Correlation between temporal response of fMRI and fast reaction time in a language task

Liu, Ho Ling; Liao, Wan Ting; Fang, Shin Yi; Chu, T. L.; Tan, Li Hai

doi:10.1016/j.mri.2004.01.003

Cited by 21 publications

(32 citation statements)

References 20 publications

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“…Low sampling rate (Goebel et al, 2003;Deshpande et al, 2010) and variable HRF latency across different brain regions (David et al, 2008) are two often-cited factors for concern. For the first concern, fMRI onset latencies have been shown to correlate well with reaction time and stimulus presentation time (Liu et al, 2004) and can be used to determine the origin of processing delays during cognitive or perceptual tasks with a temporal accuracy of tens of milliseconds (Menon et al, 1998). When applied to simulated fMRI data generated by convolving HRF with local field potentials recorded from the macaque (Deshpande et al, 2010), Granger causality is found to be able to detect delays as short as several hundred milliseconds.…”

Section: Methodological Considerationsmentioning

confidence: 99%

Causal Interactions in Attention Networks Predict Behavioral Performance

Wen¹,

Yao²,

Liu³

et al. 2012

J. Neurosci.

144

140

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Lesion and functional brain imaging studies have suggested that there are two anatomically nonoverlapping attention networks. The dorsal frontoparietal network controls goal-oriented top-down deployment of attention; the ventral frontoparietal network mediates stimulus-driven bottom-up attentional reorienting. The interaction between the two networks and its functional significance has been considered in the past but no direct test has been carried out. We addressed this problem by recording fMRI data from human subjects performing a trial-by-trial cued visual spatial attention task in which the subject had to respond to target stimuli in the attended hemifield and ignore all stimuli in the unattended hemifield. Correlating Granger causal influences between regions of interest with behavioral performance, we report two main results. First, stronger Granger causal influences from the dorsal attention network (DAN) to the ventral attention network (VAN), i.e., DAN3 VAN, are generally associated with enhanced performance, with right intraparietal sulcus (IPS), left IPS, and right frontal eye field being the main sources of behavior-enhancing influences. Second, stronger Granger causal influences from VAN to DAN, i.e., VAN3 DAN, are generally associated with degraded performance, with right temporal-parietal junction being the main sources of behavior-degrading influences. These results support the hypothesis that signals from DAN to VAN suppress and filter out unimportant distracter information, whereas signals from VAN to DAN break the attentional set maintained by the dorsal attention network to enable attentional reorienting.

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Section: Methodological Considerationsmentioning

confidence: 99%

Causal Interactions in Attention Networks Predict Behavioral Performance

Wen¹,

Yao²,

Liu³

et al. 2012

J. Neurosci.

144

140

View full text Add to dashboard Cite

show abstract

“…This hypothesis has been little explored physiologically. Voluntary motor response decisions are usually thought to be triggered primarily in frontal "executive" brain areas, including orbitofrontal cortex (Liu et al, 2004), medial prefrontal cortex, and posterior, central, and dorsal anterior cingulate cortex (Naito et al, 2000;Mesulam et al, 2001;Ullsperger and von Cramon, 2003). Although a few functional magnetic resonance imaging (fMRI) studies have reported correlations between single-trial blood-oxygen level dependant (BOLD) signal changes and RTs in frontopolar and anterior cingulate cortex (Naito et al, 2000;Mesulam et al, 2001;Liu et al, 2004), the slow time course and poor time resolution of BOLD signals makes it difficult to determine whether the concerned brain activity precedes or follows the motor response.…”

Section: Introductionmentioning

confidence: 99%

Medial Prefrontal Theta Bursts Precede Rapid Motor Responses during Visual Selective Attention

Delorme¹,

Westerfield²,

Makeig³

2007

J. Neurosci.

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After visual target stimuli presented infrequently at a covertly attended location, quicker speeded button presses immediately followed a larger positive (P3f) ramp in averaged electroencephalographic (EEG) recordings from the forehead. We show this peak in the mean response time locked to the button press to be principally composed of triphasic, primarily low-theta band (4.5 Hz) complexes preceding but only partially phase-locked to the button press, with larger complexes preceding quicker motor responses. For 10 of 15 subjects, independent component analysis of the unaveraged 31-channel data identified a temporally independent medial frontal EEG process contributing to these phenomena. Low-resolution tomographic modeling localized related components of two 253-channel data sets to medial frontal polar cortex (BA32/10). The far-frontal low-theta complexes and concomitant mean P3f positivity may index cortical activity induced by paralimbic processes involved in disinhibiting impulsive motor responses to rewarding or goal-fulfilling stimuli or events.

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“…All this may lay a basis to suggest that using any criteria, individually and blindly, is not enough for constructing an effective automatic procedure for selecting the regularization parameter, and that additional information or a mixture of techniques would probably be required. The shape of the fMRI response has been previously found to be of separate interest as its temporal characteristics were demonstrated to correlate with the behavioral measures of cognitive tasks [5,6]. The FDA smoothing techniques allow temporal features of the hemodynamic response, such as the width or the onset time, to be easily computed for possible correlation with task accuracy and reaction time.…”

Section: Discussionmentioning

confidence: 99%

Characterizing the functional MRI response using Tikhonov regularization

Vakorin

Borowsky

Sarty

2007

Statistics in Medicine

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The problem of evaluating an averaged functional magnetic resonance imaging (fMRI) response for repeated block design experiments was considered within a semiparametric regression model with autocorrelated residuals. We applied functional data analysis (FDA) techniques that use a least-squares fitting of B-spline expansions with Tikhonov regularization. To deal with the noise autocorrelation, we proposed a regularization parameter selection method based on the idea of combining temporal smoothing with residual whitening. A criterion based on a generalized chi(2)-test of the residuals for white noise was compared with a generalized cross-validation scheme. We evaluated and compared the performance of the two criteria, based on their effect on the quality of the fMRI response. We found that the regularization parameter can be tuned to improve the noise autocorrelation structure, but the whitening criterion provides too much smoothing when compared with the cross-validation criterion. The ultimate goal of the proposed smoothing techniques is to facilitate the extraction of temporal features in the hemodynamic response for further analysis. In particular, these FDA methods allow us to compute derivatives and integrals of the fMRI signal so that fMRI data may be correlated with behavioral and physiological models. For example, positive and negative hemodynamic responses may be easily and robustly identified on the basis of the first derivative at an early time point in the response. Ultimately, these methods allow us to verify previously reported correlations between the hemodynamic response and the behavioral measures of accuracy and reaction time, showing the potential to recover new information from fMRI data.

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Correlation between temporal response of fMRI and fast reaction time in a language task

Cited by 21 publications

References 20 publications

Causal Interactions in Attention Networks Predict Behavioral Performance

Causal Interactions in Attention Networks Predict Behavioral Performance

Medial Prefrontal Theta Bursts Precede Rapid Motor Responses during Visual Selective Attention

Characterizing the functional MRI response using Tikhonov regularization

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