Neural modeling, functional brain imaging, and cognition

Author(s): Tagamets, M. A., McIntosh, A. R.

Journal/Book: Trends Cogn Sci. 1999; 3: the Boulevard Langford Lane, Kidlington, Oxford Ox5 1GB, Oxon, England. Elsevier Sci Ltd. 91-98.

Abstract: The richness and complexity of data sets acquired from PET or fMRI studies of human cognition have not been exploited until recently by computational neuronal modeling methods. In this article, two neural-modeling approaches for use with functional brain imaging data are described. One, which uses structural equation modeling, estimates the functional strengths of the anatomical connections between various brain regions during specific cognitive tasks. The second employs large-scale neural modeling to relate functional neuroimaging signals in multiple, interconnected brain regions to the underlying neurobiological time-varying activities in each region. Delayed match-to-sample (visual working memory for form) tasks are used to illustrate these models.

Note: Review Horwitz B, NIA, Neurosci Lab, NIH, Bldg 10, Rm 6C414, 9000 Rockville Pike, Bethesda,MD 20892 USA

Keyword(s): PRINCIPAL-COMPONENT ANALYSIS; WORKING-MEMORY; PREFRONTAL CORTEX; DATA SETS; PET; PATHWAYS; SYSTEM; IMAGES; OBJECT; TASK

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