Multiband acquisition, also called simultaneous multislice, has become a popular technique in resting-state functional connectivity studies. (MB) acceleration leads to higher temporal resolution but spatially heterogeneous noise amplification, suggesting the costs may be greater areas such as subcortex. We evaluate MB factors of 2, 3, 4, 6, 8, 9, and 12 with 2 mm isotropic voxels, additionally 3.3 single-band acquisitions, on 32-channel head coil. Noise amplification was deeper brain...

We propose a novel deep neural network methodology for density estimation on product Riemannian manifold domains. In our approach, the directly parameterizes unknown function and is trained using penalized maximum likelihood framework, with penalty term formed differential operators. The architecture algorithm are carefully designed to handle challenges of high-dimensional domains, effectively mitigating curse dimensionality that limits traditional kernel basis expansion estimators, as well...

Hi-C technology has been developed to profile genome-wide chromosome conformation. So far data have generated from a large compendium of different cell types and tissue types. Among chromatin conformation units, loops were found play key role in gene regulation across While many loop calling algorithms developed, most callers identified shared as opposed cell-type-specific loops. We propose SSSHiC, new algorithm based on significance scale space, which can be used understand at levels...

This article focuses on the problem of studying shared- and individual-specific structure in replicated networks or graph-valued data. In particular, observed data consist $n$ graphs, $G_{i},i=1,\ldots,n$, with each graph consisting a collection edges between $V$ nodes. brain connectomics, for an individual corresponds to set interconnections among regions. Such can be organized as $V\times V$ binary adjacency matrix $A_{i}$ $i$, ones indicating edge pair nodes zeros no edge. When have...

There is an increasing interest in learning a set of small outcome-relevant subgraphs network-predictor regression. The extracted signal can greatly improve the interpretation association between network predictor and response. In brain connectomics, for individual corresponds to interconnections among regions there strong linking connectome human cognitive traits. Modern neuroimaging technology allows very fine segmentation brain, producing large structural networks. Therefore, accurate...

Adaptation capacity is critical for maintaining cognition, yet it understudied in groups at risk dementia. Autonomic nervous system (ANS) neurovisceral integration and a key contributor to adaptation capacity. To determine the central system's top-down regulation of ANS, we conducted mechanistic randomized controlled trial study, using 6-week processing speed attention (PS/A)-targeted intervention. Eighty-four older adults with amnestic mild cognitive impairment (aMCI) were PS/A-targeted...

We present a Riemannian framework for analyzing signals and images in manner that is invariant to their level of blurriness, under Gaussian blurring. Using well known relation between blurring the heat equation, we establish an action group on image space define orthogonal section this represent compare at same blur level. This comparison based geodesic distances manifold which, turn, are computed using path-straightening algorithm. The actual implementations use coefficients truncated...

Abstract A major challenge in the cognitive training field is inducing broad, far‐transfer effects. Thus far, little known about neural mechanisms underlying broad Here, we tested a set of competitive hypotheses regarding role brain integration versus segregation effect. We retrospectively analyzed data from randomized controlled trial comparing neurocognitive effects vision‐based speed processing (VSOP) and an active control consisting mental leisure activities (MLA) older adults with MCI....

Statistical classification of actions in videos is mostly performed by extracting relevant features, particularly covariance from image frames and studying time series associated with temporal evolutions these features. A natural mathematical representation activity form parameterized trajectories on the manifold, i.e. set symmetric, positive-definite matrices (SPDMs). The variable execution-rates implies parameterizations resulting trajectories, complicates their classification. Since...

Effective learning in old age, particularly those at risk for dementia, is essential prolonging independent living. Individual variability learning, however, remarkable; that is, months of cognitive training to improve may be beneficial some individuals but not others. So far, little known about which neurophysiological mechanisms account the observed induced by older adults. By combining Lövdén et al.'s (2010, A theoretical framework study adult plasticity. Psychological Bulletin, 136,...

Abstract There has been increasing interest in jointly studying structural connectivity (SC) and functional (FC) derived from diffusion MRI. Previous connectome integration studies almost exclusively required predefined atlases. However, there are many potential atlases to choose this choice heavily affects all subsequent analyses. To avoid such an arbitrary choice, we propose a novel atlas‐free approach, named Surface‐Based Connectivity Integration (SBCI), more accurately study the...

This article develops a novel spatial quantile function-on-scalar regression model, which studies the conditional distribution of high-dimensional functional response given scalar predictors. With strength both and copula modeling, we are able to explicitly characterize or image on whole domain. Our method provides comprehensive understanding effect covariates responses across different levels also gives practical way generate new images for covariate values. Theoretically, establish minimax...

We consider a single‐server constant retrial queueing system with Poisson arrival process and exponential service times, in which the server may break down when it is working. The lifetime of assumed to be exponentially distributed once breaks down, will sent for repair immediately time also distributed. There no waiting space front arriving customers decide whether enter orbit or balk depending on available information they get upon arrival. In paper, Nash equilibrium analysis customers’...

The problems of analysis and modeling spherical trajectories, that is, continuous longitudinal data on S2, are important in several disciplines. These challenging for two reasons: (1) nonlinear geometry S2 (2) the presence phase variability given data. This article develops a geometric framework separating from leaving only shape or amplitude variability. key idea is to represent each trajectory with pair variables, starting point, transported square-root velocity curve (TSRVC), tangent...

Structural brain networks constructed from diffusion MRI are important biomarkers for understanding human structure and its relation to cognitive functioning. There is increasing interest in learning differences structural between groups of subjects neuroimaging studies, leading a variable selection problem network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized select edges...

Effective cognitive training must improve cognition beyond the trained domain (show a transfer effect) and be applicable to dementia-risk populations, e.g., amnesic mild impairment (aMCI). Theories suggest should target processes that 1) show robust engagement, 2) are domain-general, 3) reflect long-lasting changes in brain organization. Brain regions connect many different networks (i.e., high participation coefficient; PC) known support integration. This capacity is relatively preserved...