scholarly journals Hyperedge bundling: A practical solution to spurious interactions in MEG/EEG source connectivity analyses

2017 ◽  
Author(s):  
Sheng H. Wang ◽  
Muriel Lobier ◽  
Felix Siebenhühner ◽  
Tuomas Puoliväli ◽  
Satu Palva ◽  
...  
Keyword(s):  
False Positive ◽  
Large Scale ◽  
True Positive Rate ◽  
Brain Regions ◽  
High Temporal Resolution ◽  
List Type ◽  
True Positive ◽  
Positive Ratio ◽  
Novel Approach ◽  
Eeg Data

AbstractInter-areal functional connectivity (FC), neuronal synchronization in particular, is thought to constitute a key systems-level mechanism for coordination of neuronal processing and communication between brain regions. Evidence to support this hypothesis has been gained largely using invasive electrophysiological approaches. In humans, neuronal activity can be non-invasively recorded only with magneto- and electroencephalography (MEG/EEG), which have been used to assess FC networks with high temporal resolution and whole-scalp coverage. However, even in source-reconstructed MEG/EEG data, signal mixing, or “source leakage”, is a significant confounder for FC analyses and network localization.Signal mixing leads to two distinct kinds of false-positive observations: artificial interactions (AI) caused directly by mixing and spurious interactions (SI) arising indirectly from the spread of signals from true interacting sources to nearby false loci. To date, several interaction metrics have been developed to solve the AI problem, but the SI problem has remained largely intractable in MEG/EEG all-to-all source connectivity studies. Here, we advance a novel approach for correcting SIs in FC analyses using source-reconstructed MEG/EEG data.Our approach is to bundle observed FC connections into hyperedges by their adjacency in signal mixing. Using realistic simulations, we show here that bundling yields hyperedges with good separability of true positives and little loss in the true positive rate. Hyperedge bundling thus significantly decreases graph noise by minimizing the false-positive to true-positive ratio. Finally, we demonstrate the advantage of edge bundling in the visualization of large-scale cortical networks with real MEG data. We propose that hypergraphs yielded by bundling represent well the set of true cortical interactions that are detectable and dissociable in MEG/EEG connectivity analysis.HighlightsA true interaction often is “ghosted” into a multitude of spurious edges (SI)Effective in controlling and illustrating SIHyperedges have much improved TPR and graph qualityAdvantages in visualizing connectivity

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals The Yale Department of Medicine COVID-19 Data Explorer and Repository (DOM-CovX): An Innovative Approach to Promoting Collaborative Scholarship During a Pandemic

2021 ◽  
Author(s):  
Tanima Arora ◽  
Michael Simonov ◽  
Jameel Alausa ◽  
Labeebah Subair ◽  
Brett Gerber ◽  
...  
Keyword(s):  
Clinical Data ◽  
Large Scale ◽  
Innovative Approach ◽  
Academic Productivity ◽  
List Type ◽  
Digital Revolution ◽  
Institutional Climate ◽  
Novel Approach ◽  
Dissemination Of Research ◽  
Academic Faculty

ABSTRACTBackgroundThe COVID-19 pandemic has led to an explosion of research publications spanning epidemiology, basic and clinical science. While a digital revolution has allowed for open access to large datasets enabling real-time tracking of the epidemic, detailed, locally-specific clinical data has been less readily accessible to a broad range of academic faculty and their trainees. This perpetuates the separation of the primary missions of clinically-focused and primary research faculty resulting in lost opportunities for improved understanding of the local epidemic; expansion of the scope of scholarship; limitation of the diversity of the research pool; lack of creation of initiatives for growth and dissemination of research skills needed for the training of the next generation of clinicians and faculty.ObjectivesCreate a common, easily accessible and up-to-date database that would promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise. By providing a robust dataset, a broad range of researchers (faculty, trainees) and clinicians are encouraged to explore and collaborate on novel clinically relevant research questions.MethodsWe constructed a research platform called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), to house cleaned, highly granular, de-identified, continually-updated data from over 7,000 patients hospitalized with COVID-19 (1/2020-present) across the Yale New Haven Health System. This included a front-end user interface for simple data visualization of aggregate data and more detailed clinical datasets for researchers after a review board process. The goal is to promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise.Expected OutcomesAccelerate generation of new knowledge and increase scholarly productivity with particular local relevanceImprove the institutional academic climate by:Broadening research scopeExpanding research capability to more diverse group of stakeholders including clinical and research-based faculty and traineesEnhancing interdepartmental collaborationsConclusionsThe DOM-CovX Data Explorer and Repository have great potential to increase academic productivity. By providing an accessible tool for simple data analysis and access to a consistently updated, standardized and large-scale dataset, it overcomes barriers for a wide variety of researchers. Beyond academic productivity, this innovative approach represents an opportunity to improve the institutional climate by fostering collaboration, diversity of scholarly pursuits and expanding medical education. It provides a novel approach that can be expanded to other diseases beyond COVID 19.

The performance of delta check methods.

1979 ◽  
Vol 25 (12) ◽  
pp. 2034-2037 ◽  
Author(s):  
L B Sheiner ◽  
L A Wheeler ◽  
J K Moore
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
True Positive Rate ◽  
True Positive ◽  
Discriminant Functions ◽  
Linear Discriminant ◽  
Operating Rate ◽  
Positive Rate ◽  
The Relationship

Abstract The percentage of mislabeled specimens detected (true-positive rate) and the percentage of correctly labeled specimens misidentified (false-positive rate) were computed for three previously proposed delta check methods and two linear discriminant functions. The true-positive rate was computed from a set of pairs of specimens, each having one member replaced by a member from another pair chosen at random. The relationship between true-positive and false-positive rates was similar among the delta check methods tested, indicating equal performance for all of them over the range of false-positive rate of interest. At a practical false-positive operating rate of about 5%, delta check methods detect only about 50% of mislabeled specimens; even if the actual mislabeling rate is moderate (e.g., 1%), only abot 10% of specimens flagged a by a delta check will actually have been mislabeled.

scholarly journals Convergent neural representations of acute nociceptive pain in healthy volunteers: A large-scale fMRI meta-analysis

2019 ◽  
Author(s):  
Anna Xu ◽  
Bart Larsen ◽  
Erica B. Baller ◽  
J. Cobb Scott ◽  
Vaishnavi Sharma ◽  
...  
Keyword(s):  
Acute Pain ◽  
Error Control ◽  
Large Scale ◽  
Meta Analysis ◽  
Brain Regions ◽  
Type I ◽  
List Type ◽  
Midcingulate Cortex ◽  
Core Set ◽  
Healthy Participants

ABSTRACTCharacterizing a reliable, pain-related neural signature is critical for translational applications. Many prior fMRI studies have examined acute pain-related brain activation in healthy participants. However, synthesizing these data to identify convergent patterns of activation can be challenging due to the heterogeneity of experimental designs and samples. To address this challenge, we conducted a comprehensive meta-analysis of fMRI studies of stimulus-induced pain in healthy participants. Following pre-registration, two independent reviewers evaluated 4,927 abstracts returned from a search of 8 databases, with 222 fMRI experiments meeting inclusion criteria. We analyzed these experiments using Activation Likelihood Estimation with rigorous type I error control (voxel height p < 0.001, cluster p < 0.05 FWE-corrected) and found a convergent, largely bilateral pattern of pain-related activation in the secondary somatosensory cortex, insula, midcingulate cortex, and thalamus. Notably, these regions were consistently recruited regardless of stimulation technique, location of induction, and participant sex. These findings suggest a highly-conserved core set of pain-related brain areas, encouraging applications as a biomarker for novel therapeutics targeting acute pain.HIGHLIGHTSPain stimulation recruits a core set of pain-related brain regions.This core set includes thalamus, SII, insula and mid-cingulate cortex.These regions were recruited regardless of stimulus modality and stimulus location.

scholarly journals Actor Critic Deep Reinforcement Learning for Neural Malware Control

2020 ◽  
Vol 34 (01) ◽  
pp. 1005-1012
Author(s):  
Yu Wang ◽  
Jack Stokes ◽  
Mady Marinescu
Keyword(s):  
Operating System ◽  
Reinforcement Learning ◽  
False Positive ◽  
Partial Information ◽  
False Positive Rate ◽  
True Positive Rate ◽  
True Positive ◽  
New Model ◽  
Positive Rate ◽  
Execution Speed

In addition to using signatures, antimalware products also detect malicious attacks by evaluating unknown files in an emulated environment, i.e. sandbox, prior to execution on a computer's native operating system. During emulation, a file cannot be scanned indefinitely, and antimalware engines often set the number of instructions to be executed based on a set of heuristics. These heuristics only make the decision of when to halt emulation using partial information leading to the execution of the file for either too many or too few instructions. Also this method is vulnerable if the attackers learn this set of heuristics. Recent research uses a deep reinforcement learning (DRL) model employing a Deep Q-Network (DQN) to learn when to halt the emulation of a file. In this paper, we propose a new DRL-based system which instead employs a modified actor critic (AC) framework for the emulation halting task. This AC model dynamically predicts the best time to halt the file's execution based on a sequence of system API calls. Compared to the earlier models, the new model is capable of handling adversarial attacks by simulating their behaviors using the critic model. The new AC model demonstrates much better performance than both the DQN model and antimalware engine's heuristics. In terms of execution speed (evaluated by the halting decision), the new model halts the execution of unknown files by up to 2.5% earlier than the DQN model and 93.6% earlier than the heuristics. For the task of detecting malicious files, the proposed AC model increases the true positive rate by 9.9% from 69.5% to 76.4% at a false positive rate of 1% compared to the DQN model, and by 83.4% from 41.2% to 76.4% at a false positive rate of 1% compared to a recently proposed LSTM model.

scholarly journals Evaluation of novel LCI CAD EYE system for real time detection of colon polyps

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255955
Author(s):  
Helmut Neumann ◽  
Andreas Kreft ◽  
Visvakanth Sivanathan ◽  
Fareed Rahman ◽  
Peter R. Galle
Keyword(s):  
Real Time ◽  
False Positive ◽  
True Positive Rate ◽  
Colorectal Polyp ◽  
Frame Rate ◽  
True Positive ◽  
Polyp Detection ◽  
Cad System ◽  
Positive Rate ◽  
Serrated Lesions

Background Linked color imaging (LCI) has been shown to be effective in multiple randomized controlled trials for enhanced colorectal polyp detection. Recently, artificial intelligence (AI) with deep learning through convolutional neural networks has dramatically improved and is increasingly recognized as a promising new technique for enhancing colorectal polyp detection. Aim This study aims to evaluate a newly developed computer-aided detection (CAD) system in combination with LCI for colorectal polyp detection. Methods First, a convolutional neural network was trained for colorectal polyp detection in combination with the LCI technique using a dataset of anonymized endoscopy videos. For validation, 240 polyps within fully recorded endoscopy videos in LCI mode, covering the entire spectrum of adenomatous histology, were used. Sensitivity (true-positive rate per lesion) and false-positive frames in a full procedure were assessed. Results The new CAD system used in LCI mode could process at least 60 frames per second, allowing for real-time video analysis. Sensitivity (true-positive rate per lesion) was 100%, with no lesion being missed. The calculated false-positive frame rate was 0.001%. Among the 240 polyps, 34 were sessile serrated lesions. The detection rate for sessile serrated lesions with the CAD system used in LCI mode was 100%. Conclusions The new CAD system used in LCI mode achieved a 100% sensitivity per lesion and a negligible false-positive frame rate. Note that the new CAD system used in LCI mode also specifically allowed for detection of serrated lesions in all cases. Accordingly, the AI algorithm introduced here for the first time has the potential to dramatically improve the quality of colonoscopy.

scholarly journals Intracranial brain stimulation modulates fMRI-based network switching

2021 ◽  
Author(s):  
Mangor Pedersen ◽  
Andrew Zalesky
Keyword(s):  
Clinical Efficacy ◽  
Brain Stimulation ◽  
Large Scale ◽  
Brain Networks ◽  
Network Models ◽  
Brain Regions ◽  
Brain Network ◽  
Intracranial Stimulation ◽  
List Type ◽  
Network Modelling

SummaryThe extent to which resting-state fMRI (rsfMRI) reflects direct neuronal changes remains unknown. Using 160 simultaneous rsfMRI and intracranial brain stimulation recordings acquired in 26 individuals with epilepsy (with varying electrode locations), we tested whether brain networks dynamically change during intracranial brain stimulation, aiming to establish whether switching between brain networks is reduced during intracranial brain stimulation. As the brain spontaneously switches between a repertoire of intrinsic functional network configurations and the rate of switching is typically increased in brain disorders, we hypothesised that intracranial stimulation would reduce the brain’s switching rate, thus potentially normalising aberrant brain network dynamics. To test this hypothesis, we quantified the rate that brain regions changed networks over time in response to brain stimulation, using network switching applied to multilayer modularity analysis of time-resolved rsfMRI connectivity. Network switching was significantly decreased during epochs with brain stimulation compared to epochs with no brain stimulation. The initial stimulation onset of brain stimulation was associated with the greatest decrease in network switching, followed by a more consistent reduction in network switching throughout the scans. These changes were most commonly observed in cortical networks spatially distant from the stimulation targets. Our results suggest that neuronal perturbation is likely to modulate large-scale brain networks, and multilayer network modelling may be used to inform the clinical efficacy of brain stimulation in neurological disease.HighlightsrsfMRI network switching is attenuated during intracranial brain stimulationStimulation-induced switching is observed distant from electrode targetsOur results are validated across a range of network parametersNetwork models may inform clinical efficacy of brain stimulation

scholarly journals Detecting Malware Based on DNS Graph Mining

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Futai Zou ◽  
Siyu Zhang ◽  
Weixiong Rao ◽  
Ping Yi
Keyword(s):  
Real World ◽  
False Positive ◽  
Graph Mining ◽  
Belief Propagation ◽  
False Positive Rate ◽  
Malware Detection ◽  
True Positive Rate ◽  
True Positive ◽  
Detection Approach ◽  
Positive Rate

Malware remains a major threat to nowadays Internet. In this paper, we propose a DNS graph mining-based malware detection approach. A DNS graph is composed of DNS nodes, which represent server IPs, client IPs, and queried domain names in the process of DNS resolution. After the graph construction, we next transform the problem of malware detection to the graph mining task of inferring graph nodes’ reputation scores using the belief propagation algorithm. The nodes with lower reputation scores are inferred as those infected by malwares with higher probability. For demonstration, we evaluate the proposed malware detection approach with real-world dataset. Our real-world dataset is collected from campus DNS servers for three months and we built a DNS graph consisting of 19,340,820 vertices and 24,277,564 edges. On the graph, we achieve a true positive rate 80.63% with a false positive rate 0.023%. With a false positive of 1.20%, the true positive rate was improved to 95.66%. We detected 88,592 hosts infected by malware or C&C servers, accounting for the percentage of 5.47% among all hosts. Meanwhile, 117,971 domains are considered to be related to malicious activities, accounting for 1.5% among all domains. The results indicate that our method is efficient and effective in detecting malwares.

scholarly journals A novel approach to the functional classification of retinal ganglion cells

2021 ◽  
Author(s):  
Gerrit Hilgen ◽  
Evgenia Kartsaki ◽  
Viktoriia Kartysh ◽  
Bruno Cessac ◽  
Evelyne Sernagor
Keyword(s):  
Large Scale ◽  
Ganglion Cells ◽  
Brain Regions ◽  
Designer Drugs ◽  
Retinal Ganglion ◽  
Novel Approach ◽  
Functional Features ◽  
Novel Strategy ◽  
Post Hoc ◽  
Multiple Clusters

Retinal neurons come in remarkable diversity based on structure, function and genetic identity. Classifying these cells is a challenging task, requiring multimodal methodology. Here, we introduce a novel approach for retinal ganglion cell (RGC) classification, based on pharmacogenetics combined with immunohistochemistry and large-scale retinal electrophysiology. Our novel strategy allows grouping of cells sharing gene expression and understanding how these cell classes respond to basic and complex visual scenes. Our approach consists of increasing the firing level of RGCs co-expressing a certain gene (Scnn1a or Grik4) using excitatory DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) and then correlate the location of these cells with post hoc immunostaining, to unequivocally characterize anatomical and functional features of these two groups. We grouped these isolated RGC responses into multiple clusters based on the similarity of the spike trains. With our approach, and accompanied by immunohistochemistry, we were able to extend the pre-existing list of Grik4 expressing RGC types to a total of 8 RGC types and, for the first time, we provide a phenotypical description of 14 Scnn1a-expressing RGCs. The insights and methods gained here can guide RGC classification but also neuronal classification challenges in other brain regions.

scholarly journals Optimizing Android Malware Detection Via Ensemble Learning

2020 ◽  
Vol 14 (09) ◽  
pp. 61
Author(s):  
Abikoye Oluwakemi Christianah ◽  
Benjamin Aruwa Gyunka ◽  
Akande Noah Oluwatobi
Keyword(s):  
Random Forest ◽  
False Positive ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Ensemble Model ◽  
True Positive ◽  
Android Malware ◽  
Detection Model ◽  
Android Malware Detection ◽  
Positive Rate

<p>Android operating system has become very popular, with the highest market share, amongst all other mobile operating systems due to its open source nature and users friendliness. This has brought about an uncontrolled rise in malicious applications targeting the Android platform. Emerging trends of Android malware are employing highly sophisticated detection and analysis avoidance techniques such that the traditional signature-based detection methods have become less potent in their ability to detect new and unknown malware. Alternative approaches, such as the Machine learning techniques have taken the lead for timely zero-day anomaly detections.  The study aimed at developing an optimized Android malware detection model using ensemble learning technique. Random Forest, Support Vector Machine, and k-Nearest Neighbours were used to develop three distinct base models and their predictive results were further combined using Majority Vote combination function to produce an ensemble model. Reverse engineering procedure was employed to extract static features from large repository of malware samples and benign applications. WEKA 3.8.2 data mining suite was used to perform all the learning experiments. The results showed that Random Forest had a true positive rate of 97.9%, a false positive rate of 1.9% and was able to correctly classify instances with 98%, making it a strong base model. The ensemble model had a true positive rate of 98.1%, false positive rate of 1.8% and was able to correctly classify instances with 98.16%. The finding shows that, although the base learners had good detection results, the ensemble learner produced a better optimized detection model compared with the performances of those of the base learners.</p>

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