Longitudinal volumetric evaluation of hippocampus and amygdala subregions in recent trauma survivors.

The hippocampus and the amygdala play a central role in post-traumatic stress disorder (PTSD) pathogenesis. While alternations in volumes of both regions have been consistently observed in individuals with PTSD, it remains unknown whether these reflect pre-trauma vulnerability traits or acquired post-trauma consequences of the disorder. Here, we conducted a longitudinal panel study of adult civilian trauma survivors admitted to a general hospital emergency department (ED). One hundred eligible participants (mean age = 32.97 ± 10.97, n = 56 females) completed both clinical interviews and structural MRI scans at 1-, 6-, and 14-months after ED admission (alias T1, T2, and T3). While all participants met PTSD diagnosis at T1, only n = 29 still met PTSD diagnosis at T3 (a "non-Remission" Group), while n = 71 did not (a "Remission" Group). Bayesian multilevel modeling analysis showed robust evidence for smaller right hippocampus volume (P+ of ~0.014) and moderate evidence for larger left amygdala volume (P+ of ~0.870) at T1 in the "non-Remission" group, compared to the "Remission" group. Subregion analysis further demonstrated robust evidence for smaller volume in the subiculum and right CA1 hippocampal subregions (P+ of ~0.021-0.046) in the "non-Remission" group. No time-dependent volumetric changes (T1 to T2 to T3) were observed across all participants or between groups. Results support the "vulnerability trait" hypothesis, suggesting that lower initial volumes of specific hippocampus subregions are associated with non-remitting PTSD. The stable volume of all hippocampal and amygdala subregions does not support the idea of consequential, progressive, stress-related atrophy during the first critical year following trauma exposure.

Assessment of early neurocognitive functioning increases the accuracy of predicting chronic PTSD risk.

Post-traumatic stress disorder (PTSD) is a protracted and debilitating consequence of traumatic events. Identifying early predictors of PTSD can inform the disorder's risk stratification and prevention. We used advanced computational models to evaluate the contribution of early neurocognitive performance measures to the accuracy of predicting chronic PTSD from demographics and early clinical features. We consecutively enrolled adult trauma survivors seen in a general hospital emergency department (ED) to a 14-month long prospective panel study. Extreme Gradient Boosting algorithm evaluated the incremental contribution to 14 months PTSD risk of demographic variables, 1-month clinical variables, and concurrent neurocognitive performance. The main outcome variable was PTSD diagnosis, 14 months after ED admission, obtained by trained clinicians using the Clinician-Administered PTSD Scale (CAPS). N = 138 trauma survivors (mean age = 34.25 ± 11.73, range = 18-64; n = 73 [53%] women) were evaluated 1 month after ED admission and followed for 14 months, at which time n = 33 (24%) met PTSD diagnosis. Demographics and clinical variables yielded a discriminatory accuracy of AUC = 0.68 in classifying PTSD diagnostic status. Adding neurocognitive functioning improved the discriminatory accuracy (AUC = 0.88); the largest contribution emanating from poorer cognitive flexibility, processing speed, motor coordination, controlled and sustained attention, emotional bias, and higher response inhibition, and recall memory. Impaired cognitive functioning 1-month after trauma exposure is a significant and independent risk factor for PTSD. Evaluating cognitive performance could improve early screening and prevention.

Deep learning model of fMRI connectivity predicts PTSD symptom trajectories in recent trauma survivors.

Early intervention following exposure to a traumatic life event could change the clinical path from the development of post traumatic stress disorder (PTSD) to recovery, hence the interest in early detection and underlying biological mechanisms involved in the development of post traumatic sequelae. We introduce a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD symptoms at one-, six- and fourteen-months after the exposure. FMRI data, as well as PTSD status and symptoms, were collected from adults at risk for PTSD development, after admission to emergency room following a traumatic event. Our computational method utilized a per-region encoder to extract brain regions embedding, which were subsequently updated by applying the algorithmic technique of pairwise attention. The affinities obtained between each pair of regions were combined to create a pairwise co-activation map used to perform multi-label classification. The results demonstrate that the novel method's performance in predicting PTSD symptoms, in a prospective manner, outperforms previous analytical techniques reported in the fMRI literature, all trained on the same dataset. We further show a high predictive ability for predicting PTSD symptom clusters and PTSD persistence. To the best of our knowledge, this is the first deep learning method applied on fMRI data with respect to prospective clinical outcomes, to predict PTSD status, severity and symptom clusters. Future work could further delineate the mechanisms that underlie such a prediction, and potentially improve single patient characterization.

Current progress in real-time functional magnetic resonance-based neurofeedback: Methodological challenges and achievements.

Neurofeedback (NF) is a research and clinical technique, characterized by live demonstration of brain activation to the subject. The technique has become increasingly popular as a tool for the training of brain self-regulation, fueled by the superiority in spatial resolution and fidelity brought along with real-time analysis of fMRI (functional magnetic resonance imaging) data, compared to the more traditional EEG (electroencephalography) approach. NF learning is a complex phenomenon and a controversial discussion on its feasibility and mechanisms has arisen in the literature. Critical aspects of the design of fMRI-NF studies include the localization of neural targets, cognitive and operant aspects of the training procedure, personalization of training, and the definition of training success, both through neural effects and (for studies with therapeutic aims) through clinical effects. In this paper, we argue that a developmental perspective should inform neural target selection particularly for pediatric populations, and different success metrics may allow in-depth analysis of NF learning. The relevance of the functional neuroanatomy of NF learning for brain target selection is discussed. Furthermore, we address controversial topics such as the role of strategy instructions, sometimes given to subjects in order to facilitate learning, and the timing of feedback. Discussion of these topics opens sight on problems that require further conceptual and empirical work, in order to improve the impact that fMRI-NF could have on basic and applied research in future.

Volitional limbic neuromodulation exerts a beneficial clinical effect on Fibromyalgia.

Volitional neural modulation using neurofeedback has been indicated as a potential treatment for chronic conditions that involve peripheral and central neural dysregulation. Here we utilized neurofeedback in patients suffering from Fibromyalgia - a chronic pain syndrome that involves sleep disturbance and emotion dysregulation. These ancillary symptoms, which have an amplificating effect on pain, are known to be mediated by heightened limbic activity. In order to reliably probe limbic activity in a scalable manner fit for EEG-neurofeedback training, we utilized an Electrical Finger Print (EFP) model of amygdala-BOLD signal (termed Amyg-EFP), that has been successfully validated in our lab in the context of volitional neuromodulation. We anticipated that Amyg-EFP-neurofeedback training aimed at limbic down modulation would improve chronic pain in patients suffering from Fibromyalgia, by reducing sleep disorder improving emotion regulation. We further expected that improved clinical status would correspond with successful training as indicated by improved down modulation of the Amygdala-EFP signal. Thirty-Four Fibromyalgia patients (31F; age 35.6 ±â€¯11.82) participated in a randomized placebo-controlled trial with biweekly Amyg-EFP-neurofeedback sessions or sham neurofeedback (n = 9) for a total duration of five consecutive weeks. Following training, participants in the real-neurofeedback group were divided into good (n = 13) or poor (n = 12) modulators according to their success in the neurofeedback training. Before and after treatment, self-reports on pain, depression, anxiety, fatigue and sleep quality were obtained, as well as objective sleep indices. Long-term clinical follow-up was made available, within up to three years of the neurofeedback training completion. REM latency and objective sleep quality index were robustly improved following the treatment course only in the real-neurofeedback group (time × group p < 0.05) and to a greater extent among good modulators (time × sub-group p < 0.05). In contrast, self-report measures did not reveal a treatment-specific response at the end of the neurofeedback training. However, the follow-up assessment revealed a delayed improvement in chronic pain and subjective sleep experience, evident only in the real-neurofeedback group (time × group p < 0.05). Moderation analysis showed that the enduring clinical effects on pain evident in the follow-up assessment were predicted by the immediate improvements following training in objective sleep and subjective affect measures. Our findings suggest that Amyg-EFP-neurofeedback that specifically targets limbic activity down modulation offers a successful principled approach for volitional EEG based neuromodulation treatment in Fibromyalgia patients. Importantly, it seems that via its immediate sleep improving effect, the neurofeedback training induced a delayed reduction in the target subjective symptom of chronic pain, far and beyond the immediate placebo effect. This indirect approach to chronic pain management reflects the substantial link between somatic and affective dysregulation that can be successfully targeted using neurofeedback.