Effects of childhood maltreatment on the neural correlates of stress- and drug cue-induced cocaine craving: Trauma and cocaine craving

Childhood adversity negatively influences all stages of the addiction process and is associated with persistent alterations in neuroendocrine, autonomic and brain responses to stress. We sought to characterize the impact of childhood abuse and neglect on the neural correlates of stress- and drug cue-induced drug craving associated with cocaine addiction. Cocaine-dependent men with (n=20) and without (n=18) moderate to severe childhood maltreatment histories underwent fMRI during script-guided mental imagery of personalized stress, drug use, and neutral experiences. Compared to the neutral script, the stress and drug use scripts activated striatal, prefrontal, posterior cingulate, temporal and cerebellar regions consistent with prior studies of induced states of stress and drug craving. For the stress script, maltreated men exhibited reduced activation of the anterior precuneus and supplementary motor area (SMA); the interaction of maltreatment severity and stress-induced craving responses predicted lesser rostral anterior cingulate cortex activation. For the drug use script, maltreated men exhibited greater left dorsolateral prefrontal cortex activation. The interaction of maltreatment severity and craving responses was associated with greater activation of the visual cortex and SMA, whereas a maltreatment-by-anxiety interaction effect included lesser ventromedial prefrontal cortex activation. The outcomes indicate an association of childhood maltreatment with a heightened appetitive anticipatory response to drug cues and a diminished engagement of regulatory and controlled action selection processes in response to stress- or drug cue-induced drug craving and anxiety responses for cocaine-dependent men. These findings provide novel insights into possible brain mechanisms by which childhood maltreatment heightens risk for relapse in drug-dependent individuals

Decoding the Traumatic Memory among Women with PTSD: Implications for Neurocircuitry Models of PTSD and Real-Time fMRI Neurofeedback.

Posttraumatic Stress Disorder (PTSD) is characterized by intrusive recall of the traumatic memory. While numerous studies have investigated the neural processing mechanisms engaged during trauma memory recall in PTSD, these analyses have only focused on group-level contrasts that reveal little about the predictive validity of the identified brain regions. By contrast, a multivariate pattern analysis (MVPA) approach towards identifying the neural mechanisms engaged during trauma memory recall would entail testing whether a multivariate set of brain regions is reliably predictive of (i.e., discriminates) whether an individual is engaging in trauma or non-trauma memory recall. Here, we use a MVPA approach to test 1) whether trauma memory vs neutral memory recall can be predicted reliably using a multivariate set of brain regions among women with PTSD related to assaultive violence exposure (N=16), 2) the methodological parameters (e.g., spatial smoothing, number of memory recall repetitions, etc.) that optimize classification accuracy and reproducibility of the feature weight spatial maps, and 3) the correspondence between brain regions that discriminate trauma memory recall and the brain regions predicted by neurocircuitry models of PTSD. Cross-validation classification accuracy was significantly above chance for all methodological permutations tested; mean accuracy across participants was 76% for the methodological parameters selected as optimal for both efficiency and accuracy. Classification accuracy was significantly better for a voxel-wise approach relative to voxels within restricted regions-of-interest (ROIs); classification accuracy did not differ when using PTSD-related ROIs compared to randomly generated ROIs. ROI-based analyses suggested the reliable involvement of the left hippocampus in discriminating memory recall across participants and that the contribution of the left amygdala to the decision function was dependent upon PTSD symptom severity. These results have methodological implications for real-time fMRI neurofeedback of the trauma memory in PTSD and conceptual implications for neurocircuitry models of PTSD that attempt to explain core neural processing mechanisms mediating PTSD

Large-scale brain organization during facial emotion processing as a function of early life trauma among adolescent girls

Background: A wealth of research has investigated the impact of early life trauma exposure on functional brain activation during facial emotion processing and has often demonstrated amygdala hyperactivity and weakened connectivity between amygdala and medial PFC (mPFC). There have been notably limited investigations linking these previous node-specific findings into larger-scale network models of brain organization. Method: To address these gaps, we applied graph theoretical analyses to fMRI data collected during a facial emotion processing task among 88 adolescent girls (n=59 exposed to direct physical or sexual assault; n=29 healthy controls), aged 11–17, during fMRI. Large-scale organization indices of modularity, assortativity, and global efficiency were calculated for stimulus-specific functional connectivity using an 883 region-of-interest parcellation. Results: Among the entire sample, more severe early life trauma was associated with more modular and assortative, but less globally efficient, network organization across all stimulus categories. Among the assaulted girls, severity of early life trauma and PTSD diagnoses were both simultaneously related to increased modular brain organization. We also found that more modularized network organization was related both to amygdala hyperactivation and weakened connectivity between amygdala and medial PFC. Conclusions: These results demonstrate that early life trauma is associated with enhanced brain organization during facial emotion processing and that this pattern of brain organization might explain the commonly observed association between childhood trauma and amygdala hyperactivity and weakened connectivity with mPFC. Implications of these results for neurocircuitry models are discussed

Implicit emotion regulation in adolescent girls: An exploratory investigation of Hidden Markov Modeling and its neural correlates.

Numerous data demonstrate that distracting emotional stimuli cause behavioral slowing (i.e. emotional conflict) and that behavior dynamically adapts to such distractors. However, the cognitive and neural mechanisms that mediate these behavioral findings are poorly understood. Several theoretical models have been developed that attempt to explain these phenomena, but these models have not been directly tested on human behavior nor compared. A potential tool to overcome this limitation is Hidden Markov Modeling (HMM), which is a computational approach to modeling indirectly observed systems. Here, we administered an emotional Stroop task to a sample of healthy adolescent girls (N = 24) during fMRI and used HMM to implement theoretical behavioral models. We then compared the model fits and tested for neural representations of the hidden states of the most supported model. We found that a modified variant of the model posited by Mathews et al. (1998) was most concordant with observed behavior and that brain activity was related to the model-based hidden states. Particularly, while the valences of the stimuli themselves were encoded primarily in the ventral visual cortex, the model-based detection of threatening targets was associated with increased activity in the bilateral anterior insula, while task effort (i.e. adaptation) was associated with reduction in the activity of these areas. These findings suggest that emotional target detection and adaptation are accomplished partly through increases and decreases, respectively, in the perceived immediate relevance of threatening cues and also demonstrate the efficacy of using HMM to apply theoretical models to human behavior

Mean classification accuracy across participants, trained using three runs, as a function of the brain mask used to select voxels: all GM voxels, only voxels within PTSD-related ROIs, all GM voxels except the PTSD-related voxels, and voxels within randomly generated ROIs.

<p>Mean classification accuracy across participants, trained using three runs, as a function of the brain mask used to select voxels: all GM voxels, only voxels within PTSD-related ROIs, all GM voxels except the PTSD-related voxels, and voxels within randomly generated ROIs.</p

Mean classification accuracy across participants (and standard error) as a function of number of runs used to train the model and spatial smoothing.

<p>Mean classification accuracy across participants (and standard error) as a function of number of runs used to train the model and spatial smoothing.</p

Demographic and clinical characteristics of the 16 adult women in this sample.

<p>Note. PCL = Posttraumatic Checklist—civilian version; BDI-II = Beck Depression Inventory-II.</p><p>Demographic and clinical characteristics of the 16 adult women in this sample.</p

Modes of Large-Scale Brain Network Organization during Threat Processing and Posttraumatic Stress Disorder Symptom Reduction during TF-CBT among Adolescent Girls - Fig 2

<p>Bar graphs comparing the network organization indices of modularity (panel A), global efficiency (panel B), and assortativity (panel C) between patients with steep (n = 10) and shallow (n = 10) treatment responses. The pre-treatment comparisons additionally include the mean network indices for a healthy comparison group of 15 adolescent girls. Error bars indicate standard errors. * indicates <i>p</i> < .05; ** indicates <i>p</i> = .076; *** indicates <i>p</i> = .059.</p

Graphical depiction of the group-level community structure.

<p>Modules were identified using the group-level median 824x824 functional connectivity matrix across all task stimuli. The modular are displayed over the ICBM 452 template brain.</p

Demographic, clinical characteristics, and treatment response of the samples.

<p>Demographic, clinical characteristics, and treatment response of the samples.</p