Biology subjects

Yushkevich, P. A.

Publications and source records attributed to Yushkevich, P. A..

8 recordsLinked to original sources

bioRxiv2026

Cortical reconstruction and anatomical parcellation of high-resolution multi-modal postmortem ex vivo MRI of the human infant brain

High-resolutionpostmortemmagneticresonanceimagingen- ables detailed examination of brain anatomy at spatial scales not achiev- able in vivo and provides a unique opportunity to link morphomet- ric measurements with the underlying pathology. Despite these advan- tages, robust computational tools for automated anatomical segmen- tation and cortical surface reconstruction remain limited, particularly in postmortem infant brains. Incomplete myelination, thinner cortical ribbons, small-scale neuroanatomy, evolving tissue contrast, fixation- induced signal alterations, and variability in postmortem preparation make standard neuroimaging pipelines unsuitable for postmortem in- fant MRI. In this work, we introduce a unique high-resolution multi- sequence postmortem infant MRI dataset and a unified computational framework that combines deep learning-based volumetric segmentation with surface-based cortical reconstruction and anatomical parcellation in native subject-space resolution. The framework is designed to general- ize across diverse postmortem MRI acquisition protocols, spatial resolu- tions, tissue preparation conditions, and specimen characteristics while remaining robust to substantial variability in image contrast, tissue de- formation, fixation-induced intensity changes, background signal char- acteristics, and anatomical variability encountered in postmortem in- fant MRI. We benchmark our framework against widely used contrast- agnostic and foundational brain segmentation models, demonstrating improved anatomical consistency and segmentation performance across heterogeneous high-resolution postmortem infant datasets. Our method enables morphometric analysis in native postmortem space, providing the same downstream quantitative analyses routinely available for in vivo developmental neuroimaging. The complete framework is released as open-source software with command-line workflows, containers, and comprehensive documentation to facilitate reproducible postmortem in- fant MRI analysis as part of the purple-mri package: https://purple-mri.readthedocs.io

neuroscience↗

bioRxiv2025

Postmortem brain MRI reveals differential associations of subcortical and limbic volumes with cortical thinning and neuropathology patterns

Structured AbstractO_ST_ABSIntroductionC_ST_ABSThe impact of different neuropathologies on deep brain structures remains to be understood. We examine subcortical and limbic volumetry in neurodegenerative diseases involving p-tau, -synuclein and TDP-43. MethodsWe acquired neuropathological measures and brain segmentations from postmortem analysis of 132 donors with Alzheimers disease (AD), Lewy body disease (LBD), Frontotemporal Lobar Degeneration with TDP-43 (FTLD-TDP) and FTLD-Tau. ResultsLBD had the least subcortical, limbic and cortical atrophy compared to AD, FTLD-TDP and FTLD-Tau. In donors with both AD and LBD pathologies, primary LBD was associated with less atrophy than primary AD. While AD had cortico-subcortical and cortico-limbic morphometric associations, LBD had more limited parieto-occipital cortico-limbic associations. FTLD-TDP had cortico-subcortical while FTLD-Tau had cortico-subcortical and cortico-limbic associations. In AD and FTLD-Tau, hippocampal volumes correlated with p-tau burden, neuron loss and gliosis. In LBD, thalamic -synuclein severity was associated with subcortical/limbic volumes. DiscussionPostmortem neuroimaging reveals disease- and region-specific structure-pathology relationships.

neuroscience↗

bioRxiv2025

Harmonized Protocol for Segmentation of the Hippocampal Tail on High-Resolution in vivo MRI from the Hippocampal Subfields Group (HSG)

The hippocampus is a heterogeneous structure with cytoarchitectonically distinct subfields that exhibit heterogeneous lifespan trajectories and are differentially susceptible to diseases. Advances in high-resolution imaging have accelerated research on these structures, yet variability in segmentation protocols limits cross-study comparability. The Hippocampal Subfields Group (HSG) is an international consortium addressing this challenge by developing a reliable, accessible, and freely available segmentation protocol for high-resolution T2-weighted 3 tesla MRI scans (http://www.hippocampalsubfields.com). Here, we present the harmonized protocol for the posterior portion of the hippocampus (the "tail"), complementing the previously established "body" protocol, and with an anterior "head" protocol under development. The tail protocol provides standardized definitions of the external boundaries for the posterior-most extent of the hippocampus, facilitating consistent segmentation from surrounding tissues. The research community was extensively involved through an online survey that incorporated comprehensive protocol details, feasibility assessments, tutorial videos, and illustrative segmentations. Through this collaborative process, consensus emerged to exclude subfield labeling in the hippocampal tail due to limited visibility of internal landmarks and substantial anatomical variability in this region. All proposed boundary guidelines were deemed clear and agreed upon via a Delphi procedure. The harmonized tail protocol has high intra-(Averaged ICC(2,1) > 0.98; Averaged Dice Similarity Coefficient = 0.92) and inter-rater reliability (Averaged ICC(2,k) > 0.98; Averaged Dice Similarity Coefficient = 0.86) and offers a practical framework for replicable segmentation. By establishing standardized guidelines, this protocol enhances comparability of findings across developmental, aging, and clinical research and is compatible with ongoing technological advances.

neuroscience↗

bioRxiv2024

Automatic segmentation of medial temporal lobe subregions in multi-scanner, multi-modality MRI of variable quality

ObjectivesVolumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimers disease. However, poor quality MR images can lead to unreliable segmentation of MTL subregions. Considering that different MRI contrast mechanisms and field strengths (jointly referred to as "modalities" here) offer distinct advantages in imaging different parts of the MTL, we developed a muti-modality segmentation model using both 7 tesla (7T) and 3 tesla (3T) structural MRI to obtain robust segmentation in poor-quality images. MethodMRI modalities including 3T T1-weighted, 3T T2-weighted, 7T T1-weighted and 7T T2-weighted (7T-T2w) of 197 participants were collected from a longitudinal aging study at the Penn Alzheimers Disease Research Center. Among them, 7T-T2w was used as the primary modality, and all other modalities were rigidly registered to the 7T-T2w. A model derived from nnU-Net took these registered modalities as input and outputted subregion segmentation in 7T-T2w space. 7T-T2w images most of which had high quality from 25 selected training participants were manually segmented to train the multi-modality model. Modality augmentation, which randomly replaced certain modalities with Gaussian noise, was applied during training to guide the model to extract information from all modalities. ResultsThe multi-modality model delivered good performance regardless of 7T-T2w quality, while the single-modality model under-segmented subregions in poor-quality images. The multi-modality model generally demonstrated stronger discrimination of A+MCI versus A-CU. Intra-class correlation and Bland-Altman plots demonstrate that the multi-modality model had higher longitudinal segmentation consistency in all subregions while the single-modality model had low consistency in poor-quality images. ConclusionThe multi-modality MRI segmentation model provides an improved biomarker for neurodegeneration in the MTL that is robust to image quality. It also provides a framework for other studies which may benefit from multimodal imaging.

bioengineering↗

bioRxiv2024

Medial temporal lobe atrophy patterns in early- versus late-onset amnestic Alzheimer's disease

BackgroundThe medial temporal lobe (MTL) is hypothesized to be relatively spared in early-onset Alzheimers disease (EOAD). Yet, detailed examination of MTL subfield volumes and drivers of atrophy in amnestic EOAD is lacking. MethodsBioFINDER-2 participants with memory impairment, abnormal amyloid-{beta} status and tau-PET were included. Forty-one EOAD individuals aged [≥]65 years and, as comparison, late-onset AD (LOAD, [≤]70 years, n=154) and A{beta}-negative cognitively unimpaired controls were included. MTL subregions and biomarkers of (co-)pathologies were measured. ResultsAD groups showed smaller MTL subregions compared to controls. Atrophy patterns were similar across AD groups, although LOAD showed thinner entorhinal cortices compared to EOAD. EOAD showed lower WMH compared to LOAD. No differences in MTL tau-PET or transactive response DNA binding protein 43-proxy positivity was found. ConclusionsWe found in vivo evidence for MTL atrophy in amnestic EOAD and overall similar levels to LOAD of MTL tau pathology and co-pathologies.

neuroscience↗

bioRxiv2023

A (Sub)field Guide to Quality Control in Hippocampal Subfield Segmentation on High-resolution T2-weighted MRI

Inquiries into properties of brain structure and function have progressed due to developments in magnetic resonance imaging (MRI). To sustain progress in investigating and quantifying neuroanatomical details in vivo, the reliability and validity of brain measurements are paramount. Quality control (QC) is a set of procedures for mitigating errors and ensuring the validity and reliability of brain measurements. Despite its importance, there is little guidance on best QC practices and reporting procedures. The study of hippocampal subfields in vivo is a critical case for QC because of their small size, inter-dependent boundary definitions, and common artifacts in the MRI data used for subfield measurements. We addressed this gap by surveying the broader scientific community studying hippocampal subfields on their views and approaches to QC. We received responses from 37 investigators spanning 10 countries, covering different career stages, and studying both healthy and pathological development and aging. In this sample, 81% of researchers considered QC to be very important or important, and 19% viewed it as fairly important. Despite this, only 46% of researchers reported on their QC processes in prior publications. In many instances, lack of reporting appeared due to ambiguous guidance on relevant details and guidance for reporting, rather than absence of QC. Here, we provide recommendations for correcting errors to maximize reliability and minimize bias. We also summarize threats to segmentation accuracy, review common QC methods, and make recommendations for best practices and reporting in publications. Implementing the recommended QC practices will collectively improve inferences to the larger population, as well as have implications for clinical practice and public health.

neuroscience↗

bioRxiv2021

3D Mapping of Neurofibrillary Tangle Burden in the Human Medial Temporal Lobe

Tau protein neurofibrillary tangles (NFT) are closely linked to neuronal/synaptic loss and cognitive decline in Alzheimers disease (AD) and related dementias. Our knowledge of the pattern of NFT progression in the human brain, critical to the development of imaging biomarkers and interpretation of in vivo imaging studies in AD, is based on conventional 2D histology studies that only sample the brain sparsely. To address this limitation, ex vivo MRI and dense serial histological imaging in 18 human medial temporal lobe (MTL) specimens were used to construct 3D quantitative maps of NFT burden in the MTL at individual and group levels. These maps reveal significant variation in NFT burden along the anterior-posterior axis. While early NFT pathology is thought to be confined to the transentorhinal region, we find similar levels of NFT burden in this region and other MTL subregions, including amygdala, temporopolar cortex, and subiculum/CA1.

neuroscience↗

bioRxiv2019

Neural and behavioral correlates of declarative memory are associated with temporal discounting in older adults

When facing decisions involving trade-offs between smaller, sooner and larger, delayed rewards, people tend to discount the value of future rewards. There are substantial individual differences in this tendency toward temporal discounting, however. One neurocognitive system that may underlie these individual differences is episodic memory, given the overlap in the neural circuitry involved in imagining the future and remembering the past. Here we tested this hypothesis in older adults, including both those that were cognitively normal and those with amnestic mild cognitive impairment (MCI). We found that performance on neuropsychological measures of episodic memory retrieval was associated with temporal discounting, such that people with better memory discounted delayed rewards less. This relationship was specific to episodic memory and temporal discounting, since executive function (another cognitive ability) was unrelated to temporal discounting, and episodic memory was unrelated to risk tolerance (another decision-making preference). We also examined cortical thickness and volume in medial temporal lobe regions critical for episodic memory. Entorhinal cortical thickness was associated with reduced temporal discounting, with episodic memory performance partially mediating this association. The inclusion of MCI participants was critical to revealing these associations between episodic memory and entorhinal cortical thickness and temporal discounting. These effects were larger in the MCI group, reduced after controlling for MCI status, and statistically significant only when including MCI participants in analyses. Overall, these findings suggest that individual differences in temporal discounting are driven by episodic memory function, and that a decline in medial temporal lobe structural integrity may impact temporal discounting.

neuroscience↗