Florian Ferreri

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.

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.

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.

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.

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.

UNSTRUCTURED 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.

BACKGROUND Mood disorder is 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 allow one 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 for the diagnosis of mood disorder, introducing for the first time paraclinical testing 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 digital phenotypes applied to mood disorders. METHODS We conducted a selective review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with the relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence. RESULTS 858 articles were included for evaluation, 43 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, body temperature). For depressive episodes, the main finding is the decrease in terms of functional and biological parameters (decrease in activities and walking, decrease in the number of calls and SMS, decrease in temperature and HRV) while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV). CONCLUSIONS The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders

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.