Consistent Brain Entropy Changes During Rumination Across Three Magnetic Resonance Imaging Scanners

Rumination, characterized by repetitive and self-referential thinking, is closely linked to depression and a broad range of psychiatric disorders. However, previous research has predominantly relied on task activation or network-level approaches, leaving critical gaps in our understanding of local brain activity dynamics during ruminative states. To address this, we employed brain entropy (BEN), a novel functional magnetic resonance imaging (fMRI)-based neuroimaging measure, to quantify the temporal irregularity and complexity of local brain activity during rumination. We analyzed a publicly available dataset comprising 41 healthy adults who completed identical fMRI tasks across three MRI scanners. Each scanning session included four conditions: resting state, sad memory, rumination, and distraction. Voxel-wise BEN analyses were conducted to examine condition-related differences, with brain regions showing consistent patterns across all three scanners at p < 0.05 considered statistically significant. Our findings revealed distinct neural signatures associated with different cognitive states. Compared to sad memory, rumination was associated with decreased BEN in the visual cortex (VC), whereas distraction was associated with decreased BEN in the posterior cingulate cortex/precuneus (PCC/PCu). Furthermore, rumination exhibited significantly increased BEN in the PCC/PCu relative to distraction. These results suggest that rumination is characterized by heightened temporal irregularity in regions supporting internal self-referential processing, accompanied by reduced engagement with external environmental information. The present study demonstrates the utility of BEN in elucidating the neural mechanisms of rumination, providing novel insights into how cognitive states are reflected in local brain activity patterns. These findings carry implications for both theoretical frameworks of ruminative cognition and the development of neuroimaging biomarkers for clinical applications in depression and related disorders.