Constrained Principal Component Analysis Reveals Functionally Connected Load-Dependent Networks Involved in Multiple Stages of Working Memory

Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.
Human Brain Mapping (Impact Factor: 5.97). 06/2011; 32(6):856-71. DOI: 10.1002/hbm.21072
Source: PubMed


Constrained principal component analysis (CPCA) with a finite impulse response (FIR) basis set was used to reveal functionally connected networks and their temporal progression over a multistage verbal working memory trial in which memory load was varied. Four components were extracted, and all showed statistically significant sensitivity to the memory load manipulation. Additionally, two of the four components sustained this peak activity, both for approximately 3 s (Components 1 and 4). The functional networks that showed sustained activity were characterized by increased activations in the dorsal anterior cingulate cortex, right dorsolateral prefrontal cortex, and left supramarginal gyrus, and decreased activations in the primary auditory cortex and "default network" regions. The functional networks that did not show sustained activity were instead dominated by increased activation in occipital cortex, dorsal anterior cingulate cortex, sensori-motor cortical regions, and superior parietal cortex. The response shapes suggest that although all four components appear to be invoked at encoding, the two sustained-peak components are likely to be additionally involved in the delay period. Our investigation provides a unique view of the contributions made by a network of brain regions over the course of a multiple-stage working memory trial.

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Available from: Yoshio Takane, Oct 07, 2015
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    • "thecasefortheothersubsequentcomponents(i.e.,Q>2).Therefore,weconcentrateon interpretingthefirstdimensionalsolution. Figure2representsfivesliceimagesconstructedfromdominant5%ofthefirst- dimensionalobjectscoresoftheentirevoxels.Thesagittalsliceattherightendofthefigures showswhichaxialplainofsectionwaschosentobedisplayed.Theseimagesrepresentthe functionalbrainnetworkbydisplayingfunctionallyconnectingactivatedregionsamongthefour subjectswhileperformingtheworkingmemorytask.Previousstudiesonworkingmemoryhave reportedevidencefortwodifferentconnectedbrainregions,alsoknownasbrainnetworks:one isatask-positivenetworkthatinvolvesincrementalactivityinfronto-parietalbrainregions;and theotherisatask-negativenetworkinvolvingsuppressedactivityinventro-medialbrainregions (Foxetal.,2005;Metzaketal.,2011a). "
    Psychometrika 08/2015; · 1.09 Impact Factor
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    • "In practice, however, the FIR technique is usually applied to only a few a priori brain regions, because task-evoked dynamics are too rich to be reviewed and interpreted at every possible location in the brain. Multivariate techniques such as independent component analysis, principal component analysis, or clustering approaches can be used to reduce the spatial dimensionality of task-evoked brain activity (Calhoun et al. 2001; Svensén et al. 2002; d'Avossa et al. 2003; Smolders et al. 2007; Metzak et al. 2011). However, these studies only reported coarse levels of decomposition at the group level, that is, describing around 5 task-evoked networks. "
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    • "Constrained PCA For a description of the CPCA methodology, see Takane and Shibayama (1991) for its original proposal. In Metzak et al. (2011a, 2011b), the CPCA application for functional magnetic resonance imaging data is described and compared with related methodologies. This implementation provided the basis for the model used here. "
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