656 research outputs found
Inferring individual differences in fMRI : finding brain regions with significant within subject correlation
No full textFunctional magnetic resonance imaging studies answer questions about activation effects in populations of subjects. To begin with, this involves appropriate modelling of the fMRI data at the within-subject level. This is followed by extending the model to multiple subjects. There have been several attempts toward this extension, all of which have focused on inference on a single effect of interest (e.g., fMRI response for one type of working memory). However, the existing literature does not seem to say much about the relevant inferential procedures when multiple effects are of interest (e.g., response for four different types of working memory). In particular, the within subject dependence of one activation effect on another is an important issue with a multivariate repeated measures model. While most standard statistical methods regard such correlation as a nuisance, to be adjusted for and then ignored, we develop two simple and intuitive tests to make inference on the existence of such correlation. We demonstrate use of these tests by application to an fMRI study of attention switching. These tests are different not only from conventional tests for sphericity but also, more importantly, from the likelihood ratio test (LRT) of the relevant hypothesis. We also discuss what prompts us to look for tests different from the LRT
Multivariate brain prediction of heart rate and skin conductance responses to social threat
No full textBainter_Final_Supplementary_Methods – Supplemental material for Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain
No full textSupplemental material, Bainter_Final_Supplementary_Methods for Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain by Sierra A. Bainter, Thomas G. McCauley, Tor Wager and Elizabeth A. Reynolds Losin in Advances in Methods and Practices in Psychological Science</p
Bainter_AMPPSOpenPracticesDisclosure-v1.0 – Supplemental material for Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain
No full textSupplemental material, Bainter_AMPPSOpenPracticesDisclosure-v1.0 for Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain by Sierra A. Bainter, Thomas G. McCauley, Tor Wager and Elizabeth A. Reynolds Losin in Advances in Methods and Practices in Psychological Science</p
bmrk3_6levels_pain_dataset.mat
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This dataset includes 33 participants, with brain responses to six levels
of heat (non-painful and painful).
The file format is an fmri_data object from the CANlab Core Tools repository for neuroimaging data analysis.
See https://canlab.github.io/
Aspects of this data appear in these papers:
Wager, T.D., Atlas, L.T., Lindquist, M.A., Roy, M., Choong-Wan, W., Kross, E. (2013).
An fMRI-Based Neurologic Signature of Physical Pain. The New England Journal of Medicine. 368:1388-1397
(Study 2)
Woo, C. -W., Roy, M., Buhle, J. T. & Wager, T. D. (2015). Distinct brain systems
mediate the effects of nociceptive input and self-regulation on pain. PLOS Biology. 13(1):
e1002036. doi:10.1371/journal.pbio.1002036
Lindquist, Martin A., Anjali Krishnan, Marina López-Solà, Marieke Jepma, Choong-Wan Woo,
Leonie Koban, Mathieu Roy, et al. 2015. ?Group-Regularized Individual Prediction:
Theory and Application to Pain.? NeuroImage.
http://www.sciencedirect.com/science/article/pii/S1053811915009982.
</p
2014_Woo_DPSP_Pain_Rejection
No full textFinished product with complete results and scripts folders pertaining to previously published dataset. This folder acts to supplement 2014_Woo_DPSP_Pain_Rejection_contrast_images by providing an example of a complete analysis pipeline and by demonstrating what contents should be present in the results directory following analysis.The primary paper on this dataset is:Woo, C. W., Koban, L., Kross, E., Lindquist, M. A., Banich, M. T., Ruzic, L., . . . Wager, T. D. (2014). Separate neural representations for physical pain and social rejection. Nature communications, 5, 5380. doi:10.1038/ncomms6380All output were generated with the CANlab help examples second-level batch scripts. See canlab.github.io for more information.</div
2014_Woo_DPSP_Pain_Rejection_contrast_images
No full textThis dataset contains sample fMRI data from pain and rejection. The primary publication is referenced below. Woo, C. W., Koban, L., Kross, E., Lindquist, M. A., Banich, M. T., Ruzic, L., . . . Wager, T. D. (2014). Separate neural representations for physical pain and social rejection. Nature communications, 5, 5380. doi:10.1038/ncomms6380Using CANlab toolboxes, one should be able to recreate the results and scripts included with this file set, and the sample HTML reports. These were created with the canlab_help_examples second-level batch scripts. See canlab.github.io for more information ('batch' section).</div
The challenges of forecasting resilience
No full textDeveloping prospective models of resilience using the translational and transdiagnostic framework proposed in the target article is a challenging endeavor and will require large-scale data sets with dense intraindividual temporal sampling and innovative analytic methods
Placebo Effects on the Neurologic Pain Signature: A Meta-analysis of Individual Participant Functional Magnetic Resonance Imaging Data
No full textImportance: Placebo effects reduce pain and contribute to clinical analgesia, but after decades of research, it remains unclear whether placebo treatments mainly affect nociceptive processes or other processes associated with pain evaluation. Objective: We conducted a systematic, participant-level meta-analysis to test the effect of placebo treatments on pain-associated functional neuroimaging responses in the neurologic pain signature (NPS), a multivariate brain pattern tracking nociceptive pain. Data Sources: Medline (PubMed) was searched from inception to May 2015; the search was augmented with results from previous meta-analyses and expert recommendations. Study Selection: Eligible studies were original investigations that were published in English in peer-reviewed journals and that involved functional neuroimaging of the human brain with evoked pain delivered under stimulus intensity-matched placebo and control conditions. The authors of all eligible studies were contacted and asked to provide single-participant data. Data Extraction and Synthesis: Data were collected between December 2015 and November 2017 following the Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data guidelines. Results were summarized across participants and studies in a random-effects model. Main Outcomes and Measures: The main, a priori outcome was NPS response; pain reports were assessed as a secondary outcome. Results: We obtained data from 20 of 28 identified eligible studies, resulting in a total sample size of 603 healthy individuals. The NPS responses to painful stimulation compared with baseline conditions were positive in 575 participants (95.4%), with a very large effect size (g = 2.30 [95% CI, 1.92 to 2.69]), confirming its sensitivity to nociceptive pain in this sample. Placebo treatments showed significant behavioral outcomes on pain ratings in 17 of 20 studies (85%) and in the combined sample (g = -0.66 [95% CI, -0.80 to -0.53]). However, placebo effects on the NPS response were significant in only 3 of 20 studies (15%) and were very small in the combined sample (g = -0.08 [95% CI, -0.15 to -0.01]). Similarly, analyses restricted to studies with low risk of bias (g = -0.07 [95% CI, -0.15 to 0.00]) indicated very small effects, and analyses of just placebo responders (g = -0.22 [95% CI, -0.34 to -0.11]) indicated small effects, as well. Conclusions and Relevance: Placebo treatments have moderate analgesic effects on pain reports. The very small effects on NPS, a validated measure that tracks levels of nociceptive pain, indicate that placebo treatments affect pain via brain mechanisms largely independent of effects on bottom-up nociceptive processing
Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data
No full textNow over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised withmeta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in thearticle are available for Coordinate-Based Meta-Ana lysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas ofconsistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). Tosimultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci fromeach study as a doubly stochastic Poisson process, where the study-speci?c log intensity function is characterized as a linearcombination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factormodeling of the basis coe?cients. Within our framework, it is also possible to account for the e?ect of study-level covariates(meta-regression), signi?cantly expanding the capabilities of the current neuroimaging meta-analysis methods available. Weapply our methodology to synthetic data and neuroimaging meta-analysis datasets
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