Biology subjects

Warfield, S. K.

Publications and source records attributed to Warfield, S. K..

10 recordsLinked to original sources

bioRxiv2025

Insights into Temporal and Spatial Dynamics of Short Association Fiber Formation in the Human Fetal Brain

Short association fibers (SAFs) form the local scaffold of cortical connectivity, supporting early functional specialization and marking sites of neurodevelopmental vulnerability. However, their development before birth remains largely unknown. Leveraging advanced fetal diffusion MRI and a histology-validated framework, we present the first in-utero reconstruction of SAF pathways in the human brain. We tracked their volumetric and microstructural developmental trajectories in 243 fetuses spanning a critical period when the brains connectome is rapidly forming. We found that SAFs emerge before sulcal folding, initially as flat, loosely arranged pathways along the subplate-white matter interface, and later reorganize into coherent U-shaped bundles. Their maturation followed a sensorimotor-to-association gradient, paralleling cortical development. Nonlinear, bundle-specific trajectories captured multiphasic maturation, with subplate dynamics preceding increases in axonal coherence and early myelination. By filling a missing link in lifespan brain connectivity, this study establishes a prenatal reference for cortical wiring and provides tools for investigating the origins of neurodevelopmental disorders.

neuroscience↗

bioRxiv2025

Charting the Normal Development of Structural Brain Connectivity in Utero using Diffusion MRI

Understanding the structural connectivity of the human brain during fetal life is critical for uncovering the early foundations of neural function and vulnerability to developmental disorders. Diffusion-weighted MRI (dMRI) enables non-invasive mapping of white matter pathways and construction of the brains structural connectome, but its application to the fetal brain has been limited by data scarcity and technical difficulties in analyzing fetal dMRI data. Here, we present the largest study to date of in utero brain connectivity, analyzing high-quality dMRI data from 198 fetuses between 22 and 37 gestational weeks from the Developing Human Connectome Project. We employed advanced fetal-specific tools for brain segmentation and parcellation, and used ensemble tractography to encourage more complete reconstruction of various white matter tracts. For connection weighting, we relied on the notion of fiber bundle capacity. We reconstructed individual structural connectomes and characterized the developmental trajectories. Graph-theoretical analysis revealed consistent increases in integration and segregation metrics over gestation, while bootstrapping confirmed the robustness of nodal and edgewise developmental patterns. Furthermore, we proposed a novel method for constructing age-specific connectome templates based on aggregation of individual subject connectomes. The new method follows an optimization-based approach to ensure that the connectome templates closely represent individual subject connectomes, are temporally consistent, and proportionally preserve short and long connections. Our analysis shows that this approach is superior to spatial alignment and averaging of the data in image space, with the resulting connectome templates supporting accurate prediction of the gestational age of individual fetuses (mean error= 0.90 {+/-} 0.81 weeks). In summary, this study provides robust normative benchmarks for fetal brain structural connectivity and demonstrates that connectome-based measures capture meaningful developmental signatures, offering a framework for future studies of early brain development.

neuroscience↗

bioRxiv2024

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brains cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

bioengineering↗

bioRxiv2024

White matter tract crossing and bottleneck regions in the fetal brain

There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been extensively researched for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 59 fetal brain scans and extracted a set of 51 distinct white tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75-80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. The results of this study highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.

neuroscience↗

bioRxiv2024

A detailed spatio-temporal atlas of the white matter tracts for the fetal brain

This study presents the construction of a comprehensive spatiotemporal atlas detailing the development of white matter tracts in the fetal brain using diffusion magnetic resonance imaging (dMRI). Our research leverages data collected from fetal MRI scans conducted between 22 and 37 weeks of gestation, capturing the dynamic changes in the brains microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), reflecting key neurodevelopmental processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.

neuroscience↗

bioRxiv2022

Heterogeneous migration of neuronal progenitors to the insula shapes the human brain

The human cerebrum consists of a precise and stereotyped arrangement of lobes, gyri, and connectivity that underlies human cognition. The development of this arrangement is less clear. Current models of radial glial cell migration explain individual gyral formation but fail to explain the global configuration of the cerebral lobes. Moreover, the insula, buried in the depths of the Sylvian fissure, belies conventional models. Here, we show that the insula has unique morphology in adults, that insular morphology and slow volumetric growth emerge during fetal development, and that a novel theory of curved migration is required to explain these findings. We calculated morphologic data in the insula and other lobes in adults (N=107) and in an in utero fetal brain atlas (N=81 healthy fetuses). In utero, the insula grows an order of magnitude slower than the other lobes and demonstrates shallower sulci, less curvature, and less surface complexity both in adults and progressively throughout fetal development. Novel spherical projection analysis demonstrates that the lenticular nuclei obstruct 60-70% of radial pathways from the ventricular zone (VZ) to the insula, forcing a curved migration path to the insula in contrast to a direct radial pathway. Using fetal diffusion tractography, we identify streams of putative progenitor cells that originate from the VZ and migrate tangentially around the lenticular nuclei to form the insula. These results challenge existing models of radial migration to the cortex, provide an alternative model for insular and cerebral development, and lay the groundwork to understand cerebral malformations, insular functional connectivity, and insular pathologies.

developmental biology↗

bioRxiv2018

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-the-art algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, ...), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

neuroscience↗

bioRxiv2018

A three year follow-up study of gadolinium enhanced and non-enhanced regions in multiple sclerosis lesions using a multi-compartment T2 relaxometry model

Demyelination, axonal damage and inflammation are critical indicators of the onset and progress of neurodegenerative diseases such as multiple sclerosis (MS) in patients. Due to physical limitations of imaging such as acquisition time and imaging resolution, a voxel in a MR image is heterogeneous in terms of tissue microstructure such as myelin, axons, intra and extra cellular fluids and free water. We present a multi-compartment tissue model which estimates the water fraction (WF) of tissues with short, medium and high T2 relaxation times in a T2 relaxometry MRI voxel. The proposed method is validated on test-retest data of healthy controls. This model was then used to study longitudinal trends of the tissue microstructures for two sub-regions of the lesions: gadolinium enhanced (E+) and non-enhanced (L-) regions of MS lesions in 10 MS patients over a period of three years. The water fraction values in E+ and L- regions were found to be significantly different (p < 0.05) over the period of first three months. The results of this study also showed that the estimates of the proposed T2 relaxometry model on brain tissue microstructures have potential to distinguish between regions undergoing active blood brain barrier breakdown from the other regions of the lesion.

neuroscience↗

bioRxiv2018

Presurgical language fMRI: Current technical practices in epilepsy surgical planning

Little is known about how language functional MRI (fMRI) is executed in clinical practice in spite of its widespread use. Here we comprehensively documented its execution in surgical planning in epilepsy. A questionnaire focusing on cognitive design, imaging acquisition, analysis and interpretation and practical considerations was developed. Individuals responsible for collecting, analyzing, and interpreting clinical language fMRI data at 63 epilepsy surgical programs responded. The central finding was of marked heterogeneity in all aspects of fMRI. Most programs use multiple tasks, with a fifth routinely using 2, 3, 4 or 5 tasks with a modal run duration of five minutes. Variants of over fifteen protocols are in routine use with forms of noun-verb generation, verbal fluency, and semantic decision-making used most often. Nearly all aspects of data acquisition and analysis vary markedly. Neither of the two best-validated protocols were used by more than 10% of respondents. Preprocessing steps are broadly consistent across sites, language-related blood flow is most often identified using general linear modeling (76% of respondents), and statistical thresholding typically varies by patient (79%). The software SPM is most often used. fMRI programs inconsistently include input from experts with all required skills (imaging, cognitive assessment, MR physics, statistical analysis, brain-behavior relationships). These data highlight marked gaps between the evidence supporting fMRI and its clinical application. Teams performing language fMRI may benefit from evaluating practice with reference to the best-validated protocols to date and ensuring individuals trained in all aspects of fMRI are involved to optimize patient care.

neuroscience↗

bioRxiv2017

Presurgical language fMRI: Current practices and patient outcomes in epilepsy surgical planning

The goal of this study was to document current clinical practice and report patient outcomes in presurgical language functional MRI (fMRI) for epilepsy surgery. Epilepsy surgical programs worldwide were surveyed as to the utility, implementation, and efficacy of language fMRI in the clinic; 82 programs responded between July 2015 and January 2016. Respondents were predominantly from the US (61%), were academic programs (85%), and predominantly evaluated adults (44%), both adults and children (40%), or children only (16%). Nearly all (96%) respondents reported using language fMRI. fMRI is used for language lateralization (100%) and for localizing (44%) language cortex to guide surgical margins. While typically considered useful, programs often reported at least one instance of disagreement with other measures (56%). When used to localize language cortex, direct brain stimulation typically confirmed fMRI findings (74%) but instances of unpredicted decline were reported by 17% of programs and unexpected preservation of function were reported by 54%. Programs reporting unexpected decline did not clearly differ from those which did not. Clinicians using fMRI to guide surgical margins typically map Brocas and Wernickes areas but not other known language areas. Language fMRI is widely used for lateralizing language cortex in patients with medically intractable epilepsy. It is also frequently used to localize language cortex though it is not yet well validated for this purpose. Many centers report cases of unexpected language preservation when fMRI activation is resected, and cases of language decline when it is not. Care will almost certainly be improved by standardizing protocols and accurately detailing the relationship between fMRI-positive areas and post-surgical language decline.

neuroscience↗