ArticleBiological psychiatry global open science2026
Functional Magnetic Resonance Imaging Signal Typically Viewed as "Noise" Has Clinical Relevance in Psychiatry.
Article in Biological psychiatry global open science, 2026. 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.
- A non-neuronal fMRI signal: both a confound and an opportunity for insight.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- One Scientist's Noise Is Another's Signal: Rethinking Physiological Noise in Addiction Functional Magnetic Resonance Imaging.Biological psychiatry global open science · 2026Article
- Human striatal population state dynamics.bioRxiv : the preprint server for biology · 2026Article
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Authors and funding
7 authors.
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Abstract
Background: Functional magnetic resonance imaging (fMRI) aims to identify biomarkers of neuropsychiatric illnesses, including substance use disorders; however, fMRI's clinical utility remains limited. One underexplored aspect of the fMRI signal is the systemic low-frequency oscillation (sLFO), a global physiological signal historically treated as noise but linked to vascular and autonomic function. Methods: Across 4 independent datasets, we used Regressor Interpolation at Progressive Time Delays analyses to extract the sLFO from resting-state and task-based fMRI data. We evaluated relationships between sLFO amplitude and acute and chronic indices of nicotine use. We further assessed the effects of nicotine and methylphenidate on the sLFO and related task performance. Results: In individuals who smoked tobacco, higher average sLFO amplitude during cigarette cue exposure was negatively associated with nicotine dependence ( Conclusions: These findings demonstrate that the sLFO encodes biologically meaningful information related to substance use, consistent with its role as an index of physiological arousal. Importantly, because the sLFO can be extracted directly from existing fMRI datasets, it offers a powerful and complementary approach to enhance the clinical relevance of fMRI research beyond substance use.
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