Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping.

Optical topography (OT) relies on the near infrared spectroscopy (NIRS) technique to provide noninvasively a spatial map of functional brain activity. OT has advantages over conventional fMRI in terms of its simple approach to measuring the hemodynamic response, its ability to distinguish between changes in oxy- and deoxy-hemoglobin and the range of human participants that can be readily investigated. We offer a new software tool, functional optical signal analysis (fOSA), for analyzing the spatially resolved optical signals that provides statistical inference capabilities about the distribution of brain activity in space and time and by experimental condition.

Seeing or doing? Influence of visual and motor familiarity in action observation.

The human brain contains specialized circuits for observing and understanding actions. Previous studies have not distinguished whether this "mirror system" uses specialized motor representations or general processes of visual inference and knowledge to understand observed actions. We report the first neuroimaging study to distinguish between these alternatives.

Discrete and analogue quantity processing in the parietal lobe: a functional MRI study.

The human intraparietal sulcus (IPS) is implicated in processing symbolic number information and possibly in nonsymbolic number information. Specific IPS activity for discrete quantities (numerosities) as compared with continuous, analogue quantity has not been demonstrated. Here we use a stimulus-driven paradigm to distinguish automatic estimation of "how many things" from "how much" and "how long.

Conjunction revisited.

The aim of this note is to revisit the analysis of conjunctions in imaging data. We review some conceptual issues that have emerged from recent discussion (Nichols, T., Brett, M.

A heuristic for the degrees of freedom of statistics based on multiple variance parameters.

In neuroimaging, data are often modeled using general linear models. Here, we focus on GLMs with error covariances which are modeled as a linear combination of multiple variance/covariance components. Each of these components is weighted by one variance parameter.