@article {806,
	title = {Acute lesions that impair affective empathy.},
	journal = {Brain},
	volume = {136},
	year = {2013},
	month = {2013 Aug},
	pages = {2539-49},
	abstract = {<p>Functional imaging studies of healthy participants and previous lesion studies have provided evidence that empathy involves dissociable cognitive functions that rely on at least partially distinct neural networks that can be individually impaired by brain damage. These studies converge in support of the proposal that affective empathy--making inferences about how another person feels--engages at least the following areas: prefrontal cortex, orbitofrontal gyrus, anterior insula, anterior cingulate cortex, temporal pole, amygdala and temporoparietal junction. We hypothesized that right-sided lesions to any one of these structures, except temporoparietal junction, would cause impaired affective empathy (whereas bilateral damage to temporoparietal junction would be required to disrupt empathy). We studied 27 patients with acute right hemisphere ischaemic stroke and 24 neurologically intact inpatients on a test of affective empathy. Acute impairment of affective empathy was associated with infarcts in the hypothesized network, particularly temporal pole and anterior insula. All patients with impaired affective empathy were also impaired in comprehension of affective prosody, but many patients with impairments in prosodic comprehension had spared affective empathy. Patients with impaired affective empathy were older, but showed no difference in performance on tests of hemispatial neglect, volume of infarct or sex distribution compared with patients with intact affective empathy.</p>},
	keywords = {Adult, Aged, Brain, Brain Ischemia, brain mapping, Cognition, Emotions, Empathy, Female, Functional Laterality, Humans, Male, Middle Aged, Nerve Net, Social Perception, Stroke},
	issn = {1460-2156},
	doi = {10.1093/brain/awt177},
	author = {Leigh, Richard and Oishi, Kenichi and Hsu, John and Lindquist, Martin and Gottesman, Rebecca F and Jarso, Samson and Crainiceanu, Ciprian and Mori, Susumu and Hillis, Argye E}
}
@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 = {<p>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.</p>},
	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}
}
