A review of heterogeneous data mining for brain disorder identification <s> Unsupervised learning <s> Findingobjective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a long-standing problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for multiple hypothesis testing (e.g., Bonferroni) tend to not be sensitive enough to be useful in this context. This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR). Recent theoretical work in statistics suggests that FDR-controlling procedures will be effective for the analysis of neuroimaging data. These procedures operate simultaneously on all voxelwise test statistics to determine which tests should be considered statistically significant. The innovation of the procedures is that they control the expected proportion of the rejected hypotheses that are falsely rejected. We demonstrate this approach using both simulations and functional magnetic resonance imaging data from two simple experiments. <s> BIB001 </s> A review of heterogeneous data mining for brain disorder identification <s> Unsupervised learning <s> The connection matrix of the human brain (the human “connectome”) represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact. <s> BIB002 </s> A review of heterogeneous data mining for brain disorder identification <s> Unsupervised learning <s> Background: Many recent studies have separately investigated functional and white matter (WM) based structural connectivity, yet their relationship remains less understood. In this paper, we proposed the functional-by-structural hierarchical (FSH) mapping to integrate multimodal connectome data from resting state fMRI (rsfMRI) and the whole brain tractography-derived connectome. Methods: FSH first observes that the level of resting-state functional correlation between any two regions in general decreases as the graph distance of the corresponding structural connectivity matrix between them increases. As not all white matter tracts are actively in use (i.e., “utilized”) during resting state, FSH thus models the rsfMRI correlation as an exponential decay function of the graph distance of the rsfMRI-informed structural connectivity or rsSC. rsSC is mathematically computed by multiplying entry-by-entry the tractography-derived structural connectivity matrix with a binary white matter “utilization matrix” U. U thus encodes whether any specific WM tract is being utilized during rsFMRI, and is estimated using simulated annealing. We applied this technique and investigated the hierarchical modular structure of rsSC from 7 depressed subjects and 7 age/gender matched controls. Results: No significant group differences were detected in the modular structures of either the resting state functional connectome or the whole brain tractography-derived connectome. By contrast, FSH results revealed significantly different patterns of association in the bilateral posterior cingulate cortex and right precuneus, with the depressed group exhibiting stronger associations among regions instrumental in self-referential operations. Discussion: The results of this study support that enhanced sensitivity can be obtained by integrating multimodal imaging data using FSH, a novel computational technique that may increase power to detect group differences in brain connectomes. <s> BIB003 </s> A review of heterogeneous data mining for brain disorder identification <s> Unsupervised learning <s> We pose the problem of network discovery which involves simplifying spatio-temporal data into cohesive regions (nodes) and relationships between those regions (edges). Such problems naturally exist in fMRI scans of human subjects. These scans consist of activations of thousands of voxels over time with the aim to simplify them into the underlying cognitive network being used. We propose supervised and semi-supervised variations of this problem and postulate a constrained tensor decomposition formulation and a corresponding alternating least squares solver that is easy to implement. We show this formulation works well in controlled experiments where supervision is incomplete, superfluous and noisy and is able to recover the underlying ground truth network. We then show that for real fMRI data our approach can reproduce well known results in neurology regarding the default mode network in resting-state healthy and Alzheimer affected individuals. Finally, we show that the reconstruction error of the decomposition provides a useful measure of the network strength and is useful at predicting key cognitive scores both by itself and with clinical information. <s> BIB004 </s> A review of heterogeneous data mining for brain disorder identification <s> Unsupervised learning <s> Following the recent advances in neuroimaging technology, the research on brain network analysis becomes an emerging area in data mining community. Brain network data pose many unique challenges for data mining research. For example, in brain networks, the nodes (i.e., the brain regions) and edges (i.e., relationships between brain regions) are usually not given, but should be derived from the neuroimaging data. The network structure can be very noisy and uncertain. Therefore, innovative methods are required for brain network analysis. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as brain network extraction, graph mining, neuroimaging data analysis. In this paper, we review some recent data mining methods which are used in the literature for mining brain network data. <s> BIB005 </s> A review of heterogeneous data mining for brain disorder identification <s> Unsupervised learning <s> Given a simple noun such as {\em apple}, and a question such as "is it edible?", what processes take place in the human brain? More specifically, given the stimulus, what are the interactions between (groups of) neurons (also known as functional connectivity) and how can we automatically infer those interactions, given measurements of the brain activity? Furthermore, how does this connectivity differ across different human subjects? In this work we present a simple, novel good-enough brain model, or GeBM in short, and a novel algorithm Sparse-SysId, which are able to effectively model the dynamics of the neuron interactions and infer the functional connectivity. Moreover, GeBM is able to simulate basic psychological phenomena such as habituation and priming (whose definition we provide in the main text). We evaluate GeBM by using both synthetic and real brain data. Using the real data, GeBM produces brain activity patterns that are strikingly similar to the real ones, and the inferred functional connectivity is able to provide neuroscientific insights towards a better understanding of the way that neurons interact with each other, as well as detect regularities and outliers in multi-subject brain activity measurements. <s> BIB006 </s> A review of heterogeneous data mining for brain disorder identification <s> Unsupervised learning <s> Investigations into brain connectivity aim to recover networks of brain regions connected by anatomical tracts or by functional associations. The inference of brain networks has recently attracted much interest due to the increasing availability of high-resolution brain imaging data. Sparse inverse covariance estimation with lasso and group lasso penalty has been demonstrated to be a powerful approach to discover brain networks. Motivated by the hierarchical structure of the brain networks, we consider the problem of estimating a graphical model with tree-structural regularization in this paper. The regularization encourages the graphical model to exhibit a brain-like structure. Specifically, in this hierarchical structure, hundreds of thousands of voxels serve as the leaf nodes of the tree. A node in the intermediate layer represents a region formed by voxels in the subtree rooted at that node. The whole brain is considered as the root of the tree. We propose to apply the tree-structural regularized graphical model to estimate the mouse brain network. However, the dimensionality of whole-brain data, usually on the order of hundreds of thousands, poses significant computational challenges. Efficient algorithms that are capable of estimating networks from high-dimensional data are highly desired. To address the computational challenge, we develop a screening rule which can quickly identify many zero blocks in the estimated graphical model, thereby dramatically reducing the computational cost of solving the proposed model. It is based on a novel insight on the relationship between screening and the so-called proximal operator that we first establish in this paper. We perform experiments on both synthetic data and real data from the Allen Developing Mouse Brain Atlas; results demonstrate the effectiveness and efficiency of the proposed approach. <s> BIB007 </s> A review of heterogeneous data mining for brain disorder identification <s> Unsupervised learning <s> This paper explores the idea of using deep neural network architecture with dynamically programmed layers for brain connectome prediction problem. Understanding the brain connectome structure is a very interesting and a challenging problem. It is critical in the research for epilepsy and other neuropathological diseases. We introduce a new deep learning architecture that exploits the spatial and temporal nature of the neuronal activation data. The architecture consists of a combination of Convolutional layer and a Recurrent layer for predicting the connectome of neurons based on their time-series of activation data. The key contribution of this paper is a dynamically programmed layer that is critical in determining the alignment between the neuronal activations of pair-wise combinations of neurons. <s> BIB008
Modern imaging techniques have allowed us to study the human brain as a complex system by modeling it as a network BIB003 . For example, the fMRI scans consist of activations of thousands of voxels over time embedding a complex interaction of signals and noise BIB001 , which naturally presents the problem of eliciting the underlying network from brain activities in the spatio-temporal tensor data. A brain connectivity network, also called a connectome BIB002 , consists of nodes (gray matter regions) and edges (white matter tracts in structural networks or correlations between two BOLD time series in functional networks). Although the anatomical atlases in the brain have been extensively studied for decades, task/subject specific networks have still not been completely explored with consideration of functional or structural connectivity information. An anatomically parcellated region may contain subregions that are characterized by dramatically different functional or structural connectivity patterns, thereby significantly limiting the utility of the constructed networks. There are usually trade-offs between reducing noise and preserving utility in brain parcellation BIB005 . Thus, investigating how to directly construct brain networks from tensor imaging data and understanding how they develop, deteriorate, and vary across individuals will benefit disease diagnosis BIB004 . Davidson et al. pose the problem of network discovery from fMRI data which involves simplifying spatio-temporal data into regions of the brain (nodes) and relationships between those regions (edges) BIB004 . Here the nodes represent collections of voxels that are known to behave cohesively over time; the edges can indicate a number of properties between nodes such as facilitation/inhibition (increases/decreases activity) or probabilistic (synchronized activity) relationships; and the weight associated with each edge encodes the strength of the relationship. A tensor can be decomposed into several factors. However, unconstrained tensor decomposition results of the fMRI data may not be good for node discovery because each factor is typically not a spatially contiguous region nor does it necessarily match an anatomical region. That is to say, many spatially adjacent voxels in the same structure are not active in the same factor which is anatomically impossible. Therefore, to achieve the purpose of discovering nodes while preserving anatomical adjacency, known anatomical regions in the brain are used as masks and constraints are added to enforce that the discovered factors should closely match these masks BIB004 . Yang et al. investigate the inference of mouse brain networks and propose a hierarchical graphical model framework with tree-structural regularization BIB007 . In the hierarchical structure, voxels serve as the leaf nodes of the tree, and a node in the intermediate layer represents a region formed by voxels in the subtree rooted at that node. For edge discovery problem, Papalexakis et al. leverage control theory to model the dynamics of neuron interactions and infer the functional connectivity BIB006 . It is assumed that in addition to the linear influence of the input stimulus, there are hidden neuron regions of the brain, which interact with each other, causing the voxel activities. Veeriah et al. propose a deep learning algorithm for predicting if the two brain neurons are causally connected given their activation time-series data BIB008 . It reveals that the exploitation of the deep architecture is critical, which jointly extracts sequences of salient patterns of activation and aligns them to predict neural connections. Overall, current research on tensor imaging analysis presents two directions: (1) supervised: for a particular brain disorder, a classifier can be trained by modeling the relationship between a set of neuroimages and their associated labels (disease status or clinical response); (2) unsupervised: regardless of brain disorders, a brain network can be discovered from a given neuroimage.