Search Results for « Neuroscientists »

Epilepsy is one of the most common and disabling neurologic conditions, yet we have an incomplete understanding of the detailed pathophysiology and, thus, treatment rationale for much of epilepsy. This article reviews the clinical aspects of seizures and epilepsy with the goal of providing neuroscientists an introduction to aspects that might be amenable to scientific investigation. Seizures and epilepsy are defined, diagnostic methods are reviewed, various clinical syndromes are discussed, and aspects of differential diagnosis, treatment, and prognosis are considered to enable neuroscientists to formulate basic and translational research questions.

Abstract There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods. Author Summary Neuroscience is held back by the fact that it is hard to evaluate if a conclusion is correct; the complexity of the systems under study and their experimental inaccessability make the assessment of algorithmic and data analytic technqiues challenging at best. We thus argue for testing approaches using known artifacts, where the correct interpretation is known. Here we present a microprocessor platform as one such test case. We find that many approaches in neuroscience, when used na•vely, fall short of producing a meaningful understanding.

Although social neuroscience is concerned with understanding how the brain interacts with its social environment, prevailing research in the field has primarily considered the human brain in isolation, deprived of its rich social context. Emerging work in social neuroscience that leverages tools from network analysis has begun to advance knowledge of how the human brain influences and is influenced by the structures of its social environment. In this paper, we provide an overview of key theory and methods in network analysis (especially for social systems) as an introduction for social neuroscientists who are interested in relating individual cognition to the structures of an individual's social environments. We also highlight some exciting new work as examples of how to productively use these tools to investigate questions of relevance to social neuroscientists. We include tutorials to help with practical implementations of the concepts that we discuss. We conclude by highlighting a broad range of exciting research opportunities for social neuroscientists who are interested in using network analysis to study social systems.

Whilst the origins of the words “neurology” and “neurologist” may be traced to the seventeenth and nineteenth centuries respectively, “neuroscience” and “neuroscientist” prove to have been of much more recent coinage, probably in the third quarter of the twentieth century. This article briefly explores those origins and then asks whether it is legitimate to fit the terms retrospectively to practice and practitioners investigating the nervous system in earlier times.

The purpose of this contribution is threefold. First, is to acquaint neuroscientists with the area of psychology known as comparative psychology. Comparative psychology is the oldest of the organized social sciences with the term appearing as early as 1808. Many of the myriad issues of experimental design routinely faced by comparative psychologists are directly applicable to neuroscience. These issues include consistent definitions of psychological phenomena, the use of Morgan's canon to reduce unbridled anthropomorphism, and observation oriented modeling as a new statistical procedure to increase replication. Second, is a discussion of early comparative methods that may be of value to contemporary neuroscientists. Third, how the comparative approach can help the neuroscientist limit unfounded generalizations across species and develop more animal-friendly behavioral testing options tailored for the species or strain of interest. The articles closes with some recommendations on how comparative psychologists and neuroscientists can work more closely together.