Search

Background: Time‐sequenced magnetic resonance imaging (MRI) of the stomach is an emerging technique for non‐invasive assessment of gastric emptying and motility. However, an automated and systematic image processing pipeline for analyzing dynamic 3D (ie, 4D) gastric MRI data has not been established. This study uses an MRI protocol for imaging the stomach with high spatiotemporal resolution and provides a pipeline for assessing gastric emptying and motility. Methods: Diet contrast‐enhanced MRI images were acquired from seventeen healthy humans after they consumed a naturalistic contrast meal. An automated image processing pipeline was developed to correct for respiratory motion, to segment and compartmentalize the lumen‐enhanced stomach, to quantify total gastric and compartmental emptying, and to compute and visualize gastric motility on the luminal surface of the stomach. Key Results: The gastric segmentation reached an accuracy of 91.10 ± 0.43% with the Type‐I error and Type‐II error being 0.11 ± 0.01% and 0.22 ± 0.01%, respectively. Gastric volume decreased 34.64 ± 2.8% over 1 h where the emptying followed a linear‐exponential pattern. The gastric motility showed peristaltic patterns with a median = 4 wave fronts (range 3–6) and a mean frequency of 3.09 ± 0.07 cycles per minute. Further, the contractile amplitude was stronger in the antrum than in the corpus (antrum vs. corpus: 5.18 ± 0.24 vs. 3.30 ± 0.16 mm; p < 0.001). Conclusions & Inferences: Our analysis pipeline can process dynamic 3D MRI images and produce personalized profiles of gastric motility and emptying. It will facilitate the application of MRI for monitoring gastric dynamics in research and clinical settings. [ABSTRACT FROM AUTHOR]

Background: Vagus nerve stimulation (VNS) is an emerging bioelectronic therapy for regulating food intake and controlling gastric motility. However, the effects of different VNS parameters and polarity on postprandial gastric motility remain incompletely characterized. Methods: In anesthetized rats (N = 3), we applied monophasic electrical stimuli to the left cervical vagus and recorded compound nerve action potential (CNAP) as a measure of nerve response. We evaluated to what extent afferent or efferent pathway could be selectively activated by monophasic VNS. In a different group of rats (N = 13), we fed each rat a gadolinium‐labeled meal and scanned the rat stomach with oral contrast‐enhanced magnetic resonance imaging (MRI) while the rat was anesthetized. We evaluated the antral and pyloric motility as a function of pulse amplitude (0.13, 0.25, 0.5, 1 mA), width (0.13, 0.25, 0.5 ms), frequency (5, 10 Hz), and polarity of VNS. Key Results: Monophasic VNS activated efferent and afferent pathways with about 67% and 82% selectivity, respectively. Primarily afferent VNS increased antral motility across a wide range of parameters. Primarily efferent VNS induced a significant decrease in antral motility as the stimulus intensity increased (R = −.93, P <.05 for 5 Hz, R = −.85, P <.05 for 10 Hz). The VNS with either polarity tended to promote pyloric motility to a greater extent given increasing stimulus intensity. Conclusions and Inferences: Monophasic VNS biased toward the afferent pathway is potentially more effective for facilitating occlusive contractions than that biased toward the efferent pathway. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) is commonly thought to be too slow to capture any neural dynamics faster than 0.1 Hz. However, recent findings demonstrate the feasibility of detecting fMRI activity at higher frequencies beyond 0.2 Hz. The origin, reliability, and generalizability of fast fMRI responses are still under debate and await confirmation through animal experiments with fMRI and invasive electrophysiology. Here, we acquired single-echo and multi-echo fMRI, as well as local field potentials, from anesthetized rat brains given gastric electrical stimulation modulated at 0.2, 0.4 and 0.8 Hz. Such gastric stimuli could drive widespread fMRI responses at corresponding frequencies from the somatosensory and cingulate cortices. Such fast fMRI responses were linearly dependent on echo times and thus indicative of blood oxygenation level dependent nature (BOLD). Local field potentials recorded during the same gastric stimuli revealed transient and phase-locked broadband neural responses, preceding the fMRI responses by as short as 0.5 s. Taken together, these results suggest that gastric stimulation can drive widespread and rapid fMRI responses of BOLD and neural origin, lending support to the feasibility of using fMRI to detect rapid changes in neural activity up to 0.8 Hz under visceral stimulation. • Gastric electrical stimuli could drive fast-fMRI responses up to 0.8 Hz. • The fast fMRI responses are linearly dependent on echo time, and thus blood oxygenation level dependent. • The fast fMRI responses follow transient and broadband neural responses in the somatosensory cortex. [ABSTRACT FROM AUTHOR]

During complex tasks, patterns of functional connectivity differ from those in the resting state. However, what accounts for such differences remains unclear. Brain activity during a task reflects an unknown mixture of spontaneous and task‐evoked activities. The difference in functional connectivity between a task state and the resting state may reflect not only task‐evoked functional connectivity, but also changes in spontaneously emerging networks. Here, we characterized the differences in apparent functional connectivity between the resting state and when human subjects were watching a naturalistic movie. Such differences were marginally explained by the task‐evoked functional connectivity involved in processing the movie content. Instead, they were mostly attributable to changes in spontaneous networks driven by ongoing activity during the task. The execution of the task reduced the correlations in ongoing activity among different cortical networks, especially between the visual and non‐visual sensory or motor cortices. Our results suggest that task‐evoked activity is not independent from spontaneous activity, and that engaging in a task may suppress spontaneous activity and its inter‐regional correlation. [ABSTRACT FROM AUTHOR]

Vagus nerve stimulation (VNS) is a therapy for epilepsy and depression. However, its efficacy varies and its mechanism remains unclear. Prior studies have used functional magnetic resonance imaging (fMRI) to map brain activations with VNS in human brains, but have reported inconsistent findings. The source of inconsistency is likely attributable to the complex temporal characteristics of VNS-evoked fMRI responses that cannot be fully explained by simplified response models in the conventional model-based analysis for activation mapping. To address this issue, we acquired 7-Tesla blood oxygenation level dependent fMRI data from anesthetized Sprague–Dawley rats receiving electrical stimulation at the left cervical vagus nerve. Using spatially independent component analysis, we identified 20 functional brain networks and detected the network-wise activations with VNS in a data-driven manner. Our results showed that VNS activated 15 out of 20 brain networks, and the activated regions covered >76% of the brain volume. The time course of the evoked response was complex and distinct across regions and networks. In addition, VNS altered the strengths and patterns of correlations among brain networks relative to those in the resting state. The most notable changes in network-network interactions were related to the limbic system. Together, such profound and widespread effects of VNS may underlie its unique potential for a wide range of therapeutics to relieve central or peripheral conditions. [ABSTRACT FROM AUTHOR]

The assessment of gastric emptying and motility in humans and animals typically requires radioactive imaging or invasive measurements. Here, we developed a robust strategy to image and characterize gastric emptying and motility in rats based on contrast-enhanced magnetic resonance imaging (MRI) and computer-assisted image processing. The animals were trained to naturally consume a gadolinium-labeled dietgel while bypassing any need for oral gavage. Following this test meal, the animals were scanned under low-dose anesthesia for high-resolution T1-weighted MRI in 7 Tesla, visualizing the time-varying distribution of the meal with greatly enhanced contrast against non-gastrointestinal (GI) tissues. Such contrast-enhanced images not only depicted the gastric anatomy, but also captured and quantified stomach emptying, intestinal filling, antral contraction, and intestinal absorption with fully automated image processing. Over four postingestion hours, the stomach emptied by 27%, largely attributed to the emptying of the forestomach rather than the corpus and the antrum, and most notable during the first 30 min. Stomach emptying was accompanied by intestinal filling for the first 2 h, whereas afterward intestinal absorption was observable as cumulative contrast enhancement in the renal medulla. The antral contraction was captured as a peristaltic wave propagating from the proximal to distal antrum. The frequency, velocity, and amplitude of the antral contraction were on average 6.34 ± 0.07 contractions per minute, 0.67 ± 0.01 mm/s, and 30.58 ± 1.03%, respectively. These results demonstrate an optimized MRI-based strategy to assess gastric emptying and motility in healthy rats, paving the way for using this technique to understand GI diseases, or test new therapeutics in rat models. [ABSTRACT FROM PUBLISHER]

Large-scale functional networks have been extensively studied using resting state functional magnetic resonance imaging (fMRI). However, the pattern, organization, and function of fine-scale network activity remain largely unknown. Here, we characterized the spontaneously emerging visual cortical activity by applying independent component (IC) analysis to resting state fMRI signals exclusively within the visual cortex. In this subsystem scale, we observed about 50 spatially ICs that were reproducible within and across subjects, and analyzed their spatial patterns and temporal relationships to reveal the intrinsic parcellation and organization of the visual cortex. The resulting visual cortical parcels were aligned with the steepest gradient of cortical myelination, and were organized into functional modules segregated along the dorsal/ventral pathways and foveal/peripheral early visual areas. Cortical distance could partly explain intra-hemispherical functional connectivity, but not interhemispherical connectivity; after discounting the effect of anatomical affinity, the fine-scale functional connectivity still preserved a similar visual-stream-specific modular organization. Moreover, cortical retinotopy, folding, and cytoarchitecture impose limited constraints to the organization of resting state activity. Given these findings, we conclude that spontaneous activity patterns in the visual cortex are primarily organized by visual streams, likely reflecting feedback network interactions. Hum Brain Mapp 38:4613-4630, 2017. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Despite the wide applications of functional magnetic resonance imaging (fMRI) to mapping brain activation and connectivity in cortical gray matter, it has rarely been utilized to study white-matter functions. In this study, we investigated the spatiotemporal characteristics of fMRI data within the white matter acquired from humans both in the resting state and while watching a naturalistic movie. By using independent component analysis and hierarchical clustering, resting-state fMRI data in the white matter were de-noised and decomposed into spatially independent components, which were further assembled into hierarchically organized axonal fiber bundles. Interestingly, such components were partly reorganized during natural vision. Relative to resting state, the visual task specifically induced a stronger degree of temporal coherence within the optic radiations, as well as significant correlations between the optic radiations and multiple cortical visual networks. Therefore, fMRI contains rich functional information about the activity and connectivity within white matter at rest and during tasks, challenging the conventional practice of taking white-matter signals as noise or artifacts. [ABSTRACT FROM AUTHOR]

Complex, sustained, dynamic, and naturalistic visual stimulation can evoke distributed brain activities that are highly reproducible within and across individuals. However, the precise origins of such reproducible responses remain incompletely understood. Here, we employed concurrent functional magnetic resonance imaging (fMRI) and eye tracking to investigate the experimental and behavioral factors that influence fMRI activity and its intra- and inter-subject reproducibility during repeated movie stimuli. We found that widely distributed and highly reproducible fMRI responses were attributed primarily to the high-level natural content in the movie. In the absence of such natural content, low-level visual features alone in a spatiotemporally scrambled control stimulus evoked significantly reduced degree and extent of reproducible responses, which were mostly confined to the primary visual cortex (V1). We also found that the varying gaze behavior affected the cortical response at the peripheral part of V1 and in the oculomotor network, with minor effects on the response reproducibility over the extrastriate visual areas. Lastly, scene transitions in the movie stimulus due to film editing partly caused the reproducible fMRI responses at widespread cortical areas, especially along the ventral visual pathway. Therefore, the naturalistic nature of a movie stimulus is necessary for driving highly reliable visual activations. In a movie-stimulation paradigm, scene transitions and individuals’ gaze behavior should be taken as potential confounding factors in order to properly interpret cortical activity that supports natural vision. [ABSTRACT FROM AUTHOR]

Axonal characterizations of connectomes in healthy and disease phenotypes are surprisingly incomplete and biased because unmyelinated axons, the most prevalent type of fibers in the nervous system, have largely been ignored as their quantitative assessment quickly becomes unmanageable as the number of axons increases. Herein, we introduce the first prototype of a high-throughput processing pipeline for automated segmentation of unmyelinated fibers. Our team has used transmission electron microscopy images of vagus and pelvic nerves in rats. All unmyelinated axons in these images are individually annotated and used as labeled data to train and validate a deep instance segmentation network. We investigate the effect of different training strategies on the overall segmentation accuracy of the network. We extensively validate the segmentation algorithm as a stand-alone segmentation tool as well as in an expert-in-the-loop hybrid segmentation setting with preliminary, albeit remarkably encouraging results. Our algorithm achieves an instance-level F 1 score of between 0.7 and 0.9 on various test images in the stand-alone mode and reduces expert annotation labor by 80% in the hybrid setting. We hope that this new high-throughput segmentation pipeline will enable quick and accurate characterization of unmyelinated fibers at scale and become instrumental in significantly advancing our understanding of connectomes in both the peripheral and the central nervous systems. [ABSTRACT FROM AUTHOR]

Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with a five-layer encoder and a five-layer decoder to learn visual representations from a diverse set of unlabeled images. Using the trained VAE, we predicted and decoded cortical activity observed with functional magnetic resonance imaging (fMRI) from three human subjects passively watching natural videos. Compared to CNN, VAE could predict the video-evoked cortical responses with comparable accuracy in early visual areas, but relatively lower accuracy in higher-order visual areas. The distinction between CNN and VAE in terms of encoding performance was primarily attributed to their different learning objectives, rather than their different model architecture or number of parameters. Despite lower encoding accuracies, VAE offered a more convenient strategy for decoding the fMRI activity to reconstruct the video input, by first converting the fMRI activity to the VAE's latent variables, and then converting the latent variables to the reconstructed video frames through the VAE's decoder. This strategy was more advantageous than alternative decoding methods, e.g. partial least squares regression, for being able to reconstruct both the spatial structure and color of the visual input. Such findings highlight VAE as an unsupervised model for learning visual representation, as well as its potential and limitations for explaining cortical responses and reconstructing naturalistic and diverse visual experiences. • Variational auto-encoder implements 1 an unsupervised model of "Bayesian brain". • Variational auto-encoder explains and predicts fMRI responses to natural videos. • Variational auto-encoder decodes fMRI responses to directly reconstruct visual input. • Convolutional neural networks trained for image classification better predict fMRI responses than variational auto-encoder trained for image reconstruction. [ABSTRACT FROM AUTHOR]

• In rats, mild blast traumatic brain injury led to a psychosocial learning deficit without confounding behavioral changes. • Behavioral impairment correlated with a known oxidative stress metabolite, 3-hydroxypropylmercapturic acid (3-HPMA). • Resting state fMRI captured altered connectivity in a vmPFC-OFC-amygdala network at 1 day and 1 week post-injury. • Blast-induced psychosocial behavior deficits, in rats, may be related to altered mGluR1/5 expression i the OFC. Traumatic brain injury (TBI) is associated with increased risk for mental health disorders, impacting post-injury quality of life and societal reintegration. TBI is also associated with deficits in psychosocial processing, defined as the cognitive integration of social and emotional behaviors, however little is known about how these deficits manifest and their contributions to post-TBI mental health. In this pre-clinical investigation using rats, a single mild blast TBI (mbTBI) induced impairment of psychosocial processing in the absence of confounding physical polytrauma, post-injury motor deficits, affective abnormalities, or deficits in non-social behavior. Impairment severity correlated with acute upregulations of a known oxidative stress metabolite, 3-hydroxypropylmercapturic acid (3-HPMA), in urine. Resting state fMRI alterations in the acute post-injury period implicated key brain regions known to regulate psychosocial behavior, including orbitofrontal cortex (OFC), which is congruent with our previous report of elevated acrolein, a marker of neurotrauma and 3-HPMA precursor, in this region following mbTBI. OFC of mbTBI-exposed rats demonstrated elevated mRNA expression of metabotropic glutamate receptors 1 and 5 (mGluR1/5) and injection of mGluR1/5-selective agonist in OFC of uninjured rats approximated mbTBI-induced psychosocial processing impairment, demonstrating a novel role for OFC in this psychosocial behavior. Furthermore, OFC may serve as a hotspot for TBI-induced disruption of psychosocial processing and subsequent mental health disorders. [ABSTRACT FROM AUTHOR]