Cognitive-behavior therapy (CBT) is a well-established first-line intervention for anxiety-related disorders, including specific phobia, social anxiety disorder, panic disorder/agoraphobia, generalized anxiety disorder, obsessive-compulsive disorder, and posttraumatic stress disorder. Several neural predictors of CBT outcome for anxiety-related disorders have been proposed, but previous results are inconsistent.

We conducted a systematic review and meta-analysis of task-based functional magnetic resonance imaging (fMRI) studies investigating whole-brain predictors of CBT outcome in anxiety-related disorders (17 studies, n = 442).

Across different tasks, we observed that brain response in a network of regions involved in salience and interoception processing, encompassing fronto-insular (the right inferior frontal gyrus-anterior insular cortex) and fronto-limbic (the dorsomedial prefrontal cortex-dorsal anterior cingulate cortex) cortices was strongly associated with a positive CBT outcome.

Our results suggest that there are robust neural predictors of CBT outcome in anxiety-related disorders that may eventually lead (probably in combination with other data) to develop personalized approaches for the treatment of these mental disorders.

Suppression of the rostral anterior cingulate cortex (rACC) has shown promise as a prognostic biomarker for depression. We aimed to use machine learning to characterise its ability to predict depression remission.

Data were obtained from 81 15- to 25-year-olds with a major depressive disorder who had participated in the YoDA-C trial, in which they had been randomised to receive cognitive behavioural therapy plus either fluoxetine or placebo. Prior to commencing treatment patients performed a functional magnetic resonance imaging (fMRI) task to assess rACC suppression. Support vector machines were trained on the fMRI data using nested cross-validation, and were similarly trained on clinical data. We further tested our fMRI model on data from the YoDA-A trial, in which participants had completed the same fMRI paradigm.

Thirty-six of 81 (44%) participants in the YoDA-C trial achieved remission. Our fMRI model was able to predict remission status (AUC = 0.777 [95% confidence interval (CI) 0.638–0.916], balanced accuracy = 67%, negative predictive value = 74%, p < 0.0001). Clinical models failed to predict remission status at better than chance levels. Testing the model on the alternative YoDA-A dataset confirmed its ability to predict remission (AUC = 0.776, balanced accuracy = 64%, negative predictive value = 70%, p < 0.0001).

We confirm that rACC activity acts as a prognostic biomarker for depression. The machine learning model can identify patients who are likely to have difficult-to-treat depression, which might direct the earlier provision of enhanced support and more intensive therapies.

Depression is commonly associated with fronto-amygdala dysfunction during the processing of emotional face expressions. Interactions between these regions are hypothesized to contribute to negative emotional processing biases and as such have been highlighted as potential biomarkers of treatment response. This study aimed to investigate depression associated alterations to directional connectivity and assess the utility of these parameters as predictors of treatment response.

Ninety-two unmedicated adolescents and young adults (mean age 20.1; 56.5% female) with moderate-to-severe major depressive disorder and 88 healthy controls (mean age 19.8; 61.4% female) completed an implicit emotional face processing fMRI task. Patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or placebo. Using dynamic causal modelling, we examined functional relationships between six brain regions implicated in emotional face processing, comparing both patients and controls and treatment responders and non-responders.

Depressed patients demonstrated reduced inhibition from the dlPFC to vmPFC and reduced excitation from the dlPFC to amygdala during sad expression processing. During fearful expression processing patients showed reduced inhibition from the vmPFC to amygdala and reduced excitation from the amygdala to dlPFC. Response was associated with connectivity from the amygdala to dlPFC during sad expression processing and amygdala to vmPFC connectivity during fearful expression processing.

Our study clarifies the nature of face processing network alterations in adolescents and young adults with depression, highlighting key interactions between the amygdala and prefrontal cortex. Moreover, these findings highlight the potential utility of these interactions in predicting treatment response.

Establishing neurobiological markers of posttraumatic stress disorder (PTSD) is essential to aid in diagnosis and treatment development. Fear processing deficits are central to PTSD, and their neural signatures may be used as such markers.

Here, we conducted a meta-analysis of seven Pavlovian fear conditioning fMRI studies comparing 156 patients with PTSD and 148 trauma-exposed healthy controls (TEHC) using seed-based d-mapping, to contrast neural correlates of experimental phases, namely conditioning, extinction learning, and extinction recall.

Patients with PTSD, as compared to TEHCs, exhibited increased activation in the anterior hippocampus (extending to the amygdala) and medial prefrontal cortex during conditioning; in the anterior hippocampus-amygdala regions during extinction learning; and in the anterior hippocampus-amygdala and medial prefrontal areas during extinction recall. Yet, patients with PTSD have shown an overall decreased activation in the thalamus during all phases in this meta-analysis.

Findings from this metanalysis suggest that PTSD is characterized by increased activation in areas related to salience and threat, and lower activation in the thalamus, a key relay hub between subcortical areas. If replicated, these fear network alterations may serve as objective diagnostic markers for PTSD, and potential targets for novel treatment development, including pharmacological and brain stimulation interventions. Future longitudinal studies are needed to examine whether these observed network alteration in PTSD are the cause or the consequence of PTSD.

The aetiological boundary between obsessive–compulsive related disorders (OCRDs) including obsessive–compulsive disorder (OCD) and anxiety disorders is unclear and continues to generate debate.

To determine the genetic overlap and the pattern of causal relationships among OCRDs and anxiety disorders.

Multivariate twin modelling methods and a new regression analysis to infer causation were used, involving 2495 male and female twins.

The amount of common genetic liability observed for OCD symptoms was higher when considering anxiety disorders and OCRDs in the model v. modelling OCRD symptoms alone. OCD symptoms emerged as risk factors for the presence of generalised anxiety, panic and hoarding symptoms, whereas social phobia appeared as a risk factor for OCD symptoms.

OCD represents a complex phenotype that includes important shared features with anxiety disorders and OCRDs. The novel patterns of risk identified between OCD and anxiety disorder may help to explain their frequent co-occurrence.

Although severe hoarding symptoms have been considered rare among obsessive-compulsive disorder (OCD) samples, the prevalence of animal hoarding in OCD is unknown. To help clarifying this issue, we searched for cases of animal hoarding among patients attending a university OCD clinic (n=420).

Chart review.

Only two patients from our sample exhibited animal hoarding (<0.5%) and only one of them presented additional obsessive-compulsive symptoms. Both cases also collected inanimate objects, presented low insight, exhibited poor response to serotonin reuptake inhibitors and did not adhere to therapy.

There seems to be a lack of relationship between animal hoarding and OCD. However, further studies with larger numbers of patients are needed to better define their psychopathological profile and more appropriate nosological insertion.

Despite knowledge of amygdala involvement in fear and anxiety, its contribution to the pathophysiology of obsessive–compulsive disorder (OCD) remains controversial. In the context of neuroimaging studies, it seems likely that the heterogeneity of the disorder might have contributed to a lack of consistent findings.

To assess the influence of OCD symptom dimensions on amygdala responses to a well-validated emotional face-matching paradigm.

Cross-sectional functional magnetic resonance imaging (fMRI) study of 67 patients with OCD and 67 age-, gender- and education-level matched healthy controls.

The severity of aggression/checking and sexual/religious symptom dimensions were significantly associated with heightened amygdala activation in those with OCD when responding to fearful faces, whereas no such correlations were seen for other symptom dimensions.

Amygdala functional alterations in OCD appear to be specifically modulated by symptom dimensions whose origins may be more closely linked to putative amygdala-centric processes, such as abnormal fear processing.