The patterns of brain activity when people with major depressive disorder (MDD) engage in repetitive negative thinking can predict how prone they are to such thoughts, according to new research published in the Journal of Affective Disorders. The findings provide new insights into the neurobiological processes underlying depression.
Depression is a widespread mental health condition that affects millions of people worldwide. While it’s known that depression can lead to persistent negative thinking patterns, the exact mechanisms underlying this process have remained a puzzle. The new study aimed to delve deeper into this matter by examining how the brain functions during both rest and rumination, offering a more comprehensive understanding of depression and its neural underpinnings.
“Repetitive negative thinking (RNT), a cognitive process characterized by a passive, repetitive, and evaluative focus on distressing thoughts, is associated with significant distress in several mental disorders, including major depressive disorder,” said study author Masaya Misaki, an associate investigator at the Laureate Institute for Brain Research.
“Brain function related to RNT in depression is often assessed using resting-state functional magnetic resonance imaging (fMRI). However, I believe that the resting state might not accurately represent RNT in depression. Hence, I conducted a machine learning analysis to predict individual RNT trait scores based on whole-brain functional connectivity patterns during both resting and induced negative thinking states.”
The study included 28 healthy individuals and 42 individuals diagnosed with MDD. These latter participants all met the criteria for unipolar MDD and were experiencing depressive symptoms at the time of the study. However, the researchers excluded a few participants who exhibited excessive head movement during the tasks to ensure the data’s accuracy.
To peer into the inner workings of the brain, the researchers conducted MRI scans on all participants. These scans included several components, such as an anatomical scan, a resting-state session, and a rumination-inducing negative thinking (NT) task. During the resting state session, the researchers asked participants to clear their minds and relax, simulating a neutral, non-task-oriented mental state. In contrast, the NT task required participants to recall a recent memory where they experienced rejection, prompting them to contemplate why they reacted the way they did.
The study’s first objective was to determine if resting-state and negative thinking functional connectivity patterns could distinguish individuals with MDD from healthy counterparts. The researchers found that both resting-state functional connectivity (RSFC) and negative thinking functional connectivity (NTFC) effectively differentiated between the two groups.
This means that when individuals with MDD think negative thoughts, their brain activity patterns are distinct from those of healthy individuals. However, there was a significant difference between RSFC and NTFC patterns, indicating that the brain’s response during resting moments might not directly reflect its activity when processing negative thoughts.
Another intriguing finding emerged when the researchers examined the ability of these brain activity patterns to predict trait rumination, refers to a person’s characteristic tendency or disposition to engage in repetitive and persistent thinking about negative events, a key aspect of depression. NTFC exhibited predictive capability for trait rumination in individuals with MDD, whereas RSFC did not.
In simpler terms, when people with depression engage in negative thinking, their brain connectivity patterns can be used to predict how much they tend to ruminate in general. This suggests that the resting state may not fully capture the complexity of rumination in depression, and that understanding brain activity during rumination is crucial to predicting depressive symptoms.
“My results indicated that while brain activation in the resting state could not predict RNT trait scores, the induced negative thinking state could,” Misaki said. “Notably, the brain functional connectivity pattern in the induced negative thinking state varied among depressed individuals, and those with higher trait RNT exhibited involvement of broader brain network regions.”
The study also employed an approach called Connectome-based Predictive Modeling (CPM) and Connectome-wide Association Analysis (CWA) to examine connectivity patterns across the brain. These analyses revealed a remarkable difference in functional connectivity patterns between resting and NT states in individuals with depression.
During rumination, areas of the visual cortex and cerebellum showed heightened connectivity and connected differently with regions associated with the default mode network (DMN). Reduced connectivity between certain regions of the brain, such as the bilateral subgenual anterior cingulate cortex (sgACC), was also evident in individuals with MDD compared to healthy individuals during rumination.
“Repetitive negative thinking is not merely a state of negative thinking; it’s a dynamic process that engages multiple brain regions,” Misaki told PsyPost. “Addressing RNT might necessitate interventions that target this dynamic circuitry rather than focusing solely on the state of negative thinking.”
While this study provides valuable insights into depression, there are some limitations to consider. The sample size was relatively small, which might not fully represent the diverse nature of depression.
“Our study didn’t track the time course of this dynamic process but instead evaluated the activation correlation between whole brain regions over an extended period,” Misaki said. “Future research should aim to develop a method to trace the dynamic trajectory of the negative thought state using measured brain activations. This would help identify specific circuits that sustain the negative thought state and hinder its resolution.”
“We are currently working on developing a machine learning decoder for the negative mindset. This tool will enable us to monitor various mental states, such as rumination, worry, positive thinking, and resting states, based on whole-brain activation patterns.”
The study, “Trait repetitive negative thinking in depression is associated with functional connectivity in negative thinking state rather than resting state“, was authored by Masaya Misaki, Aki Tsuchiyagaito, Salvador M. Guinjoan, Michael L. Rohan, and Martin P. Paulus.