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

Purpose: 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. Approach: We outline a t -distributed stochastic neighbor embedding (t-SNE)-based postprocessing technique that can be used for visualization of temporal information from a 4D fMRI data. Apart from visualization, we also show its utility in detection of major changes in the brain meta-states during the scan duration. Results: The t-SNE approach is able to detect brain-state changes from task to rest and vice versa for single- and multitask fMRI data. A temporal visualization can also be obtained for task and resting state fMRI data for assessing the temporal dynamics during the scan duration. Additionally, hemodynamic delay can be quantified by comparison of the detected brain-state changes with the experiment paradigm for task fMRI data. Conclusion: The t-SNE visualization can visualize help identify major brain-state changes from fMRI data. Such visualization can provide information about the degree of involvement and attentiveness of the subject during the scan and can be potentially utilized as a quality control for subject's performance during the scan.

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. MATERIALS AND METHODS: The plasma concentration and effect level under one dosing regimen were used to train the LSTM model. The developed LSTM model was used to predict the individual PK/PD data under other dosing regimens. RESULTS: The optimized LSTM model captured temporal dependencies and predicted PD profiles accurately for the simulated indirect PK-PD relationship. CONCLUSION: The results demonstrated that the generic LSTM model can approximate the complex underlying mechanistic biological processes.

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

Purpose: 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. Our main purpose is to utilize the potentials of deep learning techniques for fMRI data for clinical use. Approach: We present one such synergy of fMRI and deep learning, where we apply a simplified yet accurate method using a modified 3D convolutional neural networks (CNN) to resting-state fMRI data for feature extraction and classification of Alzheimer's disease (AD). The CNN is designed in such a way that it uses the fMRI data with much less preprocessing, preserving both spatial and temporal information. Results: Once trained, the network is successfully able to classify between fMRI data from healthy controls and AD subjects, including subjects in the mild cognitive impairment (MCI) stage. We have also extracted spatiotemporal features useful for classification. Conclusion: This CNN can detect and differentiate between the earlier and later stages of MCI and AD and hence, it may have potential clinical applications in both early detection and better diagnosis of Alzheimer's disease.

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.e., 10s of seconds long). To test this hypothesis, 25 subjects were scanned continuously for 25 min while they performed and transitioned between four different tasks: working memory, visual attention, math, and rest. First, we estimated dFC patterns by using a sliding window approach. Next, we extracted two engagement-specific FC patterns representing active engagement and passive engagement by using k-means clustering. Then, we derived three metrics from whole-brain dFC patterns to track engagement level, that is, dissimilarity between dFC patterns and engagement-specific FC patterns, and the level of brainwide integration level. Finally, those engagement markers were evaluated against windowed task performance by using a linear mixed effects model. Significant relationships were observed between abovementioned metrics and windowed task performance for the working memory task only. These findings partially confirm our hypothesis and underscore the potential of whole-brain dFC to track short-term task engagement levels.

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. Numerous methodologies have been proposed to characterize dFC during rest and task, but few studies have compared them in terms of their efficacy to capture behavioral and clinically relevant dynamics. This is mostly due to lack of a well-defined ground truth, especially for rest scans. In this study, with a multitask dataset (rest, memory, video, and math) serving as ground truth, we investigated the efficacy of several dFC estimation techniques at capturing cognitively relevant dFC modulation induced by external tasks. We evaluated two framewise methods (dFC estimates for a single time point): dynamic conditional correlation (DCC) and jackknife correlation (JC); and five window-based methods: sliding window correlation (SWC), sliding window correlation with L1-regularization (SWC_L1), a combination of DCC and SWC called moving average DCC (DCC_MA), multiplication of temporal derivatives (MTD), and a variant of jackknife correlation called delete-d jackknife correlation (dJC). The efficacy is defined as each dFC metric's ability to successfully subdivide multitask scans into cognitively homogenous segments (even if those segments are not temporally continuous). We found that all window-based dFC methods performed well for commonly used window lengths (WL ≥ 30sec), with sliding window methods (SWC, SWC_L1) as well as the hybrid DCC_MA approach performing slightly better. For shorter window lengths (WL ≤ 15sec), DCC_MA and dJC produced the best results. Neither framewise method (i.e., DCC and JC) led to dFC estimates with high accuracy.

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. METHODS: A total of 11 young females with obesity, BMI = 34.13 (± 3.16) kg/m2 (n = 5 control, n = 6 intervention) completed the study. Absolute strength was assessed using one repetition maximum test (1RM) for bench press (BP) and leg press (LP), and relative strength was calculated using body weight (BW) as BP-to-BW and LP-to-BW ratio. Magnetic resonance was used to quantify epicardial and paracardial adipose tissue (EAT and PAT) volume, and applanation tonometry was used to assess arterial stiffness by estimating pulse wave velocity (PWV). RESULTS: EAT and PAT volumes (ml) showed significant interaction effects (p = 0.037 and p = 0.031), and very large changes (d > 1) of EAT (p = 0.006) and PAT (p = 0.036) in the intervention group. In addition, strength was significantly improved, including BP (p = 0.003), LP (p = 0.001), BP-to-BW ratio (p = 0.001), and LP-to-BW ratio (p = 0.002), while no changes were found in PWV. CONCLUSIONS: High-intensity, moderate-volume RT, designed to enhance muscular endurance following the recommendations reduces EAT and PAT volumes, improves physical fitness in females with obesity, and has no negative effects on 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. We posit that the mean FC information for a given subject represents a significant contribution to the group-level FC dynamics. We show that the subject-specific FC profile, termed as FC individuality, can be removed to increase sensitivity to cognitively relevant FC states. To assess the impact of the FC individuality and task-specific FC modulation on the group-level FC dynamics analysis, we generate and analyze group studies of four subjects engaging in four cognitive conditions (rest, simple math, two-back memory, and visual attention task). We also propose a model to quantitatively evaluate the effect of two factors, namely, subject-specific and task-specific modulation on FC dynamics. We show that FC individuality is a predominant factor in group-level FC variability, and the embedded cognitively relevant FC states are clearly visible after removing the individual's connectivity profile. Our results challenge the current understanding of FC states and emphasize the importance of individual heterogeneity in connectivity dynamics analysis.

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. Among imaging technologies including computerized axial tomography (CAT or CT), magnetic resonance imaging (MRI), and magnetic resonance spectroscopy (MRS), MRI and MRS seem to be the best emerging techniques and together are being considered as the gold standard for body fat content and distribution. This paper reviews studies up to the present time involving different methodologies of these two emerging technologies and presents the basic concepts of MRI and MRS with required novel image analysis techniques in accurate, quantitative, and direct assessment of body fat content and distribution. This article is part of a Special Issue entitled: Oxidative Stress and Mitochondrial Quality in Diabetes/Obesity and Critical Illness Spectrum of Diseases - edited by P. Hemachandra Reddy.

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 BiCuSe1-xSxO 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. The band gap of the materials linearly increases between 0.8 eV for BiCuSeO and 1.1 eV in BiCuSO, and the covalent character of the Cu-Ch (Ch = chalcogen element, namely S or Se here) bond slightly decreases when increasing the sulfur fraction. The thermal conductivity of the end members is nearly the same, but a significant decrease is observed for the samples belonging to the solid solution, which can be explained by point defect scattering due to atomic mass and radii fluctuations between Se and S. When increasing the sulfur fraction, the electrical resistivity of the samples strongly increases, which could be linked to an evolution of the energy of formation of copper vacancies, which act as acceptor dopants in these materials.

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. METHOD: In two navigation experiments, observers fed a borescope through an object until it reached a target. Task completion time and object shape judgments were measured. In a motion perception experiment, observers reported the direction of a line that moved behind an aperture. A motion illusion associated with reduced FOV was measured. RESULTS: Navigation through an object was faster when a preview of the object's exterior was provided. Judgments about the object's shape were more accurate with a preview (compared with none) and with active viewing (compared with passive viewing). The motion illusion decreased with a rectangular or rotating octagonal viewing aperture (compared with circular). CONCLUSIONS: Navigation performance may be enhanced when surgeons develop a mental model of the surgical environment, when surgeons (rather than assistants) control the camera, and when the shape of the image is designed to reduce visual illusions. APPLICATION: Unintentional contact between surgical tools and healthy tissues may be reduced during MIS when (a) visual aids permit surgeons to maintain a mental model of the surgical environment, (b) images are bound by noncircular apertures, and (c) surgeons manually control the camera.

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. CONCLUSIONS: Digitized cervical images can be altered to facilitate research of cervical neoplasia.

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. Those pictures were scanned at 5 resolution levels from 1,550 to 4,000 dots per inch (dpi) and were presented to 4 expert colposcopists to assess image quality. After the ideal resolution level was determined, pictures were compressed at 7 compression ratios from 20:1 to 80:1 to determine the optimal level of compression that permitted full assessment of key visual details. RESULTS: There were no statistically significant differences between the 3,000 and 4,000 dpi pictures. At 2,000 dpi resolution, only one colposcopist found a slightly statistically significant difference (p = 0.02) compared with the gold standard. There was a clear loss of quality of the pictures at 1,660 dpi. At compression ratio 60:1, 3 of 4 evaluators found statistically significant differences when comparing against the gold standard. CONCLUSIONS: Our results suggest that 2,000 dpi is the optimal level for digitizing cervigrams, and the optimal compression ratio is 50:1 using a novel wavelet-based technology. At these parameters, pictures have no significant differences with the gold standard.

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. We have developed an algorithm, combining power cepstrum and zero-mean-normalized cross correlation techniques, which extracts depth information using coarse-to-fine disparity between corresponding windows in a stereo pair. The gray level encoded sparse disparity matrix is subjected to a cubic B-spline operation to generate smooth representations of the optic cup/disc surfaces and new three-dimensional (3-D) metrics from isodisparity contours. Despite the challenges involved in 3-D surface recovery, the robustness of our algorithm in finding disparities within the constraints used has been validated using stereo pairs with known disparities. In a preliminary longitudinal study of glaucoma patients, a strong correlation is found between the computer-generated quantitative cup/disc volume metrics and manual metrics commonly used in a clinic. The computer generated new metrics, however, eliminate the subjective variability and greatly reduce the time and cost involved in manual metric generation in follow-up studies of glaucoma.