I knew that I wanted to write about depression today for the A to Z Challenge, but I didn’t want to basically rewrite yesterday’s post. Nor did I want to post a bunch of statistics about depression. Most of us know, not only the statistics, but people near and dear to us who suffer from depression. I’m also sure a good number of us suffer from the disease. We know the general stuff. So what would I write about today about depression? Google to the rescue. After a basic search I found a fascinating link between my husband’s current computer obsession and my depression.
The Hubby is a total computer nerd. He is a software engineer, and has spent most of his career writing software (those search results Google brings up: he helped write that software). His current obsession is machine learning. What is machine learning you ask? I think WhatIs.com has the best definition in normal words that normal people will understand: “Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.”
What does that have to do with depression? Psychologists are now using machine learning hoping to come up with more accurate, medically based ways to diagnose depression and treat it more effectively. Last month Science Daily published the results of Dr. David Schnyer’s research at The University of Texas in Austin. Dr. Schnyer and his team are “using Magnetic Resonance Imaging (MRI) brain scans, genomics data and other relevant factors, to provide accurate predictions of risk for those with depression and anxiety.”
In other words they are using A LOT of data–so much data that it’s impossible for human beings to correlate the information in a reasonable amount of time. That is where machine learning comes in. Dr. Schnyer is receiving help from the Texas Advanced Computing Center to feed information into their supercomputer, Stampede:
The type of machine learning that Schnyer and his team tested is called Support Vector Machine Learning. The researchers provided a set of training examples, each marked as belonging to either healthy individuals or those who have been diagnosed with depression. Schnyer and his team labelled features in their data that were meaningful, and these examples were used to train the system. A computer then scanned the data, found subtle connections between disparate parts, and built a model that assigns new examples to one category or the other.
Schnyer and his team then went on to scan the brains of 52 people with depression and 45 healthy people in a control group. Stampede was able to diagnose those with depression with a 75% accuracy rate.
The thing that struck me the most about this study is that the researchers discovered that depression does not effect just one area of the brain (one of the reason past studies have failed to come up with a way to diagnose depression using brains scans). Depression affects multiple regions of the brain and how the brain works.
In Scientific American I came across another study. Dr. Conor Liston of Weill Cornell Medicine does not like how people with vast, multiple symptoms are grouped together under the umbrella diagnosis: depression. He is using machine learning to diagnose depression into four subsets that will make treatment, both with and without drugs, more effective.
I’m looking forward to keeping an eye out for more studies. This is a new frontier for depression diagnosis and treatment, and I’m interested in seeing if they will be able to replicate findings in different studies, and if they do what that means for being able to more accurately diagnose the disease. I’m very interested in what this will mean for future treatments. Right now treatment for depression is a hit or miss approach to see what works for each individual. In the future brain scans and machine learning might help, not only to diagnosis depression and its subsets, but suggest treatments that will be much more effective for those of us with depression.