Integrating functional connectivity and MVPA through a multiple constraint network analysis.

Traditional general linear model-based brain mapping efforts using functional neuroimaging are complemented by more recent multivariate pattern analyses (MVPA) that apply machine learning techniques to identify the cognitive states associated with regional BOLD activation patterns, and by connectivity analyses that identify networks of interacting regions that support particular cognitive processes. We introduce a novel analysis representing the union of these approaches, and explore the insights gained when MVPA and functional connectivity analyses are allowed to mutually constrain each other within a single model. We explored multisensory semantic representations of concrete object concepts using a self-paced multisensory imagery task.

Dyslexia on a continuum: A complex network approach.

We investigated the efficacy of graph-theoretic metrics of task-related functional brain connectivity in predicting reading difficulty and explored the hypothesis that task conditions emphasizing audiovisual integration would be especially diagnostic of reading difficulty. An fMRI study was conducted in which 24 children (8 to 14 years old) who were previously diagnosed with dyslexia completed a rhyming judgment task under three presentation modality conditions. Regression analyses found that characteristic connectivity metrics of the reading network showed a presentation modality dependent relationship with reading difficulty: Children with more segregated reading networks and those that used fewer of the available connections were those with the least severe reading difficulty.