ArticleImaging neuroscience (Cambridge, Mass.)2025
Electrophysiological signatures of ongoing thoughts during naturalistic behavior.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- An fMRI dataset of verbalized spontaneous thought with annotated transcripts and self-report trait measures.bioRxiv : the preprint server for biology · 2026Article
- Capturing naturalistic thoughts using a precision experience sampling idiographic approach.Cognitive research: principles and implications · 2026Article
- A multi-session simultaneous EEG-fMRI dataset with repeated experience sampling.bioRxiv : the preprint server for biology · 2026Article
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5 authors.
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Abstract
Humans engage in a continuous stream of ongoing mental experience. Recent work examining the neural correlates of several dimensions of thoughts has revealed their functional connectivity patterns using fMRI during constrained experimental tasks. Less is known about the electrophysiological basis of various thoughts dimensions in more naturalistic settings. To address this, we first examined the electrophysiological signatures of ongoing thoughts during naturalistic tasks in seven participants across seven recording sessions. We then combined deep learning algorithms with electrophysiological data to determine the utility of these signals in predicting thought dimensions. Based on a total of 49 data sets, our results revealed distinct oscillatory markers of 7 dimensions of ongoing thought as participants completed any computer-based activities they wished to perform. In addition to identifying electrophysiological markers consistent with those observed in experimental settings for internally oriented thoughts and freely moving thoughts, we found novel patterns not previously reported for off-task thoughts, goal-oriented thoughts, and sticky thoughts, primarily characterized by spectral activity in canonical theta, alpha, and beta bands. Importantly, applying deep learning algorithms on electrophysiological data reliably detected all seven thought dimensions at above chance levels for both within-participant (MCC = 0.22-0.43) and across-participant (MCC = 0.14-0.31) approaches. Together, these results established the electrophysiological signatures of seven dimensions of ongoing thought, assembling a comprehensive set of brain-to-experience mapping of the phenomenological features of thoughts. Our findings provide an important step toward predicting thought patterns in the real world with clinical implications for establishing biomarkers of typical and atypical thought patterns.
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