When change is the only constant:The promise of longitudinal neuroimaging in understanding social anxiety disorder

Longitudinal studies offer a unique window into developmental change. Yet, most of what we know about the pathophysiology of psychiatric disorders is based on cross-sectional work. Here, we highlight the importance of adopting a longitudinal approach in order to make progress into the identification of neurobiological mechanisms of social anxiety disorder (SAD). Using examples, we illustrate how longitudinal data can uniquely inform SAD etiology and timing of interventions. The brain’s inherently adaptive quality requires that we model risk correlates of disorders as dynamic in their expression. Developmental theories regarding timing of environmental events, cascading effects and (mal)adaptations of the developing brain will be crucial components of comprehensive, integrative models of SAD. We close by discussing analytical considerations in working with longitudinal, developmental data

End-to-end anomaly detection in stream data

Nowadays, huge volumes of data are generated with increasing velocity through various systems, applications, and activities. This increases the demand for stream and time series analysis to react to changing conditions in real-time for enhanced efficiency and quality of service delivery as well as upgraded safety and security in private and public sectors. Despite its very rich history, time series anomaly detection is still one of the vital topics in machine learning research and is receiving increasing attention. Identifying hidden patterns and selecting an appropriate model that fits the observed data well and also carries over to unobserved data is not a trivial task. Due to the increasing diversity of data sources and associated stochastic processes, this pivotal data analysis topic is loaded with various challenges like complex latent patterns, concept drift, and overfitting that may mislead the model and cause a high false alarm rate. Handling these challenges leads the advanced anomaly detection methods to develop sophisticated decision logic, which turns them into mysterious and inexplicable black-boxes. Contrary to this trend, end-users expect transparency and verifiability to trust a model and the outcomes it produces. Also, pointing the users to the most anomalous/malicious areas of time series and causal features could save them time, energy, and money. For the mentioned reasons, this thesis is addressing the crucial challenges in an end-to-end pipeline of stream-based anomaly detection through the three essential phases of behavior prediction, inference, and interpretation. The first step is focused on devising a time series model that leads to high average accuracy as well as small error deviation. On this basis, we propose higher-quality anomaly detection and scoring techniques that utilize the related contexts to reclassify the observations and post-pruning the unjustified events. Last but not least, we make the predictive process transparent and verifiable by providing meaningful reasoning behind its generated results based on the understandable concepts by a human. The provided insight can pinpoint the anomalous regions of time series and explain why the current status of a system has been flagged as anomalous. Stream-based anomaly detection research is a principal area of innovation to support our economy, security, and even the safety and health of societies worldwide. We believe our proposed analysis techniques can contribute to building a situational awareness platform and open new perspectives in a variety of domains like cybersecurity, and health

Real-time spatio-temporal coherence estimation for autonomous mode identification and invariance tracking

A general method of anomaly detection from time-correlated sensor data is disclosed. Multiple time-correlated signals are received. Their cross-signal behavior is compared against a fixed library of invariants. The library is constructed during a training process, which is itself data-driven using the same time-correlated signals. The method is applicable to a broad class of problems and is designed to respond to any departure from normal operation, including faults or events that lie outside the training envelope

An empirical and computational investigation of variable outcomes in Autism Spectrum Disorder

This thesis had two aims. The first was to investigate variability observed in the profiles of young children with autism spectrum disorder (ASD), and our ability to predict this variability based on measures in infancy. The second aim was to identify the underlying mechanisms that generate this variability. I combined analyses of clinical data sets and data from computational models to investigate the influences shaping atypical developmental trajectories in ASD. The first aim was addressed using secondary data analysis from a prospective longitudinal dataset, the British Autism Study of Infant Siblings. Clinical, behavioural, and parental report data were collected on 54 infants at risk of ASD (by virtue of having an older sibling with the disorder) and 50 low-risk controls at 7, 14, 24 and 36 months. Chapter 2 investigates whether variability differed at a group level, evaluating whether heterogeneity was exaggerated in highrisk groups versus low-risk controls. Cognitive variability scores distinguished infants with ASD at 36 months. Intra-subject variability was then assessed. A more uneven cognitive profile at 24 months was predictive of lower cognitive abilities at 36 months in high-risk infants overall. In Chapter 3, behavioural measures at 14 months were identified as predictors of diagnostic outcome at 36 months in high-risk infants. Initial results highlighted the importance of environmental factors and social and communicative performance. The predictive power of the subsequent statistical regression equations was validated against recently available data from Phase 2 of the BASIS study, with 125 at-risk infants, demonstrating 71% specificity and 81% sensitivity in predicting ASD characteristics at 24 months. In the second half of the thesis, potential mechanisms generating variability in ASD behavioural profiles were investigated via computational modelling. Thomas, Knowland and Karmiloff-Smith (2011) developed a computational model targeting the regressive sub-type of autism based on the hypothesis that regression could be caused by Over-Pruning of brain connectivity. In Chapter 4, this model is extended to capture other observed developmental trajectories in ASD. Regressive and non-regressive subgroups were identified, and each was reliably distinguished by a distinct pattern of neurocomputational parameters. Regression and early onset of pruning were indicative of poorer developmental outcomes overall. Non-regressive subgroups, both typical and atypical, were then used to investigate response to remediation via behavioural intervention. The simulation work represents the first application of populationlevel models of atypical development to intervention. Small but reliable intervention effects were identified, following a discrete phase of intervention. However, the results indicated a limited scope to intervene, with the greater success using compensatory rather than normalisation techniques. The overall results are discussed with reference to the need for convergent methods to shed light on the constraints shaping atypical developmental trajectories in ASD

Hierarchical network structure as the source of power-law frequency spectra (state-trait continua) in living and non-living systems: how physical traits and personalities emerge from first principles in biophysics

What causes organisms to have different body plans and personalities? We address this question by looking at universal principles that govern the morphology and behavior of living systems. Living systems display a small-world network structure in which many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a power-law cluster size distribution. Their dynamics show similar qualities: the timeseries of inner message passing and overt behavior contain high frequencies or 'states' that are nested within lower frequencies or 'traits'. Here, we argue that the nested modular (power-law) dynamics of living systems results from their nested modular (power-law) network structure: organisms 'vertically encode' the deep spatiotemporal structure of their environments, so that high frequencies (states) are produced by many small clusters at the base of a nested-modular hierarchy and lower frequencies (traits) are produced by fewer larger clusters at its top. These include physical as well as behavioral traits. Nested-modular structure causes higher frequencies to be embedded in lower frequencies, producing power-law dynamics. Such dynamics satisfy the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.g. earthquake dynamics, stock market fluctuations). Thus, we provide a single explanation for power-law frequency spectra in both living and non-living systems. If hierarchical structure indeed produces hierarchical dynamics, the development (e.g. during maturation) and collapse (e.g. during disease) of hierarchical structure should leave specific traces in power-law frequency spectra that may serve as early warning signs to system failure. The applications of this idea range from embryology and personality psychology to sociology, evolutionary biology and clinical medicine

Task switching in the prefrontal cortex

The overall goal of this dissertation is to elucidate the cellular and circuit mechanisms underlying flexible behavior in the prefrontal cortex. We are often faced with situations in which the appropriate behavior in one context is inappropriate in others. If these situations are familiar, we can perform the appropriate behavior without relearning how the context relates to the behavior — an important hallmark of intelligence. Neuroimaging and lesion studies have shown that this dynamic, flexible process of remapping context to behavior (task switching) is dependent on prefrontal cortex, but the precise contributions and interactions of prefrontal subdivisions are still unknown. This dissertation investigates two prefrontal areas that are thought to be involved in distinct, but complementary executive roles in task switching — the dorsolateral prefrontal cortex (dlPFC) and the anterior cingulate cortex (ACC). Using electrophysiological recordings from macaque monkeys, I show that synchronous network oscillations in the dlPFC provide a mechanism to flexibly coordinate context representations (rules) between groups of neurons during task switching. Then, I show that, wheras the ACC neurons can represent rules at the cellular level, they do not play a significant role in switching between contexts — rather they seem to be more related to errors and motivational drive. Finally, I develop a set of web-enabled interactive visualization tools designed to provide a multi-dimensional integrated view of electrophysiological datasets. Taken together, these results contribute to our understanding of task switching by investigating new mechanisms for coordination of neurons in prefrontal cortex, clarifying the roles of prefrontal subdivisions during task switching, and providing visualization tools that enhance exploration and understanding of large, complex and multi-scale electrophysiological data

Robust and secure resource management for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.Modern vehicles are examples of complex cyber-physical systems with tens to hundreds of interconnected Electronic Control Units (ECUs) that manage various vehicular subsystems. With the shift towards autonomous driving, emerging vehicles are being characterized by an increase in the number of hardware ECUs, greater complexity of applications (software), and more sophisticated in-vehicle networks. These advances have resulted in numerous challenges that impact the reliability, security, and real-time performance of these emerging automotive systems. Some of the challenges include coping with computation and communication uncertainties (e.g., jitter), developing robust control software, detecting cyber-attacks, ensuring data integrity, and enabling confidentiality during communication. However, solutions to overcome these challenges incur additional overhead, which can catastrophically delay the execution of real-time automotive tasks and message transfers. Hence, there is a need for a holistic approach to a system-level solution for resource management in automotive cyber-physical systems that enables robust and secure automotive system design while satisfying a diverse set of system-wide constraints. ECUs in vehicles today run a variety of automotive applications ranging from simple vehicle window control to highly complex Advanced Driver Assistance System (ADAS) applications. The aggressive attempts of automakers to make vehicles fully autonomous have increased the complexity and data rate requirements of applications and further led to the adoption of advanced artificial intelligence (AI) based techniques for improved perception and control. Additionally, modern vehicles are becoming increasingly connected with various external systems to realize more robust vehicle autonomy. These paradigm shifts have resulted in significant overheads in resource constrained ECUs and increased the complexity of the overall automotive system (including heterogeneous ECUs, network architectures, communication protocols, and applications), which has severe performance and safety implications on modern vehicles. The increased complexity of automotive systems introduces several computation and communication uncertainties in automotive subsystems that can cause delays in applications and messages, resulting in missed real-time deadlines. Missing deadlines for safety-critical automotive applications can be catastrophic, and this problem will be further aggravated in the case of future autonomous vehicles. Additionally, due to the harsh operating conditions (such as high temperatures, vibrations, and electromagnetic interference (EMI)) of automotive embedded systems, there is a significant risk to the integrity of the data that is exchanged between ECUs which can lead to faulty vehicle control. These challenges demand a more reliable design of automotive systems that is resilient to uncertainties and supports data integrity goals. Additionally, the increased connectivity of modern vehicles has made them highly vulnerable to various kinds of sophisticated security attacks. Hence, it is also vital to ensure the security of automotive systems, and it will become crucial as connected and autonomous vehicles become more ubiquitous. However, imposing security mechanisms on the resource constrained automotive systems can result in additional computation and communication overhead, potentially leading to further missed deadlines. Therefore, it is crucial to design techniques that incur very minimal overhead (lightweight) when trying to achieve the above-mentioned goals and ensure the real-time performance of the system. We address these issues by designing a holistic resource management framework called ROSETTA that enables robust and secure automotive cyber-physical system design while satisfying a diverse set of constraints related to reliability, security, real-time performance, and energy consumption. To achieve reliability goals, we have developed several techniques for reliability-aware scheduling and multi-level monitoring of signal integrity. To achieve security objectives, we have proposed a lightweight security framework that provides confidentiality and authenticity while meeting both security and real-time constraints. We have also introduced multiple deep learning based intrusion detection systems (IDS) to monitor and detect cyber-attacks in the in-vehicle network. Lastly, we have introduced novel techniques for jitter management and security management and deployed lightweight IDSs on resource constrained automotive ECUs while ensuring the real-time performance of the automotive systems

Models of atypical development must also be models of normal development

Functional magnetic resonance imaging studies of developmental disorders and normal cognition that include children are becoming increasingly common and represent part of a newly expanding field of developmental cognitive neuroscience. These studies have illustrated the importance of the process of development in understanding brain mechanisms underlying cognition and including children ill the study of the etiology of developmental disorders