Using new analytical techniques, scientists studied brain images of more than 6,000 children and were able to identify connectivity patterns common to people with attention-deficit/hyperactivity disorder (ADHD). Ta.
Most of our behaviors are controlled by coordinated communication between neurons in different areas of the brain. By observing neural activity in resting-state functional magnetic resonance imaging (rs-fMRI) scans, neuroscientists can understand how different regions of the brain coordinate complex functions.
“Rest state” means exactly what it sounds like. These scans are performed while the subject is at rest and are not asked to perform specific cognitive tasks or think specific thoughts. Assuming you’re not claustrophobic and don’t mind staying completely still, it can be a pretty fun experience.
The data obtained from rs-fMRI scans is invaluable to scientists studying a whole range of neurological diseases and diseases. For example, by comparing scans of individuals with conditions such as ADHD with scans of neurotypical people, the hope is to identify patterns that may explain some of the characteristics of these conditions.
However, this type of research on ADHD has so far been hampered by small sample sizes and inconsistent methods, making it difficult to draw firm conclusions. A recent study led by Michael Mooney of Oregon Health and Science University sought to change all that.
Using several large datasets, the team developed a new method to analyze image data that covers a wider range of brain regions than previously possible. They called this the Polyneural Score (PNRS).
“Our findings demonstrate strong associations between brain-wide connectivity patterns (PNRS) and 554 ADHD symptoms in two independent cohorts,” they explain in their paper. .
The authors go on to explain how their approach can be used to glean better insights from even small datasets, and how their approach can be used to glean better insights from even small datasets, and how they can potentially be used across a variety of neurological and psychiatric conditions. We explain that it may also be used to identify certain mechanisms. Does her PNRS, typical of ADHD, predict symptoms of depression? This may help identify patients at risk for comorbidities.
Although ADHD diagnoses are on the rise and more research is being done on the condition every day, there are still some major gaps in our knowledge of the underlying neurobiology. Collecting large amounts of image data is just one piece of the puzzle. You also need a way to use that data to answer your questions. The authors of this study hope their method will make that more achievable for ADHD and many other conditions.
This research neuroscience journal.