Personalized Depression Treatment

For many people gripped by depression, traditional therapies and medication are ineffective. The individual approach to treatment could be the solution.

Cue is an intervention platform for digital devices that converts passively collected sensor data from smartphones into personalised micro-interventions to improve mental health. We analyzed the best treatment for severe depression-fitting personalized ML models for each individual, using Shapley values, in order to understand their features and predictors. This revealed distinct features that were deterministically changing mood over time.

Predictors of Mood

deep depression treatment is the leading cause of mental illness across the world.1 Yet only half of those suffering from the condition receive treatment. In order to improve outcomes, clinicians need to be able to identify and treat patients with the highest likelihood of responding to specific treatments.

Personalized depression treatment is one method to achieve this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will gain the most from certain treatments. They use sensors on mobile phones, a voice assistant with artificial intelligence as well as other digital tools. Two grants worth more than $10 million will be used to determine biological and behavioral predictors of response.

So far, the majority of research on factors that predict depression treatment effectiveness has focused on clinical and sociodemographic characteristics. These include factors that affect the demographics like age, sex and educational level, clinical characteristics like symptom severity and comorbidities, and biological markers like neuroimaging and genetic variation.

Few studies have used longitudinal data to determine mood among individuals. A few studies also consider the fact that mood can vary significantly between individuals. Therefore, it is crucial to develop methods that allow for the determination of the individual differences in mood predictors and the effects of treatment.

The team's new approach uses daily, in-person evaluations of mood and lifestyle variables using a smartphone app called AWARE, a cognitive evaluation with the BiAffect app and electroencephalography -- an imaging technique that monitors brain activity. This allows the team to create algorithms that can detect various patterns of behavior and emotion that are different between people.

In addition to these modalities the team also developed a machine-learning algorithm to model the changing predictors of each person's depressed mood. The algorithm combines these individual characteristics into a distinctive "digital phenotype" for each participant.

This digital phenotype was linked to CAT DI scores that are a psychometrically validated symptoms severity scale. The correlation was low however (Pearson r = 0,08, P-value adjusted for BH = 3.55 10 03) and varied significantly between individuals.

Predictors of symptoms

Depression is the most common reason for disability across the world1, however, it is often not properly diagnosed and treated. In addition, a lack of effective treatments and stigma associated with depression disorders hinder many individuals from seeking help.

To facilitate personalized treatment in order to provide a more personalized treatment refractory depression, identifying predictors of symptoms is important. However, the current methods for predicting symptoms are based on the clinical interview, which has poor reliability and only detects a small number of features related to depression.2

Machine learning can be used to integrate continuous digital behavioral phenotypes of a person captured through smartphone sensors and an online mental health tracker (the Computerized Adaptive Testing Depression Inventory, the CAT-DI) together with other predictors of symptom severity could improve the accuracy of diagnosis and the effectiveness of treatment for depression. Digital phenotypes can provide continuous, high-resolution measurements as well as capture a wide variety of distinct behaviors and patterns that are difficult to record with interviews.

The study involved University of California Los Angeles (UCLA) students who were suffering from mild to severe depression symptoms. who were enrolled in the Screening and Treatment for Anxiety and Depression (STAND) program29 developed under the UCLA Depression Grand Challenge. Participants were routed to online support or in-person clinical treatment depending on their depression severity. Those with a CAT-DI score of 35 or 65 were assigned online support with a coach and those with scores of 75 patients were referred to in-person clinics for psychotherapy.

Participants were asked a series questions at the beginning of the study about their demographics and psychosocial traits. The questions covered age, sex and education, financial status, marital status and whether they were divorced or not, the frequency of suicidal thoughts, intentions or attempts, as well as the frequency with which they consumed alcohol. Participants also scored their level of depression symptom severity on a 0-100 scale using the CAT-DI. The CAT-DI test was conducted every two weeks for participants who received online support and weekly for those who received in-person care.

Predictors of Treatment Response

Personalized depression treatment is currently a top research topic and a lot of studies are aimed to identify predictors that enable clinicians to determine the most effective medication for each individual. Particularly, pharmacogenetics can identify genetic variants that influence the way that the body processes antidepressants. This lets doctors select the medication that are likely to be the most effective for every patient, minimizing the time and effort needed for trial-and-error treatments and avoid any negative side negative effects.

Another promising method is to construct models of prediction using a variety of data sources, combining the clinical information with neural imaging data. These models can then be used to determine the most appropriate combination of variables that are predictive of a particular outcome, such as whether or not a medication is likely to improve symptoms and mood. These models can also be used to predict the response of a patient to a treatment they are currently receiving and help doctors maximize the effectiveness of the current treatment.

A new type of research employs machine learning techniques, such as supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of multiple variables and increase predictive accuracy. These models have been shown to be effective in predicting treatment outcomes, such as response to antidepressants. These methods are becoming popular in psychiatry, and it is likely that they will become the standard for the future of clinical practice.

Research into inpatient depression treatment centers's underlying mechanisms continues, as do predictive models based on ML. Recent findings suggest that the disorder is connected with neural dysfunctions that affect specific circuits. This suggests that the treatment for depression will be individualized built around targeted therapies that target these circuits in order to restore normal functioning.

One method of doing this is by using internet-based programs that can provide a more individualized and tailored experience for patients. One study found that a program on the internet was more effective than standard treatment in alleviating symptoms and ensuring the best quality of life for those with MDD. Furthermore, a randomized controlled study of a personalised treatment for depression demonstrated sustained improvement and reduced adverse effects in a large number of participants.

Predictors of side effects

A major challenge in personalized depression treatment is predicting the antidepressant medications that will have minimal or no side effects. Many patients experience a trial-and-error approach, using various medications prescribed before finding one that is effective and tolerable. Pharmacogenetics offers a new and exciting method of selecting antidepressant medicines that are more effective and specific.

There are many predictors that can be used to determine the antidepressant to be prescribed, including genetic variations, phenotypes of patients such as gender or ethnicity and comorbidities. However it is difficult to determine the most reliable and valid predictors for a particular treatment will probably require controlled, randomized trials with much larger samples than those normally enrolled in clinical trials. This is due to the fact that it can be more difficult to identify moderators or interactions in trials that comprise only one episode per participant instead of multiple episodes over time.

Additionally the estimation of a patient's response to a specific medication will likely also require information on symptoms and comorbidities in addition to the patient's prior subjective experiences with the effectiveness and tolerability of the medication. Presently, only a handful of easily assessable sociodemographic and clinical variables appear to be reliably associated with the severity of MDD, such as gender, age, race/ethnicity and SES, BMI and the presence of alexithymia, and the severity of depression symptoms.

Many challenges remain in the use of pharmacogenetics in the treatment of depression. First, it is important to have a clear understanding and definition of the genetic mechanisms that cause depression, as well as an accurate definition of an accurate indicator of the response to treatment. Ethics such as privacy and the responsible use of genetic information are also important to consider. Pharmacogenetics can be able to, over the long term help reduce stigma around mental health treatments and improve treatment outcomes. But, like all approaches to psychiatry, careful consideration and application is essential. For now, it is ideal to offer patients an array of depression medications that work and encourage patients to openly talk with their doctor.