Cumulative vascularity maps (n = 7; TR = 1030 ms) of z_f-intervals.

<p><b>row A</b>: Ι_a = {v |−2≤z_f(TTP_v) <−1}, <b>row B</b>: Ι_p = {v |−1≤z_f(TTP_v) ≤+1} and <b>row C</b>: Ι_v = {v |+1 f(TTP_v) ≤+2} (same as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114999#pone-0114999-g002" target="_blank">Fig. 2</a>). Using the proposed z_f-intervals, independently of the clearly longer TR, regions mainly containing arterial vessels (<b>maps in row A</b>), regions containing capillary and small cerebral vessels (<b>maps in row B</b>), and regions mainly containing veins and sinuses (<b>maps in row C</b>), could be readily distinguished from each other. (Note that due to the smaller number of patients in this group the maps appear less homogeneous and more noisy than those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114999#pone-0114999-g002" target="_blank">Fig. 2</a>.)</p

When overlaid within comparable time intervals, the raw time-to-peak distribution curves (TDC) showed considerable variance, but their similarity of the basic structure is evident, which is also reflected by the moderate correlation across patients (ρ = 0.86; Spearman rank correlation; n = 61).

<p>The mean curve (red line) and the interval: mean −/+1*SD (green dashed lines) are shown.</p

Cumulative vascularity maps (n = 54; TR = 689 ms) of z_f-intervals.

<p><b>Row A</b>: Ι_a = {v | −2≤z_f(TTP_v) <−1}, <b>row B</b>: Ι_p = {v |−1≤z_f(TTP_v) ≤+1} and <b>row C</b>: Ι_v = {v |+1 f(TTP_v) ≤+2}. Using the proposed z_f-intervals regions containing mainly arterial vessels (<b>maps in row A</b>), regions containing mainly capillary and small cerebral vessels (<b>maps in row B</b>), and regions mainly containing veins and sinuses (<b>maps in row C</b>), could be easily differentiated. Note, that the interval durations for the different maps were solely derived from the global TTP-distribution, which could be used for individual adjustments of vessel segment specific thresholds. An excellent agreement (shown in green – white colors) between the different exams could be demonstrated, especially for the parenchyma. Anatomical variability between patients, which is expected to be greater concerning the individual shape of the arterial and venous vessel tree, as well as the known technical limitations of echo planar imaging are precluding a 100%-match of the z_f-score based compartment-classification in the tested samples.</p

Distribution of durations found for the interval Ι_p = {v |−1≤z_f(TTP_v) ≤+1} are depicted in relation to the patient age.

<p>An increase of approx. 30 ms per year on average was obtained from the linear regression analysis (red trend line). The dashed horizontal, blue lines mark the 5%- (lower) and 95%- (upper) percentiles of the Ι_p - interval, where the median shows at 4.3 s (green dashed horizontal line).</p

Exemplary use of TDC-modeling.

<p>TDC-modeling adapts thresholds automatically, since the same z_f-score corresponds to individually different absolute thresholds. The TDC interval: [+1< = z_f< = +4] (overlain with correlated diffusion weighted imaging [DWI]) is shown in <b>rows A</b> and <b>C</b>. Areas colored in yellow in the TDC-maps display z_f-scores> = +2 (putative critical perfusion), while red colored regions mark areas with z_f-scores <+2 (putative tolerable hypoperfusion). In <b>rows A</b> and <b>B</b> acute cardioembolic stroke is depicted, where only few yellow areas with critical perfusion (<b>row A</b>) match well with the DWI-alterations (<b>row B</b>). The same behavior was found in the patient shown in <b>rows C</b> and <b>D</b>, who suffered thromboembolic stroke due to acute occlusion of the internal carotid artery on the right and a high grade stenosis on the left side. Again a good match of the TDC prediction (<b>row C</b>) with real ischemic injury in DWI-images (<b>row D</b>) on both sides was found. Note that although the same z_f-scores were used in both cases and no severe overestimations for critically perfused areas occurred, the automatically calculated individual critical thresholds were 9.7 s for case 1 (<b>rows A & B</b>) and 5.8 s for case 2 (<b>rows C & D</b>).</p

Example of a global perfusion event derived from DSC-MRI.

<p><b>A</b>) The time-to-Peak (TTP)-distribution curve (TDC) describes the chronological arrangement of the relative amount of voxels showing their peak enhancement at a certain time step during the examination (grey solid lower line). In parallel the mean signal curve (MSC) (blue dotted upper line) for all voxels with the same TTP-value is displayed in relation to the TDC. Mind the characteristic double peak structure of the MSC that was always arranged around the main TDC-peak. The red vertical lines mark the estimated beginning (dotted) and end (solid) of the wash in and wash out phase, respectively. <b>B</b>) After extraction of irrelevant fluctuations in the mean signal curve (blue solid line) the residual components were used to define an arterio-venous transit interval (marked by vertical dotted and solid red lines). Within this interval a double Gaussian model was fitted to the main TDC-peak (gold solid line). <b>C</b>) In the last step of the analysis, based on the fitted double Gaussian model z_f-scores were computed and the time points (marked by vertical lines) for integer scores of z_f = −/+3 (green dashed lines), −/+2 (red dashed lines), −/+1 (black dashed lines) and 0 (orange dashed – extended line) were estimated. These z_f-scores, which were derived from the fitted model, served then to create vascularity maps, which allowed a differentiation between large arteries and veins as well as parenchymal vessels without any a priori defined absolute time thresholds.</p

Dilemma decisions.

<p>Percentage of utilitarian decisions (i.e., single person is sacrificed to save numerous others threatened by imminent death or injury) in dilemmas involving Humanized or Neutral persons. Error bars denote standard errors of the mean.</p

The Human Factor: Behavioral and Neural Correlates of Humanized Perception in Moral Decision Making

<div><p>The extent to which people regard others as full-blown individuals with mental states (“humanization”) seems crucial for their prosocial motivation towards them. Previous research has shown that decisions about moral dilemmas in which one person can be sacrificed to save multiple others do not consistently follow utilitarian principles. We hypothesized that this behavior can be explained by the potential victim’s perceived humanness and an ensuing increase in vicarious emotions and emotional conflict during decision making. Using fMRI, we assessed neural activity underlying moral decisions that affected fictitious persons that had or had not been experimentally humanized. In implicit priming trials, participants either engaged in mentalizing about these persons (Humanized condition) or not (Neutral condition). In subsequent moral dilemmas, participants had to decide about sacrificing these persons’ lives in order to save the lives of numerous others. Humanized persons were sacrificed less often, and the activation pattern during decisions about them indicated increased negative affect, emotional conflict, vicarious emotions, and behavioral control (pgACC/mOFC, anterior insula/IFG, aMCC and precuneus/PCC). Besides, we found enhanced effective connectivity between aMCC and anterior insula, which suggests increased emotion regulation during decisions affecting humanized victims. These findings highlight the importance of others’ perceived humanness for prosocial behavior - with aversive affect and other-related concern when imagining harming more “human-like” persons acting against purely utilitarian decisions.</p> </div

Experimental time course.

<p>Top panel: overview of the four blocks constituting the fMRI experiment. Each block started with a Humanized (H) and a Neutral (N) priming trial (this order was pseudorandomized over experimental blocks), followed by three Humanized and three Neutral dilemmas in randomized order. Middle panel: example of a block starting with a Neutral priming trial, in which “Person A” is primed, followed by a Humanized priming trial, priming “Person B”. Each primed fictitious person featured in three of the subsequently presented dilemmas. Bottom panel left: time course of one Humanized priming trial, starting with (1) a screen showing the photo, name, and text describing the fictitious person, followed a by a button press-triggered (2) screen in which a question and two response options were additionally shown; a button response (left or right) triggered (3) a screen in which a second question and two response options replaced the first question; a button response (left or right) ended the trial. Bottom panel right: time course of one Humanized dilemma trial in which “Person B” is included. Each dilemma trial started with (1) a screen showing the photo and the name of the person. After a button press (2) a text describing the emergency situation was added to the screen. A second button press triggered (3) a screen in which the first text was replaced by a second text, describing the respondent’s options to act and the associated consequences for the persons. A new button press added (4) a decision question of the type “will you (perform the action)?” to the screen, along with the response options (“yes” or “no”). A right or left button press ended the dilemma trial. Trials were separated by a variable delay of 3.7–6.9 seconds.</p

Statistical parametric maps (SPMs) showing increased activity during the question phase of Humanized (H) as compared to Neutral (N) priming trials, requiring either mentalizing (H) or not (N).

<p>(A) bilateral precuneus/PCC and dorsomedial prefrontal cortex (PFC) (B) right middle temporal gyrus (MTG)/temporal pole; a cluster of comparable size was also present in the left hemisphere (C) bilateral temporo-parietal junction (TPJ). SPMs are displayed in neurological convention on the high-resolution structural MRI template brain provided in SPM8, threshold P = 0.05, corrected for multiple comparisons at the cluster-level.</p