Synchronous low-frequency oscillation in the resting human brain has been found to form networks of functionally associated areas and hence has been widely used to map the functional connectivity of ...the brain using techniques such as resting-state functional MRI (rsfMRI). Interestingly, similar resting-state networks can also be detected in the anesthetized rodent brain, including the default mode-like network. This opens up opportunities for understanding the neurophysiological basis of the rsfMRI signal, the behavioral relevance of the network characteristics, connectomic deficits in diseases and treatment effects on brain connectivity using rodents, particularly transgenic mouse models. In this review, we will provide an overview on the resting-state networks in the rat and mouse brains, the effects of pharmacological agents, brain stimulation, structural connectivity, genetics on these networks, neuroplasticity after behavioral training and applications in models of neurological disease and psychiatric disorders. The influence of anesthesia, strain difference, and physiological variation on the rsfMRI-based connectivity measure will be discussed.
•Resting-state network in rodent brain has high similarity to that in human.•RSN change by manipulations on receptor, neural area, behavior, and gene.•RSN in disease models provide understanding of mechanisms.•Imaging RSN in rodent brain is a powerful translational tool.
Stroke leads to both regional brain functional disruptions and network reorganization. However, how brain functional networks reconfigure as task demand increases in stroke patients and whether such ...reorganization at baseline would facilitate post-stroke motor recovery are largely unknown. To address this gap, brain functional connectivity (FC) were examined at rest and motor tasks in eighteen chronic subcortical stroke patients and eleven age-matched healthy controls. Stroke patients underwent a 2-week intervention using a motor imagery-assisted brain computer interface-based (MI-BCI) training with or without transcranial direct current stimulation (tDCS). Motor recovery was determined by calculating the changes of the upper extremity component of the Fugl-Meyer Assessment (FMA) score between pre- and post-intervention divided by the pre-intervention FMA score. The results suggested that as task demand increased (i.e., from resting to passive unaffected hand gripping and to active affected hand gripping), patients showed greater FC disruptions in cognitive networks including the default and dorsal attention networks. Compared to controls, patients had lower task-related spatial similarity in the somatomotor-subcortical, default-somatomotor, salience/ventral attention-subcortical and subcortical-subcortical connections, suggesting greater inefficiency in motor execution. Importantly, higher baseline network-specific FC strength (e.g., dorsal attention and somatomotor) and more efficient brain network reconfigurations (e.g., somatomotor and subcortical) from rest to active affected hand gripping at baseline were related to better future motor recovery. Our findings underscore the importance of studying functional network reorganization during task-free and task conditions for motor recovery prediction in stroke.
In this study, MnFe2O4 nanoparticle (MFNP)‐decorated graphene oxide nanocomposites (MGONCs) are prepared through a simple mini‐emulsion and solvent evaporation process. It is demonstrated that the ...loading of magnetic nanocrystals can be tuned by varying the ratio of graphene oxide/magnetic nanoparticles. On top of that, the hydrodynamic size range of the obtained nanocomposites can be optimized by varying the sonication time during the emulsion process. By fine‐tuning the sonication time, MGONCs as small as 56.8 ± 1.1 nm, 55.0 ± 0.6 nm and 56.2 ± 0.4 nm loaded with 6 nm, 11 nm, and 14 nm MFNPs, respectively, are successfully fabricated. In order to improve the colloidal stability of MGONCs in physiological solutions (e.g., phosphate buffered saline or PBS solution), MGONCs are further conjugated with polyethylene glycol (PEG). Heating by exposing MGONCs samples to an alternating magnetic field (AMF) show that the obtained nanocomposites are efficient hyperthermia agents. At concentrations as low as 0.1 mg Fe mL−1 and under an 59.99 kA m−1 field, the highest specific absorption rate (SAR) recorded is 1588.83 W g−1 for MGONCs loaded with 14 nm MFNPs. It is also demonstrated that MGONCs are promising as magnetic resonance imaging (MRI) T2 contrast agents. A T2 relaxivity value (r2) as high as 256.2 (mM Fe)−1 s−1 could be achieved with MGONCs loaded with 14 nm MFNPs. The cytotoxicity results show that PEGylated MGONCs exhibit an excellent biocompatibility that is suitable for biomedical applications.
Manganese ferrite‐decorated graphene oxide nanocomposites are prepared by a simple mini‐emulsion/solvent evaporation technique. Such a process involves hydrophobic manganese ferrite nanocrystals and oleylamine‐modified graphene oxide. The as‐synthesized water soluble magnetic nanocomposites still retain the original manganese ferrite superparamagnetic properties, and therefore the composite is suitable for various biomedical applications.
Increasing spatial and temporal resolutions of functional MRI (fMRI) measurement has been shown to benefit the study of neural dynamics and functional interaction. However, acceleration of rodent ...brain fMRI using parallel and simultaneous multi-slice imaging techniques is hampered by the lack of high-density phased-array coils for the small brain. To overcome this limitation, we adapted phase-offset multiplanar and blipped-controlled aliasing echo planar imaging (EPI) to enable simultaneous multi-slice fMRI of the mouse brain using a single loop coil on a 9.4T scanner. Four slice bands of 0.3 × 0.3 × 0.5 mm3 resolution can be simultaneously acquired to cover the whole brain at a temporal resolution of 300 ms or the whole cerebrum in 150 ms. Instead of losing signal-to-noise ratio (SNR), both spatial and temporal SNR can be increased due to the increased k-space sampling compared to a standard single-band EPI. Task fMRI using a visual stimulation shows close to 80% increase of z-score and 4 times increase of activated area in the visual cortex using the multiband EPI due to the highly increased temporal samples. Resting-state fMRI shows reliable detection of bilateral connectivity by both single-band and multiband EPI, but no significant difference was found. Without the need of a dedicated hardware, we have demonstrated a practical method that can enable unparallelly fast whole-brain fMRI for preclinical studies. This technique can be used to increase sensitivity, distinguish transient response or acquire high spatiotemporal resolution fMRI.
•Multiple slices can be acquired simultaneously without coil array.•Whole brain fMRI of TR as short as 300ms was achieved.•Visual fMRI showed 77% increase of z-score and 4-fold increase of activated area.•No enhancement of resting-state functional connectivity except regions of low SNR.•Ultrafast fMRI enables sensitivity enhancement, detection of transient response or high spatiotemporal resolution imaging.
Omni-channel marketing is an enhanced cross-channel business model involving shared data that allows enterprises to enhance and facilitate customer experience. Omni-channel opportunities shape retail ...business and shopper behaviours by coordinating data across all channel platforms while enabling their simultaneous use. Artificial intelligence (AI) has played an increasingly critical role in marketing analysis. With the proper training, AI can predict consumer preferences and provide recommendations based on historical data to achieve precision marketing in e-commerce. At present, however, the existent chatbots on many product-ordering platforms lack AI refinement, resulting in the need to ask customers multiple questions before generating a reliable suggestion, yet an effective way to incorporate AI in an omni-channel platform has remained vague. Hence, the aim of this study was to develop an omni-channel chatbot that incorporates iOS, Android, and web components. The chatbot was designed to achieve personalised service and precision marketing using convolutional neural networks (CNNs). A shared kitchen case study demonstrates the advantages of the proposed method, which is transferable to other consumer applications such as clothing selection or personalised services. The number of food offerings and the quality of image classifiers set the research limitations, pointing toward the direction of future research.
Magnetic Resonance Imaging (MRI) has been widely used to acquire structural and functional information about the brain. In a group- or voxel-wise analysis, it is essential to correct the bias field ...of the radiofrequency coil and to extract the brain for accurate registration to the brain template. Although automatic methods have been developed, manual editing is still required, particularly for echo-planar imaging (EPI) due to its lower spatial resolution and larger geometric distortion. The needs of user interventions slow down data processing and lead to variable results between operators. Deep learning networks have been successfully used for automatic postprocessing. However, most networks are only designed for a specific processing and/or single image contrast (e.g., spin-echo or gradient-echo). This limitation markedly restricts the application and generalization of deep learning tools. To address these limitations, we developed a deep learning network based on the generative adversarial net (GAN) to automatically correct coil inhomogeneity and extract the brain from both spin- and gradient-echo EPI without user intervention. Using various quantitative indices, we show that this method achieved high similarity to the reference target and performed consistently across datasets acquired from rodents. These results highlight the potential of deep networks to integrate different postprocessing methods and adapt to different image contrasts. The use of the same network to process multimodality data would be a critical step toward a fully automatic postprocessing pipeline that could facilitate the analysis of large datasets with high consistency.
Abstract
Memory consolidation after learning involves spontaneous, brain-wide network reorganization during rest and sleep, but how this is achieved is still poorly understood. Current theory ...suggests that the hippocampus is pivotal for this reshaping of connectivity. Using fMRI in male mice, we identify that a different set of spontaneous networks and their hubs are instrumental in consolidating memory during post-learning rest. We found that two types of spatial memory training invoke distinct functional connections, but that a network of the sensory cortex and subcortical areas is common for both tasks. Furthermore, learning increased brain-wide network integration, with the prefrontal, striatal and thalamic areas being influential for this network-level reconfiguration. Chemogenetic suppression of each hub identified after learning resulted in retrograde amnesia, confirming the behavioral significance. These results demonstrate the causal and functional roles of resting-state network hubs in memory consolidation and suggest that a distributed network beyond the hippocampus subserves this process.
Diffusion tensor imaging (DTI) of the brain provides essential information on the white matter integrity and structural connectivity. However, it suffers from a low signal‐to‐noise ratio (SNR) and ...requires a long scan time to achieve high spatial and/or diffusion resolution and wide brain coverage. With recent advances in parallel and simultaneous multislice (multiband) imaging, the SNR efficiency has been improved by reducing the repetition time (TR). However, due to the limited number of RF coil channels available on preclinical MRI scanners, simultaneous multislice acquisition has not been practical. In this study, we demonstrate the ability of multiband DTI to acquire high‐resolution data of the mouse brain with 84 slices covering the whole brain in 0.2 mm isotropic resolution without a coil array at 9.4 T. Hadamard‐encoding four‐band pulses were used to acquire four slices simultaneously, with the reduction in the TR maximizing the SNR efficiency. To overcome shot‐to‐shot phase variations, Hadamard decoding with a self‐calibrated phase was developed. Compared with single‐band DTI acquired with the same scan time, the multiband DTI leads to significantly increased SNR by 40% in the white matter. This SNR gain resulted in reduced variations in fractional anisotropy, mean diffusivity, and eigenvector orientation. Furthermore, the cerebrospinal fluid signal was attenuated, leading to reduced free‐water contamination. Without the need for a high‐density coil array or parallel imaging, this technique enables highly efficient preclinical DTI that will facilitate connectome studies.
Simultaneous multislice imaging can improve the SNR efficiency of diffusion tensor imaging (DTI) by reducing the repetition time (TR). However, it has not been practical on preclinical MRI scanners due to limited coil channels. Using Hadamard encoding, we demonstrated four‐band DTI of the mouse brain without an array coil. Compared with single‐band DTI acquired with the same scan time, the multiband method leads to significantly increased SNR, which results in reduced variations in fractional anisotropy.
Brain extraction is an important preprocessing step for further processing (e.g., registration and morphometric analysis) of brain MRI data. Due to the operator-dependent and time-consuming nature of ...manual extraction, automated or semi-automated methods are essential for large-scale studies. Automatic methods are widely available for human brain imaging, but they are not optimized for rodent brains and hence may not perform well. To date, little work has been done on rodent brain extraction. We present an extended pulse-coupled neural network algorithm that operates in 3-D on the entire image volume. We evaluated its performance under varying SNR and resolution and tested this method against the brain-surface extractor (BSE) and a level-set algorithm proposed for mouse brain. The results show that this method outperforms existing methods and is robust under low SNR and with partial volume effects at lower resolutions. Together with the advantage of minimal user intervention, this method will facilitate automatic processing of large-scale rodent brain studies.
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