Analyzing Complex Problem Solving by Dynamic Brain Networks.

Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely and .

Gender classification using mesh networks on multiresolution multitask fMRI data.

Brain connectivity networks have been shown to represent gender differences under a number of cognitive tasks. Recently, it has been conjectured that fMRI signals decomposed into different resolutions embed different types of cognitive information. In this paper, we combine multiresolution analysis and connectivity networks to study gender differences under a variety of cognitive tasks, and propose a machine learning framework to discriminate individuals according to their gender.

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

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.

Hierarchical multi-resolution mesh networks for brain decoding.

Human brain is supposed to process information in multiple frequency bands. Therefore, we can extract diverse information from functional Magnetic Resonance Imaging (fMRI) data by processing it at multiple resolutions. We propose a framework, called Hierarchical Multi-resolution Mesh Networks (HMMNs), which establishes a set of brain networks at multiple resolutions of fMRI signal to represent the underlying cognitive process.

CEREBRA: a 3-D visualization tool for brain network extracted from fMRI data.

In this paper, we introduce a new tool, CEREBRA, to visualize the 3D network of human brain, extracted from the fMRI data. The tool aims to analyze the brain connectivity by representing the selected voxels as the nodes of the network. The edge weights among the voxels are estimated by considering the relationships among the voxel time series.

Ensembling brain regions for brain decoding.

In this study, we propose a new method which ensembles the brain regions for brain decoding. The ensemble is generated by clustering the fMRI images recorded during an experimental set-up which measures the cognitive states associated to semantic categories. Initially, voxel clusters are formed by using hierarchical agglomerative clustering with correlation as the similarity metric.

Functional Mesh Model with Temporal Measurements for brain decoding.

We propose a method called Functional Mesh Model with Temporal Measurements (FMM-TM) to estimate a functional relationship among voxels using temporal data, and employ these relationships for brain decoding. For each sample, we measure Blood Oxygenation Level Dependent (BOLD) responses from each voxel, and construct a functional mesh around each voxel (called seed voxel) with its nearest neighbors selected using distance metrics namely Pearson correlation, cosine similarity and Euclidean distance. Then, we represent the BOLD response of a seed voxel in terms of linear combination of BOLD responses of its p-nearest neighbors.

Machine Learning Methods for Attack Detection in the Smart Grid.

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach.

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

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.

Analyzing the information distribution in the fMRI measurements by estimating the degree of locality.

In this study, we propose a new method for analyzing and representing the distribution of discriminative information for data acquired via functional Magnetic Resonance Imaging (fMRI). For this purpose, we form a spatially local mesh with varying size, around each voxel, called the seed voxel. The relationship among each seed voxel and its neighbors is estimated using a linear regression model by minimizing the square error.