Experimental Designs for fMRI Studies in Small Samples

Bikas K. Sinha ()
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Bikas K. Sinha: Indian Statistical Institute

A chapter in Data Science and SDGs, 2021, pp 93-100 from Springer

Abstract: Abstract Functional Magnetic Resonance Imaging (fMRI) is a technology for studying how our brains respond to mental stimuli. At the design stage, one is interested in developing the best sequence of mental stimuli for collecting the most informative data in order to render the most precise inference about the ‘unknown parameters’ under an assumed statistical model. The simplest such model incorporates linear relation between mean response and the parameters describing the effects of the stimuli, applied at regularly spaced time points during the study period. In this paper, we introduce the linear model and discuss estimation issues and related concepts such as ‘orthogonality’ and ‘balance’.

Keywords: fMRI; Linear model; Spring balance; Bias; h-parameters; Estimability; Balanced structure; Orthogonality; Information matrix; Generalized variance (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-1919-9_8

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DOI: 10.1007/978-981-16-1919-9_8

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