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"Frank, Michael J."
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Computational psychiatry as a bridge from neuroscience to clinical applications
The complexity of problems and data in psychiatry requires powerful computational approaches. Computational psychiatry is an emerging field encompassing mechanistic theory-driven models and theoretically agnostic data-driven analyses that use machine-learning techniques. Clinical applications will benefit from relating theoretically meaningful process variables to complex psychiatric outcomes through data-driven techniques.
Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ, the brain, and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data driven and theory driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.
Journal Article
Explaining the history of American foreign relations
\"Explaining the History of American Foreign Relations, 3rd Edition presents substantially revised and new essays on traditional themes such as national security, corporatism, borderlands history, and international relations theory. The book also highlights such innovative conceptual approaches and analytical methods as computational analysis, symbolic borders, modernization and technopolitics, nationalism, non-state actors, domestic politics, exceptionalism, legal history, nation branding, gender, race, political economy, memory, psychology, emotions, and the senses.\"--Provided by publisher.
Book
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies
Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate individual variation in parameters over a population of subjects, leveraging their population-level distributions. Second, candidate models are themselves compared, and individual variation in the expressed model estimated, according to the fits of the models to each subject. The interdependence between these two problems arises because the relevant population for estimating parameters of a model depends on which other subjects express the model. Here, we propose a hierarchical Bayesian inference (HBI) framework for concurrent model comparison, parameter estimation and inference at the population level, combining previous approaches. We show that this framework has important advantages for both parameter estimation and model comparison theoretically and experimentally. The parameters estimated by the HBI show smaller errors compared to other methods. Model comparison by HBI is robust against outliers and is not biased towards overly simplistic models. Furthermore, the fully Bayesian approach of our theory enables researchers to make inference on group-level parameters by performing HBI t-test.
Journal Article
L.A. River
Three centuries ago, the Los Angeles River meandered through marshes and forests of willow and sycamore. Trout spawned in its waters, and grizzly bears roamed its shores in search of food. The river and its adjacent woodlands helped support one of the largest concentrations of indigenous peoples in North America, and it also largely determined the location of the first Spanish Pueblo and ultimately the city of Los Angeles. The river was also the city's sole source of water for more than a century before flood-control projects made the L.A. River what it is today. 0Michael Kolster, in 'L.A. River', relies on a nineteenth-century photographic technology to render the Los Angeles River today, from its headwaters in Canoga Park and the suburbs of the San Fernando Valley to its mouth at the Pacific Ocean in Long Beach. Coincidentally, the founding of the city of Los Angeles and California's achievement of statehood in 1850 coincide historically with the invention of the wet-plate photographic process, forever linking the city and state with the centrality of photography. The moving images that define L.A. River show a feature of the city's landscape that initially attracted native peoples to its banks and gave rise to the formation of our nation's second-largest city.
Book
Advances in the computational understanding of mental illness
Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. It encompasses both data-driven and theory-driven efforts. Here, recent advances in theory-driven work are reviewed. We argue that the brain is a computational organ. As such, an understanding of the illnesses arising from it will require a computational framework. The review divides work up into three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian inference and reinforcement learning. We discuss both general and specific challenges for the field, and suggest ways forward.
Journal Article
International handbook of globalization and world cities
\"This Handbook offers an unrivaled overview of current research into how globalization is affecting the external relations and internal structures of major cities in the world. By treating cities at a global scale, it focuses on the 'stretching' of urban functions beyond specific place locations, without losing sight of the multiple divisions in contemporary world cities. The book firmly bases city networks in their historical context, critically discusses contemporary concepts and key empirical measures, and analyzes major issues relating to world city infrastructures, economies, governance and divisions. The variety of urban outcomes in contemporary globalization is explored through detailed case studies. Edited by leading scholars of the Globalization and World Cities (GaWC) Research Network and written by over 60 experts in the field, the Handbook is a unique resource for students, researchers and academics in urban and globalization studies as well as for city professionals in planning and policy.\"--Page 4 of cover.
Book
From reinforcement learning models to psychiatric and neurological disorders
Reinforcement learning models have provided insight into the functions of dopamine and cortico-basal ganglia-thalamo-cortical circuits. Here the authors review the literature suggesting that these models can also be applied to improving our understanding of dysfunction in this system, particularly in the context of disease.
Over the last decade and a half, reinforcement learning models have fostered an increasingly sophisticated understanding of the functions of dopamine and cortico-basal ganglia-thalamo-cortical (CBGTC) circuits. More recently, these models, and the insights that they afford, have started to be used to understand important aspects of several psychiatric and neurological disorders that involve disturbances of the dopaminergic system and CBGTC circuits. We review this approach and its existing and potential applications to Parkinson's disease, Tourette's syndrome, attention-deficit/hyperactivity disorder, addiction, schizophrenia and preclinical animal models used to screen new antipsychotic drugs. The approach's proven explanatory and predictive power bodes well for the continued growth of computational psychiatry and computational neurology.
Journal Article
On the normative advantages of dopamine and striatal opponency for learning and choice
The basal ganglia (BG) contribute to reinforcement learning (RL) and decision-making, but unlike artificial RL agents, it relies on complex circuitry and dynamic dopamine modulation of opponent striatal pathways to do so. We develop the OpAL* model to assess the normative advantages of this circuitry. In OpAL*, learning induces opponent pathways to differentially emphasize the history of positive or negative outcomes for each action. Dynamic DA modulation then amplifies the pathway most tuned for the task environment. This efficient coding mechanism avoids a vexing explore–exploit tradeoff that plagues traditional RL models in sparse reward environments. OpAL* exhibits robust advantages over alternative models, particularly in environments with sparse reward and large action spaces. These advantages depend on opponent and nonlinear Hebbian plasticity mechanisms previously thought to be pathological. Finally, OpAL* captures risky choice patterns arising from DA and environmental manipulations across species, suggesting that they result from a normative biological mechanism.
Journal Article