Relationships Between Brain Intrinsic Connectivity Networks and Measures of Cognition and Emotion: A Study of the Human Connectome Project Data.

Growing evidence suggests that variations in cognitive and emotional behavior are associated with variations in brain function. To achieve a more comprehensive assessment, data-driven techniques, specifically independent component analysis (ICA), can be employed to generate outcome variables that describe unique but complementary aspects of functional connectivity within and between networks. In this study, resting-state fMRI and behavioral data were collected from 50 healthy participants in the Human Connectome Project. The neuropsychological battery evaluated performance in various domains, including episodic memory, fluid intelligence, attention, working memory, executive function, cognitive flexibility, inhibition, and processing speed. Emotional measures were also included to assess emotion recognition and negative affects (sadness, fear, and anger). A multivariate approach was adopted to evaluate the association between cognitive abilities and emotional correlates on spatiotemporal features of intrinsic connectivity networks (ICNs). The results were explored at a false discovery rate-corrected threshold of p < 0.05. There was a significant positive association between within-network connectivity of the left central executive network (CEN) and inhibitory control and attention, and a significant negative association between within-network connectivity of the right CEN and episodic memory. Furthermore, increased within-network connectivity of the default-mode network (DMN) was linked to higher fluid intelligence, while within-network connectivity in the salience network (SN) and dorsal attention network (DAN) was associated with cognitive flexibility. Anger was found to be significantly related to increased functional network connectivity between SN and CEN. Sadness and fear were associated with increased within-network connectivity of the right CEN. Additionally, fear was associated with low-frequency spectral power in SN and DMN. These findings offer new insights into the intricate relation between ICN features and cognitive and emotional functions.

Altered Dynamic Functional Network Connectivity in Healthy Adults with Acute Pain: Findings from the Human Connectome Project.

Characterizing the neural signature of pain and its modulation is critical for assessing treatment efficacy and conducting translational clinical research. However, the dynamics of pain processing in the brain have remained largely unknown. In this study, we employed independent component analysis (ICA) as a data-driven clustering method on resting-state functional magnetic resonance imaging (fMRI) to obtain intrinsic connectivity networks (ICNs) in a cohort of healthy adults from the Human Connectome Project (HCP) who were identified as having acute pain. We examined the temporal dynamic functional network connectivity (dFNC) with sliding time window correlation and k-means clustering, and compared dFNC state properties and meta-state metrics between groups. Results showed that acute pain had a significant impact on dFNC in a common connectivity state (dynamic state 5) among several ICN pairs, including the salience network, default mode network, central executive, dorsal attention networks, and basal ganglia (false discovery rate [FDR]-corrected p of 0.05). Furthermore, healthy adults with and without acute pain exhibited differences in mean dwell time (dynamic state 3), which indicated that individuals with acute pain spent more time in particular states than those without pain. Meta-state dynamic analysis further indicated significant group differences in the number of states (i.e., unique time windows for each subject), changes between states (i.e., number of times each subject changes from one meta-state to other), and total travelled distances. These preliminary results provide new information about time-varying properties of pain states related to acute pain and advocate for further state-based analyses of pain for future pain biomarker discovery and development.

An ICA Investigation into the Effect of Physiological Noise Correction on Dynamic Functional Network Connectivity and Meta-state Metrics.

Physiological fluctuations such as cardiac pulsations (heart rate) and respiratory rhythm (breathing) have been studied in the resting state functional magnetic resonance imaging (rs-fMRI) studies as the potential sources of confounds in functional connectivity. Independent component analysis (ICA) provides a data driven approach to investigate functional connectivity at the network level. However, the effect of physiological noise correction on the dynamic of ICA-derived networks has not yet been studied. The goal of this study was to investigate the effect of retrospective correction of cardiorespiratory artifacts on the time-varying aspects of functional network connectivity. Blood oxygenation-level dependent (BOLD) rs-fMRI data were collected from healthy subjects using a 3.0T MRI scanner. Whole-brain dynamic functional network connectivity (dFNC) was computed using sliding time window correlation, and k-means clustering of windowed correlation matrices. Results showed significant effects of physiological denoising on dFNC between several network pairs in particular the subcortical, and cognitive/attention networks (false discovery rate [FDR]-corrected p < 0.01). Meta-state dynamics further revealed significant changes in the number of unique windows for each subject, number of times each subject changes from one meta-state to other, and sum of L1 distances between successive meta-states. In conclusion, removal of artifacts is important for achieving reliable fMRI results, however a more cautious approach should be adapted in regressing such "noise" in ICA functional connectivity approach. More experiments are needed to investigate impact of denoising on dFNC especially across different datasets.

An ICA Investigation into the Effect of Physiological Noise Correction on Dimensionality and Spatial Maps of Intrinsic Connectivity Networks.

Physiological processes such as cardiac pulsations and respiration can induce signal modulations in functional magnetic resonance imaging (fMRI) time series, and confound inferences made about neural processing from analyses of the blood oxygenation level-dependent (BOLD) signals. Retrospective image space correction of physiological noise (RETROICOR) is a widely used approach to reduce physiological signals in data. Independent component analysis (ICA) is a valuable blind source separation method for analyzing brain networks, referred to as intrinsic connectivity networks (ICNs). Previously, we showed that temporal properties of the ICA-derived networks such as spectral power and functional network connectivity could be impacted by RETROICOR corrections. The goal of this study is to investigate the effect of retrospective correction of physiological artifacts on the ICA dimensionality (model order) and intensities of ICN spatial maps. To this aim, brain BOLD fMRI, heartbeat, and respiration were measured in 22 healthy subjects during resting state. ICA dimensionality was estimated using minimum description length (MDL) based on i.i.d. data samples and smoothness FWHM kernel, and entropy-rate based order selection by finite memory length model (ER-FM) and autoregressive model (ER-AR). Differences in spatial maps between the raw and denoised data were compared using the paired t-test and false discovery rate (FDR) thresholding was used to correct for multiple comparisons. Results showed that ICA dimensionality was greater in the raw data compared to the denoised data. Significant differences were found in the intensities of spatial maps for three ICNs: basal ganglia, precuneus, and frontal network. These preliminary results indicate that the retrospective physiological noise correction can induce change in the resting state spatial map intensity related to the within-network connectivity. More research is needed to understand this effect.

The Influence of Spatial Smoothing Kernel Size on the Temporal Features of Intrinsic Connectivity Networks.

Spatial smoothing is a common preprocessing step in the analysis of functional magnetic resonance imaging (fMRI) data. However, little is known about the effect of spatial smoothing kernel size on the temporal properties of functional brain networks. This study presents a pilot investigation on the influence of spatial smoothing using independent component analysis (ICA) as a data-driven technique to extract functional networks of brain in the form of intrinsic connectivity networks (ICNs). BOLD resting state fMRI data were collected from 22 healthy subjects on a 3.0 T MRI scanner. 3D spatial smoothing was applied using a Gaussian filter with full width at half maximum (FWHM) kernel sizes of 4 mm, 8 mm, and 12 mm in the preprocessing step. Group ICA with the Infomax algorithm was performed at 75-IC decomposition. Network temporal features including functional network connectivity (FNC) and BOLD power spectra were calculated and compared pairwise using a paired t-test with a false discovery rate (FDR) correction for multiple comparisons. Results revealed robust effects of smoothing kernel size on FNC measures of most ICNs, largely indicating a decrease in inter-network connectivity as the smoothing kernel size decreased. Power spectra analysis showed increased high-frequency power (0.15 - 0.25 Hz) but decreased low-frequency power (0.01 - 0.10 Hz) with a decrease in the smoothing kernel size (corrected p< 0.01). These findings provide a preliminary observation on the effect of spatial smoothing kernel size on the FNC and power spectra.

The Influence of Spatial Smoothing Kernel Size on the Whole-brain Dynamic Functional Network Connectivity and Meta-state Parameters.

In functional magnetic resonance imaging (fMRI), spatial smoothing procedure is generally a stable step in the preprocessing stream. Previous research (including ours) suggested dependency of the static functional connectivity on the size of the spatial smoothing kernel size. But its impact on the time-varying patterns of functional connectivity has not been investigated. Here, we sought to identify the effects of spatial smoothing on brain dynamics by performing dynamic functional network connectivity (dFNC) and meta-state analysis, a unique approach capable of examining a higher-dimensional temporal dynamism of whole-brain functional connectivity. Gaussian smoothing kernel with different widths at half of the maximum of the height of the Gaussian (4, 8, and 12 mm FWHM) were used during preprocessing prior to the group independent component analysis (ICA) with a relatively high model order of 75. dFNC was conducted using the sliding-time window approach and k-means clustering algorithm. Meta-state dynamics method was performed by reducing the number of windowed FNC correlations using principal components analysis (PCA), temporal and spatial ICA and k-means. Results revealed robust effects of spatial smoothing on the connectivity dynamics of several network pairs including a variety of cognitive/attention networks in a connectivity state with the highest occurrence (FDR corrected-p < 0.01). Meta-state analyses indicated significant changes in meta-state metrics including the number of meta-states, meta-state changes, meta-state span, and the total distance. These changes were particularly pronounced when we compared resting state data smoothed with 8 vs. 12 mm FWHM. Our preliminary findings give insights into the effects of spatial smoothing kernel size on the dynamics of functional connectivity and its consequences on meta-state parameters. It also provides further indication of the importance of evaluating variance associated with preprocessing steps on analysis outcomes.

Examining the Influence of Spatial Smoothing on Spatiotemporal Features of Intrinsic Connectivity Networks at Low ICA Model Order.

Using a relatively high model order of independent component analysis (ICA with 75 ICs) of functional magnetic resonance imaging (fMRI) data, we have reported a clear effect of spatial smoothing Gaussian kernel size on spatiotemporal properties of intrinsic connectivity networks (ICNs). However, many if not the majority of ICA fMRI studies are usually performed at low model order, e.g., 20-IC decomposition, as such low order is generally enough to extract the few networks of interest such as the default-mode network (DMN). The aim of this study is to investigate if we can replicate the spatial smoothing effects on spatiotemporal features of ICNs at low ICA model order. Same resting state fMRI data that we used with 75-IC analysis were used here. Spatial smoothing using an isotropic Gaussian filter kernel with full width at half maximum (FWHM) of 4, 8, and 12 mm was applied during preprocessing. ICNs were identified from 20-IC decomposition and evaluated in terms of three primary features: spatial map intensity, functional network connectivity (FNC), and power spectra. The results identified similar effects of spatial smoothing on spatial map intensities and power spectra at p < 0.01, false discovery rate (FDR) corrected for multiple comparisons. Reduced spatial smoothing kernel size resulted in decreased spatial map intensities as well as a generally decreased low-frequency power (0.01 - 0.10 Hz) but increased high-frequency power (0.15 - 0.25 Hz). FNC, however, did not show a uniform change in correlation values with the size of smoothing kernel. Notably, FNC between DMNs decreased but FNC between central executive and visual networks increased with an increase in smoothing kernel size. These preliminary findings confirm spatial smoothing influences ICN features regardless of model order. The discussion focuses on differences between observed changes at low and high ICA model orders.

The Effects of Cardiorespiratory and Motor Skill Fitness on Intrinsic Functional Connectivity of Neural Networks in Individuals with Parkinson's Disease.

BACKGROUND: Studies in aging older adults have shown the positive association between cognition and exercise related fitness, particularly cardiorespiratory fitness. These reports have also demonstrated the association of high cardiorespiratory fitness, as well as other types of fitness, on the reversal of age-related decline in neural network connectivity, highlighting the potential role of fitness on age- and disease-related brain changes. While the clinical benefits of exercise are well-documented in Parkinson's disease (PD), the extent to which cardiorespiratory fitness (assessed by estimated VO2max testing) or motor skill fitness (assessed by the Physical Performance Test (PPT)) affects neural network connectivity in PD remains to be investigated. The purpose of this study was to explore the hypothesis that higher fitness level is associated with an increase in the intrinsic network connectivity of cognitive networks commonly affected in PD. METHODS: In this cross-sectional resting state fMRI, we used a multivariate statistical approach based on high-dimensional independent component analysis (ICA) to investigate the association between two independent fitness metrics (estimated VO2max and PPT) and resting state network connectivity. RESULTS: We found that increased estimated VO2max was associated with increased within network connectivity in cognitive networks known to be impaired in PD, including those sub-serving memory and executive function. There was a similar trend for high levels of PPT to be associated with increased within network connectivity in distinct resting state networks. The between functional network connectivity analysis revealed that cardiorespiratory fitness was associated with increased functional connectivity between somatosensory motor network and several cognitive networks sub-serving memory, attention, and executive function. CONCLUSION: This study provides important empirical data supporting the potential association between two forms of fitness and multiple resting state networks impacting PD cognition. Linking fitness to circuit specific modulation of resting state network connectivity will help establish a neural basis for the positive effects of fitness and specific exercise modalities and provide a foundation to identify underlying mechanisms to promote repair.

Thalamic volume mediates associations between cardiorespiratory fitness (VO2max) and cognition in Parkinson's disease.

INTRODUCTION: Cognitive deficits occur in Parkinson's disease (PD). Cardiorespiratory fitness (CRF) is associated with better cognitive performance in aging especially in executive function (EF) and memory. The association between CRF and cognitive performance is understudied in people with PD. Brain structures underlying associations also remains unknown. This cross-sectional study examined the associations between CRF and cognitive performance in PD. We also examined associations between CRF and brain structures impacted in PD. Mediation analysis were conducted to examine whether brain structures impacted in PD mediate putative associations between CRF and cognitive performance. METHODS: Individuals with PD (N = 33) underwent magnetic resonance imaging (MRI), CRF evaluation (estimated VO2max), and neuropsychological assessment. Composite cognitive scores of episodic memory, EF, attention, language, and visuospatial functioning were generated. Structural equation models were constructed to examine whether MRI volume estimates (thalamus and pallidum) mediated associations between CRF and cognitive performance (adjusting for age, education, PD disease duration, sex, MDS-UPDRS motor score, and total intracranial volume). RESULTS: Higher CRF was associated with better episodic memory (Standardized ß = 0.391; p = 0.008), EF (Standardized ß = 0.324; p = 0.025), and visuospatial performance (Standardized ß = 0.570; p = 0.005). Higher CRF was associated with larger thalamic (Standardized ß = 0.722; p = 0.004) and pallidum (Standardized ß = 0.635; p = 0.004) volumes. Thalamic volume mediated the association between higher CRF and better EF (Indirect effect = 0.309) and episodic memory (Indirect effect = 0.209) performance (p < 0.05). The pallidum did not significantly mediate associations between CRF and cognitive outcomes. CONCLUSION: The thalamus plays an important role in the association between CRF and both EF and episodic memory in PD.

Effects of Visceral Interoception on Topological Properties of the Brain - A Graph Theory Analysis of Resting state fMRI.

Recent neuroimaging studies have employed graph theory as a data-driven approach to describe topological organization of the brain under different neurological disorders or task conditions and across life span. In this exploratory study, we tested whether subtle differences in interoception related to intravesical fullness can alter brain topological architecture in healthy participants. 17 right-handed women underwent a series of resting state fMRI scans that included catheterization and partial bladder filling. Using a whole brain regions of interest (ROIs), we computed several graph theory metrics to assess the efficiency of brain-wide information exchange. Results showed that brain network's topological properties significantly changed in many brain regions when we binary compared different interoceptive resting state conditions. Notably, we observed changes in global efficiency in the salience network, the central executive network, anterior dorsal attention network and the posterior default-mode network (DMN) as bladder became full and interoceptive signals intensified. Moreover, degree (the number of connections for each node), and betweenness centrality (how connected a particular region is to other regions) differed between the empty bladder, the catheterized empty bladder, and the catheterized and partially filled bladder. Comparing resting state data before and after an interoceptive task (repeated intravesical infusion and drainage) further showed increased average path length for the salience networks and decreased clustering coefficient of the DMN. These results suggest visceral interoception influences brain topological properties of resting state networks.

Impact of Intravesical Cold Sensation on Functional Network Connectivity Estimated Using ICA at Rest & During Interoceptive Task.

Afferent nerves that carry interoceptive signals from the viscera to the brain include Aδ and C-fibers. Previously, we examined the effects of detrusor distention (conveyed mainly by Aδ fibers) on the static functional network connectivity (FNC) of the brain using independent component analysis (ICA) of fMRI time series. In the present study, we investigate the impact of intravesical cold sensation (thought to be conveyed by C-fibers) on brain FNC using similar ICA approach. Thirteen healthy women were scanned on a 3.0T MRI scanner during a resting state scan and an intravesical cold sensation task fMRI. High dimensional ICA (n = 75) were used to decompose the fMRI data into several intrinsic connectivity networks (ICNs) including the default-mode (DMN), subcortical (SCN; amygdala, thalamus), salience (SN), central executive (CEN), sensorimotor (SMN), and cerebellar/brainstem (CBN) networks. Results demonstrate significant FNC differences in several ICN pairs primarily between the SCN and cognitive networks such as CEN, as well as between SN and CBN and DMN when intravesical cold water condition was compared to rest (FDR-corrected p-value of 0.05). Significant increases in FNC between CBN and between SMN were also observed during interoceptive condition. The results indicate significant impact of Aδ and C-fiber-originated interoceptive signals on the brain connectivity when compared to the baseline rest.

Mild cognitive impairment, psychiatric symptoms, and executive functioning in patients with Parkinson's disease.

OBJECTIVE: Mild cognitive impairment (MCI) and psychiatric symptoms (anxiety, depression, and apathy) are common in Parkinson's disease (PD). While studies have supported the association between psychiatric symptoms and cognitive performance in PD, it is unclear if the magnitude of link between psychiatric symptoms and cognitive health is stronger by MCI status. The purpose of this study was to examine the association between cognitive performance and psychiatric symptoms in PD and whether MCI status moderates this association. METHODS/DESIGN: Participants (N = 187) completed a comprehensive neuropsychological assessment that included measures of attention, language, executive function (EF), visuospatial ability, episodic memory, and psychiatric symptoms. Participants were classified as PD-MCI (N = 73) or PD-normal cognition (NC; N = 114). Linear regression analyses were conducted to examine the association between psychiatric symptoms and cognitive performance and the moderating effect of PD-MCI status. RESULTS: There were no differences in mean psychiatric symptoms between PD-MCI and PD-NC. Psychiatric symptoms were predominantly associated with worse EF. The magnitude of the association between anxiety and worse EF was larger in participants with PD-MCI compared with PD-NC. A multivariable regression analysis examining the independent contributions of each symptom demonstrated the most robust association between EF and anxiety. CONCLUSIONS: Symptoms of anxiety, depression, and apathy are associated with worse executive functioning in individuals with PD. PD-MCI may be important in moderating the association between cognitive performance, specifically anxiety, and EF. Factors that promote cognitive resilience may serve as key therapeutic modalities in managing neuropsychiatric symptoms in PD.

The Nature of the Task Influences Intrinsic Connectivity Networks: An Exploratory fMRI Study in Healthy Subjects.

Task-induced variations in neural activity and their effects on the topological architecture of intrinsic connectivity networks (ICNs) of the brain are still a matter of ongoing research. In this exploratory study, we used spatial independent component analysis (ICA) as a data-driven technique to characterize ICNs related to two different tasks in healthy subjects who underwent 3T blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI). The fMRI tasks consisted of (a) a viscerosensory stimulation of an internal organ (interoceptive task), and (b) passive viewing of emotionally expressive faces and pictures from the International Affective Picture System (exteroceptive emotion task). Comparison of the network volumes and peak activations during each task condition demonstrated that changes in ICN volume and corresponding peak activation differed between the interoceptive and exteroceptive emotion tasks when compared to the baseline rest. Further, salience network was the most task-activated ICN for both fMRI task conditions. However, different spatial characteristics were observed between the salience networks derived from the interoceptive task and the one derived from the exteroceptive emotion task. This study is a step in the direction of better understanding the influence of task condition on ICN topology. Future research with a larger sample size and task variations should delve deeper into what aspects of network topology really matter, with further investigations regarding the observed differences due to gender and age.

Cold Water Pressor Test Differentially Modulates Functional Network Connectivity in Fibromyalgia Patients Compared with Healthy Controls.

Fibromyalgia is a multifaceted chronic pain condition of unknown etiology. Conditioned pain modulation (CPM) such as cold water pressor test of the foot, is widely documented as being disrupted in patients with fibromyalgia. To date, the mechanisms underlying such dysregulation of the descending control of pain in fibromyalgia remain poorly understood. In this study, we used ICA-based network analysis to comprehensively compare differences in functional network connectivity among relevant (nonartifactual) intrinsic connectivity brain networks during the resting state before and after cold pressor test in patients with fibromyalgia and healthy controls. The results revealed significant differences in functional connectivity between the two groups that included the networks that integrate cognitive control and attention systems with memory, emotion and brainstem regions. Specifically, functional connectivity involving central executive network was absent in patients with fibromyalgia compared with controls. Patients showed significant functional connectivity changes involving subcortical and brainstem networks with the sensorimotor and dorsal attention networks. Accordingly, aberrant CPM in patients with fibromyalgia may be due to the differences in functional connectivity involving the subcortical/brainstem regions, and is facilitated by the recruitment of the dorsal attention network in lieu of the central executive network. Future research replicating the present findings with larger sample size can shed more light on neurobiology of endogenous pain modulation in fibromyalgia.

Characterizing the Effects of MR Image Quality Metrics on Intrinsic Connectivity Brain Networks: A Multivariate Approach.

Motion-induced artifact detection has become a fixture in the assessment of functional magnetic resonance imaging (fMRI) quality control. However, the effects of other MR image quality (IQ) metrics on intrinsic connectivity brain networks are largely unexplored. Accordingly, we report herein the initial assessment of the effects of a comprehensive list of IQ metrics on resting state networks using a multivariate analysis of covariance (MANCOVA) approach based on high-order spatial independent component analysis (ICA). Three categories of MR IQ metrics were considered: (1) metrics for artifacts including the AFNI outlier ratio and quality index, framewise displacement, and ghost to signal ratio, (2) metrics for the temporal quality of MRI data including the temporal framewise change in global BOLD signals (DVARS), global correlation of time-series, and temporal signal to noise ratio, (3) metrics for the structural quality of MRI data including the entropy focus criterion, foreground-background energy ratio, full-width half maximum smoothness, and static signal to noise ratio. After FDR-correction for multiple comparisons, results showed significant effects of the static and temporal signal to noise ratios on the spatial map intensities of the basal ganglia, default-mode and cerebellar networks. AFNI outlier ratio, framewise displacement and DVARS exhibited significant effects on the BOLD power spectra of sensorimotor networks. The global correlation of time-series displayed wide-spread modulation of the spectral power in most networks. Further investigations of the effect of IQ metrics on the characteristics of intrinsic connectivity brain networks allow more accurate interpretation of the fMRI results.

Measuring the Influence of Physiological Noise Corrections on ICA Derived Intrinsic Connectivity Brain Networks in Rest and Task fMRI.

Physiological noise corrections using RETROICOR algorithm has been shown to increase signal sensitivity in resting state networks such as the default-mode network. However, independent component analysis (ICA)-based network approach may suffer from such corrections especially if there is any overlap between two sources in the decomposition domain. To address the extent the physiological noise corrections may impact ICA derived intrinsic connectivity brain networks, we measured network features including functional network connectivity (FNC), power spectra, and network spatial maps in the resting state and task functional magnetic resonance imaging (fMRI) data that were acquired in the same visit from a group of healthy volunteers. Statistical analysis showed functional connectivity between several networks were significantly changed after RETROICOR corrections in both rest and task fMRI. Significant FNC alterations were found in the subcortical, basal ganglia, salience, and default-mode networks. Power spectra analysis showed a trend toward lower power spectra in the subcortical and salience networks at [0.20 and 0.24] Hz after RETROICOR corrections in both rest and task fMRI. Furthermore, physiological noise corrections led to volumetric decrease in the resting state networks that included the subcortical, basal ganglia, salience, and default-mode networks, and volumetric enlargement in the sensorimotor and cerebellar networks. In task fMRI data, physiological noise corrections generally resulted in the expansion of networks except for task-activated networks including the anterior salience, central executive, dorsal attention, and cerebellar networks. If confirmed with larger sample sizes, these results suggest that physiological noise corrections alter some network features, and that such alterations are different between resting state and task fMRI data.

Investigating the BOLD spectral power of the intrinsic connectivity networks in fibromyalgia patients: A resting-state fMRI study.

Recent advances in multivariate statistical analysis of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) have provided novel insights into the network organization of the human brain. Here, we applied group independent component analysis, a well-established approach for detecting brain intrinsic connectivity networks, to examine the spontaneous BOLD fluctuations in patients with fibromyalgia and healthy controls before and after exposure to a stressor. The BOLD spectral power characteristics of component time courses were calculated using the fast Fourier transform (FFT) algorithm, and group comparison was performed at six frequency bins between 0 and 0.24 Hz at 0.04 Hz intervals. Relative to controls, patients with fibromyalgia displayed significant BOLD spectral power differences in the default-mode, salience, and subcortical networks at the baseline level (PBon ferroni-corrected <; 0.05). Multivariate analysis of covariance (MANCOVA) further revealed significant effects of the cold water temperature, and pain rating on the spectral power of the sensorimotor, salience, and prefrontal networks, while the diagnosis of fibromyalgia influenced the BOLD spectral power of the salience and subcortical networks (PFDR-corrected <; 0.05). Since the BOLD spectral power reflects the degree of fluctuations within a network, future studies of the correlation between BOLD spectral power and pain processing can cast additional light on the nature of the central nervous system dysfunction in patients with chronic pain syndromes.

A novel infusion-drainage device to assess lower urinary tract function in neuro-imaging.

OBJECTIVE: To evaluate the applicability and precision of a novel infusion-drainage device (IDD) for standardized filling paradigms in neuro-urology and functional magnetic resonance imaging (fMRI) studies of lower urinary tract (LUT) function/dysfunction. SUBJECTS/PATIENTS AND METHODS: The IDD is based on electrohydrostatic actuation which was previously proven feasible in a prototype setup. The current design includes hydraulic cylinders and a motorized slider to provide force and motion. Methodological aspects have been assessed in a technical application laboratory as well as in healthy subjects (n=33) and patients with LUT dysfunction (n=3) undergoing fMRI during bladder stimulation. After catheterization, the bladder was pre-filled until a persistent desire to void was reported by each subject. The scan paradigm comprised automated, repetitive bladder filling and withdrawal of 100 mL body warm (37 °C) saline, interleaved with rest and sensation rating. Neuroimaging data were analysed using Statistical Parametric Mapping version 12 (SMP12). RESULTS: Volume delivery accuracy was between 99.1±1.2% and 99.9±0.2%, for different flow rates and volumes. Magnetic resonance (MR) compatibility was demonstrated by a small decrease in signal-to-noise ratio (SNR), i.e. 1.13% for anatomical and 0.54% for functional scans, and a decrease of 1.76% for time-variant SNR. Automated, repetitive bladder-filling elicited robust (P = 0.05, family-wise error corrected) brain activity in areas previously reported to be involved in supraspinal LUT control. There was a high synchronism between the LUT stimulation and the blood oxygenation level-dependent (BOLD) signal changes in such areas. CONCLUSION: We were able to develop an MR-compatible and MR-synchronized IDD to routinely stimulate the LUT during fMRI in a standardized manner. The device provides LUT stimulation at high system accuracy resulting in significant supraspinal BOLD signal changes in interoceptive and LUT control areas in synchronicity to the applied stimuli. The IDD is commercially available, portable and multi-configurable. Such a device may help to improve precision and standardization of LUT tasks in neuro-imaging studies on supraspinal LUT control, and may therefore facilitate multi-site studies and comparability between different LUT investigations in the future.

Design and Application of a New Automated Fluidic Visceral Stimulation Device for Human fMRI Studies of Interoception.

Mapping the brain centers that mediate the sensory-perceptual processing of visceral afferent signals arising from the body (i.e., interoception) is useful both for characterizing normal brain activity and for understanding clinical disorders related to abnormal processing of visceral sensation. Here, we report a novel closed-system, electrohydrostatically driven master-slave device that was designed and constructed for delivering controlled fluidic stimulations of visceral organs and inner cavities of the human body within the confines of a 3T magnetic resonance imaging (MRI) scanner. The design concept and performance of the device in the MRI environment are described. In addition, the device was applied during a functional MRI (fMRI) investigation of visceral stimulation related to detrusor distention in two representative subjects to verify its feasibility in humans. System evaluation tests demonstrate that the device is MR-compatible with negligible impact on imaging quality [static signal-to-noise ratio (SNR) loss <2.5% and temporal SNR loss <3.5%], and has an accuracy of 99.68% for flow rate and 99.27% for volume delivery. A precise synchronization of the stimulus delivery with fMRI slice acquisition was achieved by programming the proposed device to detect the 5 V transistor-transistor logic (TTL) trigger signals generated by the MRI scanner. The fMRI data analysis using the general linear model analysis with the standard hemodynamic response function showed increased activations in the network of brain regions that included the insula, anterior and mid-cingulate and lateral prefrontal cortices, and thalamus in response to increased distension pressure on viscera. The translation from manually operated devices to an MR-compatible and MR-synchronized device under automatic control represents a useful innovation for clinical neuroimaging studies of human interoception.

Identifying the effects of visceral interoception on human brain connectome: A multivariate analysis of covariance of fMRI data.

Sources of variations in the neural circuitry of the human brain and interrelationship between intrinsic connectivity networks (ICNs) are still a matter of debate and ongoing research. Here, we applied a multivariate analysis of covariance (MANCOVA) based on high-dimensional independent component analysis (ICA) to identify the effects of interoception and related variables on human brain connectome. Fifteen healthy right-handed subjects (all females, age range 21 - 48 years; mean age = 30.3, SD = 8.7 years) underwent a blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) that included continuous intravesical saline infusion and drainage. The design matrix included the intravesical fullness, subject fullness rating, normalized right and left insula thickness, age, and neuropsychological assessments (Mini-Mental State Exam; MMSE, and Hospital Anxiety and Depression Scale; HADS) as covariates of interest. Univariate tests were also performed with a reduced design matrix (p <; 0.05, corrected for multiple comparisons using false discovery rate) to study the nature and extent of the relationship between these covariates and three ICA outcome measures, namely, the spatial map intensity, frequency spectral power, and functional network connectivity. Results showed significant effects of interoception (intravesical fullness) on spatial map intensity of the salience network (anchored by insula and anterior cingulate cortex) and the frontoparietal central executive network, The left and right insula thickness influenced the spatial map intensity of the subcortical network, and the attention/cognitive and default-mode networks, respectively. The intravesical fullness also showed an effect on the spectral power of the subcortical network. Further investigations of the effect of internal (bodily) sensations on the ICN properties can provide an invaluable tool for understanding the role of interoception in health and illness.