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Personalized Depression Treatment

Traditional therapies and medications don't work for a majority of people suffering from depression. The individual approach to treatment could be the answer.

Cue is an intervention platform that transforms passively acquired sensor data from smartphones into customized micro-interventions for improving mental health. We analysed the best-fit personalized ML models for each subject using Shapley values to understand their feature predictors and uncover distinct features that deterministically change mood with time.

Predictors of Mood

Depression is among the leading causes of mental illness.1 However, only half of those who have the disorder receive treatment1. To improve the outcomes, doctors must be able to recognize and treat patients most likely to respond to certain treatments.

Personalized depression treatment is one method to achieve this. Utilizing sensors on mobile phones, an artificial intelligence voice assistant, and other digital tools researchers at the University of Illinois Chicago (UIC) are working on new ways to determine which patients will benefit from which treatments. Two grants were awarded that total more than $10 million, they will use these tools to identify the biological and behavioral factors that determine the response to antidepressant medication and psychotherapy.

So far, the majority of research on predictors for depression treatment ect treatment effectiveness has centered on clinical and sociodemographic characteristics. These include factors that affect the demographics like age, sex and educational level, clinical characteristics like the severity of symptoms and comorbidities and biological indicators such as neuroimaging and genetic variation.

A few studies have utilized longitudinal data to predict mood of individuals. Few studies also take into consideration the fact that mood can be very different between individuals. Therefore, it is important to develop methods that permit the determination and quantification of the individual differences in mood predictors and medical treatment for depression effects, for instance.

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. The team can then develop algorithms to recognize patterns of behavior and emotions that are unique to each individual.

In addition to these modalities the team developed a machine-learning algorithm to model the changing predictors of each person's depressed mood. The algorithm combines the individual differences to create a unique "digital genotype" for each participant.

This digital phenotype has been associated with CAT DI scores, a psychometrically validated symptom severity scale. The correlation was not strong, however (Pearson r = 0,08; P-value adjusted by BH 3.55 x 10 03) and varied greatly between individuals.

Predictors of symptoms

Depression is the leading reason for disability across the world1, but it is often misdiagnosed and untreated2. In addition the absence of effective interventions and stigmatization associated with depression disorders hinder many individuals from seeking help.

To aid in the development of a personalized treatment, it is important to identify predictors of symptoms. Current prediction methods rely heavily on clinical interviews, which are unreliable and only identify a handful of features associated with depression.

Machine learning can improve the accuracy of diagnosis and treatment for depression by combining continuous, digital behavioral patterns gathered from sensors on smartphones along with a verified mental health tracker online (the Computerized Adaptive Testing Depression Inventory CAT-DI). Digital phenotypes can provide continuous, high-resolution measurements. They also capture a wide variety of distinctive behaviors and activity patterns that are difficult to capture with interviews.

The study involved University of California Los Angeles students who had mild to severe depression symptoms who were enrolled in the Screening and Treatment for Anxiety and Depression program29 that was developed as part of the UCLA Depression Grand Challenge. Participants were directed to online support or in-person clinical depression treatments treatment according to the severity of their depression. Participants who scored a high on the CAT-DI of 35 or 65 were assigned to online support via a peer coach, while those who scored 75 were sent to clinics in-person for psychotherapy.

At the beginning of the interview, participants were asked the answers to a series of questions concerning their personal demographics and psychosocial characteristics. These included sex, age education, work, and financial status; whether they were divorced, partnered or single; their current suicidal thoughts, intentions or attempts; as well as the frequency with which they drank alcohol. Participants also rated their level of depression severity on a scale ranging from 0-100 using the CAT-DI. The CAT-DI assessment was carried out every two weeks for those who received online support, and weekly for those who received in-person assistance.

Predictors of the Reaction to Treatment

Personalized depression treatment is currently a research priority and a lot of studies are aimed to identify predictors that help clinicians determine the most effective medications for each individual. Pharmacogenetics in particular is a method of identifying genetic variations that affect how the human body metabolizes drugs. This enables doctors to choose drugs that are likely to work best drug to treat anxiety and depression for each patient, minimizing the time and effort required in trials and errors, while avoid any adverse effects that could otherwise hinder advancement.

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 be used to identify the most effective combination of variables that is predictive of a particular outcome, like whether or not a drug is likely to improve the mood and symptoms. These models can also be used to predict a patient's response to a natural treatment for depression they are currently receiving which allows doctors to maximize the effectiveness of their current treatment.

A new generation of studies utilizes machine learning techniques like supervised learning and classification algorithms (like regularized logistic regression or tree-based methods) to combine the effects of multiple variables to improve predictive accuracy. These models have proven to be useful in predicting treatment outcomes such as the response to antidepressants. These approaches are gaining popularity in psychiatry and it is likely that they will become the standard for the future of clinical practice.

Research into depression's underlying mechanisms continues, in addition to ML-based predictive models. Recent research suggests that depression is related to dysfunctions in specific neural networks. This suggests that individualized depression treatment will be built around targeted therapies that target these circuits in order to restore normal functioning.

Internet-delivered interventions can be an option to achieve this. They can provide a more tailored and individualized experience for patients. For instance, one study found that a program on the internet was more effective than standard treatment in alleviating symptoms and ensuring an improved quality of life for people suffering from MDD. Additionally, a randomized controlled study of a customized approach to depression treatment showed an improvement in symptoms and fewer adverse effects in a large number of participants.

Predictors of adverse effects

A major issue in personalizing depression treatment involves identifying and predicting the antidepressant medications that will have minimal or no side effects. Many patients are prescribed a variety of medications before settling on a treatment that is effective and tolerated. Pharmacogenetics provides an exciting new avenue for a more effective and precise approach to choosing antidepressant medications.

There are a variety of variables that can be used to determine the antidepressant that should be prescribed, including gene variations, patient phenotypes like gender or ethnicity and the presence of comorbidities. However it is difficult to determine the most reliable and valid predictors for a particular treatment will probably require randomized controlled trials of considerably larger samples than those that are typically part of clinical trials. This is because it may be more difficult to detect interactions or moderators in trials that only include one episode per person instead of multiple episodes spread over a period of time.

Furthermore the prediction of a patient's response will likely require information on the comorbidities, symptoms profiles and the patient's own perception of the effectiveness and tolerability. Presently, only a handful of easily measurable sociodemographic and clinical variables seem to be reliably associated with response to MDD factors, including age, gender, race/ethnicity and SES BMI, the presence of alexithymia, and the severity of depression symptoms.

The application of pharmacogenetics to depression treatment is still in its early stages and there are many hurdles to overcome. First is a thorough understanding of the genetic mechanisms is needed as well as a clear definition of what is a reliable predictor of treatment response. Ethics like privacy, and the ethical use of genetic information are also important to consider. The use of pharmacogenetics may, in the long run, reduce stigma surrounding mental health treatments and improve treatment outcomes. However, as with any other psychiatric treatment, careful consideration and planning is required. At present, the most effective method is to provide patients with an array of effective depression medication options and encourage them to speak freely with their doctors about their concerns and experiences.