Biology subjects

Casto, C.

Publications and source records attributed to Casto, C..

6 recordsLinked to original sources

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↗

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

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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bioRxiv2024

Information-making processes in the speaker's brain drive human conversations forward

Natural human conversation is driven by the exchange of information-rich messages that surprise the listener and deviate from predictable context. While extensive research has characterized how the brain processes unexpected linguistic input during comprehension, the neural mechanisms underlying the generation of such information by the speaker remain poorly understood. In this study, we hypothesize that the speakers brain actively creates information through extra neural computation, transforming internal thoughts into novel, meaningful linguistic outputs. Utilizing a unique 24/7 dataset of continuous electrocorticography (ECoG) comprising approximately 100 hours of spontaneous natural conversations, we contrast the neural basis of speech production and comprehension within the same participants. Behaviorally, we find that speakers take longer pausing for an additional 100-150 milliseconds before producing information-rich, improbable words, even when controlling for word identity and frequency. Neurally, we provide converging evidence for a previously unreported process: generating information-rich words elicits significantly stronger neural activity (ERP) and enhanced neural encoding and decoding in language-related areas starting 100-500 ms before articulation. This pattern contrasts sharply with speech comprehension, where enhanced neural activity for predictable words occurs before onset, while responses to improbable words emerge only after onset as prediction errors. Furthermore, we demonstrate that large language models (LLMs) mirror this biological process, requiring deeper internal computation across layers to generate improbable versus probable words. Together, these results reveal that the speakers brain is not merely a transmission channel but an active generator of information, recruiting extra neural resources to create novel content that diverges from listener expectations.

neuroscience↗

bioRxiv2022

Intracranial recordings reveal three distinct neural response patterns in the language network

Despite long knowing what brain areas support language comprehension, our knowledge of the neural computations that these frontal and temporal regions implement remains limited. One important unresolved question concerns functional differences among the neural populations that comprise the language network. Leveraging the high spatiotemporal resolution of intracranial recordings, we examined responses to sentences and linguistically degraded conditions and discovered three response profiles that differ in their temporal dynamics. These profiles appear to reflect different temporal receptive windows (TRWs), with average TRWs of about 1, 4, and 6 words, as estimated with a simple one-parameter model. Neural populations exhibiting these profiles are interleaved across the language network, which suggests that all language regions have direct access to distinct, multi-scale representations of linguistic input--a property that may be critical for the efficiency and robustness of language processing.

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bioRxiv2020

Thinking ahead: prediction in context as a keystone of language in humans and machines

Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models are trained to generate appropriate linguistic responses in a given context. We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process natural language: 1) both are engaged in continuous next-word prediction before word-onset; 2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise (i.e., prediction error signals); 3) both represent words as a function of the previous context. In support of these three principles, our findings indicate that: a) the neural activity before word-onset contains context-dependent predictive information about forthcoming words, even hundreds of milliseconds before the words are perceived; b) the neural activity after word-onset reflects the surprise level and prediction error; and c) autoregressive DLM contextual embeddings capture the neural representation of context-specific word meaning better than arbitrary or static semantic embeddings. Together, our findings suggest that autoregressive DLMs provide a novel and biologically feasible computational framework for studying the neural basis of language.

neuroscience↗