High-field downfield MR spectroscopic imaging in the human brain.

PURPOSE: To investigate the feasibility of downfield MR spectroscopic imaging (DF-MRSI) in the human brain at 7T. METHODS: A 7T DF-MRSI pulse sequence was implemented based on the previously described methodology at 3T, with 3D phase-encoding, 1 3 ‾ 3 1 ‾ $$ 1\overline{3}3\overline{1} $$ spectral-spatial excitation, and frequency selective refocusing. Data were pre-processed followed by analysis using the "LCModel" software package, and metabolite maps created from the LCModel results. Total scan time, including brain MRI and a water-reference MRSI, was 24 min. The sequence was tested in 10 normal volunteers. Estimated metabolite levels and uncertainty values (Cramer Rao lower bounds, CRLBs) for nine downfield peaks were compared between seven different brain regions, anterior cingulate cortex (ACC), centrum semiovale (CSO), corpus callosum (CC), cerebellar vermis (CV), dorsolateral prefrontal cortex (DLPFC), posterior cingulate cortex (PCC), and thalamus (Thal). RESULTS: DF peaks were relatively uniformly distributed throughout the brain, with only a small number of peaks showing any significant regional variations. Most DF peaks had average CRLB<25% in most brain regions. Average SNR values were higher for the brain regions ACC and DLPFC (˜7 ± 0.95, mean ± SD) while in a range of 3.4-6.0 for other brain regions. Average linewidth (FWHM) values were greater than 35 Hz in the ACC, CV, and Thal, and 22 Hz in CC, CSO, DLPFC, and PCC. CONCLUSION: High-field DF-MRSI is able to spatially map exchangeable protons in the human brain at high resolution and with near whole-brain coverage in acceptable scan times, and in the future may be used to study metabolism of brain tumors or other neuropathological disorders.

Downfield proton MRSI with whole-brain coverage at 3T.

PURPOSE: To develop a 3D downfield (DF) MRSI protocol with whole brain coverage and post-processing pipeline for creation of metabolite maps. METHODS: A 3D, circularly phase-encoded version of the previously developed 2D DF MRSI sequence with 1 3 ‾ 3 1 ‾ $$ 1\overline{3}3\overline{1} $$ spectral-spatial excitation and frequency selective refocusing was implemented and tested in five healthy volunteers at 3T. The DF metabolite maps with a nominal spatial resolution of 0.7 cm3 were recorded in eight slices at 3T in a scan time of 22 m 40 s. An MRSI post-processing pipeline was developed to create DF metabolite maps. Metabolite concentrations and uncertainty estimates were compared between region differences for nine DF peaks. RESULTS: LCModel analysis showed Cramer Rao lower bounds average values of 3%-4% for protein amide resonances in the three selected regions (anterior cingulate, dorsolateral prefrontal cortex, and centrum semiovale); Cramer Rao lower bounds were somewhat higher for individual peaks but for the most part were less than 20%. While DF concentration maps were visually quite homogeneous throughout the brain, general linear regression analysis corrected for multiple comparisons found significant differences between centrum semiovale and dorsolateral prefrontal cortex for peaks at 7.09 ppm (p = 0.014), 7.90 ppm (p = 0.009), 8.18 ppm (p = 0.009), combined amides (p = 0.009), and between anterior cingulate and dorsolateral prefrontal cortex for the 7.30 ppm peak (p = 0.020). Cramer Rao lower bounds values were not significantly different between brain regions for any of the DF peaks. CONCLUSION: The 3D DF MRSI of the human brain at 3T with wide spatial coverage for the mapping of exchangeable amide and other resonances is feasible at a nominal spatial resolution of 0.7 cm3 .

Information-Theoretic Evidence for Predictive Coding in the Face-Processing System.

Predictive coding suggests that the brain infers the causes of its sensations by combining sensory evidence with internal predictions based on available prior knowledge. However, the neurophysiological correlates of (pre)activated prior knowledge serving these predictions are still unknown. Based on the idea that such preactivated prior knowledge must be maintained until needed, we measured the amount of maintained information in neural signals via the active information storage (AIS) measure. AIS was calculated on whole-brain beamformer-reconstructed source time courses from MEG recordings of 52 human subjects during the baseline of a Mooney face/house detection task. Preactivation of prior knowledge for faces showed as α-band-related and ß-band-related AIS increases in content-specific areas; these AIS increases were behaviorally relevant in the brain's fusiform face area. Further, AIS allowed decoding of the cued category on a trial-by-trial basis. Our results support accounts indicating that activated prior knowledge and the corresponding predictions are signaled in low-frequency activity (<30 Hz).SIGNIFICANCE STATEMENT Our perception is not only determined by the information our eyes/retina and other sensory organs receive from the outside world, but strongly depends also on information already present in our brains, such as prior knowledge about specific situations or objects. A currently popular theory in neuroscience, predictive coding theory, suggests that this prior knowledge is used by the brain to form internal predictions about upcoming sensory information. However, neurophysiological evidence for this hypothesis is rare, mostly because this kind of evidence requires strong a priori assumptions about the specific predictions the brain makes and the brain areas involved. Using a novel, assumption-free approach, we find that face-related prior knowledge and the derived predictions are represented in low-frequency brain activity.

Downfield Proton MRSI at 3 Tesla: A Pilot Study in Human Brain Tumors.

PURPOSE: To investigate the use of 3D downfield proton magnetic resonance spectroscopic imaging (DF-MRSI) for evaluation of tumor recurrence in patients with glioblastoma (GBM). METHODS: Seven patients (4F, age range 44-65 and mean ± standard deviation 59.3 ± 7.5 years) with previously treated GBM were scanned using a recently developed 3D DF-MRSI sequence at 3T. Short TE 3D DF-MRSI and water reference 3D-MRSI scans were collected with a nominal spatial resolution of 0.7 cm3. DF volume data in eight slices covered 12 cm of brain in the cranio-caudal axis. Data were analyzed using the 'LCModel' program and a basis set containing nine peaks ranging in frequency between 6.83 to 8.49 ppm. The DF8.18 (assigned to amides) and DF7.90 peaks were selected for the creation of metabolic images and statistical analysis. Longitudinal MR images and clinical history were used to classify brain lesions as either recurrent tumor or treatment effect, which may include necrosis. DF-MRSI data were compared between lesion groups (recurrent tumor, treatment effect) and normal-appearing brain. RESULTS: Of the seven brain tumor patients, two were classified as having recurrent tumor and the rest were classified as treatment effect. Amide metabolite levels from recurrent tumor regions were significantly (p < 0.05) higher compared to both normal-appearing brain and treatment effect regions. Amide levels in lesion voxels classified as treatment effect were significantly lower than normal brain. CONCLUSIONS: 3D DF-MRSI in human brain tumors at 3T is feasible and was well tolerated by all patients enrolled in this preliminary study. Amide levels measured by 3D DF-MRSI were significantly different between treatment effect and tumor regrowth.

Downfield Proton MRSI with whole-brain coverage at 3T.

Purpose: To develop a 3D downfield magnetic resonance spectroscopic imaging (DF-MRSI) protocol with whole brain coverage and post-processing pipeline for creation of metabolite maps. Methods: A 3D, circularly phase-encoded version of the previously developed 2D DF-MRSI sequence with spectral-spatial excitation and frequency selective refocusing was implemented and tested in 5 healthy volunteers at 3T. Downfield metabolite maps with a nominal spatial resolution of 0.7 cm 3 were recorded in 8 slices at 3T in a scan time of 22m 40s. An MRSI post-processing pipeline was developed to create DF metabolite maps. Metabolite concentrations and uncertainty estimates were compared between region differences for nine downfield peaks. Results: LCModel analysis showed CRLB average values of 3-4% for protein amide resonances in the three selected regions (anterior cingulate (ACC), dorsolateral prefrontal cortex (DLPFC), and centrum semiovale (CSO)); CRLBs were somewhat higher for individual peaks but for the most part were less than 20%. While DF concentration maps were visually quite homogeneous throughout the brain, general linear regression analysis corrected for multiple comparisons found significant differences between CSO and DLPFC for peaks at 7.09 ppm (p= 0.014), 7.90 ppm (p=0.009), 8.18 ppm (p=0.009), combined amides (p=0.009), and between ACC and DLPFC for the 7.30 ppm peak (p=0.020). CRLB values were not significantly different between brain regions for any of the DF peaks. Conclusion: 3D DF-MRSI of the human brain at 3T with wide spatial coverage for the mapping of exchangeable amide and other resonances is feasible at a nominal spatial resolution of 0.7 cm 3 .

Metabolites of progesterone in pregnancy: Associations with perinatal anxiety.

BACKGROUND: Anxiety disorders are the most common psychiatric disorder during the perinatal period and one of the major risk factors for postpartum depression, yet we know little about biological factors in the etiology of perinatal anxiety. A growing literature points to neuroactive steroid (NAS) dysregulation in perinatal mental illness, but directionality has not been clearly demonstrated, results are not consistent, and no studies have investigated NAS in a population with pure anxiety without comorbid depression. We aimed to add to the limited literature by examining the association between anxiety without comorbid depression and metabolic pathways of NAS longitudinally across the peripartum. METHODS: We measured anxiety symptoms by psychological scales and NAS levels using Gas Chromatography-Mass Spectrometry (GC-MS) at the second and third trimester (T2 and T3) and week 6 postpartum (W6) in n = 36 women with anxiety and n = 38 healthy controls. The anxiety group was determined by a data-driven approach, and cross-sectional and longitudinal statistical methods were used to examine the relationship between the study population and NAS. RESULTS: We found that anxiety had a significant moderating effect on the relationship between progesterone and allopregnanolone, with no such effect for the relationships between progesterone and the intermediate (5α-DHP) or isomeric (isoallopregnanolone) compounds in this pathway, and no effects on the corresponding pathway converting progesterone to pregnanolone and epipregnanolone. We also found a less precipitous decline in the ratio of allopregnanolone to progesterone between T3 and W6 in the anxiety group compared to the non-anxiety group. A genotype analysis of a single-nucleotide polymorphism in the AKR1C2 gene demonstrated that the relationship of allopregnanolone to the intermediate metabolite, 5α-DHP, differed by genotype. CONCLUSION: Our exploratory findings indicate that, for pregnant people with anxiety, metabolism is shunted more aggressively toward the endpoint of the progesterone to allopregnanolone metabolic pathway than it is for those without anxiety.

Application of a 1H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts.

Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10 normed-MSE of -1.79, an improvement of almost two orders of magnitude.

Endothelial ERα promotes glucose tolerance by enhancing endothelial insulin transport to skeletal muscle.

The estrogen receptor (ER) designated ERα has actions in many cell and tissue types that impact glucose homeostasis. It is unknown if these include mechanisms in endothelial cells, which have the potential to influence relative obesity, and processes in adipose tissue and skeletal muscle that impact glucose control. Here we show that independent of impact on events in adipose tissue, endothelial ERα promotes glucose tolerance by enhancing endothelial insulin transport to skeletal muscle. Endothelial ERα-deficient male mice are glucose intolerant and insulin resistant, and in females the antidiabetogenic actions of estradiol (E2) are absent. The glucose dysregulation is due to impaired skeletal muscle glucose disposal that results from attenuated muscle insulin delivery. Endothelial ERα activation stimulates insulin transcytosis by skeletal muscle microvascular endothelial cells. Mechanistically this involves nuclear ERα-dependent upregulation of vesicular trafficking regulator sorting nexin 5 (SNX5) expression, and PI3 kinase activation that drives plasma membrane recruitment of SNX5. Thus, coupled nuclear and non-nuclear actions of ERα promote endothelial insulin transport to skeletal muscle to foster normal glucose homeostasis.

Three-dimensional visualization and improved quantification with super-resolution ultrasound imaging - validation framework for analysis of microvascular morphology using a chicken embryo model.

The purpose of this study was to improve the morphological analysis of microvascular networks depicted in three-dimensional (3D) super-resolution ultrasound (SR-US) images. This was supported by qualitative and quantitative validation by comparison to matched brightfield microscopy and traditional B-mode ultrasound (US) images. Contrast-enhanced US (CEUS) images were collected using a preclinical US scanner (Vevo 3100, FUJIFILM VisualSonics Inc.) equipped with an MX250 linear array transducer. CEUS imaging was performed after administration of a microbubble (MB) contrast agent into the vitelline network of a developing chicken embryo. Volume data was collected by mechanically scanning the US transducer throughout a tissue volume-of-interest in 90µm step increments. CEUS images were collected at each increment and stored as in-phase/quadrature data (2000 frames at 152 frames per sec). SR-US images were created for each cross-sectional plane using established data processing methods. All SR-US images were then used to reconstruct a final 3D volume for vessel diameter (VD) quantification and for surface rendering. VD quantification from the 3D SR-US data exhibited an average error of 6.1% ± 6.0% when compared with matched brightfield microscopy images, whereas measurements from B-mode US images had an average error of 77.1% ± 68.9%. Volume and surface renderings in 3D space enabled qualitative validation and improved visualization of small vessels below the axial resolution of the US system. Overall, 3D SR-US image reconstructions depicted the microvascular network of the developing chicken embryos. Improved visualization of isolated vessels and quantification of microvascular morphology from SR-US images achieved a considerably greater accuracy compared to B-mode US measurements.

Morphological image processing for multiscale analysis of super-resolution ultrasound images of tissue microvascular networks.

Diabetes is a major disease and known to impair microvascular recruitment due to insulin resistance. Previous quantifications of the changes in microvascular networks at the capillary level were being performed with either full or manually selected region-of-interests (ROIs) from super-resolution ultrasound (SR-US) images. However, these approaches were imprecise, time-consuming, and unsuitable for automated processes. Here we provided a custom software solution for automated multiscale analysis of SR-US images of tissue microvascularity patterns. An Acuson Sequoia 512 ultrasound (US) scanner equipped with a 15L8-S linear array transducer was used in a nonlinear imaging mode to collect all data. C57BL/6J male mice fed standard chow and studied at age 13-16 wk comprised the lean group (N = 14), and 24-31 wk-old mice who received a high-fat diet provided the obese group (N = 8). After administration of a microbubble (MB) contrast agent, the proximal hindlimb adductor muscle of each animal was imaged (dynamic contrast-enhanced US, DCE-US) for 10 min at baseline and again at 1 h and towards the end of a 2 h hyperinsulinemic-euglycemic clamp. Vascular structures were enhanced with a multiscale vessel enhancement filter and binary vessel segments were delineated using Otsu's global threshold method. We then computed vessel diameters by employing morphological image processing methods for quantitative analysis. Our custom software enabled automated multiscale image examination by defining a diameter threshold to limit the analysis at the capillary level. Longitudinal changes in AUC, IPK, and MVD were significant for lean group (p < 0.02 using Full-ROI and p < 0.01 using 150 µm-ROI) and for obese group (p < 0.02 using Full-ROI, p < 0.03 using 150 µm-ROI). By eliminating large vessels from the ROI (above 150 µm in diameter), perfusion parameters were more sensitive to changes exhibited by the smaller vessels, that are known to be more impacted by disease and treatment.

Efficacy of doxycycline release collagen membrane on surgically created and contaminated defects in rat tibiae: A histopathological and microbiological study.

BACKGROUND: The effects of systemic antibiotics on controlling infective pathogens after guided bone regeneration(GBR) procedures especially in membrane exposures are limited. However, local administrations of antibiotics are rare in GBR techniques. AIM: The aim of this study was to investigate the osteogenesis potential and the antibacterial effect of a doxycycline releasing collagen membrane in surgically created and contaminated defects in rat tibiae. MATERIAL AND METHODS: Defects were created in 20 rats that were randomly divided in to two groups: control group (defect contaminated by Porphyromonas gingivalis, filled with bone graft and covered by collagen membrane); test group (defect contaminated by P. gingivalis filled with bone graft and covered by collagen membrane containing 1mg/cm(2) doxycycline. Animals were sacrificed post surgically on the 14th day for microbiologic evaluation and on the 28th day for histopathological evaluation. RESULTS: The degree of osteogenesis in the test group was seen to be significantly higher than control group (p: 0.011; p<0.05). Furthermore in test group, no bacterial growth was observed. The bacteria counts were determined between 1×104 and 268×104CFU/g with a median of 1.32×104 for control group. CONCLUSIONS: Within the limitations of this study, the results of the present study suggests that the use of a doxycycline releasing membrane has a positive effect on contaminated GBR procedures for limiting P. gingivalis infections leading to bone formation following GBR procedures in a rat model.