Tootfinder

Robust Containerization of the High Angular Resolution Functional Imaging (HARFI) Pipeline
Zhiyuan Li, Kurt G. Schilling, Bennett A. Landman
https://arxiv.org/abs/2507.07010

Robust Containerization of the High Angular Resolution Functional Imaging (HARFI) Pipeline
Historically, functional magnetic resonance imaging (fMRI) of the brain has focused primarily on gray matter, particularly the cortical gray matter and associated nuclei. However, recent work has demonstrated that functional activity in white matter also plays a meaningful role in both cognition and learning. In previous work, we introduced the High Angular Resolution Functional Imaging (HARFI) pipeline, which demonstrated both local and global patterns of functional correlation in white matter…

Identifying Neural Signatures from fMRI using Hybrid Principal Components Regression
Jared Rieck, Julia Wrobel, Joshua L. Gowin, Yue Wang, Martin Paulus, Ryan Peterson
https://arxiv.org/abs/2509.07300 …

Identifying Neural Signatures from fMRI using Hybrid Principal Components Regression
Recent advances in neuroimaging analysis have enabled accurate decoding of mental state from brain activation patterns during functional magnetic resonance imaging scans. A commonly applied tool for this purpose is principal components regression regularized with the least absolute shrinkage and selection operator (LASSO PCR), a type of multi-voxel pattern analysis (MVPA). This model presumes that all components are equally likely to harbor relevant information, when in fact the task-related si…

@… Yes, exactly! And I think that is closer to what Alexander meant about patterns, TBH.
But honestly, I don’t think he did a stellar job explaining patterns, and his other work is way better.
IIRC A City Is Not A Tree covers changes in the system over time (and how observing the system changes the system), even though its main point is that you …

Progress and new challenges in image-based profiling
Erik Serrano, John Peters, Jesko Wagner, Rebecca E. Graham, Zhenghao Chen, Brian Feng, Gisele Miranda, Alexandr A. Kalinin, Loan Vulliard, Jenna Tomkinson, Cameron Mattson, Michael J. Lippincott, Ziqi Kang, Divya Sitani, Dave Bunten, Srijit Seal, Neil O. Carragher, Anne E. Carpenter, Shantanu Singh, Paula A. Marin Zapata, Juan C. Caicedo, Gregory P. Way

Progress and new challenges in image-based profiling
For over two decades, image-based profiling has revolutionized cellular phenotype analysis. Image-based profiling processes rich, high-throughput, microscopy data into unbiased measurements that reveal phenotypic patterns powerful for drug discovery, functional genomics, and cell state classification. Here, we review the evolving computational landscape of image-based profiling, detailing current procedures, discussing limitations, and highlighting future development directions. Deep learning h…

Single-Shot Multispectral Encoding: Advancing Optical Lithography for Encryption and Spectroscopy
Hyewon Shim, Geonwoong Park, Hyunsuk Yun, Sunmin Ryu, Yong-Young Noh, Cheol-Joo Kim
https://arxiv.org/abs/2508.05645

Single-Shot Multispectral Encoding: Advancing Optical Lithography for Encryption and Spectroscopy
Most modern optical display and sensing devices utilize a limited number of spectral units within the visible range, based on human color perception. In contrast, the rapid advancement of machine-based pattern recognition and spectral analysis could facilitate the use of multispectral functional units, yet the challenge of creating complex, high-definition, and reproducible patterns with an increasing number of spectral units limits their widespread application. Here, we report a technique for …

Patterns of imbalance states between sub-brain regimes during development in the resting state
Fahimeh Ahmadi, Zahra Moradimanesh, Reza Khosrowabadi, G. Reza Jafari
https://arxiv.org/abs/2508.01307

Patterns of imbalance states between sub-brain regimes during development in the resting state
The functional brain network is the result of neurons and different areas working together in a coordinated manner. The findings indicate that the spatial distribution of anticorrelations in the brain network is not inherent. In this study, using Heider balance theory, we examine the network of imbalanced triangles with negative links in the brain, which undergoes dynamic changes across development. By analyzing the age-related transformations in the connectivity of imbalanced triangles at the …

When Lifetimes Liberate: A Type System for Arenas with Higher-Order Reachability Tracking
Siyuan He, Songlin Jia, Yuyan Bao, Tiark Rompf
https://arxiv.org/abs/2509.04253 https:/…

When Lifetimes Liberate: A Type System for Arenas with Higher-Order Reachability Tracking
Static resource management in higher-order functional languages remains elusive due to tensions between control, expressiveness, and flexibility. Region-based systems [Grossman et al. 2002; Tofte et al. 2001] offer control over lifetimes and expressive in-region sharing, but restrict resources to lexical scopes. Rust, an instance of ownership types [Clarke et al. 2013], offers non-lexical lifetimes and robust safety guarantees, yet its global invariants make common sharing patterns hard to expr…

Functional Renormalization for Signal Detection: Dimensional Analysis and Dimensional Phase Transition for Nearly Continuous Spectra Effective Field Theory
Riccardo Finotello, Vincent Lahoche, Dine Ousmane Samary
https://arxiv.org/abs/2507.01064

Functional Renormalization for Signal Detection: Dimensional Analysis and Dimensional Phase Transition for Nearly Continuous Spectra Effective Field Theory
Signal detection is one of the main challenges of data science. According to the nature of the data, the presence of noise may corrupt measurements and hinder the discovery of significant patterns. A wide range of techniques aiming at extracting the relevant degrees of freedom from data has been thus developed over the years. However, signal detection in almost continuous spectra, for small signal-to-noise ratios, remains a known difficult issue. This paper develops over recent advancements pro…

Distinct Modes of Functional Neural Organization in Autism: Insights from Dynamical Systems Analysis of Resting-State EEG
Sungwoo Ahn, Leonid L Rubchinsky, Evie A Malaia
https://arxiv.org/abs/2506.23013

Distinct Modes of Functional Neural Organization in Autism: Insights from Dynamical Systems Analysis of Resting-State EEG
While differences in patterns of functional connectivity and neural synchronization have been reported between individuals on the autism spectrum and neurotypical peers at various age stages, these differences appear to be subtle and may not be captured by typical quantitative measures of EEG. We used the dynamical systems approach to analyze resting-state EEG to investigate fine-grained spatiotemporal organization of brain networks in autistic and neurotypical young adults. While power spectra…

Auditory gamma-frequency entrainment abolishes working memory deficits in a rodent model of autism #40Hz sensory entra…

Auditory Gamma-Frequency Entrainment Abolishes Working Memory Deficits in a Rodent Model of Autism
Individuals on the autism spectrum often exhibit atypical responses to sensory stimuli and difficulties with behavioral regulation, reflecting altered functional patterns in core sensory-motor circuits. These sensory processing differences suggest the potential for using patterned auditory stimulation to modulate neural activity and mitigate symptoms. While impairments in higher cognitive functions are a hallmark of autism, they remain underexplored in rodent models compared to basic sensory-mo…

MeloKids: Multisensory VR System to Enhance Speech and Motor Coordination in Children with Hearing Loss
Yichen Yu, Qiaoran Wang
https://arxiv.org/abs/2507.18619 https://

MeloKids: Multisensory VR System to Enhance Speech and Motor Coordination in Children with Hearing Loss
Children with hearing impairments face ongoing challenges in language and motor development. This study explores how multi-sensory feedback technology based on virtual reality (VR), integrating auditory, visual, and tactile stimuli, can enhance rehabilitation outcomes. Using functional near-infrared spectroscopy (fNIRS) technology, we assessed cortical activation patterns in children during pitch-matching tasks across different interaction modes. Our findings aim to provide evidence for designi…

Signed Higher-Order Interactions for Brain Disorder Diagnosis via Multi-Channel Transformers
Dengyi Zhao, Zhiheng Zhou, Guiying Yan, Dongxiao Yu, Xingqi Qi
https://arxiv.org/abs/2507.20205

Signed Higher-Order Interactions for Brain Disorder Diagnosis via Multi-Channel Transformers
Accurately characterizing higher-order interactions of brain regions and extracting interpretable organizational patterns from Functional Magnetic Resonance Imaging data is crucial for brain disease diagnosis. Current graph-based deep learning models primarily focus on pairwise or triadic patterns while neglecting signed higher-order interactions, limiting comprehensive understanding of brain-wide communication. We propose HOI-Brain, a novel computational framework leveraging signed higher-orde…

The Morpho-Kinetic Landscape of Macrophage Modes During Wound Healing in Zebrafish
Seol Ah Park, Giulia Lupi, Resul Ozbilgic, Michal Kollar, Aneta A. Ozvat, Mai Nguyen-Chi, Karol Mikula
https://arxiv.org/abs/2507.19314

The Morpho-Kinetic Landscape of Macrophage Modes During Wound Healing in Zebrafish
Macrophages play an essential role in wound healing due to their dynamic nature and functional plasticity, exhibiting highly heterogeneous morpho-kinetic behaviors depending on their activation states. However, quantitative analysis of macrophage behavior in in vivo settings remains limited, largely due to the complexity of their diverse morphologies and motility patterns over time. In this study, we present an analytic workflow to investigate macrophage dynamics in zebrafish. By computing a co…

Distinct neurodynamics of functional brain networks in Alzheimer's disease and frontotemporal dementia as revealed by EEG
Sungwoo Ahn, Evie A. Malaia, Leonid L Rubchinsky
https://arxiv.org/abs/2507.08728

Distinct neurodynamics of functional brain networks in Alzheimer's disease and frontotemporal dementia as revealed by EEG
Objective While Alzheimer's disease (AD) and frontotemporal dementia (FTD) show some common memory deficits, these two disorders show partially overlapping complex spatiotemporal patterns of neural dynamics. The objective of this study is to characterize these patterns to better understand the general principles of neurodynamics in these conditions. Methods A comprehensive array of methods to study brain rhythms and functional brain networks are used in the study, from spectral power measures…

Gender Similarities Dominate Mathematical Cognition at the Neural Level: A Japanese fMRI Study Using Advanced Wavelet Analysis and Generative AI
Tatsuru Kikuchi
https://arxiv.org/abs/2507.21140

Gender Similarities Dominate Mathematical Cognition at the Neural Level: A Japanese fMRI Study Using Advanced Wavelet Analysis and Generative AI
Recent large scale behavioral studies suggest early emergence of gender differences in mathematical performance within months of school entry. However, these findings lack direct neural evidence and are constrained by cultural contexts. We conducted functional magnetic resonance imaging (fMRI) during mathematical tasks in Japanese participants (N = 156), employing an advanced wavelet time frequency analysis to examine dynamic brain processes rather than static activation patterns. Wavelet decom…

Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data
Zixia Zhou, Junyan Liu, Wei Emma Wu, Ruogu Fang, Sheng Liu, Qingyue Wei, Rui Yan, Yi Guo, Qian Tao, Yuanyuan Wang, Md Tauhidul Islam, Lei Xing
https://arxiv.org/abs/2508.11672

Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data
Dynamic brain data, teeming with biological and functional insights, are becoming increasingly accessible through advanced measurements, providing a gateway to understanding the inner workings of the brain in living subjects. However, the vast size and intricate complexity of the data also pose a daunting challenge in reliably extracting meaningful information across various data sources. This paper introduces a generalizable unsupervised deep manifold learning for exploration of neurocognitive…

Data-Efficient Neural Training with Dynamic Connectomes
Yutong Wu, Peilin He, Tananun Songdechakraiwut
https://arxiv.org/abs/2508.06817 https://arxiv.org/p…

Data-Efficient Neural Training with Dynamic Connectomes
The study of dynamic functional connectomes has provided valuable insights into how patterns of brain activity change over time. Neural networks process information through artificial neurons, conceptually inspired by patterns of activation in the brain. However, their hierarchical structure and high-dimensional parameter space pose challenges for understanding and controlling training dynamics. In this study, we introduce a novel approach to characterize training dynamics in neural networks by…