On the Specificity and Permanence of Electroencephalography Functional Connectivity

Functional connectivity, representing a statistical coupling relationship between different brain regions or electrodes, is an influential concept in clinical medicine and cognitive neuroscience. Electroencephalography-derived functional connectivity (EEG-FC) provides relevant characteristic information about individual differences in cognitive tasks and personality traits. However, it remains unclear whether these individual-dependent EEG-FCs remain relatively permanent across long-term sessions. This manuscript utilizes machine learning algorithms to explore the individual specificity and permanence of resting-state EEG connectivity patterns. We performed six recordings at different intervals during a six-month period to examine the variation and permanence of resting-state EEG-FC over a long period. The results indicated that the EEG-FC networks are quite subject-specific with a high-precision identification accuracy of greater than 90%. Meanwhile, the individual specificity remained stable and only varied slightly after six months. Furthermore, the specificity is mainly derived from the internal connectivity of the frontal lobe. Our work demonstrates the existence of specific and permanent EEG-FC patterns in the brain, providing potential information for biometric applications.

Modeling Musculoskeletal Dynamics during Gait: Evaluating the Best Personalization Strategy through Model Anatomical Consistency

No consensus exists on how to model human articulations within MSK models for the analysis of gait dynamics. We propose a method to evaluate joint models and we apply it to three models with different levels of personalization. The method evaluates the joint model’s adherence to the MSK hypothesis of negligible joint work by quantifying ligament and cartilage deformations resulting from joint motion; to be anatomically consistent, these deformations should be minimum. The contrary would require considerable external work to move the joint, violating a strong working hypothesis and raising concerns about the credibility of the MSK outputs. Gait analysis and medical resonance imaging (MRI) from ten participants were combined to build lower limb subject-specific MSK models. MRI-reconstructed anatomy enabled three levels of personalization using different ankle joint models, in which motion corresponded to different ligament elongation and cartilage co-penetration. To estimate the impact of anatomical inconsistency in MSK outputs, joint internal forces resulting from tissue deformations were computed for each joint model and MSK simulations were performed ignoring or considering their contribution. The three models differed considerably for maximum ligament elongation and cartilage co-penetration (between 5.94 and 50.69% and between −0.53 and −5.36 mm, respectively). However, the model dynamic output from the gait simulations were similar. When accounting for the internal forces associated with tissue deformation, outputs changed considerably, the higher the personalization level the smaller the changes. Anatomical consistency provides a solid method to compare different joint models. Results suggest that consistency grows with personalization, which should be tailored according to the research question. A high level of anatomical consistency is recommended when individual specificity and the behavior of articular structures is under investigation.

Comparative analysis of the effects of collection methods on salivary steroids

Background Steroid hormone test for saliva was a promising area of research, however the impact of different collection methods on salivary steroids was underexplored so far. This study was designed to compare the effects of different collection methods (unstimulated or stimulated by chewing paraffin, forepart or midstream) on salivary flow rate, concentrations and secretion rates of steroids in saliva. Methods Whole-saliva samples were collected from 10 systemically and orally healthy participants, whose forepart and midstream segments of saliva were collected under unstimulated and stimulated conditions, with the salivary flow rate of each sample recorded. The concentrations and secretion rates of salivary steroids including testosterone, dehydroepiandrosterone (DHEA) and progesterone were measured by ELISA, with the multiple of change calculated. Results The results indicated mechanical stimulation used in collection of saliva samples could affect concentrations and secretion rates of steroids, whereas forepart and midstream segments had little differences in levels of salivary steroids, which effects could be partly influenced by individual specificity. The asynchronism in change of secretion rate of steroids with that of salivary flow rate might play an important role during this course. Conclusion Based on these findings, we suggested to use the same collection method throughout one analytical study on salivary steroids or in longitudinal observations to ensure the comparability of the saliva samples collected.

Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction

file:///C:/Users/MWF/Downloads/Arabzadehghahyazi, Negar.Pre-retrieval Query Performance Prediction (QPP) methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the set of documents retrieved for the query. Among pre-retrieval query performance predictors, specificity-based metrics investigate how corpus, query and corpus-query level statistics can be used to predict the performance of the query. In this thesis, we explore how neural embeddings can be utilized to define corpus-independent and semantics-aware specificity metrics. Our metrics are based on the intuition that a term that is closely surrounded by other terms in the embedding space is more likely to be specific while a term surrounded by less closely related terms is more likely to be generic. On this basis, we leverage geometric properties between embedded terms to define four groups of metrics: (1) neighborhood-based, (2) graph-based, (3) cluster-based and (4) vector-based metrics. Moreover, we employ learning-to-rank techniques to analyze the importance of individual specificity metrics. To evaluate the proposed metrics, we have curated and publicly share a test collection of term specificity measurements defined based on Wikipedia category hierarchy and DMOZ taxonomy. We report on our extensive experiments on the effectiveness of our metrics through metric comparison, ablation study and comparison against the state-of-the-art baselines. We have shown that our proposed set of pre-retrieval QPP metrics based on the properties of pre-trained neural embeddings are more effective for performance prediction compared to the state-of-the-art methods. We report our findings based on Robust04, ClueWeb09 and Gov2 corpora and their associated TREC topics.

Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction

file:///C:/Users/MWF/Downloads/Arabzadehghahyazi, Negar.Pre-retrieval Query Performance Prediction (QPP) methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the set of documents retrieved for the query. Among pre-retrieval query performance predictors, specificity-based metrics investigate how corpus, query and corpus-query level statistics can be used to predict the performance of the query. In this thesis, we explore how neural embeddings can be utilized to define corpus-independent and semantics-aware specificity metrics. Our metrics are based on the intuition that a term that is closely surrounded by other terms in the embedding space is more likely to be specific while a term surrounded by less closely related terms is more likely to be generic. On this basis, we leverage geometric properties between embedded terms to define four groups of metrics: (1) neighborhood-based, (2) graph-based, (3) cluster-based and (4) vector-based metrics. Moreover, we employ learning-to-rank techniques to analyze the importance of individual specificity metrics. To evaluate the proposed metrics, we have curated and publicly share a test collection of term specificity measurements defined based on Wikipedia category hierarchy and DMOZ taxonomy. We report on our extensive experiments on the effectiveness of our metrics through metric comparison, ablation study and comparison against the state-of-the-art baselines. We have shown that our proposed set of pre-retrieval QPP metrics based on the properties of pre-trained neural embeddings are more effective for performance prediction compared to the state-of-the-art methods. We report our findings based on Robust04, ClueWeb09 and Gov2 corpora and their associated TREC topics.

Use of gadoxetic acid-enhanced MRI to predict the development of postoperative pancreatic fistulas by estimating the degree of pancreatic fibrosis

Background: Postoperative pancreatic fistula (POPF) can be life-threatening, and gadoxetic acid-enhanced MRI is routinely performed in patients undergoing pancreatic surgery. However, previous reports have not investigated if gadoxetic acid-enhanced MRI can be used to predict POPF risk. Objective: This study aims to explore if gadoxetic acid-enhanced MRI can predict pancreatic fibrosis and the need for POPF treatment before surgery. Method: We retrospectively analyzed gadoxetic acid-enhanced MR images from 142 patients who underwent pancreatic surgery between January 1, 2011, and April 30, 2018. Pre-dynamic signal intensity (SI) and values for portal, transitional, and hepatobiliary phase standardized based on pre-dynamic study values were analyzed. The diameter of the main pancreatic duct (DMPD) was measured, and the degree of pancreatic fibrosis was classified as F0–F3. We defined POPF higher than grade B as significant. Results: Odds ratios for combinations that led to any degree of fibrosis higher than grade B were defined as significant risk factors. The highest odds ratio was obtained for F0 vs. F1–F3 (p = 0.038). DMPD (p<0.001), pre-SI (p=0.008), portal-SI/pre-SI (p<0.001), transitional-SI/pre-SI (p<0.001), and hepatobiliary-SI/pre-SI (p = 0.012) were significantly correlated with the presence of fibrosis. The presence of fibrosis was best detected by DMPD (AUC=0.777). Individual specificity values of transitional-SI/pre-SI and DMPD were 95.5% and 86.6%, respectively, and their combined specificity was 97.7%. Conclusion: The absence of pancreatic fibrosis is a risk factor for developing POPF higher than grade B. DMPD was the most useful diagnostic indicator of the presence of fibrosis among our analysis, and its specificity increased when combined with transitional-SI/pre-SI.

Self-knowledge, Perception, and Margaret Cavendish’s Metaphysics of the Individual

For Margaret Cavendish, every single part of matter has self-knowledge, and almost every part has perceptive knowledge. This paper asks what is at stake for Cavendish in ascribing self-knowing and perceptive properties to matter. Whereas many commentators take perception and self-knowledge to be guides to Cavendish’s epistemology, this paper takes them to be guides to her metaphysics, in that it shows that these categories account for individual specificity and for relationality. A part of matter is a unique individual insofar as it is self-knowing – and it is a part in relation to other parts, and to the whole of matter, insofar as it is a perceptive part. This is so because self-knowledge is purely self-referential and complete, while perceptive knowledge is purely relational.

Relieff Matching Feature Selection for Emotion Recognition Based on EEG Signal

ReliefF Matching Feature Selection (RMFS) is proposed in the paper, which can solve the problem of individual specificity and global threshold mismatch of emotion recognition. Firstly, EEG was decomposed into six emotion-related bands by wavelet packet, then EMD was employed for extracting the 10 categories of features of wavelet coefficient and IMF component of the reconstructed signal; Secondly, the optimization formula of the feature group weight was proposed based on feature sets selected by ReliefF, and it can get the weights of different test features, which were the global optimal matching feature group and the corresponding matching channel, so it can eliminate the redundant information and solve the problem of individual specificity. Finally, SVM was employed to identify the test feature group data to obtain emotional recognition results. The experimental results show that the average correct rates of RMFS for two-category of the valence and the arousal are 93.28% and 93.32%, and the four-categories are higher than 83%. The efficiency of the single subject using RMFS is improved by 42.65%, which is better than the traditional ReliefF algorithm.