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Several recent multi-compartment diffusion MRI investigations and modeling strategies have utilized the orientationally-averaged, or spherical mean, diffusion-weighted signal to study tissue microstructure of the central nervous system. Most experimental designs sample a large number of diffusion weighted directions in order to calculate the spherical mean signal, however, sampling a subset of these directions may increase scanning efficiency and enable either a decrease in scan time or the ability to sample more diffusion weightings. Here, we aim to determine the minimum number of gradient directions needed for a robust measurement of the spherical mean signal. We used computer simulations to characterize the variation of the measured spherical mean signal as a function of the number of gradient directions, while also investigating the effects of diffusion weighting (b-value), signal-to-noise ratio (SNR), available hardware, and spherical mean fitting strategy. We then utilize empirically acquired data in the brain and spinal cord to validate simulations, showing experimental results are in good agreement with simulations. We summarize these results by providing an intuitive lookup table to facilitate the determination of the minimal number of sampling directions needed for robust spherical mean measurements, and give recommendations based on SNR and experimental conditions.

Neurite Orientation Dispersion and Density Imaging (NODDI) has been gaining prominence for estimating multiple diffusion compartments from MRI data acquired in a clinically feasible time. To establish a pathway for adoption of NODDI in clinical studies, it is important to understand the sensitivity and reproducibility of NODDI metrics on empirical data in the context of acquisition protocol and brain anatomy. Previous studies addressed reproducibility across the 3 T scanners and within session and between subject reproducibility at 1.5 T and 3 T. However, empirical reproducibility on the performance of NODDI metrics based on b-value and diffusion-sensitized directions has not yet been addressed. In this study, we investigate a high angular resolution dataset with 11 repeats of a study with five b-values shells (1000, 1500, 2000, 2500 and 3000 s/mm2) and 96 directions per shell on a single subject. We validated the findings with a dataset from second subject with 10 repeats and 3 b-value shells (1000, 2000, 3000 s/mm2). The NODDI model was estimated using Accelerated Microstructure Imaging via Convex Optimization (AMICO) for different b-values and gradient directions on two-shell High Angular Resolution Density Imaging (HARDI) data fixing the lower shell at b = 1000 s/mm2. NODDI model applied to all acquired imaging data was used as a baseline gold standard for comparison. Additionally, we characterize orientation dispersion index (ODI) reproducibility using single-shell data. The experimental findings confirmed the sensitivity of intracellular volume fraction (Vic) with the choice of outer shell b-value more than with the choice of gradient directions. On the other hand, ODI is more sensitive to the number of gradient directions compared to b-value selection. Single-shell results for ODI are more comparable to 2-shell data at lower b-values than higher b-values. Recommended settings by region of interest and acquisition time are reported for the researchers considering using NODDI in human studies and/or comparing results across acquisition protocols.

A new target-field approach to generating uniform radio frequency (RF) fields within the human body for high field MRI is described. The method involves producing a set of external fields which, after interaction with a dielectric object, superimpose to produce a traveling plane wave, exposing all spins to the same RF amplitude (B1) over a cycle of the harmonic field. Conceptually this is similar to conventional RF shimming, but uses a different RF source design, input data, and objective function. The method requires a detailed knowledge of the coupling between exterior field modes, produced by an array of RF sources, and field modes within the body. Given an estimate of the coupling matrix, the linear superposition of external modes that produces a desired internal target field can be determined. The new method is termed Traveling Internal Plane-wave Synthesis (TIPS). A simple design of a coil array is described that can, in principle, generate the required field modes. Simulations demonstrate that radio frequency magnetic fields of nearly uniform (

Functional MRI (fMRI) signals are robustly detectable in white matter (WM) but they have been largely ignored in the fMRI literature. Their nature, interpretation, and relevance as potential indicators of brain function remain under explored and even controversial. Blood oxygenation level dependent (BOLD) contrast has for over 25 years been exploited for detecting localized neural activity in the cortex using fMRI. While BOLD signals have been reliably detected in grey matter (GM) in a very large number of studies, such signals have rarely been reported from WM. However, it is clear from our own and other studies that although BOLD effects are weaker in WM, using appropriate detection and analysis methods they are robustly detectable both in response to stimuli and in a resting state. BOLD fluctuations in a resting state exhibit similar temporal and spectral profiles in both GM and WM, and their relative low frequency (0.01–0.1 Hz) signal powers are comparable. They also vary with baseline neural activity e.g. as induced by different levels of anesthesia, and alter in response to a stimulus. In previous work we reported that BOLD signals in WM in a resting state exhibit anisotropic temporal correlations with neighboring voxels. On the basis of these findings, we derived functional correlation tensors that quantify the correlational anisotropy in WM BOLD signals. We found that, along many WM tracts, the directional preferences of these functional correlation tensors in a resting state are grossly consistent with those revealed by diffusion tensors, and that external stimuli tend to enhance visualization of specific and relevant fiber pathways. These findings support the proposition that variations in WM BOLD signals represent tract-specific responses to neural activity. We have more recently shown that sensory stimulations induce explicit BOLD responses along parts of the projection fiber pathways, and that task-related BOLD changes in WM occur synchronously with the temporal pattern of stimuli. WM tracts also show a transient signal response following short stimuli analogous to but different from the hemodynamic response function (HRF) characteristic of GM. Thus there is converging and compelling evidence that WM exhibits both resting state fluctuations and stimulus-evoked BOLD signals very similar (albeit weaker) to those in GM. A number of studies from other laboratories have also reported reliable observations of WM activations. Detection of BOLD signals in WM has been enhanced by using specialized tasks or modified data analysis methods. In this mini-review we report summaries of some of our recent studies that provide evidence that BOLD signals in WM are related to brain functional activity and deserve greater attention by the neuroimaging community.

Purpose Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance for characterizing in-vivo white matter. Models relating microarchitecture to observed DW-MRI signals as a function of diffusion sensitization are the lens through which DW-MRI data are interpreted. Numerous modern approaches offer opportunities to assess more complex intra-voxel structures. Nevertheless, there remains a substantial gap between intra-voxel estimated structures and ground truth captured by 3-D histology. Methods Herein, we propose a novel data-driven approach to model the non-linear mapping between observed DW-MRI signals and ground truth structures using a sequential deep neural network regression using residual block deep neural network (ResDNN). Training was performed on two 3-D histology datasets of squirrel monkey brains and validated on a third. A second validation was performed using scan-rescan datasets of 12 subjects from Human Connectome Project. The ResDNN was compared with multiple micro-structure reconstruction methods and super resolved-constrained spherical deconvolution (sCSD) in particular as baseline for both the validations. Results Angular correlation coefficient (ACC) is a correlation/similarity measure and can be interpreted as accuracy when compared with a ground truth. The median ACC of ResDNN is 0.82 and median ACC's of different variants of CSD are 0.75, 0.77, 0.79. The mean, median and std. of ResDNN & sCSD ACC across 12 subjects from HCP are 0.74, 0.88, 0.31 and 0.61, 0.71, 0.31 respectively. Conclusion This work highlights the ability of deep learning to capture linkages between ex-vivo ground truth data with feasible MRI sequences. The data-driven approach is applicable to human in-vivo data and results in intriguingly high reproducibility of orientation structure.

Neuroimaging often involves acquiring high-resolution anatomical images along with other low-resolution image modalities, like diffusion and functional magnetic resonance imaging. Performing gray matter statistics with low-resolution image modalities is a challenge due to registration artifacts and partial volume effects. Gray matter surface based spatial statistics (GS-BSS) has been shown to provide higher sensitivity using gray matter surfaces compared to that of skeletonization approach of gray matter based spatial statistics which is adapted from tract based spatial statistics in diffusion studies. In this study, we improve upon GS-BSS incorporating neurite orientation dispersion and density imaging (NODDI) based search (denoted N-GSBSS) by 1) enhancing metrics mapping from native space, 2) incorporating maximum orientation dispersion index (ODI) search along surface normal, and 3) proposing applicability to other modalities, such as functional MRI (fMRI). We evaluated the performance of N-GSBSS against three baseline pipelines: volume-based registration, FreeSurfer’s surface registration and ciftify pipeline for fMRI and simulation studies. First, qualitative mean ODI results are shown for N-GSBSS with and without NODDI based search in comparision with ciftify pipeline. Second, we conducted one-sample t-tests on working memory activations in fMRI to show that the proposed method can aid in the analysis of low resolution fMRI data. Finally we performed a sensitivity test in a simulation study by varying percentage change of intensity values within a region of interest in gray matter probability maps. N-GSBSS showed higher sensitivity in the simulation test compared to the other methods capturing difference between the groups starting at 10 percent change in the intensity values. The computational time of N-GSBSS is 68 times faster than that of traditional surface-based or 86 times faster than that of ciftify pipeline analysis.

Diffusion magnetic resonance images typically suffer from spatial distortions due to susceptibility induced off-resonance fields, which may affect the geometric fidelity of the reconstructed volume and cause mismatches with anatomical images. State-of-the art susceptibility correction (for example, FSL's TOPUP algorithm) typically requires data acquired twice with reverse phase encoding directions, referred to as blip-up blip-down acquisitions, in order to estimate an undistorted volume. Unfortunately, not all imaging protocols include a blip-up blip-down acquisition, and cannot take advantage of the state-of-the art susceptibility and motion correction capabilities. In this study, we aim to enable TOPUP-like processing with historical and/or limited diffusion imaging data that include only a structural image and single blip diffusion image. We utilize deep learning to synthesize an undistorted non-diffusion weighted image from the structural image, and use the non-distorted synthetic image as an anatomical target for distortion correction. We evaluate the efficacy of this approach (named Synb0-DisCo) and show that our distortion correction process results in better matching of the geometry of undistorted anatomical images, reduces variation in diffusion modeling, and is practically equivalent to having both blip-up and blip-down non-diffusion weighted images.

Functional magnetic resonance imaging usually detects changes in blood oxygenation level dependent (BOLD) signals from T2*-sensitive acquisitions, and is most effective in detecting activity in brain cortex which is irrigated by rich vasculature to meet high metabolic demands. We recently demonstrated that MRI signals from T2*-sensitive acquisitions in a resting state exhibit structure-specific temporal correlations along white matter tracts. In this report we validate our preliminary findings and introduce spatio-temporal functional correlation tensors to characterize the directional preferences of temporal correlations in MRI signals acquired at rest. The results bear a remarkable similarity to data obtained by diffusion tensor imaging but without any diffusion-encoding gradients. Just as in gray matter, temporal correlations in resting state signals may reflect intrinsic synchronizations of neural activity in white matter. Here we demonstrate that functional correlation tensors are able to visualize long range white matter tracts as well as short range sub-cortical fibers imaged at rest, and that evoked functional activities alter these structures and enhance the visualization of relevant neural circuitry. Furthermore, we explore the biophysical mechanisms underlying these phenomena by comparing pulse sequences, which suggest that white matter signal variations are consistent with hemodynamic (BOLD) changes associated with neural activity. These results suggest new ways to evaluate MRI signal changes within white matter.

Purpose The diffusion MRI “b-vector” table describing the diffusion sensitization direction can be flipped and permuted in dimension due to different orientation conventions used in scanners and incorrect or improperly utilized file formats. This can lead to incorrect fiber orientation estimates and subsequent tractography failure. Here, we present an automated quality control procedure to detect when the b-table is flipped and/or permuted incorrectly. Methods We define a “fiber coherence index” to describe how well fibers are connected to each other, and use it to automatically detect the correct configuration of b-vectors. We examined the performance on 3981 research subject scans (Baltimore Longitudinal Study of Aging), 1065 normal subject scans of high image quality (Human Connectome Project), and 202 patient scans (Vanderbilt University Medical Center), as well as 9 in-vivo and 9 ex-vivo animal data. Results The coherence index resulted in a 99.9% (3979/3981) and 100% (1065/1065) success rate in normal subject scans, 98% (198/202) in patient scans, and 100% (18/18) in both in-vivo and ex-vivo animal data in detecting the correct gradient table in datasets without severe image artifacts. The four failing cases (4/202) in patient scans, and two failures in healthy subject scans (2/3981), all showed prominent motion or signal dropout artifacts. Conclusions The fiber coherence measure can be used as an automatic quality assurance check in any diffusion analysis pipeline. Additionally, the success of this fiber coherence measure suggests potential broader applications, including evaluating data quality, or even providing diagnostic value as a biomarker of white matter integrity.

Diffusion MRI (dMRI) fiber tractography has become a pillar of the neuroimaging community due to its ability to noninvasively map the structural connectivity of the brain. Despite widespread use in clinical and research domains, these methods suffer from several potential drawbacks or limitations. Thus, validating the accuracy and reproducibility of techniques is critical for sound scientific conclusions and effective clinical outcomes. Towards this end, a number of international benchmark competitions, or "challenges", has been organized by the diffusion MRI community in order to investigate the reliability of the tractography process by providing a platform to compare algorithms and results in a fair manner, and evaluate common and emerging algorithms in an effort to advance the state of the field. In this paper, we summarize the lessons from a decade of challenges in tractography, and give perspective on the past, present, and future "challenges" that the field of diffusion tractography faces.

The ability to evaluate empirical diffusion MRI acquisitions for quality and to correct the resulting imaging metrics allows for improved inference and increased replicability. Previous work has shown promise for estimation of bias and variance of generalized fractional anisotropy (GFA) but comes at the price of computational complexity. This paper aims to provide methods for estimating GFA, bias of GFA and standard deviation of GFA quickly and accurately. In order to provide a method for bias and variance estimation that can return results faster than the previously studied statistical techniques, three deep, fully-connected neural networks are developed for GFA, bias of GFA, and standard deviation of GFA. The results of these networks are compared to the observed values of the metrics as well as those fit from the statistical techniques (i.e. Simulation Extrapolation (SIMEX) for bias estimation and wild bootstrap for variance estimation). Our GFA network provides predictions that are closer to the true GFA values than a Q-ball fit of the observed data (root-mean-square error (RMSE) 0.0077 vs 0.0082, p

Diffusion weighted MRI (DWMRI) and the myriad of analysis approaches (from tensors to spherical harmonics and brain tractography to body multi-compartment models) depend on accurate quantification of the apparent diffusion coefficient (ADC). Signal drift during imaging (e.g., due to b0 drift associated with heating) can cause systematic non-linearities that manifest as ADC changes if not corrected. Herein, we present a case study on two phantoms on one scanner. Different scan protocols exhibit different degrees of drift during similar scans and may be sensitive to the order of scans within an exam. Vos et al. recently reviewed the effects of signal drift in DWMRI acquisitions and proposed a temporal model for correction. We propose a novel spatial-temporal model to correct for higher order aspects of the signal drift and derive a statistically robust variant. We evaluate the Vos model and propose a method using two phantoms that mimic the ADC of the relevant brain tissue (0.36–2.2 × 10–3 mm2/s) on a single 3 T scanner. The phantoms are (1) a spherical isotropic sphere consisting of a single concentration of polyvinylpyrrolidone (PVP) and (2) an ice-water phantom with 13 vials of varying PVP concentrations. To characterize the impact of interspersed minimally weighted volumes (“b0's”), image volumes with b-value equal to 0.1 s/mm2 are interspersed every 8, 16, 32, 48, and 96 diffusion weighted volumes in different trials. Signal drift is found to have spatially varying effects that are not accounted for with temporal-only models. The novel model captures drift more accurately (i.e., reduces the overall change per-voxel over the course of a scan) and results in more consistent ADC metrics.

Q-ball imaging (QBI) is a popular high angular resolution diffusion imaging (HARDI) technique used to study brain architecture in vivo. Simulation and phantom-based studies suggest that QBI results are affected by the b-value, the number of diffusion weighting directions, and the signal-to-noise ratio (SNR). However, optimal acquisition schemes for QBI in clinical settings are largely undetermined given empirical (observed) imaging considerations. In this study, we acquire a HARDI dataset at five b-values with 11 repetitions on a single subject to investigate the effects of acquisition scheme and subsequent analysis models on the accuracy and precision of measures of tissue composition and fiber orientation derived from clinically feasible QBI at 3T. Clinical feasibility entails short scan protocols - less than five minutes in the current study - resulting in lower SNR, lower b-values, and fewer diffusion directions than are typical in most QBI protocols with research applications, where time constraints are less prevalent. In agreement with previous studies, we find that the b-value and number of diffusion directions impact the magnitude and variation of QBI indices in both white matter and gray matter regions; however, QBI indices are most heavily dependent on the maximum order of the spherical harmonic (SH) series used to represent the diffusion orientation distribution function (ODF). Specifically, to ensure numerical stability and reduce the occurrence of false peaks and inflated anisotropy, we recommend oversampling by at least 8–12 more diffusion directions than the number of estimated coefficients for a given SH order. In addition, in an equal scan time comparison of QBI accuracy, we find that increasing the directional resolution of the HARDI dataset is preferable to repeating observations; however, our results indicate that as few as 32 directions at a low b-value (1,000 s/mm2) captures most of the angular information in the q-ball ODF. Our findings provide guidance for determining an optimal acquisition scheme for QBI in the low SNR and low scan time regime, and suggest that care must be taken when choosing the basis functions used to represent the QBI ODF.

Resting state functional magnetic resonance imaging (rsfMRI) has been widely used to measure functional connectivity between cortical regions of the brain. However, there have been minimal reports of bold oxygenation level dependent (BOLD) signals in white matter, and even fewer attempts to detect resting state connectivity. Recently, there has been growing evidence that suggests that reliable detection of white matter BOLD signals may be possible. We have previously shown that nearest neighbor inter-voxel correlations of resting state BOLD signal fluctuations in white matter are anisotropic and can be represented by a functional correlation tensor, but the biophysical origins of these signal variations are not clear. We aimed to assess whether MRI signal fluctuations in white matter vary for different baseline levels of neural activity. We performed imaging studies on live squirrel monkeys under different levels of isoflurane anesthesia at 9.4T. We found 1) the fractional power (0.01-0.08Hz) in white matter was between 60 to 75% of the level in gray matter; 2) the power in both gray and white matter low frequencies decreased monotonically in similar manner with increasing levels of anesthesia; 3) the distribution of fractional anisotropy values of the functional tensors in white matter were significantly higher than those in gray matter; and 4) the functional tensor eigenvalues decreased with increasing level of anesthesia. Our results suggest that as anesthesia level changes baseline neural activity, white matter signal fluctuations behave similarly to those in gray matter, and functional tensors in white matter are affected in parallel.

Purpose Animal models are needed to better understand the relationship between diffusion MRI (dMRI) and the underlying tissue microstructure. One promising model for validation studies is the common squirrel monkey, Saimiri sciureus . This study aims to determine (1) the reproducibility of in vivo diffusion measures both within and between subjects; (2) the agreement between in vivo and ex vivo data acquired from the same specimen and (3) normal diffusion values and their variation across brain regions. Methods Data were acquired from three healthy squirrel monkeys, each imaged twice in vivo and once ex vivo . Reproducibility of fractional anisotropy (FA), mean diffusivity (MD), and principal eigenvector (PEV) was assessed, and normal values were determined both in vivo and ex vivo . Results The calculated coefficients of variation (CVs) for both intra-subject and inter-subject MD were below 10% (low variability) while FA had a wider range of CVs, 2–14% intra-subject (moderate variability), and 3–31% inter-subject (high variability). MD in ex vivo tissue was lower than in vivo (30%–50% decrease), while FA values increased in all regions (30–39% increase). The mode of angular differences between in vivo and ex vivo PEVs was 12 degrees. Conclusion This study characterizes the diffusion properties of the squirrel monkey brain and serves as the groundwork for using the squirrel monkey, both in vivo and ex vivo , as a model for diffusion MRI studies.

Diffusion tensor imaging (DTI) was used to investigate the involvement of brain white matter in Williams syndrome (WS), a genetic neurodevelopmental disorder. Whole-brain DTIs were obtained from 16 young adults with WS and 16 normal controls. A voxel-based analysis was performed to compare fractional anisotropy (FA) values between the two groups. A tract-based analysis was also performed to compare FA values between the two groups along two major white matter tracts that pass through the external capsule: the uncinate and inferior fronto-occipital fasciculi. Several regions of both increased and decreased FA were found within major white matter tracts that connect functional regions that have previously been implicated in the cognitive and neurological symptoms of the syndrome. The tract-based analysis provided additional insight into the involvement of specific white matter tracts implicated in the voxel-based analysis within the external capsule. The results from this study support previously reported changes in white matter diffusion properties in WS and demonstrate the potential usefulness for tract-based analysis in future studies of the disorder.

A multi-step procedure was developed to register magnetic resonance imaging (MRI) and histological data from the same sample in the light microscopy image space, with the ultimate goal of allowing quantitative comparisons of the two datasets. The fixed brain of an owl monkey was used to develop and test the procedure. In addition to the MRI and histological data, photographic images of the brain tissue block acquired during sectioning were assembled into a blockface volume to provide an intermediate step for the overall registration process. The MR volume was first registered to the blockface volume using a combination of linear and nonlinear registration, and two dimensional (2D) blockface sections were registered to corresponding myelin stained sections using a combination of linear and nonlinear registration. Before this 2D registration, two major types of tissue distortions were corrected: tissue tearing and independent movement of different parts of the brain, both introduced during histological processing of the sections. The correction procedure utilized a 2D method to close tissue tears and a multiple iterative closest point (ICP) algorithm to reposition separate pieces of tissue in the image. The accuracy of the overall MR to micrograph registration procedure was assessed by measuring the distance between registered landmarks chosen in the MR image space and the corresponding landmarks chosen in the micrograph space. The average error distance of the MR data registered to micrograph data was 0.324 ± 0.277 mm, only 8% larger than the width of the MRI voxel (0.3 mm).

To improve the accuracy of structural and architectural characterization of living tissue with diffusion tensor imaging, an efficient smoothing algorithm is presented for reducing noise in diffusion tensor images. The algorithm is based on anisotropic diffusion filtering, which allows both image detail preservation and noise reduction. However, traditional numerical schemes for anisotropic filtering have the drawback of inefficiency and inaccuracy due to their poor stability and first order time accuracy. To address this, an unconditionally stable and second order time accuracy semi-implicit Craig-Sneyd scheme is adapted in our anisotropic filtering. By using large step size, unconditional stability allows this scheme to take much fewer iterations and thus less computation time than the explicit scheme to achieve a certain degree of smoothing. Second order time accuracy makes the algorithm reduce noise more effectively than a first order scheme with the same total iteration time. Both the efficiency and effectiveness are quantitatively evaluated based on synthetic and in vivo human brain diffusion tensor images, and these tests demonstrate that our algorithm is an efficient and effective tool for denoising diffusion tensor images.

Most studies investigating mental numerical processing involve adult participants and little is known about the functioning of these systems in children. The current study used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of numeracy and the influence of age on these correlates with a group of adults and a group of third graders who had average to above average mathematical ability. Participants performed simple and complex versions of exact and approximate calculation tasks while in the magnet. Like adults, children activated a network of brain regions in the frontal and parietal lobes during the calculation tasks, and they recruited additional brain regions for the more complex versions of the tasks. However, direct comparisons between adults and children revealed significant differences in level of activation across all tasks. In particular, patterns of activation in the parietal lobe were significantly different as a function of age. Findings support previous claims that the parietal lobe becomes more specialized for arithmetic tasks with age.

Diffusion tensor imaging (DTI)-based fiber tractography holds great promise in delineating neuronal fiber tracts and, hence, providing connectivity maps of the neural networks in the human brain. An array of image-processing techniques has to be developed to turn DTI tractography into a practically useful tool. To this end, we have developed a suite of image-processing tools for fiber tractography with improved reliability. This article summarizes the main technical developments we have made to date, which include anisotropic smoothing, anisotropic interpolation, Bayesian fiber tracking and automatic fiber bundling. A primary focus of these techniques is the robustness to noise and partial volume averaging, the two major hurdles to reliable fiber tractography. Performance of these techniques has been comprehensively examined with simulated and in vivo DTI data, demonstrating improvements in the robustness and reliability of DTI tractography.

Quantitative characterization of multiple sclerosis (MS) lesion load is of considerable interest to clinical follow-up studies. Based on fuzzy clustering of multiparameter magnetic resonance images, we have developed a computer-assisted system for volumetric quantification of brain tissue. Tests on patient data show that the system is very efficient, and volumetric measurements characterized are highly reproducible. The high reproducibility and efficiency offer the potential of routine laboratory and clinical use for quantification of MS lesion load.

There is considerable interest in the use of MR imaging to study brain function. Recently, it has been demonstrated that small changes in signal intensity occur in the visual cortex in T2*-weighted imaging in response to appropriate visual stimuli. Similar responses to activation have also been recorded in motor cortex as well as frontal lobes. To date most of these studies have been carried out at very high field strength or they have used echo planar imaging. We report our preliminary results showing that the effects of activation of visual and motor areas of the brains of normal volunteers can be recorded using conventional MR imaging methods on a standard 1.5 T clinical scanner. Using gradient-echo imaging sequences, we have been able to map activated visual and motor cortex with high spatial resolution in multiple planes, and are using this technique to examine the relationship between physiological response and stimulus parameters. Signal changes of the order of 2-12% in images with TE = 45 msec, TR = 120 msec, and alpha = 40 degrees, permit excellent depiction of the regions affected.

Multi-echo pulse sequences for producing T2-weighted images in much reduced imaging times have recently been developed for routine clinical use. A number of recent articles have described the contrast obtained with fast spin-echo (FSE) sequences and have generally indicated that they depict tissues very similarly to conventional spin-echo (SE) imaging. There are, however, some important differences in contrast between some tissues in FSE images. This work presents a detailed study of the contrast obtained with FSE imaging sequences and examines the image sequence and tissue parameters which influence contrast. The use of multiple refocusing pulses produces several subtle effects not seen in conventional SE imaging sequences, and in this study the precise nature and extent of such effects are described. The relative contributions to image contrast of magnetization transfer, the decoupling of J-modulation effects, the production of stimulated echoes and direct saturation effects, of diffusion and of the effects of the differential attenuation of different spatial frequencies, are each quantified. The mechanisms responsible for the brighter fat signal seen in FSE images, as well as the loss of signal from some other tissues, are explained. Computer simulations, phantom experiments, and clinical images are all used to support the conclusions.

Functional magnetic resonance imaging (fMRI) was used to measure the effects of acute hypoglycemia caused by passive sensory stimulation on brain activation. Visual stimulation was used to generate blood-oxygen-level-dependent (BOLD) contrast, which was monitored during hyperinsulinemic hypoglycemic and euglycemic clamp studies. Hypoglycemia (50 +/- 1 mg glucose/dl) decreased the fMRI signal relative to euglycemia in 10 healthy human subjects: the fractional signal change was reduced by 28 +/- 12% (P.05). These changes were reversed when euglycemia was restored. These data provide a basis of comparison for studies that quantify hypoglycemia-related changes in fMRI activity during cognitive tasks based on visual stimuli and demonstrate that variations in blood glucose levels may modulate BOLD signals in the healthy brain.

The objective of this study was to detect auditory cortical activation in non-sedated neonates employing functional magnetic resonance imaging (fMRI). Using echo-planar functional brain imaging, subjects were presented with a frequency-modulated pure tone; the BOLD signal response was mapped in 5 mm-thick slices running parallel to the superior temporal gyrus. Twenty healthy neonates (13 term, 7 preterm) at term and 4 adult control subjects. Blood oxygen level-dependent (BOLD) signal in response to auditory stimulus was detected in all 4 adults and in 14 of the 20 neonates. FMRI studies of adult subjects demonstrated increased signal in the superior temporal regions during auditory stimulation. In contrast, signal decreases were detected during auditory stimulation in 9 of 14 newborns with BOLD response. fMRI can be used to detect brain activation with auditory stimulation in human infants.

Diffusion-weighted imaging was used to study the relationship between apparent diffusion coefficient (ADC) and cell volume fraction in cell suspensions and packed arrays. Cell volume fraction was varied by changing extracellular fluid osmolarity (for human glial cells) and by changing cell density (for human glial and red blood cells). In packed arrays of glial cells, ADC increased 10% when cells shrank and decreased 13% when cells swelled. ADC decreased 34% as cell density increased from 0 to 72%. In erythrocyte suspensions, ADC decreased 90% as the cell density increased from 0 to 89%. These results agree with theoretical predictions.