Similarity-driven multi-view embeddings from high-dimensional biomedical data

Avants, Brian; Tustison, Nicholas J.; Stone, James L.

doi:10.1038/s43588-021-00029-8

Cited by 18 publications

(11 citation statements)

References 83 publications

(95 reference statements)

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“…Finally, although VDP has proven to be a compelling quantity for clinical studies, the results from the diagnostic prediction evaluation and the previous discussion imply that this popular measure does not fully leverage the spatial information of the segmentation information from any of these algorithms. Perhaps the results of this work, in addition to pointing to the need for rethinking algorithm innovation direction, also point to possibly investigating differentiating spatial patterns within the images as evidence of disease and/ or growth and correlations with non-imaging data using sophisticated voxel-scale statistical techniques, which intrinsically leverage spatial information (eg, similarity-driven multivariate linear reconstruction 52,53 ).…”

Section: Discussionmentioning

confidence: 97%

Image‐ versus histogram‐based considerations in semantic segmentation of pulmonary hyperpolarized gas images

Tustison

Altes

Qing

et al. 2021

Magnetic Resonance in Med

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Purpose To characterize the differences between histogram‐based and image‐based algorithms for segmentation of hyperpolarized gas lung images. Methods Four previously published histogram‐based segmentation algorithms (ie, linear binning, hierarchical k‐means, fuzzy spatial c‐means, and a Gaussian mixture model with a Markov random field prior) and an image‐based convolutional neural network were used to segment 2 simulated data sets derived from a public (n = 29 subjects) and a retrospective collection (n = 51 subjects) of hyperpolarized 129Xe gas lung images transformed by common MRI artifacts (noise and nonlinear intensity distortion). The resulting ventilation‐based segmentations were used to assess algorithmic performance and characterize optimization domain differences in terms of measurement bias and precision. Results Although facilitating computational processing and providing discriminating clinically relevant measures of interest, histogram‐based segmentation methods discard important contextual spatial information and are consequently less robust in terms of measurement precision in the presence of common MRI artifacts relative to the image‐based convolutional neural network. Conclusions Direct optimization within the image domain using convolutional neural networks leverages spatial information, which mitigates problematic issues associated with histogram‐based approaches and suggests a preferred future research direction. Further, the entire processing and evaluation framework, including the newly reported deep learning functionality, is available as open source through the well‐known Advanced Normalization Tools ecosystem.

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Section: Discussionmentioning

confidence: 97%

Image‐ versus histogram‐based considerations in semantic segmentation of pulmonary hyperpolarized gas images

Tustison

Altes

Qing

et al. 2021

Magnetic Resonance in Med

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“…Such measurements can be inherently noisy and incomplete while simultaneously mutually overlapping and complementary. A recently developed statistical framework, known as similarity-driven multi-view linear reconstruction (SiMLR), 46 has been employed for exploring and analyzing such data. Successful application includes a recent investigation into a career breacher cohort characterized by repetive low-level blast exposure 8 where the use of SiMLR permitted identification of significant group effects spanning multiple modalities, including those mentioned previously.…”

Section: Methodsmentioning

confidence: 99%

Neurological Effects of Repeated Blast Exposure in Special Operations Personnel

Stone,

Avants,

Tustison

et al. 2024

Journal of Neurotrauma

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Exposure to blast overpressure has been a pervasive feature of combat-related injuries. Studies exploring the neurological correlates of repeated low-level blast exposure in career “breachers” demonstrated higher levels of tumor necrosis factor alpha (TNFα) and interleukin (IL)-6 and decreases in IL-10 within brain-derived extracellular vesicles (BDEVs). The current pilot study was initiated in partnership with the U.S. Special Operations Command (USSOCOM) to explore whether neuroinflammation is seen within special operators with prior blast exposure. Data were analyzed from 18 service members (SMs), inclusive of 9 blast-exposed special operators with an extensive career history of repeated blast exposures and 9 controls matched by age and duration of service. Neuroinflammation was assessed utilizing positron emission tomography (PET) imaging with [ 18 F]DPA-714. Serum was acquired to assess inflammatory biomarkers within whole serum and BDEVs. The Blast Exposure Threshold Survey (BETS) was acquired to determine blast history. Both self-report and neurocognitive measures were acquired to assess cognition. Similarity-driven Multi-view Linear Reconstruction (SiMLR) was used for joint analysis of acquired data. Analysis of BDEVs indicated significant positive associations with a generalized blast exposure value (GBEV) derived from the BETS. SiMLR-based analyses of neuroimaging demonstrated exposure-related relationships between GBEV, PET-neuroinflammation, cortical thickness, and volume loss within special operators. Affected brain networks included regions associated with memory retrieval and executive functioning, as well as visual and heteromodal processing. Post hoc assessments of cognitive measures failed to demonstrate significant associations with GBEV. This emerging evidence suggests neuroinflammation may be a key feature of the brain response to blast exposure over a career in operational personnel. The common thread of neuroinflammation observed in blast-exposed populations requires further study.

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“…Another step in multimodal integration is integrated data processing, with co-registration of two or more modalities and summary metrics extracted from common regions (Dennis et al, 2018). Lastly, some techniques involve statistical integration of data using advanced machine learning and statistical methods, which allow for multi-dimensional examination of data (Avants et al, 2021; Zavaliangos-Petropulu et al, 2017).…”

Section: Funding Trendsmentioning

confidence: 99%

The Role of Neuroimaging in Evolving TBI Research and Clinical Practice

Dennis

Keleher

Tate

et al. 2023

Preprint

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Neuroimaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) have been widely adopted in the clinical diagnosis and management of traumatic brain injury (TBI), particularly at the more acute and severe levels of injury. Additionally, a number of advanced applications of MRI have been employed in TBI-related clinical research with great promise, and researchers have used these techniques to better understand underlying mechanisms, progression of secondary injury and tissue perturbation over time, and relation of focal and diffuse injury to later outcome. However, the acquisition and analysis time, the cost of these and other imaging modalities, and the need for specialized expertise have represented historical barriers in extending these tools in clinical practice. While group studies are important in detecting patterns, heterogeneity among patient presentation and limited sample sizes from which to compare individual level data to well-developed normative data have also played a role in the limited translatability of imaging to wider clinical application. Fortunately, the field of TBI has benefitted from increased public and scientific awareness of the prevalence and impact of TBI, particularly in head injury related to recent military conflicts and sport-related concussion. This awareness parallels an increase in federal funding in the United States and other countries allocated to investigation in these areas. In this article we summarize funding and publication trends since the mainstream adoption of imaging in TBI to elucidate evolving trends and priorities in the application of different techniques and patient populations. We also review recent and ongoing efforts to advance the field through promoting reproducibility, data sharing, big data analytic methods, and team science. Finally, we discuss international collaborative efforts to combine and harmonize neuroimaging, cognitive, and clinical data, both prospectively and retrospectively. Each of these represent unique, but related, efforts that facilitate closing gaps between the use of advanced imaging solely as a research tool and the use of it in clinical diagnosis, prognosis, and treatment planning and monitoring.

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Similarity-driven multi-view embeddings from high-dimensional biomedical data

Cited by 18 publications

References 83 publications

Image‐ versus histogram‐based considerations in semantic segmentation of pulmonary hyperpolarized gas images

Image‐ versus histogram‐based considerations in semantic segmentation of pulmonary hyperpolarized gas images

Neurological Effects of Repeated Blast Exposure in Special Operations Personnel

The Role of Neuroimaging in Evolving TBI Research and Clinical Practice

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