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.

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).

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.

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.

Childhood maltreatment is associated with a sex-dependent functional reorganization of a brain inhibitory control network.

Childhood adversity represents a major risk factor for drug addiction and other mental disorders. However, the specific mechanisms by which childhood adversity impacts human brain organization to confer greater vulnerability for negative outcomes in adulthood is largely unknown. As an impaired process in drug addiction, inhibitory control of behavior was investigated as a target of childhood maltreatment (abuse and neglect). Forty adults without Axis-I psychiatric disorders (21 females) completed a Childhood Trauma Questionnaire (CTQ) and underwent functional MRI (fMRI) while performing a stop-signal task. A group independent component analysis identified a putative brain inhibitory control network. Graph theoretical analyses and structural equation modeling investigated the impact of childhood maltreatment on the functional organization of this neural processing network. Graph theory outcomes revealed sex differences in the relationship between network functional connectivity and inhibitory control which were dependent on the severity of childhood maltreatment exposure. A network effective connectivity analysis indicated that a maltreatment dose-related negative modulation of dorsal anterior cingulate (dACC) activity by the left inferior frontal cortex (IFC) predicted better response inhibition and lesser attention deficit hyperactivity disorder (ADHD) symptoms in females, but poorer response inhibition and greater ADHD symptoms in males. Less inhibition of the right IFC by dACC in males with higher CTQ scores improved inhibitory control ability. The childhood maltreatment-related reorganization of a brain inhibitory control network provides sex-dependent mechanisms by which childhood adversity may confer greater risk for drug use and related disorders and by which adaptive brain responses protect individuals from this risk factor.

Merging clinical neuropsychology and functional neuroimaging to evaluate the construct validity and neural network engagement of the n-back task.

The n-back task is a widely used neuroimaging paradigm for studying the neural basis of working memory (WM); however, its neuropsychometric properties have received little empirical investigation. The present study merged clinical neuropsychology and functional magnetic resonance imaging (fMRI) to explore the construct validity of the letter variant of the n-back task (LNB) and to further identify the task-evoked networks involved in WM. Construct validity of the LNB task was investigated using a bootstrapping approach to correlate LNB task performance across clinically validated neuropsychological measures of WM to establish convergent validity, as well as measures of related but distinct cognitive constructs (i.e., attention and short-term memory) to establish discriminant validity. Independent component analysis (ICA) identified brain networks active during the LNB task in 34 healthy control participants, and general linear modeling determined task-relatedness of these networks. Bootstrap correlation analyses revealed moderate to high correlations among measures expected to converge with LNB (|ρ|≥ 0.37) and weak correlations among measures expected to discriminate (|ρ|≤ 0.29), controlling for age and education. ICA identified 35 independent networks, 17 of which demonstrated engagement significantly related to task condition, controlling for reaction time variability. Of these, the bilateral frontoparietal networks, bilateral dorsolateral prefrontal cortices, bilateral superior parietal lobules including precuneus, and frontoinsular network were preferentially recruited by the 2-back condition compared to 0-back control condition, indicating WM involvement. These results support the use of the LNB as a measure of WM and confirm its use in probing the network-level neural correlates of WM processing.

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.

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.

Altered resting-state effective connectivity of fronto-parietal motor control systems on the primary motor network following stroke.

Previous brain imaging work suggests that stroke alters the effective connectivity (the influence neural regions exert upon each other) of motor execution networks. The present study examines the intrinsic effective connectivity of top-down motor control in stroke survivors (n=13) relative to healthy participants (n=12). Stroke survivors exhibited significant deficits in motor function, as assessed by the Fugl-Meyer Motor Assessment. We used structural equation modeling (SEM) of resting-state fMRI data to investigate the relationship between motor deficits and the intrinsic effective connectivity between brain regions involved in motor control and motor execution. An exploratory adaptation of SEM determined the optimal model of motor execution effective connectivity in healthy participants, and confirmatory SEM assessed stroke survivors' fit to that model. We observed alterations in spontaneous resting-state effective connectivity from fronto-parietal guidance systems to the motor network in stroke survivors. More specifically, diminished connectivity was found in connections from the superior parietal cortex to primary motor cortex and supplementary motor cortex. Furthermore, the paths demonstrated large individual variance in stroke survivors but less variance in healthy participants. These findings suggest that characterizing the deficits in resting-state connectivity of top-down processes in stroke survivors may help optimize cognitive and physical rehabilitation therapies by individually targeting specific neural pathway.

A whole brain fMRI atlas generated via spatially constrained spectral clustering.

Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/.

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.

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.

Exploratory structural equation modeling of resting-state fMRI: applicability of group models to individual subjects.

The extension of group-level connectivity methods to individual subjects remains a hurdle for statistical analyses of neuroimaging data. Previous group analyses of positron emission tomography data in clinically depressed patients, for example, have shown that resting-state connectivity prior to therapy predicts how patients eventually respond to pharmacological and cognitive-behavioral therapy. Such applications would be considerably more informative for clinical decision making if these connectivity methods could be extended into the individual subject domain. To test such an extension, 46 treatment-naïve depressed patients were enrolled in an fMRI study to model baseline resting-state functional connectivity. Resting-state fMRI scans were acquired and submitted to exploratory structural equation modeling (SEM) to derive the optimal group connectivity model. Jackknife and split sample tests confirm that group model was highly reproducible, and path weights were consistent across the best five group models. When this model was applied to data from individual subjects, 85% of patients fit the group model. Histogram analysis of individual subjects' paths indicate that some paths are better representative of group membership. These results suggest that exploratory SEM is a viable technique for neuroimaging connectivity analyses of individual subjects' resting-state fMRI data.

Changes in resting state effective connectivity in the motor network following rehabilitation of upper extremity poststroke paresis.

BACKGROUND: A promising paradigm in human neuroimaging is the study of slow (<0.1 Hz) spontaneous fluctuations in the hemodynamic response measured by functional magnetic resonance imaging (fMRI). Spontaneous activity (i.e., resting state) refers to activity that cannot be attributed to specific inputs or outputs, that is, activity intrinsically generated by the brain. METHOD: This article presents pilot data examining neural connectivity in patients with poststroke hemiparesis before and after 3 weeks of upper extremity rehabilitation in the Accelerated Skill Acquisition Program (ASAP). Resting-state fMRI data acquired pre and post therapy were analyzed using an exploratory adaptation of structural equation modeling (SEM) to evaluate therapy-related changes in motor network effective connectivity. RESULTS: Each ASAP patient showed behavioral improvement. ASAP patients also showed increased influence of the affected hemisphere premotor cortex (a-PM) upon the unaffected hemisphere premotor cortex (u-PM) following therapy. The influence of a-PM on affected hemisphere primary motor cortex (a-M1) also increased with therapy for 3 of 5 patients, including those with greatest behavioral improvement. CONCLUSIONS: Our findings suggest that network analyses of resting-state fMRI constitute promising tools for functional characterization of functional brain disorders, for intergroup comparisons, and potentially for assessing effective connectivity within single subjects; all of which have important implications for stroke rehabilitation.

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.

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.

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.

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.