Parallel imaging using phased array coils facilitates accelerated magnetic resonance imaging (MRI) and spectroscopy (MRS). Parallel data reconstruction requires the combination of data from individual coil elements, but limited combination algorithms currently exist for higher-order phased arrays and MRS data. Here, we present a systematic framework for identifying coil proximity-related signal inhomogeneities and noise levels in phased array coils that may affect sensitivity of parallel MRS. Single-voxel MRS was acquired in nine voxel positions in a brain spectroscopy phantom on a 3 T whole-body MR scanner using commercially available 64-, 32-, and 20-channel phased array coils. Spectra produced by individual coil elements were combined using both a signal-to-noise ratio (SNR) threshold and based on the position of individual coil elements. SNR and metabolite Cramer-Rao lower bounds (CRLBs) from the final combined spectra were used as metrics to compare combination strategies and the effects of the phased array geometry and individual coil proximity. Comparisons were performed using one-way repeated measures ANOVA and post-hoc Tukey's range test (p < 0.05). The 32-channel phased array coil produced the highest overall SNR compared to the 64-channel (p = 0.0009) or 20-channel coils (p = 0.003). Low SNR spectra from individual coil elements in the 64-channel coil can reduce the overall SNR when simply combining spectra from all elements. SNR varied significantly as a function of voxel position (F = 58.3, p < 0.0001) and SNR threshold for all phased arrays (p < 0.05 for 64-, 32-, and 20-channel coils). Metabolite CRLBs were dependent on the combination strategy. We demonstrate the importance of the sampling voxel position and coil proximity on overall SNR in parallel MRS data acquisition, with significant SNR improvements after selectively filtering individual spectra based on pre-determined SNR thresholds which must be optimized for each phased array coil element and volume of interest.
Multi-channel phased receive arrays have been widely adopted for magnetic resonance imaging (MRI) and spectroscopy (MRS). An important step in the use of receive arrays for MRS is the combination of spectra collected from individual coil channels. The goal of this work was to implement an improved strategy termed OpTIMUS (i.e., optimized truncation to integrate multi-channel MRS data using rank-R singular value decomposition) for combining data from individual channels. OpTIMUS relies on spectral windowing coupled with a rank-R decomposition to calculate the optimal coil channel weights. MRS data acquired from a brain spectroscopy phantom and 11 healthy volunteers were first processed using a whitening transformation to remove correlated noise.
Whitened spectra were then iteratively windowed or truncated, followed by a rank-R singular value decomposition (SVD) to empirically determine the coil channel weights. Spectra combined using the vendor-supplied method, signal/noise2 weighting, previously reported whitened SVD (rank-1), and OpTIMUS were evaluated using the signal-to-noise ratio (SNR). Significant increases in SNR ranging from 6% to 33% (P ≤ 0.05) were observed for brain MRS data combined with OpTIMUS compared with the three other combination algorithms. The assumption that a rank-1 SVD maximizes SNR was tested empirically, and a higher rank-R decomposition, combined with spectral windowing prior to SVD, resulted in increased SNR.
Understanding and subsequently controlling the non-specific interactions between engineered nanomaterials and the biological environment have become increasingly important for further developing and advancing nanotechnology for biomedical applications. Such non-specific interactions, also known as the biofouling effect, mainly associate with the adsorption of biomolecules (such as proteins, DNAs, RNAs, and peptides) onto the surface of nanomaterials and the adhesion or uptake of nanomaterials by various cells. By altering the surface properties of nanomaterials, the biofouling effect can lead to in situ changes of physicochemical properties, pharmacokinetics, functions, and toxicity of nanomaterials. This review provides discussions on the current understanding of the biofouling effect, the factors that affect the non-specific interactions associated with biofouling, and the impact of the biofouling effect on the performance and functions of nanomaterials. An overview of the development and applications of various anti-biofouling coating materials to preserve and improve the properties and functions of engineered nanomaterials for intended biomedical applications is also provided.
Background: Molecular markers for classification of gliomas include isocitrate dehydrogenase (IDH) mutations and codeletion of chromosomal arms 1p and 19q (1p/19q). While mutations in IDH enzymes result in the well-characterized production of oncometabolite 2-hydroxyglutarate, dysregulation of other metabolites in IDH tumors is less characterized. Similarly, the effects of 1p/19q codeletion on cellular metabolism are also unclear. Aim: This study aimed to quantify changes in tumor metabolites in human glioma tissue as a function of both IDH mutation and 1p/19q codeletion. Methods and Results: Deidentified human glioma tissue and associated clinical data were obtained from the Emory University Winship Cancer Institute tissue biobank from 14 patients (WHO grades II, III, and IV; seven female and seven male). Proton (1H) high-resolution magic angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy data were acquired using a 600 MHz Bruker AVANCE III NMR spectrometer. Metabolite concentrations were calculated using LCModel. Differences in metabolite concentrations as a function of IDH mutation, 1p/19q codeletion, and survival status were determined using Mann–Whitney U tests. Concentrations of alanine, glutamine, and glutamate were significantly lower in glioma tissue with IDH mutations compared to tissue with IDH wildtype. Additionally, glutamate concentration was significantly lower in glioma tissue with 1p/19q codeletion compared to intact 1p/19q. Exploratory analysis revealed alanine concentration varied significantly as a function of survival status. Conclusions: Given the emerging landscape of glioma treatments that target metabolic dysregulation, an improved understanding of altered metabolism in molecular sub-types of gliomas, including those with IDH mutation and 1p/19q codeletion, is an important consideration for treatment stratification and personalized medicine.
The glutamine pathway is emerging as an important marker of cancer prognosis and a target for new treatments. In gliomas, the most common type of brain tumors, metabolic reprogramming leads to abnormal consumption of glutamine as an energy source, and increased glutamine concentrations are associated with treatment resistance and proliferation. A key challenge in the development of glutamine-based biomarkers and therapies is the limited number of in vivo tools to noninvasively assess local glutamine metabolism and monitor its changes. In this review, we describe the importance of glutamine metabolism in gliomas and review the current landscape of translational and emerging imaging techniques to measure glutamine in the brain. These techniques include MRS, PET, SPECT, and preclinical methods such as fluorescence and mass spectrometry imaging. Finally, we discuss the roadblocks that must be overcome before incorporating glutamine into a personalized approach for glioma management.
Elevated expression of β-amyloid (Aβ1-42) and tau are considered risk-factors for Alzheimer's disease in healthy older adults. We investigated the effect of aging and cerebrospinal fluid levels of Aβ1-42 and tau on 1) frontal metabolites measured with proton magnetic resonance spectroscopy (MRS) and 2) cognition in cognitively normal older adults (n = 144; age range 50-85). Levels of frontal gamma aminobutyric acid (GABA+) and myo-inositol relative to creatine (mI/tCr) were predicted by age. Levels of GABA+ predicted cognitive performance better than mI/tCr. Additionally, we found that frontal levels of n-acetylaspartate relative to creatine (tNAA/tCr) were predicted by levels of t-tau. In cognitively normal older adults, levels of frontal GABA+ and mI/tCr are predicted by aging, with levels of GABA+ decreasing with age and the opposite for mI/tCr. These results suggest that age- and biomarker-related changes in brain metabolites are not only located in the posterior cortex as suggested by previous studies and further demonstrate that MRS is a viable tool in the study of aging and biomarkers associated with pathological aging and Alzheimer's disease.
Diversity of participants in biomedical research with respect to race, ethnicity, and biological sex is crucial, particularly given differences in disease prevalence, recovery, and survival rates between demographic groups. The objective of this systematic review was to report on the demographics of neuroimaging studies using magnetic resonance imaging (MRI). The Web of Science database was used and data collection was performed between June 2021 to November 2021; all articles were reviewed independently by at least two researchers. Articles utilizing MR data acquired in the United States, with n ≥ 10 human subjects, and published between 2010–2020 were included. Non-primary research articles and those published in journals that did not meet a quality control check were excluded. Of the 408 studies meeting inclusion criteria, approximately 77% report sex, 10% report race, and 4% report ethnicity. Demographic reporting also varied as function of disease studied, participant age range, funding, and publisher. We anticipate quantitative data on the extent, or lack, of reporting will be necessary to ensure inclusion of diverse populations in biomedical research.
Background and Purpose: Many patients with chronic pain report hypersensitivity not only to noxious stimuli, but also to other modalities including innocuous touch, sound, and light, possibly due to differences in the processing of these stimuli. The goal of this study was to characterize functional connectivity (FC) differences between subjects with temporomandibular disorders (TMD) and pain-free controls during a visual functional magnetic resonance imaging (fMRI) task that included an unpleasant, strobing visual stimulus. We hypothesized the TMD cohort would exhibit maladaptations in brain networks consistent with multisensory hypersensitivities observed in TMD patients. Methods: This pilot study included 16 subjects, 10 with TMD and 6 pain-free controls. Clinical pain was characterized using self-reported questionnaires. Visual task-based fMRI data were collected on a 3T MR scanner and used to determine differences in FC via group independent component analysis. Results: Compared to controls, subjects with TMD exhibited abnormally increased FC between the default mode network and lateral prefrontal areas involved in attention and executive function, and impaired FC between the frontoparietal network and higher order visual processing areas. Conclusions: The results indicate maladaptation of brain functional networks, likely due to deficits in multisensory integration, default mode network function, and visual attention and engendered by chronic pain mechanisms.
Brain temperature is an understudied parameter relevant to brain injury and ischemia. To advance our understanding of thermal dynamics in the human brain, combined with the challenges of routine experimental measurements, a biophysical modeling framework was developed to facilitate individualized brain temperature predictions. Model-predicted brain temperatures using our fully conserved model were compared with whole brain chemical shift thermometry acquired in 30 healthy human subjects (15 male and 15 female, age range 18–36 years old). Magnetic resonance (MR) thermometry, as well as structural imaging, angiography, and venography, were acquired prospectively on a Siemens Prisma whole body 3 T MR scanner. Bland–Altman plots demonstrate agreement between model-predicted and MR-measured brain temperatures at the voxel-level. Regional variations were similar between predicted and measured temperatures (< 0.55 °C for all 10 cortical and 12 subcortical regions of interest), and subcortical white matter temperatures were higher than cortical regions. We anticipate the advancement of brain temperature as a marker of health and injury will be facilitated by a well-validated computational model which can enable predictions when experiments are not feasible.
Temporomandibular disorders (TMD) involve chronic pain in the masticatory muscles and jaw joints, but the mechanisms underlying the pain are heterogenous and vary across individuals. In some cases, structural, functional, and metabolic changes in the brain may underlie the condition. In the present study, we evaluated the functional connectivity between 86 regions of interest (ROIs), which were chosen based on previously reported neuroimaging studies of pain and differences in brain morphology identified in an initial surface-based morphometry analysis. Our main objectives were to investigate the topology of the network formed by these ROIs and how it differs between individuals with TMD and chronic pain (n = 16) and pain-free control participants (n = 12). In addition to a true resting state functional connectivity scan, we also measured functional connectivity during a 6-min application of a noxious cuff stimulus applied to the left leg. Our principal finding is individuals with TMD exhibit more suprathreshold correlations (higher nodal degree) among all ROIs but fewer "hub" nodes (i.e., decreased betweenness centrality) across conditions and across all pain pathways. These results suggest is this pain-related network of nodes may be "over-wired" in individuals with TMD and chronic pain compared to controls, both at rest and during experimental pain.