Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls.

OBJECTIVE: Electroconvulsive therapy (ECT) is effective for major depressive episodes. Understanding of underlying mechanisms has been increased by examining changes of brain connectivity but studies often do not correct for test-retest variability in healthy controls (HC). In this study, we investigated changes in resting-state networks after ECT in a multicenter study. METHODS: Functional resting-state magnetic resonance imaging data, acquired before start and within one week after ECT, from 90 depressed patients were analyzed, as well as longitudinal data of 24 HC. Group-information guided independent component analysis (GIG-ICA) was used to spatially restrict decomposition to twelve canonical resting-state networks. Selected networks of interest were the default mode network (DMN), salience network (SN), and left and right frontoparietal network (LFPN, and RFPN). Whole-brain voxel-wise analyses were used to assess group differences at baseline, group by time interactions, and correlations with treatment effectiveness. In addition, between-network connectivity and within-network strengths were computed. RESULTS: Within-network strength of the DMN was lower at baseline in ECT patients which increased after ECT compared to HC, after which no differences were detected. At baseline, ECT patients showed lower whole-brain voxel-wise DMN connectivity in the precuneus. Increase of within-network strength of the LFPN was correlated with treatment effectiveness. We did not find whole-brain voxel-wise or between-network changes. CONCLUSION: DMN within-network connectivity normalized after ECT. Within-network increase of the LFPN in ECT patients was correlated with higher treatment effectiveness. In contrast to earlier studies, we found no whole-brain voxel-wise changes, which highlights the necessity to account for test-retest effects.

Sparse Bayesian modeling of hierarchical independent component analysis: Reliable estimation of individual differences in brain networks.

Independent component analysis (ICA) is one of the leading approaches for studying brain functional networks. There is increasing interest in neuroscience studies to investigate individual differences in brain networks and their association with demographic characteristics and clinical outcomes. In this work, we develop a sparse Bayesian group hierarchical ICA model that offers significant improvements over existing ICA techniques for identifying covariate effects on the brain network. Specifically, we model the population-level ICA source signals for brain networks using a Dirichlet process mixture. To reliably capture individual differences on brain networks, we propose sparse estimation of the covariate effects in the hierarchical ICA model via a horseshoe prior. Through extensive simulation studies, we show that our approach performs considerably better in detecting covariate effects in comparison with the leading group ICA methods. We then perform an ICA decomposition of a between-subject meditation study. Our method is able to identify significant effects related to meditative practice in brain regions that are consistent with previous research into the default mode network, whereas other group ICA approaches find few to no effects.

BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks.

Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGB website at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.

FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation.

Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential of functional brain networks constructed from fMRI data for clinical predictions. Traditional functional brain networks, however, are noisy and unaware of downstream prediction tasks, while also incompatible with the deep graph neural network (GNN) models. In order to fully unleash the power of GNNs in network-based fMRI analysis, we develop FBNETGEN, a task-aware and interpretable fMRI analysis framework via deep brain network generation. In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks. Along with the process, the key novel component is the graph generator which learns to transform raw time-series features into task-oriented brain networks. Our learnable graphs also provide unique interpretations by highlighting prediction-related brain regions. Comprehensive experiments on two datasets, i.e., the recently released and currently largest publicly available fMRI dataset Adolescent Brain Cognitive Development (ABCD), and the widely-used fMRI dataset PNC, prove the superior effectiveness and interpretability of FBNETGEN. The implementation is available at https://github.com/Wayfear/FBNETGEN.

How tufted capuchin monkeys (Sapajus spp.) and humans (Homo sapiens) handle a jointed tool.

The embodied theory of tooling predicts that when using a grasped object as a tool, individuals accommodate their actions to manage the altered degrees of freedom in the body-plus-object system. We tested predictions from this theory by studying how 3 tufted capuchin monkeys (Sapajus spp.) and 6 humans (Homo sapiens) used a hoe to retrieve a token. The hoe's handle was rigid, had 2 segments with 1 planar joint, or had 3 segments with 2 (orthogonal) planar joints. When jointed, rotating the handle could render it rigid. The monkeys used more actions to retrieve the token when the handle had 1 joint than when it had no joints or 2 joints. They did not use exploratory actions frequently nor in a directed manner in any condition. Although they sometimes rotated the handle of the hoe, they did not make it rigid. In a follow-up study, we explored whether humans would rotate the handle to use a 2-jointed hoe in a conventional manner, as predicted both by the embodied theory and theories of functional fixedness in humans. Two people rotated the handle to use the hoe conventionally, but 4 people did not; instead, they used the hoe as it was presented, as did the monkeys. These results confirm some predictions but also highlight shortcomings of the embodied theory with respect to specifying the consequences of adding multiple degrees of freedom. The study of species' perceptual sensitivity to jointed object's inertial properties could help to refine the embodied theory of tooling. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Association of Blood Donor Sex and Age With Outcomes in Very Low-Birth-Weight Infants Receiving Blood Transfusion.

Importance: There are conflicting data on the association between blood donor characteristics and outcomes among patients receiving transfusions. Objective: To evaluate the association of blood donor sex and age with mortality or serious morbidity in very low-birth-weight (VLBW) infants receiving blood transfusions. Design, Setting, and Participants: This is a cohort study using data collected from 3 hospitals in Atlanta, Georgia. VLBW infants (≤1500 g) who received red blood cell (RBC) transfusion from exclusively male or female donors were enrolled from January 2010 to February 2014. Infants received follow-up until 90 days, hospital discharge, transfer to a non-study-affiliated hospital, or death. Data analysis was performed from July 2019 to December 2020. Exposures: Donor sex and mean donor age. Main Outcomes and Measures: The primary outcome was a composite outcome of death, necrotizing enterocolitis (Bell stage II or higher), retinopathy of prematurity (stage III or higher), or moderate-to-severe bronchopulmonary dysplasia. Modified Poisson regression, with consideration of covariate interactions, was used to estimate the association between donor sex and age with the primary outcome, with adjustment for the total number of transfusions and birth weight. Results: In total, 181 infants were evaluated, with a mean (SD) birth weight of 919 (253) g and mean (SD) gestational age of 27.0 (2.2) weeks; 56 infants (31%) received RBC transfusion from exclusively female donors. The mean (SD) donor age was 46.6 (13.7) years. The primary outcome incidence was 21% (12 of 56 infants) among infants receiving RBCs from exclusively female donors, compared with 45% (56 of 125 infants) among those receiving RBCs from exclusively male donors. Significant interactions were detected between female donor and donor age (P for interaction = .005) and between female donor and number of transfusions (P for interaction < .001). For the typical infant, who received a median (interquartile range) of 2 (1-3) transfusions, RBC transfusion from exclusively female donors, compared with male donors, was associated with a lower risk of the primary outcome (relative risk, 0.29; 95% CI, 0.16-0.54). The protective association between RBC transfusions from female donors, compared with male donors, and the primary outcome increased as the donor age increased, but decreased as the number of transfusions increased. Conclusions and Relevance: These findings suggest that RBC transfusion from female donors, particularly older female donors, is associated with a lower risk of death or serious morbidity in VLBW infants receiving transfusion. Larger studies confirming these findings and examining potential mechanisms are warranted.

Bayesian Joint Modeling of Multiple Brain Functional Networks.

Investigating the similarity and changes in brain networks under different mental conditions has become increasingly important in neuroscience research. A standard separate estimation strategy fails to pool information across networks and hence has reduced estimation accuracy and power to detect between-network differences. Motivated by a fMRI Stroop task experiment that involves multiple related tasks, we develop an integrative Bayesian approach for jointly modeling multiple brain networks that provides a systematic inferential framework for network comparisons. The proposed approach explicitly models shared and differential patterns via flexible Dirichlet process-based priors on edge probabilities. Conditional on edges, the connection strengths are modeled via Bayesian spike and slab prior on the precision matrix off-diagonals. Numerical simulations illustrate that the proposed approach has increased power to detect true differential edges while providing adequate control on false positives and achieves greater network estimation accuracy compared to existing methods. The Stroop task data analysis reveals greater connectivity differences between task and fixation that are concentrated in brain regions previously identified as differentially activated in Stroop task, and more nuanced connectivity differences between exertion and relaxed task. In contrast, penalized modeling approaches involving computationally burdensome permutation tests reveal negligible network differences between conditions that seem biologically implausible.

Feasibility of Auricular Field Stimulation in Fibromyalgia: Evaluation by Functional Magnetic Resonance Imaging, Randomized Trial.

OBJECTIVE: To evaluate the feasibility of recruitment, preliminary efficacy, and acceptability of auricular percutaneous electrical nerve field stimulation (PENFS) for the treatment of fibromyalgia in veterans, using neuroimaging as an outcome measure and a biomarker of treatment response. DESIGN: Randomized, controlled, single-blind. SETTING: Government hospital. SUBJECTS: Twenty-one veterans with fibromyalgia were randomized to standard therapy (ST) control or ST with auricular PENFS treatment. METHODS: Participants received weekly visits with a pain practitioner over 4 weeks. The PENFS group received reapplication of PENFS at each weekly visit. Resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) data were collected within 2 weeks prior to initiating treatment and 2 weeks following the final treatment. Analysis of rs-fcMRI used a right posterior insula seed. Pain and function were assessed at baseline and at 2, 6, and 12 weeks post-treatment. RESULTS: At 12 weeks post-treatment, there was a nonsignificant trend toward improved pain scores and significant improvements in pain interference with sleep among the PENFS treatment group as compared with the ST controls. Neuroimaging data displayed increased connectivity to areas of the cerebellum and executive control networks in the PENFS group as compared with the ST control group following treatment. CONCLUSIONS: There was a trend toward improved pain and function among veterans with fibromyalgia in the ST + PENFS group as compared with the ST control group. Pain and functional outcomes correlated with altered rs-fcMRI network connectivity. Neuroimaging results differed between groups, suggesting an alternative underlying mechanism for PENFS analgesia.

HINT: A hierarchical independent component analysis toolbox for investigating brain functional networks using neuroimaging data.

BACKGROUND: Independent component analysis (ICA) is a popular tool for investigating brain organization in neuroscience research. In fMRI studies, an important goal is to study how brain networks are modulated by subjects' clinical and demographic variables. Existing ICA methods and toolboxes don't incorporate subjects' covariates effects in ICA estimation of brain networks, which potentially leads to loss in accuracy and statistical power in detecting brain network differences between subjects' groups. NEW METHOD: We introduce a Matlab toolbox, HINT (Hierarchical INdependent component analysis Toolbox), that provides a hierarchical covariate-adjusted ICA (hc-ICA) for modeling and testing covariate effects and generates model-based estimates of brain networks on both the population- and individual-level. HINT provides a user-friendly Matlab GUI that allows users to easily load images, specify covariate effects, monitor model estimation via an EM algorithm, specify hypothesis tests, and visualize results. HINT also has a command line interface which allows users to conveniently run and reproduce the analysis with a script. COMPARISON TO EXISTING METHODS: HINT implements a new multi-level probabilistic ICA model for group ICA. It provides a statistically principled ICA modeling framework for investigating covariate effects on brain networks. HINT can also generate and visualize model-based network estimates for user-specified subject groups, which greatly facilitates group comparisons. RESULTS: We demonstrate the steps and functionality of HINT with an fMRI example data to estimate treatment effects on brain networks while controlling for other covariates. Results demonstrate estimated brain networks and model-based comparisons between the treatment and control groups. In comparisons using synthetic fMRI data, HINT shows desirable statistical power in detecting group differences in networks especially in small sample sizes, while maintaining a low false positive rate. HINT also demonstrates similar or increased accuracy in reconstructing both population- and individual-level source signal maps as compared to some state-of-the-art group ICA methods. CONCLUSION: HINT can provide a useful tool for both statistical and neuroscience researchers to evaluate and test differences in brain networks between subject groups.

A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer's Disease Data.

There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility.

Vitamin D for the Immune System in Cystic Fibrosis (DISC): a double-blind, multicenter, randomized, placebo-controlled clinical trial.

BACKGROUND: Patients with cystic fibrosis (CF) have increased risk of vitamin D deficiency owing to fat malabsorption and other factors. Vitamin D deficiency has been associated with increased risk of pulmonary exacerbations of CF. OBJECTIVES: The primary objective of this study was to examine the impact of a single high-dose bolus of vitamin D3 followed by maintenance treatment given to adults with CF during an acute pulmonary exacerbation on future recurrence of pulmonary exacerbations. METHODS: This was a multicenter, double-blind, placebo-controlled, intent-to-treat clinical trial. Subjects with CF were randomly assigned to oral vitamin D3 given as a single dose of 250,000 International Units (IU) or to placebo within 72 h of hospital admission for an acute pulmonary exacerbation, followed by 50,000 IU of vitamin D3 or an identically matched placebo pill taken orally every other week starting at 3 mo after random assignment. The primary outcome was the composite endpoint of the time to next pulmonary exacerbation or death within 1 y. The secondary outcomes included circulating concentrations of the antimicrobial peptide cathelicidin and recovery of lung function as assessed by the percentage of predicted forced expiratory volume in 1 s (FEV1%). RESULTS: A total of 91 subjects were enrolled in the study. There were no differences between the vitamin D3 and placebo groups in time to next pulmonary exacerbation or death at 1 y. In addition, there were no differences in serial recovery of lung function after pulmonary exacerbation by FEV1% or in serial concentrations of plasma cathelicidin. CONCLUSIONS: Vitamin D3 initially given at the time of pulmonary exacerbation of CF did not alter the time to the next pulmonary exacerbation, 12-mo mortality, serial lung function, or serial plasma cathelicidin concentrations. This trial was registered at clinicaltrials.gov as NCT01426256.