Biology subjects

Tuckute, G.

Publications and source records attributed to Tuckute, G..

15 recordsLinked to original sources

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↗

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.

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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.

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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.

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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.

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bioRxiv2024

Disentangling signal and noise in neural responses through generative modeling

Measurements of neural responses to identically repeated experimental events often exhibit large amounts of variability. This noise is distinct from signal, operationally defined as the average expected response across repeated trials for each given event. Accurately distinguishing signal from noise is important, as each is a target that is worthy of study (many believe noise reflects important aspects of brain function) and it is important not to confuse one for the other. Here, we describe a principled modeling approach in which response measurements are explicitly modeled as the sum of samples from multivariate signal and noise distributions. In our proposed method--termed Generative Modeling of Signal and Noise (GSN)--the signal distribution is estimated by subtracting the estimated noise distribution from the estimated data distribution. Importantly, GSN improves estimates of the signal distribution, but does not provide improved estimates of responses to individual events. We validate GSN using ground-truth simulations and show that it compares favorably with related methods. We also demonstrate the application of GSN to empirical fMRI data to illustrate a simple consequence of GSN: by disentangling signal and noise components in neural responses, GSN denoises principal components analysis and improves estimates of dimensionality. We end by discussing other situations that may benefit from GSNs characterization of signal and noise, such as estimation of noise ceilings for computational models of neural activity. A code toolbox for GSN is provided with both MATLAB and Python implementations.

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bioRxiv2023

Lexical semantic content, not syntactic structure, is the main contributor to ANN-brain similarity of fMRI responses in the language network

Representations from artificial neural network (ANN) language models have been shown to predict human brain activity in the language network. To understand what aspects of linguistic stimuli contribute to ANN-to-brain similarity, we used an fMRI dataset of responses to n=627 naturalistic English sentences (Pereira et al., 2018) and systematically manipulated the stimuli for which ANN representations were extracted. In particular, we i) perturbed sentences word order, ii) removed different subsets of words, or iii) replaced sentences with other sentences of varying semantic similarity. We found that the lexical semantic content of the sentence (largely carried by content words) rather than the sentences syntactic form (conveyed via word order or function words) is primarily responsible for the ANN-to-brain similarity. In follow-up analyses, we found that perturbation manipulations that adversely affect brain predictivity also lead to more divergent representations in the ANNs embedding space and decrease the ANNs ability to predict upcoming tokens in those stimuli. Further, results are robust to whether the mapping model is trained on intact or perturbed stimuli, and whether the ANN sentence representations are conditioned on the same linguistic context that humans saw. The critical result--that lexical- semantic content is the main contributor to the similarity between ANN representations and neural ones--aligns with the idea that the goal of the human language system is to extract meaning from linguistic strings. Finally, this work highlights the strength of systematic experimental manipulations for evaluating how close we are to accurate and generalizable models of the human language network.

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bioRxiv2023

Driving and suppressing the human language network using large language models

Transformer models such as GPT generate human-like language and are highly predictive of human brain responses to language. Here, using fMRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of brain response associated with each sentence. Then, we use the model to identify new sentences that are predicted to drive or suppress responses in the human language network. We show that these model-selected novel sentences indeed strongly drive and suppress activity of human language areas in new individuals. A systematic analysis of the model-selected sentences reveals that surprisal and well-formedness of linguistic input are key determinants of response strength in the language network. These results establish the ability of neural network models to not only mimic human language but also noninvasively control neural activity in higher-level cortical areas, like the language network.

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bioRxiv2022

Many but not all deep neural network audio models capture brain responses and exhibit hierarchical region correspondence

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system, and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system, but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models, and thus how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on four different tasks. Most tested models out-predicted previous filter-bank models of auditory cortex, and exhibited systematic model-brain correspondence: middle stages best predicted primary auditory cortex while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.

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bioRxiv2022

LanA (Language Atlas): A probabilistic atlas for the language network based on fMRI data from >800 individuals

Two analytic traditions characterize fMRI language research. One relies on averaging activations voxel-wise across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, a location in a common brain space cannot be meaningfully linked to function. An alternative approach relies on identifying language areas in each individual using a functional localizer. Because of its greater sensitivity, functional resolution, and interpretability, functional localization is gaining popularity, but it is not always feasible, and cannot be applied retroactively to past studies. We provide a solution for bridging these currently disjoint approaches in the form of a probabilistic functional atlas created from fMRI data for an extensively validated language localizer in 806 individuals. This atlas enables estimating the probability that any given location in a common brain space belongs to the language network, and thus can help interpret group-level peaks and meta-analyses of such peaks, and lesion locations in patient investigations. More meaningful comparisons of findings across studies should increase robustness and replicability in language research.

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bioRxiv2021

Frontal language areas do not emerge in the absence of temporal language areas: A case study of an individual born without a left temporal lobe

Language relies on a left-lateralized fronto-temporal brain network. How this network emerges ontogenetically remains debated. We asked whether frontal language areas emerge in the absence of temporal language areas through a deep-data investigation of an individual (EG) born without her left temporal lobe. Using fMRI methods that have been validated to elicit reliable individual-level responses, we find that--as expected for early left hemisphere damage--EG has a fully functional language network in her right hemisphere (comparable to that in n=145 controls) and performs normally on language assessments. However, we detect no response to language in EGs left frontal lobe (replicated across two sessions, 3 years apart). Another network--the multiple demand network--is robustly present in frontal lobes bilaterally, suggesting that EGs left frontal cortex can support non-linguistic cognition. The existence of temporal language areas therefore appears to be a prerequisite for the emergence of the frontal language areas.

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bioRxiv2020

Artificial Neural Networks Accurately Predict Language Processing in the Brain

The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models, this approach yields novel insights into cognitive and neural mechanisms in the target domain. We here present a first systematic study taking this approach to higher-level cognition: human language processing, our species signature cognitive skill. We find that the most powerful transformer models predict nearly 100% of explainable variance in neural responses to sentences and generalize across different datasets and imaging modalities (fMRI, ECoG). Models neural fits ( brain score) and fits to behavioral responses are both strongly correlated with model accuracy on the next-word prediction task (but not other language tasks). Model architecture appears to substantially contribute to neural fit. These results provide computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain. SignificanceLanguage is a quintessentially human ability. Research has long probed the functional architecture of language processing in the mind and brain using diverse brain imaging, behavioral, and computational modeling approaches. However, adequate neurally mechanistic accounts of how meaning might be extracted from language are sorely lacking. Here, we report an important first step toward addressing this gap by connecting recent artificial neural networks from machine learning to human recordings during language processing. We find that the most powerful models predict neural and behavioral responses across different datasets up to noise levels. Models that perform better at predicting the next word in a sequence also better predict brain measurements - providing computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain.

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bioRxiv2019

A framework for closed-loop neurofeedback for real-time EEG decoding

Neurofeedback based on real-time brain imaging allows for targeted training of brain activity with demonstrated clinical applications. A rapid technical development of electroen-cephalography (EEG)-based systems and an increasing interest in cognitive training has lead to a call for accessible and adaptable software frameworks. Here, we present and outline the core components of a novel open-source neurofeedback framework based on scalp EEG signals for real-time neuroimaging, state classification and closed-loop feedback.\n\nThe software framework includes real-time signal preprocessing, adaptive artifact rejection, and cognitive state classification from scalp EEG. The framework is implemented using exclusively Python source code to allow for diverse functionality, high modularity, and easy extendibility of software development depending on the experimenters needs.\n\nAs a proof of concept, we demonstrate the functionality of our new software framework by implementing an attention training paradigm using a consumer-grade, dry-electrode EEG system. Twenty-two participants were trained on a single neurofeedback session with behavioral pre- and post-training sessions within three consecutive days. We demonstrate a mean decoding error rate of 34.3% (chance=50%) of subjective attentional states. Hence, cognitive states were decoded in real-time by continuously updating classification models on recently recorded EEG data without the need for any EEG recordings prior to the neurofeedback session.\n\nThe proposed software framework allows a wide range of users to actively engage in the development of novel neurofeedback tools to accelerate improvements in neurofeedback as a translational and therapeutic tool.

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bioRxiv2019

Chronic paroxetine blunts stress response and normalizes adverse behavioral effects seen acutely in nulliparous rats

Selective serotonin reuptake inhibitors (SSRI) are widely used antidepressants and their effect is partly mediated by restoring stress axis dynamics, which may depend on sex and hormonal states. In the present study, we investigate the effect of daily injections of the SSRI paroxetine (5 mg/kg s.c.) on swim stress-induced corticosterone (CORT) response, depressive-like behavior (forced swim test) and anxiety-like behavior (open field test) in nulliparous Sprague Dawley rats. Data were acquired after either 1-3 (acute PXT) or 11-13 (chronic PXT) injections. We found that chronic, but not acute, paroxetine blunted the swim stress-induced CORT response. We observed an increase in depressive-like and anxiety-like behavior following acute PXT, and a normalization after chronic PXT treatment. Intriguingly, our findings of rapid recovery from adverse SSRI effects differ from corresponding studies performed by our group in postpartum rats. Thus, the study emphasizes that mechanisms of action and efficacy of SSRIs differ according to reproductive states, which if translated to humans may inform treatment strategies, beyond SSRIs alone, for hormone transition related depressive states.

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bioRxiv2018

Single Trial Decoding of Scalp EEG Under Naturalistic Stimuli

There is significant current interest in decoding mental states from electro-encephalography (EEG) recordings. EEG signals are subject-specific, sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that Support Vector Machine (SVM) classifiers trained on a relatively small set of de-noised (averaged) pseudo-trials perform on par with classifiers trained on a large set of noisy single-trial samples. For visualization of EEG signatures exploited by SVM classifiers, we propose a novel method for computing sensitivity maps of EEG-based SVM classifiers. Moreover, we apply the NPAIRS resampling framework for estimation of map uncertainty and show that effect sizes of sensitivity maps for classifiers trained on small samples of de-noised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudo-trial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization and unbiased performance evaluation in machine learning approaches for brain decoding.

neuroscience↗