Cerebrum Winter 2021

The same approaches Google and Facebook use to leverage the concept of networks to improve search engines and social interactions began to be applied to the brain, which can be thought of as a network of networks.

predict response to antidepressants versus mood stabilizers in adolescents with mood disorders). Beyond these patterns, they also appear to be predictive of risk for psychiatric disorders. However, we are still only scratching the surface in the prediction era, as these approaches tend to require large amounts of data, and algorithms are still not able to learn well without a good “ground truth” (i.e., If we already know the answer it is not hard to train a computer to recognize it, but with mental illness we are still unsure about even the diagnostic categories). Existing techniques with the data we have are still unable to tell us clearly whether the psychiatric diagnoses accurately reflect the underlying disorder. Thankfully, algorithms are getting smarter, and more data are arriving all the time. The Era of Big Data We are now firmly in the era of big data for neuroimaging and psychiatry. The number of large, shared data sets has dramatically increased over the past few years. Several studies, e.g., Aging Brain Cognition and Development and UK Biobank , are scanning tens of thousands of individuals over time (although these are mostly individuals without psychiatric problems, but the assessed measures can be used to study psychiatric issues as a spectrum). There is interest in considering mental illness as manifested by otherwise everyday human traits that lie outside typical ranges of behavior and process. For example, someone may be anxious about an upcoming deadline, but when anxiety becomes constant and independent of circumstances, it impacts our quality of life and might be considered a psychiatric disorder. To study mental illness requires vast amounts of data and flexible models that can handle all its complexity. A promising class of powerful models (deep learning models), like those that were used to beat international experts in the game of Go, or that Alexa uses to recognize your commands, have been shown to be very powerful. But they also require a lot of data. The application of deep learning (deep artificial neural networks) to neuroimaging has shown great promise and will likely be a major force in advancing our knowledge and understanding of the data. These approaches require considerable computational resources as the complexity of models and the amount of data continue to grow. Challenges, Testing, and Discovery Given that the criteria used to decide who has a mental disorder are largely based on self-reported symptoms and not biologically based, we have a wicked chicken-and-egg problem. Do predictions of mental disorder based on non- biological characterizations provide useful and actionable

addition, fancy new analytical tools that can assess brain activity coming from many regions at once (e.g., multivariate methods and techniques based on graph theory), similar to those used by many software engineering companies, were introduced and are now being used with increasing regularity. But although these approaches have taught us much about how psychiatric disorders impact brain connectivity, they have not yet led to clinical tools. For that we need more precise and individualized information. The “Prediction” Era The vast majority of brain imaging researchers focus on describing central tendencies and group results, rather than findings with individual subjects, and the case studies that are reported typically concentrate on explaining the data at hand rather than predicting unseen data from an individual (i.e., Can I use brain imaging to predict a future diagnosis, or to determine if that individual will respond well to a certain medication?). While this may seem like a small distinction, it is quite critical, as the results for these two approaches (studying averages versus studying individuals) often differ. A focus on individual level prediction and forecasting of future trajectories relevant to an individual person is arguably the most important goal if brain imaging is to translate into practical solutions to improve the quality of life and enhance technological development. Prediction studies typically utilize advanced computational approaches and algorithms that can learn from data (i.e., machine learning). The field has experienced a large growth in studies using machine-learning approaches to make individualized predictions (i.e., informed guesses) of symptoms, cognitive scores, medication response information, and more. Studies of brain function and structure that focus on these estimates have revealed whole brain patterns that show potential to be able to predict and identify mental disorders and to predict treatment response (e.g., using resting fMRI to

24 DANA FOUNDATION CEREBRUM | Winter 2021

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