Why Does Psychotherapy Work and for Whom? Hormonal Answers

The questions of for whom and why psychotherapy is effective have been the focus of five decades of research. Most of this knowledge is based on self-report measures. Following the biopsychosocial model of mental disorders, this article explores the potential of hormones in answering these questions. The literature on cortisol, oxytocin, and oestradiol in psychotherapy was systematically searched, focusing on (a) baseline hormonal predictors of who may benefit from psychotherapy and (b) hormonal changes as indicators of therapeutic change. The search was limited to depression and anxiety disorders. In sum, the findings show that, of all three hormones, the role of cortisol is most established and that both cortisol and oxytocin are implicated in psychotherapy, although a causal role is still waiting to be demonstrated. Moreover, there is a differential role of hormones in the psychotherapy of depression versus anxiety. The directions of research mapped in this article may elucidate how psychotherapy can be selected to match patients’ endocrine states and how hormonal levels can be manipulated to improve outcomes

I see you as recognizing me; therefore, I trust you: Operationalizing epistemic trust in psychotherapy

Epistemic trust (ET) is one’s ability to trust others and relies on the information they convey as being relevant and generalizable. This concept has received considerable theoretical and clinical attention, suggesting it is a promising factor in effective psychotherapy, possibly consisting of three elements: sharing, we-mode, and learning. However, for it to be used in clinical practice and research, it is imperative to (a) enhance our clinical understanding of how ET may manifest in the context of treatment and (b) understand how the process of change may occur in the course of treatment. The present study aims to identify patients’ trait-like ET characteristics upon initiating treatment and explore the possible state-like changes in ET characteristics throughout treatment. Taking a discovery-oriented approach, we examined how therapists can identify a patient’s level of ET at the beginning of treatment. We also examined how, within a treatment for individuals with poor pretreatment ET, the therapist and patient work interactively to bring about a positive change in ET. Identifying the process in which the therapist implements techniques in response to the patient’s reactions may enable the active mechanism to be isolated and promote the first formulation of the way changes in ET occur in sequence. (PsycInfo Database Record (c) 2023 APA, all rights reserved

If you believe that breaking is possible, believe also that fixing is possible: a framework for ruptures and repairs in child psychotherapy

Safran and Muran’s classic theoretical framework of alliance rupture and repair suggests effective techniques for repairing alliance ruptures. Accumulating empirical evidence suggests that successful processes of rupture and repair result in better therapeutic outcome and reduced dropout rates. Although ruptures in the alliance in child psychotherapy are frequent, little is known about how to repair them. The present paper proposes a model for identifying and repairing ruptures in child psychotherapy based on Safran and Muran. It consists of four phases: i) identifying the rupture and understanding its underlying communication message, ii) indicating the presence of the rupture, iii) accepting responsibility over the therapists’ part in the rupture and emphasizing the children’s active role as communicators of their distress, and iv) resolving the rupture using change strategies and meta-communication by constructing a narrative story. The theoretical rationale of each phase is explained in detail, and practical clinical guidelines are provided. Empirical studies are needed to examine the effectiveness of the proposed framework

Not Just Nonspecific Factors: The Roles of Alliance and Expectancy in Treatment, and Their Neurobiological Underpinnings

Therapeutic factors such as alliance and expectancy have been found to greatly affect treatment outcome in both psychotherapy and psychopharmacotherapy. Often, these factors are referred to as nonspecific because of their common roles across treatment modalities. Here we argue that conceptualizing such factors as nonspecific is not accurate at best, misleading at worst and may undermine treatment outcome across various modalities. We argue that alliance and expectancy contain both a trait-like common factor component and a state-like specific effect, and that it is clinically, conceptually and methodologically critical to disentangle the two. In other words, both alliance and expectancy may also function as active ingredients of treatment, leading to better outcome. We review the literature regarding the neurobiological underpinnings of alliance and of the expectancy effect, and suggest how future studies on the neurobiological basis of these effects can shed further light on the potentially distinct mechanisms of the trait-like and state-like components of each therapeutic factor

Intrusive Traumatic Re-Experiencing Domain (ITRED) – Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

Background Intrusive Traumatic Re-Experiencing Domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective. Methods Data was collected from nine sites taking part in the ENIGMA-PTSD Consortium (n=584) and included itemized PTSD symptoms scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and Trauma-exposed (TE)-only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. Random forest classification model was built on a training set using cross-validation (CV), and the averaged CV model performance for classification was evaluated using area-under-the-curve (AUC). The model was tested using a fully independent portion of the data (test dataset), and the test AUC was evaluated. Results RsFC signatures differentiated TE-only participants from PTSD and from ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and from ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontal-parietal network, differentiated TE-only participants from one group (PTSD or ITRED-only), but to a lesser extent from the other. Conclusion Neural network connectivity supports ITRED as a novel neurobiologically-based approach to classifying post-trauma psychopathology

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p

Intrusive Traumatic Re-Experiencing Domain: Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

Background: Intrusive traumatic re-experiencing domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective. Methods: Data were collected from 9 sites taking part in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) PTSD Consortium (n = 584) and included itemized PTSD symptom scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and trauma-exposed (TE)–only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. A random forest classification model was built on a training set using cross-validation, and the averaged cross-validation model performance for classification was evaluated using the area under the curve. The model was tested using a fully independent portion of the data (test dataset), and the test area under the curve was evaluated. Results: rsFC signatures differentiated TE-only participants from PTSD and ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and ITRED-only participants mainly involved default mode network–related pathways. Some unique features, such as connectivity within the frontoparietal network, differentiated TE-only participants from one group (PTSD or ITRED-only) but to a lesser extent from the other group. Conclusions: Neural network connectivity supports ITRED as a novel neurobiologically based approach to classifying posttrauma psychopathology