Heart Rate Variability Features as Predictors of Intermittent Theta-Burst Stimulation Response in Posttraumatic Stress Disorder.

BACKGROUND: Posttraumatic stress disorder (PTSD) is associated with autonomic dysfunction as indicated by deficits in the sympathetic and parasympathetic nervous systems. These abnormalities are expressed as elevated heart rate and reduced heart rate variability (HRV), respectively. Intermittent theta-burst stimulation (iTBS), a form of transcranial magnetic stimulation, has demonstrated effectiveness in PTSD. Nevertheless, it remains unclear whether HRV may be an iTBS biomarker for PTSD and whether iTBS impacts autonomic activity. MATERIALS AND METHODS: Fifty veterans with PTSD participated in a randomized controlled trial, receiving ten daily sessions of sham-controlled iTBS (right dorsolateral prefrontal cortex, 1800 pulses/day, 80% active motor threshold, 9.5 min). With a usable dataset (N = 47), HRV parameters were assessed as predictors of clinical response immediately after stimulation. iTBS effects on autonomic response (mean RR interval, root mean square of successive differences [RMSSD], total power [TP], and low-frequency/high-frequency [LF/HF] ratio) were evaluated using an ultra-short approach. RESULTS: TP and RMSSD were significant predictors of acute clinical response to iTBS. Individuals with higher TP had better response to iTBS with improved symptoms on the Clinician-Administered PTSD Scale (rs = -0.58, p = 0.004), and higher functionality on the Social and Occupational Function Scale (rs = 0.43, p = 0.04). Similarly, higher RMSSD was associated with superior outcomes (rs = -0.44, p = 0.04). No other significant changes in HRV metrics were observed (p ≥ 0.05). CONCLUSIONS: Our findings indicate that autonomic activity is a potential low-cost and technically simple predictive biomarker of iTBS response in PTSD. Less autonomic dysfunction was associated with superior clinical improvements with iTBS. Future studies might consider HRV acquisition during iTBS, as well as prospective testing of these findings in patients with elevated hyperarousal.

Mapping PTSD symptoms to brain networks: a machine learning study.

Posttraumatic Stress Disorder (PTSD) is a prevalent and debilitating condition with complex and variable presentation. While PTSD symptom domains (intrusion, avoidance, cognition/mood, and arousal/reactivity) correlate highly, the relative importance of these symptom subsets often differs across patients. In this study, we used machine learning to derive how PTSD symptom subsets differ based upon brain functional connectivity. We acquired resting-state magnetic resonance imaging in a sample (N = 50) of PTSD patients and characterized clinical features using the PTSD Checklist for DSM-5 (PCL-5). We compared connectivity among 100 cortical and subcortical regions within the default mode, salience, executive, and affective networks. We then used principal component analysis and least-angle regression (LARS) to identify relationships between symptom domain severity and brain networks. We found connectivity predicted PTSD symptom profiles. The goodness of fit (R2) for total PCL-5 score was 0.29 and the R2 for intrusion, avoidance, cognition/mood, and arousal/reactivity symptoms was 0.33, 0.23, -0.01, and 0.06, respectively. The model performed significantly better than chance in predicting total PCL-5 score (p = 0.030) as well as intrusion and avoidance scores (p = 0.002 and p = 0.034). It was not able to predict cognition and arousal scores (p = 0.412 and p = 0.164). While this work requires replication, these findings demonstrate that this computational approach can directly link PTSD symptom domains with neural network connectivity patterns. This line of research provides an important step toward data-driven diagnostic assessments in PTSD, and the use of computational methods to identify individual patterns of network pathology that can be leveraged toward individualized treatment.

A review of prospective memory in individuals with acquired brain injury.

OBJECTIVE: Prospective memory (PM) deficits have emerged as an important predictor of difficulty in daily life for individuals with acquired brain injury (BI). This review examines the variables that have been found to influence PM performance in this population. In addition, current methods of assessment are reviewed with a focus on clinical measures. Finally, cognitive rehabilitation therapies are reviewed, including compensatory, restorative and metacognitive approaches. METHOD: Preferred reporting items for systematic reviews and meta-analyses guidelines were used to identify studies. Studies were added that were identified from the reference lists of these. RESULTS: Research has begun to elucidate the contributing variables to PM deficits after BI, such as attention, executive function and retrospective memory components. Imaging studies have identified prefrontal deficits, especially in the region of BA10 as contributing to these deficits. There are now several clinical measures available with good psychometric properties. Rehabilitation techniques have mostly focused on compensatory strategies, but, in addition, some restorative and metacognitive approaches have shown preliminary promise. CONCLUSIONS: PM deficits are a common and important deficit after BI. Clinical evaluation is recommended and further understanding of rehabilitation techniques is needed.