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Introduction: Anti-GABABR encephalitis is a rare disease reported to be often associated with tumors. The current study aims to summarize the clinical characteristics, imaging features, treatments, outcomes and explore the potential prognosis risk factors of patients with anti-GABABR encephalitis., Methods: Patients tested positive for anti-GABABR were retrospective studied from a single medical center in China over a period of 3 years. They were followed up for a maximum period of 18 months. Clinical data were summarized and prognostic factors including demographic characteristics, laboratory tests, and neurological functions were compared between survived and deceased patients at 18 months follow-up., Results: Twenty-six patients, 10 females (38.5%) and 16 males (61.5%), diagnosed with anti-GABABR encephalitis were studied. The median age was 58 years. Of the 23 cases with complete clinical data, their main manifestations were epileptic seizures (65%), mental and behavioral abnormalities (52%), and cognitive impairment (48%). 7 (30.4%) cases had tumors: 5 small cell lung cancer (SCLC), 1 rectum adenocarcinoma (moderately differentiated) and 1 esophageal squamous cell carcinoma. MRI showed 5 (22%) cases had T2 FLAIR increased signals in cortex but with different regions affected. One of the two patients scanned for PET-CT showed hypermetabolism in the left temporal lobe region. The disease course ranged from 5 days to 3 years. 2 patients (one had esophageal carcinoma) without immunotherapy and 3 patients (one had SCLC) that did not response to immunotherapy died soon after diagnosis. 18 patients improved after immunotherapy while 3 (all had SCLC) died after relapses. The prevalence of epileptic seizures and malignancies was significantly lower in the survival group than in the deceased group at 18-months follow-up, the same as the admission mRs score. Serum fibrinogen, cerebrospinal fluid immunoglobulin G quotient, and 24-hour intrathecal synthesis rate were significantly lower in the survival groups as well., Conclusions: Cortex T2 FLAIR abnormalities were only observed in a small proportion of anti-GABABR encephalitis patients with heterogeneous MRI phenotypes. High mRS score at admission, epileptic seizures and the presence of a tumor indicated a poor prognosis, while the underlying mechanism of the later two factors should be investigated further., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Zong, Yao, Molenaar, Damoiseaux, Li, Rouhl and Martinez-Martinez.)

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

The present study examined genetic, prenatal, and postnatal environmental pathways in the intergenerational transmission of anxiety and depressive symptoms from parents to early adolescents (when these symptoms start to increase), while considering timing effects of exposure to parent anxiety and depressive symptoms postnatally. The sample was from the Early Growth and Development Study, including 561 adopted children (57% male, 55% White, 13% Black/African American, 11% Hispanic/Latine, 20% multiracial, 1% other; 407 provided data in early adolescence) and their birth (BP) and adoptive parents (AP). Using a trait-state-occasion model with eight assessments from child ages 9 months to 11 years, we partitioned trait-like AP anxiety and depressive symptoms from time-specific fluctuations of AP anxiety and depressive symptoms. Offspring anxiety and depressive symptoms were assessed at 11 years (while controlling for similar symptoms at 4.5 years). Results suggested that time-specific fluctuations of AP1 (mostly mothers) anxiety/depressive symptoms in infancy (9 months) were indirectly associated with offspring anxiety/depressive symptoms at 11 years via offspring anxiety/depressive symptoms at 4.5 years; time-specific fluctuations of AP1 anxiety/depressive symptoms at child age 11 years were concurrently associated with offspring anxiety/depressive symptoms at 11 years. AP2 (mostly fathers) anxiety/depressive symptoms were not associated with offspring symptoms. Genetic and prenatal influences measured by BP internalizing problems were not associated with offspring symptoms. Results suggested infancy and early adolescence as developmental periods when children are susceptible to influences of parent anxiety and depressive symptoms. Preventive interventions should consider time-specific fluctuations in parent anxiety and depressive symptoms during these developmental periods. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Rapid developments over the last several decades have brought increased focus and attention to the role of time scales and heterogeneity in the modeling of human processes. To address these emerging questions, subgrouping methods developed in the discrete-time framework-such as the vector autoregression (VAR)-have undergone widespread development to identify shared nomothetic trends from idiographic modeling results. Given the dependence of VAR-based parameters on the measurement intervals of the data, we sought to clarify the strengths and limitations of these methods in recovering subgroup dynamics under different measurement intervals. Building on the work of Molenaar and collaborators for subgrouping individual time-series by means of the subgrouped chain graphical VAR (scgVAR) and the subgrouping option in the group iterative multiple model estimation (S-GIMME), we present results from a Monte Carlo study aimed at addressing the implications of identifying subgroups using these discrete-time methods when applied to continuous-time data. Results indicate that discrete-time subgrouping methods perform well at recovering true subgroups when the measurement intervals are large enough to capture the full range of a system's dynamics, either via lagged or contemporaneous effects. Further implications and limitations are discussed therein.

Background: Anti-GAD65 autoantibodies (GAD65-Abs) may occur in patients with epilepsy and other neurological disorders, but the clinical significance is not clear-cut. Whereas high levels of GAD65-Abs are considered pathogenic in neuropsychiatric disorders, low or moderate levels are only considered as mere bystanders in, e.g., diabetes mellitus type 1 (DM1). The value of cell-based assays (CBA) and immunohistochemistry (IHC) for GAD65-Abs detection has not been clearly evaluated in this context., Objective: To re-evaluate the assumption that high levels of GAD65-Abs are related to neuropsychiatric disorders and lower levels only to DM1 and to compare ELISA results with CBA and IHC to determine the additional value of these tests., Methods: 111 sera previously assessed for GAD65-Abs by ELISA in routine clinical practice were studied. Clinical indications for testing were, e.g., suspected autoimmune encephalitis or epilepsy (neuropsychiatric cohort; n  = 71, 7 cases were initially tested positive for GAD65-Abs by ELISA), and DM1 or latent autoimmune diabetes in adults (DM1/LADA cohort ( n  = 40, all were initially tested positive)). Sera were re-tested for GAD65-Abs by ELISA, CBA, and IHC. Also, we examined the possible presence of GAD67-Abs by CBA and of other neuronal autoantibodies by IHC. Samples that showed IHC patterns different from GAD65 were further tested by selected CBAs., Results: ELISA retested GAD65-Abs level in patients with neuropsychiatric diseases was higher than in patients with DM1/LADA (only retested positive samples were compared; 6 vs. 38; median 47,092 U/mL vs. 581 U/mL; p  = 0.02). GAD-Abs showed positive both by CBA and IHC only if antibody levels were above 10,000 U/mL, without a difference in prevalence between the studied cohorts. We found other neuronal antibodies in one patient with epilepsy (mGluR1-Abs, GAD-Abs negative), and in a patient with encephalitis, and two patients with LADA., Conclusion: GAD65-Abs levels are significantly higher in patients with neuropsychiatric disease than in patients with DM1/LADA, however, positivity in CBA and IHC only correlates with high levels of GAD65-Abs, and not with the underlying diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zong, Vinke, Du, Hoffmann, Mané-Damas, Molenaar, Damoiseaux, Losen, Rouhl and Martinez-Martinez.)

Researchers across varied fields increasingly are collecting and analyzing intensive longitudinal data (ILD) to examine processes across time at the individual level. Two types of relations are typically examined: lagged and contemporaneous. Lagged relations capture how variables at a prior time point can be used to explain variance in variables at a later time point. These are always modeled using auto- and cross-regressions by means of vector autoregression (VAR). By contrast, there are two types of relations commonly used to model the contemporaneous relations, which model how variables relate instantaneously. Until now, researchers must opt to either model contemporaneous relations as undirected relations among residuals (e.g., partial or full correlations) or as directed relations among the variables (e.g., paths or regressions). The choice for how to model contemporaneous relations has implications for inferences as well as the potential to introduce bias in the VAR lagged relations if the wrong type of relation is used. This article introduces a novel data-driven method, hybrid-group iterative multiple model estimation (GIMME), that provides a solution to the problem of having to choose one or the other type of contemporaneous relation to model. The modeling framework utilized in hybrid-GIMME allows for both types of contemporaneous relations in addition to the standard VAR relations. Both simulated and empirical data were used to test the performance of hybrid-GIMME. Results suggest this is a robust method for recovering contemporaneous relations in an exploratory manner, particularly with an ample number of time points per person. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

That standardized measurement procedures are a sine qua non of "good" science is generally not questioned. Here we examine the meaning and use of standardized measurement in behavioral science. Procedures and methods of measurement that have served the physical sciences so well should not blindly be assumed to work in the same manner and with the same effectiveness in behavioral science. There seems to be general agreement that social/behavioral science is "different" among the sciences. Problems arising from how behavioral science is "different" begin, we believe, with measurement. We put forward the argument that the source of the difference is unique to animate objects and is first evident at the stage of measuring the behavioral attributes of interest. It is at that point in conducting scientific inquiry that the matters raised might be resolved by developing and applying alternatives to standardized measurement. One such alternative discussed is the idiographic filter (Nesselroade, Gerstorf, Hardy, & Ram, 2007)., Competing Interests: The authors declare that there is no conflict of interest., (© Person-Oriented Research.)

We introduce a discrete-time dynamical system method, the Boolean network method, that may be useful for modeling, studying, and controlling nonlinear dynamics in multivariate systems, particularly when binary time-series are available. We introduce the method in three steps: inference of the temporal relations as Boolean functions, extraction of attractors and assignment of desirability based on domain knowledge, and design of network control to direct a psychological system toward a desired attractor. To demonstrate how the Boolean network can describe and prescribe control for emotion regulation dynamics, we applied this method to data from a study of how children use bidding to an adult and/or distraction to regulate their anger during a frustrating task ( N  = 120, T  = 480 seconds). Network control strategies were designed to move the child into attractors where anger is OFF. The sample shows heterogeneous emotion regulation dynamics across children in 22 distinct Boolean networks, and heterogeneous control strategies regarding which behavior to perturb and how to perturb it. The Boolean network method provides a novel method to describe nonlinear dynamics in multivariate psychological systems and is a method with potential to eventually inform the design of interventions that can guide those systems toward desired goals.

The presence of autoantibodies directed against the muscle nicotinic acetylcholine receptor (AChR) is the most common cause of myasthenia gravis (MG). These antibodies damage the postsynaptic membrane of the neuromuscular junction and cause muscle weakness by depleting AChRs and thus impairing synaptic transmission. As one of the best-characterized antibody-mediated autoimmune diseases, AChR-MG has often served as a reference model for other autoimmune disorders. Classical pharmacological treatments, including broad-spectrum immunosuppressive drugs, are effective in many patients. However, complete remission cannot be achieved in all patients, and 10% of patients do not respond to currently used therapies. This may be attributed to production of autoantibodies by long-lived plasma cells which are resistant to conventional immunosuppressive drugs. Hence, novel therapies specifically targeting plasma cells might be a suitable therapeutic approach for selected patients. Additionally, in order to reduce side effects of broad-spectrum immunosuppression, targeted immunotherapies and symptomatic treatments will be required. This review presents established therapies as well as novel therapeutic approaches for MG and related conditions, with a focus on AChR-MG., (Copyright © 2022. Published by Elsevier B.V.)

Education can be viewed as a control theory problem in which students seek ongoing exogenous input-either through traditional classroom teaching or other alternative training resources-to minimize the discrepancies between their actual and target (reference) performance levels. Using illustrative data from [Formula: see text] Dutch elementary school students as measured using the Math Garden, a web-based computer adaptive practice and monitoring system, we simulate and evaluate the outcomes of using off-line and finite memory linear quadratic controllers with constraintsto forecast students' optimal training durations. By integrating population standards with each student's own latent change information, we demonstrate that adoption of the control theory-guided, person- and time-specific training dosages could yield increased training benefits at reduced costs compared to students' actual observed training durations, and a fixed-duration training scheme. The control theory approach also outperforms a linear scheme that provides training recommendations based on observed scores under noisy and the presence of missing data. Design-related issues such as ways to determine the penalty cost of input administration and the size of the control horizon window are addressed through a series of illustrative and empirically (Math Garden) motivated simulations., (© 2022. The Author(s) under exclusive licence to The Psychometric Society.)

Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, otherwise only a fraction of the system dynamics will be recovered. In this paper we introduce a Square-Root Second-Order Extended Kalman Filtering approach for estimating smoothly time-varying parameters. This approach is capable of handling dynamic factor models where the relations between variables underlying the processes of interest change in a manner that may be difficult to specify in advance. We examine the performance of our approach in a Monte Carlo simulation and show the proposed algorithm accurately recovers the unobserved states in the case of a bivariate dynamic factor model with time-varying dynamics and treatment effects. Furthermore, we illustrate the utility of our approach in characterizing the time-varying effect of a meditation intervention on day-to-day emotional experiences.

Introduction: Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at functional connectivity maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This article introduces HRF-GIMME, an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on functional magnetic resonance imaging data collected by using event-related designs. Critically, hemodynamic response function (HRF)-GIMME incorporates person-specific modeling of the HRF to accommodate known variability in onset delay and shape. Methods: After an introduction of the technical aspects of HRF-GIMME, the performance of HRF-GIMME is evaluated via both a simulation study and application to empirical data. The simulation study assesses the sensitivity and specificity of HRF-GIMME by using data simulated from one slow and two rapid event-related designs, and HRF-GIMME is then applied to two empirical data sets from similar designs to evaluate performance in recovering known neural circuitry. Results: HRF-GIMME showed high sensitivity and specificity across all simulated conditions, and it performed well in the recovery of expected relations between convolved task vectors and brain regions in both simulated and empirical data, particularly for the slow event-related design. Conclusion: Results from simulated and empirical data indicate that HRF-GIMME is a powerful new tool for obtaining directed functional connectivity maps of intrinsic and task-related connections that is able to uncover what is common across the sample as well as crucial individual-level path connections and estimates. Impact statement Group iterative multiple model estimation (GIMME) is a reliable method for creating functional connectivity maps of the connections between brain regions across time, and it is able to detect what is common across the sample and what is shared between subsets of participants, as well as individual-level path estimates. However, historically, GIMME does not model task-related effects. The novel HRF-GIMME algorithm enables the modeling of direct and modulatory task effects through individual-level estimation of the hemodynamic response function (HRF), presenting a powerful new tool for assessing task effects on functional connectivity networks in functional magnetic resonance imaging data.

Wayne Velicer is remembered for a mind where mathematical concepts and calculations intrigued him, behavioral science beckoned him, and people fascinated him. Born in Green Bay, Wisconsin on March 4, 1944, he was raised on a farm, although early influences extended far beyond that beginning. His Mathematics BS and Psychology minor at Wisconsin State University in Oshkosh, and his PhD in Quantitative Psychology from Purdue led him to a fruitful and far-reaching career. He was honored several times as a high-impact author, was a renowned scholar in quantitative and health psychology, and had more than 300 scholarly publications and 54,000+ citations of his work, advancing the arenas of quantitative methodology and behavioral health. In his methodological work, Velicer sought out ways to measure, synthesize, categorize, and assess people and constructs across behaviors and time, largely through principal components analysis, time series, and cluster analysis. Further, he and several colleagues developed a method called Testing Theory-based Quantitative Predictions, successfully applied to predicting outcomes and effect sizes in smoking cessation, diet behavior, and sun protection, with the potential for wider applications. With $60,000,000 in external funding, Velicer also helped engage a large cadre of students and other colleagues to study methodological models for a myriad of health behaviors in a widely applied Transtheoretical Model of Change. Unwittingly, he has engendered indelible memories and gratitude to all who crossed his path. Although Wayne Velicer left this world on October 15, 2017 after battling an aggressive cancer, he is still very present among us.

Understanding patterns of symptom co-occurrence is one of the most difficult challenges in psychopathology research. Do symptoms co-occur because of a latent factor, or might they directly and causally influence one another? Motivated by such questions, there has been a surge of interest in network analyses that emphasize the putatively direct role symptoms play in influencing each other. In this critical paper, we highlight conceptual and statistical problems with using centrality measures in cross-sectional networks. In particular, common network analyses assume that there are no unmodeled latent variables that confound symptom co-occurrence. The traditions of clinical taxonomy and test development in psychometric theory, however, greatly increase the possibility that latent variables exist in symptom data. In simulations that include latent variables, we demonstrate that closeness and betweenness are vulnerable to spurious covariance among symptoms that connect subgraphs (e.g., diagnoses). We further show that strength is redundant with factor loading in several cases. Finally, if a symptom reflects multiple latent causes, centrality metrics reflect a weighted combination, undermining their interpretability in empirical data. Our results suggest that it is essential for network psychometric approaches to examine the evidence for latent variables prior to analyzing or interpreting patterns at the symptom level. Failing to do so risks identifying spurious relationships or failing to detect causally important effects. Altogether, we argue that centrality measures do not provide solid ground for understanding the structure of psychopathology when latent confounding exists.

The etiology of psychotic disorders is still unknown, but in a subgroup of patients symptoms might be caused by an autoimmune reaction. In this study, we tested patterns of autoimmune reactivity against potentially novel hippocampal antigens. Serum of a cohort of 621 individuals with psychotic disorders and 257 controls were first tested for reactivity on neuropil of rat brain sections. Brain reactive sera (67 diseased, 27 healthy) were further tested for antibody binding to glutamic acid decarboxylase (GAD) isotype 65 and 67 by cell-based assay (CBA). A sub-cohort of 199 individuals with psychotic disorders and 152 controls was tested for the prevalence of anti-nuclear antibodies (ANA) on HEp2-substrate as well as for reactivity to double-stranded DNA, ribosomal P (RPP), and cardiolipin (CL). Incubation of rat brain with serum resulted in unidentified hippocampal binding patterns in both diseased and control groups. Upon screening with GAD CBA, one of these patterns was identified as GAD65 in one individual with schizophrenia and also in one healthy individual. Two diseased and two healthy individuals had low antibody levels targeting GAD67 by CBA. Antibody reactivity on HEp-2-substrate was increased in patients with schizoaffective disorder, but only in 3 patients did antibody testing hint at a possible diagnosis of systemic lupus erythematosus. Although reactivity of serum to intracellular antigens might be increased in patients with psychotic disorder, no specific targets could be identified. GAD antibodies are very rare and do not seem increased in serum of patients with psychotic disorders., Competing Interests: Declaration of competing interest None., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Neuronal surface autoantibodies (NSAbs) against various antigens cause autoimmune encephalitis. Some of these antigens are also involved in the pathology of depression and anxiety. To study whether NSAbs are more common in plasma of individuals with depression and anxiety than in controls, and to investigate if NSAbs correlate with disease status, plasma samples of 819 individuals with a current diagnosis of depression and/or anxiety, 920 in remission and 492 individuals without these disorders were included in this study. Samples were tested by a combination of immunohistochemistry (IHC), staining on live rat hippocampus neurons and cell-based assay (CBA). By IHC, 50 (2.2%) samples showed immunoreactivity to rat brain tissue, with no significant differences between the aforementioned groups (22/819 vs 18/920 vs 11/492, P > 0.99). In addition, eight IHC positive samples were positive for NSAbs on live neurons (7/819 vs 0/920 vs 1/492, P = 0.006). The IHC-staining patterns of these eight samples were atypical for autoimmune encephalitis and accordingly, they tested negative for known NSAbs by CBA. No obvious difference in the clinical characteristics between individuals with or without NSAbs was observed. In conclusion, novel NSAbs were rare but predominately found in patients with current anxiety or depression indicating they might affect mental health in a small group of patients.

Borderline personality disorder (BPD) involves instability in self-concept, emotions, and behavior. However, the dynamic, longitudinal relations among BPD symptoms and between these symptoms and other problematic emotional experiences are poorly understood. It is also unclear whether these dynamics are the same across persons (including across diagnostic boundaries), specific to individuals with BPD, or idiographic. The current study uses ecological momentary assessment and group iterative multiple model estimation, a novel, data-driven approach to identifying dynamic patterns in time-series data at group, subgroup, and individual levels, to investigate the dynamic connections among select features of BPD (anger, impulsivity, and identity disturbance) and anxiety-related experiences. Forty-two psychiatric outpatients diagnosed with BPD ( n = 27) or with an anxiety disorder, but not BPD ( n = 15), rated their anger, identity disturbance, impulsivity, anxiety, stress, and calmness states 6 times per day for 21 days, providing a total of 4,699 surveys. Only 1 dynamic link between symptoms was identified that applied at the group level, and group iterative multiple model estimation did not reveal stable subgroups of individuals with distinct symptom dynamics. Instead, these dynamics differed from individual to individual. These results suggest that connections among these BPD and anxiety symptoms do not depend on diagnosis and are somewhat idiographic. Case examples are used to illustrate the clinical utility of within-person symptom models as a supplement to traditional diagnostic information. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches-GIMME, uSEM, and LASSO gVAR-in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three approaches dynamic networks provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches' respective strengths and limitations.

An individual's emotions system can be conceived of as a synchronized, coordinated, and/or emergent combination of physiology, experience, and behavioral components. Together, the interplay among these components produce emotional experiences through coordinated excitatory positive feedback (i.e., the mutual amplification of emotion concordance) and/or inhibitory negative feedback (i.e., the damping of emotion regulation) processes. Different system configurations produce differential psychophysiological reactivity profiles, and by implication, differential moment-to-moment emotional experience and long-term development. Applying dynamic systems models to second-by-second psychophysiological and experience time-series data collected from 130 adolescents (age 12.0 to 16.7 years) completing a social stress-inducing speech task, we describe the configuration of adolescents' emotion systems, and examine how differences in the dynamic outputs of those systems (psychophysiological reactivity profile) are related to individual differences in trait anxiety. We found substantial heterogeneity in the coordination patterns of these adolescents. Some individuals' emotion systems were characterized by negative feedback loops (emotion regulation processes), many by unidirectionally connected or independent components, and a few by positive feedback loops (emotion concordance). The reactivity dynamics of respiratory sinus arrhythmia were related to adolescents' level of trait anxiety. Results highlight how dynamic systems models may contribute to our understanding of interindividual and developmental differences. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Psychotic disorders are debilitating mental illnesses associated with abnormalities in various neurotransmitter systems. The development of disease-modifing therapies has been hampered by the mostly unknown etiologies and pathophysiologies. Autoantibodies against several neuronal antigens are responsible for autoimmune encephalitis. These autoantibodies disrupt neurotransmission within the brain, resulting in a wide range of psychiatric and neurologic manifestations, including psychosis. The overlap of symptoms of autoimmune encephalitis with psychotic disorders raised the question as to whether autoantibodies against a number of receptors, ion channel and associated proteins could ultimately be responsible for some forms of psychosis. Here we review our current knowledge, on antibody mediated autoimmunity in psychotic disorders, the different diagnostic methods and their limitations, as well as on varying therapeutic approaches targeting the immune system., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

There is increasing appreciation that network-level interactions among regions produce components of face processing previously ascribed to individual regions. Our goals were to use an exhaustive data-driven approach to derive and quantify the topology of directed functional connections within a priori defined nodes of the face processing network and evaluate whether the topology is category-specific. Young adults were scanned with fMRI as they viewed movies of faces, objects, and scenes. We employed GIMME to model effective connectivity among core and extended face processing regions, which allowed us to evaluate all possible directional connections, under each viewing condition (face, object, place). During face processing, we observed directional connections from the right posterior superior temporal sulcus to both the right occipital face area and right fusiform face area (FFA), which does not reflect the topology reported in prior studies. We observed connectivity between core and extended regions during face processing, but this limited to a feed-forward connection from the FFA to the amygdala. Finally, the topology of connections was unique to face processing. These findings suggest that the pattern of directed functional connections within the face processing network, particularly in the right core regions, may not be as hierarchical and feed-forward as described previously. Our findings support the notion that topologies of network connections are specialized, emergent, and dynamically responsive to task demands., (© 2019 Wiley Periodicals, Inc.)

The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.

Objective: This study examined the role of dispositional mindfulness in impacting the relational benefits of yoga in novice practitioners in an undergraduate academic yoga course., Participants: 21 college students enrolled in a yoga course throughout a 15-week academic semester (January-April 2016)., Methods: Participants attended lab assessments at the beginning and end of the semester and were asked to complete eight consecutive Internet-based daily surveys across six separate bursts, yielding 48 data points for each participant., Results: Multivariate time-series analyses revealed within-person subgroup differences such that in general, those in the high trait mindfulness group gleaned benefits from yoga practice with respect to relational outcomes., Conclusions: This study demonstrated trait mindfulness impacts relational outcomes in novice yoga practitioners, suggesting those low in trait mindfulness may not garner immediate relational benefits from yoga and could benefit from additional strategies to bolster against any negative influences of initiating yoga practice.

Myasthenia gravis (MG) is an autoimmune disease of the neuromuscular junction. Most patients have pathogenic autoantibodies against the acetylcholine receptor (AChR). In the last years a novel subpopulation of MG patients has been described that harbors antibodies against low-density lipoprotein receptor-related protein 4 (Lrp4), another postsynaptic neuromuscular antigen. In early-onset AChR MG (EOMG), the thymus plays an important role in immunopathogenesis, and early thymectomy is beneficial. It is still unknown if the thymus plays any role in Lrp4-MG. In this pilot study, we compared thymus samples from four patients with Lrp4-MG (one pre-treated with immunosuppressive drugs), four non-MG controls and five EOMG patients (not pretreated with immunosuppressive drugs). Immunohistochemistry of the Lrp4-MG thymi revealed normal architecture, with normal numbers and distribution of B-cells, lymphoid follicles and Hassall's corpuscles. Primary CD23 + lymphoid follicles were similarly infrequent in Lrp4-MG and control thymic sections. In none of the control or Lrp4-MG thymi did we find secondary follicles with CD10 + germinal centers. These were evident in 2 of the 5 EOMG thymi, where primary lymphoid follicles were also more frequent on average, thus showing considerable heterogeneity between patients. Even if characteristic pathological thymic changes were not observed in the Lrp4 subgroup, we cannot exclude a role for the thymus in Lrp4-MG pathogenesis, since one Lrp4-MG patient went into clinical remission after thymectomy alone (at one year follow-up) and one more improved after thymectomy in combination with immunosuppressive therapy., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

By taking advantage of the natural variation in genetic relatedness among identical (monozygotic: MZ) and fraternal (dizygotic: DZ) twins, twin studies are able to estimate genetic and environmental contributions to complex human behaviors. Recently concerns have been raised about the accuracy of twin studies in light of findings of genetic and epigenetic changes in twins. One of the concerns raised is that MZ twins are not 100% genetically and epigenetically similar because they show variations in their genomes and epigenomes leading to inaccurate estimates of heritability. This article presents findings from a simulation study that examined the degree of bias in estimates of heritability and environmentality when the genetic and epigenetic similarity of MZ twins differs from 1.00 and when the genetic and epigenetic similarity of DZ twins differs from 0.50. The findings suggest that in the standard biometric model when MZ or DZ twin similarity differs from 1.00 or 0.50, respectively, the variance that should be attributed to genetic influences is instead attributed to nonshared environmental influences, thus deflating the estimates of genetic influences and inflating the estimates of nonshared environmental influences. Although estimates of genetic and nonshared environmental influences from the standard biometric model were found to deviate from "true" values, the bias was usually smaller than 10% points indicating that the interpretations of findings from previous twin studies are mostly correct.

Socioemotional processes engaged in daily life may afford and/or constrain individuals' emotion regulation in ways that affect psychological health. Recent findings from experience sampling studies suggest that persistence of negative emotions (emotion inertia), the strength of relations among an individual's negative emotions (density of the emotion network), and cycles of negative/aggressive interpersonal transactions are related to psychological health. Using multiple bursts of intensive experience sampling data obtained from 150 persons over one year, person-specific analysis, and impulse response analysis, this study quantifies the complex and interconnected socioemotional processes that surround individuals' daily social interactions and on-going regulation of negative emotion in terms of recovery time. We also examine how this measure of regulatory inefficiency is related to interindividual differences and intraindividual change in level of depressive symptoms. Individuals with longer recovery times had higher overall level of depressive symptoms. As well, during periods where recovery time of sadness was longer than usual, individuals' depressive symptoms were also higher than usual, particularly among individuals who experienced higher overall level of stressful life events. The findings and analysis highlight the utility of a person-specific network approach to study emotion regulation, how regulatory processes change over time, and potentially how planned changes in the configuration of individuals' systems may contribute to psychological health.

We report here the sequence and functional characterization of a recombinantly expressed autoantibody (mAb 131) previously isolated from a myasthenia gravis patient by immortalization of thymic B cells using Epstein-Barr virus and TLR9 activation. The antibody is characterized by a high degree of somatic mutations as well as a 6 amino acid insertion within the VHCDR2. The recombinant mAb 131 is specific for the γ-subunit of the fetal AChR to which it bound with sub-nanomolar apparent affinity, and detected the presence of fetal AChR on a number of rhabdomyosarcoma cell lines. Mab 131 blocked one of the two α-bungarotoxin binding sites on the fetal AChR, and partially blocked the binding of an antibody (mAb 637) to the α-subunit of the AChR, suggesting that both antibodies bind at or near one ACh binding site at the α/γ subunit interface. However, mAb 131 did not reduce fetal AChR ion channel currents in electrophysiological experiments. These results indicate that mAb 131, although generated from an MG patient, is unlikely to be pathogenic and may make it a potentially useful reagent for studies of myasthenia gravis, rhabdomyosarcoma and arthrogryposis multiplex congenita which can be caused by fetal-specific AChR-blocking autoantibodies.

Autoantibodies against ion channels are the cause of numerous neurologic autoimmune disorders. Frequently, such pathogenic autoantibodies have a restricted epitope-specificity. In such cases, competing antibody formats devoid of pathogenic effector functions (blocker antibodies) have the potential to treat disease by displacing autoantibodies from their target. Here, we have used a model of the neuromuscular autoimmune disease myasthenia gravis in rhesus monkeys (Macaca mulatta) to test the therapeutic potential of a new blocker antibody: MG was induced by passive transfer of pathogenic acetylcholine receptor-specific monoclonal antibody IgG1-637. The effect of the blocker antibody (IgG4Δhinge-637, the hinge-deleted IgG4 version of IgG1-637) was assessed using decrement measurements and single-fiber electromyography. Three daily doses of 1.7 mg/kg IgG1-637 (cumulative dose 5 mg/kg) induced impairment of neuromuscular transmission, as demonstrated by significantly increased jitter, synaptic transmission failures (blockings) and a decrease in the amplitude of the compound muscle action potentials during repeated stimulations (decrement), without showing overt symptoms of muscle weakness. Treatment with three daily doses of 10 mg/kg IgG4Δhinge-637 significantly reduced the IgG1-637-induced increase in jitter, blockings and decrement. Together, these results represent proof-of principle data for therapy of acetylcholine receptor-myasthenia gravis with a monovalent antibody format that blocks binding of pathogenic autoantibodies.

The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.

Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.

Autoimmunity mediated by IgG4 subclass autoantibodies is an expanding field of research. Due to their structural characteristics a key feature of IgG4 antibodies is the ability to exchange Fab-arms with other, unrelated, IgG4 molecules, making the IgG4 molecule potentially monovalent for the specific antigen. However, whether those disease-associated antigen-specific IgG4 are mono- or divalent for their antigens is unknown. Myasthenia gravis (MG) with antibodies to muscle specific kinase (MuSK-MG) is a well-recognized disease in which the predominant pathogenic IgG4 antibody binds to extracellular epitopes on MuSK at the neuromuscular junction; this inhibits a pathway that clusters the acetylcholine (neurotransmitter) receptors and leads to failure of neuromuscular transmission. In vitro Fab-arm exchange-inducing conditions were applied to MuSK antibodies in sera, purified IgG4 and IgG1-3 sub-fractions. Solid-phase cross-linking assays were established to determine the extent of pre-existing and inducible Fab-arm exchange. Functional effects of the resulting populations of IgG4 antibodies were determined by measuring inhibition of agrin-induced AChR clustering in C2C12 cells. To confirm the results, κ/κ, λ/λ and hybrid κ/λ IgG4s were isolated and tested for MuSK antibodies. At least fifty percent of patients had IgG4, but not IgG1-3, MuSK antibodies that could undergo Fab-arm exchange in vitro under reducing conditions. Also MuSK antibodies were found in vivo that were divalent (monospecific for MuSK). Fab-arm exchange with normal human IgG4 did not prevent the inhibitory effect of serum derived MuSK antibodies on AChR clustering in C2C12 mouse myotubes. The results suggest that a considerable proportion of MuSK IgG4 could already be Fab-arm exchanged in vivo. This was confirmed by isolating endogenous IgG4 MuSK antibodies containing both κ and λ light chains, i.e. hybrid IgG4 molecules. These new findings demonstrate that Fab-arm exchanged antibodies are pathogenic., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background/objectives: Invariance of intelligence across age is often assumed but infrequently explicitly tested. Horn and McArdle (1992) tested measurement invariance of intelligence, providing adequate model fit but might not consider all relevant aspects such as sub-test differences. The goal of the current paper is to explore age-related invariance of the WAIS-R using an alternative model that allows direct tests of age on WAIS-R subtests., Methods: Cross-sectional data on 940 participants aged 16-75 from the WAIS-R normative values were used. Subtests examined were information, comprehension, similarities, vocabulary, picture completion, block design, picture arrangement, and object assembly. The two intelligence factors considered were fluid and crystallized intelligence. Self-reported ages were divided into young (16-22, n = 300), adult (29-39, n = 275), middle (40-60, n = 205), and older (61-75, n = 160) adult groups., Results: Results suggested partial metric invariance holds. Although most of the subtests reflected fluid and crystalized intelligence similarly across different ages, invariance did not hold for block design on fluid intelligence and picture arrangement on crystallized intelligence for older adults. Additionally, there was evidence of a correlated residual between information and vocabulary for the young adults only. This partial metric invariance model yielded acceptable model fit compared to previously-proposed invariance models of Horn and McArdle (1992)., Conclusion: Almost complete metric invariance holds for a two-factor model of intelligence. Most of the subtests were invariant across age groups, suggesting little evidence for age-related bias in the WAIS-R. However, we did find unique relationships between two subtests and intelligence. Future studies should examine age-related differences in subtests when testing measurement invariance in intelligence.

The experiment was setup to investigate the control of human quiet standing through the manipulation of augmented visual information feedback of selective properties of the motion of two primary variables in postural control: center of pressure (COP) and center of mass (COM). Five properties of feedback information were contrasted to a no feedback dual-task (watching a movie) control condition to determine the impact of visual real-time feedback on the coordination of the joint motions in postural control in both static and dynamic one-leg standing postures. The feedback information included 2D COP or COM position and macro variables derived from the COP and COM motions, namely virtual time-to-contact (VTC) and the COP-COM coupling. The findings in the static condition showed that the VTC and COP-COM coupling feedback conditions decreased postural motion more than the 2D COP or COM positional information. These variables also induced larger sway amplitudes in the dynamic condition showing a more progressive search strategy in exploring the stability limits. Canonical correlation analysis (CCA) found that COP-COM coupling contributed less than the other feedback variables to the redundancy of the system reflected in the common variance between joint motions and properties of sway motion. The COP-COM coupling had the lowest weighting of the motion properties to redundancy under the feedback conditions but overall the qualitative pattern of the joint motion structures was preserved within the respective static and dynamic balance conditions.

Most behavioural genetic studies focus on genetic and environmental influences on inter-individual phenotypic differences at the population level. The growing collection of intensive longitudinal data in social and behavioural science offers a unique opportunity to examine genetic and environmental influences on intra-individual phenotypic variability at the individual level. The current study introduces a novel idiographic approach and one novel method to investigate genetic and environmental influences on intra-individual variability by a simple empirical demonstration. Person-specific non-shared environmental influences on intra-individual variability of daily school feelings were estimated using time series data from twenty-one pairs of monozygotic twins (age = 10 years, 16 female pairs) over two consecutive weeks. Results showed substantial inter-individual heterogeneity in person-specific non-shared environmental influences. The current study represents a first step in investigating environmental influences on intra-individual variability with an idiographic approach, and provides implications for future behavioural genetic studies to examine developmental processes from a microscopic angle.

The nomothetic approach (i.e., the study of interindividual variation) dominates analyses of clinical data, even though its assumption of homogeneity across people and time is often violated. The idiographic approach (i.e., the study of intraindividual variation) is best suited for analyses of heterogeneous clinical data, but its person-specific methods and results have been criticized as unwieldy. Group iterative multiple model estimation (GIMME) combines the assets of the nomothetic and idiographic approaches by creating person-specific maps that contain a group-level structure. The maps show how intensively measured variables predict and are predicted by each other at different time scales. In this article, GIMME is introduced conceptually and mathematically, and then applied to an empirical data set containing the negative affect, detachment, disinhibition, and hostility composite ratings from the daily diaries of 25 individuals with personality pathology. Results are discussed with the aim of elucidating GIMME's potential for clinical research and practice., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Primarily from a measurement standpoint, we question some basic beliefs and procedures characterizing the scientific study of human behavior. The relations between observed and unobserved variables are key to an empirical approach to building explanatory theories and we are especially concerned about how the former are used as proxies for the latter. We believe that behavioral science can profitably reconsider the prevailing version of this arrangement because of its vulnerability to limiting idiosyncratic aspects of observed/unobserved variable relations. We describe a general measurement approach that takes into account idiosyncrasies that should be irrelevant to the measurement process but can intrude and may invalidate it in ways that distort and weaken relations among theoretically important variables. To clarify further our major concerns, we briefly describe one version of the measurement approach that fundamentally supports the individual as the primary unit of analysis orientation that we believe should be preeminent in the scientific study of human behavior.

Three commentaries on the Nesselroade and Molenaar target article in this issue are responded to in the interest of elaborating and defending the points of view expressed in our article. The commentaries feature philosophy of science, general structural modeling, and broad behavioral research perspectives. Responding to the commentaries afforded us the opportunity to clarify further matters that we deem critical to the fundamental matter of measurement in behavioral science, especially as it emphasizes (properly, we believe) the individual as the primary unit of analysis.

Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample., (Published by Elsevier Inc.)

Psychiatric diagnostic covariation suggests that the underlying structure of psychopathology is not one of circumscribed disorders. Quantitative modeling of individual differences in diagnostic patterns has uncovered several broad domains of mental disorder liability, of which the Internalizing and Externalizing spectra have garnered the greatest support. These dimensions have generally been estimated from lifetime or past-year comorbidity patters, which are distal from the covariation of symptoms and maladaptive behavior that ebb and flow in daily life. In this study, structural models are applied to daily diary data (Median = 94 days) of maladaptive behaviors collected from a sample (N = 101) of individuals diagnosed with personality disorders (PDs). Using multilevel and unified structural equation modeling, between-person, within-person, and person-specific structures were estimated from 16 behaviors that are encompassed by the Internalizing and Externalizing spectra. At the between-person level (i.e., individual differences in average endorsement across days) we found support for a two-factor Internalizing-Externalizing model, which exhibits significant associations with corresponding diagnostic spectra. At the within-person level (i.e., dynamic covariation among daily behavior pooled across individuals) we found support for a more differentiated, four-factor, Negative Affect-Detachment-Hostility-Disinhibition structure. Finally, we demonstrate that the person-specific structures of associations between these four domains are highly idiosyncratic.

A posteriori model validation for the temporal order of neural directed functional connectivity maps is rare. This is striking because models that require sequential independence among residuals are regularly implemented. The aim of the current study was (a) to apply to directed functional connectivity maps of functional magnetic resonance imaging data an a posteriori model validation procedure (i.e., white noise tests of one-step-ahead prediction errors combined with decision criteria for revising the maps based upon Lagrange Multiplier tests), and (b) to demonstrate how the procedure applies to single-subject simulated, single-subject task-related, and multi-subject resting state data. Directed functional connectivity was determined by the unified structural equation model family of approaches in order to map contemporaneous and first order lagged connections among brain regions at the group- and individual-levels while incorporating external input, then white noise tests were run. Findings revealed that the validation procedure successfully detected unmodeled sequential dependencies among residuals and recovered higher order (greater than one) simulated connections, and that the procedure can accommodate task-related input. Findings also revealed that lags greater than one were present in resting state data: With a group-level network that contained only contemporaneous and first order connections, 44% of subjects required second order, individual-level connections in order to obtain maps with white noise residuals. Results have broad methodological relevance (e.g., temporal validation is necessary after directed functional connectivity analyses because the presence of unmodeled higher order sequential dependencies may bias parameter estimates) and substantive implications (e.g., higher order lags may be common in resting state data).

Myasthenia gravis (MG) with antibodies against the acetylcholine receptor (AChR) is characterized by a chronic, fatigable weakness of voluntary muscles. The production of autoantibodies involves the dysregulation of T cells which provide the environment for the development of autoreactive B cells. The symptoms are caused by destruction of the postsynaptic membrane and degradation of the AChR by IgG autoantibodies, predominantly of the G1 and G3 subclasses. Active immunization of animals with AChR from mammalian muscles, AChR from Torpedo or Electrophorus electric organs, and recombinant or synthetic AChR fragments generates a chronic model of MG, termed experimental autoimmune myasthenia gravis (EAMG). This model covers cellular mechanisms involved in the immune response against the AChR, e.g. antigen presentation, T cell-help and regulation, B cell selection and differentiation into plasma cells. Our aim is to define standard operation procedures and recommendations for the rat EAMG model using purified AChR from the Torpedo californica electric organ, in order to facilitate more rapid translation of preclinical proof of concept or efficacy studies into clinical trials and, ultimately, clinical practice., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Despite exciting advances in the functional imaging of the brain, it remains a challenge to define regions of interest (ROIs) that do not require investigator supervision and permit examination of change in networks over time (or plasticity). Plasticity is most readily examined by maintaining ROIs constant via seed-based and anatomical-atlas based techniques, but these approaches are not data-driven, requiring definition based on prior experience (e.g., choice of seed-region, anatomical landmarks). These approaches are limiting especially when functional connectivity may evolve over time in areas that are finer than known anatomical landmarks or in areas outside predetermined seeded regions. An ideal method would permit investigators to study network plasticity due to learning, maturation effects, or clinical recovery via multiple time point data that can be compared to one another in the same ROI while also preserving the voxel-level data in those ROIs at each time point. Data-driven approaches (e.g., whole-brain voxelwise approaches) ameliorate concerns regarding investigator bias, but the fundamental problem of comparing the results between distinct data sets remains. In this paper we propose an approach, aggregate-initialized label propagation (AILP), which allows for data at separate time points to be compared for examining developmental processes resulting in network change (plasticity). To do so, we use a whole-brain modularity approach to parcellate the brain into anatomically constrained functional modules at separate time points and then apply the AILP algorithm to form a consensus set of ROIs for examining change over time. To demonstrate its utility, we make use of a known dataset of individuals with traumatic brain injury sampled at two time points during the first year of recovery and show how the AILP procedure can be applied to select regions of interest to be used in a graph theoretical analysis of plasticity.

This paper investigated the organization of the postural control system in human upright stance. To this aim the shared variance between joint and 3D total body center of mass (COM) motions was analyzed using multivariate canonical correlation analysis (CCA). The CCA was performed as a function of established models of postural control that varied in their joint degrees of freedom (DOF), namely, an inverted pendulum ankle model (2DOF), ankle-hip model (4DOF), ankle-knee-hip model (5DOF), and ankle-knee-hip-neck model (7DOF). Healthy young adults performed various postural tasks (two-leg and one-leg quiet stances, voluntary AP and ML sway) on a foam and rigid surface of support. Based on CCA model selection procedures, the amount of shared variance between joint and 3D COM motions and the cross-loading patterns we provide direct evidence of the contribution of multi-DOF postural control mechanisms to human balance. The direct model fitting of CCA showed that incrementing the DOFs in the model through to 7DOF was associated with progressively enhanced shared variance with COM motion. In the 7DOF model, the first canonical function revealed more active involvement of all joints during more challenging one leg stances and dynamic posture tasks. Furthermore, the shared variance was enhanced during the dynamic posture conditions, consistent with a reduction of dimension. This set of outcomes shows directly the degeneracy of multivariate joint regulation in postural control that is influenced by stance and surface of support conditions.

Background: Sustained recovery from substance abuse is a dynamic intraindividual-level process., Objectives: We argue that research on recovery process will benefit from a theoretical approach that captures both the dynamic and the idiographic nature of substance abuse recovery. In addition to setting out why we believe that research on recovery can benefit from such an approach, we provide a demonstration of idiographic within-individual analyses of between- and within-day associations among negative affect, substance use craving, and positive social experiences., Design and Subjects: The data used were drawn from 39 abstinent young adults in 12-step recovery from substance abuse (mean age = 22.9, females = 12). Participants provided an average of 26.7 days of daily diary data by end-of-day collections. Unified first-order structural equation models were fit individually to predict daily levels of craving and negative affect from the previous day's same two variables as well as from both the previous day's and the same day's positive social experiences., Results: Model estimates demonstrated substantial interindividual heterogeneity in their day-to-day associations in both direction and magnitude, highlighting the importance of applying idiographic approach to understanding recovery. Cluster analyses were subsequently applied to individual model estimates to identify homogeneous subgroups that demonstrated similar day-to-day association patterns, revealing two distinct subgroups that appeared to manage daily abstinence through different mechanisms., Conclusions: The idiographic approach presented provides the potential value of framing recovery as an idiosyncratic dynamic process and provides targets for tailored and adaptive treatment and recovery supporting intervention in future design and evaluation., (© The Author(s) 2015.)

This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.

The first few days of an attempt to quit smoking are marked by impairments in cognitive domains, such as working memory and attention. These cognitive impairments have been linked to increased risk for relapse. Little is known about individual differences in the cognitive impairments that accompany deprivation or the neural processing reflected in those differences. In order to address this knowledge gap, we collected functional magnetic resonance imaging (fMRI) data from 118 nicotine-deprived smokers while they performed a verbal n-back task. We predicted better performance would be associated with more efficient patterns of brain activation and effective connectivity. Results indicated that performance was positively related to load-related activation in the left dorsolateral prefrontal cortex and the left lateral premotor cortex. Additionally, effective connectivity patterns differed as a function of performance, with more accurate participants having simpler, more parsimonious network models than did worse participants. Cognitive efficiency is typically thought of as less neural activation for equal or superior behavioral performance. Taken together, findings suggest cognitive efficiency should not be viewed solely in terms of amount of activation but that both the magnitude of activation within and degree of covariation between task-critical structures must be considered. This research highlights the benefit of combining traditional fMRI analysis with newer methods for modeling brain connectivity. These results suggest a possible role for indices of network functioning in assessing relapse risk in quitting smokers as well as offer potentially useful targets for novel intervention strategies., (© 2013 Society for the Study of Addiction.)