Colored noise and computational inference in neurophysiological (fMRI) time series analysis: Resampling methods in time and wavelet domains

Bullmore, Edward T.; Long, Chris; Suckling, John; Fadili, Jalal M.; Calvert, Gemma A.; Zelaya, Fernando; Carpenter, T. Adrian; Brammer, Mick

doi:10.1002/1097-0193(200102)12:2<61::aid-hbm1004>3.0.co;2-w

Cited by 560 publications

(478 citation statements)

References 58 publications

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“…This addresses the problem inherent in the use of the F-statistic that the residual degrees of freedom are often unknown in fMRI time series due to the presence of colored noise in the signal. Following computation of the observed SSQ ratio at each voxel, the data were permuted by the wavelet-based method described and extensively characterized in Bullmore et al, 33 which permits the data-driven calculation of the null distribution of SSQ ratios under the assumption of no experimentally determined response. This distribution can then be used to threshold the activation maps at any desired type I error rate.…”

Section: Discussionmentioning

confidence: 99%

“…The detection of activated regions was extended from voxel to cluster level using the method described in detail by Bullmore et al 33 The observed One patient in non-hoarding OCD group had missing data in the following variables: SI-R, OCI-R washing, OCI-R checking, OCI-R order and STAI-S. Values are given in means (standard deviations) unless otherwise specified.…”

Section: Discussionmentioning

confidence: 99%

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To discard or not to discard: the neural basis of hoarding symptoms in obsessive-compulsive disorder

et al. 2008

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Preliminary neuroimaging studies suggest that patients with the 'compulsive hoarding syndrome' may be a neurobiologically distinct variant of obsessive-compulsive disorder (OCD) but further research is needed. A total of 29 OCD patients (13 with and 16 without prominent hoarding symptoms) and 21 healthy controls of both sexes participated in two functional magnetic resonance imaging experiments consisting of the provocation of hoarding-related and symptom-unrelated (aversive control) anxiety. In response to the hoarding-related (but not symptom-unrelated) anxiety provocation, OCD patients with prominent hoarding symptoms showed greater activation in bilateral anterior ventromedial prefrontal cortex (VMPFC) than patients without hoarding symptoms and healthy controls. In the entire patient group (n = 29), provoked anxiety was positively correlated with activation in a frontolimbic network that included the anterior VMPFC, medial temporal structures, thalamus and sensorimotor cortex. Negative correlations were observed in the left dorsal anterior cingulate gyrus, bilateral temporal cortex, bilateral dorsolateral/medial prefrontal regions, basal ganglia and parieto-occipital regions. These results were independent from the effects of age, sex, level of education, state anxiety, depression, comorbidity and use of medication. The findings are consistent with the animal and lesion literature and several landmark clinical features of compulsive hoarding, particularly decision-making difficulties. Whether the results are generalizable to hoarders who do not meet criteria for OCD remains to be investigated.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

To discard or not to discard: the neural basis of hoarding symptoms in obsessive-compulsive disorder

et al. 2008

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“…16 http://go.warwick.ac.uk/tenichols/software/snpm 17 More flexible wavelet decorrelation can whiten better (Bullmore et al, 2001), but can have problems with simple block designs (Friman & Westin, 2005). Also note that a randomized experimental design justifies a randomization test with any data (Raz et al, 2003), though this has limited application.…”

Section: Permutationmentioning

confidence: 99%

Multiple testing corrections, nonparametric methods, and random field theory

2012

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“…However, constructing surrogate data by simply permuting the original data will destroy the background correlations in the data which we wish to retain within our null distribution. Bullmore et al (2001) show that resampling the detail coefficients of discrete wavelet transformed data and reconstructing by the inverse discrete wavelet transform preserves much of the correlations of the original data while spatially rearranging the exact correlations. Permuting a subset of the wavelet coefficients corresponding to the intracranial region of the brain allows us to construct surrogate data for which a large portion of the energy of the original data is retained in the intracranial space of the surrogate data .…”

Section: Introductionmentioning

confidence: 99%

Determining significant connectivity by 4D spatiotemporal wavelet packet resampling of functional neuroimaging data

2006

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An active area of neuroimaging research involves examining functional relationships between spatially remote brain regions. When determining whether two brain regions exhibit significant correlation due to true functional connectivity, one must account for the background spatial correlation inherent in neuroimaging data. We define background correlation as spatiotemporal correlation in the data caused by factors other than neurophysiologically based functional associations such as scanner induced correlations and image preprocessing. We develop a 4D spatiotemporal wavelet packet resampling method which generates surrogate data that preserves only the average background spatial correlation within an axial slice, across axial slices, and through each voxel time series, while excluding the specific correlations due to true functional relationships. We also extend an amplitude adjustment algorithm which adjusts our surrogate data to closely match the amplitude distribution of the original data. Our method improves upon existing wavelet-based methods and extends them to 4D. We apply our resampling technique to determine significant functional connectivity from resting state and motor task fMRI datasets. D

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Colored noise and computational inference in neurophysiological (fMRI) time series analysis: Resampling methods in time and wavelet domains

Cited by 560 publications

References 58 publications

To discard or not to discard: the neural basis of hoarding symptoms in obsessive-compulsive disorder

To discard or not to discard: the neural basis of hoarding symptoms in obsessive-compulsive disorder

Multiple testing corrections, nonparametric methods, and random field theory

Determining significant connectivity by 4D spatiotemporal wavelet packet resampling of functional neuroimaging data

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