@article {841,
	title = {Dissociable influences of opiates and expectations on pain.},
	journal = {J Neurosci},
	volume = {32},
	year = {2012},
	month = {2012 Jun 6},
	pages = {8053-64},
	abstract = {<p>Placebo treatments and opiate drugs are thought to have common effects on the opioid system and pain-related brain processes. This has created excitement about the potential for expectations to modulate drug effects themselves. If drug effects differ as a function of belief, this would challenge the assumptions underlying the standard clinical trial. We conducted two studies to directly examine the relationship between expectations and opioid analgesia. We administered the opioid agonist remifentanil to human subjects during experimental thermal pain and manipulated participants{\textquoteright} knowledge of drug delivery using an open-hidden design. This allowed us to test drug effects, expectancy (knowledge) effects, and their interactions on pain reports and pain-related responses in the brain. Remifentanil and expectancy both reduced pain, but drug effects on pain reports and fMRI activity did not interact with expectancy. Regions associated with pain processing showed drug-induced modulation during both Open and Hidden conditions, with no differences in drug effects as a function of expectation. Instead, expectancy modulated activity in frontal cortex, with a separable time course from drug effects. These findings reveal that opiates and placebo treatments both influence clinically relevant outcomes and operate without mutual interference.</p>},
	keywords = {Analgesics, Opioid, Anticipation, Psychological, Behavior, brain mapping, Dose-Response Relationship, Drug, Female, Hemodynamics, Hot Temperature, Humans, Image Processing, Computer-Assisted, Injections, Intravenous, Linear Models, Magnetic Resonance Imaging, Male, Pain, Pain Measurement, Piperidines, Young Adult},
	issn = {1529-2401},
	doi = {10.1523/JNEUROSCI.0383-12.2012},
	author = {Atlas, Lauren Y and Whittington, Robert A and Lindquist, Martin A and Wielgosz, Joe and Sonty, Nomita and Wager, Tor D}
}
@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 = {<p>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.</p>},
	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}
}
