Language processing in posterior fossa tumour patients: Psycholinguistic insights into the word-finding ability

Language processing in posterior fossa tumour patients: Psycholinguistic insights into the word-finding ability | 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 Language processing in posterior fossa tumour patients: Psycholinguistic insights into the word-finding ability Rida Ahmed, Aliene Reinders, Cheyenne Svaldi, Annet Kingma, Karin Persson, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8200894/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Word finding - the ability to retrieve and produce appropriate words in response to prompts or visual stimuli - is impaired in some patients with a posterior fossa tumour. Yet, few studies use preoperative assessment as a baseline, and an in-depth linguistic analysis of tasks assessing word-finding ability remains limited. The current study aims to fill this knowledge gap by analysing pre- and postoperative word-finding ability and identifying its linguistic predictors. Method 38 English-speaking patients (19 males and 19 females), aged between 2,5 and 17,6 years and diagnosed with posterior fossa tumours were assessed before and after surgery. Performance was assessed using a picture-naming task, Wordrace, measuring both accuracy and reaction times. These measures were interpreted in terms of their correlation with linguistic levels (i.e., lexical, semantic, phonological). Results Patients exhibited a significant slowing in word-finding speed following surgery, while accuracy remained stable across assessment points. Despite this decline in speed, the influence of psycholinguistic factors on word-finding ability remained consistent. Lexical-semantic variables predicted word-finding speed, whereas accuracy was influenced only by lexical variables. Conclusion The findings suggest that although general performance declined postoperatively, the underlying linguistic processes engaged during word finding were preserved. The study emphasises the importance of longitudinal assessment in patients with posterior fossa tumours and the need to compare patient performance against normative data. Infratentorial Neoplasms Posterior Fossa Tumors Language Disorders Psycholinguistics Semantics Posterior Fossa Syndrome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Central nervous system tumours are the second most common group of childhood neoplasms after leukaemia and continue to cause the highest number of childhood deaths related to cancer [ 1 ]. These statistics still hold even though survival rates of children with cancer have increased significantly over the last few decades [ 2 ]. Around half of childhood brain tumours arise in the posterior fossa that contains the cerebellum and the brainstem [ 3 ]. With improving treatment and care standards, it is important to ensure that survivors lead socially engaged lives with minimal functional impairments. Children with posterior fossa tumours (PFTs) present with language impairments at all processing levels, from producing individual words to forming sentences and using language appropriately in social situations, prompting further investigation into the nature of these impairments [ 4 ]. Since speech and language impairments create a barrier to effective social engagement [ 5 ], investigating important skills such as word-finding abilities is useful in gaining insights into language deficits affecting different linguistic processes [ 6 , 7 ]. The current study explores the nature of language processing underlying word-finding abilities in the PFT population using a picture-naming task. Furthermore, it examines potential changes in the factors influencing naming performance, measured before and after tumour surgery. The linguistic cerebellum The cerebellum has traditionally been attributed a role in motor function [ 8 ]. Nonetheless, by now, many studies using clinical and neuroimaging methods, mostly with adults, have confirmed its role in cognitive functions, including a variety of language functions, indexed by tasks such as verbal fluency [ 9 , 10 ], lexical decision [ 11 ], verb generation [ 12 ], and reading aloud [ 11 ]. Specific to lexical access - a process of retrieving words from the mental dictionary, right cerebellar involvement has been shown in phonemic fluency [ 13 ] and lexical decision [ 11 ]. The right cerebellum also showed activation when participants were asked to pay attention to the semantic relations between words [ 14 ]. Moreover, a recent study showed that electrical stimulation of the posterior cerebellum bilaterally facilitated the retrieval of semantically related words, confirming the cerebellum's role in word retrieval in healthy participants [ 15 ]. In addition, both hemispheres of the cerebellum have shown to be sensitive to distinct word properties during spoken language comprehension [ 16 ]. Specifically, higher word frequency was positively associated with activation in bilateral Crus I (extending into Crus II - subregions in the lateral posterior cerebellum), and superior lobule VI and IX. Moreover, lower phonological neighbourhood density (i.e., fewer similar-sounding words) were linked to greater activation in bilateral Crus I/II regions, with a stronger effect in the right hemisphere. Impairments in children with PFTs Surgical resection of a PFT may be followed by an array of motor, linguistic, and neurobehavioral/affective symptoms [ 17 ]. These have been collectively named 'post-operative pediatric cerebellar mutism syndrome (ppCMS)’ [ 18 , p. 1199], or Posterior Fossa Syndrome (PFS) [ 19 ]. A hallmark of this syndrome is a temporary period of absence of speech (mutism, PFS1) or severely reduced speech (PFS2), which may occur on its own or follow a period of mutism; together, these are referred to as post-operative speech impairment (POSI) [ 20 , 21 ]. Young age at diagnosis, high-grade tumour, and central midline location lead to an increased risk of developing POSI. Importantly, not all post-surgical impairments fall under the formal diagnosis of PFS/pCMS. Even in the absence of mutism, children may show fine and gross motor impairments [ 22 ] and cognitive sequelae, for example, low IQ, and poor memory, attention, and executive functioning [ 23 ]. Dysarthria, especially of the ataxic type, is also commonly reported, whether or not mutism is present [ 24 , 25 ]. Furthermore, several studies have reported impairments in language abilities after surgery, regardless of whether the child experienced a phase of mutism or reduced speech [ 4 , 26 ]. Yet, this literature is characterized by a great inconsistency, as some studies did not report group differences in language ability when comparing groups of PFT survivors and healthy controls (see [ 27 ] vs. [ 28 ]). A recent systematic review suggests that this inconsistency may partly reflect the substantial inter-individual variability in which aspects of language are impaired, as well as substantial methodological differences across studies, highlighting the need to study language in greater granularity with precise methods and attention to underlying linguistic mechanisms [ 4 ]. This systematic review of individuals reported in the literature showed that PFT survivors may show varying language impairment profiles, with individuals presenting many possible combinations of impaired and spared ability across levels of language processing [ 29 , 30 ], for example in semantics (knowledge of meanings) compared to morphosyntax (grammar), or pragmatics (language use in context). Word-finding difficulties in the PFT population When looking more specifically at studies examining word-finding abilities (see the Appendix for a summary of the literature), eight studies found significantly low performance of PFT patients in expressive vocabulary and word-finding tests [ 26 , 31 – 37 ], four showed patients’ performance comparable to control participants or norms [ 28 , 38 – 40 ], and the remaining ten had their results complicated due to various factors (for example, no control comparison [ 41 ]; merging naming scores with other tests to report a single measure of verbal intelligence [ 42 ]. Thus, the literature is divided on the lexical retrieval abilities in PFT patients, mainly because of a lack of experimental control. This can be expected in patient populations like these since testing specific language functions is neither practical due to fatigue and affective symptoms nor a priority. While these studies focused on whether word-finding deficits were present, one recent study investigated the nature of errors produced by children, providing insight into what may go wrong in language processing, leading to poor performance [ 35 ]. Their error analysis found that PFT survivors mostly produced semantically side-oriented errors, that is, a closely related word in meaning (e.g., pear instead of apple). The pre- vs. post-surgical onset of impairments Although the studies described above report on post-surgical impairments, children with PFTs may show impairments due to the presence of the tumour itself (before surgery), and also as a consequence of the necessary surgery and post-surgical treatments (i.e., radio- and/or chemotherapy). For instance, one study found that all patients who developed CMS after surgery had pathological naming preoperatively [ 32 ]. Another study reported slow (37%), inaccurate (24%), or slow and inaccurate (16%) word finding in PFT survivors already before surgery [ 35 ]. Moreover, when analysing word-finding abilities of PFT patients both before and after surgery, patients were found to be substantially impaired in comparison to controls in terms of naming speed at both timepoints [ 36 ]. Upon close inspection of the individual data, variability is reported such that some patients’ word-finding ability worsened after surgery while it improved for others. Importantly, the causal nature of word-finding abilities might change from pre- to post-surgery. First, impairments may exist because the tumour affects the anatomical substrates that have a role in word finding but depending on tumour type (low-grade or high-grade), this may happen at varying speeds, sometimes allowing for the language system to adapt to the anatomical changes via neuroplastic mechanisms [ 43 ]. This disruption may occur locally within the cerebellum due to its contribution to language processing [ 44 ], or through diaschisis, where tumour-induced compression causes disrupted connectivity with supratentorial language regions [ 45 , 46 ]. In addition, language impairment can result from local disruption to supratentorial areas themselves, due to tumour-related hydrocephalus [ 47 ]. On the other hand, surgery, while necessary for survival, causes a sudden, acute lesion as well as post-surgical edema in the posterior fossa region, which affects structures beyond the tumoral tissue, potentially affecting additional language processes. Hence, the neurocognitive nature of the word-finding difficulty may change from pre- to post-surgery. The chain of processes in word finding Psycholinguistic models of language processing suggest that word retrieval may fail or become challenging due to language processing impairments, which can happen at different stages of the word retrieval process. As illustrated in Fig. 1, model adapted from [ 48 , 49 ], picture naming comprises different sequential processes: Object Recognition is initiated when a person sees an object in a picture or in real life. Then follows non-verbal conceptual information about the object, such as knowing that an apple is something to eat, accessed through the level called Object Concepts . The information is then processed in the Semantic System , which is defined as the elements of knowledge that collectively compose the meaning of words. The next stage in the word retrieval process is the retrieval of the spoken word forms from the Phonological Output Lexicon , where known vocabulary is stored as combinations of speech sounds. Once a phonological form has been activated from the output lexicon, a phoneme string is generated by the Phonological Assembly . Finally, the phonemes from this sequence are fed into the Articulatory Programming stage, which generates neuromuscular commands from these phonemes, and the word is ultimately articulated. Factors that influence speed and accuracy of naming in individuals with language disorders have received much attention in previous literature [ 50 , 51 ]. Among these factors are the psycholinguistic properties of words that are to be named, such as word frequency, age of acquisition, and familiarity. Some of them are said to accelerate the retrieval of words (i.e., word frequency [ 52 ]; age of acquisition [ 53 ]), while others slow it down (i.e., phonological neighbourhood density [ 54 ]; word length [ 55 ]). This effect of word properties on lexical retrieval can be explained by the differential involvement of each language processing level (e.g., semantics, phonology) in language production. Importantly, the critical variable framework postulates that when a given variable affects performance in word retrieval, it is considered evidence of impaired language processing at the level of functioning relating to that specific variable [ 56 ]. In what follows, variables associated with specific levels of language processing shown in Fig. 1 will be described. It should be noted that the assignment of psycholinguistic variables to specific language processing levels is not always consistent. For example, the effects of age of acquisition of a word could be of a lexical origin (increased exposure to a word helps in its fast retrieval) or a semantic origin (early acquired words are processed quicker because they are positioned more centrally in the semantic network) [ 57 ]. The following section outlines the most commonly reported associations in the literature. Semantic variables Several semantic variables have been found to influence the lexical retrieval process at the Semantic System level shown in Fig. 1. For instance, imageability, which refers to the extent to which a word evokes a mental image, has been shown to facilitate word retrieval in both neurotypical individuals and people with aphasia [ 58 – 60 ]. Similarly, concreteness, which is defined as the degree to which a noun can be touched or visualized, supports the word retrieval process by creating strong semantic associations [ 61 ]. The number of semantic features, defined as the number of distinct conceptual attributes associated with a word, also facilitates naming speed and accuracy in neurotypical individuals by increasing semantic and lexical activation [ 62 , 63 ]. It has also been found to be a key predictor of naming performance in people with aphasia [ 64 ]. Lexical variables The lexical level of processing is reflected in the Phonological Output Lexicon in Fig. 1 and is influenced by variables relating to the way words are organized in the mental lexicon. One such variable is word frequency. It refers to how often language users are likely to encounter a word in everyday use and has a well-established facilitatory effect on naming in neurotypical individuals [ 65 – 68 ]. Secondly, age of acquisition (AoA) of a word also affects naming in neurotypical individuals, with early-acquired words retrieved faster [ 69 – 72 ]. Word familiarity is a more subjective measure of how often a person encounters a word - calculated based on participants’ ratings of how familiar a word feels to them - and it correlates strongly with word frequency and similarly facilitates naming [ 71 , 73 , 74 ]. It has also been shown to distinguish between variants of primary progressive aphasia [ 60 ]. Lastly, the phonological neighbourhood density (PND) is the number of words that can be formed by replacing, deleting, or adding one phoneme to the original word [ 75 ]. It is important to note that, although based on sound similarity, phonological neighbourhood density is considered a lexical-level variable because it reflects how words are organized in the mental lexicon [ 76 ]. It facilitates naming in people with aphasia [ 77 ], but shows variable effects in children depending on language development status [ 78 ]. These effects are often explained by interactive activation models, where activation spreads between phonemes and words, resulting in either competition or facilitation [ 75 , 79 ]. Phonological variables Phonological variables like word length influence lexical retrieval at stages prior to articulation. Specifically, the phonological output buffer precedes articulatory programming, temporarily storing speech sounds. Word length is measured by either the number of phonemes or syllables in a word. In some populations, such as people with aphasia, longer words result in more errors in naming [ 59 ], although a reverse effect has also been reported where longer words are more easily retrieved due to fewer phonological neighbours [ 80 ]. Specifically, longer word length in syllables has been shown to correlate with longer naming latencies in individuals with developmental language disorder [ 81 ]. Lastly, the number of consonant clusters in a word affects the Articulatory Programming stage of word production [ 82 ]. The increase in the number of consonant clusters has been associated with a higher number of errors in naming in children with DLD [ 83 , 84 ]. Previous studies with participants with language disorders have employed analysis of the psycholinguistic properties of the words produced. For instance, when checking for the psycholinguistic properties of the words produced by cerebellar tumour patients, impairments were observed at multiple levels of language processing [ 85 ]. Similarly, in spontaneous speech in bilingual children with developmental language disorder, it was found that the difference in phonological neighbourhood density between nouns and verbs was more pronounced in sequential bilingual children with DLD than in typically developing children [ 86 ]. Using a similar approach, a study investigating naming in individuals with aphasia found that the ‘lexical usage’ factor (composed of word frequency, familiarity, and age of acquisition) significantly differentiated between patients with fluent and non-fluent aphasia regarding naming accuracy [ 87 ]. Lastly, a role of word familiarity has been reported in driving naming performance in patients with primary progressive aphasia [ 60 ]. Present investigation Research gap Not only is the previous literature divided on the presence/absence of word-retrieval deficits in PFT patients, but the linguistic nature of impairments behind these deficits has been scarcely investigated. Psycholinguistic properties of words provide a window to investigate the processing dynamics that may be related to the performance in word finding even when the word is ultimately named correctly (e.g., through relations with reaction times (RTs)). This has not been explored in previous studies with the PFT population. Clinically, such an investigation is significant for two reasons: 1) identifying whether deficits stem from semantic, lexical, or phonological stages can inform targeted therapies (e.g., semantic feature training vs. phonological cueing), 2) subtle psycholinguistic effects (e.g., slowed RTs for low-frequency words) may serve as markers of cerebellar-cortical circuit disruption in patients with otherwise intact accuracy, potentially identifying cases at risk of presenting worse language performance. Moreover, the early postoperative stage in patients who undergo surgery in supratentorial language-eloquent areas is characterised by a decline in all language functions, including those involved in lexical retrieval [ 88 , 89 ]. This is attributed to the significant tissue damage as a result of surgery. Since several studies have identified a role of the cerebellum in language, and lesions in this region have been associated with subsequent language impairments [ 90 ], the current study also explored whether linguistic processing behind word finding in PFT patients is affected by the tumour surgery by comparing performance between the preoperative and the early postoperative periods. Research questions and hypotheses The current study aims to address three main questions. First, we examine whether word-finding ability changes from before to after surgery in patients with PFTs. It is expected that both the speed and accuracy of word retrieval will decline postoperatively, reflecting the potential impact of tumour removal on neural structures involved in language processing. Second, we explore which underlying linguistic processes drive word-finding abilities in the PFT patients. Based on prior findings, such as the prevalence of semantically related errors in PFT patients [ 35 ] and reports of semantic paraphasias in cerebellar stroke patients [ 90 ] - we hypothesize that semantic and lexical-semantic levels will play a primary role in driving word-finding performance. The exploration for the rest of the linguistic levels is exploratory. Finally, the study investigates whether the underlying linguistic processes differ in predicting word-finding abilities pre- and postoperatively in PFT patients. We anticipate that damage to brain tissue following resection may alter the relative influence of psycholinguistic levels on word-finding ability, indicating a shift in the mechanisms supporting word-finding ability. Methods Participants Thirty-eight English-speaking patients, 19 males and 19 females from the United Kingdom, included in the European Study of the Cerebellar Mutism Syndrome [ 91 ] formed the participant group for the current study. The European Study of CMS is a large multicenter study initiated in 2014 to investigate the incidence, symptoms, risk factors, and prognosis of CMS in children following surgery for posterior fossa tumours. The patients included in the current sub-study did not present with a history of developmental language disorder (DLD) or other speech-language disorders. Table 1 presents the demographic, tumour, and postoperative speech characteristics of participants. Table 1 Demographic, tumour, and postoperative speech characteristics of participants (n = 38) Characteristic Included cohort (n = 38) Sex 19 males (50) 19 females (50) Age at surgery (years) Mean = 8,8 Range = 2,5–17,6 SD = 4 Tumor histology Pilocytic astrocytoma: 20 (≈ 53) Medulloblastoma: 8 (≈ 21) Ependymoma: 6 (≈ 16) Ganglioglioma: 1 (≈ 3) Unknown: 3 (≈ 8) POSI status Habitual speech: 30 (≈ 79) Mute: 4 (≈ 10) Reduced speech: 2 (≈ 5) Unknown: 2 (≈ 5) POSI = Postoperative speech impairment. Categorical variables in n (%) Materials The study used audio recordings of the Wordrace task to determine accuracy and reaction times. Wordrace is a picture-naming task especially designed for the European study of CMS to test word-finding ability [ 92 ]. It contains 25 pictures that are shown either on screen or on paper one by one, and which need to be named as fast as possible. The test has been normed for the Swedish language and has shown a high test-retest reliability for measuring accuracy (r = .894) and speed (r = .627) [ 93 ]. It is a good alternative to traditional naming tests as it puts minimal demands on executive functioning, thus helping in measuring just the word-finding speed and accuracy [ 35 ]. For some target words, alternatives are accepted: for instance, ship for boat and chicken for rooster. Procedure The study used retrospective Wordrace data collected from patients at UK sites participating in the European Study of CMS, including Alder Hey Children’s Hospital (Liverpool), Great Ormond Street Hospital (London), and centers in Manchester, Bristol, and Nottingham. We used data from two assessment points, i.e., preoperatively (assessment point 1) and 1–4 weeks postoperatively (assessment point 2). The tester explained the task to the participants before administering it and then turned the pages or swiped on the tablet screen after the participant named each picture. As per the instructions, if a picture was not named after 5 seconds, the tester was supposed to move on to the next one, but this was not followed in all the testing sessions. The tester also kept track of the total time taken to complete the test with a stopwatch. Only minimal feedback was provided, such as ‘good job’, or the tester asked ‘what’s that?’. Data processing Calculating reaction times Reaction time and accuracy were determined for each item on Wordrace using Praat [ 94 ], a software used for phonetic analysis of audio data. See Fig. 2 for an example of what the TextGrid looked like for each speech sample (an interval is created just before a word is articulated and labeled for easier extraction of reaction times and accuracy for each label using Praat script). A response was considered correct if the target word was eventually named, even if the participant named a semantically related or unrelated word before naming the target word. Only correct responses were considered for reaction time analyses. Since the database only had audio recordings and the task was administered in different formats (screen or paper) in different centres across the UK, we defined specific markers of the start of the reaction time: an audible tap on the keyboard by the examiner for centres that conducted it on screen and the start of page flipping sound for centres that conducted it on paper. A tap or page flip was not audible for some audio recordings that probably administered the task on a tablet. For these cases, the pause between the naming of the previous item and the next item served as the reaction time. These different modes of task administration were adjusted for later in the analysis. The endpoint of the reaction time was marked at the onset of the acoustic marker of the first phoneme produced in the response (voicing for voiced consonants and vowels; or, for example, the onset of the plosive or fricative signal), as soon as this was visible in the waveform corresponding to the participant starting to name the picture. This ensures we only measured the time the participant took to retrieve the word, not other unrelated delays, such as phoneme elongation or interrupted articulation. Some participants named the pictures starting with the articles ‘a’, ‘the’ and ‘an’ while some started with the word ‘some’, such as ‘some bread’. Reaction time was noted after the article or the quantifier since participants may elongate these when struggling to retrieve words. Moreover, if the participant hesitated to say a word by articulating the first phoneme and then pausing and saying the whole word, the reaction time was noted until they said the word itself. In some cases, the participant said a semantically related word before ultimately naming the target word. In such cases, reaction time was noted until the target word was named. Sometimes, the participants coughed while naming a picture. Their reaction time was noted before they began coughing only if they had already articulated 20% of the phonemes of the word. Otherwise, the item was not considered for reaction time. The items for which the child could only say the target word upon hearing it from the tester were excluded and marked as ‘no response’. Figure 2 Praat TextGrid for each speech file Coding words for psycholinguistic properties To answer research questions 2 and 3, the study considered psycholinguistic word property norms for British English. Table 2 shows databases that are specifically normed for British English that were used for the present study. Consonant clusters and word length in syllables were defined manually. Each word in the Wordrace test was coded for each word property. Table 2 Psycholinguistic Properties of Words Word property Database Linguistic level Rating Scale Word frequency SUBTLEX-UK [ 95 ] Lexical Zipf scale (1–7) 1–3: Low frequency 4–7: High frequency Phonological neighbourhood density CLEARPOND (EnglishPOND) [ 96 ] Lexical Number of phonological neighbours after adding/deleting/substituting 1 phoneme Age of acquisition (AoA) The Bristol norms for age of acquisition [ 97 ] Lexical Average AoA in years across participants Semantic features Semantic feature production norms [ 62 ] Semantic No. of distinct features listed for each concept Concreteness The Glasgow Norms [ 98 ] Semantic 1–7 scale (1 = concrete, 7 = abstract - we used mean concreteness rating across participants) Imageability The Glasgow Norms [ 98 ] Semantic 1–7 scale (1 = very unimageable, 7 = very imageable) Familiarity The Bristol norms for familiarity [ 97 ] Semantic/lexical 1–7 scale (1 = very unfamiliar, 7 = very familiar) Length in phonemes CLEARPOND (EnglishPOND) [ 96 ] Phonological No. of phonemes Length in Syllables Self-determined Phonological No. of syllables Consonant clusters Self-determined Phonological No. of consonant clusters Dimensionality reduction via Principal Component Analysis The variables in Table 2 were to be used as predictors of word-finding speed and accuracy in analyses addressing the research questions 2 and 3. However, these make a large number of predictors and our interest was in the role of language processing levels, and not specific variables. Given that several variables are expected to reflect overlapping linguistic processing levels, variables were clustered together when they could be merged. This was done using a Principal Component Analysis (PCA) with values of all included psycholinguistic variables in Table 2 for the nouns that the participants used (n = 40); this included all the different synonyms used. A similar approach has been used by previous studies (see for example, [ 87 ]). The PCA was done in RStudio [ 99 ]. All variables were inserted into the PCA at once (not per level of language processing). This way, their clustering was entirely data-driven and not based on previous assumptions. To prepare for PCA, all the values were first z -scaled to get a uniform scale because different variables were originally rated on varied scales. For example, familiarity is rated on a scale of one to seven, whereas the phonological neighbourhood density is in the form of continuous numbers, so not standardizing can result in an artificial dominance of one variable with a larger scale. The number of principal components to be retained was determined by two criteria based on previous research [ 85 , 100 ], i.e., the cumulative variance explained should be more than 70%, and the components should have an eigenvalue above 1.0. The first four components explained 81.8% variance in the data; all of them have an eigenvalue higher than 1.0 (see Table 4 for explained variance and Fig. 3 for the scree plot for eigenvalues of components). Table 3 shows the loadings of different values on the components where a value equal to or higher than 0.45 is considered a meaningful contribution and is bolded. Table 3 Component Loadings C1 C2 C3 C4 Length in syllables -0.27 0.50 0.23 0.24 Length in phonemes 0.87 -0.3 -0.12 -0.17 Phonological neighbourhood -0.88 0.07 0.05 0.23 Consonant clusters 0.95 0.02 -0.04 0.01 Imageability -0.03 0.96 0.15 -0.06 Semantic features -0.13 0.10 -0.10 0.92 Age of acquisition 0.24 -0.14 -0.84 0.29 Familiarity 0.09 0.10 0.86 0.22 Frequency -0.39 -0.12 0.51 0.67 Concreteness -0.06 0.94 -0.06 0.01 Table 4 Summary C1 C2 C3 C4 SS loadings 2.750 2.210 1.811 1.561 Proportion variance 0.275 0.221 0.181 0.156 Cumulative variance 0.275 0.496 0.677 0.833 Figure 3 Scree plot of the principal component analysis Based on the values of component loadings (i.e., > 0.45), three variables, namely consonant clusters, length in phonemes, and phonological neighbourhood density loaded onto the first component. This is a phonological component since all these variables are reported in the literature to relate to phonological processing, either at the level of the output lexicon or the phonological output buffer. Since these variables are separated from the more classical lexical variables (e.g., frequency), their combination into a component may reflect post-lexical phonological processing related to the functioning of the output buffer or articulatory programming and execution. The second component constitutes imageability, concreteness, and length in syllables – an unexpectedly mixed grouping that we interpret as predominantly semantic due to the strong loadings of the first two variables (i.e., 0.96 and 0.94 respectively). Next is the lexical component, consisting of age of acquisition, familiarity, and frequency. Lastly, the number of semantic features and frequency made a significant contribution to the last component reflecting a lexical-semantic component. The structure of the C4 component can be explained by the fact that the number of semantic features and frequency go hand in hand (see for example, [ 101 ] about the interaction between number of semantic features and word frequency). The number of semantic features represents the variety of contexts an object can be encountered in (for example, a chicken is something you find in a farm, is a bird, is a type of food, makes a sound, can be used as a metaphor, etc.). The higher the number of semantic features of an item, the higher its frequency rating would be. A new dataset was created by averaging the scaled values of each variable that made a significant contribution to a component. For example, the values of length in phonemes, phonological neighbourhood density, and consonant clusters of each word were averaged to attain a value for C1 - the phonological component of that word. The same was done for C2-4 in relation to the variables with meaningful contributions. Since the age of acquisition and phonological neighbourhood density had negative loadings as a result of the PCA, their values were multiplied by -1 prior to averaging, to ensure all variables contributed in the same direction. The resulting dataset was used for all subsequent analyses. Statistical analyses The statistical analyses were performed in RStudio [ 99 ] and followed three steps. First, validity of the reaction time data was established since it was acquired with a non-traditional method (i.e., manually marking pause boundaries from retrospective audio recordings). Also, Wordrace has not been standardized for English. Construct validity can be evaluated by correlating a test measure with another measure known to vary in relation to that construct [ 102 ]. Therefore, a linear regression analysis was run using age and log-transformed RTs as the predictor and outcome variables, respectively. Moreover, a logistic regression analysis was employed to see how age correlated with naming accuracy. Second, the change in RTs and accuracy from the first assessment point to the second was investigated by running a linear mixed effects model and a generalized mixed effects model with RTs and accuracy as outcome variables, respectively, and the assessment point as the predictor variable. Both models used participants as random intercepts because we expect a lot of inter-individual variability in the severity of naming difficulty. Third, to answer research question 2, a mixed-effects regression analysis was employed, and the correlation between principal components and RTs was investigated. Participants were added as random intercepts. Log-transformed RTs for correct responses were put as the outcome variable in the model. A separate model took naming accuracy as the outcome variable. This model employed a generalized mixed-effects regression analysis to examine the relationship between principal components and naming accuracy (1 = correct, 0 = incorrect). Lastly, to investigate the change in the way principal components predict performance across assessment points, the study employed two mixed-effects regression analyses similar to the previous models for research question 2, but now with interaction terms between each component and each assessment point (pre- vs. post-surgery). This determined whether the correlation between a certain component and naming accuracy and/or RT depended on assessment time. Results Validation step A linear regression analysis showed a negative and significant correlation between the participants' age and the naming reaction times (β = -0.02, 95% CI =-0.03, -0.02, p < .001), meaning that children get faster at naming pictures with increasing age. Further, the logistic regression analysis yielded a significant positive correlation between age and naming accuracy, (odds ratio of 1.55, 95% CI = 1.42, 1.70, p < .001). Figure 4a and 4b show these correlations. Figure 4 The effect of age on naming speed (a) and probability of correct naming (b) Change in word-finding ability from pre- to post-surgery Reaction times increased significantly from pre- to post-surgery (β = 0.05, 95% CI = 0.01–0.09, p = 0.011), while naming accuracy remained constant (Odds Ratio = 1.03, 95% CI = 0.64–1.64, p = 0.917), staying near ceiling levels. The estimate of 0.05 reflects the increase in log-transformed reaction times from pre- to post-surgery. When back-transformed, this corresponds to a 5.1% increase, or approximately 70 milliseconds, based on the pre-surgery mean of 1375 milliseconds. Table 5 presents the results of the mixed-effects regression analysis for reaction times and generalized mixed-effects regression for naming accuracy. Figure 5a illustrates the confidence intervals for reaction times plotted across both assessment points, and Fig. 5b features a bar graph showing that the accuracy scores remained consistently close to 1 across assessment moments. Table 5 Results of mixed-effects regression for change in reaction times and generalized mixed-effects regression for change in naming accuracy across assessments Outcome variable Predictors Estimates CI p Reaction times (Intercept) 7.10 6.99–7.21 < 0.001 assessment point [ 2 ] 0.05 0.01–0.09 0.011 Odds ratios Accuracy (Intercept) 123.64 38.09–401.31 < 0.001 assessment point [ 2 ] 1.03 0.64–1.64 0.917 Figure 5 Naming speed (a) and accuracy (b) across assessment moments Language processing underlying word-finding ability The regression analyses for principal components and the outcome variables (i.e., RTs and accuracy) yielded the results shown in Table 6. C4 (a lexical-semantic component) significantly predicted naming reaction times, suggesting that the lexical-semantic properties of words drive naming speed in PFT patients ( p = 0.025, see Fig. 6a). As for naming accuracy, the lexical component (C3) predicted the performance significantly ( p < 0.001, see Fig. 6b). Reaction times were slightly shorter for the assessments done on the screen, which was expected because it took less time to swipe on the screen than to flip a page ( p = 0.093). The interclass coefficient (ICC) of both models (i.e., 0.42 and 0.37) show that a substantial proportion of the total variance is attributable to differences between individuals. Table 6 Mixed-effects regression results with reaction times for correct responses and principal components Outcome variable Predictors Coefficients CI p Fixed effects Reaction times (Intercept) 7.37 7.10–7.65 < 0.001 C1 (phonological) -0.02 -0.05–0.00 0.062 C2 (semantic) -0.02 -0.04–0.01 0.132 C3 (lexical) -0.00 -0.03–0.03 0.797 C4 (lexical-semantic) -0.04 -0.08 – -0.01 0.025 mode [screen] -0.12 -0.26 – -0.02 0.093 age -0.03 -0.06 – -0.00 0.037 Sex [female] 0.06 -0.16–0.28 0.606 Random effects σ² 0.16 τ₀₀ 0.11 ICC 0.42 Fixed effects Accuracy (Intercept) 1.71 0.39–9.86 0.503 C1 (phonological) 1.09 0.81–1.46 0.577 C2 (semantic) 1.02 0.75–1.40 0.877 C3 (lexical) 2.12 1.50–3.00 < 0.001 C4(lexical-semantic) 0.90 0.56–1.45 0.665 age 1.60 1.29–1.99 < 0.001 Sex [female] 1.94 0.53–7.11 0.318 Random effects σ² 3.29 τ₀₀ 1.90 ICC 0.37 σ² = residual variance; τ₀₀ = random intercept variance; ICC = interclass correlation coefficient Figure 6 Regression of psycholinguistic components across naming speed (a) and accuracy (b) Change in language processing from pre- to post-surgery For question 3, the study examined the change in how various linguistic levels predicted naming speed and accuracy across assessment points 1, preoperative, and 2, 1–4 weeks postoperative assessment. As shown in Table 7, none of the interactions between principal components representing the linguistic levels and the assessment moment were significant. Table 7 Influence of principal components on naming reaction times and accuracy across assessment points Outcome variable Interactions Estimates CI p Reaction times C1 × assessment point [ 2 ] 0.03 -0.02–0.08 0.196 C2 × assessment point [ 2 ] -0.01 -0.06–0.04 0.722 C3 × assessment point [ 2 ] -0.02 -0.08–0.03 0.426 C4 × assessment point [ 2 ] 0.05 -0.02–0.12 0.192 Odds ratios Accuracy C1 × assessment point [ 2 ] 1.62 0.89–2.95 0.111 C2 × assessment point [ 2 ] 0.76 0.39–1.48 0.421 C3 × assessment point [ 2 ] 1.70 0.85–3.39 0.134 C4 × assessment point [ 2 ] 0.92 0.35–2.41 0.866 C1 = Phonological component; C2 = Semantic component; C3 = Lexical component; C4 = Lexical-semantic component Discussion We report that word-finding speed is slower after surgery, while accuracy does not change. Furthermore, word-finding speed is predicted by the lexical-semantic level (i.e., number of semantic features and word frequency), while the lexical-only component (age of acquisition, familiarity, and frequency) predicts word-finding accuracy. Lastly, there is no difference in how the above-mentioned variables predicted word-finding ability across assessment points. Method validation Wordrace has not been standardized for English, so it is important to establish the validity of this task as best as we can through the available data. Previous literature on healthy populations has shown that word retrieval gets faster with increasing age as the lexical representations get stronger [ 35 ], and the current analysis found the same, i.e., a significant positive correlation between patients’ age and word-finding speed. Secondly, the analysis of naming accuracy revealed that age positively and significantly predicted the probability of producing a correct response. As evident from the plot in Fig. 4, this correlation linearly increased until around ten years of age and then remained constant afterward. This aligns with the literature confirming that naming accuracy on confrontational naming tests increases linearly in the initial years (from one to five years) and then remains constant in unimpaired populations [ 103 ]. Word-finding ability pre- and post-surgery The increase in naming reaction times post-surgery indicates a worsening of word-finding speed and aligns with previous studies that reported a decline in language function following surgical removal in supratentorial language-eloquent areas in adults [ 104 , 105 ]. Particularly in the naming latency literature, decline in adult patients' performance has been observed on the Boston Naming Test (BNT) after surgery compared to baseline performance before surgery [ 43 ]. Literature on cerebellar degeneration also shows that these adult patients present with significantly slowed word finding. The local involvement of the cerebellum in word-finding ability as evidenced by studies on lexical decision [ 11 ], phonemic fluency [ 13 ], and semantic tasks [ 14 , 15 ], may be the primary reason for slow word-finding after surgery. While slow word finding does correlate with motor impairments, motor impairments are not the sole underlying cause of this speed reduction, as the severed connections of the cerebellum with the cerebrum may also explain word-finding difficulties [ 106 ]. Therefore, surgery might increase the lesion size compared to the lesion due to the tumour only, thus resulting in slowed word finding after surgery. Language processing underlying word-finding ability Effects of word properties on language performance were investigated as these may be used to characterise the nature of language impairments in clinical populations [ 48 , 56 ], but these inferences are based on the observation that the same variables affect performance in healthy individuals [ 53 , 55 , 58 ]. The significant effect of the lexical-semantic linguistic level on word-finding speed could thus signal the effective use of lexical and semantic resources to achieve accurate naming in patients. Future studies including controls can test if this effect is atypical in any way (larger or smaller than in controls). Atypical effects could signal impairment, in line with the studies reviewed by [ 4 ], where joint impairments of lexical and semantic processing have been reported in PFT patients in 26% of the studies that tested for it through expressive vocabulary or naming tasks. Regarding the psycholinguistic nature of impairment in patients undergoing cerebellar tumour surgery, the lexical level was found to be impaired in three out of twelve patients [ 85 ]. The lexical level (C3) also significantly predicted response accuracy. This level consisted of frequency, age of acquisition, and familiarity. Words frequently occurring in the corpus, more familiar to people due to exposure, and learned early on in the acquisition process are more likely to be retrieved correctly in healthy individuals and as such their effect could signal effective lexical processing supporting fast naming, while atypical effects of these variables in patients may signal lexical impairment [ 71 , 73 , 107 ]. In the current study, other linguistic levels did not significantly predict word retrieval speed, which can be attributed to the task's easy nature when considering the participants' age. The pictures included in the task do not vary much in complexity and are of highly concrete, imageable, and frequent nouns, which may result in relatively easier access to the representations at levels such as semantics and phonology. As noted in a previous study, picture-based tasks constrain the range of possible verbal responses and psycholinguistic variability, especially when stimuli are designed for use with children [ 85 ]. Moreover, we had expected a significant effect of the mainly semantic level (C2) in driving word-finding ability, but did not find such an effect. It could be that in order to find such an effect, the items of the test need to be carefully controlled for semantic similarity and complexity, which could be a direction for future studies. Moreover, the effect seen at the lexical-semantic level may also have trickled down from the semantic level. For instance, competition between semantic neighbours may result in difficulty in selecting the target concept at the semantic level, leading to the same sort of difficulty in lexical selection [ 48 ]. Whether patients show an atypical effect of these variables (which is a clear indication of impairment) can only be determined by future studies including healthy controls. However, if the influence of variables changes in patients as a consequence of surgery, along with a change in performance, then this could signal a change into impaired processing at a certain level of processing, as evaluated in the next section. Language processing pre- and post-surgery The way the psycholinguistic levels affect word-finding ability did not change in our PFT sample across the two assessment points. This shows that language processing ability did not change at any particular linguistic level due to the surgery, albeit a worsening in the existing skills. The lack of additional types of impairment probably reflect that the anatomical substrates affected by surgery are mainly determined by the location of the tumour [ 108 ], diaschisis via tumour compression [ 45 , 46 ], and/or supratentorial disruption due to hydrocephalus [ 47 ], and thus relevant impairments are already present before surgery, albeit in a less severe form [ 88 ]. This would imply that different kinds of tumour could lead to a distinct pattern: for example, in adult patients with supratentorial tumours, tumour grade is an important factor to consider when investigating the change in performance pre- and post-surgery since patients with low-grade glioma tend to have already gone through neuroplastic changes due to the slow progression of the tumour and would be expected not to show as much decline after surgery [ 43 ]. On the other hand, high-grade glioma patients show more drastic changes right after surgery because the lesion affects parts of their eloquent brain networks adversely due to the functions not having enough time to relocate to a different brain region. Another potential explanation for a decay in naming speed in the absence of changes within the language processing system, is that naming speed may be reduced in the context of a more general cognitive mechanism, such as processing speed, which critically interacts with other cognitive functions (e.g., language), and is shown to be frequently impaired in children with posterior fossa tumors [ 109 ]. Although most studies assessing processing speed have compared patients with and without administration of radiotherapy, one study showed that PFT patients who did not undergo adjuvant radiotherapy also presented with slower information processing speed compared to a non-CNS tumour control group [ 110 ]. This could explain a general decay in language performance (i.e., specifically word-finding speed in the current study) which is nonspecific to any particular linguistic level. Future studies may also focus on studying language ability at later time points and on the effects that adjuvant radiotherapy may have in leading to additional linguistic impairments or changing the psycholinguistic nature of word-finding ability. Such a pattern has been shown by in an investigation of rapid picture naming as part of information processing speed which revealed that the difference in processing speed was more pronounced after the irradiation therapy [ 111 ]. Limitations and future directions The current study did not have control data to compare the word-finding ability of PFT patients. This limited the interpretation of the linguistic levels that significantly impacted accuracy and reaction times in terms of their contribution to the impairment status. Future studies should recruit age-matched controls and replicate the current study to find if their word-finding ability is influenced by the same linguistic levels as the PFT sample. The approach used in previous studies for narrative data can also be employed to check whether principal component analysis with the psycholinguistic variables and word-finding ability helps differentiate between controls and patients [ 85 ]. Wordrace needs to be standardized in English and other languages, adding to the validity of the test. An even better approach would be to rethink the test design, balancing items on visual complexity and psycholinguistic properties of the target words. Potentially significant factors, such as the selection of stimuli and their order, are usually overlooked in picture naming tests [ 112 ]. For instance, the effect of semantic categories can obscure performance [ 113 ]. Lastly, more assessment points should be investigated to examine the trajectory of word-finding ability over time after surgery in terms of improvement or worsening. As reported in a recent systematic review [ 4 ], some studies that tested language ability longitudinally in the PFT sample, revealed inconsistent results, with some finding no impairment shortly after surgery but finding phonological and pragmatic deficits at one-year follow-up [ 114 ], while others reported persistent lexical-semantic difficulties at all assessment points after surgery [ 115 ]. A longitudinal study would also help pinpoint how radiotherapy affects word retrieval abilities and how this evolution might differ depending on specific patient characteristics such as age, sex, language background (i.e., monolingual/bilingual), and/or socioeconomic status. Conclusion In conclusion, this study provides important insights into the relationship between linguistic processing levels and word-finding ability in patients with posterior fossa tumours (PFT), both before and after surgical intervention. By examining the influence of psycholinguistic levels on word-finding accuracy and speed, we found that lexical-semantic processing significantly predicted word-finding speed, while only lexical processing predicted accuracy. Moreover, the observed decline in word-finding speed post-surgery suggests a deterioration in lexical retrieval, consistent with previous studies on language function post-surgery in language-eloquent areas. However, the stability of the predictive influence of psycholinguistic levels across assessment points indicates that while surgery may exacerbate existing impairments, it does not necessarily alter the underlying linguistic processing mechanisms, and we cannot ascribe the decay in language ability specifically to lexical processing. Rather, this may be explained by more general cognitive processing limitations (e.g., in processing speed). Importantly, method validation confirmed the Wordrace test's validity, aligning with known developmental patterns in naming abilities, thus establishing its utility for assessing word-finding performance. Future research should address the limitations identified, such as the lack of control data and the need for a standardized word-finding test. Additionally, longitudinal studies with more assessment points would provide a clearer trajectory of word-finding ability over time, considering factors like radiotherapy and patient characteristics. These steps will further help us understand the complex dynamics of language processing in PFT patients, ultimately guiding better clinical practices and rehabilitation strategies. 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). Rida Ahmed received funding from the research master program, European Master’s in Clinical Linguistics (EMCL project 2020-2026), with grant number 619668-EPP-1-2020-1-NL-EPPKA1-JMD-MOB. Karin Persson received funding from The Swedish Childhood Cancer Foundation, Queen Silvia’s Jubilee Fund, Jonas Foundation. Ditte Boeg Thomsen and Jonathan Kjær Grønbæk received funding from the Inge Lehmann grant (grant number 10.46540/4302-00027B) from the Independent Research Fund Denmark. Aske Foldbjerg Laustsen received funding from The Danish Childhood Cancer Foundation (grant number: 2021-7343). Ethical considerations The current study used patient data from the European Study of CMS. The Research Ethics Committees of the Capital Region (H-6–2014–002) in Denmark gave their approval for the collection of data for this project, and the study was authorized in the UK afterwards. Contributions Conceptualization : Rida Ahmed and Vânia de Aguiar; Methodology : Rida Ahmed, Vânia de Aguiar, Cheyenne Svaldi; Formal analysis : Rida Ahmed, Cheyenne Svaldi; Resources : René Mathiasen, Marianne Juhler, Barry Pizer, Kristian Aquilina, Greg Fellows, Ian Kamaly; Data curation : Aliene Reinders; Supervision : Vânia de Aguiar; Writing - original draft : Rida Ahmed; Writing - review & editing : Vânia de Aguiar, Cheyenne Svaldi, Karin Persson, Ditte Boeg Thomson, Jonathan Kjær Grønbæk, Aske Foldbjerg Laustsen, Barry Pizer, Roel Jonkers, Annet Kingma, Rida Ahmed; Funding acquisition : Vânia de Aguiar, Ditte Boeg Thomson. Acknowledgements We thank all participants for their valuable time. References Cancer Research UK. Children’s cancer statistics [Internet]. Cancer Research UK; 2024. https://www.cancerresearchuk.org/health-professional/cancer-statistics/childrens-cancers Zahnreich S, Schmidberger H. 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06:44:45","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":349462,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8200894/v1/a0d382832d89fef98e6e3848.html"},{"id":97674334,"identity":"3f94e09e-07d6-4d16-99ac-c14c34662c04","added_by":"auto","created_at":"2025-12-08 09:43:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLanguage Processing Model for Production of Single Words. \u003c/em\u003eAdapted from \u003ca href=\"https://www.zotero.org/google-docs/?broken=WrVOCJ\"\u003e[48]\u003c/a\u003e.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8200894/v1/f9b77f9012daa7c6d4adbf6d.jpg"},{"id":97674549,"identity":"38578e8b-601a-434f-b634-ffd3c28f5f86","added_by":"auto","created_at":"2025-12-08 09:43:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePraat TextGrid for each speech file\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8200894/v1/f311cdd0a9da29e080f07d80.jpg"},{"id":97674080,"identity":"55e5ea93-b0f6-4eef-8e89-37b8566ed423","added_by":"auto","created_at":"2025-12-08 09:42:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eScree plot of the principal component analysis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8200894/v1/eeb0f6c8390a540e01c3ebeb.jpg"},{"id":97652833,"identity":"f109468a-4183-4502-bffe-9380034a3c10","added_by":"auto","created_at":"2025-12-08 06:44:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe effect of age on naming speed (a) and probability of correct naming (b)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8200894/v1/530cd983a8c31273e8b6adbe.jpg"},{"id":97674298,"identity":"541a9851-a69c-4033-93d1-e60d78da295d","added_by":"auto","created_at":"2025-12-08 09:42:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNaming speed (a) and accuracy (b) across assessment moments\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8200894/v1/2dca45853665b6e4c8fda1ff.jpg"},{"id":97652831,"identity":"1982cb62-15c7-4d26-bd46-73f2ea512a89","added_by":"auto","created_at":"2025-12-08 06:44:45","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":29323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRegression of psycholinguistic components across naming speed (a) and accuracy (b)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8200894/v1/05d1e28e63e2e6297891d87a.jpg"},{"id":97678893,"identity":"40e72b84-c6c1-4369-9422-2b72bd975c87","added_by":"auto","created_at":"2025-12-08 09:56:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1895226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8200894/v1/2166c3ce-e496-456c-ac63-d94ab5430e82.pdf"},{"id":97652828,"identity":"e1b22492-34c7-4934-bfac-0628b5b55b97","added_by":"auto","created_at":"2025-12-08 06:44:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21041,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8200894/v1/6da6415e5c188f05c5df301f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Language processing in posterior fossa tumour patients: Psycholinguistic insights into the word-finding ability","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCentral nervous system tumours are the second most common group of childhood neoplasms after leukaemia and continue to cause the highest number of childhood deaths related to cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These statistics still hold even though survival rates of children with cancer have increased significantly over the last few decades [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Around half of childhood brain tumours arise in the posterior fossa that contains the cerebellum and the brainstem [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. With improving treatment and care standards, it is important to ensure that survivors lead socially engaged lives with minimal functional impairments. Children with posterior fossa tumours (PFTs) present with language impairments at all processing levels, from producing individual words to forming sentences and using language appropriately in social situations, prompting further investigation into the nature of these impairments [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Since speech and language impairments create a barrier to effective social engagement [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], investigating important skills such as word-finding abilities is useful in gaining insights into language deficits affecting different linguistic processes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The current study explores the nature of language processing underlying word-finding abilities in the PFT population using a picture-naming task. Furthermore, it examines potential changes in the factors influencing naming performance, measured before and after tumour surgery.\u003c/p\u003e\n\u003ch3\u003eThe linguistic cerebellum\u003c/h3\u003e\n\u003cp\u003eThe cerebellum has traditionally been attributed a role in motor function [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nonetheless, by now, many studies using clinical and neuroimaging methods, mostly with adults, have confirmed its role in cognitive functions, including a variety of language functions, indexed by tasks such as verbal fluency [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], lexical decision [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], verb generation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and reading aloud [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Specific to lexical access - a process of retrieving words from the mental dictionary, right cerebellar involvement has been shown in phonemic fluency [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and lexical decision [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The right cerebellum also showed activation when participants were asked to pay attention to the semantic relations between words [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, a recent study showed that electrical stimulation of the posterior cerebellum bilaterally facilitated the retrieval of semantically related words, confirming the cerebellum's role in word retrieval in healthy participants [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, both hemispheres of the cerebellum have shown to be sensitive to distinct word properties during spoken language comprehension [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Specifically, higher word frequency was positively associated with activation in bilateral Crus I (extending into Crus II - subregions in the lateral posterior cerebellum), and superior lobule VI and IX. Moreover, lower phonological neighbourhood density (i.e., fewer similar-sounding words) were linked to greater activation in bilateral Crus I/II regions, with a stronger effect in the right hemisphere.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eImpairments in children with PFTs\u003c/h2\u003e\u003cp\u003eSurgical resection of a PFT may be followed by an array of motor, linguistic, and neurobehavioral/affective symptoms [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These have been collectively named 'post-operative pediatric cerebellar mutism syndrome (ppCMS)\u0026rsquo; [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, p. 1199], or Posterior Fossa Syndrome (PFS) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A hallmark of this syndrome is a temporary period of absence of speech (mutism, PFS1) or severely reduced speech (PFS2), which may occur on its own or follow a period of mutism; together, these are referred to as post-operative speech impairment (POSI) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Young age at diagnosis, high-grade tumour, and central midline location lead to an increased risk of developing POSI.\u003c/p\u003e\u003cp\u003eImportantly, not all post-surgical impairments fall under the formal diagnosis of PFS/pCMS. Even in the absence of mutism, children may show fine and gross motor impairments [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and cognitive sequelae, for example, low IQ, and poor memory, attention, and executive functioning [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Dysarthria, especially of the ataxic type, is also commonly reported, whether or not mutism is present [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, several studies have reported impairments in language abilities after surgery, regardless of whether the child experienced a phase of mutism or reduced speech [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Yet, this literature is characterized by a great inconsistency, as some studies did not report group differences in language ability when comparing groups of PFT survivors and healthy controls (see [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] vs. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]). A recent systematic review suggests that this inconsistency may partly reflect the substantial inter-individual variability in which aspects of language are impaired, as well as substantial methodological differences across studies, highlighting the need to study language in greater granularity with precise methods and attention to underlying linguistic mechanisms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This systematic review of individuals reported in the literature showed that PFT survivors may show varying language impairment profiles, with individuals presenting many possible combinations of impaired and spared ability across levels of language processing [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], for example in semantics (knowledge of meanings) compared to morphosyntax (grammar), or pragmatics (language use in context).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eWord-finding difficulties in the PFT population\u003c/h3\u003e\n\u003cp\u003eWhen looking more specifically at studies examining word-finding abilities (see the Appendix for a summary of the literature), eight studies found significantly low performance of PFT patients in expressive vocabulary and word-finding tests [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35 CR36\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], four showed patients\u0026rsquo; performance comparable to control participants or norms [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and the remaining ten had their results complicated due to various factors (for example, no control comparison [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]; merging naming scores with other tests to report a single measure of verbal intelligence [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Thus, the literature is divided on the lexical retrieval abilities in PFT patients, mainly because of a lack of experimental control. This can be expected in patient populations like these since testing specific language functions is neither practical due to fatigue and affective symptoms nor a priority. While these studies focused on whether word-finding deficits were present, one recent study investigated the nature of errors produced by children, providing insight into what may go wrong in language processing, leading to poor performance [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Their error analysis found that PFT survivors mostly produced semantically side-oriented errors, that is, a closely related word in meaning (e.g., pear instead of apple).\u003c/p\u003e\n\u003ch3\u003eThe pre- vs. post-surgical onset of impairments\u003c/h3\u003e\n\u003cp\u003eAlthough the studies described above report on post-surgical impairments, children with PFTs may show impairments due to the presence of the tumour itself (before surgery), and also as a consequence of the necessary surgery and post-surgical treatments (i.e., radio- and/or chemotherapy). For instance, one study found that all patients who developed CMS after surgery had pathological naming preoperatively [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Another study reported slow (37%), inaccurate (24%), or slow and inaccurate (16%) word finding in PFT survivors already before surgery [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Moreover, when analysing word-finding abilities of PFT patients both before and after surgery, patients were found to be substantially impaired in comparison to controls in terms of naming speed at both timepoints [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Upon close inspection of the individual data, variability is reported such that some patients\u0026rsquo; word-finding ability worsened after surgery while it improved for others.\u003c/p\u003e\u003cp\u003eImportantly, the causal nature of word-finding abilities might change from pre- to post-surgery. First, impairments may exist because the tumour affects the anatomical substrates that have a role in word finding but depending on tumour type (low-grade or high-grade), this may happen at varying speeds, sometimes allowing for the language system to adapt to the anatomical changes via neuroplastic mechanisms [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This disruption may occur locally within the cerebellum due to its contribution to language processing [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], or through diaschisis, where tumour-induced compression causes disrupted connectivity with supratentorial language regions [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In addition, language impairment can result from local disruption to supratentorial areas themselves, due to tumour-related hydrocephalus [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. On the other hand, surgery, while necessary for survival, causes a sudden, acute lesion as well as post-surgical edema in the posterior fossa region, which affects structures beyond the tumoral tissue, potentially affecting additional language processes. Hence, the neurocognitive nature of the word-finding difficulty may change from pre- to post-surgery.\u003c/p\u003e\n\u003ch3\u003eThe chain of processes in word finding\u003c/h3\u003e\n\u003cp\u003ePsycholinguistic models of language processing suggest that word retrieval may fail or become challenging due to language processing impairments, which can happen at different stages of the word retrieval process. As illustrated in Fig.\u0026nbsp;1, model adapted from [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], picture naming comprises different sequential processes: \u003cem\u003eObject Recognition\u003c/em\u003e is initiated when a person sees an object in a picture or in real life. Then follows non-verbal conceptual information about the object, such as knowing that an apple is something to eat, accessed through the level called \u003cem\u003eObject Concepts\u003c/em\u003e. The information is then processed in the \u003cem\u003eSemantic System\u003c/em\u003e, which is defined as the elements of knowledge that collectively compose the meaning of words.\u003c/p\u003e\u003cp\u003eThe next stage in the word retrieval process is the retrieval of the spoken word forms from the \u003cem\u003ePhonological Output Lexicon\u003c/em\u003e, where known vocabulary is stored as combinations of speech sounds. Once a phonological form has been activated from the output lexicon, a phoneme string is generated by the \u003cem\u003ePhonological Assembly\u003c/em\u003e. Finally, the phonemes from this sequence are fed into the \u003cem\u003eArticulatory Programming\u003c/em\u003e stage, which generates neuromuscular commands from these phonemes, and the word is ultimately articulated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFactors that influence speed and accuracy of naming in individuals with language disorders have received much attention in previous literature [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Among these factors are the psycholinguistic properties of words that are to be named, such as word frequency, age of acquisition, and familiarity. Some of them are said to accelerate the retrieval of words (i.e., word frequency [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]; age of acquisition [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]), while others slow it down (i.e., phonological neighbourhood density [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]; word length [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]). This effect of word properties on lexical retrieval can be explained by the differential involvement of each language processing level (e.g., semantics, phonology) in language production. Importantly, the critical variable framework postulates that when a given variable affects performance in word retrieval, it is considered evidence of impaired language processing at the level of functioning relating to that specific variable [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn what follows, variables associated with specific levels of language processing shown in Fig.\u0026nbsp;1 will be described. It should be noted that the assignment of psycholinguistic variables to specific language processing levels is not always consistent. For example, the effects of age of acquisition of a word could be of a lexical origin (increased exposure to a word helps in its fast retrieval) or a semantic origin (early acquired words are processed quicker because they are positioned more centrally in the semantic network) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The following section outlines the most commonly reported associations in the literature.\u003c/p\u003e\n\u003ch3\u003eSemantic variables\u003c/h3\u003e\n\u003cp\u003eSeveral semantic variables have been found to influence the lexical retrieval process at the Semantic System level shown in Fig.\u0026nbsp;1. For instance, imageability, which refers to the extent to which a word evokes a mental image, has been shown to facilitate word retrieval in both neurotypical individuals and people with aphasia [\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Similarly, concreteness, which is defined as the degree to which a noun can be touched or visualized, supports the word retrieval process by creating strong semantic associations [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The number of semantic features, defined as the number of distinct conceptual attributes associated with a word, also facilitates naming speed and accuracy in neurotypical individuals by increasing semantic and lexical activation [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. It has also been found to be a key predictor of naming performance in people with aphasia [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLexical variables\u003c/h2\u003e\u003cp\u003eThe lexical level of processing is reflected in the Phonological Output Lexicon in Fig.\u0026nbsp;1 and is influenced by variables relating to the way words are organized in the mental lexicon. One such variable is word frequency. It refers to how often language users are likely to encounter a word in everyday use and has a well-established facilitatory effect on naming in neurotypical individuals [\u003cspan additionalcitationids=\"CR66 CR67\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Secondly, age of acquisition (AoA) of a word also affects naming in neurotypical individuals, with early-acquired words retrieved faster [\u003cspan additionalcitationids=\"CR70 CR71\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Word familiarity is a more subjective measure of how often a person encounters a word - calculated based on participants\u0026rsquo; ratings of how familiar a word feels to them - and it correlates strongly with word frequency and similarly facilitates naming [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. It has also been shown to distinguish between variants of primary progressive aphasia [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Lastly, the phonological neighbourhood density (PND) is the number of words that can be formed by replacing, deleting, or adding one phoneme to the original word [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. It is important to note that, although based on sound similarity, phonological neighbourhood density is considered a lexical-level variable because it reflects how words are organized in the mental lexicon [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. It facilitates naming in people with aphasia [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], but shows variable effects in children depending on language development status [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. These effects are often explained by interactive activation models, where activation spreads between phonemes and words, resulting in either competition or facilitation [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhonological variables\u003c/h3\u003e\n\u003cp\u003ePhonological variables like word length influence lexical retrieval at stages prior to articulation. Specifically, the phonological output buffer precedes articulatory programming, temporarily storing speech sounds. Word length is measured by either the number of phonemes or syllables in a word. In some populations, such as people with aphasia, longer words result in more errors in naming [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], although a reverse effect has also been reported where longer words are more easily retrieved due to fewer phonological neighbours [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Specifically, longer word length in syllables has been shown to correlate with longer naming latencies in individuals with developmental language disorder [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Lastly, the number of consonant clusters in a word affects the Articulatory Programming stage of word production [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. The increase in the number of consonant clusters has been associated with a higher number of errors in naming in children with DLD [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e Previous studies with participants with language disorders have employed analysis of the psycholinguistic properties of the words produced. For instance, when checking for the psycholinguistic properties of the words produced by cerebellar tumour patients, impairments were observed at multiple levels of language processing [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Similarly, in spontaneous speech in bilingual children with developmental language disorder, it was found that the difference in phonological neighbourhood density between nouns and verbs was more pronounced in sequential bilingual children with DLD than in typically developing children [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Using a similar approach, a study investigating naming in individuals with aphasia found that the \u0026lsquo;lexical usage\u0026rsquo; factor (composed of word frequency, familiarity, and age of acquisition) significantly differentiated between patients with fluent and non-fluent aphasia regarding naming accuracy [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Lastly, a role of word familiarity has been reported in driving naming performance in patients with primary progressive aphasia [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003ePresent investigation\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eResearch gap\u003c/h2\u003e\u003cp\u003eNot only is the previous literature divided on the presence/absence of word-retrieval deficits in PFT patients, but the linguistic nature of impairments behind these deficits has been scarcely investigated. Psycholinguistic properties of words provide a window to investigate the processing dynamics that may be related to the performance in word finding even when the word is ultimately named correctly (e.g., through relations with reaction times (RTs)). This has not been explored in previous studies with the PFT population. Clinically, such an investigation is significant for two reasons: 1) identifying whether deficits stem from semantic, lexical, or phonological stages can inform targeted therapies (e.g., semantic feature training vs. phonological cueing), 2) subtle psycholinguistic effects (e.g., slowed RTs for low-frequency words) may serve as markers of cerebellar-cortical circuit disruption in patients with otherwise intact accuracy, potentially identifying cases at risk of presenting worse language performance.\u003c/p\u003e\u003cp\u003eMoreover, the early postoperative stage in patients who undergo surgery in supratentorial language-eloquent areas is characterised by a decline in all language functions, including those involved in lexical retrieval [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. This is attributed to the significant tissue damage as a result of surgery. Since several studies have identified a role of the cerebellum in language, and lesions in this region have been associated with subsequent language impairments [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], the current study also explored whether linguistic processing behind word finding in PFT patients is affected by the tumour surgery by comparing performance between the preoperative and the early postoperative periods.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eResearch questions and hypotheses\u003c/h2\u003e\u003cp\u003eThe current study aims to address three main questions.\u003c/p\u003e\u003cp\u003eFirst, we examine whether word-finding ability changes from before to after surgery in patients with PFTs. It is expected that both the speed and accuracy of word retrieval will decline postoperatively, reflecting the potential impact of tumour removal on neural structures involved in language processing.\u003c/p\u003e\u003cp\u003eSecond, we explore which underlying linguistic processes drive word-finding abilities in the PFT patients. Based on prior findings, such as the prevalence of semantically related errors in PFT patients [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and reports of semantic paraphasias in cerebellar stroke patients [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e] - we hypothesize that semantic and lexical-semantic levels will play a primary role in driving word-finding performance. The exploration for the rest of the linguistic levels is exploratory.\u003c/p\u003e\u003cp\u003eFinally, the study investigates whether the underlying linguistic processes differ in predicting word-finding abilities pre- and postoperatively in PFT patients. We anticipate that damage to brain tissue following resection may alter the relative influence of psycholinguistic levels on word-finding ability, indicating a shift in the mechanisms supporting word-finding ability.\u003c/p\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThirty-eight English-speaking patients, 19 males and 19 females from the United Kingdom, included in the European Study of the Cerebellar Mutism Syndrome [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e] formed the participant group for the current study. The European Study of CMS is a large multicenter study initiated in 2014 to investigate the incidence, symptoms, risk factors, and prognosis of CMS in children following surgery for posterior fossa tumours. The patients included in the current sub-study did not present with a history of developmental language disorder (DLD) or other speech-language disorders. Table\u0026nbsp;1 presents the demographic, tumour, and postoperative speech characteristics of participants.\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\u003eDemographic, tumour, and postoperative speech characteristics of participants (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncluded cohort (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 males (50)\u003c/p\u003e\u003cp\u003e19 females (50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at surgery (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;=\u0026thinsp;8,8\u003c/p\u003e\u003cp\u003eRange\u0026thinsp;=\u0026thinsp;2,5\u0026ndash;17,6\u003c/p\u003e\u003cp\u003eSD\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor histology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePilocytic astrocytoma: 20 (\u0026asymp;\u0026thinsp;53)\u003c/p\u003e\u003cp\u003eMedulloblastoma: 8 (\u0026asymp;\u0026thinsp;21)\u003c/p\u003e\u003cp\u003eEpendymoma: 6 (\u0026asymp;\u0026thinsp;16)\u003c/p\u003e\u003cp\u003eGanglioglioma: 1 (\u0026asymp;\u0026thinsp;3)\u003c/p\u003e\u003cp\u003eUnknown: 3 (\u0026asymp;\u0026thinsp;8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOSI status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHabitual speech: 30 (\u0026asymp;\u0026thinsp;79)\u003c/p\u003e\u003cp\u003eMute: 4 (\u0026asymp;\u0026thinsp;10)\u003c/p\u003e\u003cp\u003eReduced speech: 2 (\u0026asymp;\u0026thinsp;5)\u003c/p\u003e\u003cp\u003eUnknown: 2 (\u0026asymp;\u0026thinsp;5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePOSI\u0026thinsp;=\u0026thinsp;Postoperative speech impairment. Categorical variables in n (%)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMaterials\u003c/h2\u003e\u003cp\u003eThe study used audio recordings of the Wordrace task to determine accuracy and reaction times. Wordrace is a picture-naming task especially designed for the European study of CMS to test word-finding ability [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. It contains 25 pictures that are shown either on screen or on paper one by one, and which need to be named as fast as possible. The test has been normed for the Swedish language and has shown a high test-retest reliability for measuring accuracy (r\u0026thinsp;=\u0026thinsp;.894) and speed (r\u0026thinsp;=\u0026thinsp;.627) [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. It is a good alternative to traditional naming tests as it puts minimal demands on executive functioning, thus helping in measuring just the word-finding speed and accuracy [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. For some target words, alternatives are accepted: for instance, ship for boat and chicken for rooster.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eProcedure\u003c/h2\u003e\u003cp\u003e The study used retrospective Wordrace data collected from patients at UK sites participating in the European Study of CMS, including Alder Hey Children\u0026rsquo;s Hospital (Liverpool), Great Ormond Street Hospital (London), and centers in Manchester, Bristol, and Nottingham. We used data from two assessment points, i.e., preoperatively (assessment point 1) and 1\u0026ndash;4 weeks postoperatively (assessment point 2). The tester explained the task to the participants before administering it and then turned the pages or swiped on the tablet screen after the participant named each picture. As per the instructions, if a picture was not named after 5 seconds, the tester was supposed to move on to the next one, but this was not followed in all the testing sessions. The tester also kept track of the total time taken to complete the test with a stopwatch. Only minimal feedback was provided, such as \u0026lsquo;good job\u0026rsquo;, or the tester asked \u0026lsquo;what\u0026rsquo;s that?\u0026rsquo;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eData processing\u003c/h2\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003eCalculating reaction times\u003c/h2\u003e\u003cp\u003eReaction time and accuracy were determined for each item on Wordrace using Praat [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e], a software used for phonetic analysis of audio data. See Fig.\u0026nbsp;2 for an example of what the TextGrid looked like for each speech sample (an interval is created just before a word is articulated and labeled for easier extraction of reaction times and accuracy for each label using Praat script). A response was considered correct if the target word was eventually named, even if the participant named a semantically related or unrelated word before naming the target word. Only correct responses were considered for reaction time analyses. Since the database only had audio recordings and the task was administered in different formats (screen or paper) in different centres across the UK, we defined specific markers of the start of the reaction time: an audible tap on the keyboard by the examiner for centres that conducted it on screen and the start of page flipping sound for centres that conducted it on paper. A tap or page flip was not audible for some audio recordings that probably administered the task on a tablet. For these cases, the pause between the naming of the previous item and the next item served as the reaction time. These different modes of task administration were adjusted for later in the analysis. The endpoint of the reaction time was marked at the onset of the acoustic marker of the first phoneme produced in the response (voicing for voiced consonants and vowels; or, for example, the onset of the plosive or fricative signal), as soon as this was visible in the waveform corresponding to the participant starting to name the picture. This ensures we only measured the time the participant took to retrieve the word, not other unrelated delays, such as phoneme elongation or interrupted articulation.\u003c/p\u003e\u003cp\u003eSome participants named the pictures starting with the articles \u0026lsquo;a\u0026rsquo;, \u0026lsquo;the\u0026rsquo; and \u0026lsquo;an\u0026rsquo; while some started with the word \u0026lsquo;some\u0026rsquo;, such as \u0026lsquo;some bread\u0026rsquo;. Reaction time was noted after the article or the quantifier since participants may elongate these when struggling to retrieve words. Moreover, if the participant hesitated to say a word by articulating the first phoneme and then pausing and saying the whole word, the reaction time was noted until they said the word itself. In some cases, the participant said a semantically related word before ultimately naming the target word. In such cases, reaction time was noted until the target word was named. Sometimes, the participants coughed while naming a picture. Their reaction time was noted before they began coughing only if they had already articulated 20% of the phonemes of the word. Otherwise, the item was not considered for reaction time. The items for which the child could only say the target word upon hearing it from the tester were excluded and marked as \u0026lsquo;no response\u0026rsquo;.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;2\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePraat TextGrid for each speech file\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eCoding words for psycholinguistic properties\u003c/h2\u003e\u003cp\u003eTo answer research questions 2 and 3, the study considered psycholinguistic word property norms for British English. Table\u0026nbsp;2 shows databases that are specifically normed for British English that were used for the present study. Consonant clusters and word length in syllables were defined manually. Each word in the Wordrace test was coded for each word property.\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\u003ePsycholinguistic Properties of Words\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWord property\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDatabase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLinguistic level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRating Scale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWord frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSUBTLEX-UK\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLexical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZipf scale (1\u0026ndash;7)\u003c/p\u003e\u003cp\u003e1\u0026ndash;3: Low frequency\u003c/p\u003e\u003cp\u003e4\u0026ndash;7: High frequency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhonological neighbourhood density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCLEARPOND (EnglishPOND)\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLexical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of phonological neighbours after adding/deleting/substituting 1 phoneme\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of acquisition (AoA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe Bristol norms for age of acquisition\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLexical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage AoA in years across participants\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemantic features\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSemantic feature production norms\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemantic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo. of distinct features listed for each concept\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcreteness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe Glasgow Norms\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemantic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;7 scale (1\u0026thinsp;=\u0026thinsp;concrete, 7\u0026thinsp;=\u0026thinsp;abstract - we used mean concreteness rating across participants)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImageability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe Glasgow Norms\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemantic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;7 scale (1\u0026thinsp;=\u0026thinsp;very unimageable, 7\u0026thinsp;=\u0026thinsp;very imageable)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamiliarity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe Bristol norms for familiarity\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSemantic/lexical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;7 scale (1\u0026thinsp;=\u0026thinsp;very unfamiliar, 7\u0026thinsp;=\u0026thinsp;very familiar)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength in phonemes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCLEARPOND (EnglishPOND)\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePhonological\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo. of phonemes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength in Syllables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-determined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePhonological\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo. of syllables\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsonant clusters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-determined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePhonological\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo. of consonant clusters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eDimensionality reduction via Principal Component Analysis\u003c/h2\u003e\u003cp\u003eThe variables in Table\u0026nbsp;2 were to be used as predictors of word-finding speed and accuracy in analyses addressing the research questions 2 and 3. However, these make a large number of predictors and our interest was in the role of language processing levels, and not specific variables. Given that several variables are expected to reflect overlapping linguistic processing levels, variables were clustered together when they could be merged. This was done using a Principal Component Analysis (PCA) with values of all included psycholinguistic variables in Table\u0026nbsp;2 for the nouns that the participants used (n\u0026thinsp;=\u0026thinsp;40); this included all the different synonyms used. A similar approach has been used by previous studies (see for example, [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]). The PCA was done in RStudio [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. All variables were inserted into the PCA at once (not per level of language processing). This way, their clustering was entirely data-driven and not based on previous assumptions.\u003c/p\u003e\u003cp\u003eTo prepare for PCA, all the values were first \u003cem\u003ez\u003c/em\u003e-scaled to get a uniform scale because different variables were originally rated on varied scales. For example, familiarity is rated on a scale of one to seven, whereas the phonological neighbourhood density is in the form of continuous numbers, so not standardizing can result in an artificial dominance of one variable with a larger scale. The number of principal components to be retained was determined by two criteria based on previous research [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e], i.e., the cumulative variance explained should be more than 70%, and the components should have an eigenvalue above 1.0. The first four components explained 81.8% variance in the data; all of them have an eigenvalue higher than 1.0 (see Table\u0026nbsp;4 for explained variance and Fig.\u0026nbsp;3 for the scree plot for eigenvalues of components). Table\u0026nbsp;3 shows the loadings of different values on the components where a value equal to or higher than 0.45 is considered a meaningful contribution and is bolded.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eComponent Loadings\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=\"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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength in syllables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength in phonemes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhonological neighbourhood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsonant clusters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.95\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImageability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.96\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemantic features\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of acquisition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.84\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamiliarity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcreteness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.94\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eSummary\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=\"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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSS loadings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.561\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section4\"\u003e\u003ch2\u003e\u003c/h2\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;3\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eScree plot of the principal component analysis\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the values of component loadings (i.e., \u0026gt;\u0026thinsp;0.45), three variables, namely consonant clusters, length in phonemes, and phonological neighbourhood density loaded onto the first component. This is a phonological component since all these variables are reported in the literature to relate to phonological processing, either at the level of the output lexicon or the phonological output buffer. Since these variables are separated from the more classical lexical variables (e.g., frequency), their combination into a component may reflect post-lexical phonological processing related to the functioning of the output buffer or articulatory programming and execution. The second component constitutes imageability, concreteness, and length in syllables \u0026ndash; an unexpectedly mixed grouping that we interpret as predominantly semantic due to the strong loadings of the first two variables (i.e., 0.96 and 0.94 respectively). Next is the lexical component, consisting of age of acquisition, familiarity, and frequency. Lastly, the number of semantic features and frequency made a significant contribution to the last component reflecting a lexical-semantic component. The structure of the C4 component can be explained by the fact that the number of semantic features and frequency go hand in hand (see for example, [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e] about the interaction between number of semantic features and word frequency). The number of semantic features represents the variety of contexts an object can be encountered in (for example, a chicken is something you find in a farm, is a bird, is a type of food, makes a sound, can be used as a metaphor, etc.). The higher the number of semantic features of an item, the higher its frequency rating would be.\u003c/p\u003e\u003cp\u003eA new dataset was created by averaging the scaled values of each variable that made a significant contribution to a component. For example, the values of length in phonemes, phonological neighbourhood density, and consonant clusters of each word were averaged to attain a value for C1 - the phonological component of that word. The same was done for C2-4 in relation to the variables with meaningful contributions. Since the age of acquisition and phonological neighbourhood density had negative loadings as a result of the PCA, their values were multiplied by -1 prior to averaging, to ensure all variables contributed in the same direction. The resulting dataset was used for all subsequent analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003eStatistical analyses\u003c/h2\u003e\u003cp\u003eThe statistical analyses were performed in RStudio [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e] and followed three steps. First, validity of the reaction time data was established since it was acquired with a non-traditional method (i.e., manually marking pause boundaries from retrospective audio recordings). Also, Wordrace has not been standardized for English. Construct validity can be evaluated by correlating a test measure with another measure known to vary in relation to that construct [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. Therefore, a linear regression analysis was run using age and log-transformed RTs as the predictor and outcome variables, respectively. Moreover, a logistic regression analysis was employed to see how age correlated with naming accuracy.\u003c/p\u003e\u003cp\u003eSecond, the change in RTs and accuracy from the first assessment point to the second was investigated by running a linear mixed effects model and a generalized mixed effects model with RTs and accuracy as outcome variables, respectively, and the assessment point as the predictor variable. Both models used participants as random intercepts because we expect a lot of inter-individual variability in the severity of naming difficulty.\u003c/p\u003e\u003cp\u003eThird, to answer research question 2, a mixed-effects regression analysis was employed, and the correlation between principal components and RTs was investigated. Participants were added as random intercepts. Log-transformed RTs for correct responses were put as the outcome variable in the model. A separate model took naming accuracy as the outcome variable. This model employed a generalized mixed-effects regression analysis to examine the relationship between principal components and naming accuracy (1\u0026thinsp;=\u0026thinsp;correct, 0\u0026thinsp;=\u0026thinsp;incorrect).\u003c/p\u003e\u003cp\u003eLastly, to investigate the change in the way principal components predict performance across assessment points, the study employed two mixed-effects regression analyses similar to the previous models for research question 2, but now with interaction terms between each component and each assessment point (pre- vs. post-surgery). This determined whether the correlation between a certain component and naming accuracy and/or RT depended on assessment time.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003eValidation step\u003c/h2\u003e\u003cp\u003eA linear regression analysis showed a negative and significant correlation between the participants' age and the naming reaction times (β = -0.02, 95% CI =-0.03, -0.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), meaning that children get faster at naming pictures with increasing age. Further, the logistic regression analysis yielded a significant positive correlation between age and naming accuracy, (odds ratio of 1.55, 95% CI\u0026thinsp;=\u0026thinsp;1.42, 1.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Figure\u0026nbsp;4a and 4b show these correlations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;4\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003eThe effect of age on naming speed (a) and probability of correct naming (b)\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eChange in word-finding ability from pre- to post-surgery\u003c/h3\u003e\n\u003cp\u003eReaction times increased significantly from pre- to post-surgery (β\u0026thinsp;=\u0026thinsp;0.05, 95% CI\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), while naming accuracy remained constant (Odds Ratio\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;0.64\u0026ndash;1.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.917), staying near ceiling levels. The estimate of 0.05 reflects the increase in log-transformed reaction times from pre- to post-surgery. When back-transformed, this corresponds to a 5.1% increase, or approximately 70 milliseconds, based on the pre-surgery mean of 1375 milliseconds. Table\u0026nbsp;5 presents the results of the mixed-effects regression analysis for reaction times and generalized mixed-effects regression for naming accuracy. Figure\u0026nbsp;5a illustrates the confidence intervals for reaction times plotted across both assessment points, and Fig.\u0026nbsp;5b features a bar graph showing that the accuracy scores remained consistently close to 1 across assessment moments.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eResults of mixed-effects regression for change in reaction times and generalized mixed-effects regression for change in naming accuracy across assessments\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOutcome variable\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCI\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\u003eReaction times\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.99\u0026ndash;7.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eassessment point [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u0026ndash;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\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\u003eOdds ratios\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.09\u0026ndash;401.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eassessment point [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.64\u0026ndash;1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;5\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eNaming speed (a) and accuracy (b) across assessment moments\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003eLanguage processing underlying word-finding ability\u003c/h2\u003e\u003cp\u003eThe regression analyses for principal components and the outcome variables (i.e., RTs and accuracy) yielded the results shown in Table\u0026nbsp;6. C4 (a lexical-semantic component) significantly predicted naming reaction times, suggesting that the lexical-semantic properties of words drive naming speed in PFT patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025, see Fig.\u0026nbsp;6a). As for naming accuracy, the lexical component (C3) predicted the performance significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, see Fig.\u0026nbsp;6b). Reaction times were slightly shorter for the assessments done on the screen, which was expected because it took less time to swipe on the screen than to flip a page (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.093). The interclass coefficient (ICC) of both models (i.e., 0.42 and 0.37) show that a substantial proportion of the total variance is attributable to differences between individuals.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eMixed-effects regression results with reaction times for correct responses and principal components\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\u003cp\u003e\u003cem\u003eOutcome variable\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCoefficients\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCI\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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFixed effects\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReaction times\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.10\u0026ndash;7.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eC1 (phonological)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.05\u0026ndash;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.062\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\u003eC2 (semantic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.04\u0026ndash;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.132\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\u003eC3 (lexical)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u0026ndash;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.797\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\u003eC4 (lexical-semantic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.08\u0026nbsp;\u0026ndash;\u0026nbsp;-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\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\u003emode [screen]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.26\u0026nbsp;\u0026ndash;\u0026nbsp;-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.093\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\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.06\u0026nbsp;\u0026ndash;\u0026nbsp;-0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\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\u003eSex [female]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.16\u0026ndash;0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom effects\u003c/b\u003e\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\u003eσ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003eτ₀₀\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003eICC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFixed effects\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39\u0026ndash;9.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.503\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\u003eC1 (phonological)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.81\u0026ndash;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.577\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\u003eC2 (semantic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75\u0026ndash;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.877\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\u003eC3 (lexical)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.50\u0026ndash;3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eC4(lexical-semantic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56\u0026ndash;1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.665\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\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.29\u0026ndash;1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003eSex [female]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u0026ndash;7.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom effects\u003c/b\u003e\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\u003eσ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003eτ₀₀\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003eICC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eσ\u0026sup2; = residual variance; τ₀₀ = random intercept variance; ICC\u0026thinsp;=\u0026thinsp;interclass correlation coefficient\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;6\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section3\"\u003e\u003ch2\u003eRegression of psycholinguistic components across naming speed (a) and accuracy (b)\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eChange in language processing from pre- to post-surgery\u003c/h3\u003e\n\u003cp\u003eFor question 3, the study examined the change in how various linguistic levels predicted naming speed and accuracy across assessment points 1, preoperative, and 2, 1\u0026ndash;4 weeks postoperative assessment. As shown in Table\u0026nbsp;7, none of the interactions between principal components representing the linguistic levels and the assessment moment were significant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eInfluence of principal components on naming reaction times and accuracy across assessment points\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOutcome variable\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eInteractions\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCI\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\u003eReaction times\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC1 \u0026times; assessment point [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.02\u0026ndash;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.196\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\u003eC2 \u0026times; assessment point [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.06\u0026ndash;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.722\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\u003eC3 \u0026times; assessment point [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.08\u0026ndash;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.426\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\u003eC4 \u0026times; assessment point [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.02\u0026ndash;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.192\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\u003eOdds ratios\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC1 \u0026times; assessment point [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u0026ndash;2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.111\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\u003eC2 \u0026times; assessment point [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.39\u0026ndash;1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.421\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\u003eC3 \u0026times; assessment point [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85\u0026ndash;3.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.134\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\u003eC4 \u0026times; assessment point [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.35\u0026ndash;2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eC1\u0026thinsp;=\u0026thinsp;Phonological component; C2\u0026thinsp;=\u0026thinsp;Semantic component; C3\u0026thinsp;=\u0026thinsp;Lexical component; C4\u0026thinsp;=\u0026thinsp;Lexical-semantic component\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe report that word-finding speed is slower after surgery, while accuracy does not change. Furthermore, word-finding speed is predicted by the lexical-semantic level (i.e., number of semantic features and word frequency), while the lexical-only component (age of acquisition, familiarity, and frequency) predicts word-finding accuracy. Lastly, there is no difference in how the above-mentioned variables predicted word-finding ability across assessment points.\u003c/p\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003eMethod validation\u003c/h2\u003e\u003cp\u003eWordrace has not been standardized for English, so it is important to establish the validity of this task as best as we can through the available data. Previous literature on healthy populations has shown that word retrieval gets faster with increasing age as the lexical representations get stronger [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and the current analysis found the same, i.e., a significant positive correlation between patients\u0026rsquo; age and word-finding speed. Secondly, the analysis of naming accuracy revealed that age positively and significantly predicted the probability of producing a correct response. As evident from the plot in Fig.\u0026nbsp;4, this correlation linearly increased until around ten years of age and then remained constant afterward. This aligns with the literature confirming that naming accuracy on confrontational naming tests increases linearly in the initial years (from one to five years) and then remains constant in unimpaired populations [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec35\" class=\"Section2\"\u003e\u003ch2\u003eWord-finding ability pre- and post-surgery\u003c/h2\u003e\u003cp\u003eThe increase in naming reaction times post-surgery indicates a worsening of word-finding speed and aligns with previous studies that reported a decline in language function following surgical removal in supratentorial language-eloquent areas in adults [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. Particularly in the naming latency literature, decline in adult patients' performance has been observed on the Boston Naming Test (BNT) after surgery compared to baseline performance before surgery [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Literature on cerebellar degeneration also shows that these adult patients present with significantly slowed word finding. The local involvement of the cerebellum in word-finding ability as evidenced by studies on lexical decision [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], phonemic fluency [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and semantic tasks [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], may be the primary reason for slow word-finding after surgery. While slow word finding does correlate with motor impairments, motor impairments are not the sole underlying cause of this speed reduction, as the severed connections of the cerebellum with the cerebrum may also explain word-finding difficulties [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]. Therefore, surgery might increase the lesion size compared to the lesion due to the tumour only, thus resulting in slowed word finding after surgery.\u003c/p\u003e\u003cdiv id=\"Sec36\" class=\"Section3\"\u003e\u003ch2\u003eLanguage processing underlying word-finding ability\u003c/h2\u003e\u003cp\u003eEffects of word properties on language performance were investigated as these may be used to characterise the nature of language impairments in clinical populations [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], but these inferences are based on the observation that the same variables affect performance in healthy individuals [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The significant effect of the lexical-semantic linguistic level on word-finding speed could thus signal the effective use of lexical and semantic resources to achieve accurate naming in patients. Future studies including controls can test if this effect is atypical in any way (larger or smaller than in controls). Atypical effects could signal impairment, in line with the studies reviewed by [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], where joint impairments of lexical and semantic processing have been reported in PFT patients in 26% of the studies that tested for it through expressive vocabulary or naming tasks. Regarding the psycholinguistic nature of impairment in patients undergoing cerebellar tumour surgery, the lexical level was found to be impaired in three out of twelve patients [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe lexical level (C3) also significantly predicted response accuracy. This level consisted of frequency, age of acquisition, and familiarity. Words frequently occurring in the corpus, more familiar to people due to exposure, and learned early on in the acquisition process are more likely to be retrieved correctly in healthy individuals and as such their effect could signal effective lexical processing supporting fast naming, while atypical effects of these variables in patients may signal lexical impairment [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e In the current study, other linguistic levels did not significantly predict word retrieval speed, which can be attributed to the task's easy nature when considering the participants' age. The pictures included in the task do not vary much in complexity and are of highly concrete, imageable, and frequent nouns, which may result in relatively easier access to the representations at levels such as semantics and phonology. As noted in a previous study, picture-based tasks constrain the range of possible verbal responses and psycholinguistic variability, especially when stimuli are designed for use with children [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Moreover, we had expected a significant effect of the mainly semantic level (C2) in driving word-finding ability, but did not find such an effect. It could be that in order to find such an effect, the items of the test need to be carefully controlled for semantic similarity and complexity, which could be a direction for future studies. Moreover, the effect seen at the lexical-semantic level may also have trickled down from the semantic level. For instance, competition between semantic neighbours may result in difficulty in selecting the target concept at the semantic level, leading to the same sort of difficulty in lexical selection [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhether patients show an atypical effect of these variables (which is a clear indication of impairment) can only be determined by future studies including healthy controls. However, if the influence of variables changes in patients as a consequence of surgery, along with a change in performance, then this could signal a change into impaired processing at a certain level of processing, as evaluated in the next section.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eLanguage processing pre- and post-surgery\u003c/h3\u003e\n\u003cp\u003eThe way the psycholinguistic levels affect word-finding ability did not change in our PFT sample across the two assessment points. This shows that language processing ability did not change at any particular linguistic level due to the surgery, albeit a worsening in the existing skills. The lack of additional types of impairment probably reflect that the anatomical substrates affected by surgery are mainly determined by the location of the tumour [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e], diaschisis via tumour compression [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and/or supratentorial disruption due to hydrocephalus [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and thus relevant impairments are already present before surgery, albeit in a less severe form [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. This would imply that different kinds of tumour could lead to a distinct pattern: for example, in adult patients with supratentorial tumours, tumour grade is an important factor to consider when investigating the change in performance pre- and post-surgery since patients with low-grade glioma tend to have already gone through neuroplastic changes due to the slow progression of the tumour and would be expected not to show as much decline after surgery [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. On the other hand, high-grade glioma patients show more drastic changes right after surgery because the lesion affects parts of their eloquent brain networks adversely due to the functions not having enough time to relocate to a different brain region.\u003c/p\u003e\u003cp\u003eAnother potential explanation for a decay in naming speed in the absence of changes within the language processing system, is that naming speed may be reduced in the context of a more general cognitive mechanism, such as processing speed, which critically interacts with other cognitive functions (e.g., language), and is shown to be frequently impaired in children with posterior fossa tumors [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. Although most studies assessing processing speed have compared patients with and without administration of radiotherapy, one study showed that PFT patients who did not undergo adjuvant radiotherapy also presented with slower information processing speed compared to a non-CNS tumour control group [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. This could explain a general decay in language performance (i.e., specifically word-finding speed in the current study) which is nonspecific to any particular linguistic level.\u003c/p\u003e\u003cp\u003eFuture studies may also focus on studying language ability at later time points and on the effects that adjuvant radiotherapy may have in leading to additional linguistic impairments or changing the psycholinguistic nature of word-finding ability. Such a pattern has been shown by in an investigation of rapid picture naming as part of information processing speed which revealed that the difference in processing speed was more pronounced after the irradiation therapy [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eLimitations and future directions\u003c/h3\u003e\n\u003cp\u003eThe current study did not have control data to compare the word-finding ability of PFT patients. This limited the interpretation of the linguistic levels that significantly impacted accuracy and reaction times in terms of their contribution to the impairment status. Future studies should recruit age-matched controls and replicate the current study to find if their word-finding ability is influenced by the same linguistic levels as the PFT sample. The approach used in previous studies for narrative data can also be employed to check whether principal component analysis with the psycholinguistic variables and word-finding ability helps differentiate between controls and patients [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWordrace needs to be standardized in English and other languages, adding to the validity of the test. An even better approach would be to rethink the test design, balancing items on visual complexity and psycholinguistic properties of the target words. Potentially significant factors, such as the selection of stimuli and their order, are usually overlooked in picture naming tests [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. For instance, the effect of semantic categories can obscure performance [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLastly, more assessment points should be investigated to examine the trajectory of word-finding ability over time after surgery in terms of improvement or worsening. As reported in a recent systematic review [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], some studies that tested language ability longitudinally in the PFT sample, revealed inconsistent results, with some finding no impairment shortly after surgery but finding phonological and pragmatic deficits at one-year follow-up [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e], while others reported persistent lexical-semantic difficulties at all assessment points after surgery [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. A longitudinal study would also help pinpoint how radiotherapy affects word retrieval abilities and how this evolution might differ depending on specific patient characteristics such as age, sex, language background (i.e., monolingual/bilingual), and/or socioeconomic status.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides important insights into the relationship between linguistic processing levels and word-finding ability in patients with posterior fossa tumours (PFT), both before and after surgical intervention. By examining the influence of psycholinguistic levels on word-finding accuracy and speed, we found that lexical-semantic processing significantly predicted word-finding speed, while only lexical processing predicted accuracy. Moreover, the observed decline in word-finding speed post-surgery suggests a deterioration in lexical retrieval, consistent with previous studies on language function post-surgery in language-eloquent areas. However, the stability of the predictive influence of psycholinguistic levels across assessment points indicates that while surgery may exacerbate existing impairments, it does not necessarily alter the underlying linguistic processing mechanisms, and we cannot ascribe the decay in language ability specifically to lexical processing. Rather, this may be explained by more general cognitive processing limitations (e.g., in processing speed). Importantly, method validation confirmed the Wordrace test's validity, aligning with known developmental patterns in naming abilities, thus establishing its utility for assessing word-finding performance.\u003c/p\u003e\u003cp\u003eFuture research should address the limitations identified, such as the lack of control data and the need for a standardized word-finding test. Additionally, longitudinal studies with more assessment points would provide a clearer trajectory of word-finding ability over time, considering factors like radiotherapy and patient characteristics. These steps will further help us understand the complex dynamics of language processing in PFT patients, ultimately guiding better clinical practices and rehabilitation strategies.\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). Rida Ahmed received funding from the research master program, European Master\u0026rsquo;s in Clinical Linguistics (EMCL project 2020-2026), with grant number 619668-EPP-1-2020-1-NL-EPPKA1-JMD-MOB. Karin Persson received funding from The Swedish Childhood Cancer Foundation, Queen Silvia\u0026rsquo;s Jubilee Fund, Jonas Foundation. Ditte Boeg Thomsen and Jonathan Kj\u0026aelig;r Gr\u0026oslash;nb\u0026aelig;k received funding from the Inge Lehmann grant (grant number 10.46540/4302-00027B) from the Independent Research Fund Denmark. Aske Foldbjerg Laustsen received funding from The Danish Childhood Cancer Foundation (grant number: 2021-7343).\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 Study of CMS. The Research Ethics Committees of the Capital Region (H-6\u0026ndash;2014\u0026ndash;002) in Denmark gave their approval for the collection of data for this project, and the study was authorized in the UK afterwards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization\u003c/strong\u003e: Rida Ahmed and Vânia de Aguiar; \u003cstrong\u003eMethodology\u003c/strong\u003e: Rida Ahmed, Vânia de Aguiar, Cheyenne Svaldi; \u003cstrong\u003eFormal analysis\u003c/strong\u003e: Rida Ahmed, Cheyenne Svaldi; \u003cstrong\u003eResources\u003c/strong\u003e: Ren\u0026eacute; Mathiasen, Marianne Juhler, Barry Pizer, Kristian Aquilina, Greg Fellows, Ian Kamaly; \u003cstrong\u003eData curation\u003c/strong\u003e: Aliene Reinders; \u003cstrong\u003eSupervision\u003c/strong\u003e: Vânia de Aguiar; \u003cstrong\u003eWriting - original draft\u003c/strong\u003e: Rida Ahmed; \u003cstrong\u003eWriting - review \u0026amp; editing\u003c/strong\u003e: Vânia de Aguiar, Cheyenne Svaldi, Karin Persson, Ditte Boeg Thomson, Jonathan Kj\u0026aelig;r Gr\u0026oslash;nb\u0026aelig;k, Aske Foldbjerg Laustsen, Barry Pizer, Roel Jonkers, Annet Kingma, Rida Ahmed; \u003cstrong\u003eFunding acquisition\u003c/strong\u003e: Vânia de Aguiar, Ditte Boeg Thomson.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants for their valuable time.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCancer Research UK. 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Verb generation in children and adolescents with acute cerebellar lesions. Neuropsychologia. 2007;45:977\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuropsychologia.2006.09.002\u003c/span\u003e\u003cspan address=\"10.1016/j.neuropsychologia.2006.09.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"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":"Infratentorial Neoplasms, Posterior Fossa Tumors, Language Disorders, Psycholinguistics, Semantics, Posterior Fossa Syndrome","lastPublishedDoi":"10.21203/rs.3.rs-8200894/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8200894/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWord finding - the ability to retrieve and produce appropriate words in response to prompts or visual stimuli - is impaired in some patients with a posterior fossa tumour. Yet, few studies use preoperative assessment as a baseline, and an in-depth linguistic analysis of tasks assessing word-finding ability remains limited. The current study aims to fill this knowledge gap by analysing pre- and postoperative word-finding ability and identifying its linguistic predictors.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003e38 English-speaking patients (19 males and 19 females), aged between 2,5 and 17,6 years and diagnosed with posterior fossa tumours were assessed before and after surgery. Performance was assessed using a picture-naming task, Wordrace, measuring both accuracy and reaction times. These measures were interpreted in terms of their correlation with linguistic levels (i.e., lexical, semantic, phonological).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePatients exhibited a significant slowing in word-finding speed following surgery, while accuracy remained stable across assessment points. Despite this decline in speed, the influence of psycholinguistic factors on word-finding ability remained consistent. Lexical-semantic variables predicted word-finding speed, whereas accuracy was influenced only by lexical variables.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe findings suggest that although general performance declined postoperatively, the underlying linguistic processes engaged during word finding were preserved. The study emphasises the importance of longitudinal assessment in patients with posterior fossa tumours and the need to compare patient performance against normative data.\u003c/p\u003e","manuscriptTitle":"Language processing in posterior fossa tumour patients: Psycholinguistic insights into the word-finding ability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 06:44:40","doi":"10.21203/rs.3.rs-8200894/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T07:11:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T00:39:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132153841874407966609555338656915927733","date":"2025-12-24T05:36:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T16:06:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268355878374986467383038160017354497058","date":"2025-12-09T17:16:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T09:32:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-26T03:14:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-26T03:12:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Cerebellum","date":"2025-11-25T08:34:09+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":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T11:24:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 06:44:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8200894","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8200894","identity":"rs-8200894","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}