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Functional neuroimaging as a catalyst for integrated neuroscience
Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.
This Perspective reviews successful applications of functional magnetic resonance imaging (fMRI) and presents a case for fMRI as a central hub on which to integrate the dispersed subfields of systems, cognitive, computational and clinical neuroscience.
Journal Article
Data-driven computational neuroscience : machine learning and statistical models
\"Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered\"-- Provided by publisher.
BOOK
A study of problems encountered in Granger causality analysis from a neuroscience perspective
Granger causality methods were developed to analyze the flow of information between time series. These methods have become more widely applied in neuroscience. Frequency-domain causality measures, such as those of Geweke, as well as multivariate methods, have particular appeal in neuroscience due to the prevalence of oscillatory phenomena and highly multivariate experimental recordings. Despite its widespread application in many fields, there are ongoing concerns regarding the applicability of Granger causality methods in neuroscience. When are these methods appropriate? How reliably do they recover the system structure underlying the observed data? What do frequency-domain causality measures tell us about the functional properties of oscillatory neural systems? In this paper, we analyze fundamental properties of Granger–Geweke (GG) causality, both computational and conceptual. Specifically, we show that (i) GG causality estimates can be either severely biased or of high variance, both leading to spurious results; (ii) even if estimated correctly, GG causality estimates alone are not interpretable without examining the component behaviors of the system model; and (iii) GG causality ignores critical components of a system’s dynamics. Based on this analysis, we find that the notion of causality quantified is incompatible with the objectives of many neuroscience investigations, leading to highly counterintuitive and potentially misleading results. Through the analysis of these problems, we provide important conceptual clarification of GG causality, with implications for other related causality approaches and for the role of causality analyses in neuroscience as a whole.
Journal Article
The neuroscience of you : how every brain is different and how to understand yours
\"From University of Washington professor Chantel Prat comes The Neuroscience of You, a rollicking adventure into the human brain that reveals the surprising truth about neuroscience, shifting our focus from what's average to an understanding of how every brain is different, exactly why our quirks are important, and what this means for each of us. With style and wit, Chantel Prat takes us on a tour of the meaningful ways that our brains are dissimilar from one another. Using real-world examples, along with take-them-yourself tests and quizzes, she shows you how to identify the strengths and weakness of your own brain, while learning what might be going on in the brains of those who are unlike you. With sections like \"Focus,\" \"Navigate,\" and \"Connect,\" The Neuroscience of You helps us see how brains that are engineered differently ultimately take diverse paths when it comes time to prioritize information, use what they've learned from experience, relate to other people, and so much more. While other scientists focus on how \"the\" brain works \"on average,\" Prat argues that our obsession with commonalities has slowed our progress toward understanding the very things that make each of us unique and interesting. Her field-leading research, employing cutting-edge technology, reveals the truth: Complicated as it may be, no two brains are alike. And individual differences in brain functioning are as pervasive as they are fundamental to defining what \"normal\" looks like. Adages such as, \"I'm not wired that way\" intuitively point to the fact that the brains we're piloting, educating, and parenting are wonderfully distinct, explaining a whole host of phenomena, from how easily a person might learn a second language in adulthood to whether someone feels curious or threatened when faced with new information. This book invites the reader to understand themselves and others by zooming in so close that we all look gray and squishy\"-- Provided by publisher.
Book
Recent advances in neurotechnologies with broad potential for neuroscience research
Interest in deciphering the fundamental mechanisms and processes of the human mind represents a central driving force in modern neuroscience research. Activities in support of this goal rely on advanced methodologies and engineering systems that are capable of interrogating and stimulating neural pathways, from single cells in small networks to interconnections that span the entire brain. Recent research establishes the foundations for a broad range of creative neurotechnologies that enable unique modes of operation in this context. This review focuses on those systems with proven utility in animal model studies and with levels of technical maturity that suggest a potential for broad deployment to the neuroscience community in the relatively near future. We include a brief summary of existing and emerging neuroscience techniques, as background for a primary focus on device technologies that address associated opportunities in electrical, optical and microfluidic neural interfaces, some with multimodal capabilities. Examples of the use of these technologies in recent neuroscience studies illustrate their practical value. The vibrancy of the engineering science associated with these platforms, the interdisciplinary nature of this field of research and its relevance to grand challenges in the treatment of neurological disorders motivate continued growth of this area of study.This review summarizes advances in electrical, optical and microfluidic neural interfaces with characteristics that suggest near-term potential for broad deployment to the neuroscience community.
Journal Article
The science of the brain
Explores the human brain using simple flowcharts to break down difficult concepts into accessible chunks.
Book
Social cognitive network neuroscience
Abstract
Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks—collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach—which leverages methods from the field of network neuroscience and graph theory—can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.
Journal Article