Summary: By reducing the difficulty of imaging data, scientists have reduced the number of secure patterns of brain activity across more than 300 people. These constant participant-to-person activity patterns may provide insight into specific behavioral differences and biomarkers for medical disorders.
The results may be used to develop diagnostic and scientific methods to understand the neural foundation of mental health conditions. The analysis team is now trying to find brain patterns certain to medical illnesses by applying this technique to scientific populations.
Important Information:
- Using imaging data, scientists identified recurring mental activity patterns across 337 people.
- These designs were linked to cognitive work, feeling rules, and substance use.
- The results may provide better diagnostics and indicators for psychiatric disorders.
Origin: Yale
The fact that brain activity, measured by functional magnetic resonance imaging ( fMRI), is incredibly complex is a key challenge in the effort to link brain activity with behavior. Due to the complexity of the situation, it may be challenging to identify consistent exercise designs between different individuals or within people.
In a new study, Yale researchers were able to get imaging data, lessen its complexity, and in doing so, reveal firm patterns of activity shared across more than 300 unique people.
The results, experts say, are a promising step ahead in uncovering markers for psychiatric problems.
The research was  , published , Sept. 24 in the journal , PLOS Biology.
” Because human , head activity , is so complicated, it can be unreliable, especially when you’re aiming for reproducibility”, said Kangjoo Lee, head author of the study and an associate study professor in the Department of Psychiatry at Yale School of Medicine.
” We wanted to get functions of brain action that were related to features of human actions and were also continuous across various people,” according to the authors of this study.
To do this, the analysts used ultrasound data from 337 healthier younger adults, each of whom underwent four 15-minute mri scans.
” These pictures, which were taken during a resting position, were largely images of mental activity”, said Lee. ” But over time, we were able to see moment-to-moment changes in brain exercise”.
Each screenshot represented mental activity at a specific time, which included numerous different mind networks engaged in a variety of processes, which contributed to the complexity of the data.
To show shared patterns in the data, the researchers applied an approach called data aspect decline, which, Lee says, basically takes high-dimensional, difficult data like mental activity and reduces it to a lower dimensional space. The concept resembles presenting a complicated party pattern through a few simple movements.
The scientists discovered three expressed patterns of brain activity that were “highly recurring across members and within individuals” after reducing the difficulty of the information.
Additionally, while these designs were found among all members, there were also distinctions among the individuals. For example, the researchers observed differences in words of which of the three states , various individuals , spent the most time, how much they lingered in particular state, and which states individuals transitioned between.
These findings suggest that these kinds of patterns could provide insight into something that is shared between different people, such as a behavior, as well as individual differences related to those behaviors or how they change over time.
The researchers are now considering how this approach might be applied to psychiatric disorders.
” Here we looked at , healthy adults, but if we ran a similar analysis in a clinical population, we may find recurring brain patterns that are shared among that population but not among healthy individuals”, said co-senior author John Murray, a former professor of psychiatry and physics at Yale, now a professor at Dartmouth College.
These shared patterns may therefore be useful in clinical settings as biomarkers of psychiatric illness.
That idea is supported by the current study. The researchers discovered that cognitive function, emotion regulation, and alcohol and substance use were related to the patterns that people used more time and the patterns that they changed.
” Uncovering recurring brain patterns in clinical populations could tell us something about the , neural activity , associated with specific symptoms and how it differs between individuals”, said Lee.
About this news about neuroscience and behavior research
Author: Mallory Locklear
Source: Yale
Contact: Mallory Locklear – Yale
Image: The image is credited to Neuroscience News
Original Research: Open access.
Kangjoo Lee and al. claim that “human brain state dynamics are highly reproducible and related to neural and behavioral characteristics.” PLOS Biology
Abstract
The dynamics of a human brain state are incredibly reproducible and linked to neural and behavioral characteristics.
Between individuals and between individuals ( traits ), there are variations in both neural activity and behavior. However, the mapping of state-trait neural variation to behavior is not well understood.
To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. We discover reproducible spatiotemporal features of co-activation patterns at the single-subject level in healthy young adults.
We demonstrate that state-specific and general neural features that exhibit day-to-day variability can be found in a joint analysis of state-trait neural variations and feature reduction.
The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use.
The neural and behavioral characteristics that determine a person’s likelihood of occupying a particular co-activation pattern are also reproducible.
This combined analysis of state-trait variations offers the potential to create reproducible neuroimaging indicators of individual life’s functional outcome.
