Discerning nonlinear brain dynamics from EEG:an application to autistic spectrum disorder in young children

A challenging goal in neuroscience is that of identifying specific brain patterns characterising autistic spectrum disorder (ASD). Genetic studies, together with investigations based on magnetic resonance imaging (MRI) and functional MRI, support the idea that distinctive structural features could exist in the ASD brain. In the developing brains of babies and small children, structural differences could provide the basis for different brain connectivity, giving rise to macroscopic effects detectable by e.g. electroencephalography (EEG). A significant body of research has already been conducted in this direction, mainly computing spectral power and coherence. Perhaps due to methodological limitations, together with high variability within and between the cohorts investigated, results have not been in complete agreement, and it is therefore still the case that the diagnosis of ASD is based on behavioural tests and interviews. This thesis describes a step-by-step characterisation and comparison of brain dynamics from ASD and neurotypical subjects, based on the analysis of multi-probe EEG time-series from male children aged 3-5 years. The methods applied are all ones that take explicit account of the intrinsically non-linear, open, and time-variable nature of the system. Time-frequency representations were first computed from the time-series to evaluate the spectral power and to categorise the ranges encompassing different activities as low-frequency (LF, 0.8-3.5 Hz), mid-range-frequency (MF, 3.5-12 Hz) or high-frequency (HF, 12-48 Hz). The spatial pathways for the propagation of neuronal activity were then investigated by calculation of wavelet phase coherence. Finally, deeper insight into brain connectivity was achieved by computation of the dynamical cross-frequency coupling between triplets of spatially distributed phases. In doing so, dynamical Bayesian inference was used to find the coupling parameters between the oscillators in the spatially-distributed network. The sets of parameters extracted by this means allowed evaluation of the strength of particular coupling components of the triplet LF, MF→HF, and enabled reconstruction of the coupling functions. By investigation of the form of the coupling functions, the thesis goes beyond conventional measures like the directionality and strength of an interaction, and reveals subtler features of the underlying mechanism. The measured power distributions highlight differences between ASD and typically developing children in the preferential frequency range for local synchronisation of neuronal activity: the relative power is generally higher at LF and HF, and lower at MF, in the ASD case. The phase coherence maps from ASD subjects also exhibited differences, with lower connectivity at LF and MF in the frontal and fronto-occipital pairs, and higher coherence at high frequencies for central links. There was higher inter-subject variability in a comparison of the forms of coupling functions in the ASD group; and a weaker coupling in their theta-gamma range, which can be linked with the cognitive features of the disorder. In conclusion, the approach developed in this thesis gave promising preliminary results, suggesting that a biomarker for ASD could be defined in terms of the described patterns of functional and effective connectivity computed from EEG measurements

Optimisation approaches for data mining in biological systems

The advances in data acquisition technologies have generated massive amounts of data that present considerable challenge for analysis. How to efficiently and automatically mine through the data and extract the maximum value by identifying the hidden patterns is an active research area, called data mining. This thesis tackles several problems in data mining, including data classification, regression analysis and community detection in complex networks, with considerable applications in various biological systems. First, the problem of data classification is investigated. An existing classifier has been adopted from literature and two novel solution procedures have been proposed, which are shown to improve the predictive accuracy of the original method and significantly reduce the computational time. Disease classification using high throughput genomic data is also addressed. To tackle the problem of analysing large number of genes against small number of samples, a new approach of incorporating extra biological knowledge and constructing higher level composite features for classification has been proposed. A novel model has been introduced to optimise the construction of composite features. Subsequently, regression analysis is considered where two piece-wise linear regression methods have been presented. The first method partitions one feature into multiple complementary intervals and ts each with a distinct linear function. The other method is a more generalised variant of the previous one and performs recursive binary partitioning that permits partitioning of multiple features. Lastly, community detection in complex networks is investigated where a new optimisation framework is introduced to identify the modular structure hidden in directed networks via optimisation of modularity. A non-linear model is firstly proposed before its linearised variant is presented. The optimisation framework consists of two major steps, including solving the non-linear model to identify a coarse initial partition and a second step of solving repeatedly the linearised models to re fine the network partition

Robot-assisted fMRI assessment of early brain development

Preterm birth can interfere with the intra-uterine mechanisms driving cerebral development during the third trimester of gestation and often results in severe neuro-developmental impairments. Characterizing normal/abnormal patterns of early brain maturation could be fundamental in devising and guiding early therapeutic strategies aimed at improving clinical outcome by exploiting the enhanced early neuroplasticity. Over the last decade the morphology and structure of the developing human brain has been vastly characterized; however the concurrent maturation of brain function is still poorly understood. Task-dependent fMRI studies of the preterm brain can directly probe the emergence of fundamental neuroscientific notions and also provide clinicians with much needed early diagnostic and prognostic information. To date, task-fMRI studies of the preterm population have however been hampered by methodological challenges leading to inconsistent and contradictory results. In this thesis I present a modular and flexible system to provide clinicians and researchers with a simple and reliable solution to deliver computer-controlled stimulation patterns to preterm infants during task-fMRI experiments. The system is primarily aimed at studying the developing sensori-motor system as preterm infants are often affected by neuro-motor dysfunctions such as cerebral palsy. Wrist and ankle robotic stimulators were developed and firstly used to study the emerging somatosensory “homunculus”. The wrist robotic stimulator was then used to characterize the development of the sensori-motor system throughout the mid-to-late preterm period. An instrumented pacifier system was also developed to explore the potential sensorimotor modulation of early sucking activity; however, despite its clear potential to be employed in future fMRI studies, results have not yet been obtained on preterm infants. Functional difficulties associated with prematurity are likely to extend to multi-sensory integration, and the olfactory system currently remains under-investigated despite its clear developmental importance. A custom olfactometer was developed and used to assess its early functionality.Open Acces

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

COMMUNITY DETECTION IN GRAPHS

Thesis (Ph.D.) - Indiana University, Luddy School of Informatics, Computing, and Engineering/University Graduate School, 2020Community detection has always been one of the fundamental research topics in graph mining. As a type of unsupervised or semi-supervised approach, community detection aims to explore node high-order closeness by leveraging graph topological structure. By grouping similar nodes or edges into the same community while separating dissimilar ones apart into different communities, graph structure can be revealed in a coarser resolution. It can be beneficial for numerous applications such as user shopping recommendation and advertisement in e-commerce, protein-protein interaction prediction in the bioinformatics, and literature recommendation or scholar collaboration in citation analysis. However, identifying communities is an ill-defined problem. Due to the No Free Lunch theorem [1], there is neither gold standard to represent perfect community partition nor universal methods that are able to detect satisfied communities for all tasks under various types of graphs. To have a global view of this research topic, I summarize state-of-art community detection methods by categorizing them based on graph types, research tasks and methodology frameworks. As academic exploration on community detection grows rapidly in recent years, I hereby particularly focus on the state-of-art works published in the latest decade, which may leave out some classic models published decades ago. Meanwhile, three subtle community detection tasks are proposed and assessed in this dissertation as well. First, apart from general models which consider only graph structures, personalized community detection considers user need as auxiliary information to guide community detection. In the end, there will be fine-grained communities for nodes better matching user needs while coarser-resolution communities for the rest of less relevant nodes. Second, graphs always suffer from the sparse connectivity issue. Leveraging conventional models directly on such graphs may hugely distort the quality of generate communities. To tackle such a problem, cross-graph techniques are involved to propagate external graph information as a support for target graph community detection. Third, graph community structure supports a natural language processing (NLP) task to depict node intrinsic characteristics by generating node summarizations via a text generative model. The contribution of this dissertation is threefold. First, a decent amount of researches are reviewed and summarized under a well-defined taxonomy. Existing works about methods, evaluation and applications are all addressed in the literature review. Second, three novel community detection tasks are demonstrated and associated models are proposed and evaluated by comparing with state-of-art baselines under various datasets. Third, the limitations of current works are pointed out and future research tracks with potentials are discussed as well

Generative models for group fMRI data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 151-174).In this thesis, we develop an exploratory framework for design and analysis of fMRI studies. In our framework, the experimenter presents subjects with a broad set of stimuli/tasks relevant to the domain under study. The analysis method then automatically searches for likely patterns of functional specificity in the resulting data. This is in contrast to the traditional confirmatory approaches that require the experimenter to specify a narrow hypothesis a priori and aims to localize areas of the brain whose activation pattern agrees with the hypothesized response. To validate the hypothesis, it is usually assumed that detected areas should appear in consistent anatomical locations across subjects. Our approach relaxes the conventional anatomical consistency constraint to discover networks of functionally homogeneous but anatomically variable areas. Our analysis method relies on generative models that explain fMRI data across the group as collections of brain locations with similar profiles of functional specificity. We refer to each such collection as a functional system and model it as a component of a mixture model for the data. The search for patterns of specificity corresponds to inference on the hidden variables of the model based on the observed fMRI data. We also develop a nonparametric hierarchical Bayesian model for group fMRI data that integrates the mixture model prior over activations with a model for fMRI signals. We apply the algorithms in a study of high level vision where we consider a large space of patterns of category selectivity over 69 distinct images. The analysis successfully discovers previously characterized face, scene, and body selective areas, among a few others, as the most dominant patterns in the data. This finding suggests that our approach can be employed to search for novel patterns of functional specificity in high level perception and cognition.by Danial Lashkari.Ph.D

The role of autistic traits in the perception of emotion from faces and voices: a behavioural and fMRI investigation

This thesis combined behavioural and fMRI approaches in the study of the role of autistic traits in the perception of emotion from faces and voices, addressing research questions concerning: behavioural recognition of a full range of six basic emotions across multiple domains (face, voice, and face-voice); neural correlates during the processing of a wide range of emotional expressions from the face, the voice and the combination of both; neural circuity in responding to an incongruence effect (incongruence vs. congruence). The behavioural study investigated the effects of autistic traits as quantified by the Autism- Spectrum Quotient (AQ) on emotional processing in forms of unimodal (faces, voices) and crossmodal (emotionally congruent face-voice expressions) presentations. In addition, by taking into account the degree of anxiety, the role of co-morbid anxiety on emotion recognition in autistic traits was also explored. Compared to an age and gender-matched group of individuals with low levels of autistic traits (LAQ), a trend of no general deficit was found in individuals with high levels of autistic traits (HAQ) in recognizing emotions presented in faces and voice, regardless of their co-morbid anxiety. However, co-morbid anxiety did moderate the relationship between autistic traits and the recognition of emotions (e.g., fear, surprise, and anger), and this effect tended to be different for the two groups. Specifically, with greater anxiety, individuals with HAQ were found to show less probility of correct response in recognizing the emotion of fear. In contrast, individuals with LAQ showed greater probability of correct response in recognizing fear expressions. For response time, anxiety symptoms tended to be significantly associated with greater response latency in the HAQ group but less response latency in the LAQ group in the recognition of emotional expressions, negative emotions in particular (e.g., anger, fear, and sadness); and this effect of anxiety was not restricted to specific modalities. Despite the absence of finding a general emotion recognition deficit in individuals with considerable autistic traits compared to those with low levels of autistic traits, it did not necessarily mean that these two groups shared same neural network when processing emotions. Therefore, it was useful to explore the neural correlates engaged in processing of emotional expressions in individuals with high levels of autistic traits. Results of this investigation tended to suggest a hypo activation of brain areas dedicated to multimodal integration, particularly for displays showing happiness and disgust. However, both the HAQ group and LAQ group showed similar patterns of brain response (mainly in temporal regions) in response to face-voice combination. In response to emotional stimuli in single modality, the HAQ group activated a number of frontal and temporal regions (e.g., STG, MFG, IFG); these differences may suggested a more effortful and less automatic processing in individual with HAQ. In everyday life, emotional information is often conveyed by both the face and voice. Consequently, concurrently presented information by one source can alter the way that information from the other source is perceived and leads to emotional incongruence if information from the two sources was incongruent. Using fMRI, the present work also examined the neural circuity involved in responding to an incongruence effect (incongruence vs. congruence) from face-voice pairs in a group of individuals with considerable autistic traits. In addition, the differences in brain responses for emotional incongruity between explicit instructions to attend to facial expression and explicit instructions to attend to tone of voice in autistic traits was also explored. It was found that there was no significant incongruence effect between groups, given that individuals with a high level of autistic traits are able to recruit more normative neural networks for processing incongruence as individuals with a low level of autistic traits, regardless of instructions. Though no between group differences, individuals with HAQ showed negative activation in regions involved in the default- mode network. However, taken into account changes of instructions, a stronger incongruence effect was more likely to be occurred in the voice-attend condition for individuals with HAQ while in the face-attend condition for individuals with LAQ