Biology subjects

Fedorenko, E.

Publications and source records attributed to Fedorenko, E..

At least 19 recordsLinked to original sources

bioRxiv2026

The language network does not support music perception across a range of music experience

Music and language share many similarities, yet music does not elicit a response in the language brain areas in adults with typical amounts of music experience, and patients with aphasia show preserved music ability. However, this dissociation might not generalize across development or expertise: it may not yet be present in children, who have limited experience with language and music, or it may be diminished in musicians with extensive music training. To evaluate these possibilities, we here used a precision fMRI approach to examine the responses in the language areas of children (aged 4-16 years, n=80) and adult musicians (n=10) to music. We found that the results obtained previously for non-musician adults replicate in two new control samples and, critically, generalize to both of the newly tested populations: the language areas are already selective for language relative to music in young children, showing no age-related change from early to late childhood, and remain selective despite extensive music training in highly experienced musicians. Thus, it appears that the amount of music exposure and practice have little effect on how the language brain areas respond to music.

neuroscience↗

bioRxiv2026

Behavioral and brain responses to language reflect different levels of linguistic representation

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.

neuroscience↗

bioRxiv2026

The cerebellum specializes for language even in the absence of contralateral neocortical inputs

Long considered a structure dedicated primarily to motor control, the cerebellum is now known to contain regions that respond selectively to language. However, how cerebellar language specialization emerges during development remains unknown. The prevailing proposal is that cerebellar functional specialization critically depends on inputs from the contralateral neocortex, received through well-established reciprocal cortico-cerebellar connections. Here, we test this proposal in two individuals with atypical brain development: EG, a right-handed woman who lacks most of her left temporal lobe (presumably from birth) and whose neocortical language network resides in her right hemisphere, and C1, an individual missing the entire left cerebral hemisphere from birth. Using precision functional MRI in EG and a cohort of 74 typically-developing adults, we find that EG's cerebellar language network shows a strong left-hemispheric bias, mirroring the atypical lateralization of language in her cerebral cortex, while preserving canonical topography and response profiles of the language-dominant cerebellar regions. Critically, however, EG's right cerebellar hemisphere also responds to language and even contains a language-selective region despite the absence of language regions in the neocortical left hemisphere. We replicate these cerebellar findings in C1, a more extreme case who is missing the entire left hemisphere. These results show that the cerebellum develops language sensitivity despite the absence of contralateral inputs in the face of early injury. Together, these findings challenge the view that cerebellar specialization critically requires contralateral neocortical inputs, and point instead to some degree of intrinsic neocortex-independent cerebellar organization.

neuroscience↗

bioRxiv2026

Monosynaptic connections link functionally similar regions in human cortex

The functional organization of human cortex has been mapped in considerable detail1-5, yet the axonal connections linking these areas remain largely unknown. This gap precludes inferring either the computational pathways through cortical networks or the principles governing why each area connects to its particular target regions. Here, in patients undergoing intracranial monitoring for epilepsy, we used concurrent electrical stimulation and functional magnetic resonance imaging (es-fMRI), validated previously against tracer studies in nonhuman primates6, to map anatomical connectivity of cortical sites across the whole brain, and combined these maps with task and resting-state fMRI in the same individuals. Es-fMRI revealed four main findings. First, connectivity followed an asymmetric functional-similarity principle: connected sites tended to share similar functional profiles, but, because connectivity is sparse, most pairs of functionally similar sites were not connected. Second, although connectivity also declines with distance7, the functional profile explained three times as much variance in connectivity as distance did, and long-range connections were the most functionally specific. Third, es-fMRI revealed direct monosynaptic links between established functional regions, including between the fusiform face area (FFA)2 and the temporoparietal junction (TPJ)3 for social cognition. Fourth, resting-state functional connectivity (rsFC) of a stimulation site was only weakly correlated with that sites es-fMRI connectivity. Together, these results provide a first principled link between anatomical connectivity and functional organization in the human cortex, and establish es-fMRI as a scalable approach to building a tracer-grade connectome of the human brain.

neuroscience↗

bioRxiv2026

The emergence of the language system in the toddler brain

Toddlerhood is a time when childrens language competence undergoes a dramatic transformation, making it a key period to examine the emergence of the brains language system. Functional magnetic resonance imaging data from 29 awake toddlers (19-36 months) scanned on a child-friendly language task reveal that, already at this young age, areas in the left frontal and temporal cortex respond more to comprehensible language (i.e., videos of puppets speaking in English) than the control condition (matched videos with incomprehensible audio). Although the magnitude of response to language is substantially weaker than in adults, the topography is already adult-like, challenging claims that the frontal component or left-hemispheric dominance do not emerge until later in life.

neuroscience↗

bioRxiv2025

A distinct set of brain areas process prosody--the melody of speech

Human speech carries information beyond the words themselves: pitch, loudness, duration, and pauses--jointly referred to as prosody--emphasize critical words, help group words into phrases, and convey emotional and other socially-relevant information. Using a novel fMRI paradigm, we identify a set of prosody-responsive brain areas and then richly characterize them across 8 experiments (25 experimental conditions; n=51 participants). These areas--located on the lateral temporal surface bilaterally and in the frontal lobe--respond to the presence of prosody in both meaningful and meaningless speech, and are distinct from nearby temporal pitch- and speech-perception areas and from frontal areas sensitive to general cognitive and attentional demands. They are also dissociable from--but show partial overlap with--the language areas and with the area that processes dynamic facial expressions. Thus, prosodic information is processed by a distinct set of brain areas, which may help integrate linguistic meanings with non-verbal communicative signals.

neuroscience↗

bioRxiv2025

Semantic reasoning takes place largely outside the language network

The brains language network is often implicated in the representation and manipulation of abstract semantic knowledge. However, this view is inconsistent with a large body of evidence suggesting that language processing is neurally distinct from the rest of cognition. Here, we use precision brain imaging to uncover a set of brain regions, separate from the language network, that are engaged in semantic processing of both linguistic and pictorial stimuli. In three fMRI experiments, participants (total n=41 tested across 49 sessions) viewed sentences and pictures depicting simple events. In separate blocks, they performed either a semantic task or a difficulty-matched perceptual task. Across all three experiments, several areas in left lateral prefrontal cortex, left temporo-parietal cortex, and right cerebellum responded to semantic tasks for both sentences and pictures. These semantic processing areas are spatially and functionally distinct from the nearby language-selective areas, as well as from the multiple demand and default mode networks, exhibiting a unique response profile. Our results provide evidence for a new kind of selectivity in the human brain and pave the way for future explorations of the neural mechanisms that underlie semantic reasoning.

neuroscience↗

bioRxiv2025

The language network responds robustly to sentences across diverse tasks

A network of left frontal and temporal brain areas supports language comprehension and production, implementing computations related to word retrieval and combinatorial linguistic processing. Here, we ask: to what extent are responses to language in this language network stable across task contexts, and how does this stability compare to task sensitivity in the domain-general multiple demand (MD) network? Participants (n=52) read sentences and nonword lists under six task conditions, including passive reading, reading with a memory probe after each stimulus, and reading and answering questions that require deep semantic engagement. The sentences>nonwords contrast isolated the same set of language-responsive voxels across all tasks; the locations of those voxels were participant-specific, highlighting the value of individual-specific functional localization. We therefore conclude that language localization is robust to task variation. We then examined the magnitudes and fine-grained activation patterns in these language-responsive voxels (the language network) and in the domain-general MD network, to test whether task demands modulate linguistic computations and/or recruit a distinct brain system. The language network responded robustly to sentences across all tasks, with somewhat higher responses to semantically engaging tasks. In contrast, the MD network responded to both sentences and nonwords in the presence of a task, which warrants caution when using language paradigms that include task demands, as such paradigms engage two independent networks. A multivariate analysis further revealed that stimulus information is more easily decodable in the language network, whereas task information is more decodable in the MD network. These results suggest that the language and MD networks perform complementary functions during task-driven language comprehension, with the language network primarily extracting information from linguistic input and the MD network determining the appropriate response to the task.

neuroscience↗

bioRxiv2025

A human brain network specialized for abstract formal reasoning

Humans stand out in the animal kingdom for their ability to reason in highly abstract ways. Using a deep-data precision fMRI approach, we identify and richly characterize a network of frontal brain areas that support abstract formal reasoning. This abstract reasoning network robustly dissociates from the domain-general Multiple Demand network--the current leading candidate substrate of fluid intelligence--as well as from three other networks supporting high-level cognition: the language network, the intuitive physical reasoning network, and the social reasoning network. Finally, the areas of this network respond robustly during both deductive and inductive reasoning, during classic matrix reasoning problems, and when solving multiplication and division problems. This network may therefore support the most abstract forms of reasoning, possibly constituting a human-specific adaptation.

neuroscience↗

bioRxiv2025

A two-dimensional space of linguistic representations shared across individuals

Our ability to extract meaning from linguistic inputs and package ideas into word sequences is supported by a network of left-hemisphere frontal and temporal brain areas. Despite extensive research, previous attempts to discover differences among these language areas have not revealed clear dissociations or spatial organization. All areas respond similarly during controlled linguistic experiments as well as during naturalistic language comprehension. To search for finer-grained organizational principles of language processing, we applied data-driven decomposition methods to ultra-high-field (7T) fMRI responses from eight participants listening to 200 linguistically diverse sentences. Using a cross-validation procedure that identifies shared structure across individuals, we find that two components successfully generalize across participants, together accounting for about 32% of the explainable variance in brain responses to sentences. The first component corresponds to processing difficulty, and the second--to meaning abstractness; we formally support this interpretation through targeted behavioral experiments and information-theoretic measures. Furthermore, we find that the two components are systematically organized within frontal and temporal language areas, with the meaning-abstractness component more prominent in the temporal regions. These findings reveal an interpretable, low-dimensional, spatially structured representational basis for language processing, and advance our understanding of linguistic representations at a detailed, fine-scale organizational level.

neuroscience↗

bioRxiv2025

The cerebellar components of the human language network

The cerebellums capacity for neural computation is arguably unmatched. Yet despite now ample evidence of cerebellar contributions to cognition, including language, its precise role in language processing remains debated. Here, we systematically characterize cerebellar language-responsive regions using precision fMRI. We identify four cerebellar regions that respond to language across modalities (Experiments 1a-b, n=754). One region--spanning Crus I/II/lobule VIIb--is selective for language relative to diverse non-linguistic perceptual, cognitive, and motor tasks (Experiments 2a-f, n=732), and the rest exhibit mixed-selective profiles, responding strongly to language but also to one or more of the non-linguistic conditions. Similar to the neocortical language system, the language-selective region is engaged by sentence-level meanings during comprehension and production (Experiments 3a-b, n=100) and shows fine-grained sensitivity to linguistic processing difficulty (Experiment 3c, n=5). Further, this regions response to language is not due to the frequent presence of social content in language, as it is strongly engaged by both social and nonsocial sentences (Experiment 3d, n=10). Finally, all four regions, but especially Crus I/II/VIIb, are functionally connected to the neocortical language system (Experiment 4, n=85). We propose that these cerebellar regions constitute components of the extended language network, with one region supporting linguistic semantic processing and closely mirroring the selectivity of the neocortical language network, and the other three plausibly integrating information from diverse neocortical regions.

neuroscience↗

bioRxiv2025

The extended language network: Language selective brain areas whose contributions to language remain to be discovered

Although language neuroscience has largely focused on core left frontal and temporal brain areas and their right-hemisphere homotopes, numerous other areas--cortical and subcortical--have been implicated in linguistic processing. However, these areas contributions to language remain unclear given that the evidence for their recruitment comes from diverse paradigms, many of which conflate language processing with perceptual, motor, or task-related cognitive processes. Using fMRI data from 772 participants (438 females, 334 males) performing an extensively validated language localizer paradigm that isolates language processing from other processes, we a) delineate a comprehensive set of areas that respond reliably to language across written and auditory modalities, and b) evaluate these areas selectivity for language relative to a demanding non-linguistic task. In line with prior claims, many areas outside the core fronto-temporal network respond during language processing, and most of them show selectivity for language relative to general task demands. These language-selective areas of the extended language network include areas around the temporal poles, in the medial frontal cortex, in the hippocampus, and in the cerebellum, among others. Although distributed across many parts of the brain, the extended language-selective network still only comprises a small fraction (<5%) of the grey matter volume, challenging the view that the entire brain processes language. These newly identified language-selective areas can now be systematically characterized to decipher their contributions to language processing, including testing whether these contributions differ from those of the core language areas. Significance statementLanguage processing consistently recruits a left-lateralized fronto-temporal brain network, but language tasks often additionally engage areas outside this core system. In an fMRI dataset of 772 participants performing a validated language localizer task, we identified 17 brain areas outside the core fronto-temporal network that respond to both auditory and written language. Most of these areas show selectivity for language, including regions in the temporal poles, medial frontal cortex, hippocampus, and cerebellum. Despite its large number of components, this extended language network still only takes up a small fraction of the grey matter volume, challenging the view that the entire brain processes language. These findings lay the foundation for systematic characterization of these newly identified non-canonical language areas.

neuroscience↗

bioRxiv2025

A language network in the individualized functional connectomes of over 1,000 human brains doing arbitrary tasks

A century and a half of neuroscience has yielded many divergent theories of the neurobiology of language. Two factors that likely contribute to this situation include (a) conceptual disagreement about language and its component processes, and (b) intrinsic inter-individual variability in the topography of language areas. Recent functional magnetic resonance imaging (fMRI) studies of small numbers of intensively scanned individuals have argued that a language-selective brain network emerges bottom-up from correlations (individualized functional connectomics, iFC) in task-free (e.g., rest) or task-regressed activation timecourses. Here we test this hypothesis at scale and evaluate its practical utility for task-agnostic language localization: we apply iFC separately to each of 1,957 (fMRI) scanning sessions (1,199 unique brains), each consisting of diverse tasks. We find that iFC indeed reveals a largely left-hemisphere-dominant frontotemporal network that is more stable within individuals than between them, robust to the granularity of the parcellation, and selective for language. These results support the hypothesis that this network is a key structure in the functional organization of the adult brain and show that it can be recovered retrospectively from arbitrary imaging data, with implications for neuroscience, neurosurgery, and neural engineering.

neuroscience↗

bioRxiv2025

Multilingual Computational Models Reveal Shared Brain Responses to 21 Languages

At the heart of language neuroscience lies a fundamental question: How does the brain process the rich variety of languages? Multilingual neural network models offer a way to answer this question by representing linguistic content across languages in a shared space. Leveraging these advances, we evaluated the similarity of linguistic representations in speakers of 21 languages. We combined existing (12 languages across 4 language families) and newly collected fMRI data (9 languages across 4 families) to test encoding models predicting brain activity in the language network using representations from multilingual models. Model representations reliably predicted brain responses within each language. Critically, encoding models can be transferred zero-shot across languages, so that a model trained to predict brain activity in a set of languages can account for responses in a held-out language. These results imply a shared cross-lingual component, which appears to be related to a shared meaning space.

neuroscience↗

bioRxiv2024

Universality of representation in biological and artificial neural networks

Many artificial neural networks (ANNs) trained with ecologically plausible objectives on naturalistic data align with behavior and neural representations in biological systems. Here, we show that this alignment is a consequence of convergence onto the same representations by high-performing ANNs and by brains. We developed a method to identify stimuli that systematically vary the degree of inter-model representation agreement. Across language and vision, we then showed that stimuli from high-and low-agreement sets predictably modulated model-to-brain alignment. We also examined which stimulus features distinguish high-from low-agreement sentences and images. Our results establish representation universality as a core component in the model-to-brain alignment and provide a new approach for using ANNs to uncover the structure of biological representations and computations.

neuroscience↗

bioRxiv2024

Intuitive physical reasoning is not mediated by linguistic nor exclusively domain-general abstract representations

The ability to reason about the physical world is a critical tool in the human cognitive toolbox, but the nature of the representations that mediate physical reasoning remains debated. Here, we use fMRI to illuminate this question by investigating the relationship between the physical-reasoning system and two well-characterized systems: a) the domain-general Multiple Demand (MD) system, which supports abstract reasoning, including mathematical and logical reasoning, and b) the language system, which supports linguistic computations and has been hypothesized to mediate some forms of thought. We replicate prior findings of a network of frontal and parietal areas that are robustly engaged by physical reasoning and identify an additional physical-reasoning area in the left frontal cortex, which also houses components of the MD and language systems. Critically, direct comparisons with tasks that target the MD and the language systems reveal that the physical-reasoning system overlaps with the MD system, but is dissociable from it in fine-grained activation patterns, which replicates prior work. Moreover, the physical-reasoning system does not overlap with the language system. These results suggest that physical reasoning does not rely on linguistic representations, nor exclusively on the domain-general abstract reasoning that the MD system supports.

neuroscience↗

bioRxiv2024

The language network ages well: Preserved selectivity, lateralization, and within-network functional synchronization in older brains

Healthy aging is associated with structural and functional brain changes. However, cognitive abilities vary in how they change with age: whereas executive functions, like working memory, show age-related decline, aspects of linguistic processing remain relatively preserved. The heterogeneity of the cognitive-behavioral landscape in aging predicts differences among brain networks in whether and how they should change with age. To evaluate this prediction, we used individual-subject fMRI analyses (precision fMRI) to examine the language-selective network and--for control purposes--the Multiple Demand (MD) network, which supports executive functions, in older adults (n=64) relative to young controls (n=483). In line with past claims, relative to young adults, the MD network of older adults shows weaker, less spatially extensive, and more topographically variable activations during an executive function task and reduced within-network functional connectivity. However, in stark contrast to the MD network, we find remarkable preservation of the language network in older adults. Their language network responds during language comprehension as strongly and selectively as in younger adults, and shows a similar degree of left-hemispheric lateralization and within-network functional connectivity. Our findings suggest that the language network remains young-like--at least on standard measures of function and connectivity--and align with behavioral preservation of language comprehension in healthy aging.

neuroscience↗

bioRxiv2024

A 3.5-minute-long reading-based fMRI localizer for the language network

The field of human cognitive neuroscience is increasingly acknowledging inter-individual differences in the precise locations of functional areas and the corresponding need for individual-level analyses in fMRI studies. One approach to identifying functional areas and networks within individual brains is based on robust and extensively validated localizer paradigms--contrasts of conditions that aim to isolate some mental process of interest. Here, we present a new version of a localizer for the fronto-temporal language-selective network. This localizer is similar to a commonly-used localizer based on the reading of sentences and nonword sequences (Fedorenko et al., 2010) but uses speeded presentation (200ms per word/nonword). Based on a direct comparison between the standard version (450ms per word/nonword) and the speeded versions of the language localizer in 24 participants, we show that a single run of the speeded localizer (3.5 min) is highly effective at identifying the language-selective areas: indeed, it is more effective than the standard localizer given that it leads to an increased response to the critical (sentence) condition and a decreased response to the control (nonwords) condition. This localizer may therefore become the version of choice for identifying the language network in neurotypical adults or special populations (as long as they are proficient readers), especially when time is of essence.

neuroscience↗