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Fast Brain sentence examples within Detect Fast Brain

Detect Fast Brain

Thus, developing approaches able to adequately detect fast brain dynamics is critical.

Detecting modular brain states in rest and task

Detect Fast Brain

Thus, developing approaches able to adequately detect fast brain dynamics is critical.

Detecting M/EEG modular brain states in rest and task

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Fast Brain sentence examples within Characterize Fast Brain

Characterize Fast Brain

We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants.

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

Characterize Fast Brain

We found that: first, the brain activity signal is more compactly represented by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants.

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

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Fast Brain sentence examples within fast brain dynamic

fast brain dynamic

We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants.

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

fast brain dynamic

We found that: first, the brain activity signal is more compactly represented by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants.

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

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Fast Brain sentence examples within fast brain activity

fast brain activity

Collectively, both stimulus/task intensity and image resolution can affect the sensitivity of fMRI to fast brain activity, which may partly explain recent observations of fast fMRI signals.

Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics

fast brain activity

Here, the authors show that fast brain activity at posterior sites is nested into prefrontal slow brain waves, with cognitive demand determining the slow wave phase involved.

Dynamic regulation of interregional cortical communication by slow brain oscillations during working memory

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Fast Brain sentence examples within fast brain storm

fast brain storm

This paper proposes refrigerated showcase fault detection by a correntropy based Artificial Neural Network (ANN) using Fast Brain Storm Optimization (FBSO).

Refrigerated Showcase Fault Detection by a Correntropy Based Artificial Neural Network Using Fast Brain Storm Optimization

fast brain storm

The proposed CANN using MBSO is verified to be more accurate than the conventional least square error (LSE) based ANNs (LANNs) using stochastic gradient descent (SGD) and the conventional CANNs using fast brain storm optimization (FBSO) with actual measurement data of showcase.

Refrigerated Showcase Fault Detection by a Correntropy Based Artificial Neural Network Using Modified Brain Storm Optimization

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Fast Brain sentence examples within fast brain magnetic

fast brain magnetic

Recently, a fast brain magnetic resonance imaging (fbMRI) protocol has been employed for rapid imaging of hydrocephalus in pediatric patients.

Utility of a pediatric fast magnetic resonance imaging protocol as surveillance scanning for traumatic brain injury

fast brain magnetic

OBJECTIVE The objective of this study was to determine the accuracy of fast brain magnetic resonance imaging (MRI) in the detection of intra- and extra-axial intracranial hemorrhage compared with standard-of-care computed tomography (CT) or MRI in pediatric patients.

Fast Brain Magnetic Resonance Imaging With Half-Fourier Acquisition With Single-Shot Turbo Spin Echo Sequence in Detection of Intracranial Hemorrhage and Skull Fracture in General Pediatric Patients: Preliminary Results.

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Fast Brain sentence examples within fast brain tumor

fast brain tumor

Threshold-based techniques are efficient for fast brain tumor extraction.

Brain Tumor Extraction using Adaptive Threshold Selection Network

fast brain tumor

From above problems a proposed method can be made by Adaptive KWFCM method to segment accurate and fast brain tumor.

Brain Tumor Segmentation Using clustering Approach

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Fast Brain sentence examples within fast brain mrus

fast brain mrus

CONCLUSION The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols.

Evaluation of the Aggregated Time Savings in Adopting Fast Brain MRI Techniques for Outpatient Brain MRI.

fast brain mrus

Conversely, fast brain MRI permits image acquisition without the need for sedation, although its short pulse sequences, susceptibility to motion artifact, and contrast resolution have limited its use to assessing ventricular size or major structural variations.

Feasibility of fast brain diffusion MRI to quantify white matter injury in pediatric hydrocephalus.

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10.1128/JVI.01200-21

There is fast brain uptake with 0.

The HIV-1 matrix protein p17 does cross the Blood-Brain Barrier.

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10.1016/j.neuroimage.2021.118658

Collectively, both stimulus/task intensity and image resolution can affect the sensitivity of fMRI to fast brain activity, which may partly explain recent observations of fast fMRI signals.

Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics

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10.1007/s00247-021-05117-5

Ultrafast brain imaging with wave-CAIPI MP-RAGE is feasible in awake pediatric patients, providing a substantial reduction in scan time at a cost of subjectively increased image noise.

Comparison of ultrafast wave-controlled aliasing in parallel imaging (CAIPI) magnetization-prepared rapid acquisition gradient echo (MP-RAGE) and standard MP-RAGE in non-sedated children: initial clinical experience

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10.3174/ajnr.A7065

At the institution described by Koral et al, 86% of the 679 fast brain MRIs ordered for ventricular size evaluation in 2016 were performed on an outpatient basis, while only 3% were ordered by the emergency department, and 22 patients received at least 5 head CTs for shunt evaluation during the year.

Improving Access to Fast MRI for Emergent Shunt Evaluation

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10.1016/j.neuroimage.2021.118611

We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants.

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

More Fast Brain sentence examples
10.3171/2020.8.PEDS20496

Recently, a fast brain magnetic resonance imaging (fbMRI) protocol has been employed for rapid imaging of hydrocephalus in pediatric patients.

Utility of a pediatric fast magnetic resonance imaging protocol as surveillance scanning for traumatic brain injury

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10.1016/j.acra.2021.07.011

CONCLUSION The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols.

Evaluation of the Aggregated Time Savings in Adopting Fast Brain MRI Techniques for Outpatient Brain MRI.

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10.1101/2021.05.10.443362

The use of simple/automatized heuristics may also lead to increased reliance on fast brain processes, whereas fuzzy heuristic may lead to lingering uncertainty.

Neural correlates of individual differences in aversion to risk and choice inconsistencies

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10.1101/2021.03.03.433561

We found that: first, the brain activity signal is more compactly represented by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants.

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

More Fast Brain sentence examples
10.1109/ACCESS.2019.2948621

In this paper, we propose an end-to-end fast brain extraction and visualization framework.

A Fast 3D Brain Extraction and Visualization Framework Using Active Contour and Modern OpenGL Pipelines

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10.1007/978-3-030-17795-9_30

In this paper, we train a state-of-the-art deep neural network segmentation model to do fast brain volumetric segmentation from T1 MRI scans.

Fast Brain Volumetric Segmentation from T1 MRI Scans

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10.1038/s41467-019-12057-0

Here, the authors show that fast brain activity at posterior sites is nested into prefrontal slow brain waves, with cognitive demand determining the slow wave phase involved.

Dynamic regulation of interregional cortical communication by slow brain oscillations during working memory

More Fast Brain sentence examples
10.1162/netn_a_00090

Thus, developing approaches able to adequately detect fast brain dynamics is critical.

Detecting modular brain states in rest and task

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10.1007/978-3-030-26369-0_27

This paper proposes refrigerated showcase fault detection by a correntropy based Artificial Neural Network (ANN) using Fast Brain Storm Optimization (FBSO).

Refrigerated Showcase Fault Detection by a Correntropy Based Artificial Neural Network Using Fast Brain Storm Optimization

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10.1101/856179

Understanding these fast network dynamics is fundamental to understanding how brains work, but up to now it has proven very difficult to model fast brain dynamics for various methodological reasons.

Modeling time-varying brain networks with a self-tuning optimized Kalman filter

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10.1101/510727

Thus, developing approaches able to adequately detect fast brain dynamics is critical.

Detecting M/EEG modular brain states in rest and task

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10.1097/PEC.0000000000001949

OBJECTIVE The objective of this study was to determine the accuracy of fast brain magnetic resonance imaging (MRI) in the detection of intra- and extra-axial intracranial hemorrhage compared with standard-of-care computed tomography (CT) or MRI in pediatric patients.

Fast Brain Magnetic Resonance Imaging With Half-Fourier Acquisition With Single-Shot Turbo Spin Echo Sequence in Detection of Intracranial Hemorrhage and Skull Fracture in General Pediatric Patients: Preliminary Results.

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10.1109/icesip46348.2019.8938318

Threshold-based techniques are efficient for fast brain tumor extraction.

Brain Tumor Extraction using Adaptive Threshold Selection Network

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10.17163/SOPH.N26.2019.03

Epstein for the occurrence of Phrenoblysis (periods of fast brain growth), interspersed with periods of slow growth and that occurring in correlation with the Piagetian stages.

Epistemología genética y la (in)visibilidad de los enfoques constructivistas en neurociencias

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10.1109/i-PACT44901.2019.8959996

From above problems a proposed method can be made by Adaptive KWFCM method to segment accurate and fast brain tumor.

Brain Tumor Segmentation Using clustering Approach

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10.3171/2019.5.PEDS18596

Conversely, fast brain MRI permits image acquisition without the need for sedation, although its short pulse sequences, susceptibility to motion artifact, and contrast resolution have limited its use to assessing ventricular size or major structural variations.

Feasibility of fast brain diffusion MRI to quantify white matter injury in pediatric hydrocephalus.

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10.1109/SSCI44817.2019.9003017

The proposed CANN using MBSO is verified to be more accurate than the conventional least square error (LSE) based ANNs (LANNs) using stochastic gradient descent (SGD) and the conventional CANNs using fast brain storm optimization (FBSO) with actual measurement data of showcase.

Refrigerated Showcase Fault Detection by a Correntropy Based Artificial Neural Network Using Modified Brain Storm Optimization

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10.1109/EMBC.2019.8857617

To realize asynchronous control of a brain-computer interface (BCI) system, a fast brain switch with low false positive rate (FPR) is required.

A Fast Brain Switch Based on Multi-Class Code-Modulated VEPs *

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Fast Brain
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What is/are Fast Brain?

Fast Brain - There is fast brain uptake with 0. [1] Collectively, both stimulus/task intensity and image resolution can affect the sensitivity of fMRI to fast brain activity, which may partly explain recent observations of fast fMRI signals. [2] Ultrafast brain imaging with wave-CAIPI MP-RAGE is feasible in awake pediatric patients, providing a substantial reduction in scan time at a cost of subjectively increased image noise. [3] At the institution described by Koral et al, 86% of the 679 fast brain MRIs ordered for ventricular size evaluation in 2016 were performed on an outpatient basis, while only 3% were ordered by the emergency department, and 22 patients received at least 5 head CTs for shunt evaluation during the year. [4] We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. [5] Recently, a fast brain magnetic resonance imaging (fbMRI) protocol has been employed for rapid imaging of hydrocephalus in pediatric patients. [6] CONCLUSION The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols. [7] The use of simple/automatized heuristics may also lead to increased reliance on fast brain processes, whereas fuzzy heuristic may lead to lingering uncertainty. [8] We found that: first, the brain activity signal is more compactly represented by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. [9] In this paper, we propose an end-to-end fast brain extraction and visualization framework. [10] In this paper, we train a state-of-the-art deep neural network segmentation model to do fast brain volumetric segmentation from T1 MRI scans. [11] Here, the authors show that fast brain activity at posterior sites is nested into prefrontal slow brain waves, with cognitive demand determining the slow wave phase involved. [12] Thus, developing approaches able to adequately detect fast brain dynamics is critical. [13] This paper proposes refrigerated showcase fault detection by a correntropy based Artificial Neural Network (ANN) using Fast Brain Storm Optimization (FBSO). [14] Understanding these fast network dynamics is fundamental to understanding how brains work, but up to now it has proven very difficult to model fast brain dynamics for various methodological reasons. [15] Thus, developing approaches able to adequately detect fast brain dynamics is critical. [16] OBJECTIVE The objective of this study was to determine the accuracy of fast brain magnetic resonance imaging (MRI) in the detection of intra- and extra-axial intracranial hemorrhage compared with standard-of-care computed tomography (CT) or MRI in pediatric patients. [17] Threshold-based techniques are efficient for fast brain tumor extraction. [18] Epstein for the occurrence of Phrenoblysis (periods of fast brain growth), interspersed with periods of slow growth and that occurring in correlation with the Piagetian stages. [19] From above problems a proposed method can be made by Adaptive KWFCM method to segment accurate and fast brain tumor. [20] Conversely, fast brain MRI permits image acquisition without the need for sedation, although its short pulse sequences, susceptibility to motion artifact, and contrast resolution have limited its use to assessing ventricular size or major structural variations. [21] The proposed CANN using MBSO is verified to be more accurate than the conventional least square error (LSE) based ANNs (LANNs) using stochastic gradient descent (SGD) and the conventional CANNs using fast brain storm optimization (FBSO) with actual measurement data of showcase. [22] To realize asynchronous control of a brain-computer interface (BCI) system, a fast brain switch with low false positive rate (FPR) is required. [23]

Detect Fast Brain

Thus, developing approaches able to adequately detect fast brain dynamics is critical. [1] Thus, developing approaches able to adequately detect fast brain dynamics is critical. [2]

Characterize Fast Brain

We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. [1] We found that: first, the brain activity signal is more compactly represented by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. [2]

fast brain dynamic

We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. [1] We found that: first, the brain activity signal is more compactly represented by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. [2] Thus, developing approaches able to adequately detect fast brain dynamics is critical. [3] Understanding these fast network dynamics is fundamental to understanding how brains work, but up to now it has proven very difficult to model fast brain dynamics for various methodological reasons. [4] Thus, developing approaches able to adequately detect fast brain dynamics is critical. [5]

fast brain activity

Collectively, both stimulus/task intensity and image resolution can affect the sensitivity of fMRI to fast brain activity, which may partly explain recent observations of fast fMRI signals. [1] Here, the authors show that fast brain activity at posterior sites is nested into prefrontal slow brain waves, with cognitive demand determining the slow wave phase involved. [2]

fast brain storm

This paper proposes refrigerated showcase fault detection by a correntropy based Artificial Neural Network (ANN) using Fast Brain Storm Optimization (FBSO). [1] The proposed CANN using MBSO is verified to be more accurate than the conventional least square error (LSE) based ANNs (LANNs) using stochastic gradient descent (SGD) and the conventional CANNs using fast brain storm optimization (FBSO) with actual measurement data of showcase. [2]

fast brain magnetic

Recently, a fast brain magnetic resonance imaging (fbMRI) protocol has been employed for rapid imaging of hydrocephalus in pediatric patients. [1] OBJECTIVE The objective of this study was to determine the accuracy of fast brain magnetic resonance imaging (MRI) in the detection of intra- and extra-axial intracranial hemorrhage compared with standard-of-care computed tomography (CT) or MRI in pediatric patients. [2]

fast brain tumor

Threshold-based techniques are efficient for fast brain tumor extraction. [1] From above problems a proposed method can be made by Adaptive KWFCM method to segment accurate and fast brain tumor. [2]

fast brain mrus

CONCLUSION The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols. [1] Conversely, fast brain MRI permits image acquisition without the need for sedation, although its short pulse sequences, susceptibility to motion artifact, and contrast resolution have limited its use to assessing ventricular size or major structural variations. [2]