Liana Portugal

Is tonic immobility the core sign among conventional peritraumatic signs and symptoms listed for PTSD

BACKGROUND Previous studies suggested the importance of peritraumatic reactions as predictors of PSTD symptoms severity. Despite mounting evidence that tonic immobility occurs under intense life threats its role as predictor of PTSD severity remains by and large understudied. The objective of this study was to investigate the role of peritraumatic reactions (tonic immobility, panic and dissociation) as predictors of PTSD symptoms severity. METHODS Participants were 32 victims of urban violence with PTSD diagnosed through the SCID-I. In order to evaluate PTSD symptoms at baseline, we used the Post-Traumatic Stress Disorder Checklist-Civilian Version. To assess peritraumatic reactions we employed the Physical Reactions Scale, the Peritraumatic Dissociative Experiences Questionnaire and Tonic Immobility questions. As confounding variables, we considered negative affect (measured by the Positive and Negative Affect Schedule-Trait Version), sex and time elapsed since trauma. RESULTS Tonic immobility was the only predictor of PTSD symptoms severity that kept the statistical significance after controlling for potential confounders. LIMITATIONS This study was based on a relatively small sample recruited in a tertiary clinic, a fact that may limit the generalizability of its findings. The retrospective design may have predisposed to recall bias. CONCLUSIONS Our study provides good reason to conduct more research on tonic immobility in PTSD with other samples and with different time frames in an attempt to replicate these stimulating results.

Sparse network-based models for patient classification using fMRI

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has been successful at discriminating psychiatric patients from healthy subjects. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. As is generally accepted, many psychiatric disorders, such as depression and schizophrenia, are brain connectivity disorders. Therefore, pattern recognition based on network models should provide more scientific insight and potentially more powerful predictions than voxel-based approaches. Here, we build a sparse network-based discriminative modelling framework, based on Gaussian graphical models and L1-norm regularised linear Support Vector Machines (SVM). The proposed framework provides easier pattern interpretation in terms of underlying network changes between groups, and we illustrate our technique by classifying patients with depression and controls, using fMRI data from a sad facial processing task.

EEG correlates of the severity of posttraumatic stress symptoms: A systematic review of the dimensional PTSD literature

BACKGROUND Considering the Research Domain Criteria (RDoC) framework, it is crucial to investigate posttraumatic stress disorder (PTSD) as a spectrum that ranges from normal to pathological. This dimensional approach is especially important to aid early PTSD detection and to guide better treatment options. In recent years, electroencephalography (EEG) has been used to investigate PTSD; however, reviews regarding EEG data related to PTSD are lacking, especially considering the dimensional approach. This systematic review examined the literature regarding EEG alterations in trauma-exposed people with posttraumatic stress symptoms (PTSS) to identify putative EEG biomarkers of PTSS severity. METHOD A systematic review of EEG studies of trauma-exposed participants with PTSS that reported dimensional analyses (e.g., correlations or regressions) between PTSS and EEG measures was performed. RESULTS The literature search yielded 1178 references, of which 34 studies were eligible for inclusion. Despite variability among the reviewed studies, the PTSS severity was often associated with P2, P3-family event-related potentials (ERPs) and alpha rhythms. LIMITATIONS The search was limited to articles published in English; no information about non-published studies or studies reported in other languages was obtained. Another limitation was the heterogeneity of studies, which made meta-analysis challenging. CONCLUSIONS EEG provides promising candidates to act as biomarkers, although further studies are required to confirm the findings. Thus, EEG, in addition to being cheaper and easier to implement than other central techniques, has the potential to reveal biomarkers of PTSS severity.

How you perceive threat determines your behavior

The prioritization of processing emotional stimuli usually produces deleterious effects on task performance when it distracts from a task. One common explanation is that brain resources are consumed by emotional stimuli, diverting resources away from executing the task. Viewing unpleasant stimuli also generates defensive reactions, and these responses may be at least partially responsible for the effect of the emotional modulation observed in various reaction time (RT) paradigms. We investigated whether modulatory effects on RT vary if we presented threat stimuli to prompt different defensive responses. To trigger different responses, we manipulated threat perception by moving the direction of threatening stimuli. Threatening or neutral stimuli were presented as distractors during a bar orientation discrimination task. The results demonstrated that threat stimuli directed toward the observer produced a decrease in RT; in contrast, threat stimuli directed away from the observer produced an increase in RT, when compared to neutral stimuli. Accelerated RT during directed toward threat stimuli was attributed to increased motor preparation resulting from strong activation of the defense response cascade. In contrast, directed away threat stimuli likely activated the defense cascade, but less intensively, prompting immobility. Different threat stimuli produced varying effects, which was interpreted as evidence that the modulation of RT by emotional stimuli represents the summation of attentional and motivational effects. Additionally, participants who had been previously exposed to diverse types of violent crime were more strongly influenced by threat stimuli directed toward the observer. In sum, our data support the concept that emotions are indeed action tendencies.

Peritraumatic tonic immobility is associated with posttraumatic stress symptoms in undergraduate Brazilian students

OBJECTIVE Tonic immobility is a defensive reaction occurring under extreme life threats. Patients with posttraumatic stress disorder (PTSD) reporting peritraumatic tonic immobility show the most severe symptoms and a poorer response to treatment. This study investigated the predictive value of tonic immobility for posttraumatic stress symptoms in a non-clinical sample. METHODS One hundred and ninety-eight college students exposed to various life threatening events were selected to participate. The Posttraumatic Stress Disorder Checklist - Civilian Version (PCL-C) and tonic immobility questions were used. Linear regression models were fitted to investigate the association between peritraumatic tonic immobility and PCL-C scores. Peritraumatic dissociation, peritraumatic panic reactions, negative affect, gender, type of trauma, and time since trauma were considered as confounding variables. RESULTS We found significant association between peritraumatic tonic immobility and PTSD symptoms in a non-clinical sample exposed to various traumas, even after regression controlled for confounding variables (β = 1.99, p = 0.017). CONCLUSIONS This automatic reaction under extreme life threatening stress, although adaptive for defense, may have pathological consequences as implied by its association with PTSD symptoms.

Localization of p27kip1 in the developing avian retina: sustained expression in the mature tissue.

p27kip1 is a cyclin/CDK inhibitor that is expressed in cells that exit cell cycle and turn post-mitotic. Here, we characterized the expression and localization of p27kip1 during the development of the chick embryo retina. Expression of p27kip1 in this tissue begins at embryonic day 5 (E5), increasing as development proceeds. In contrast to the expression in the developing rat retina that markedly decreases after postnatal day 6, expression of p27kip1 in the chick retina decreases only slightly ( approximately 30%) after E12. Thereafter, it remains highly expressed in the tissue. p27kip1 expression increases in an orderly succession. By E5, immunoreactivity was observed over beta-tubulin III (TUJ-1) positive cell bodies located in the prospective Ganglion Cell Layer. By E7, p27kip1 was also detected over elongated cell nuclei located in the inner and outer portions of the Neuroblastic Layer and over cell bodies in the middle of the Inner Plexiform Layer. By E12, besides labeled cell bodies, labeled processes from amacrine cells and from cells at the GCL in the IPL were identified. In retinas from post-hatched chicken, immunoreactivity was observed over cell bodies located at all nuclear layers. Several differentiated ChAT positive cholinergic cells were labeled for p27kip1. Our data suggest that, as in the retina of other species, p27kip1 is expressed in cells that are exiting cell cycle and differentiating in the early developing chick embryo retina. However, as opposed to rodents and amphibians, neuronal expression of p27kip1 is sustained in the adult chick retina, indicating that its expression is differently regulated during development in this specie.

Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging

Introduction High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points. Methods A sample of fifty-seven youth (mean age: 14.5 years; 32 males) was selected from a multi-site study of youth with parent-reported behavioral and emotional dysregulation. Participants performed a block-design reward paradigm during functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Relevance Vector Regression (RVR) and two cross-validation strategies implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Medication was treated as a binary confounding variable. Decoded and actual clinical scores were compared using Pearson’s correlation coefficient (r) and mean squared error (MSE) to evaluate the models. Permutation test was applied to estimate significance levels. Results Relevance Vector Regression identified patterns of neural activity associated with symptoms of behavioral and emotional dysregulation at the initial study screen and close to the fMRI scanning session. The correlation and the mean squared error between actual and decoded symptoms were significant at the initial study screen and close to the fMRI scanning session. However, after controlling for potential medication effects, results remained significant only for decoding symptoms at the initial study screen. Neural regions with the highest contribution to the pattern regression model included cerebellum, sensory-motor and fronto-limbic areas. Conclusions The combination of pattern regression models and neuroimaging can help to determine the severity of behavioral and emotional dysregulation in youth at different time points.

Exposure to trauma-relevant pictures is associated with tachycardia in victims who had experienced an intense peritraumatic defensive response: the tonic immobility.

Tonic immobility is an involuntary, last-ditch defensive reaction characterized by physical inactivity in a context of inescapable threat that has been described in many species, including humans. The occurrence of this defensive response is a predictor of the severity of psychiatric disorders and may be considered as an index of an intense reaction to a traumatic event. Here, we investigated whether the retrospective reports of peritraumatic tonic immobility reaction in participants exposed to a traumatic event would modify their cardiac responses to pictures related to their trauma. Using a questionnaire of life-threating events, we selected students who experienced violent crime as their most intense trauma and students who had never experienced a violent crime trauma, but experienced other traumatic events. All participants completed a questionnaire that estimated the intensity of tonic immobility during their most intense trauma. Electrocardiographic recordings were collected during exposure to pictures. Participants viewed emotional pictures (human attack with guns) and neutral pictures. These emotional stimuli were selected to be trauma-relevant to the violent crime group and non trauma-relevant to the no violent crime trauma group. Violent crime group showed a positive correlation between heart rate changes after viewing trauma-related pictures and tonic immobility scores. We observed that low tonic immobility scores were associated with bradycardia and high scores with tachycardia in response to trauma-relevant pictures. For the no violent crime group, no significant correlation was detected. These results suggest that the relevance of the stimuli and the magnitude of the defensive response during a previous trauma event were important factors triggering more intense defensive responses.

Sparse Network-Based Models for Patient Classification Using fMRI

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has been successful at discriminating psychiatric patients from healthy subjects. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. As is generally accepted, many psychiatric disorders, such as depression and schizophrenia, are brain connectivity disorders. Therefore, pattern recognition based on network models should provide more scientific insight and potentially more powerful predictions than voxel-based approaches. Here, we build a sparse network-based discriminative modelling framework, based on Gaussian graphical models and L1-norm regularised linear Support Vector Machines (SVM). The proposed framework provides easier pattern interpretation in terms of underlying network changes between groups, and we illustrate our technique by classifying patients with depression and controls, using fMRI data from a sad facial processing task.

Embedding anatomical or functional knowledge in whole-brain multiple kernel learning models

Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In this work, we present a novel approach that uses group sparsity to model the whole brain multivariate pattern as a combination of regional patterns. More specifically, we use a sparse version of Multiple Kernel Learning (MKL) to simultaneously learn the contribution of each brain region, previously defined by an atlas, to the decision function. Our application of MKL provides two beneficial features: (1) it can lead to improved overall generalisation performance when the grouping structure imposed by the atlas is consistent with the data; (2) it can identify a subset of relevant brain regions for the predictive model. In order to investigate the effect of the grouping in the proposed MKL approach we compared the results of three different atlases using three different datasets. The method has been implemented in the new version of the open-source Pattern Recognition for Neuroimaging Toolbox (PRoNTo).