Amy Catherine Janes

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.

Characterizing individual variation in brain network organization is essential for developing fMRI-based biomarkers in precision psychiatry. We show that arousal, assessed by systemic low-frequency oscillation (sLFO) amplitude in the fMRI signal, is strongly associated with interindividual variability in functional brain network properties (e.g., R = 0.70 for default mode network (DMN)–dorsal attention network (DAN) connectivity; R = −0.63 for DMN dynamics), with replication across independent sessions and samples. These relationships persist after regressing the sLFO out of the BOLD signal and are replicated using established physiological arousal indices, indicating that arousal-related physiology significantly contributes to variability in brain network measures. Pharmacological manipulation shows that increased arousal under catecholaminergic agonism is associated with enhanced network differentiation, whereas antagonism produces the opposite effect. These findings highlight arousal-related physiological processes as a major contributor to individual differences in functional brain network properties as estimated with fMRI and indicate that catecholaminergic modulation is associated with these effects via arousal-related processes.

Functional magnetic resonance imaging (fMRI) is a powerful tool for probing neuronal activity in vivo, but fMRI data are inherently noisy. To mitigate this, a wide range of denoising strategies have been developed, including volume censoring, anatomical component‐based noise correction (aCompCor), ICA‐based methods (e.g., AROMA, FIX), and multi‐echo approaches (e.g., ME‐ICA, tedana). These techniques are often applied in different combinations and have been predominantly evaluated on single‐echo resting‐state fMRI data—typically without incorporating more recent methodological advances known to improve modeling, such as order‐independent “1‐step regression”, modeling temporal autocorrelation (pre‐whitening), and temporal shifting of physiological nuisance regressors. To fill this gap, we used a framework that incorporates these methods and benchmarked a range of denoising pipelines across task and resting‐state, single‐ and multi‐band, and single‐ and multi‐echo fMRI datasets, using different combinations of standard denoising confounds. Pipeline performance was evaluated using temporal signal‐to‐noise ratio (tSNR) and percentage remaining degrees‐of‐freedom (DoF), effectiveness of motion correction, and effectiveness of signal preservation. While pipelines only using ICA were insufficient, those that incorporated physiological nuisance regressors performed well. Additional improvements were observed when temporally shifted physiological regressors were accounted for. Based on these results, we provide recommendations for selecting denoising pipelines and emphasize the need for continued benchmarking as new methods are developed or applied in novel contexts.

Functional magnetic resonance imaging (fMRI) is central to studying neurobiological mechanisms, yet fMRI has limited clinical utility, highlighting the need for novel approaches. We show that a component of the fMRI signal—the systemic low-frequency oscillation (sLFO), linked to blood flow and physiological measures of arousal—indexes trait- and state-level drug use phenotypes. In individuals who chronically use nicotine, sLFO amplitude increased during abstinence and correlated with heightened dependence severity and cue-induced craving. In healthy participants, acute methylphenidate—but not nicotine—reduced sLFO amplitude in a manner that corresponded with improved behavioral performance. These findings demonstrate that the sLFO, typically treated as noise, carries biologically meaningful information. Evaluating the sLFO offers a complementary perspective to traditional fMRI analyses, thus enhancing clinical relevance. Broadly, the sensitivity of sLFO signals to drug administration, cues, and abstinence underscores the need to account for this signal’s contribution when interpreting fMRI responses across experimental conditions.

The neurobiological mechanisms of long-term tobacco use have generated significant interest due to the substantial harm posed by smoking.^1,2 Clinically, functional magnetic resonance imaging (fMRI) has identified ways in which tobacco use is associated with brain function, including inherent resting-state functional organization.³ Such investigations have primarily centered on static connectivity measures, which calculate correlated brain activity over time.⁴ In contrast, emerging evidence indicates that dynamic resting state analyses capture time-varying or dynamic patterns of functional coordination among brain systems,⁵ providing insight into the essential features of large-scale network function⁶ underlying cognition and psychopathology.^7,8 Thus far, to our knowledge, there has been no evaluation into how temporal dynamic profiles differ between those who do and do not smoke, nor is there evidence indicating how smoking-related risk factors, such as emotional dysregulation or childhood trauma, influence temporal dynamics.

Global signal regression (GSR) is a controversial analysis method, since its removal of signal has been observed to reduce the reliability of functional connectivity estimates. Here, we used test–retest reliability to characterize potential differences in spatial patterns between conventional, static GSR (sGSR) and a novel dynamic form of GSR (dGSR). In contrast with sGSR, dGSR models the global signal at a time delay to correct for blood arrival time. Thus, dGSR accounts for greater variation in global signal, removes blood‐flow‐related nuisance signal, and leaves higher quality neuronal signal remaining. We used intraclass correlation coefficients (ICCs) to estimate the reliability of functional connectivity in 462 healthy controls from the Human Connectome Project. We tested across two factors: denoising method used (control, sGSR, and dGSR), and interacquisition interval (between days, or within session while varying phase encoding direction). Reliability was estimated regionally to identify topographic patterns for each condition. sGSR and dGSR provided global reductions in reliability compared with the non‐GSR control. Test–retest reliability was highest in the frontoparietal and default mode regions, and lowest in sensorimotor cortex for all conditions. dGSR provides more effective denoising in regions where both strategies greatly reduce reliability. Both GSR methods substantially reduced test–retest reliability, which was most evident in brain regions that had low reliability prior to denoising. These findings suggest that reliability of interregional correlation is likely inflated by the global signal, which is thought to primarily reflect dynamic blood flow.

Background: Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). While there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participant characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work is difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting, and review the FDCR literature to assess the reporting of items that are deemed important.Methods: Fifty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group based on a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review.Results: By the final round, 38 items reached the consensus threshold and were classified under 7 major categories: “Participant Characteristics”, “General fMRI Information”, “General Task Information”, “Cue Information”, “Craving Assessment Inside Scanner”, “Craving Assessment Outside Scanner” and “Pre- and Post- Scanning Considerations”. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, while items in the “general fMRI reporting” category were reported in 90.5% of the reviewed papers, items in the “pre- and post-scanning considerations” category were reported by only 44.7% of reviewed FDCR studies.Conclusion: Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting, and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.

Nicotine enhances the reinforcement of non-drug rewards by increasing nucleus accumbens (NAcc) reactivity to anticipatory cues. This anticipatory effect is selective as no clear evidence has emerged showing that nicotine acutely changes reward receipt reactivity. However, repeated rewarding experiences shifts peak brain reactivity from hedonic reward outcome to the motivational anticipatory cue yielding more habitual cue-induced behavior. Given nicotine’s influence on NAcc reactivity and connectivity, it is plausible that nicotine acutely induces this shift and alters NAcc functional connectivity during reward processing. To evaluate this currently untested hypothesis, a randomized crossover design was used in which healthy non-smokers were administered placebo and nicotine (2-mg lozenge). Brain activation to monetary reward anticipation and outcome was evaluated with functional magnetic resonance imaging. Relative to placebo, nicotine induced more NAcc reactivity to reward anticipation. Greater NAcc activation during anticipation was significantly associated with lower NAcc activation to outcome. During outcome, nicotine reduced NAcc functional connectivity with cortical regions including the anterior cingulate cortex, orbitofrontal cortex, and insula. These regions showed the same negative relationship between reward anticipation and outcome as noted in the NAcc. The current findings significantly improve our understanding of how nicotine changes corticostriatal circuit function and communication during distinct phases of reward processing and critically show that these alterations happen acutely following a single dose. The implications of this work explain nicotinic modulation of general reward function, which offer insights into the initial drive to smoke and the subsequent difficulty in cessation.