Biology subjects

Mancini, S.

Publications and source records attributed to Mancini, S..

3 recordsLinked to original sources

bioRxiv2026

Low-frequency neural responses synchronize to distinct structural rather than lexical features during sentence comprehension

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

neuroscience↗

bioRxiv2024

GPT-4 based AI agents - the new expert system for detection of antimicrobial resistance mechanisms?

BackgroundEUCAST recommends a two-step process for beta-lactamases in Gram-negative bacteria. Screening with minimal inhibitory concentrations (MICs) or inhibition zone diameters for potential extended spectrum beta-lactamase (ESBL), plasmid-mediated AmpC beta-lactamase, or carbapenemase production is followed by confirmatory tests. GPT-4 and its newly released customized GPT-agent may support the initial EUCAST-screening process. We aimed to validate a customized GPT-agent to identify potential resistance mechanisms. MethodsWe used 225 Gram-negative isolates. Based on phenotypic resistances against beta-lactam antibiotics, we formed four categories: "none", "ESBL", "AmpC", or "carbapenemase". We included 862 phenotypic categories. Next, we customized a GPT-agent with EUCAST-guidelines, expert rules, and EUCAST-breakpoint table (v13.1). We compared routine diagnostic outputs (reference) to (i) EUCAST-GPT-expert, (ii) medical microbiologists, and (iii) GPT-4 without customization. We determined performance as sensitivities and specificities to flag suspect resistance mechanisms. ResultsThree human readers showed concordance in 814/862 (94.4%) phenotypic categories and used in median eight words (IQR 4-11) for reasoning. Median sensitivity and specificity for ESBL, AmpC, and carbapenemase were 98%/99.1%, 96.8%/97.1%, and 95.5%/98.5%, respectively. Three independent prompting rounds of the GPT-agent showed concordance in 706/862 (81.9%) categories but used in median 158 words (IQR 140-174) for reasoning. Median sensitivity and specificity for ESBL, AmpC, and carbapenemase prediction were 95.4%/69.23%, 96.9%/86.3%, and 100%/98.8%, respectively. In the non-customized GPT-4, 169/862 (19.6%) categories could be interpreted. Of these 137/169 (81.1%) categories agreed with routine diagnostic. The non-customized GPT-4 used in median 85 words (IQR 72-105) for reasoning. ConclusionHuman experts showed higher concordance and shorter argumentations compared to GPT-agents. Human experts showed comparable median sensitivities and higher specificities compared to GPT-agents. GPT-agents showed more unspecific flagging of ESBL and AmpC, potentially, resulting in additional testing, diagnostic delays, and higher costs. GPT-4 and GPT-agents are not IVDR/FDA-approved, but validation of LLMs is critical and datasets for benchmarking are needed.

microbiology↗

bioRxiv2020

Linguistic input drives brain network configuration during language comprehension.

Assessing the synchrony and interplay between distributed neural regions is critical to understanding how language is processed. Here, we investigated possible neuro-functional links between form and meaning during sentence comprehension combining a classical whole-brain approach, which characterizes patterns of brain activation resulting from our experimental manipulation, and a novel multivariate network-based approach, which uses graph-theory measures to unravel the architectural configuration of the language system. Capitalizing on the Spanish gender agreement system, we experimentally manipulated formal and conceptual factors: whether the noun-adjective grammatical gender relationship was congruent or not and whether the noun gender type was semantically informative or strictly formal. Left inferior and middle frontal gyri, as well as left MTG/STG emerged as critical areas for the computation of grammatical relations. We demonstrate how the interface between formal and conceptual features depends on the synergic articulation of brain areas divided in three subnetworks that extend beyond the classical left-lateralized perisylvian language circuit. Critically, we isolated a subregion of the left angular gyrus showing a significant interaction between gender congruency and gender type. The functional interplay between the angular gyrus and left perisylvian language-specific circuit proves crucial for constructing coherent and meaningful messages. Importantly, using graph theory we show that this complex system is functionally malleable: the role each node plays within the network changes depending on the available linguistic cues. Significance StatementNeural networks can be described as graphs comprising distributed and interconnected nodes. These nodes share functional properties but also differ in terms of functional specialization and the number of interconnections mediating the efficient transfer of information. Previous work has shown functional connectivity differences based on graph-theory properties between typical and atypical samples. However, here we have used concepts from graph theory to characterize connectivity during language processing using task-related fMRI. This approach allowed us to demonstrate how linguistic input drives brain network configuration during language comprehension. This is the first evidence of functional flexibility in language networks: the communicative capacity of each hub changes depending on whether the linguistic input grants access to meaning or is purely formal.

neuroscience↗