ArXiv最新文献

{"title":"Accurate identification of communication between multiple interacting neural populations.","authors":"Belle Liu, Jacob Sacks, Matthew D Golub","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Neural recording technologies now enable simultaneous recording of population activity across many brain regions, motivating the development of data-driven models of communication between brain regions. However, existing models can struggle to disentangle the sources that influence recorded neural populations, leading to inaccurate portraits of inter-regional communication. Here, we introduce Multi-Region Latent Factor Analysis via Dynamical Systems (MR-LFADS), a sequential variational autoencoder designed to disentangle inter-regional communication, inputs from unobserved regions, and local neural population dynamics. We show that MR-LFADS outperforms existing approaches at identifying communication across dozens of simulations of task-trained multi-region networks. When applied to large-scale electrophysiology, MR-LFADS predicts brain-wide effects of circuit perturbations that were held out during model fitting. These validations on synthetic and real neural data position MR-LFADS as a promising tool for discovering principles of brain-wide information processing.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12393237/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radial spoke energy for self-navigated motion detection and position-ordered dynamic musculoskeletal MRI.","authors":"Enping Lin, Fatih Calakli, Musa Tunç Arslan, Giovani Schulte Farina, Simon Keith Warfield","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Motion remains a key challenge in MRI, as both involuntary (e.g., head motion) and voluntary (e.g., joint motion) movement can degrade image quality or provide opportunities for dynamic assessment. Existing motion sensing methods, such as external tracking or navigator sequences, often require additional hardware, increase SAR, or demand sequence modification, which limits clinical flexibility. We propose a computationally efficient, self-navigated motion sensing technique based on spoke energy derived from 3D radial k-space data. Using the Fourier Slice and Parseval's theorems, spoke energy captures object-coil alignment and can be computed without altering the sequence. A sliding window summation improves robustness, and a second principal component analysis (2ndPCA) strategy yields a unified motion-sensitive signal. Beyond conventional head motion correction, we demonstrate the novel application of this method in enhancing dynamic 4D MRI of the ankle and knee under a continuous movement protocol. By sorting spokes based on position rather than time, we achieve motion-resolved reconstructions with improved anatomical clarity. This approach enables real-time motion detection and supports broader adoption of motion-aware dynamic MRI.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Desiderata for a biomedical knowledge network: opportunities, challenges and future Directions.","authors":"Chunlei Wu, Hongfang Liu, Jason Flannick, Mark A Musen, Andrew I Su, Lawrence Hunter, Thomas M Powers, Cathy H Wu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Knowledge graphs, collectively as a knowledge network, have become critical tools for knowledge discovery in computable and explainable knowledge systems. Due to the semantic and structural complexities of biomedical data, these knowledge graphs need to enable dynamic reasoning over large evolving graphs and support fit-for-purpose abstraction, while establishing standards, preserving provenance and enforcing policy constraints for actionable discovery. A recent meeting of leading scientists discussed the opportunities, challenges and future directions of a biomedical knowledge network. Here we present six desiderata inspired by the meeting: (1) inference and reasoning in biomedical knowledge graphs need domain-centric approaches; (2) harmonized and accessible standards are required for knowledge graph representation and metadata; (3) robust validation of biomedical knowledge graphs needs multi-layered, context-aware approaches that are both rigorous and scalable; (4) the evolving and synergistic relationship between knowledge graphs and large language models is essential in empowering AI-driven biomedical discovery; (5) integrated development environments, public repositories, and governance frameworks are essential for secure and reproducible knowledge graph sharing; and (6) robust validation, provenance, and ethical governance are critical for trustworthy biomedical knowledge graphs. Addressing these key issues will be essential to realize the promises of a biomedical knowledge network in advancing biomedicine.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases.","authors":"Mathew J Koretsky, Maya Willey, Adi Asija, Owen Bianchi, Chelsea X Alvarado, Tanay Nayak, Nicole Kuznetsov, Sungwon Kim, Mike A Nalls, Daniel Khashabi, Faraz Faghri","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks. However, current text-to-SQL systems often struggle to map qualitative scientific questions into executable SQL, particularly when implicit domain reasoning is required. We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world biomedical knowledge base. BiomedSQL comprises 68,000 question/SQL query/answer triples grounded in a harmonized BigQuery knowledge base that integrates gene-disease associations, causal inference from omics data, and drug approval records. Each question requires models to infer domain-specific criteria, such as genome-wide significance thresholds, effect directionality, or trial phase filtering, rather than rely on syntactic translation alone. We evaluate a range of open- and closed-source LLMs across prompting strategies and interaction paradigms. Our results reveal a substantial performance gap: GPT-o3-mini achieves 59.0% execution accuracy, while our custom multi-step agent, BMSQL, reaches 62.6%, both well below the expert baseline of 90.0%. BiomedSQL provides a new foundation for advancing text-to-SQL systems capable of supporting scientific discovery through robust reasoning over structured biomedical knowledge bases. Our dataset is publicly available at https://huggingface.co/datasets/NIH-CARD/BiomedSQL, and our code is open-source at https://github.com/NIH-CARD/biomedsql.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12478439/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction.","authors":"Matt Y Cheung, Tucker J Netherton, Laurence E Court, Ashok Veeraraghavan, Guha Balakrishnan","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Modern deep learning reconstruction algorithms generate impressively realistic scans from sparse inputs, but can often produce significant inaccuracies. This makes it difficult to provide statistically guaranteed claims about the true state of a subject from scans reconstructed by these algorithms. In this study, we propose a framework for computing provably valid prediction bounds on claims derived from probabilistic black-box image reconstruction algorithms. The key insights behind our framework are to represent reconstructed scans with a derived clinical metric of interest, and to calibrate bounds on the ground truth metric with conformal prediction (CP) using a prior calibration dataset. These bounds convey interpretable feedback about the subject's state, and can also be used to retrieve nearest-neighbor reconstructed scans for visual inspection. We demonstrate the utility of this framework on sparse-view computed tomography (CT) for fat mass quantification and radiotherapy planning tasks. Results show that our framework produces bounds with better semantical interpretation than conventional pixel-based bounding approaches. Furthermore, we can flag dangerous outlier reconstructions that look plausible but have statistically unlikely metric values.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11071610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140853860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the control of recurrent neural networks using constant inputs.","authors":"Cyprien Tamekue, Ruiqi Chen, ShiNung Ching","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This paper investigates the controllability of a broad class of recurrent neural networks widely used in theoretical neuroscience, including models of large-scale human brain dynamics. Motivated by emerging applications in non-invasive neurostimulation such as transcranial direct current stimulation (tDCS), we study the control synthesis of these networks using constant and piecewise constant inputs. The neural model considered is a continuous-time Hopfield-type system with nonlinear activation functions and arbitrary input matrices representing inter-regional brain interactions. Our main contribution is the formulation and solution of a control synthesis problem for such nonlinear systems using specific solution representations. These representations yield explicit algebraic conditions for synthesizing constant and piecewise constant controls that solve a two-point boundary value problem in state space up to higher-order corrections with respect to the time horizon. In particular, the input is constructed to satisfy a tractable small-time algebraic relation involving the Jacobian of the nonlinear drift, ensuring that the synthesis reduces to verifying conditions on the system matrices. For canonical input matrices that directly actuate $k$ nodes, this implies that the reachable set (with constant inputs) of a given initial state is an affine subspace whose dimension equals the input rank and whose basis can be computed efficiently using a thin QR factorization. Numerical simulations illustrate the theoretical results and demonstrate the effectiveness of the proposed synthesis in guiding the design of brain stimulation protocols for therapeutic and cognitive applications.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11537334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142585131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal Machine Learning Analysis of Empirical Relative Biological Effectiveness (RBE) for Mandible Osteoradionecrosis in Head and Neck Cancer Radiotherapy.","authors":"Jingyuan Chen, Zhong Liu, Yunze Yang, Olivia M Muller, Zhengliang Liu, Tianming Liu, Lei Zeng, Robert L Foote, Daniel J Ma, Samir H Patel, Wei Liu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Mandible Osteoradionecrosis (ORN) is one of the most severe adverse events (AEs) for head and neck (H&N) cancer radiotherapy. Previous retrospective investigations on real-world data relied on conventional statistical models that primarily elucidate correlation rather than establishing causal relationships. Through the novel causal machine learning, we aim to obtain empirical relative biological effectiveness (RBE) for ORN in H&N cancer patients treated with pencil-beam-scanning proton therapy (PBSPT). 335 patients treated by PBSPT and 931 patients treated by volumetric-modulated arc therapy (VMAT) were included. We use 1:1 case-matching to minimize the imbalance in clinical factors between PBSPT and VMAT. The bias test of standardized mean differences (SMD) was applied on the case-matched patient cohorts. The causal machine learning method, causal forest (CF), was adopted to investigate the causal effects between dosimetric factors and the incidence of ORN. The dose volume constraints (DVCs) for VMAT and PBSPT were derived based on causal effects. RBE values were further empirically derived based on tolerance curves formed from DVCs. 335 VMAT patients were case-matched to 335 PBSPT patients; however, SMD analysis revealed persistent covariate imbalances within each group, indicating residual confounding influence. Using CF modeling, we identified DVCs of mandible ORN and found that PBSPT had lower critical volumes than those of VMAT, leading to empirical RBE exceeding 1.1 in the moderate dose range (1.61 at 40 Gy[RBE=1.1], 1.30 at 50 Gy, and 1.13 at 60 Gy). This study presents a novel application of causal machine learning to evaluate mandible ORN in radiotherapy. The results indicate that proton RBE may significantly exceed 1.1 in the moderate dose range, underscoring the importance of incorporating the variable RBE into PBSPT treatment planning to mitigate the risk of ORN.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-invasive Reversible Software-based Configuration of a Clinically Used Linear Accelerator for Preclinical Electron FLASH Radiobiology.","authors":"Stavros Melemenidis, Dixin Chen, Cody Jensen, Joseph B Schulz, Murat Surucu, Amy S Yu, Edward E Graves, Mengying Shi, Peter G Maxim, Andrew Currell, Billy W Loo Jr, Lawrie Skinner, M Ramish Ashraf","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Configuring clinical linear accelerators (linacs) for ultra-high dose rate (UHDR) electron experiments typically requires invasive hardware manipulation and/or irreversible manufacturer modifications, limiting broader implementation. We present an independently developed UHDR electron configuration of a clinical TrueBeam linac that allows reversible switching between preclinical UHDR and conventional (CONV) modes using only non-invasive software settings. UHDR mode was achieved via service mode software with RF and beam current settings typical of a photon beam, the photon target and monitor chamber retracted, and a clinically unused low-energy scattering foil inserted. An external AC current transformer (ACCT) for beam monitoring, anatomy-specific collimator, and sample holder were mounted on the accessory tray, with external ion chamber in solid water for exit dose monitoring. Percent depth dose (PDD) was measured for UHDR and CONV beams. Dose-per-pulse (DPP) was varied by adjusting gun voltage and quantified with radiochromic film at different source-to-surface distances (SSD). Beam profiles assessed dose uniformity and usable field size. Dose calibration was established between film, ACCT, and ion chamber, and day-to-day reproducibility was tested. PDD confirmed similar energies for UHDR (12.8MeV) and CONV (11.9MeV) beams with matching profiles through mouse thickness. Maximum DPP exceeded 0.5Gy, reaching ~1.5Gy for collimated in vivo setups and ~0.7Gy at extended SSD for tissue culture. Field flatness and symmetry were maintained, supporting organ-specific irradiations and up to 5cm fields for culture. Calibration showed strong linearity across detectors, and output variation was <4%. We demonstrated accurate, reproducible UHDR delivery on a widely available clinical linac with no invasive hardware manipulation, enabling preclinical FLASH research on a clinical treatment machine.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transabdominal Fetal Oximetry via Diffuse Optics: Principled Analysis and Demonstration in Pregnant Ovine Models.","authors":"Weitai Qian, Rishad Raiyan Joarder, Randall Fowler, Begum Kasap, Mahya Saffarpour, Kourosh Vali, Tailai Lihe, Aijun Wang, Diana Farmer, Soheil Ghiasi","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Diffuse optics has the potential to offer a substantial advancement in fetal health monitoring via enabling continuous measurement of fetal blood oxygen saturation (fSpO$_2$). Aiming to enhance the sensing accuracy and to elucidate the foundational limits of Transabdominal Fetal Oximetry (TFO) via diffuse optics, we introduce a theoretical derivation, and a comprehensive pipeline for fSpO$_2$ estimation from non-invasively sensed diffuse light intensity values, which are leveraged to analyze datasets obtained through both simulations and in-vivo experiments in gold standard large animal model of pregnancy. We propose the Exponential Pulsation Ratio (EPR) as a key feature, and develop machine-learning models to fuse the information collected across multiple detectors. Our proposed method demonstrates a Mean Absolute Error (MAE) of 4.81% and 6.85% with a Pearson's r correlation of 0.81 (p<0.001) and 0.71 (p<0.001) for estimation of fSpO$_2$ in simulated dataset and in-vivo dataset, respectively. Across both datasets, our method outperforms the existing approaches, enhancing the accuracy of the fSpO$_2$ estimation and demonstrates its viability as a supplemental technology for intrapartum fetal monitoring.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Roadmap towards Personalized Approaches and Safety Considerations in Non-Ionizing Radiation: From Dosimetry to Therapeutic and Diagnostic Applications.","authors":"Ilkka Laakso, Margarethus Marius Paulides, Sachiko Kodera, Seungyoung Ahn, Christopher L Brace, Marta Cavagnaro, Ji Chen, Zhi-De Deng, Valerio De Santis, Yinliang Diao, Lourdes Farrugia, Mauro Feliziani, Serena Fiocchi, Francesco Fioranelli, Takashi Hikage, Sergey Makaroff, Maya Mizuno, Alexander Opitz, Emma Pickwell-MacPherson, Punit Prakash, Dario B Rodrigues, Kensuke Sasaki, Takuya Sakamoto, Zachary Taylor, Hubregt J Visser, Desmond T B Yeo, Akimasa Hirata","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This roadmap provides a comprehensive and forward-looking perspective on the individualized application and safety of non-ionizing radiation (NIR) dosimetry in diagnostic and therapeutic medicine. Covering a wide range of frequencies, i.e., from low-frequency to terahertz, this document provides an overview of the current state of the art and anticipates future research needs in selected key topics of NIR-based medical applications. It also emphasizes the importance of personalized dosimetry, rigorous safety evaluation, and interdisciplinary collaboration to ensure safe and effective integration of NIR technologies in modern therapy and diagnosis.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}