Biology subjects

Geniesse, C.

Publications and source records attributed to Geniesse, C..

7 recordsLinked to original sources

bioRxiv2026

Claim-Level Transparency Analysis of LLM-Generated Diagnostic Reports: A Metabolic and Endocrine Biomarker Study

Large language models are increasingly deployed in clinical decision-support contexts, yet systematic evaluation of their factual reliability in generating patient-specific diagnostic reports remains sparse, particularly for laboratory interpretation tasks. This study presents a controlled transparency experiment in which four frontier LLMs -- Claude Sonnet 4.6, Claude Opus 4.6, GPT-5.2, and Gemini 3.1 Pro -- each generated diagnostic reports for 36 patients (29 female, 7 male; aged 27-64) with biomarker profiles spanning metabolic, endocrine, and nutritional markers. A transparency engine1 extracted up to 50 claims per report (3,035 total), searched for supporting scientific evidence, and classified each claim as supported by science, plausible, or unsupported. Unsupported claims were uncommon: the transparency engine classified 2.7% of claims as unsupported (hereafter, the pipeline-measured hallucination rate; naive claim-level 95% Wilson CI: 2.2%-3.4%), with GPT-5.2 at the lowest observed rate (1.7%) and Claude Opus 4.6 at the highest (3.6%). However, mechanistic verification revealed a much larger plausibility gap: 915 claims (30.2%) were biologically reasonable but lacked a fully verified evidence chain, bringing the share of claims not fully supported by direct evidence to 32.9%. Gemini 3.1 Pro produced the highest plausible proportion (39.6%), suggesting a more conservative but less fully grounded reasoning profile. Although coarse support-level distributions were broadly similar across models (Cramers V = 0.081), claim-level analysis revealed substantial narrative divergence: 61.2% of claims were unique to a single model, and matched-claim agreement was low (Cohens kappa = 0.233), indicating that models generate substantively different clinical narratives for the same patient data despite comparable aggregate support profiles. These findings show that hallucination metrics alone understate the share of claims not fully verified under this protocol, and that claim-level mechanistic verification is needed to distinguish the proven from the merely plausible in metabolic and endocrine laboratory interpretation, with generalizability to other clinical domains requiring further study.

systems biology↗

bioRxiv2025

Topological data analysis reveals rigid brain-state dynamics during self-viewing in trait rumination

Rumination--repetitive, negatively valenced, self-focused thought--is a maladaptive cognitive style linked to emotional dysregulation and psychiatric risk. To investigate its neural underpinnings in a naturalistic context, we developed an fMRI paradigm in which participants observed and reflected on videos of their own past group-based problem-solving sessions, a naturalistic self-relevant context rarely examined in fMRI studies. Thirty-two adults (mean age = 30.4 {+/-} 5.4 years; 13 F) were recorded during collaborative design-thinking tasks in triads. In a subsequent scanning session, each participant viewed two self-relevant team videos and one control team video, followed by a structured reflection period. We assessed trait rumination using the Rumination-Reflection Questionnaire (RRQ) and applied Topological Data Analysis (TDA) via the Mapper algorithm to model individual-level whole-brain dynamics during the task. Mapper shape graphs captured temporal transitions between brain states, allowing us to quantify the similarity of timepoints across the session. Individuals with higher trait rumination showed significantly higher temporal similarity, indicating reduced brain-state variability, during self-relevant conditions (r = 0.46, p = 0.018). This effect was not observed during the control condition. These findings suggest that rumination is associated with rigid brain dynamics during self-observation and evaluative processing. Traditional GLM and inter-subject correlation (ISC) analyses confirmed task engagement of key self-referential and social-evaluative regions, while Mapper revealed dynamic features not captured by static or group-averaged methods. Together, these findings demonstrate that trait rumination is associated with rigid large-scale brain dynamics during self-relevant cognition and highlight the value of combining naturalistic paradigms with topological approaches to capture behaviorally meaningful signatures. Significance StatementRumination--repetitive, self-focused thought--is a core cognitive feature of depression and anxiety. However, the neural dynamics underlying this mental inflexibility remain poorly understood, especially during ecologically valid, self-relevant experiences. Using topological data analysis and a naturalistic self-reflection fMRI paradigm, we identify a novel neural marker: individuals with higher rumination traits exhibit reduced flexibility in brain state transitions when watching videos of themselves. This signature is uniquely captured by Mapper, a data-driven geometric framework, and not detected by traditional neuroimaging methods. Our findings offer a new window into how maladaptive thought patterns manifest in brain dynamics, paving the way for personalized diagnostics and interventions in mood disorders.

neuroscience↗

bioRxiv2024

A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria (RDoC) Framework

Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns, we employed a latent variable approach using bifactor analysis. We examined 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with 6,192 participants. A curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set, and the remaining held-out maps formed the internal validation set. External validation was conducted using 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Here, we show that a bifactor model incorporating a task-general domain and splitting the cognitive systems domain better fits the examined corpus of tfMRI data than the current RDoC framework. We also identify the domain of arousal and regulatory systems as underrepresented. Our data-driven validation supports revising the RDoC framework to reflect underlying brain circuitry more accurately.

neuroscience↗

bioRxiv2023

Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data

Capturing and tracking large-scale brain activity dynamics holds the potential to deepen our understanding of cognition. Previously, tools from Topological Data Analysis, especially Mapper, have been successfully used to mine brain activity dynamics at the highest spatiotemporal resolutions. Even though it is a relatively established tool within the field of Topological Data Analysis, Mapper results are highly impacted by parameter selection. Given that non-invasive human neuroimaging data (e.g., from fMRI) is typically fraught with artifacts and no gold standards exist regarding "true" state transitions, we argue for a thorough examination of Mapper parameter choices to better reveal their impact. Using synthetic data (with known transition structure) and real fMRI data, we explore a variety of parameter choices for each Mapper step, thereby providing guidance and heuristics for the field. We also release our parameter-exploration toolbox as a software package to make it easier for scientists to investigate and apply Mapper on any dataset.

neuroscience↗

bioRxiv2021

Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking

Despite substantial progress in the quest of demystifying the brain basis of creativity, several questions remain open. One such issue concerns the relationship between two latent cognitive modes during creative thinking, i.e., deliberate goal-directed cognition and spontaneous thought generation. Although an interplay between deliberate and spontaneous thinking is often indirectly implicated in the creativity literature (e.g., dual-process models), a bottom-up data-driven validation of the cognitive processes associated with creative thinking is still lacking. Here, we attempted to capture the latent modes of creative thinking by utilizing a data-driven approach on a novel continuous multitask paradigm (CMP) that widely sampled a hypothetical two-dimensional cognitive plane of deliberate and spontaneous thinking in a single fMRI session. The CMP consisted of eight task blocks ranging from undirected mind wandering to goal-directed working memory task, while also including two of the widely used creativity tasks, i.e., alternate uses task (AUT) and remote association task (RAT). Using data-driven eigen-connectivity (EC) analysis on the multitask whole-brain functional connectivity (FC) patterns, we embedded the multitask FCs into a low-dimensional latent space. The first two latent components, as revealed by the EC analysis, broadly mapped onto the two cognitive modes of deliberate and spontaneous thinking, respectively. Further, in this low-dimensional space, both creativity tasks were located in the upper right corner of high deliberate and spontaneous thinking (creative cognitive space). Neuroanatomically, the creative cognitive space was represented by not only increased intra-network connectivity within executive control and default mode networks, but also by a higher inter-network coupling between the two. Further, individual differences reflected in the low-dimensional connectivity embeddings were related to differences in deliberate and spontaneous thinking abilities. Altogether, using a continuous multitask paradigm and data-driven approach, we provide direct empirical evidence for the contribution of both deliberate and spontaneous modes of cognition during creative thinking.

neuroscience↗

bioRxiv2021

Synaptopodin is required for stress fiber and contractomere assembly at the epithelial junction

The apical junction of epithelial cells can generate force to control cell geometry and perform contractile processes while maintaining barrier function and adhesion. Yet, the structural basis for force generation at the apical junction is not fully understood. Here, we describe 2 synaptopodin-dependent actomyosin structures that are spatially, temporally, and structurally distinct. The first structure is formed by retrograde flow of synaptopodin initiated at the apical junction, creating sarcomeric stress fibers that lie parallel to the junction and insert into junctional complexes on the apical plane. Retrograde flow of synaptopodin is also seen at vinculin-decorated basal junctions on the basal plane. Contractions of apical stress fibers is associated with clustering of membrane complexes via side-on synaptopodin linkers whereas contractions of stress fibers inserted at the apical junction via head-on synaptopodin linkers results in junction shortening. Upon junction maturation, apical stress fibers are disassembled. In mature epithelial monolayer, a motorized "contractomere" complex capable of "walking the junction" is formed at junction vertices. Contractomere motility results in changes in junctional length, altering the overall shape of the cell and packing geometry within the monolayer. We propose a model of epithelial homeostasis that utilizes contractomere motility to preserve the permeability barrier during intercellular movement and junctional processes. Summary StatementSynaptopodin retrograde flows initiate the assembly of apical and basal stress fibers from the apical and basal junctions. In mature apical junction, a motorized junctional complex, we termed the contractomere, allows the apical junction to change length and organize cell geometry within a confluent monolayer.

cell biology↗

bioRxiv2017

Blind prediction of noncanonical RNA structure at atomic accuracy

Prediction of RNA structure from nucleotide sequence remains an unsolved grand challenge of biochemistry and requires distinct concepts from protein structure prediction. Despite extensive algorithmic development in recent years, modeling of noncanonical base pairs of new RNA structural motifs has not been achieved in blind challenges. We report herein a stepwise Monte Carlo (SWM) method with a unique add-and-delete move set that enables predictions of noncanonical base pairs of complex RNA structures. A benchmark of 82 diverse motifs establishes the methods general ability to recover noncanonical pairs ab initio, including multistrand motifs that have been refractory to prior approaches. In a blind challenge, SWM models predicted nucleotide-resolution chemical mapping and compensatory mutagenesis experiments for three in vitro selected tetraloop/receptors with previously unsolved structures (C7.2, C7.10, and R1). As a final test, SWM blindly and correctly predicted all noncanonical pairs of a Zika virus double pseudoknot during a recent community-wide RNA-puzzle. Stepwise structure formation, as encoded in the SWM method, enables modeling of noncanonical RNA structure in a variety of previously intractable problems.

biochemistry↗