Echo planar imaging (EPI) is the method of choice for the majority of functional magnetic resonance imaging (fMRI), yet EPI is prone to geometric distortions and thus misaligns with conventional anatomical reference data. The poor geometric correspondence between functional and anatomical data can lead to severe misplacements and corruption of detected activation patterns. However, recent advances in imaging technology have provided EPI data with increasing quality and resolution. Here we present a framework for deriving cortical surface reconstructions directly from high-resolution EPI-based reference images that provide anatomical models exactly geometric distortion-matched to the functional data. Anatomical EPI data with 1mm isotropic voxel size were acquired using a fast multiple inversion recovery time EPI sequence (MI-EPI) at 7T, from which quantitative T1 maps were calculated. Using these T1 maps, volumetric data mimicking the tissue contrast of standard anatomical data were synthesized using the Bloch equations, and these T1-weighted data were automatically processed using FreeSurfer. The spatial alignment between T2(⁎)-weighted EPI data and the synthetic T1-weighted anatomical MI-EPI-based images was improved compared to the conventional anatomical reference. In particular, the alignment near the regions vulnerable to distortion due to magnetic susceptibility differences was improved, and sampling of the adjacent tissue classes outside of the cortex was reduced when using cortical surface reconstructions derived directly from the MI-EPI reference. The MI-EPI method therefore produces high-quality anatomical data that can be automatically segmented with standard software, providing cortical surface reconstructions that are geometrically matched to the BOLD fMRI data.

Diffusion magnetic resonance imaging (MRI) methods for axon diameter mapping benefit from higher maximum gradient strengths than are currently available on commercial human scanners. Using a dedicated high-gradient 3T human MRI scanner with a maximum gradient strength of 300 mT/m, we systematically studied the effect of gradient strength on in vivo axon diameter and density estimates in the human corpus callosum. Pulsed gradient spin echo experiments were performed in a single scan session lasting approximately 2h on each of three human subjects. The data were then divided into subsets with maximum gradient strengths of 77, 145, 212, and 293 mT/m and diffusion times encompassing short (16 and 25 ms) and long (60 and 94 ms) diffusion time regimes. A three-compartment model of intra-axonal diffusion, extra-axonal diffusion, and free diffusion in cerebrospinal fluid was fitted to the data using a Markov chain Monte Carlo approach. For the acquisition parameters, model, and fitting routine used in our study, it was found that higher maximum gradient strengths decreased the mean axon diameter estimates by two to three fold and decreased the uncertainty in axon diameter estimates by more than half across the corpus callosum. The exclusive use of longer diffusion times resulted in axon diameter estimates that were up to two times larger than those obtained with shorter diffusion times. Axon diameter and density maps appeared less noisy and showed improved contrast between different regions of the corpus callosum with higher maximum gradient strength. Known differences in axon diameter and density between the genu, body, and splenium of the corpus callosum were preserved and became more reproducible at higher maximum gradient strengths. Our results suggest that an optimal q-space sampling scheme for estimating in vivo axon diameters should incorporate the highest possible gradient strength. The improvement in axon diameter and density estimates that we demonstrate from increasing maximum gradient strength will inform protocol development and encourage the adoption of higher maximum gradient strengths for use in commercial human scanners.

The neurophysiological basis of variability in the latency of evoked neural responses has been of interest for decades. We describe a method to identify white matter pathways that may contribute to inter-individual variability in the timing of neural activity. We investigated the relation of the latency of peak visual responses in occipital cortex as measured by magnetoencephalography (MEG) to fractional anisotropy (FA) in the entire brain as measured with diffusion tensor imaging (DTI) in eight healthy young adults. This method makes no assumptions about the anatomy of white matter connections. Visual responses were evoked during a saccadic paradigm and were time-locked to arrival at a saccadic goal. The latency of the peak visual response was inversely related to FA in bilateral parietal and right lateral frontal white matter adjacent to cortical regions that modulate early visual responses. These relations suggest that biophysical properties of white matter affect the timing of early visual responses. This preliminary report demonstrates a non-invasive, unbiased method to relate the timing information from evoked-response experiments to the biophysical properties of white matter measured with DTI.

Axon diameter mapping using high-gradient diffusion MRI has generated great interest as a noninvasive tool for studying trends in axonal size in the human brain. One of the main barriers to mapping axon diameter across the whole brain is accounting for complex white matter fiber configurations (e.g., crossings and fanning), which are prevalent throughout the brain. Here, we present a framework for generalizing axon diameter index estimation to the whole brain independent of the underlying fiber orientation distribution using the spherical mean technique (SMT). This approach is shown to significantly benefit from the use of real-valued diffusion data with Gaussian noise, which reduces the systematic bias in the estimated parameters resulting from the elevation of the noise floor when using magnitude data with Rician noise. We demonstrate the feasibility of obtaining whole-brain orientationally invariant estimates of axon diameter index and relative volume fractions in six healthy human volunteers using real-valued diffusion data acquired on a dedicated high-gradient 3-Tesla human MRI scanner with 300 mT/m maximum gradient strength. The trends in axon diameter index are consistent with known variations in axon diameter from histology and demonstrate the potential of this generalized framework for revealing coherent patterns in axonal structure throughout the living human brain. The use of real-valued diffusion data provides a viable solution for eliminating the Rician noise floor and should be considered for all spherical mean approaches to microstructural parameter estimation.

Strong gradient systems can improve the signal-to-noise ratio of diffusion MRI measurements and enable a wider range of acquisition parameters that are beneficial for microstructural imaging. We present a comprehensive diffusion MRI dataset of 26 healthy participants acquired on the MGH-USC 3 T Connectome scanner equipped with 300 mT/m maximum gradient strength and a custom-built 64-channel head coil. For each participant, the one-hour long acquisition systematically sampled the accessible diffusion measurement space, including two diffusion times (19 and 49 ms), eight gradient strengths linearly spaced between 30 mT/m and 290 mT/m for each diffusion time, and 32 or 64 uniformly distributed directions. The diffusion MRI data were preprocessed to correct for gradient nonlinearity, eddy currents, and susceptibility induced distortions. In addition, scan/rescan data from a subset of seven individuals were also acquired and provided. The MGH Connectome Diffusion Microstructure Dataset (CDMD) may serve as a test bed for the development of new data analysis methods, such as fiber orientation estimation, tractography and microstructural modelling.

The temporal monocular crescent (TMC) is the most peripheral portion of the visual field whose perception relies solely on input from the ipsilateral eye. According to a handful of post-mortem histological studies in humans and non-human primates, the TMC is represented visuotopically within the most anterior portion of the primary visual cortical area (V1). However, functional evidence of the TMC visuotopic representation in human visual cortex is rare, mostly due to the small size of the TMC representation (~6% of V1) and due to the technical challenges of stimulating the most peripheral portion of the visual field inside the MRI scanner. In this study, by taking advantage of custom-built MRI-compatible visual stimulation goggles with curved displays, we successfully stimulated the TMC region of the visual field in eight human subjects, half of them right-eye dominant, inside a 3 ​T MRI scanner. This enabled us to localize the representation of TMC, along with the blind spot representation (another visuotopic landmark in V1), in all volunteers, which match the expected spatial pattern based on prior anatomical studies. In all hemispheres, the TMC visuotopic representation was localized along the peripheral border of V1, within the most anterior portion of the calcarine sulcus, without any apparent extension into the second visual area (V2). We further demonstrate the reliability of this localization within/across experimental sessions, and consistency in the spatial location of TMC across individuals after accounting for inter-subject structural differences.

Obesity is a metabolic dysfunction characterized by excessive body fat deposition as a consequence of an energy imbalance. Novel therapeutic strategies have emerged that are safe and have comparatively low side effects for obesity treatment. Functional foods and nutraceuticals have recently received a great deal of attention because of their components with the properties of antimetabolic syndrome. Based on our previous in vitro and in vivo investigations on anti-adipogenesis activity and improved body fat accumulation in serials, the combination of three ingredients (including bainiku-ekisu, black garlic, and Mesona procumbens Hemsl), comprising the Mei-Gin formula (MGF), was eventually selected as a novel inhibitor that exhibited preventive effects against obesity. Herein, we verify the anti-obesity effects of MGF in obese rats induced by a high-fat diet and discuss the potential molecular mechanisms underlying obesity development. Oral administration of MGF significantly suppressed the final body weight, weight change, energy and water intake, subcutaneous and visceral fat mass, liver weight, hepatic total lipids and triglycerides (TG), and serum levels of TG, triglycerides (TC), low-density lipoprotein cholesterol (LDL-C), alanine transaminase (AST), uric acid, and ketone bodies and augmented fecal total lipids, TG, and cholesterol excretion in the high-dose MGF-supplemented groups. Furthermore, the corresponding lipid metabolic pathways revealed that MGF supplementation effectively increased lipolysis and fatty acid oxidation gene expression and attenuated fatty acid synthesis gene expression in the white adipose tissue (WAT) and liver and it also increased mitochondrial activation and thermogenic gene expression in the brown adipose tissue (BAT) of rats with obesity induced by a high-fat diet (HFD). These results demonstrate that the intake of MGF can be beneficial for the suppression of HFD-induced obesity in rats through the lipolysis, fatty oxidation, and thermogenesis pathway. In conclusion, these results demonstrate the anti-obesity efficacy of MGF in vivo and suggest that MGF may act as a potential therapeutic agent against obesity.

Physiological noise arising from a variety of sources can significantly degrade the detection of task-related activity in BOLD-contrast fMRI experiments. If whole head spatial coverage is desired, effective suppression of oscillatory physiological noise from cardiac and respiratory fluctuations is quite difficult without external monitoring, since traditional EPI acquisition methods cannot sample the signal rapidly enough to satisfy the Nyquist sampling theorem, leading to temporal aliasing of noise. Using a combination of high speed magnetic resonance inverse imaging (InI) and digital filtering, we demonstrate that it is possible to suppress cardiac and respiratory noise without auxiliary monitoring, while achieving whole head spatial coverage and reasonable spatial resolution. Our systematic study of the effects of different moving average (MA) digital filters demonstrates that a MA filter with a 2 s window can effectively reduce the variance in the hemodynamic baseline signal, thereby achieving 57%-58% improvements in peak z-statistic values compared to unfiltered InI or spatially smoothed EPI data (FWHM = 8.6 mm). In conclusion, the high temporal sampling rates achievable with InI permit significant reductions in physiological noise using standard temporal filtering techniques that result in significant improvements in hemodynamic response estimation.

Human eosinophil derived neurotoxin (EDN), a granule protein secreted by activated eosinophils, is a biomarker for asthma in children. EDN belongs to the human RNase A superfamily possessing both ribonucleolytic and antiviral activities. EDN interacts with heparin oligosaccharides and heparin sulfate proteoglycans on bronchial epithelial Beas-2B cells. In this study, we demonstrate that the binding of EDN to cells requires cell surface glycosaminoglycans (GAGs), and the binding strength between EDN and GAGs depends on the sulfation levels of GAGs. Furthermore, in silico computer modeling and in vitro binding assays suggest critical roles for the following basic amino acids located within heparin binding regions (HBRs) of EDN 34QRRCKN39 (HBR1), 65NKTRKN70 (HBR2), and 113NRDQRRD119 (HBR3) and in particular Arg35, Arg36, and Arg38 within HBR1, and Arg114 and Arg117 within HBR3. Our data suggest that sulfated GAGs play a major role in EDN binding, which in turn may be related to the cellular effects of EDN.

Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.

The MGH-USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing a magnetic field gradient of up to 300 mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH-USC Adult Diffusion Dataset (N=35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU-Minn Connectome Database (ConnectomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH-Harvard-USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH-USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography.

This paper compares a range of compartment models for diffusion MRI data on in vivo human acquisitions from a standard 60mT/m system (Philips 3T Achieva) and a unique 300mT/m system (Siemens Connectom). The key aim is to determine whether both systems support broadly the same models or whether the Connectom higher gradient system supports significantly more complex models. A single volunteer underwent 8h of acquisition on each system to provide uniquely wide and dense sampling of the available space of pulsed-gradient spin-echo (PGSE) measurements. We select a set of promising models from the wide set of possible three-compartment models for in vivo white matter (WM) that previous work and preliminary experiments suggest as strong candidates, but extend them to fit for compartmental T2 and diffusivity. We focus on the corpus callosum where the WM fibre architecture is simplest and compare their ability to explain the measured data, using Akaike's information criterion (AIC), and to predict unseen data, using cross-validation. We also compare the stability of parameter estimates in the presence of i) noise, using bootstrapping, and ii) spatial variation, using visual assessment and comparison with anatomical knowledge. Broadly similar models emerge from the AIC and cross-validation experiments in both data sets. Specifically, a three-compartment model consisting of either a Bingham distribution of sticks or a Cylinder for the intracellular compartment, an anisotropic diffusion tensor (DT) model for the extracellular compartment, as well as an isotropic CSF compartment, performs consistently well. However, various other models also perform well and no single model emerges as clear winner. The WM data (with virtually no CSF contamination) do not support compartmental T2 but partially support compartmental diffusivity. Evaluation of parameter stability favours simpler models than those identified by AIC or cross-validation. They suggest that the level of complexity in models underpinning currently popular microstructure imaging techniques such as NODDI, CHARMED, or ActiveAx, where the number of free parameters is about 4 or 5 rather than 10 or 11, may reflect the level of complexity achievable for a useful technique on current systems, although the 300mT/m data may support more complex models.

The parameter selection for diffusion MRI experiments is dominated by the "k-q tradeoff" whereby the Signal to Noise Ratio (SNR) of the images is traded for either high spatial resolution (determined by the maximum k-value collected) or high diffusion sensitivity (effected by b-value or the q vector) but usually not both. Furthermore, different brain regions (such as gray matter and white matter) likely require different tradeoffs between these parameters due to the size of the structures to be visualized or the length-scale of the microstructure being probed. In this case, it might be advantageous to combine information from two scans - a scan with high q but low k (high angular resolution in diffusion but low spatial resolution in the image domain) to provide maximal information about white matter fiber crossing, and one low q but high k (low angular resolution but high spatial resolution) for probing the cortex. In this study, we propose a method, termed HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging, for acquiring and combining the information from these two complementary types of scan with the goal of studying diffusion in the cortex without compromising white matter fiber information. The white-gray boundary and pial surface obtained from anatomical scans are incorporated as prior information to guide the fusion. We study the complementary advantages of the fused datasets, and assess the quality of the HIBRID data compared to either alone.

Prenatal exposure to buprenorphine renders offspring vulnerable to cerebral impairments. In this study, our data demonstrate, for the first time, that prenatal exposure to buprenorphine escalates astrocyte activation concurrent with indications of endoplasmic reticulum (ER) stress in the hippocampi of neonates, and this can be prevented by the coadministration of dextromethorphan with buprenorphine. Furthermore, dextromethorphan can inhibit the accumulation of GPR37 in the hippocampus of newborns caused by buprenorphine and is accompanied by the proapoptotic ER stress response that involves the procaspase-3/CHOP pathway. Primary astrocyte cultures derived from the neonates of the buprenorphine group also displayed aberrant ER calcium mobilization and elevated basal levels of cyclooxygenase-2 (COX-2) at 14 days in vitro while showing sensitivity to lipopolysaccharide-activated expression of COX-2. Similarly, these long-lasting defects in the hippocampus and astrocytes were abolished by dextromethorphan. Our findings suggest that prenatal exposure to buprenorphine might instigate long-lasting effects on hippocampal and astrocytic functions. The beneficial effects of prenatal coadministration of dextromethorphan might be, at least in part, attributed to its properties in attenuating astrocyte activation and hippocampal ER stress in neonates.

Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories worldwide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a mouse default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. RSN spatial specificity was enhanced in datasets acquired at higher field strength, with cryoprobes, in ventilated animals, and under medetomidine-isoflurane combination sedation. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

To investigate the potential anti-obesity properties of an innovative functional formula (called the Mei-Gin formula: MGF) consisting of bainiku-ekisu, Prunus mume (70% ethanol extract), black garlic (water extract), and Mesona procumbens Hemsl. (40% ethanol extract) for reducing lipid accumulation in 3T3-L1 adipocytes in vitro and obese rats in vivo.

Functional MRI (fMRI) has become an important translational tool for studying brain activity and connectivity in animal models and humans. For accurate and reliable measurement of functional connectivity, nuisance removal strategies developed for human brain, such as regressing motion parameters, cerebrospinal fluid (CSF)/white matter-derived signals and the global signal, have been applied to rodent. However, due to the very different anatomy, with the majority of the rodent brain being gray matter, and experimental conditions, in which animals are anesthetized and head-fixed, these methods may not be suitable for rodent fMRI. In this study, we assessed various nuisance regression methods and the effects of motion correction on a large dataset of both task and resting fMRI of anesthetized rat brain. Sensitivity and specificity were assessed in the somatosensory pathway under forepaw stimulation and resting state. Reproducibility at various sample sizes was simulated by randomly subsampling the dataset. To overcome the difficulty in extracting nuisance from the brain, a method using principal components estimated from tissues outside the brain was evaluated. Our results showed that neither detrend, motion correction, motion regression nor CSF signal regression could improve specificity despite increasing temporal signal-to-noise ratios. Although global signal regression increased the specificity of task activation and functional connectivity, the sensitivity and connectivity strength was drastically reduced, likely due to its strong correlation with the cortical signal. Motion parameters also correlated with task activation and the global signal, indicating that motion correction detected intensity variations in the brain. The nuisance estimated from tissues outside the brain produced a moderate improvement in specificity. In conclusion, nuisance removal suitable for human fMRI may not be optimal for rodents. While further development is needed, estimating nuisance from tissues outside the brain may be an alternative.

Naturalistic stimuli such as movies are increasingly used to engage cognitive and emotional processes during fMRI of brain hemodynamic activity. However, movies have been little utilized during magnetoencephalography (MEG) and EEG that directly measure population-level neuronal activity at a millisecond resolution. Here, subjects watched a 17-min segment from the movie Crash (Lionsgate Films, 2004) twice during simultaneous MEG/EEG recordings. Physiological noise components, including ocular and cardiac artifacts, were removed using the DRIFTER algorithm. Dynamic estimates of cortical activity were calculated using MRI-informed minimum-norm estimation. To improve the signal-to-noise ratio (SNR), principal component analyses (PCA) were employed to extract the prevailing temporal characteristics within each anatomical parcel of the Freesurfer Desikan-Killiany cortical atlas. A variety of alternative inter-subject correlation (ISC) approaches were then utilized to investigate the reliability of inter-subject synchronization during natural viewing. In the first analysis, the ISCs of the time series of each anatomical region over the full time period across all subject pairs were calculated and averaged. In the second analysis, dynamic ISC (dISC) analysis, the correlation was calculated over a sliding window of 200 ms with 3.3 ms steps. Finally, in a between-run ISC analysis, the between-run correlation was calculated over the dynamic ISCs of the two different runs after the Fisher z-transformation. Overall, the most reliable activations occurred in occipital/inferior temporal visual and superior temporal auditory cortices as well as in the posterior cingulate, precuneus, pre- and post-central gyri, and right inferior and middle frontal gyri. Significant between-run ISCs were observed in superior temporal auditory cortices and inferior temporal visual cortices. Taken together, our results show that movies can be utilized as naturalistic stimuli in MEG/EEG similarly as in fMRI studies.

Recently proposed dynamic magnetic resonance (MR) inverse imaging (InI) is a novel parallel imaging reconstruction technique capable of improving the temporal resolution of blood-oxygen level-dependent (BOLD) contrast functional MRI (fMRI) to the order of milliseconds at the cost of moderate spatial resolution. Volumetric InI reconstructs spatial information from projection data by solving ill-posed inverse problems using simultaneous acquisitions from a RF coil array. Previously a spatial filtering technique based on linearly constrained minimum variance (LCMV) beamformer was suggested to localize the hemodynamic changes of dynamic InI data with improved spatial resolution and sensitivity. Here we report an advancement of the spatial filtering method, which combines the eigenspace projection of the measured data and the L1-norm minimization of the spatial filters' output noise amplitude, to further improve the detection power of BOLD contrast fMRI data. Using numerical simulation and in vivo data, we demonstrate that this eigenspace linearly constrained minimum amplitude (eLCMA) beamformer can reconstruct spatiotemporal hemodynamic signals with high statistical significance values and high spatial resolution in event-related two-choice reaction time visuomotor experiments.

Based on the infamous left-lateralized neglect syndrome, one might hypothesize that the dominating right parietal cortex has a bilateral representation of space, whereas the left parietal cortex represents only the contralateral right hemispace. Whether this principle applies to human auditory attention is not yet fully clear. Here, we explicitly tested the differences in cross-hemispheric functional coupling between the intraparietal sulcus (IPS) and auditory cortex (AC) using combined magnetoencephalography (MEG), EEG, and functional MRI (fMRI). Inter-regional pairwise phase consistency (PPC) was analyzed from data obtained during dichotic auditory selective attention task, where subjects were in 10-s trials cued to attend to sounds presented to one ear and to ignore sounds presented in the opposite ear. Using MEG/EEG/fMRI source modeling, parietotemporal PPC patterns were (a) mapped between all AC locations vs. IPS seeds and (b) analyzed between four anatomically defined AC regions-of-interest (ROI) vs. IPS seeds. Consistent with our hypothesis, stronger cross-hemispheric PPC was observed between the right IPS and left AC for attended right-ear sounds, as compared to PPC between the left IPS and right AC for attended left-ear sounds. In the mapping analyses, these differences emerged at 7-13Hz, i.e., at the theta to alpha frequency bands, and peaked in Heschl's gyrus and lateral posterior non-primary ACs. The ROI analysis revealed similarly lateralized differences also in the beta and lower theta bands. Taken together, our results support the view that the right parietal cortex dominates auditory spatial attention.