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Karl J Friston - One of the best experts on this subject based on the ideXlab platform.
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analysis of family wise error rates in Statistical Parametric Mapping using random field theory
Human Brain Mapping, 2019Co-Authors: Guillaume Flandin, Karl J FristonAbstract:: This technical report revisits the analysis of family-wise error rates in Statistical Parametric Mapping-using random field theory-reported in (Eklund et al. []: arXiv 1511.01863). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of Parametric assumptions-and random field theory-in the analysis of functional neuroimaging data. We briefly rehearse the advantages Parametric analyses offer over nonParametric alternatives and then unpack the implications of (Eklund et al. []: arXiv 1511.01863) for Parametric procedures. Hum Brain Mapp, 40:2052-2054, 2019. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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analysis of family wise error rates in Statistical Parametric Mapping using random field theory
arXiv: Applications, 2016Co-Authors: Guillaume Flandin, Karl J FristonAbstract:This technical report revisits the analysis of family-wise error rates in Statistical Parametric Mapping - using random field theory - reported in (Eklund et al., 2015). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of Parametric assumptions - and random field theory - in the analysis of functional neuroimaging data. We briefly rehearse the advantages Parametric analyses offer over nonParametric alternatives and then unpack the implications of (Eklund et al., 2015) for Parametric procedures.
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The problem of low variance voxels in Statistical Parametric Mapping; a new hat avoids a 'haircut'
NeuroImage, 2011Co-Authors: Gerard R. Ridgway, Karl J Friston, Guillaume Flandin, Vladimir Litvak, William D. PennyAbstract:Statistical Parametric Mapping (SPM) locates significant clusters based on a ratio of signal to noise (a ‘contrast’ of the parameters divided by its standard error) meaning that very low noise regions, for example outside the brain, can attain artefactually high Statistical values. Similarly, the commonly applied preprocessing step of Gaussian spatial smoothing can shift the peak Statistical significance away from the peak of the contrast and towards regions of lower variance. These problems have previously been identified in positron emission tomography (PET) (Reimold et al., 2006) and voxel-based morphometry (VBM) (Acosta-Cabronero et al., 2008), but can also appear in functional magnetic resonance imaging (fMRI) studies. Additionally, for source-reconstructed magneto- and electro-encephalography (M/EEG), the problems are particularly severe because sparsity-favouring priors constrain meaningfully large signal and variance to a small set of compactly supported regions within the brain. (Acosta-Cabronero et al., 2008) suggested adding noise to background voxels (the ‘haircut’), effectively increasing their noise variance, but at the cost of contaminating neighbouring regions with the added noise once smoothed. Following theory and simulations, we propose to modify – directly and solely – the noise variance estimate, and investigate this solution on real imaging data from a range of modalities.
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Statistical Parametric Mapping (SPM)
Scholarpedia, 2008Co-Authors: Guillaume Flandin, Karl J FristonAbstract:Statistical Parametric Mapping is the application of Random Field Theory to make inferences about the topological features of Statistical processes that are continuous functions of space or time. It is usually used to identify regionally specific effects (e.g., brain activations) in neuroimaging data to characterize functional anatomy and disease-related changes.
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Statistical Parametric Mapping - Voxel-based morphometry
Statistical Parametric Mapping, 2007Co-Authors: John Ashburner, Karl J FristonAbstract:At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of regional grey-matter ‘density’ between two groups of subjects. The procedure is relatively straightforward, and involves spatially normalizing and segmenting high-resolution magnetic resonance (MR) images into the same stereotaxic space. These grey-matter segments are then smoothed to a spatial scale at which differences are expressed (usually about 12 mm). Voxel-wise Parametric Statistical tests are performed, which compare the smoothed grey-matter images from the groups using Statistical Parametric Mapping. Corrections for multiple comparisons are generally made using the theory of random fields.
Dong-soo Lee - One of the best experts on this subject based on the ideXlab platform.
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metabolic connectivity by interregional correlation analysis using Statistical Parametric Mapping spm and fdg brain pet methodological development and patterns of metabolic connectivity in adults
European Journal of Nuclear Medicine and Molecular Imaging, 2008Co-Authors: Dong-soo Lee, Jae Sung Lee, Hyejin Kang, Heejung Kim, Hyojin Park, Myung-chul LeeAbstract:Purpose Regionally connected areas of the resting brain can be detected by fluorodeoxyglucose-positron emission tomography (FDG-PET). Voxel-wise metabolic connectivity was examined, and normative data were established by performing interregional correlation analysis on Statistical Parametric Mapping of FDG-PET data.
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Characteristics of glucose metabolism in the visual cortex of amblyopes using positron-emission tomography and Statistical Parametric Mapping.
Journal of pediatric ophthalmology and strabismus, 2002Co-Authors: Mi Young Choi, Dong-soo Lee, Jeong Min Hwang, Dong Gyu Choi, Kyoung Min Lee, Ki-ho ParkAbstract:Background: The effects of amblyopia on the glucose metabolism in the visual cortex in the resting state are evaluated, the asymmetry of glucose metabolism in the ipsilateral and the contralateral occipital lobes was examined by comparing the number of hypometabolic pixels in both occipital lobes, and the correlation between this asymmetry and the results of the ophthalmologic tests was evaluated. Methods: Eleven amblyopes (7 anisometropic and 4 strabismic) and 12 normal subjects were studied with their eyes open, but without any further visual stimulus using F-18-fluorodeoxyglucose positron-emission tomography (PET) and Statistical Parametric Mapping. Ophthalmologic tests including stereoacuity, contrast sensitivity function, monocular optokinetic nystagmus, and visual evoked potential (VEP) were measured. Results: Compared to normal subjects, glucose metabolism decreased in Brodmann area (BA) 17, BAs 18/19, both inferior temporal lobes (BAs 37 and 20), and the superior parietal lobe (BA 7) in amblyopic patients, regardless of strabismic or anisometropic amblyopia. The laterality index of the hypometabolic pixels in the occipital lobe closely correlated with the asymmetry in the latency time of VEP (r = 0.82, P
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Different uptake of (99m)Tc-ECD and (99m)Tc-HMPAO in the same brains: analysis by Statistical Parametric Mapping.
European journal of nuclear medicine, 2001Co-Authors: In-young Hyun, Jae Sung Lee, Joung-ho Rha, Il Keun Lee, Dong-soo LeeAbstract:The purpose of this study was to investigate the differences between technetium-99m ethyl cysteinate dimer (99mTc-ECD) and technetium-99m hexamethylpropylene amine oxime (99mTc-HMPAO) uptake in the same brains by means of Statistical Parametric Mapping (SPM) analysis. We examined 20 patients (9 male, 11 female, mean age 62±12 years) using 99mTc-ECD and 99mTc-HMPAO single-photon emission tomography (SPET) and magnetic resonance imaging (MRI) of the brain less than 7 days after onset of stroke. MRI showed no cortical infarctions. Infarctions in the pons (6 patients) and medulla (1), ischaemic periventricular white matter lesions (13) and lacunar infarction (7) were found on MRI. Split-dose and sequential SPET techniques were used for 99mTc-ECD and 99mTc-HMPAO brain SPET, without repositioning of the patient. All of the SPET images were spatially transformed to standard space, smoothed and globally normalized. The differences between the 99mTc-ECD and 99mTc-HMPAO SPET images were Statistically analysed using Statistical Parametric Mapping (SPM) 96 software. The difference between two groups was considered significant at a threshold of uncorrected P values less than 0.01. Visual analysis showed no hypoperfused areas on either 99mTc-ECD or 99mTc-HMPAO SPET images. SPM analysis revealed significantly different uptake of 99mTc-ECD and 99mTc-HMPAO in the same brains. On the 99mTc-ECD SPET images, relatively higher uptake was observed in the frontal, parietal and occipital lobes, in the left superior temporal lobe and in the superior region of the cerebellum. On the 99mTc-HMPAO SPET images, relatively higher uptake was observed in the medial temporal lobes, thalami, periventricular white matter and brain stem. These differences in uptake of the two tracers in the same brains on SPM analysis suggest that interpretation of cerebral perfusion is possible using SPET with 99mTc-ECD and 99mTc-HMPAO.
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Functional Brain Mapping Using $H_2^{15}O$ Positron Emission Tomography ( I ): Statistical Parametric Mapping Method
1998Co-Authors: Dong-soo Lee, Jae Sung Lee, Kyeong-min Kim, June-key Chung, Myung-chul LeeAbstract:Purpose: We investigated the Statistical methods to compose the functional brain map of human working memory and the principal factors that have an effect on the methods for localization. Materials and Methods: Repeated PET scans with successive four tasks, which consist of one control and three different activation tasks, were performed on six right-handed normal volunteers for 2 minutes after bolus injections of 925 MBq at the intervals of 30 minutes. Image data were analyzed using SPM96 (Statistical Parametric Mapping) implemented with Matlab (Mathworks Inc., U.S.A.). Images from the same subject were spatially registered and were normalized using linear and nonlinear transformation methods. Significant difference between control and each activation state was estimated at every voxel based on the general linear model. Differences of global counts were removed using analysis of covariance (ANCOVA) with global activity as covariate. Using the mean and variance for each condition which was adjusted using ANCOVA, t-statistics was performed on every voxel To interpret the results more easily, t-values were transformed to the standard Gaussian distribution (Z-score). Results: All the subjects carried out the activation and control tests successfully. Average rate of correct answers was 95%. The numbers of activated blobs were 4 for verbal memory I, 9 for verbal memory II, 9 for visual memory, and 6 for conjunctive activation of these three tasks. The verbal working memory activates predominantly left-sided structures, and the visual memory activates the right hemisphere. Conclusion: We conclude that rCBF PET imaging and Statistical Parametric Mapping method were useful in the localization of the brain regions for verbal and visual working memory.
Guillaume Flandin - One of the best experts on this subject based on the ideXlab platform.
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analysis of family wise error rates in Statistical Parametric Mapping using random field theory
Human Brain Mapping, 2019Co-Authors: Guillaume Flandin, Karl J FristonAbstract:: This technical report revisits the analysis of family-wise error rates in Statistical Parametric Mapping-using random field theory-reported in (Eklund et al. []: arXiv 1511.01863). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of Parametric assumptions-and random field theory-in the analysis of functional neuroimaging data. We briefly rehearse the advantages Parametric analyses offer over nonParametric alternatives and then unpack the implications of (Eklund et al. []: arXiv 1511.01863) for Parametric procedures. Hum Brain Mapp, 40:2052-2054, 2019. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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analysis of family wise error rates in Statistical Parametric Mapping using random field theory
arXiv: Applications, 2016Co-Authors: Guillaume Flandin, Karl J FristonAbstract:This technical report revisits the analysis of family-wise error rates in Statistical Parametric Mapping - using random field theory - reported in (Eklund et al., 2015). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of Parametric assumptions - and random field theory - in the analysis of functional neuroimaging data. We briefly rehearse the advantages Parametric analyses offer over nonParametric alternatives and then unpack the implications of (Eklund et al., 2015) for Parametric procedures.
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The problem of low variance voxels in Statistical Parametric Mapping; a new hat avoids a 'haircut'
NeuroImage, 2011Co-Authors: Gerard R. Ridgway, Karl J Friston, Guillaume Flandin, Vladimir Litvak, William D. PennyAbstract:Statistical Parametric Mapping (SPM) locates significant clusters based on a ratio of signal to noise (a ‘contrast’ of the parameters divided by its standard error) meaning that very low noise regions, for example outside the brain, can attain artefactually high Statistical values. Similarly, the commonly applied preprocessing step of Gaussian spatial smoothing can shift the peak Statistical significance away from the peak of the contrast and towards regions of lower variance. These problems have previously been identified in positron emission tomography (PET) (Reimold et al., 2006) and voxel-based morphometry (VBM) (Acosta-Cabronero et al., 2008), but can also appear in functional magnetic resonance imaging (fMRI) studies. Additionally, for source-reconstructed magneto- and electro-encephalography (M/EEG), the problems are particularly severe because sparsity-favouring priors constrain meaningfully large signal and variance to a small set of compactly supported regions within the brain. (Acosta-Cabronero et al., 2008) suggested adding noise to background voxels (the ‘haircut’), effectively increasing their noise variance, but at the cost of contaminating neighbouring regions with the added noise once smoothed. Following theory and simulations, we propose to modify – directly and solely – the noise variance estimate, and investigate this solution on real imaging data from a range of modalities.
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Statistical Parametric Mapping (SPM)
Scholarpedia, 2008Co-Authors: Guillaume Flandin, Karl J FristonAbstract:Statistical Parametric Mapping is the application of Random Field Theory to make inferences about the topological features of Statistical processes that are continuous functions of space or time. It is usually used to identify regionally specific effects (e.g., brain activations) in neuroimaging data to characterize functional anatomy and disease-related changes.
Todd C Pataky - One of the best experts on this subject based on the ideXlab platform.
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analysis of three dimensional knee kinematics during stair descent two decades post acl rupture data revisited using Statistical Parametric Mapping
Journal of Electromyography and Kinesiology, 2017Co-Authors: Gisela Sole, Todd C Pataky, Eva Tengman, Charlotte HägerAbstract:Changes in movement patterns following knee injuries have generally used analyses of pre-defined discrete event-related variables, whereas Statistical Parametric Mapping (SPM) assesses continuous d ...
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Analysis of three-dimensional knee kinematics during stair descent two decades post-ACL rupture – Data revisited using Statistical Parametric Mapping
Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology, 2016Co-Authors: Gisela Sole, Todd C Pataky, Eva Tengman, Charlotte HägerAbstract:Changes in movement patterns following knee injuries have generally used analyses of pre-defined discrete event-related variables, whereas Statistical Parametric Mapping (SPM) assesses continuous d ...
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Pooling sexes when assessing ground reaction forces during walking: Statistical Parametric Mapping versus traditional approach.
Journal of biomechanics, 2015Co-Authors: Marcelo Peduzzi De Castro, Todd C Pataky, Gisela Sole, João Paulo Vilas-boasAbstract:Ground reaction force (GRF) data from men and women are commonly pooled for analyses. However, it may not be justifiable to pool sexes on the basis of discrete parameters extracted from continuous GRF gait waveforms because this can miss continuous effects. Forty healthy participants (20 men and 20 women) walked at a cadence of 100 steps per minute across two force plates, recording GRFs. Two Statistical methods were used to test the null hypothesis of no mean GRF differences between sexes: (i) Statistical Parametric Mapping—using the entire three-component GRF waveform; and (ii) traditional approach—using the first and second vertical GRF peaks. Statistical Parametric Mapping results suggested large sex differences, which post-hoc analyses suggested were due predominantly to higher anterior– posterior and vertical GRFs in early stance in women compared to men. Statistically significant differences were observed for the first GRF peak and similar values for the second GRF peak. These contrasting results emphasise that different parts of the waveform have different signal strengths and thus that one may use the traditional approach to choose arbitrary metrics and make arbitrary conclusions. We suggest that researchers and clinicians consider both the entire gait waveforms and sex-specificity when analysing GRF data.
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one dimensional Statistical Parametric Mapping in python
Computer Methods in Biomechanics and Biomedical Engineering, 2012Co-Authors: Todd C PatakyAbstract:Statistical Parametric Mapping (SPM) is a topological methodology for detecting field changes in smooth n-dimensional continua. Many classes of biomechanical data are smooth and contained within discrete bounds and as such are well suited to SPM analyses. The current paper accompanies release of ‘SPM1D’, a free and open-source Python package for conducting SPM analyses on a set of registered 1D curves. Three example applications are presented: (i) kinematics, (ii) ground reaction forces and (iii) contact pressure distribution in probabilistic finite element modelling. In addition to offering a high-level interface to a variety of common Statistical tests like t tests, regression and ANOVA, SPM1D also emphasises fundamental concepts of SPM theory through stand-alone example scripts. Source code and documentation are available at: www.tpataky.net/spm1d/.
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generalized n dimensional biomechanical field analysis using Statistical Parametric Mapping
Journal of Biomechanics, 2010Co-Authors: Todd C PatakyAbstract:A variety of biomechanical data are sampled from smooth n-dimensional spatiotemporal fields. These data are usually analyzed discretely, by extracting summary metrics from particular points or regions in the continuum. It has been shown that, in certain situations, such schemes can compromise the spatiotemporal integrity of the original fields. An alternative methodology called Statistical Parametric Mapping (SPM), designed specifically for continuous field analysis, constructs Statistical images that lie in the original, biomechanically meaningful sampling space. The current paper demonstrates how SPM can be used to analyze both experimental and simulated biomechanical field data of arbitrary spatiotemporal dimensionality. Firstly, 0-, 1-, 2-, and 3-dimensional spatiotemporal datasets derived from a pedobarographic experiment were analyzed using a common linear model to emphasize that SPM procedures are (practically) identical irrespective of the data's physical dimensionality. Secondly two probabilistic finite element simulation studies were conducted, examining heel pad stress and femoral strain fields, respectively, to demonstrate how SPM can be used to probe the significance of field-wide simulation results in the presence of uncontrollable or induced modeling uncertainty. Results were biomechanically intuitive and suggest that SPM may be suitable for a wide variety of mechanical field applications. SPM's main theoretical advantage is that it avoids problems associated with a priori assumptions regarding the spatiotemporal foci of field signals. SPM's main practical advantage is that a unified framework, encapsulated by a single linear equation, affords comprehensive Statistical analyses of smooth scalar fields in arbitrarily bounded n-dimensional spaces.
Jaeduck Jang - One of the best experts on this subject based on the ideXlab platform.
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nirs spm Statistical Parametric Mapping for near infrared spectroscopy
NeuroImage, 2009Co-Authors: Jong Chul Ye, Kwang Eun Jang, Jinwook Jung, Jaeduck JangAbstract:Near infrared spectroscopy (NIRS) is a non-invasive method to measure brain activity via changes in the degree of hemoglobin oxygenation through the intact skull. As optically measured hemoglobin signals strongly correlate with BOLD signals, simultaneous measurement using NIRS and fMRI promises a significant mutual enhancement of temporal and spatial resolutions. Although there exists a powerful Statistical Parametric Mapping tool in fMRI, current public domain Statistical tools for NIRS have several limitations related to the quantitative analysis of simultaneous recording studies with fMRI. In this paper, a new public domain Statistical toolbox known as NIRS-SPM is described. It enables the quantitative analysis of NIRS signal. More specifically, NIRS data are Statistically analyzed based on the general linear model (GLM) and Sun's tube formula. The p-values are calculated as the excursion probability of an inhomogeneous random field on a representation manifold that is dependent on the structure of the error covariance matrix and the interpolating kernels. NIRS-SPM not only enables the calculation of activation maps of oxy-, deoxy-hemoglobin and total hemoglobin, but also allows for the super-resolution localization, which is not possible using conventional analysis tools. Extensive experimental results using finger tapping and memory tasks confirm the viability of the proposed method.
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NIRS-SPM: Statistical Parametric Mapping for near-infrared spectroscopy.
NeuroImage, 2008Co-Authors: Sungho Tak, Kwang Eun Jang, Jinwook Jung, Jaeduck JangAbstract:Near infrared spectroscopy (NIRS) is a non-invasive method to measure brain activity via changes in the degree of hemoglobin oxygenation through the intact skull. As optically measured hemoglobin signals strongly correlate with BOLD signals, simultaneous measurement using NIRS and fMRI promises a significant mutual enhancement of temporal and spatial resolutions. Although there exists a powerful Statistical Parametric Mapping tool in fMRI, current public domain Statistical tools for NIRS have several limitations related to the quantitative analysis of simultaneous recording studies with fMRI. In this paper, a new public domain Statistical toolbox known as NIRS-SPM is described. It enables the quantitative analysis of NIRS signal. More specifically, NIRS data are Statistically analyzed based on the general linear model (GLM) and Sun's tube formula. The p-values are calculated as the excursion probability of an inhomogeneous random field on a representation manifold that is dependent on the structure of the error covariance matrix and the interpolating kernels. NIRS-SPM not only enables the calculation of activation maps of oxy-, deoxy-hemoglobin and total hemoglobin, but also allows for the super-resolution localization, which is not possible using conventional analysis tools. Extensive experimental results using finger tapping and memory tasks confirm the viability of the proposed method.
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NIRS-SPM: Statistical Parametric Mapping for near infrared spectroscopy
Multimodal Biomedical Imaging III, 2008Co-Authors: Sungho Tak, Kwang Eun Jang, Jinwook Jung, Jaeduck Jang, Yong JeongAbstract:Even though there exists a powerful Statistical Parametric Mapping (SPM) tool for fMRI, similar public domain tools are not available for near infrared spectroscopy (NIRS). In this paper, we describe a new public domain Statistical toolbox called NIRS-SPM for quantitative analysis of NIRS signals. Specifically, NIRS-SPM Statistically analyzes the NIRS data using GLM and makes inference as the excursion probability which comes from the random field that are interpolated from the sparse measurement. In order to obtain correct inference, NIRS-SPM offers the pre-coloring and pre-whitening method for temporal correlation estimation. For simultaneous recording NIRS signal with fMRI, the spatial Mapping between fMRI image and real coordinate in 3-D digitizer is estimated using Horn's algorithm. These powerful tools allows us the super-resolution localization of the brain activation which is not possible using the conventional NIRS analysis tools.
