A simple and objective method for reproducible resting state network (RSN) detection in fMRI

Spatial Independent Component Analysis (ICA) decomposes the time by space functional MRI (fMRI) matrix into a set of 1-D basis time courses and their associated 3-D spatial maps that are optimized for mutual independence. When applied to resting state fMRI (rsfMRI), ICA produces several spatial independent components (ICs) that seem to have biological relevance - the so-called resting state networks (RSNs). The ICA problem is well posed when the true data generating process follows a linear mixture of ICs model in terms of the identifiability of the mixing matrix. However, the contrast function used for promoting mutual independence in ICA is dependent on the finite amount of observed data and is potentially non-convex with multiple local minima. Hence, each run of ICA could produce potentially different IC estimates even for the same data. One technique to deal with this run-to-run variability of ICA was proposed by Yang et al. (2008) in their algorithm RAICAR which allows for the selection of only those ICs that have a high run-to-run reproducibility. We propose an enhancement to the original RAICAR algorithm that enables us to assign reproducibility p-values to each IC and allows for an objective assessment of both within subject and across subjects reproducibility. We call the resulting algorithm RAICAR-N (N stands for null hypothesis test), and we have applied it to publicly available human rsfMRI data (http://www.nitrc.org). Our reproducibility analyses indicated that many of the published RSNs in rsfMRI literature are highly reproducible. However, we found several other RSNs that are highly reproducible but not frequently listed in the literature.Comment: 54 pages, 13 figure

Colocalized Structural and Functional Changes in the Cortex of Patients with Trigeminal Neuropathic Pain

Background: Recent data suggests that in chronic pain there are changes in gray matter consistent with decreased brain volume, indicating that the disease process may produce morphological changes in the brains of those affected. However, no study has evaluated cortical thickness in relation to specific functional changes in evoked pain. In this study we sought to investigate structural (gray matter thickness) and functional (blood oxygenation dependent level – BOLD) changes in cortical regions of precisely matched patients with chronic trigeminal neuropathic pain (TNP) affecting the right maxillary (V2) division of the trigeminal nerve. The model has a number of advantages including the evaluation of specific changes that can be mapped to known somatotopic anatomy. Methodology/Principal Findings: Cortical regions were chosen based on sensory (Somatosensory cortex (SI and SII), motor (MI) and posterior insula), or emotional (DLPFC, Frontal, Anterior Insula, Cingulate) processing of pain. Both structural and functional (to brush-induced allodynia) scans were obtained and averaged from two different imaging sessions separated by 2–6 months in all patients. Age and gender-matched healthy controls were also scanned twice for cortical thickness measurement. Changes in cortical thickness of TNP patients were frequently colocalized and correlated with functional allodynic activations, and included both cortical thickening and thinning in sensorimotor regions, and predominantly thinning in emotional regions. Conclusions: Overall, such patterns of cortical thickness suggest a dynamic functionally-driven plasticity of the brain. These structural changes, which correlated with the pain duration, age-at-onset, pain intensity and cortical activity, may be specific targets for evaluating therapeutic interventions