288 research outputs found

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

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    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

    SMART: A statistical framework for optimal design matrix generation with application to fMRI

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    The general linear model (GLM) is a well established tool for analyzing functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM proceed in a massively univariate fashion where the same design matrix is used for analyzing data from each voxel. A major limitation of this approach is the locally varying nature of signals of interest as well as associated confounds. This local variability results in a potentially large bias and uncontrolled increase in variance for the contrast of interest. The main contributions of this paper are two fold (1) We develop a statistical framework called SMART that enables estimation of an optimal design matrix while explicitly controlling the bias variance decomposition over a set of potential design matrices and (2) We develop and validate a numerical algorithm for computing optimal design matrices for general fMRI data sets. The implications of this framework include the ability to match optimally the magnitude of underlying signals to their true magnitudes while also matching the "null" signals to zero size thereby optimizing both the sensitivity and specificity of signal detection. By enabling the capture of multiple profiles of interest using a single contrast (as opposed to an F-test) in a way that optimizes for both bias and variance enables the passing of first level parameter estimates and their variances to the higher level for group analysis which is not possible using F-tests. We demonstrate the application of this approach to in vivo pharmacological fMRI data capturing the acute response to a drug infusion, to task-evoked, block design fMRI and to the estimation of a haemodynamic response function (HRF) response in event-related fMRI. Our framework is quite general and has potentially wide applicability to a variety of disciplines.Comment: 68 pages, 34 figure

    Diffuse Optical Tomography Activation in the Somatosensory Cortex: Specific Activation by Painful vs. Non-Painful Thermal Stimuli

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    Background: Pain is difficult to assess due to the subjective nature of self-reporting. The lack of objective measures of pain has hampered the development of new treatments as well as the evaluation of current ones. Functional MRI studies of pain have begun to delineate potential brain response signatures that could be used as objective read-outs of pain. Using Diffuse Optical Tomography (DOT), we have shown in the past a distinct DOT signal over the somatosensory cortex to a noxious heat stimulus that could be distinguished from the signal elicited by innocuous mechanical stimuli. Here we further our findings by studying the response to thermal innocuous and noxious stimuli. Methodology/Principal Findings: Innocuous and noxious thermal stimuli were applied to the skin of the face of the first division (ophthalmic) of the trigeminal nerve in healthy volunteers (N = 6). Stimuli temperatures were adjusted for each subject to evoke warm (equivalent to a 3/10) and painful hot (7/10) sensations in a verbal rating scale (0/10 = no/max pain). A set of 26 stimuli (5 sec each) was applied for each temperature with inter-stimulus intervals varied between 8 and 15 sec using a Peltier thermode. A DOT system was used to capture cortical responses on both sides of the head over the primary somatosensory cortical region (S1). For the innocuous stimuli, group results indicated mainly activation on the contralateral side with a weak ipsilateral response. For the noxious stimuli, bilateral activation was observed with comparable amplitudes on both sides. Furthermore, noxious stimuli produced a temporal biphasic response while innocuous stimuli produced a monophasic response. Conclusions/Significance: These results are in accordance with fMRI and our other DOT studies of innocuous mechanical and noxious heat stimuli. The data indicate the differentiation of DOT cortical responses for pain vs. innocuous stimuli that may be useful in assessing objectively acute pain
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