Odor information is transmitted from the olfactory bulb to several primary olfactory cortical regions in parallel, including the anterior olfactory nucleus (AON) and piriform cortex (Pir). However, the specific roles of the olfactory bulb and cortical outputs in wider interactions with other interconnected regions throughout the brain remain unclear due to the lack of suitable in vivo techniques. Furthermore, emerging associations between olfactory-related dysfunctions and neurological disorders underscore the need for examining olfactory networks at the systems level. Using optogenetics, fMRI, and computational modeling, we interrogated the spatiotemporal properties of brain-wide neural interactions in olfactory networks. We observed distinct downstream recruitment patterns. Specifically, stimulation of excitatory projection neurons in OB predominantly activates primary olfactory network regions, while stimulation of OB afferents in AON and Pir primarily orthodromically activates hippocampal/striatal and limbic networks, respectively. Temporally, repeated OB or AON stimulation diminishes neural activity propagation brain-wide in contrast to Pir stimulation. Dynamic causal modeling analysis reveals a robust inhibitory effect of AON outputs on striatal and limbic network regions. In addition, experiments in aged rat models show decreased brain-wide activation following OB stimulation, particularly in the primary olfactory and limbic networks. Modeling analysis identifies a dysfunctional AON to Pir connection, indicating the impairment of this primary olfactory cortical circuit that disrupts the downstream long-range propagation. Our study delineates the spatiotemporal properties of olfactory neural activity propagation in brain-wide networks for the first time and distinguishes the roles of primary olfactory cortical, AON and Pir, outputs in shaping neural interactions at the systems level.

PURPOSE:To demonstrate magnetization transfer (MT) effects with low specific absorption rate (SAR) on ultra-low-field (ULF) MRI. METHODS:MT imaging was implemented by using sinc-modulated RF pulse train (SPT) modules to provide bilateral off-resonance irradiation. They were incorporated into 3D gradient echo (GRE) and fast spin echo (FSE) protocols on a shielding-free 0.055T head scanner. MT effects were first verified using phantoms. Brain MT imaging was conducted in both healthy subjects and patients. RESULTS:MT effects were clearly observed in phantoms using six SPT modules with total flip angle 3600° at central primary saturation bands of approximate offset ±786 Hz, even in the presence of large relative B0 inhomogeneity. For brain, strong MT effects were observed in gray matter, white matter, and muscle in 3D GRE and FSE imaging using six and sixteen SPT modules with total flip angle 3600° and 9600°, respectively. Fat, cerebrospinal fluid, and blood exhibited relatively weak MT effects. MT preparation enhanced tissue contrasts in T2-weighted and FLAIR-like images, and improved brain lesion delineation. The estimated MT SAR was 0.0024 and 0.0008 W/kg for two protocols, respectively, which is far below the US Food and Drug Administration (FDA) limit of 3.0 W/kg. CONCLUSION:Robust MT effects can be readily obtained at ULF with extremely low SAR, despite poor relative B0 homogeneity in ppm. This unique advantage enables flexible MT pulse design and implementation on low-cost ULF MRI platforms to achieve strong MT effects in brain and beyond, potentially augmenting their clinical utility in the future.

At present, MRI scans are performed inside a fully-enclosed RF shielding room, posing stringent installation requirement and unnecessary patient discomfort. We aim to develop an electromagnetic interference (EMI) cancellation strategy for MRI with no or incomplete RF shielding. In this study, a simultaneous sensing and deep learning driven EMI cancellation strategy is presented to model, predict and remove EMI signals from acquired MRI signals. Specifically, during each MRI scan, separate EMI sensing coils placed in various spatial locations are utilized to simultaneously sample environmental and internal EMI signals within two windows (for both conventional MRI signal acquisition and EMI characterization acquisition). Then a CNN model is trained using the EMI characterization data to relate EMI signals detected by EMI sensing coils to EMI signals in MRI receive coil. This model is utilized to retrospectively predict and remove EMI signals components detected by MRI receive coil during the MRI signal acquisition window. We implemented and demonstrated this strategy for various EMI sources on a mobile ultra-low-field 0.055 T permanent magnet MRI scanner and a 1.5 T superconducting magnet MRI scanner with no or incomplete RF shielding. Our experimental results demonstrate that the method is highly effective and robust in predicting and removing various EMI sources from both external environments and internal scanner electronics at both 0.055 T (2.3 MHz) and 1.5 T (64 MHz), producing final image signal-to-noise ratios that are comparable to those obtained using a fully enclosed RF shielding. Our proposed strategy enables MRI operation with no or incomplete RF shielding, alleviating MRI installation and operational requirements. It is also potentially applicable to other scenarios of accurate RF signal detection or discrimination in presence of external and internal EMI or RF sources.

We aim to explore the feasibility of head and neck time-of-flight (TOF) magnetic resonance angiography (MRA) at ultra-low-field (ULF). TOF MRA was conducted on a highly simplified 0.05 T MRI scanner with no radiofrequency (RF) and magnetic shielding. A flow-compensated three-dimensional (3D) gradient echo (GRE) sequence with a tilt-optimized nonsaturated excitation RF pulse, and a flow-compensated multislice two-dimensional (2D) GRE sequence, were implemented for cerebral artery and vein imaging, respectively. For carotid artery and jugular vein imaging, flow-compensated 2D GRE sequences were utilized with venous and arterial blood presaturation, respectively. MRA was performed on young healthy subjects. Vessel-to-background contrast was experimentally observed with strong blood inflow effect and background tissue suppression. The large primary cerebral arteries and veins, carotid arteries, jugular veins, and artery bifurcations could be identified in both raw GRE images and maximum intensity projections. The primary brain and neck arteries were found to be reproducible among multiple examination sessions. These preliminary experimental results demonstrated the possibility of artery TOF MRA on low-cost 0.05 T scanners for the first time, despite the extremely low MR signal. We expect to improve the quality of ULF TOF MRA in the near future through sequence development and optimization, ongoing advances in ULF hardware and image formation, and the use of vascular T1 contrast agents.

Low magnetic field magnetic resonance imaging (MRI) ( B 0 $$ {B}_0 $$  < 1 T) is regaining interest in the magnetic resonance (MR) community as a complementary, more flexible, and cost-effective approach to MRI diagnosis. Yet, the impaired signal-to-noise ratio (SNR) per square root of time, or SNR efficiency, leading in turn to prolonged acquisition times, still challenges its relevance at the clinical level. To address this, researchers investigate various hardware and software solutions to improve SNR efficiency at low field, including the leveraging of latest advances in computing hardware. However, there may not be a single recipe for improving SNR at low field, and it is key to embrace the challenges and limitations of each proposed solution. In other words, suitable solutions depend on the final objective or application envisioned for a low-field scanner and, more importantly, on the characteristics of a specific low B 0 $$ {B}_0 $$ field. In this review, we aim to provide an overview on software solutions to improve SNR efficiency at low field. First, we cover techniques for efficient k -space sampling and reconstruction. Then, we present post-acquisition techniques that enhance MR images such as denoising and super-resolution. In addition, we summarize recently introduced electromagnetic interference cancellation approaches showing great promises when operating in shielding-free environments. Finally, we discuss the advantages and limitations of these approaches that could provide directions for future applications.

Purpose: To develop a new electromagnetic interference (EMI) elimination strategy for RF shielding-free MRI via active EMI sensing and deep learning direct MR signal prediction (Deep-DSP). Methods: Deep-DSP is proposed to directly predict EMI-free MR signals. During scanning, MRI receive coil and EMI sensing coils simultaneously sample data within two windows (i.e., for MR data and EMI characterization data acquisition, respectively). Afterward, a residual U-Net model is trained using synthetic MRI receive coil data and EMI sensing coil data acquired during EMI signal characterization window, to predict EMI-free MR signals from signals acquired by MRI receive and EMI sensing coils. The trained model is then used to directly predict EMI-free MR signals from data acquired by MRI receive and sensing coils during the MR signal-acquisition window. This strategy was evaluated on an ultralow-field 0.055T brain MRI scanner without any RF shielding and a 1.5T whole-body scanner with incomplete RF shielding. Results: Deep-DSP accurately predicted EMI-free MR signals in presence of strong EMI. It outperformed recently developed EDITER and convolutional neural network methods, yielding better EMI elimination and enabling use of few EMI sensing coils. Furthermore, it could work well without dedicated EMI characterization data. Conclusion: Deep-DSP presents an effective EMI elimination strategy that outperforms existing methods, advancing toward truly portable and patient-friendly MRI. It exploits electromagnetic coupling between MRI receive and EMI sensing coils as well as typical MR signal characteristics. Despite its deep learning nature, Deep-DSP framework is computationally simple and efficient.

Despite a half-century of advancements, global magnetic resonance imaging (MRI) accessibility remains limited and uneven, hindering its full potential in health care. Initially, MRI development focused on low fields around 0.05 Tesla, but progress halted after the introduction of the 1.5 Tesla whole-body superconducting scanner in 1983. Using a permanent 0.05 Tesla magnet and deep learning for electromagnetic interference elimination, we developed a whole-body scanner that operates using a standard wall power outlet and without radiofrequency and magnetic shielding. We demonstrated its wide-ranging applicability for imaging various anatomical structures. Furthermore, we developed three-dimensional deep learning reconstruction to boost image quality by harnessing extensive high-field MRI data. These advances pave the way for affordable deep learning–powered ultra-low-field MRI scanners, addressing unmet clinical needs in diverse health care settings worldwide.

As a key oscillatory activity in the brain, thalamic spindle activities are long believed to support memory consolidation. However, their propagation characteristics and causal actions at systems level remain unclear. Using functional MRI (fMRI) and electrophysiology recordings in male rats, we found that optogenetically-evoked somatosensory thalamic spindle-like activities targeted numerous sensorimotor (cortex, thalamus, brainstem and basal ganglia) and non-sensorimotor limbic regions (cortex, amygdala, and hippocampus) in a stimulation frequency- and length-dependent manner. Thalamic stimulation at slow spindle frequency (8 Hz) and long spindle length (3 s) evoked the most robust brain-wide cross-modal activities. Behaviorally, evoking these global cross-modal activities during memory consolidation improved visual-somatosensory associative memory performance. More importantly, parallel visual fMRI experiments uncovered response potentiation in brain-wide sensorimotor and limbic integrative regions, especially superior colliculus, periaqueductal gray, and insular, retrosplenial and frontal cortices. Our study directly reveals that thalamic spindle activities propagate in a spatiotemporally specific manner and that they consolidate associative memory by strengthening multi-target memory representation.

The advent of portable, low-field MRI (LF-MRI) heralds new opportunities in neuroimaging. Low power requirements and transportability have enabled scanning outside the controlled environment of a conventional MRI suite, enhancing access to neuroimaging for indications that are not well suited to existing technologies. Maximizing the information extracted from the reduced signal-to-noise ratio of LF-MRI is crucial to developing clinically useful diagnostic images. Progress in electromagnetic noise cancellation and machine learning reconstruction algorithms from sparse k -space data as well as new approaches to image enhancement have now enabled these advancements. Coupling technological innovation with bedside imaging creates new prospects in visualizing the healthy brain and detecting acute and chronic pathological changes. Ongoing development of hardware, improvements in pulse sequences and image reconstruction, and validation of clinical utility will continue to accelerate this field. As further innovation occurs, portable LF-MRI will facilitate the democratization of MRI and create new applications not previously feasible with conventional systems.

Despite its prominence in learning and memory, hippocampal influence in early auditory processing centers remains unknown. Here, we examined how hippocampal activity modulates sound-evoked responses in the auditory midbrain and thalamus using optogenetics and functional MRI (fMRI) in rodents. Ventral hippocampus (vHP) excitatory neuron stimulation at 5 Hz evoked robust hippocampal activity that propagates to the primary auditory cortex. We then tested 5 Hz vHP stimulation paired with either natural vocalizations or artificial/noise acoustic stimuli. vHP stimulation enhanced auditory responses to vocalizations (with a negative or positive valence) in the inferior colliculus, medial geniculate body, and auditory cortex, but not to their temporally reversed counterparts (artificial sounds) or broadband noise. Meanwhile, pharmacological vHP inactivation diminished response selectivity to vocalizations. These results directly reveal the large-scale hippocampal participation in natural sound processing at early centers of the ascending auditory pathway. They expand our present understanding of hippocampus in global auditory networks.

PurposeTo develop a robust parallel imaging reconstruction method using spatial nulling maps (SNMs).MethodsParallel reconstruction using null operations (PRUNO) is a k-space reconstruction method where a k-space nulling system is derived using null-subspace bases of the calibration matrix. ESPIRiT reconstruction extends the PRUNO subspace concept by exploiting the linear relationship between signal-subspace bases and spatial coil sensitivity characteristics, yielding a hybrid-domain approach. Yet it requires empirical eigenvalue thresholding to mask the coil sensitivity information and is sensitive to signal- and null-subspace division. In this study, we combine the concepts of null-subspace PRUNO and hybrid-domain ESPIRiT to provide a more robust reconstruction method that extracts null-subspace bases of calibration matrix to calculate image-domain SNMs. Multi-channel images are reconstructed by solving an image-domain nulling system formed by SNMs that contain both coil sensitivity and finite image support information, therefore, circumventing the masking-related procedure. The proposed method was evaluated with multi-channel 2D brain and knee data and compared to ESPIRiT.ResultsThe proposed hybrid-domain method produced quality reconstruction highly comparable to ESPIRiT with optimal manual masking. It involved no masking-related manual procedure and was tolerant of the actual division of null- and signal-subspace. Spatial regularization could be also readily incorporated to reduce noise amplification as in ESPIRiT.ConclusionWe provide an efficient hybrid-domain reconstruction method using multi-channel SNMs that are calculated from coil calibration data. It eliminates the need for coil sensitivity masking and is relatively insensitive to subspace separation, therefore, presenting a robust parallel imaging reconstruction procedure in practice.

Magnetic resonance imaging is a key diagnostic tool in modern healthcare, yet its accessibility is low with the vast majority of clinical MRI scanners being placed in highly specialized radiology departments, large centralized imaging centers, and housed on ground floors of hospitals and clinics. In this study, we deployed our recently developed 0.055T brain ultra-low-field MRI scanner to demonstrate preliminary feasibility in diagnosing tumor and stroke cases with direct comparisons to 3T clinical MRI results. The development of such ULF MRI technologies will enable patient-centric and site-agnostic MRI scanners to fulfill the unmet clinical needs across various global healthcare sites.

At present, MRI scans are typically performed inside fully enclosed radiofrequency (RF) shielding rooms, posing stringent installation requirements and causing patient discomfort. We aim to eliminate electromagnetic interference (EMI) for MRI with no or incomplete RF shielding. In this study, a method of active sensing and deep learning EMI prediction is presented to model, predict, and remove EMI signal components from acquired MRI signals. Specifically, during each MRI scan, separate EMI-sensing coils placed in various locations are utilized to simultaneously sample external and internal EMI signals within two windows (for both conventional MRI signal acquisition and EMI characterization acquisition). A convolution neural network model is trained using the EMI characterization data to relate EMI signals detected by EMI-sensing coils to EMI signals in the MRI receive coil. This model is then used to retrospectively predict and remove EMI signal components detected by the MRI receive coil during the MRI signal acquisition window. This strategy was implemented on a low-cost ultralow-field 0.055 T permanent magnet MRI scanner without RF shielding. It produced final image signal-to-noise ratios that were comparable with those obtained using a fully enclosed RF shielding cage, and outperformed existing analytical EMI elimination methods (i.e., spectral domain transfer function and external dynamic interference estimation and removal [EDITER] methods). A preliminary experiment also demonstrated its applicability on a 1.5 T superconducting magnet MRI scanner with incomplete RF shielding. Altogether, the results demonstrated that the proposed method was highly effective in predicting and removing various EMI signals from both external environments and internal scanner electronics at both 0.055 T (2.3 MHz) and 1.5 T (63.9 MHz). The proposed strategy enables shielding-free MRI. The concept is relatively simple and is potentially applicable to other RF signal detection scenarios in the presence of external and/or internal EMI.

Thalamic reticular nucleus (TRN) has been shown to gate sensory thalamo-cortical interactions and selectively modulate thalamic sensory information processing according to behavioral demands. However, whether TRN can exert long-range, i.e., beyond thalamus, cross-modal modulation of sensory processing remains unclear. In this fMRI study, we demonstrate that optogenetic excitation of somatosensory-specific TRN enhances cross-modal excitatory sensory inputs but suppresses cross-modal competing inputs at somatosensory cortices. Our work provides insight into how TRN differentially gate the processing of distinct cross-modal sensory information at large-scale, which may be critical for ensuring the balance between various task demands.

External denoising, also known as reference-based denoising, utilizes information from clean reference images, yielding more robust results than internal denoising, especially in situations with high noise levels. The effectiveness of reference-based denoising relies on the similarity between reference and noisy images. In this paper, we introduce a novel external denoising framework for magnetic resonance imaging (MRI) that takes advantage of the inherent anatomical structure similarities across subjects. Our framework employs noise-resistant neural networks to extract deep features, which facilitate searches for high-quality reference images from a large external database. This versatile framework is compatible with all reference-based denoising algorithms, obviating the need for acquiring additional reference images from the subject under examination.

PurposeRecent development of ultra‐low‐field (ULF) MRI presents opportunities for low‐power, shielding‐free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large‐scale publicly available 3T brain data.MethodsA dual‐acquisition 3D superresolution model is developed for ULF brain MRI at 0.055 T. It consists of deep cross‐scale feature extraction, attentional fusion of two acquisitions, and reconstruction. Models for T1‐weighted and T2‐weighted imaging were trained with 3D ULF image data sets synthesized from the high‐resolution 3T brain data from the Human Connectome Project. They were applied to 0.055T brain MRI with two repetitions and isotropic 3‐mm acquisition resolution in healthy volunteers, young and old, as well as patients.ResultsThe proposed approach significantly enhanced image spatial resolution and suppressed noise/artifacts. It yielded high 3D image quality at 0.055 T for the two most common neuroimaging protocols with isotropic 1.5‐mm synthetic resolution and total scan time under 20 min. Fine anatomical details were restored with intrasubject reproducibility, intercontrast consistency, and confirmed by 3T MRI.ConclusionThe proposed dual‐acquisition 3D superresolution approach advances ULF MRI for quality brain imaging through deep learning of high‐field brain data. Such strategy can empower ULF MRI for low‐cost brain imaging, especially in point‐of‐care scenarios or/and in low‐income and mid‐income countries.

Recently, low-field magnetic resonance imaging (MRI) has gained renewed interest to promote MRI accessibility and affordability worldwide. The presented M4Raw dataset aims to facilitate methodology development and reproducible research in this field. The dataset comprises multi-channel brain k-space data collected from 183 healthy volunteers using a 0.3 Tesla whole-body MRI system, and includes T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) images with in-plane resolution of ~1.2 mm and through-plane resolution of 5 mm. Importantly, each contrast contains multiple repetitions, which can be used individually or to form multi-repetition averaged images. After excluding motion-corrupted data, the partitioned training and validation subsets contain 1024 and 240 volumes, respectively. To demonstrate the potential utility of this dataset, we trained deep learning models for image denoising and parallel imaging tasks and compared their performance with traditional reconstruction methods. This M4Raw dataset will be valuable for the development of advanced data-driven methods specifically for low-field MRI. It can also serve as a benchmark dataset for general MRI reconstruction algorithms.

PURPOSE:To develop a truly calibrationless reconstruction method that derives An Eigenvalue Approach to Autocalibrating Parallel MRI (ESPIRiT) maps from uniformly-undersampled multi-channel MR data by deep learning.METHODS:ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. To utilize subject-coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi-slice axial stack. The performance of the approach was evaluated using publicly available T1-weighed brain and cardiac data.RESULTS:The proposed model robustly predicted multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k-space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps.CONCLUSION:A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly-undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from the coil and protocol-specific data.

Low-rank technique has emerged as a powerful calibrationless alternative for parallel magnetic resonance (MR) imaging. Calibrationless low-rank reconstruction, such as low-rank modeling of local k-space neighborhoods (LORAKS), implicitly exploits both coil sensitivity modulations and the finite spatial support constraint of MR images through an iterative low-rank matrix recovery process. Although powerful, this slow iteration process is computationally demanding and reconstruction requires empirical rank optimization, hampering its robust applications for high-resolution volume imaging. This paper proposes a fast and calibrationless low-rank reconstruction of undersampled multi-slice MR brain data, based on the finite spatial support constraint reformulation with a direct deep learning estimation of spatial support maps. The iteration process of low-rank reconstruction is unrolled into a complex-valued network by training on fully-sampled multi-slice axial brain datasets acquired from the same MR coil system. To utilize coil-subject geometric parameters available for datasets, the model minimizes a hybrid loss on two sets of spatial support maps, corresponding to brain data at the original slice locations as actually acquired and nearby locations within the standard reference coordinate. This deep learning framework was integrated with LORAKS reconstruction and was evaluated with publically available gradient-echo T1-weighted brain datasets. It directly produced high-quality multi-channel spatial support maps from undersampled data, enabling rapid reconstruction without iteration. Moreover, it led to effective reductions of artifacts and noise amplification at high acceleration. In summary, our proposed deep learning framework offers a new strategy to advance the existing calibrationless low-rank reconstruction, rendering it computationally efficient, simple, and robust in practice.

Declining global DNA methylation and cognitive impairment are reported to occur in the normal aging process. It is not known if DNA methylation plays a role in the efficacy of memory-enhancing therapies. In this study, aged animals were administered prelimbic cortical deep brain stimulation (PrL DBS) and/or L-methionine (MET) treatment. We found that PrL DBS and MET (MET-PrL DBS) co-administration resulted in hippocampal-dependent spatial memory enhancements in aged animals. Molecular data suggested MET-PrL DBS induced DNA methyltransferase DNMT3a-dependent methylation, robust synergistic upregulation of neuroplasticity-related genes, and simultaneous inhibition of the memory-suppressing gene calcineurin in the hippocampus. We further found that MET-PrL DBS also activated the PKA-CaMKIIα-BDNF pathway, increased hippocampal neurogenesis, and enhanced dopaminergic and serotonergic neurotransmission. We next inhibited the activity of DNA methyltransferase (DNMT) by RG108 infusion in the hippocampus of young animals to establish a causal relationship between DNMT activity and the effects of PrL DBS. Hippocampal DNMT inhibition in young animals was sufficient to recapitulate the behavioral deficits observed in aged animals and abolished the memory-enhancing and molecular effects of PrL DBS. Our findings implicate hippocampal DNMT as a therapeutic target for PrL DBS and pave way for the potential use of non-invasive neuromodulation modalities against dementia.