Neurobiology of Language

Irony and sarcasm are complex forms of non-literal language that hinge on a misalignment between surface meaning and speaker intent, requiring listeners to integrate contextual, semantic, and prosodic cues. While prior neuroimaging studies have implicated a broad network--including the temporal cortex, the inferior frontal gyrus, and the medial prefrontal cortex--in the comprehension of ironic and sarcastic speech, the precise neural mechanisms underlying the integration of semantic and prosodic information remain unclear. In the present study, we addressed this gap by employing voxel-wise encoding models to systematically identify brain regions specifically involved in combining prosodic and semantic cues during non-literal language comprehension. Participants listened to naturalistic auditory dialogues in which both discourse context and target utterance semantics and prosody were systematically manipulated. We derived custom text embeddings using transformer-based models to capture context-sensitive semantic representations of ironic statements, alongside acoustic features characterizing affective prosody. Ridge regression models were fitted to predict BOLD responses at the voxel level using semantic, prosodic, and combined features, and we identified integration as voxels in which each modality contributed predictive information beyond the other, using a permutation-based conjunction test. The regions integrating prosody and semantics depended on whether discourse context was modeled: integration was confined to the bilateral temporal speech cortex when statements were encoded in isolation, but additionally engaged the left inferior frontal gyrus pars orbitalis (IFGorb) when each statement was weighted by its relevance to the preceding context. These findings indicate that the left IFGorb integrates prosody with context-dependent meaning, engaging beyond the temporal speech cortex specifically when comprehension requires combining semantic, prosodic, and contextual cues--as in irony and sarcasm.

Second language (L2) processing engages both language-specific and domain-general control systems, yet how these systems vary with L2 proficiency remains unclear. We used functional magnetic resonance imaging (fMRI) to examine neural activity during L2 English processing in Korean-English (K-E) bilinguals across three proficiency levels (beginner, intermediate, advanced). Participants performed rhyme and spelling judgement tasks manipulating orthographic-phonological conflict. Behaviourally, conflict conditions reduced accuracy, with proficiency effects observed selectively in the rhyme task. fMRI results showed that conflict processing recruited frontoparietal control regions, including inferior frontal and parietal cortices, accompanied by deactivation in default mode network regions. Critically, proficiency-related effects differed by task. During rhyme judgement, advanced bilinguals showed greater activation in the left supramarginal gyrus (SMG) and cerebellum, whereas intermediate bilinguals exhibited greater recruitment of the left middle orbital gyrus and dorsomedial prefrontal cortex. During spelling judgement, advanced bilinguals showed greater thalamic activation alongside greater deactivation of the right dorsolateral prefrontal cortex. Activity in the left SMG and cerebellum was positively associated with L2 reading score, and cerebellar activity was also associated with rhyme-task performance, whereas right DLPFC activity was negatively associated with the scores. These findings suggest that increasing L2 proficiency is associated less with altered recruitment of core reading regions than with task-specific shifts in the balance between phonological-specialized, subcortical attentional, and domain-general control systems supporting L2 processing.

Human language processing can be studied through both behavior and brain activity, yet it remains unclear whether these two data types reflect sensitivity to the same information. One influential view holds that both behavioral and neural responses are largely determined by processing effort, often estimated by word surprisal together with the context-independent properties of word frequency and length. At the same time, neural responses have been shown to encode richer aspects of linguistic content, including meaning. Here, we use neural network language models to operationalize these alternatives and systematically compare, within the same analytic computational framework, the predictive power of low-dimensional effort-based predictors and high-dimensional embedding representations that encode contextualized linguistic content, including meaning. Across 8 behavioral datasets and 5 neural datasets (4 fMRI and 1 ERP), we find that processing effort captures substantial variance in both behavioral and neural measures of language processing, in line with much previous work. However, for brain responses---but not for behavioral measures---embedding representations carry substantial predictive power beyond the estimates of processing effort. These results therefore suggest that neural data provide access to rich, high-dimensional dynamics of language comprehension, whereas behavioral data reflect a bottlenecking of these dynamics into a small set of theoretically motivated properties of contextualized linguistic input.

Language comprehension requires the continuous transformation of speech into a hierarchy of linguistic units, from phonemes to syllables to words. Because speech unfolds rapidly, listeners are thought to predict upcoming content to keep pace. Previous research has provided empirical evidence for predictive processes operating at multiple linguistic levels during naturalistic listening, including words and phonemes. However, it remains unclear whether prediction also operates concurrently at other levels, such as syllabic and phrasal representation. Here we use Mandarin Chinese to examine the neural signatures of predictive processing across multiple levels of linguistic granularity during natural speech comprehension. Mandarin comprises four representational levels: phoneme, sub-syllabic, character and word, and its lexical identity is largely constrained at the sub-syllabic level, potentially redistributing predictive weight across linguistic representations. We recorded magnetoencephalography (MEG) data while 34 native Mandarin speakers (21 females) listened to a naturalistic audiobook and applied linear regression modeling to examine how linguistic features modulated neural activity. We found that the brain activity of listeners segmented speech into hierarchical units, and that surprisal modulated responses simultaneously across sub-syllabic, character and word levels. In contrast to findings from Indo-European languages, however, we did not observe unique surprisal effects at the lowest, phonemic level. Furthermore, the surprisal of lexical tone in Mandarin modulated brain activity only when integrated with its phonological components. These findings suggest that predictive processing during Mandarin speech comprehension operates concurrently across multiple (though not necessarily all) levels of linguistic granularity, with its implementation shaped by language-specific structural properties. Significance statementLanguage comprehension involves segmenting a continuous acoustic stream into multiple linguistic units, from phonemes to words, and generating predictions at these levels. However, direct neural evidence remains limited regarding how segmentation and prediction operate simultaneously across levels of linguistic granularity, particularly outside Indo-European languages. Using temporal response function analysis of magnetoencephalography data recorded during naturalistic Mandarin listening, we show that predictive processing occurs across multiple levels of linguistic granularity. Specifically, we find evidence for prediction-related neural responses at sub-syllabic, character, and word levels, but not a reliable unique effect at the phonemic level. These results indicate that predictive processing also operates during Mandarin speech comprehension, and its neural implementation is shaped by language-specific structural properties.

How internal speech relates to overt speech remains a fundamental question in speech production: do different forms of speech preserve a common neural representation of the intended word, or does that representation change as speech becomes articulated? We used time-resolved electroencephalography to characterize representations of 10 Chinese words during imagined, silent, and overt speech. Word identity was reliably decodable in all three modes, but its temporal dynamics differed: imagined-speech representations peaked earlier and were less temporally stable, whereas silent and overt speech showed stronger and more sustained representations. Cross-mode decoding revealed word-discriminative information shared across all three mode pairs, with substantially stronger generalization between silent and overt speech. However, direct comparison of word-level representational geometry revealed robust correspondence only between silent and overt speech, indicating that transferable information across modes does not necessarily imply preservation of the broader relational structure among words. Representational similarity analyses further showed distinct visual-form, semantic, and phonetic dynamics across speech modes, with late visual-form and phonetic information contributing uniquely to the geometry shared by silent and overt speech. Controlling for time-matched surface electromyography preserved the overall silent-overt neural correspondence and within-mode phonetic representations, while eliminating the unique phonetic contribution to their shared geometry, suggesting that peripheral articulation accounts for part, but not all, of this common structure. Together, these findings show that imagined, silent, and overt speech share word representations at different levels and suggest that representational geometry and temporal stability are progressively reorganized as internal speech is translated into articulation.

Language comprehension involves the integration of single words (lexical units) into phrases and sentences (multi-word structures). Previous frequency-tagging studies have found that low-frequency neural responses synchronize to the frequency of multi-word structures. However, it is currently unclear how exactly structural and lexical processes jointly impact these synchronization findings. The present magnetoencephalography experiment implemented the frequency-tagging paradigm in the visual modality with written words to investigate neural synchronization to multi-word sentences varying in internal structure (reversed word orders between verb-initial Spanish and verb-final Basque sentences) and in lexical content (real words and pseudo words). We find converging evidence that neural responses largely synchronize to structural rather than lexical features. This was observed as robust phase synchronization strength to the frequency of sentences containing reversed structures, with certain lexical modulations depending on language-specific structural features. Crucially, we also found shifted phase angle dynamics between the reversed structures of Spanish and Basque sentences independently of word-level lexical characteristics. Together, these findings suggest that neural synchronization to multi-word structures is largely driven by distinct structural features operating via two segregated neural dimensions: frequency coding for the coarser aspects (i.e., timescale/duration) and phase representing the finer-grained aspects (i.e., internal structure) of multi-word structures. Our findings thus advance key insights into the core components of the neural mechanisms supporting language comprehension. HighlightsO_LINeural synchronization to sentences is driven by structural (not lexical) features. C_LIO_LIRobust sentence-frequency synchronization across languages varying in structure. C_LIO_LIPhase angle is selectively sensitive to cross-linguistic structural differences. C_LIO_LILexical modulations depend on language-specific structure. C_LIO_LIStructure synchronization segregates into two dimensions: frequency and phase. C_LI

Can grammatical gender in a bilingual's first language shape gender-stereotype processing in a second language? Spanish (L1)-English (L2) bilinguals (n = 28) and English monolinguals (n = 28) completed an event-related potential (ERP) priming task in which English pronouns (SHE/HE) followed gender-stereotyped English nouns, half of which had gender-marked Spanish translation equivalents (e.g., NURSE 'enfermera/o', SURGEON 'cirujana/o'), and half unmarked translation equivalents (e.g., SINGER 'cantante', JANITOR 'conserje'). Both groups showed asymmetric stereotype priming: male pronouns elicited a larger N400 for incongruent than congruent primes, whereas female pronouns elicited a larger P300 for incongruent than congruent primes. Crucially, only bilinguals showed modulation by Spanish grammatical gender marking: the N400 effect for male pronouns was larger for primes with gender-marked than unmarked Spanish translations. These findings provide neural evidence that grammatical gender in a bilingual's first language can influence gender-stereotype processing in a second language, linking cross-linguistic activation to social cognition.

Traditional views assume that pragmatic deficits after stroke, which compromise the interpretation of communicative intentions and non-literal meanings, follow damage to the right hemisphere (RHD), with left-hemisphere damage (LHD) primarily linked to aphasia and structural language impairment. To examine hemispheric contributions to post-stroke pragmatic profiles, we assessed 99 stroke patients (40 LHD, including 14 with aphasia of minimal-to-moderate severity; 59 RHD) and 60 healthy controls with the Assessment of Pragmatic Abilities and Cognitive Substrates (APACS). While stroke patients overall performed worse than controls, LHD and RHD profiles were largely comparable across three converging analyses: (1) permutation tests revealed no hemispheric differences except on the two tasks requiring expressive components (Interview and Figurative Language 2), which in turn lowered the composites (APACS Production and Total); (2) equivalence testing established equivalence for most measures, with only these same tasks and composites remaining inconclusive; and (3) unsupervised clustering did not group patients by lesion side. Theory of Mind was robustly associated with pragmatic performance in both groups, whereas structural language abilities related specifically to LHD performance and general cognition only to RHD. Excluding aphasic LHD patients strengthened the evidence for comparable profiles, indicating that aphasic LHD patients largely drove the residual differences. In conclusion, primary pragmatic impairment, especially in the receptive domain, emerged comparably after LHD and RHD, with the only residual LHD disadvantage limited to tasks demanding open verbal output. These findings challenge the assumption of right-hemispheric specialization for pragmatics, stressing the need for pragmatic assessment in all post-stroke patients.

Adults who stutter can speak fluently when speech is not addressed to another person, but stuttering emerges when they aim to convey information to a listener. The value of the information being conveyed to the listener also affects the likelihood of stuttering. Why should the mere absence of a listener neutralise a profound motor deficit, and why does a word's predictability affect whether it is spoken fluently? To resolve this socio-motor paradox, we develop a computational model of stuttering within an active inference architecture. The model represents the communicative context, including whether a listener is present and whether the agent is speaking or listening. It was designed around two candidate mechanisms for stuttering, a prior for silence and rigid phoneme sequencing precision. Using both, the model produced fluent private speech and more stuttering-like events during social speech. In the same parameter regime, the model also showed more stuttering-like events on words with higher information value, and produced a word-length effect, in which disfluency increased with longer words. To our knowledge, this is the first model of stuttering to generate both the private speech and the information-value effect from inferred communicative context. By representing the listener as a hidden state that makes the sensory consequences of resuming speech ambiguous, the model offers a computational link between social cognition and speech-motor instability, and suggests that speech fluency depends on whether the speaker believes anyone is present. Clinically, it may offer testable hypotheses and a route to personalising treatment, since the same overt severity can arise from different combinations of parameters.

Theories of predictive coding propose that perception is the process of inferring the causes of our sensory input by comparing that input with predictions derived from our internal models of the world. Such predictive processes are thought to play a central role in language comprehension, however, robust neurophysiological evidence for such processes, particularly during natural speech perception, remains limited. Previous work has suggested that the early auditory encoding of words in natural speech is influenced by their preceding linguistic context. However, it remains unclear whether this effect is driven by prediction per se or dynamic modulations of attention based on contextual uncertainty. To distinguish between these alternatives, we recorded electroencephalography from 17 healthy adults while they listened to slightly changed audiobook. Specifically, we identified and replaced several unsurprising content words with more surprising words. We quantified the early auditory encoding of words using speech-envelope reconstruction accuracy within 100-ms time window after word onset and examined its relationship to word surprisal and contextual uncertainty. We found that more surprising words showed enhanced early auditory encoding despite matched contextual constraint. Moreover, the temporal profile of this enhancement depended on when the incoming speech signal diverged from the predicted phonological sequence, consistent with the emergence of prediction-error responses. Linear mixed-effects modeling further revealed that word surprisal had a substantially stronger influence on early auditory encoding than contextual uncertainty. Together, these findings indicate that the early auditory encoding of words during naturalistic speech perception is more strongly associated with predictive computations than with uncertainty-driven attentional gain. Significance StatementDuring natural speech comprehension, contextual information influences how the brain processes incoming sensory input. However, whether this context-dependent modulation of the early auditory encoding of words reflects predictive computations or dynamic changes in attentional gain has remained unresolved. By combining a naturalistic speech paradigm with a stimulus manipulation that varies word surprisal while controlling contextual uncertainty, we show that the early auditory encoding of words is driven by word surprisal under matched contextual constraint. Moreover, the temporal dynamics of this modulation closely follow the point at which the incoming speech signal departs from the predicted phonological sequence. These findings provide neurophysiological evidence that predictive computations contribute to the context- dependent modulation of early auditory processing during natural speech comprehension.

Encoding models offer a principled framework for linking computational representations of language to neural activity, but most electroencephalography (EEG) evidence for brain-language alignment comes from tightly controlled, word-by-word reading paradigms. Whether such alignment is detectable during naturalistic reading, and how it is affected by lapses in attention, remains unclear. We addressed these questions using ROAMM, a multimodal dataset containing simultaneous EEG and eye-tracking recordings with time-resolved mind-wandering (MW) annotations from 44 participants reading naturalistic texts. Ridge regression encoding models were trained to predict fixation-aligned EEG spectral power and fixation-related potentials (FRPs) from five word-embedding models (GloVe, word2vec, BERT, GPT-2, and Llama 3). Using permutation testing with false discovery rate correction, we found statistically reliable brain-language alignment across both feature types, with contextual embeddings outperforming static embeddings. Spectral alignment was strongest in the alpha and low-beta bands over parietal electrodes, while FRP-based alignment peaked 200-300 ms after fixation onset over central and parietal-occipital regions. Leveraging ROAMMs span-level MW annotations, we further show that brain-language alignment is systematically reduced during MW, an effect that was substantially larger for oscillatory (PSD) than for event-related (FRP) features. These findings demonstrate that modern language-model representations are reflected in EEG activity during naturalistic reading despite the modalitys inherent noise, and that fluctuations in attention constitute an underappreciated source of variability in brain-language encoding studies.

Connected speech is essential for everyday communication, but clinical constraints and patient fatigue limit detailed evaluation in acute stroke (<1-week post-stroke). Bedside assessments may sample discourse but rarely quantify language impairment (LI) in connected speech, leaving patient communication poorly characterized. We analyzed brief story retellings from 86 patients with left-hemisphere stroke (~4 days post-stroke; 63 classified with LI using composite clinical and naming criteria). From transcripts generated with automatic speech recognition, we derived discrete linguistic features and embeddings with Large Language Models (LLMs). An ensemble of embedding-based classifiers distinguished patients with and without LI with 90% balanced accuracy (79% sensitivity, 100% specificity), outperforming independent embedding and discrete-linguistic-based classifiers, showing distinct LLMs contributed complementary information. Adding the discrete-linguistic-based classifier to the ensemble did not improve balanced accuracy but modestly increased sensitivity at the expense of specificity. We provide proof of concept for a fast, largely automated discourse screener of acute LI.

The human brain must rapidly construct hierarchical structures to organize complex sequential behavior, yet the neural dynamics supporting this process during natural behavior remain poorly understood. Spoken language provides a powerful model system for investigating this computation, requiring rapid transformation of conceptual intent into structured sequential output. Using rare intracranial stereo-electroencephalography (SEEG) recordings from patients producing extended spontaneous speech, we examined how syntactic planning unfolds over time using measures of constituency, dependency structure, and probabilistic syntactic categories. We identified a hierarchical planning architecture in which global sentence structure and core syntactic categories (nouns and verbs) were specified before more local planning operations. Neural representations of these categories emerged up to 1 s before articulation and persisted throughout the planning period, whereas optional modifiers, including adjectives and adverbs, were recruited only closer to speech onset. These observations support a model of hierarchical incremental planning in which abstract sentence structure precedes the incremental specification of individual sentence elements. While core syntactic categories engaged a broader fronto-temporo-parietal network than other word classes, syntactic-depth-related activity emerged in parallel across cortical regions and the hippocampus, suggesting that hippocampal relational representations contribute to sentence structure building. Together, these findings support a cortico-hippocampal model of speech production in which hierarchical sentence structure and core syntactic categories are planned before secondary syntactic elements are incrementally incorporated into the evolving sentence plan. These results provide a neural account of how abstract linguistic structure is transformed into fluent speech.

Integrating auditory and visual cues is a hallmark of human speech perception, yet adults from East Asian backgrounds show less reliance on visual speech than their Western counterparts. The origins of this cultural difference, however, remain unknown. To investigate whether this divergence is established early in infancy, we examined audiovisual integration in 6- to 12- month-old White Canadian (n=111) and Chinese (n=115) infants using a novel paradigm measuring their perception of the McGurk effect. Across four experiments, we found a clear developmental divergence: Canadian infants showed a stable McGurk effect from 6 months onward, whereas Chinese infants showed a more protracted developmental trajectory, a cultural pattern that was further highlighted when their integration was challenged by other-race faces. These findings provide the first direct evidence that cultural differences in multisensory speech perception are established within the first year of life, suggesting that the brains strategy for binding sight and sound is shaped by early experience, with broad implications for theories of language acquisition and developmental science.

Developmental dyslexia is a specific, highly prevalent and often debilitating reading and spelling disorder with unknown neurocomputational mechanisms. Here we discovered, in a preregistered functional magnetic resonance imaging study optimized for the subcortical sensory pathway, that dyslexia is characterized by altered predictive coding in left-hemispheric auditory sensory pathway nuclei. The neurocomputational alterations were related to one of the two main dyslexia risk scores, indicating a crucial role for dyslexia pathophysiology.

Background and Objectives: Word-finding difficulty is common in healthy aging and in neurologic disorders, including temporal lobe epilepsy (TLE) and early Alzheimer disease. Standard language measures have limited sensitivity for detecting subtle or longitudinal changes in spontaneous speech. We examined whether natural language processing and acoustic analysis of spoken biographical recall could identify lexical and temporal speech features associated with language and memory performance in TLE. Methods: We conducted a cross-sectional observational study of spoken recall during a Famous Faces biographical memory task. Adults with TLE and healthy controls (HCs) viewed 20 famous faces and spontaneously recalled biographical details. Speech was transcribed and diarized using automated tools. Lexical measures included word counts and lexical index (ratio of rare to common words). Acoustic measures included utterance and pause duration and pause frequency. Features were compared between groups and correlated with neuropsychological measures, including Montreal Cognitive Assessment (MoCA), Boston Naming Test (BNT), delayed recall, education, and biographical recall accuracy Results: Eighty-one adults participated (51 TLE, 30 HCs). Lexical measures did not differ between groups. In TLE, lexical index correlated with BNT performance (rs=0.62) and MoCA score (rs=0.35). Compared with HCs, participants with TLE produced shorter utterances (5.54 {+/-} 2.50 vs. 6.59 {+/-} 2.63; Cohen's d=0.41, 95% CI -0.05 to 0.86, p=0.041), shorter pauses (0.61 {+/-} 0.21 vs 0.67 {+/-} 0.22, Cohen's d=0.29, 95% CI -0.16 to 0.74, p=0.043), and more frequent pauses (10.81 {+/-} 3.30 vs 9.29 {+/-} 3.59, Cohen's d=-0.45, 95% CI -0.90 to 0.01, p=0.036). Higher education was associated with longer utterances, longer pauses, and lower pause frequency, without evidence of a diagnosis-by-education interaction. Faster utterance rate was associated with better biographical recall in both groups, while higher pause rate was associated with worse recall in TLE. Discussion: Speech-derived lexical and temporal features from naturalistic recall capture clinically relevant variation in language and memory-related performance. Although lexical output did not distinguish TLE from HCs, lexical richness tracked naming and global cognition in TLE, while temporal speech features related to recall performance. These findings support the potential of automated speech analysis as a digital behavioral biomarker for word-finding difficulty in neurologic populations.

Background and aimVery preterm birth (VPT; [&le;]32 weeks gestation) is associated with an increased risk of later psychiatric disorders, including psychosis. Although psychosis typically emerges in adulthood, subclinical early signs along the psychosis continuum, such as psychotic-like experiences (PLEs), can be observed much earlier. We therefore aimed to study PLEs in childhood in VPT individuals recruited from a clinical cohort compared with full-term (FT) controls. We subsequently investigated whether findings could be replicated in an independent population-based cohort. MethodsPrimary analyses were conducted in the Brain, Immunity and Psychopathology (BIPP) study, including 197 children born VPT recruited through Neonatal Intensive Care Units and 72 FT controls assessed at a mean age of 10.50{+/-}1.77 years. Between-group differences in PLEs were then examined in the Adolescent Brain Cognitive Development (ABCD) study, including 149 children born VPT and 9519 FT controls assessed at a mean age of 9.94 {+/-} 0.63 years. PLEs were assessed using the Prodromal Questionnaire-Brief Child Version (PQ-BC), yielding frequency and distress-related scores for both the total scale and three specific domains (unusual thought content, perceptual abnormalities, disorganised speech). Regression models tested associations between birth status (VPT and control) and PQ-BC scores adjusting for age, sex, and socio-economic status, with secondary models additionally adjusting for cognitive ability and broader psychopathology. Pooled analyses examined cohort effects (BIPP and ABCD) and cohort-by-group status (VPT and control) interactions. ResultsIn BIPP, VPT birth was associated with higher PQ-BC total ({beta}=1.61, p=0.004) and distress scores ({beta}=0.78, p=0.039), with the strongest and most consistent associations observed for perceptual abnormalities across total score (sum of endorsed items), distressing items, and distress severity scores (all p[&le;]0.01). These associations were attenuated but largely persisted after adjustment for cognitive ability and broader psychopathology, particularly for perceptual abnormalities. In ABCD, VPT birth was not significantly associated with global or domain-specific PQ-BC outcomes. DiscussionVPT birth is associated with increased vulnerability to PLEs in childhood, particularly in the domain of perceptual abnormalities. The lack of clear replication in the population-based ABCD cohort may reflect differences in the composition of its VPT subgroup, which may not fully represent VPT individuals typically seen in clinical cohorts.

Self-repairs, in-utterance revisions in which a speaker abandons and reformulates their speech, are a promising interpretable marker for speech-based cognitive screening. Detecting them automatically is difficult because a self-repair is defined by its relationship to surrounding speech rather than by fixed lexical cues. On the DementiaBank ADReSS corpus, we compared the capability of generative LLMs under a five-condition prompt ablation against a fine-tuned DistilBERT token classifier at detecting self-repairs. GPT-5 performed best (test F1 = 0.73) and was largely insensitive to prompt design, whereas the LLaMA (open-weight alternative) was both weaker and far more prompt-sensitive (test F1 = 0.47). DistilBERT, nearly 100 times smaller, matched the open-weight LLM at a fraction of the computational cost. These results suggest that a locally deployable encoder, given sufficient in-domain annotation, is a more plausible route to clinical self-repair detection than scaling model size or prompt complexity.

Brain regions specialized for language have been extensively described, yet their arrangement into one or multiple networks remains debated. Using precision functional mapping across three independent cohorts of intensively scanned individuals (22 individuals scanned over 216 separate MRI sessions), we dissociated two nested left-lateralized perisylvian networks: an intermediate language network (intLANG) and an anatomically distinct association language network (aLANG). intLANG is anchored to precentral speech areas and the Sylvian parietal-temporal area (Spt), whereas aLANG surrounds intLANG and extends into higher-order prefrontal and temporal association cortices. The two networks can be fully recapitulated by functional connectivity from adjacent cerebellar regions, indicating that they are segregated, brain-wide networks. Task-based analyses further reveal that intLANG and aLANG are functionally distinct: intLANG responds robustly during rhyme judgments and nonword reading that emphasize phonology, whereas aLANG is preferentially recruited during meaning-based sentence processing. These findings indicate that human language engages nested distributed networks each specialized for distinct components of language processing: a lower-order network biased toward phonology, and a surrounding association network that subserves higher-order syntax and semantics. This nested organization is similar to other brain systems suggesting a shared hierarchical motif that may give rise to specialized cognitive functions across the human brain.

Background: Infant muscle tone reflects early central nervous system integrity and has been associated with later motor and cognitive development, including autism traits. The cerebellum regulates both motor control and higher-order socio-cognitive functions and has been repeatedly implicated in autism, but its role in linking infant muscle tone to adolescent autistic traits has not previously been studied in a large, prospective population cohort. Methods: We used data from the prospective Generation R Study. Infant muscle tone (hypotonia and hypertonia) was assessed via Prechtl examination, and third-trimester fetal transcerebellar diameter was measured using ultrasound n=6,842). Cerebellar morphology at ages 6, 10, and 14 years (n=4,861) was measured using structural MRI. Linear mixed-effects models tested associations between infant muscle tone and 35 anatomical and 10 functional cerebellar regions. Causal mediation models tested whether cerebellar volume mediated associations between infant muscle tone and adolescent autistic traits at age 14 (Social Responsiveness Scale). Results: Hypotonia predicted larger vermis IX volumes across childhood (beta=0.037, pFDR =0.043). Hypertonia showed an age-dependent association with left lateral lobule IX (beta=-0.0027, pFDR =0.041), with differences diminishing with age. Third-trimester transcerebellar diameter did not predict postnatal muscle tone. Given its significant main effect, vermis IX volume was tested as a mediator, but did not mediate the pathway to adolescent autistic traits. However, infant hypotonia showed a small direct association with elevated autistic traits at age 14, specific to girls (beta=0.0255, p=0.020). Conclusions: Infant muscle tone is associated with localized differences in cerebellar volumes. These associations are specific to vermal and left hemispheric lobule IX, a region commonly implicated in spinocerebellar postural control, axial stability, and higher-order sensorimotor integration. Furthermore, infant muscle tone was not predicted by prenatal cerebellar diameter, and cerebellar volumes did not mediate the association between infant hypotonia and adolescent autistic traits in our study. Future research should further investigate these findings in clinical populations, integrating longitudinal whole-brain, multi-modal imaging to clarify the association between infant muscle tone, the cerebellar functioning, and autistic traits.