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Statistical techniques are needed to analyze data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which capture dependencies in complex biological systems, are often summarized by graph-theoretical features. These features, which lend themselves to outcome modeling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation-based adjacency matrices often need to be sparsified before meaningful graph-theoretical features can be extracted, requiring the data analysts to determine an optimal threshold. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph-theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph-theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modeling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modeling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome-generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting-state fMRI data to predict biological age in children. Our study establishes the flexible modeling approach as a statistically principled, serious competitor to ad-hoc methods with superior performance., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Given the vulnerability of older adults to chronic disease and physical disability, coupled with the threat that obesity poses to healthy aging, there is an urgent need to understand the causes of positive energy balance and the struggle that many older adults face with intentional weight loss. This paper focuses on neural vulnerabilities related to overeating in older adults, and moderating variables that can have either favorable or unfavorable effect these vulnerabilities. Research from our laboratory on older adults with obesity suggests that they are prone to similar neural vulnerabilities for overeating that have been observed in younger and middle-aged populations. In addition, following brief postabsorptive states, functional brain networks both in the resting state and in response to active imagery of desired food are associated with 6-month weight loss. Data reviewed suggest that the sensorimotor network is a central hub in the process of valuation and underscores the central role played by habits in overeating. Finally, we demonstrate how research on the neural vulnerabilities for overeating offers a useful framework for guiding clinical decision-making in weight management., Competing Interests: Conflict of Interest The authors declare no conflict of interest.

Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed-effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole-brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering-based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets., (© 2018 Wiley Periodicals, Inc.)

Background and Purpose: Post-traumatic stress disorder is associated with connectivity changes in the default mode, central executive, and salience networks, and other brain regions. This study evaluated changes in network connectivity associated with usage of High-resolution, relational, resonance-based electroencephalic mirroring (HIRREM ® ; Brain State Technologies, Scottsdale, AZ), a closed-loop, allostatic, acoustic stimulation neurotechnology, for military-related traumatic stress., Methods: Eighteen participants (17 males, mean age 41 years [SD = 7], 15 active duty) enrolled in an IRB approved pilot trial for symptoms of military-related traumatic stress. Participants received 19.5 (1.1) HIRREM sessions over 12 days. Symptoms, physiological and functional measures, and whole brain resting MRI were collected before and after HIRREM. Six whole brain functional networks were evaluated using summary variables and community structure of predefined networks. Pre to postintervention change was analyzed using paired-sample statistical tests., Results: Postintervention, there was an overall increase in connectivity of the default mode network (P = .0094). There were decreases of community structure in both the anterior portion of the default mode (medial prefrontal cortex, P = .0097) and in the sensorimotor (P = .005) network. There were no statistically significant changes at the whole brain level, or in the central executive, salience, or other networks analyzed. Participants demonstrated significant improvements in clinical symptoms, as well as autonomic cardiovascular regulation, which have been reported previously., Conclusions: Use of closed-loop, allostatic, acoustic stimulation neurotechnology (HIRREM) was associated with connectivity changes in the default mode and sensorimotor networks, in directions that may have explained the subjects' clinical improvements., (© 2018 The Authors. Journal of Neuroimaging published by Wiley Periodicals, Inc. on behalf of American Society of Neuroimaging.)