Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks. Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks, we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder (PTSD).
Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical "template" ICA model where source signals—including known population brain networks and subject-specific signals—are represented as latent variables. For estimation, we derive an expectation–maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75–250% higher intra-subject reliability. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Fibromyalgia is a chronic pain syndrome that presents with a constellation of broad symptoms, including decreased physical function, fatigue, cognitive disturbances, and other somatic complaints. Available therapies are often insufficient in treating symptoms, with inadequate pain control commonly leading to opioid usage for attempted management. Cranial electrical stimulation (CES) is a promising non-pharmacologic treatment option for pain conditions that uses pulsed electrical current stimulation to modify brain function via transcutaneous electrodes. These neural mechanisms and the applications of CES in fibromyalgia symptom relief require further exploration. A total of 50 participants from the Atlanta Veterans Affairs Healthcare System (VAHCS) diagnosed with fibromyalgia were enrolled and then block-randomized into either a placebo plus standard therapy or active CES plus standard therapy group. Baseline assessments were obtained prior to the start of treatment. Both interventions occurred over 12 weeks, and participants were assessed at 6 weeks and 12 weeks after treatment initiation. The primary outcome investigated whether pain and functional improvements occur with the application of CES. Additionally, baseline and follow-up resting state functional connectivity magnetic resonance imaging (rs-fcMRI) were obtained at the 6-week and 12-week time points to assess for clinical applications of neural connectivity biomarkers and the underlying neural associations related to treatment effects. This is a randomized, placebo-controlled trial to determine the efficacy of CES for improving pain and function in fibromyalgia and further develop rs-fcMRI as a clinical tool to assess the neural correlates and mechanisms of chronic pain and analgesic response.
Abstract Collecting neuroimaging data in the form of tensors (i.e. multidimensional arrays) has become more common in mental health studies, driven by an increasing interest in studying the associations between neuroimaging phenotypes and clinical disease manifestation. Motivated by a neuroimaging study of post-traumatic stress disorder (PTSD) from the Grady Trauma Project, we study a tensor response quantile regression framework, which enables novel analyses that confer a detailed view of the potentially heterogeneous association between a neuroimaging phenotype and relevant clinical predictors. We adopt a sensible low-rank structure to represent the association of interest, and propose a simple two-step estimation procedure which is easy to implement with existing software. We provide rigorous theoretical justifications for the intuitive two-step procedure. Simulation studies demonstrate good performance of the proposed method with realistic sample sizes in neuroimaging studies. We conduct the proposed tensor response quantile regression analysis of the motivating PTSD study to investigate the association between fMRI resting-state functional connectivity and PTSD symptom severity. Our results uncover non-homogeneous effects of PTSD symptoms on brain functional connectivity, which cannot be captured by existing tensor response methods.
Recent neuroimaging studies have highlighted the importance of network-centric brain analysis, particularly with functional magnetic resonance imaging. The emergence of Deep Neural Networks has fostered a substantial interest in predicting clinical outcomes and categorizing individuals based on brain networks. However, the conventional approach involving static brain network analysis offers limited potential in capturing the dynamism of brain function. Although recent studies have attempted to harness dynamic brain networks, their high dimensionality and complexity present substantial challenges. This paper proposes a novel methodology, Dynamic bRAin Transformer (DART), which combines static and dynamic brain networks for more effective and nuanced brain function analysis. Our model uses the static brain network as a baseline, integrating dynamic brain networks to enhance performance against traditional methods. We innovatively employ attention mechanisms, enhancing model explainability and exploiting the dynamic brain network's temporal variations. The proposed approach offers a robust solution to the low signal-to-noise ratio of blood-oxygen-level-dependent signals, a recurring issue in direct DNN modeling. It also provides valuable insights into which brain circuits or dynamic networks contribute more to final predictions. As such, DRAT shows a promising direction in neuroimaging studies, contributing to the comprehensive understanding of brain organization and the role of neural circuits.
Familial hypercholesterolemia (FH) is a genetic disease with very high levels of circulating low density lipoprotein cholesterol (LDL-C) levels that leads to accelerated atherosclerosis. Lipoprotein apheresis is an effective treatment option for patients with FH and results in reduced cardiovascular morbidity and mortality. Circulating progenitor cells (CPCs) are markers of overall vascular health and diminished levels have been associated with decreased reparative potential and worse outcomes. We assessed the short-term change in CPC levels following a single lipoprotein apheresis session in FH patients who are already on stable lipoprotein apheresis therapy. We hypothesized that in addition to a reduction in atherogenic lipids, the cardiovascular benefit from lipoprotein apheresis therapy is mediated by enhanced vascular reparative capacity through mobilization of CPCs.Eight FH patients (1 homozygous and 7 heterozygous) on stable lipoprotein apheresis therapy for at least three months had CPCs measured at baseline (prior to apheresis) and two hours after apheresis. Results were compared with data from age-matched hyperlipidemic (HLP) patients on statin therapy and healthy volunteers.FH patients had higher baseline circulating levels of CD34+/CD133+ and CD34+/CD133+/CXCR4+ cells compared to HLP and healthy subjects. There was no significant change in CPCs after apheresis in FH patients.FH patients had higher CPC counts at baseline compared to age-matched HLP and healthy controls, suggesting activation of reparative mechanism in this high risk population. Larger studies are needed to better characterize differences in CPC counts between FH subjects and HLP patients over time.
Abstract Due to the performance overhead of virtual machines, traditional virtualization solutions are generally not suitable for artificial intelligence applications. Container-based virtualization technology represented by Docker can provide a customized environment, good portability, good compatibility and low overhead. In this article, we give a performance comparison among Docker, Singularity, and Charliecloud containers. In particular, CPU and GPU are used in each container to run a real application of artificial intelligence for the performance comparison. Finally, the experimental results are given and discussed.
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