The coming era of computational psychiatry

This is a column I wrote for the Georgetown Interdisciplinary Program in Neuroscience newsletter about computational psychiatry. My aim was to write a broad overview of the field for a lay audience, so it should be pretty accessible. Here is a link to the newsletter.

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Fifty percent of Americans will develop at least one form of mental illness in their lifetime1, a staggering statistic. Despite such high disease burden, our understanding of psychiatric disorders significantly lags behind that of other medical conditions. Psychiatric investigation is difficult: the brain is an incredibly complex system embedded in an unpredictable environment. Equipped with rapidly advancing technologies, basic neuroscience has recently begun to overcome this complexity to reveal brainNet copyfundamental
mechanisms of brain function. A bottleneck exists, however, in translating discoveries in the lab into meaningful clinical improvements for patients. The nascent field of computational psychiatry, the confluence of computational, statistical, and clinical investigation, may change that. Computational psychiatry crosses disciplinary borders to imagine a future in which clinicians, aided by powerful algorithms, are able to use a picture of a patient’s brain and its connections to better detect and treat psychiatric disease.

One reason for the paucity of success stories in psychiatry is that diagnosing mental illness is difficult. Physicians are trained to integrate observations from patint interviews, physical exams, and laboratory tests to make a diagnosis. Medical students are taught that some clinical signs are pathognomonic, or characteristic for a particular disease. In psychiatry, diagnosis is far more subtle and complex. First, laboratory tests and physical exams, indispensible tools for clinicians, are less useful in the evaluation of mental illness. Instead, diagnostic classification is primarily symptom-based. The Diagnostic and Statistical Manual of Mental Disorders (DSM), often referred to as the “Bible” of the field, is used to diagnose patients on the basis of clusters of symptoms, while remaining agnostic to the underlying pathophysiology. Very few symptoms in psychiatry are pathognomonic, however; most symptoms are characteristic of multiple disorders. Individuals often suffer from comorbidity, or the co-occurrence of several diseases, further complicating diagnostic classification. Clinicians need new tools to deal with this complexity.

Machine learning (ML), a subfield of artificial intelligence, is a system of algorithms that learns patterns in data. You encounter these algorithms every day: Siri on your iPhone and Facebook’s image recognition software are both powered by ML. Recently, researchers have begun applying ML algorithms to neuroimaging data of psychiatric patients with the goal of discovering potential biomarkers for disease diagnosis and progression. Here’s how it works: we begin with a cohort of individuals with schizophrenia, and a group of healthy controls. We collect data using functional magnetic resonance imaging (fMRI), a technique that measures blood flow in the brain as a proxy for neuronal activity. Using a statistical technique known as independent component analysis, we can characterize the functional activity of various networks of brain regions, each of which carries clinically useful information. These data are far too large and complex for psychiatrists to parse, however. Rather, we feed these datasets, or examples, into a type of ML algorithm know as a classifier, which scours millions of data points to learn the relevant characteristics, or features, that predict the class of each example. In other words, the classifier is a data-driven technique for classifying individuals with and without schizophrenia on the basis of pictures of their brain.

In 2014, at the Machine Learning for Signal Processing competition, teams from all around the world put their algorithms to the test in an effort to successfully diagnose individuals with schizophrenia on the basis of functional and structural MRI scans2. The winning group achieved an area under the curve for disease classification of 0.89, an impressive feat. ML algorithms have also been successfully applied to other psychiatric diseases including autism and post-traumatic stress disorder, in some studies achieving diagnostic accuracies exceeding 90%. ML can be applied to treatment selection, too. In the first clinical trial of its kind, an automatic treatment-selection algorithm outperformed standard clinical protocol in the management of depression4. Medications were selected for each patient by comparing their electroencephalogram (EEG) to a reference database of EEG biomarkers. The database was constructed by mining a dataset of EEGs for over 2,000 individuals, combined with information about treatment-response in each patient, to determine which EEG features successfully predict treatment outcomes.

Computational psychiatry conferences and academic departments are sprouting up all over the world. An eponymous journal will publish its first issue in Spring 2016. The future looks promising, but important hurdles remain before these techniques can be regularly deployed in clinical practice. One important challenge concerns comorbid conditions. Published studies have primarily focused on diagnosing individuals with a single condition, such as depression. Major depressive disorder commonly presents with comorbid anxiety disorder, however, and most current ML implementations are not optimized to deal with multiple diagnoses for each individual. The increasingly collaborative intersection between the computational and medical fields is uniquely positioned to solve these challenges. The new tools produced will help guide clinical decision-making, resulting in better outcomes for patients.

References

  1. http://www.cdc.gov/mentalhealthsurveillance/fact_sheet.html
  2. Silva, R.F. et al. The tenth annual MLSP competition: schizophrenia classification challenge. IEEE Int. Workshop Mach. Learn. Signal Process. 1–6 (2014).
  3. Wolfers, T et al. From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci. Biobehav. Rev. 57, 328–349
  4. DeBattista, C. et al. The use of referenced-EEG (rEEG) in assisting medication selection for the treatment of depression. J. Psychiatr. Res. 45, 64–75 (2011).

 

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