A New Statistical Approach to Detecting Significant Activation in Functional MRI

Marchini, Jonathan; Ripley, B. D.

doi:10.1006/nimg.2000.0628

Cited by 124 publications

(98 citation statements)

References 19 publications

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“…However, each group revealed one additional component showing predominantly positive activity in a strikingly similar pattern across the groups, and which did not appear to be artifactual by virtue of its spatial distribution and the frequency spectra (Marchini and Ripley, 2000;Moritz et al, 2003). Interestingly, this secondary component of interest (Sec-C) did not show a significant temporal relationship with the paradigm timing (NC, R 2 ϭ 0.08; low-SB MCI, R 2 ϭ 0.02; high-SB MCI, R 2 ϭ 0.13; AD, R 2 ϭ 0.13), but rather appeared to be a more constant "state" of high-frequency activity maintained throughout the experiment.…”

Section: Secondary Component Of Interest: Within-group Analysismentioning

confidence: 74%

Alterations in Memory Networks in Mild Cognitive Impairment and Alzheimer's Disease: An Independent Component Analysis

Celone¹,

Calhoun²,

Dickerson³

et al. 2006

J. Neurosci.

639

560

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Memory function is likely subserved by multiple distributed neural networks, which are disrupted by the pathophysiological process of Alzheimer's disease (AD). In this study, we used multivariate analytic techniques to investigate memory-related functional magnetic resonance imaging (fMRI) activity in 52 individuals across the continuum of normal aging, mild cognitive impairment (MCI), and mild AD. Independent component analyses revealed specific memory-related networks that activated or deactivated during an associative memory paradigm. Across all subjects, hippocampal activation and parietal deactivation demonstrated a strong reciprocal relationship. Furthermore, we found evidence of a nonlinear trajectory of fMRI activation across the continuum of impairment. Less impaired MCI subjects showed paradoxical hyperactivation in the hippocampus compared with controls, whereas more impaired MCI subjects demonstrated significant hypoactivation, similar to the levels observed in the mild AD subjects. We found a remarkably parallel curve in the pattern of memory-related deactivation in medial and lateral parietal regions with greater deactivation in less-impaired MCI and loss of deactivation in more impaired MCI and mild AD subjects. Interestingly, the failure of deactivation in these regions was also associated with increased positive activity in a neocortical attentional network in MCI and AD. Our findings suggest that loss of functional integrity of the hippocampal-based memory systems is directly related to alterations of neural activity in parietal regions seen over the course of MCI and AD. These data may also provide functional evidence of the interaction between neocortical and medial temporal lobe pathology in early AD.

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Section: Secondary Component Of Interest: Within-group Analysismentioning

confidence: 74%

Alterations in Memory Networks in Mild Cognitive Impairment and Alzheimer's Disease: An Independent Component Analysis

Celone¹,

Calhoun²,

Dickerson³

et al. 2006

J. Neurosci.

639

560

View full text Add to dashboard Cite

show abstract

“…At each voxel, the posterior of β g is approximated as a multivariate T with noncentrality parameter β g , variance parameters Cov(β g ) [see (13) and (14)], and degrees of freedom ν g . The noncentrality and variance parameters depend on the unknown mixed-effects covariance Vg.…”

Section: Reml Starts With a Different Random Variablementioning

confidence: 99%

“…In that setting, σ 2 g Vg will be the identity multiplied by a scalar, and the second-level estimate (13) reduces to the OLS estimate.…”

Section: Spm2mentioning

confidence: 99%

Modeling and inference of multisubject fMRI data

Mumford

Nichols

2006

IEEE Eng. Med. Biol. Mag.

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“…In general, choosing the optimal number of terms in such expansions can become problematic if the stimulus is not a simple blocked design, raising concerns respecting proper determination of drift from task-related activity. Other trend removal strategies such as running-lines smoothers are equally effective, if not preferable (e.g., Marchini and Ripley, 2000) to these regression models but may have similar drawbacks related to the choice of tuning or smoothing parameter. The estimation of b p t in Eq.…”

Section: Voxelwise Modelingmentioning

confidence: 99%

Spatiotemporal wavelet analysis for functional MRI

et al. 2004

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Characterizing the spatiotemporal behavior of the BOLD signal in functional Magnetic Resonance Imaging (fMRI) is a central issue in understanding brain function. While the nature of functional activation clusters is fundamentally heterogeneous, many current analysis approaches use spatially invariant models that can degrade anatomic boundaries and distort the underlying spatiotemporal signal. Furthermore, few analysis approaches use true spatiotemporal continuity in their statistical formulations. To address these issues, we present a novel spatiotemporal wavelet procedure that uses a stimulus-convolved hemodynamic signal plus correlated noise model. The wavelet fits, computed by spatially constrained maximum-likelihood estimation, provide efficient multiscale representations of heterogeneous brain structures and give well-identified, parsimonious spatial activation estimates that are modulated by the temporal fMRI dynamics. In a study of both simulated data and actual fMRI memory task experiments, our new method gave lower mean-squared error and seemed to result in more localized fMRI activation maps compared to models using standard wavelet or smoothing techniques. Our spatiotemporal wavelet framework suggests a useful tool for the analysis of fMRI studies. D

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A New Statistical Approach to Detecting Significant Activation in Functional MRI

Cited by 124 publications

References 19 publications

Alterations in Memory Networks in Mild Cognitive Impairment and Alzheimer's Disease: An Independent Component Analysis

Alterations in Memory Networks in Mild Cognitive Impairment and Alzheimer's Disease: An Independent Component Analysis

Modeling and inference of multisubject fMRI data

Spatiotemporal wavelet analysis for functional MRI

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