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Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing., Competing Interests: Declaration of Competing Interest SHK receives payment from Mendi Innovations AB, Stockholm, Sweden., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Purpose: We report a new postprocessing procedure that uses Fourier-domain data analyses to improve the accuracy and reliability of phase unwrapping for MRI data of low SNR., Methods: The developed method first identifies the Fourier-domain energy peak locations corresponding to different image-domain areas from which a robust measurement of image-domain phase gradients can be obtained even for MRI data of low SNR. The phase-gradient information measured from critical brain regions using the above-mentioned Fourier-domain analysis is then combined with the conventional temporal-domain or spatial-domain phase-unwrapping procedure to remove phase wraps. The developed method was tested with MRI data obtained from 30 healthy adult volunteers, and its performance was quantitatively evaluated., Results: The developed Fourier-domain analysis could robustly quantify image-domain phase gradients even for MRI data with low SNR (e.g., SNR ≃ 2). Experimental results show that the Fourier-domain analyses could further reduce phase wrap artifact in data produced by the conventional temporal-domain or spatial-domain phase-unwrapping procedures., Conclusion: Our results demonstrate that the developed phase-unwrapping method can reduce residual phase wraps resulting from conventional procedures in critical brain regions (e.g., near the air-tissue interfaces) and should prove valuable for studies that require accurate measurements of MRI phase values, such as QSM, B 0 field mapping, and temperature mapping., (© 2019 International Society for Magnetic Resonance in Medicine.)

MRI parametric mapping, including T2 mapping, can quantitatively characterize tissue properties and is an important MRI procedure in biomedical research and studies of diseases [1-3]. However, the accuracy, quality, and signal-to-noise ratio (SNR) of MRI parametric mapping may be negatively impacted by Rician noise in multi-contrast MRI data [4]. As such, it is important to develop a post-processing method to minimize the negative impact of Rician noise. In this study, we report a new parametric-contrast-matched principal component analysis (PCM-PCA) denoising method that involves 1) identifying voxels with similar T2 decay characteristics and 2) using the principal component analysis (PCA) to denoise multi-contrast MRI data along the echo time (TE) dimension. We additionally evaluated the effects of integrating Rician bias correction and the new PCM-PCA method. In this study, we mathematically added Rician noise at various levels to human brain MRI data and performed different combinations of denoising and Rician bias correction on the magnitude-valued images. We found that MRI denoising using the PCM-PCA method resulted in improved image quality, SNR, and accuracy of the measured T2 relaxation time constants. Additionally, we found that for data with low SNR ( e.g ., 1.5 or lower), Rician bias correction further improved image quality and T2 mapping accuracy. In summary, our experimental results demonstrated that the new PCM-PCA denoising method and Rician bias correction adequately improve multi-contrast MRI quality and T2 parametric mapping accuracy.

Purpose: We report an approach, termed Repeated k-t-subsampling and artifact-minimization (ReKAM), for removing motion artifacts in free-breathing abdominal MRI. The method is particularly valuable for challenging patients who may not hold their breath for a long time or have irregular respiratory rate., Methods: The ReKAM framework comprises one acquisition module and two reconstruction modules. A fast MRI sequence is used to repeatedly acquire multiple sets of k-t space data. Motion artifacts are then minimized by two reconstruction modules: (a) a bootstrapping module in k-t-space is used to identify a low-artifact image; (b) a constrained reconstruction module that integrates projection onto convex set (POCS) and multiplexed sensitivity encoding (MUSE), termed POCSMUSE, is applied to further remove residual artifact. The ReKAM framework is compatible with different pulse sequences, and generally applicable to irregular data sampling patterns in k-space. Free-breathing fast spin-echo MRI data, acquired from healthy volunteers and patients, were used to evaluate the developed ReKAM method., Results: Experimental results show that the ReKAM technique can produce high-quality free-breathing images with the artifact levels comparable to that of breath-holding MRI., Conclusion: The ReKAM framework improves the quality of free-breathing abdominal MRI data, and is compatible with various MRI pulse sequences., (© 2017 American Association of Physicists in Medicine.)

Purpose: To develop a high-speed T 2 mapping protocol that is capable of accurately measuring T 2 relaxation time constants from a single-shot acquisition., Theory: A new echo-split single-shot gradient-spin-echo (GRASE) pulse sequence is developed to acquire multicontrast data while suppressing signals from most nonprimary echo pathways in Carr-Purcell-Meiboom-Gill (CPMG) echoes. Residual nonprimary pathway signals are taken into consideration when performing T 2 mapping using a parametric multiplexed sensitivity encoding based on projection onto convex sets (parametric-POCSMUSE) reconstruction method that incorporates extended phase graph modeling of GRASE signals., Methods: The single-shot echo-split GRASE-based T 2 mapping procedure was evaluated in human studies at 3 Tesla. The acquired data were compared with reference data obtained with a more time-consuming interleaved spin-echo echo planar imaging protocol. T 2 maps derived from conventional single-shot GRASE scans, in which nonprimary echo pathways were not appropriately addressed, were also evaluated., Results: Using the developed single-shot T 2 mapping protocol, quantitatively accurate T 2 maps can be obtained with a short scan time (<0.2 seconds per slice)., Conclusion: Accurate T 2 mapping with minimal signal contamination from CPMG high-order echo pathways can be achieved by the developed method that integrates single-shot echo-split GRASE acquisition and parametric-POCSMUSE reconstruction. Magn Reson Med 79:383-393, 2018. © 2017 International Society for Magnetic Resonance in Medicine., (© 2017 International Society for Magnetic Resonance in Medicine.)

Major advances in resting-state functional magnetic resonance imaging (fMRI) techniques in the last two decades have provided a tool to better understand the functional organization of the brain both in health and illness. Despite such developments, characterizing regulation and cerebral representation of mind wandering, which occurs unavoidably during resting-state fMRI scans and may induce variability of the acquired data, remains a work in progress. Here, we demonstrate that a decrease or decoupling in functional connectivity involving the caudate nucleus, insula, medial prefrontal cortex and other domain-specific regions was associated with more sustained mind wandering in particular thought domains during resting-state fMRI. Importantly, our findings suggest that temporal and between-subject variations in functional connectivity of above-mentioned regions might be linked with the continuity of mind wandering. Our study not only provides a preliminary framework for characterizing the maintenance and cerebral representation of different types of mind wandering, but also highlights the importance of taking mind wandering into consideration when studying brain organization with resting-state fMRI in the future.

Background: Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades., New Method: A spatially regularized support vector machine (SVM) technique was developed for the reliable brain mapping in task- and resting-state. Unlike most existing SVM-based brain mapping techniques, which implement supervised classifications of specific brain functional states or disorders, the proposed method performs a semi-supervised classification for the general brain function mapping where spatial correlation of fMRI is integrated into the SVM learning. The method can adapt to intra- and inter-subject variations induced by fMRI nonstationarity, and identify a true boundary between active and inactive voxels, or between functionally connected and unconnected voxels in a feature space., Results: The method was evaluated using synthetic and experimental data at the individual and group level. Multiple features were evaluated in terms of their contributions to the spatially regularized SVM learning. Reliable mapping results in both task- and resting-state were obtained from individual subjects and at the group level., Comparison With Existing Methods: A comparison study was performed with independent component analysis, general linear model, and correlation analysis methods. Experimental results indicate that the proposed method can provide a better or comparable mapping performance at the individual and group level., Conclusions: The proposed method can provide accurate and reliable mapping of brain function in task- and resting-state, and is applicable to a variety of quantitative fMRI studies., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Measurements of plaque stiffness may provide important prognostic and diagnostic information to help clinicians distinguish vulnerable plaques containing soft lipid pools from more stable, stiffer plaques. In this preliminary study, we compare in vivo ultrasonic Acoustic Radiation Force Impulse (ARFI) imaging derived measures of carotid plaque stiffness with composition determined by spatially registered Magnetic Resonance Imaging (MRI) in five human subjects with stenosis > 50%. Ultrasound imaging was implemented on a commercial diagnostic scanner with custom pulse sequences to collect spatially registered 2D longitudinal B-mode and ARFI images. A standardized, multi-contrast weighted MRI sequence was used to obtain 3D Time of Flight (TOF), T1 weighted (T1W), T2 weighted (T2W), and Proton Density Weighted (PDW) transverse image stacks of volumetric data. The MRI data was segmented to identify lipid, calcium, and normal loose matrix components using commercially available software. 3D MRI segmented plaque models were rendered and spatially registered with 2D B-mode images to create fused ultrasound and MRI volumetric images for each subject. ARFI imaging displacements in regions of interest (ROIs) derived from MRI segmented contours of varying composition were compared. Regions of calcium and normal loose matrix components identified by MRI presented as homogeneously stiff regions of similarly low (typically ≈ 1 μm) displacement in ARFI imaging. MRI identified lipid pools > 2 mm(2), found in three out of five subjects, presented as softer regions of increased displacement that were on average 1.8 times greater than the displacements in adjacent regions of loose matrix components in spatially registered ARFI images. This work provides early evidence supporting the use of ARFI imaging to noninvasively identify lipid regions in carotid artery plaques in vivo that are believed to increase the propensity of a plaque to rupture. Additionally, the results provide early training data for future studies and aid in the interpretation and possible clinical utility of ARFI imaging for identifying the elusive vulnerable plaque.

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive neuromodulation technique that has been used to treat neurological and psychiatric conditions. Although results of rTMS intervention are promising, so far, little is known about the rTMS effect on brain functional networks in clinical populations. In this study, we used a whole-brain connectivity analysis of resting-state functional magnetic resonance imaging data to uncover changes in functional connectivity following rTMS intervention and their association with motor symptoms in patients with multiple system atrophy (MSA). Patients were randomized to active rTMS or sham rTMS groups and completed a 10-session 5-Hz rTMS treatment over the left primary motor area. The results showed significant rTMS-related changes in motor symptoms and functional connectivity. Specifically, (1) significant improvement of motor symptoms was observed in the active rTMS group, but not in the sham rTMS group; and (2) several functional links involving the default mode, cerebellar, and limbic networks exhibited positive changes in functional connectivity in the active rTMS group. Moreover, the positive changes in functional connectivity were associated with improvement in motor symptoms for the active rTMS group. The present findings suggest that rTMS may improve motor symptoms by modulating functional links connecting to the default mode, cerebellar, and limbic networks, inferring a future therapeutic candidate for patients with MSA.

Purpose: Imaging of midbrain nuclei using T2- or T2*-weighted MRI often entails long echo time, leading to long scan time. In this study, an inverse double-echo steady-state (iDESS) technique is proposed for efficiently depicting midbrain nuclei., Methods: Thirteen healthy subjects participated in this study. iDESS was performed along with two sets of T2*-weighted spoiled gradient-echo images (SPGR1, with scan time identical to iDESS and SPGR2, using clinical scanning parameters as a reference standard) for comparison. Generation of iDESS composite images combining two echo signals was optimized for maximal contrast-to-noise ratio (CNR) between the red nuclei and surrounding tissues. Signal-to-noise ratios (SNRs) were calculated from the occipital lobe. Comparison was also made using phase-enhanced images as in standard susceptibility-weighted imaging (SWI)., Results: The iDESS images present significantly higher SNR efficiency (171.3) than SPGR1 (158.7, p = 0.013) and SPGR2 (95.5, p < 10(-8)). iDESS CNR efficiency (19.2) is also significantly greater than SPGR1 (6.9, p < 10(-6)) and SPGR2 (14.3, p = 0.0016). Compared with DESS, iDESS provides further advantage on enhanced phase information and hence improved contrast on SWI-processed images., Conclusions: iDESS efficiently depicts midbrain nuclei with improved CNR efficiency, increased SNR efficiency, and reduced scan time and is less prone to susceptibility signal loss from air-tissue interfaces.

Purpose: Current four dimensional magnetic resonance imaging (4D-MRI) techniques lack sufficient temporal/spatial resolution and consistent tumor contrast. To overcome these limitations, this study presents the development and initial evaluation of a new strategy for 4D-MRI which is based on retrospective k-space reordering., Methods: We simulated a k-space reordered 4D-MRI on a 4D digital extended cardiac-torso (XCAT) human phantom. A 2D echo planar imaging MRI sequence [frame rate (F) = 0.448 Hz; image resolution (R) = 256 × 256; number of k-space segments (NKS) = 4] with sequential image acquisition mode was assumed for the simulation. Image quality of the simulated "4D-MRI" acquired from the XCAT phantom was qualitatively evaluated, and tumor motion trajectories were compared to input signals. In particular, mean absolute amplitude differences (D) and cross correlation coefficients (CC) were calculated. Furthermore, to evaluate the data sufficient condition for the new 4D-MRI technique, a comprehensive simulation study was performed using 30 cancer patients' respiratory profiles to study the relationships between data completeness (Cp) and a number of impacting factors: the number of repeated scans (NR), number of slices (NS), number of respiratory phase bins (NP), NKS, F, R, and initial respiratory phase at image acquisition (P0). As a proof-of-concept, we implemented the proposed k-space reordering 4D-MRI technique on a T2-weighted fast spin echo MR sequence and tested it on a healthy volunteer., Results: The simulated 4D-MRI acquired from the XCAT phantom matched closely to the original XCAT images. Tumor motion trajectories measured from the simulated 4D-MRI matched well with input signals (D = 0.83 and 0.83 mm, and CC = 0.998 and 0.992 in superior-inferior and anterior-posterior directions, respectively). The relationship between Cp and NR was found best represented by an exponential function (CP=1001-e(-0.18NR) , when NS = 30, NP = 6). At a CP value of 95%, the relative error in tumor volume was 0.66%, indicating that NR at a CP value of 95% (NR,95%) is sufficient. It was found that NR,95% is approximately linearly proportional to NP (r = 0.99), and nearly independent of all other factors. The 4D-MRI images of the healthy volunteer clearly demonstrated respiratory motion in the diaphragm region with minimal motion induced noise or aliasing., Conclusions: It is feasible to generate respiratory correlated 4D-MRI by retrospectively reordering k-space based on respiratory phase. This new technology may lead to the next generation 4D-MRI with high spatiotemporal resolution and optimal tumor contrast, holding great promises to improve the motion management in radiotherapy of mobile cancers.

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Functional magnetic resonance imaging (fMRI) technique with blood oxygenation level dependent (BOLD) contrast is a powerful tool for noninvasive mapping of brain function under task and resting states. The removal of cardiac- and respiration-induced physiological noise in fMRI data has been a significant challenge as fMRI studies seek to achieve higher spatial resolutions and characterize more subtle neuronal changes. The low temporal sampling rate of most multi-slice fMRI experiments often causes aliasing of physiological noise into the frequency range of BOLD activation signal. In addition, changes of heartbeat and respiration patterns also generate physiological fluctuations that have similar frequencies with BOLD activation. Most existing physiological noise-removal methods either place restrictive limitations on image acquisition or utilize filtering or regression based post-processing algorithms, which cannot distinguish the frequency-overlapping BOLD activation and the physiological noise. In this work, we address the challenge of physiological noise removal via the kernel machine technique, where a nonlinear kernel machine technique, kernel principal component analysis, is used with a specifically identified kernel function to differentiate BOLD signal from the physiological noise of the frequency. The proposed method was evaluated in human fMRI data acquired from multiple task-related and resting state fMRI experiments. A comparison study was also performed with an existing adaptive filtering method. The results indicate that the proposed method can effectively identify and reduce the physiological noise in fMRI data. The comparison study shows that the proposed method can provide comparable or better noise removal performance than the adaptive filtering approach., (© 2013.)

The purpose of this work was to evaluate a previously proposed approach that aims to improve the point spread function (PSF) of MR spectroscopic imaging (MRSI) to avoid corruption by lipid signal arising from neighboring voxels. Retrospective spatial filtering can be used to alter the PSF; however, this either reduces spatial resolution or requires extending the acquisition in k-space at the cost of increased imaging time. Alternatively, the method evaluated here, PSF-choice, can modify the PSF localization to reduce the contamination from adjacent lipids by conforming the signal response more closely to the desired MRSI voxel grid. This is done without increasing scan time or degrading SNR of important metabolites. PSF-choice achieves improvements in spatial localization through modifications to the radiofrequency excitation pulses. An implementation of this method is reported for MRSI of the prostate, where it is demonstrated that, in 13 of 16 pilot prostate MRSI scans, intravoxel spectral contamination from lipid was significantly reduced when using PSF-choice. Phantom studies were also performed that demonstrate, compared with MRSI with standard Fourier phase encoding, out-of-voxel signal contamination of spectra was significantly reduced in MRSI with PSF-choice.

Object: To develop an ultrafast MRI-based temperature monitoring method for application during rapid ultrasound exposures in moving organs., Materials and Methods: A slice selective 90° - 180° pair of RF pulses was used to solicit an echo from a column, which was then sampled with a train of gradient echoes. In a gel phantom, phase changes of each echo were compared to standard gradient-echo thermometry, and temperature monitoring was tested during focused ultrasound sonications. Signal-to-noise ratio (SNR) performance was evaluated in vivo in a rabbit brain, and feasibility was tested in a human heart., Results: The correlation between each echo in the acquisition and MRI-based temperature measurements was good (R = 0.98 ± 0.03). A temperature sampling rate of 19 Hz was achieved at 3T in the gel phantom. It was possible to acquire the water frequency in the beating heart muscle with 5-Hz sampling rate during a breath hold., Conclusion: Ultrafast thermometry via phase or frequency monitoring along single columns was demonstrated. With a temporal resolution around 50 ms, it may be possible to monitor focal heating produced by short ultrasound pulses.

Functional magnetic resonance imaging (fMRI) techniques enable noninvasive studies of brain functional activity under task and resting states. However, the analysis of brain activity could be significantly affected by the cardiac- and respiration-induced physiological noise in fMRI data. In most multi-slice fMRI experiments, the temporal sampling rates are not high enough to critically sample the physiological noise, and the noise is aliased into frequency bands where useful brain functional signal exists, compromising the analysis. Most existing approaches cannot distinguish between the aliased noise and signal if they overlap in the frequency domain. In this work, we further developed a kernel principal component analysis based physiological removal method based on our previous work. Specifically, two kernel functions were evaluated based on a newly proposed criterion that can measure the capability of a kernel to separate the aliased physiological noise from fMRI signal. In addition, a mutual information based criterion was designed to select principal components for noise removal. The method was evaluated by human experimental fMRI studies, and the results demonstrate that the proposed method can effectively identify and attenuate the aliased physiological noise in fMRI data.

Spiral imaging is vulnerable to spatial and temporal variations of the amplitude of the static magnetic field (B(0)) caused by susceptibility effects, eddy currents, chemical shifts, subject motion, physiological noise, and system instabilities, resulting in image blurring. Here, a novel off-resonance correction method is proposed to address these issues. A k-space energy spectrum analysis algorithm is first applied to inherently and dynamically generate a B(0) map from the k-space data at each time point, without requiring any additional data acquisition, pulse sequence modification, or phase unwrapping. A simulated phase evolution rewinding algorithm and an automatic residual deblurring algorithm are then used to correct for the blurring caused by both spatial and temporal B(0) variations, resulting in a high spatial and temporal fidelity. This method is validated against conventional B(0) mapping and deblurring methods, and its advantages for dynamic MRI applications are demonstrated in functional MRI studies.

Functional connectivity (FC) reflects the coherence of spontaneous, low-frequency fluctuations in functional magnetic resonance imaging (fMRI) data. We report a behavior-based connectivity analysis method, in which whole-brain data are used to identify behaviorally relevant, intrinsic FC networks. Nineteen younger adults (20-28 years) and 19 healthy, older adults (63-78 years) were assessed with fMRI and diffusion tensor imaging (DTI). Results indicated that FC involving a distributed network of brain regions, particularly the inferior frontal gyri, exhibited age-related change in the correlation with perceptual-motor speed (choice reaction time; RT). No relation between FC and RT was evident for younger adults, whereas older adults exhibited a significant age-related slowing of perceptual-motor speed, which was mediated by decreasing FC. Older adults' FC values were in turn associated positively with white matter integrity (from DTI) within the genu of the corpus callosum. The developed FC analysis illustrates the value of identifying connectivity by combining structural, functional, and behavioral data.

Surgical planning for deep brain stimulation implantation procedures requires T1-weighted imaging (T1WI) for stereotactic navigation. Because the subthalamic nucleus, the main target for deep brain stimulation, and other midbrain nuclei cannot be visualized on the stereotactic guidance T1WI, additional T2-weighted imaging (T2WI) is generally obtained and registered to the T1WI for surgical targeting. Surgical planning based on the registration of the 2 data sets is subject to error resulting from inconsistent geometric distortions and any subject movement between the 2 scans. In this article, we propose a new method to produce susceptibility-enhanced, contrast-optimized T1-weighted 3-dimensional spoiled gradient recalled acquisition in steady state images with enhanced contrast for midbrain nuclei within the volumetric T1WI data set itself, eliminating the need for additional T2WI. The scan parameters of 3-dimensional spoiled gradient recalled acquisition in steady state are chosen in a way that T1WI can be obtained from conventional magnitude reconstruction and images with improved contrast between midbrain nuclei and surrounding tissues can be produced from the same data by performing susceptibility-weighted imaging reconstruction on a chosen region of interest. In addition, our preliminary experience suggests that the resulting contrast between the midbrain nuclei is superior to the current state-of-the-art fast spin echo T2WI in depicting the subthalamic nucleus as distinct from the substantia nigra pars reticulata and clear depiction of the nucleus ventrointermedius externus of thalamus.

We report a functional magnetic resonance imaging method to deliver task-specific brain activities as biofeedback signals to guide individuals to increase cortical activity in auditory areas during sound stimulation. A total of 11 study participants underwent multiple functional magnetic resonance imaging scan sessions, while the changes in the activated cortical volume within the primary and secondary auditory areas were fed back to them between scan sessions. On the basis of the feedback information, participants attempted to increase the number of significant voxels during the subsequent trial sessions by adjusting their level of attention to the auditory stimuli. Results showed that the group of individuals who received the feedback were able to increase the activation volume and blood oxygenation level-dependent signal to a greater degree than the control group.

Computation of B1 fields is a key issue for determination and correction of intensity nonuniformity in magnetic resonance images. This paper presents a new method for computing transmitted and received B1 fields. Our method combines a modified MRI acquisition protocol and an estimation technique based on the Levenberg-Marquardt algorithm and spatial filtering. It enables accurate estimation of transmitted and received B1 fields for both homogeneous and heterogeneous objects. The method is validated using numerical simulations and experimental data from phantom and human scans. The experimental results are in agreement with theoretical expectations.

Deposition of iron in the brain is proposed to play a role in the pathophysiology of the normal aging process and neurodegenerative diseases. Whereas iron is required for normal neuronal metabolism, excessive levels can contribute to the formation of free radicals, leading to lipid peroxidation and neurotoxicity. Magnetic resonance imaging (MRI) is a powerful tool to detect excessive iron in the brain and longitudinally monitor changes in iron levels. Iron deposition is associated with a reduction in the T2 relaxation time, leading to hypointensity on spin-echo and gradient-echo T2-weighted images. The MRI changes associated with iron deposition have been observed both in normal aging and in various chronic neurological diseases, including multiple sclerosis, Alzheimer disease, and Parkinson disease. Magnetic resonance imaging metrics providing information about iron concentrations include R2, R2', and R2*. The purpose of this review is to discuss the role of iron and its detection by MRI in various neurological disorders. We will review the basic biochemical properties of iron and its influence on MRI signal. We will also summarize the sensitivity and specificity of MRI techniques in detecting iron. The MRI and pathological findings pertaining to brain iron will be reviewed with respect to normal aging and a variety of neurological disorders. Finally, the biochemistry and pathophysiology surrounding iron, oxidative stress, free radicals, and lipid peroxidation in the brain will be discussed, including therapeutic implications. The potential role of iron deposition and its assessment by MRI provides exciting potential applications to the diagnosis, longitudinal monitoring, and therapeutic development for disorders of the brain.

Parallel MRI techniques reconstruct full-FOV images from undersampled k-space data by using the uncorrelated information from RF array coil elements. One disadvantage of parallel MRI is that the image signal-to-noise ratio (SNR) is degraded because of the reduced data samples and the spatially correlated nature of multiple RF receivers. Regularization has been proposed to mitigate the SNR loss originating due to the latter reason. Since it is necessary to utilize static prior to regularization, the dynamic contrast-to-noise ratio (CNR) in parallel MRI will be affected. In this paper we investigate the CNR of regularized sensitivity encoding (SENSE) acquisitions. We propose to implement regularized parallel MRI acquisitions in functional MRI (fMRI) experiments by incorporating the prior from combined segmented echo-planar imaging (EPI) acquisition into SENSE reconstructions. We investigated the impact of regularization on the CNR by performing parametric simulations at various BOLD contrasts, acceleration rates, and sizes of the active brain areas. As quantified by receiver operating characteristic (ROC) analysis, the simulations suggest that the detection power of SENSE fMRI can be improved by regularized reconstructions, compared to unregularized reconstructions. Human motor and visual fMRI data acquired at different field strengths and array coils also demonstrate that regularized SENSE improves the detection of functionally active brain regions., (2005 Wiley-Liss, Inc)

Technical aspects of two general fast spectroscopic imaging (SI) strategies, one based on gradient echo trains and the other on spin echo trains, are reviewed within the context of potential applications in the field of functional magnetic resonance imaging (fMRI). Fast spectroscopic imaging of water may prove useful for identifying mechanisms underlying the blood oxygenation level dependence (BOLD) of the water signal during brain activation studies. Reasonably rapid mapping of changes in proton signals from brain metabolites, like lactate, creatine or even neurotransmitter associated metabolites like GABA, is substantially more challenging but technically feasible particularly as higher field strengths become available. Fast spectroscopic methods directed towards the 31P signals from phosphocreatine (PCr) and adenosine tri-phosphates (ATP) are also technically feasible and may prove useful for studying cerebral energetics within fMRI contexts.

A brain-computer interface (BCI) is a way of conveying an individual's thoughts to control computer or electromechanical hardware. Capitalizing on the ability to characterize brain activity in a reproducible manner, we explored the possibility of using real-time fMRI to interpret the spatial distribution of brain function as BCI commands. Using a high-field (3T) MRI scanner, brain activities associated with four distinct covert functional tasks were detected and subsequently translated into predetermined computer commands for moving four directional cursors. The proposed fMRI-BCI method allowed volunteer subjects to navigate through a simple 2D maze solely through their thought processes.

A new functional MRI protocol that integrates variable echo time (TE) acquisition and a block-design paradigm is proposed and evaluated with finger-tapping motor task. Simulations and experimental data show that the blood oxygenation level-dependent (BOLD) sensitivity achieved with this approach is comparable to that achieved using a conventional constant-TE protocol. The proposed variable-TE fMRI protocol provides valuable information that cannot be obtained with the constant-TE protocol. First, a field inhomogeneity map can be derived from the multi-TE data and used to correct EPI geometric distortions. Second, changes of T2* values due to the BOLD effect can be quantified. Third, for brain regions with pronounced susceptibility field gradients, the reduced BOLD sensitivity may be compensated for when the acquired multi-TE data are processed appropriately (e.g., with weighted summation). Fourth, large venules and veins may possibly be identified (depending on the vessel orientation and volume fraction) by evaluating the phase values of the multi-TE data. Finally, magnetic field drift over time can be measured from dynamic field maps available with this protocol.

The impact of voxel geometry on the blood oxygenation level-dependent (BOLD) signal detectability in the presence of field inhomogeneity is assessed and a quantitative approach to selecting appropriate voxel geometry is developed in this report. Application of the developed technique to BOLD sensitivity improvement of the human amygdala is presented. Field inhomogeneity was measured experimentally at 1.5 T and 3 T and the dominant susceptibility field gradient in the human amygdala was observed approximately along the superior-inferior direction. Based on the field mapping studies, an optimal selection for the slice orientation would be an oblique pseudo-coronal plane with its frequency-encoding direction parallel to the field gradient measured from each subject. Experimentally this was confirmed by comparing the normalized standard deviation of time-series echo-planar imaging signals acquired with different slice orientations, in the absence of a functional stimulus. A further confirmation with a carefully designed functional magnetic resonance imaging study is needed. Although the BOLD sensitivity may generally be improved by a voxel size commensurable with the activation volume, our quantitative analysis shows that the optimal voxel size also depends on the susceptibility field gradient and is usually smaller than the activation volume. The predicted phenomenon is confirmed with a hybrid simulation, in which the functional activation was mathematically added to the experimentally acquired rest-period echo-planar imaging data.

Brain iron dyshomeostasis disrupts various critical cellular functions, and age-related iron accumulation may contribute to deficient neurotransmission and cell death. While recent studies have linked excessive brain iron to cognitive function in the context of neurodegenerative disease, little is known regarding the role of brain iron accumulation in cognitive aging in healthy adults. Further, previous studies have focused primarily on deep gray matter regions, where the level of iron deposition is highest. However, recent evidence suggests that cortical iron may also contribute to cognitive deficit and neurodegenerative disease. Here, we used quantitative susceptibility mapping (QSM) to measure brain iron in 67 healthy participants 18-78 years of age. Speed-dependent (fluid) cognition was assessed from a battery of 12 psychometric and computer-based tests. From voxelwise QSM analyses, we found that QSM susceptibility values were negatively associated with fluid cognition in the right inferior temporal gyrus, bilateral putamen, posterior cingulate gyrus, motor, and premotor cortices. Mediation analysis indicated that susceptibility in the right inferior temporal gyrus was a significant mediator of the relation between age and fluid cognition, and similar effects were evident for the left inferior temporal gyrus at a lower statistical threshold. Additionally, age and right inferior temporal gyrus susceptibility interacted to predict fluid cognition, such that brain iron was negatively associated with a cognitive decline for adults over 45 years of age. These findings suggest that iron may have a mediating role in cognitive decline and may be an early biomarker of neurodegenerative disease.

Background Human immunodeficiency virus (HIV)–associated neurocognitive impairment remains a prevalent comorbidity that impacts daily functioning and increases morbidity. While HIV infection is known to cause widespread disruptions in the brain, different magnetic resonance imaging (MRI) modalities have not been effectively integrated. In this study, we applied 3-way supervised fusion to investigate how structural and functional coalterations affect cognitive function. Methods Participants (59 people living with HIV and 58 without HIV) completed comprehensive neuropsychological testing and multimodal MRI scanning to acquire high-resolution anatomical, diffusion-weighted, and resting-state functional images. Preprocessed data were reduced using voxel-based morphometry, probabilistic tractography, and regional homogeneity, respectively. We applied multimodal canonical correlation analysis with reference plus joint independent component analysis using global cognitive functioning as the reference. Results Compared with controls, participants living with HIV had lower global cognitive functioning. One joint component was both group discriminating and correlated with cognitive function. This component included the following covarying regions: fractional anisotropy in the corpus callosum, short and long association fiber tracts, and corticopontine fibers; gray matter volume in the thalamus, prefrontal cortex, precuneus, posterior parietal regions, and occipital lobe; and functional connectivity in frontoparietal and visual processing regions. Component loadings for fractional anisotropy also correlated with immunosuppression. Conclusions These results suggest that coalterations in brain structure and function can distinguish people with and without HIV and may drive cognitive impairment. As MRI becomes more commonplace in HIV care, multimodal fusion may provide neural biomarkers to support diagnosis and treatment of cognitive impairment. [ABSTRACT FROM AUTHOR]

Marijuana use, which is disproportionately prevalent among human immunodeficiency virus (HIV)-infected persons, can alter activity in fronto-parietal regions during cognitively demanding tasks. While HIV is also associated with altered neural activation, it is not known how marijuana may further affect brain function in this population. Our study examined the independent and additive effects of HIV infection and regular marijuana use on neural activation during a cognitive interference task. The sample included 93 adults who differed on marijuana (MJ) and HIV statuses (20 MJ+/HIV+, 19 MJ+/HIV-, 29 MJ-/HIV+, 25 MJ-/HIV-). Participants completed a counting Stroop task during a functional magnetic resonance imaging scan. Main and interactive effects on neural activation during interference versus neutral blocks were examined using a mixed-effects analysis. The sample showed the expected Stroop effect for both speed and accuracy. There were main effects of MJ in the right and left inferior parietal lobules, with the left cluster extending into the posterior middle temporal gyrus and a main effect of HIV in the dorsal anterior cingulate cortex. There was an interaction in the left fronto-insular cortex, such that the MJ+/HIV+ group had the largest increase in activation compared with other groups. Among MJ+, signal change in this cluster correlated positively with cumulative years of regular marijuana use. These results suggest that comorbid HIV and marijuana use is associated with complex neural alterations in multiple brain regions during cognitive interference. Follow-up research is needed to determine how marijuana-related characteristics may moderate HIV neurologic disease and impact real-world functioning. [ABSTRACT FROM AUTHOR]

Despite increased rates of disease, disability, and social losses with aging, seniors consistently report higher levels of subjective well-being (SWB), a construct closely related to happiness, than younger adults. In this exploratory study, we utilized an available dataset to investigate how aspects of health commonly deteriorating with age, including sensory (i.e., vision and hearing) and cognitive status, relate to variability in self-described contributors to happiness. Community-dwelling seniors (n = 114) responded to a single-item prompt: "name things that make people happy." 1731 responses were categorized into 13 domains of SWB via structured content analysis. Sensory health and cognition were assessed by Snellen visual acuity, pure-tone audiometry, and in-person administration of the Brief Test of Adult Cognition by Telephone (BTACT) battery. A subset of eligible participants (n = 57) underwent functional magnetic resonance imaging (fMRI) to assess resting state functional connectivity (FC) within a previously described dopaminergic network associated with reward processing. SWB response patterns were relatively stable across gender, sensory status, and cognitive performance with few exceptions. For example, hearing-impaired participants listed fewer determinants of SWB (13.59 vs. 17.16; p<0.001) and were less likely to name things in the "special events" category. Participants with a higher proportion of responses in the "accomplishments" domain (e.g., winning, getting good grades) demonstrated increased FC between the ventral tegmental area and nucleus accumbens, regions implicated in reward and motivated behavior. While the framework for determinants of happiness among seniors was largely stable across the factors assessed here, our findings suggest that subtle changes in this construct may be linked to sensory loss. The possibility that perceptions about determinants of happiness might relate to differences in intrinsic connectivity within reward-related brain networks also warrants further investigation. [ABSTRACT FROM AUTHOR]

Over the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real- and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real- and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real- and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real- and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) is a non-invasive and powerful imaging tool for detecting brain activities. The majority of fMRI studies are performed with single-shot echo-planar imaging (EPI) due to its high temporal resolution. Recent studies have demonstrated that, by increasing the spatial-resolution of fMRI, previously unidentified neuronal networks can be measured. However, it is challenging to improve the spatial resolution of conventional single-shot EPI based fMRI. Although multi-shot interleaved EPI is superior to single-shot EPI in terms of the improved spatial-resolution, reduced geometric distortions, and sharper point spread function (PSF), interleaved EPI based fMRI has two main limitations: 1) the imaging throughput is lower in interleaved EPI; 2) the magnitude and phase signal variations among EPI segments (due to physiological noise, subject motion, and B0 drift) are translated to significant in-plane aliasing artifact across the field of view (FOV). Here we report a method that integrates multiple approaches to address the technical limitations of interleaved EPI-based fMRI. Firstly, the multiplexed sensitivity-encoding (MUSE) post-processing algorithm is used to suppress in-plane aliasing artifacts resulting from time-domain signal instabilities during dynamic scans. Secondly, a simultaneous multi-band interleaved EPI pulse sequence, with a controlled aliasing scheme incorporated, is implemented to increase the imaging throughput. Thirdly, the MUSE algorithm is then generalized to accommodate fMRI data obtained with our multi-band interleaved EPI pulse sequence, suppressing both in-plane and through-plane aliasing artifacts. The blood-oxygenation-level-dependent (BOLD) signal detectability and the scan throughput can be significantly improved for interleaved EPI-based fMRI. Our human fMRI data obtained from 3 Tesla systems demonstrate the effectiveness of the developed methods. It is expected that future fMRI studies requiring high spatial-resolvability and fidelity will largely benefit from the reported techniques. [ABSTRACT FROM AUTHOR]

ABSTRACT BACKGROUND AND PURPOSE The combination of phase demodulation and field mapping is a practical method to correct echo planar imaging (EPI) geometric distortion. However, since phase dispersion accumulates in each phase-encoding step, the calculation complexity of phase modulation is Ny-fold higher than conventional image reconstructions. Thus, correcting EPI images via phase demodulation is generally a time-consuming task. METHODS Parallel computing by employing general-purpose calculations on graphics processing units (GPU) can accelerate scientific computing if the algorithm is parallelized. This study proposes a method that incorporates the GPU-based technique into phase demodulation calculations to reduce computation time. The proposed parallel algorithm was applied to a PROPELLER-EPI diffusion tensor data set. RESULTS The GPU-based phase demodulation method reduced the EPI distortion correctly, and accelerated the computation. The total reconstruction time of the 16-slice PROPELLER-EPI diffusion tensor images with matrix size of 128 × 128 was reduced from 1,754 seconds to 101 seconds by utilizing the parallelized 4-GPU program. CONCLUSIONS GPU computing is a promising method to accelerate EPI geometric correction. The resulting reduction in computation time of phase demodulation should accelerate postprocessing for studies performed with EPI, and should effectuate the PROPELLER-EPI technique for clinical practice. [ABSTRACT FROM AUTHOR]

Object: To develop an ultrafast MRI-based temperature monitoring method for application during rapid ultrasound exposures in moving organs. Materials and methods: A slice selective 90° − 180° pair of RF pulses was used to solicit an echo from a column, which was then sampled with a train of gradient echoes. In a gel phantom, phase changes of each echo were compared to standard gradient-echo thermometry, and temperature monitoring was tested during focused ultrasound sonications. Signal-to-noise ratio (SNR) performance was evaluated in vivo in a rabbit brain, and feasibility was tested in a human heart. Results: The correlation between each echo in the acquisition and MRI-based temperature measurements was good ( R = 0.98 ± 0.03). A temperature sampling rate of 19 Hz was achieved at 3T in the gel phantom. It was possible to acquire the water frequency in the beating heart muscle with 5-Hz sampling rate during a breath hold. Conclusion: Ultrafast thermometry via phase or frequency monitoring along single columns was demonstrated. With a temporal resolution around 50 ms, it may be possible to monitor focal heating produced by short ultrasound pulses. [ABSTRACT FROM AUTHOR]

The investigation of the reproducibility in functional MRI (fMRI) is an important step in the quantification and analysis of paradigm-related brain activation. This article reports on reproducibility of cortical activation characterized by repeated fMRI runs (10 times) during the performance of a motor imagery and a passive auditory stimulation as a control task. Two parameters, the size of activation and BOLD signal contrast, were measured from regions-of-interest for 10 subjects across different threshold conditions. The variability of these parameters was normalized with respect to the mean obtained from 10 runs, and represented as the intrasession variability. It was found that the variability was significantly lower in the measurement of BOLD signal contrast as compared to the measurement of the size of activation. The variability of the activation volume measurement was greater in the motor imagery task than in the auditory tasks across all thresholds. This task-dependent difference was not apparent from the measurement of the BOLD signal contrast. The presence of threshold dependence in the variability measurement was also examined, but no such dependency was found. The results suggest that a measurement of BOLD signal itself is a more reliable indicator of paradigm-related brain activation during repeated fMRI scans. [ABSTRACT FROM AUTHOR]

Line-scan echo planar spectroscopic imaging (LSEPSI) is applied to quickly measure the T2 and T2∗ relaxation time constants in pre-selected 2D or 3D regions. Results from brain imaging studies at 3T suggest that the proposed method may prove valuable for both basic research (e.g., quantifying the changes of T2/T2∗ values in functional MRI with blood oxygenation level-dependent contrast) and clinical studies (e.g., measuring the T2′ shortening due to iron deposition). The proposed spatially selective T2 and T2∗ mapping technique is especially well suited for studies, where T2/T2∗ quantification needs to be performed dynamically in a pre-selected 2D or 3D region. [Copyright &y& Elsevier]

Background: Both HIV infection and chronic cocaine use alter the neural circuitry of decision making, but the interactive effects of these commonly comorbid conditions have not been adequately examined. This study tested how cocaine moderates HIV-related neural activation during an intertemporal decision-making task.Methods: The sample included 73 participants who differed on cocaine and HIV status (18 COC+/HIV+, 19 COC+/HIV-, 19 COC-/HIV+, 17 COC-/HIV-). Participants made choices between smaller, sooner and larger, delayed rewards while undergoing functional MRI. Choices varied in difficulty based on subjective value: hard (equivalently valued), easy (disparately valued), and control choices. A mixed-effects model controlling for education and smoking identified main and interactive effects of HIV and COC during hard relative to easy choices (difficulty contrast).Results: COC+ status was associated with lower activation in bilateral frontal gyri and right insular and posterior parietal cortices. HIV+ status was associated with higher activation in the visual cortex, but lower activation in bilateral prefrontal cortices and cerebellum and left posterior parietal cortex. COC moderated the effects of HIV in several clusters centered in the bilateral prefrontal cortices and cerebellum. In post-hoc analyses, there were significant effects of HIV status on activation for COC+, but not COC-, participants; interaction effects remained after controlling for polysubstance use.Conclusion: Cocaine use may diminish the compensatory neural activation often seen among HIV+ samples during decision making. Our results highlight the importance of examining the neuropsychiatric effects of comorbid medical conditions to identify potential neural targets for cognitive remediation interventions. [ABSTRACT FROM AUTHOR]

Age-related decline in fluid cognition can be characterized as a disconnection among specific brain structures, leading to a decline in functional efficiency. The potential sources of disconnection, however, are unclear. We investigated imaging measures of cerebral white-matter integrity, resting-state functional connectivity, and white-matter hyperintensity volume as mediators of the relation between age and fluid cognition, in 145 healthy, community-dwelling adults 19–79 years of age. At a general level of analysis, with a single composite measure of fluid cognition and single measures of each of the 3 imaging modalities, age exhibited an independent influence on the cognitive and imaging measures, and the imaging variables did not mediate the age-cognition relation. At a more specific level of analysis, resting-state functional connectivity of sensorimotor networks was a significant mediator of the age-related decline in executive function. These findings suggest that different levels of analysis lead to different models of neurocognitive disconnection, and that resting-state functional connectivity, in particular, may contribute to age-related decline in executive function. [ABSTRACT FROM AUTHOR]

Advances in molecular and structural and functional neuroimaging are rapidly expanding the complexity of neurobiological understanding of Parkinson's disease (PD). This review article begins with an introduction to PD neurobiology as a foundation for interpreting neuroimaging findings that may further lead to more integrated and comprehensive understanding of PD. Diverse areas of PD neuroimaging are then reviewed and summarized, including positron emission tomography, single photon emission computed tomography, magnetic resonance spectroscopy and imaging, transcranial sonography, magnetoencephalography, and multimodal imaging, with focus on human studies published over the last five years. These included studies on differential diagnosis, co-morbidity, genetic and prodromal PD, and treatments from l -DOPA to brain stimulation approaches, transplantation and gene therapies. Overall, neuroimaging has shown that PD is a neurodegenerative disorder involving many neurotransmitters, brain regions, structural and functional connections, and neurocognitive systems. A broad neurobiological understanding of PD will be essential for translational efforts to develop better treatments and preventive strategies. Many questions remain and we conclude with some suggestions for future directions of neuroimaging of PD. [ABSTRACT FROM AUTHOR]

Abstract: In this article we review recent research on diffusion tensor imaging (DTI) of white matter (WM) integrity and the implications for age-related differences in cognition. Neurobiological mechanisms defined from DTI analyses suggest that a primary dimension of age-related decline in WM is a decline in the structural integrity of myelin, particularly in brain regions that myelinate later developmentally. Research integrating behavioral measures with DTI indicates that WM integrity supports the communication among cortical networks, particularly those involving executive function, perceptual speed, and memory (i.e., fluid cognition). In the absence of significant disease, age shares a substantial portion of the variance associated with the relation between WM integrity and fluid cognition. Current data are consistent with one model in which age-related decline in WM integrity contributes to a decreased efficiency of communication among networks for fluid cognitive abilities. Neurocognitive disorders for which older adults are at risk, such as depression, further modulate the relation between WM and cognition, in ways that are not as yet entirely clear. Developments in DTI technology are providing a new insight into both the neurobiological mechanisms of aging WM and the potential contribution of DTI to understanding functional measures of brain activity. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease. [Copyright &y& Elsevier]