The specific neural systems underlying the subjective feeling of fear are debated in affective neuroscience. Here, we combine functional MRI with machine learning to identify and evaluate a sensitive and generalizable neural signature predictive of the momentary self-reported subjective fear experience across discovery (n = 67), validation (n = 20) and generalization (n = 31) cohorts. We systematically demonstrate that accurate fear prediction crucially requires distributed brain systems, with important contributions from cortical (e.g., prefrontal, midcingulate and insular cortices) and subcortical (e.g., thalamus, periaqueductal gray, basal forebrain and amygdala) regions. We further demonstrate that the neural representation of subjective fear is distinguishable from the representation of conditioned threat and general negative affect. Overall, our findings suggest that subjective fear, which exhibits distinct neural representation with some other aversive states, is encoded in distributed systems rather than isolated ‘fear centers’.

OBJECTIVE The authors aimed to evaluate the technical feasibility of a mixed-reality neuronavigation (MRN) system with a wearable head-mounted device (HMD) and to determine its clinical application and accuracy. METHODS A semiautomatic registration MRN system on HoloLens smart glasses was developed and tested for accuracy and feasibility. Thirty-seven patients with intracranial lesions were prospectively identified. For each patient, multimodal imaging–based holograms of lesions, markers, and surrounding eloquent structures were created and then imported to the MRN HMD. After a point-based registration, the holograms were projected onto the patient's head and observed through the HMD. The contour of the holograms was compared with standard neuronavigation (SN). The projection of the lesion boundaries perceived by the neurosurgeon on the patient's scalp was then marked with MRN and SN. The distance between the two contours generated by MRN and SN was measured so that the accuracy of MRN could be assessed. RESULTS MRN localization was achieved in all patients. The mean additional time required for MRN was 36.3 ± 6.3 minutes, in which the mean registration time was 2.6 ± 0.9 minutes. A trend toward a shorter time required for preparation was observed with the increase of neurosurgeon experience with the MRN system. The overall median deviation was 4.1 mm (IQR 3.0 mm–4.7 mm), and 81.1% of the lesions localized by MRN were found to be highly consistent with SN (deviation < 5.0 mm). There was a significant difference between the supine position and the prone position (3.7 ± 1.1 mm vs 5.4 ± 0.9 mm, p = 0.001). The magnitudes of deviation vectors did not correlate with lesion volume (p = 0.126) or depth (p = 0.128). There was no significant difference in additional operating time between different operators (37.4 ± 4.8 minutes vs 34.6 ± 4.8 minutes, p = 0.237) or in localization deviation (3.7 ± 1.0 mm vs 4.6 ± 1.5 mm, p = 0.070). CONCLUSIONS This study provided a complete set of a clinically applicable workflow on an easy-to-use MRN system using a wearable HMD, and has shown its technical feasibility and accuracy. Further development is required to improve the accuracy and clinical efficacy of this system.

Fundamental and clinical neuroscience has benefited tremendously from the development of automated computational analyses. In excess of 600 human neuroimaging papers using Voxel-based Morphometry (VBM) are now published every year and a number of different automated processing pipelines are used, although it remains to be systematically assessed whether they come up with the same answers. Here we examined variability between four commonly used VBM pipelines in two large brain structural datasets. Spatial similarity and between-pipeline reproducibility of the processed gray matter brain maps were generally low between pipelines. Examination of sex-differences and age-related changes revealed considerable differences between the pipelines in terms of the specific regions identified. Machine learning-based multivariate analyses allowed accurate predictions of sex and age, however accuracy differed between pipelines. Our findings suggest that the choice of pipeline alone leads to considerable variability in brain structural markers which poses a serious challenge for reproducibility and interpretation.

Background: Dual-energy computed tomography (DECT) has been widely applied to detect lymph node (LN) and lymph node metastasis (LNM) in various cancers, including papillary thyroid carcinoma (PTC). This study aimed to quantitatively evaluate metastatic cervical lymph nodes (LNs) <0.5 cm in patients with PTC using DECT, which has not been done in previous studies. Methods: Preoperative DECT data of patients with pathologically confirmed PTC were retrospectively collected and analyzed between May 2016 and June 2018. A total of 359 LNs from 52 patients were included. Diameter, iodine concentration (IC), normalized iodine concentration (NIC), and the slope of the energy spectrum curve (λ HU ) of LNs in the arterial and the venous phases were compared between metastatic and nonmetastatic LNs. The optimal parameters were obtained from the receiver operating characteristic (ROC) curves. The generalized estimation equation (GEE) model was used to evaluate independent diagnostic factors for LNM. Results: A total of 139 metastatic and 220 non-metastatic LNs were analyzed. There were statistical differences of quantitative parameters between the two groups (P value 0.000-0.007). The optimal parameter for diagnosing LNM was IC in the arterial phase, and its area under the curve (AUC), sensitivity, and specificity were 0.775, 71.9%, and 73.6%, respectively. When the three parameters of diameter, IC in the arterial phase, and NIC in the venous phase were combined, the prediction efficiency was better, and the AUC was 0.819. The GEE results showed that LNs located in level VIa [odds ratio (OR) 2.030, 95% confidence interval (CI): 1.134-3.634, P=0.017], VIb (OR 2.836, 95% CI: 1.597-5.038, P=0.000), diameter (OR 2.023, 95% CI: 1.158-3.532, P=0.013), IC in the arterial phase (OR 4.444, 95% CI: 2.808-7.035, P=0.000), and IC in the venous phase (OR 5.387, 95% CI: 3.449-8.413, P=0.000) were independent risk factors for LNM in patients with PTC. Conclusions: DECT had good diagnostic performance in the differentiation of cervical metastatic LNs <0.5 cm in patients with PTC.