A Nonparametric Graphical Model for Functional Data With Application to Brain Networks Based on fMRI

2018 ◽  
Vol 113 (524) ◽  
pp. 1637-1655 ◽  
Author(s):  
Bing Li ◽  
Eftychia Solea
Keyword(s):  
Functional Data ◽  
Graphical Model ◽  
Brain Networks

scholarly journals Sparse Graphical Models for Functional Connectivity Networks: Best Methods and the Autocorrelation Issue

2017 ◽  
Author(s):  
Yunan Zhu ◽  
Ivor Cribben
Keyword(s):  
Graphical Models ◽  
Graphical Model ◽  
Brain Networks ◽  
Information Criterion ◽  
Selection Method ◽  
Absolute Deviation ◽  
Data Set ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
The Impact

AbstractSparse graphical models are frequently used to explore both static and dynamic functional brain networks from neuroimaging data. However, the practical performance of the models has not been studied in detail for brain networks. In this work, we have two objectives. First, we compare several sparse graphical model estimation procedures and several selection criteria under various experimental settings, such as different dimensions, sample sizes, types of data, and sparsity levels of the true model structures. We discuss in detail the superiority and deficiency of each combination. Second, in the same simulation study, we show the impact of autocorrelation and whitening on the estimation of functional brain networks. We apply the methods to a resting-state functional magnetic resonance imaging (fMRI) data set. Our results show that the best sparse graphical model, in terms of detection of true connections and having few false-positive connections, is the smoothly clipped absolute deviation (SCAD) estimating method in combination with the Bayesian information criterion (BIC) and cross-validation (CV) selection method. In addition, the presence of autocorrelation in the data adversely affects the estimation of networks but can be helped by using the CV selection method. These results question the validity of a number of fMRI studies where inferior graphical model techniques have been used to estimate brain networks.

scholarly journals Network Structure During Encoding Predicts Working Memory Performance

2018 ◽  
Author(s):  
Anirudh Wodeyar ◽  
Ramesh Srinivasan
Keyword(s):  
Working Memory ◽  
Graphical Model ◽  
Structural Characteristics ◽  
Brain Activity ◽  
Memory Performance ◽  
Memory Function ◽  
Brain Networks ◽  
Memory Task ◽  
Functional Clustering ◽  
Network Properties

ABSTRACTWorking memory operates through networks that integrate distributed modular brain activity. We characterize the structure of networks in different electroencephalographic frequency bands while individuals perform a working memory task. The objective was to identify network properties that support working memory function during the encoding, maintenance, and retrieval of memory. In each EEG frequency band, we estimated a complex-valued Gaussian graphical model to characterize the structure of brain networks using measures from graph theory. Critically, the structural characteristics of brain networks that facilitate performance are all established during encoding, suggesting that they reflect the effect of attention on the quality of the representation in working memory. Segregation of networks in the alpha and beta bands during encoding increased with accuracy. In the theta band, greater integration of functional clusters involving the temporal lobe with other cortical areas predicted faster response time, starting in the encoding interval and persisting throughout the task, indicating that functional clustering facilitates rapid memory manipulation.

Doubly functional graphical models in high dimensions

Biometrika ◽  
2020 ◽  
Vol 107 (2) ◽  
pp. 415-431
Author(s):  
Xinghao Qiao ◽  
Cheng Qian ◽  
Gareth M James ◽  
Shaojun Guo
Keyword(s):  
Graphical Models ◽  
Functional Data ◽  
Graphical Model ◽  
Convergence Rates ◽  
High Dimensions ◽  
Conditional Dependence ◽  
Large P Small N ◽  
Concentration Bounds ◽  
Dependence Relationship ◽  
Functional Principal Components

Summary We consider estimating a functional graphical model from multivariate functional observations. In functional data analysis, the classical assumption is that each function has been measured over a densely sampled grid. However, in practice the functions have often been observed, with measurement error, at a relatively small number of points. We propose a class of doubly functional graphical models to capture the evolving conditional dependence relationship among a large number of sparsely or densely sampled functions. Our approach first implements a nonparametric smoother to perform functional principal components analysis for each curve, then estimates a functional covariance matrix and finally computes sparse precision matrices, which in turn provide the doubly functional graphical model. We derive some novel concentration bounds, uniform convergence rates and model selection properties of our estimator for both sparsely and densely sampled functional data in the high-dimensional large-$p$, small-$n$ regime. We demonstrate via simulations that the proposed method significantly outperforms possible competitors. Our proposed method is applied to a brain imaging dataset.

scholarly journals Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes

Biometrika ◽  
2021 ◽  
Author(s):  
J Zapata ◽  
S Y Oh ◽  
A Petersen
Keyword(s):  
Graphical Models ◽  
Functional Data ◽  
Graphical Model ◽  
Multivariate Data ◽  
Efficient Estimation ◽  
Covariance Operator ◽  
Gaussian Graphical Models ◽  
Partial Separability ◽  
Fixed Dimension ◽  
Multivariate Functional Data

Abstract The covariance structure of multivariate functional data can be highly complex, especially if the multivariate dimension is large, making extensions of statistical methods for standard multivariate data to the functional data setting challenging. For example, Gaussian graphical models have recently been extended to the setting of multivariate functional data by applying multivariate methods to the coefficients of truncated basis expansions. However, a key difficulty compared to multivariate data is that the covariance operator is compact, and thus not invertible. The methodology in this paper addresses the general problem of covariance modelling for multivariate functional data, and functional Gaussian graphical models in particular. As a first step, a new notion of separability for the covariance operator of multivariate functional data is proposed, termed partial separability, leading to a novel Karhunen–Loève-type expansion for such data. Next, the partial separability structure is shown to be particularly useful in order to provide a well-defined functional Gaussian graphical model that can be identified with a sequence of finite-dimensional graphical models, each of identical fixed dimension. This motivates a simple and efficient estimation procedure through application of the joint graphical lasso. Empirical performance of the method for graphical model estimation is assessed through simulation and analysis of functional brain connectivity during a motor task.

Lessons From the Neural Bases of Speech and Voice

2011 ◽  
Vol 21 (1) ◽  
pp. 5-14
Author(s):  
Christy L. Ludlow
Keyword(s):  
Brain Networks ◽  
Neural Substrates ◽  
Voice Behavior ◽  
Therapeutic Approaches ◽  
Neural Bases ◽  
Sound Foundation ◽  
The Brain ◽  
Normal Speech

The premise of this article is that increased understanding of the brain bases for normal speech and voice behavior will provide a sound foundation for developing therapeutic approaches to establish or re-establish these functions. The neural substrates involved in speech/voice behaviors, the types of muscle patterning for speech and voice, the brain networks involved and their regulation, and how they can be externally modulated for improving function will be addressed.

Epilepsi dan Attention-Deficit / Hyperactivity Disorder (ADHD) pada Anak

2017 ◽  
Vol 10 (2) ◽  
pp. 80
Author(s):  
Riki Sukiandra
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Brain Networks ◽  
Seizure Threshold ◽  
Childhood Epilepsy ◽  
Adhd Symptoms ◽  
Hyperactivity Disorder ◽  
Neurologic Disturbance ◽  
Inattentive Type ◽  
Child Age

Attention-deficit / hyperactivity disorder (ADHD) has been associated with childhood epilepsy. Epilepsy are themost common neurologic disturbance in child age. Children with epilepsy tend to get one or more ADHD symptoms,its related to lack of norepinephrine neurotransmitter in brain, that cause attenuate the effect of GABA and disruptionto fronto-striatal brain networks, these same brain networks are disrupted by seizures or the structural brainabnormalities that can cause seizures. Children with epilepsy especially absance, tend to get inattentive type ofADHD more than other types. Abnormalities of electro-encephalography found in inattentive type of ADHD withhigh focus activities in all lobe area. No data published that methylphenidate can lower seizure threshold or act asproconvulsant. Children with epilepsy tend to get one or more symptoms of ADHD in the following days.

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