Blood Oxygen Level-dependent Signal Research Articles

NIMG-26. GLIOBLASTOMA TISSUE INVASION MAPPING WITH NOVEL HYPOXIA-TARGETED BLOOD OXYGENATION-LEVEL DEPENDENT MRI

Abstract BACKGROUND Glioblastoma is a devastating incurable malignant primary brain tumor exhibiting an erratic growth pattern and brain tissue invasion exceeding the typically depicted contrast-enhancing lesion on MRI. This behaviour is strongly associated with the presence of aberrant vasculature. Advanced clinical neuroimaging techniques, however, have not provided convincing imaging markers to determine glioblastoma tissue infiltration and monitor treatment effects. We investigated a new imaging technique based on blood oxygenation-level dependent (BOLD) MRI contrast with precise hypoxic respiratory targeting in order to better determine tumor tissue invasion not restricted to the typically depicted focal glioblastoma lesion on contrast-enhanced MRI. METHODS A computer-controlled gas blender was used to induce transient and standardized hypoxic stimulus in isocapnic conditions during echo-planar-imaging acquisition in patients with brain tumors.8,9 Data were preprocessed using SPM and in-house written MATLAB scripts. The induced BOLD signal changes were used to calculate %BOLD signal change during hypoxic stimulus with respect to the baseline, contrast to noise (CNR) ratio and goodness-of-fit (Rsquared) for each voxel in the whole-brain and automatically segmented region of interest (contrast-enhancement, necrosis, edema) and overlayed using color-maps onto anatomical high resolution T1. RESULTS Hypoxic targeting during gas modulation could be performed with high reproducibility, caused no discomfort in patients with brain tumors and was able to induce strong signal change during hypoxic stimulus with high contrast-noise ratio and Rsquared. In particular, when compared with other tumors, BOLD hypoxia glioblastoma tissue invasion maps show very distinct hypoxic signal responses in some cases extending beyond the focal -contrast-enhancing- glioblastoma lesion. CONCLUSION The ability to map at voxel level BOLD hypoxia signal patterns may determine a novel therapeutic imaging marker based on structural vascular alterations in the tumour tissue infiltration zone before these become appreciable with standard neuroimaging. This may support a refinement and tailoring of tumor resection and improve follow-up after resection and during treatment i.e. chemoradiotherapy.

NIMG-23. A NOVEL APPROACH TO PERFUSION MRI STUDIES IN BRAIN TUMOR PATIENTS USING ENDOGENOUS DEOXYHEMOGLOBIN AS A CONTRAST AGENT: A PROOF-OF-CONCEPT STUDY

Abstract BACKGROUND Assessment of resting perfusion in brain tumors is clinically relevant and relies on dynamic susceptibility contrast magnetic resonance imaging using gadolinium contrast. Preliminary studies have introduced the use of controlled hemoglobin desaturation by means of a transient hypoxic stimulus during echo-planar imaging(EPI) as a surrogate for gadolinium for resting perfusion assessment in healthy subjects. In this proof of concept study, we investigated whether this approach is feasible in brain tumors and warrant further validation. METHODS A computer controlled gas blender was used to induce transient and standardized hypoxic stimulus in isocapnic conditions during EPI acquisition in a heterogenous cohort of tumor patients. The induced blood-oxygen-level-dependent(BOLD) signal changes determined by transit of a bolus of deoxygenated blood in the brain vasculature were used as a surrogate of gadolinium to calculate cerebral blood volume(CBV), cerebral blood flow(CBF) and mean transit time(MTT) using a global arterial input functions obtained through the BOLD signal measured over the middle cerebral artery as previously described by Poublanc et al. Perfusion parameters were calculated in the whole-brain voxel-by-voxel and in automatically segmented region of interest(ROI) using SPM, AFNI, Verbena softwares and MATLAB in-house written scripts. RESULTS Nine patients were included. MTT, rCBV, rCBF were calculated voxel-per-voxel. Obtained maps were overlayed on the co-registered T1 MRI scans and compared to T1CE and FLAIR to identify perfusion patterns in tumor ROIs (contrast enhancement, edema, necrosis). The deoxy-hemoglobin BOLD MRI perfusion were compared to clinical DSC-MRI when available and showed high qualitative agreement. CONCLUSION Transient and precise hemoglobin desaturation by controlled hypoxic gas modulation is safe and repeatable in brain tumor patients. The induced BOLD signal change allows measurement of resting perfusion, which qualitatively closely mimic gadolinium perfusion in different brain tumors. Deoxyhemoglobin-based perfusion warrant further quantitative validation against gadolinium in a larger cohort of heterogenous tumor patients.

A comparative analysis of the sensitivity and BOLD contamination of the VASO response at 3 Tesla: ME-DEPICTING vs. ME-EPI readouts

Abstract ‘Non-BOLD fMRI’ data acquired at non-zero echo time (TE) suffer from contamination by the Blood Oxygenation Level Dependent (BOLD) signal due to the unavoidable signal decay caused by transverse relaxation. This contamination further reduces their already low inherent functional sensitivities and makes their correction essential. The Slice-Saturation Slab-Inversion Vascular Space Occupancy (SS-SI–VASO), for instance, cancels out BOLD contributions from VASO data, reflecting cerebral blood volume (CBV) changes, via a dynamic division approach. Alternatively, multi-echo (ME) data provide the possibility of extrapolating to TE=0. Acquisitions at very short TE would minimize the need for such corrections. The center-out EPI variant (‘DEPICTING’) is one such readout which allows for short TE. The ME 2D DEPICTING was compared here against a traditional ME 2D EPI for its sensitivity to functional changes in the VASO signal. The two BOLD-correction schemes were also evaluated. Clear differences in functional sensitivity were observed for the uncorrected VASO data obtained from the first echo, TE1, of the two readouts. VASO data corrected by ME extrapolation were, however, found to be almost identical in their sensitivity for detecting CBV changes for both readouts. An excessively high increase in VASO signal sensitivity observed with the dynamic division correction for both readouts revealed a near-perfect linear dependence on TE of VASO signal changes. This could be attributed to the substantial intravascular BOLD contributions at 3 T. In the present data, extravascular ΔR2* fraction was found to be around ~50–60%. ME extrapolation is, hence, recommended to avoid overestimation of functional CBV changes at commonly used TEs.

Neuroimaging Findings for the Overnight Consolidation of Learned Non-native Speech Sounds

Abstract Research over the past two decades has documented the importance of sleep to language learning. Sleep has been suggested to play a role in establishing new speech representations as well; however, the neural mechanisms corresponding to sleep-mediated effects on speech perception behavior are unknown. In this study, we trained monolingual English-speaking adults to perceive differences between the Hindi dental vs. retroflex speech contrast in the evening. We examined the blood oxygen level dependent signal using functional magnetic resonance imaging during perceptual tasks on both the trained talker and on an untrained talker shortly after training, and again the next morning. We also employed diffusion tensor imaging to determine if individual differences in white matter structure could predict variability in overnight consolidation. We found greater activity in cortical regions associated with language processing (e.g., left insula) on the second day. Fractional anisotropy values in the anterior thalamic radiation and the uncinate fasciculus was associated with the magnitude of overnight change in perceptual behavior on the generalization (untrained) talker, after controlling for differences in sleep duration and initial learning. Our findings suggest that speech-perceptual information is subject to an overnight transfer of information to the cortex. Moreover, neural structure appears to be linked to individual differences in efficiency of overnight consolidation.

Feasibility of submillimeter functional quantitative susceptibility mapping using 3D echo planar imaging at 7 T.

Quantitative susceptibility mapping (QSM) is a tool for mapping tissue susceptibility. Using QSM for functional brain mapping, it is possible to directly quantify blood-oxygen-level-dependent (BOLD) susceptibility changes. This study presents a submillimeter functional QSM (fQSM) approach compared to BOLD fMRI from data acquired with 3D gradient-echo echo planar imaging (EPI) at ultra-high field. Complex EPI data were acquired in nine healthy subjects with varying temporal and spatial resolutions and used for BOLD fMRI and for fQSM. Right-hand finger tapping experiments were performed as well as one measurement with intentional subject movement. Susceptibility maps were computed using 3D path-based unwrapping, the variable-kernel sophisticated harmonic artifact reduction for phase data, and the streaking artifact reduction for QSM algorithm. Functional data analysis included general linear modeling and computation of z-scores. Submillimeter data were denoised using NOise reduction with DIstribution Corrected (NORDIC), which improved z-scores in the motor cortex for fQSM and fMRI. An expected increase in BOLD fMRI signal and corresponding decrease in magnetic susceptibility was observed in sensorimotor areas during active periods. For all experiments, fQSM showed smaller activation regions compared with fMRI. The percentage of high negative t-values localized in the cortex was higher for fQSM (52%) than for positive or negative t-values for fMRI (45%). For the scans with intentional motion, movement exceeded the size of a voxel, but paradigm dependent signal evolution could be recovered using motion correction. In conclusion, this study demonstrates the feasibility of submillimeter whole-brain fQSM with voxel volume of 0.53 μL. In comparison to traditional BOLD fMRI, fQSM provided improved localization of brain activation within the cortex, especially in submillimeter 3D EPI sequences.

Highfield imaging of the subgenual anterior cingulate cortex in uni- and bipolar depression.

The subgenual Anterior Cingulate Cortex (sgACC), as a part of the Anterior Cingulate Cortex and the limbic system plays a crucial role in mood regulation. Previous structural and functional brain imaging studies of the sgACC have revealed alterations of Gray Matter (GM) volumes and Blood Oxygenation Level Dependent signals (BOLD) in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD), suggesting potential biomarker traits for affective disorders. In this study we investigated the gray matter volume of the sgACC in 3 different patient groups: 40 MDD patients, of which 20 were medicated (MDDm) and 20 were unmedicated (MDDu), and 21 medicated BD patients, and compared them with 23 healthy volunteers. We examined GM volume alteration using high-resolution 7T Magnetic Resonance Imaging (MRI) which produced quantitative maps of the spin-lattice relaxation time (T1). T1 maps provide high contrast between gray and white matter, and at 7 Tesla voxels with submillimeter resolution can be acquired in a reasonable scan time. We developed a semi-automatic segmentation protocol based on refined landmarks derived from previous volumetric studies using quantitative T1 maps as raw input data for automatic tissue segmentation of GM, WM and CSF (cerebrospinal fluid) tissue. The sgACC ROI was then superimposed on these tissue probability maps and traced manually by two independent raters (F.B., M.M.) following our semi-automatic segmentation protocol. Interrater reliability was calculated on a subset of 10 brain scans for each hemisphere, showing an Intra-Class Correlation coefficient (ICC) r = 0.96 for left sgACC and r = 0.84 for right sgACC respectively. In summary, we have developed a reproducible and reliable semi-automatic segmentation protocol to measure gray matter volume in the sgACC. Based on previous findings from meta-analyses on morphometric studies of the sgACC, we hypothesized that patients with MDD would have lower gray matter sgACC volumes compared to healthy subjects. Post-hoc analysis revealed smaller subgenual volumes for the left hemisphere in both the medicated (MDDm) and non-medicated (MDDu) group versus healthy controls (p = .001, p = .008) respectively. For the right hemisphere, the (MDDu) and BD group exhibited significantly lower subgenual volumes than healthy controls (p < .001, p = .004) respectively. To our knowledge, this is the first morphometric MRI study using T1 maps obtained in high-resolution 7 Tesla MRI to compare MDD and BD patients with healthy controls.

7625 Functional and structural neuronal changes of dulaglutide compared to placebo during smoking cessation - a randomized placebo-controlled trial

Abstract Disclosure: C.O. Sailer: None. S. Lengsfeld: None. D. Coynel: None. F. Baur: None. K. Bologna: None. T. Vukajlovic: None. N.A. Jeanloz: None. C. Bathelt: None. D. Zanchi: None. M. Christ-Crain: None. B.F. Winzeler: None. Introduction: Nicotine activates reward-related brain regions and chronic cigarette smoking can lead to functional and structural neuronal changes. Glucagon-like peptide-1 (GLP-1) receptor agonists (RAs), known for their anti-obesity effects, have emerged as potential treatments for addictive disorders. A pilot study of exenatide vs placebo revealed increased short-term smoking abstinence rates and reduce smoking craving. We previously reported the results of a clinical trial (n=255) investigating the effect of dulaglutide vs placebo on smoking cessation. Herein, we report the functional magnetic resonance imaging (fMRI) results of a subset of participants. This substudy aimed to investigate the influence of dulaglutide on smoking craving, functional and structural neuronal changes of smokers aiming to quit smoking. Methods: This single-center, randomized, double-blind, placebo-controlled trial enrolled 71 participants with at least a moderate nicotine dependence, all committed to quitting smoking. Of these, 35 participants received 12 weeks dulaglutide 1.5mg, and 36 participants placebo, alongside varenicline treatment and behavioral counselling. An fMRI session was conducted at baseline and 12 weeks. The primary outcomes included smoking craving assessed on a visual analogue scale (VAS, range 0-7), differences in blood oxygen level dependent (BOLD) signal (whole brain and regions of interests, e.g., nucleus accumbens, amygdala, thalamus) and gray matter changes between sessions and groups when exposed to smoking and neutral video cues. Secondary outcomes assessed the above in quitters vs persistent smokers. Results: In line with the primary study findings, abstinence rates at 12 weeks were comparable in the dulaglutide (54%) and placebo (68%) groups (p=0.27). In a mixed effect model, smoking craving significantly decreased at 12 weeks vs baseline (-2.13 (95% CI -2.46, -1.80), p&amp;lt;0.001) and were lower for neutral vs smoking cues (-2.02 (-2.34, 1.70,), p&amp;lt;0.001). However, no significant inter-group difference was observed (-0.09 (-0.53, 0.33), p=0.65). Persistent smokers exhibited higher smoking craving at 12 weeks vs quitters (0.48 (0.06, 0.90), p=0.028), particularly when exposed to smoking cues (2.02 (1.70, 2.34), p&amp;lt;0.001). No significant differences were found in functional or structural analyses between interventions or smoking status. Conclusions: The smoking cessation intervention led to a significant reduction in smoking craving at 12 weeks without in-between group differences in behavioral, functional, or structural outcomes. Our findings challenge the hypothesis that GLP-1 RAs modulates smoking craving and behavior. It is plausible that a potential GLP-1 effect might have been masked by varenicline treatment and the unexpectedly high abstinence rate in the placebo group. Future studies should explore more potent GLP-1 RAs or dual agonists. Presentation: 6/1/2024

Reassessing the Functional Significance of Blood Oxygen Level Dependent Signal Variability.

BOLD signal variability (SDBOLD) has emerged as a unique measure of the adaptive properties of neural systems that facilitate fast, stable responding, based on claims that SDBOLD is independent of mean BOLD signal (meanBOLD) and is a powerful predictor of behavioral performance. We challenge these two claims. First, the apparent independence of SDBOLD and meanBOLD may reflect the presence of deactivations; we hypothesize that although SDBOLD may not be related to raw meanBOLD, it will be linearly related to "absolute" meanBOLD. Second, the observed relationship between SDBOLD and performance may be an artifact of using fixed-length trials longer than RTs. Such designs provide opportunities to toggle between on- and off-task states, and fast responders likely engage in more frequent state-switching, thereby artificially elevating SDBOLD. We hypothesize that SDBOLD will be higher and more strongly related to performance when using such fixed-length trials relative to self-paced trials that terminate upon a response. We test these two hypotheses in an fMRI study using blocks of fixed-length or self-paced trials. Results confirmed both hypotheses: (1) SDBOLD was robustly related with absolute meanBOLD, and (2) toggling between on- and off-task states during fixed-length trials reliably contributed to SDBOLD. Together, these findings suggest that a reappraisal of the functional significance of SDBOLD as a unique marker of cognitive performance is warranted.

Association between neurovascular coupling and neural signals in the resting state as revealed by a combined fMRI and magnetoencephalography study in humans.

The blood oxygenation level-dependent (BOLD) activation reflects hemodynamic events mediated by neurovascular coupling. During task performance, the BOLD hemodynamic response in a relevant area is mainly driven by the high levels of synaptic activity (reflected in local field potentials, LFPs) but, in contrast, during a task-free, resting state, the contribution to BOLD of such neural events is small, as expected by the comparatively (to the task state) low level of neural events. Concomitant recording of BOLD and LFP at rest in animal experiments has estimated the neural contribution to BOLD to ∼10%. Such experiments have not been performed in humans. As an approximation, we recorded (in the same subject, n = 57 healthy participants) at a task-free, resting state the BOLD signal and, in a different session, the magnetoencephalographic (MEG) signal, which reflects purely neural (synaptic) events. We then calculated the turnover of these signals by computing the successive moment-to-moment difference in the BOLD and MEG time series and retaining the median of the absolute value of the differenced series (BOLD and TMEG, respectively). The correlation between normalized turnovers of BOLD (TBOLD) and turnovers of MEG (TMEG) was r = 0.336 (r2 = 0.113; P = 0.011). These results estimate that 11.3% of the variance in TBOLD can be explained by the variance in TMEG. This estimate is close to the aforementioned estimate obtained by direct recordings in animal experiments. NEW & NOTEWORTHY Here, we report on a weak positive association between turnovers of blood oxygenation level-dependent (TBOLD) and magnetoencephalographic (TMEG) signals in 57 healthy human subjects in a resting, task-free state. More specifically, we found that the purely neural TMEG accounted for 11.1% of the TBOLD, a percentage remarkably close to that found between resting-state local field potentials (LFPs) and BOLD recorded concurrently in animal experiments.

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.

A Randomization-Based, Model-Free Approach to Functional Neuroimaging: A Proof of Concept.

Functional neuroimaging analysis takes noisy multidimensional measurements as input and produces statistical inferences regarding the functional properties of brain regions as output. Such inferences are most commonly model-based, in that they assume a model of how neural activity translates to the measured signal (blood oxygenation level-dependent signal in the case of functional MRI). The use of models increases statistical sensitivity and makes it possible to ask fine-grained theoretical questions. However, this comes at the cost of making theoretical assumptions about the underlying data-generating process. An advantage of model-free approaches is that they can be used in cases where model assumptions are known not to hold. To this end, we introduce a randomization-based, model-free approach to functional neuroimaging. TWISTER randomization makes it possible to infer functional selectivity from correlations between experimental runs. We provide a proof of concept in the form of a visuomotor mapping experiment and discuss the possible strengths and limitations of this new approach in light of our empirical results.

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.

Dynamic causal modelling highlights the importance of decreased self-inhibition of the sensorimotor cortex in motor fatigability.

Motor fatigability emerges when challenging motor tasks must be maintained over an extended period of time. It is frequently observed in everyday life and affects patients as well as healthy individuals. Motor fatigability can be measured using simple tasks like finger tapping at maximum speed for 30s. This typically results in a rapid decrease of tapping frequency, a phenomenon called motor slowing. In a previous study (Bächinger et al, eLife, 8 (September), https://doi.org/10.7554/eLife.46750 , 2019), we showed that motor slowing goes hand in hand with a gradual increase in blood oxygen level dependent signal in the primary sensorimotor cortex (SM1), supplementary motor area (SMA), and dorsal premotor cortex (PMd). It is unclear what drives the activity increase in SM1 caused by motor slowing and whether motor fatigability affects the dynamic interactions between SM1, SMA, and PMd. Here, we performed dynamic causal modelling (DCM) on data of 24 healthy young participants collected during functional magnetic resonance imaging to answer this question. The regions of interest (ROI) were defined based on the peak activation within SM1, SMA, and PMd. The model space consisted of bilateral connections between all ROI, with intrinsic self-modulation as inhibitory, and driving inputs set to premotor areas. Our findings revealed that motor slowing was associated with a significant reduction in SM1 self-inhibition, as uncovered by testing the maximum à posteriori against 0 (t(23)=-4.51, p < 0.001). Additionally, the model revealed a significant decrease in the driving input to premotor areas (t(23) > 2.71, p < 0.05) suggesting that structures other than cortical motor areas may contribute to motor fatigability.

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.

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.

Lumbosacral spinal cord functional connectivity at rest: From feasibility to reliability

Abstract In the past decade, exploration of spontaneous blood-oxygen-level-dependent (BOLD) signal fluctuations has expanded beyond the brain to include the spinal cord. While most studies have predominantly focused on the cervical region, the lumbosacral segments play a crucial role in motor control and sensory processing of the lower limbs. Addressing this gap, the aims of the current study were two-fold: first, confirming the presence and nature of organized spontaneous BOLD signals in the human lumbosacral spinal cord; second, systematically assessing the impact of various denoising strategies on signal quality and functional connectivity (FC) patterns. Given the susceptibility of spinal cord fMRI to noise, this step is pivotal to ensure the robustness of intrinsic FC. Our findings uncovered bilateral FC between the ventral and dorsal horns. Importantly, these patterns were consistently observed across denoising methods and demonstrating fair to excellent split-half temporal stability. Importantly, the evaluation of diverse denoising strategies highlighted the efficacy of physiological noise modeling (PNM)-based pipelines in cleaning the signal while preserving the strength of connectivity estimates. Together, our results provide evidence of robust FC patterns in the lumbosacral spinal cord, thereby paving the way for future studies probing caudal spinal activity.

Improving the Sensitivity of Task-Based Multi-Echo Functional Magnetic Resonance Imaging via T2* Mapping Using Synthetic Data-Driven Deep Learning.

(1) Background: Functional magnetic resonance imaging (fMRI) utilizing multi-echo gradient echo-planar imaging (ME-GE-EPI) has demonstrated higher sensitivity and stability compared to utilizing single-echo gradient echo-planar imaging (SE-GE-EPI). The direct derivation of T2* maps from fitting multi-echo data enables accurate recording of dynamic functional changes in the brain, exhibiting higher sensitivity than echo combination maps. However, the widely employed voxel-wise log-linear fitting is susceptible to inevitable noise accumulation during image acquisition. (2) Methods: This work introduced a synthetic data-driven deep learning (SD-DL) method to obtain T2* maps for multi-echo (ME) fMRI analysis. (3) Results: The experimental results showed the efficient enhancement of the temporal signal-to-noise ratio (tSNR), improved task-based blood oxygen level-dependent (BOLD) percentage signal change, and enhanced performance in multi-echo independent component analysis (MEICA) using the proposed method. (4) Conclusion: T2* maps derived from ME-fMRI data using the proposed SD-DL method exhibit enhanced BOLD sensitivity in comparison to T2* maps derived from the LLF method.

Leveraging Multi-Echo EPI to Enhance BOLD Sensitivity in Task-based Olfactory fMRI.

Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) contrast relies on gradient echo echo-planar imaging (GE-EPI) to quantify dynamic susceptibility changes associated with the hemodynamic response to neural activity. However, acquiring BOLD fMRI in human olfactory regions is particularly challenging due to their proximity to the sinuses where large susceptibility gradients induce magnetic field distortions. BOLD fMRI of the human olfactory system is further complicated by respiratory artifacts that are highly correlated with event onsets in olfactory tasks. Multi-echo EPI (ME-EPI) acquires gradient echo data at multiple echo times (TEs) during a single acquisition and can leverage signal evolution over the multiple echo times to enhance BOLD sensitivity and reduce artifactual signal contributions. In the current study, we developed a ME-EPI acquisition protocol for olfactory task-based fMRI and demonstrated significant improvement in BOLD signal sensitivity over conventional single-echo EPI (1E-EPI). The observed improvement arose from both an increase in BOLD signal changes through a T 2 * -weighted echo combination and a reduction in non-BOLD artifacts through the application of the Multi-Echo Independent Components Analysis (ME-ICA) denoising method. This study represents one of the first direct comparisons between 1E-EPI and ME-EPI in high-susceptibility regions and provides compelling evidence in favor of using ME-EPI for future task-based fMRI studies.