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488
Nonparametric Permutation Tests for Functional Neuroimaging: A Primer with Examples. Human Brain Mapping
, 2001
"... The statistical analyses of functional mapping experiments usually proceeds at the voxel level, involving the formation and assessment of a statistic image: at each voxel a statistic indicating evidence of the experimental effect of interest, at that voxel, is computed, giving an image of statistics ..."
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Cited by 376 (10 self)
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The statistical analyses of functional mapping experiments usually proceeds at the voxel level, involving the formation and assessment of a statistic image: at each voxel a statistic indicating evidence of the experimental effect of interest, at that voxel, is computed, giving an image of statistics, a statistic
Machine learning classifiers and fmri: A tutorial overview
- NeuroImage
, 2009
"... Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviors and other variables of interest from fM ..."
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Cited by 145 (5 self)
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Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviors and other variables of interest from fMRI data and thereby show the data contain enough information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of ‘is there information about a variable of interest ’ (pattern discrimination), classifiers can be used to tackle other classes of question, namely ‘where is the information ’ (pattern localization) and ‘how is that information encoded ’ (pattern characterization). 1
Mapping directed influence over the brain using Granger causality and fMRI
- NEUROIMAGE. 25:230--242
, 2005
"... We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguish ..."
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Cited by 111 (4 self)
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We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity.
Classical and Bayesian inference in neuroimaging: applications
- NeuroImage
"... introduced empirical Bayes as a potentially useful way to estimate and make inferences about effects in hierarchical models. In this paper we present a series of models that exemplify the diversity of problems that can be addressed within this framework. In hierarchical linear observation models, bo ..."
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Cited by 106 (13 self)
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introduced empirical Bayes as a potentially useful way to estimate and make inferences about effects in hierarchical models. In this paper we present a series of models that exemplify the diversity of problems that can be addressed within this framework. In hierarchical linear observation models, both classical and empirical Bayesian approaches can be framed in terms of covariance component estimation (e.g., variance partitioning). To illustrate the use of the expectation– maximization (EM) algorithm in covariance component estimation we focus first on two important problems in fMRI: nonsphericity induced by (i) serial or temporal correlations among errors and (ii) variance components caused by the hierarchical nature of multisubject studies. In hierarchical observation models,
The neural correlates of maternal and romantic love
- Neuroimage
, 2004
"... Romantic and maternal love are highly rewarding experiences. Both are linked to the perpetuation of the species and therefore have a closely linked biological function of crucial evolutionary importance. Yet almost nothing is known about their neural correlates in the human. We therefore used fMRI t ..."
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Cited by 73 (1 self)
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Romantic and maternal love are highly rewarding experiences. Both are linked to the perpetuation of the species and therefore have a closely linked biological function of crucial evolutionary importance. Yet almost nothing is known about their neural correlates in the human. We therefore used fMRI to measure brain activity in mothers while they viewed pictures of their own and of acquainted children, and of their best friend and of acquainted adults as additional controls. The activity specific to maternal attachment was compared to that associated to romantic love described in our earlier study and to the distribution of attachment-mediating neurohormones established by other studies. Both types of attachment activated regions specific to each, as well as overlapping regions in the brain’s reward system that coincide with areas rich in oxytocin and vasopressin receptors. Both deactivated a common set of regions associated with negative emotions, social judgment and ‘mentalizing’, that is, the assessment of other people’s intentions and emotions. We conclude that human attachment employs a push–pull mechanism that overcomes social distance by deactivating networks used for critical social assessment and negative emotions, while it bonds individuals through the involvement of the reward circuitry, explaining the power of love to motivate and exhilarate.
Framework for the statistical shape analysis of brain structures using spharm-pdm
- In Insight Journal, Special Edition on the Open Science Workshop at MICCAI
, 2006
"... Abstract — Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical ..."
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Cited by 55 (5 self)
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Abstract — Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology. The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling T 2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives. I.
An evaluation of thresholding techniques in fMRI analysis
, 2004
"... This paper reviews and compares individual voxel-wise thresholding methods for identifying active voxels in single-subject fMRI datasets. Different error rates are described which may be used to calibrate activation thresholds. We discuss methods which control each of the error rates at a prespecifi ..."
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Cited by 51 (20 self)
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This paper reviews and compares individual voxel-wise thresholding methods for identifying active voxels in single-subject fMRI datasets. Different error rates are described which may be used to calibrate activation thresholds. We discuss methods which control each of the error rates at a prespecified level a, including simple procedures which ignore spatial correlation among the test statistics as well as more elaborate ones which incorporate this correlation information. The operating characteristics of the methods are shown through a simulation study, indicating that the error rate used has an important impact on the sensitivity of the thresholding method, but that accounting for correlation has little impact. Therefore, the simple procedures described work well for thresholding most single-subject fMRI experiments and are recommended. The methods are illustrated with a real bilateral finger tapping experiment
Metacognitive evaluation, self-relevance, and the right prefrontal cortex
- Neuroimage
, 2004
"... The capability to foster metacognitive evaluations (MEs) of oneself and others represents a major component of conscious awareness. Separate emerging lines of brain activation research examining ME have converged on the medial prefrontal cortex as a common finding. The current functional magnetic re ..."
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Cited by 46 (1 self)
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The capability to foster metacognitive evaluations (MEs) of oneself and others represents a major component of conscious awareness. Separate emerging lines of brain activation research examining ME have converged on the medial prefrontal cortex as a common finding. The current functional magnetic resonance imaging (fMRI) study utilized a task that directly compared ME associated with two referentially discrete targets: oneself and a significant other (e.g., close friend or relative). Nineteen healthy young adult participants (mean age 24; 9 female, 10 male) were required to make yes/no decisions based on individually presented trait adjectives across two separate referential conditions and a nonreferential control condition: self-evaluation (SE), significant other-evaluation (OE), and semantic positivity-evaluation (SPE), respectively. Results of random-effects group analyses indicated a common area of medial prefrontal activation during the ME conditions of self- and other-evaluation versus the baseline semantic positivity-evaluation condition. A direct comparison of brain activation between the self and other evaluative conditions revealed a right dorsolateral prefrontal response that was significantly more active when making evaluations about the self. The present study extends upon the prior findings of separate research domains by directly comparing the cerebral response to ME about the self and others, and finding right PFC activation increases as a function of self-relevance.
Transmodal sensorimotor networks during action observation in professional pianists
- J. Cogn. Neurosci
, 2005
"... & Audiovisual perception and imitation are essential for musical learning and skill acquisition. We compared profes-sional pianists to musically naive controls with fMRI while observing piano playing finger–hand movements and serial finger–thumb opposition movements both with and without synchro ..."
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Cited by 43 (0 self)
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& Audiovisual perception and imitation are essential for musical learning and skill acquisition. We compared profes-sional pianists to musically naive controls with fMRI while observing piano playing finger–hand movements and serial finger–thumb opposition movements both with and without synchronous piano sound. Pianists showed stronger activa-tions within a fronto-parieto-temporal network while observing piano playing compared to controls and contrasted to per-ception of serial finger–thumb opposition movements. Observation of silent piano playing additionally recruited auditory areas in pianists. Perception of piano sounds coupled with serial finger–thumb opposition movements evoked increased activation within the sensorimotor net-work. This indicates specialization of multimodal auditory– sensorimotor systems within a fronto-parieto-temporal net-work by professional musical training. Musical ‘‘language,’’ which is acquired by observation and imitation, seems to be tightly coupled to this network in accord with an observation– execution system linking visual and auditory perception to motor performance. &
Generalized Tensor-Based Morphometry of HIV/AIDS Using Multivariate Statistics on Deformation Tensors
"... Abstract—This paper investigates the performance of a new multivariate method for tensor-based morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical temp ..."
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Cited by 43 (10 self)
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Abstract—This paper investigates the performance of a new multivariate method for tensor-based morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical template via 3-D nonlinear registration; the resulting deformation fields are analyzed statistically to identify group differences in anatomy. Rather than study the Jacobian determinant (volume expansion factor) of these deformations, as is common, we retain the full deformation tensors and apply a manifold version of Hotelling’s 2 test to them, in a Log-Euclidean domain. In 2-D and 3-D magnetic resonance imaging (MRI) data from 26 HIV/AIDS patients and 14 matched healthy subjects, we compared multivariate tensor analysis versus univariate tests of simpler tensor-derived indices: the Jacobian determinant, the trace, geodesic anisotropy, and eigenvalues of the deformation tensor, and the angle of rotation of its eigenvectors. We detected consistent, but more extensive patterns of structural abnormalities, with multivariate tests on the full tensor manifold. Their improved power was established by analyzing cumulative-value plots using false discovery rate (FDR) methods, appropriately controlling for false positives. This increased detection sensitivity may empower drug trials and large-scale studies of disease that use tensor-based morphometry. Index Terms—Brain, image analysis, Lie groups, magnetic resonance imaging (MRI), statistics. I.
