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Machine Learning and Interpretation in Neuroimaging

Overview of attention for book
Cover of 'Machine Learning and Interpretation in Neuroimaging'

Table of Contents

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    Book Overview
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    Chapter 1 A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding
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    Chapter 2 Beyond Brain Reading: Randomized Sparsity and Clustering to Simultaneously Predict and Identify
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    Chapter 3 Searchlight Based Feature Extraction
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    Chapter 4 Looking Outside the Searchlight
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    Chapter 5 Population Codes Representing Musical Timbre for High-Level fMRI Categorization of Music Genres
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    Chapter 6 Induction in Neuroscience with Classification: Issues and Solutions
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    Chapter 7 A New Feature Selection Method Based on Stability Theory – Exploring Parameters Space to Evaluate Classification Accuracy in Neuroimaging Data
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    Chapter 8 Identification of OCD-Relevant Brain Areas through Multivariate Feature Selection
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    Chapter 9 Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain
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    Chapter 10 Decoding Complex Cognitive States Online by Manifold Regularization in Real-Time fMRI
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    Chapter 11 Modality Neutral Techniques for Brain Image Understanding
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    Chapter 12 How Does the Brain Represent Visual Scenes? A Neuromagnetic Scene Categorization Study
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    Chapter 13 Finding Consistencies in MEG Responses to Repeated Natural Speech
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    Chapter 14 Categorized EEG Neurofeedback Performance Unveils Simultaneous fMRI Deep Brain Activation
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    Chapter 15 Predicting Clinically Definite Multiple Sclerosis from Onset Using SVM
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    Chapter 16 MKL-Based Sample Enrichment and Customized Outcomes Enable Smaller AD Clinical Trials
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    Chapter 17 Pairwise Analysis for Longitudinal fMRI Studies
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    Chapter 18 Non-separable Spatiotemporal Brain Hemodynamics Contain Neural Information
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    Chapter 19 The Dynamic Beamformer
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    Chapter 20 Covert Attention as a Paradigm for Subject-Independent Brain-Computer Interfacing
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    Chapter 21 The Neural Dynamics of Visual Processing in Monkey Extrastriate Cortex: A Comparison between Univariate and Multivariate Techniques
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    Chapter 22 Statistical Learning for Resting-State fMRI: Successes and Challenges
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    Chapter 23 Relating Brain Functional Connectivity to Anatomical Connections: Model Selection
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    Chapter 24 Information-Theoretic Connectivity-Based Cortex Parcellation
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    Chapter 25 Inferring Brain Networks through Graphical Models with Hidden Variables
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    Chapter 26 Pitfalls in EEG-Based Brain Effective Connectivity Analysis
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    Chapter 27 Data-Driven Modeling of BOLD Drug Response Curves Using Gaussian Process Learning
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    Chapter 28 Machine Learning and Interpretation in Neuroimaging
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    Chapter 29 Identification of Functional Clusters in the Striatum Using Infinite Relational Modeling
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    Chapter 30 Machine Learning and Interpretation in Neuroimaging
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    Chapter 31 Real-Time Functional MRI Classification of Brain States Using Markov-SVM Hybrid Models: Peering Inside the rt-fMRI Black Box
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    Chapter 32 Restoring the Generalizability of SVM Based Decoding in High Dimensional Neuroimage Data
Attention for Chapter 14: Categorized EEG Neurofeedback Performance Unveils Simultaneous fMRI Deep Brain Activation
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  • Average Attention Score compared to outputs of the same age and source

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Chapter title
Categorized EEG Neurofeedback Performance Unveils Simultaneous fMRI Deep Brain Activation
Chapter number 14
Book title
Machine Learning and Interpretation in Neuroimaging
Published in
Lecture notes in computer science, January 2016
DOI 10.1007/978-3-642-34713-9_14
Book ISBNs
978-3-64-234712-2, 978-3-64-234713-9
Authors

Sivan Kinreich, Ilana Podlipsky, Nathan Intrator, Talma Hendler, Kinreich, Sivan, Podlipsky, Ilana, Intrator, Nathan, Hendler, Talma

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 2%
China 1 2%
Unknown 45 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 28%
Student > Master 10 21%
Student > Ph. D. Student 10 21%
Student > Postgraduate 3 6%
Student > Doctoral Student 2 4%
Other 4 9%
Unknown 5 11%
Readers by discipline Count As %
Psychology 9 19%
Neuroscience 7 15%
Computer Science 7 15%
Engineering 4 9%
Medicine and Dentistry 4 9%
Other 5 11%
Unknown 11 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 November 2013.
All research outputs
#14,515,646
of 24,953,268 outputs
Outputs from Lecture notes in computer science
#4,006
of 8,154 outputs
Outputs of similar age
#197,058
of 408,088 outputs
Outputs of similar age from Lecture notes in computer science
#293
of 515 outputs
Altmetric has tracked 24,953,268 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,154 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 408,088 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 515 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.