Greta Tuckute

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

The paper shows that the cerebellum can develop language sensitivity without contralateral neocortical inputs, using cases with atypical brain development.

The cerebellar components of the human language network

This study identifies four cerebellar regions involved in language processing using fMRI, with one region showing selectivity for language and functional connections to the neocortical language system.

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

This paper introduces a faster fMRI language localizer that identifies language-selective areas in 3.5 minutes, outperforming the standard 450ms per item version in 24 participants.

Disentangling signal and noise in neural responses through generative modeling

This paper introduces GSN, a method to disentangle signal and noise in neural responses, showing it improves dimensionality estimates in fMRI data.

A two-dimensional space of linguistic representations shared across individuals

The paper identifies two interpretable dimensions of linguistic processing using fMRI data, showing they generalize across individuals and relate to processing difficulty and meaning abstractness.

Multilingual Computational Models Capture a Shared Meaning Component in Brain Responses across 21 Languages

This study evaluates how multilingual computational models capture shared meaning components in brain responses across 21 languages using fMRI data.

Driving and suppressing the human language network using large language models

The paper uses a GPT-based model to predict and control brain responses to language in humans using fMRI data.

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions

The paper evaluates how well deep neural networks model auditory brain responses and finds that some models better predict brain activity than others, with task-specific effects on prediction quality.

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

This study shows that lexical semantic content, not syntax, drives similarity between ANN language model representations and fMRI brain responses in the language network.

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

The paper introduces a probabilistic atlas for the language network using fMRI data from 806 individuals to bridge gaps between group-level and individual-level language research.

The neural architecture of language: Integrative modeling converges on predictive processing

This paper shows that powerful transformer models predict neural and behavioral responses during language processing across datasets and modalities, supporting predictive processing as a core mechanism.

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

The paper investigates whether frontal language areas develop without temporal language areas by studying an individual born without a left temporal lobe, finding that frontal language areas do not emerge in its absence.

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

The paper introduces an open-source neurofeedback framework for real-time EEG decoding and demonstrates its ability to classify cognitive states with a 34.3% decoding error rate.

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

This study shows chronic paroxetine reduces stress-induced corticosterone and normalizes behavioral effects in nulliparous rats, unlike acute treatment.

Single Trial Decoding of Scalp EEG Under Natural Conditions

This study decodes mental states from scalp EEG under natural conditions using SVM classifiers and shows that small sets of de-noised data perform similarly to large sets of noisy data.