Biology subjects

Kai, J.

Publications and source records attributed to Kai, J..

9 recordsLinked to original sources

bioRxiv2026

AFIDs-Validator: An Open-Access AI-Guided Platform for Learning Anatomical Landmark Placement

Accurate localization of anatomical landmarks is a foundational skill in anatomy and imaging that is often taught informally through expert mentorship, requiring access to data and desktop software. There is no openly accessible, interactive resource that teaches neuroanatomy with quantitative feedback. We present the AFIDs-Validator (validator.afids.io), an open-access, browser-based platform that pairs guided instruction with quantitative assessment. The platform combines (1) a learning mode in which a language-model neuroanatomy tutor operates inside an MRI viewer, giving anatomy-first instruction that responds to the learner's current image slice, orientation, and cursor position; and (2) a validation engine that accepts a learner's landmark file and returns per-landmark Euclidean error against expert-annotated references spanning 21 brain templates. To make the feedback interpretable, we analyzed 15,000 landmark annotations across 132 human subjects and found that landmark difficulty varies fourfold (median error ranged from 0.37 mm at the anterior commissure to 1.50 mm at the temporal horns) with heavy-tailed distributions at every landmark. These distributions are compiled into per-landmark reliability priors, so learners are scored against the empirical spread of trained raters rather than an arbitrary threshold, and difficult landmarks are not mistaken for poor performance. The AFIDs-Validator requires no installation, licensed software, or local data, and all code, reference data, and tutor design are openly released.

scientific communication and education↗

bioRxiv2026

An anatomically distinct dopaminergic cell population of the zona incerta as evidence for the human A13 nucleus

The zona incerta (ZI) is a functionally and molecularly diverse deep brain region increasingly recognized as an integrative hub. The rodent ZI contains the dopaminergic A13 nucleus, with studies supporting a central role in modulating sensorimotor integration, nociception, and confers resilience in models of neurodegeneration; however, to date, no clear human homologue has been identified. Here, we identified a tyrosine hydroxylase-positive subregion in the human ZI consistent with the A13, and refined its borders using ex vivo MRI and histology. We translate these findings to in vivo MRI, demonstrating the A13 is distinguishable from surrounding regions by elevated T1 and T2* values. We further revealed a principal rostral-caudal axis of molecular and structural connectivity variation in the ZI, and identified the A13 as a localized molecular specialization. Altogether, we report a distinct ZI subregion corresponding to the A13 nucleus, providing an anatomical foundation for further mechanistic and translational investigation.

neuroscience↗

bioRxiv2025

Styx: A multi-language API Generator for Command-Line Tools

In numerous scientific domains, established tools have often been developed with complex command-line interfaces. Such is the case for brain imaging and bioinformatics, making the use of powerful legacy tools in modern workflow paradigms challenging. We present (i) Styx, a compiler for generating language-native wrapper functions from static tool metadata, leading to seamless integration of command-line tools within the data science ecosystem. Alongside Styx, we have created (ii) NiWrap, a collection of more than 1900 neuroimaging command-line function descriptions as a proof-of-concept implementation. These interfaces, available in Python, R, and TypeScript (available at https://github.com/styx-api), significantly reduce the complexity of writing and interpreting software pipelines, particularly when composing workflows across packages with distinct API standards. The compiler architecture of Styx facilitates maintainability and portability across computing environments. As with all metadata-dependent infrastructure, creating sufficient metadata annotations remains a barrier to adoption. Accordingly, NiWrap demonstrates approaches that lower this barrier through direct source code extraction and LLM-assisted documentation parsing. Together, Styx and NiWrap offer a sustainable solution for interfacing diverse command-line tools with modern data science ecosystems. This modular approach enhances reproducibility and efficiency in pipeline development while ensuring portability across computing environments and programming languages.

bioinformatics↗

bioRxiv2024

Mapping the topographic organization of the human zona incerta using diffusion MRI

The zona incerta (ZI) is a deep brain region originally described by Auguste Forel as an "immensely confusing area about which nothing can be said." Despite the elusive nature of this structure, mounting evidence supports the role of the ZI and surrounding regions across a diverse range of brain functions and as a candidate target for neuromodulatory therapies. Using in vivo diffusion MRI and data-driven connectivity, we identify a topographic organization between the ZI and neocortex. Specifically, our methods identify a rostral-caudal gradient predominantly connecting the frontopolar and ventral prefrontal cortices with the rostral ZI, and the primary sensorimotor cortices with the caudal ZI. Moreover, we demonstrate how clustering and gradient approaches build complementary evidence including facilitating the mapping of a central region of the ZI, connected with the dorsal prefrontal cortex. These results were shown to be replicable across multiple datasets and at the individual subject level, building evidence for the important role of the ZI in mediating frontal lobe-associated tasks, ranging from motor to cognitive to emotional control. Finally, we consider the impact of this topographic organization on the refinement of neuromodulatory targets. These results pave the way for an increasingly detailed understanding of ZI substructures, and considerations for in vivo targeting of the ZI for neuromodulation.

neuroscience↗

bioRxiv2022

Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging templates and datasets. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (n = 132 subjects for a total of 15,232 fiducials) and more than 10 brain templates (4,288 fiducials), compiling over 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1-2 mm (using a total of 50,336 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure (BIDS) allowing for facile incorporation into modern neuroimaging analysis pipelines. Data is accessible on GitHub (https://github.com/afids/afids-data).

neuroscience↗

bioRxiv2022

An automated BIDS-App for brain segmentation of human fetal functional MRI data

Fetal functional magnetic resonance imaging (fMRI) offers critical insight into the developing brain and could aid in predicting developmental outcomes. As the fetal brain is surrounded by heterogeneous tissue, it is not possible to use adult- or child-based segmentation toolboxes. Manually-segmented masks can be used to extract the fetal brain; however, this comes at significant time costs. Here, we present a new BIDS App for masking fetal fMRI, funcmasker-flex, that overcomes these issues with a robust 3D convolutional neural network (U-net) architecture implemented in an extensible and transparent Snakemake workflow. Open-access fetal fMRI data with manual brain masks from 159 fetuses (1103 total volumes) were used for training and testing the U-net model. We also tested generalizability of the model using 82 locally acquired functional scans from 19 fetuses, which included over 2300 manually segmented volumes. Dice metrics were used to compare performance of funcmasker-flex to the ground truth manually segmented volumes, and segmentations were consistently robust (all Dice metrics [≥]0.74). The tool is freely available and can be applied to any BIDS dataset containing fetal bold sequences. funcmasker-flex reduces the need for manual segmentation, even when applied to novel fetal functional datasets, resulting in significant time-cost savings for performing fetal fMRI analysis.

neuroscience↗

bioRxiv2022

Detection of local growth patterns in longitudinally imaged low-grade gliomas

BackgroundDiffuse low-grade gliomas (LGGs) are primary brain tumors with infiltrative, anisotropic growth related to surrounding white and grey matter structures. In this study, we illustrate the use of deformation-based morphometry (DBM) as a simple and objective method to study the local change in growth patterns of LGGs. MethodsAn imaging pipeline was developed involving the creation of patient-specific average templates and nonlinear registration of pre-treatment follow-up MRIs to the average template. Jacobian maps were derived and analyzed to identify areas of tissue expansion and contraction over time. ResultsOur analysis demonstrates that tissue expansion occurs primarily around the edges of the tumor, while the lesion core and areas adjacent to obstacles, such as the skull, show no significant growth. Tumors also appeared to grow faster and predominantly in areas of white matter. Regions of the brain surrounding the lesion showed slight contraction over time, likely representing compression due to mass effect of the tumor. ConclusionsWe demonstrate that DBM is a useful clinical tool to understand the long-term clinical course of an individuals tumor and identify areas of rapid growth, which can explain the clinical signs and symptoms, predict future symptoms, and guide targeted diagnostics and therapy. HighlightsO_LILow-grade glioma expansion occurs primarily around the edges of the tumor. C_LIO_LITumor cores and tissue next to obstacles show no significant growth over time. C_LIO_LIDBM provides a clinically valuable assessment of local tumor growth and activity. C_LI

cancer biology↗

bioRxiv2022

Mapping the subcortical connectome using in vivo diffusion MRI: feasability and reliability

Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo.

neuroscience↗

bioRxiv2019

Assessing white matter pathway reproducibility from human whole-brain tractography clustering

Diffusion MRI, together with tractography techniques, is a non-invasive tool to investigate the brains structural pathways (tracts). These tracts join together different regions of the brain and tract identification often involves the use of manual ROIs or automated techniques such as clustering. By studying these connections, current understanding of the connectome can be improved and changes due to disease in patient populations may be identified. We developed a tool to automatically identify all pathways in the human brain, including the short-range, U-shaped tracts, and map quantitative scalar metrics along the pathway trajectory for subsequent analysis. Pathways are identified via a spectral clustering technique on two datasets: Human Connectome Project (intersubject) and MyConnectome Project (intrasubject) and the reliability of the extracted tract and scalar values are evaluated. Average Euclidean distances and volumetric overlap were computed and indicated good spatial reliability. Intraclass correlations of the fractional anisotropy value mapped along the tract was calculated and exhibited good reproducibility within each dataset. Additionally, these evaluation metrics, together with the coefficient of variation of the mean streamline count is used to determine reliably identified U-shaped tracts across the datasets. The developed tract identification tool is an additional resource to studying the human connectome with increased confidence in the results. The identified reliable U-shaped tracts contributes to the identification of common structural connections across individuals and aids in advancing our understanding of the brains short-range pathways.

bioinformatics↗