Element-centric clustering comparison unifies overlaps and hierarchy

Clustering is one of the most universal approaches for understanding complex data. A pivotal aspect of clustering analysis is quantitatively comparing clusterings; clustering comparison is the basis for many tasks such as clustering evaluation, consensus clustering, and tracking the temporal evolution of clusters. In particular, the extrinsic evaluation of clustering methods requires comparing the uncovered clusterings to planted clusterings or known metadata. Yet, as we demonstrate, existing clustering comparison measures have critical biases which undermine their usefulness, and no measure accommodates both overlapping and hierarchical clusterings. Here we unify the comparison of disjoint, overlapping, and hierarchically structured clusterings by proposing a new element-centric framework: elements are compared based on the relationships induced by the cluster structure, as opposed to the traditional cluster-centric philosophy. We demonstrate that, in contrast to standard clustering similarity measures, our framework does not suffer from critical biases and naturally provides unique insights into how the clusterings differ. We illustrate the strengths of our framework by revealing new insights into the organization of clusters in two applications: the improved classification of schizophrenia based on the overlapping and hierarchical community structure of fMRI brain networks, and the disentanglement of various social homophily factors in Facebook social networks. The universality of clustering suggests far-reaching impact of our framework throughout all areas of science

Resolving Structure in Human Brain Organization: Identifying Mesoscale Organization in Weighted Network Representations

Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.Comment: Comments welcom

Novel Biomarker Identification Approaches for Schizophrenia using fMRI and Retinal Electrophysiology

University of Minnesota Ph.D. dissertation. November 2017. Major: Biomedical Engineering. Advisors: Kelvin Lim, Theoden Netoff. 1 computer file (PDF); vi, 109 pages.Schizophrenia is a chronic mental illness. The exact cause if schizophrenia is not yet known. Extensive research has been done to identify robust biomarkers for the disease using non-invasive brain imaging techniques. A robust biomarker can be informative about pathophysiology of the disease and can guide clinicians into developing more effective interventions. The aim of this dissertation is two folds. First, we seek to identify robust biomarkers using resting state fMRI activity from a cohort of schizophrenic and healthy subjects in a purely data driven approach. We will calculate multivariate network measures and use them as features for classification of the subjects into healthy and diseased. The network measures will be calculated using nodes defined by the AAL anatomical atlas as well as a functional atlas constructed from the fMRI activity. Network measures with high classification rate may be used as potential biomarkers. We will employ double cross-validation to estimate generalizability of our results to a new population of subjects that were not used in biomarker identification. Second, we seek to identify biomarkers using electroretinogram (ERG). We will use a data driven approach to classify individuals based on the pattern of retinal activity they exhibit in response to visual stimulation. Characteristics of the ERG result in high classification rate are presented as potential biomarkers of schizophrenia

Application of graph theoretical methods to the functional connectome of human brain.

During the past decade, there has been a great interest in creating mathematical models to describe the properties of connectivity in the human brain. One of the established tools to describe these interactions among regions of the brain is graph theory. However, graph theoretical methods were mainly designed for the analysis of single network which is problematic for neuroscientists wishing to study groups of subjects. Specifically, studies using the Rich Club (RC) graph measure require cumbersome methods to make statistical inferences. In the first part of this work, we propose a framework to analyse the inter-subject variability in Rich Club organisation. The proposed framework is used to identify the changes in RC coefficient and RC organisation in patients with schizophrenia relative to healthy control. We follow this work by proposing a novel method, named Rich Block (RB), which is a combination of the tradition Rich Club and Stochastic Block Models (SBM). We show that using RBs can not only facilitate an inter-subject statistical inference, it can also account for differences in profile of connectivity, and control for subject-level covariates. We validate the Rich Block approach by simulating networks of different size and structure. We find that RB accurately estimates RC coefficients and RC organisations, specifically, in network with large number of nodes and blocks. With real data we use RB to identify changes in coefficient and organisation of highly connected sub-graphs of hub blocks in schizophrenia. In the final portion of this work, we examine the methods used to define each edge in networks formed from resting-state functional magnetic resonance imaging (rs-fMRI). The standard approach in rs-fMRI is to divide the brain into regions, extract time series, and compute the temporal correlation between each region. These correlations are assumed to follow standard results, when in fact serial autocorrelation in the time series can corrupt these results. While some authors have proposed corrections to account for autocorrelation, they are poorly documented and always assume homogeneity of autocorrelation over brain regions. Thus we propose a method to account for bias in interregion correlation estimates due to autocorrelation. We develop an exact method and an approximate, more computationally efficient method that adjusts for the sampling variability in the correlation coefficient. We use inter-subject scrambled real-data to validate the proposed methods under a null setting, and intact real-data to examine the impact of our method on graph theoretical measures. We find that the standard methods fail to practically correct the sensitivity and specificity level due to over-simplifying the temporal structure of BOLD time series, while even our approximate method is substantially more accurate

Development and evaluation of optimization based data mining techniques analysis of brain data

Neuroscience is an interdisciplinary science which deals with the study of structure and function of the brain and nervous system. Neuroscience encompasses disciplines such as computer science, mathematics, engineering, and linguistics. The structure of the healthy brain and representation of information by neural activity are among most challenging problems in neuroscience. Neuroscience is experiencing exponentially growing volumes of data obtained by using different technologies. The investigation of such data has tremendous impact on developing new and improving existing models of both healthy and diseased brains. Various techniques have been used for collecting brain data sets for addressing neuroscience problems. These data sets can be categorized into two main groups: resting-state and state-dependent data sets. Resting-state data is based on recording the brain activity when a subject does not think about any specific concept while state-dependent data is based on recording brain activity related to specific tasks. In general, brain data sets contain a large number of features (e.g. tens of thousands) and significantly fewer samples (e.g. several hundred). Such data sets are sparse and noisy. In addition to these problems, brain data sets have a few number of subjects. Brains are very complex systems and data about any brain activity reflects very complex relationship between neurons as well as different parts of the brain. Such relationships are highly nonlinear and general purpose data mining algorithms are not always efficient for their study. The development of machine learning techniques for brain data sets is an emerging research area in neuroscience. Over the last decade, various machine learning techniques have been developed for application to brain data sets. In the meantime, some well-known algorithms such as feature selection and supervised classification have been modified for analysis of brain data sets. Support vector machines, logistic regression, and Gaussian Naive Bayes classifiers are widely used for application to brain data sets. However, Support vector machines and logistic regression algorithms are not efficient for sparse and noisy data sets and Gaussian Naive Bayes classifiers do not give high accuracy. The aim of this study is to develop new and modify the existing data mining algorithms for the analysis brain data sets. Our contribution in this thesis can be listed as follow: 1. Development of new algorithms: 1.1. Development of new voxel (feature) selection algorithms for Functional magnetic resonance imaging (fMRI) data sets, and evaluation of these algorithms on the Haxby and Science 2008 data sets. 1.2. Development of new feature selection algorithm based on the catastrophe model for regression analysis problems. 2. Development and evaluation of different versions of the adaptive neuro-fuzzy model for the analysis of the spike-discharge as a function of other neuronal parameters. 3. Development and evaluation of the modified global k-means clustering algorithm for investigation of the structure of the healthy brain. 4. Development and evaluation of region of interest (ROI) method for analysis of brain functionalconnectivity in healthy subjects and schizophrenia patients.Doctor of Philosoph

Dynamic correlations in ongoing neuronal oscillations in humans - perspectives on brain function and its disorders

This Thesis is involved with neuronal oscillations in the human brain and their coordination across time, space and frequency. The aim of the Thesis was to quantify correlations in neuronal oscillations over these dimensions, and to elucidate their significance in cognitive processing and brain disorders. Magnetoencephalographic (MEG) recordings of major depression patients revealed that long-range temporal correlations (LRTC) were decreased, compared to control subjects, in the 5 Hz oscillations in a manner that was dependent on the degree of the disorder. While studying epileptic patients, on the other hand, it was found that the LRTC in neuronal oscillations recorded intracranially with electroencephalography (EEG) were strengthened in the seizure initiation region. A novel approach to map spatial correlations between cortical regions was developed. The method is based on parcellating the cortex to patches and estimating phase synchrony between all patches. Mapping synchrony from inverse-modelled MEG / EEG data revealed wide-spread phase synchronization during a visual working memory task. Furthermore, the network architectures of task-related synchrony were found to be segregated over frequency. Cross-frequency interactions were investigated with analyses of nested brain activity in data recorded with full-bandwidth EEG during a somatosensory detection task. According to these data, the phase of ongoing infra-slow fluctuations (ISF), which were discovered in the frequency band of 0.01-0.1 Hz, was correlated with the amplitude of faster > 1 Hz neuronal oscillations. Strikingly, the behavioral detection performance displayed similar dependency on the ISFs as the > 1 Hz neuronal oscillations. The studies composing this Thesis showed that correlations in neuronal oscillations are functionally related to brain disorders and cognitive processing. Such correlations are suggested to reveal the coordination of neuronal oscillations across time, space and frequency. The results contribute to system-level understanding of brain function

Diagnosing Autism Spectrum Disorder through Brain Functional Magnetic Resonance Imaging

Autism spectrum disorder (ASD) is a neurodevelopmental condition that can be debilitating to social functioning. Previous functional Magnetic Resonance Imaging (fMRI) classification studies have included only small subject sample sizes (n 50) and have seen high classification accuracy. The recent release of the Autism Brain Imaging Data Exchange (ABIDE) provides fMRI data for over 1,100 subjects. In our research, we derive a subject\u27s functional network connectivity (FNC) from their fMRI data and develop a regularized logistic classifier to determine whether a subject has autism. We obtained up to 65% classification accuracy, similar to other studies using the ABIDE dataset, suggesting that generalizing a classifier over a large number of subjects is much more difficult than smaller studies. The connectivity among several brain regions of ASD subjects were highlighted in the model as abnormal compared to the control subjects which potentially warrants future investigations about how these regions affect ASD. Although the classification accuracy was lower than what could be considered as clinically applicable, this research contributes to the continuing development of an automated classifier for diagnosing autism

Functional neuroimaging in subjects at high genetic risk of schizophrenia

Schizophrenia is an incapacitating psychiatric disorder characterized by hallucinations and delusions with a lifetime risk of around 1% worldwide. It is a highly heritable disorder which generally becomes manifest in early adult life. The established condition has been associated with structural and functional brain abnormalities, principally in prefrontal and temporal lobes, but it is unclear whether such abnormalities are related to inherited vulnerability, medication effects, or the presence of symptoms. Furthermore, the mechanisms by which the pre-morbid state switches into florid psychosis are unknown. The Edinburgh High Risk Study is designed to address these issues. The first phase (1994-1999) employed repeated clinical, neuropsychological assessments and structural imaging. In the current phase (1999-2004) functional magnetic resonance imaging (fMRI) has been added to the tests used previously.As part of the Edinburgh High Risk Study, this study used a covert verbal initiation fMRI task (the Hayling Sentence Completion Test) known to elicit frontal and temporal activation, to examine a large number of young participants at high risk of developing schizophrenia for genetic reasons, in comparison with a matched group of healthy controls. Subjects were scanned at baseline, and after approximately one year. At the time of the baseline scan none of the participants met criteria for any psychiatric disorder, however, a number of subjects reported isolated psychotic symptoms on direct questioning. Over the course of the entire study (1994-2004), 21 individuals developed schizophrenia according to standard diagnostic criteria. Four of these subjects made the transition over the course of the current study (1999-2004), i.e. subsequent to the baseline functional scanThere were three main aims of the current study (i) to use fMRI to identify the neural correlates of state and trait effects in high risk individuals, (ii) to determine ifit is possible to distinguish those who subsequently become ill from those who remain well using functional imaging, and (iii) to determine if patterns of brain activity change with the transition to illness, or vary with changes in symptomatic status of these individuals.Regarding the first aim, group differences of apparent genetic origin were found in prefrontal, thalamic, cerebellar regions, and differences in activation in those with symptoms were found in the parietal lobe. Functional connectivity analysis examining interactions between these regions also indicated similar abnormalities. These results may therefore reflect inherited deficits, and the earliest changes associated with the psychotic state, respectively. Although only a small number of subjects became ill over the course of the current study («=4), initial findings suggested abnormalities in medial prefrontal and medial temporal regions (with an indication of parietal lobe dysfunction) were able to distinguish those who later became ill versus those that remained well. Finally, there were also indications of changes in activation patterns over time in a subgroup of subjects with varying symptomatic status.To conclude, these results are consistent with previous findings in the Edinburgh High Risk Study - what is inherited by the high risk individuals is a state of heightened vulnerability manifesting, in the case of functional imaging, as abnormalities in activation and/or connectivity in preffontal-thalamiccerebellar and prefrontal-parietal regions. These finding also suggest that there are additional differences seen in those with psychotic symptoms, and to some extent in those who subsequently go on to develop the disorder. These results are not confounded by anti-psychotic medication since all subjects were anti-psychotic naive at the time of assessment. The lack of findings traditionally associated with the established illness (dorsolateral prefrontal cortex and lateral temporal lobe) indicate these may be specifically associated with the established state, or when performance differences become manifest. Overall therefore these findings reveal information regarding the pathophysiology of the state of vulnerability to the disorder and about the mechanisms involved in the development of schizophrenia or schizophrenic symptomatology