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648
Mapping motor inhibition: Conjunctive brain activations across different versions of go/no-go and stop tasks. NeuroImage 13: 250–261
, 2001
"... Conjunction analysis methods were used in functional magnetic resonance imaging to investigate brain regions commonly activated in subjects performing different versions of go/no-go and stop tasks, differing in probability of inhibitory signals and/or contrast conditions. Generic brain activation ma ..."
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Cited by 120 (0 self)
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Conjunction analysis methods were used in functional magnetic resonance imaging to investigate brain regions commonly activated in subjects performing different versions of go/no-go and stop tasks, differing in probability of inhibitory signals and/or contrast conditions. Generic brain activation maps highlighted brain regions commonly activated in (a) two different go/no-go task versions, (b) three different stop task versions, and (c) all 5 inhibition task versions. Comparison between the generic activation maps of stop and go/no-go task versions revealed inhibitory mechanisms specific to go/no-go or stop task performance in 15 healthy, right-handed, male adults. In the go/no-go task a motor response had to be selectively executed or inhibited in either 50 % or 30 % of
Separating processes within a trial in event-related functional MRI. I. The method. NeuroImage 13
, 2001
"... Many cognitive processes occur on time scales that can significantly affect the shape of the blood oxygenation level-dependent (BOLD) response in eventrelated functional MRI. This shape can be estimated from event related designs, even if these processes occur in a fixed temporal sequence (J. M. Oll ..."
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Cited by 113 (3 self)
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Many cognitive processes occur on time scales that can significantly affect the shape of the blood oxygenation level-dependent (BOLD) response in eventrelated functional MRI. This shape can be estimated from event related designs, even if these processes occur in a fixed temporal sequence (J. M. Ollinger, G. L. Shulman, and M. Corbetta. 2001. NeuroImage 13: 210–217). Several important considerations come into play when interpreting these time courses. First, in single subjects, correlations among neighboring time points give the noise a smooth appearance that can be confused with changes in the BOLD response. Second, the variance and degree of correlation among estimated time courses are strongly influenced by the timing of the experimental design. Simulations show that optimal results are obtained if the intertrial intervals are as short as possible, if they follow an exponential distribution with at least three distinct values, and if 40 % of the trials are partial trials. These results are not particularly sensitive to the fraction of partial trials, so accurate estimation of time courses can be obtained with lower percentages of partial trials (20–25%). Third, statistical maps can be formed from F statistics computed with the extra sum of square principle or by t statistics computed from the cross-correlation of the time courses with a model for the hemodynamic response. The latter method relies on an accurate model for the hemodynamic response. The most robust model among those tested was a single gamma function. Finally, the power spectrum of the measured BOLD signal in rapid event-related paradigms is similar to that of the noise. Nevertheless, highpass filtering is desirable if the appropriate model
Spiral-in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts
- Magn. Reson. Med
, 2001
"... BOLD fMRI is hampered by dropout of signal in the orbitofrontal and parietal brain regions due to magnetic field gradients near air-tissue interfaces. This work reports the use of spiral-in trajectories that begin at the edge of k-space and end at the origin, and spiral in/out trajectories in which ..."
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Cited by 108 (6 self)
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BOLD fMRI is hampered by dropout of signal in the orbitofrontal and parietal brain regions due to magnetic field gradients near air-tissue interfaces. This work reports the use of spiral-in trajectories that begin at the edge of k-space and end at the origin, and spiral in/out trajectories in which a spiral-in readout is followed by a conventional spiral-out trajectory. The spiral-in trajectory reduces the dropout and increases the BOLD contrast. The spiral-in and spiral-out images can be combined in several ways to simultaneously achieve increased signal-tonoise ratio (SNR) and reduced dropout artifacts. Activation experiments employing an olfaction task demonstrate significantly increased activation volumes due to reduced dropout, and overall increased SNR in all regions. Magn Reson Med 46: 515–522, 2001. © 2001 Wiley-Liss, Inc. Key words: functional magnetic resonance imaging; spiral in/
A parametric manipulation of factors affecting task-induced deactivation in functional neuroimaging
- Journal of Cognitive Neuroscience
, 2003
"... & Task-induced deactivation (TID) refers to a regional decrease in blood flow during an active task relative to a ‘‘resting’ ’ or ‘‘passive’ ’ baseline. We tested the hypothesis that TID results from a reallocation of processing resources by parametrically manipulating task difficulty within thr ..."
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Cited by 102 (5 self)
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& Task-induced deactivation (TID) refers to a regional decrease in blood flow during an active task relative to a ‘‘resting’ ’ or ‘‘passive’ ’ baseline. We tested the hypothesis that TID results from a reallocation of processing resources by parametrically manipulating task difficulty within three factors: target discriminability, stimulus presentation rate, and short-term memory load. Subjects performed an auditory target detection task during functional magnetic resonance imaging (fMRI), responding to a single target tone or, in the short-term memory load conditions, to target sequences. Seven task conditions (a common version and two additional levels for each of the three factors) were each alternated with ‘‘rest’ ’ in a block design. Analysis of covariance identified brain regions in which TID occurred. Analyses of variance identified seven regions (left anterior cingulate/superior frontal gyrus, left middle frontal gyrus, right anterior cingulate gyrus, left and right posterior cingulate gyrus, left posterior parieto-occipital cortex, and right precuneus) in which TID magnitude varied across task levels within a factor. Follow-up tests indicated that for each of the three factors, TID magnitude increased with task difficulty. These results suggest that TID represents reallocation of processing resources from areas in which TID occurs to areas involved in task performance. Short-term memory load and stimulus rate also predict suppression of spontaneous thought, and many of the brain areas showing TID have been linked with semantic processing, supporting claims that TID may be due in part to suspension of spontaneous semantic processes that occur during ‘‘rest’’ (Binder et al., 1999). The concept that the typical ‘‘resting state’ ’ is actually a condition characterized by rich cognitive activity has important implications for the design and analysis of neuroimaging studies. &
An investigation of functional and anatomical connectivity using magnetic resonance imaging
- Neuroimage
"... This article examines functional and anatomical connectivity in healthy human subjects measured with magnetic resonance imaging methods. Anatomical connectivity in white matter is obtained from measurements of the diffusion tensor. A Monte-Carlo simulation determines the probability that a particle ..."
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Cited by 94 (3 self)
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This article examines functional and anatomical connectivity in healthy human subjects measured with magnetic resonance imaging methods. Anatomical connectivity in white matter is obtained from measurements of the diffusion tensor. A Monte-Carlo simulation determines the probability that a particle diffuses between two points, with the probability of a jump in a particular direction from a given voxel being based on the local value of the diffusion tensor components. Functional connectivity between grey matter pixels is assessed without recourse to a specific activation paradigm, by calculating the correlation coefficient between random fluctuations in the blood oxygenation level-dependent signal time course in different pixels. The methods are used to examine the
Regional homogeneity approach to fMRI data analysis
- NeuroImage
, 2004
"... Kendall’s coefficient concordance (KCC) can measure the similarity of a number of time series. It has been used for purifying a given cluster in functional MRI (fMRI). In the present study, a new method was developed based on the regional homogeneity (ReHo), in which KCC was used to measure the simi ..."
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Cited by 88 (9 self)
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Kendall’s coefficient concordance (KCC) can measure the similarity of a number of time series. It has been used for purifying a given cluster in functional MRI (fMRI). In the present study, a new method was developed based on the regional homogeneity (ReHo), in which KCC was used to measure the similarity of the time series of a given voxel to those of its nearest neighbors in a voxel-wise way. Six healthy subjects performed left and right finger movement tasks in event-related design; five of them were additionally scanned in a rest condition. KCC was compared among the three conditions (left finger movement, right finger movement, and the rest). Results show that bilateral primary motor cortex (M1) had higher KCC in either left or right finger movement condition than in rest condition. Contrary to prediction and to activation pattern, KCC of ipsilateral M1 is significantly higher than contralateral M1 in unilateral finger movement conditions. These results support the previous electrophysiologic findings of increasing ipsilateral M1 excitation during unilateral movement. ReHo can consider as a complementary method to model-driven method, and it could help reveal the complexity of the human brain function. More work is needed to understand the neural mechanism underlying ReHo.
Modeling the hemodynamic response to brain activation
- Neuroimage
, 2004
"... Neural activity in the brain is accompanied by changes in cerebral blood flow (CBF) and blood oxygenation that are detectable with functional magnetic resonance imaging (fMRI) techniques. In this paper, recent mathematical models of this hemodynamic response are reviewed and integrated. Models are d ..."
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Cited by 84 (4 self)
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Neural activity in the brain is accompanied by changes in cerebral blood flow (CBF) and blood oxygenation that are detectable with functional magnetic resonance imaging (fMRI) techniques. In this paper, recent mathematical models of this hemodynamic response are reviewed and integrated. Models are described for: (1) the blood oxygenation level dependent (BOLD) signal as a function of changes in cerebral oxygen extraction fraction (E) and cerebral blood volume (CBV); (2) the balloon model, proposed to describe the transient dynamics of CBV and deoxyhemoglobin (Hb) and how they affect the BOLD signal; (3) neurovascular coupling, relating the responses in CBF and cerebral metabolic rate of oxygen (CMRO2) to the neural activity response; and (4) a simple model for the temporal nonlinearity of the neural response itself. These models are integrated into a mathematical framework describing the steps linking a stimulus to the measured BOLD and CBF responses. Experimental results examining transient features of the BOLD response (post-stimulus undershoot and initial dip), nonlinearities of the hemodynamic response, and the role of the physiologic baseline state in altering the BOLD signal are discussed in the context of the proposed models. Quantitative modeling of the hemodynamic response, when combined with experimental data measuring both the BOLD and CBF responses, makes possible a more specific and quantitative assessment of brain physiology than is possible with standard BOLD imaging alone. This approach has the potential to enhance numerous studies of brain function in development, health, and disease.
Unaliasing by Fourier-Encoding the Overlaps Using the Temporal Dimension (UNFOLD), Applied to Cardiac Imaging and fMRI
- MAGNETIC RESONANCE IN MEDICINE 42:813–828 (1999)
, 1999
"... In several applications, MRI is used to monitor the time behavior of the signal in an organ of interest; e.g., signal evolution because of physiological motion, activation, or contrast-agent accumulation. Dynamic applications involve acquiring data in a k–t space, which contains both temporal and s ..."
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Cited by 81 (3 self)
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In several applications, MRI is used to monitor the time behavior of the signal in an organ of interest; e.g., signal evolution because of physiological motion, activation, or contrast-agent accumulation. Dynamic applications involve acquiring data in a k–t space, which contains both temporal and spatial information. It is shown here that in some dynamic applications, the t axis of k–t space is not densely filled with information. A method is introduced that can transfer information from the k axes to the t axis, allowing a denser, smaller k–t space to be acquired, and leading to significant reductions in the acquisition time of the temporal frames. Results are presented for cardiac-triggered imaging and functional MRI (fMRI), and are compared with data obtained in a conventional way. The temporal resolution was increased by nearly a factor of two in the cardiac-triggered study, and by as much as a factor of eight in the fMRI study. This increase allowed the acquisition of fMRI activation maps, even when the acquisition time for a single full time frame was actually longer than the paradigm cycle period itself. The new method can be used to significantly reduce the acquisition time of the individual temporal frames in certain dynamic studies. This can be used, for example, to increase the temporal or spatial resolution, increase the spatial coverage, decrease the total imaging time, or alter sequence parameters
Real-time functional magnetic resonance imaging
- Magnetic Resonance in Medicine
, 1995
"... A recursive algorithm suitable for functional magnetic reso-nance imaging (FMRI) calculations is presented. The correla-tion coefficient of a time course of images with a reference time series, with the mean and any linear trend projected out, may be computed with 22 operations per voxel, per image; ..."
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Cited by 67 (2 self)
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A recursive algorithm suitable for functional magnetic reso-nance imaging (FMRI) calculations is presented. The correla-tion coefficient of a time course of images with a reference time series, with the mean and any linear trend projected out, may be computed with 22 operations per voxel, per image; the storage overhead is four numbers per voxel. A statistical model for the FMRI signal is presented, and thresholds for the correlation coefficient are derived from it. Selected images from the first real-time functional neuroimaging experiment (at 3 Tesla) are presented. Using a 50-MHz workstation equipped with a 1Cbt analog-to-digital converter, each echo planar image was acquired, reconstructed, correlated, thresh-olded, and displayed in pseudocolor (highlighting active re-gions in the brain) within 500 ms of the RF pulse. Key words: functional MRI; recursive image processing.
How good is good enough in path analysis of fMRI data? Neuroimage 2000
"... This paper is concerned with the problem of evaluating goodness-of-fit of a path analytic model to an interregional correlation matrix derived from functional magnetic resonance imaging (fMRI) data. We argue that model evaluation based on testing the null hypothesis that the correlation matrix predi ..."
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Cited by 64 (6 self)
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This paper is concerned with the problem of evaluating goodness-of-fit of a path analytic model to an interregional correlation matrix derived from functional magnetic resonance imaging (fMRI) data. We argue that model evaluation based on testing the null hypothesis that the correlation matrix predicted by the model equals the population correlation matrix is problematic because P values are conditional on asymptotic distributional results (which may not be valid for fMRI data acquired in less than 10 min), as well as arbitrary specification of residual variances and effective degrees of freedom in each regional fMRI time series. We introduce an alternative approach based on an algorithm for automatic identification of the best fitting model that can be found to account for
