Electronic Publishing -- a Journal and its Production

The use of the term "Electronic Publishing" transcends any notions of the paperless office and of a purely electronic transfer and dissemination of information over networks. It now encompasses all computer-assisted methods for the production of documents and includes the imaging of a document on paper as one of the options to be provided by an integrated processing scheme. Electronic publishing draws heavily on techniques from computer science and information technology but technical, legal, financial and organisational problems have to be overcome before it can replace traditional publication mechanisms. These problems are illustrated with reference to the publication arrangements for the journal `Electronic Publishing Origination, Dissemination and Design'. The authors of this paper are the co-editors of this journal, which appears in traditional form and relies on a wide variety of support from electronic technologies in the pre-publication phase.

Parcellation of fMRI datasets with ICA and PLS: a data driven approach

Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM) and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.