Advances in Metabolic Mapping Techniques for Brain Imaging of Behavioral and Learning Functions 1992
DOI: 10.1007/978-94-011-2712-7_7
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Covariance Analysis of Functional Interactions in the Brain Using Metabolic and Blood Flow Data

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Cited by 42 publications
(38 citation statements)
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“…The SEM network approach described here is an example of a covariance-based approach (Horwitz & McIntosh, 1993;Horwitz, Soncrant, & Haxby, 1992). The basic hypothesis of different covariance based approaches is that the intrinsic variability in the neural response, during a cognitive state or particular task, will emulate the relevant functional interactions or effective connectivity (Friston, 1994;Friston, 1995).…”
Section: Some Assumptions and Limitations Of The Network Approachmentioning
confidence: 99%
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“…The SEM network approach described here is an example of a covariance-based approach (Horwitz & McIntosh, 1993;Horwitz, Soncrant, & Haxby, 1992). The basic hypothesis of different covariance based approaches is that the intrinsic variability in the neural response, during a cognitive state or particular task, will emulate the relevant functional interactions or effective connectivity (Friston, 1994;Friston, 1995).…”
Section: Some Assumptions and Limitations Of The Network Approachmentioning
confidence: 99%
“…Several sources of interregional covariances have been proposed (Horwitz et al, 1992) and the actual sources of the observed covariances are largely unknown. However, some of the proposed sources may give rise to spurious correlations that are necessarily confounded with correlations arising because of the effective connectivity or the functional architecture (e.g., adaptation, fatigue, attentional drift; cf.…”
Section: Covariance Sourcesmentioning
confidence: 99%
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“…To complement the subtraction technique, we have developed a method for examining the covariance, or functional connectivity, between regions during the performance of individual tasks by using data obtained from positron emission tomography (PET) (8)(9)(10). The main assumption of this method is that subject-to-subject differences in using a systems-level neural network result in strongly correlated neural activity [indexed by regional cerebral blood flow (rCBF)] between network elements.…”
mentioning
confidence: 99%
“…For these animals, we simply subtracted a background value measured in the dorsal striatum to correct for background (ensuring that the relationships we obtained in the first analysis were not an artifact of the residualization). The values were then entered into the original equation (i.e., they were multiplied through the weights of the original equation) to obtain predicted behavioral values, and the predicted versus observed correlation was evaluated accordingly (Horwitz et al, 1992): the Pearson's r value was converted to Fisher's z value (Fisher's r-z transformation) using the formula:…”
Section: Predictive Validity Analysismentioning
confidence: 99%