Commentary and Opinion: II. Statistical Parametric Mapping: Ontology and Current Issues

Friston, Karl J.

doi:10.1038/jcbfm.1995.45

Cited by 252 publications

(137 citation statements)

References 35 publications

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“…The statistical model included high frequency signal filtering (high pass filter = 128 seconds) and the AR1 method of estimating temporal autocorrelation. The time series data were statistically analyzed using the general linear model (Friston, 1995). Two subtractive contrasts were specified per individual analysis: SA versus AVA (contrast coding: 1 1; referred to as SA forthwith) and the inverse, AVA versus SA (contrast coding: -1 -1 ).…”

Section: Discussionmentioning

confidence: 99%

Self-appraisal decisions evoke dissociated dorsal—ventral aMPFC networks

2006

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The anterior medial prefrontal cortex (aMPFC) is consistently active during personally salient decisions, yet the differential contributory processes of this region along the dorsal-ventral axis are less understood. Using a self-appraisal decision-making task and functional magnetic resonance imaging, we demonstrated task-dependent connectivity of ventral aMPFC with amygdala, insula, and nucleus accumbens, and dorsal aMPFC connectivity with dorsolateral PFC and bilateral hippocampus. These aMPFC networks appear to subserve distinct contributory processes inherent to self-appraisal decisions, specifically a dorsally mediated cognitive and a ventrally mediated affective/self-relevance network.

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Section: Discussionmentioning

confidence: 99%

Self-appraisal decisions evoke dissociated dorsal—ventral aMPFC networks

2006

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“…Activation/Deactivation Maps-In order to assess the selective activations and deactivations in individual participants elicited by the combined protocol-driven visual experiments, we applied the standard General Linear Model analysis (Friston, 1995). A boxcar predictor with hemodynamic delay of 3 seconds was constructed for all visual conditions and the model was independently fitted to the time course of each voxel.…”

Section: Discussionmentioning

confidence: 99%

Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems

Golland²,

et al. 2008

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Global organizational principles are critical for understanding cortical functionality. Recently we proposed a global sub-division of the posterior cortex into two large-scale systems. One system, labeled extrinsic, comprises the sensory-motor cortex, and is associated with the external environment. The second system, labeled intrinsic, overlaps substantially with the previously described "default-mode" network, and is likely associated with inner-oriented processing. This global partition of the cerebral cortex emerged from hemodynamic imaging data the analysis of which was constrained by pre-determined hypotheses. Here we applied a hypothesis-free, unsupervised two-class clustering algorithm (k-means) to a large set of fMRI data. The two clusters delineated by this unsupervised hypothesis-free procedure showed high anatomical consistency across individuals, and their cortical topography coincided largely with the previously determined extrinsic and intrinsic systems. These new clustering-based results confirm that the intrinsic-extrinsic subdivision constitutes a fundamental cortical divide.

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“…Difference images (fenfluramine minus placebo) were formed, and unpaired t-tests compared patients with volunteers; the threshold of significance was established through a resampling method (Haznedar et al 1997). The t-test manipulation is like that of Friston (1995) but uses an image standardization method utilizing visually identified anatomical landmarks and empirically derived tests of significance based on concurrent data.…”

Section: Statistical Probability Mapping (Spm)mentioning

confidence: 99%