Visualizing temporal brain-state changes for fMRI using t-distributed stochastic neighbor embedding.

Currently, functional magnetic resonance imaging (fMRI) is the most commonly used technique for obtaining dynamic information about the brain. However, because of the complexity of the data, it is often difficult to directly visualize the temporal aspect of the fMRI data. We outline a -distributed stochastic neighbor embedding (t-SNE)-based postprocessing technique that can be used for visualization of temporal information from a 4D fMRI data.

Long short-term memory recurrent neural network for pharmacokinetic-pharmacodynamic modeling.

Objective: Recurrent neural network (RNN) has been demonstrated as a powerful tool for analyzing various types of time series data. There is limited knowledge about the application of the RNN model in the area of pharmacokinetic (PK) and pharmacodynamic (PD) analysis. In this paper, a specific variation of RNN, long short-term memory (LSTM) network, is presented to analyze the simulated PK/PD data of a hypothetical drug.

Spatiotemporal feature extraction and classification of Alzheimer's disease using deep learning 3D-CNN for fMRI data.

Through the last three decades, functional magnetic resonance imaging (fMRI) has provided immense quantities of information about the dynamics of the brain, functional brain mapping, and resting-state brain networks. Despite providing such rich functional information, fMRI is still not a commonly used clinical technique due to inaccuracy involved in analysis of extremely noisy data. However, ongoing developments in deep learning techniques suggest potential improvements and better performance in many different domains.

Time-varying whole-brain functional network connectivity coupled to task engagement.

Brain functional connectivity (FC), as measured by blood oxygenation level-dependent (BOLD) signal, fluctuates at the scale of 10s of seconds. It has recently been found that whole-brain dynamic FC (dFC) patterns contain sufficient information to permit identification of ongoing tasks. Here, we hypothesize that dFC patterns carry fine-grained information that allows for tracking short-term task engagement levels (i.

Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information.

Given the dynamic nature of the human brain, there has been an increasing interest in investigating short-term temporal changes in functional connectivity, also known as dynamic functional connectivity (dFC), i.e., the time-varying inter-regional statistical dependence of blood oxygenation level-dependent (BOLD) signal within the constraints of a single scan.

Effects of resistance training on MRI-derived epicardial fat volume and arterial stiffness in women with obesity: a randomized pilot study.

Aim: To date, few studies have analyzed the effects of exercise on cardiac adipose tissue. Overall, exercise programs did not meet the recommendations for significant weight loss, the utilization of resistance training was minimal, and the conclusions derived from these studies have diminished exercise as a strategy for cardiac fat loss.

Purpose: The objective of this pilot study was to analyze the effects of 3-week high-intensity, moderate-volume muscular endurance resistance training (RT) on cardiac fat and arterial stiffness.

Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study.

Functional connectivity (FC) has been widely used to study the functional organization of temporally correlated and spatially distributed brain regions. Recent studies of FC dynamics, quantified by windowed correlations, provide new insights to analyze dynamic, context-dependent reconfiguration of brain networks. A set of reoccurring whole-brain connectivity patterns at rest, referred to as FC states, have been identified, hypothetically reflecting underlying cognitive processes or mental states.

The role of MRI in understanding the underlying mechanisms in obesity associated diseases.

Obesity and its possible association with diseases including diabetes and cardiovascular diseases have been studied for decades for its impact on healthcare. Recent studies clearly indicate the need for developing accurate and reproducible methodologies for assessing body fat content and distribution. Body fat distribution plays a significant role in developing an insight in the underlying mechanisms in which adipose tissue is linked with various diseases.

Structure and Transport Properties of the BiCuSeO-BiCuSO Solid Solution.

In this paper, we report on the crystal structure and the electrical and thermal transport properties of the BiCuSeSO series. From the evolution of the structural parameters with the substitution rate, we can confidently conclude that a complete solid solution exists between the BiCuSeO and BiCuSO end members, without any miscibility gap. However, the decrease of the stability of the materials when increasing the sulfur fraction, with a simultaneous volatilization, makes it difficult to obtain S-rich samples in a single phase.

Toward the improvement of image-guided interventions for minimally invasive surgery: three factors that affect performance.

Objectives: The objectives were to measure the impact of specific features of imaging devices on tasks relevant to minimally invasive surgery (MIS) and to investigate cognitive and perceptual factors in such tasks.

Background: Although image-guided interventions used in MIS provide benefits for patients, they pose drawbacks for surgeons, including degraded depth perception and reduced field of view (FOV). It is important to identify design factors that affect performance.

Preparing digitized cervigrams for colposcopy research and education: determination of optimal resolution and compression parameters.

Objective: Visual assessment of digitized cervigrams through the Internet needs to be optimized. The National Cancer Institute and National Library of Medicine are involved in a large effort to improve colposcopic assessment and, in preparation, are conducting methodologic research.

Materials And Methods: We selected 50 cervigrams with diagnoses ranging from normal to cervical intraepithelial neoplasia 3 or invasive cancer.

Digitized cervical images: problems, solutions, and potential medical impact.

Objective: To demonstrate compression, illumination enhancement, registration, segmentation, automated classification and steganography using digitized cervical images.

Materials And Methods: The Hybrid Multi-Scale Vector Quantization algorithm developed at Texas Technological University and other automated systems were used to improve digitized cervical images.

Results: We demonstrated high levels of image compression, illumination enhancement, registration, automated segmentation and classification and steganography of digitized cervical images.

Digital stereo image analyzer for generating automated 3-D measures of optic disc deformation in glaucoma.

The major limitations of precise evaluation of retinal structures in present clinical situations are the lack of standardization, the inherent subjectivity involved in the interpretation of retinal images, and intra- as well as interobserver variability. While evaluating optic disc deformation in glaucoma, these limitations could be overcome by using advanced digital image analysis techniques to generate precise metrics from stereo optic disc image pairs. A digital stereovision system for visualizing the topography of the optic nerve head from stereo optic disc images is presented.