Decoding Repetitive Negative Thoughts: Machine Learning Predicts Rumination Neuroscience News

Summary: A team of researchers has developed a predictive model for recognizing patterns of persistent negative thinking, or rumination, using machine learning.

The researchers hypothesized that the variance in dynamic connectivity between some brain regions, such as the dorsal medial prefrontal cortex (dmPFC), could be associated with rumination. Brain activity was measured in the participants using functional magnetic resonance imaging (fMRI).

This innovative model may provide a valuable biomarker for depression, aiding in early diagnosis and monitoring treatment progress.

Main aspects:

1. The research team successfully trained machine learning models to approximate rumination scores based on participants’ fMRI data.
2. Of all the regions in the default mode network, only the dorsal medial prefrontal cortex (dmPFC)-based model was able to predict rumination scores.
3. The model was also successful in predicting depression scores in real patients with major depressive disorder (MDD), highlighting its potential as a valuable biomarker for depression.

Source: Institute for Basic Science

Our minds often get caught up in repetitive thoughts, such as past mistakes, regrets, insecurities, or unresolved conflicts. This persistent negative thought pattern, called rumination, can have detrimental effects on mental health, leading to conditions such as depression and anxiety.

Recognizing rumination as an important risk factor for depression, researchers have worked to identify its neural signature and develop methods of early detection.

A team of scientists led by KIM Jungwoo of the Center for Neuroscience Imaging Research (CNIR) within the Institute for Basic Science (IBS), in collaboration with researchers from the University of Arizona and Dartmouth College, conducted a study to develop a predictive model of rumination using the power of machine learning.

Previous research has linked a network of brain regions called the default mode network (DMN) to rumination. However, the specific region responsible for individual differences in rumination has remained unclear.

The team hypothesized that dynamic connectivity variance, which measures the stability of interactions between brain regions over time, could be associated with rumination due to its temporal persistence.

To test this, they used functional magnetic resonance imaging (fMRI) to measure brain activity in healthy participants at rest. Using the variance of dynamic connectivity between each DMN region and brain regions throughout the brain as inputs and self-report measures of rumination scores as outputs, the researchers trained machine learning models to approximate rumination scores based on the data fMRI of the participants.

Of all the DMN regions, only the dorsal medial prefrontal cortex (dmPFC)-based model was able to predict rumination scores in healthy participants.

Furthermore, the dynamic connectivity between the dmPFC and the inferior frontal gyrus, as well as the cerebellum, was found to be particularly important in predicting rumination.

These findings highlight the significance of the dmPFC in rumination and depression, which is in line with previous research linking that region with high-level reflective processes in individuals.

Notably, the model was also successful in predicting depression scores in real patients with major depressive disorder (MDD). So the model shows promise as a valuable biomarker for depression, aiding in the identification of people at risk and monitoring treatment progress.

By shedding light on the neural basis of rumination and its relevance to depression, this study contributes to the advancement of mental health research and may lead to more effective interventions and better outcomes for people with depression.

Professor WOO Choong-Wan, the lead author, said: ‘Dynamic patterns of natural thought streams greatly influence our mood and emotional states.

“Rumination is one of the most important thought patterns and this study shows that the tendency to ruminate could be decoded by brain connectivity measured with fMRI.

“We hope that this research will continue to advance and that in the future neuroimaging can be used to monitor and manage mental health.

Going forward, the researchers plan to validate and refine the predictive model using larger and more diverse populations. They also aim to explore the potential applications of this model in the clinical setting, integrating it with existing diagnostic and therapeutic approaches.

Continuing research in this area has the potential to lead to personalized interventions that target rumination and depression more effectively, ultimately improving the lives of those affected by these conditions.

About this news about machine learning research and rumination

Author: William Su
Source: Institute for Basic Science
Contact: William Suh – Institute for Basic Science
Image: The image is credited to Neuroscience News

Original research: Free access.
“A dynamic functional rumination model based on the dorsomedial prefrontal cortex” by KIM Jungwoo et al. Nature communications

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Abstract

A dynamic model of functional connectivity based on the dorsomedial prefrontal cortex of rumination

Rumination is a cognitive style characterized by repetitive thoughts about one’s negative internal states and is a common symptom of depression. Previous studies have linked trait rumination to alterations in the default mode network, but brain markers predictive of rumination are lacking.

Here, we adopt a predictive modeling approach to develop a neuroimaging rumination marker based on the variance of resting-state dynamic functional connectivity and test it on 5 different subclinical and clinical samples (totalNo=288).

A whole-brain marker based on dynamic connectivity with the dorsomedial prefrontal cortex (dmPFC) emerges as generalizable across subclinical datasets. A refined marker consisting of the most important features of a virtual lesion analysis further predicts the depression scores of adults with major depressive disorder (No=35).

This study highlights the role of dmPFC in tract rumination and provides a dynamic functional connectivity indicator for rumination.