marginal maximum likelihood estimation, dichotomous data, ordered and unordered categorical data, pair comparison data,

Euclidian model, INDSCAL, measurement, similarities, data analysis, similarities data, quantification, successive block algorithm,

Path analysis with components, partial least squares, alternating least squares,

orthogonal projection operator, trace-orthogonality, generalized singular value decomposition (GSVD), QR decomposition, vector preference models, two-way CANDELINC, dual scaling, redundancy analysis, GMANOVA (growth curve models),

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No abstract

No abstract

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.