@article {828, title = {Axial 3D gradient-echo imaging for improved multiple sclerosis lesion detection in the cervical spinal cord at 3T.}, journal = {Neuroradiology}, volume = {55}, year = {2013}, month = {2013 Mar}, pages = {431-9}, abstract = {

INTRODUCTION: In multiple sclerosis (MS), spinal cord imaging can help in diagnosis and follow-up evaluation. However, spinal cord magnetic resonance imaging (MRI) is technically challenging, and image quality, particularly in the axial plane, is typically poor compared to brain MRI. Because gradient-recalled echo (GRE) images might offer improved contrast resolution within the spinal cord at high magnetic field strength, both without and with a magnetization transfer prepulse, we compared them to T2-weighted fast-spin-echo (T2-FSE) images for the detection of MS lesions in the cervical cord at 3T.

METHODS: On a clinical 3T MRI scanner, we studied 62 MS cases and 19 healthy volunteers. Axial 3D GRE sequences were performed without and with off-resonance radiofrequency irradiation. To mimic clinical practice, all images were evaluated in conjunction with linked images from a sagittal short tau inversion recovery scan, which is considered the gold standard for lesion detection in MS. Two experienced observers recorded image quality, location and size of focal lesions, atrophy, swelling, and diffuse signal abnormality independently at first and then in consensus.

RESULTS: The number and volume of lesions detected with high confidence was more than three times as high on both GRE sequences compared to T2-FSE (p < 0.0001). Approximately 5 \% of GRE scans were affected by artifacts that interfered with image interpretation, not significantly different from T2W-FSE.

CONCLUSIONS: Axial 3D GRE sequences are useful for MS lesion detection when compared to 2D T2-FSE sequences in the cervical spinal cord at 3T and should be considered when examining intramedullary spinal cord lesions.

}, keywords = {Adult, Aged, Algorithms, Cervical Vertebrae, Echo-Planar Imaging, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Male, Middle Aged, Multiple Sclerosis, Reproducibility of Results, Sensitivity and Specificity, Spinal Cord}, issn = {1432-1920}, doi = {10.1007/s00234-012-1118-5}, author = {Ozturk, Arzu and Aygun, Nafi and Smith, Seth A and Caffo, Brian and Calabresi, Peter A and Reich, Daniel S} } @article {839, title = {An fMRI-based neurologic signature of physical pain.}, journal = {N Engl J Med}, volume = {368}, year = {2013}, month = {2013 Apr 11}, pages = {1388-97}, abstract = {

BACKGROUND: Persistent pain is measured by means of self-report, the sole reliance on which hampers diagnosis and treatment. Functional magnetic resonance imaging (fMRI) holds promise for identifying objective measures of pain, but brain measures that are sensitive and specific to physical pain have not yet been identified.

METHODS: In four studies involving a total of 114 participants, we developed an fMRI-based measure that predicts pain intensity at the level of the individual person. In study 1, we used machine-learning analyses to identify a pattern of fMRI activity across brain regions--a neurologic signature--that was associated with heat-induced pain. The pattern included the thalamus, the posterior and anterior insulae, the secondary somatosensory cortex, the anterior cingulate cortex, the periaqueductal gray matter, and other regions. In study 2, we tested the sensitivity and specificity of the signature to pain versus warmth in a new sample. In study 3, we assessed specificity relative to social pain, which activates many of the same brain regions as physical pain. In study 4, we assessed the responsiveness of the measure to the analgesic agent remifentanil.

RESULTS: In study 1, the neurologic signature showed sensitivity and specificity of 94\% or more (95\% confidence interval [CI], 89 to 98) in discriminating painful heat from nonpainful warmth, pain anticipation, and pain recall. In study 2, the signature discriminated between painful heat and nonpainful warmth with 93\% sensitivity and specificity (95\% CI, 84 to 100). In study 3, it discriminated between physical pain and social pain with 85\% sensitivity (95\% CI, 76 to 94) and 73\% specificity (95\% CI, 61 to 84) and with 95\% sensitivity and specificity in a forced-choice test of which of two conditions was more painful. In study 4, the strength of the signature response was substantially reduced when remifentanil was administered.

CONCLUSIONS: It is possible to use fMRI to assess pain elicited by noxious heat in healthy persons. Future studies are needed to assess whether the signature predicts clinical pain. (Funded by the National Institute on Drug Abuse and others.).

}, keywords = {Adult, Analgesics, Opioid, Artificial Intelligence, Brain, brain mapping, Female, Hot Temperature, Humans, Magnetic Resonance Imaging, Male, Pain, Pain Measurement, Piperidines, ROC Curve, Sensitivity and Specificity, Young Adult}, issn = {1533-4406}, doi = {10.1056/NEJMoa1204471}, author = {Wager, Tor D and Atlas, Lauren Y and Lindquist, Martin A and Roy, Mathieu and Woo, Choong-Wan and Kross, Ethan} } @article {844, title = {Estimating and testing variance components in a multi-level GLM.}, journal = {Neuroimage}, volume = {59}, year = {2012}, month = {2012 Jan 2}, pages = {490-501}, abstract = {

Most analysis of multi-subject fMRI data is concerned with determining whether there exists a significant population-wide {\textquoteright}activation{\textquoteright} in a comparison between two or more conditions. This is typically assessed by testing the average value of a contrast of parameter estimates (COPE) against zero in a general linear model (GLM) analysis. However, important information can also be obtained by testing whether there exist significant individual differences in effect magnitude between subjects, i.e. whether the variance of a COPE is significantly different from zero. Intuitively, such a test amounts to testing whether inter-individual differences are larger than would be expected given the within-subject error variance. We compare several methods for estimating variance components, including a) a na{\"\i}ve estimate using ordinary least squares (OLS); b) linear mixed effects in R (LMER); c) a novel Matlab implementation of iterative generalized least squares (IGLS) and its restricted maximum likelihood variant (RIGLS). All methods produced reasonable estimates of within- and between-subject variance components, with IGLS providing an attractive balance between sensitivity and appropriate control of false positives. Finally, we use the IGLS method to estimate inter-subject variance in a perfusion fMRI study (N=18) of social evaluative threat, and show evidence for significant inter-individual differences in ventromedial prefrontal cortex (VMPFC), amygdala, hippocampus and medial temporal lobes, insula, and brainstem, with predicted inverse coupling between VMPFC and the midbrain periaqueductal gray only when high inter-individual variance was used to define the seed for functional connectivity analyses. In sum, tests of variance provides a way of selecting regions that show significant inter-individual variability for subsequent analyses that attempt to explain those individual differences.

}, keywords = {Brain, brain mapping, Humans, Image Interpretation, Computer-Assisted, Linear Models, Magnetic Resonance Imaging, Models, Neurological, Sensitivity and Specificity}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2011.07.077}, author = {Lindquist, Martin A and Spicer, Julie and Asllani, Iris and Wager, Tor D} }