Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome.

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.

Neurocognitive risk phenotyping to predict mood symptoms in adolescence.

Predicting mood disorders in adolescence is a challenge that motivates research to identify neurocognitive predictors of symptom expression and clinical profiles. This study used machine learning to test whether neurocognitive variables predicted future manic or anhedonic symptoms in two adolescent samples risk-enriched for lifetime mood disorders (Sample 1, n = 73, ages = 13-25, M [SD] = 19.22 [2.49] years, 68% lifetime mood disorder) or familial mood disorders (Sample 2, n = 154, ages = 13-21, M [SD] = 16.46 [1.95] years, 62% first-degree family history of mood disorder). Participants completed cognitive testing and functional magnetic resonance imaging at baseline, for behavioral and neural measures of reward processing and executive functioning. Next, participants completed a daily diary procedure for 8-16 weeks. Penalized mixed-effects models identified neurocognitive predictors of future mood symptoms and stress-reactive changes in mood symptoms. Results included the following. In both samples, adolescents showing ventral corticostriatal reward hyposensitivity and lower reward performance reported more severe stress-reactive anhedonia. Poorer executive functioning behavior was associated with heightened anhedonia overall in Sample 1, but lower stress-reactive anhedonia in both samples. In Sample 1, adolescents showing ventral corticostriatal reward hypersensitivity and poorer executive functioning reported more severe stress-reactive manic symptoms. Clustering analyses identified, and replicated, five neurocognitive subgroups. Adolescents characterized by neural or behavioral reward hyposensitivities together with average-to-poor executive functioning reported unipolar symptom profiles. Adolescents showing neural reward hypersensitivity together with poor behavioral executive functioning reported a bipolar symptom profile (Sample 1 only). Together, neurocognitive phenotypes may hold value for predicting symptom expression and profiles of mood pathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The dimensional structure of internalizing psychopathology: Relation to diagnostic categories.

Recent approaches aim to represent the dimensional structure of psychopathology, but relatively little research has rigorously tested sub-dimensions within internalizing psychopathology. This study tests pre-registered models of the dimensional structure of internalizing psychopathology, and their relations with current and lifetime depressive and anxiety disorders diagnostic data, in adult samples harmonized across three sites (n=427). Across S-1 bifactor and hierarchical models, we found converging evidence for both general and specific internalizing dimensions. Depression, generalized anxiety disorder (GAD), social anxiety disorder (SAD), and panic attacks were all associated with a general internalizing factor that we posit primarily represents motivational anhedonia. GAD was also associated with a specific anxious apprehension factor, and SAD with specific anxious apprehension and low positive affect factors. We suggest that dimensional approaches capturing shared and specific internalizing symptom facets more accurately describe the structure of internalizing psychopathology and provide useful alternatives to categorical diagnoses to advance clinical science.

Mood Symptom Dimensions and Developmental Differences in Neurocognition in Adolescence.

Adolescence is critical period of neurocognitive development as well as increased prevalence of mood pathology. This cross-sectional study replicated developmental patterns of neurocognition and tested whether mood symptoms moderated developmental effects. Participants were 419 adolescents (n=246 with current mood disorders) who completed reward learning and executive functioning tasks, and reported on age, puberty, and mood symptoms. Structural equation modeling revealed a quadratic relationship between puberty and reward learning performance that was moderated by symptom severity: in early puberty, adolescents reporting higher manic symptoms exhibited heightened reward learning performance (better maximizing of rewards on learning tasks), whereas adolescents reporting elevated anhedonia showed blunted reward learning performance. Models also showed a linear relationship between age and executive functioning that was moderated by manic symptoms: adolescents reporting higher mania showed poorer executive functioning at older ages. Findings suggest neurocognitive development is altered in adolescents with mood pathology and suggest directions for longitudinal studies.

Longitudinal Relations Between Emotion Regulation and Internalizing Symptoms in Emerging Adults During the Covid-19 Pandemic.

Background: Maladaptive and adaptive emotion regulation are putative risk and protective factors for depression and anxiety, but most prior research does not differentiate within-person effects from between-person individual differences. The current study does so during the early part of the Covid-19 pandemic when internalizing symptoms were high. Methods: A sample of emerging adult undergraduate students (N = 154) completed online questionnaires bi-weekly on depression, anxiety, and emotion regulation across eight weeks during the early days of the Covid-19 pandemic (April 2nd to June 27th, 2020). Results: Depression demonstrated significantly positive between-person correlations with overall maladaptive emotion regulation, catastrophizing, and self-blame, and negative correlations with overall adaptive emotion regulation and reappraisal. Anxiety demonstrated significantly positive between-person correlations with overall maladaptive emotion regulation, rumination, and catastrophizing, and a negative correlation with reappraisal. After controlling for these between-person associations, however, there were generally no within-person associations between emotion regulation and internalizing symptoms. Conclusions: Emotion regulation and internalizing symptoms might be temporally stable individual differences that cooccur with one another as opposed to having a more dynamic relation. Alternatively, these dynamic mechanisms might operate over much shorter or longer periods compared to the two-week time lag in the current study. Supplementary Information: The online version contains supplementary material available at 10.1007/s10608-023-10366-9.

Amygdala and nucleus accumbens activation during reward anticipation moderates the association between life stressor frequency and depressive symptoms.

BACKGROUND: Life stressors confer risk for depressive symptoms, but individuals vary in the extent of their sensitivity to life stressors. One protective factor may be an individual's level of reward sensitivity, e.g., a stronger neurobiological response to environmental rewards may mitigate emotional responses to stressors. However, the nature of neurobiological reward sensitivity that corresponds with stress resilience is unknown. Further, this model is untested in adolescence, when life stressor frequency and depression increase. METHODS: We tested the hypothesis that stronger reward-related activation in the left and right nucleus accumbens (NAc), amygdala, and medial prefrontal cortex (mPFC) attenuates the strength of the stress-depression relation. We measured BOLD activation throughout Win and Lose blocks of a monetary reward task, as well as during anticipation and outcome phases of the task. Participants (N = 151, ages 13-19) were recruited to be stratified on risk for mood disorders to enhance variance in depressive symptoms. RESULTS: Activation during anticipation of rewards in the bilateral amygdala and NAc, but not mPFC, buffered the association between life stressors and depressive symptoms. This buffering effect was not found for reward outcome activation or activation across Win blocks. CONCLUSIONS: Results highlight the importance of reward anticipation activation of subcortical structures in attenuating the stress-depression link, suggesting that reward motivation may be a cognitive mechanism through which this stress buffering occurs.

Stress and reward: A multimodal assessment of childhood sexual abuse.

Background: Childhood adversity has been found to impact stress and brain reward systems but it is unclear whether interactions between these systems might explain resilient vs. non-resilient trajectories following childhood sexual abuse (CSA). To address this gap, we adopted a multimodal approach in which cortisol reactivity to an acute stressor was assessed in conjunction with behavioral and neural measures of reward responsiveness in females with major depressive disorder (MDD) or no psychiatric disorders (i.e., resilient) who experienced CSA compared to females with and without MDD who did not experience abuse. Methods: Latent Class Mixed Modelling (LCMM) identified classes of adults (n = 62; MAge = 26.48, SD = 5.68) characterized by distinct cortisol trajectories in response to a combined social evaluative cold pressor task. Classes were examined for their history of CSA and resilience as well as behavioral and neural measures of reward responsiveness using 128-channel electroencephalography (event-related potentials and source localization analysis). Results: LCMM analysis identified two distinct classes of individuals with increased (Responders) or blunted (Non-Responders) cortisol reactivity to an acute stressor. Unlike Responders, Non-Responders did not modulate reward responses throughout the stress manipulation. No differences emerged between Responders and Non-Responders in terms of CSA or resilience. However, exploratory results showed that blunted cortisol response and non-modulation of reward responses emerged for those who experienced CSA at a younger age. Conclusions: Co-occurring blunted stress and reward reactivity emerged irrespective of adults' experience of CSA or resilience. However, preliminary findings showed that CSA ending during peripubertal development was associated with blunted cortisol and reward responsiveness. Future research needs to replicate findings in larger samples and could investigate if increasing reward responsiveness during critical times of neurodevelopment could normalize stress reactivity to future stressors and thus promote resilience.

Resting state brain dynamics: Associations with childhood sexual abuse and major depressive disorder.

Early life stress (ELS) and major depressive disorder (MDD) share neural network abnormalities. However, it is unclear how ELS and MDD may separately and/or jointly relate to brain networks, and whether neural differences exist between depressed individuals with vs without ELS. Moreover, prior work evaluated static versus dynamic network properties, a critical gap considering brain networks show changes in coordinated activity over time. Seventy-one unmedicated females with and without childhood sexual abuse (CSA) histories and/or MDD completed a resting state scan and a stress task in which cortisol and affective ratings were collected. Recurring functional network co-activation patterns (CAPs) were examined and time in CAP (number of times each CAP is expressed) and transition frequencies (transitioning between different CAPs) were computed. The effects of MDD and CSA on CAP metrics were examined and CAP metrics were correlated with depression and stress-related variables. Results showed that MDD, but not CSA, related to CAP metrics. Specifically, individuals with MDD (N = 35) relative to HCs (N = 36), spent more time in a posterior default mode (DMN)-frontoparietal network (FPN) CAP and transitioned more frequently between posterior DMN-FPN and prototypical DMN CAPs. Across groups, more time spent in a posterior DMN-FPN CAP and greater DMN-FPN and prototypical DMN CAP transition frequencies were linked to higher rumination. Imbalances between the DMN and the FPN appear central to MDD and might contribute to MDD-related cognitive dysfunction, including rumination. Unexpectedly, CSA did not modulate such dysfunctions, a finding that needs to be replicated by future studies with larger sample sizes.

Coping with COVID Stress: Maladaptive and Adaptive Response Styles Predicting College Student Internalizing Symptom Dimensions.

The COVID-19 pandemic significantly disrupted daily life for undergraduates and introduced new stressors (e.g., campus closures). How individuals respond to stressors can interact with stress to increase disorder risk in both unique and transdiagnostic ways. The current study examined how maladaptive and adaptive stress response styles moderated the perceived severity of COVID-related stressors effect on general and specific internalizing dimensions at the beginning of the COVID-19 pandemic in a combined undergraduate sample across two universities (N = 451) using latent bifactor modeling and LASSO modeling to identify optimal predictors. Results showed that perceived stress severity and maladaptive response styles (not adaptive response styles or interactions between stress and response styles) were associated with both common and specific internalizing dimensions. Results suggest additive associations of stress severity and maladaptive coping with internalizing symptoms during the pandemic's beginning, and provide important insights for screening, prevention, and intervention during future public health crises. Supplementary Information: The online version contains supplementary material available at 10.1007/s10862-022-09975-7.

Dynamic Resting-State Network Biomarkers of Antidepressant Treatment Response.

BACKGROUND: Delivery of effective antidepressant treatment has been hampered by a lack of objective tools for predicting or monitoring treatment response. This study aimed to address this gap by testing novel dynamic resting-state functional network markers of antidepressant response. METHODS: The Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study randomized adults with major depressive disorder to 8 weeks of either sertraline or placebo, and depression severity was evaluated longitudinally. Participants completed resting-state neuroimaging pretreatment and again after 1 week of treatment (n = 259 eligible for analyses). Coactivation pattern analyses identified recurrent whole-brain states of spatial coactivation, and computed time spent in each state for each participant was the main dynamic measure. Multilevel modeling estimated the associations between pretreatment network dynamics and sertraline response and between early (pretreatment to 1 week) changes in network dynamics and sertraline response. RESULTS: Dynamic network markers of early sertraline response included increased time in network states consistent with canonical default and salience networks, together with decreased time in network states characterized by coactivation of cingulate and ventral limbic or temporal regions. The effect of sertraline on depression recovery was mediated by these dynamic network changes. In contrast, early changes in dynamic functioning of corticolimbic and frontoinsular-default networks were related to patterns of symptom recovery common across treatment groups. CONCLUSIONS: Dynamic resting-state markers of early antidepressant response or general recovery may assist development of clinical tools for monitoring and predicting effective intervention.

General and Specific Dimensions of Mood Symptoms Are Associated With Impairments in Common Executive Function in Adolescence and Young Adulthood.

Both unipolar and bipolar depression have been linked with impairments in executive functioning (EF). In particular, mood symptom severity is associated with differences in common EF, a latent measure of general EF abilities. The relationship between mood disorders and EF is particularly salient in adolescence and young adulthood when the ongoing development of EF intersects with a higher risk of mood disorder onset. However, it remains unclear if common EF impairments have associations with specific symptom dimensions of mood pathology such as blunted positive affect, mood instability, or physiological arousal, or if differences in common EF more broadly relate to what is shared across various symptom domains, such as general negative affect or distress. To address this question, bifactor models can be applied to simultaneously examine the shared and unique contributions of particular mood symptom dimensions. However, no studies to our knowledge have examined bifactor models of mood symptoms in relation to measures of common EF. This study examined associations between common EF and general vs. specific symptom dimensions (anhedonia, physiological arousal, and mania) using structural equation modeling in adolescents and young adults with varying severity of mood symptoms (n = 495, ages = 13-25 years, 68.69% female). A General Depression factor capturing shared variance across symptoms statistically predicted lower Common EF. Additionally, a factor specific to physiological arousal was associated with lower Common EF. Anhedonia-specific and Mania-specific factors were not significantly related to Common EF. Altogether, these results indicate that deficits in common EF are driven by, or reflect, general features of mood pathology that are shared across symptom dimensions but are also specifically associated with physiological arousal.

Executive Functions and Impulsivity as Transdiagnostic Correlates of Psychopathology in Childhood: A Behavioral Genetic Analysis.

Executive functions (EFs) and impulsivity are dimensions of self-regulation that are both related to psychopathology. However, self-report measures of impulsivity and laboratory EF tasks typically display small correlations, and existing research indicates that impulsivity and EFs may tap separate aspects of self-regulation that independently statistically predict psychopathology in adulthood. However, relationships between EFs, impulsivity, and psychopathology may be different in childhood compared to adulthood. Here, we examine whether these patterns hold in the baseline assessment of the Adolescent Brain and Cognitive Development (ABCD) sample, a national sample of over 11,000 children (including 749 twin pairs) ages 9-10 years. We examine the phenotypic and genetic relationships among latent variables for different components of EFs and multiple facets of impulsivity. Additionally, we assess how EFs and impulsivity relate to composite measures and latent variables of psychopathology derived from parent report. EFs were weakly correlated with impulsivity, and the strength varied by impulsivity facet, emphasizing their separability. We did not identify significant genetic and environmental correlations between EFs and impulsivity. Moreover, controlling for their small relationships with each other, both EFs and some facets of impulsivity statistically predicted an Externalizing factor, attention problems, and social problems, and twin analyses suggested these relationships were genetic in origin. These findings indicate that EFs and impulsivity represent phenotypically and genetically separable aspects of self-regulation that are both transdiagnostic correlates of psychopathology in childhood.

Alcohol- and non-alcohol-related interference: An fMRI study of treatment-seeking adults with alcohol use disorder.

BACKGROUND: Individuals with alcohol use disorder (AUD) have difficulty diverting attention away from alcohol-related stimuli and towards non-alcohol-related goals (i.e., alcohol-related attention interference). It remains unclear whether regulatory brain function differs during alcohol and non-alcohol-related interference. This study compares brain reactivity during the alcohol and classic Stroop and whether such brain function relates to AUD severity. METHODS: 46 participants with AUD completed alcohol and classic color-word Stroop tasks during fMRI. Brain activity was compared during alcohol and classic Stroop interference in the rostral and dorsal anterior cingulate cortices (rACC and dACC) and correlated with self-reported AUD severity. Exploratory whole-brain analyses were also conducted. RESULTS: Behavioral interference (i.e., slower reaction times) was observed during alcohol and classic Stroop. rACC activity was significantly higher during the alcohol > neutral contrast versus the incongruent > congruent contrast. dACC activity did not differ between the Stroop tasks. dACC activity during incongruent > congruent was positively associated with AUD severity. CONCLUSIONS: Activity in ACC subregions differed during alcohol and non-alcohol interference. Increased alcohol-related activity in the rACC, a region linked to emotional conflict resolution, suggests an interfering effect of self-relevant alcohol cues on non-alcohol-related processing. AUD severity was related to greater dACC reactivity during classic Stroop interference, suggesting that non-drug-related cognitive control impairments are more pronounced in those with more problematic alcohol use.

The Emotional Word-Emotional Face Stroop task in the ABCD study: Psychometric validation and associations with measures of cognition and psychopathology.

Characterizing the interactions among attention, cognitive control, and emotion during adolescence may provide important insights into why this critical developmental period coincides with a dramatic increase in risk for psychopathology. However, it has proven challenging to develop a single neurobehavioral task that simultaneously engages and differentially measures these diverse domains. In the current study, we describe properties of performance on the Emotional Word-Emotional Face Stroop (EWEFS) task in the Adolescent Brain Cognitive Development (ABCD) Study, a task that allows researchers to concurrently measure processing speed/attentional vigilance (i.e., performance on congruent trials), inhibitory control (i.e., Stroop interference effect), and emotional information processing (i.e., difference in performance on trials with happy as compared to angry distracting faces). We first demonstrate that the task manipulations worked as designed and that Stroop performance is associated with multiple cognitive constructs derived from different measures at a prior time point. We then show that Stroop metrics tapping these three domains are preferentially associated with aspects of externalizing psychopathology and inattention. These results highlight the potential of the EWEFS task to help elucidate the longitudinal dynamics of attention, inhibitory control, and emotion across adolescent development, dynamics which may be altered by level of psychopathology.

Nicotine acutely alters temporal properties of resting brain states.

BACKGROUND: Nicotine-dependent individuals have altered activity in neurocognitive networks such as the default mode (DMN), salience (SN) and central executive networks (CEN). One theory suggests that, among chronic tobacco smokers, nicotine abstinence drives more DMN-related internal processing while nicotine replacement suppresses DMN and enhances SN and CEN. Whether acute nicotine impacts network dynamics in non-smokers is, however, unknown. METHODS: In a randomized double-blind crossover study, 17 healthy non-smokers (8 females) were administered placebo and nicotine (2-mg lozenge) on two different days prior to collecting resting-state functional magnetic resonance imaging (fMRI). Previously defined brain states in 462 individuals that spatially overlap with well-characterized resting-state networks including the DMN, SN, and CEN were applied to compute state-specific dynamics at rest: total time spent in state, persistence in each state after entry, and frequency of state transitions. We examined whether nicotine acutely alters these resting-state dynamics. RESULTS: A significant drug-by-state interaction emerged; post-hoc analyses clarified that, relative to placebo, nicotine suppressed time spent in a frontoinsular-DMN state (posterior cingulate cortex, medial prefrontal cortex, anterior insula, striatum and orbitofrontal cortex) and enhanced time spent in a SN state (anterior cingulate cortex and insula). No significant findings were observed for persistence and frequency. CONCLUSIONS: In non-smokers, nicotine biases resting-state brain function away from the frontoinsular-DMN and toward the SN, which may reduce internally focused cognition and enhance salience processing. While past work suggests nicotine impacts DMN activity, the current work shows nicotinic influences on a specific DMN-like network that has been linked with rumination and depression.

Behavioral mediators of stress-related mood symptoms in adolescence & young adulthood.

BACKGROUND: Stress is a risk factor for unipolar and bipolar mood disorders, but the mechanisms linking stress to specific symptoms remain elusive. Behavioral responses to stress, such as impulsivity and social withdrawal, may mediate the associations between stress and particular mood symptoms. METHODS: This study evaluated behavioral mediators of the relationship between self-reported intensity of daily stress and mood symptoms over up to eight weeks of daily diary surveys. The sample included individuals with unipolar or bipolar disorders, or with no psychiatric history (n = 113, ages 15-25). RESULTS: Results showed that higher daily stress was related to higher severity of mania, and this pathway was mediated by impulsive behaviors. Higher stress also predicted higher severity of anhedonic depression, and social withdrawal mediated this relationship. A k-means clustering analysis revealed six subgroups with divergent profiles of stress-behavior-symptom pathways. LIMITATIONS: Given the observational study design, analyses cannot determine causal relationships amongst these variables. Further work is needed to determine how relationships between these variables may vary based on stressor type, at different timescales, and within different populations. CONCLUSIONS: Findings support a theoretical model in which impulsivity and social withdrawal act as behavioral mediators of the relationship between stress and mood symptoms. Additionally, distinct patterns of reactivity distinguished subgroups of people vulnerable to particular types of mood symptoms. These results provide novel information about how stress-reactive behaviors relate to specific mood symptoms, which may have clinical relevance as targets of intervention.

Sex differences in functional network dynamics observed using coactivation pattern analysis.

Sex differences in the organization of large-scale resting-state brain networks have been identified using traditional static measures, which average functional connectivity over extended time periods. In contrast, emerging dynamic measures have the potential to define sex differences in network changes over time, providing additional understanding of neurobiological sex differences. To meet this goal, we used a Coactivation Pattern Analysis (CAP) using resting-state functional magnetic resonance imaging data from 181 males and 181 females from the Human Connectome Project. Significant main effects of sex were observed across two independent imaging sessions. Relative to males, females spent more total time in two transient network states (TNSs) spatially overlapping with the dorsal attention network and occipital/sensory-motor network. Greater time spent in these TNSs was related to females making more frequent transitions into these TNSs compared to males. In contrast, males spent more total time in TNSs spatially overlapping with the salience network, which was related to males staying for longer periods once entering these TNSs compared to females. State-to-state transitions also significantly differed between sexes: females transitioned more frequently from default mode network (DMN) states to the dorsal attention network state, whereas males transitioned more frequently from DMN states to salience network states. Results show that males and females spend differing amounts of time at rest in two distinct attention-related networks and show sex-specific transition patterns from DMN states into these attention-related networks. This work lays the groundwork for future investigations into the cognitive and behavioral implications of these sex-specific network dynamics.

Functional Segregation of Human Brain Networks Across the Lifespan: An Exploratory Analysis of Static and Dynamic Resting-State Functional Connectivity.

Prior research has shown that during development, there is increased segregation between, and increased integration within, prototypical resting-state functional brain networks. Functional networks are typically defined by static functional connectivity over extended periods of rest. However, little is known about how time-varying properties of functional networks change with age. Likewise, a comparison of standard approaches to functional connectivity may provide a nuanced view of how network integration and segregation are reflected across the lifespan. Therefore, this exploratory study evaluated common approaches to static and dynamic functional network connectivity in a publicly available dataset of subjects ranging from 8 to 75 years of age. Analyses evaluated relationships between age and static resting-state functional connectivity, variability (standard deviation) of connectivity, and mean dwell time of functional network states defined by recurring patterns of whole-brain connectivity. Results showed that older age was associated with decreased static connectivity between nodes of different canonical networks, particularly between the visual system and nodes in other networks. Age was not significantly related to variability of connectivity. Mean dwell time of a network state reflecting high connectivity between visual regions decreased with age, but older age was also associated with increased mean dwell time of a network state reflecting high connectivity within and between canonical sensorimotor and visual networks. Results support a model of increased network segregation over the lifespan and also highlight potential pathways of top-down regulation among networks.

Temporal Dynamics of Large-Scale Networks Predict Neural Cue Reactivity and Cue-Induced Craving.

BACKGROUND: Cue reactivity, a core characteristic of substance use disorders, commonly recruits brain regions that are key nodes in neurocognitive networks, including the default mode network (DMN) and salience network (SN). Whether resting-state temporal dynamic properties of these networks relate to subsequent cue reactivity and cue-induced craving is unknown. METHODS: The resting-state data of 46 nicotine-dependent participants were assessed to define temporal dynamic properties of DMN and SN states. Temporal dynamics focused on the total time across the scan session that brain activity resides in these specific states. Using regression models, we examined how the total time in each state related to neural reactivity to smoking cues within key DMN (posterior cingulate cortex, medial prefrontal cortex) or SN (anterior insula, dorsal anterior cingulate cortex) nodes. Mediation analyses were subsequently conducted to study how neural cue reactivity mediates the relationship between total time in state at rest and subjective cue-induced craving. RESULTS: Increased time spent in the DMN state and decreased time spent in the SN state predicted subsequent cue-induced increases in the anterior insula and dorsal anterior cingulate cortex, respectively. Cue-induced anterior insula and dorsal anterior cingulate cortex activity significantly mediated the relationship between time spent in DMN/SN and cue-induced subjective craving. CONCLUSIONS: Our findings showed a significant relationship between resting-state dynamics of the DMN/SN and task-activated SN nodes that together predicted cue-induced craving changes in nicotine-dependent individuals. These findings propose a neurobiological pathway for cue-induced craving that begins with resting-state temporal dynamics, suggesting that brain responding to external stimuli is driven by resting temporal dynamics.

Machine Learning Identifies Large-Scale Reward-Related Activity Modulated by Dopaminergic Enhancement in Major Depression.

BACKGROUND: Theoretical models have emphasized systems-level abnormalities in major depressive disorder (MDD). For unbiased yet rigorous evaluations of pathophysiological mechanisms underlying MDD, it is critically important to develop data-driven approaches that harness whole-brain data to classify MDD and evaluate possible normalizing effects of targeted interventions. Here, using an experimental therapeutics approach coupled with machine learning, we investigated the effect of a pharmacological challenge aiming to enhance dopaminergic signaling on whole-brain response to reward-related stimuli in MDD. METHODS: Using a double-blind, placebo-controlled design, we analyzed functional magnetic resonance imaging data from 31 unmedicated MDD participants receiving a single dose of 50 mg amisulpride (MDDAmisulpride), 26 MDD participants receiving placebo (MDDPlacebo), and 28 healthy control subjects receiving placebo (HCPlacebo) recruited through two independent studies. An importance-guided machine learning technique for model selection was used on whole-brain functional magnetic resonance imaging data probing reward anticipation and consumption to identify features linked to MDD (MDDPlacebo vs. HCPlacebo) and dopaminergic enhancement (MDDAmisulpride vs. MDDPlacebo). RESULTS: Highly predictive classification models emerged that distinguished MDDPlacebo from HCPlacebo (area under the curve = 0.87) and MDDPlacebo from MDDAmisulpride (area under the curve = 0.89). Although reward-related striatal activation and connectivity were among the most predictive features, the best truncated models based on whole-brain features were significantly better relative to models trained using striatal features only. CONCLUSIONS: Results indicate that in MDD, enhanced dopaminergic signaling restores abnormal activation and connectivity in a widespread network of regions. These findings provide new insights into the pathophysiology of MDD and pharmacological mechanism of antidepressants at the system level in addressing reward processing deficits among depressed individuals.