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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.

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. •ICA applied to white-matter fMRI signals reveals reproducible and hierarchical patterns.•White-matter ICA components are mostly preserved, but are in part distinct between the resting state and the task state.•The distinction is specific to the axonal fibers involved in the task execution.•White-matter fMRI data are not noise or artifacts, but instead are signals of likely neuronal origin.

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.

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.

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.

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.

Musical imagery is the human experience of imagining music without actually hearing it. The neural basis of this mental ability is unclear, especially for musicians capable of engaging in accurate and vivid musical imagery. Here, we created a visualization of an 8-minute symphony as a silent movie and used it as real-time cue for musicians to continuously imagine the music for repeated and synchronized sessions during functional magnetic resonance imaging (fMRI). The activations and networks evoked by musical imagery were compared with those elicited by the subjects directly listening to the same music. Musical imagery and musical perception resulted in overlapping activations at the anterolateral belt and Wernicke’s area, where the responses were correlated with the auditory features of the music. Whereas Wernicke’s area interacted within the intrinsic auditory network during musical perception, it was involved in much more complex networks during musical imagery, showing positive correlations with the dorsal attention network and the motor-control network and negative correlations with the default-mode network. Our results highlight the important role of Wernicke’s area in forming vivid musical imagery through bilateral and anti-correlated network interactions, challenging the conventional view of segregated and lateralized processing of music versus language.

Objective: Consuming less water (systemic dehydration) has long been thought to dehydrate the vocal folds. An \"in vivo,\" repeated measures study tested the assumption that systemic dehydration causes vocal fold dehydration. Proton density (PD)-weighted magnetic resonance imaging (MRI) of rat vocal folds was employed to investigate (a) whether varying magnitudes of systemic dehydration would dehydrate the vocal folds and (b) whether systemic rehydration would rehydrate the vocal folds. Method: Male (n = 25) and female (n = 14) Sprague Dawley rats were imaged with 7T MRI, and normalized PD-weighted signal intensities were obtained at predehydration, following dehydration, and following rehydration. Animals were dehydrated to 1 of 3 levels by water withholding to induce body weight loss: mild (< 6% body weight loss), moderate (6%-10% body weight loss), and marked (> 10% body weight loss). Results: There was a significant decrease in vocal fold signal intensities after moderate and marked dehydration (p < 0.0167). Rehydration increased the normalized signal intensity to predehydration levels for only the moderate group (p < 0.0167). Normalized signal intensity did not significantly change after mild dehydration or when the mildly dehydrated animals were rehydrated. Additionally, there were no significant differences in PD-weighted MRI normalized signal intensity between male and female rats (p > 0.05). Conclusion: This study provides evidence supporting clinical voice recommendations for rehydration by increasing water intake after an acute, moderate systemic dehydration event. However, acute systemic dehydration of mild levels did not dehydrate the vocal folds as observed by PD-weighted MRI. Future programmatic research will focus on chronic, recurring systemic dehydration.

Abstract Background Radiogenomics is an emerging tool with applications in screening for molecular biomarkers in diagnostic and prognostic assessment through the extraction of quantitative data from medical images (Shui et al., Front Oncol 2021). In this study, we aim to develop machine learning (ML) models to predict the most common genomic alterations present in our subset of renal cell carcinoma patients (pts). Methods Retrospectively, pts with tissue genomic testing from CT-guided biopsy samples were identified. Genomic testing was done via GEM ExTra assay, a CAP-accredited, CLIA-certified test encompassing tumor whole exome sequencing and whole transcriptome sequencing (TGen; Phoenix, AZ). Biopsy sample collection sites were identified from pre-biopsy contrast CT images, and the lesions were segmented with ITK-SNAP software, from which 510 radiomic features were extracted with Pyradiomics. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the most relevant features. Logistic regression (LR) and support vector machine (SVM) classifiers were built for the prediction of PBRM1, VHL, and SETD2 gene alterations. Multiple metrics were used to evaluate the predictive performance via leave-one-out cross-validation, including the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). Feature importance was evaluated with Shapley additive explanations (SHAP) method. Results A total of 14 RCC pts (10:4 M:F) with genomic testing from CT-guided biopsies were identified. The majority of pts were White (85.7%) and had clear cell histology (71.4%). The most common locations for the CT-guided biopsy were lung (18.2%), soft tissue (13.6%), kidney (9.1%), and bone (9.1%). The most common alterations were seen in PBRM1 (50%), VHL (43.9%), and SETD2 (35.7%) genes. The PBRM1 gene was predicted with the highest AUROC (0.84) and AUPRC (0.88) with the SVM classifier, followed by the SETD2 gene (AUROC=0.78 and AUPRC=0.66) with LR classifier and VHL gene (AUROC=0.56 and AUPRC=0.65) with SVM classifier. Notably, all three models showed good sensitivity in classifying gene mutation status (PBRM1: 0.86; VHL: 0.88; SETD2: 0.89). Among all radiomic features, first-order features, Gray Level Size Zone Matrix features, and Gray Level Dependence Matrix features were found to be the most important features for predictions. Conclusions Using a CT-based radiomics analysis of the biopsy area, we showed that SVM and LR prediction models could predict PBRM1, VHL, and SETD2 mutations with high accuracy. These models may assist in identifying potentially actionable alterations and yield ease in treatment selection for RCC. Further extensive studies are warranted to validate our findings and improve our model. CDMRP DOD Funding: no

During complex tasks, patterns of functional connectivity (FC) differ from those in the resting state. What accounts for such differences remains unclear. Brain activity during a task reflects an unknown mixture of spontaneous activity and task-evoked responses. The difference in FC between a task state and resting state may reflect not only task-evoked connectivity, but also changes in spontaneously emerging networks. Here, we characterized the difference in apparent functional connectivity between the resting state and when human subjects were watching a naturalistic movie. Such differences were marginally (3-15%) explained by the task-evoked networks directly involved in processing the movie content, but 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 cortices. Our results suggest that the interaction between spontaneous and task-evoked activities is not mutually independent or linearly additive, and that engaging in a task may suppress ongoing activity.