Correction: Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study.

[This corrects the article DOI: 10.2196/31862.].

Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies.

BACKGROUND: Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological and behavioral substrates are associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning (ML) shows promise in predicting treatment response in MDD, but its application is limited by challenges to the clinical interpretability of ML models, and clinicians often lack confidence in model results. In order to improve the interpretability of ML models in clinical practice, our goal was to demonstrate the derivation of treatment-relevant patient profiles comprised of clinical and demographic information using a novel ML approach. METHODS: We analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a ML model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Prototypes were evaluated for interpretability by assessing differences in feature distributions (e.g. age, sex, symptom severity) and treatment-specific outcomes. RESULTS: A 3-prototype model achieved an area under the receiver operating curve of 0.66 and an expected absolute improvement in remission rate for those receiving the best predicted treatment of 6.5% (relative improvement of 15.6%) compared to the population remission rate. We identified three treatment-relevant patient clusters. Cluster A patients tended to be younger, to have increased levels of fatigue, and more severe symptoms. Cluster B patients tended to be older, female, have less severe symptoms, and the highest remission rates. Cluster C patients had more severe symptoms, lower remission rates, more psychomotor agitation, more intense suicidal ideation, and more somatic genital symptoms. CONCLUSION: It is possible to produce novel treatment-relevant patient profiles using ML models; doing so may improve interpretability of ML models and the quality of precision medicine treatments for MDD.

Amphetamine disrupts dopamine axon growth in adolescence by a sex-specific mechanism in mice.

Initiating drug use during adolescence increases the risk of developing addiction or other psychopathologies later in life, with long-term outcomes varying according to sex and exact timing of use. The cellular and molecular underpinnings explaining this differential sensitivity to detrimental drug effects remain unexplained. The Netrin-1/DCC guidance cue system segregates cortical and limbic dopamine pathways in adolescence. Here we show that amphetamine, by dysregulating Netrin-1/DCC signaling, triggers ectopic growth of mesolimbic dopamine axons to the prefrontal cortex, only in early-adolescent male mice, underlying a male-specific vulnerability to enduring cognitive deficits. In adolescent females, compensatory changes in Netrin-1 protect against the deleterious consequences of amphetamine on dopamine connectivity and cognitive outcomes. Netrin-1/DCC signaling functions as a molecular switch which can be differentially regulated by the same drug experience as function of an individual's sex and adolescent age, and lead to divergent long-term outcomes associated with vulnerable or resilient phenotypes.

The scheduling of adolescence with Netrin-1 and UNC5C.

Dopamine axons are the only axons known to grow during adolescence. Here, using rodent models, we examined how two proteins, Netrin-1 and its receptor, UNC5C, guide dopamine axons towards the prefrontal cortex and shape behaviour. We demonstrate in mice ( Mus musculus ) that dopamine axons reach the cortex through a transient gradient of Netrin-1 expressing cells - disrupting this gradient reroutes axons away from their target. Using a seasonal model (Siberian hamsters; Phodopus sungorus ) we find that mesocortical dopamine development can be regulated by a natural environmental cue (daylength) in a sexually dimorphic manner - delayed in males, but advanced in females. The timings of dopamine axon growth and UNC5C expression are always phase-locked. Adolescence is an ill-defined, transitional period; we pinpoint neurodevelopmental markers underlying this period.

Evaluating the perceived utility of an artificial intelligence-powered clinical decision support system for depression treatment using a simulation center.

Aifred is a clinical decision support system (CDSS) that uses artificial intelligence to assist physicians in selecting treatments for major depressive disorder (MDD) by providing probabilities of remission for different treatment options based on patient characteristics. We evaluated the utility of the CDSS as perceived by physicians participating in simulated clinical interactions. Twenty physicians who were either staff or residents in psychiatry or family medicine completed a study in which they had three 10-minute clinical interactions with standardized patients portraying mild, moderate, and severe episodes of MDD. During these scenarios, physicians were given access to the CDSS, which they could use in their treatment decisions. The perceived utility of the CDSS was assessed through self-report questionnaires, scenario observations, and interviews. 60% of physicians perceived the CDSS to be a useful tool in their treatment-selection process, with family physicians perceiving the greatest utility. Moreover, 50% of physicians would use the tool for all patients with depression, with an additional 35% noting that they would reserve the tool for more severe or treatment-resistant patients. Furthermore, clinicians found the tool to be useful in discussing treatment options with patients. The efficacy of this CDSS and its potential to improve treatment outcomes must be further evaluated in clinical trials.

Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study.

BACKGROUND: Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows. OBJECTIVE: This study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction. METHODS: Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews. RESULTS: Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change. CONCLUSIONS: Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies. TRIAL REGISTRATION: ClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642.

Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician-patient interaction.

BACKGROUND: Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction. AIMS: Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician-patient interaction. METHOD: Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback. RESULTS: All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician-patient interaction. CONCLUSIONS: The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician-patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

Low-cost conditioned place preference setup including video recording and analysis of behaviour.

The conditioned place preference (CPP) paradigm is widely used in rodent research to test the rewarding and aversive properties of different stimuli. Despite its relative simplicity, commercially available CPP systems are often costly. Here we describe the construction of a CPP setup and a behavioral data analysis pipeline incorporating: • a CPP box which can be built in a single day by using widely available and affordable materials. • an open source computer system for data acquisition (based on Raspberry Pi). • a freely available behavioural analysis software. The behavioural analysis allows for measurement of both locomotor activity and time spent in a zone of interest. Including all components, our setup costs ~1/10 of the cost of the least expensive commercially available CPP boxes alone (not including data acquisition and analysis). We validated the setup by showing that a 4 mg/kg dose of amphetamine increases locomotor activity acutely in adolescent male mice and induces conditioned preference for the drug-paired compartment in the CPP test.

DCC-related developmental effects of abused- versus therapeutic-like amphetamine doses in adolescence.

The guidance cue receptor DCC controls mesocortical dopamine development in adolescence. Repeated exposure to an amphetamine regimen of 4 mg/kg during early adolescence induces, in male mice, downregulation of DCC expression in dopamine neurons by recruiting the Dcc microRNA repressor, microRNA-218 (miR-218). This adolescent amphetamine regimen also disrupts mesocortical dopamine connectivity and behavioral control in adulthood. Whether low doses of amphetamine in adolescence induce similar molecular and developmental effects needs to be established. Here, we quantified plasma amphetamine concentrations in early adolescent mice following a 4 or 0.5 mg/kg dose and found peak levels corresponding to those seen in humans following recreational and therapeutic settings, respectively. In contrast to the high doses, the low amphetamine regimen does not alter Dcc mRNA or miR-218 expression; instead, it upregulates DCC protein levels. Furthermore, high, but not low, drug doses downregulate the expression of the DCC receptor ligand, Netrin-1, in the nucleus accumbens and prefrontal cortex. Exposure to the low-dose regimen did not alter the expanse of mesocortical dopamine axons or their number/density of presynaptic sites in adulthood. Strikingly, adolescent exposure to the low-dose drug regimen does not impair behavioral inhibition in adulthood; instead, it induces an overall increase in performance in a go/no-go task. These results show that developmental consequences of exposure to therapeutic- versus abused-like doses of amphetamine in adolescence have dissimilar molecular signatures and opposite behavioral effects. These findings have important clinical relevance since amphetamines are widely used for therapeutic purposes in youth.

Acetylations of Ftz-F1 and histone H4K5 are required for the fine-tuning of ecdysone biosynthesis during Drosophila metamorphosis.

The molting during Drosophila development is tightly regulated by the ecdysone hormone. Several steps of the ecdysone biosynthesis have been already identified but the regulation of the entire process has not been clarified yet. We have previously reported that dATAC histone acetyltransferase complex is necessary for the steroid hormone biosynthesis process. To reveal possible mechanisms controlled by dATAC we made assumptions that either dATAC may influence directly the transcription of Halloween genes involved in steroid hormone biosynthesis or it may exert an indirect effect on it by acetylating the Ftz-F1 transcription factor which regulates the transcription of steroid converting genes. Here we show that the lack of dATAC complex results in increased mRNA level and decreased protein level of Ftz-F1. In this context, decreased mRNA and increased protein levels of Ftz-F1 were detected upon treatment of Drosophila S2 cells with histone deacetylase inhibitor trichostatin A. We showed that Ftz-F1, the transcriptional activator of Halloween genes, is acetylated in S2 cells. In addition, we found that ecdysone biosynthetic Halloween genes are transcribed in S2 cells and their expression can be influenced by deacetylase inhibitors. Furthermore, we could detect H4K5 acetylation at the regulatory regions of disembodied and shade Halloween genes, while H3K9 acetylation is absent on these genes. Based on our findings we conclude that the dATAC HAT complex might play a dual regulatory role in Drosophila steroid hormone biosynthesis through the acetylation of Ftz-F1 protein and the regulation of the H4K5 acetylation at the promoters of Halloween genes.