BOLD Signal Research Articles

Effects of Locus Coeruleus Activation on n-Back Performance and Frontoparietal Activity.

Previous studies have linked working memory capacity to restricted hemodynamic responses within critical nodes of the frontoparietal network. Emerging evidence suggests a potential role of the locus coeruleus (LC) in modulating activation of key regions essential for working memory function. This study investigated this hypothesis by examining changes in BOLD signal within the LC and cortex during a parametrically designed verbal working memory task (n-back). fMRI revealed load-dependent task activation, with maximum activation of presumed LC neurons positively correlating with working memory capacity. Furthermore, increased hemodynamic responses in the superior parietal lobes and dorsolateral pFC corresponded with the magnitude of LC activation near working memory capacity limits. An exploratory functional connectivity analysis suggests improvements in working memory performance rely on negative functional connectivity between the LC and cortical regions not primarily involved in task completion. On the basis of previous evidence, this association may represent inhibitory input from cortical regions, enabling phasic bursts of activity from LC neurons, thereby facilitating enhanced cortical activation. This result may also indicate noradrenergic suppression of cortical regions that are not crucial for task completion, leading to enhanced network efficiency. These findings suggest a mechanism by which the LC may improve verbal working memory performance by facilitating enhanced activation in regions critical for visual working memory capacity and active maintenance, potentially enhancing network efficiency.

Subjective cognitive decline is associated with altered patterns of brain activity and connectivity during performance of an old/new recognition memory task

IntroductionSubjective Cognitive Decline (SCD) is considered a preclinical stage within the AD continuum. Knowledge about the functional changes in the brain associated with episodic memory retrieval and novelty recognition in people with SCD is currently very limited. MethodThe study aimed to evaluate behavioural and neurofunctional changes in individuals with SCD, measured relative to a control group, during successful episodic memory retrieval and novelty recognition, as well as to compare the functional connectivity patterns related to these cognitive processes within the Default Mode Network (DMN) in both groups. Participants performed an old/new recognition memory task with words while the BOLD signal was acquired. ResultsNo between-group differences were observed in the performance of the episodic memory task. However, during the successful recognition of old words, the SCD group showed brain hypoactivity in the right rolandic operculum and reduced functional connectivity between the DMN and the fronto-parietal control network (FPCN). During the correct identification of new words, the SCD group also showed reduced connectivity between the DMN and the FPCN, and lower connectivity within the DMN. ConclusionDespite the absence of objective evidence of cognitive impairment, people with SCD display several changes in brain activity and connectivity associated with episodic memory retrieval and novelty recognition.

High-resolution awake mouse fMRI at 14 Tesla.

High-resolution awake mouse fMRI remains challenging despite extensive efforts to address motion-induced artifacts and stress. This study introduces an implantable radiofrequency (RF) surface coil design that minimizes image distortion caused by the air/tissue interface of mouse brains while simultaneously serving as a headpost for fixation during scanning. Furthermore, this study provides a thorough acclimation method used to accustom animals to the MRI environment minimizing motion induced artifacts. Using a 14T scanner, high-resolution fMRI enabled brain-wide functional mapping of visual and vibrissa stimulation at 100×100×200μm resolution with a 2s per frame sampling rate. Besides activated ascending visual and vibrissa pathways, robust BOLD responses were detected in the anterior cingulate cortex upon visual stimulation and spread through the ventral retrosplenial area (VRA) with vibrissa air-puff stimulation, demonstrating higher-order sensory processing in association cortices of awake mice. In particular, the rapid hemodynamic responses in VRA upon vibrissa stimulation showed a strong correlation with the hippocampus, thalamus, and prefrontal cortical areas. Cross-correlation analysis with designated VRA responses revealed early positive BOLD signals at the contralateral barrel cortex (BC) occurring 2 seconds prior to the air-puff in awake mice with repetitive stimulation, which was not detected using a randomized stimulation paradigm. This early BC activation indicated a learned anticipation through the vibrissa system and association cortices in awake mice under continuous training of repetitive air-puff stimulation. This work establishes a high-resolution awake mouse fMRI platform, enabling brain-wide functional mapping of sensory signal processing in higher association cortical areas.

Real-Time fMRI Neurofeedback Training of Selective Attention in Older Adults.

Selective attention declines with age, due to age-related functional changes in dorsal anterior cingulate cortex (dACC). Real-time functional magnetic resonance imaging (rtfMRI) neurofeedback has been used in young adults to train volitional control of brain activity, including in dACC. For the first time, this study used rtfMRI neurofeedback to train 19 young and 27 older adults in volitional up- or down-regulation of bilateral dACC during a selective attention task. Older participants in the up-regulation condition (experimental group) showed greater reward points and dACC BOLD signal across training sessions, reflective of neurofeedback training success; and faster reaction time and better response accuracy, suggesting behavioral benefits on selective attention. These effects were not observed for older participants in the down-regulation condition (inverse condition control group), supporting specificity of volitional dACC up-regulation training in older adults. These effects were, unexpectedly, also not observed for young participants in the up-regulation condition (age control group), perhaps due to a lack of motivation to continue the training. These findings provide promising first evidence of functional plasticity in dACC in late life via rtfMRI neurofeedback up-regulation training, enhancing selective attention, and demonstrate proof of concept of rtfMRI neurofeedback training in cognitive aging.

Breakdown of TMS evoked EEG signal propagation within the default mode network in Alzheimer’s disease

BackgroundThe neural activity of the Default Mode Network (DMN) is disrupted in patients with In Alzheimer’s disease (AD). ObjectivesWe used a novel multimodal approach to track neural signal propagation within the DMN in AD patients. MethodsTwenty mild to moderate AD patients were recruited. We used transcranial magnetic stimulation (TMS) pulses to probe with a millisecond time resolution the propagation of evoked electroencephalography (EEG) signal following the neural activation of the Precuneus (PC), which is a key hub area of the DMN. Moreover, functional and structural magnetic resonance imaging (MRI) data were collected to reconstruct individual features of the DMN. ResultsIn AD patients a probe TMS pulse applied over the PC evokes an increased local activity unmasking underlying hyperexcitability. In contrast, the EEG evoked neural signal did not propagate efficiently within the DMN showing a remarkable breakdown of signal propagation. fMRI and structural tractography showed that impaired signal propagation was related to the same connectivity matrices derived from DMN BOLD signal and transferred by specific white matter bundles forming the cingulum. These features were not detectable stimulating other areas (left dorsolateral prefrontal cortex) or for different networks (fronto-parietal network). Finally, connectivity breakdown was associated with cognitive impairment, as measured with the Clinical Dementia Rating Scale sum of boxes (CDR-SB). ConclusionsTMS-EEG in AD shows both local hyperexcitability and a lack of signal propagation within the DMN. These neurophysiological features also correlate with structural and cognitive attributes of the patients. SignificanceNeuronavigated TMS-EEG may be used as a novel neurophysiological biomarker of DMN connectivity in AD patients.

Feedback to V1 strongly influences BOLD signal during contextual modulation: Evidence from laminar fMRI

Feedback to V1 strongly influences BOLD signal during contextual modulation: Evidence from laminar fMRI

Reinforcement learning processes as forecasters of depression remission

BackgroundAspects of reinforcement learning have been associated with specific depression symptoms and may inform the course of depressive illness. MethodsWe applied support vector machines to investigate whether blood‑oxygen-level dependent (BOLD) responses linked with neural prediction error (nPE) and neural expected value (nEV) from a probabilistic learning task could forecast depression remission. We investigated whether predictions were moderated by treatment use or symptoms. Participants included 55 individuals (n = 39 female) with a depression diagnosis at baseline; 36 of these individuals completed standard cognitive behavioral therapy and 19 were followed during naturalistic course of illness. All participants were assessed for depression diagnosis at a follow-up visit. ResultsBoth nPE and nEV classifiers forecasted remission significantly better than null classifiers. The nEV classifier performed significantly better than the nPE classifier. We found no main or interaction effects of treatment status on nPE or nEV accuracy. We found a significant interaction between nPE-forecasted remission status and anhedonia, but not for negative affect or anxious arousal, when controlling for nEV-forecasted remission status. LimitationsOur sample size, while comparable to that of other studies, limits options for maximizing and evaluating model performance. We addressed this with two standard methods for optimizing model performance (90:10 train and test scheme and bootstrapped sampling). ConclusionsResults support nEV and nPE as relevant biobehavioral signals for understanding depression outcome independent of treatment status, with nEV being stronger than nPE as a predictor of remission. Reinforcement learning variables may be useful components of an individualized medicine framework for depression healthcare.

Impact of analytic decisions on test–retest reliability of individual and group estimates in functional magnetic resonance imaging: A multiverse analysis using the monetary incentive delay task

Abstract Empirical studies reporting low test–retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction, and contrast selection, may improve estimates of test–retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual- and group-level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (Ns: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options), and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 – .55 across the 3 samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization, and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared with using the Neutral cue. This effect was largest for the Cue Model parameterization; however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test–retest reliability may not always improve interpretability of the estimated BOLD signal.

Exploring the Non-Invasive Methods of Brain- Computer Interface: A Comprehensive Review of their Advances and Applications

The brain-computer interface technology allows the human brain to control external devices directly without using the brain’s output channels or peripheral nerves. It helps individuals with motor impairments to use mechanical and external devices to communicate with the outside world. Non-invasive BCIs allow communication between the human brain and external devices without the need for surgeries or invasive procedures. Methods like EEG, MEG, fMRI, and fNIRS are used. EEG enables the acquisition of electrical activity along the scalp by measuring voltage fluctuations and neurotransmission activity in the brain. The electrodes are attached to a cap-like device and are placed on the scalp to record the electrical current generated by the brain. Unlike MEG, which necessitates specially constructed rooms, EEG is portable. Lab-grade EEG is expensive but cheaper than other forms of BCI. MEG uses magnetometers to measure magnetic fields produced by electric currents occurring naturally in the brain. MEG is better than EEG at measuring high-frequency activity. MEG signals are less distorted by the skull layer. FMRI records blood oxygen level-dependent (BOLD) signals with high spatial resolution across the entire brain. It does this by tracking the hemodynamic response, which is the increase in blood flow to active brain areas. It does this using the principle of nuclear magnetic resonance, where hydrogen atoms in water molecules in the blood emit signals when subjected to a strong magnetic field. It has an advantage over EEG due to its superior spatial specificity and resolution. FNIRS measures the blood flow and oxygenation in the blood associated with neural activity. It gains insight into the brain's hemodynamic response, which is essential for understanding neural functioning during BCI tasks.

Hyperglycemia selectively increases cerebral non-oxidative glucose consumption without affecting blood flow.

Multiple studies have shown that hyperglycemia increases the cerebral metabolic rate of glucose (CMRglc) in subcortical white matter. This observation remains unexplained. Using positron emission tomography (PET) and euinsulinaemic glucose clamps, we found, for the first time, that acute hyperglycemia increases non-oxidative CMRglc (i.e., aerobic glycolysis (AG)) in subcortical white mater as well as in medial temporal lobe structures, cerebellum and brainstem, all areas with low euglycemic CMRglc. Surprisingly, hyperglycemia did not change regional cerebral blood flow (CBF), the cerebral metabolic rate of oxygen (CMRO2), or the blood-oxygen-level-dependent (BOLD) response. Regional gene expression data reveal that brain regions where CMRglc increased have greater expression of hexokinase 2 (HK2). Simulations of glucose transport revealed that, unlike hexokinase 1, HK2 is not saturated at euglycemia, thus accommodating increased AG during hyperglycemia.

Validity and specificity of BOLD effects and their correction in 1H-fMRS.

This study aimed to characterize blood oxygen level-dependent (BOLD) effects in proton magnetic resonance (1H-MR) spectra obtained during optogenetic activation of the rat forelimb cortex to correct and estimate the accurate changes in metabolite concentration. For a more comprehensive understanding of BOLD effects detected with functional magnetic resonance spectroscopy (fMRS) and to optimize the correction method, a 1 Hz line-narrowing effect was simulated. Then, proton functional magnetic resonance spectroscopy (1H-fMRS) data acquired using stimulated echo acquisition mode (STEAM) at 9.4T in rats (n = 8) upon optogenetic stimulation of the primary somatosensory cortex were utilized. The data were analyzed using MATLAB routines and LCModel. Uncorrected and corrected 1H-MR spectra from the simulated and in vivo data were quantified and compared. BOLD-corrected difference spectra were also calculated and analyzed. Additionally, the effects of stimulated and non-stimulated water on the quantification of metabolite concentration swere investigated. Significant mean increases in water and N-acetylaspartate (NAA) peak heights (+1.1% and +4.5%, respectively) were found to be accompanied by decreased linewidths (-0.5 Hz and -2.8%) upon optogenetic stimulation. These estimates were used for further defining an accurate line-broadening (lb) factor. The usage of a non-data-driven lb introduced false-positive errors in the metabolite concentration change estimates, thereby altering the specificity of the findings. The water and metabolite BOLD contributions were separated using different water scalings within LCModel. The linewidth-matching procedure using a precise lb factor remains the most effective approach for accurately quantifying small (±0.3 μmol/g) metabolic changes in 1H-fMRS studies. A simple and preliminary compartmentation of BOLD effects was proposed, but it will require validation.

Differences in subcortical functional connectivity in patients with epilepsy.

Epilepsy is a disease characterized by abnormal paroxysmal bioelectrical activity in the brain cortex and subcortical structures. Seizures per se change brain metabolism in epileptic focus and in distal parts of the brain. However, interictal phenomena can also affect functional connectivity (FC) and brain metabolism in other parts of the brain. We hypothesised that epilepsy affects functional connectivity not only among cortical, but also between subcortical, structures of the brain in a resting state condition. Investigating functional connectivity in patients with epilepsy could provide insights into the underlying pathophysiological mechanisms. Better understanding may lead to more effective treatment strategies. Functional connectivity was analysed in 35 patients with epilepsy and in 28 healthy volunteers. The group of patients was divided into generalised and focal epilepsy (temporal and extratemporal subgroups). Each patient and healthy volunteer underwent an fMRI resting-state session. During the study, EEG signals were simultaneously recorded with fMRI to facilitate the subsequent detection of potential interictal epileptiform discharges (IEDs). Their potential impact on BOLD signals was mitigated through linear regression. The data was processed and correlation coefficients (FC values) between the BOLD signal from selected structures of the central nervous system were determined and compared between study groups. The results were presented as significant differences in correlation coefficients between brain/subcortical structures in the epilepsy and control groups. Lower FC values for the epilepsy group compared to the control group were shown for connections related to the MPFC, hippocampus, thalamus, amygdala, and the parahippocampal gyrus. Epilepsy alters the functional connectivity of resting state subcortical networks. Patterns of pathological changes of FC differ between epilepsy subtypes, with predominant lower FC between the hippocampus, parahippocampal gyrus, amygdala and thalamus in patients with epilepsy. This study suggests that epilepsy affects subcortical structures. Identifying distinct patterns of altered FC in epilepsy subtypes may help to tailor treatment strategies. Changes in FC detected by fMRI may precede clinical symptoms, aiding in the early diagnosis of cognitive and emotional disorders in focal epilepsy.

Estimation of cerebrovascular reactivity amplitude and lag using breath-holding fMRI and the global BOLD signal: Application in diabetes and hypertension.

In this work, we demonstrate a data-driven approach for estimating cerebrovascular reactivity (CVR) amplitude and lag from breathhold (BH) fMRI data alone. Our approach employs a frequency-domain approach that is independent of external recordings. CVR amplitude is estimated from the BOLD frequency spectrum and CVR lag is estimated from the Fourier phase using the global-mean BOLD signal as reference. Unlike referencing to external recordings, these lags are specific to the brain. We demonstrated our method in detecting regional CVR amplitude and lag differences across healthy (CTL), hypertensive (HT) and hypertension-plus-type-2-diabetes (HT + DM) groups of similar ages and sex ratios, with a total N of 49. We found CVR amplitude to be significantly higher in CTL compared to HT + DM, with minimal difference between CTL and HT. Also, voxelwise CVR lag estimated in the Fourier domain is a more sensitive marker of vascular dysfunction than CVR amplitude. CVR lag in HT is significantly shorter than in CTL, with minimal difference between CTL and HT + DM. Our results support the importance of joint CVR amplitude and lag assessments in clinical applications.

Нейровизуализационный подход для выявления биомаркеров рабочей памяти у больных с хронической ишемией мозга

Verbal working memory (VWM) is a fundamental function responsible for temporary storage and short-term handling of verbal information. The study was aimed to determine the working memory biomarker associated with imaging of the source of infra-slow electrical activity in patients with chronic cerebral ischemia (CCI). A total of 50 patients with CCI took part in the study: 16 males and 34 females aged 50–85 years. VWM was evaluated by the Luria test. The subjects were divided into two groups matched by age with the VWM below and above the average level for the studied sample. The infra-slow, below 0.1 Hz, electrical activity, otherwise known as the DC potentials (DCPs) of the brain, was recorded with five monopolar leads: frontal, central, occipital, right and left temporal. The resting state fMRI was used to analyze brain regions with the activated BOLD (blood-oxygen-level-dependent) signal that were associated with the brain regions responsible for VWM and the DCP generation sources recorded with the non-polarizable electrodes. The differences in BOLD signal activation and infra-slow activity amplitude were found in two VWM groups. These resting-state neural networks, VWM and the neural network responsible for DCP generation, overlapped in frontal regions. There were significant differences in DCP recorded with the frontal lead in two VWM groups (р = 0.00004). In patients with CCI, infra-slow activity, recorded with the frontal lead that is generated by the neural network fragment representing an intersection of the VWM network and the part of the brain responsible for DCP generation in the frontal region, is a VWM biomarker.

A Plug-In Graph Neural Network to Boost Temporal Sensitivity in fMRI Analysis.

Learning-based methods offer performance leaps over traditional methods in classification analysis of high-dimensional functional MRI (fMRI) data. In this domain, deep-learning models that analyze functional connectivity (FC) features among brain regions have been particularly promising. However, many existing models receive as input temporally static FC features that summarize inter-regional interactions across an entire scan, reducing the temporal sensitivity of classifiers by limiting their ability to leverage information on dynamic FC features of brain activity. To improve the performance of baseline classification models without compromising efficiency, here we propose a novel plug-in based on a graph neural network, GraphCorr, to provide enhanced input features to baseline models. The proposed plug-in computes a set of latent FC features with enhanced temporal information while maintaining comparable dimensionality to static features. Taking brain regions as nodes and blood-oxygen-level-dependent (BOLD) signals as node inputs, GraphCorr leverages a node embedder module based on a transformer encoder to capture dynamic latent representations of BOLD signals. GraphCorr also leverages a lag filter module to account for delayed interactions across nodes by learning correlational features of windowed BOLD signals across time delays. These two feature groups are then fused via a message passing algorithm executed on the formulated graph. Comprehensive demonstrations on three public datasets indicate improved classification performance for several state-of-the-art graph and convolutional baseline models when they are augmented with GraphCorr.

Lifetime depression and mania/hypomania risk predicted by neural markers in three independent young adult samples during working memory and emotional regulation.

Objective markers of pathophysiological processes underlying lifetime depression and mania/hypomania risk can provide biologically informed targets for novel interventions to help prevent the onset of affective disorders in individuals with subsyndromal symptoms. Greater activity within and functional connectivity (FC) between the central executive network (CEN), supporting emotional regulation (ER) subcomponent processes such as working memory (WM), the default mode network (DMN), supporting self-related information processing, and the salience network (SN), is thought to interfere with cognitive functioning and predispose to depressive disorders. Using an emotional n-back paradigm designed to examine WM and ER capacity, we examined in young adults: (1) relationships among activity and FC in these networks and lifetime depression and mania/hypomania risk; (2) the extent to which these relationships were specific to lifetime depression risk versus lifetime mania/hypomania risk; (3) whether findings in a first, Discovery sample n = 101, 63 female, age = 23.85 (2.9) could be replicated in a two independent Test samples of young adults: Test sample 1: n = 90, 60 female, age = 21.7 (2.0); Test sample 2: n = 96, 65 female, age = 21.6 (2.1). The Mood Spectrum Self-Report (MOODS-SR-L) assessed lifetime mania/hypomania risk and depression risk. We showed significant clusters of activity to each contrast in similar locations in the anatomic mask in each Test sample as in the Discovery sample, and, using extracted mean BOLD signal from these clusters as IVs, we showed similar patterns of IV-DV relationships in each Test sample as in the Discovery sample. Specifically, in the Discovery sample, greater DMN activity during WM was associated with greater lifetime depression risk. This finding was specific to depression and replicated in both independent samples (all ps<0.05 qFDR). Greater CEN activity during ER was associated with increased lifetime depression risk and lifetime mania/hypomania risk in all three samples (all ps< 0.05 qFDR). These replicated findings provide promising objective, neural markers to better identify, and guide and monitor early interventions for, depression and mania/hypomania risk in young adults.

Real-time fMRI neurofeedback of the anterior insula using arterial spin labelling.

Arterial spin labelling (ASL) is the only non-invasive technique that allows absolute quantification of perfusion and is increasingly used in brain activation studies. Contrary to the blood oxygen level-dependent (BOLD) effect ASL measures the cerebral blood flow (CBF) directly. However, the ASL signal has a lower signal-to-noise ratio (SNR), than the BOLD signal, which constrains its utilization in neurofeedback studies. If successful, ASL neurofeedback can be used to aid in the rehabilitation of health conditions with impaired blood flow, for example, stroke. We provide the first ASL-based neurofeedback study incorporating a double-blind, sham-controlled design. A pseudo-continuous ASL (pCASL) approach with background suppression and 3D GRASE readout was combined with a real-time post-processing pipeline. The real-time pipeline allows to monitor the ASL signal and provides real-time feedback on the neural activity to the subject. In total 41 healthy adults (19-56 years) divided into three groups underwent a neurofeedback-based emotion imagery training of the left anterior insula. Two groups differing only in the explicitness level of instruction received real training and a third group received sham feedback. Only those participants receiving real feedback with explicit instruction showed significantly higher absolute CBF values in the trained region during neurofeedback than participants receiving sham feedback. However, responder analyses of percent signal change values show no differences in activation between the three groups. Persisting limitations, such as the lower SNR, confounding effects of arterial transit time and partial volume effects still impact negatively the implementation of ASL neurofeedback.

Ex Vivo Simultaneous H215O Positron Emission Tomography and Magnetic Resonance Imaging of Porcine Kidneys-A Feasibility Study.

Pig kidneys (n = 7) were connected to a NMP circuit. PET/MRI was performed at two different pump flow levels: a blood-oxygenation-level-dependent (BOLD) MRI sequence performed simultaneously with a H215O PET sequence for determination of RBP. RBP was measured using H215O PET in all kidneys (flow 1: 0.42-0.76 mL/min/g, flow 2: 0.7-1.6 mL/min/g). We found a linear correlation between changes in delivered blood flow from the perfusion pump and changes in the measured RBP using PET imaging (r2 = 0.87). Our study demonstrated the feasibility of combined H215O PET/MRI during NMP of isolated porcine kidneys with tissue oxygenation being stable over time. The introduction of H215O PET/MRI in nephrological research could be highly relevant for future pre-transplant kidney evaluation and as a tool for studying renal physiology in healthy and diseased kidneys.

Dynamics of human serotonin synthesis differentially link to reward anticipation and feedback.

Serotonin (5-HT) plays an essential role in reward processing, however, the possibilities to investigate 5-HT action in humans during emotional stimulation are particularly limited. Here we demonstrate the feasibility of assessing reward-specific dynamics in 5-HT synthesis using functional PET (fPET), combining its molecular specificity with the high temporal resolution of blood oxygen level dependent (BOLD) fMRI. Sixteen healthy volunteers underwent simultaneous fPET/fMRI with the radioligand [11C]AMT, a substrate for tryptophan hydroxylase. During the scan, participants completed the monetary incentive delay task and arterial blood samples were acquired for quantifying 5-HT synthesis rates. BOLD fMRI was recorded as a proxy of neuronal activation, allowing differentiation of reward anticipation and feedback. Monetary gain and loss resulted in substantial increases in 5-HT synthesis in the ventral striatum (VStr, +21% from baseline) and the anterior insula (+41%). In the VStr, task-specific 5-HT synthesis was further correlated with BOLD signal changes during reward feedback (ρ = -0.65), but not anticipation. Conversely, 5-HT synthesis in the anterior insula correlated with BOLD reward anticipation (ρ = -0.61), but not feedback. In sum, we provide a robust tool to identify task-induced changes in 5-HT action in humans, linking the dynamics of 5-HT synthesis to distinct phases of reward processing in a regionally specific manner. Given the relevance of altered reward processing in psychiatric disorders such as addiction, depression and schizophrenia, our approach offers a tailored assessment of impaired 5-HT signaling during cognitive and emotional processing.

Reliability of task-based fMRI in the dorsal horn of the human spinal cord

Abstract The application of functional magnetic resonance imaging (fMRI) to the human spinal cord is still a relatively small field of research and faces many challenges. Here we aimed to probe the limitations of task-based spinal fMRI at 3T by investigating the reliability of spinal cord blood oxygen level dependent (BOLD) responses to repeated nociceptive stimulation across 2 consecutive days in 40 healthy volunteers. We assessed the test–retest reliability of subjective ratings, autonomic responses, and spinal cord BOLD responses to short heat-pain stimuli (1 s duration) using the intraclass correlation coefficient (ICC). At the group level, we observed robust autonomic responses as well as spatially specific spinal cord BOLD responses at the expected location, but no spatial overlap in BOLD response patterns across days. While autonomic indicators of pain processing showed good-to-excellent reliability, both β-estimates and z-scores of task-related BOLD responses showed poor reliability across days in the target region (gray matter of the ipsilateral dorsal horn). When taking into account the sensitivity of gradient-echo echo planar imaging (GE-EPI) to draining vein signals by including the venous plexus in the analysis, we observed BOLD responses with fair reliability across days. Taken together, these results demonstrate that heat-pain stimuli as short as 1 s are able to evoke a robust and spatially specific BOLD response, which is, however, strongly variable within participants across time, resulting in low reliability in the dorsal horn gray matter. Further improvements in data acquisition and analysis techniques are thus necessary before event-related spinal cord fMRI as used here can be reliably employed in longitudinal designs or clinical settings.