summary: A team of researchers used machine learning to develop a predictive model that recognizes patterns of persistent negative thoughts, or ruminations.
The researchers hypothesized that differences in dynamic connectivity between specific brain regions, such as the dorsomedial prefrontal cortex (dmPFC), may be related to rumination. Participants’ brain activity was measured using functional magnetic resonance imaging (fMRI).
This innovative model provides a valuable biomarker for depression and may aid in early detection and monitoring of treatment progress.
- The research team successfully trained a machine learning model to approximate rumination scores based on participants’ fMRI data.
- Of all default mode network regions, only models based on the dorsal medial prefrontal cortex (dmPFC) successfully predicted rumination scores.
- The model also successfully predicted depression scores in real-world patients with major depressive disorder (MDD), highlighting its potential as a valuable biomarker of depression.
sauce: Basic Science Research Institute
Our minds are often trapped in recurring thoughts of past failures, regrets, anxieties, and unresolved conflicts. This persistent pattern of negative thinking, called rumination, can negatively impact mental health and lead to symptoms such as depression and anxiety.
Recognizing that rumination is a major risk factor for depression, researchers have worked to identify its neural signatures and develop early detection methods.
A team of scientists led by KIM Jungwoo at the Center for Neuroscience Imaging Research (CNIR) within the Institute for Basic Science (IBS) is working with researchers at the University of Arizona and Dartmouth College to develop predictive models that: was carried out. Harness the power of machine learning to ruminate.
Previous research has associated a network of brain regions called the ‘default mode network’ (DMN) with rumination. However, the specific areas responsible for individual differences in rumination remained unclear.
The researchers hypothesized that variations in dynamic connectivity, which measures the stability of interactions between brain regions over time, may be associated with rumination due to their temporal persistence. .
To test this, they utilized functional magnetic resonance imaging (fMRI) to measure resting brain activity in healthy participants. The researchers used the variance of dynamic connectivity between each DMN region and brain regions across the brain as input, and the self-reported measure of rumination score as output, and calculated rumination scores based on participants’ fMRI data. A machine learning model was trained to approximate.
Of all DMN regions, only models based on the dorsal medial prefrontal cortex (dmPFC) successfully predicted rumination scores in healthy participants.
Furthermore, dynamic connections between the dmPFC and the inferior frontal gyrus and cerebellum were found to be particularly important in predicting rumination.
These findings highlight the importance of the dmPFC in rumination and depression and are consistent with previous studies linking this region with high-level reflex processes in individuals.
Remarkably, the model also successfully predicted depression scores in real patients with major depressive disorder (MDD). This model therefore holds promise as a valuable biomarker for depression, helping to identify at-risk individuals and monitor treatment progress.
By clarifying the neural underpinnings of rumination and its association with depression, this study may contribute to advances in mental health research and lead to more effective interventions and improved outcomes for depressed patients. .
Professor WOO Choong-Wan, lead author, said: “The dynamic patterns of our natural thought flow have a profound effect on our moods and emotional states.
“Rumination is one of the most important thought patterns, and this study shows that brain connectivity measured by fMRI can be used to decipher rumination tendencies.
“We hope that this research will continue to advance so that in the future neuroimaging can be used to monitor and manage mental health.”
In the future, the researchers plan to use larger and more diverse populations to validate and refine the predictive model. They also aim to integrate this model with existing diagnostic and therapeutic approaches to explore potential applications of this model in clinical settings.
Continued research in this area may lead to personalized interventions that target rumination and depression more effectively, ultimately improving the lives of individuals affected by these conditions. I have.
About this machine learning and rumination research news
author: William Sue
sauce: Basic Science Research Institute
contact: William Suh – Institute for Basic Science
image: Image credited to Neuroscience News
Original research: open access.
“A dynamic functional connection model of rumination based on the dorsomedial prefrontal cortexWritten by Kim Jeong Woo et al. Nature Communications
A dynamic functional connection model of rumination based on the dorsomedial prefrontal cortex
Rumination, a cognitive style characterized by recurrent thoughts about one’s negative inner states, is a common symptom of depression. Previous studies have associated rumination traits with alterations in default mode networks, but brain markers that predict rumination are lacking.
Here, we employ a predictive modeling approach to develop a neuroimaging marker of rumination based on the variance of dynamic resting-state functional connectivity and test it across five diverse subclinical and clinical samples (total). To do. n= 288).
A whole-brain marker based on dynamic connectivity with the dorsomedial prefrontal cortex (dmPFC) was found to be generalizable across asymptomatic datasets. Refined markers composed of the most important features from virtual lesion analysis further predict depression scores in adults with major depressive disorder (n= 35).
This study highlights the role of dmPFC in trait rumination and provides a dynamic functional connectivity marker for rumination.