Functional magnetic resonance imaging (FMRI) of the human brain

DeYoe, Edgar A.; Bandettini, Peter A.; Neitz, Jay; Miller, David A.; Winans, Paula

doi:10.1016/0165-0270(94)90191-0

Cited by 340 publications

(206 citation statements)

References 35 publications

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“…This is a robust test that reliably produces a response independent of cognitive or learning processes (DeYoe et al 1994). Each study consisted of 31 alternating control and checkerboard blocks beginning and ending with a control block.…”

Section: Protocolmentioning

confidence: 99%

Functional Tissue Pulsatility Imaging of the Brain During Visual Stimulation

Kucewicz

Dunmire

Leotta

et al. 2007

Ultrasound in Medicine & Biology

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Functional tissue pulsatility imaging (fTPI) is a new ultrasonic technique being developed to map brain function by measuring changes in tissue pulsatility due to changes in blood flow with neuronal activation. The technique is based in principle on plethysmography, an older, non-ultrasound technology for measuring expansion of a whole limb or body part due to perfusion. Perfused tissue expands by a fraction of a percent early in each cardiac cycle when arterial inflow exceeds venous outflow and relaxes later in the cardiac cycle when venous drainage dominates. Tissue pulsatility imaging (TPI) uses tissue Doppler signal processing methods to measure this pulsatile "plethysmographic" signal from hundreds or thousands of sample volumes in an ultrasound image plane. A feasibility study was conducted to determine if TPI could be used to detect regional brain activation during a visual contrast-reversing checkerboard block paradigm study. During a study, ultrasound data were collected transcranially from the occipital lobe as a subject viewed alternating blocks of a reversing checkerboard (stimulus condition) and a static, gray screen (control condition). Multivariate Analysis of Variance (MANOVA) was used to identify sample volumes with significantly different pulsatility waveforms during the control and stimulus blocks. In 7 out 14 studies, consistent regions of activation were detected from tissue around the major vessels perfusing the visual cortex.

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Section: Protocolmentioning

confidence: 99%

Functional Tissue Pulsatility Imaging of the Brain During Visual Stimulation

Kucewicz

Dunmire

Leotta

et al. 2007

Ultrasound in Medicine & Biology

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“…The BOLD signal reaches its maximum approximately 5 to 8 s after stimulus onset and remains increased beyond the duration of the stimulus (Blamire et al, 1992;DeYoe et al, 1994;Menon et al, 1995). However, a number of studies have shown that this general time course following stimulation can vary considerably when different cortical regions and subjects are compared.…”

Section: Introductionmentioning

confidence: 99%

“…The minimum and the maximum activation and their respective time lags with reference to stimulation onset, t min and t max , were sought in the time range of 0 to 5 s and 3 to 8 s, respectively, with t min Ͻ t max . These time ranges were chosen in accordance with earlier reports about the usual time ranges of time-to-onset and time-to-peak (Blamire et al, 1992;DeYoe et al, 1994;Menon et al, 1995;Buckner et al, 1996). In addition, the first and second derivatives of the time courses at each time step were calculated using Taylor polynomial approximations…”

Section: Analysis Of Bold Dynamicsmentioning

confidence: 99%

“…When examining fMRI time courses, an increase in the BOLD signal is often observed about 2 s after stimulus onset (DeYoe et al, 1994;Buckner et al, 1996;Buckner, 1998;Menon and Kim, 1999). The BOLD signal reaches its maximum approximately 5 to 8 s after stimulus onset and remains increased beyond the duration of the stimulus (Blamire et al, 1992;DeYoe et al, 1994;Menon et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

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Within-subject variability of BOLD response dynamics

et al. 2003

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In this paper we investigate the within-subject variability of dynamical aspects of the BOLD response obtained in a series of fMRI sessions several days apart. Five different parameters describing the temporal behavior of trial-averaged time courses, such as time-to-peak and time-to-onset, were estimated and analyzed with respect to their variability across nine sessions. Results show that small variances of the estimated parameters can be found, provided that the analysis is restricted to voxels activated in all individual sessions. Among the investigated parameters, time-to-peak shows the most stable behavior. These results were obtained using two different analysis methods, the estimation of the parameters directly from trial-averaged time courses and fitting trial-averaged time courses to an assumed hemodynamic response function. Both methods yield comparable results.

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“…In humans, retinotopy can be reliably demonstrated in fMRI by using slowly moving, phase-encoded retinotopic stimuli (Engel et al 1994(Engel et al , 1997Deyoe et al 1994;Sereno et al 1995).…”

Section: Mri Of the Monkey Brainmentioning

confidence: 99%

The neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal

Logothetis

2002

Phil. Trans. R. Soc. Lond. B

817

516

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Magnetic resonance imaging (MRI) has rapidly become an important tool in clinical medicine and biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is currently the most widely used method for brain mapping and studying the neural basis of human cognition. While the method is widespread, there is insufficient knowledge of the physiological basis of the fMRI signal to interpret the data confidently with respect to neural activity. This paper reviews the basic principles of MRI and fMRI, and subsequently discusses in some detail the relationship between the blood-oxygenlevel-dependent (BOLD) fMRI signal and the neural activity elicited during sensory stimulation. To examine this relationship, we conducted the first simultaneous intracortical recordings of neural signals and BOLD responses. Depending on the temporal characteristics of the stimulus, a moderate to strong correlation was found between the neural activity measured with microelectrodes and the BOLD signal averaged over a small area around the microelectrode tips. However, the BOLD signal had significantly higher variability than the neural activity, indicating that human fMRI combined with traditional statistical methods underestimates the reliability of the neuronal activity. To understand the relative contribution of several types of neuronal signals to the haemodynamic response, we compared local field potentials (LFPs), single-and multi-unit activity (MUA) with high spatio-temporal fMRI responses recorded simultaneously in monkey visual cortex. At recording sites characterized by transient responses, only the LFP signal was significantly correlated with the haemodynamic response. Furthermore, the LFPs had the largest magnitude signal and linear systems analysis showed that the LFPs were better than the MUAs at predicting the fMRI responses. These findings, together with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input and processing of neuronal information within a region and not the output signal transmitted to other brain regions.

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Functional magnetic resonance imaging (FMRI) of the human brain

Cited by 340 publications

References 35 publications

Functional Tissue Pulsatility Imaging of the Brain During Visual Stimulation

Functional Tissue Pulsatility Imaging of the Brain During Visual Stimulation

Within-subject variability of BOLD response dynamics

The neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal

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