The extracted information from brain imaging datasets provides more accurate inferences about diseases, causes and which features would be useful for diagnostics. Can experts in Big Data look at brain scans from patients living with schizophrenia and healthy controls and determine which person is living with schizophrenia and which is not?
The brains of people with schizophrenia are different in many ways from those of people living without the condition. The differences are likely due to multiple genetic variants affecting the brain. The goal is not to replace psychiatrists, but to help psychiatrists do what they are already doing. Big Data can help to connect the dots between molecules and mental ill-health. Even the best computational work cannot uncover small abnormalities without a large collection of data. Are there specific regions in the brain that make you more likely to commit suicide? Or to become depressed? If we find a brain marker that is predictive we might be able to come up with some sort of test to take advantage of this finding.
Mental ill-health and neurological conditions have touched nearly everyone on the planet. With the further use of computational techniques, depression will no longer be simply depression. People with traditional psychopathology can be sub-classified into different categories; this can lead to much more appropriate treatment and to addressing the symptoms of mental ill-health more rapidly as well as more effectively.
Investigations of Bipolar Disorder (BD) that integrate pediatric and adult populations will be required to sufficiently enroll adequate numbers of people at risk for BD to understand the opportunities of Big Data in the prediction and prevention of BD. Advances in bioinformatics utilizing a Big Data approach may provide opportunities for novel insights with respect to the causes of mental illness. Data from clinical, administrative, imaging and “omics”, patient internet activities, sensors, and monitoring tools will provide outstanding opportunities for psychiatry. In spite of numerous technical challenges, new methodologies are quickly being developed that will permit the utilization of big datasets to increase understanding of existing as well as new questions in psychiatry.
These findings highlight the need for proper methods to analyze large datasets with clinical disease and biological information. Indeed, recent technological advances in laboratory work have brought a substantial increase in data. The challenge is to combine these Big Data with biology and medicine. Understanding how genetic and environmental interactions impact brain structure and function is now an important question in medicine - even more so in neuropsychiatry. A question that Big Data might help to answer.
