Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

A platform for visual learning dc by Charles C. Kemp.

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 77-78).M.Eng

Finding Nonlinear Relationships in Functional Magnetic Resonance Imaging Data with Genetic Programming

The human brain is a complex, nonlinear dynamic chaotic system that is poorly understood. When faced with these difficult to understand systems, it is common to observe the system and develop models such that the underlying system might be deciphered. When observing neurological activity within the brain with functional magnetic resonance imaging (fMRI), it is common to develop linear models of functional connectivity; however, these models are incapable of describing the nonlinearities we know to exist within the system. A genetic programming (GP) system was developed to perform symbolic regression on recorded fMRI data. Symbolic regression makes fewer assumptions than traditional linear tools and can describe nonlinearities within the system. Although GP is a powerful form of machine learning that has many drawbacks (computational cost, overfitting, stochastic), it may provide new insights into the underlying system being studied. The contents of this thesis are presented in an integrated article format. For all articles, data from the Human Connectome Project were used. In the first article, nonlinear models for 507 subjects performing a motor task were created. These nonlinear models generated by GP contained fewer ROI than what would be found with traditional, linear tools. It was found that the generated nonlinear models would not fit the data as well as the linear models; however, when compared to linear models containing a similar number of ROI, the nonlinear models performed better. Ten subjects performing 7 tasks were studied in article two. After improvements to the GP system, the generated nonlinear models outperformed the linear models in many cases and were never significantly worse than the linear models. Forty subjects performing 7 tasks were studied in article three. Newly generated nonlinear models were applied to unseen data from the same subject performing the same task (intrasubject generalization) and many nonlinear models generalized to unseen data better than the linear models. The nonlinear models were applied to unseen data from other subjects performing the same task (intersubject generalization) and were not capable of generalizing as well as the linear

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments

Long-term neural and physiological phenotyping of a single human

Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders

Data-Driven Framework for Understanding & Modeling Ride-Sourcing Transportation Systems

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for all trips made within the city since November 1, 2018. The data comprises the trip ends (pick-up and drop-off locations), trip timestamps, trip length and duration, fare including tipping amounts, and whether the trip was authorized to be shared (pooled) with another passenger or not. Therefore, the main goal of this dissertation is to develop a comprehensive data-driven framework to understand and model the system using this data from Chicago, in a reproducible and transferable fashion. Using data fusion approach, sociodemographic, economic, parking supply, transit availability and accessibility, built environment and crime data are collected from open sources to develop this framework. The framework is predicated on three pillars of analytics: (1) explorative and descriptive analytics, (2) diagnostic analytics, and (3) predictive analytics. The dissertation research framework also provides a guide on the key spatial and behavioral explanatory variables shaping the utility of the mode, driving the demand, and governing the interdependencies between the demand’s willingness to share and surge price. Thus, the key findings can be readily challenged, verified, and utilized in different geographies. In the explorative and descriptive analytics, the ride-sourcing system’s spatial and temporal dimensions of the system are analyzed to achieve two objectives: (1) explore, reveal, and assess the significance of spatial effects, i.e., spatial dependence and heterogeneity, in the system behavior, and (2) develop a behavioral market segmentation and trend mining of the willingness to share. This is linked to the diagnostic analytics layer, as the revealed spatial effects motivates the adoption of spatial econometric models to analytically identify the ride-sourcing system determinants. Multiple linear regression (MLR) is used as a benchmark model against spatial error model (SEM), spatially lagged X (SLX) model, and geographically weighted regression (GWR) model. Two innovative modeling constructs are introduced deal with the ride-sourcing system’s spatial effects and multicollinearity: (1) Calibrated Spatially Lagged X Ridge Model (CSLXR) and Calibrated Geographically Weighted Ridge Regression (CGWRR) in the diagnostic analytics layer. The identified determinants in the diagnostic analytics layer are then fed into the predictive analytics one to develop an interpretable machine learning (ML) modeling framework. The system’s annual average weekday origin-destination (AAWD OD) flow is modeled using the following state-of-the-art ML models: (1) Multilayer Perceptron (MLP) Regression, (2) Support Vector Machines Regression (SVR), and (3) Tree-based ensemble learning methods, i.e., Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost). The innovative modeling construct of CGWRR developed in the diagnostic analytics is then validated in a predictive context and is found to outperform the state-of-the-art ML models in terms of testing score of 0.914, in comparison to 0.906 for XGBoost, 0.84 for RFR, 0.89 for SVR, and 0.86 for MLP. The CGWRR exhibits outperformance as well in terms of the root mean squared error (RMSE) and mean average error (MAE). The findings of this dissertation partially bridge the gap between the practice and the research on ride-sourcing transportation systems understanding and integration. The empirical findings made in the descriptive and explorative analytics can be further utilized by regional agencies to fill practice and policymaking gaps on regulating ride-sourcing services using corridor or cordon toll, optimally allocating standing areas to minimize deadheading, especially during off-peak periods, and promoting the ride-share willingness in disadvantage communities. The CGWRR provides a reliable modeling and simulation tool to researchers and practitioners to integrate the ride-sourcing system in multimodal transportation modeling frameworks, simulation testbed for testing long-range impacts of policies on ride-sourcing, like improved transit supply, congestions pricing, or increased parking rates, and to plan ahead for similar futuristic transportation modes, like the shared autonomous vehicles

Development Of Human Brain Network Architecture Underlying Executive Function

The transition from late childhood to adulthood is characterized by refinements in brain structure and function that support the dynamic control of attention and goal-directed behavior. One broad domain of cognition that undergoes particularly protracted development is executive function, which encompasses diverse cognitive processes including working memory, inhibitory control, and task switching. Delineating how white matter architecture develops to support specialized brain circuits underlying individual differences in executive function is critical for understanding sources of risk-taking behavior and mortality during adolescence. Moreover, neuropsychiatric disorders are increasingly understood as disorders of brain development, are marked by failures of executive function, and are linked to the disruption of evolving brain connectivity. Network theory provides a parsimonious framework for modeling how anatomical white matter pathways support synchronized fluctuations in neural activity. However, only sparse data exists regarding how the maturation of white matter architecture during human brain development supports coordinated fluctuations in neural activity underlying higher-order cognitive ability. To address this gap, we capitalize on multi-modal neuroimaging and cognitive phenotyping data collected as part of the Philadelphia Neurodevelopmental Cohort (PNC), a large community-based study of brain development. First, diffusion tractography methods were applied to characterize how the development of structural brain network topology supports domain-specific improvements in cognitive ability (n=882, ages 8-22 years old). Second, structural connectivity and task-based functional connectivity approaches were integrated to describe how the development of anatomical constraints on functional communication support individual differences in executive function (n=727, ages 8-23 years old). Finally, the systematic impact of head motion artifact on measures of structural connectivity were characterized (n=949, ages 8-22 years old), providing important guidelines for studying the development of structural brain network architecture. Together, this body of work expands our understanding of how developing white matter connectivity in youth supports the emergence of functionally specialized circuits underlying executive processing. As diverse types of psychopathology are increasingly linked to atypical brain maturation, these findings could collectively lead to earlier diagnosis and personalized interventions for individuals at risk for developing mental disorders

Sensorimotor Modulations by Cognitive Processes During Accurate Speech Discrimination: An EEG Investigation of Dorsal Stream Processing

Internal models mediate the transmission of information between anterior and posterior regions of the dorsal stream in support of speech perception, though it remains unclear how this mechanism responds to cognitive processes in service of task demands. The purpose of the current study was to identify the influences of attention and working memory on sensorimotor activity across the dorsal stream during speech discrimination, with set size and signal clarity employed to modulate stimulus predictability and the time course of increased task demands, respectively. Independent Component Analysis of 64–channel EEG data identified bilateral sensorimotor mu and auditory alpha components from a cohort of 42 participants, indexing activity from anterior (mu) and posterior (auditory) aspects of the dorsal stream. Time frequency (ERSP) analysis evaluated task-related changes in focal activation patterns with phase coherence measures employed to track patterns of information flow across the dorsal stream. ERSP decomposition of mu clusters revealed event-related desynchronization (ERD) in beta and alpha bands, which were interpreted as evidence of forward (beta) and inverse (alpha) internal modeling across the time course of perception events. Stronger pre-stimulus mu alpha ERD in small set discrimination tasks was interpreted as more efficient attentional allocation due to the reduced sensory search space enabled by predictable stimuli. Mu-alpha and mu-beta ERD in peri- and post-stimulus periods were interpreted within the framework of Analysis by Synthesis as evidence of working memory activity for stimulus processing and maintenance, with weaker activity in degraded conditions suggesting that covert rehearsal mechanisms are sensitive to the quality of the stimulus being retained in working memory. Similar ERSP patterns across conditions despite the differences in stimulus predictability and clarity, suggest that subjects may have adapted to tasks. In light of this, future studies of sensorimotor processing should consider the ecological validity of the tasks employed, as well as the larger cognitive environment in which tasks are performed. The absence of interpretable patterns of mu-auditory coherence modulation across the time course of speech discrimination highlights the need for more sensitive analyses to probe dorsal stream connectivity