Effects of Sub-threshold Transcutaneous Auricular Vagus Nerve Stimulation on Cingulate Cortex and Insula Resting-state Functional Connectivity.

Transcutaneous auricular vagus nerve stimulation (taVNS), a non-invasive alternative to vagus nerve stimulation (VNS) with implantable devices, has shown promise in treating disorders such as depression, migraine, and insomnia. Studies of these disorders with resting-state functional magnetic resonance imaging (MRI) (rsfMRI) have found sustained changes in resting-state functional connectivity (rsFC) in patients treated with low frequency (1-20 Hz) taVNS. A recent study has reported reductions in pain scores in patients with rheumatoid arthritis after a 12-week treatment of high-frequency (20 kHz) sub-threshold taVNS. However, no studies to date have examined the effects of high-frequency sub-threshold taVNS on rsFC. The objective of this study was to determine whether high-frequency sub-threshold taVNS induces changes in rsFC using seed regions from the cingulate cortex and insula, brain regions that play a key role in interoception and processing of pain. With a single-blind placebo-controlled repeated measures experimental design, rsfMRI scans were acquired before and after 15 min of either sub-threshold taVNS treatment or a sham control. Significant taVNS-related changes in functional connections to the cingulate cortex were detected between the anterior cingulate cortex and right superior temporal gyrus and between the midcingulate cortex and right inferior parietal lobule. In addition, significant changes in functional connections to the insula were detected between the posterior insula and right precuneus and between the anterior insula and right cuneus gyrus. These results suggest that high-frequency sub-threshold taVNS can lead to sustained effects on the rsFC of brain regions involved in interoception and processing of pain in a cohort of healthy subjects. This study lays the foundation for future rsfMRI studies of high-frequency sub-threshold taVNS in clinical populations.

Effects of sub-threshold transcutaneous auricular vagus nerve stimulation on cerebral blood flow.

Transcutaneous auricular vagus nerve stimulation (taVNS) has shown promise as a non-invasive alternative to vagus nerve stimulation (VNS) with implantable devices, which has been used to treat drug-resistant epilepsy and treatment-resistant depression. Prior work has used functional MRI to investigate the brain response to taVNS, and more recent work has also demonstrated potential therapeutic effects of high-frequency sub-threshold taVNS in rheumatoid arthritis. However, no studies to date have measured the effects of high-frequency sub-threshold taVNS on cerebral blood flow (CBF). The objective of this study was to determine whether high-frequency (20 kHz) sub-threshold taVNS induces significant changes in CBF, a promising metric for the assessment of the sustained effects of taVNS. Arterial spin labeling (ASL) MRI scans were performed on 20 healthy subjects in a single-blind placebo-controlled repeated measures experimental design. The ASL scans were performed before and after 15 min of either sub-threshold taVNS treatment or a sham control. taVNS induced significant changes in CBF in the superior posterior cerebellum that were largely localized to bilateral Crus I and Crus II. Post hoc analyses showed that the changes were driven by a treatment-related decrease in CBF. Fifteen minutes of high-frequency sub-threshold taVNS can induce sustained CBF decreases in the bilateral posterior cerebellum in a cohort of healthy subjects. This study lays the foundation for future studies in clinical populations, and also supports the use of ASL measures of CBF for the assessment of the sustained effects of taVNS.

Author Correction: Distinct thalamocortical network dynamics are associated with the pathophysiology of chronic low back pain.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Distinct thalamocortical network dynamics are associated with the pathophysiology of chronic low back pain.

Thalamocortical dysrhythmia is a key pathology of chronic neuropathic pain, but few studies have investigated thalamocortical networks in chronic low back pain (cLBP) given its non-specific etiology and complexity. Using fMRI, we propose an analytical pipeline to identify abnormal thalamocortical network dynamics in cLBP patients and validate the findings in two independent cohorts. We first identify two reoccurring dynamic connectivity states and their associations with chronic and temporary pain. Further analyses show that cLBP patients have abnormal connectivity between the ventral lateral/posterolateral nucleus (VL/VPL) and postcentral gyrus (PoCG) and between the dorsal/ventral medial nucleus and insula in the less frequent connectivity state, and temporary pain exacerbation alters connectivity between the VL/VPL and PoCG and the default mode network in the more frequent connectivity state. These results extend current findings on thalamocortical dysfunction and dysrhythmia in chronic pain and demonstrate that cLBP pathophysiology and clinical pain intensity are associated with distinct thalamocortical network dynamics.

Vigilance Effects in Resting-State fMRI.

Measures of resting-state functional magnetic resonance imaging (rsfMRI) activity have been shown to be sensitive to cognitive function and disease state. However, there is growing evidence that variations in vigilance can lead to pronounced and spatially widespread differences in resting-state brain activity. Unless properly accounted for, differences in vigilance can give rise to changes in resting-state activity that can be misinterpreted as primary cognitive or disease-related effects. In this paper, we examine in detail the link between vigilance and rsfMRI measures, such as signal variance and functional connectivity. We consider how state changes due to factors such as caffeine and sleep deprivation affect both vigilance and rsfMRI measures and review emerging approaches and methodological challenges for the estimation and interpretation of vigilance effects.

The Effects of Global Signal Regression on Estimates of Resting-State Blood Oxygen-Level-Dependent Functional Magnetic Resonance Imaging and Electroencephalogram Vigilance Correlations.

Global signal regression (GSR) is a commonly used although controversial preprocessing approach in the analysis of resting-state blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data. Although the effects of GSR on resting-state functional connectivity measures have received much attention, there has been relatively little attention devoted to its effects on studies looking at the relationship between resting-state BOLD measures and independent measures of brain activity. In this study, we used simultaneously acquired electroencephalogram (EEG)-fMRI data in humans to examine the effects of GSR on the correlation between resting-state BOLD fluctuations and EEG vigilance measures. We show that GSR leads to a positive shift in the correlation between the BOLD and vigilance measures. This shift leads to a reduction in the spatial extent of negative correlations in widespread brain areas, including the visual cortex, but leads to the appearance of positive correlations in other areas, such as the cingulate gyrus. The results obtained using GSR are consistent with those of a temporal censoring process in which the correlation is computed using a temporal subset of the data. Since the data from these retained time points are unaffected by the censoring process, this finding suggests that the positive correlations in cingulate gyrus are not simply an artifact of GSR.

Template-based prediction of vigilance fluctuations in resting-state fMRI.

Changes in vigilance or alertness during a typical resting state fMRI scan are inevitable and have been found to affect measures of functional brain connectivity. Since it is not often feasible to monitor vigilance with EEG during fMRI scans, it would be of great value to have methods for estimating vigilance levels from fMRI data alone. A recent study, conducted in macaque monkeys, proposed a template-based approach for fMRI-based estimation of vigilance fluctuations. Here, we use simultaneously acquired EEG/fMRI data to investigate whether the same template-based approach can be employed to estimate vigilance fluctuations of awake humans across different resting-state conditions. We first demonstrate that the spatial pattern of correlations between EEG-defined vigilance and fMRI in our data is consistent with the previous literature. Notably, however, we observed a significant difference between the eyes-closed (EC) and eyes-open (EO) conditions, finding stronger negative correlations with vigilance in regions forming the default mode network and higher positive correlations in thalamus and insula in the EC condition when compared to the EO condition. Taking these correlation maps as "templates" for vigilance estimation, we found that the template-based approach produced fMRI-based vigilance estimates that were significantly correlated with EEG-based vigilance measures, indicating its generalizability from macaques to humans. We also demonstrate that the performance of this method was related to the overall amount of variability in a subject's vigilance state, and that the template-based approach outperformed the use of the global signal as a vigilance estimator. In addition, we show that the template-based approach can be used to estimate the variability across scans in the amplitude of the vigilance fluctuations. We discuss the benefits and tradeoffs of using the template-based approach in future fMRI studies.

The global signal in fMRI: Nuisance or Information?

The global signal is widely used as a regressor or normalization factor for removing the effects of global variations in the analysis of functional magnetic resonance imaging (fMRI) studies. However, there is considerable controversy over its use because of the potential bias that can be introduced when it is applied to the analysis of both task-related and resting-state fMRI studies. In this paper we take a closer look at the global signal, examining in detail the various sources that can contribute to the signal. For the most part, the global signal has been treated as a nuisance term, but there is growing evidence that it may also contain valuable information. We also examine the various ways that the global signal has been used in the analysis of fMRI data, including global signal regression, global signal subtraction, and global signal normalization. Furthermore, we describe new ways for understanding the effects of global signal regression and its relation to the other approaches.

Underconnected, But Not Broken? Dynamic Functional Connectivity MRI Shows Underconnectivity in Autism Is Linked to Increased Intra-Individual Variability Across Time.

Autism spectrum disorder (ASD) is characterized by core sociocommunicative impairments. Atypical intrinsic functional connectivity (iFC) has been reported in numerous studies of ASD. A majority of findings has indicated long-distance underconnectivity. However, fMRI studies have thus far exclusively examined static iFC across several minutes of scanning. We examined temporal variability of iFC, using sliding window analyses in selected high-quality (low-motion) consortium datasets from 76 ASD and 76 matched typically developing (TD) participants (Study 1) and in-house data from 32 ASD and 32 TD participants. Mean iFC and standard deviation of the sliding window correlation (SD-iFC) were computed for regions of interest (ROIs) from default mode and salience networks, as well as amygdala and thalamus. In both studies, ROI pairings with significant underconnectivity (ASD

A pilot study investigating changes in neural processing after mindfulness training in elite athletes.

The ability to pay close attention to the present moment can be a crucial factor for performing well in a competitive situation. Training mindfulness is one approach to potentially improve elite athletes' ability to focus their attention on the present moment. However, virtually nothing is known about whether these types of interventions alter neural systems that are important for optimal performance. This pilot study examined whether an intervention aimed at improving mindfulness [Mindful Performance Enhancement, Awareness and Knowledge (mPEAK)] changes neural activation patterns during an interoceptive challenge. Participants completed a task involving anticipation and experience of loaded breathing during functional magnetic resonance imaging recording. There were five main results following mPEAK training: (1) elite athletes self-reported higher levels of interoceptive awareness and mindfulness and lower levels of alexithymia; (2) greater insula and anterior cingulate cortex (ACC) activation during anticipation and post-breathing load conditions; (3) increased ACC activation during the anticipation condition was associated with increased scores on the describing subscale of the Five Facet Mindfulness Questionnaire; (4) increased insula activation during the post-load condition was associated with decreases in the Toronto Alexithymia Scale identifying feelings subscale; (5) decreased resting state functional connectivity between the PCC and the right medial frontal cortex and the ACC. Taken together, this pilot study suggests that mPEAK training may lead to increased attention to bodily signals and greater neural processing during the anticipation and recovery from interoceptive perturbations. This association between attention to and processing of interoceptive afferents may result in greater adaptation during stressful situations in elite athletes.

Game controller modification for fMRI hyperscanning experiments in a cooperative virtual reality environment.

Hyperscanning, an emerging technique in which data from multiple interacting subjects' brains are simultaneously recorded, has become an increasingly popular way to address complex topics, such as "theory of mind." However, most previous fMRI hyperscanning experiments have been limited to abstract social interactions (e.g. phone conversations). Our new method utilizes a virtual reality (VR) environment used for military training, Virtual Battlespace 2 (VBS2), to create realistic avatar-avatar interactions and cooperative tasks. To control the virtual avatar, subjects use a MRI compatible Playstation 3 game controller, modified by removing all extraneous metal components and replacing any necessary ones with 3D printed plastic models. Control of both scanners' operation is initiated by a VBS2 plugin to sync scanner time to the known time within the VR environment. Our modifications include:•Modification of game controller to be MRI compatible.•Design of VBS2 virtual environment for cooperative interactions.•Syncing two MRI machines for simultaneous recording.

Subject specific BOLD fMRI respiratory and cardiac response functions obtained from global signal.

Subtle changes in either breathing pattern or cardiac pulse rate alter blood oxygen level dependent functional magnetic resonance imaging signal (BOLD fMRI). This is problematic because such fluctuations could possibly not be related to underlying neuronal activations of interest but instead the source of physiological noise. Several methods have been proposed to eliminate physiological noise in BOLD fMRI data. One such method is to derive a template based on average multi-subject data for respiratory response function (RRF) and cardiac response function (CRF) by simultaneously utilizing an external recording of cardiac and respiratory waveforms with the fMRI. Standard templates can then be used to model, map, and remove respiration and cardiac fluctuations from fMRI data. Utilizing these does not, however, account for intra-subject variations in physiological response. Thus, performing a more individualized approach for single subject physiological noise correction becomes more desirable, especially for clinical purposes. Here we propose a novel approach that employs subject-specific RRF and CRF response functions obtained from the whole brain or brain tissue-specific global signals (GS). Averaging multiple voxels in global signal computation ensures physiological noise dominance over thermal and system noise in even high-spatial-resolution fMRI data, making the GS suitable for deriving robust estimations of both RRF and CRF for individual subjects. Using these individualized response functions instead of standard templates based on multi-subject averages judiciously removes physiological noise from the data, assuming that there is minimal neuronal contribution in the derived individualized filters. Subject-specific physiological response functions obtained from the GS better maps individuals' physiological characteristics.