Analysis of presurgical language in children with posterior fossa tumours relative to postoperative speech outcomes: findings from the European CMS study

Analysis of presurgical language in children with posterior fossa tumours relative to postoperative speech outcomes: findings from the European CMS study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of presurgical language in children with posterior fossa tumours relative to postoperative speech outcomes: findings from the European CMS study Aliene Reinders, Cheyenne Svaldi, Annet Kingma, Jonathan Kjær Grønbæk, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8387537/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2026 Read the published version in The Cerebellum → Version 1 posted 10 You are reading this latest preprint version Abstract Background. Cerebellar Mutism Syndrome (CMS) is a common complication in children following posterior fossa tumour (PFT) surgery, typically marked by transient postoperative speech impairment (POSI; i.e., mutism or reduced speech). Differences in language performance between children with and without POSI have been observed postoperatively, but it remains unclear to what extent these language difficulties exist preoperatively and whether preoperative difficulties are related to POSI. This study provides the first comprehensive analysis of preoperative language samples, using data from the European CMS study. The aim was to compare patients who did or did not develop POSI to identify preoperative language characteristics that may be associated with POSI. Method. Preoperative language samples of 34 patients aged 3–17 years were analysed, including 16 who later developed POSI and 18 who did not. An analysis was performed to compare sample characteristics and language performance across four levels: semantics, lexical, morphosyntax, and phonology. Results. No significant preoperative language differences were found between the groups for the levels of language processing (all p -values > .137). Children who developed POSI produced more unintelligible speech preoperatively (β = -14.455, p = .024), but their intelligibility improved with age (age×group: β = 0.152, p = .007), whereas intelligibility in children without POSI remained relatively stable across age. Conclusion. These findings suggest that risk factors for POSI within the domain of verbal output may lie more in preoperative speech than in language. A comprehensive analysis of preoperative speech may provide valuable insight into speech characteristics potentially related to POSI. cerebellar mutism syndrome mutism posterior fossa syndrome infratentorial neoplasms preoperative language impairment language disorders Figures Figure 1 Introduction Approximately half of the brain tumours in children occur in the posterior fossa [ 1 ]. Treatment for Posterior Fossa Tumours (PFTs) generally entails neurosurgical resection which is often followed by chemo- and radiotherapy [ 2 ]. Cerebellar Mutism Syndrome (CMS), also referred to as paediatric cerebellar mutism syndrome , posterior fossa syndrome (PFS) , or mutism with subsequent dysarthria (MSD) , is a common complication following PFT surgery in children. It typically arises within days after the neurosurgical resection and affects around 24–34% of patients [ 3 – 6 ]. The most defining symptom of CMS is transient mutism or severely reduced speech, and it is further characterized by emotional lability, hypotonia, and a wide range of motor and cognitive deficits [ 7 ]. Although the mutism or reduced speech is transient, speech and language problems can persist later in life [ 8 ]. The profound acute and long-term impact of the complication highlights the importance of accurately predicting which patients are at risk of developing it. Research identifying risk factors for the emergence of mutism or reduced speech primarily focuses on demographic (such as age, sex, and handedness) and clinical factors (such as tumour type and location and surgical approach; [ 9 – 11 ]). Risk factors in the domain of language have been studied [ 12 ], but their full scope remains underexplored. In this study, we therefore perform a comprehensive analysis of narrative language samples of children diagnosed with a PFT before they undergo neurosurgical resection. Their language performance will be related to their postoperative speech status (mutism or reduced speech vs. habitual speech), to identify if there are language characteristics that may be related to the emergence of mutism or reduced speech. Speech and language after mutism or reduced speech Long-term impairments in speech and language may be observed in paediatric PFT survivors regardless of whether they experienced mutism or reduced speech or not [ 13 – 15 ]. Nonetheless, several studies suggest that there may be differences in motor and cognitive skills, and specifically speech and language abilities in patients who experienced mutism or reduced speech and patients who did not [ 16 – 19 ]. When mutism or reduced speech subsides, patients will often present with motor speech disorders, a pattern collectively termed mutism with subsequent dysarthria [ 19 ]. They may also have more impairments in the initiation of voluntary movement, including speech initiation [ 17 ]. De Smet et al. [ 16 ] reported a pattern of adynamic spontaneous output (i.e., very little initiation) in patients suffering from mutism, which was still observed two years after neurosurgical resection. Additionally, several studies suggest that there may be differences in language processing in patients who did and did not experience mutism or reduced speech. Cámara et al. [ 13 ] found that patients who experienced mutism had poorer outcomes than those who did not across several general cognitive and language abilities, including verbal comprehension, receptive and expressive language, verbal memory, and verbal fluency. Additionally, the presence of mutism or reduced speech was found to be related to slower reading pace [ 20 ] and poorer verbal learning [ 21 ]. A comprehensive linguistic analysis of spontaneous language by Svaldi et al. [ 15 ] also suggested that while individual language profiles varied a lot, there was a differential pattern of long-term impairment in children who did and did not have mutism. Children who experienced mutism showed language impairments predominantly in the morphosyntactic and semantic domains of language, while children who did not develop mutism showed a wider spread in language impairments across all language domains. However, in a recent study investigating postoperative word-finding abilities in a large sample, Persson et al. [ 14 ] did not find a relation between postoperative speech impairment (POSI, consisting of mutism or reduced speech) and poorer postoperative word-finding abilities. Still, all children who had experienced a complete absence of speech exhibited a postoperative decline in word finding. Studies examining language differences between patients with mutism or reduced speech and those without included either small participant numbers [ 15 ], or focused on a specific aspect of language [ 14 ]. Furthermore, most studies did not account for preoperative language impairment (with the exception of [ 14 ]). This is important, as it may be that preoperative language characteristics can be linked to the emergence of mutism or reduced speech. Risk factors for mutism or reduced speech Extensive research has been done on the risk factors related to the emergence of mutism or reduced speech, see [ 10 ] for an overview. Several studies show that the incidence of mutism or reduced speech decreases with age [ 4 , 5 , 22 ], with rare occurrence in adults [ 23 ]. İldan et al. [ 24 ] proposed that the higher incidence of mutism in younger children could be related to the incomplete maturation of the brain that renders younger children more prone to developing the complication. Additionally, some tumour locations are associated with higher risk of mutism or reduced speech, such as vermal and midline tumours [ 3 , 4 , 25 ] and brainstem tumours [ 6 ]. Cerebellar hemisphere tumours, on the other hand, generate a lower risk [ 4 , 6 ]. Concerning tumour type, patients with high-grade medulloblastomas have consistently been reported to develop mutism or reduced speech more often than children with low-grade astrocytomas [ 3 , 4 ]. Preoperative risk factors in the domain of language are underexplored, as previous research primarily focuses on differential postoperative language outcomes in patients with mutism or reduced speech. Nonetheless, Di Rocco et al. [ 12 ] found that children in their sample without preoperative language problems did not develop mutism, while all children in their study who developed mutism presented with preoperative language problems. Preoperative language impairments were characterised by a shorter Mean Length of Utterance (MLU) and problems with verbal fluency and lexical naming. Bianchi et al. [ 8 ] extended the findings of Di Rocco et al. by enlarging the patient cohort, showing that 20 out of 70 patients with PFTs who developed mutism presented with preoperative language impairments. In this study, phonological impairments appeared to be most common. Persson et al. [ 26 ] found that patients with PFTs experience word finding difficulties before neurosurgical resection, characterized by slow and/or inaccurate word finding, but they did not examine the relationship between preoperative word finding difficulties and the emergence of mutism or reduced speech. Research into the preoperative language abilities of children diagnosed with a PFT is thus limited, and to date, no research has compared the language profiles of children who do and do not experience mutism or reduced speech comprehensively, that is, providing a picture across different levels of language processing. Identifying language impairments through connected language The existing literature on language disorders in child survivors of PFTs suggests that these may affect all language domains (e.g., phonology, morphosyntax, lexical and semantic knowledge, pragmatics) and may present themselves in variable combinations and severity across children [ 15 , 27 ]. Preoperative language abilities should thus be evaluated comprehensively in every patient, including all language domains. A highly productive approach to evaluate multiple domains of language is through the evaluation of language samples, either elicited in conversations/interactions, picture descriptions, or through the (re)telling of narratives [ 15 , 28 , 29 ]. This approach has been used previously in children with PFTs, focusing on the macro- and microstructural aspects of language after neurosurgical resection and revealing differences from healthy controls [ 15 , 30 ] as well as differences within patient subgroups [ 31 ]. This approach combines the use of standard measures (e.g., Type-Token Ratio) and the critical variable approach by Shallice [ 32 ], similarly to Svaldi et al. [ 15 ], see also the Supplementary Material for a detailed description of this approach. In the critical variable approach, Shallice describes that properties of words (i.e., psycholinguistic properties) can impact language performance, and that these properties, which reflect functioning of specific levels of language processing, can reveal impairments at their respective levels. Levels of language processing Across models of language processing [e.g., 33–35] it is commonly agreed that conveying a message (e.g., ‘The cat meows’) through spoken language requires processing at several levels of language. At the semantic level, conceptual information related to the meanings of words is being stored and retrieved (e.g., the knowledge that a cat meows; Levelt, 1989). At the lexical level, the word forms which make up our mental dictionary, or lexicon, are stored and retrieved [ 35 ]. The morphosyntactic level relates to the internal structure of words (morphology) and sentences (syntax) [ 36 ]. Morphological rules govern the internal structure of words, and establish a relation to other units in the syntactic structure (e.g., the addition of ‘s’ to the verb-stem ‘meow’, to match the third person singular of the subject). Syntax consists of grammatical information such as word classes (e.g., ‘cat’ is a noun, ‘meowing’ is a verb), as well as rules concerning sentence structure (e.g., the subject ‘cat’ must come before the verb ‘meows’). Furthermore, at the phonological level, segmental phonological information is retrieved. This entails information of individual speech sounds, which need to be ordered correctly and stored in phonological short-term memory in preparation for and during speech [ 35 ]. Breakdowns at each of these levels can lead to characteristic patterns of errors [see 37]. For each of these levels, there are linguistic variables which may be extracted from narrative language to study their functioning. For example, semantic representations may be easier to retrieve depending on how easily a concept evokes a mental image (imageability) [ 38 ] or how concrete/abstract it is [ 39 ], so patients with semantic disorders may be biased to use the words which are highly imageable and concrete [e.g., 15,40]. Other variables that tap into the semantic system are familiarity [ 41 ] or instrumentality of verbs [ 42 ]. At the lexical level, vocabulary size can be estimated with the ratio of different to total words in a sample (Type-token ratio, TTR), or lexical properties, such as lexical accuracy, corpus-based word frequency [ 43 ], or word age of acquisition [ 44 ] may be studied. Morphosyntactic ability may be assessed with measures such as the Mean Length of Utterances [e.g., 45], as well as grammatical accuracy [ 46 ], and proportion of finite verbs (i.e., inflected verbs such as walks, as opposed to walk), with the latter two showing impairment in children with PFTs [ 15 ]. Other word properties such as verb transitivity, unaccusativity, and regularity of inflectional paradigms, can also be used to detect atypical verb usage in populations with language impairment [e.g., 47,48]. At the phonological level, impairments may be revealed by phonological errors [ 49 ], or a bias to produce less complex articulatory patterns (e.g., string vs. sing) [ 50 , 51 ]. Furthermore, a tendency to produce short words may be indicative of phonological short-term memory difficulties [ 52 ]. While most of these variables were included in the work by Svaldi et al. [ 15 ] on postoperative language, such a comprehensive analysis of narrative language has not been reported in studies concerning preoperative language abilities of PFT patients. Current study In summary, postoperatively, there appear to be language characteristics that distinguish children who have experienced postoperative speech impairment from those who have not. Research into the preoperative stage is limited but suggests that linguistic differences between patients with mutism or reduced speech compared to those with habitual speech might already be present before neurosurgical resection. However, knowledge on the exact nature of these preoperative impairments and what language characteristics are related to the emergence of mutism or reduced speech remains unclear. This study will be the first to extensively analyse the preoperative language samples of patients who underwent neurosurgical resection for a PFT and did or did not develop postoperative speech impairment. Language samples of 34 patients will be analysed, replicating and expanding on language analysis procedures used by Svaldi et al. [ 15 ]. We aim to investigate which preoperative semantic, lexical, morphosyntactic, and phonological characteristics may be related to the emergence of mutism or reduced speech. Strengthening our understanding of preoperative language abilities as a risk factor for the emergence of this complication will help predict postoperative outcomes and allows better preparation of patients and their parents regarding potential difficulties that might await them after surgery. Method Participants Data from children who underwent surgery for a PFT were retrieved through a database part of the prospective European CMS Study [ 53 ]. This study was registered on November 24, 2014, with ClinicalTrials.gov (registration number: NCT02300766). Within the European CMS Study, postoperative mutism or reduced speech is referred to as Postoperative Speech Impairment (POSI), a term we adopt in the current study. Between 2013 and 2024, 794 patients participated in the study. We included patients from treatment centres in The Netherlands, The United Kingdom and Italy, due to the availability of research personnel proficient in the languages spoken in these countries (Dutch, English and Italian, respectively). Criteria for inclusion in the current study were: (1) the availability of a language sample collected before the first neurosurgical resection of a PFT, (2) the availability of information on the presence of POSI, (3) the absence of previous neurological, neuropsychological, psychiatric and/or speech problems, and (4) the absence of previous chemotherapy and/or radiotherapy treatment. In addition to the inclusion criteria, several clinical measures were obtained to further characterise the patient group, including POSI status, tumor location and histology, and preoperative hydrocephalus, dysarthria and oculomotor abnormalities. POSI was reported by a clinician as present if the patient presented for at least one day post-surgery with mutism (i.e., no production of words or short sentences) or severely reduced speech (i.e., limited to single words or short sentences which can only be elicited after vigorous stimulation). Tumor location was reported by a clinician after surgical resection, and could include the cerebellar vermis, the right and left hemispheres, the fourth ventricle and/or the brainstem. Tumor histology was reported as medulloblastoma, pilocytic astrocytoma, ependymoma or other. Preoperative hydrocephalus was reported as present or absent. Preoperative dysarthria and oculomotor difficulties were rated by a clinician using a five- and three-point scale, respectively, which we reclassified for the current study as absent (original score 0), or present (any higher score). Participant selection is summarised in Fig. 1. Sixty-six patients met the inclusion criteria, of whom 16 developed POSI and 50 did not. Given our specific interest in language difficulties in patients with POSI, we performed an additional selection step to create a matched control group of patients without POSI, considering several additional clinical characteristics. Eighteen patients who did not develop POSI were matched at the group level to the 16 patients with POSI based on age, sex, country, and language background (i.e., mono- or multilingual), as well as preoperative hydrocephalus, dysarthria, and oculomotor difficulties. Further clinical characteristics were not considered in the matching procedure to avoid creating atypical samples, given the relationship between POSI and, for instance, tumour histology and location (Grønbæk et al., 2023). The final groups did not differ in tumour histology and tumour location, although there was a tendency for tumour location towards a group difference. See Table 1 for an overview of demographic and clinical characteristics of the groups, and Appendix A for individual demographic and clinical information. Figure 1 Process of participant inclusion Table 1 Demographic and clinical background information per group POSI (n = 16) no POSI (n = 18) χ 2a / t p Age (Y;M) t = - 0.214 .832 M ( SD ) 8;10 (3;3) 9;1 (3;5) Range 3;9–14;2 3;5–16;0 Sex , n (% b ) χ 2 = 0.007 1.000 Male 10 (63) 11 (61) female 6 (37) 7 (39) Country , n (%) χ 2 = 0.092 1.000 Italy 7 (44) 8 (44) The Netherlands 3 (19) 4 (22) United Kingdom 6 (38) 6 (33) Language background , n (%) χ 2 = 0.993 1.000 Monolingual 13 (81) 14 (78) Bi- and multilingual 3 (19) 3 (17) Unknown 0 (0) 1 (6) Tumour location , n (%) χ 2 = 8.543 .065 Left cerebellar hemisphere 1 (6) 1 (6) Right cerebellar hemisphere 0 (0) 6 (38) Vermis 5 (28) 7 (44) Fourth ventricle 13 (72) 7 (44) Brainstem 4 (22) 7 (44) Tumour histology , n (%) χ 2 = 6.359 .156 Medulloblastoma 10 (63) 6 (33) Pilocytic astrocytoma 2 (13) 9 (50) Ependymoma 1 (6) 1 (6) Other 2 (13) 2 (11) Unknown 1 (6) 0 (0) Pre-op dysarthria , n (%) χ 2 = 0.694 .834 Present 2 (13) 1 (6) Absent 13 (81) 15 (83) Unknown 1 (6) 2 (11) Pre-op hydrocephalus , n (%) χ 2 = 0.034 1.000 Present 12 (75) 13 (72) Absent 4 (25) 5 (28) Pre-op oculomotor difficulties , n (%) χ 2 = 0.174 1.000 Present 9 (56) 9 (50) Absent 5 (31) 6 (33) Unknown 2 (13) 3 (17) Note. POSI = Postoperative Speech Impairment, defined as mutism or severely reduced speech; No POSI = habitual speech; Y;M = age in years and months; Other = Atypical Teratoid/Rhabdoid Tumour and other tumour types; Pre-op = preoperative. a Chi square test was performed using Monte Carlo simulation (10,000 replicates) to account for small cell counts and limited sample size. b Percentages may not total exactly 100% due to rounding. Materials and Procedures The language samples were collected by a clinician or speech and language pathologist, typically from a few days before surgery to the day of surgery. To elicit a narrative language sample, the ERRNI – Fish Story [ 54 ] was used. In this task, the child was presented with a picture storybook about a boy who goes to a pet store and encounters various situations along the way. The examiner was instructed to let the child go through the book, viewing all the pictures from beginning to end without saying anything. Afterwards, the child was asked to tell the story using the picture book. The examiner was further instructed to interfere minimally but was allowed to provide encouragement to continue, such as ‘Mhm,’ or general prompts like ‘What happened next?’. However, examiners frequently intervened despite these instructions (to be explained in more detail below). The story told by the child was recorded using an audio recorder. Data coding Data preparation: Global sample characteristics The narrative language samples were transcribed using the transcription and annotation software ELAN [ 55 ], following a detailed protocol based on the Spontaneous Speech Analysis Procedure (STAP) [ 56 ] and the ERRNI – Fish Story [ 54 ]. Utterances that contained 20% or more unintelligible or hard-to-understand words were excluded from further analysis. Additionally, utterances in which the child mimicked the tester, general comments unrelated to the story and questions asked to the examiner were excluded from further analysis. If an utterance was interrupted (e.g., by the examiner or a long break) but the child continued their syntactic structure after the interruption, both parts were analysed as one utterance. All remaining utterances were included in the analyses described hereafter. As mentioned above, examiners were instructed not to prompt patients, but occasionally deviated from these instructions. Utterances were therefore coded into three categories: (1) elliptic utterances , if they were syntactically dependent on a prompt given by the tester (e.g., Examiner: “What does the mom give to the boy?” , Participant: “Money” ); (2) prompted utterances , if they were prompted by the tester but syntactically independent (e.g., Examiner: “ What is the girl doing with the bags?” , Participant: “The girl is swapping the doll and the fish." ); and (3) free utterances , if the child produced them spontaneously or in response to a general, neutral prompt (e.g., Examiner: “ And what do you see here?” , Participant: “ The boy is walking to the pet store.” ). Typically, elliptic and prompted utterances are excluded from further analysis because they do not reflect the child’s independent ability to formulate a sentence and may affect language measures. For example, elliptic answers could lower MLU, and prompted responses might encourage the child to use words they would not have produced spontaneously. However, in the current study, neither elliptic nor prompted utterances were excluded, as doing so would result in the loss of a significant portion of data. From this pre-processing step, we calculated several global sample characteristics, which could help enhance the interpretation and contextualization of the results. First, we calculated the sample size in number of words and number of utterances . Furthermore, the percentage of unclear speech was calculated, reflecting the percentage of utterances that had to be removed from the sample because they contained too many hard-to-understand or unintelligible words. Finally, the percentage of prompted utterances was calculated to reflect the amount of utterances produced with support from the examiner (either elliptic or prompted). (Psycho)linguistic analyses After preparing the data, a (psycho)linguistic language sample analysis was performed, similar to the procedures reported by Svaldi et al. [ 15 ]. A total of 24 language measures were extracted from the samples on four levels of language processing (i.e., semantic, lexical, morphosyntactic, and phonological). See Table 2 for an overview of variables per level of language processing. Table 2 Overview of language measures Level of language processing Standard language sample measures Additional (psycho)linguisticf variables Semantics - Concreteness* Familiarity* Imageability* Verb instrumentality (proportion) Lexical Lexical diversity (TTR)* Age of acquisition* Lexical accuracy (percentage) Word frequency* Morphosyntax Mean length of utterance (in words) Verb transitivity (proportion) Grammatical accuracy (percentage) Unaccusativity (proportion) Finiteness index Verb regularity (proportion) Phonology Phonological errors Word length (in phonemes)* Cluster index Note. Variables marked with * were analysed separately for nouns and verbs. TTR = Type-Token Ratio. See Supplementary Material for detailed descriptions of each variable. Standard language sample measures . The standard language measures at the lexical level included Type-Token Ratio ( TTR ) and lexical accuracy . TTR was calculated by dividing the number of unique nouns/verbs in the sample by the total number of nouns/verbs, including those uttered as part of hesitations, repetitions and self-corrections. TTR scores could range from 0 to 1, with scores closer to 1 expressing better performance. Lexical accuracy was determined based on whether an utterance contained lexical errors (e.g., semantic paraphasias), and was expressed as the percentage of lexically correct utterances. Morphosyntactic standard language measures included mean length of utterance ( MLU ), grammatical accuracy and finiteness index . MLU was calculated by dividing the total number of words in a language sample by the total number of utterances, with higher scores indicating more syntactically complex language. Grammatical accuracy was determined based on whether an utterance contained grammatical errors (e.g., errors in word order or missing elements), and was expressed as the percentage of grammatically correct utterances. The finiteness index was calculated by dividing the number of correctly produced inflected verbs (e.g., ‘walks’ that matches the third person singular of the subject in ‘the man walks’) by the total number of required inflected verbs. Scores for the finiteness index could range from 0 to 1, with scores closer to 1 expressing better performance. At the phonological level, the proportion of phonological errors in the sample was calculated by dividing the total number of errors by the total number of words in the sample. Additional (psycholinguistic) variables. For every unique noun and verb produced by the child, ratings for multiple psycholinguistics variables (e.g., imageability, frequency) were extracted, reflecting different levels of language processing. At the semantic level, concreteness, familiarity, and imageability ratings were extracted. On the lexical level, AoA and word frequency were considered. For the variables verb instrumentality, transitivity, unaccusativity and regularity , at the morphosyntactic level, a trained linguist determined whether the given property could be attributed to every verb produced (e.g., ‘1’ if the verb was instrumental, ‘0’ if the verb was not). Thereafter, the proportion of instrumental/transitive/regular verbs to the total number of verbs was calculated. Unaccusativity was calculated as the proportion of unaccusative verbs to the total number of intransitive verbs. On the phonological level, word length in phonemes was extracted from a database or determined by a trained linguist, in case a word was not included in the database. Additionally, the cluster index was calculated by dividing, per utterance, the total number of correctly produced clusters by the total number of required clusters. Scores could range from 0 to 1, with scores closer to 1 reflecting a more accurate production of clusters. To ensure accuracy and consistency, the aforementioned data coding was validated by the first and/or second author. See Appendix B for an overview of the language-specific databases used. Analyses To evaluate the variables extracted from the language samples, we performed a Principal Component Analysis (PCA) [ 57 ]. This multivariate technique was applied to reduce the number of comparisons to be performed, given the high number of variables included. In a PCA, variables contributing similarly to the variability in the data are clustered together into components. As we had a relatively high number of variables (24) in relation to the sample size (34), we performed four separate PCAs: one for each level of language processing. This way, we could reduce the dimensionality of the data, while taking the PCA’s sensitivity for the participant-variable ratio into account. Because we were extracting psycholinguistic variables from databases and those were not always complete, we had to deal with a small number of missing values. A PCA cannot be performed when variables contain missing values. Therefore, three patients were excluded from the semantic PCA because none of their produced verbs had available concreteness or imageability ratings. Subsequently, the variables were evaluated for suitability for the PCA, using the Kaiser–Meyer–Olkin Measure of Sampling Adequacy (KMO) and Bartlett’s test of sphericity. A variable was considered suitable for inclusion in the PCA if its KMO value exceeded approximately 0.5[1] and if the assumption of sphericity was not violated. Based on the eigenvalues (> 1.0) of the components and the elbow method, we determined how many components to retain, see [ 57 ] for further explanation. Variables with a loading greater than 0.45 or less than − 0.45 were considered to contribute significantly to the variability explained by the component. All variables were then normalized and the normalized scores of the variables that clustered together in the component were averaged. Where necessary, variables were recoded so that higher scores consistently reflected better performance. For example, a higher score on phonological errors originally indicated more errors (worse performance) and was therefore inverted so that a higher score represented better performance. Those combined variables were then used for further analyses[2] . Several variables were not eligible for inclusion in the PCA due to insufficient KMO values, indicating that these variables did not contribute to the variance in the same way as other variables did, and thus should not be combined in a given component. Those variables were therefore compared between groups separately. Every extracted component from the PCA and separate variables excluded from the PCA because of insufficient KMO values were compared between groups separately using linear models [ 58 ], including group (POSI or no POSI) as a predictor and age and country as covariates. Additionally, we added group × age interactions to the models, given the higher risk of developing POSI for younger children. For components including TTR nouns and/or TTR verbs, we added number of words as a fixed effect to the model, given TTR’s known sensitivity to sample size [ 59 ]. Additionally, we ran linear models for the global sample characteristics (i.e., sample size in words and utterances, intelligibility and prompting). All statistical analyses were performed using RStudio [ 60 ]. Results Global sample characteristics In Table 4 , the results of the analysis of several global sample characteristics are reported. The analyses showed that the percentage of unclear speech (i.e., speech that was hard to understand or unintelligible) and thus the proportion of data that had to be excluded from further analyses, were significantly higher in the group who later developed POSI compared to the group who did not (β = -14.455, p = .024). We also found a significant interaction between group and age. In the POSI group, intelligibility was lower in younger children but improved with increasing age, whereas in children who did not develop POSI, intelligibility was relatively similar across ages (β = .152, p = .007). Additionally, a near-significant difference was observed in the proportion of utterances produced with examiner support (either prompted or elliptic; β = 48.754, p = .064), indicating a tendency for children who later developed POSI to receive more prompts from the examiner. No differences were found in the number of words or utterances that the child produced telling the story. See Appendix D for the results of the complete models including predictors and covariates. Table 4 Linear models for global sample characteristics POSI No POSI group β (SE, p ) age × group β (SE, p ) R² #Words 105 132 -42.75 (55.20, .445) .016 (0.48, .739) .18 #Utterances 18.8 19.9 2.91 (5.57, .605) -0.03 (0.05, .484) .17 %Unclear 3.9% 1.9% -14.46 (6.06, .024) 0.15 (0.05, .007) .22 %Prompted 23.6% 11.6% 48.75 (25.31, .064) -0.35 (0.22, .127) .23 Note. POSI = postoperative speech impairment, defined as mutism or severely reduced speech; No POSI = habitual speech; #Words = sample size in number of words; #Utterances = sample size in number of utterances; %Unclear = percentage of hard-to-understand and unintelligible speech; SE = standard error. (Psycho)linguistic analysis Principal Component Analysis In the Principal Component Analysis, two components (C1 and C2) were extracted per level of language processing. At the semantic level, all variables were included in the PCA. For Semantics C1, the variables with a significant loading were concreteness verbs, imageability nouns, imageability verbs , and instrumentality verbs , and for Semantics C2 these were concreteness nouns and familiarity nouns . At the lexical level, the variables AoA verbs and frequency nouns were not suitable for inclusion in the PCA due to insufficient KMO values. The components extracted from the remaining variables were C1, where lexical correctness, AoA nouns and frequency verbs had a significant contribution, and C2, containing the variables TTR verbs and TTR nouns . At the morphosyntactic level, unaccusativity was excluded from further analysis, as only 6 children produced unaccusative verbs. Regularity appeared not to be suitable for inclusion in the PCA, due to an insufficient KMO value. Two components were extracted from the remaining variables: C1 containing grammatical correctness and finiteness index , and C2 containing MLU and transitivity . At the phonological level, all variables were included in the PCA. In C1, word length verbs and word length nouns had a significant loading. In C2, the variables with a significant loading were phonological errors and cluster index . See Appendix C for the full overview of the variables and their loadings in the components. Group comparisons Linear models were constructed for each component and for the separate variables not included in the PCA, including number of words (C2), age and country as covariates, group as a predictor, and the interaction group × age . See Table 5 for an overview of the models for each component and separate variable and see Appendix D for the results of the complete models including predictors and covariates. Table 5 Linear model per component or separate variable Component/variable group β (SE, p ) age × group β (SE, p ) R² Semantic C1 : Concr. V + Imageab. V+ Instrum. V + Imageab. N -0.19 (0.12, .103) -0.00 (0.00, .666) .54 C2 : Concr. N + Fam. N -0.30 (0.57, .605) 0.00 (0.00, .706) .65 Lexical C1 : Lex. cor + AoA N+ Freq. V -0.73 (0.41, .086) 0.01 (0.00, .137) .77 C2 : TTR N + TTR V 0.51 (0.77, .517) -0.00 (.01, .722) .31 AoA V -0.22 (0.64, .739) 0.00 (.01, .535) .63 Freq. N 1.25 (1.02, .232) -0.01 (.01, .143) .07 Morphosyntax C1 : Gram. cor.+Finite. ind. -0.96 (0.84, .261) 0.01 (.01, .307) .06 C2 : MLU + Trans. V -1.23 (0.95, .206) 0.01 (.01, .162) .15 Regularity V -0.08 (0.74, .916) 0.00 (.01, .994) .52 Phonology C1 : Length V + Length N 0.05 (0.22, .810) -0.00 (.00, .895) .95 C2 : Phon. err.+Clust. ind. -0.05 (0.04, .257) -0.00 (.76, .451) .17 Note. C = Component; N = Nouns; V = Verbs; Concr. = Concreteness; Imageab. = Imageability; Instrum. = Instrumentality; Fam. = Familiarity; Lex. cor. = Lexical correctness; AoA = Age of acquisition; Freq. = Frequency; TTR = Type-Token Ratio; Gram. cor. = grammatical correctness; Finite. ind. = finiteness index; Trans. = Transitivity; Phon. err. = Phonological errors; Clust. ind. = Cluster index; SE = standard error. Country had a significant effect on the components/variables Semantic C1, Semantic C2, Lexical C1, AoA verbs, verb regularity and Phonology C1 and C2. Additionally, age significantly impacted Frequency nouns, and number of words had a significant effect on the component Lexical C2, including TTR nouns and verbs. Group appeared not to be a significant predictor of the scores for any of the components or separate variables. No significant age × group interactions were found for any of the components or separate variables. Discussion To identify risk factors in the domain of language related to the emergence of POSI, we performed an extensive linguistic analysis of the preoperative narrative language samples of 34 patients who received neurosurgical resection for a PFT. We compared some global language sample characteristics of a group of 16 children who developed POSI after neurosurgical resection with 18 patients who did not develop the complication. Thereafter, we performed an extensive (psycho)linguistic analysis of language samples, using a PCA in which we extracted two components for each level of language processing (i.e., the semantic, lexical, morphosyntactic, and phonological level). We compared the groups using the combined variables derived from the PCA results, as well as the separate variables that were not included in the PCA. Children who later developed POSI tended to produce a higher proportion of unintelligible or hard-to-understand speech, and this effect interacted with age: the younger the child developing POSI, the higher the proportion of unclear speech. Group comparisons with the components and separate variables showed no differences between the two groups in the linguistic measures reflecting semantic, lexical, morphosyntactic, and phonological processing, and no interactions between group and age. In this section, we will discuss the group comparisons and how this relates to previous research on this population. Global sample characteristics Considering the global sample characteristics, the percentage of unclear speech that had to be excluded from further analysis was higher in the patients who later developed POSI compared to those who did not. This is in line with results from Bianchi et al. [ 8 ], who characterized preoperative impairments in the vast majority of patients who went on to develop mutism as a phonological disorder, while comprehension and lexical naming appeared relatively intact (NB: they also refer to these difficulties as phonetic disorder and apraxia of speech , which are motor-speech disorders, which leaves some uncertainty about the exact nature of these difficulties). While unclear speech was excluded from further analysis, motor-speech related factors potentially contributed to the speech being unintelligible, such as respiration, phonation, resonance, prosody, articulation and speech rate [ 61 , 62 ]. Several studies reported preoperative difficulties in the domain of speech in children who developed mutism, such as dysarthria [ 12 , 62 ], ataxia [ 62 – 64 ] and apraxia of speech [ 12 ], although these did not extensively assess the nature of these speech difficulties. A more systematic, in-depth analysis of speech, intelligibility and phonological errors could provide more insight into the phonological and/or speech impairments these patients experience and if these could be a risk factor for the development of POSI. Interestingly, the effect of age on the proportion of unclear speech differed between the two groups. In the group who later developed POSI, the proportion of unclear speech was higher in younger children and decreased with increasing age, whereas age was not associated with intelligibility in the group who did not develop POSI. This finding aligns with research showing a higher risk of developing mutism or reduced speech in younger children [ 4 , 5 ]. Mutism or reduced speech is hypothesized to be a form of cerebello-cerebral diaschisis, characterized by damage to the connections between the cerebellum and cerebrum, resulting in hypoactivity of the cerebral hemispheres [ 65 , 66 ]. İldan et al. [ 24 ] suggest that these connections are more vulnerable to damage in children, due to the incomplete maturation of the brain, resulting in a higher incidence of mutism or reduced speech. The presence of this age effect already before neurosurgical resection suggests that not only the surgical intervention [ 10 ], but also the tumour itself may have a stronger impact on these pathways in younger children. Additionally, we observed an imbalance in the amount of prompting provided by the testers between groups, albeit just above the significance threshold. This occurred despite instructions to interfere minimally and only provide general prompts when the child needed encouragement [ 54 ]. Although the effect did not reach statistical significance, there seems to be a tendency toward more frequent prompting in the group that later developed POSI. This pattern might suggest that the adynamic language pattern often observed after neurosurgical resection [ 16 ] could already be present to some extent in the preoperative stage, prompting testers to provide more support. However, this prompting should be investigated more systematically, in greater detail and in a larger group of patients before firm conclusions can be drawn. (Psycho)linguistic analysis In our (psycho)linguistic analysis, none of the language components or separate variables preoperatively differentiated children who developed POSI from those who did not. These results are in contrast with previous research by Di Rocco et al. [ 12 ], who preoperatively found a lower MLU and problems with lexical naming and verbal fluency for children who developed mutism, and Bianchi et al. [ 8 ], who expanded Di Rocco’s sample, suggested a relationship between preoperative language performance and mutism. Critically, MLU was defined by Di Rocco et al. [ 12 ] based on parental report [ 67 , 68 ]. While ratings of sentence length are also employed in perceptual speech analysis, this measure may not be entirely comparable to the typical MLU calculation in linguistics studies, which reflects the length of syntactic units and distinctions between devices such as conjunction vs. subordination, which would be difficult for parents to judge [ 69 ]. Furthermore, sentences may be judged by parents as shorter due to motor speech symptoms (rather than language). For instance, difficulties with speech motor control and in particular coordination of respiration-phonation-articulation may lead to more frequent pauses which appear as shorter sentences, if length does not consider linguistic structures. In line with this, a perceptual analysis of speech carried out in the European CMS Study [ 70 ] showed that children who went on to develop POSI speak in shorter phrases before neurosurgical resection (as rated by speech language pathologists). Similarly, performance on language tasks such as picture naming or verbal fluency may also be influenced by poor motor speech, especially when the tasks are timed (as is the case of verbal fluency) or reaction times are taken into account, as done in some picture naming tests [ 71 , 72 ]. Additionally, performance on language tasks is not solely determined by language or speech abilities. Verbal fluency, for instance, also relies on neuropsychological processes such as executive functioning and processing speed [ 73 , 74 ]. The group differences in verbal fluency found by Di Rocco and colleagues could therefore also be driven by difficulties in these domains. Horne et al. [ 75 ] and Cámara et al. [ 13 ] reported postoperative impairments in executive functioning and processing speed. Given the overall poorer preoperative neuropsychological status of patients who developed mutism reported by Mariën et al. [ 65 ], these difficluties are likely at least to some extent already present before neurosurgical resection. Combined, this suggests that children with and without mutism or reduced speech may differ in their (motor) speech or overall neuropsychological status before neurosurgical resection, which may affect their performance on language tasks, while differences in language itself may be absent. Nevertheless, children with PFTs in general may still exhibit preoperative language impairments due to tumor presence and growth. Further research comparing patients’ language samples to those of healthy peers, as was done for lexical naming by Persson et al. [ 26 ], could provide more insight into the extent of difficulty and the specific level of language affected. Something that has to be taken into account when interpreting the results is the type of tumour diagnosed in patients. The groups were not matched on tumour type, as this would create atypical groups, given the relatedness of tumour type to emergence of mutism or reduced speech [ 3 , 10 ]. It should be noted, however, that the proportion of medulloblastomas is relatively (albeit non-significantly) higher in our POSI group (63%), compared to the no POSI group (33%). Medulloblastomas have been linked to the emergence of mutism or reduced speech, but Persson et al. [ 26 ] did not find a relation with preoperative word-finding difficulties. They hypothesize that the emergence of mutism or reduced speech is not related to the tumour itself, but rather to the high-risk surgery medulloblastomas require. This might explain why no relationship between medulloblastomas and language performance was observed preoperatively. This hypothesis aligns with the results of the current study, in which a higher proportion of medulloblastomas was found in the group that later developed POSI, but no group differences were found before neurosurgical resection. Further research on the relationship between tumour type, location, and preoperative language performance is needed to better understand the impact of tumour characteristics on language performance. Limitations and suggestions for future research A limitation of this study concerns the type of test (i.e., telling a story based on a picture book) that was used to collect a language sample, which might have affected the quality of the language samples extracted. In a picture-description task, it is largely predetermined what the child will talk about, which may have limited children in using their linguistic abilities to their full potential. Qiu [ 76 ], for instance, showed an increased lexical complexity for a story generation task where no pictures were used, compared to a picture-description task. In the same line, the task might have limited the potential range of psycholinguistic variable values, such as AoA, imageability and word length. Svaldi et al. [ 15 ] also suggested an impact of the task after analysing language samples obtained using two elicitation methods: conversation and picture description. The picture-description task was found to identify fewer atypical language profiles than the conversation task. Although we believe differences between the two groups might primarily be in the domain of speech, the task used might have made it difficult to capture subtle language differences between groups, especially in the semantic domain [ 15 ]. Opting for more naturalistic approaches (e.g., parent-child or examiner-child interactions; see, for instance, Ellis Weismer et al. [ 77 ]) may provide a more ecologically valid and comprehensive picture of language abilities. Nonetheless, it should be noted that the European CMS study is a large-scale study across many centres and languages. The ERRNI procedure [ 54 ], using a picture-book-based narrative, creates a setting where language samples can be gathered in a very standardized way, despite the large variability in settings and languages. Other, more naturalistic or interview-based data collection procedures might introduce much more variability and require even greater expertise from those gathering the data, which may have a detrimental effect on the feasibility of the study or the quality of the data. Furthermore, the way patients were classified as experiencing mutism or reduced speech may have impacted our results. In the European CMS study, decisions about whether a child showed mutism or reduced speech were not made by speech and language therapists, but by clinicians such as surgeons, pediatricians or, in some cases, nursing staff. While we may assume relatively strong agreement on identifying mutism as a complete absence of speech, the categorisation of reduced speech is likely more variable. What one rater considers a clinically significant reduction in speech may not be judged as such by another. As a result, some children classified as having POSI (reduced speech) might have been categorised as having no POSI by other raters, and vice versa. This heterogeneity may have introduced noise into the POSI/no POSI variable, potentially making it more difficult to detect associations between preoperative language abilities and postoperative speech outcomes. Future research may benefit from standardised criteria or rater training, or from involving speech and language therapists in the diagnostic process, to improve the reliability of POSI classification. Another limitation concerns the inclusion procedure of participants. Because we performed a language sample analysis in our study, a criterion for inclusion in the study was the availability of a preoperative language sample. This led to the exclusion of 86 patients for whom there was no sample available. Information on the reason for not performing the language task was often not available, but this could be related to the characteristics of the patients or their neuropsychological status. Mariën et al. [ 65 ] proposed a relationship between neuropsychological status and the emergence of mutism, making the excluded group particularly interesting for future research. Although language test administration is difficult in this group, future research could focus on suitable tests to gain a better understanding of the language abilities of children with worse neuropsychological status. Conclusion This study aimed to identify risk factors in the domain of language that could be related to the emergence of POSI, using a comprehensive analysis of preoperative language samples. The global sample characteristics and the linguistic abilities of patients who developed POSI were compared to those of patients who did not develop POSI. Results revealed a higher proportion of unintelligible speech in the group that later developed POSI. The linguistic analysis of the language samples did not reveal any group differences. Our results indicate that preoperative differences between the two groups may be primarily related to motor speech rather than to the microstructural aspects of language assessed through our (psycho)linguistic analyses. The language differences between patients with and without POSI observed postoperatively may thus be correlated with the effects of neurosurgical tumor resection. This study adds to the limited body of preoperative research performed in this population and suggests that already at the preoperative stage, there might be speech characteristics that are related to the emergence of postoperative mutism or reduced speech. Additional research is needed to further explore the predictive value of speech characteristics (which we predict will show more prominent group differences, given our findings on intelligibility). Such advances will help form an increasingly accurate risk prediction of the development of mutism or reduced speech. Declarations Funding This publication is supported by funding awarded to project Verb Processing and Verb Learning in Children With Paediatric Posterior Fossa Tumours (with file number VI.Vidi.201.003) of the research program NWO-Talentprogramma Vidi SGW 2020 financed by the Dutch Research Council (NWO). Jonathan Kjær Grønbæk and Ditte Boeg Thomsen received funding from the Inge Lehmann grant (grant number 10.46540/4302-00027B) from the Independent Research Fund Denmark. Karin Persson received funding from The Swedish Childhood Cancer Foundation, Queen Silvia’s Jubilee Fund, Jonas Foundation. Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethical considerations The current study used patient data from the European CMS Study. Data collection for this project was approved by the Research Ethics Committees of the Capital Region in Denmark (H-6-2014-002). The first participation of Dutch centers was approved by the Medical Ethics Review Committee (CMO) of Radboud University Medical Center, Nijmegen (NL55516.091.15). Following a temporary discontinuation, the second participation of Dutch centers received ethical approval from the Medical Ethics Review Committee NedMec (METC NedMec; NL81967.041.22). Participation of centers in the UK was approved by the North West - Liverpool East Research Ethics Committee (16/NW/0633). Participation of the Italian center was approved by the Ethical Committee of the IRCCS Bambino Gesù Children’s Hospital (1923/2019). All procedures were conducted in accordance with the Declaration of Helsinki. Human ethics and consent to participate Written informed consent was obtained from all individual participants or from a parent of the participating children. Data availability The paper reports a secondary analysis of data from the European CMS study. Requests for access to and reuse of the data should be directed to the principal investigator of the European CMS study, René Mathiasen ( [email protected] ). Author constributions Conceptualization: Aliene Reinders and Vânia de Aguiar; Methodology: Aliene Reinders, Vânia de Aguiar and Cheyenne Svaldi; Project administration and resources: Jonathan Kjær Grønbæk, René Mathiasen, Christine Dahl, Marianne Juhler, Barry Pizer, Colin Thorbinson, Kristian Aquilina, Eelco Hoving, Andrea Carai, Angela Mastrunozzi and Vânia de Aguiar; Investigation: Aliene Reinders, Cheyenne Svaldi and Bianca Andreozzi; Data curation and formal analysis: Aliene Reinders and Cheyenne Svaldi; Supervision: Vânia de Aguiar and Roel Jonkers; Writing - original draft preparation: Aliene Reinders; Writing - review and editing: all authors reviewed the manuscript; Funding acquisition: Vânia de Aguiar, Ditte Boeg Thomson, Marianne Juhler and Jonathan Kjær Grønbæk Acknowledgements We thank the CMS study team for their sustained efforts in patient recruitment and longitudinal follow-up, and the patients whose time and participation made this research possible. 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Brain Lang. 2005;92:153–67. https://doi.org/10.1016/j.bandl.2004.06.015 . Marien P, Engelborghs S, Fabbro F, De Deyn PP. The Lateralized Linguistic Cerebellum: A Review and a New Hypothesis. Brain Lang. 2001;79:580–600. https://doi.org/10.1006/brln.2001.2569 . Van Baarsen KM, Grotenhuis JA. The anatomical substrate of cerebellar mutism. Med Hypotheses. 2014;82:774–80. https://doi.org/10.1016/j.mehy.2014.03.023 . Fenson L. MacArthur Communicative Development Inventories: User’s guide and technical manual. Paul H. Brookes; 2002. Caselli MC, Bello A, Rinaldi P, Pasqualetti P. Il primo vocabolario del bambino: gesti, parole e frasi. Forme lunghe e forme brevi del questionario e valori di riferimento per la fascia 8–36 mesi. Milano, Italy: Franco Angeli; 2015. Eisenberg SL, Fersko TM, Lundgren C. The Use of MLU for Identifying Language Impairment in Preschool Children. Am J Speech Lang Pathol. American Speech-Language-Hearing Association; 2001;10:323–42. https://doi.org/10.1044/1058-0360(2001/028) Ahmed R, Boll-Avetisyan N, Kjær Grønbæk J, Boeg Thomsen D, Mathiasen R, De Aguiar V. Preoperative Speech Deviations and Postoperative Impairment in Children with Poster Fossa Tumors [Poster abstract]. Budapest, Hungary; 2025. Coady JA. Rapid Naming by Children With and Without Specific Language Impairment. J Speech Lang Hear Res. 2013;56:604–17. https://doi.org/10.1044/1092-4388(2012/10-0144) . Miller CA, Leonard LB, Kail RV, Zhang X, Tomblin JB, Francis DJ. Response Time in 14-Year-Olds With Language Impairment. J Speech Lang Hear Res. American Speech-Language-Hearing Association; 2006;49:712–28. https://doi.org/10.1044/1092-4388(2006/052) Elgamal SA, Roy EA, Sharratt MT. Age and Verbal Fluency: The Mediating Effect of Speed of Processing. Can Geriatr J CGJ. 2011;14:66–72. https://doi.org/10.5770/cgj.v14i3.17 . Shao Z, Janse E, Visser K, Meyer AS. What do verbal fluency tasks measure? Predictors of verbal fluency performance in older adults. Front Psychol [Internet] Front. 2014. https://doi.org/10.3389/fpsyg.2014.00772 . [cited 2025 Jan 30];5. Horne BM, Attanayake AA, Aquilina K, Murphy T, Malcolm CP. The Neurocognitive Profile of Post-operative Paediatric Cerebellar Mutism Syndrome: A Systematic Review [Internet]. medRxiv; 2025 [cited 2025 Oct 8]. p. 2025.02.21.25322700. https://doi.org/10.1101/2025.02.21.25322700 . Qiu X. Picture or non-picture? The influence of narrative task types on lower- and higher-proficiency EFL learners’ oral production. Int Rev Appl Linguist Lang Teach De Gruyter Mouton. 2022;60:383–409. https://doi.org/10.1515/iral-2017-0094 . Ellis Weismer S, Venker CE, Evans JL, Moyle MJ. Fast mapping in late-talking toddlers. Appl Psycholinguist. 2013;34:69–89. https://doi.org/10.1017/S0142716411000610 . Footnotes We choose to be lenient with this cut-off. The main goal of performing the PCA was dimensionality reduction. Therefore, if there were any variables that had a slightly lower (e.g., 0.48) KMO value, but did cluster together with other variables in a meaningful way later on in the analyses, we decided to include those variables in the analyses. In a PCA, every variable has a loading onto every component, some stronger than others (expressed by the loading value). It is possible to extract components that incorporate all variables while considering their specific loadings. However, this leads to less interpretable data, as each component becomes a mixture of all variables. Therefore, we choose to use the PCA as a guide to identify which variables can be clustered together and we averaged the scores of only those variables that made a significant contribution to the component. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialAnalysisofpresurgicallanguageinchildrenwithposteriorfossatumoursrelativetopostoperativespeechoutcomesfindingsfromtheEuropeanCMSstudy.docx Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2026 Read the published version in The Cerebellum → Version 1 posted Editorial decision: Revision requested 29 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviews received at journal 15 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 31 Dec, 2025 Reviewers invited by journal 31 Dec, 2025 Editor assigned by journal 25 Dec, 2025 Submission checks completed at journal 25 Dec, 2025 First submitted to journal 17 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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15:16:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":44800,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8387537/v1/90c908568574b2b81c079f91.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of presurgical language in children with posterior fossa tumours relative to postoperative speech outcomes: findings from the European CMS study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eApproximately half of the brain tumours in children occur in the posterior fossa [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Treatment for Posterior Fossa Tumours (PFTs) generally entails neurosurgical resection which is often followed by chemo- and radiotherapy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Cerebellar Mutism Syndrome (CMS), also referred to as \u003cem\u003epaediatric cerebellar mutism syndrome\u003c/em\u003e, \u003cem\u003eposterior fossa syndrome (PFS)\u003c/em\u003e, or \u003cem\u003emutism with subsequent dysarthria (MSD)\u003c/em\u003e, is a common complication following PFT surgery in children. It typically arises within days after the neurosurgical resection and affects around 24\u0026ndash;34% of patients [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The most defining symptom of CMS is transient mutism or severely reduced speech, and it is further characterized by emotional lability, hypotonia, and a wide range of motor and cognitive deficits [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although the mutism or reduced speech is transient, speech and language problems can persist later in life [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The profound acute and long-term impact of the complication highlights the importance of accurately predicting which patients are at risk of developing it. Research identifying risk factors for the emergence of mutism or reduced speech primarily focuses on demographic (such as age, sex, and handedness) and clinical factors (such as tumour type and location and surgical approach; [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]). Risk factors in the domain of language have been studied [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], but their full scope remains underexplored. In this study, we therefore perform a comprehensive analysis of narrative language samples of children diagnosed with a PFT before they undergo neurosurgical resection. Their language performance will be related to their postoperative speech status (mutism or reduced speech vs. habitual speech), to identify if there are language characteristics that may be related to the emergence of mutism or reduced speech.\u003c/p\u003e\n\u003ch3\u003eSpeech and language after mutism or reduced speech\u003c/h3\u003e\n\u003cp\u003eLong-term impairments in speech and language may be observed in paediatric PFT survivors regardless of whether they experienced mutism or reduced speech or not [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Nonetheless, several studies suggest that there may be differences in motor and cognitive skills, and specifically speech and language abilities in patients who experienced mutism or reduced speech and patients who did not [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. When mutism or reduced speech subsides, patients will often present with motor speech disorders, a pattern collectively termed mutism with subsequent dysarthria [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. They may also have more impairments in the initiation of voluntary movement, including speech initiation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. De Smet et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] reported a pattern of adynamic spontaneous output (i.e., very little initiation) in patients suffering from mutism, which was still observed two years after neurosurgical resection.\u003c/p\u003e \u003cp\u003eAdditionally, several studies suggest that there may be differences in \u003cem\u003elanguage\u003c/em\u003e processing in patients who did and did not experience mutism or reduced speech. C\u0026aacute;mara et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] found that patients who experienced mutism had poorer outcomes than those who did not across several general cognitive and language abilities, including verbal comprehension, receptive and expressive language, verbal memory, and verbal fluency. Additionally, the presence of mutism or reduced speech was found to be related to slower reading pace [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and poorer verbal learning [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A comprehensive linguistic analysis of spontaneous language by Svaldi et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] also suggested that while individual language profiles varied a lot, there was a differential pattern of long-term impairment in children who did and did not have mutism. Children who experienced mutism showed language impairments predominantly in the morphosyntactic and semantic domains of language, while children who did not develop mutism showed a wider spread in language impairments across all language domains. However, in a recent study investigating postoperative word-finding abilities in a large sample, Persson et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] did not find a relation between postoperative speech impairment (POSI, consisting of mutism or reduced speech) and poorer postoperative word-finding abilities. Still, all children who had experienced a complete absence of speech exhibited a postoperative decline in word finding.\u003c/p\u003e \u003cp\u003eStudies examining language differences between patients with mutism or reduced speech and those without included either small participant numbers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], or focused on a specific aspect of language [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, most studies did not account for preoperative language impairment (with the exception of [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]). This is important, as it may be that preoperative language characteristics can be linked to the emergence of mutism or reduced speech.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors for mutism or reduced speech\u003c/h2\u003e \u003cp\u003eExtensive research has been done on the risk factors related to the emergence of mutism or reduced speech, see [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] for an overview. Several studies show that the incidence of mutism or reduced speech decreases with age [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], with rare occurrence in adults [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. İldan et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] proposed that the higher incidence of mutism in younger children could be related to the incomplete maturation of the brain that renders younger children more prone to developing the complication. Additionally, some tumour locations are associated with higher risk of mutism or reduced speech, such as vermal and midline tumours [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and brainstem tumours [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Cerebellar hemisphere tumours, on the other hand, generate a lower risk [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Concerning tumour type, patients with high-grade medulloblastomas have consistently been reported to develop mutism or reduced speech more often than children with low-grade astrocytomas [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePreoperative risk factors in the domain of language are underexplored, as previous research primarily focuses on differential postoperative language outcomes in patients with mutism or reduced speech. Nonetheless, Di Rocco et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] found that children in their sample without preoperative language problems did not develop mutism, while all children in their study who developed mutism presented with preoperative language problems. Preoperative language impairments were characterised by a shorter Mean Length of Utterance (MLU) and problems with verbal fluency and lexical naming. Bianchi et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] extended the findings of Di Rocco et al. by enlarging the patient cohort, showing that 20 out of 70 patients with PFTs who developed mutism presented with preoperative language impairments. In this study, phonological impairments appeared to be most common. Persson et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] found that patients with PFTs experience word finding difficulties before neurosurgical resection, characterized by slow and/or inaccurate word finding, but they did not examine the relationship between preoperative word finding difficulties and the emergence of mutism or reduced speech. Research into the preoperative language abilities of children diagnosed with a PFT is thus limited, and to date, no research has compared the language profiles of children who do and do not experience mutism or reduced speech comprehensively, that is, providing a picture across different levels of language processing.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentifying language impairments through connected language\u003c/h3\u003e\n\u003cp\u003eThe existing literature on language disorders in child survivors of PFTs suggests that these may affect all language domains (e.g., phonology, morphosyntax, lexical and semantic knowledge, pragmatics) and may present themselves in variable combinations and severity across children [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Preoperative language abilities should thus be evaluated comprehensively in every patient, including all language domains. A highly productive approach to evaluate multiple domains of language is through the evaluation of language samples, either elicited in conversations/interactions, picture descriptions, or through the (re)telling of narratives [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This approach has been used previously in children with PFTs, focusing on the macro- and microstructural aspects of language after neurosurgical resection and revealing differences from healthy controls [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] as well as differences within patient subgroups [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis approach combines the use of standard measures (e.g., Type-Token Ratio) and the critical variable approach by Shallice [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], similarly to Svaldi et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], see also the Supplementary Material for a detailed description of this approach. In the critical variable approach, Shallice describes that properties of words (i.e., psycholinguistic properties) can impact language performance, and that these properties, which reflect functioning of specific levels of language processing, can reveal impairments at their respective levels.\u003c/p\u003e\n\u003ch3\u003eLevels of language processing\u003c/h3\u003e\n\u003cp\u003e Across models of language processing [e.g., 33\u0026ndash;35] it is commonly agreed that conveying a message (e.g., \u0026lsquo;The cat meows\u0026rsquo;) through spoken language requires processing at several levels of language. At the semantic level, conceptual information related to the meanings of words is being stored and retrieved (e.g., the knowledge that a cat meows; Levelt, 1989). At the lexical level, the word forms which make up our mental dictionary, or lexicon, are stored and retrieved [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The morphosyntactic level relates to the internal structure of words (morphology) and sentences (syntax) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Morphological rules govern the internal structure of words, and establish a relation to other units in the syntactic structure (e.g., the addition of \u0026lsquo;s\u0026rsquo; to the verb-stem \u0026lsquo;meow\u0026rsquo;, to match the third person singular of the subject). Syntax consists of grammatical information such as word classes (e.g., \u0026lsquo;cat\u0026rsquo; is a noun, \u0026lsquo;meowing\u0026rsquo; is a verb), as well as rules concerning sentence structure (e.g., the subject \u0026lsquo;cat\u0026rsquo; must come before the verb \u0026lsquo;meows\u0026rsquo;). Furthermore, at the phonological level, segmental phonological information is retrieved. This entails information of individual speech sounds, which need to be ordered correctly and stored in phonological short-term memory in preparation for and during speech [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Breakdowns at each of these levels can lead to characteristic patterns of errors [see 37].\u003c/p\u003e \u003cp\u003eFor each of these levels, there are linguistic variables which may be extracted from narrative language to study their functioning. For example, semantic representations may be easier to retrieve depending on how easily a concept evokes a mental image (imageability) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] or how concrete/abstract it is [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], so patients with semantic disorders may be biased to use the words which are highly imageable and concrete [e.g., 15,40]. Other variables that tap into the semantic system are familiarity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] or instrumentality of verbs [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. At the lexical level, vocabulary size can be estimated with the ratio of different to total words in a sample (Type-token ratio, TTR), or lexical properties, such as lexical accuracy, corpus-based word frequency [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], or word age of acquisition [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] may be studied. Morphosyntactic ability may be assessed with measures such as the Mean Length of Utterances [e.g., 45], as well as grammatical accuracy [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and proportion of finite verbs (i.e., inflected verbs such as walks, as opposed to walk), with the latter two showing impairment in children with PFTs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Other word properties such as verb transitivity, unaccusativity, and regularity of inflectional paradigms, can also be used to detect atypical verb usage in populations with language impairment [e.g., 47,48]. At the phonological level, impairments may be revealed by phonological errors [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], or a bias to produce less complex articulatory patterns (e.g., string vs. sing) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Furthermore, a tendency to produce short words may be indicative of phonological short-term memory difficulties [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. While most of these variables were included in the work by Svaldi et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] on postoperative language, such a comprehensive analysis of narrative language has not been reported in studies concerning preoperative language abilities of PFT patients.\u003c/p\u003e\n\u003ch3\u003eCurrent study\u003c/h3\u003e\n\u003cp\u003eIn summary, postoperatively, there appear to be language characteristics that distinguish children who have experienced postoperative speech impairment from those who have not. Research into the preoperative stage is limited but suggests that linguistic differences between patients with mutism or reduced speech compared to those with habitual speech might already be present before neurosurgical resection. However, knowledge on the exact nature of these preoperative impairments and what language characteristics are related to the emergence of mutism or reduced speech remains unclear. This study will be the first to extensively analyse the preoperative language samples of patients who underwent neurosurgical resection for a PFT and did or did not develop postoperative speech impairment. Language samples of 34 patients will be analysed, replicating and expanding on language analysis procedures used by Svaldi et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We aim to investigate which preoperative semantic, lexical, morphosyntactic, and phonological characteristics may be related to the emergence of mutism or reduced speech. Strengthening our understanding of preoperative language abilities as a risk factor for the emergence of this complication will help predict postoperative outcomes and allows better preparation of patients and their parents regarding potential difficulties that might await them after surgery.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eData from children who underwent surgery for a PFT were retrieved through a database part of the prospective European CMS Study [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This study was registered on November 24, 2014, with ClinicalTrials.gov (registration number: NCT02300766). Within the European CMS Study, postoperative mutism or reduced speech is referred to as Postoperative Speech Impairment (POSI), a term we adopt in the current study. Between 2013 and 2024, 794 patients participated in the study. We included patients from treatment centres in The Netherlands, The United Kingdom and Italy, due to the availability of research personnel proficient in the languages spoken in these countries (Dutch, English and Italian, respectively). Criteria for inclusion in the current study were: (1) the availability of a language sample collected before the first neurosurgical resection of a PFT, (2) the availability of information on the presence of POSI, (3) the absence of previous neurological, neuropsychological, psychiatric and/or speech problems, and (4) the absence of previous chemotherapy and/or radiotherapy treatment.\u003c/p\u003e \u003cp\u003eIn addition to the inclusion criteria, several clinical measures were obtained to further characterise the patient group, including POSI status, tumor location and histology, and preoperative hydrocephalus, dysarthria and oculomotor abnormalities. POSI was reported by a clinician as present if the patient presented for at least one day post-surgery with mutism (i.e., no production of words or short sentences) or severely reduced speech (i.e., limited to single words or short sentences which can only be elicited after vigorous stimulation). Tumor location was reported by a clinician after surgical resection, and could include the cerebellar vermis, the right and left hemispheres, the fourth ventricle and/or the brainstem. Tumor histology was reported as medulloblastoma, pilocytic astrocytoma, ependymoma or other. Preoperative hydrocephalus was reported as present or absent. Preoperative dysarthria and oculomotor difficulties were rated by a clinician using a five- and three-point scale, respectively, which we reclassified for the current study as absent (original score 0), or present (any higher score).\u003c/p\u003e \u003cp\u003eParticipant selection is summarised in Fig.\u0026nbsp;1. Sixty-six patients met the inclusion criteria, of whom 16 developed POSI and 50 did not. Given our specific interest in language difficulties in patients with POSI, we performed an additional selection step to create a matched control group of patients without POSI, considering several additional clinical characteristics. Eighteen patients who did not develop POSI were matched at the group level to the 16 patients with POSI based on age, sex, country, and language background (i.e., mono- or multilingual), as well as preoperative hydrocephalus, dysarthria, and oculomotor difficulties. Further clinical characteristics were not considered in the matching procedure to avoid creating atypical samples, given the relationship between POSI and, for instance, tumour histology and location (Gr\u0026oslash;nb\u0026aelig;k et al., 2023). The final groups did not differ in tumour histology and tumour location, although there was a tendency for tumour location towards a group difference. See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for an overview of demographic and clinical characteristics of the groups, and Appendix A for individual demographic and clinical information.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcess of participant inclusion\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDemographic and clinical background information per group\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOSI (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eno POSI (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2a\u003c/sup\u003e/\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Y;M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003et = -\u003c/em\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8;10 (3;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9;1 (3;5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3;9\u0026ndash;14;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3;5\u0026ndash;16;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCountry\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe Netherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLanguage background\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonolingual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBi- and multilingual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumour location\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;8.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft cerebellar hemisphere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight cerebellar hemisphere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVermis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth ventricle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrainstem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumour histology\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedulloblastoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePilocytic astrocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpendymoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-op dysarthria\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-op hydrocephalus\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-op oculomotor\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003edifficulties\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e POSI\u0026thinsp;=\u0026thinsp;Postoperative Speech Impairment, defined as mutism or severely reduced speech; No POSI\u0026thinsp;=\u0026thinsp;habitual speech; Y;M\u0026thinsp;=\u0026thinsp;age in years and months; Other\u0026thinsp;=\u0026thinsp;Atypical Teratoid/Rhabdoid Tumour and other tumour types; Pre-op\u0026thinsp;=\u0026thinsp;preoperative.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003eChi square test was performed using Monte Carlo simulation (10,000 replicates) to account for small cell counts and limited sample size.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003ePercentages may not total exactly 100% due to rounding.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMaterials and Procedures\u003c/h2\u003e \u003cp\u003eThe language samples were collected by a clinician or speech and language pathologist, typically from a few days before surgery to the day of surgery. To elicit a narrative language sample, the ERRNI \u0026ndash; Fish Story [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] was used. In this task, the child was presented with a picture storybook about a boy who goes to a pet store and encounters various situations along the way. The examiner was instructed to let the child go through the book, viewing all the pictures from beginning to end without saying anything. Afterwards, the child was asked to tell the story using the picture book. The examiner was further instructed to interfere minimally but was allowed to provide encouragement to continue, such as \u0026lsquo;Mhm,\u0026rsquo; or general prompts like \u0026lsquo;What happened next?\u0026rsquo;. However, examiners frequently intervened despite these instructions (to be explained in more detail below). The story told by the child was recorded using an audio recorder.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData coding\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eData preparation: Global sample characteristics\u003c/h2\u003e \u003cp\u003eThe narrative language samples were transcribed using the transcription and annotation software ELAN [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], following a detailed protocol based on the Spontaneous Speech Analysis Procedure (STAP) [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] and the ERRNI \u0026ndash; Fish Story [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Utterances that contained 20% or more unintelligible or hard-to-understand words were excluded from further analysis. Additionally, utterances in which the child mimicked the tester, general comments unrelated to the story and questions asked to the examiner were excluded from further analysis. If an utterance was interrupted (e.g., by the examiner or a long break) but the child continued their syntactic structure after the interruption, both parts were analysed as one utterance. All remaining utterances were included in the analyses described hereafter.\u003c/p\u003e \u003cp\u003eAs mentioned above, examiners were instructed not to prompt patients, but occasionally deviated from these instructions. Utterances were therefore coded into three categories: (1) \u003cem\u003eelliptic utterances\u003c/em\u003e, if they were syntactically dependent on a prompt given by the tester (e.g., Examiner: \u003cem\u003e\u0026ldquo;What does the mom give to the boy?\u0026rdquo;\u003c/em\u003e, Participant: \u003cem\u003e\u0026ldquo;Money\u0026rdquo;\u003c/em\u003e); (2) \u003cem\u003eprompted utterances\u003c/em\u003e, if they were prompted by the tester but syntactically independent (e.g., Examiner: \u0026ldquo;\u003cem\u003eWhat is the girl doing with the bags?\u0026rdquo;\u003c/em\u003e, Participant: \u003cem\u003e\u0026ldquo;The girl is swapping the doll and the fish.\"\u003c/em\u003e); and (3) \u003cem\u003efree utterances\u003c/em\u003e, if the child produced them spontaneously or in response to a general, neutral prompt (e.g., Examiner: \u0026ldquo;\u003cem\u003eAnd what do you see here?\u0026rdquo;\u003c/em\u003e, Participant: \u0026ldquo;\u003cem\u003eThe boy is walking to the pet store.\u0026rdquo;\u003c/em\u003e). Typically, elliptic and prompted utterances are excluded from further analysis because they do not reflect the child\u0026rsquo;s independent ability to formulate a sentence and may affect language measures. For example, elliptic answers could lower MLU, and prompted responses might encourage the child to use words they would not have produced spontaneously. However, in the current study, neither elliptic nor prompted utterances were excluded, as doing so would result in the loss of a significant portion of data.\u003c/p\u003e \u003cp\u003eFrom this pre-processing step, we calculated several global sample characteristics, which could help enhance the interpretation and contextualization of the results. First, we calculated the sample size in \u003cem\u003enumber of words\u003c/em\u003e and \u003cem\u003enumber of utterances\u003c/em\u003e. Furthermore, the \u003cem\u003epercentage of unclear speech\u003c/em\u003e was calculated, reflecting the percentage of utterances that had to be removed from the sample because they contained too many hard-to-understand or unintelligible words. Finally, the \u003cem\u003epercentage of prompted utterances\u003c/em\u003e was calculated to reflect the amount of utterances produced with support from the examiner (either elliptic or prompted).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e(Psycho)linguistic analyses\u003c/h2\u003e \u003cp\u003eAfter preparing the data, a (psycho)linguistic language sample analysis was performed, similar to the procedures reported by Svaldi et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A total of 24 language measures were extracted from the samples on four levels of language processing (i.e., semantic, lexical, morphosyntactic, and phonological). See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for an overview of variables per level of language processing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eOverview of language measures\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of language processing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard language sample measures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdditional (psycho)linguisticf variables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSemantics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConcreteness*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFamiliarity*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImageability*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVerb instrumentality (proportion)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLexical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLexical diversity (TTR)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge of acquisition*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLexical accuracy (percentage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWord frequency*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMorphosyntax\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean length of utterance (in words)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVerb transitivity (proportion)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrammatical accuracy (percentage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnaccusativity (proportion)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFiniteness index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVerb regularity (proportion)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhonology\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhonological errors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWord length (in phonemes)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote.\u003c/em\u003e Variables marked with * were analysed separately for nouns and verbs. TTR\u0026thinsp;=\u0026thinsp;Type-Token Ratio. See Supplementary Material for detailed descriptions of each variable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStandard language sample measures\u003c/b\u003e. The standard language measures at the lexical level included \u003cem\u003eType-Token Ratio\u003c/em\u003e (\u003cem\u003eTTR\u003c/em\u003e) and \u003cem\u003elexical accuracy\u003c/em\u003e. TTR was calculated by dividing the number of unique nouns/verbs in the sample by the total number of nouns/verbs, including those uttered as part of hesitations, repetitions and self-corrections. TTR scores could range from 0 to 1, with scores closer to 1 expressing better performance. Lexical accuracy was determined based on whether an utterance contained lexical errors (e.g., semantic paraphasias), and was expressed as the percentage of lexically correct utterances. Morphosyntactic standard language measures included \u003cem\u003emean length of utterance\u003c/em\u003e (\u003cem\u003eMLU\u003c/em\u003e), \u003cem\u003egrammatical accuracy\u003c/em\u003e and \u003cem\u003efiniteness index\u003c/em\u003e. MLU was calculated by dividing the total number of words in a language sample by the total number of utterances, with higher scores indicating more syntactically complex language. Grammatical accuracy was determined based on whether an utterance contained grammatical errors (e.g., errors in word order or missing elements), and was expressed as the percentage of grammatically correct utterances. The \u003cem\u003efiniteness index\u003c/em\u003e was calculated by dividing the number of correctly produced inflected verbs (e.g., \u0026lsquo;walks\u0026rsquo; that matches the third person singular of the subject in \u0026lsquo;the man walks\u0026rsquo;) by the total number of required inflected verbs. Scores for the finiteness index could range from 0 to 1, with scores closer to 1 expressing better performance. At the phonological level, the proportion of \u003cem\u003ephonological errors\u003c/em\u003e in the sample was calculated by dividing the total number of errors by the total number of words in the sample.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdditional (psycholinguistic) variables.\u003c/b\u003e For every unique noun and verb produced by the child, ratings for multiple psycholinguistics variables (e.g., imageability, frequency) were extracted, reflecting different levels of language processing. At the semantic level, concreteness, familiarity, and imageability ratings were extracted. On the lexical level, AoA and word frequency were considered. For the variables \u003cem\u003everb instrumentality, transitivity, unaccusativity\u003c/em\u003e and \u003cem\u003eregularity\u003c/em\u003e, at the morphosyntactic level, a trained linguist determined whether the given property could be attributed to every verb produced (e.g., \u0026lsquo;1\u0026rsquo; if the verb was instrumental, \u0026lsquo;0\u0026rsquo; if the verb was not). Thereafter, the proportion of instrumental/transitive/regular verbs to the total number of verbs was calculated. \u003cem\u003eUnaccusativity\u003c/em\u003e was calculated as the proportion of unaccusative verbs to the total number of intransitive verbs. On the phonological level, word length in phonemes was extracted from a database or determined by a trained linguist, in case a word was not included in the database. Additionally, the \u003cem\u003ecluster index\u003c/em\u003e was calculated by dividing, per utterance, the total number of correctly produced clusters by the total number of required clusters. Scores could range from 0 to 1, with scores closer to 1 reflecting a more accurate production of clusters. To ensure accuracy and consistency, the aforementioned data coding was validated by the first and/or second author. See Appendix B for an overview of the language-specific databases used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalyses\u003c/h2\u003e \u003cp\u003eTo evaluate the variables extracted from the language samples, we performed a Principal Component Analysis (PCA) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This multivariate technique was applied to reduce the number of comparisons to be performed, given the high number of variables included. In a PCA, variables contributing similarly to the variability in the data are clustered together into components. As we had a relatively high number of variables (24) in relation to the sample size (34), we performed four separate PCAs: one for each level of language processing. This way, we could reduce the dimensionality of the data, while taking the PCA\u0026rsquo;s sensitivity for the participant-variable ratio into account. Because we were extracting psycholinguistic variables from databases and those were not always complete, we had to deal with a small number of missing values. A PCA cannot be performed when variables contain missing values. Therefore, three patients were excluded from the semantic PCA because none of their produced verbs had available concreteness or imageability ratings.\u003c/p\u003e \u003cp\u003eSubsequently, the variables were evaluated for suitability for the PCA, using the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin Measure of Sampling Adequacy (KMO) and Bartlett\u0026rsquo;s test of sphericity. A variable was considered suitable for inclusion in the PCA if its KMO value exceeded approximately 0.5[1]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e and if the assumption of sphericity was not violated. Based on the eigenvalues (\u0026gt;\u0026thinsp;1.0) of the components and the elbow method, we determined how many components to retain, see [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] for further explanation. Variables with a loading greater than 0.45 or less than \u0026minus;\u0026thinsp;0.45 were considered to contribute significantly to the variability explained by the component. All variables were then normalized and the normalized scores of the variables that clustered together in the component were averaged. Where necessary, variables were recoded so that higher scores consistently reflected better performance. For example, a higher score on \u003cem\u003ephonological errors\u003c/em\u003e originally indicated more errors (worse performance) and was therefore inverted so that a higher score represented better performance. Those combined variables were then used for further analyses[2]\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003cp\u003eSeveral variables were not eligible for inclusion in the PCA due to insufficient KMO values, indicating that these variables did not contribute to the variance in the same way as other variables did, and thus should not be combined in a given component. Those variables were therefore compared between groups separately. Every extracted component from the PCA and separate variables excluded from the PCA because of insufficient KMO values were compared between groups separately using linear models [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], including \u003cem\u003egroup\u003c/em\u003e (POSI or no POSI) as a predictor and \u003cem\u003eage\u003c/em\u003e and \u003cem\u003ecountry\u003c/em\u003e as covariates. Additionally, we added \u003cem\u003egroup\u003c/em\u003e \u0026times; \u003cem\u003eage\u003c/em\u003e interactions to the models, given the higher risk of developing POSI for younger children. For components including TTR nouns and/or TTR verbs, we added \u003cem\u003enumber of words\u003c/em\u003e as a fixed effect to the model, given TTR\u0026rsquo;s known sensitivity to sample size [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Additionally, we ran linear models for the global sample characteristics (i.e., sample size in words and utterances, intelligibility and prompting). All statistical analyses were performed using RStudio [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGlobal sample characteristics\u003c/h2\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the results of the analysis of several global sample characteristics are reported. The analyses showed that the percentage of unclear speech (i.e., speech that was hard to understand or unintelligible) and thus the proportion of data that had to be excluded from further analyses, were significantly higher in the group who later developed POSI compared to the group who did not (β = -14.455, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.024). We also found a significant interaction between group and age. In the POSI group, intelligibility was lower in younger children but improved with increasing age, whereas in children who did not develop POSI, intelligibility was relatively similar across ages (β\u0026thinsp;=\u0026thinsp;.152, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.007). Additionally, a near-significant difference was observed in the proportion of utterances produced with examiner support (either prompted or elliptic; β\u0026thinsp;=\u0026thinsp;48.754, p\u0026thinsp;=\u0026thinsp;.064), indicating a tendency for children who later developed POSI to receive more prompts from the examiner. No differences were found in the number of words or utterances that the child produced telling the story. See Appendix D for the results of the complete models including predictors and covariates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eLinear models for global sample characteristics\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo POSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003cp\u003eβ (SE, \u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eage \u0026times; group\u003c/p\u003e \u003cp\u003eβ (SE, \u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#Words\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-42.75 (55.20, .445)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e.016 (0.48, .739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#Utterances\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.91 (5.57, .605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03 (0.05, .484)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e%Unclear\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-14.46 (6.06, .024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15 (0.05, .007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e%Prompted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.75 (25.31, .064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.35 (0.22, .127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e POSI\u0026thinsp;=\u0026thinsp;postoperative speech impairment, defined as mutism or severely reduced speech; No POSI\u0026thinsp;=\u0026thinsp;habitual speech; #Words\u0026thinsp;=\u0026thinsp;sample size in number of words; #Utterances\u0026thinsp;=\u0026thinsp;sample size in number of utterances; %Unclear\u0026thinsp;=\u0026thinsp;percentage of hard-to-understand and unintelligible speech; SE\u0026thinsp;=\u0026thinsp;standard error.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e(Psycho)linguistic analysis\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003ePrincipal Component Analysis\u003c/h2\u003e \u003cp\u003eIn the Principal Component Analysis, two components (C1 and C2) were extracted per level of language processing. At the semantic level, all variables were included in the PCA. For Semantics C1, the variables with a significant loading were \u003cem\u003econcreteness verbs, imageability nouns, imageability verbs\u003c/em\u003e, and \u003cem\u003einstrumentality verbs\u003c/em\u003e, and for Semantics C2 these were \u003cem\u003econcreteness nouns\u003c/em\u003e and \u003cem\u003efamiliarity nouns\u003c/em\u003e. At the lexical level, the variables \u003cem\u003eAoA verbs\u003c/em\u003e and \u003cem\u003efrequency nouns\u003c/em\u003e were not suitable for inclusion in the PCA due to insufficient KMO values. The components extracted from the remaining variables were C1, where \u003cem\u003elexical correctness, AoA nouns\u003c/em\u003e and \u003cem\u003efrequency verbs\u003c/em\u003e had a significant contribution, and C2, containing the variables \u003cem\u003eTTR verbs\u003c/em\u003e and \u003cem\u003eTTR nouns\u003c/em\u003e. At the morphosyntactic level, \u003cem\u003eunaccusativity\u003c/em\u003e was excluded from further analysis, as only 6 children produced unaccusative verbs. \u003cem\u003eRegularity\u003c/em\u003e appeared not to be suitable for inclusion in the PCA, due to an insufficient KMO value. Two components were extracted from the remaining variables: C1 containing \u003cem\u003egrammatical correctness\u003c/em\u003e and \u003cem\u003efiniteness index\u003c/em\u003e, and C2 containing \u003cem\u003eMLU\u003c/em\u003e and \u003cem\u003etransitivity\u003c/em\u003e. At the phonological level, all variables were included in the PCA. In C1, \u003cem\u003eword length verbs\u003c/em\u003e and \u003cem\u003eword length nouns\u003c/em\u003e had a significant loading. In C2, the variables with a significant loading were \u003cem\u003ephonological errors\u003c/em\u003e and \u003cem\u003ecluster index\u003c/em\u003e. See Appendix C for the full overview of the variables and their loadings in the components.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGroup comparisons\u003c/h2\u003e \u003cp\u003eLinear models were constructed for each component and for the separate variables not included in the PCA, including \u003cem\u003enumber of words\u003c/em\u003e (C2), \u003cem\u003eage\u003c/em\u003e and \u003cem\u003ecountry\u003c/em\u003e as covariates, \u003cem\u003egroup\u003c/em\u003e as a predictor, and the interaction \u003cem\u003egroup\u003c/em\u003e \u0026times; \u003cem\u003eage\u003c/em\u003e. See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e for an overview of the models for each component and separate variable and see Appendix D for the results of the complete models including predictors and covariates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eLinear model per component or separate variable\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent/variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003cp\u003eβ (SE, \u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eage \u0026times; group\u003c/p\u003e \u003cp\u003eβ (SE, \u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemantic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC1\u003c/b\u003e: Concr. V\u0026thinsp;+\u0026thinsp;Imageab. V+\u003c/p\u003e \u003cp\u003eInstrum. V\u0026thinsp;+\u0026thinsp;Imageab. N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.19 (0.12, .103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.00 (0.00, .666)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC2\u003c/b\u003e: Concr. N\u0026thinsp;+\u0026thinsp;Fam. N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.30 (0.57, .605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00 (0.00, .706)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLexical\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC1\u003c/b\u003e: Lex. cor\u0026thinsp;+\u0026thinsp;AoA N+\u003c/p\u003e \u003cp\u003eFreq.\u0026nbsp;V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.73 (0.41, .086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01 (0.00, .137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC2\u003c/b\u003e: TTR N\u0026thinsp;+\u0026thinsp;TTR V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.77, .517)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.00 (.01, .722)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAoA V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.22 (0.64, .739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00 (.01, .535)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreq.\u0026nbsp;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.25 (1.02, .232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.01 (.01, .143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMorphosyntax\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC1\u003c/b\u003e: Gram. cor.+Finite. ind.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.96 (0.84, .261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01 (.01, .307)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC2\u003c/b\u003e: MLU\u0026thinsp;+\u0026thinsp;Trans. V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.23 (0.95, .206)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01 (.01, .162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegularity V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.08 (0.74, .916)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00 (.01, .994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhonology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC1\u003c/b\u003e: Length V\u0026thinsp;+\u0026thinsp;Length N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.22, .810)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.00 (.00, .895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC2\u003c/b\u003e: Phon. err.+Clust. ind.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05 (0.04, .257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.00 (.76, .451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e C\u0026thinsp;=\u0026thinsp;Component; N\u0026thinsp;=\u0026thinsp;Nouns; V\u0026thinsp;=\u0026thinsp;Verbs; Concr. = Concreteness; Imageab. = Imageability; Instrum. = Instrumentality; Fam. = Familiarity; Lex. cor. = Lexical correctness; AoA\u0026thinsp;=\u0026thinsp;Age of acquisition; Freq.\u0026nbsp;= Frequency; TTR\u0026thinsp;=\u0026thinsp;Type-Token Ratio; Gram. cor. = grammatical correctness; Finite. ind. = finiteness index; Trans. = Transitivity; Phon. err. = Phonological errors; Clust. ind. = Cluster index; SE\u0026thinsp;=\u0026thinsp;standard error.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCountry\u003c/em\u003e had a significant effect on the components/variables Semantic C1, Semantic C2, Lexical C1, AoA verbs, verb regularity and Phonology C1 and C2. Additionally, \u003cem\u003eage\u003c/em\u003e significantly impacted Frequency nouns, and \u003cem\u003enumber of words\u003c/em\u003e had a significant effect on the component Lexical C2, including TTR nouns and verbs. \u003cem\u003eGroup\u003c/em\u003e appeared not to be a significant predictor of the scores for any of the components or separate variables. No significant \u003cem\u003eage\u003c/em\u003e \u0026times; \u003cem\u003egroup\u003c/em\u003e interactions were found for any of the components or separate variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo identify risk factors in the domain of language related to the emergence of POSI, we performed an extensive linguistic analysis of the preoperative narrative language samples of 34 patients who received neurosurgical resection for a PFT. We compared some global language sample characteristics of a group of 16 children who developed POSI after neurosurgical resection with 18 patients who did not develop the complication. Thereafter, we performed an extensive (psycho)linguistic analysis of language samples, using a PCA in which we extracted two components for each level of language processing (i.e., the semantic, lexical, morphosyntactic, and phonological level). We compared the groups using the combined variables derived from the PCA results, as well as the separate variables that were not included in the PCA. Children who later developed POSI tended to produce a higher proportion of unintelligible or hard-to-understand speech, and this effect interacted with age: the younger the child developing POSI, the higher the proportion of unclear speech. Group comparisons with the components and separate variables showed no differences between the two groups in the linguistic measures reflecting semantic, lexical, morphosyntactic, and phonological processing, and no interactions between group and age. In this section, we will discuss the group comparisons and how this relates to previous research on this population.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eGlobal sample characteristics\u003c/h2\u003e \u003cp\u003eConsidering the global sample characteristics, the percentage of unclear speech that had to be excluded from further analysis was higher in the patients who later developed POSI compared to those who did not. This is in line with results from Bianchi et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], who characterized preoperative impairments in the vast majority of patients who went on to develop mutism as a phonological disorder, while comprehension and lexical naming appeared relatively intact (NB: they also refer to these difficulties as \u003cem\u003ephonetic disorder\u003c/em\u003e and \u003cem\u003eapraxia of speech\u003c/em\u003e, which are motor-speech disorders, which leaves some uncertainty about the exact nature of these difficulties). While unclear speech was excluded from further analysis, motor-speech related factors potentially contributed to the speech being unintelligible, such as respiration, phonation, resonance, prosody, articulation and speech rate [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Several studies reported preoperative difficulties in the domain of speech in children who developed mutism, such as dysarthria [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], ataxia [\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] and apraxia of speech [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], although these did not extensively assess the nature of these speech difficulties. A more systematic, in-depth analysis of speech, intelligibility and phonological errors could provide more insight into the phonological and/or speech impairments these patients experience and if these could be a risk factor for the development of POSI.\u003c/p\u003e \u003cp\u003eInterestingly, the effect of age on the proportion of unclear speech differed between the two groups. In the group who later developed POSI, the proportion of unclear speech was higher in younger children and decreased with increasing age, whereas age was not associated with intelligibility in the group who did not develop POSI. This finding aligns with research showing a higher risk of developing mutism or reduced speech in younger children [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Mutism or reduced speech is hypothesized to be a form of cerebello-cerebral diaschisis, characterized by damage to the connections between the cerebellum and cerebrum, resulting in hypoactivity of the cerebral hemispheres [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. İldan et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] suggest that these connections are more vulnerable to damage in children, due to the incomplete maturation of the brain, resulting in a higher incidence of mutism or reduced speech. The presence of this age effect already before neurosurgical resection suggests that not only the surgical intervention [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], but also the tumour itself may have a stronger impact on these pathways in younger children.\u003c/p\u003e \u003cp\u003eAdditionally, we observed an imbalance in the amount of prompting provided by the testers between groups, albeit just above the significance threshold. This occurred despite instructions to interfere minimally and only provide general prompts when the child needed encouragement [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Although the effect did not reach statistical significance, there seems to be a tendency toward more frequent prompting in the group that later developed POSI. This pattern might suggest that the adynamic language pattern often observed after neurosurgical resection [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] could already be present to some extent in the preoperative stage, prompting testers to provide more support. However, this prompting should be investigated more systematically, in greater detail and in a larger group of patients before firm conclusions can be drawn.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e(Psycho)linguistic analysis\u003c/h2\u003e \u003cp\u003eIn our (psycho)linguistic analysis, none of the language components or separate variables preoperatively differentiated children who developed POSI from those who did not. These results are in contrast with previous research by Di Rocco et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], who preoperatively found a lower MLU and problems with lexical naming and verbal fluency for children who developed mutism, and Bianchi et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], who expanded Di Rocco\u0026rsquo;s sample, suggested a relationship between preoperative language performance and mutism. Critically, MLU was defined by Di Rocco et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] based on parental report [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. While ratings of sentence length are also employed in perceptual speech analysis, this measure may not be entirely comparable to the typical MLU calculation in linguistics studies, which reflects the length of syntactic units and distinctions between devices such as conjunction vs. subordination, which would be difficult for parents to judge [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Furthermore, sentences may be judged by parents as shorter due to motor speech symptoms (rather than language). For instance, difficulties with speech motor control and in particular coordination of respiration-phonation-articulation may lead to more frequent pauses which appear as shorter sentences, if length does not consider linguistic structures. In line with this, a perceptual analysis of speech carried out in the European CMS Study [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] showed that children who went on to develop POSI speak in shorter phrases before neurosurgical resection (as rated by speech language pathologists). Similarly, performance on language tasks such as picture naming or verbal fluency may also be influenced by poor motor speech, especially when the tasks are timed (as is the case of verbal fluency) or reaction times are taken into account, as done in some picture naming tests [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, performance on language tasks is not solely determined by language or speech abilities. Verbal fluency, for instance, also relies on neuropsychological processes such as executive functioning and processing speed [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. The group differences in verbal fluency found by Di Rocco and colleagues could therefore also be driven by difficulties in these domains. Horne et al. [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] and C\u0026aacute;mara et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported postoperative impairments in executive functioning and processing speed. Given the overall poorer preoperative neuropsychological status of patients who developed mutism reported by Mari\u0026euml;n et al. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], these difficluties are likely at least to some extent already present before neurosurgical resection.\u003c/p\u003e \u003cp\u003eCombined, this suggests that children with and without mutism or reduced speech may differ in their (motor) speech or overall neuropsychological status before neurosurgical resection, which may affect their performance on language tasks, while differences in language itself may be absent. Nevertheless, children with PFTs in general may still exhibit preoperative language impairments due to tumor presence and growth. Further research comparing patients\u0026rsquo; language samples to those of healthy peers, as was done for lexical naming by Persson et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], could provide more insight into the extent of difficulty and the specific level of language affected.\u003c/p\u003e \u003cp\u003eSomething that has to be taken into account when interpreting the results is the type of tumour diagnosed in patients. The groups were not matched on tumour type, as this would create atypical groups, given the relatedness of tumour type to emergence of mutism or reduced speech [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It should be noted, however, that the proportion of medulloblastomas is relatively (albeit non-significantly) higher in our POSI group (63%), compared to the no POSI group (33%). Medulloblastomas have been linked to the emergence of mutism or reduced speech, but Persson et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] did not find a relation with preoperative word-finding difficulties. They hypothesize that the emergence of mutism or reduced speech is not related to the tumour itself, but rather to the high-risk surgery medulloblastomas require. This might explain why no relationship between medulloblastomas and language performance was observed preoperatively. This hypothesis aligns with the results of the current study, in which a higher proportion of medulloblastomas was found in the group that later developed POSI, but no group differences were found before neurosurgical resection. Further research on the relationship between tumour type, location, and preoperative language performance is needed to better understand the impact of tumour characteristics on language performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and suggestions for future research\u003c/h2\u003e \u003cp\u003eA limitation of this study concerns the type of test (i.e., telling a story based on a picture book) that was used to collect a language sample, which might have affected the quality of the language samples extracted. In a picture-description task, it is largely predetermined what the child will talk about, which may have limited children in using their linguistic abilities to their full potential. Qiu [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], for instance, showed an increased lexical complexity for a story generation task where no pictures were used, compared to a picture-description task. In the same line, the task might have limited the potential range of psycholinguistic variable values, such as AoA, imageability and word length. Svaldi et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] also suggested an impact of the task after analysing language samples obtained using two elicitation methods: conversation and picture description. The picture-description task was found to identify fewer atypical language profiles than the conversation task. Although we believe differences between the two groups might primarily be in the domain of speech, the task used might have made it difficult to capture subtle language differences between groups, especially in the semantic domain [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Opting for more naturalistic approaches (e.g., parent-child or examiner-child interactions; see, for instance, Ellis Weismer et al. [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]) may provide a more ecologically valid and comprehensive picture of language abilities. Nonetheless, it should be noted that the European CMS study is a large-scale study across many centres and languages. The ERRNI procedure [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], using a picture-book-based narrative, creates a setting where language samples can be gathered in a very standardized way, despite the large variability in settings and languages. Other, more naturalistic or interview-based data collection procedures might introduce much more variability and require even greater expertise from those gathering the data, which may have a detrimental effect on the feasibility of the study or the quality of the data.\u003c/p\u003e \u003cp\u003eFurthermore, the way patients were classified as experiencing mutism or reduced speech may have impacted our results. In the European CMS study, decisions about whether a child showed mutism or reduced speech were not made by speech and language therapists, but by clinicians such as surgeons, pediatricians or, in some cases, nursing staff. While we may assume relatively strong agreement on identifying mutism as a complete absence of speech, the categorisation of reduced speech is likely more variable. What one rater considers a clinically significant reduction in speech may not be judged as such by another. As a result, some children classified as having POSI (reduced speech) might have been categorised as having no POSI by other raters, and vice versa. This heterogeneity may have introduced noise into the POSI/no POSI variable, potentially making it more difficult to detect associations between preoperative language abilities and postoperative speech outcomes. Future research may benefit from standardised criteria or rater training, or from involving speech and language therapists in the diagnostic process, to improve the reliability of POSI classification.\u003c/p\u003e \u003cp\u003eAnother limitation concerns the inclusion procedure of participants. Because we performed a language sample analysis in our study, a criterion for inclusion in the study was the availability of a preoperative language sample. This led to the exclusion of 86 patients for whom there was no sample available. Information on the reason for not performing the language task was often not available, but this could be related to the characteristics of the patients or their neuropsychological status. Mari\u0026euml;n et al. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] proposed a relationship between neuropsychological status and the emergence of mutism, making the excluded group particularly interesting for future research. Although language test administration is difficult in this group, future research could focus on suitable tests to gain a better understanding of the language abilities of children with worse neuropsychological status.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to identify risk factors in the domain of language that could be related to the emergence of POSI, using a comprehensive analysis of preoperative language samples. The global sample characteristics and the linguistic abilities of patients who developed POSI were compared to those of patients who did not develop POSI. Results revealed a higher proportion of unintelligible speech in the group that later developed POSI. The linguistic analysis of the language samples did not reveal any group differences. Our results indicate that preoperative differences between the two groups may be primarily related to motor speech rather than to the microstructural aspects of language assessed through our (psycho)linguistic analyses. The language differences between patients with and without POSI observed postoperatively may thus be correlated with the effects of neurosurgical tumor resection.\u003c/p\u003e \u003cp\u003eThis study adds to the limited body of preoperative research performed in this population and suggests that already at the preoperative stage, there might be speech characteristics that are related to the emergence of postoperative mutism or reduced speech. Additional research is needed to further explore the predictive value of speech characteristics (which we predict will show more prominent group differences, given our findings on intelligibility). Such advances will help form an increasingly accurate risk prediction of the development of mutism or reduced speech.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis publication is supported by funding awarded to project Verb Processing and Verb Learning in Children With Paediatric Posterior Fossa Tumours (with file number VI.Vidi.201.003) of the research program NWO-Talentprogramma Vidi SGW 2020 financed by the Dutch Research Council (NWO). Jonathan Kjær Grønbæk and Ditte Boeg Thomsen received funding from the Inge Lehmann grant (grant number 10.46540/4302-00027B) from the Independent Research Fund Denmark. Karin Persson received funding from The Swedish Childhood Cancer Foundation, Queen Silvia’s Jubilee Fund, Jonas Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study used patient data from the European CMS Study. Data collection for this project was approved by the Research Ethics Committees of the Capital Region in Denmark (H-6-2014-002). The first participation of Dutch centers was approved by the Medical Ethics Review Committee (CMO) of Radboud University Medical Center, Nijmegen (NL55516.091.15). Following a temporary discontinuation, the second participation of Dutch centers received ethical approval from the Medical Ethics Review Committee NedMec (METC NedMec; NL81967.041.22). Participation of centers in the UK was approved by the North West - Liverpool East Research Ethics Committee (16/NW/0633). Participation of the Italian center was approved by the Ethical Committee of the IRCCS Bambino Gesù Children’s Hospital (1923/2019). All procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman ethics and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all individual participants or from a parent of the participating children.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe paper reports a secondary analysis of data from the European CMS study. Requests for access to and reuse of the data should be directed to the principal investigator of the European CMS study, René Mathiasen ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor constributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u003c/strong\u003e Aliene Reinders and Vânia de Aguiar; \u003cstrong\u003eMethodology:\u003c/strong\u003e Aliene Reinders, Vânia de Aguiar and Cheyenne Svaldi; \u003cstrong\u003eProject administration\u003c/strong\u003e \u003cstrong\u003eand resources:\u003c/strong\u003e Jonathan Kjær Grønbæk, René Mathiasen, Christine Dahl, Marianne Juhler, Barry Pizer, Colin Thorbinson, Kristian Aquilina, Eelco Hoving, Andrea Carai, Angela Mastrunozzi and Vânia de Aguiar; \u003cstrong\u003eInvestigation:\u003c/strong\u003e Aliene Reinders, Cheyenne Svaldi and Bianca Andreozzi; \u003cstrong\u003eData curation and formal analysis:\u003c/strong\u003e Aliene Reinders and Cheyenne Svaldi; \u003cstrong\u003eSupervision:\u003c/strong\u003e Vânia de Aguiar and Roel Jonkers; \u003cstrong\u003eWriting - original draft preparation:\u003c/strong\u003e Aliene Reinders; \u003cstrong\u003eWriting - review and editing:\u003c/strong\u003e all authors reviewed the manuscript; \u003cstrong\u003eFunding acquisition:\u003c/strong\u003e Vânia de Aguiar, Ditte Boeg Thomson, Marianne Juhler and Jonathan Kjær Grønbæk\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the CMS study team for their sustained efforts in patient recruitment and longitudinal follow-up, and the patients whose time and participation made this research possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRickert CH, Paulus W. 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Int Rev Appl Linguist Lang Teach De Gruyter Mouton. 2022;60:383\u0026ndash;409. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/iral-2017-0094\u003c/span\u003e\u003cspan address=\"10.1515/iral-2017-0094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllis Weismer S, Venker CE, Evans JL, Moyle MJ. Fast mapping in late-talking toddlers. Appl Psycholinguist. 2013;34:69\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0142716411000610\u003c/span\u003e\u003cspan address=\"10.1017/S0142716411000610\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e We choose to be lenient with this cut-off. The main goal of performing the PCA was dimensionality reduction. Therefore, if there were any variables that had a slightly lower (e.g., 0.48) KMO value, but did cluster together with other variables in a meaningful way later on in the analyses, we decided to include those variables in the analyses.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e In a PCA, every variable has a loading onto every component, some stronger than others (expressed by the loading value). It is possible to extract components that incorporate all variables while considering their specific loadings. However, this leads to less interpretable data, as each component becomes a mixture of all variables. Therefore, we choose to use the PCA as a guide to identify which variables can be clustered together and we averaged the scores of only those variables that made a significant contribution to the component.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-cerebellum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cere","sideBox":"Learn more about [The Cerebellum](http://link.springer.com/journal/12311)","snPcode":"12311","submissionUrl":"https://submission.nature.com/new-submission/12311/3","title":"The Cerebellum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"cerebellar mutism syndrome, mutism, posterior fossa syndrome, infratentorial neoplasms, preoperative language impairment, language disorders","lastPublishedDoi":"10.21203/rs.3.rs-8387537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8387537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eCerebellar Mutism Syndrome (CMS) is a common complication in children following posterior fossa tumour (PFT) surgery, typically marked by transient postoperative speech impairment (POSI; i.e., mutism or reduced speech). Differences in language performance between children with and without POSI have been observed postoperatively, but it remains unclear to what extent these language difficulties exist preoperatively and whether preoperative difficulties are related to POSI. This study provides the first comprehensive analysis of preoperative language samples, using data from the European CMS study. The aim was to compare patients who did or did not develop POSI to identify preoperative language characteristics that may be associated with POSI.\u003c/p\u003e\u003ch2\u003eMethod.\u003c/h2\u003e \u003cp\u003ePreoperative language samples of 34 patients aged 3\u0026ndash;17 years were analysed, including 16 who later developed POSI and 18 who did not. An analysis was performed to compare sample characteristics and language performance across four levels: semantics, lexical, morphosyntax, and phonology.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eNo significant preoperative language differences were found between the groups for the levels of language processing (all \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026gt;\u0026thinsp;.137). Children who developed POSI produced more unintelligible speech preoperatively (β = -14.455, p\u0026thinsp;=\u0026thinsp;.024), but their intelligibility improved with age (age\u0026times;group: β\u0026thinsp;=\u0026thinsp;0.152, p\u0026thinsp;=\u0026thinsp;.007), whereas intelligibility in children without POSI remained relatively stable across age.\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e \u003cp\u003e These findings suggest that risk factors for POSI within the domain of verbal output may lie more in preoperative speech than in language. A comprehensive analysis of preoperative speech may provide valuable insight into speech characteristics potentially related to POSI.\u003c/p\u003e","manuscriptTitle":"Analysis of presurgical language in children with posterior fossa tumours relative to postoperative speech outcomes: findings from the European CMS study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 15:16:11","doi":"10.21203/rs.3.rs-8387537/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-29T10:06:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T00:28:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-15T13:09:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26832757674800555647326883889165386628","date":"2026-01-13T09:24:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258777126469825357166420245721303293461","date":"2026-01-12T23:39:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111271066987913132807580708536128698291","date":"2025-12-31T11:18:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-31T10:18:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-26T00:24:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-26T00:24:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Cerebellum","date":"2025-12-17T15:47:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-cerebellum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cere","sideBox":"Learn more about [The Cerebellum](http://link.springer.com/journal/12311)","snPcode":"12311","submissionUrl":"https://submission.nature.com/new-submission/12311/3","title":"The Cerebellum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"14b037ba-751b-4df4-8036-85d568cbcf85","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:13:41+00:00","versionOfRecord":{"articleIdentity":"rs-8387537","link":"https://doi.org/10.1007/s12311-026-01987-3","journal":{"identity":"the-cerebellum","isVorOnly":false,"title":"The Cerebellum"},"publishedOn":"2026-04-10 15:58:34","publishedOnDateReadable":"April 10th, 2026"},"versionCreatedAt":"2026-03-13 15:16:11","video":"","vorDoi":"10.1007/s12311-026-01987-3","vorDoiUrl":"https://doi.org/10.1007/s12311-026-01987-3","workflowStages":[]},"version":"v1","identity":"rs-8387537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8387537","identity":"rs-8387537","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}