Nonstationary cluster-size inference with random field and permutation methods

Hayasaka, Satoru; Phan, K. Luan; Liberzon, Israel; Worsley, Keith J.; Nichols, Thomas E.

doi:10.1016/j.neuroimage.2004.01.041

Cited by 626 publications

(520 citation statements)

References 20 publications

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“…In the setting of low degrees of freedom, non-parametric statistics could have been used to analyze the fMRI data (Hayasaka et al, 2004). However, the power of non-parametric statistics may also be reduced by the small number of subjects (n=6), which would have only allowed a limited number of resamplings (2 6 = 64).…”

Section: Discussionmentioning

confidence: 99%

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Sleep deprivation alters functioning within the neural network underlying the covert orienting of attention

Mander¹,

Reid²,

Davuluri³

et al. 2008

Brain Research

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One function of spatial attention is to enable goal-directed interactions with the environment through the allocation of neural resources to motivationally relevant parts of space. Studies have shown that responses are enhanced when spatial attention is predictively biased towards locations where significant events are expected to occur. Previous studies suggest that the ability to bias attention predictively is related to posterior cingulate cortex (PCC) activation (Small et al., 2003). Sleep deprivation (SD) impairs selective attention and reduces PCC activity (Thomas et al., 2000). Based on these findings, we hypothesized that SD would affect PCC function and alter the ability to predictively allocate spatial attention. Seven healthy, young adults underwent functional magnetic resonance imaging (fMRI) following normal rest and 34-36 hours of SD while performing a task in which attention was shifted in response to peripheral targets preceded by spatially informative (valid), misleading (invalid), or uninformative (neutral) cues. When rested, but not when sleep-deprived, subjects responded more quickly to targets that followed valid cues than those after neutral or invalid cues. Brain activity during validly cued trials with a reaction time benefit was compared to activity in trials with no benefit. PCC activation was greater during trials with a reaction time benefit following normal rest. In contrast, following SD, reaction time benefits were associated with activation in the left intraparietal sulcus, a region associated with receptivity to stimuli at unexpected locations. These changes may render sleep-deprived individuals less able to anticipate the locations of upcoming events, and more susceptible to distraction by stimuli at irrelevant locations.

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Section: Discussionmentioning

confidence: 99%

“…the average number of voxels containing BOLD signals that significantly correlate with each other (Hayasaka et al, 2004). Under lower degrees of freedom, this value is more likely to vary across the image.…”

Section: Fmri Datamentioning

confidence: 99%

Sleep deprivation alters functioning within the neural network underlying the covert orienting of attention

Mander¹,

Reid²,

Davuluri³

et al. 2008

Brain Research

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“…The main effect of Zygosity and the Zygosity Â Risk interaction was examined using F-contrasts. Nonisotropic smoothness was corrected for by using the VBM5 toolbox (Hayasaka et al, 2004). A false discovery rate (FDR) threshold of 0.05 was used to correct for multiple comparisons (Genovese et al, 2002).…”

Section: Statistical Analysesmentioning

confidence: 99%

Hippocampal volume changes in healthy subjects at risk of unipolar depression

Baaré

Vinberg

Knudsen

et al. 2010

Journal of Psychiatric Research

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a b s t r a c tUnipolar depression is moderately heritable. It is unclear whether structural brain changes associated with unipolar depression are present in healthy persons at risk of the disorder. Here we investigated whether a genetic predisposition to unipolar depression is associated with structural brain changes. A priori, hippocampal volume reductions were hypothesized. Using a high-risk study design, magnetic resonance imaging brain scans were obtained from 59 healthy high-risk subjects having a co-twin with unipolar depression, and 53 healthy low-risk subjects without a first-degree family history of major psychiatric disorder. High-risk twins had smaller hippocampal volumes than low-risk twins (p < 0.04). The finding was most pronounced in DZ twins. Groups did not differ on global brain tissue volumes or regional tissue volumes assessed in exploratory voxel-wise whole cerebrum analyses. In conclusion, hippocampal volume reduction may index a predisposition to develop depression and thus may be predictive of future onset of the disorder. Further studies are needed to elucidate the role of (shared) environmental and genetic factors.

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“…In order for RFT to work properly, various assumptions need to be met, including smooth random fields, sufficiently large search volume relative to the FWHM of images, and uniform smoothness within images Nichols and Hayasaka 2003;Worsley, et al 1992). As for uniform smoothness, there are some approaches to overcome the violation in this assumption (Hayasaka, et al 2004;. However, such approaches require estimating smoothness at each voxel and introduce great variability in statistical inference (Hayasaka, et al 2004).…”

Section: Discussionmentioning

confidence: 99%

“…As for uniform smoothness, there are some approaches to overcome the violation in this assumption (Hayasaka, et al 2004;. However, such approaches require estimating smoothness at each voxel and introduce great variability in statistical inference (Hayasaka, et al 2004). Thus such corrections for non-uniform smoothness may not be appropriate for a typical pilot data with a limited number of scans.…”

Section: Discussionmentioning

confidence: 99%

Power and sample size calculation for neuroimaging studies by non-central random field theory

et al. 2007

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Determining power and sample size in neuroimaging studies is a challenging task because of the massive multiple comparisons among tens of thousands of correlated voxels. To facilitate this task, we propose a power analysis method based on random field theory (RFT) by modeling signal areas within images as non-central random field. With this framework, power can be calculated for specific areas of anticipated signals within the brain while accounting for the 3D nature of signals. This framework can also be extended to visualize local variability in sensitivity as a power map and a sample size map. We validated our non-central RFT framework based on Monte-Carlo simulations. Moreover, we applied our method to a blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) data set with a small sample size in order to demonstrate its use in study planning. From the simulations, we found that our method was able to estimate power quite accurately. In the fMRI data analysis, despite the small sample size, we were able to determine power and the number of subjects required to detect signals.

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Nonstationary cluster-size inference with random field and permutation methods

Cited by 626 publications

References 20 publications

Sleep deprivation alters functioning within the neural network underlying the covert orienting of attention

Sleep deprivation alters functioning within the neural network underlying the covert orienting of attention

Hippocampal volume changes in healthy subjects at risk of unipolar depression

Power and sample size calculation for neuroimaging studies by non-central random field theory

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