Search Results (19)

Background: Depression that is resistant to two or more adequate treatment trials—treatment-resistant depression (TRD)—is a prevalent clinical challenge. Although psychotherapies have been recommended by clinical guidelines as an alternative or adjunctive treatment strategy, the effectiveness of psychotherapy in individuals with TRD has not yet been evaluated through meta-analytic methods, primarily due to a limited number of trials. This highlights the necessity of personalized research targeting this specific population. This systematic review and meta-analysis aimed to summarize the evidence on psychotherapy in treating TRD. Methods: A systematic search was conducted following the Guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles were included if they quantitatively examined the efficacy of psychotherapy on depression symptoms in individuals diagnosed with depression who had not responded to at least two prior treatments (i.e., pharmacotherapy and/or psychotherapy). Results: A total of 12 studies were included. The quality of evidence was evaluated as being globally moderate. When pooling all psychotherapies, a small-to-moderate, but significant, effect on depressive symptoms was observed compared to the control group (SMD = −0.49, CI = −0.63; −0.34). The observed effect remained unchanged after removing the outlier (SMD = −0.47, CI = −0.62; −0.32). When examining depressive symptoms by type of psychotherapy, Mindfulness-Based Cognitive Therapy (SMD = −0.51, CI = −0.76; −0.25), Cognitive Behavioral Therapy (SMD = −0.53, CI = −0.92; −0.14), and Cognitive Therapy (SMD = −0.51, CI = −1.01; −0.01) showed a moderately significant effect on depressive symptoms compared to the control group. Conclusions: Although this potentially represents the first meta-analysis in this area, the number of studies specifically addressing this complex population remains limited, and the existing literature is still in its early stages. Research focusing on TRD is notably sparse compared to the broader body of work on depression without treatment resistance. Consequently, it was not possible to conduct meta-analyses by type of psychotherapy across all treatment modalities and by type of control group. Due to several study limitations, there is currently limited evidence available about the effectiveness of psychotherapy for TRD, and further trials are needed. Beyond the treatments usually offered for depression, it is possible that TRD requires a personalized medicine approach. Full article

Background and Clinical Significance: Avatar Therapy (AT) for individuals with treatment-resistant auditory verbal hallucinations (AVHs) in schizophrenia aims to address emotional responses, beliefs about voices, self-perception, and coping strategies. This study focuses on three participants who, during AT, shifted their belief about the origin of their most distressing voice from an external source to a self-generated one. Case Presentation: The objective of this study was to explore the evolution of the reattribution of the participants’ most distressing voice to oneself during AT and the patients’ perception of this reattribution. Immersive sessions and semi-structured interviews were transcribed and qualitatively described to provide a session-by-session account of the evolution of each participant’s AVH reattribution to themselves during the course of AT, along with their perceptions of this reattribution. This process led to the recognition that initially perceived as external voices were internally generated thoughts, reflecting how participants viewed themselves. Two participants reported a reduction in AVH severity. All three described positive changes in how they related to their voices and self-perception. Additional improvements were observed in emotional regulation, social functioning, and engagement in personal projects. Conclusions: This reassignment of the voice from an external source to an internal one suggests that AT can modify how individuals relate to their voices and may empower them to regain control over their hallucinations. However, given the exploratory nature of this study, the results should be interpreted as examples. Full article

Background: Previous syntheses on the neural effects of alcohol have been restricted to tasks assessing craving, cognitive control, and reward processing. Despite extensive research, a comprehensive synthesis of functional magnetic resonance imaging (fMRI) findings on alcohol use disorder (AUD) remains lacking. This study aimed to identify consistent brain activation alterations across all cognitive and emotional tasks administered to individuals with AUD while distinguishing between short-term and long-term abstinence and using activation likelihood estimation meta-analysis. Sub-analyses on task types were performed. Methods: A systematic review identified 67 fMRI studies on participants with an AUD. Results: The meta-analysis revealed significant alterations in brain activity, including both hypo- and hyperactivation in the left putamen across all AUD participants. These alterations were observed more frequently during decision-making and reward tasks. Short-term abstinent individuals exhibited hypoactivation in the right middle frontal gyrus (MFG), corresponding to the dorsolateral prefrontal cortex. In contrast, long-term abstinent individuals displayed hypoactivation in the right superior frontal gyrus (SFG) and dorsal anterior cingulate cortex (dACC). This meta-analysis highlights critical neural alterations in AUD, particularly in regions associated with reward processing (putamen), executive functions (MFG and SFG), and attentional salience (dACC). Putamen changes were predominantly observed during short-term abstinence and in decision-making, as well as reward processing tasks. dACC and SFG hypoactivation were specific to long-term abstinence, while MFG hypoactivation was specific to short-term abstinence. Conclusions: These findings support prior research indicating a motivational imbalance and persistent executive dysfunctions in AUD. Standardizing consumption metrics and expanding task diversity in future research is essential to further refine our understanding of the neural effects of AUD. Full article

Background/Objectives: Violent behaviors are uncommon in patients with schizophrenia (Sch), but when present, exacerbate stigma and challenge treatment. The following study aimed to identify the structural abnormalities associated with violent behaviors in Sch by implementing a validated tool specifically designed to evaluate violent behaviors in psychiatric populations, as well as by performing region-of-interest neuroimaging analyses, focused on areas commonly associated with the neurobiology of violence and aggression. Methods: Eighty-three participants were divided into three groups: Sch with violent behaviors (Sch+V, n = 34), Sch without violent behaviors (Sch-V, n = 28), and healthy controls (HC, n = 21). Structural neuroimaging analyses were performed across groups to assess gray matter volume (GMV) and cortical thickness (CT) differences in regions previously implicated in aggressive behaviors. Results: The data revealed significant reductions in GMV in the right amygdala and diminished cortical thickness (CT) in the bilateral dorsolateral prefrontal cortices (dlPFC) in patients with Sch+V compared to patients with Sch-V and HCs. Right amygdalar volume also demonstrated a negative correlational trend with hostility scores in patients with Sch+V. Conclusions: These findings underscore disruptions in the structural integrity of the dlPFC—responsible for inhibitory control—and the amygdala—central to emotional processing in violent patients with Sch. Future research should aim to investigate potential functional interactions at a network level to gain a deeper understanding of the neurobiological underpinnings of violent behaviors in this population. Full article

Background: The advent of virtual reality in psychiatry presents a wealth of opportunities for a variety of psychopathologies. Avatar Interventions are dialogic and experiential treatments integrating personalized medicine with virtual reality (VR), which have shown promising results by enhancing the emotional regulation of their participants. Notably, Avatar Therapy for the treatment of auditory hallucinations (i.e., voices) allows patients to engage in dialogue with an avatar representing their most persecutory voice. In addition, Avatar Intervention for cannabis use disorder involves an avatar representing a significant person in the patient’s consumption. In both cases, the main goal is to modify the problematic relationship and allow patients to regain control over their symptoms. While results are promising, its potential to be applied to other psychopathologies, such as major depression, is an exciting area for further exploration. In an era where VR interventions are gaining popularity, the present study aims to investigate whether technological advancements could overcome current limitations, such as avatar realism, and foster a deeper immersion into virtual environments, thereby enhancing participants’ sense of presence within the virtual world. A newly developed virtual reality platform was compared to the current platform used by our research team in past and ongoing studies. Methods: This study involved 43 subjects: 20 healthy subjects and 23 subjects diagnosed with severe mental disorders. Each participant interacted with an avatar using both platforms. After each immersive session, questionnaires were administered by a graduate student in a double-blind manner to evaluate technological advancements and user experiences. Results: The findings indicate that the new technological improvements allow the new platform to significantly surpass the current platform as per multiple subjective parameters. Notably, the new platform was associated with superior realism of the avatar (d = 0.574; p < 0.001) and the voice (d = 1.035; p < 0.001), as well as enhanced lip synchronization (d = 0.693; p < 0.001). Participants reported a significantly heightened sense of presence (d = 0.520; p = 0.002) and an overall better immersive experience (d = 0.756; p < 0.001) with the new VR platform. These observations were true in both healthy subjects and participants with severe mental disorders. Conclusions: The technological improvements generated a heightened sense of presence among participants, thus improving their immersive experience. These two parameters could be associated with the effectiveness of VR interventions and future studies should be undertaken to evaluate their impact on outcomes. Full article

Background: Psychotherapeutic approaches such as Avatar Therapy (AT) are novel therapeutic attempts to help patients diagnosed with treatment-resistant schizophrenia. Qualitative analyses of immersive sessions of AT have been undertaken to enhance and refine the existing interventions taking place in this therapy. To account for the time-consuming and costly nature and potential misclassification biases, prior implementation of a Linear Support Vector Classifier provided helpful insight. Single model implementation for text classification is often limited, especially for datasets containing imbalanced data. The main objective of this study is to evaluate the change in accuracy of automated text classification machine learning algorithms when using an ensemble approach for immersive session verbatims of AT. Methods: An ensemble model, comprising five machine learning algorithms, was implemented to conduct text classification for avatar and patient interactions. The models included in this study are: Multinomial Naïve Bayes, Linear Support Vector Classifier, Multi-layer perceptron classifier, XGBClassifier and the K-Nearest-Neighbor model. Accuracy, precision, recall and f1-score were compared for the individual classifiers and the ensemble model. Results: The ensemble model performed better than its individual counterparts for accuracy. Conclusion: Using an ensemble methodological approach, this methodology might be employed in future research to provide insight into the interactions being categorized and the therapeutical outcome of patients based on their experience with AT with optimal precision. Full article

This meta-analysis investigates auditory steady-state responses (ASSRs) as potential biomarkers of schizophrenia, focusing on previously unexplored clinical populations, frequencies, and variables. We examined 37 studies, encompassing a diverse cohort of 1788 patients with schizophrenia, including 208 patients with first-episode psychosis, 281 at-risk individuals, and 1603 healthy controls. The results indicate moderate reductions in 40 Hz ASSRs in schizophrenia patients, with significantly greater reductions in first-episode psychosis patients and minimal changes in at-risk individuals. These results call into question the expected progression of ASSR alterations across all stages of schizophrenia. The analysis also revealed the sensitivity of ASSR alterations at 40 Hz to various factors, including stimulus type, level of analysis, and attentional focus. In conclusion, our research highlights ASSRs, particularly at 40 Hz, as potential biomarkers of schizophrenia, revealing varied implications across different stages of the disorder. This study enriches our understanding of ASSRs in schizophrenia, highlighting their potential diagnostic and therapeutic relevance, particularly in the early stages of the disease. Full article

Considering the harmful effects of cannabis on individuals with a severe mental disorder and the limited effectiveness of current interventions, this case report showcases the beneficial results of a 10-session Avatar intervention for cannabis use disorder (CUD) on a polysubstance user with a comorbid schizoaffective disorder. Virtual reality allowed the creation of an Avatar representing a person significantly related to the patient’s drug use. Avatar intervention for CUD aims to combine exposure, relational, and cognitive behavioral therapies while practicing real-life situations and learning how to manage negative emotions and cravings. Throughout therapy and later on, Mr. C managed to maintain abstinence from all substances. Also, an improvement in the severity of CUD, as well as a greater motivation to change consumption, was observed after therapy. As observed by his mother, his psychiatrist, and himself, the benefits of Avatar intervention for CUD extended to other spheres of his life. The drastic results observed in this patient could be promising as an alternative to the current treatment available for people with a dual diagnosis of cannabis use disorder and psychotic disorder, which generally lack effectiveness. A single-blind randomized control trial comparing the treatment with a classical intervention in a larger sample is currently underway to evaluate whether the results are reproducible on a larger sample. Full article

(1) Background: Approximately 30% of schizophrenia patients are known to be treatment-resistant. For these cases, more personalized approaches must be developed. Virtual reality therapeutic approaches such as avatar therapy (AT) are currently undergoing investigations to address these patients’ needs. To further tailor the therapeutic trajectory of patients presenting with this complex presentation of schizophrenia, quantitative insight about the therapeutic process is warranted. The aim of the study is to combine a classification model with a regression model with the aim of predicting the therapeutic outcomes of patients based on the interactions taking place during their first immersive session of virtual reality therapy. (2) Methods: A combination of a Linear Support Vector Classifier and logistic regression was conducted over a dataset comprising 162 verbatims of the immersive sessions of 18 patients who previously underwent AT. As a testing dataset, 17 participants, unknown to the dataset, had their first immersive session presented to the combinatory model to predict their clinical outcome. (3) Results: The model accurately predicted the clinical outcome for 15 out of the 17 participants. Classification of the therapeutic interactions achieved an accuracy of 63%. (4) Conclusion: To our knowledge, this is the first attempt to predict the outcome of psychotherapy patients based on the content of their interactions with their therapist. These results are important as they open the door to personalization of psychotherapy based on quantitative information about the interactions taking place during AT. Full article

Background: Fibromyalgia (FM) and major depression disorder (MDD) frequently co-occur. Both disorders may share common serotonergic alterations, although there is less evidence of such alterations in FM. It is also unclear as to whether these alterations are persistent over time or transient. The objectives of this study were to (i) examine the changes in mRNA expression of serotonin transporter (SERT) on the surface of peripheral blood mononuclear cells (PBMCs) in FM, MDD, and the FM + MDD subjects compared to healthy controls, and to (ii) evaluate the effect of drug treatment on SERT expression. Methods: PBMCs were isolated from FM, MDD, FM + MDD, and control subjects. SERT expression was analyzed at the mRNA level via quantitative real-time polymerase chain reaction. Statistical analyses were performed using analyses of variance and linear mixed-effects models. Results: SERT mRNA expression was significantly reduced in MDD subjects compared to controls (p < 0.001), but not in FM nor in FM + MDD subjects. Although the drug treatments improved symptoms in FM, MDD, and FM + MDD subjects, they had no significant effect on SERT mRNA expression. Conclusions: These results corroborate the role of the SERT in the pathophysiology of MDD, but not in FM, and show that the decreased mRNA expression of SERT is a persistent, rather than transient, phenomenon. Full article

(1) Background: Avatar Therapy (AT) is currently being studied to help patients suffering from treatment-resistant schizophrenia. Facilitating annotations of immersive verbatims in AT by using classification algorithms could be an interesting avenue to reduce the time and cost of conducting such analysis and adding objective quantitative data in the classification of the different interactions taking place during the therapy. The aim of this study is to compare the performance of machine learning algorithms in the automatic annotation of immersive session verbatims of AT. (2) Methods: Five machine learning algorithms were implemented over a dataset as per the Scikit-Learn library: Support vector classifier, Linear support vector classifier, Multinomial Naïve Bayes, Decision Tree, and Multi-layer perceptron classifier. The dataset consisted of the 27 different types of interactions taking place in AT for the Avatar and the patient for 35 patients who underwent eight immersive sessions as part of their treatment in AT. (3) Results: The Linear SVC performed best over the dataset as compared with the other algorithms with the highest accuracy score, recall score, and F1-Score. The regular SVC performed best for precision. (4) Conclusions: This study presented an objective method for classifying textual interactions based on immersive session verbatims and gave a first comparison of multiple machine learning algorithms on AT. Full article

(1) Background: The therapeutic mechanisms underlying psychotherapeutic interventions for individuals with treatment-resistant schizophrenia are mostly unknown. One of these treatment techniques is avatar therapy (AT), in which the patient engages in immersive sessions while interacting with an avatar representing their primary persistent auditory verbal hallucination. The aim of this study was to conduct an unsupervised machine-learning analysis of verbatims of treatment-resistant schizophrenia patients that have followed AT. The second aim of the study was to compare the data clusters obtained from the unsupervised machine-learning analysis with previously conducted qualitative analysis. (2) Methods: A k-means algorithm was performed over the immersive-session verbatims of 18 patients suffering from treatment-resistant schizophrenia who followed AT to cluster interactions of the avatar and the patient. Data were pre-processed using vectorization and data reduction. (3): Results: Three clusters of interactions were identified for the avatar’s interactions whereas four clusters were identified for the patient’s interactions. (4) Conclusion: This study was the first attempt to conduct unsupervised machine learning on AT and provided a quantitative insight into the inner interactions that take place during immersive sessions. The use of unsupervised machine learning could yield a better understanding of the type of interactions that take place in AT and their clinical implications. Full article

Cannabis use disorder (CUD) is a complex issue, even more so when it is comorbid with a severe mental disorder (SMD). Available interventions are at best slightly effective, and their effects are not maintained over time. Therefore, the integration of virtual reality (VR) may increase efficacy; however, it has not yet been investigated in the treatment of CUD. A novel approach, avatar intervention for CUD, uses existing therapeutic techniques from other recommended therapies (e.g., cognitive behavioral methods, motivational interviewing) and allows participants to practice them in real-time. During immersive sessions, participants are invited to interact with an avatar representing a significant person related to their drug use. This pilot clinical trial aimed to evaluate the short-term efficacity of avatar intervention for CUD on 19 participants with a dual diagnosis of SMD and CUD. Results showed a significant moderate reduction in the quantity of cannabis use (Cohen’s d = 0.611, p = 0.004), which was confirmed via urinary quantification of cannabis use. Overall, this unique intervention shows promising results. Longer-term results, as well as comparison with classical interventions in a larger sample, are warranted through a future single-blind randomized controlled trial. Full article

(1) Background: Very little is known about the inner therapeutic processes of psychotherapy interventions for patients suffering from treatment-resistant schizophrenia. Avatar therapy (AT) is one such modalities in which the patient is undergoing immersive sessions in which they interact with an Avatar representing their main persistent auditory verbal hallucination. The aim of this study is to identify the most prevalent dyadic interactions between the patient and the Avatar in AT for patient’s suffering from TRS. (2) Methods: A content analysis of 256 verbatims originating from 32 patients who completed AT between 2017 and 2022 at the Institut universitaire en santé mentale de Montréal was conducted to identify dyadic interactions between the patients and their Avatar. (3) Results: Five key dyads were identified to occur on average more than 10 times for each participant during the immersive sessions across their AT: (Avatar: Reinforcement, Patient: Self-affirmation), (Avatar: Provocation, Patient: Self-affirmation), (Avatar: Coping mechanisms, Patient: Prevention), (Patient: Self-affirmation, Avatar: Reinforcement), and (Patient: Self-appraisal, Avatar: Reinforcement). (4) Conclusion: These dyads offer a first qualitative insight to the interpersonal dynamics and patient-avatar relationships taking place during AT. Future studies on the implication of such dyadic interactions with the therapeutic outcome of AT should be conducted considering the importance of dyadic relationships in psychotherapy. Full article

Avatar Therapy has a significant impact on symptoms, beliefs, and quality of life of patients with treatment-resistant schizophrenia. However, little is known about how these changes are implemented into their lives and to which aspects of their lives these improvements relate. Ten consecutive patients enrolled in an ongoing clinical trial were assessed using semi-guided interviews before as well as three months after Avatar Therapy. These encounters have been recorded and transcribed so that the discourse could be thoroughly analyzed, leading to the generation of an extensive theme grid. As the cases were analyzed, the grid was adapted in a back-and-forth manner until data saturation occurred. The content analysis allowed the identification of nine main themes representing different aspects of the patients’ lives, each of which was subdivided into more specific codes. By analyzing the evolution of their frequency, it was observed that, following therapy, patients presented with fewer psychotic symptoms, better self-esteem, more hobbies and projects, and an overall improved lifestyle and mood. Finally, investigating the impact of Avatar Therapy on quality of life allows for a deeper understanding of how people with treatment-resistant schizophrenia can achieve meaningful changes and move towards a certain recovery process. Full article