The three-prong method: a novel assessment of residual stress in laser powder bed fusion

<p><b>Boxplots of quantitative parameters</b> included <b>a)</b> ratio of N-acetylaspartate and N-acetylaspartylglutamate (NAA) to creatine and phosphocreatine (Cr) both for chemical shift imaging (CSI) and single voxel (SV) measurements, <b>b)</b> ratio of choline containing compounds (Cho) to Cr both for CSI and SV, <b>c)</b> myelin water fraction (MWF), <b>d)</b> magnetization transfer ratio (MTR), <b>e)</b> quantitative susceptibility mapping (QSM), and <b>f)</b> R2*. Parameters were measured in frontal white matter (WM) and two parameters within the cortico-spinal tract (CST): at the level of the posterior limb of internal capsule (PLIC) and at the level of the centrum semiovale (CS), see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167274#pone.0167274.g001" target="_blank">Fig 1</a>.</p

Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia

Background: White matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research. Methods: We used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS). Results: Across tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions. Conclusion: To conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia

Gender difference in tuning to faces.

<p>In the Face-n-Food task, females more readily recognize images as a face than males. Vertical bars represent SEM.</p

Proportion of face responses to each image in the Face-n-Food task for female and male participants.

<p>The image number reflects its recognizability as a face (1—the least recognizable, 10—the most recognizable). Fitted trend curves represent moving average of face response proportion across the images. Females not only earlier report seeing a face and give on overall more face responses, but also faster reach a ceiling level of performance.</p

Examples of the Face-n-Food images.

<p>The least resembling face (left panel) and most resembling face (right panel) images from the Face-n-Food task.</p

Examples of the Giuseppe Arcimboldo style.

<p>Left: “Reversible Head with Basket of Fruit” painting by Giuseppe Arcimboldo (1526–1593), an Italian painter best known for creating fascinating imaginative portraits composed entirely of fruits, vegetables, plants and flowers (image source Artdaily.org; public domain). Right: The portrait of one of the authors of this paper (ANS) created by another author (MAP) in a manner slightly bordering on the style of Giuseppe Arcimboldo.</p

Ratiometric Method for Rapid Monitoring of Biological Processes Using Bioresponsive MRI Contrast Agents

Bioresponsive magnetic resonance imaging (MRI) contrast agents hold great potential for noninvasive tracking of essential biological processes. Consequently, a number of MR sensors for several imaging protocols have been developed, attempting to produce the maximal signal difference for a given event. Here we introduce an approach which could substantially improve the detection of physiological events with fast kinetics. We developed a nanosized, calcium-sensitive dendrimeric probe that changes longitudinal and transverse relaxation times with different magnitudes. The change in their ratio is rapidly recorded by means of a balanced steady-state free precession (bSSFP) imaging protocol. The employed methodology results in an almost four times greater signal gain per unit of time as compared to conventional T₁-weighted imaging with small sized contrast agents. Furthermore, it is suitable for high resolution functional MRI at high magnetic fields. This methodology could evolve into a valuable tool for rapid monitoring of various biological events

Task-dependent dynamic reconfiguration of whole-brain networks.

<p>We depict the reconfiguration using hubness maps on basis of fMRI data fingertapping data of the Human Connectome Project. The hubness maps indicate the number of network edges that feature a significant change between the two experimental conditions. The top row (A) contrasts right hand minus left hand tapping, the bottom row (B) shows the reverse contrast. The colours encode the number of edges with <i>Fdr</i> < 0.05 having one of their endpoints in the respective colour-coded voxel and ranges from 1 to 1000. This number can be interpreted as a measure of “hubness”. Thus, red values in the above figure indicate hubs where many edges accumulate in a voxel. See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158185#pone.0158185.s001" target="_blank">S1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158185#pone.0158185.s002" target="_blank">S2</a> Figs.</p

Illustration of a potential problem in correlation-based statistics.

<p>(A) Hypothetical time courses of two pairs of voxels (<i>i</i>, <i>j</i>) and (<i>m</i>, <i>n</i>) in three experimental trials of the same condition are shown here. It is clearly visible that the voxel pair (<i>i</i>, <i>j</i>) has a low inter-trial consistency, while the pair (<i>m</i>, <i>n</i>) has a high one. (B) In standard correlation-based statistics, the correlation between pairs of voxels is computed for each trial. Here, the <i>correlations</i> between (<i>i</i>, <i>j</i>) are consistent across trials, but their temporal profiles are not. In correlation-based statistics (CBS), only the consistency of correlation values is considered while the consistency of the temporal profiles within a trial is ignored. Therefore, in CBS one might erroneously conclude that voxels <i>i</i>, <i>j</i> belong to the same task-related network. The voxel pair (<i>m</i>, <i>n</i>) on the other hand also shows consistent temporal profiles and is therefore more likely to belong to the same network. (C) We propose a new measure of synchronization based on effect sizes, taking into account the inter-trial consistency. Our measure is able to separate between the voxel pairs (<i>i</i>, <i>j</i>) and (<i>m</i>, <i>n</i>); the voxel pair with low inter-trial consistency receives low scores.</p

Exemplary lesion detection results in an MRI-negative patient.

(A) Overlays of the prediction statistic on the inflated template surface for different selected univariate and multivariate approaches. The colorscale is aligned according to the false-positive fraction in each case. The green outline depicts the border of the lobar hypothesis label. (B, C) Original image data with overlaid tracings of the reconstructed pial surface, colored according to the cortical thickness GLM z-score, showing the maximum value in the temporal hypothesis label (B) and frontopolar (C). Overall, the results agree well with the clinical constellation in this patient: Whereas the hypothesis was formally defined as right insular, upon intracranial EEG also seizures originating from right frontal were recorded; in the end, the multifocal localization contraindicated surgery.</p