Decoding neural reactivation of threat during fear learning, extinction, and recall in a randomized clinical trial of L-DOPA among women with PTSD.

BACKGROUND: Laboratory paradigms are widely used to study fear learning in posttraumatic stress disorder (PTSD). Recent basic science models demonstrate that, during fear learning, patterns of activity in large neuronal ensembles for the conditioned stimuli (CS) begin to reinstate neural activity patterns for the unconditioned stimuli (US), suggesting a direct way of quantifying fear memory strength for the CS. Here, we translate this concept to human neuroimaging and test the impact of post-learning dopaminergic neurotransmission on fear memory strength during fear acquisition, extinction, and recall among women with PTSD in a re-analysis of previously reported data. METHODS: Participants (N = 79) completed a context-dependent fear acquisition and extinction task on day 1 and extinction recall tests 24 h later. We decoded activity patterns in large-scale functional networks for the US, then applied this decoder to activity patterns toward the CS on day 1 and day 2. RESULTS: US decoder output for the CS+ increased during acquisition and decreased during extinction in networks traditionally implicated in human fear learning. The strength of US neural reactivation also predicted individuals skin conductance responses. Participants randomized to receive L-DOPA (n = 43) following extinction on day 1 demonstrated less US neural reactivation on day 2 relative to the placebo group (n = 28). CONCLUSION: These results support neural reactivation as a measure of memory strength between competing memories of threat and safety and further demonstrate the role of dopaminergic neurotransmission in the consolidation of fear extinction memories.

Smartphone intervention to optimize medication-assisted treatment outcomes for opioid use disorder: study protocol for a randomized controlled trial.

BACKGROUND: Opioids accounted for 75% of drug overdoses in the USA in 2020, with rural states particularly impacted by the opioid crisis. While medication-assisted treatment (MAT) with Suboxone remains one of the more efficacious treatments for opioid use disorder (OUD), approximately 40% of people receiving Suboxone for outpatient MAT for OUD (MOUD) relapse within the first 6 months of treatment. We developed the smartphone app-based intervention OptiMAT as an adjunctive intervention to improve MOUD outcomes. The aims of this study are to (1) evaluate the efficacy of adjunctive OptiMAT use in reducing opioid misuse among people receiving MOUD and (2) evaluate the role of specific OptiMAT features in reducing opioid misuse, including the use of GPS-driven just-in-time intervention. METHODS: We will conduct a two-arm, single-blind, randomized controlled trial of adults receiving outpatient MOUD in the greater Little Rock AR area. Participants are English-speaking adults ages 18 or older recently enrolled in outpatient MOUD at one of our participating study clinics. Participants will be allocated via 1:1 randomized block design to (1) MOUD with adjunctive use of OptiMAT (MOUD+OptiMAT) or (2) MOUD without OptiMAT (MOUD-only). Our blinded research statistician will evaluate differences between the two groups in opioid misuse (as determined by quantitative urinalysis conducted by clinical lab staff blinded to group membership) during the 6-months following study enrolment. Secondary analyses will evaluate if OptiMAT-usage patterns within the MOUD+OptiMAT group predict opioid misuse or continued abstinence. DISCUSSION: This study will test if adjunctive use of OptiMAT improve MOUD outcomes. Study findings could lead to expansion of OptiMAT into rural clinical settings, and the identification of OptiMAT features which best predict positive clinical outcome could lead to refinement of this and similar smartphone app-based interventions. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05336188 , registered March 21, 2022.

Smartphone intervention to optimize medication assisted treatment outcomes for opioid use disorder: study protocol for a randomized controlled trial.

Background: Opioids accounted for 75% of drug overdoses in the United States in 2020, with rural states particularly impacted by the opioid crisis. While medication assisted treatment (MAT) with Suboxone remains one of the more efficacious treatments for opioid use disorder (OUD), approximately 40% of people receiving Suboxone for outpatient MAT for OUD (MOUD) relapse within the first 6 months of treatment. We developed the smartphone app-based intervention OptiMAT as an adjunctive intervention to improve MOUD outcomes. The aims of this study are to (1) evaluate the efficacy of adjunctive OptiMAT use in reducing opioid misuse among people receiving MOUD; and (2) evaluate the role of specific OpitMAT features in reducing opioid misuse, including the use of GPS-driven just-in-time intervention. Methods: We will conduct a two-arm, single-blind, randomized controlled trial of adults receiving outpatient MOUD in the greater Little Rock AR area. Participants are English-speaking adults ages 18 or older recently enrolled in outpatient MOUD at one of our participating study clinics. Participants will be allocated via 1:1 randomized block design to (1) MOUD with adjunctive use of OptiMAT (MOUD+OptiMAT) or (2) MOUD without OptiMAT (MOUD-only). Our blinded research statistician will evaluate differences between the two groups in opioid misuse (as determined by quantitative urinalysis conducted by clinical lab staff blinded to group membership) during the 6-months following study enrolment. Secondary analyses will evaluate if OptiMAT-usage patterns within the MOUD+OptiMAT group predict opioid misuse or continued abstinence. Discussion: This study will test if adjunctive use of OptiMAT improve MOUD outcomes. Study findings could lead to expansion of OptiMAT into rural clinical settings, and the identification of OptiMAT features which best predict positive clinical outcome could lead to refinement of this and similar smartphone appbased interventions. Trial registration: ClinicalTrials.gov identifier: NCT05336188, registered March 21, 2022, https://clinicaltrials.gov/ct2/show/NCT05336188.

Action-value processing underlies the role of the dorsal anterior cingulate cortex in performance monitoring during self-regulation of affect.

In this study, we merged methods from engineering control theory, machine learning, and human neuroimaging to critically test the putative role of the dorsal anterior cingulate cortex (dACC) in goal-directed performance monitoring during an emotion regulation task. Healthy adult participants (n = 94) underwent cued-recall and re-experiencing of their responses to affective image stimuli with concurrent functional magnetic resonance imaging and psychophysiological response recording. During cued-recall/re-experiencing trials, participants engaged in explicit self-regulation of their momentary affective state to match a pre-defined affective goal state. Within these trials, neural decoding methods measured affect processing from fMRI BOLD signals across the orthogonal affective dimensions of valence and arousal. Participants' affective brain states were independently validated via facial electromyography (valence) and electrodermal activity (arousal) responses. The decoded affective states were then used to contrast four computational models of performance monitoring (i.e., error, predicted response outcome, action-value, and conflict) by their relative abilities to explain emotion regulation task-related dACC activation. We found that the dACC most plausibly encodes action-value for both valence and arousal processing. We also confirmed that dACC activation directly encodes affective arousal and also likely encodes recruitment of attention and regulation resources. Beyond its contribution to improving our understanding of the roles that the dACC plays in emotion regulation, this study introduced a novel analytical framework through which affect processing and regulation may be functionally dissociated, thereby permitting mechanistic analysis of real-world emotion regulation strategies, e.g., distraction and reappraisal, which are widely employed in cognitive behavioral therapy to address clinical deficits in emotion regulation.

Value estimation and latent-state update-related neural activity during fear conditioning predict posttraumatic stress disorder symptom severity.

Learning theories of posttraumatic stress disorder (PTSD) purport that fear-learning processes, such as those that support fear acquisition and extinction, are impaired. Computational models designed to capture specific processes involved in fear learning have primarily assessed model-free, or trial-and-error, reinforcement learning (RL). Although previous studies indicated that aspects of model-free RL are disrupted among individuals with PTSD, research has yet to identify whether model-based RL, which is inferential and contextually driven, is impaired. Given empirical evidence of aberrant contextual modulation of fear in PTSD, the present study sought to identify whether model-based RL processes are altered during fear conditioning among women with interpersonal violence (IPV)-related PTSD (n = 85) using computational modeling. Model-free, hybrid, and model-based RL models were applied to skin conductance responses (SCR) collected during fear acquisition and extinction, and the model-based RL model was found to provide the best fit to the SCR data. Parameters from the model-based RL model were carried forward to neuroimaging analyses (voxel-wise and independent component analysis). Results revealed that reduced activity within visual processing regions during model-based updating uniquely predicted higher PTSD symptoms. Additionally, after controlling for model-based updating, greater value estimation encoding within the left frontoparietal network during fear acquisition and reduced value estimation encoding within the dorsomedial prefrontal cortex during fear extinction predicted greater PTSD symptoms. Results provide evidence of disrupted RL processes in women with assault-related PTSD, which may contribute to impaired fear and safety learning, and, furthermore, may relate to treatment response (e.g., poorer response to exposure therapy).

Unique neurocircuitry activation profiles during fear conditioning and extinction among women with posttraumatic stress disorder.

BACKGROUND: Neurocircuitry models of posttraumatic stress disorder (PTSD) suggest specific alterations in brain structures linked with fear conditioning and extinction. Most models assume a unitary pattern of neurocircuitry dysfunction in PTSD and little attention has focused on defining unique profiles of neurocircuitry engagement (i.e., biotypes), despite known clinical heterogeneity in PTSD. Here, we aim to address this gap using a data-driven approach to characterize unique neurocircuitry profiles among women with PTSD. METHODS: Seventy-six women with PTSD related to assaultive violence exposure competed a task during fMRI that alternated between fear conditioning, where a geometric shape predicted the occurrence of an electric shock, and fear extinction, where the geometric shape no longer predicted electric shock. A multivariate clustering analysis was applied to neurocircuitry patterns constrained within an a priori mask of structures linked with emotion processing. Resulting biotypes were compared on clinical measures of neurocognition, trauma exposure, general mental health symptoms, and PTSD symptoms and on psychophysiological responding during the task. RESULTS: The clustering analysis identified three biotypes (BT), differentiated by patterns of engagement within salience, default mode, and visual processing networks. BT1 was characterized by higher working memory, fewer general mental health symptoms, and low childhood sexual abuse, and lower PTSD symptom severity. BT2 was characterized by lower verbal IQ but better extinction learning as defined by psychophysiology and threat expectancy. BT3 was characterized by low childhood sexual abuse, anxious arousal, and re-experiencing symptoms. CONCLUSION: This data demonstrates unique profiles of neurocircuitry engagement in PTSD, each associated with different clinical characteristics, and suggests further research defining distinct biotypes of PTSD. Clinicaltrials.gov, https://clinicaltrials.gov/ct2/home, NCT02560389.

Combining Physiological and Neuroimaging Measures to Predict Affect Processing Induced by Affectively Valent Image Stimuli.

The importance of affect processing to human behavior has long driven researchers to pursue its measurement. In this study, we compared the relative fidelity of measurements of neural activation and physiology (i.e., heart rate change) in detecting affective valence induction across a broad continuum of conveyed affective valence. We combined intra-subject neural activation based multivariate predictions of affective valence with measures of heart rate (HR) deceleration to predict predefined normative affect rating scores for stimuli drawn from the International Affective Picture System (IAPS) in a population (n = 50) of healthy adults. In sum, we found that patterns of neural activation and HR deceleration significantly, and uniquely, explain the variance in normative valent scores associated with IAPS stimuli; however, we also found that patterns of neural activation explain a significantly greater proportion of that variance. These traits persisted across a range of stimulus sets, differing by the polar-extremity of their positively and negatively valent subsets, which represent the positively and negatively valent polar-extremity of stimulus sets reported in the literature. Overall, these findings support the acquisition of heart rate deceleration concurrently with fMRI to provide convergent validation of induced affect processing in the dimension of affective valence.

The neural correlates of low social integration as a risk factor for suicide.

Low social integration is commonly described in acutely suicidal individuals. Neural mechanisms underlying low social integration are poorly understood in depressed and suicidal patients. We sought to characterize the neural response to low social integration in acutely suicidal patients. Adult depressed patients within 3 days of a suicide attempt (n = 10), depressed patients with suicidal ideation (n = 9), non-suicidal depressed patients (n = 15), and healthy controls (N = 18) were administered the Cyberball Game while undergoing functional magnetic resonance imaging. We used complementary functional connectivity and region of interest data analysis approaches. There were no group differences in functional connectivity within neural network involving the pain matrix, nor in insula neural activity or the insula during either social inclusion. Superior anterior insula activity exhibited an inverted U-shaped curve across the suicide risk spectrum during social inclusion. Superior insula activity during social inclusion correlated with depression severity and psychological pain. Dorsal anterior cingulate cortex activity during social exclusion correlated with physical pain severity. Neural responses in the anterior insula significantly correlated with depression severity and with psychological pain during social inclusion, whereas dACC activity significantly correlated with physical pain during social exclusion. Recent suicidal behavior seems associated with a distinct neural response to social exclusion independently of presence of depression or suicidal thoughts.

Intertemporal decision-making-related brain states predict adolescent drug abuse intervention responses.

Adolescent drug misuse represents a major risk factor for long-term drug use disorders. However, wide individual differences in responses to first-line behavioral therapies targeting adolescent drug misuse limit critical early intervention. Identifying the neural signatures of those adolescents most likely to respond to an intervention would potentially guide personalized strategies for reducing drug misuse. Prior to a 14-week evidence-based intervention involving combinations of contingency management, motivational enhancement, and cognitive behavioral therapy, thirty adolescent alcohol and/or cannabis users underwent fMRI while performing a reward delay discounting (DD) task tapping an addiction-related cognition. Intervention responses were longitudinally characterized by both urinalysis and self-report measures of the percentage of days used during treatment and in post-treatment follow-up. Group independent component analysis (ICA) of task fMRI data identified neural processing networks related to DD task performance. Separate measures of wholesale recruitment during immediate reward choices and within-network functional connectivity among selective networks significantly predicted intervention-related changes in drug misuse frequency. Specifically, heightened pre-intervention engagement of a temporal lobe "reward motivation" network for impulsive choices on the DD task predicted poorer intervention outcomes, while modes of functional connectivity within the reward motivation network, a prospection network, and a posterior insula network demonstrated robust associations with intervention outcomes. Finally, the pre-intervention functional organization of the prospection network also predicted post-intervention drug use behaviors for up to 6 months of follow-up. Multiple functional variations in the neural processing networks supporting preference for immediate and future rewards signal individual differences in readiness to benefit from an effective behavioral therapy for reducing adolescent drug misuse. The implications for efforts to boost therapy responses are discussed.

Individual differences in rate of acquiring stable neural representations of tasks in fMRI.

Task-related functional magnetic resonance imaging (fMRI) is a widely-used tool for studying the neural processing correlates of human behavior in both healthy and clinical populations. There is growing interest in mapping individual differences in fMRI task behavior and neural responses. By utilizing neuroadaptive task designs accounting for such individual differences, task durations can be personalized to potentially optimize neuroimaging study outcomes (e.g., classification of task-related brain states). To test this hypothesis, we first retrospectively tracked the volume-by-volume changes of beta weights generated from general linear models (GLM) for 67 adult subjects performing a stop-signal task (SST). We then modeled the convergence of the volume-by-volume changes of beta weights according to their exponential decay (ED) in units of half-life. Our results showed significant differences in beta weight convergence estimates of optimal stopping times (OSTs) between go following successful stop trials and failed stop trials for both cocaine dependent (CD) and control group (Con), and between go following successful stop trials and go following failed stop trials for Con group. Further, we implemented support vector machine (SVM) classification for 67 CD/Con labeled subjects and compared the classification accuracies of fMRI-based features derived from (1) the full fMRI task versus (2) the fMRI task truncated to multiples of the unit of half-life. Among the computed binary classification accuracies, two types of task durations based on 2 half-lives significantly outperformed the accuracies using fully acquired trials, supporting this length as the OST for the SST. In conclusion, we demonstrate the potential of a neuroadaptive task design that can be widely applied to personalizing other task-based fMRI experiments in either dynamic real-time fMRI applications or within fMRI preprocessing pipelines.

The neural representation of the association between comorbid drug use disorders and childhood maltreatment.

BACKGROUND: Comorbidity of drug use disorders (DUD) with other psychopathology is associated with worse functional and treatment outcomes than DUD alone. The present study sought to identify altered functional neural circuitry underlying DUD comorbidity with other psychiatric disorders, and model the relationship of these alterations to childhood trauma (Childhood Trauma Questionnaire) and negative self-beliefs (Beck Depression Inventory). METHODS: A sample of adult men and women (mean = 36.8 years) with childhood maltreatment histories (n = 81) was allocated into the following groups based on psychiatric diagnoses and drug use history: no current or past psychiatric disorders (trauma control sample, n = 20), DUD only (n = 22), psychopathology only (n = 20), and DUD comorbid with other psychiatric illness (DCoP, n = 25). RESULTS: Multiple regression of seed-based resting-state fMRI, controlling for age and sex, identified a functional connection between the right rostral anterior cingulate cortex (rACC) and left temporoparietal junction (TPJ) that was significantly increased in DCoP females, relative to the other clinical and control groups. Within the DCoP female sample, mediation analysis demonstrated that strength of connectivity between the subgenual cingulate cortex and both the right anterior insula and rostral lateral prefrontal cortex significantly mediated the relationship between increasing physical abuse and self-criticism with age as a moderator. CONCLUSIONS: This study related sex-dependent alterations in functional organization of the prefrontal cortex with DCoP that are, in turn, related to magnitude of negative self-beliefs to childhood trauma exposure. Additionally, DCoP-selective alterations in rACC connectivity suggest that the neural correlates of DCoP do not represent linear additive contributions from two independent disorders.

Personality variables modify the relationship between childhood maltreatment history and poor functional outcomes.

Childhood maltreatment history is a prevalent risk factor for substance use disorder and has lifelong adverse consequences on psychiatric wellbeing. The role of personality variations in determining childhood maltreatment-associated outcomes is poorly understood. This study sought to test neuroticism and agreeableness as mediator and moderator, respectively, of functional outcomes associated with having a history of childhood maltreatment and presence/absence of cocaine dependence. Ninety-four participants completed the Structured Clinical Interview for DSM-IV (SCID-IV), Childhood Trauma Questionnaire (CTQ), NEO-Five Factor Inventory (NEO-FFI), and the Addiction Severity Index (ASI). The distribution-of-the-product strategy tested if neuroticism mediated the relationship between CTQ and ASI scores. Agreeableness was tested as a moderator using bootstrapped multiple regression analyses with agreeableness*CTQ interaction terms as predictors of ASI scores. Analyses covaried for cocaine dependence to determine its influence. Neuroticism mediated the relationship between severity of childhood maltreatment history and family (ASI-Family) and psychiatric (ASI-Psychiatric) dysfunction in adulthood, independent of cocaine dependence. Agreeableness negatively moderated the effect of childhood maltreatment severity on family dysfunction. Exposure to emotional neglect and abuse selectively drove the mediation and moderation effects. Personality-directed interventions that reduce neuroticism or increase agreeableness may be promising approaches to uncouple childhood maltreatment history from lifelong social and psychiatric dysfunction.

Brain States That Encode Perceived Emotion Are Reproducible but Their Classification Accuracy Is Stimulus-Dependent.

The brain state hypothesis of image-induced affect processing, which posits that a one-to-one mapping exists between each image stimulus and its induced functional magnetic resonance imaging (fMRI)-derived neural activation pattern (i.e., brain state), has recently received support from several multivariate pattern analysis (MVPA) studies. Critically, however, classification accuracy differences across these studies, which largely share experimental designs and analyses, suggest that there exist one or more unaccounted sources of variance within MVPA studies of affect processing. To explore this possibility, we directly demonstrated strong inter-study correlations between image-induced affective brain states acquired 4 years apart on the same MRI scanner using near-identical methodology with studies differing only by the specific image stimuli and subjects. We subsequently developed a plausible explanation for inter-study differences in affective valence and arousal classification accuracies based on the spatial distribution of the perceived affective properties of the stimuli. Controlling for this distribution improved valence classification accuracy from 56% to 85% and arousal classification accuracy from 61% to 78%, which mirrored the full range of classification accuracy across studies within the existing literature. Finally, we validated the predictive fidelity of our image-related brain states according to an independent measurement, autonomic arousal, captured via skin conductance response (SCR). Brain states significantly but weakly (r = 0.08) predicted the SCRs that accompanied individual image stimulations. More importantly, the effect size of brain state predictions of SCR increased more than threefold (r = 0.25) when the stimulus set was restricted to those images having group-level significantly classifiable arousal properties.

Modes of Resting Functional Brain Organization Differentiate Suicidal Thoughts and Actions: A Preliminary Study.

OBJECTIVE: A major target in suicide prevention is interrupting the progression from suicidal thoughts to action. Use of complex algorithms in large samples has identified individuals at very high risk for suicide. We tested the ability of data-driven pattern classification analysis of brain functional connectivity to differentiate recent suicide attempters from patients with suicidal ideation. METHODS: We performed a cross-sectional study using resting-state functional magnetic resonance imaging in depressed inpatients and outpatients of both sexes recruited from a university hospital between March 2014 and June 2016: recent suicide Attempters within 3 days of an attempt (n = 10), Suicidal Ideators (n = 9), Depressed Non-Suicidal Controls (n = 17), and Healthy Controls (n = 18). All depressed patients fulfilled DSM-IV-TR criteria for major depressive episode and either major depressive disorder, bipolar disorder, or depression not otherwise specified. A subset of suicide attempters (n = 7) were rescanned within 7 days. We used a support vector machine data-driven neural pattern classification analysis of resting-state functional connectivity to characterize recent suicide attempters and then tested the classifier's specificity. RESULTS: A binary classifier trained to discriminate patterns of resting-state functional connectivity robustly differentiated Suicide Attempters from Suicidal Ideators (mean accuracy = 0.788, signed rank test: P = .002; null hypothesis: area under the curve = 0.5), with distinct functional connectivity between the default mode and the limbic, salience, and central executive networks. The classifier did not discriminate stable Suicide Attempters from Suicidal Ideators (mean accuracy = 0.58, P = .33) or presence from absence of lifetime suicidal behavior (mean accuracy = 0.543, P = .348) and was not improved by modeling clinical variables (mean accuracy = 0.736, P = .002). CONCLUSIONS: Measures of intrinsic brain organization may have practical value as objective measures of suicide risk and its underlying mechanisms. Further incorporation of serum or cognitive markers and use of a prospective study design are needed to validate and refine the clinical relevance of this candidate biomarker of suicide risk.

Dynamic changes in large-scale functional network organization during autobiographical memory retrieval.

Autobiographical memory (AM), episodic memory for life events, involves the orchestration of multiple dynamic cognitive processes, including memory access and subsequent elaboration. Previous neuroimaging studies have contrasted memory access and elaboration processes in terms of regional brain activation and connectivity within large, multi-region networks. Although interactions between key memory-related regions such as the hippocampus and prefrontal cortex (PFC) have been shown to play an important role in AM retrieval, it remains unclear how such connectivity between specific, individual regions involved in AM retrieval changes dynamically across the retrieval process and how these changes relate to broader memory networks throughout the whole brain. The present functional magnetic resonance imaging (fMRI) study sought to assess the specific changes in interregional connectivity patterns across the AM retrieval processes to understand network level mechanisms of AM retrieval and further test current theoretical accounts of dynamic AM retrieval processes. We predicted that dynamic connections would reflect two hypothesized memory processes, with initial processes reflecting memory-access related connections between regions such as the anterior hippocampal and ventrolateral PFC regions, and later processes reflecting elaboration-related connections between dorsolateral frontal working memory regions and parietal-occipital visual imagery regions. One week prior to fMRI scanning, fifteen healthy adult participants generated AMs using personally selected cue words. During scanning, participants were cued to retrieve the AMs. We used a moving-window functional connectivity analysis and graph theoretic measures to examine dynamic changes in the strength and centrality of connectivity among regions involved in AM retrieval. Consistent with predictions, early, access-related processing primarily involved a ventral frontal to temporal-parietal network associated with strategic search and initial reactivation of specific episodic memory traces. In addition, neural network connectivity during later retrieval processes was associated with strong connections between occipital-parietal regions and dorsal fronto-parietal regions associated with mental imagery, reliving, and working memory processes. Taken together, these current findings help refine and extend dynamic neural processing models of AM retrieval by providing evidence of the specific connections throughout the brain that change in their synchrony with one another as processing progresses from access of specific event memories to elaborative reliving of the past event.

Tai Chi for Posttraumatic Stress Disorder and Chronic Musculoskeletal Pain: A Pilot Study.

PURPOSE: Explore the feasibility of a Tai Chi intervention to improve musculoskeletal pain, emotion, cognition, and physical function in individuals with posttraumatic stress disorder. DESIGN: Two-phase, one-arm quasi-experimental design. METHOD: Phase 1: 11 participants completed one Tai Chi session, feasibility questionnaire, and were offered participation in Phase 2, a 12-week Tai Chi intervention. Ten participants participated in Phase 2. Pain intensity, interference, physical function scales, an emotional battery, and cognition tests were used for pre- and postintervention outcome measures. Paired t tests and thematic analysis were used for analysis. FINDINGS: In Phase 1, most felt Tai Chi would benefit health (90.9%) and expressed interest in continuing Tai Chi (6.73 out of 7). Phase 2 results showed improvement in fear-affect (raw t = -2.64, p = .03; age adjusted t = -2.90, p = .02), fear-somatic arousal (raw t = -2.53, p = .035), List Sorting Working Memory (raw t = 2.62, p = .031; age adjusted t = 2.96, p = .018), 6-Minute Walk Test ( t = 3.541, p = .008), and current level of Pain Intensity ( t = -4.00, p = .004). CONCLUSIONS: Tai Chi is an acceptable, holistic treatment to individuals with musculoskeletal pain and posttraumatic stress disorder. It may reduce pain, improve emotion, memory, and physical function.

Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect.

Recent evidence suggests that emotions have a distributed neural representation, which has significant implications for our understanding of the mechanisms underlying emotion regulation and dysregulation as well as the potential targets available for neuromodulation-based emotion therapeutics. This work adds to this evidence by testing the distribution of neural representations underlying the affective dimensions of valence and arousal using representational models that vary in both the degree and the nature of their distribution. We used multi-voxel pattern classification (MVPC) to identify whole-brain patterns of functional magnetic resonance imaging (fMRI)-derived neural activations that reliably predicted dimensional properties of affect (valence and arousal) for visual stimuli viewed by a normative sample (n = 32) of demographically diverse, healthy adults. Inter-subject leave-one-out cross-validation showed whole-brain MVPC significantly predicted (p < 0.001) binarized normative ratings of valence (positive vs. negative, 59% accuracy) and arousal (high vs. low, 56% accuracy). We also conducted group-level univariate general linear modeling (GLM) analyses to identify brain regions whose response significantly differed for the contrasts of positive versus negative valence or high versus low arousal. Multivoxel pattern classifiers using voxels drawn from all identified regions of interest (all-ROIs) exhibited mixed performance; arousal was predicted significantly better than chance but worse than the whole-brain classifier, whereas valence was not predicted significantly better than chance. Multivoxel classifiers derived using individual ROIs generally performed no better than chance. Although performance of the all-ROI classifier improved with larger ROIs (generated by relaxing the clustering threshold), performance was still poorer than the whole-brain classifier. These findings support a highly distributed model of neural processing for the affective dimensions of valence and arousal. Finally, joint error analyses of the MVPC hyperplanes encoding valence and arousal identified regions within the dimensional affect space where multivoxel classifiers exhibited the greatest difficulty encoding brain states - specifically, stimuli of moderate arousal and high or low valence. In conclusion, we highlight new directions for characterizing affective processing for mechanistic and therapeutic applications in affective neuroscience.

The neural correlates of reciprocity are sensitive to prior experience of reciprocity.

Reciprocity is central to human relationships and is strongly influenced by multiple factors including the nature of social exchanges and their attendant emotional reactions. Despite recent advances in the field, the neural processes involved in this modulation of reciprocal behavior by ongoing social interaction are poorly understood. We hypothesized that activity within a discrete set of neural networks including a putative moral cognitive neural network is associated with reciprocity behavior. Nineteen healthy adults underwent functional magnetic resonance imaging scanning while playing the trustee role in the Trust Game. Personality traits and moral development were assessed. Independent component analysis was used to identify task-related functional brain networks and assess their relationship to behavior. The saliency network (insula and anterior cingulate) was positively correlated with reciprocity behavior. A consistent array of brain regions supports the engagement of emotional, self-referential and planning processes during social reciprocity behavior.

Functional independence in resting-state connectivity facilitates higher-order cognition.

Growing evidence suggests that intrinsic functional connectivity (i.e. highly structured patterns of communication between brain regions during wakeful rest) may encode cognitive ability. However, the generalizability of these findings is limited by between-study differences in statistical methodology and cognitive domains evaluated. To address this barrier, we evaluated resting-state neural representations of multiple cognitive domains within a relatively large normative adult sample. Forty-four participants (mean(sd) age=31(10) years; 18 male and 26 female) completed a resting-state functional MRI scan and neuropsychological assessments spanning motor, visuospatial, language, learning, memory, attention, working memory, and executive function performance. Robust linear regression related cognitive performance to resting-state connectivity among 200 a priori determined functional regions of interest (ROIs). Only higher-order cognitions (such as learning and executive function) demonstrated significant relationships between brain function and behavior. Additionally, all significant relationships were negative - characterized by moderately positive correlations among low performers and weak to moderately negative correlations among high performers. These findings suggest that functional independence among brain regions at rest facilitates cognitive performance. Our interpretation is consistent with graph theoretic analyses which represent the brain as independent functional nodes that undergo dynamic reorganization with task demand. Future work will build upon these findings by evaluating domain-specific variance in resting-state neural representations of cognitive impairment among patient populations.