Key result
Separating sLFO from fMRI data provides cleaner neuronal signals and indexes behaviorally relevant physiological arousal.
Why the study?
Interpretation of fMRI BOLD signals is complicated because they reflect both neuronal activity and vascular physiology, particularly in neuropsychopharmacology where pharmacological and psychiatric effects are accompanied by physiological shifts.
Separating the systemic low-frequency oscillation (sLFO) from fMRI data provides a cleaner neuronal signal and a complementary index of physiological arousal.
Vascular confounds may bias BOLD fMRI in neuropsychopharmacology; leaves open optimal correction methods for routine use.
Functional magnetic resonance imaging (fMRI) is widely used to investigate brain function. However, interpretation of the blood oxygen level-dependent (BOLD) signal is complicated by the fact that it reflects both neuronal activity and vascular physiology. This problem is especially relevant in neuropsychopharmacology research because pharmacological and psychiatric effects on brain function are often accompanied by physiological shifts. Here, we discuss recent evidence for an approach to disentangling neuronal and physiological components of the BOLD signal that enhances the interpretability and clinical utility of fMRI. Converging findings suggest that the systemic low-frequency oscillation (sLFO), a major component of the global signal, indexes cardiovascular manifestations of arousal rather than neuronal function. As such, retaining the sLFO in fMRI data can substantially distort functional connectivity estimates, leading to the misinterpretation of physiological fluctuations in arousal as neuronal effects. At the same time, the sLFO itself tracks physiological arousal level, behavioral performance, drug craving, pharmacological modulation, and large-scale brain network organization. Collectively, we suggest that when interpreting fMRI data, sLFO-indexed physiological arousal should both be modeled and considered separately, as extracting this signal from fMRI data provides dual benefits: a cleaner neuronal signal and a complementary index of behaviorally relevant physiology.
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Janes et al. (2026) conducted a review in fMRI methodology in neuropsychopharmacology. Systemic low-frequency oscillation (sLFO) modeling vs. Standard fMRI preprocessing without sLFO modeling was evaluated. Modeling and separating the systemic low-frequency oscillation (sLFO) from fMRI data provides a cleaner neuronal signal and a complementary index of behaviorally relevant physiological arousal.
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