Ordinal Outcome State-Space Models for Intensive Longitudinal Data.

Intensive longitudinal (IL) data are increasingly prevalent in psychological science, coinciding with technological advancements that make it simple to deploy study designs such as daily diary and ecological momentary assessments. IL data are characterized by a rapid rate of data collection (1+ collections per day), over a period of time, allowing for the capture of the dynamics that underlie psychological and behavioral processes. One powerful framework for analyzing IL data is state-space modeling, where observed variables are considered measurements for underlying states (i.e., latent variables) that change together over time. However, state-space modeling has typically relied on continuous measurements, whereas psychological data often come in the form of ordinal measurements such as Likert scale items. In this manuscript, we develop a general estimation approach for state-space models with ordinal measurements, specifically focusing on a graded response model for Likert scale items. We evaluate the performance of our model and estimator against that of the commonly used "linear approximation" model, which treats ordinal measurements as though they are continuous. We find that our model resulted in unbiased estimates of the state dynamics, while the linear approximation resulted in strongly biased estimates of the state dynamics. Finally, we develop an approximate standard error, termed slice standard errors and show that these approximate standard errors are more liberal than true standard errors (i.e., smaller) at a consistent bias.

Altered Lateral Prefrontal Cortex Functioning During Emotional Interference Resistance Is Associated With Affect Lability in Adults With Persisting Symptoms of Attention-Deficit/Hyperactivity Disorder From Childhood.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by inattention and/or impulsivity/hyperactivity. ADHD, especially when persisting into adulthood, often includes emotional dysregulation, such as affect lability; however, the neural correlates of emotionality in adults with heterogeneous ADHD symptom persistence remain unclear. METHODS: The present study sought to determine shared and distinct functional neuroanatomical profiles of neural circuitry during emotional interference resistance using the emotional face n-back task in adult participants with persisting (n = 47), desisting (n = 93), or no (n = 42) childhood ADHD symptoms while undergoing functional magnetic resonance imaging. RESULTS: Participants without any lifetime ADHD diagnosis performed significantly better (faster and more accurately) than participants with ADHD diagnoses on trials with high cognitive loads (2-back) that included task-irrelevant emotional distractors, tapping into executive functioning and emotion regulatory processes. In participants with persisting ADHD symptoms, more severe emotional symptoms were related to worse task performance. Heightened dorsolateral and ventrolateral prefrontal cortex activation was associated with more accurate and faster performance on 2-back emotional faces trials, respectively. Reduced activation was associated with greater affect lability in adults with persisting ADHD, and dorsolateral prefrontal cortex activation mediated the relationship between affect lability and task accuracy. CONCLUSIONS: These findings suggest that alterations in dorsolateral prefrontal cortex function associated with greater interference in cognitive processes from emotion could represent a marker of risk for problems with emotional dysregulation in individuals with persisting ADHD and thus represent a potential therapeutic target for those with greater emotional symptoms of ADHD.

Pathophysiological Responses to Bloodstream Infection in Critically Ill Transplant Recipients Compared With Non-Transplant Recipients.

BACKGROUND: Identification of bloodstream infection (BSI) in transplant recipients may be difficult due to immunosuppression. Accordingly, we aimed to compare responses to BSI in critically ill transplant and non-transplant recipients and to modify systemic inflammatory response syndrome (SIRS) criteria for transplant recipients. METHODS: We analyzed univariate risks and developed multivariable models of BSI with 27 clinical variables from adult intensive care unit (ICU) patients at the University of Virginia (UVA) and at the University of Pittsburgh (Pitt). We used Bayesian inference to adjust SIRS criteria for transplant recipients. RESULTS: We analyzed 38.7 million hourly measurements from 41 725 patients at UVA, including 1897 transplant recipients with 193 episodes of BSI and 53 608 patients at Pitt, including 1614 transplant recipients with 768 episodes of BSI. The univariate responses to BSI were comparable in transplant and non-transplant recipients. The area under the receiver operating characteristic curve (AUC) was 0.82 (95% confidence interval [CI], .80-.83) for the model using all UVA patient data and 0.80 (95% CI, .76-.83) when using only transplant recipient data. The UVA all-patient model had an AUC of 0.77 (95% CI, .76-.79) in non-transplant recipients and 0.75 (95% CI, .71-.79) in transplant recipients at Pitt. The relative importance of the 27 predictors was similar in transplant and non-transplant models. An upper temperature of 37.5°C in SIRS criteria improved reclassification performance in transplant recipients. CONCLUSIONS: Critically ill transplant and non-transplant recipients had similar responses to BSI. An upper temperature of 37.5°C in SIRS criteria improved BSI screening in transplant recipients.

A purely data-driven framework for prediction, optimization, and control of networked processes.

Networks are landmarks of many complex phenomena where interweaving interactions between different agents transform simple local rule-sets into nonlinear emergent behaviors. While some recent studies unveil associations between the network structure and the underlying dynamical process, identifying stochastic nonlinear dynamical processes continues to be an outstanding problem. Here, we develop a simple data-driven framework based on operator-theoretic techniques to identify and control stochastic nonlinear dynamics taking place over large-scale networks. The proposed approach requires no prior knowledge of the network structure and identifies the underlying dynamics solely using a collection of two-step snapshots of the states. This data-driven system identification is achieved by using the Koopman operator to find a low-dimensional representation of the dynamical patterns that evolve linearly. Further, we use the global linear Koopman model to solve critical control problems by applying to model predictive control (MPC)-typically, a challenging proposition when applied to large networks. We show that our proposed approach tackles this by converting the original nonlinear programming into a more tractable optimization problem that is both convex (quadratic programming) and with far fewer variables.

Fronto-amygdala resting state functional connectivity is associated with anxiety symptoms among adolescent girls more advanced in pubertal maturation.

Early adolescence, with the onset of puberty, is an important period when sex differences in anxiety emerge, with girls reporting significantly higher anxiety symptoms than boys. This study examined the role of puberty on fronto-amygdala functional connectivity and risk of anxiety symptoms in 70 girls (age 11-13) who completed a resting state fMRI scan, self-report measures of anxiety symptoms and pubertal status, and provided basal testosterone levels (64 girls). Resting state fMRI data were preprocessed using fMRIPrep and connectivity indices were extracted from ventromedial prefrontal cortex (vmPFC) and amygdala regions-of-interest. We tested moderated mediation models and hypothesized that vmPFC-amygdala would mediate the relation between three indices of puberty (testosterone and adrenarcheal/gonadarcheal development) and anxiety, with puberty moderating the relation between connectivity and anxiety. Results showed a significant moderation effect of testosterone and adrenarcheal development in the right amygdala and a rostral/dorsal area of the vmPFC and of gonadarcheal development in the left amygdala and a medial area of the vmPFC on anxiety symptoms. Simple slope analyses showed that vmPFC-amygdala connectivity was negatively associated with anxiety only in girls more advanced in puberty suggesting that sensitivity to the effects of puberty on fronto-amygdala function could contribute to risk for anxiety disorders among adolescent girls.

Effect of methylphenidate on functional controllability: a preliminary study in medication-naïve children with ADHD.

Methylphenidate (MPH) is the recommended first-line treatment for attention-deficit/hyperactivity disorder (ADHD). While MPH's mechanism of action as a dopamine and noradrenaline transporter blocker is well known, how this translates to ADHD-related symptom mitigation is still unclear. As functional connectivity is reliably altered in ADHD, with recent literature indicating dysfunctional connectivity dynamics as well, one possible mechanism is through altering brain network dynamics. In a double-blind, placebo-controlled MPH crossover trial, 19 medication-naïve children with ADHD underwent two functional MRI scanning sessions (one on MPH and one on placebo) that included a resting state scan and two inhibitory control tasks; 27 typically developing (TD) children completed the same protocol without medication. Network control theory, which quantifies how brain activity reacts to system inputs based on underlying connectivity, was used to assess differences in average and modal functional controllability during rest and both tasks between TD children and children with ADHD (on and off MPH) and between children with ADHD on and off MPH. Children with ADHD on placebo exhibited higher average controllability and lower modal controllability of attention, reward, and somatomotor networks than TD children. Children with ADHD on MPH were statistically indistinguishable from TD children on almost all controllability metrics. These findings suggest that MPH may stabilize functional network dynamics in children with ADHD, both reducing reactivity of brain organization and making it easier to achieve brain states necessary for cognitively demanding tasks.

The affective harm account (AHA) of moral judgment: Reconciling cognition and affect, dyadic morality and disgust, harm and purity.

Moral psychology has long debated whether moral judgment is rooted in harm versus affect. We reconcile this debate with the affective harm account (AHA) of moral judgment. The AHA understands harm as an intuitive perception (i.e., perceived harm), and divides "affect" into two: embodied visceral arousal (i.e., gut feelings) and stimulus-directed affective appraisals (e.g., ratings of disgustingness). The AHA was tested in a randomized, double-blind pharmacological experiment with healthy young adults judging the immorality, harmfulness, and disgustingness of everyday moral scenarios (e.g., lying) and unusual purity scenarios (e.g., sex with a corpse) after receiving either a placebo or the ß-blocker propranolol (a drug that dampens visceral arousal). Results confirmed the three key hypotheses of the AHA. First, perceived harm and affective appraisals are neither competing nor independent but intertwined. Second, although both perceived harm and affective appraisals predict moral judgment, perceived harm is consistently relevant across all scenarios (in line with the theory of dyadic morality), whereas affective appraisals are especially relevant in unusual purity scenarios (in line with affect-as-information theory). Third, the "gut feelings" of visceral arousal are not as important to morality as often believed. Dampening visceral arousal (via propranolol) did not directly impact moral judgment, but instead changed the relative contribution of affective appraisals to moral judgment-and only in unusual purity scenarios. By embracing a constructionist view of the mind that blurs traditional dichotomies, the AHA reconciles historic harm-centric and current affect-centric theories, parsimoniously explaining judgment differences across various moral scenarios without requiring any "moral foundations." (PsycInfo Database Record (c) 2022 APA, all rights reserved).

On the Control of Psychological Networks.

The combination of network theory and network psychometric methods has opened up a variety of new ways to conceptualize and study psychological disorders. The idea of psychological disorders as dynamic systems has sparked interest in developing interventions based on results of network analytic tools. However, simply estimating a network model is not sufficient for determining which symptoms might be most effective to intervene upon, nor is it sufficient for determining the potential efficacy of any given intervention. In this paper, we attempt to remedy this gap by introducing fundamental concepts of control theory to both psychometricians and applied psychologists. We introduce two controllability statistics to the psychometric literature, average and modal controllability, to facilitate selecting the best set of intervention targets. Following this introduction, we show how intervention scientists can probe the effects of both theoretical and empirical interventions on networks derived from real data and demonstrate how simulations can account for intervention cost and the desire to reduce specific symptoms. Every step is based on rich clinical EMA data from a sample of subjects undergoing treatment for complicated grief, with a focus on the outcome suicidal ideation. All methods are implemented in an open-source R package netcontrol, and complete code for replicating the analyses in this manuscript are available online.

Path and Directionality Discovery in Individual Dynamic Models: A Regularized Unified Structural Equation Modeling Approach for Hybrid Vector Autoregression.

There recently has been growing interest in the study of psychological and neurological processes at an individual level. One goal in such endeavors is to construct person-specific dynamic assessments using time series techniques such as Vector Autoregressive (VAR) models. However, two problems exist with current VAR specifications: (1) VAR models are restricted in that contemporaneous relations are typically modeled either as undirected relations among residuals or directed relations among observed variables, but not both; (2) current estimation frameworks are limited by the reliance on stepwise model building procedures. This study adopts a new modeling approach. We first extended the current unified SEM (uSEM) framework, a widely used structural VAR model, to a hybrid representation (i.e., "huSEM") to include both undirected and directed contemporaneous effects, and then replaced the stepwise modeling with a LASSO-type regularization for a global search of the optimal sparse model. Our simulation study showed that regularized huSEM performed uniformly the best over alternative VAR representations and/or modeling approaches, with respect to accurately recovering the presence and directionality of hybrid relations and reliably removing false relations when the data are generated to have two types of contemporaneous relations. The present study to our knowledge is the first application of the recently developed regularized SEM technique to the estimation of huSEM, which points to a promising future for statistical learning in psychometric models.

Aging bodies, aging emotions: Interoceptive differences in emotion representations and self-reports across adulthood.

Bodily sensations are closely linked to emotional experiences. However, most research assessing the body-emotion link focuses on young adult samples. Inspired by prior work showing age-related declines in autonomic reactivity and interoception, we present 2 studies investigating age-related differences in the extent to which adults (18-75 years) associate interoceptive or internal bodily sensations with emotions. Study 1 (N = 150) used a property association task to assess age effects on adults' tendencies to associate interoceptive sensations, relative to behaviors or situations, with negative emotion categories (e.g., anger, sadness). Study 2 (N = 200) used the Day Reconstruction experience sampling method to assess the effect of age on adults' tendencies to report interoceptive sensations and emotional experiences in daily life. Consistent with prior literature suggesting that older adults have more muted physiological responses and interoceptive abilities than younger adults, we found that older adults' mental representations (Study 1) and self-reported experiences (Study 2) of emotion are less associated with interoceptive sensations than are those of younger adults. Across both studies, age effects were most prominent for high arousal emotions (e.g., anger, fear) and sensations (e.g., racing heart) that are often associated with peripheral psychophysiological concomitants in young adults. These findings are consistent with psychological constructionist models and a "maturational dualism" account of emotional aging, suggesting additional pathways by which emotions may differ across adulthood. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Bridging global and local topology in whole-brain networks using the network statistic jackknife.

Whole-brain network analysis is commonly used to investigate the topology of the brain using a variety of neuroimaging modalities. This approach is notable for its applicability to a large number of domains, such as understanding how brain network organization relates to cognition and behavior and examining disrupted brain network organization in disease. A benefit to this approach is the ability to summarize overall brain network organization with a single metric (e.g., global efficiency). However, important local differences in network structure might exist without any corresponding observable differences in global topology, making a whole-brain analysis strategy unlikely to detect relevant local findings. Conversely, using local network metrics can identify local differences, but are not directly informative of differences in global topology. Here, we propose the network statistic (NS) jackknife framework, a simulated lesioning method that combines the utility of global network analysis strategies with the ability to detect relevant local differences in network structure. We evaluate the NS jackknife framework with a simulation study and an empirical example comparing global efficiency in children with attention-deficit/hyperactivity disorder (ADHD) and typically developing (TD) children. The NS jackknife framework has been implemented in a public, open-source R package, netjack, available at https://cran.r-project.org/package=netjack.

Modeling Heterogeneous Peer Assortment Effects Using Finite Mixture Exponential Random Graph Models.

This article develops a class of models called sender/receiver finite mixture exponential random graph models (SRFM-ERGMs). This class of models extends the existing exponential random graph modeling framework to allow analysts to model unobserved heterogeneity in the effects of nodal covariates and network features without a block structure. An empirical example regarding substance use among adolescents is presented. Simulations across a variety of conditions are used to evaluate the performance of this technique. We conclude that unobserved heterogeneity in effects of nodal covariates can be a major cause of misfit in network models, and the SRFM-ERGM approach can alleviate this misfit. Implications for the analysis of social networks in psychological science are discussed.

Emotion semantics show both cultural variation and universal structure.

Many human languages have words for emotions such as "anger" and "fear," yet it is not clear whether these emotions have similar meanings across languages, or why their meanings might vary. We estimate emotion semantics across a sample of 2474 spoken languages using "colexification"-a phenomenon in which languages name semantically related concepts with the same word. Analyses show significant variation in networks of emotion concept colexification, which is predicted by the geographic proximity of language families. We also find evidence of universal structure in emotion colexification networks, with all families differentiating emotions primarily on the basis of hedonic valence and physiological activation. Our findings contribute to debates about universality and diversity in how humans understand and experience emotion.

The Impact of Military Status on Cognitive Processing Therapy Outcomes in the Community.

Military-affiliated individuals (i.e., active duty personnel and veterans) exhibit high rates of posttraumatic stress disorder (PTSD). Although existing evidence-based treatments for PTSD, such as cognitive processing therapy (CPT), have demonstrated effectiveness with military-affiliated patients, there is evidence to suggest these individuals do not benefit as much as civilians. However, few studies have directly compared the effects of PTSD treatment between civilian and military-affiliated participants. The current study compared treatment outcomes of military-affiliated and civilian patients receiving CPT. Participants with PTSD who were either civilians (n = 136) or military-affiliated (n = 63) received CPT from community-based providers in training for CPT. Results indicated that military-affiliated participants were equally likely to complete treatment, Log odds ratio (OR) = 0.14, p = .648. Although military-affiliated participants exhibited reductions in PTSD, B = -2.53, p < .001; and depression symptoms, B = -0.65, p < .001, they experienced smaller reductions in symptoms relative to civilians: B = 1.15, p = .015 for PTSD symptoms and B = 0.29, p = .029 for depression symptoms. Furthermore, variability estimates indicated there was more variability in providers' treatment of military-affiliated versus civilian participants (i.e., completion rates and symptom reduction). These findings suggest that military-affiliated patients can be successfully retained in trauma-focused treatment in the community at the same rate as civilian patients, and they significantly improve in PTSD and depression symptoms although not as much as civilians. These findings also highlight community providers' variability in treatment of military-affiliated patients, providing support for more military-cultural training.

Spanish Abstracts by Asociación Chilena de Estrés Traumático (ACET) El impacto del estatus militar en los resultados de la terapia de procesamiento cognitivo en la comunidad TERAPIA DE PROCESAMIENTO COGNITIVO Y ESTATUS MILITAR Los individuos afiliados a los militares (es decir, personal en servicio activo y veteranos) exhiben altas tasas de trastorno de estrés postraumático (TEPT). Si bien los tratamientos basados ​​en la evidencia existentes para el TEPT, como la terapia de procesamiento cognitivo (CPT en sus siglas en inglés), han demostrado ser efectivos con los pacientes afiliados a las fuerzas armadas, existen evidencias que sugieren que estas personas no se benefician tanto como los civiles. Sin embargo, pocos estudios han comparado directamente los efectos del tratamiento de TEPT entre participantes civiles y afiliados a los militares. El presente estudio comparó los resultados del tratamiento de los pacientes civiles y afiliados a los militares que recibieron CPT. Participantes con TEPT que eran civiles (n = 136) o afiliados a los militares (n = 63) recibieron CPT de proveedores comunitarios en entrenamiento de CPT. Los resultados indicaron que los participantes afiliados a las fuerzas armadas tenían la misma probabilidad de completar el tratamiento, razón de probabilidades de registro (OR) = 0.14, p = .648. Aunque los participantes afiliados a los militares mostraron reducciones en el TEPT, B = -2.53, p <.001; y los síntomas de depresión, B = - 0.65, p <.001, experimentaron reducciones más pequeñas en los síntomas en relación con los civiles: B = 1.15, p = .015 para los síntomas de TEPT y B = 0.29, p = .029 para los síntomas de depresión. Además, las estimaciones de variabilidad indicaron que había una mayor variabilidad en proveedores de tratamiento de los participantes afiliados a los militares en comparación con los civiles (es decir, las tasas de finalización y la reducción de los síntomas). Estos hallazgos sugieren que los pacientes afiliados a las fuerzas armadas pueden ser retenidos con éxito en el tratamiento centrado en el trauma en la comunidad al mismo ritmo que los pacientes civiles, y mejoran significativamente en los síntomas de TEPT y depresión, aunque no tanto como los civiles. Estos hallazgos también resaltan la variabilidad de los proveedores comunitarios en el tratamiento de los pacientes afiliados a las fuerzas armadas, brindando apoyo para una mayor capacitación de la cultura militar.

Assessing the robustness of cluster solutions obtained from sparse count matrices.

Psychological researchers often seek to obtain cluster solutions from sparse count matrices (e.g., social networks; counts of symptoms that are in common for 2 given individuals; structural brain imaging). Increasingly, community detection methods are being used to subset the data in a data-driven manner. While many of these approaches perform well in simulation studies and thus offer some improvement upon traditional clustering approaches, there is no readily available approach for evaluating the robustness of these solutions in empirical data. Researchers have no way of knowing if their results are due to noise. We describe here 2 approaches novel to the field of psychology that enable evaluation of cluster solution robustness. This tutorial also explains the use of an associated R package, perturbR, which provides researchers with the ability to use the methods described herein. In the first approach, the cluster assignment from the original matrix is compared against cluster assignments obtained by randomly perturbing the edges in the matrix. Stable cluster solutions should not demonstrate large changes in the presence of small perturbations. For the second approach, Monte Carlo simulations of random matrices that have the same properties as the original matrix are generated. The distribution of quality scores ("modularity") obtained from the cluster solutions from these matrices are then compared with the score obtained from the original matrix results. From this, one can assess if the results are better than what would be expected by chance. perturbR automates these 2 methods, providing an easy-to-use resource for psychological researchers. We demonstrate the utility of this package using benchmark simulated data generated from a previous study and then apply the methods to publicly available empirical data obtained from social networks and structural neuroimaging. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Social Context of Sexual Minority Adolescents and Relationship to Alcohol Use.

PURPOSE: Peer relationships are especially relevant during adolescence and may contribute to sexuality-based disparities in substance use. This study uses social network analysis to examine how social networks may serve as risk or protective factors for sexual minority youth in the context of alcohol use. METHODS: Social network analysis was applied to 11th to 12th graders in three diverse high schools in a rural area of the Southeast United States. The network consists of 1,179 students, 607 of whom were participants in the study and nominated friends. Regression models were used to examine how potential predictors of alcohol use may function differently for sexual minority and majority students. RESULTS: Approximately one fourth of students were classified as sexual minorities, inclusive of students who self-identified or reported any same-sex romantic attraction or sexual experience. These students did not use alcohol in greater amounts than students in the sexual majority. They received fewer incoming friendship nominations (p < .05) although a higher percentage of friendships were reciprocated (p < .05). They exhibited lower eigenvector centrality (p = .01), and their networks were less cohesive (p < .001). However, low centrality and low density did not predict greater alcohol consumption. Sexual minorities appeared to be influenced less strongly by peers' alcohol use, and friendships with sexual minorities further mitigated peer influence. CONCLUSION: Sexual minorities occupied less prominent positions within their social networks. However, these network differences did not place sexual minorities at increased risk of alcohol use.

Comparing directed functional connectivity between groups with confirmatory subgrouping GIMME.

Connectivity modeling in functional neuroimaging has become widely used method of analysis for understanding functional architecture. One method for deriving directed connectivity models is Group Iterative Multiple Model Estimation (GIMME; Gates and Molenaar, 2012). GIMME looks for commonalities across the sample to detect signal from noise and arrive at edges that exist across the majority in the group ("group-level edges") and individual-level edges. In this way, GIMME obtains generalizable results via the group-level edges while also allowing for between subject heterogeneity in connectivity, moving the field closer to obtaining reliable personalized connectivity maps. In this article, we present a novel extension of GIMME, confirmatory subgrouping GIMME, which estimates subgroup-level edges for a priori known groups (e.g. typically developing controls vs. clinical group). Detecting edges that consistently exist for individuals within predefined subgroups aids in interpretation of the heterogeneity in connectivity maps and allows for subgroup-specific inferences. We describe this algorithm, as well as several methods to examine the results. We present an empirical example that finds similarities and differences in resting state functional connectivity among four groups of children: typically developing controls (TDC), children with autism spectrum disorder (ASD), children with Inattentive (ADHD-I) and Combined (ADHD-C) Type ADHD. Findings from this study suggest common involvement of the left Broca's area in all the clinical groups, as well as several unique patterns of functional connectivity specific to a given disorder. Overall, the current approach and proof of principle findings highlight a novel and reliable tool for capturing heterogeneity in complex mental health disorders.

Age and Gender Effects on Intrinsic Connectivity in Autism Using Functional Integration and Segregation.

BACKGROUND: The objective of this study was to examine intrinsic whole-brain functional connectivity in autism spectrum disorder (ASD) using the framework of functional segregation and integration. Emphasis was given to potential gender and developmental effects as well as identification of specific networks that may contribute to the global results. METHODS: We leveraged an open data resource (N = 1587) of resting-state functional magnetic resonance imaging data in the Autism Brain Imaging Data Exchange (ABIDE) initiative, combining data from more than 2100 unique cross-sectional datasets in ABIDE I and ABIDE II collected at different sites. Modularity and global efficiency were utilized to assess functional segregation and integration, respectively. A meta-analytic approach for handling site differences was used. The effects of age, gender, and diagnostic category on segregation and integration were assessed using linear regression. RESULTS: Modularity decreased nonlinearly in the ASD group with age, as evidenced by an increase and then decrease over development. Global efficiency had an opposite relationship with age by first decreasing and then increasing in the ASD group. Both modularity and global efficiency remained largely stable in the typically developing control group during development, representing a significantly different effect than seen in the ASD group. Age effects on modularity were localized to the somatosensory network. Finally, a marginally significant interaction between age, gender, and diagnostic category was found for modularity. CONCLUSIONS: Our results support prior work that suggested a quadratic effect of age on brain development in ASD, while providing new insights about the specific characteristics of developmental and gender effects on intrinsic connectivity in ASD.

Concordance networks and application to clustering cancer symptomology.

Symptoms of complex illnesses such as cancer often present with a high degree of heterogeneity between patients. At the same time, there are often core symptoms that act as common drivers for other symptoms, such as fatigue leading to depression and cognitive dysfunction. These symptoms are termed bridge symptoms and when combined with heterogeneity in symptom presentation, are difficult to detect using traditional unsupervised clustering techniques. This article develops a method for identifying patient communities based on bridge symptoms termed concordance network clustering. An empirical study of breast cancer symptomatology is presented, and demonstrates the applicability of this method for identifying bridge symptoms.