Background: Functional magnetic resonance imaging (fMRI) is a widely available method and is therefore progressively utilized in neurosurgical practice. This study was carried out to determine fMRI sensitivity and specificity and to emphasize the threshold dependence of fMRI data.
Methods: A total of 17 consecutive patients, scheduled for surgery on intracerebral lesions near eloquent brain areas, underwent preoperative motor (N = 12) and language (N = 5) fMRI. Functional data were analyzed with SPM software and displayed on a neuronavigation system for intraoperative guidance. High-risk maps for motor and language deficits obtained from direct electric cortical stimulation (DECS) were used for validation of functional activated areas. In a first analysis step, sensitivity and specificity were calculated in terms of a side-by-side correlation. The next step, the threshold dependence of fMRI data sensitivity and specificity, was estimated according to four statistical thresholds (p1 < 0.05, p2 < 0.0005, p3 < 0.00001, p4 < 0.0000001).
Results: Both functional imaging and DECS revealed definite results for the investigated areas in all patients. Calculation of sensitivity and specificity resulted in 100 % and 68 % for the motor group and a sensitivity of 75 % and specificity of 68 % for the language group at the fixed threshold analysis. Threshold-dependent analysis of the obtained data revealed a sensitivity/specificity relationship from 100 %/0 % at threshold (p1), 100 %/5 % at (p2), 74 %/9 % at (p3), and 37 %/36 % at (p4) for the motor group. Evaluation of threshold-dependent sensitivity and specificity for the language group resulted in 78 %/51 % at threshold (p1), 67 %/75 % at (p2), 50 %/78 % at (p3), and 33 %/89 % at (p4).
Conclusions: The present findings on the threshold dependence of fMRI data demonstrate why individualized thresholds should be obtained in case of fMRI evaluation. Although the results are satisfying in most cases, fMRI is apparently not sufficient for critical intraoperative decision-making.
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http://dx.doi.org/10.1007/s00701-013-1624-1 | DOI Listing |
JMIR Public Health Surveill
January 2025
Center for Global Health, University of New Mexico Health Sciences Center, Albuquerque, NM, United States.
Background: Numerous studies have assessed the risk of SARS-CoV-2 exposure and infection among health care workers during the pandemic. However, far fewer studies have investigated the impact of SARS-CoV-2 on essential workers in other sectors. Moreover, guidance for maintaining a safely operating workplace in sectors outside of health care remains limited.
View Article and Find Full Text PDFBackground: The atherogenic index of plasma (AIP) is a newly identified metabolic marker for atherosclerosis. However, there are inconsistent conclusions regarding the relationship between AIP and hypertension.
Methods: The study subjects were sourced from the National Health and Nutrition Examination Survey (NHANES) database from 2017 to 2020.
PLoS One
January 2025
Manchester Cancer Research Centre, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.
Non-covalent protein-protein interactions are one of the most fundamental building blocks in cellular signalling pathways. Despite this, they have been historically hard to identify using conventional methods due to their often weak and transient nature. Using genetic code expansion and incorporation of commercially available unnatural amino acids, we have developed a highly accessible method whereby interactions between biotinylated ubiquitin-like protein (UBL) probes and their binding partners can be stabilised using ultraviolet (UV) light-induced crosslinks.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Pathology, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Background: Ras-GTPase-activating protein (GAP)-binding protein 1 (G3BP1) emerges as a pivotal oncogenic gene across various malignancies, notably including nasopharyngeal carcinoma (NPC). The use of automated image analysis tools for immunohistochemical (IHC) staining of particular proteins is highly beneficial, as it could reduce the burden on pathologists. Interestingly, there have been no prior studies that have examined G3BP1 IHC staining using digital pathology.
View Article and Find Full Text PDFIn 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to high false positive rates, which is unacceptable in disease monitoring and early warning.
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