Detecting Task-Dependent Functional Connectivity in Group Iterative Multiple Model Estimation with Person-Specific Hemodynamic Response Functions

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.

Problems with Centrality Measures in Psychopathology Symptom Networks: Why Network Psychometrics Cannot Escape Psychometric Theory

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 Hierarchical Taxonomy of Psychopathology (HiTOP): A Quantitative Nosology Based on Consensus of Evidence

Traditional diagnostic systems went beyond empirical evidence on the structure of mental health. Consequently, these diagnoses do not depict psychopathology accurately, and their validity in research and utility in clinicalpractice are therefore limited. The Hierarchical Taxonomy of Psychopathology (HiTOP) consortium proposed a model based on structural evidence. It addresses problems of diagnostic heterogeneity, comorbidity, and unreliability. We review the HiTOP model, supporting evidence, and conceptualization of psychopathology in this hierarchical dimensional framework. The system is not yet comprehensive, and we describe the processes for improving and expanding it. We summarize data on the ability of HiTOP to predict and explain etiology (genetic, environmental, and neurobiological), risk factors, outcomes, and treatment response. We describe progress in the development of HiTOP-based measures and in clinical implementation of the system. Finally, we review outstanding challenges and the research agenda. HiTOP is of practical utility already, and its ongoing development will produce a transformative map of psychopathology.

Dispositional attachment style moderates the effects of physiological coregulation on short-term changes in attachment anxiety and avoidance

Individuals with personality disorders often experience romantic relationship dysfunction and have an insecure attachment style. Here, we examined attachment dynamics in dyadic interactions, focusing specifically on the role of physiological coregulation in state attachment processes in couples oversampled for personality pathology. A total of 121 couples completed a 10-minute discussion about an area of disagreement in their relationship and a 5-minute discussion in which they planned an event together. We used a dynamical model of heart rate changes to estimate coregulation. We found that (a) increases in state attachment avoidance were associated with contrarian coregulation (heart rate becoming misaligned with the partner's physiology) and (b) conversely, increases in state attachment anxiety were associated with dependent coregulation (heart rate becoming aligned with the partners' physiology). Dispositional attachment insecurity moderated the effects of state attachment insecurity on physiological coregulation. Whereas dispositional anxiety predicted individuals exhibiting dependent coregulation in response to state insecurity, dispositional avoidance predicted contrarian coregulation in response to state insecurity. This work provides insight into the role of physiological coregulation in attachment dynamics among couples oversampled for personality pathology, suggesting that disruptions to coregulation contribute to impaired emotion regulation during romantic conflicts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Differential reinforcement encoding along the hippocampal long axis helps resolve the explore-exploit dilemma

When making decisions, should one exploit known good options or explore potentially better alternatives? Exploration of spatially unstructured options depends on the neocortex, striatum, and amygdala. In natural environments, however, better options often cluster together, forming structured value distributions. The hippocampus binds reward information into allocentric cognitive maps to support navigation and foraging in such spaces. Here we report that human posterior hippocampus (PH) invigorates exploration while anterior hippocampus (AH) supports the transition to exploitation on a reinforcement learning task with a spatially structured reward function. These dynamics depend on differential reinforcement representations in the PH and AH. Whereas local reward prediction error signals are early and phasic in the PH tail, global value maximum signals are delayed and sustained in the AH body. AH compresses reinforcement information across episodes, updating the location and prominence of the value maximum and displaying goal cell-like ramping activity when navigating toward it.

From Description to Explanation: Integrating Across Multiple Levels of Analysis to Inform Neuroscientific Accounts of Dimensional Personality Pathology

Dimensional approaches to psychiatric nosology are rapidly transforming the way researchers and clinicians conceptualize personality pathology, leading to a growing interest in describing how individuals differ from one another. Yet, in order to successfully prevent and treat personality pathology, it is also necessary to explain the sources of these individual differences. The emerging field of personality neuroscience is well-positioned to guide the transition from description to explanation within personality pathology research. However, establishing comprehensive, mechanistic accounts of personality pathology will require personality neuroscientists to move beyond atheoretical studies that link trait differences to neural correlates without considering the algorithmic processes that are carried out by those correlates. We highlight some of the dangers we see in overpopulating personality neuroscience with brain-trait associational studies and offer a series of recommendations for personality neuroscientists seeking to build explanatory theories of personality pathology.

Disinhibition and Detachment in Adolescence: A Developmental Cognitive Neuroscience Perspective on the Alternative Model for Personality Disorders

Personality pathology often emerges during adolescence, but attempts to understand its neurocognitive basis have traditionally been undermined by problems associated with the categorical classification of personality disorders. In contrast, dimensional models of personality pathology, such as the Alternative Model for Personality Disorders (AMPD) in DSM-5, may provide a stronger foundation for neurobiological investigations of maladaptive individual differences in personality. As an example, we review studies of the adolescent development of reward processing and cognitive control and connect these systems to the normal personality hierarchy and to two dimensions included in the AMPD - Detachment and Disinhibition. We argue that by linking developmental changes in these systems to the AMPD, researchers will be better positioned to understand the relationship between neurocognitive development and the expression of personality pathology in adolescence and early adulthood.

The Emotional Bank Account and the Four Horsemen of the Apocalypse in Romantic Relationships of People with Borderline Personality Disorder: A Dyadic Observational Study

Few studies have examined behaviors in romantic relationships associated with borderline personality disorder (BPD). We assessed critical variables from marital research: the "emotional bank account" (positive-to-negative behaviors; Gottman, 1993) and the "four horsemen of the apocalypse" (criticism, defensiveness, contempt, and stonewalling; Gottman & Silver, 1999; Gottman & Krokoff, 1989). Couples (N = 130, or 260 participants) engaged in a conflict task and reported relationship satisfaction at intake and 12-months. Clinician-rated BPD and avoidant PD (APD) criteria were examined. People with more BPD symptoms and their partners were less satisfied, which worsened by follow-up. Conflict behaviors partially explained these associations. Partners of people with more BPD symptoms had a worse emotional bank account, which then predicted (a) poorer satisfaction for both members and (b) worsening partner satisfaction. People with more BPD symptoms criticized more; their partners defended and stonewalled more. APD predicted worsening satisfaction. BPD appears to link specifically with relationship dysfunction, partly through associations with partner behavior.

Disrupted physiological coregulation during a conflict predicts short-term discord and long-term relationship dysfunction in couples with personality pathology

Interpersonal dysfunction is a core feature of personality disorders, often affecting close relationships. Nevertheless, little is known about the moment-to-moment dynamic processes by which personality pathology contributes to dysfunctional relationships. Here, we investigated the role of physiological attunement during a conflict discussion in romantic couples oversampled for personality pathology. We hypothesized that physiological coregulation would be disrupted in individuals with personality pathology, subsequently predicting short-term discord and long-term relationship dissatisfaction. One hundred twenty-one couples completed a 10-min discussion about an area of disagreement while cardiovascular physiology and behavior were recorded. We quantified coregulation using a dynamical systems model of heart rate changes. We found that greater interpersonal problem severity was associated with more contrarian coregulation, exacerbating negative affect and interpersonal perceptions. Furthermore, the extent to which coregulation was associated with increased discord prospectively predicted relationship dissatisfaction 1 year later. Altogether, this work sheds light on a pathway by which personality pathology contributes to problems in romantic relationships. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Predicting future courses of psychotherapy within a grouped LASSO framework

Objective: There is a paucity of studies examining the experience of clients who undergo multiple courses of psychotherapy. Conducted within a large practice research network, this study demonstrated that returning therapy clients comprise a considerable portion of the clinical population in university counseling settings, and identified variables associated with return to therapy. Method: Utilizing data spanning 2013 to 2017, statistical variable selection for predicting return to therapy was conducted via grouped least absolute shrinkage and selection operator (grouped LASSO) applied to logistic regression. The grouped LASSO approach is described in detail to facilitate learning and replication. The paper also addresses methodological considerations related to this approach, such as sample size, generalizability, as well as general strengths and limitations. Results: Attendance rate, duration of initial treatment course, social anxiety, perceived social support, academic distress, and alcohol use were identified as predictive of return to therapy. Conclusions: Findings could help inform more cost-effective policies for session limits (e.g., extending session limits for clients with social anxiety), referral decisions (e.g., for clients with alcohol use problems), and appointment reminders (based on the association between poor attendance rate and return to therapy). Taking into account the many reasons that can explain why clients do or do not return to therapy, these findings also could inform clinicians' early case conceptualizations and treatment interventions.

Ventromedial prefrontal value signals and functional connectivity during decision-making in suicidal behavior and impulsivity

Suicide is linked to impaired value-based decision-making and impulsivity, but whether these risk factors share neural underpinnings is unclear. Disrupted ventromedial prefrontal cortex (vmPFC) value signals may underlie this behavioral phenotype. We investigated vmPFC value signals, vmPFC-frontoparietal connectivity, and the impact of impulsivity during decision-making in depressed individuals with and without suicidal behavior. Middle-aged and older adults (n = 116; 35 with a history of suicide attempts, 25 with ideation only, 25 depressed controls with no ideation, and 31 nonpsychiatric controls) completed a decision-making task with drifting reward probabilities during fMRI. Values of choices, estimated by a reinforcement learning model, were regressed against BOLD signal. VmPFC value activation was compared between groups. Moderating effects of impulsivity on vmPFC-frontoparietal connectivity were assessed in nonpsychiatric controls and compared among patient groups. VmPFC value responses in participants with a history of suicide attempts were reduced relative to nonpsychiatric controls (p < 0.05). In nonpsychiatric controls, vmPFC-frontoparietal connectivity was negatively moderated by impulsivity (pFWE corrected < 0.05). This effect was preserved in comparison patient groups but abolished in suicide attempters (p < 0.001). This change in neural connectivity patterns also affected behavior: people with a history of suicide attempts showed a disrupted effect of vmPFC-frontoparietal connectivity, impulsivity, and reinforcement on choice quality (p < 0.001). These effects were specific to vmPFC and not to striatum. In summary, findings from this study largely support disrupted vmPFC value signals in suicidal behavior. In addition, it uncovers an altered pattern of vmPFC-frontoparietal connectivity in impulsive people with suicidal behavior, which may underlie disrupted choice processes in a suicidal crisis.

A Hierarchical Taxonomy of Psychopathology Can Transform Mental Health Research

For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system-the Hierarchical Taxonomy of Psychopathology (HiTOP)-that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.

Value-Based Choice, Contingency Learning, and Suicidal Behavior in Mid- and Late-Life Depression

BACKGROUND

Suicidal behavior is associated with impaired decision making in contexts of uncertainty. Existing studies, however, do not definitively address whether suidice attempers have 1) impairment in learning from experience or 2) impairment in choice based on comparison of estimated option values. Our reinforcement learning model-based behavioral study tested these hypotheses directly in middle-aged and older suicide attempters representative of those who die by suicide.

METHODS

Two samples (sample 1, n = 135; sample 2, n = 125) of suicide attempters with depression (n_attempters = 54 and 39, respectively), suicide ideators, nonsuicidal patients with depression, and healthy control participants completed a probabilistic three-choice decision-making task. A second experiment in sample 2 experimentally dissociated long-term learned value from reward magnitude. Analyses combined computational reinforcement learning and mixed-effects models of decision times and choices.

RESULTS

With regard to learning, suicide attempters (vs. all comparison groups) were less sensitive to one-back reinforcement, as indicated by a reduced effect on both choices and decision times. Learning deficits scaled with attempt lethality and were partially explained by poor cognitive control. With regard to value-based choice, suicide attempters (vs. all comparison groups) displayed abnormally long decision times when choosing between similarly valued options and were less able to distinguish between the best and second-best options. Group differences in value-based choice were robust to controlling for cognitive performance, comorbidities, impulsivity, psychotropic exposure, and possible brain damage from attempts.

CONCLUSIONS

Serious suicidal behavior is associated with impaired reward learning, likely undermining the search for alternative solutions. Attempted suicide is associated with impaired value comparison during the choice process, potentially interfering with the consideration of deterrents and alternatives in a crisis.

Attachment styles, social behavior, and personality functioning in romantic relationships

Personality disorders (PDs) are commonly associated with romantic relationship disturbance. However, research has seldom evaluated who people with high PD severity partner with, and what explains the link between PD severity and romantic relationship disturbance. First, we examined the degree to which people match with partners with similar levels of personality and interpersonal problems. Second, we evaluated whether the relationship between PD severity and romantic relationship satisfaction would be explained by attachment styles and demand/withdraw behavior. Couples selected for high PD severity (n = 130; 260 participants) engaged in a conflict task, were assessed for PDs and attachment using semi-structured interviews, and self-reported their relationship satisfaction. Dyad members were not similar in terms of PD severity but evidenced a small degree of similarity on specific attachment styles and were moderately similar on attachment insecurity and interpersonal problems. PD severity also moderated the degree to which one person's attachment anxiety was associated with their partner's attachment avoidance. In addition, using a dyadic analytic approach, we found attachment anxiety and actor and partner withdrawal explained some of the relationship between PD severity and relationship satisfaction. Our results indicate people often have romantic partners with similar levels of attachment disturbance and interpersonal problems and that attachment styles and related behavior explains some of the association between PD severity and relationship satisfaction. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Selective maintenance of value information helps resolve the exploration/exploitation dilemma

In natural environments with many options of uncertain value, one faces a difficult tradeoff between exploiting familiar, valuable options or searching for better alternatives. Reinforcement learning models of this exploration/exploitation dilemma typically modulate the rate of exploratory choices or preferentially sample uncertain options. The extent to which such models capture human behavior remains unclear, in part because they do not consider the constraints on remembering what is learned. Using reinforcement-based timing as a motivating example, we show that selectively maintaining high-value actions compresses the amount of information to be tracked in learning, as quantified by Shannon's entropy. In turn, the information content of the value representation controls the balance between exploration (high entropy) and exploitation (low entropy). Selectively maintaining preferred action values while allowing others to decay renders the choices increasingly exploitative across learning episodes. To adjudicate among alternative maintenance and sampling strategies, we developed a new reinforcement learning model, StrategiC ExPloration/ExPloitation of Temporal Instrumental Contingencies (SCEPTIC). In computational studies, a resource-rational selective maintenance approach was as successful as more resource-intensive strategies. Furthermore, human behavior was consistent with selective maintenance; information compression was most pronounced in subjects with superior performance and non-verbal intelligence, and in learnable vs. unlearnable contingencies. Cognitively demanding uncertainty-directed exploration recovered a more accurate representation in simulations with no foraging advantage and was strongly unsupported in our human study.

Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world

Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer's disease or depression have adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.

Interpersonal dysfunction in borderline personality: a decision neuroscience perspective

Borderline personality disorder (BPD) is characterized by disadvantageous decisions that are often expressed in close relationships and associated with intense negative emotions. Although functional neuroimaging studies of BPD have described regions associated with altered social cognition and emotion processing, these correlates do not inform an understanding of how brain activity leads to maladaptive choices. Drawing on recent research, we argue that formal models of decision-making are crucial to elaborating theories of BPD that bridge psychological constructs, behavior, and neural systems. We propose that maladaptive interactions between Pavlovian and instrumental influences play a crucial role in the expression of interpersonal problems. Finally, we articulate specific hypotheses about how clinical features of BPD may map onto neural systems that implement separable decision processes.

Modeling Heterogeneity in Momentary Interpersonal and Affective Dynamic Processes in Borderline Personality Disorder

Borderline personality disorder (BPD) is a diagnosis defined by impairments in several dynamic processes (e.g., interpersonal relating, affect regulation, behavioral control). Theories of BPD emphasize that these impairments appear in specific contexts, and emerging results confirm this view. At the same time, BPD is a complex construct that encompasses individuals with heterogeneous pathology. These features-dynamic processes, situational specificity, and individual heterogeneity-pose significant assessment challenges. In the current study, we demonstrate assessment and analytic methods that capture both between-person differences and within-person changes over time. Twenty-five participants diagnosed with BPD completed event-contingent, ambulatory assessment protocols over 21 days. We used p-technique factor analyses to identify person-specific psychological structures consistent with clinical theories of personality. Five exemplar cases are selected and presented in detail to showcase the potential utility of these methods. The presented cases' factor structures reflect not only heterogeneity but also suggest points of convergence. The factors also demonstrated significant associations with important clinical targets (self-harm, interpersonal violence).

The effect of pathological narcissism on interpersonal and affective processes in social interactions

Narcissism has significant interpersonal costs, yet little research has examined behavioral and affective patterns characteristic of narcissism in naturalistic settings. Here we studied the effect of narcissistic features on the dynamic processes of interpersonal behavior and affect in daily life. We used interpersonal theory to generate transactional models of social interaction (i.e., linkages among perceptions of others' behavior, affect, and one's own behavior) predicted to be characteristic of narcissism. Psychiatric outpatients (N = 102) completed clinical interviews and a 21-day ecological momentary assessment protocol using smartphones. After social interactions (N = 5,781), participants reported on perceptions of their interaction partner's behavior (scored along the dimensions of dominant-submissive and affiliative-quarrelsome), their own affect, and their own behavior. Multilevel structural equation modeling was used to examine dynamic links among behavior and affect across interactions, and the role of narcissism in moderating these links. Results showed that perceptions of others' dominance did not predict dominant behavior, but did predict quarrelsome behavior, and this link was potentiated by narcissism. Furthermore, the link between others' dominance and one's own quarrelsome behavior was mediated by negative affect. Moderated mediation was also found: Narcissism amplified the link between ratings of others' dominance and one's own quarrelsomeness and negative affect. Narcissism did not moderate the link between other dominance and own dominance, nor the link between other affiliation and own affiliation. These results suggest that narcissism is associated with specific interpersonal and affective processes, such that sensitivity to others' dominance triggers antagonistic behavior in daily life. (PsycINFO Database Record

Incentives facilitate developmental improvement in inhibitory control by modulating control-related networks

Adolescence is a period of heightened sensitivity to incentives and relatively weak cognitive control, which may contribute to risky behaviors. Studies of brain activity have generally identified greater activation of the ventral striatum to rewards and less activation of prefrontal regions during control tasks in adolescents compared to adults. Little is known, however, about age-related changes in the functional brain networks underlying incentive processing and cognitive control. This cross-sectional study characterized the effects of incentives on inhibitory control during an oculomotor task using whole-brain functional connectivity analyses. During an fMRI scan, one hundred forty typically developing individuals completed an incentivized antisaccade task consisting of incentive cue, preparation, and response phases. We found that task modulation of control networks increased gradually from childhood to adulthood, whereas a network including ventral striatum and ventromedial prefrontal cortex displayed an adolescent-specific peak in response to the receipt of outcomes, consistent with dual-systems models. Notably, however, greater modulation of salience and motor networks during the preparation phase mediated age-related improvements in antisaccade accuracy, whereas adolescent enhancement of value-related circuitry did not. Relative to neutral cues, both reward and loss cues enhanced task-related connectivity of the salience network when preparing to inhibit a saccade. Altogether, our findings suggest that incentives facilitate inhibitory control by enhancing the salience of one's responses and that over development, the recruitment of functional networks involved in saliency and motor preparation supports better performance.

MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus

MplusAutomation is a package for R that facilitates complex latent variable analyses in Mplus involving comparisons among many models and parameters. More specifically, MplusAutomation provides tools to accomplish three objectives: to create and manage Mplus syntax for groups of related models; to automate the estimation of many models; and to extract, aggregate, and compare fit statistics, parameter estimates, and ancillary model outputs. We provide an introduction to the package using applied examples including a large-scale simulation study. By reducing the effort required for large-scale studies, a broad goal of MplusAutomation is to support methodological developments in structural equation modeling using Mplus.

Functional connectivity differences in autism during face and car recognition: underconnectivity and atypical age-related changes

Face recognition abilities improve between adolescence and adulthood over typical development (TD), but plateau in autism, leading to increasing face recognition deficits in autism later in life. Developmental differences between autism and TD may reflect changes between neural systems involved in the development of face encoding and recognition. Here, we focused on whole-brain connectivity with the fusiform face area (FFA), a well-established face-preferential brain region. Older children, adolescents, and adults with and without autism completed the Cambridge Face Memory Test, and a matched car memory test, during fMRI scanning. We then examined task-based functional connectivity between the FFA and the rest of the brain, comparing autism and TD groups during encoding and recognition of face and car stimuli. The autism group exhibited underconnectivity, relative to the TD group, between the FFA and frontal and primary visual cortices, independent of age. Underconnectivity with the medial and rostral lateral prefrontal cortex was face-specific during encoding and recognition, respectively. Conversely, underconnectivity with the L orbitofrontal cortex was evident for both face and car encoding. Atypical age-related changes in connectivity emerged between the FFA and the R temporoparietal junction, and R dorsal striatum for face stimuli only. Similar differences in age-related changes in autism emerged for FFA connectivity with the amygdala across both face and car recognition. Thus, underconnectivity and atypical development of functional connectivity may lead to a less optimal face-processing network in the context of increasing general and social cognitive deficits in autism.

Development of White Matter Microstructure and Intrinsic Functional Connectivity Between the Amygdala and Ventromedial Prefrontal Cortex: Associations With Anxiety and Depression

BACKGROUND

Connectivity between the amygdala and ventromedial prefrontal cortex (vmPFC) is compromised in multiple psychiatric disorders, many of which emerge during adolescence. To identify to what extent the deviations in amygdala-vmPFC maturation contribute to the onset of psychiatric disorders, it is essential to characterize amygdala-vmPFC connectivity changes during typical development.

METHODS

Using an accelerated cohort longitudinal design (1-3 time points, 10-25 years old, n = 246), we characterized developmental changes of the amygdala-vmPFC subregion functional and structural connectivity using resting-state functional magnetic resonance imaging and diffusion-weighted imaging.

RESULTS

Functional connectivity between the centromedial amygdala and rostral anterior cingulate cortex (rACC), anterior vmPFC, and subgenual cingulate significantly decreased from late childhood to early adulthood in male and female subjects. Age-associated decreases were also observed between the basolateral amygdala and the rACC. Importantly, these findings were replicated in a separate cohort (10-22 years old, n = 327). Similarly, structural connectivity, as measured by quantitative anisotropy, significantly decreased with age in the same regions. Functional connectivity between the centromedial amygdala and the rACC was associated with structural connectivity in these same regions during early adulthood (22-25 years old). Finally, a novel time-varying coefficient analysis showed that increased centromedial amygdala-rACC functional connectivity was associated with greater anxiety and depression symptoms during early adulthood, while increased structural connectivity in centromedial amygdala-anterior vmPFC white matter was associated with greater anxiety/depression during late childhood.

CONCLUSIONS

Specific developmental periods of functional and structural connectivity between the amygdala and the prefrontal systems may contribute to the emergence of anxiety and depressive symptoms and may play a critical role in the emergence of psychiatric disorders in adolescence.

Protracted development of executive and mnemonic brain systems underlying working memory in adolescence: A longitudinal fMRI study

Working memory (WM), the ability to hold information on-line to guide planned behavior, improves through adolescence in parallel with continued maturation of critical brain systems supporting cognitive control. Initial developmental neuroimaging studies with one or two timepoints have provided important though varied results limiting our understanding of which and how neural systems change during this transition into mature WM. In this study, we leverage functional magnetic resonance imaging (fMRI) longitudinal data spanning up to 9 years in 129 normally developing individuals to identify which systems demonstrate growth changes that accompany improvements in WM performance. We used a memory guided saccade task that allowed us to probe encoding, pure maintenance, and retrieval neural processes of WM. Consistent with prior research, we found that WM performance continued to improve into the early 20's. fMRI region of interest (ROI) analyses revealed developmental (1) increases in sensorimotor-related (encoding/retrieval) activity in visual cortex from childhood through early adulthood that were associated with WM accuracy and (2) decreases in sustained (maintenance) activity in executive regions from childhood through mid-adolescence that were associated with response latency in childhood and early adolescence. Together these results provide compelling evidence that underlying the maturation of WM is a transition from reliance on executive systems to specialized regions related to the domain of mnemonic requirements of the task leading to optimal performance.