weir, Ware, & Boyd, 2013; Hidecker et al., 2011; Himmelmann, Lindh, & Hidecker, 2013; Parkes, Hill, Platt, & Donnelly, 2010; Queensland Cerebral Palsy Register [QCPR], 2012). Communication skills in children with CP have been shown to be associated with gross motor function, intellectual impairments, gestational age
a
Department of Rehabilitation Medicine, Severance Hospital, Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea b Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea Correspondence to Eun Sook Park: [email protected] and [email protected] Editor: Sean Redmond Associate Editor: Steve Small Received July 6, 2016 Revision received September 21, 2016 Accepted November 21, 2016 https://doi.org/10.1044/2016_JSLHR-L-16-0281
Disclosure: The authors have declared that no competing interests existed at the time of publication.
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Participants and Methods Participants This study was conducted in a university-affiliated, tertiary-care teaching hospital. We retrospectively reviewed the medical records of children with CP between January 2006 and December 2015. Of those records, 172 children who had brain MRI entered into a picture achieving and communication system and who underwent a language assessment by speech pathologists between 3 and 7 years of age were included in this study. The characteristics of the subjects are described in Table 1. The Institutional Review Board of the hospital approved this study protocol (Approval 4-2016-0185).
Language Ability Language abilities were assessed by speech-language pathologists with the Sequenced Language Scale for Infants (SELSI) or Preschool Receptive-Expressive Language Scale (PRES). Both scales are validated in the Korean population (Kim, 2000, 2002). SELSI was used for children younger than 3 years, whereas PRES was used for preschoolers aged 3 years or older. However, SELSI was conducted in some older children whose language levels were inadequate for PRES. In this study, 76 (44.2%) children were assessed with SELSI and 96 (55.8%) were assessed with PRES. The mean age for language assessment was 53 (standard deviation ± 14, range = 36–78) months. The receptive and expressive language equivalent age was obtained through SELSI and PRES. Age equivalent was divided by chronological age to obtain developmental quotients (DQs) for receptive and expressive language. Language development was categorized into five levels based Table 1. Characteristics of the participants.
Variable Sex, n (%) Male Female Gestational age (weeks) Preterm birth (< 36 weeks), n (%) Term birth (≥ 36 weeks), n (%) GMFCS, n (%) Level I Level II Level III Level IV Level V Tone abnormality, n (%) Spastic Dyskinetic Ataxic Mixed
Participant characteristics
117 (68.0) 55 (32.0) 35.2 (23–42) 70 (40.7) 102 (59.3) 47 (27.3) 43 (25.0) 30 (17.4) 26 (15.1) 26 (15.1) 139 (80.8) 21 (12.2) 5 (2.9) 7 (4.1)
Note. Values are expressed as number of participants (percentage) or mean (range). GMFCS = Gross Motor Functional Classification System.
2
on DQ (1: DQ ≥ 80, 2: 70 ≤ DQ ≤ 79, 3: 50 ≤ DQ ≤ 69, 4: 25 ≤ DQ ≤ 49, 5: DQ < 25). Articulation development was assessed by speechlanguage pathologists using the Urimal Test of Articulation and Phonology (U-TAP; Kim & Shin, 2004). The U-TAP measures the percentage of correct consonants using 23 picture cards to elicit 43 Korean consonants at word level and is standardized for use in Korean children aged 2 to 6 years. In this study, an articulation developmental problem was defined as a result less than 2 SD from the mean of agematched children.
Brain MRI Brain MRI was performed on all children using either a 1.5-T or a 3-T MRI (Achieva 1.5 T/3.0 T, Philips Medical Systems, Best, the Netherlands). Brain MRI characteristics were classified into normal, congenital malformation such as cortical dysplasia or schizencephaly, periventricular white matter lesion (PVWL), deep gray matter lesion, focal infarct, cortical/subcortical damage, and others, according to a previous study (Bax et al., 2006). According to our previous reports (Choi, Choi, Rha, & Park, 2016; Choi, Rha, & Park, 2016), the outcomes of PVWL or deep gray matter lesion varied according to severity. Therefore, PVWL patients were subgrouped into three levels: mild (hyperintensity in periventricular white matter), moderate (hyperintensity + ventricular wall irregularity), and severe (diffuse PVWL + ventricular dilatation). Deep gray matter lesion patients were grouped into hypoxic-ischemic encephalopathy (HIE) pattern or kernicterus pattern. The severity of the HIE pattern was subgrouped into focal and mild versus moderate and severe, according to previous study methods (Choi, Choi, et al., 2016). The categorical classification and severity of PVWL or deep gray matter were determined by a neuroradiologist (the second author) who was blinded to the subject’s clinical condition and neurodevelopmental status.
Neurodevelopmental Outcomes Tone abnormalities of CP were classified by spastic, dyskinetic, ataxic, and mixed type according to Surveillance of Cerebral Palsy in Europe (2000) guidelines. Neurodevelopmental outcomes were assessed with the Gross Motor Function Classification System–Expanded and Revised (GMFCS-E&R) and the Manual Ability Classification System (MACS), which are widely used for describing gross motor and upper arm function in children with CP and which have shown evidence of reliability and validity (Eliasson et al., 2006; Palisano, Rosenbaum, Bartlett, & Livingston, 2008). The GMFCS and MACS functional levels used for this study were determined based on classification at the last visit when the child was 3 years of age or older.
Neuropsychological Assessment Cognitive function was assessed using the Korean version of the Wechsler Preschool and Primary Scale of
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Intelligence, Revised Edition (K-WPPSI-R; Park, Kwak, & Park, 2002). The Korean version of the Bayley Scales of Infant Development, Second Edition (Park & Cho, 2006) was used to assess the cognitive function of children unable to complete the K-WPPSI-R. Cognitive function was categorized as normal or borderline cognitive abilities (intelligence quotient [IQ] ≥ 70 or mental developmental index [MDI] ≥ 70), mild intellectual disability (ID; 50 ≤ IQ or MDI < 70), or moderate/severe ID (IQ or MDI < 50) according to the American Psychiatric Association’s (2000) Diagnostic and Statistical Manual of Mental Disorders.
Other Accompanying Impairments By reviewing subjects’ medical records, accompanying impairments such as epilepsy, severe visual impairments representing functional blindness, or hearing impairments (HIs) requiring hearing aid and cochlear implant were assessed. Eight children had HI. The severity of HI in the better ear at diagnosis was classified using the following World Health Organization (1991) classification: mild 26 to 40 dB HI, moderate 41 to 60 dB HI, severe 61 to 80 dB HI, and profound > 80 dB HI. According to this classification, one child had mild HI, two children had moderate HI, two children had severe HI, and three children had profound HI. Two children with profound HI had undergone cochlear implantation; the other six children wore hearing aids. Five children had functional blindness in both eyes.
Statistical Analyses Statistical analysis was performed using the Statistical Package for the Social Sciences for Windows (SPSS version 23.0, IBM SPSS Incorporated, Chicago, IL). Wilcoxon signed rank test for nonparametric value or a paired t test for parametric value was used to compare the differences between receptive and expressive language DQs according to GMFCS, tone abnormality, or brain MRI characteristics. The Kruskal–Wallis test for nonparametric values or analysis of variance for parametric values was performed to compare differences in receptive and expressive language DQs among groups. Post hoc Bonferroni correction was used for all the multiple comparisons. In addition, univariable and multivariable linear regression modeling were used to identify factors significantly associated with receptive and expressive language ability including gestational age, sex, GMFCS, body part involvement, cognitive function, epilepsy history, tube feeding, visual impairment, and HI. Pearson correlation analysis was used to investigate the associations between language DQs and full-scale IQ/MDI-DQ. A p value less than .05 was considered statistically significant in all statistical tests.
Results The mean DQ in expressive language was significantly lower than the DQ in receptive language in children with
CP at every level of GMFCS. Children with spastic and dyskinetic CP had higher receptive DQs than expressive DQs. The DQs in expressive and receptive language were significantly different according to GMFCS level, MACS, and cognitive function. In addition, epilepsy, visual impairment, and tube feeding were also significantly related to expressive and receptive language DQs (see Table 2). Bonferroni-adjusted post hoc analysis revealed the significant differences in DQs between the three levels of cognitive function and also between GMFCS/MACS Level I and other GMFCS/ MACS levels, between GMFCS/ MACS Level II and IV or V, between GMFCS/MACS Level III and V, and between GMFCS/MACS Level IV and V. In addition, post hoc analysis revealed significant higher DQs in both expressive and receptive language in the children without epilepsy compared with the epilepsy groups. In addition, DQs in expressive language were statistically significantly different between types of CP. Children with ataxic CP had the highest DQs, whereas children with mixed CP had the lowest scores. Children with unilateral CP showed higher DQs in both receptive and expressive language than children with bilateral CP. The developmental levels of both receptive and expressive language are presented in Table 3. More than two thirds of the children had delayed development (DQ ≤ 70) in both receptive and expressive language. The majority of children had similar levels of receptive and expressive language development, but 61 children displayed a discrepancy between receptive and expressive language levels. Fifty children had better receptive than expressive language level. Eight children had two levels higher receptive than expressive language. Three children had three levels higher receptive than expressive language. In contrast, 11 children had better expressive than receptive language development. The children with a higher level of expressive than receptive language all had ambulatory status at GMFCS Level I to III. However, there were no significant factors relating to the presence of discrepancy on univariable analysis. Articulation problems were assessed with U-TAP in 80 children who were able to complete the test. Fifty-two (65%) children had articulation developmental problems. Among the GMFCS, MACS, types of CP, and brain MRI characteristics, only cognitive function was significantly related to presence of articulation developmental problems.
Brain MRI and Language Ability The DQs in expressive language, but not in receptive language, were significantly different between the brain MRI characteristics (see Table 4). The DQ values in both receptive and expressive language were lowest in the children with brain malformation and highest in the children with normal brain findings or focal infarct. The DQs in both receptive and expressive language were significantly different according to the severity of PVWL and deep gray matter lesion. Post hoc analysis with Bonferroni adjustment revealed significant differences in receptive and expressive DQs between PVWL I and II, between PVWL I and
Choi et al.: Language Development and Brain MRI in CP
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Table 2. Language ability according to functional level or associated impairment. Receptive language Variable GMFCS I II III IV V MACS I II III IV V Cognitive level Normal Mild ID Moderate-severe ID Epilepsy No previous seizure Controlled epilepsy without AED Controlled epilepsy with AED Epilepsy with AED or epileptic surgery Visual impairment No Yes Hearing impairment No Yes Tube feeding No Yes Tone abnormality Spastic Ataxic Dyskinetic Mixed Body part involvement Unilateral Bilateral
Expressive language
n
DQ
p
p
47 43 30 26 26
86.84 (65.98, 101.16) 64.10 (48.34, 91.90) 58.55 (39.61, 78.12) 47.39 (36.60, 60.19) 16.61 (10.09, 31.91)
< .001*
82.86 (64.89, 96.80) 61.11 (39.14, 79.99) 35.60 (22.62, 66.43) 35.91 (22.62, 44.84) 12.98 (7.83, 20.13)
< .001*
42 46 35 23 26
88.29 (64.57, 102.70) 76.18 (61.08, 94.82) 52.86 (41.98, 65.51) 39.02 (29.17, 51.85) 16.61 (10.09, 31.91)
< .001*
82.61 (69.42, 95.07) 71.83 (52.60, 91.15) 35.71 (22.85, 58.30) 23.53 (14.48, 38.36) 12.98 (7.83, 20.13)
< .001*
59 38 75
92.50 (78.10, 103.49) 62.66 (49.00, 68.24) 35.29 (16.98, 50.00)
< .001*
90.91 (75.60, 99.06) 55.01 (38.02, 68.04) 21.05 (12.83, 35.81)
< .001*
103 22 30 17
72.97 (47.47, 95.20) 49.60 (23.34, 81.75) 46.75 (22.19, 53.93) 47.62 (21.28, 63.42)
< .001*
68.29 (33.32, 91.96) 39.44 (19.89, 76.24) 31.59 (14.42, 45.11) 25.42 (15.69, 56.25)
< .001*
167 5
61.29 (39.15, 88.57) 14.00 (9.84, 20.73)
.009†
53.66 (23.20, 82.61) 11.76 (10.00, 14.63)
.017†
164 8
60.58 (38.31, 87.77) 49.43 (36.96, 91.48)
.824
48.69 (22.34, 79.74) 42.05 (15.94, 95.81)
.933
167 5
60.58 (38.31, 87.77) 49.43 (36.96, 91.48)
.001†
48.69 (22.34, 79.74) 42.05 (15.94, 95.81)
.003†
139 5 21 7
60.34 (39.13, 88.07) 95.28 (95.12, 100.89) 61.29 (22.92, 82.26) 39.06 (15.32, 70.46)
.080
54.76 (23.53, 81.76) 90.57 (80.49, 92.68) 34.68 (14.81, 54.55) 19.77 (12.16, 44.09)
.010*
41 131
71.05 (57.69, 88.00) 54.76 (33.77, 88.35)
.003†
70.00 (45.24, 89.55) 40.00 (19.64, 78.76)
.001†
DQ
Note. Values are expressed as median (25th, 75th percentile). DQ = developmental quotient; GMFCS = Gross Motor Functional Classification System; MACS = Manual Ability Classification System; ID = intellectual disability; AED = antiepileptic drug. *p < .05 by Kruskal–Wallis test. †p < .05 by Mann–Whitney test.
Table 3. Differences between receptive and expressive language ability. Language, n (%) DQ ≥ 80 70–79 50–69 25–49 < 25
Receptive
Expressive
52 (30.2) 13 (7.6) 40 (23.3) 37 (21.5) 30 (17.4)
45 (26.2) 15 (8.7) 27 (15.7) 36 (20.9) 49 (28.5)
Difference R = E (n = 111)
R > E (n = 50)
R < E (n = 11)
1 level = 39 2 level = 8 3 level = 3
1 level = 9 2 level = 2
Note. Values are expressed as number of participants (percentage). DQ = developmental quotient; R = receptive language; E = expressive language. R = E: within one level difference. R > E: DQ of receptive language is higher than expressive language by one level or more. R < E: DQ of receptive language is less than expressive language by one level or more.
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Table 4. Language ability according to brain MRI finding. Receptive language Finding Brain MRI Normal Malformation PVWL Deep gray matter Focal infarction Cortical/subcortical Others, cerebellar PVWL PVWL I PVWL II PVWL III PVWL Unilateral Bilateral Deep gray matter HIE pattern I/II HIE pattern III/IV Kernicterus
Expressive language
n
DQ
p
4 16 101 36 7 4 4
82.86 (52.86, 100.22) 39.53 (29.82, 55.05) 60.81 (41.18, 88.14) 63.30 (23.59, 89.04) 85.07 (73.03, 90.67) 60.90 (40.22, 77.67) 66.6 (41.06, 88.12)
.175
< .001*
DQ
77.14 (55.95, 87.14) 22.46 (14.07, 44.64) 58.90 (27.88, 80.49) 38.85 (16.51, 85.44) 89.55 (62.45, 96.67) 40.72 (28.64, 51.60) 58.55 (23.60, 91.40) 74.44 ± 29.35 52.22 ± 27.97 37.55 ± 23.60
p .044†
35 40 26
79.69 ± 27.98 57.46 ± 25.75 48.60 ± 25.45
< .001*
19 82
77.78 (65.98, 102.63) 55.74 (37.92, 83.87)
.006†
70.77 (61.41, 87.43) 49.62 (23.53, 78.85)
.012†
13 15 8
82.35 (62.50, 100.0) 22.92 (13.53, 63.25) 65.99 (44.56, 90.57)
.012†
69.23 (40.00, 88.57) 19.77 (12.04, 38.85) 38.50 (20.76, 92.28)
.426
Note. Values are expressed as median (25th, 75th percentile) or M ± SD. DQ = developmental quotient; MRI = magnetic resonance imaging; PVWL = periventricular white matter lesion; HIE = hypoxic-ischemic encephalopathy. *p < .05 by analysis of variance. †p < .05 by Kruskal–Wallis test or Mann–Whitney test.
III, and between Grade I/II and Grade III or IV of the HIE patterns of deep gray matter lesion. In addition, the DQs in both receptive and expressive language were significantly lower in bilateral PVWL compared with unilateral PVWL. Children with marked discrepancy between receptive and expressive language development (≥ two level differences) had PWVL or deep gray matter lesion and cortical/ subcortical lesions on brain MRI characteristics. Fifty percent of children with cortical/subcortical lesion had a marked discrepancy (see Figure 1). In addition, the mean DQs in expressive language were significantly lower than the DQs in receptive language in children with malformation, PVWL, or deep gray matter lesion.
Factors Relating to Language Development There were strong relationships between language DQs and IQ/MDI-DQ (r = .85 receptive language DQ, r = .87 expressive language DQ, correlation coefficient [r] as determined by the Pearson correlation test). On univariable analysis, GMFCS level (ambulatory vs. nonambulatory), body involvement (unilateral CP vs. bilateral CP), cognitive function, epilepsy history, tube feeding, visual impairment, and severity and laterality of PVWL were significantly related to receptive or expressive DQs (see Table 5). However, on multivariable analysis, only cognitive function continued to be significantly associated with receptive language development, whereas ambulatory status and cognitive function were strongly associated with expressive language development (see Table 6).
Discussion Previous studies have demonstrated common communication impairments in children with CP (Coleman et al., 2013; Himmelmann et al., 2013; Parkes et al., 2010), but little is known about language development in these children. According to a previous study (Hustad et al., 2010), 75% of children with CP at 4.5 years old had clinical speech and/ or language impairment. In addition, 85% of 2-year-old children with CP had clinical language delays relative to age expectations (Hustad, Allison, McFadd, & Riehle, 2014). Although the sample sizes of both studies were small (n = 27 and 34), the results suggest a high risk of language developmental delay in children with CP. In our study, more than two thirds of the children had language developmental delays in both receptive and expressive language ability (DQ < 80%). These findings support the importance of early speech and language assessment to counteract unfavorable language development in children with CP. Among the children tested with U-TAP, 65% had articulation developmental problems. However, a substantial number of the children who could not be tested with U-TAP are very likely to have articulation problems. Speech intelligibility has been a topic of considerable interest in children with CP. According to previous studies, production of vowels or vowel space is an important variable in speech intelligibility in preschool children with CP (DuHadway & Hustad, 2012; Lee & Hustad, 2013). A multiple speech subsystem approach, such as multiple acoustic variables reflecting the articulatory subsystem, is suggested for predicting speech intelligibility in children with CP (Lee, Hustad, & Weismer, 2014). Further studies on the relationships between articulation
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Figure 1. Discrepancy in language development according to brain magnetic resonance imaging findings. R = receptive language; E = expressive language. R = E: within one level difference of development quotient of receptive and expressive language.
Table 5. Univariable regression analysis of factors associated with language development. Receptive language Variable Gestational age < 32 vs. ≥ 32 weeks Female vs. male GMFCS IV–V vs. I–III level Bilateral vs. unilateral involvement Cognitive level (normal as reference group) Mild ID Moderate-severe ID Epilepsy history Tube feeding Visual impairment Hearing impairment PVWL severity (mild as reference group) Moderate Severe PVWL laterality (bilateral vs. unilateral lesion)
Expressive language
B (SE )
p
B (SE )
p
1.06 (5.30) −1.78 (5.19) −33.29 (4.61) −17.00 (5.52)
.842 .732 < .001* .002*
2.02 (5.48) −7.02 (5.34) −38.62 (4.58) −19.49 (5.68)
.713 .191 < .001* .001*
−28.77 (4.16) −55.91 (3.48) −24.14 (4.58) −16.58 (5.71) −38.33 (14.10) −1.25 (11.49)
< .001* < .001* < .001* .004* .007* .914
−33.06 (3.75) −62.29 (3.14) −25.45 (4.72) −14.06 (5.96) −31.54 (14.70) 3.27 (11.89)
< .001* < .001* < .001* .019* .033* .783
−22.23 (6.13) −31.09 (6.85) −20.40 (7.17)
< .001* < .001* .005*
−22.21 (6.35) −36.89 (7.10) −19.36 (7.65)
.001* < .001* .013*
Note. SE = standard error; GMFCS = Gross Motor Functional Classification System; ID = intellectual disability; PVWL = periventricular white matter lesion. *p < .05 by linear regression analysis.
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Table 6. Multivariable regression analysis of factors associated with language development. Receptive language Variable GMFCS IV–V vs. I–III level Bilateral vs. unilateral involvement Cognitive level (normal as reference group) Mild ID Moderate-severe ID Epilepsy history Tube feeding Visual impairment
Expressive language
B (SE )
p
B (SE )
p
−7.32 (4.06) 0.15 (3.92)
.073 .969
−10.62 (3.64) 0.06 (3.51)
.004* .986
−27.38 (4.06) −49.39 (4.56) −3.09 (3.56) −6.32 (4.20) −5.00 (10.35)
< .001* < .001* .386 .135 .630
−31.24 (3.74) −55.06 (4.08) −2.37 (3.19) −3.76 (3.76) 1.97 (9.27)
< .001* < .001* .458 .319 .832
Note. SE = standard error; GMFCS = Gross Motor Functional Classification System; ID = intellectual disability. *p < .05 by linear regression analysis.
developmental problems, including acoustic variables and speech intelligibility, are needed. There have been only three published studies investigating communication ability in relation to neuroimaging findings in children with CP. In two of these studies, communication impairments were determined by the ability to communicate verbally (Himmelmann & Uvebrant, 2011; Zhang et al., 2015), whereas the communication functional classification system was used to classify communication impairments in the other study (Himmelmann et al., 2013). In addition, both brain MRI and CT images were included for classifying neuroimaging findings in two of the studies (Himmelmann et al., 2013; Himmelmann & Uvebrant, 2011); only brain MRI were included in the other studies (Zhang et al., 2015). The categories classifying the neuroimaging findings and the statistical analyses used were different between studies, making it hard to directly compare the results. On the other hand, the study by Geytenbeek et al. (2015) tried to identify relations between brain abnormalities and spoken language comprehension in nonspeaking children with severe CP. In that study, the language comprehension was best in basal ganglia lesion whereas it was poorest in PVWL. In the literature, a cortical/subcortical lesion, basal ganglia lesion (Himmelmann et al., 2013; Himmelmann & Uvebrant, 2011; Zhang et al., 2015), and brain malformation (Geytenbeek et al., 2015; Himmelmann & Uvebrant, 2011; Zhang et al., 2015) were associated with nonverbal status in children with CP. The low DQ scores in children with these brain MRI characteristics in our study seem to be in line with these previous results. However, we did not find any significant differences in language development between groups of brain MRI characteristics. These findings suggest the limitation of categorical classification of brain MRI characteristics for predicting language developmental outcome. On the other hand, our study revealed wide ranges of language development, especially in children with PVWL or deep gray matter lesion. In the literature, the severity of PVWL was closely related to cognitive function (Choi, Rha, et al., 2016; Resic et al., 2008; Woodward, Clark, Bora, & Inder, 2012). Deep gray matter injury demonstrates heterogeneous patterns on brain MRI characteristics; thus, a wide range
of clinical outcomes according to involvement of deep gray matter lesion has been increasingly reported (Choi, Choi, et al., 2016; Krageloh-Mann et al., 2002; Martinez-Biarge et al., 2011; Martinez-Biarge, Diez-Sebastian, Rutherford, & Cowan, 2010). Extensive deep gray matter injury was shown to be associated with a high risk of severe cognitive impairment (Krageloh-Mann et al., 2002; Martinez-Biarge et al., 2010; Twomey, Twomey, Ryan, Murphy, & Donoghue, 2010). Because cognitive function was a key factor in language development in our study, the severity of PVWL or deep gray matter was significantly associated with language development. These findings suggest that lesion severity is more useful for predicting language development than categorization of brain MRI characteristics. Compared with PVWL or deep gray matter lesion, the number of cases with other brain MRI characteristics was too small to be subdivided into groups according to severity in our study. As a result, further delineation of language developmental outcomes in other brain MRI characteristics by severity is needed with larger number of cases. Recently published studies using more advanced techniques such as quantitative volumetric and diffusion tensor imaging measurements may be helpful for better understanding the underlying structural brain network or connectivity in children with CP (Pannek, Boyd, Fiori, Guzzetta, & Rose, 2014; Scheck, Pannek, Fiori, Boyd, & Rose, 2014). Nevertheless, the simple classification of MRI lesions is much friendlier for the clinician than rigorous and sophisticated methods such as volumetric analysis, diffusion tensor tractography, and so forth. In this context, our study is worthy of providing the general pictures of the language development of the children with CP. Interestingly, more than one third of the children had a discrepancy between receptive and expressive language development, with 7.5% of children having marked differences (≥ two level differences). This discrepancy has become a challenging issue for clinicians and/or parents dealing with language developmental problems. In our study, the children with marked discrepancy of two or more level differences had PVWL, deep gray matter lesion, or cortical/ subcortical lesion. The number of children with PVWL or deep gray matter lesion was far greater than other brain
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MRI characteristics in our study; thus, it was likely that some of the children with PVWL or deep gray matter lesion had language development discrepancy. However, despite the small sample size, the high incidence of marked discrepancy in children with cortical/subcortical lesion suggests that cortical/subcortical lesions are likely to be associated with such discrepancy. Further studies with larger sample sizes of cortical/subcortical lesion are needed to address this issue. According to previous studies, cognitive function is closely related to communication ability (Compagnone et al., 2014; Himmelmann et al., 2013; Vos et al., 2014). On the other hand, children with more severe motor deficits or more accompanying impairments are likely to have more communication impairments (Compagnone et al., 2014; Himmelmann & Uvebrant, 2011; Sigurdardottir & Vik, 2011; Zhang et al., 2015). However, the relationships between these factors have not been analyzed in prior studies. According to a previous longitudinal study, the development of expressive communication was found to be mostly related to motor disorder type, whereas the development of receptive communication was found to be most closely related to cognitive function (Vos et al., 2014). Our study results also suggest that cognitive function plays a key role in language development. Severe motor impairment can lead to more deficits in expressive language development. These findings are in line with the previous longitudinal study. The limitation of our study was its retrospective nature, with some children lost to follow-up. In general, children with good outcomes were more likely to be lost to follow-up than children with severe impairments. However, the distribution of GMFCS levels in our subjects seems to be compatible with the distributions of GMFCS levels of previous population-based studies (QCPR, 2012; Reid, Carlin, & Reddihough, 2011); thus, we believe that the lost cases did not significantly affect our results. Another limitation of our study is the distribution of brain MRI characteristics. Compared with PVWL or deep gray matter lesion, the sample sizes of patients with other brain findings were far smaller; thus, the data on language development in the groups with these small sample sizes should be cautiously interpreted. In addition, the measures for cognitive performances used in this study rely heavily on verbal abilities. Therefore, the strong relation between cognitive performance and language development should be cautiously interpreted because it might be possible from the verbal nature of the measures of cognitive performance. In addition, there are many factors affecting language developmental outcomes such as socioeconomic status, educational level of parents, parental attitude, and neonatal morbidity. Further studies are needed to delineate the outcomes with consideration of or control for such confounding factors. The strength of our study was the use of standardized tools for language developmental outcomes and cognitive function assessments and the demonstration of the relationship of language developmental outcomes with brain
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MRI characteristics. As far as we know, our study has the largest sample of PVWL on brain MRI characteristics, resulting in the identification of a relationship between language development and the severity of PVWL in children with CP.
Conclusions Our study showed a strong association between language development and cognition. In addition, severity of motor impairment was also related to expressive language development. Language developmental discrepancy was not uncommon in the children with CP, with 7.5% of children having marked discrepancy (≥ two level differences). Children with cortical/subcortical lesion were at high risk of marked language discrepancy. Wide ranges of language development in PVWL or deep gray matter lesion suggest the limited value of predicting language developmental outcome based on categorical classification of brain findings. These findings suggest that the severity of each categorical classification of brain MRI characteristics might provide useful information for language development in children with CP.
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