Retrospective estimation and correction of physiological artifacts in fMRI by direct extraction of physiological activity from MR data

Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing

Functional MRI (fMRI) data may be contaminated by artifacts arising from a myriad of sources, including subject head motion, respiration, heartbeat, scanner drift, and thermal noise. These artifacts cause deviations from common distributional assumptions, introduce spatial and temporal outliers, and reduce the signal-to-noise ratio of the data-all of which can have negative consequences for the accuracy and power of downstream statistical analysis. Scrubbing is a technique for excluding fMRI volumes thought to be contaminated by artifacts and generally comes in two flavors. Motion scrubbing based on subject head motion-derived measures is popular but suffers from a number of drawbacks, among them the need to choose a threshold, a lack of generalizability to multiband acquisitions, and high rates of censoring of individual volumes and entire subjects. Alternatively, data-driven scrubbing methods like DVARS are based on observed noise in the processed fMRI timeseries and may avoid some of these issues. Here we propose "projection scrubbing", a novel data-driven scrubbing method based on a statistical outlier detection framework and strategic dimension reduction, including independent component analysis (ICA), to isolate artifactual variation. We undertake a comprehensive comparison of motion scrubbing with data-driven projection scrubbing and DVARS. We argue that an appropriate metric for the success of scrubbing is maximal data retention subject to reasonable performance on typical benchmarks such as the validity, reliability, and identifiability of functional connectivity. We find that stringent motion scrubbing yields worsened validity, worsened reliability, and produced small improvements to fingerprinting. Meanwhile, data-driven scrubbing methods tend to yield greater improvements to fingerprinting while not generally worsening validity or reliability. Importantly, however, data-driven scrubbing excludes a fraction of the number of volumes or entire sessions compared to motion scrubbing. The ability of data-driven fMRI scrubbing to improve data retention without negatively impacting the quality of downstream analysis has major implications for sample sizes in population neuroscience research.

Unsupervised physiological noise correction of functional magnetic resonance imaging data using phase and magnitude information (PREPAIR)

Of the sources of noise affecting blood oxygen level-dependent functional magnetic resonance imaging (fMRI), respiration and cardiac fluctuations are responsible for the largest part of the variance, particularly at high and ultrahigh field. Existing approaches to removing physiological noise either use external recordings, which can be unwieldy and unreliable, or attempt to identify physiological noise from the magnitude fMRI data. Data-driven approaches are limited by sensitivity, temporal aliasing, and the need for user interaction. In the light of the sensitivity of the phase of the MR signal to local changes in the field stemming from physiological processes, we have developed an unsupervised physiological noise correction method using the information carried in the phase and the magnitude of echo-planar imaging data. Our technique, Physiological Regressor Estimation from Phase and mAgnItude, sub-tR (PREPAIR) derives time series signals sampled at the slice TR from both phase and magnitude images. It allows physiological noise to be captured without aliasing, and efficiently removes other sources of signal fluctuations not related to physiology, prior to regressor estimation. We demonstrate that the physiological signal time courses identified with PREPAIR agree well with those from external devices and retrieve challenging cardiac dynamics. The removal of physiological noise was as effective as that achieved with the most used approach based on external recordings, RETROICOR. In comparison with widely used recording-free physiological noise correction tools-PESTICA and FIX, both performed in unsupervised mode-PREPAIR removed significantly more respiratory and cardiac noise than PESTICA, and achieved a larger increase in temporal signal-to-noise-ratio at both 3 and 7 T.

A k-space-based method to measure and correct for temporal B0 field variations in MR temperature imaging

PURPOSE

Present a method to use change in phase in repeated Cartesian k-space measurements to monitor the change in magnetic field for dynamic MR temperature imaging.

METHODS

The method is applied to focused ultrasound heating experiments in a gelatin phantom and an ex vivo salt pork sample, without and with simulated respiratory motion.

RESULTS

In each experiment, phase variations due to B0 field drift and respiration were readily apparent in the measured phase difference. With correction, the SD of the temperature over time was reduced from 0.18°C to 0.14°C (no breathing) and from 0.81°C to 0.22°C (with breathing) for the gelatin phantom, and from 0.68°C to 0.13°C (no breathing) and from 1.06°C to 0.17°C (with breathing) for the pork sample. The accuracy in nonheated regions, assessed as the RMS error deviation from 0°C, improved from 1.70°C to 1.11°C (no breathing) and from 4.73°C to 1.47°C (with breathing) for the gelatin phantom, and from 5.95°C to 0.88°C (no breathing) and from 13.40°C to 1.73°C (with breathing) for the pork sample. The correction did not affect the temperature measurement accuracy in the heated regions.

CONCLUSION

This work demonstrates that phase changes resulting from variations in B0 due to drift and respiration, commonly seen in MR thermometry applications, can be measured directly from 3D Cartesian acquisition methods. The correction of temporal field variations using the presented technique improved temperature accuracy, reduced variability in nonheated regions, and did not reduce accuracy in heated regions.

Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI

Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.

Reprint of 'Noise contributions to the fMRI signal: An Overview'

The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.

Methods for cleaning the BOLD fMRI signal

Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

Amide proton transfer CEST of the cervical spinal cord in multiple sclerosis patients at 3T

PURPOSE

The ability to evaluate pathological changes in the spinal cord in multiple sclerosis (MS) is limited because T₁ - and T₂ -w MRI imaging are not sensitive to biochemical changes in vivo. Amide proton transfer (APT) chemical exchange saturation transfer (CEST) can indirectly detect amide protons associated with proteins and peptides, potentially providing more pathological specificity. Here, we implement APT CEST in the cervical spinal cord of healthy and MS cohorts at 3T.

METHODS

APT CEST of the cervical spinal cord was obtained in a cohort of 10 controls and 10 MS patients using a novel respiratory correction methodology. APT was quantified using two methods: 1) APT_w , based off the conventional magnetization transfer ratio asymmetry, and 2) ΔAPT, a spatial characterization of APT changes in MS patients relative to the controls.

RESULTS

Respiratory correction yielded highly reproducible z-spectra in white matter (intraclass correlation coefficient = 0.82). APT_w signals in normal-appearing white matter (NAWM) of MS patients were significantly different from healthy controls (P = 0.04), whereas ΔAPT of MS patients highlighted large APT differences in NAWM.

CONCLUSION

Respiration correction in the spinal cord is necessary to accurately quantify APT CEST, which can provide unique biochemical information regarding disease processes within the spinal cord. Magn Reson Med 79:806-814, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Impact of physiological noise correction on detecting blood oxygenation level-dependent contrast in the breast

Physiological fluctuations are expected to be a dominant source of noise in blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) experiments to assess tumour oxygenation and angiogenesis. This work investigates the impact of various physiological noise regressors: retrospective image correction (RETROICOR), heart rate (HR) and respiratory volume per unit time (RVT), on signal variance and the detection of BOLD contrast in the breast in response to a modulated respiratory stimulus. BOLD MRI was performed at 3 T in ten volunteers at rest and during cycles of oxygen and carbogen gas breathing. RETROICOR was optimized using F-tests to determine which cardiac and respiratory phase terms accounted for a significant amount of signal variance. A nested regression analysis was performed to assess the effect of RETROICOR, HR and RVT on the model fit residuals, temporal signal-to-noise ratio, and BOLD activation parameters. The optimized RETROICOR model accounted for the largest amount of signal variance ([Formula: see text]  =  3.3  ±  2.1%) and improved the detection of BOLD activation (P  =  0.002). Inclusion of HR and RVT regressors explained additional signal variance, but had a negative impact on activation parameter estimation (P  <  0.001). Fluctuations in HR and RVT appeared to be correlated with the stimulus and may contribute to apparent BOLD signal reactivity.

Noise contributions to the fMRI signal: An overview

The ability to discriminate signal from noise plays a key role in the analysis and interpretation of functional magnetic resonance imaging (fMRI) measures of brain activity. Over the past two decades, a number of major sources of noise have been identified, including system-related instabilities, subject motion, and physiological fluctuations. This article reviews the characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal. Approaches for distinguishing signal from noise and the associated challenges are also reviewed. These challenges reflect the fact that some noise sources, such as respiratory activity, are generated by the same underlying brain networks that give rise to functional signals that are of interest.

Functional Magnetic Resonance Imaging Methods

Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.

Respiration artifact correction in three-dimensional proton resonance frequency MR thermometry using phase navigators

PURPOSE

To develop reliable three-dimensional (3D) segmented echo planar imaging (seg-EPI) proton resonance frequency (PRF) temperature monitoring in the presence of respiration-induced B0 variation.

METHODS

A free induction decay (FID) phase navigator was inserted into a 3D seg-EPI sequence before and after EPI readout to monitor B0 field variations. Using the field change estimates, the phase of each k-space line was adjusted to remove the additional phase from the respiratory induced off-resonance. This correction technique was evaluated while heating with MR-guided focused ultrasound (MRgFUS) in phantoms with simulated breathing and during nonheating conditions in healthy in vivo breasts.

RESULTS

With k-space phase correction, the standard deviation of magnitude images and PRF temperature measurements in breast from five volunteers improved by an average factor of 1.5 and 2.1, respectively. Improved accuracy of temperature estimates was observed after correction while heating with MRgFUS in phantoms.

CONCLUSION

Phase correction based on two FID navigators placed before and after the echo train provides promising results for implementing 3D monitoring of thermal therapy treatments in the presence of field variations due to respiration. Magn Reson Med 76:206-213, 2016. © 2015 Wiley Periodicals, Inc.

Vobi One: a data processing software package for functional optical imaging

Optical imaging is the only technique that allows to record the activity of a neuronal population at the mesoscopic scale. A large region of the cortex (10-20 mm diameter) is directly imaged with a CCD camera while the animal performs a behavioral task, producing spatio-temporal data with an unprecedented combination of spatial and temporal resolutions (respectively, tens of micrometers and milliseconds). However, researchers who have developed and used this technique have relied on heterogeneous software and methods to analyze their data. In this paper, we introduce Vobi One, a software package entirely dedicated to the processing of functional optical imaging data. It has been designed to facilitate the processing of data and the comparison of different analysis methods. Moreover, it should help bring good analysis practices to the community because it relies on a database and a standard format for data handling and it provides tools that allow producing reproducible research. Vobi One is an extension of the BrainVISA software platform, entirely written with the Python programming language, open source and freely available for download at https://trac.int.univ-amu.fr/vobi_one.

Characterization and reduction of cardiac- and respiratory-induced noise as a function of the sampling rate (TR) in fMRI

It has recently been shown that both high-frequency and low-frequency cardiac and respiratory noise sources exist throughout the entire brain and can cause significant signal changes in fMRI data. It is also known that the brainstem, basal forebrain and spinal cord areas are problematic for fMRI because of the magnitude of cardiac-induced pulsations at these locations. In this study, the physiological noise contributions in the lower brain areas (covering the brainstem and adjacent regions) are investigated and a novel method is presented for computing both low-frequency and high-frequency physiological regressors accurately for each subject. In particular, using a novel optimization algorithm that penalizes curvature (i.e. the second derivative) of the physiological hemodynamic response functions, the cardiac- and respiratory-related response functions are computed. The physiological noise variance is determined for each voxel and the frequency-aliasing property of the high-frequency cardiac waveform as a function of the repetition time (TR) is investigated. It is shown that for the brainstem and other brain areas associated with large pulsations of the cardiac rate, the temporal SNR associated with the low-frequency range of the BOLD response has maxima at subject-specific TRs. At these values, the high-frequency aliased cardiac rate can be eliminated by digital filtering without affecting the BOLD-related signal.

MRI under hyperbaric air and oxygen: effects on local magnetic field and relaxation times

PURPOSE

Hyperbaric oxygen therapy has shown efficacies in the treatment of a number of diseases. The goal of this study was to develop a rodent hyperbaric chamber for MRI studies and to investigate the effects of hyperbaric air and hyperbaric oxygen on local magnetic field (B0 ) and MRI relaxation parameters in the rat brain.

METHODS

A hyperbaric chamber, constructed to fit inside an animal MRI scanner, was pressurized with air to four atmospheres, while oxygen was delivered locally via nose cone. B0 , T2 , T2 *, and T1 maps in the rat brain were evaluated under normobaric air, hyperbaric air, and hyperbaric oxygen at 7T.

RESULTS

Under hyperbaric oxygen, images exhibited artifacts and temporal instability, attributable to fluctuating oxygen concentration from air and oxygen mixing near the imaging region. Physically shielding the imaging region from fluctuating oxygen concentration resolved the problems. With increasing oxygen at hyperbaric pressure, B0 was shifted downfield with increased inhomogeneity near the ear canals and nose. Brain T2 and T2 * were lengthened, and T1 was shortened.

CONCLUSION

This study establishes the means to perform MRI on rodents under hyperbaric conditions. Hyperbaric air and hyperbaric oxygen have significant effects on B0 and tissue relaxation parameters compared with normobaric air.

Modeling and suppression of respiration induced B0-fluctuations in non-balanced steady-state free precession sequences at 7 Tesla

OBJECT

To develop and evaluate a model for describing the S1 (S+) and S2 (S-) phase in the presence of off-resonance frequency fluctuations, and to evaluate the performance of a novel interleaved navigator echo scheme.

MATERIALS AND METHODS

Using the extended phase graph model, a linear phase term was added to the evolution of transverse states. An approximation for the total S2 phase was derived with one fit parameter τl, which serves as an effective lifetime of the S2 signal. The model was evaluated using synthetic and in vivo phase evolution data. In addition, a novel interleaved phase correction scheme for the nb-SSFP sequence was applied to BOLD-fMRI data, and the number of activated voxels before and after phase correction was determined.

RESULTS

The phases of S1 and S2 signals are significantly different from each other. The proposed nb-SSFP phase model provided a good description of the measured phase evolution data, and the approximate model for the S2 phase provided both at good fit to the data, as well as an effective lifetime of the S2 signal. In some subjects the phase contribution from older pathways was underestimated. In the BOLD-fMRI data, a twofold increase of the number of activated voxels for the S2 signal was observed, compared to no correction and a conventional navigator echo method.

CONCLUSION

The different phase evolution of S1 and S2 signals can be qualitatively described by the proposed model, and detrimental phase history effects are significant at 7 Tesla when not appropriately corrected.

The story of the initial dip in fMRI

Over the past 20 years much attention has been given to characterizing the spatial accuracy of fMRI based signals and to techniques that improve on its co-localization with neuronal activity. While the vast majority of fMRI studies have always used the conventional positive BOLD signal, alternative contrast options have demonstrated superior spatial specificity. One of these options surfaced shortly after the initial BOLD fMRI demonstrations and was motivated by optical imaging studies which revealed an early signal change that was much smaller but spatially more specific than the delayed positive response. This early signal change was attributed to oxygenation changes prior to any subsequent blood flow increases. After observation of this biphasic hemodynamic response in fMRI, because this early response resulted in a small MR signal decrease prior to the onset of the large signal increase, it became known as the "initial dip". While the initial dip in fMRI was subsequently reported by many studies, including those in humans, monkeys, and cats, there were conflicting views about the associated mechanisms and whether it could be generalized across brain regions or species, in addition to whether or not it would prove fruitful for neuroscience. These discrepancies, along with the implications that the initial dip might increase the spatial specificity of BOLD fMRI from 2 to 3mm to something more closely associated with neural activity, resulted in lot of buzz and controversy in the community for many years. In this review, the authors provide an account of the story of the initial dip in MR based functional imaging from the Minnesota perspective, where the first demonstrations, characterizations, and applications of the initial dip commenced.

Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER

In this article we introduce the DRIFTER algorithm, which is a new model based Bayesian method for retrospective elimination of physiological noise from functional magnetic resonance imaging (fMRI) data. In the method, we first estimate the frequency trajectories of the physiological signals with the interacting multiple models (IMM) filter algorithm. The frequency trajectories can be estimated from external reference signals, or if the temporal resolution is high enough, from the fMRI data. The estimated frequency trajectories are then used in a state space model in combination of a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoother, which separates the signal into an activation related cleaned signal, physiological noise, and white measurement noise components. Using experimental data, we show that the method outperforms the RETROICOR algorithm if the shape and amplitude of the physiological signals change over time.

Detection of physiological noise in resting state fMRI using machine learning

We present a technique for predicting cardiac and respiratory phase on a time point by time point basis, from fMRI image data. These predictions have utility in attempts to detrend effects of the physiological cycles from fMRI image data. We demonstrate the technique both in the case where it can be trained on a subject's own data, and when it cannot. The prediction scheme uses a multiclass support vector machine algorithm. Predictions are demonstrated to have a close fit to recorded physiological phase, with median Pearson correlation scores between recorded and predicted values of 0.99 for the best case scenario (cardiac cycle trained on a subject's own data) down to 0.83 for the worst case scenario (respiratory predictions trained on group data), as compared to random chance correlation score of 0.70. When predictions were used with RETROICOR--a popular physiological noise removal tool--the effects are compared to using recorded phase values. Using Fourier transforms and seed based correlation analysis, RETROICOR is shown to produce similar effects whether recorded physiological phase values are used, or they are predicted using this technique. This was seen by similar levels of noise reduction noise in the same regions of the Fourier spectra, and changes in seed based correlation scores in similar regions of the brain. This technique has a use in situations where data from direct monitoring of the cardiac and respiratory cycles are incomplete or absent, but researchers still wish to reduce this source of noise in the image data.

Phase stability in fMRI time series: effect of noise regression, off-resonance correction and spatial filtering techniques

Although the majority of fMRI studies exploit magnitude changes only, there is an increasing interest regarding the potential additive information conveyed by the phase signal. This integrated part of the complex number furnished by the MR scanners can also be used for exploring direct detection of neuronal activity and for thermography. Few studies have explicitly addressed the issue of the available signal stability in the context of phase time-series, and therefore we explored the spatial pattern of frequency specific phase fluctuations, and evaluated the effect of physiological noise components (heart beat and respiration) on the phase signal. Three categories of retrospective noise reduction techniques were explored and the temporal signal stability was evaluated in terms of a physiologic noise model, for seven fMRI measurement protocols in eight healthy subjects at 3T, for segmented CSF, gray and white matter voxels. We confirmed that for most processing methods, an efficient use of the phase information is hampered by the fact that noise from physiological and instrumental sources contributes significantly more to the phase than to the magnitude instability. Noise regression based on the phase evolution of the central k-space point, RETROICOR, or an orthonormalized combination of these were able to reduce their impact, but without bringing phase stability down to levels expected from the magnitude signal. Similar results were obtained after targeted removal of scan-to-scan variations in the bulk magnetic field by the dynamic off-resonance in k-space (DORK) method and by the temporal off-resonance alignment of single-echo time series technique (TOAST). We found that spatial high-pass filtering was necessary, and in vivo a Gaussian filter width of 20mm was sufficient to suppress physiological noise and bring the phase fluctuations to magnitude levels. Stronger filters brought the fluctuations down to levels dictated by thermal noise contributions, and for 62.5mm(3) voxels the phase stability was as low as 5 mrad (0.27°). In conditions of low SNR(o) and high temporal sampling rate (short TR); we achieved an upper bound for the phase instabilities at 0.0017 ppm, which is close to the dHb contribution to the GM/WM phase contrast.

Neural correlates of interference control in adolescents with traumatic brain injury: functional magnetic resonance imaging study of the counting stroop task

Difficulty in inhibition or cognitive control is a common and significant sequela of pediatric traumatic brain injury (TBI). The present study used functional MRI to examine one specific inhibitory function, interference control, in 11 adolescents, aged 12-16 years, (mean age, 15.7 years) with TBI who were at least 1 year postinjury and 11 age-matched typically developing control participants (TC) (mean age, 15.2 years). Participants completed a Counting Stroop task with 2 main conditions: (1) a neutral condition requiring the counting of animal words and (2) an interference condition in which mismatched number words were counted. Both TBI and TC adolescents activated similar networks of brain regions relevant to interference control, but the TBI group showed higher levels of activation relative to the TC group in multiple brain areas within this network, including predominantly right frontal and parietal regions. Findings of greater activation of the relevant neural network in the TBI group are consistent with recent fMRI findings using other interference control paradigms with individuals with a history of TBI.

Respiratory noise correction using phase information

Respiratory noise is a confounding factor in functional magnetic resonance imaging (MRI) data analysis. A novel method called Respiratory noise Correction using Phase information is proposed to retrospectively correct for the respiratory noise in functional MRI (fMRI) time series. It is demonstrated that the respiratory movement and the phase of functional MRI images are highly correlated in time. The signal fluctuation due to respiratory movements can be effectively estimated from the phase variation and removed from the functional MRI time series using a Wiener filtering technique. In our experiments, this new method is compared with RETROICOR, which requires recording respiration signal simultaneously in an fMRI experiment. The two techniques show comparable performance with respect to the respiratory noise correction for fMRI time series. However, this technique is more advantageous because there is no need for monitoring the subjects' respiration or changing functional MRI protocols. This technique is also potentially useful for correcting respiratory noise from abnormal breathing or when the respiration is not periodic.

Evaluation of preprocessing steps to compensate for magnetic field distortions due to body movements in BOLD fMRI

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is currently the dominant technique for non-invasive investigation of brain functions. One of the challenges with BOLD fMRI, particularly at high fields, is compensation for the effects of spatiotemporally varying magnetic field inhomogeneities (DeltaB(0)) caused by normal subject respiration and, in some studies, movement of the subject during the scan to perform tasks related to the functional paradigm. The presence of DeltaB(0) during data acquisition distorts reconstructed images and introduces extraneous fluctuations in the fMRI time series that decrease the BOLD contrast-to-noise ratio. Optimization of the fMRI data-processing pipeline to compensate for geometric distortions is of paramount importance to ensure high quality of fMRI data. To investigate DeltaB(0) caused by subject movement, echo-planar imaging scans were collected with and without concurrent motion of a phantom arm. The phantom arm was constructed and moved by the experimenter to emulate forearm motions while subjects remained still and observed a visual stimulation paradigm. These data were then subjected to eight different combinations of preprocessing steps. The best preprocessing pipeline included navigator correction, a complex phase regressor and spatial smoothing. The synergy between navigator correction and phase regression reduced geometric distortions better than either step in isolation and preconditioned the data to make them more amenable to the benefits of spatial smoothing. The combination of these steps provided a 10% increase in t-statistics compared to only navigator correction and spatial smoothing and reduced the noise and false activations in regions where no legitimate effects would occur.

Three-dimensional, in vivo MRI with self-gating and image coregistration in the mouse

Motion during magnetic resonance imaging (MRI) scans routinely results in undesirable image artifact or blurring. Since high-resolution, three-dimensional (3D) imaging of the mouse requires long scan times for satisfactory signal-to-noise ratio (SNR) and image quality, motion-related artifacts are likely over much of the body and limit applications of mouse MRI. In this investigation, we explored the use of self-gated imaging methods and image coregistration for improving image quality in the presence of motion. Self-gated signal results from a modified 3D gradient-echo sequence showed detection of periodic respiratory and cardiac motion in the adult mouse-with excellent comparison to traditional measurements, sensitivity to respiration-induced tissue changes in the brain, and even detection of embryonic cardiac motion in utero. Serial image coregistration with rapidly-acquired, low-SNR volumes further enabled detection and correction of bulk changes in embryo location during in utero imaging sessions and subsequent reconstruction of high-quality images. These methods, in combination, are shown to expand the range of applications for 3D mouse MRI, enabling late-stage embryonic heart imaging and introducing the possibility of longitudinal developmental studies from embryonic stages through adulthood.

Fast joint reconstruction of dynamic R2* and field maps in functional MRI

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is conventionally done by reconstructing T(2)(*)-weighted images. However, since the images are unitless they are nonquantifiable in terms of important physiological parameters. An alternative approach is to reconstruct R(2)(*) maps which are quantifiable and have comparable BOLD contrast as T(2)(*)-weighted images. However, conventional R(2)(*) mapping involves long readouts and ignores relaxation during readout. Another problem with fMRI imaging is temporal drift/fluctuations in off-resonance. Conventionally, a field map is collected at the start of the fMRI study to correct for off-resonance, ignoring any temporal changes. Here, we propose a new fast regularized iterative algorithm that jointly reconstructs R(2)(*) and field maps for all time frames in fMRI data. To accelerate the algorithm we linearize the MR signal model, enabling the use of fast regularized iterative reconstruction methods. The regularizer was designed to account for the different resolution properties of both R(2)(*) and field maps and provide uniform spatial resolution. For fMRI data with the same temporal frame rate as data collected for T(2)(*)-weighted imaging the resulting R(2)(*) maps performed comparably to T(2)(*)-weighted images in activation detection while also correcting for spatially global and local temporal changes in off-resonance.

Long-term neural processing of attention following early childhood traumatic brain injury: fMRI and neurobehavioral outcomes

Attentional deficits are common and significant sequelae of pediatric traumatic brain injury (TBI). However, little is known about how the underlying neural processes that support different components of attention are affected. The present study examined brain activation patterns using fMRI in a group of young children who sustained a TBI in early childhood (n = 5; mean age = 9.4), and a group of age-matched control children with orthopedic injuries (OI) (n = 8) during a continuous performance task (CPT). Four children in the TBI group had moderate injuries, and one had a severe injury. Performance on the CPT task did not differ between groups. Both TBI and OI children activated similar networks of brain regions relevant to sustained attention processing, but the TBI group demonstrated several areas of significantly greater activation relative to controls, including frontal and parietal regions. These findings of over-activation of the relevant attention network in the TBI group contrast with those obtained in imaging studies of Attention-Deficit/Hyperactivity Disorder where under-activation of the attention network has been documented. This study provides evidence that young children's brains function differently following a traumatic brain injury, and that these differences persist for years after the injury.

Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal

Heart rate fluctuations occur in the low-frequency range (<0.1 Hz) probed in functional magnetic resonance imaging (fMRI) studies of resting-state functional connectivity and most fMRI block paradigms and may be related to low-frequency blood-oxygenation-level-dependent (BOLD) signal fluctuations. To investigate this hypothesis, temporal correlations between cardiac rate and resting-state fMRI signal timecourses were assessed at 3 T. Resting-state BOLD fMRI and accompanying physiological data were acquired and analyzed using cross-correlation and regression. Time-shifted cardiac rate timecourses were included as regressors in addition to established physiological regressors (RETROICOR (Glover, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 44, 162-167) and respiration volume per unit time (Birn, R.M., Diamond, J.B., Smith, M.A., Bandettini, P.A., 2006b. Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage 31, 1536-1548). Significant correlations between the cardiac rate and BOLD signal timecourses were revealed, particularly negative correlations in gray matter at time shifts of 6-12 s and positive correlations at time shifts of 30-42 s (TR=6 s). Regressors consisting of cardiac rate timecourses shifted by delays of between 0 and 24 s explained an additional 1% of the BOLD signal variance on average over the whole brain across 9 subjects, a similar additional variance to that explained by respiration volume per unit time and RETROICOR regressors, even when used in combination with these other physiological regressors. This suggests that including such time-shifted cardiac rate regressors will be beneficial for explaining physiological noise variance and will thereby improve the statistical power in future task-based and resting-state fMRI studies.

Real-time shimming to compensate for respiration-induced B0 fluctuations

In MRI of human brain, the respiratory cycle can induce B0-field fluctuations through motion of the chest and fluctuations in local oxygen concentration. The associated NMR frequency changes can affect the MRI data in various ways and lead to temporal signal fluctuations, and image artifacts such as ghosting and blurring. Since the size of the effect scales with magnetic field strength, artifacts become particularly problematic at fields above 3.0T. Furthermore, the spatial dependence of the B0-field fluctuations complicates their correction. In this work, a new method is presented that allows compensation of field fluctuations by modulating the B0 shims in real time. In this method, a reference scan is acquired to measure the spatial distribution of the B0 effect related to chest motion. During the actual scan, this information is then used, together with chest motion data, to apply compensating B0 shims in real time. The method can be combined with any type of scan without modifications to the pulse sequence. Real-time B0 shimming is demonstrated to substantially improve the phase stability of EPI data and the image quality of multishot gradient-echo (GRE) MRI at 7T.

Cortical Reorganization Following Modified Constraint-Induced Movement Therapy: A Study of 4 Patients With Chronic Stroke

OBJECTIVE

To determine whether cortical changes occur following participation in a program of modified constraint-induced movement therapy (mCIMT).

DESIGN

Pre-post, case series.

SETTING

Outpatient rehabilitation hospital.

PARTICIPANTS

Two men and 2 women with unilateral stroke occurring more than 1 year prior to study entry and moderate stable motor deficits.

INTERVENTION

Subjects participated in mCIMT, comprised of structured, 30-minute therapy sessions emphasizing affected arm use in valued activities, which occurred 3 days a week for 10 weeks. Their unaffected arms were restrained 5 days a week for 5 hours.

MAIN OUTCOME MEASURES

The Action Research Arm Test (ARAT), upper-extremity portion of the Fugl-Meyer Assessment (FMA), Motor Activity Log (MAL), and functional magnetic resonance imaging (fMRI) at 4T were administered before and after mCIMT.

RESULTS

Three subjects exhibited score increases on the MAL, ARAT, and FMA, representing increased affected arm use, impairment, and function. These subjects reported new ability to perform valued activities with the affected hand, such as writing. These subjects also displayed cortical reorganization on fMRI. One subject exhibited minimal affected arm use changes, modest function changes, and no cortical fMRI changes.

CONCLUSIONS

Increased affected arm use during mCIMT appears to induce cortical reorganization, as measured by fMRI. In patients who responded to mCIMT, cortical reorganization was positively related to degree of increase in affected arm use and ability. Because mCIMT is more easily administered than longer duration protocols, mCIMT may be a more practicable way of studying plasticity.

Effect of spatial smoothing on physiological noise in high-resolution fMRI

Physiological noise dominates the SNR of the fMRI time-course at commonly used spatial resolutions at field strengths of 3 T and above. Operating in this physiological noise dominated regime limits some benefits of high field acquisition since increases in image SNR produce only modest increases in time-course SNR. Although previous studies have shown that the physiological noise dominance can be mitigated by using higher spatial resolutions, not all functional studies require voxel sizes smaller than the thickness of the human cortex. In this study, we examine the effect of acquiring high spatial resolution, thermal noise dominated time-courses and spatially smoothing the images to lower resolutions, which would otherwise be physiological noise dominated. At high field strengths, where physiological noise is most problematic, this strategy lowered the overall time-course variance compared to direct acquisition at commonly used spatial resolution. At 7 T for example, 5 x 5 x 3 mm3 resolution images derived from smoothing 1.5 x 1.5 x 3 mm3 data improved time-course SNR by a factor of 1.89 compared to a time-series acquired at 5 x 5 x 3 mm3. Presumably, this effect was derived from the reduced physiological-to-thermal noise ratio in the high spatial resolution data followed by a smoothing operation that improves SNR without adding physiological noise. Our findings demonstrate that in contrast to conventional SNR penalties associated with spatially smoothing Fourier data, the time-course SNR of smoothed high-resolution data can be improved compared to direct acquisition at the desired resolution.

Making memories: a cross-sectional investigation of episodic memory encoding in childhood using FMRI

In adults, the neural substrate associated with encoding memories connected to a specific time and place include the prefrontal cortex and medial temporal lobe (MTL). Using functional magnetic resonance imaging, this research studied the developmental trajectory of this frontal-MTL system by comparing 7- and 8-year-old children to those who were 10 or older in conditions that promoted episodic encoding. In 1 condition, participants generated verbs from nouns heard; in another, they listened to short stories for comprehension. Regions in which brain activation predicted subsequent recognition memory performance were identified. These included the left prefrontal cortex, but not MTL, in the verb generation condition for both age groups. In the story comprehension condition, activation in left posterior MTL predicted subsequent memory performance in both age groups, and activation in left anterior MTL (including the hippocampus proper) and left prefrontal cortex predicted subsequent memory only for the older children. These results illustrate both similarities and differences in how brain systems interact in development to mediate the formation of episodic memories.

Generalized self-navigated motion detection technique: Preliminary investigation in abdominal imaging

Patient motion remains a primary obstacle to diagnostic image quality, especially in the abdomen, despite the existence of various motion artifact reduction techniques. This work presents a self-navigated motion detection method that can be generalized for most pulse sequences and k-space trajectories. Motion information is extracted directly from raw MR data, requiring no additional gradient or RF pulses, no physiologic monitoring equipment, and minimal--if any--impact on scan time. Initial feasibility results with a two-dimensional gradient echo sequence are shown in phantom studies and in vivo volunteer abdominal studies, demonstrating the sensitivity of the method to both respiratory motion and cardiovascular pulsatility. Prospectively gated images were acquired using the self-navigated data to synchronize image acquisition with motion. These preliminary results suggest that the self-navigated method is a promising technique for reducing motion artifacts in clinical abdominal and cardiac applications.

fMRI shows atypical language lateralization in pediatric epilepsy patients

PURPOSE

The goal of this study was to compare language lateralization between pediatric epilepsy patients and healthy children.

METHODS

Two groups of subjects were evaluated with functional magnetic resonance imaging (fMRI) by using a silent verb-generation task. The first group included 18 pediatric epilepsy patients, whereas the control group consisted of 18 age/gender/handedness-matched healthy subjects.

RESULTS

A significant difference in hemispheric lateralization index (LI) was found between children with epilepsy (mean LI =-0.038) and the age/gender/handedness-matched healthy control subjects (mean LI=0.257; t=6.490, p<0.0001). A dramatic difference also was observed in the percentage of children with epilepsy (77.78%) who had atypical LI (right-hemispheric or bilateral, LI<0.1) when compared with the age/gender/handedness-matched group (11.11%; chi(2)=16.02, p<0.001). A linear regression analysis showed a trend toward increasing language lateralization with age in healthy controls (R(2)=0.152; p=0.108). This association was not observed in pediatric epilepsy subjects (R(2)=0.004, p=0.80). A significant association between language LI and epilepsy duration also was found (R(2)=0.234, p<0.05).

CONCLUSIONS

This study shows that epilepsy during childhood is associated with neuroplasticity and reorganization of language function.

Strategies for improving the detection of fMRI activation in trigeminal pathways with cardiac gating

Functional magnetic resonance imaging (fMRI) has become a powerful tool for studying the normal and diseased human brain. The application of fMRI in detecting neuronal signals in the trigeminal system, however, has been hindered by low detection sensitivity due to activation artifacts caused by cardiac pulse-induced brain and brainstem movement. A variety of cardiac gating techniques have been proposed to overcome this issue, typically by phase locking the sampling to a particular time point during each cardiac cycle. We sought to compare different cardiac gating strategies for trigeminal system fMRI. In the present study, we used tactile stimuli to elicit brainstem and thalamus activation and compared the fMRI results obtained without cardiac gating and with three different cardiac gating strategies: single-echo with TR of 3 or 9 heartbeats (HBs) and dual-echo T2*-mapping EPI (TR = 2 HBs, TE = 21/55 ms). The dual-echo T2* mapping and the single-echo with TR of 2 and 3 HBs cardiac-gated fMRI techniques both increased detection rate of fMRI activation in brainstem. Activation in the brainstem and the thalamus was best detected by cardiac-gated dual-echo EPI.

Comorbid ADHD is associated with altered patterns of neuronal activation in adolescents with bipolar disorder performing a simple attention task

OBJECTIVES

Bipolar disorder is increasingly recognized as a significant source of psychiatric morbidity in children and adolescents. Younger bipolar patients symptomatically differ from adults, and frequently present with comorbid disorders, particularly attention-deficit hyperactivity disorder (ADHD). The neurophysiological relationship between these two disorders, however, remains unclear. In this study we utilized functional magnetic resonance imaging (fMRI) to compare activation patterns during performance of a simple attention task between bipolar adolescents with and without ADHD.

METHODS

Eleven bipolar adolescents with comorbid ADHD and 15 bipolar adolescents without comorbidity were recruited to participate in fMRI scans. A single-digit continuous performance task alternated with a control task in a block-design paradigm. between-group comparisons were made using voxel-by-voxel analysis. Follow-up correlations were made between performance and activation.

RESULTS

Group performance did not significantly differ in percentage correct (p = 0.36) or discriminability (p = 0.11). ADHD comorbidity was associated with less activation in the ventrolateral prefrontal cortex (Brodmann 10) and anterior cingulate, and greater activation in posterior parietal cortex and middle temporal gyrus. Comorbid ADHD was associated with substantial differences in patterns of correlation between performance and voxel-by-voxel activation.

CONCLUSIONS

Our findings suggest that comorbid ADHD in bipolar adolescents is associated with activation of alternative pathways during performance of a simple attention task. The pattern of differences suggests that bipolar adolescents with comorbid ADHD demonstrate decreased activation of prefrontal regions, compared with bipolar adolescents without ADHD, and preferentially recruit portions of posterior parietal and temporal cortex.

Modeling and suppression of respiration-related physiological noise in echo-planar functional magnetic resonance imaging using global and one-dimensional navigator echo correction

A major source of noise in functional magnetic resonance imaging (fMRI) arises from modulations in the local magnetic field in the head due to motion of the subject's chest through the respiratory cycle, and this physiologic noise can nullify the gains in statistical power expected by the use of higher magnetic fields for fMRI. In particular, fMRI data acquired using echo-planar imaging (EPI) are very sensitive to these spatially and temporally varying respiration-induced frequency offsets. In this study, accurate 3D magnetic field maps in the head were measured and used to determine the frequency offsets at the two extremes of the respiratory cycle. From these maps, spatially dependent frequency variations from about -1.0 Hz to +1.5 Hz were measured in the brain through the respiratory cycle. Simulations of a typical axial EPI fMRI experiment acquired in the presence of this measured field variation were performed, demonstrating regional image intensity variations between 1 and 5% in single pixel time series. The inadequacy of either global or 1D navigator echo corrections to measure and suppress respiratory-induced noise in fMRI time series is demonstrated. The nature of the spatial variations observed suggests that 2D approaches should be considered.

Separation of physiological very low frequency fluctuation from aliasing by switched sampling interval fMRI scans

Anesthetized children have dominant blood-oxygen-level-dependent (BOLD) signal sources presenting high-power fluctuations at very low frequencies (VLF <0.05 Hz). Aliasing of frequencies higher than critically sampled has been regarded as one probable origin of the VLF fluctuations. Aliased signal frequencies change when the sampling rate of the data is altered. In this study, the aliasing of VLF BOLD signal fluctuation was analysed by switching the repetition time (TR) of magnetic resonance (MR) images. Eleven anesthetized children were imaged at 1.5 T using TRs of 500 and 1200 ms. The BOLD signal sources were separated with independent component analysis (ICA). Occipital cortex signal sources had nonaliased VLF fluctuation ( approximately 0.03 Hz) in 9 of 11 subjects. Arterial signal sources failed to present stable power peaks at frequencies lower than 0.42 Hz presumably due to aliasing. Cerebrospinal fluid (CSF)-related signal sources showed nonaliased VLF in four subjects. In conclusion, the VLF BOLD signal fluctuation in the occipital cortex is a true physiological fluctuation, not a result of signal aliasing.

Increased sensitivity in neuroimaging analyses using robust regression

Robust regression techniques are a class of estimators that are relatively insensitive to the presence of one or more outliers in the data. They are especially well suited to data that require large numbers of statistical tests and may contain outliers due to factors not of experimental interest. Both these issues apply particularly to neuroimaging data analysis. We use simulations to compare several robust techniques against ordinary least squares (OLS) regression, and we apply robust regression to second-level (group "random effects") analyses in three fMRI datasets. Our results show that robust iteratively reweighted least squares (IRLS) at the 2nd level is a computationally efficient technique that both increases statistical power and decreases false positive rates in the presence of outliers. The benefits of IRLS are apparent with small samples (n = 10) and increase with larger sample sizes (n = 40) in the typical range of group neuroimaging experiments. When no true effects are present, IRLS controls false positive rates at an appropriate level. We show that IRLS can have substantial benefits in analysis of group data and in estimating hemodynamic response shapes from time series data. We provide software to implement IRLS in group neuroimaging analyses.

Study of temporal stationarity and spatial consistency of fMRI noise using independent component analysis

Spatial independent component analysis (ICA) was used to study the temporal stationarity and spatial consistency of structured functional MRI (fMRI) noise. Spatial correlations have been used in the past to generate filters for the removal of structured noise for each time-course in an fMRI dataset. It would be beneficial to produce a multivariate filter based on the same principles. ICA is examined to determine if it has properties that are beneficial for this type of filtering. Six fMRI baseline datasets were decomposed via spatial ICA. The time-courses associated with each component were tested for wide-sense stationarity using the wide sense stationarity quotient (WSS). Each dataset was divided into three subsets and each subset was decomposed. The components of first and third subset were matched by the strength of their correlation. The components produced by ICA were found to have largely nonstationary time-courses. Despite the temporal nonstationarity in the data, ICA was found to produce consistent spatial components. The degree of correlation among components differed depending on the amount of dimension reduction performed on the data. It was found that a relatively small number of dimensions produced components that are potentially useful for generating a spatial fMRI filter.

Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters

Previous studies have shown that under some conditions, noise fluctuations in an fMRI time-course are dominated by physiological modulations of the image intensity with secondary contributions from thermal image noise and that these two sources scale differently with signal intensity, susceptibility weighting (TE) and field strength. The SNR of the fMRI time-course was found to be near its asymptotic limit for moderate spatial resolution measurements at 3 T with only marginal gains expected from acquisition at higher field strengths. In this study, we investigate the amplitude of image intensity fluctuations in the fMRI time-course at magnetic field strengths of 1.5 T, 3 T, and 7 T as a function of image resolution, flip angle and TE. The time-course SNR was a similar function of the image SNR regardless of whether the image SNR was modulated by flip angle, image resolution, or field strength. For spatial resolutions typical of those currently used in fMRI (e.g., 3 x 3 x 3 mm(3)), increases in image SNR obtained from 7 T acquisition produced only modest increases in time-course SNR. At this spatial resolution, the ratio of physiological noise to thermal image noise was 0.61, 0.89, and 2.23 for 1.5 T, 3 T, and 7 T. At a resolution of 1 x 1 x 3 mm(3), however, the physiological to thermal noise ratio was 0.34, 0.57, and 0.91 for 1.5 T, 3 T and 7 T for TE near T2*. Thus, by reducing the signal strength using higher image resolution, the ratio of physiologic to image noise could be reduced to a regime where increased sensitivity afforded by higher field strength still translated to improved SNR in the fMRI time-series.

Motion-correction techniques for standing equine MRI

Magnetic resonance imaging (MRI) of the distal extremities of the standing, sedated horse would be desirable if diagnostic quality images could be obtained. With the availability of extremity and special purpose magnet designs on the market, a system to safely accommodate the standing horse may gain increasing popularity. This paper considers the issue of motion that will need to be addressed to achieve successful, diagnostic quality images. The motion of the carpus and tarsus of five standing, sedated horses was quantified. The obtained motion records were then used to induce motion in cadaveric joint specimens during several MRI scans. The measured dorsal-palmar/plantar, medial-lateral, and proximal-distal random wobbling motions in the standing sedated horse were several centimeters in magnitude and generated severe motion-artifacts during axial MRI of the cadaveric specimens. Two retrospective motion-correction techniques (autocorrection and navigator-based adaptive correction) were used to correct the corrupted images. The motion artifacts were nearly eliminated with the use of both techniques in series. Although significant hurdles remain, these results suggest promise for allowing diagnostic quality MRI of the carpus and tarsus in the standing horse.

Changes in neuronal activation in patients with bipolar disorder during performance of a working memory task

OBJECTIVES

Several lines of evidence suggest that deficits in cognition persist in bipolar patients during periods of euthymia. Working memory impairment has been observed in euthymic bipolar patients and noted to be a significant source of functional deficits in psychiatric disorders. Functional changes associated with these cognitive deficits however, remain poorly understood. We hypothesized that patients with bipolar disorder would demonstrate changes in neuronal activation in specific regions forming part of the working memory network.

METHODS

Fifteen euthymic bipolar patients and fifteen age- and gender-matched healthy controls were recruited. Subjects participated in fMRI scans during which a two-back working memory task alternated with a zero-back control/attention task using a block-design paradigm. Groups were analyzed separately, and intergroup comparisons were made using an exploratory, voxel-by-voxel analysis.

RESULTS

Bipolar patients performed more poorly on the cognitive tasks than did healthy controls (F = 3.77, p = 0.04). After covarying for task performance and reaction time, bipolar patients demonstrated significantly greater activation than healthy subjects in several regions including the fronto-polar prefrontal cortex, temporal cortex, basal ganglia, thalamus, and posterior parietal cortex. No areas showed a significant decrease in activation, compared with healthy controls.

CONCLUSIONS

Our findings suggest that decreased working memory performance in bipolar patients reflects specific neurofunctional deficits. These deficits may represent primary areas of neuropathology or be secondary to neuropathology elsewhere in the working memory network. Continued research utilizing other imaging modalities may further clarify the underlying neuropathology involved in these cognitive deficits.

Adaptive Denoising of Event-Related Functional Magnetic Resonance Imaging Data Using Spectral Subtraction

A new adaptive signal-preserving technique for noise suppression in event-related functional magnetic resonance imaging (fMRI) data is proposed based on spectral subtraction. The proposed technique estimates a parametric model for the power spectrum of random noise from the acquired data based on the characteristics of the Rician statistical model. This model is subsequently used to estimate a noise-suppressed power spectrum for any given pixel time course by simple subtraction of power spectra. The new technique is tested using computer simulations and real data from event-related fMRI experiments. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components in the signal. Moreover, we demonstrate that further analysis using principal component analysis and independent component analysis shows a significant improvement in both convergence and clarity of results when the new technique is used. Given its simple form, the new method does not change the statistical characteristics of the signal or cause correlated noise to be present in the processed signal. This suggests the value of the new technique as a useful preprocessing step for fMRI data analysis.

High-resolution functional MRI at 3T in healthy and epilepsy subjects: hippocampal activation with picture encoding task

Functional MRI (fMRI) studies of memory with coarse resolution of 4 x 4 x 5 mm often fail to demonstrate blood oxygenation level-dependent (BOLD) activation in the hippocampal formation. This failure occurs when nonactivating white matter is averaged with the signal from hippocampal gray matter, attenuating the total BOLD signal from a single voxel due to the "partial volume effect." In this study, we evaluated the suitability of high-resolution fMRI at 3T (voxel size 2 x 2 x 3 mm) for improved visualization of hippocampal activation during memory encoding in 21 healthy and 6 epilepsy subjects. We used a picture encoding task (block design) that involved memorization of indoor and outdoor scenes along with an appropriate resting task. Region-of-interest (ROI) analysis was performed; laterality indices (LIs) were calculated based on hippocampal ROIs (hROIs) or on global medial temporal ROIs (mtROIs). In 19 healthy subjects, robust bilateral BOLD signal changes within both ROIs were noted. The mean LI+/-SD for the hROIs is -0.12+/-0.06 and that for the medial temporal ROIs -0.12+/-0.05, with correlation between the LIs (r = 0.59, P = 0.009). Good concordance was noted between the surgical outcome and memory lateralization with the fMRI task employed in this study. The preliminary results are encouraging, and with continuing improvements in MRI scanner technology, we expect fMRI of the hippocampal formation at higher resolution to be possible and preferable. Furthermore, these results suggest that a larger study to test the utility of high-resolution fMRI in epilepsy presurgical evaluation is needed.

High field human imaging

This review article examines the state of knowledge regarding human imaging using MRI at high main magnetic field strengths. The article starts with a summary of the technical issues associated with magnetic field strengths in the range of 3-8 T, including magnet characteristics and the properties of radiofrequency magnetic fields, with special reference to sensitivity, power deposition, and homogeneity. The published data on tissue-water relaxation times in the brain is tabulated and the implications for contrast and pulse sequence implementation is elucidated. The behavior of the major fast imaging sequences, fast low angle shot (FLASH), rapid acquisition with relaxation enhancement (RARE), and echo planar imaging (EPI), is examined in this context. A number of anatomical images from 3 T systems are presented as examples. Particular attention is given to various forms of vascular imaging, namely, time of flight angiography, venography, and arterial spin labeling. The most complex changes in contrast with main magnetic field strength are in activation studies utilizing the blood oxygen level dependent mechanism, which are examined in detail. Improvements in spatial specificity are emphasized, particularly in conjunction with spin-echo imaging. The article concludes with a discussion of the current status and the potential impact of technical developments such as parallel imaging.

Model assessment and model building in fMRI

Model quality is rarely assessed in fMRI data analyses and less often reported. This may have contributed to several shortcomings in the current fMRI data analyses, including: (1) Model mis-specification, leading to incorrect inference about the activation-maps, SPM[t] and SPM[F]; (2) Improper model selection based on the number of activated voxels, rather than on model quality; (3) Under-utilization of systematic model building, resulting in the common but suboptimal practice of using only a single, pre-specified, usually over-simplified model; (4) Spatially homogenous modeling, neglecting the spatial heterogeneity of fMRI signal fluctuations; and (5) Lack of standards for formal model comparison, contributing to the high variability of fMRI results across studies and centers. To overcome these shortcomings, it is essential to assess and report the quality of the models used in the analysis. In this study, we applied images of the Durbin-Watson statistic (DW-map) and the coefficient of multiple determination (R(2)-map) as complementary tools to assess the validity as well as goodness of fit, i.e., quality, of models in fMRI data analysis. Higher quality models were built upon reduced models using classic model building. While inclusion of an appropriate variable in the model improved the quality of the model, inclusion of an inappropriate variable, i.e., model mis-specification, adversely affected it. Higher quality models, however, occasionally decreased the number of activated voxels, whereas lower quality or inappropriate models occasionally increased the number of activated voxels, indicating that the conventional approach to fMRI data analysis may yield sub-optimal or incorrect results. We propose that model quality maps become part of a broader package of maps for quality assessment in fMRI, facilitating validation, optimization, and standardization of fMRI result across studies and centers. Hum. Brain Mapping 20:227-238, 2003.