Background
It is known that respiration modulates cavopulmonary flows, but little data compare mean flows under breath-holding and free-breathing conditions to isolate the respiratory effects and effects of exercise on the respiratory modulation. Methods Real-time phase-contrast magnetic resonance combined with a novel method to track respiration on the same image acquisition was used to investigate respiratory effects on Fontan caval and aortic flows under breath-holding, free-breathing, and exercise conditions. Respiratory phasicity indices that were based on beat-averaged flow were used to quantify the respiratory effect. Results Flow during inspiration was substantially higher than expiration under the free-breathing and exercise conditions for both inferior vena cava (inspiration/expiration: 1.6 ± 0.5 and 1.8 ± 0.5, respectively) and superior vena cava (inspiration/expiration: 1.9 ± 0.6 and 2.6 ± 2.0, respectively). Changes from rest to exercise in the respiratory phasicity index for these vessels further showed the impact of respiration. Total systemic venous flow showed no significant statistical difference between the breath-holding and free-breathing conditions. In addition, no substantial difference was found between the descending aorta and inferior vena cava mean flows under either resting or exercise conditions. Conclusions This study demonstrated that inferior vena cava and superior vena cava flow time variance is dominated by respiratory effects, which can be detected by the respiratory phasicity index. However, the minimal respiration influence on net flow validates the routine use of breath-holding techniques to measure mean flows in Fontan patients. Moreover, the mean flows in the inferior vena cava and descending aorta are interchangeable.
In addition to their well characterized role in allergic inflammation, recent data confirm that mast cells play a more extensive role in a variety of immune responses. However, their contribution to autoimmune and neurologic disease processes has not been investigated. Experimental allergic encephalomyelitis (EAE) and its human disease counterpart, multiple sclerosis, are considered to be CD4+ T cell-mediated autoimmune diseases affecting the central nervous system. Several lines of indirect evidence suggest that mast cells could also play a role in the pathogenesis of both the human and murine disease. Using a myelin oligodendrocyte glycoprotein (MOG)-induced model of acute EAE, we show that mast cell-deficient W/W(v) mice exhibit significantly reduced disease incidence, delayed disease onset, and decreased mean clinical scores when compared with their wild-type congenic littermates. No differences were observed in MOG-specific T and B cell responses between the two groups, indicating that a global T or B cell defect is not present in W/W(v) animals. Reconstitution of the mast cell population in W/W(v) mice restores induction of early and severe disease to wild-type levels, suggesting that mast cells are critical for the full manifestation of disease. These data provide a new mechanism for immune destruction in EAE and indicate that mast cells play a broader role in neurologic inflammation.
Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain's dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting-state and task-active data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. Upon observing the local neighborhood of brain-states adopted subsequent to each stimulus, we may conclude that resting brain activity includes brain states that are, at times, similar to those adopted during tasks, but that are at other times distinct from task-active brain states. As task-active brain states often populate a local neighborhood, back-projection of segments of the dynamical state space onto the brain's surface reveals the patterns of brain activity that support many experimentally-defined states.
Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders.
Functional connectivity between brain regions, measured with resting state functional magnetic resonance imaging, holds great potential for understanding the basis of behavior and neuropsychiatric diseases. Recently it has become clear that correlations between the blood oxygenation level dependent (BOLD) signals from different areas vary over the course of a typical scan (6-10. min in length), though the changes are obscured by standard methods of analysis that assume the relationships are stationary. Unfortunately, because similar variability is observed in signals that share no temporal information, it is unclear which dynamic changes are related to underlying neural events. To examine this question, BOLD data were recorded simultaneously with local field potentials (LFP) from interhemispheric primary somatosensory cortex (SI) in anesthetized rats. LFP signals were converted into band-limited power (BLP) signals including delta, theta, alpha, beta and gamma. Correlation between signals from interhemispheric SI was performed in sliding windows to produce signals of correlation over time for BOLD and each BLP band. Both BOLD and BLP signals showed large changes in correlation over time and the changes in BOLD were significantly correlated to the changes in BLP. The strongest relationship was seen when using the theta, beta and gamma bands. Interestingly, while steady-state BOLD and BLP correlate with the global fMRI signal, dynamic BOLD becomes more like dynamic BLP after the global signal is regressed. As BOLD sliding window connectivity is partially reflecting underlying LFP changes, the present study suggests it may be a valuable method of studying dynamic changes in brain states.
The slow fluctuations of the blood-oxygenation-level dependent (BOLD) signal in resting-state fMRI are widely utilized as a surrogate marker of ongoing neural activity. Spontaneous neural activity includes a broad range of frequencies, from infraslow (< 0.5. Hz) fluctuations to fast action potentials. Recent studies have demonstrated a correlative relationship between the BOLD fluctuations and power modulations of the local field potential (LFP), particularly in the gamma band. However, the relationship between the BOLD signal and the infraslow components of the LFP, which are directly comparable in frequency to the BOLD fluctuations, has not been directly investigated. Here we report a first examination of the temporal relation between the resting-state BOLD signal and infraslow LFPs using simultaneous fMRI and full-band LFP recording in rat. The spontaneous BOLD signal at the recording sites exhibited significant localized correlation with the infraslow LFP signals as well as with the slow power modulations of higher-frequency LFPs (1-100. Hz) at a delay comparable to the hemodynamic response time under anesthesia. Infraslow electrical activity has been postulated to play a role in attentional processes, and the findings reported here suggest that infraslow LFP coordination may share a mechanism with the large-scale BOLD-based networks previously implicated in task performance, providing new insight into the mechanisms contributing to the resting state fMRI signal.
by
Sarah T. Plummer;
Christoph P. Hornik;
Hamilton Baker;
Gregory A. Fleming;
Susan Foerster;
Matthew Ferguson;
Andrew C. Glatz;
Russel Hirsch;
Jeffrey P. Jacobs;
Kyong-Jin Lee;
Alan B. Lewis;
Jennifer S. Li;
Mary Martin;
Diego Porras;
Wolfgang A. K. Radtke;
John F. Rhodes;
Julie A. Vincent;
Jeffrey D. Zampi;
Kevin D. Hill
Objectives: Aortic arch reconstruction in children with single ventricle lesions may predispose to circulatory inefficiency and maladaptive physiology leading to increased myocardial workload. We sought to describe neoaortic anatomy and physiology, risk factors for abnormalities, and impact on right ventricular function in patients with single right ventricle lesions after arch reconstruction. Methods: Prestage II aortic angiograms from the Pediatric Heart Network Single Ventricle Reconstruction trial were analyzed to define arch geometry (Romanesque [normal], crenel [elongated] , or gothic [angular]), indexed neoaortic dimensions, and distensibility. Comparisons were made with 50 single-ventricle controls without prior arch reconstruction. Factors associated with ascending neoaortic dilation, reduced distensibility, and decreased ventricular function on the 14-month echocardiogram were evaluated using univariate and multivariable logistic regression. Results: Interpretable angiograms were available for 326 of 389 subjects (84%). Compared with controls, study subjects more often demonstrated abnormal arch geometry (67% vs 22%, P < .01) and had increased ascending neoaortic dilation (Z score 3.8 ± 2.2 vs 2.6 ± 2.0, P < .01) and reduced distensibility index (2.2 ± 1.9 vs 8.0 ± 3.8, P < .01). Adjusted odds of neoaortic dilation were increased in subjects with gothic arch geometry (odds ratio [OR], 3.2 vs crenel geometry, P < .01) and a right ventricle-pulmonary artery shunt (OR, 3.4 vs Blalock–Taussig shunt, P < .01) but were decreased in subjects with aortic atresia (OR, 0.7 vs stenosis, P < .01) and those with recoarctation (OR, 0.3 vs no recoarctation, P = .04). No demographic, anatomic, or surgical factors predicted reduced distensibility. Neither dilation nor distensibility predicted reduced right ventricular function. Conclusions: After Norwood surgery, the reconstructed neoaorta demonstrates abnormal anatomy and physiology. Further study is needed to evaluate the longer-term impact of these features.
The field of brain connectomics develops our understanding of the brain's intrinsic organization by characterizing trends in spontaneous brain activity. Linear correlations in spontaneous blood-oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) fluctuations are often used as measures of functional connectivity (FC), that is, as a quantity describing how similarly two brain regions behave over time. Given the natural spectral scaling of BOLD-fMRI signals, it may be useful to represent BOLD-fMRI as multiple processes occurring over multiple scales. The wavelet domain presents a transform space well suited to the examination of multiscale systems as the wavelet basis set is constructed from a self-similar rescaling of a time and frequency delimited kernel. In the present study, we utilize wavelet transforms to examine fluctuations in whole-brain BOLD-fMRI connectivity as a function of wavelet spectral scale in a sample (N = 31) of resting healthy human volunteers. Information theoretic criteria measure relatedness between spectrally-delimited FC graphs. Voxelwise comparisons of between-spectra graph structures illustrate the development of preferential functional networks across spectral bands.
There have been extensive studies of intrinsic connectivity networks (ICNs) in the human brains using resting-state functional magnetic resonance imaging (fMRI) in the literature. However, the functional organization of ICNs in macaque brains has been less explored so far, despite growing interests in the field. In this work, we propose a computational framework to identify connectome-scale group-wise consistent ICNs in macaques via sparse representation of whole-brain resting-state fMRI data. Experimental results demonstrate that 70 group-wise consistent ICNs are successfully identified in macaque brains via the proposed framework. These 70 ICNs are interpreted based on two publicly available parcellation maps of macaque brains and our work significantly expand currently known macaque ICNs already reported in the literature. In general, this set of connectome-scale group-wise consistent ICNs can potentially benefit a variety of studies in the neuroscience and brain-mapping fields, and they provide a foundation to better understand brain evolution in the future.
A common assumption in the study of brain functional connectivity is that the brain network is stationary. However it is increasingly recognized that the brain organization is prone to variations across the scanning session, fueling the need for dynamic connectivity approaches. One of the main challenges in developing such approaches is that the frequency and change points for the brain organization are unknown, with these changes potentially occurring frequently during the scanning session. In order to provide greater power to detect rapid connectivity changes, we propose a fully automated two-stage approach which pools information across multiple subjects to estimate change points in functional connectivity, and subsequently estimates the brain networks within each state phase lying between consecutive change points. The number and positioning of the change points are unknown and learned from the data in the first stage, by modeling a time-dependent connectivity metric under a fused lasso approach. In the second stage, the brain functional network for each state phase is inferred via sparse inverse covariance matrices. We compare the performance of the method with existing dynamic connectivity approaches via extensive simulation studies, and apply the proposed approach to a saccade block task fMRI data.