Enhancing Local Linear Models Using Functional Connectivity for Brain State Decoding (original) (raw)

Functional Mesh Learning for pattern analysis of cognitive processes

2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing, 2013

We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. Then, the local mesh for each voxel is formed by including the functionally closest neighboring voxels in the mesh. The relationship between the voxels within a mesh is estimated by using a linear regression model. These relationship vectors, called Functional Connectivity aware Local Relational Features (FC-LRF) are then used to train a statistical learning machine. The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-Nearest Neighbor and Support Vector Machine, are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the Functional Mesh Learning model, which range in 62-68% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40-48%, for ten semantic categories.

Discriminative Functional Connectivity Measures for Brain Decoding

ArXiv, 2014

Encoding the Local Connectivity Patterns of fMRI for Cognitive State Classification

ArXiv, 2016

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher Vectors (FV), Vector of Locally Aggregated Descriptors (VLAD) and Bag-of-Words (BoW) methods. We first obtain local descriptors, called Mesh Arc Descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called \textit{brain connectivity dictionary} by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting the codewords at the mean of each component of the mixture. Codewords represent the connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using the k-Means clustering. We classify the cognitive states of Human Connectome Project (HCP) task fMRI dataset, where we train support vector machines (SVM) by the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfull...

Encoding the local connectivity patterns of fMRI for cognitive task and state classification

Brain imaging and behavior, 2018

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate t...

A New Representation of fMRI Signal by a Set of Local Meshes for Brain Decoding

IEEE Transactions on Signal and Information Processing over Networks, 2017

How neurons influence each other's firing depends on the strength of synaptic connections among them. Motivated by the highly interconnected structure of the brain, in this study, we propose a computational model to estimate the relationships among voxels and employ them as features for cognitive state classification. We represent the sequence of functional Magnetic Resonance Imaging (fMRI) measurements recorded during a cognitive stimulus by a set of local meshes. Then, we represent the corresponding cognitive state by the edge weights of these meshes each of which is estimated assuming a regularized linear relationship among voxel time series in a predefined locality. The estimated mesh edge weights provide a better representation of information in the brain for cognitive state or task classification. We examine the representative power of our mesh edge weights on visual recognition and emotional memory retrieval experiments by training a Support Vector Machine classifier. Also, we use mesh edge weights as feature vectors of inter-subject classification on Human Connectome Project task fMRI dataset, and test their performance. We observe that mesh edge weights perform better than the popular fMRI features, such as, raw voxel intensity values, pairwise correlations, features extracted using PCA and ICA, for classifying the cognitive states.

Modeling the Brain Connectivity for Pattern Analysis

2014 22nd International Conference on Pattern Recognition, 2014

An information theoretic approach is proposed to estimate the degree of connectivity for each voxel with its neighboring voxels. The neighborhood system is defined by spatial and functional connectivity metrics. Then, a local mesh of variable size is formed around each voxel using spatial or functional neighborhood. The mesh arc weights, called Mesh Arc Descriptors (MAD), are estimated by a linear regression model fitted to the voxel intensity values of the functional Magnetic Resonance Images (fMRI). Finally, the error term of the linear regression equation is used to estimate the mesh size for a voxel by optimizing Akaike's information Criterion, Bayesian Information Criterion and Rissanen's Minimum Description Length. fMRI measurements are obtained during a memory encoding and retrieval experiment performed on a subject who is exposed to the stimuli from 10 semantic categories. For each sample, a k-NN classifier is trained using the Mesh Arc Descriptors (MAD) having the variable mesh sizes. The classification performances reflect that the suggested variable-size Mesh Arc Descriptors represents the mental states better than the classical multi-voxel pattern representation. Moreover, we observe that the degree of connectivities in the brain greatly varies for each voxel.

Modelling the Brain Connectivity for Pattern Analysis

Large Scale Functional Connectivity for Brain Decoding

Biomedical Engineering / 817: Robotics Applications, 2014

Functional Magnetic Resonance Imaging (fMRI) data consists of time series for each voxel recorded during a cognitive task. In order to extract useful information from this noisy and redundant data, techniques are proposed to select the voxels that are relevant to the underlying cognitive task. We propose a simple and efficient algorithm for decoding the brain states by modelling the correlation patterns between the voxel time series. For each stimulus during the experiment, a separate functional connectivity matrix is computed in voxel level. The elements in connectivity matrices are then filtered out by making use of a minimum spanning tree formed using a global connectivity matrix for the entire experiment in order to reduce dimensionality. For a recognition memory experiment with nine subjects, functional connectivity matrices are computed for encoding and retrieval phases. The class labels of the retrieval samples are predicted within a k-nearest neighbour space constructed by the traversed entries in the functional connectivity matrices for encoding samples. The proposed method is also adapted to large scale functional connectivity tasks by making use of graphics boards. Classification performance in ten categories is comparable and even better compared to both classical and enhanced methods of multi-voxel pattern analysis techniques.

Representation of cognitive processes using the minimum spanning tree of local meshes

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013

A new graphical model called Cognitive Process Graph (CPG) is proposed, for classifying cognitive processes based on neural activation patterns which are acquired via functional Magnetic Resonance Imaging (fMRI) in brain. In the CPG, first local meshes are formed around each voxel. Second, the relationships between a voxel and its neighbors in a local mesh, which are estimated by using a linear regression model, are used to form the edges among the voxels (graph nodes) in the CPG. Then, a minimum spanning tree (MST) of the CPG which spans all the voxels in the region of interest is computed. The arc weights of the MST are used to represent the underlying cognitive processes. The proposed method reduces the curse of dimensionality problem that is caused by very large dimension of the feature space of the fMRI measurements, compared to number of instances. Finally, the arc weights computed over the path of the MST called MST-Features (MST-F) are used to train a statistical learning machine. The proposed method is tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM), are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the proposed learning modelis superior to the classical multi-voxel pattern analysis (MVPA) methods for the underlying cognitive process.

Mesh Learning for Classifying Cognitive Processes

2012

A relatively recent advance in cognitive neuroscience has been multi-voxel pattern analysis (MVPA), which enables researchers to decode brain states and/or the type of information represented in the brain during a cognitive operation. MVPA methods utilize machine learning algorithms to distinguish among types of information or cognitive states represented in the brain, based on distributed patterns of neural activity. In the current investigation, we propose a new approach for representation of neural data for pattern analysis, namely a Mesh Learning Model. In this approach, at each time instant, a star mesh is formed around each voxel, such that the voxel corresponding to the center node is surrounded by its p-nearest neighbors. The arc weights of each mesh are estimated from the voxel intensity values by least squares method. The estimated arc weights of all the meshes, called Mesh Arc Descriptors (MADs), are then used to train a classifier, such as Neural Networks, k-Nearest Neighbor, Naïve Bayes and Support Vector Machines. The proposed Mesh Model was tested on neuroimaging data acquired via functional magnetic resonance imaging (fMRI) during a recognition memory experiment using categorized word lists, employing a previously established experimental paradigm . Results suggest that the proposed Mesh Learning approach can provide an effective algorithm for pattern analysis of brain activity during cognitive processing.

Enhancing Local Linear Models Using Functional Connectivity for Brain State Decoding (original) (raw)

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