Journal ArticleDOI
Resting state dynamics meets anatomical structure: Temporal multiple kernel learning (tMKL) model
Sriniwas Govinda Surampudi,Joyneel Misra,Gustavo Deco,Raju S. Bapi,Avinash Sharma,Dipanjan Roy +5 more
TLDR
An innovative method that learns parameters specific to the latent states using a graph‐theoretic model (temporal Multiple Kernel Learning, tMKL) that inherently links dynamics to the structure and finally predicts the grand average FC of the test subjects by leveraging a state transition Markov model.About:
This article is published in NeuroImage.The article was published on 2019-01-01. It has received 17 citations till now. The article focuses on the topics: Dynamic functional connectivity & Resting state fMRI.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
Metastable Resting State Brain Dynamics.
Peter beim Graben,Antonio Jimenez-Marin,Ibai Diez,Jesus M. Cortes,Jesus M. Cortes,Mathieu Desroches,Serafim Rodrigues,Serafim Rodrigues +7 more
TL;DR: The number of metastable states is determined as a measure of complexity for all subjects and for region numbers varying from 3 to 100, and RSA convergence toward an optimal segmentation of 40 metastableStates for normalized BOLD signals, averaged over BHA modules is found.
Journal ArticleDOI
Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity
TL;DR: The results demonstrate that the predicted intrahemispheric structural connections and the weights distribution are highly consistent with the empirical SC derived from diffusion magnetic resonance imaging (dMRI) and probabilistic tractography, thus strongly supporting the model and algorithms proposed.
Journal ArticleDOI
Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning
Simon Wein,Gustavo Deco,Gustavo Deco,Ana Maria Tomé,Markus Goldhacker,Wilhelm M. Malloni,Mark W. Greenlee,Elmar Lang +7 more
TL;DR: A short survey on the recent literature on the relationship between the brain structure and its functional dynamics can be found in this article, focusing on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.
Journal ArticleDOI
Generative Models of Brain Dynamics
Mahta Ramezanian-Panahi,German Abrevaya,Jean-Christophe Gagnon-Audet,Vikram Voleti,Irina Rish,Guillaume Dumas +5 more
TL;DR: This review article presents several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
References
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Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Journal ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
Complex network measures of brain connectivity: uses and interpretations.
Mikail Rubinov,Olaf Sporns +1 more
TL;DR: Construction of brain networks from connectivity data is discussed and the most commonly used network measures of structural and functional connectivity are described, which variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, and test resilience of networks to insult.
Journal ArticleDOI
A tutorial on spectral clustering
TL;DR: In this article, the authors present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches, and discuss the advantages and disadvantages of these algorithms.
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Complex network measures of brain connectivity: uses and interpretations.
Mikail Rubinov,Olaf Sporns +1 more