Immobility reactions under threat: A contribution to human defensive cascade and PTSD

Violence exacts a burden on public health. Gun violence is a major trigger for motor defensive reactions in humans and post-traumatic stress disorder (PTSD) is its main psychiatric sequela. However, studies of the human defensive cascade, especially the motor reactions, are at an early stage. This review focuses on studies that employ stabilometry, a methodology that assesses whole body motor reactions, to address defensive behaviors to violence-related threats. Special attention is given to three reactions: "attentive immobility", "immobility under attack" and "tonic immobility", with emphasis on the latter - a peritraumatic reaction which has been strongly associated with the severity of PTSD. These reactions are characterized by reduced body sway and bradycardia, except tonic immobility that presents robust tachycardia. The advances made by investigations into the immobility reactions of the human defensive cascade contribute to helping to bridge the gap between human and non-human species. Furthermore, progresses in basic research to objectively monitor motor defensive reactions under threat can help to develop a dimensional, trans-diagnostic approach to PTSD.

Examination of the predictive value of structural magnetic resonance scans in bipolar disorder: a pattern classification approach

BACKGROUND

Bipolar disorder (BD) is one of the leading causes of disability worldwide. Patients are further disadvantaged by delays in accurate diagnosis ranging between 5 and 10 years. We applied Gaussian process classifiers (GPCs) to structural magnetic resonance imaging (sMRI) data to evaluate the feasibility of using pattern recognition techniques for the diagnostic classification of patients with BD.

METHOD

GPCs were applied to gray (GM) and white matter (WM) sMRI data derived from two independent samples of patients with BD (cohort 1: n = 26; cohort 2: n = 14). Within each cohort patients were matched on age, sex and IQ to an equal number of healthy controls.

RESULTS

The diagnostic accuracy of the GPC for GM was 73% in cohort 1 and 72% in cohort 2; the sensitivity and specificity of the GM classification were respectively 69% and 77% in cohort 1 and 64% and 99% in cohort 2. The diagnostic accuracy of the GPC for WM was 69% in cohort 1 and 78% in cohort 2; the sensitivity and specificity of the WM classification were both 69% in cohort 1 and 71% and 86% respectively in cohort 2. In both samples, GM and WM clusters discriminating between patients and controls were localized within cortical and subcortical structures implicated in BD.

CONCLUSIONS

Our results demonstrate the predictive value of neuroanatomical data in discriminating patients with BD from healthy individuals. The overlap between discriminative networks and regions implicated in the pathophysiology of BD supports the biological plausibility of the classifiers.

Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study

BACKGROUND

To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.

METHOD

One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls.

RESULTS

At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035).

CONCLUSIONS

We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data.