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Search Results for all work with filters:

  • Engineering, Electronics and Electrical

Work 1-10 of 81

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Article

Imaging Renal Urea Handling in Rats at Millimeter Resolution using Hyperpolarized Magnetic Resonance Relaxometry.

by Galen D. Reed; Cornelius von Morze; Alan S. Verkman; Bertram L. Koelsch; Myriam M. Chaumeil; Michael Lustig; Sabrina M. Ronen; Robert A. Bok; Jeff Sands; Peder E. Z. Larson; Zhen J. Wang; Jan Henrik Ardenkjær Larsen; John Kurhanewicz; Daniel B. Vigneron

2016

Subjects
  • Engineering, Biomedical
  • Engineering, Electronics and Electrical
  • Health Sciences, Radiology
  • File Download
  • View Abstract

Abstract:Close

In vivo spin spin relaxation time (T2) heterogeneity of hyperpolarized [(13)C,(15)N2]urea in the rat kidney was investigated. Selective quenching of the vascular hyperpolarized (13)C signal with a macromolecular relaxation agent revealed that a long-T2 component of the [(13)C,(15)N2]urea signal originated from the renal extravascular space, thus allowing the vascular and renal filtrate contrast agent pools of the [(13)C,(15)N2]urea to be distinguished via multi-exponential analysis. The T2 response to induced diuresis and antidiuresis was performed with two imaging agents: hyperpolarized [(13)C,(15)N2]urea and a control agent hyperpolarized bis-1,1-(hydroxymethyl)-1-(13)C-cyclopropane-(2)H8. Large T2 increases in the inner-medullar and papilla were observed with the former agent and not the latter during antidiuresis. Therefore, [(13)C,(15)N2]urea relaxometry is sensitive to two steps of the renal urea handling process: glomerular filtration and the inner-medullary urea transporter (UT)-A1 and UT-A3 mediated urea concentrating process. Simple motion correction and subspace denoising algorithms are presented to aid in the multi exponential data analysis. Furthermore, a T2-edited, ultra long echo time sequence was developed for sub-2 mm(3) resolution 3D encoding of urea by exploiting relaxation differences in the vascular and filtrate pools.

Article

Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia

by Qunfang Long; Suchita Bhinge; Vince D Calhoun; T Adali

2020

Subjects
  • Engineering, Electronics and Electrical
  • Computer Science
  • File Download
  • View Abstract

Abstract:Close

The extraction of common and distinct biomedical signatures among different populations allows for a more detailed study of the group-specific as well as distinct information of different populations. A number of subspace analysis algorithms have been developed and successfully applied to data fusion, however they are limited to joint analysis of only a couple of datasets. Since subspace analysis is very promising for analysis of multi-subject medical imaging data as well, we focus on this problem and propose a new method based on independent vector analysis (IVA) for common subspace extraction (IVA-CS) for multi-subject data analysis. IVA-CS leverages the strength of IVA in identification of a complete subspace structure across multiple datasets along with an efficient solution that uses only second-order statistics. We propose a subset analysis approach within IVA-CS to mitigate issues in estimation in IVA due to high dimensionality, both in terms of components estimated and the number of datasets. We introduce a scheme to determine a desirable size for the subset that is high enough to exploit the dependence across datasets and is not affected by the high dimensionality issue. We demonstrate the success of IVA-CS in extracting complex subset structures and apply the method to analysis of functional magnetic resonance imaging data from 179 subjects and show that it successfully identifies shared and complementary brain patterns from patients with schizophrenia (SZ) and healthy controls group. Two components with linked resting-state networks are identified to be unique to the SZ group providing evidence of functional dysconnectivity. IVA-CS also identifies subgroups of SZs that show significant differences in terms of their brain networks and clinical symptoms.

Article

Soft, wireless periocular wearable electronics for real-time detection of eye vergence in a virtual reality toward mobile eye therapies

by Saswat Mishra; Yun-Soung Kim; Jittrapol Intarasirisawat; Young-Tae Kwon; Yongkuk Lee; Musa Mahmood; Hyo-Ryoung Lim; Robert Herbert; Ki Jun Yu; Chee Siang Ang; Woon-Hong Yeo

2020

Subjects
  • Engineering, Mechanical
  • Engineering, Electronics and Electrical
  • File Download
  • View Abstract

Abstract:Close

Recent advancements in electronic packaging and image processing techniques have opened the possibility for optics-based portable eye tracking approaches, but technical and safety hurdles limit safe implementation toward wearable applications. Here, we introduce a fully wearable, wireless soft electronic system that offers a portable, highly sensitive tracking of eye movements (vergence) via the combination of skin-conformal sensors and a virtual reality system. Advancement of material processing and printing technologies based on aerosol jet printing enables reliable manufacturing of skin-like sensors, while the flexible hybrid circuit based on elastomer and chip integration allows comfortable integration with a user's head. Analytical and computational study of a data classification algorithm provides a highly accurate tool for real-time detection and classification of ocular motions. In vivo demonstration with 14 human subjects captures the potential of the wearable electronics as a portable therapy system, whose minimized form factor facilitates seamless interplay with traditional wearable hardware.

Article

Spatial subcellular organelle networks in single cells

by Mythreye Venkatesan; Nicholas Zhang; Benoit Marteau; Yukina Yajima; Nerea O De Zarate Garcia; Zhou Fang; Thomas Hu; Shuangyi Cai; Zhou Ford; Shuangyi Cai; Adam Ford; Harrison Olszewski; Andrew Borst; Ahmet F Coskun

2023

Subjects
  • Engineering, Biomedical
  • Engineering, Electronics and Electrical
  • View Abstract

Abstract:Close

Organelles play important roles in human health and disease, such as maintaining homeostasis, regulating growth and aging, and generating energy. Organelle diversity in cells not only exists between cell types but also between individual cells. Therefore, studying the distribution of organelles at the single-cell level is important to understand cellular function. Mesenchymal stem cells are multipotent cells that have been explored as a therapeutic method for treating a variety of diseases. Studying how organelles are structured in these cells can answer questions about their characteristics and potential. Herein, rapid multiplexed immunofluorescence (RapMIF) was performed to understand the spatial organization of 10 organelle proteins and the interactions between them in the bone marrow (BM) and umbilical cord (UC) mesenchymal stem cells (MSCs). Spatial correlations, colocalization, clustering, statistical tests, texture, and morphological analyses were conducted at the single cell level, shedding light onto the interrelations between the organelles and comparisons of the two MSC subtypes. Such analytics toolsets indicated that UC MSCs exhibited higher organelle expression and spatially spread distribution of mitochondria accompanied by several other organelles compared to BM MSCs. This data-driven single-cell approach provided by rapid subcellular proteomic imaging enables personalized stem cell therapeutics.

Article

Deep learning methods and applications in neuroimaging

by Jing Sui; MingXia Liu; Jong-Hwan Lee; Jun Zhang; Vince Calhoun

2020

Subjects
  • Engineering, Biomedical
  • Biology, Neuroscience
  • Engineering, Electronics and Electrical
  • File Download
  • View Abstract

Abstract:Close

Deep learning (DL) has gained considerable attention in the scientific community, breaking benchmark records in many areas such as speech and visual recognition. However, the incorporation of deep learning approaches in neuroimaging is still a challenging and promising direction, due to the high-dimensional feature dimensions and limited sample sizes (Calhoun and Sui, 2016). MRI features. Currently, advances in medical imaging technologies have enabled image acquisition at faster rates and with increased resolution. Also, multiple accessible international brain imaging datasets online facilitate the generation of neuroimaging big data. These provide wonderful testbeds for the advanced computerized tools, especially deep learning approaches, which has shown its efficacy to neuroimaging applications (Hou et al., 2019; Kim et al., 2016; Liu et al., 2018; Yan et al., 2019).

Article

Acoustic Emissions as a Non-Invasive Biomarker of the Structural Health of the Knee

by Daniel C Whittingslow; Hyeon-Ki Jeong; Venu G Ganti; Nathan J Kirkpatrick; Geza F Kogler; Omer T Inan

2020

Subjects
  • Engineering, Biomedical
  • Engineering, Electronics and Electrical
  • File Download
  • View Abstract

Abstract:Close

The longitudinal assessment of joint health is a long-standing issue in the management of musculoskeletal injuries. The acoustic emissions (AEs) produced by joint articulation could serve as a biomarker for joint health assessment, but their use has been limited by a lack of mechanistic understanding of their creation. In this paper, we investigate that mechanism using an injury model in human lower-limb cadavers, and relate AEs to joint kinematics. Using our custom joint sound recording system, we recorded the AEs from 9 cadaver legs in four stages: at baseline, after a sham surgery, after a meniscus tear, and post-meniscectomy. We compare the resulting AEs using their b-values. We then compare joint anatomy/kinematics to the AEs using the x-ray reconstruction of moving morphology (XROMM) technique. After the meniscus tear the number and amplitude of the AE peaks greatly increased from baseline and sham (b-value =1.33±0.15; p<0.05). The XROMM analysis showed a close correlation between the minimal inter-joint distances (0.251±0.082 cm during extension, 0.265±.003 during flexion, at 145°) and a large increase in the AEs. This work provides key insight into the nature of joint AEs, and details a novel technique and analysis for recording and interpreting these biosignals.

Article

Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data

by MABS Akhonda; Yuri Levin-Schwartz; Vince D Calhoun; Tülay Adali

2022

Subjects
  • Health Sciences, Public Health
  • Engineering, Electronics and Electrical
  • Computer Science
  • File Download
  • View Abstract

Abstract:Close

It is becoming increasingly common to collect multiple related neuroimaging datasets either from different modalities or from different tasks and conditions. In addition, we have non-imaging data such as cognitive or behavioral variables, and it is through the association of these two sets of data—neuroimaging and non-neuroimaging—that we can understand and explain the evolution of neural and cognitive processes, and predict outcomes for intervention and treatment. Multiple methods for the joint analysis or fusion of multiple neuroimaging datasets or modalities exist; however, methods for the joint analysis of imaging and non-imaging data are still in their infancy. Current approaches for identifying brain networks related to cognitive assessments are still largely based on simple one-to-one correlation analyses and do not use the cross information available across multiple datasets. This work proposes two approaches based on independent vector analysis (IVA) to jointly analyze the imaging datasets and behavioral variables such that multivariate relationships across imaging data and behavioral features can be identified. The simulation results show that our proposed methods provide better accuracy in identifying associations across imaging and behavioral components than current approaches. With functional magnetic resonance imaging (fMRI) task data collected from 138 healthy controls and 109 patients with schizophrenia, results reveal that the central executive network (CEN) estimated in multiple datasets shows a strong correlation with the behavioral variable that measures working memory, a result that is not identified by traditional approaches. Most of the identified fMRI maps also show significant differences in activations across healthy controls and patients potentially providing a useful signature of mental disorders.

Article

Disjoint Subspaces for Common and Distinct Component Analysis: Application to the Fusion of Multi-task FMRI Data

by MABS Akhonda; Ben Gabrielson; Suchita Bhinge; Vince D Calhoun; Tülay Adali

2021

Subjects
  • Computer Science
  • Engineering, Electronics and Electrical
  • Biology, Neuroscience
  • File Download
  • View Abstract

Abstract:Close

Background: Data-driven methods such as independent component analysis (ICA) makes very few assumptions on the data and the relationships of multiple datasets, and hence, are attractive for the fusion of medical imaging data. Two important extensions of ICA for multiset fusion are the joint ICA (jICA) and the multiset canonical correlation analysis and joint ICA (MCCA-jICA) techniques. Both approaches assume identical mixing matrices, emphasizing components that are common across the multiple datasets. However, in general, one would expect to have components that are both common across the datasets and distinct to each dataset. New method: We propose a general framework, disjoint subspace analysis using ICA (DS-ICA), which identifies and extracts not only the common but also the distinct components across multiple datasets. A key component of the method is the identification of these subspaces and their separation before subsequent analyses, which helps establish better model match and provides flexibility in algorithm and order choice. Comparison: We compare DS-ICA with jICA and MCCA-jICA through both simulations and application to multiset functional magnetic resonance imaging (fMRI) task data collected from healthy controls as well as patients with schizophrenia. Results: The results show DS-ICA estimates more components discriminative between healthy controls and patients than jICA and MCCA-jICA, and with higher discriminatory power showing activation differences in meaningful regions. When applied to a classification framework, components estimated by DS-ICA results in higher classification performance for different dataset combinations than the other two methods. Conclusion: These results demonstrate that DS-ICA is an effective method for fusion of multiple datasets.

Article

Domain adaptation for supervised integration of scRNA-seq data

by Yutong Sun; Peng Qiu

2023

Subjects
  • Engineering, Biomedical
  • Engineering, Electronics and Electrical
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Abstract:Close

Large-scale scRNA-seq studies typically generate data in batches, which often induce nontrivial batch effects that need to be corrected. Given the global efforts for building cell atlases and the increasing number of annotated scRNA-seq datasets accumulated, we propose a supervised strategy for scRNA-seq data integration called SIDA (Supervised Integration using Domain Adaptation), which uses the cell type annotations to guide the integration of diverse batches. The supervised strategy is based on domain adaptation that was initially proposed in the computer vision field. We demonstrate that SIDA is able to generate comprehensive reference datasets that lead to improved accuracy in automated cell type mapping analyses.

Article

Determining the Number of States in Dynamic Functional Connectivity Using Cluster Validity Indexes

by Victor M Vergara; Mustafa Salman; Anees Abrol; Flor A Espinoza; Vince D Calhoun

2020

Subjects
  • Engineering, Electronics and Electrical
  • Computer Science
  • Engineering, Biomedical
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Clustering analysis is employed in brain dynamic functional connectivity (dFC) to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several clustering validity index (CVI) methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined.
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