Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships between the CNN and the brain. Through the encoding models, the CNN-predicted areas covered not only the ventral stream, but also the dorsal stream, albeit to a lesser degree; single-voxel response was visualized as the specific pixel pattern that drove the response, revealing the distinct representation of individual cortical location; cortical activation was synthesized from natural images with high-throughput to map category representation, contrast, and selectivity. Through the decoding models, fMRI signals were directly decoded to estimate the feature representations in both visual and semantic spaces, for direct visual reconstruction and semantic categorization, respectively. These results corroborate, generalize, and extend previous findings, and highlight the value of using deep learning, as an all-in-one model of the visual cortex, to understand and decode natural vision.
Background The therapeutic mechanisms of deep brain stimulations (DBS) are not fully understood. Axonal block induced by high frequency stimulation (HFS) has been suggested as one possible underlying mechanism of DBS. Objective To investigate the mechanism of the generation of HFS-induced axonal block. Methods High frequency pulse trains were applied to the fiber tracts of alveus and Schaffer collaterals in the hippocampal CA1 neurons in anaesthetized rats at 50, 100 and 200 Hz. The amplitude changes of antidromic-evoked population spikes (APS) were measured to determine the degree of axonal block. The amplitude ratio of paired-pulse evoked APS was used to assess the changes of refractory period. Results There were two distinct recovery stages of axonal block following the termination of HFS. One frequency-dependent faster phase followed by another frequency-independent slower phase. Experiments with specially designed temporal patterns of stimulation showed that HFS produced an extension of the duration of axonal refractory period thereby causing a fast recovery phase of the axonal block. Thus, prolonged gaps inserted within HFS trains could eliminate the axonal block and induced large population spikes and even epileptiform activity in the upstream or downstream regions. Conclusions Extension of refractory period plays an important role on HFS induced axonal block. Stimulation pattern with properly designed pauses could be beneficial for different requirements of excitation or inhibition in DBS therapies.
The MRI protocol employed in this study is expected to enable advanced preclinical studies to understand stomach pathophysiology and its therapeutics. Results from this study suggest an electroceutical treatment approach for gastric emptying disorders using cervical VNS to control the degree of pyloric sphincter relaxation.
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.
The parasympathetic innervation to the gastrointestinal (GI) tract plays a key role in regulating, modulating, and controlling GI motility and maintaining energy homeostasis. 1,2 Because this innervation is predominantly supplied by the vagus nerve, 3,4 it has been of great interest to modulate motor neural signaling to the gut via electrical stimulation of the vagus nerve. [5][6][7] However, several morphological and functional studies have suggested that the vagus is a heterogeneous nerve consisting of distinct fiber calibers that carries both efferent and afferent traffic. 8,9 The stimulus-response relations often vary according to the site of stimulation and also the choice of stimulus parameters. As
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