@article {834,
	title = {Separate neural representations for physical pain and social rejection.},
	journal = {Nat Commun},
	volume = {5},
	year = {2014},
	month = {2014},
	pages = {5380},
	abstract = {<p>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.</p>},
	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 = {<p>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.</p>},
	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 {lindquist08,
	title = {The Statistical Analysis of fMRI Data},
	journal = {Statistical Science},
	volume = {23},
	year = {2008},
	pages = {439{\textendash}464},
	author = {Lindquist, Martin A}
}
@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}
}
