@article {836, title = {Brain mediators of the effects of noxious heat on pain.}, journal = {Pain}, volume = {155}, year = {2014}, month = {2014 Aug}, pages = {1632-48}, abstract = {

Recent human neuroimaging studies have investigated the neural correlates of either noxious stimulus intensity or reported pain. Although useful, analyzing brain relationships with stimulus intensity and behavior separately does not address how sensation and pain are linked in the central nervous system. In this study, we used multi-level mediation analysis to identify brain mediators of pain--regions in which trial-by-trial responses to heat explained variability in the relationship between noxious stimulus intensity (across 4 levels) and pain. This approach has the potential to identify multiple circuits with complementary roles in pain genesis. Brain mediators of noxious heat effects on pain included targets of ascending nociceptive pathways (anterior cingulate, insula, SII, and medial thalamus) and also prefrontal and subcortical regions not associated with nociceptive pathways per se. Cluster analysis revealed that mediators were grouped into several distinct functional networks, including the following: somatosensory, paralimbic, and striatal-cerebellar networks that increased with stimulus intensity; and 2 networks co-localized with "default mode" regions in which stimulus intensity-related decreases mediated increased pain. We also identified "thermosensory" regions that responded to increasing noxious heat but did not predict pain reports. Finally, several regions did not respond to noxious input, but their activity predicted pain; these included ventromedial prefrontal cortex, dorsolateral prefrontal cortex, cerebellar regions, and supplementary motor cortices. These regions likely underlie both nociceptive and non-nociceptive processes that contribute to pain, such as attention and decision-making processes. Overall, these results elucidate how multiple distinct brain systems jointly contribute to the central generation of pain.

}, issn = {1872-6623}, doi = {10.1016/j.pain.2014.05.015}, author = {Atlas, Lauren Y and Lindquist, Martin A and Bolger, Niall and Wager, Tor D} } @article {835, title = {Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach.}, journal = {Neuroimage}, volume = {101}, year = {2014}, month = {2014 Nov 1}, pages = {531-46}, abstract = {

To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been an increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought that this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-window technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove to be useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data.

}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2014.06.052}, author = {Lindquist, Martin A and Xu, Yuting and Nebel, Mary Beth and Caffo, Brain S} } @article {790, title = {Health effects of lesion localization in multiple sclerosis: spatial registration and confounding adjustment.}, journal = {PLoS One}, volume = {9}, year = {2014}, month = {2014}, pages = {e107263}, abstract = {

Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0107263}, author = {Eloyan, Ani and Shou, Haochang and Shinohara, Russell T and Sweeney, Elizabeth M and Nebel, Mary Beth and Cuzzocreo, Jennifer L and Calabresi, Peter A and Reich, Daniel S and Lindquist, Martin A and Crainiceanu, Ciprian M} } @article {837, title = {A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies.}, journal = {Neuroimage}, volume = {98}, year = {2014}, month = {2014 Sep}, pages = {61-72}, abstract = {

In this paper we introduce a new hierarchical model for the simultaneous detection of brain activation and estimation of the shape of the hemodynamic response in multi-subject fMRI studies. The proposed approach circumvents a major stumbling block in standard multi-subject fMRI data analysis, in that it both allows the shape of the hemodynamic response function to vary across region and subjects, while still providing a straightforward way to estimate population-level activation. An efficient estimation algorithm is presented, as is an inferential framework that allows for not only tests of activation, but also tests for deviations from some canonical shape. The model is validated through simulations and application to a multi-subject fMRI study of thermal pain.

}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2014.04.052}, author = {Degras, David and Lindquist, Martin A} } @article {834, title = {Separate neural representations for physical pain and social rejection.}, journal = {Nat Commun}, volume = {5}, year = {2014}, month = {2014}, pages = {5380}, abstract = {

Current theories suggest that physical pain and social rejection share common neural mechanisms, largely by virtue of overlapping functional magnetic resonance imaging (fMRI) activity. Here we challenge this notion by identifying distinct multivariate fMRI patterns unique to pain and rejection. Sixty participants experience painful heat and warmth and view photos of ex-partners and friends on separate trials. FMRI pattern classifiers discriminate pain and rejection from their respective control conditions in out-of-sample individuals with 92\% and 80\% accuracy. The rejection classifier performs at chance on pain, and vice versa. Pain- and rejection-related representations are uncorrelated within regions thought to encode pain affect (for example, dorsal anterior cingulate) and show distinct functional connectivity with other regions in a separate resting-state data set (N=91). These findings demonstrate that separate representations underlie pain and rejection despite common fMRI activity at the gross anatomical level. Rather than co-opting pain circuitry, rejection involves distinct affective representations in humans.

}, issn = {2041-1723}, doi = {10.1038/ncomms6380}, author = {Woo, Choong-Wan and Koban, Leonie and Kross, Ethan and Lindquist, Martin A and Banich, Marie T and Ruzic, Luka and Andrews-Hanna, Jessica R and Wager, Tor D} } @article {792, title = {Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI.}, journal = {Neuroimage}, volume = {102 Pt 2}, year = {2014}, month = {2014 Nov 15}, pages = {938-44}, abstract = {

Resting-state functional magnetic resonance imaging (rs-fMRI) is used to investigate synchronous activations in spatially distinct regions of the brain, which are thought to reflect functional systems supporting cognitive processes. Analyses are often performed using seed-based correlation analysis, allowing researchers to explore functional connectivity between data in a seed region and the rest of the brain. Using scan-rescan rs-fMRI data, we investigate how well the subject-specific seed-based correlation map from the second replication of the study can be predicted using data from the first replication. We show that one can dramatically improve prediction of subject-specific connectivity by borrowing strength from the group correlation map computed using all other subjects in the study. Even more surprisingly, we found that the group correlation map provided a better prediction of a subject{\textquoteright}s connectivity than the individual{\textquoteright}s own data. While further discussion and experimentation are required to understand how this can be used in practice, results indicate that shrinkage-based methods that borrow strength from the population mean should play a role in rs-fMRI data analysis.

}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2014.05.043}, author = {Shou, Haochang and Eloyan, Ani and Nebel, Mary Beth and Mejia, Amanda and Pekar, James J and Mostofsky, Stewart and Caffo, Brian and Lindquist, Martin A and Crainiceanu, Ciprian M} } @article {843, title = {Cloak and DAG: a response to the comments on our comment.}, journal = {Neuroimage}, volume = {76}, year = {2013}, month = {2013 Aug 1}, pages = {446-9}, abstract = {

Our original comment (Lindquist and Sobel, 2011) made explicit the types of assumptions neuroimaging researchers are making when directed graphical models (DGMs), which include certain types of structural equation models (SEMs), are used to estimate causal effects. When these assumptions, which many researchers are not aware of, are not met, parameters of these models should not be interpreted as effects. Thus it is imperative that neuroimaging researchers interested in issues involving causation, for example, effective connectivity, consider the plausibility of these assumptions for their particular problem before using SEMs. In cases where these additional assumptions are not met, researchers may be able to use other methods and/or design experimental studies where the use of unrealistic assumptions can be avoided. Pearl does not disagree with anything we stated. However, he takes exception to our use of potential outcomes{\textquoteright} notation, which is the standard notation used in the statistical literature on causal inference, and his comment is devoted to promoting his alternative conventions. Glymour{\textquoteright}s comment is based on three claims that he inappropriately attributes to us. Glymour is also more optimistic than us about the potential of using directed graphical models (DGMs) to discover causal relations in neuroimaging research; we briefly address this issue toward the end of our rejoinder.

}, keywords = {Artifacts, Brain, Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Meta-Analysis as Topic}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2011.11.027}, author = {Lindquist, Martin A and Sobel, Michael E} } @article {838, title = {Detecting functional connectivity change points for single-subject fMRI data.}, journal = {Front Comput Neurosci}, volume = {7}, year = {2013}, month = {2013}, pages = {143}, abstract = {

Recently in functional magnetic resonance imaging (fMRI) studies there has been an increased interest in understanding the dynamic manner in which brain regions communicate with one another, as subjects perform a set of experimental tasks or as their psychological state changes. Dynamic Connectivity Regression (DCR) is a data-driven technique used for detecting temporal change points in functional connectivity between brain regions where the number and location of the change points are unknown a priori. After finding the change points, DCR estimates a graph or set of relationships between the brain regions for data that falls between pairs of change points. In previous work, the method was predominantly validated using multi-subject data. In this paper, we concentrate on single-subject data and introduce a new DCR algorithm. The new algorithm increases accuracy for individual subject data with a small number of observations and reduces the number of false positives in the estimated undirected graphs. We also introduce a new Likelihood Ratio test for comparing sparse graphs across (or within) subjects; thus allowing us to determine whether data should be combined across subjects. We perform an extensive simulation analysis on vector autoregression (VAR) data as well as to an fMRI data set from a study (n = 23) of a state anxiety induction using a socially evaluative threat challenge. The focus on single-subject data allows us to study the variation between individuals and may provide us with a deeper knowledge of the workings of the brain.

}, issn = {1662-5188}, doi = {10.3389/fncom.2013.00143}, author = {Cribben, Ivor and Wager, Tor D and Lindquist, Martin A} } @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 {808, title = {Ironing out the statistical wrinkles in "ten ironic rules".}, journal = {Neuroimage}, volume = {81}, year = {2013}, month = {2013 Nov 1}, pages = {499-502}, abstract = {

The article "Ten ironic rules for non-statistical reviewers" (Friston, 2012) shares some commonly heard frustrations about the peer-review process that all researchers can identify with. Though we found the article amusing, we have some concerns about its description of a number of statistical issues. In this commentary we address these issues, as well as the premise of the article.

}, keywords = {Neuroimaging, Peer Review, Research, Research Design, Statistics as Topic}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2013.02.056}, author = {Lindquist, Martin A and Caffo, Brian and Crainiceanu, Ciprian} } @article {LS11b, title = {Cloak and DAG: A response to the comments on our comment}, journal = {NeuroImage, to appear}, year = {2012}, issn = {1053-8119}, doi = {10.1016/j.neuroimage.2011.11.027}, url = {http://www.sciencedirect.com/science/article/pii/S1053811911013085}, author = {Lindquist, Martin A and Sobel, M.E.} } @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 = {

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.

}, 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 {842, title = {Dynamic connectivity regression: determining state-related changes in brain connectivity.}, journal = {Neuroimage}, volume = {61}, year = {2012}, month = {2012 Jul 16}, pages = {907-20}, abstract = {

Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being studied are known. However, often it is hard to specify this information a priori. In this work we introduce a data-driven technique for partitioning the experimental time course into distinct temporal intervals with different multivariate functional connectivity patterns between a set of regions of interest (ROIs). The technique, called Dynamic Connectivity Regression (DCR), detects temporal change points in functional connectivity and estimates a graph, or set of relationships between ROIs, for data in the temporal partition that falls between pairs of change points. Hence, DCR allows for estimation of both the time of change in connectivity and the connectivity graph for each partition, without requiring prior knowledge of the nature of the experimental design. Permutation and bootstrapping methods are used to perform inference on the change points. The method is applied to various simulated data sets as well as to an fMRI data set from a study (N=26) of a state anxiety induction using a socially evaluative threat challenge. The results illustrate the method{\textquoteright}s ability to observe how the networks between different brain regions changed with subjects{\textquoteright} emotional state.

}, keywords = {Algorithms, Brain, brain mapping, Computer Simulation, Emotions, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Neural Pathways}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2012.03.070}, author = {Cribben, Ivor and Haraldsdottir, Ragnheidur and Atlas, Lauren Y and Wager, Tor D and Lindquist, Martin A} } @article {cribben2012dynamic, title = {Dynamic connectivity regression: Determining state-related changes in brain connectivity}, journal = {NeuroImage}, year = {2012}, publisher = {Elsevier}, author = {Cribben, Ivor and Haraldsdottir, Ragnheidur and Atlas, Lauren Y and Wager, Tor D and Lindquist, Martin A} } @article {lindquist2012estimating, title = {Estimating and testing variance components in a multi-level GLM}, journal = {Neuroimage}, volume = {59}, number = {1}, year = {2012}, pages = {490{\textendash}501}, publisher = {Elsevier}, author = {Lindquist, Martin A and Spicer, Julie and Asllani, Iris 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 = {

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} } @article {840, title = {Functional Causal Mediation Analysis With an Application to Brain Connectivity.}, journal = {J Am Stat Assoc}, volume = {107}, year = {2012}, month = {2012 Dec 21}, pages = {1297-1309}, abstract = {

Mediation analysis is often used in the behavioral sciences to investigate the role of intermediate variables that lie on the causal path between a randomized treatment and an outcome variable. Typically, mediation is assessed using structural equation models (SEMs), with model coefficients interpreted as causal effects. In this article, we present an extension of SEMs to the functional data analysis (FDA) setting that allows the mediating variable to be a continuous function rather than a single scalar measure, thus providing the opportunity to study the functional effects of the mediator on the outcome. We provide sufficient conditions for identifying the average causal effects of the functional mediators using the extended SEM, as well as weaker conditions under which an instrumental variable estimand may be interpreted as an effect. The method is applied to data from a functional magnetic resonance imaging (fMRI) study of thermal pain that sought to determine whether activation in certain brain regions mediated the effect of applied temperature on self-reported pain. Our approach provides valuable information about the timing of the mediating effect that is not readily available when using the standard nonfunctional approach. To the best of our knowledge, this work provides the first application of causal inference to the FDA framework.

}, issn = {0162-1459}, doi = {10.1080/01621459.2012.695640}, author = {Lindquist, Martin A} } @article {lindquist12, title = {Functional causal mediation analysis with an application to brain connectivity}, journal = {Journal of the American Statistical Association, to appear.}, year = {2012}, author = {Lindquist, Martin A} } @article {LS11a, title = {Graphical models, potential outcomes and causal inference: Comment on Ramsey, Spirtes and Glymour}, journal = {NeuroImage}, volume = {57}, year = {2011}, pages = {334-336}, issn = {1053-8119}, doi = {10.1016/j.neuroimage.2010.10.020}, url = {http://www.sciencedirect.com/science/article/pii/S1053811910013121}, author = {Lindquist, Martin A and Sobel, M.E.} } @article {845, title = {Graphical models, potential outcomes and causal inference: comment on Ramsey, Spirtes and Glymour.}, journal = {Neuroimage}, volume = {57}, year = {2011}, month = {2011 Jul 15}, pages = {334-6}, abstract = {

Ramsey, Spirtes and Glymour (RSG) critique a method proposed by Neumann et al. (2010) for the discovery of functional networks from fMRI meta-analysis data. We concur with this critique, but are unconvinced that directed graphical models (DGMs) are generally useful for estimating causal effects. We express our reservations using the "potential outcomes" framework for causal inference widely used in statistics.

}, keywords = {Artifacts, Brain, Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Meta-Analysis as Topic}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2010.10.020}, author = {Lindquist, Martin A and Sobel, Michael E} } @article {atlas, title = {Brain mediators of predictive cue effects on perceived pain}, journal = {J Neurosci}, volume = {30}, year = {2010}, pages = {12964-77}, author = {Atlas, L.Y. and Bolger, N. and Lindquist, Martin A and Wager, T.D.} } @article {846, title = {Brain mediators of predictive cue effects on perceived pain.}, journal = {J Neurosci}, volume = {30}, year = {2010}, month = {2010 Sep 29}, pages = {12964-77}, abstract = {

Information about upcoming pain strongly influences pain experience in experimental and clinical settings, but little is known about the brain mechanisms that link expectation and experience. To identify the pathways by which informational cues influence perception, analyses must jointly consider both the effects of cues on brain responses and the relationship between brain responses and changes in reported experience. Our task and analysis strategy were designed to test these relationships. Auditory cues elicited expectations for barely painful or highly painful thermal stimulation, and we assessed how cues influenced human subjects{\textquoteright} pain reports and brain responses to matched levels of noxious heat using functional magnetic resonance imaging. We used multilevel mediation analysis to identify brain regions that (1) are modulated by predictive cues, (2) predict trial-to-trial variations in pain reports, and (3) formally mediate the relationship between cues and reported pain. Cues influenced heat-evoked responses in most canonical pain-processing regions, including both medial and lateral pain pathways. Effects on several regions correlated with pretask expectations, suggesting that expectancy plays a prominent role. A subset of pain-processing regions, including anterior cingulate cortex, anterior insula, and thalamus, formally mediated cue effects on pain. Effects on these regions were in turn mediated by cue-evoked anticipatory activity in the medial orbitofrontal cortex (OFC) and ventral striatum, areas not previously directly implicated in nociception. These results suggest that activity in pain-processing regions reflects a combination of nociceptive input and top-down information related to expectations, and that anticipatory processes in OFC and striatum may play a key role in modulating pain processing.

}, keywords = {Adult, Brain, Cognition, Cues, Female, Hot Temperature, Humans, Male, Pain, Pain Threshold, Perception, Physical Stimulation}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.0057-10.2010}, author = {Atlas, Lauren Y and Bolger, Niall and Lindquist, Martin A and Wager, Tor D} } @article {kriegeskorte2010everything, title = {Everything you never wanted to know about circular analysis, but were afraid to ask}, journal = {Journal of Cerebral Blood Flow \& Metabolism}, volume = {30}, number = {9}, year = {2010}, pages = {1551{\textendash}1557}, publisher = {Nature Publishing Group}, author = {Kriegeskorte, Nikolaus and Lindquist, Martin A and Nichols, Thomas E and Poldrack, Russell A and Vul, Edward} } @article {847, title = {Everything you never wanted to know about circular analysis, but were afraid to ask.}, journal = {J Cereb Blood Flow Metab}, volume = {30}, year = {2010}, month = {2010 Sep}, pages = {1551-7}, abstract = {

Over the past year, a heated discussion about {\textquoteright}circular{\textquoteright} or {\textquoteright}nonindependent{\textquoteright} analysis in brain imaging has emerged in the literature. An analysis is circular (or nonindependent) if it is based on data that were selected for showing the effect of interest or a related effect. The authors of this paper are researchers who have contributed to the discussion and span a range of viewpoints. To clarify points of agreement and disagreement in the community, we collaboratively assembled a series of questions on circularity herein, to which we provide our individual current answers in }, keywords = {Brain, brain mapping, Data Interpretation, Statistical, Guidelines as Topic, Image Processing, Computer-Assisted, Neurosciences, Publications, Research Design, Selection Bias}, issn = {1559-7016}, doi = {10.1038/jcbfm.2010.86}, author = {Kriegeskorte, Nikolaus and Lindquist, Martin A and Nichols, Thomas E and Poldrack, Russell A and Vul, Edward} } @article {Wager09b, title = {Brain mediators of cardiovascular responses to social threat, Part II: Prefrontal subcortical pathways and relationship with anxiety}, journal = {NeuroImage}, volume = {47}, year = {2009}, pages = {836-851}, author = {Wager, T.D. and van Ast, V. and Davidson, M.L. and Lindquist, Martin A and Ochsner, K.N.} } @article {Wager09a, title = {Brain mediators of cardiovascular responses to social threat, Part I: Reciprocal dorsal and ventral sub-regions of the medial prefrontal cortex and heart-rate reactivity}, journal = {NeuroImage}, volume = {47}, year = {2009}, pages = {821-835}, author = {Wager, T.D. and Waugh, C.E. and Lindquist, Martin A and Noll, D.C. and Fredrickson, B.L. and Taylor, S.F.} } @article {lindquist2009correlations, title = {Correlations and multiple comparisons in functional imaging: a statistical perspective (Commentary on Vul et al., 2009)}, journal = {Perspectives on Psychological Science}, volume = {4}, number = {3}, year = {2009}, pages = {310{\textendash}313}, publisher = {SAGE Publications}, author = {Lindquist, Martin A and Gelman, Andrew} } @article {wager2009evaluating, title = {Evaluating the consistency and specificity of neuroimaging data using meta-analysis}, journal = {Neuroimage}, volume = {45}, number = {1}, year = {2009}, pages = {S210{\textendash}S221}, publisher = {Elsevier}, author = {Wager, Tor D and Lindquist, Martin A and Nichols, Thomas E and Kober, Hedy and Van Snellenberg, Jared X} } @article {lindquistJASA09, title = {Logistic Regression with Brownian-like Predictors}, journal = {Journal of the American Statistical Association}, volume = {104}, year = {2009}, pages = {1575-1585}, author = {Lindquist, Martin A and McKeague, I.W.} } @article {lindquist09, title = {Modeling the Hemodynamic Response Function in fMRI: Efficiency, Bias and Mis-modeling}, journal = {NeuroImage}, volume = {45}, year = {2009}, pages = {S187-S198}, author = {Lindquist, Martin A and Loh, J.M. and Atlas, L. and Wager, T.D.} } @article {grinband2008detection, title = {Detection of time-varying signals in event-related fMRI designs}, journal = {Neuroimage}, volume = {43}, number = {3}, year = {2008}, pages = {509{\textendash}520}, publisher = {Elsevier}, author = {Grinband, Jack and Wager, Tor D and Lindquist, Martin A and Ferrera, Vincent P and Hirsch, Joy} } @article {Wager08, title = {Prefrontal-Subcortical Pathways Mediating Successful Emotion Regulation}, journal = {Neuron}, volume = {59}, year = {2008}, pages = {1037-1050}, author = {Wager, T.D. and Davidson, M.L. and Hughes, B.L. and Lindquist, Martin A and Ochsner, K.N.} } @article {lindquist2008rapid, title = {Rapid three-dimensional functional magnetic resonance imaging}, year = {2008}, author = {Lindquist, Martin A and Zhang, Cun-Hui and Glover, Gary and Shepp, Lawrence} } @article {lindquist08, title = {The Statistical Analysis of fMRI Data}, journal = {Statistical Science}, volume = {23}, year = {2008}, pages = {439{\textendash}464}, author = {Lindquist, Martin A} } @article {wager2007meta, title = {Meta-analysis of functional neuroimaging data: current and future directions}, journal = {Social Cognitive and Affective Neuroscience}, volume = {2}, number = {2}, year = {2007}, pages = {150{\textendash}158}, publisher = {Oxford University Press}, author = {Wager, Tor D and Lindquist, Martin A and Kaplan, Lauren} } @article {lindquist2007modeling, title = {Modeling state-related fMRI activity using change-point theory}, journal = {NeuroImage}, volume = {35}, number = {3}, year = {2007}, pages = {1125{\textendash}1141}, publisher = {Elsevier}, author = {Lindquist, Martin A and Waugh, Christian and Wager, Tor D} } @article {lindquist07, title = {Validity and Power in Hemodynamic Response Modeling: A comparison study and a new approach}, journal = {Human Brain Mapping}, volume = {28}, year = {2007}, pages = {764-784}, author = {Lindquist, Martin A and Wager, T.D.} } @article {yacoub2003spin, title = {Spin-echo fMRI in humans using high spatial resolutions and high magnetic fields}, journal = {Magnetic resonance in medicine}, volume = {49}, number = {4}, year = {2003}, pages = {655{\textendash}664}, publisher = {Wiley Online Library}, author = {Yacoub, Essa and Duong, Timothy Q and De Moortele, Van and Lindquist, Martin A and Adriany, Gregor and Kim, Seong-Gi and U{\u g}urbil, K{\^a}mil and Hu, Xiaoping and others} }