Probing the neural signature of mind wandering with simultaneous fMRI-EEG and pupillometry

Groot, Josephine M; Boayue, Nya M; Csifcsák, Gábor; Boekel, Wouter; Huster, René; Forstmann, Birte U; Mittner, Matthias · 2021 · Crossref

DOI: 10.1016/j.neuroimage.2020.117412

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Summary

This study investigates the neural mechanisms underlying mind wandering, specifically task-unrelated thoughts (TUTs), by addressing the limitations of previous research that relied on single-modality imaging. While the default mode network (DMN) is known to be associated with internal mentation, its dynamic interaction with task-positive networks and its electrophysiological correlates remain poorly understood. To capture the spatiotemporal dynamics of TUTs with high precision, the authors employed simultaneous functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and pupillometry during a sustained attention to response task (SART). The experimental design involved 28 healthy adults who performed the SART while undergoing simultaneous data acquisition. Attentional states were monitored using an adaptive experience sampling method, where thought probes were triggered based on fluctuations in reaction time variability. Participants rated their attentional focus on a four-point scale, which was dichotomized into "on-task" and "off-task" states. The researchers extracted single-trial features from each modality: fMRI node activity and dynamic functional connectivity within and between the DMN and anticorrelated network (ACN); EEG prestimulus oscillatory power and event-related potential (ERP) amplitudes; and baseline and evoked pupil diameter. These features were fed into a support vector machine (SVM) classifier trained to distinguish between on-task and off-task trials. The results demonstrated that TUTs were associated with significantly worse behavioral performance, including higher reaction time variability and increased omission and commission errors. The multimodal SVM classifier achieved above-chance accuracy (65%) in predicting attentional states. Crucially, dynamic functional connectivity features were the strongest predictors of mind wandering, outperforming other modalities. The neural signature of TUTs was characterized by weaker DMN activity but elevated activity in the ACN, alongside stronger functional coupling between these networks. Electrophysiologically, mind wandering was marked by widespread increases in delta, theta, and alpha power, but not beta power, and reduced amplitudes of late, but not early, ERPs. Pupillometry revealed larger baseline pupil sizes during off-task states. These findings provide a comprehensive multimodal profile of mind wandering, highlighting the critical role of dynamic interactions between large-scale cortical networks. The study challenges the view of the DMN as a simple task-negative system, instead suggesting that mind wandering involves complex, transient coupling between the DMN and control networks. By demonstrating that dynamic connectivity is the most predictive marker, the research offers new insights into the neural substrates of attentional lapses and establishes a robust framework for detecting transient cognitive states using machine learning.

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• neuro workload indices
• mind wandering

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• Empirical Findings: physiological data