The Experts below are selected from a list of 127329 Experts worldwide ranked by ideXlab platform
Gustavo Ramirez-gonzalez - One of the best experts on this subject based on the ideXlab platform.
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Exploring DNS, HTTP, and ICMP Response Time Computations on Brain Signal/Image Databases using a Packet Sniffer Tool
IEEE Access, 2018Co-Authors: V. Elamaran, G. Venkat Babu, V. S. Balaji, Cristhian Figueroa, Nivethitha Arunkumar, Jorge Gomez, Gustavo Ramirez-gonzalezAbstract:Neurological Signal processing is of significance not only the physiologist doing research and the clinician investigating patients but also to the biomedical engineer who is needed to collect, process, and interpret the physiological Signals by prototyping systems and algorithms for their manipulations. While it is a fact that there does hold immense stuff (material) on the subject of digital neurological Signal processing, however, it is dispersed in various scientific, technological, and physiological journals, databases also in various international conference proceedings. Consequently, it is a quite hard, more time-consuming, and often tiresome job, especially to the stranger to the domain. Hence, this study concentrates on how much time would require to access the databases belong to the Brain Signal/image collections, neurological Signals, etc. The sixteen US-based Servers, ten UK-based Servers, and the five Servers from other countries are included in this study. Mainly, the domain name system, hyper text transfer protocol, and the Internet control message protocol query/response times are analyzed using a popular packet sniffer called Wireshark.
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Network Vulnerability Analysis on Brain Signal/Image Databases Using Nmap and Wireshark Tools
IEEE Access, 2018Co-Authors: G. Bagyalakshmi, V. Elamaran, N. Arunkumar, G. Rajkumar, M. Easwaran, K. Narasimhan, Mario Solarte, Ivan Hernandez, Gustavo Ramirez-gonzalezAbstract:Brain Signal processing is important for not only the physiologist doing analysis investigation, but also for the clinician inspecting patients, biomedical engineer who is responsible for collecting, processing, and interpreting the electroencephalogram Signals by modeling systems and algorithms for their manipulations. The abundant materials on the subject of Brain Signal/image processing are scattered in different scientific, technological and physiological journals, international conference proceedings, and also in various databases. Therefore, it is altogether a difficult, too time-consuming, and much tiresome work, exclusively to the newcomers in this field. Therefore, this paper focuses on providing the list of popular databases available belonging to the neurological Signals, Brain Signal/image collections, and so on. The count and the kinds of attacks across the networked computer systems have hiked the significance of computer network security. At present, network administrators use to inspect, examine, scrutinize, review, and analyze the network traffic to figure out what is going on and to set up a prompt response in the event of an identified attack. This paper analyzes the different sweep techniques such as Ping sweep, TCP sweep, and Null sweep on the popular databases about the Brain Signal/image collections. The results of the Ping sweep support status, TCP sweep times, and Null scan times on different servers are discussed finally.
V. Elamaran - One of the best experts on this subject based on the ideXlab platform.
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Revisiting computer networking protocols by wireless sniffing on Brain Signal/image portals
Neural Computing and Applications, 2020Co-Authors: B. R. Sathishkumar, V. Elamaran, V. S. Balaji, B. Sundaravadivazhagan, Betty Martin, G. Sasi, M. Chandrasekar, S. Rakesh Kumar, N. ArunkumarAbstract:Brain Signal/image processing is of significant attention not only the physiologist carrying out analysis and probe and the clinician investigating patients but further to the biomedical engineer who is vital for acquisition, processing, and interpreting the electroencephalogram Signals by designing systems and algorithms for their control. The precious, abundant materials or information in the field of Brain Signal/image processing is distributed in the diverse scientific, technological and physiological periodicals, magazines, journals, international conference proceedings, and also in various portals/databases. Security threats or attacks may happen for a portal using data interruption, information interception, content modification and fabrication with new data. This study interprets the protocol layering information for the captured packets, image reconstruction after sniffing the packets, and the DNS/rDNS response times for a given portal/IP address using a Wireshark open source tool. Also, the security assessment results such as OS fingerprinting and port sweeping on the remote machines are performed using Nmap open source tool. Results are analyzed on specific Brain Signal/image processing portals around the globe located in USA, UK, and other countries.
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Exploring DNS, HTTP, and ICMP Response Time Computations on Brain Signal/Image Databases using a Packet Sniffer Tool
IEEE Access, 2018Co-Authors: V. Elamaran, G. Venkat Babu, V. S. Balaji, Cristhian Figueroa, Nivethitha Arunkumar, Jorge Gomez, Gustavo Ramirez-gonzalezAbstract:Neurological Signal processing is of significance not only the physiologist doing research and the clinician investigating patients but also to the biomedical engineer who is needed to collect, process, and interpret the physiological Signals by prototyping systems and algorithms for their manipulations. While it is a fact that there does hold immense stuff (material) on the subject of digital neurological Signal processing, however, it is dispersed in various scientific, technological, and physiological journals, databases also in various international conference proceedings. Consequently, it is a quite hard, more time-consuming, and often tiresome job, especially to the stranger to the domain. Hence, this study concentrates on how much time would require to access the databases belong to the Brain Signal/image collections, neurological Signals, etc. The sixteen US-based Servers, ten UK-based Servers, and the five Servers from other countries are included in this study. Mainly, the domain name system, hyper text transfer protocol, and the Internet control message protocol query/response times are analyzed using a popular packet sniffer called Wireshark.
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exploring dns http and icmp response time computations on Brain Signal image databases using a packet sniffer tool
IEEE Access, 2018Co-Authors: V. Elamaran, V. S. Balaji, Cristhian Figueroa, Nivethitha Arunkumar, Jorge Gomez, Venkat G Babu, Gustavo RamirezgonzalezAbstract:Neurological Signal processing is of significance not only the physiologist doing research and the clinician investigating patients but also to the biomedical engineer who is needed to collect, process, and interpret the physiological Signals by prototyping systems and algorithms for their manipulations. While it is a fact that there does hold immense stuff (material) on the subject of digital neurological Signal processing, however, it is dispersed in various scientific, technological, and physiological journals, databases also in various international conference proceedings. Consequently, it is a quite hard, more time-consuming, and often tiresome job, especially to the stranger to the domain. Hence, this study concentrates on how much time would require to access the databases belong to the Brain Signal/image collections, neurological Signals, etc. The sixteen US-based Servers, ten UK-based Servers, and the five Servers from other countries are included in this study. Mainly, the domain name system, hyper text transfer protocol, and the Internet control message protocol query/response times are analyzed using a popular packet sniffer called Wireshark.
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Network Vulnerability Analysis on Brain Signal/Image Databases Using Nmap and Wireshark Tools
IEEE Access, 2018Co-Authors: G. Bagyalakshmi, V. Elamaran, N. Arunkumar, G. Rajkumar, M. Easwaran, K. Narasimhan, Mario Solarte, Ivan Hernandez, Gustavo Ramirez-gonzalezAbstract:Brain Signal processing is important for not only the physiologist doing analysis investigation, but also for the clinician inspecting patients, biomedical engineer who is responsible for collecting, processing, and interpreting the electroencephalogram Signals by modeling systems and algorithms for their manipulations. The abundant materials on the subject of Brain Signal/image processing are scattered in different scientific, technological and physiological journals, international conference proceedings, and also in various databases. Therefore, it is altogether a difficult, too time-consuming, and much tiresome work, exclusively to the newcomers in this field. Therefore, this paper focuses on providing the list of popular databases available belonging to the neurological Signals, Brain Signal/image collections, and so on. The count and the kinds of attacks across the networked computer systems have hiked the significance of computer network security. At present, network administrators use to inspect, examine, scrutinize, review, and analyze the network traffic to figure out what is going on and to set up a prompt response in the event of an identified attack. This paper analyzes the different sweep techniques such as Ping sweep, TCP sweep, and Null sweep on the popular databases about the Brain Signal/image collections. The results of the Ping sweep support status, TCP sweep times, and Null scan times on different servers are discussed finally.
Tobias Grossmann - One of the best experts on this subject based on the ideXlab platform.
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epigenetic tuning of Brain Signal entropy in emergent human social behavior
BMC Medicine, 2020Co-Authors: Meghan H Puglia, Kathleen M Krol, Manuela Missana, Cabell L Williams, Travis S Lillard, James P Morris, Jessica J Connelly, Tobias GrossmannAbstract:How the Brain develops accurate models of the external world and generates appropriate behavioral responses is a vital question of widespread multidisciplinary interest. It is increasingly understood that Brain Signal variability—posited to enhance perception, facilitate flexible cognitive representations, and improve behavioral outcomes—plays an important role in neural and cognitive development. The ability to perceive, interpret, and respond to complex and dynamic social information is particularly critical for the development of adaptive learning and behavior. Social perception relies on oxytocin-regulated neural networks that emerge early in development. We tested the hypothesis that individual differences in the endogenous oxytocinergic system early in life may influence social behavioral outcomes by regulating variability in Brain Signaling during social perception. In study 1, 55 infants provided a saliva sample at 5 months of age for analysis of individual differences in the oxytocinergic system and underwent electroencephalography (EEG) while listening to human vocalizations at 8 months of age for the assessment of Brain Signal variability. Infant behavior was assessed via parental report. In study 2, 60 infants provided a saliva sample and underwent EEG while viewing faces and objects and listening to human speech and water sounds at 4 months of age. Infant behavior was assessed via parental report and eye tracking. We show in two independent infant samples that increased Brain Signal entropy during social perception is in part explained by an epigenetic modification to the oxytocin receptor gene (OXTR) and accounts for significant individual differences in social behavior in the first year of life. These results are measure-, context-, and modality-specific: entropy, not standard deviation, links OXTR methylation and infant behavior; entropy evoked during social perception specifically explains social behavior only; and only entropy evoked during social auditory perception predicts infant vocalization behavior. Demonstrating these associations in infancy is critical for elucidating the neurobiological mechanisms accounting for individual differences in cognition and behavior relevant to neurodevelopmental disorders. Our results suggest that an epigenetic modification to the oxytocin receptor gene and Brain Signal entropy are useful indicators of social development and may hold potential diagnostic, therapeutic, and prognostic value.
Douglas D. Garrett - One of the best experts on this subject based on the ideXlab platform.
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Moment-to-moment Brain Signal variability reliably predicts psychiatric treatment outcome
2021Co-Authors: Kristoffer N.t. Månsson, L. Waschke, A. Manzouri, T. Furmark, H. Fischer, Douglas D. GarrettAbstract:Biomarkers of psychiatric treatment response remain elusive. Functional magnetic resonance imaging (fMRI) has shown promise, but low reliability has limited the utility of typical fMRI measures as harbingers of treatment success. Strikingly, temporal variability in Brain Signals has already proven a sensitive and reliable indicator of individual differences, but has not yet been examined in relation to psychiatric treatment outcomes. Here, 45 patients with social anxiety disorder were scanned twice (11 weeks apart) using simple task-based and resting-state fMRI to capture moment-to-moment neural variability. After fMRI test-retest, patients underwent a 9-week cognitive-behavioral therapy. Reliability-based 5-fold cross-validation showed that task-based Brain Signal variability was the strongest contributor in a treatment outcome prediction model (total rACTUAL,PREDICTED = .77) - outperforming self-reports, resting-state neural variability, and standard mean-based measures of neural activity. Notably, task-based Brain Signal variability showed excellent test-retest reliability (intraclass correlation coefficient = .80), even with a task length less than 3 minutes long. Rather than a source of undesirable "noise", moment-to-moment fMRI variability may instead serve as a highly reliable and efficient prognostic indicator of clinical outcome.
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higher performers upregulate Brain Signal variability in response to more feature rich visual input
NeuroImage, 2020Co-Authors: Douglas D. Garrett, Samira M Epp, Maike Kleemeyer, Ulman Lindenberger, Thad A PolkAbstract:Abstract The extent to which Brain responses differ across varying cognitive demands is referred to as “neural differentiation,” and greater neural differentiation has been associated with better cognitive performance in older adults. An emerging approach has examined within-person neural differentiation using moment-to-moment Brain Signal variability. A number of studies have found that Brain Signal variability differs by cognitive state; however, the factors that cause Signal variability to rise or fall on a given task remain understudied. We hypothesized that top performers would modulate Signal variability according to the complexity of sensory input, upregulating variability when processing more feature-rich stimuli. In the current study, 46 older adults passively viewed face and house stimuli during fMRI. Low-level analyses showed that house images were more feature-rich than faces, and subsequent computational modelling of ventral visual stream responses (HMAX) revealed that houses were more feature-rich especially in V1/V2-like model layers. Notably, we then found that participants exhibiting greater face-to-house upregulation of Brain Signal variability in V1/V2 (higher for house relative to face stimuli) also exhibited more accurate, faster, and more consistent behavioral performance on a battery of offline visuo-cognitive tasks. Further, control models revealed that face-house modulation of mean Brain Signal was relatively insensitive to offline cognition, providing further evidence for the importance of Brain Signal variability for understanding human behavior. We conclude that the ability to align Brain Signal variability to the richness of perceptual input may mark heightened trait-level behavioral performance in older adults.
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higher performing older adults upregulate Brain Signal variability in response to feature rich sensory input
bioRxiv, 2018Co-Authors: Douglas D. Garrett, Samira M Epp, Maike Kleemeyer, Ulman Lindenberger, Thad A PolkAbstract:Differentiation of Brain Signal variability across different cognitive states has been hypothesized to facilitate adaptation to changing task demands, but why Signal variability should be higher or lower on a given task remains unknown. We hypothesized that the level of Brain Signal variability should mirror the feature density of sensory input, especially in high performers. To test these hypotheses, we had 46 healthy older adults passively view face and house stimuli during fMRI. We first used a computational model of the ventral visual stream (HMAX) to decode the feature content of all face and house images seen by participants; model results revealed that house images were much more feature-rich than faces, particularly for V1- and V2-like model layers. Using fMRI, we then found that participants whose V1/V2 Brain Signal variability increased the most in response to more feature-rich visual input (houses vs. faces) also exhibited faster and more stable behavioral performance on a comprehensive battery of offline visual tasks. We conclude that the ability to align visuocortical Signal variability to the density of visual input may mark heightened trait-level behavioral performance in older adults.
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age differences in Brain Signal variability are robust to multiple vascular controls
Scientific Reports, 2017Co-Authors: Douglas D. Garrett, Ulman Lindenberger, Richard D Hoge, Claudine J GauthierAbstract:A host of studies support that younger, better performing adults express greater moment-to-moment blood oxygen level-dependent (BOLD) Signal variability (SDBOLD) in various cortical regions, supporting an emerging view that the aging Brain may undergo a generalized reduction in dynamic range. However, the exact physiological nature of age differences in SDBOLD remains understudied. In a sample of 29 younger and 45 older adults, we examined the contribution of vascular factors to age group differences in fixation-based SDBOLD using (1) a dual-echo BOLD/pseudo-continuous arterial spin labeling (pCASL) sequence, and (2) hypercapnia via a computer-controlled gas delivery system. We tested the hypothesis that, although SDBOLD may relate to individual differences in absolute cerebral blood flow (CBF), BOLD cerebrovascular reactivity (CVR), or maximum BOLD Signal change (M), robust age differences in SDBOLD would remain after multiple statistical controls for these vascular factors. As expected, our results demonstrated that Brain regions in which younger adults expressed higher SDBOLD persisted after comprehensive control of vascular effects. Our findings thus further establish BOLD Signal variability as an important marker of the aging Brain.
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bold variability is related to dopaminergic neurotransmission and cognitive aging
Cerebral Cortex, 2016Co-Authors: Douglas D. Garrett, Ulman Lindenberger, Marc Guitartmasip, Alireza Salami, Anna Rieckmann, Lars BackmanAbstract:Dopamine (DA) losses are associated with various aging-related cognitive deficits. Typically, higher moment-to-moment Brain Signal variability in large-scale patterns of voxels in neocortical regions is linked to better cognitive performance and younger adult age, yet the physiological mechanisms regulating Brain Signal variability are unknown. We explored the relationship among adult age, DA availability, and blood oxygen level-dependent (BOLD) Signal variability, while younger and older participants performed a spatial working memory (SWM) task. We quantified striatal and extrastriatal DA D1 receptor density with [(11)C]SCH23390 and positron emission tomography in all participants. We found that BOLD variability in a neocortical region was negatively related to age and positively related to SWM performance. In contrast, BOLD variability in subcortical regions and bilateral hippocampus was positively related to age and slower responses, and negatively related to D1 density in caudate and dorsolateral prefrontal cortex. Furthermore, BOLD variability in neocortical regions was positively associated with task-related disengagement of the default-mode network, a network whose activation needs to be suppressed for efficient SWM processing. Our results show that age-related DA losses contribute to changes in Brain Signal variability in subcortical regions and suggest a potential mechanism, by which neocortical BOLD variability supports cognitive performance.
Anthony R Mcintosh - One of the best experts on this subject based on the ideXlab platform.
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Brain Signal complexity rises with repetition suppression in visual learning
Neuroscience, 2016Co-Authors: Marc Philippe Lafontaine, Anthony R Mcintosh, Karine Lacourse, Jeanmarc Lina, Frederic Gosselin, Hugo Theoret, Sarah LippeAbstract:Neuronal activity associated with visual processing of an unfamiliar face gradually diminishes when it is viewed repeatedly. This process, known as repetition suppression (RS), is involved in the acquisition of familiarity. Current models suggest that RS results from interactions between visual information processing areas located in the occipito-temporal cortex and higher order areas, such as the dorsolateral prefrontal cortex (DLPFC). Brain Signal complexity, which reflects information dynamics of cortical networks, has been shown to increase as unfamiliar faces become familiar. However, the complementarity of RS and increases in Brain Signal complexity have yet to be demonstrated within the same measurements. We hypothesized that RS and Brain Signal complexity increase occur simultaneously during learning of unfamiliar faces. Further, we expected alteration of DLPFC function by transcranial direct current stimulation (tDCS) to modulate RS and Brain Signal complexity over the occipito-temporal cortex. Participants underwent three tDCS conditions in random order: right anodal/left cathodal, right cathodal/left anodal and sham. Following tDCS, participants learned unfamiliar faces, while an electroencephalogram (EEG) was recorded. Results revealed RS over occipito-temporal electrode sites during learning, reflected by a decrease in Signal energy, a measure of amplitude. Simultaneously, as Signal energy decreased, Brain Signal complexity, as estimated with multiscale entropy (MSE), increased. In addition, prefrontal tDCS modulated Brain Signal complexity over the right occipito-temporal cortex during the first presentation of faces. These results suggest that although RS may reflect a Brain mechanism essential to learning, complementary processes reflected by increases in Brain Signal complexity, may be instrumental in the acquisition of novel visual information. Such processes likely involve long-range coordinated activity between prefrontal and lower order visual areas.
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spatiotemporal dependency of age related changes in Brain Signal variability
Cerebral Cortex, 2014Co-Authors: Anthony R Mcintosh, Natasa Kovacevic, Vasily A Vakorin, H Wang, Andreea O Diaconescu, Andrea B. ProtznerAbstract:Recent theoretical and empirical work has focused on the variability of network dynamics in maturation. Such variability seems to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into healthy aging. Two different data sets, one EEG (total n = 48, ages 18-72) and one magnetoencephalography (n = 31, ages 20-75) were analyzed for such spatiotemporal dependency using multiscale entropy (MSE) from regional Brain sources. In both data sets, the changes in MSE were timescale dependent, with higher entropy at fine scales and lower at more coarse scales with greater age. The Signals were parsed further into local entropy, related to information processed within a regional source, and distributed entropy (information shared between two sources, i.e., functional connectivity). Local entropy increased for most regions, whereas the dominant change in distributed entropy was age-related reductions across hemispheres. These data further the understanding of changes in Brain Signal variability across the lifespan, suggesting an inverted U-shaped curve, but with an important qualifier. Unlike earlier in maturation, where the changes are more widespread, changes in adulthood show strong spatiotemporal dependence.
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Brain Signal Variability is Parametrically Modifiable
Cerebral cortex (New York N.Y. : 1991), 2013Co-Authors: Douglas D. Garrett, Anthony R Mcintosh, Cheryl L. GradyAbstract:Moment-to-moment Brain Signal variability is a ubiquitous neural characteristic, yet remains poorly understood. Evidence indicates that heightened Signal variability can index and aid efficient neural function, but it is not known whether Signal variability responds to precise levels of environmental demand, or instead whether variability is relatively static. Using multivariate modeling of functional magnetic resonance imaging-based parametric face processing data, we show here that within-person Signal variability level responds to incremental adjustments in task difficulty, in a manner entirely distinct from results produced by examining mean Brain Signals. Using mixed modeling, we also linked parametric modulations in Signal variability with modulations in task performance. We found that difficulty-related reductions in Signal variability predicted reduced accuracy and longer reaction times within-person; mean Signal changes were not predictive. We further probed the various differences between Signal variance and Signal means by examining all voxels, subjects, and conditions; this analysis of over 2 million data points failed to reveal any notable relations between voxel variances and means. Our results suggest that Brain Signal variability provides a systematic task-driven Signal of interest from which we can understand the dynamic function of the human Brain, and in a way that mean Signals cannot capture.
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moment to moment Brain Signal variability a next frontier in human Brain mapping
Neuroscience & Biobehavioral Reviews, 2013Co-Authors: Douglas D. Garrett, Anthony R Mcintosh, Ulman Lindenberger, Gregory R Samanezlarkin, Stuart W S Macdonald, Cheryl L. GradyAbstract:Neuroscientists have long observed that Brain activity is naturally variable from moment-to-moment, but neuroimaging research has largely ignored the potential importance of this phenomenon. An emerging research focus on within-person Brain Signal variability is providing novel insights, and offering highly predictive, complementary, and even orthogonal views of Brain function in relation to human lifespan development, cognitive performance, and various clinical conditions. As a result, Brain Signal variability is evolving as a bona fide Signal of interest, and should no longer be dismissed as meaningless noise when mapping the human Brain.
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relating Brain Signal variability to knowledge representation
NeuroImage, 2012Co-Authors: Jennifer J Heisz, Judith M Shedden, Anthony R McintoshAbstract:Abstract We assessed the hypothesis that Brain Signal variability is a reflection of functional network reconfiguration during memory processing. In the present experiments, we use multiscale entropy to capture the variability of human electroencephalogram (EEG) while manipulating the knowledge representation associated with faces stored in memory. Across two experiments, we observed increased variability as a function of greater knowledge representation. In Experiment 1, individuals with greater familiarity for a group of famous faces displayed more Brain Signal variability. In Experiment 2, Brain Signal variability increased with learning after multiple experimental exposures to previously unfamiliar faces. The results demonstrate that variability increases with face familiarity; cognitive processes during the perception of familiar stimuli may engage a broader network of regions, which manifests as higher complexity/variability in spatial and temporal domains. In addition, effects of repetition suppression on Brain Signal variability were observed, and the pattern of results is consistent with a selectivity model of neural adaptation.