Variability in fMRI: A re‐examination of inter‐session differences

Smith, Stephen M.; Beckmann, Christian F.; Ramnani, Narender; Woolrich, Mark W.; Bannister, P.; Jenkinson, Mark; Matthews, Paul M.; McGonigle, David J.

doi:10.1002/hbm.20080

Cited by 167 publications

(125 citation statements)

References 22 publications

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“…7 and 8). In comparing data across sessions, the use of thresholding may increase the apparent variability even when the maps are not significantly different [34]. This was in particular a problem for subject VA, where the data on Day 180 contained a number of voxels that were just below threshold but that overlapped with the other 2 data sets.…”

Section: Resultsmentioning

confidence: 99%

High‐resolution FMRI at 1.5T using balanced SSFP

Miller

Smith

Jezzard

et al. 2005

Magnetic Resonance in Med

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The resolution in conventional BOLD FMRI is considerably lower than can be achieved with other MRI methods, and is insufficient for many important applications. One major difficulty in robustly improving spatial resolution is the poor image quality in BOLD FMRI, which suffers from distortions, blurring, and signal dropout. This work considers the potential for increased resolution with a new FMRI method based on balanced SSFP. This method establishes a blood oxygenation sensitive steady-state (BOSS) signal, in which the frequency sensitivity of balanced SSFP is used to detect the frequency shift of deoxyhemoglobin. BOSS FMRI is highly SNR efficient and does not suffer from image distortions or signal dropout, making this method an excellent candidate for high-resolution FMRI. This study presents the first demonstration of high-resolution BOSS FMRI, using an efficient 3D stack-of-segmented EPI readout and combined acquisition at multiple center frequencies. BOSS FMRI is shown to enable high-resolution FMRI data (1 ؋ 1 ؋ 2 mm 3 ) in both visual and motor systems using standard hardware at 1.5 T. Currently, the major limitation of BOSS FMRI is its sensitivity to temporal and spatial field drift. BOLD FMRI has become an important tool in neuroimaging because it represents a good compromise between spatial resolution, temporal sampling, and breadth of coverage. Nevertheless, the achievable spatial resolution in conventional FMRI is poor relative to other MRI methods: Typical BOLD FMRI experiments acquire voxel volumes of 50 -100 mm 3 (e.g., 4 ϫ 4 ϫ 6 mm 3 voxels), while anatomical reference scans are regularly collected with voxel volumes of just 1-5 mm 3 . The coarse resolution of conventional FMRI is acceptable for many applications; however, these methods are inadequate to elucidate fine cortical architecture or study small gray matter nuclei. For example, small voxels are necessary to resolve the ocular dominance columns [1], or retinotopic [2] and somatotopic [3] maps. Additionally, the ability to distinguish deep brain nuclei would have a major impact on, for example, the study of memory [4] or pain [5].High-resolution BOLD FMRI is limited in part due to the coupling of functional contrast to sources of image artifacts and signal loss. Because BOLD detects signal dephasing caused by deoxyhemoglobin, functional contrast requires long echo times. Unfortunately, the use of long T E enhances signal dephasing from other sources of frequency heterogeneity, resulting in signal loss near susceptibility boundaries. Additional image artifacts are caused by the need for long readouts, which introduce distortion in frequency-shifted regions and, to a lesser extent, image blurring due to relaxation. Hence, robust functional contrast, which requires long T E and long readouts, is fundamentally at odds with image quality, which favors short T E and short readouts. This tradeoff of signal detectability for image fidelity is intrinsically tied to the use of a contrast mechanism that is based on signal dephasing, limiting the ac...

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Section: Resultsmentioning

confidence: 99%

High‐resolution FMRI at 1.5T using balanced SSFP

Miller

Smith

Jezzard

et al. 2005

Magnetic Resonance in Med

Self Cite

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“…This allowed dissociation between different sub-regions of the PPC, and adjacent coil positions served as mutual control sites. Individual coil positions were preplanned by transforming the MNI coordinates of the grid from MNI space (Mazziotta et al, 2001) to the space of the individual structural images using the linear registration (FLIRT) of FSL 4.0 (FMRIB, Oxford, UK; (Smith et al, 2004(Smith et al, , 2005). The closest coil position on the skull of every participant was determined for each coil position using custom-written MATLAB routines (The MathWorks, Natick, MA, USA) and the surface reconstruction of the skull as obtained with BrainVoyager 2000 (Brain Innovation, Maastricht, The Netherlands).…”

Section: Tms Stimulationmentioning

confidence: 99%

A key region in the human parietal cortex for processing proprioceptive hand feedback during reaching movements

et al. 2014

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Seemingly effortless, we adjust our movements to continuously changing environments. After initiation of a goal-directed movement, the motor command is under constant control of sensory feedback loops. The main sensory signals contributing to movement control are vision and proprioception. Recent neuroimaging studies have focused mainly on identifying the parts of the posterior parietal cortex (PPC) that contribute to visually guided movements. We used event-related TMS and force perturbations of the reaching hand to test whether the same sub-regions of the left PPC contribute to the processing of proprioceptive-only and of multi-sensory information about hand position when reaching for a visual target. TMS over two distinct stimulation sites elicited differential effects: TMS applied over the posterior part of the medial intraparietal sulcus (mIPS) compromised reaching accuracy when proprioception was the only sensory information available for correcting the reaching error. When visual feedback of the hand was available, TMS over the anterior intraparietal sulcus (aIPS) prolonged reaching time. Our results show for the first time the causal involvement of the posterior mIPS in processing proprioceptive feedback for online reaching control, and demonstrate that distinct cortical areas process proprioceptive-only and multi-sensory information for fast feedback corrections.

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“…Variability in needling technique, deqi sensations, design paradigm, differences in neruoimaging hardware and software, as well as data post-processing methods [62,63], may all account for many of the reported differences in brain response. Therefore, it may be helpful to define a standardized reporting system to describe details of acupuncture manipulations [64].…”

Section: Further Directions For Acupuncture Neuroimaging Researchmentioning

confidence: 99%

Spatiotemporal Modulation of Central Neural Pathway Underlying Acupuncture Action: A Systematic Review

Bai¹,

Qin²,

Liang³

et al. 2009

CMIR

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Acupuncture, an ancient therapeutic technique, is currently gaining popularity as an important modality of alternative and complementary medicine in the West world. Concurrently, scientific interests in exploring whether acupuncture is therapeutically effective are raised alongside those about the means by which it may operate. Modern neuroimaging techniques such as functional magnetic resonance imaging, positron emission tomography, electroencephalography, and magnetoencephalography provide a means to safely monitor brain activity in humans. In this review, we have summarized evidence derived from the neuroimaging studies and tried to elucidate the neurophysiological correlates of acupuncture. Previous investigations on the neural responses to acupuncture mainly focus on its acute effects and explore the correlation between the specific acupoints and cortical activations only in the spatial domain. However, abundant clinical reports and psychophysical analysis suggest the kinetics of acupuncture is longer acting as a function of time. Consequentially, an accurate interpretation of acupuncture actions depends on how effectively we can characterize the nature of temporal variations underlying neural activities, rather than simply detect the occurrence of such changes. This emerging picture indicates that both designing paradigms and statistical models involved in acupuncture studies should be applied with great care.

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Variability in fMRI: A re‐examination of inter‐session differences

Cited by 167 publications

References 22 publications

High‐resolution FMRI at 1.5T using balanced SSFP

High‐resolution FMRI at 1.5T using balanced SSFP

A key region in the human parietal cortex for processing proprioceptive hand feedback during reaching movements

Spatiotemporal Modulation of Central Neural Pathway Underlying Acupuncture Action: A Systematic Review

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