Automated speech analysis for risk detection of depression, anxiety, insomnia, and fatigue: Algorithm Development and Validation Study.

BACKGROUND: While speech analysis holds promise for mental health assessment, research often focuses on single symptoms, despite symptom co-occurrences and interactions. In addition, predictive models in mental health do not properly assess speech-based systems' limitations, such as uncertainty, or fairness for a safe clinical deployment. OBJECTIVE: We investigated the predictive potential of mobile-collected speech data for detecting and estimating depression, anxiety, fatigue, and insomnia, focusing on other factors than mere accuracy, in the general population. METHODS: We included n=865 healthy adults and recorded their answers regarding their perceived mental and sleep states. We asked how they felt and if they had slept well lately. Clinically validated questionnaires measuring depression, anxiety, insomnia, and fatigue severity were also used. We developed a novel speech and machine learning pipeline involving voice activity detection, feature extraction, and model training. We automatically analyzed participants' speech with a fully ML automatic pipeline to capture speech variability. Then, we modelled speech with pretrained deep learning models that were pre-trained on a large open free database and we selected the best one on the validation set. Based on the best speech modelling approach, we evaluated clinical threshold detection, individual score prediction, model uncertainty estimation, and performance fairness across demographics (age, sex, education). We employed a train-validation-test split for all evaluations: to develop our models, select the best ones and assess the generalizability of held-out data. RESULTS: The best model was WhisperM with a max pooling, and oversampling method. Our methods achieved good detection performance for all symptoms, depression (PHQ-9 AUC= 0.76F1=0.49, BDI AUC=0.78, F1=0,65), anxiety (GAD-7 F1=0.50, AUC=0.77) insomnia (AIS AUC=0.73, F1=0.62), and fatigue (MFI Total Score F1=0.88, AUC=0.68). These strengths were maintained for depression detection with BDI and Fatigue for abstention rates for uncertain cases (Risk-Coverage AUCs < 0.4). Individual symptom scores were predicted with good accuracy (Correlations were all significant, with Pearson strengths between 0.31 and 0.49). Fairness analysis revealed that models were consistent for sex (average Disparity Ratio (DR) = 0.86), to a lesser extent for education level (average Disparity Ratio (DR) = 0.47) and worse for age groups (average Disparity Ratio (DR) = 0.33). CONCLUSIONS: This study demonstrates the potential of speech-based systems for multifaceted mental health assessment in the general population, not only for detecting clinical thresholds but also for estimating their severity. Addressing fairness and incorporating uncertainty estimation with selective classification are key contributions that can enhance the clinical utility and responsible implementation of such systems. This approach offers promise for more accurate and nuanced mental health assessments, benefiting both patients and clinicians.

Musicotherapy mobile applications: what level of evidence and potential role in psychiatric care? A systematic review.

Context: In our times of smartphone ubiquity, mobile applications are an inescapable daily life tool, including in health care. Music therapy has already proven its worth, notably in mental health. Hence, we were interested in the mobile app format for this type of therapy, its level of evidence, how to use it in daily psychiatric care and the leads for future research and innovation. Method: This study carries out a systematic review of scientific literature of this topic on two search engines, PubMed and PubPsych, using these key-words: [(web-application) OR (web-app) OR (smartphone) OR (apps) OR (app)) AND ((music) OR (music therapy) OR (melody)]. Outcome: Out of a total of 282 studies found by keyword, 31 are included in this review. Several outcomes emerge. These studies relate to existing applications like Music Care, Calm or Unwind, on application prototypes or a potential use of music streaming applications on health care. They involve many different populations and clinical situations, including in hospital environments, for patients with chronic illnesses, different age ranges or for the general population. These musical interventions show a significant effect mainly for anxious symptoms, but also for depression, sleep disorders, pain and other psychiatric or psycho-somatic syndromes. These applications have no significant adverse effects. Conclusion: This review shows that music therapy apps have several potentials for improving mental health care. It could assist psychiatric usual care and could potentially lower medication intake. Nevertheless, the studies on the topic are limited and recent but they open prospects for future research.

Insights into medical students' perceptions of work culture during the COVID-19 pandemic: a mixed method study.

BACKGROUND: The COVID-19 pandemic brought about profound social changes that affected students worldwide. These changes had both psychological and economic consequences, and also led to the adoption of new teaching methods. It can also have an impact on work culture, which is the collective set of values, norms, and practices within a specific profession, shaping how individuals in that field behave, communicate, and identify with their work. The aim of the study was to examine medical students' perception of professional culture during the COVID-19 crisis when they voluntarily participated in the healthcare network established, outside of university placements, for the management of COVID patients. METHODS: A questionnaire study based on the vignette methodology was conducted among third-year medical students. Drawing from three scenarios in which students were variably engaged in crisis management, it included questions about their perceptions of the medical profession, their motivation, and their sense of belonging to the profession. RESULTS: 352 students responded to the survey. The pandemic had both a positive and a negative impact on students' perceptions of the medical profession. Cluster analysis using a k-means algorithm and principal component analysis revealed three clusters of students with different perceptions of the medical profession. The first cluster, which represented the majority of students, corresponded to a relatively positive perception of the profession that was reinforced during the pandemic. In the second cluster, students' perceptions were reinforced still further, and particular importance was attached to field experience. Students in the third cluster had the most negative perceptions, having been shaken the most by the pandemic, and they attached little importance to field experience. CONCLUSIONS: The analysis highlighted the importance of students being able to adapt and draw on a range of resources during the COVID-19 pandemic. This underscores the need for work cultures that support adaptability and coping. Further research is needed to understand its long-term effects on students' perceptions of the medical profession and to identify interventions that could support students in the aftermath of this difficult period.

Pharmacogenetic Guidelines for Psychotropic Drugs: Optimizing Prescriptions in Clinical Practice.

The modalities for prescribing a psychotropic (dose and choice of molecule) are currently unsatisfactory, which can lead to a lack of efficacy of the treatment associated with prolonged exposure of the patient to the symptoms of his or her illness and the side effects of the molecule. In order to improve the quality of treatment prescription, a part of the current biomedical research is dedicated to the development of pharmacogenetic tools for individualized prescription. In this guideline, we will present the genes of interest with level 1 clinical recommendations according to PharmGKB for the two major families of psychotropics: antipsychotics and antidepressants. For antipsychotics, there are CYP2D6 and CYP3A4, and for antidepressants, CYP2B6, CYP2D6, and CYP2C19. The study will focus on describing the role of each gene, presenting the variants that cause functional changes, and discussing the implications for prescriptions in clinical practice.

Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health.

The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.

Transcranial Magnetic Stimulation (rTMS) on the Precuneus in Alzheimer's Disease: A Literature Review.

The current literature review aimed to evaluate the effectiveness of rTMS on the precuneus as a potential treatment for Alzheimer's disease (AD). Although the number of studies specifically targeting the precuneus is limited, the results from this review suggest the potential benefits of this approach. Future studies should focus on exploring the long-term effects of rTMS on the precuneus in Alzheimer's disease patients, as well as determining the optimal stimulation parameters and protocols for this population. Additionally, investigating the effects of rTMS on the precuneus in combination with other brain regions implicated in AD may provide valuable insights into the development of effective treatment for this debilitating neurodegenerative disorder.

Variability in Motor Threshold during Transcranial Magnetic Stimulation Treatment for Depression: Neurophysiological Implications.

The measurement of the motor threshold (MT) is an important element in determining stimulation intensity during Transcranial Magnetic Stimulation treatment (rTMS). The current recommendations propose its realization at least once a week. The variability in this motor threshold is an important factor to consider as it could translate certain neurophysiological specificities. We conducted a retrospective naturalistic study on data from 30 patients treated for treatment-resistant depression in an rTMS-specialized center. For each patient, weekly motor-evoked potential (MEP) was performed and several clinical elements were collected as part of our clinical interviews. Regarding response to treatment (Patient Health Questionnaire-9 (PHQ-9) before and after treatment), there was a mean difference of -8.88 (-21 to 0) in PHQ9 in the Theta Burst group, of -9.00 (-18 to -1) in the High-Frequency (10 Hz) group, and of -4.66 (-10 to +2) in the Low-Frequency (1 Hz) group. The mean improvement in depressive symptoms was 47% (p < 0.001, effect-size: 1.60). The motor threshold changed over the course of the treatment, with a minimum individual range of 1 point and a maximum of 19 points (total subset), and a greater concentration in the remission group (4 to 10) than in the other groups (3 to 10 in the response group, 1 to 8 in the partial response group, 3 to 19 in the stagnation group). We also note that the difference between MT at week 1 and week 6 was statistically significant only in the remission group, with a different evolutionary profile showing an upward trend in MT. Our findings suggest a potential predictive value of MT changes during treatment, particularly an increase in MT in patients who achieve remission and a distinct "break" in MT around the 4th week, which could predict nonresponse.

Repetitive Transcranial Magnetic Stimulation (rTMS) in Post-Traumatic Stress Disorder: Study Protocol of a Nationwide Randomized Controlled Clinical Trial of Neuro-Enhanced Psychotherapy "TraumaStim".

The use of high-frequency Transcranial Magnetic Stimulation (HF-rTMS) of the right dorsolateral prefrontal cortex (DLPFC) in treating Post-traumatic Stress Disorder (PTSD) is currently regarded as a level B intervention (probable effectiveness). HF-rTMS has attracted interest as a neuromodulation therapeutic method for PTSD. Prolonged exposure and reactivation therapy are also regarded as first-line treatments for PTSD. Randomized controlled clinical studies examining the effectiveness of several HF-rTMS sessions coupled with psychotherapy have not yet been completed. In total, 102 patients with refractory PTSD will be randomly assigned (1:1) to reactivation therapy, in addition to either active HF-rTMS (20 Hz) or sham HF-rTMS, for 12 sessions in a nationwide, multicenter, double-blind controlled trial. The impact on PTSD symptoms and neurocognitive functioning will be assessed. The primary outcome is the PTSD severity score measured based on the Clinician-Administered PTSD Scale (CAPS-5) at one month. If this additional therapy is successful, it may strengthen the case for regulatory authorities to approve this additional technique of treating PTSD. Additionally, it expands the field of neurostimulation-assisted psychotherapy.

Digital phenotype of mood disorders: A conceptual and critical review.

Background: Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care. Objective: The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders. Methods: We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence. Results: Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV). Conclusion: The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.

Functional neuro-anatomy of social cognition in posttraumatic stress disorder: A systematic review.

OBJECTIVE: Post-traumatic stress disorder (PTSD) is a common mental disorder following one or more traumatic events in which patients exhibit behavioural and emotional disturbances. Recent studies report alterations in social cognition with cerebral functioning modifications. While it is now established that brain function can be modified and severely altered following successive childhood traumas, less studies have focused on brain alterations in adults with normal social cognition development. METHODS: We conducted a selective literature review by querying PubMed and Embase databases for titles of articles research on PTSD adults published from January 2000 to December 2021 focusing on adulthood traumatic events. RESULTS: Majority of studies reported frontolimbic rupture, with limbic structures like amygdala missing top-down control of frontal regulation. These cerebral dysfunctions could be observed even without overt behavioural defects on social cognition tests. CONCLUSION: These results can be analysed in light of intrinsic cerebral networks and we propose an attentional model of social threat information processing opening up perspective of social attentional rehabilitation in adjunction to usual care.

Rapid cycling bipolar disorder: Literature review on pharmacological treatment illustrated by a case report on ketamine.

INTRODUCTION: Rapid cycling bipolar disorder (RCBD) is defined as four or more affective episodes (depression, mania or hypomania) within 1 year. RCBD has a high point of prevalence (from 10% to 20% among clinical bipolar samples) and is associated with greater severity, longer illness duration, worse global functioning and higher suicidal risk, but there is no consensus on treatment option. The use of several pharmacological agents has been reported (levothyroxine, antipsychotics, antidepressants and mood stabilizers). OBJECTIVE: The main objective of this review was to propose a critical review of the literature and to rank the pharmacological agent using a level of evidence (LEO) adapted from the Center for Evidence-Based Medicine, and to illustrate it with a case report on off-label intravenous ketamine. METHOD: We conducted a review using the MeSH terms and keywords (bipolar [Title/Abstract]) AND (rapid [Title/Abstract]) AND (cycling [Title/Abstract]) AND (treatment [Title/Abstract]). Alexis Bourla and Stéphane Mouchabac screened 638 documents identified through literature search in Medline (PubMed) or by bibliographic references and 164 abstracts were then analyzed. Nonpharmacological treatments were excluded. RESULT: Seventy articles were included in the review and divided into six categories: mood stabilizers, antipsychotics, hormonal treatments, ketamine and other pharmacological treatments. DISCUSSION: Our review highlights the heterogeneity of the pharmacological treatment of RCBD and no clear consensus can emerge.

In Search of Digital Dopamine: How Apps Can Motivate Depressed Patients, a Review and Conceptual Analysis.

INTRODUCTION: Depression is highly prevalent and causes considerable suffering and disease burden despite the existence of wide-ranging treatment options. Momentary assessment is a promising tool in the management of psychiatric disorders, and particularly depression. It allows for a real-time evaluation of symptoms and an earlier detection of relapse or treatment efficacy. Treating the motivational and hedonic aspects of depression is a key target reported in the literature, but it is time-consuming in terms of human resources. Digital Applications offer a major opportunity to indirectly regulate impaired motivational circuits through dopaminergic pathways. OBJECTIVE: The main objective of this review was twofold: (1) propose a conceptual and critical review of the literature regarding the theoretical and technical principles of digital applications focused on motivation in depression, activating dopamine, and (2) suggest recommendations on the relevance of using these tools and their potential place in the treatment of depression. MATERIAL AND METHODS: A search for words related to "dopamine", "depression", "smartphone apps", "digital phenotype" has been conducted on PubMed. RESULTS: Ecological momentary interventions (EMIs) differ from traditional treatments by providing relevant, useful intervention strategies in the context of people's daily lives. EMIs triggered by ecological momentary assessment (EMA) are called "Smart-EMI". Smart-EMIs can mimic the "dopamine reward system" if the intervention is tailored for motivation or hedonic enhancement, and it has been shown that a simple reward (such as a digital badge) can increase motivation. DISCUSSION: The various studies presented support the potential interest of digital health in effectively motivating depressed patients to adopt therapeutic activation behaviors. Finding effective ways to integrate EMIs with human-provided therapeutic support may ultimately yield the most efficient and effective intervention method. This approach could be a helpful tool to increase adherence and motivation. CONCLUSION: Smartphone apps can motivate depressed patients by enhancing dopamine, offering the opportunity to enhance motivation and behavioral changes, although longer term studies are still needed.

Improving clinical decision-making in psychiatry: implementation of digital phenotyping could mitigate the influence of patient's and practitioner's individual cognitive biases.

High stake clinical choices in psychiatry can be impacted by external irrelevant factors. A strong understanding of the cognitive and behavioural mechanisms involved in clinical reasoning and decision-making is fundamental in improving healthcare quality. Indeed, the decision in clinical practice can be influenced by errors or approximations which can affect the diagnosis and, by extension, the prognosis: human factors are responsible for a significant proportion of medical errors, often of cognitive origin. Both patient's and clinician's cognitive biases can affect decision-making procedures at different time points. From the patient's point of view, the quality of explicit symptoms and data reported to the psychiatrist might be affected by cognitive biases affecting attention, perception or memory. From the clinician's point of view, a variety of reasoning and decision-making pitfalls might affect the interpretation of information provided by the patient. As personal technology becomes increasingly embedded in human lives, a new concept called digital phenotyping is based on the idea of collecting real-time markers of human behaviour in order to determine the 'digital signature of a pathology'. Indeed, this strategy relies on the assumption that behaviours are 'quantifiable' from data extracted and analysed through connected tools (smartphone, digital sensors and wearable devices) to deduce an 'e-semiology'. In this article, we postulate that implementing digital phenotyping could improve clinical reasoning and decision-making outcomes by mitigating the influence of patient's and practitioner's individual cognitive biases.

Social cognition in post-traumatic stress disorder: A systematic review.

OBJECTIVES: Post-traumatic stress disorder (PTSD) is a common psychiatric condition. Patients with PTSD have marked symptoms that significantly impair their social and emotional abilities, and numerous studies have explored this issue. We hypothesized that impairment of social cognition takes part in functional disability of individuals with PTSD. METHODS: We conducted a systematic review by querying PubMed database for the titles of articles published up to February 2018 with the terms [PTSD] [Post traumatic disorder] AND [Emotion recognition] OR [Facial expression of emotion] OR [Facial expression perception] OR [Empathy] OR [Affective empathy] OR [Mentalizing] OR [Social cognition] OR [Theory of Mind] OR [Mental state attribution] OR [Cognitive empathy] OR [Emotional empathy] OR [Social behaviour deficits]. RESULTS: Our results suggest that affective and cognitive aspect of theory of mind is comprehensively disturbed in patients with PTSD, showing a significant impairment in their ability to predict what others feel, think, or believe. They could also be massively altered in their perception of basic emotional expressions whether it is an expression of threat or happiness. Their affective empathy appears to be systematically disturbed and correlated to verbal and/or physical aggressive behaviour. CONCLUSIONS: Social cognition is disturbed in PTSD and should be regarded as an important symptom. Damages in social cognition seem to take part in the functional disability of people with PTSD. We highlight the interest of a systematic assessment of social cognition in the care of patients with PTSD and suggest which tests could be the most relevant for this evaluation. PRACTITIONER POINTS: •PTSD is no longer regarded as a subtype of anxiety disorder, but as part of a new category in the DSM-5. In clinical practice, symptoms tied to alterations in arousal and reactivity - such as irritability and vigilance - and to the disturbance of cognition and mood, are particularly closely correlated with poorer quality of life. Impaired social cognition clearly impacts the functional disability of people with PTSD. There are potential benefits of individualized cognitive remediation based on empathy and the emotional component of ToM (cognitive remediation, cognitive-behavioural therapy, therapeutic education, etc.) in PTSD people.

Are student nurses ready for new technologies in mental health? Mixed-methods study.

BACKGROUND: Technical innovations such as ecological momentary assessment (EMA), machine learning (ML), computerized adaptive testing (CAT), Digital Phenotyping, Clinical Decision Support Systems (CDSS), Algorithms, and Biomarkers have caused a paradigm shift in psychiatric care. The aim of the present study was to explore how student nurses view this paradigm shift, by assessing the acceptability of smartphone-based EMA, CAT, and biosensor-based Digital Phenotyping. We also investigated the factors affecting this acceptability. METHOD: Student nurses recruited via nursing schools participated in a quantitative study involving the screenplay method, in which they were exposed to two scenarios about depression care, one featuring EMA and CAT, the other featuring a connected wristband (CW) for Digital Phenotyping. Four acceptability domains (usefulness, usability, reliability, risk) were investigated. RESULTS: We recorded 1216 observations for the first scenario and 1106 for the second. Regarding overall acceptability, the CW was viewed less positively than CAT and EMA. Regarding reliability, whereas respondents believed that the CW could correctly detect depressive relapse, they did not think that EMA and CAT were sufficiently reliable for the accurate diagnosis of depressive disorder. More than 70% of respondents stated that they would nevertheless be interested in offering EMA, CAT or CW to their patients, but more than 60% feared that these devices might hinder the therapeutic relationship. CONCLUSION: This was the first study assessing student nurses' views of EMA, CAT and CW-based digital phenotyping. Respondents were interested in these new technologies and keen to offer them to their patients. However, our study highlighted several issues, as respondents doubted the reliability of these devices and feared that they would hinder the therapeutic relationship. Subgroup analysis revealed correspondences between acceptability profiles and demographic profiles. It is therefore essential for nurses and student nurses to receive training and become involved in the development of this new technologies.

Psychiatric Advance Directives and Artificial Intelligence: A Conceptual Framework for Theoretical and Ethical Principles.

The patient's decision-making abilities are often altered in psychiatric disorders. The legal framework of psychiatric advance directives (PADs) has been made to provide care to patients in these situations while respecting their free and informed consent. The implementation of artificial intelligence (AI) within Clinical Decision Support Systems (CDSS) may result in improvements for complex decisions that are often made in situations covered by PADs. Still, it raises theoretical and ethical issues this paper aims to address. First, it goes through every level of possible intervention of AI in the PAD drafting process, beginning with what data sources it could access and if its data processing competencies should be limited, then treating of the opportune moments it should be used and its place in the contractual relationship between each party (patient, caregivers, and trusted person). Second, it focuses on ethical principles and how these principles, whether they are medical principles (autonomy, beneficence, non-maleficence, justice) applied to AI or AI principles (loyalty and vigilance) applied to medicine, should be taken into account in the future of the PAD drafting process. Some general guidelines are proposed in conclusion: AI must remain a decision support system as a partner of each party of the PAD contract; patients should be able to choose a personalized type of AI intervention or no AI intervention at all; they should stay informed, i.e., understand the functioning and relevance of AI thanks to educational programs; finally, a committee should be created for ensuring the principle of vigilance by auditing these new tools in terms of successes, failures, security, and relevance.

How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review.

BACKGROUND: New technologies are set to profoundly change the way we understand and manage psychiatric disorders, including obsessive-compulsive disorder (OCD). Developments in imaging and biomarkers, along with medical informatics, may well allow for better assessments and interventions in the future. Recent advances in the concept of digital phenotype, which involves using computerized measurement tools to capture the characteristics of a given psychiatric disorder, is one paradigmatic example. OBJECTIVE: The impact of new technologies on health professionals' practice in OCD care remains to be determined. Recent developments could disrupt not just their clinical practices, but also their beliefs, ethics, and representations, even going so far as to question their professional culture. This study aimed to conduct an extensive review of new technologies in OCD. METHODS: We conducted the review by looking for titles in the PubMed database up to December 2017 that contained the following terms: [Obsessive] AND [Smartphone] OR [phone] OR [Internet] OR [Device] OR [Wearable] OR [Mobile] OR [Machine learning] OR [Artificial] OR [Biofeedback] OR [Neurofeedback] OR [Momentary] OR [Computerized] OR [Heart rate variability] OR [actigraphy] OR [actimetry] OR [digital] OR [virtual reality] OR [Tele] OR [video]. RESULTS: We analyzed 364 articles, of which 62 were included. Our review was divided into 3 parts: prediction, assessment (including diagnosis, screening, and monitoring), and intervention. CONCLUSIONS: The review showed that the place of connected objects, machine learning, and remote monitoring has yet to be defined in OCD. Smartphone assessment apps and the Web Screening Questionnaire demonstrated good sensitivity and adequate specificity for detecting OCD symptoms when compared with a full-length structured clinical interview. The ecological momentary assessment procedure may also represent a worthy addition to the current suite of assessment tools. In the field of intervention, CBT supported by smartphone, internet, or computer may not be more effective than that delivered by a qualified practitioner, but it is easy to use, well accepted by patients, reproducible, and cost-effective. Finally, new technologies are enabling the development of new therapies, including biofeedback and virtual reality, which focus on the learning of coping skills. For them to be used, these tools must be properly explained and tailored to individual physician and patient profiles.

[Organic and psychiatric intricacy: The complex psychiatric disorder concept, paraclinical investigations]. / Intrications organo-psychiatriques : le concept de troubles psychiatriques complexes, quels examens complémentaires ?

The purpose of this article is to describe complex psychiatric disorders, to recall "minimal classical" explorations in psychiatry, to describe the concept of "complex psychiatric disorders" and to propose a systematized method of exploration. Some organic diseases are well known for their links with psychiatric disorders (manic syndrome and hyperthyroidism, depressive syndrome and corticotropic insufficiency, anxiety disorder and heart disease, etc.). Many other neurological, autoimmune, metabolic, paraneoplastic or endocrine pathologies can have essentially psycho-behavioral manifestations before being neurological or systemic. A large number of factors (nutritional, toxic, immunological, etc.), often ignored, influence the links between organicity and psychiatric pathologies. It is necessary to optimize the medical management of these patients in whom the psychiatric diagnosis masks a curable organo-psychiatric cause.