Advances in Deep Brain Stimulation for Parkinson's

Total Page:16

File Type:pdf, Size:1020Kb

Advances in Deep Brain Stimulation for Parkinson's ADVANCES IN DEEP BRAIN STIMULATION FOR PARKINSON’S DISEASE PhD Thesis in Biomedical Engineering CARMEN CÁMARA Kevin Warwick Ernesto Pereda PhD in Electrical Engineering PhD in Neuroscience UNIVERSIDAD POLITÉCNICA DE MADRID PhD in Biomedical Engineering EscueIa Técnica Superior de Ingenieros de Telecomunicación (ETSIT UPM) Centre for Biomedical Technology (CTB) ADVANCES IN DEEP BRAIN STIMULATION FOR PARKINSON’S DISEASE Carmen Cámara Núñez MSc in Biomedical Engineering PhD in Computer Science Advisors Kevin Warwick Ernesto Pereda PhD in Electrical Engineering PhD in Neuroscience Contents Acknowledgements iii Summary ix Resumen xi Acronyms xiii I Introduction 1 1 Introduction to Parkinson’s Disease 3 1.1 Brief introduction to the brain . 4 1.2 Functioning of the basal ganglia . 8 1.3 Pathophysiology of Parkinson’s disease. 10 1.4 Treatments . 16 1.5 Deep brain stimulation . 17 2 Introduction to Neuroimaging and Analysis Methods 21 2.1 Intracraneal Recordings . 25 2.2 Magnetoencephalography . 26 2.3 Analysis Methods. 31 2.3.1 Spectral Analysis . 31 2.3.2 Synchronization Analysis . 33 2.3.3 Non-linear Dynamical Analysis . 37 2.4 Statistical Methods. 39 2.4.1 Machine Learning . 40 2.4.2 Cluster-based permutation test . 42 II DBS Mistery 45 3 DBS effects on cortico-cortical functional connectivity 47 3.1 Effects of deep brain stimulation . 48 3.2 Subjects . 50 3.3 MEG . 51 3.4 Analysis . 53 3.5 Results . 58 3.6 Conclusions . 63 III Closed-loop DBS 65 4 Closed-loop Deep Brain Stimulation 67 4.1 Dataset. 71 i ii Contents 5 Resting tremor classification and detection in Parkinson’s dis- ease patients 75 5.1 Tremor . 76 5.2 Data Preparation and feature extraction . 77 5.3 Proposed System & Results. 79 5.4 Discussion (Performance Evaluation) . 88 5.5 Conclusions . 88 6 A fuzzy inference system for closed-loop deep brain stimula- tion in Parkinson’s disease 91 6.1 Methods . 92 6.2 Fuzzy System . 93 6.3 System . 95 6.4 Results . 99 6.5 Discussion. 102 6.6 Conclusions . 103 7 Non-linear dynamical analysis of resting tremor for demand- driven deep brain stimulation 105 7.1 Data Preparation . 106 7.2 Recurrence Networks . 107 7.3 Network Measures . 109 7.4 System . 112 7.5 Discussion. 117 7.6 Conclusions . 119 8 Closed-loop deep brain stimulation based on a stream-clustering system 121 8.1 Neural oscillations versus synchronization . 122 8.2 Signal Preprocessing . 124 8.3 Synchronization analysis . 124 8.4 Data Stream Mining . 126 8.5 Stream Clustering . 128 8.6 System Description . 130 8.7 Experimental Analysis. 131 8.8 Discussion. 138 IV Conclusions & Future perspectives 143 9 General Conclusions & Future Perspectives 145 Curriculum Vitæ 151 Bibliography 153 Acknowledgements t is said that having a good first boss (in my case, supervisors) is crucial since I one will end up looking like him. If this is true, I have been fortunate. Firstly for the high worth of my tutors, but also with the centre in which this thesis has been developed. Here, I want to thank all the people who have helped me in the developing of this thesis. Kevin I discovered who would later be my supervisor, in a television program of scientific divulgation. It was a program in which the researcher interviewed showed his work to the general public. I was still studying the degree, but I thought, ”I hope some- day I can do half the impressive things this guy does”. When I decided I wanted to do the thesis, I got up the courage (of course thinking he would not answer (because he was a superstar)), and sent an email to Kevin Warwick. But he did reply (I still sometimes flip with it), and he gave me an appointment in Reading, UK, and agreed to supervise my thesis! Kevin was collaborating with Tipu Aziz, from Oxford University, in the design of intelligent systems for the treatment of Parkinson’s disease. So he proposed to me to work on that in my thesis. At the time, I only had a master’s degree in Artificial Intelligence. Since then, I have also learned Bioengineering and some Neuroimag- ing and Neuroscience. It has been a thesis’s topic that has led me to learn a little bit of quite a few things. So it turned out to be the perfect research area for the thesis. Why isn’t this, in the end, the goal of a thesis, exploration and continuous learning?. So I have to thank you, Kevin, for choosing this topic for me, because it has taken me to wonderful places. I have not been lucky enough to have Kevin in the same lab as me, nor in the same building, not even in the same country. Despite this, his influence has been inspiring; his work will always be an inspiration to me. Thank you, Kevin, for giving me this opportunity, because everything I have learned, all this adventure, started on that visit to Reading. Thanks to Tipu as well for the time he has dedicated to me and the data he has kindly lent me. Ernesto I met Ernesto at a seminar he taught at the master’s in bioengineering at UPM. He is an excellent teacher, so I had no doubt that he would be an excellent thesis tutor as well. Ernesto started supervising me in the middle of the thesis. But it has been long enough to learn from him. Like Kevin, he was not in the same geographical place as me. Maybe we have been able to see each other only once or twice a year. iii iv Acknowledgements But on those occasions, Ernesto sat down and talk to me for a long time to define the work for the next few months. He always has excellent ideas, so talking to him is very motivating. Having a skype with him always serves to renew the desire to do many cool things, so I hope they never finish. I admire very much your ability to get so much work done and commit so much to the university. I don’t know how you get there you have time for so much, but I hope part of your future time may be to keep doing cool things together. Thanks, Ernesto. Center for Biomedical Technology ++ I knew the CTB during the master’s degree in bioengineering, and I decided it was the best place to enrol my thesis. Many students want to be there, and there is no place for everyone. For that reason, I have to thank here Fernando Maestú for giving the opportunity to me. The CTB is a place full of very talented people. It thus becomes an inspiring place, which encourages the desire to learn. This is thanks to the direction of Fernando, but also of all the members of the lab, so I want to thank here all the students I’ve coincided with there. I want to give special thanks to Ricardo Bruña. My teacher of ”signals and sys- tems” during the master, my master’s final project supervisor, and my friend since then. Talking to him is a little frustrating because you realise all you have left to learn. But he always has time to sit with you in front of a blank sheet of paper and paints you drawings and mathematical formulas so that you understand things at a low level (the way I like it). Everything that I know about signal processing has been taught to me by him. And I am sure he will teach me everything I still have to know. I hope we keep working together a lot; it is the best things about the CTB. Although he is not a CTB colleague, I would like to thank Narayan here as well. He taught me Chaos Theory and his thesis was part of the basic bibliography of my bedside table (I’ve read it more times than mine). Thank you for being so impres- sive and spend time with me, I’ll never forget your wonderful tutorials at Aalto. Finally, thanks to the European Institute of Innovation and Technology (EIT Digital) for choosing me to do my thesis within their elitist doctoral school. It has allowed me to do an infinitely more complete training, with a vision of entrepreneurship and digital world (almost as in silicon valley), which is not possible outside its program. Thanks to the colleagues chosen with me, for the time together during the long training courses. We had a great time, and it was a vital experience that I will never forget. This thesis has never been merely a goal for me, but the beginning of a path, that I have enjoyed so much (I know this is not what a thesis student typically says, but I swear it is real). I hope to keep learning and doing new, inspiring, challenging and wonderful things with all of you, Kevin, Ernesto and colleagues. Thank you all. Acknowledgements v vi Acknowledgements Mi querida familia Nada de lo que una persona es o logra es por mérito exclusivo, sino una combi- nación de toda la educación recibida y una pizca de suerte. Del primer ingrediente, los únicos responsables son mis padres. Siempre nos han dicho que teníamos el potencial para hacer lo que quisieramos y nos han animado a hacerlo desde pe- queñas. Convencieron a la directora de mi primer colegio de que podía ir a clases de informatica con 6 años, con estudiantes diez años mayores que yo (quizás porque yo también era muy testaruda). Finalmente pude ir y lo hice bastante bien.
Recommended publications
  • Are Systems Neuroscience Explanations Mechanistic?
    Are Systems Neuroscience Explanations Mechanistic? Carlos Zednik [email protected] Institute of Cognitive Science, University of Osnabrück 49069 Osnabrück, Germany Paper to be presented at: Philosophy of Science Association 24th Biennial Meeting (Chicago, IL), November 2014 Abstract Whereas most branches of neuroscience are thought to provide mechanistic explanations, systems neuroscience is not. Two reasons are traditionally cited in support of this conclusion. First, systems neuroscientists rarely, if ever, rely on the dual strategies of decomposition and localization. Second, they typically emphasize organizational properties over the properties of individual components. In this paper, I argue that neither reason is conclusive: researchers might rely on alternative strategies for mechanism discovery, and focusing on organization is often appropriate and consistent with the norms of mechanistic explanation. Thus, many explanations in systems neuroscience can also be viewed as mechanistic explanations. 1 1. Introduction There is a widespread consensus in philosophy of science that neuroscientists provide mechanistic explanations. That is, they seek the discovery and description of the mechanisms responsible for the behavioral and neurological phenomena being explained. This consensus is supported by a growing philosophical literature on past and present examples from various branches of neuroscience, including molecular (Craver 2007; Machamer, Darden, and Craver 2000), cognitive (Bechtel 2008; Kaplan and Craver 2011), and computational
    [Show full text]
  • Towards Chipification: the Multifunctional Body Art of the Net Generation
    University of Wollongong Research Online Faculty of Engineering and Information Faculty of Informatics - Papers (Archive) Sciences 1-6-2006 Towards chipification: the multifunctional body art of the Net Generation Katina Michael University of Wollongong, [email protected] M G. Michael University of Wollongong, [email protected] Follow this and additional works at: https://ro.uow.edu.au/infopapers Part of the Physical Sciences and Mathematics Commons Recommended Citation Michael, Katina and Michael, M G.: Towards chipification: the multifunctional body art of the Net Generation 2006. https://ro.uow.edu.au/infopapers/372 Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: [email protected] Towards chipification: the multifunctional body art of the Net Generation Abstract This paper considers the trajectory of the microchip within the context of converging disciplines to predict the realm of likely possibilities in the shortterm future of the technology. After presenting the evolutionary development from first generation to fourth generation wearable computing, a case study on medical breakthroughs using implantable devices is presented. The findings of the paper suggest that before too long, implantable devices will become commonplace for everyday humancentric applications. The paradigm shift is exemplified in the use of microchips, from their original purpose in identifying humans and objects to its ultimate trajectory with multifunctional capabilities buried within the body. Keywords wearable computing, chip implants, emerging technologies, culture, smart clothes, biomedicine, biochips, electrophorus Disciplines Physical Sciences and Mathematics Publication Details This paper was originally published as: Michael, K & Michael, MG, Towards chipification: the multifunctional body art of the Net Generation, Cultural Attitudes Towards Technology and Communication 2006 Conference, Murdoch University, Western Australia, 2006, 622-641.
    [Show full text]
  • Systems Neuroscience of Mathematical Cognition and Learning
    CHAPTER 15 Systems Neuroscience of Mathematical Cognition and Learning: Basic Organization and Neural Sources of Heterogeneity in Typical and Atypical Development Teresa Iuculano, Aarthi Padmanabhan, Vinod Menon Stanford University, Stanford, CA, United States OUTLINE Introduction 288 Ventral and Dorsal Visual Streams: Neural Building Blocks of Mathematical Cognition 292 Basic Organization 292 Heterogeneity in Typical and Atypical Development 295 Parietal-Frontal Systems: Short-Term and Working Memory 296 Basic Organization 296 Heterogeneity in Typical and Atypical Development 299 Lateral Frontotemporal Cortices: Language-Mediated Systems 302 Basic Organization 302 Heterogeneity in Typical and Atypical Development 302 Heterogeneity of Function in Numerical Cognition 287 https://doi.org/10.1016/B978-0-12-811529-9.00015-7 © 2018 Elsevier Inc. All rights reserved. 288 15. SYSTEMS NEUROSCIENCE OF MATHEMATICAL COGNITION AND LEARNING The Medial Temporal Lobe: Declarative Memory 306 Basic Organization 306 Heterogeneity in Typical and Atypical Development 306 The Circuit View: Attention and Control Processes and Dynamic Circuits Orchestrating Mathematical Learning 310 Basic Organization 310 Heterogeneity in Typical and Atypical Development 312 Plasticity in Multiple Brain Systems: Relation to Learning 314 Basic Organization 314 Heterogeneity in Typical and Atypical Development 315 Conclusions and Future Directions 320 References 324 INTRODUCTION Mathematical skill acquisition is hierarchical in nature, and each iteration of increased proficiency builds on knowledge of a lower-level primitive. For example, learning to solve arithmetical operations such as “3 + 4” requires first an understanding of what numbers mean and rep- resent (e.g., the symbol “3” refers to the quantity of three items, which derives from the ability to attend to discrete items in the environment).
    [Show full text]
  • An Introduction to Control Systems; K. Warwick
    An Introduction to Control Systems; K. Warwick 362 pages; World Scientific, 1996; K. Warwick; 9810225970, 9789810225971; 1996; An Introduction to Control Systems; This significantly revised edition presents a broad introduction to Control Systems and balances new, modern methods with the more classical. It is an excellent text for use as a first course in Control Systems by undergraduate students in all branches of engineering and applied mathematics. The book contains: A comprehensive coverage of automatic control, integrating digital and computer control techniques and their implementations, the practical issues and problems in Control System design; the three-term PID controller, the most widely used controller in industry today; numerous in-chapter worked examples and end-of-chapter exercises. This second edition also includes an introductory guide to some more recent developments, namely fuzzy logic control and neural networks. file download wici.pdf The Breakthrough in Artificial Intelligence; While horror films and science fiction have repeatedly warned of robots running amok, Kevin Warwick takes the threats out of the realm of fiction and into the real world, truly; Computers; K. Warwick; 307 pages; ISBN:0252072235; 1997; March of the Machines Control Bruce O. Watkins; Introduction to control systems; UOM:39015002007683; Technology & Engineering; 625 pages; 1969 Robot Control; ISBN:0863411282; Jan 1, 1988; K. Warwick, Alan Pugh; Technology & Engineering; 238 pages; Theory and Applications Automatic control; ISBN:0750622989; Davinder K. Anand; 730 pages; Since the second edition of this classic text for students and engineers appeared in 1984, the use of computer-aided design software has become an important adjunct to the; Introduction to Control Systems; Jan 1, 1995 An Introduction to Control Systems pdf download 596 pages; Mar 18, 1993; STANFORD:36105004050907; based on the proceedings of a conference on Robotics, applied mathematics and computational aspects; K.
    [Show full text]
  • A Meta-Analysis of Functional MRI Results
    RESEARCH Cooperating yet distinct brain networks engaged during naturalistic paradigms: A meta-analysis of functional MRI results 1 1 1 2 Katherine L. Bottenhorn , Jessica S. Flannery , Emily R. Boeving , Michael C. Riedel , 3,4 1 2 Simon B. Eickhoff , Matthew T. Sutherland , and Angela R. Laird 1Department of Psychology, Florida International University, Miami, FL, USA 2Department of Physics, Florida International University, Miami, FL, USA 3Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany Downloaded from http://direct.mit.edu/netn/article-pdf/3/1/27/1092290/netn_a_00050.pdf by guest on 01 October 2021 4Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany an open access journal Keywords: Neuroimaging meta-analysis, Naturalistic paradigms, Clustering analysis, Neuro- informatics ABSTRACT Cognitive processes do not occur by pure insertion and instead depend on the full complement of co-occurring mental processes, including perceptual and motor functions. As such, there is limited ecological validity to human neuroimaging experiments that use Citation: Bottenhorn, K. L., Flannery, highly controlled tasks to isolate mental processes of interest. However, a growing literature J. S., Boeving, E. R., Riedel, M. C., Eickhoff, S. B., Sutherland, M. T., & shows how dynamic, interactive tasks have allowed researchers to study cognition as it more Laird, A. R. (2019). Cooperating yet naturally occurs. Collective analysis across such neuroimaging experiments may answer distinct brain networks engaged during naturalistic paradigms: broader questions regarding how naturalistic cognition is biologically distributed throughout A meta-analysis of functional MRI k results. Network Neuroscience, the brain. We applied an unbiased, data-driven, meta-analytic approach that uses -means 3(1), 27–48.
    [Show full text]
  • Human Enhancement Technologies and Our Merger with Machines
    Human Enhancement and Technologies Our Merger with Machines Human • Woodrow Barfield and Blodgett-Ford Sayoko Enhancement Technologies and Our Merger with Machines Edited by Woodrow Barfield and Sayoko Blodgett-Ford Printed Edition of the Special Issue Published in Philosophies www.mdpi.com/journal/philosophies Human Enhancement Technologies and Our Merger with Machines Human Enhancement Technologies and Our Merger with Machines Editors Woodrow Barfield Sayoko Blodgett-Ford MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin Editors Woodrow Barfield Sayoko Blodgett-Ford Visiting Professor, University of Turin Boston College Law School Affiliate, Whitaker Institute, NUI, Galway USA Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Philosophies (ISSN 2409-9287) (available at: https://www.mdpi.com/journal/philosophies/special issues/human enhancement technologies). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year, Volume Number, Page Range. ISBN 978-3-0365-0904-4 (Hbk) ISBN 978-3-0365-0905-1 (PDF) Cover image courtesy of N. M. Ford. © 2021 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.
    [Show full text]
  • Virtual Reality, Neuroscience and the Living Flesh
    Angles New Perspectives on the Anglophone World 2 | 2016 New Approaches to the Body The Brain Without the Body? Virtual Reality, Neuroscience and the Living Flesh Marion Roussel Electronic version URL: http://journals.openedition.org/angles/1872 DOI: 10.4000/angles.1872 ISSN: 2274-2042 Publisher Société des Anglicistes de l'Enseignement Supérieur Electronic reference Marion Roussel, « The Brain Without the Body? Virtual Reality, Neuroscience and the Living Flesh », Angles [Online], 2 | 2016, Online since 01 April 2016, connection on 28 July 2020. URL : http:// journals.openedition.org/angles/1872 ; DOI : https://doi.org/10.4000/angles.1872 This text was automatically generated on 28 July 2020. Angles. New Perspectives on the Anglophone World is licensed under a Creative Commons Attribution- NonCommercial-ShareAlike 4.0 International License. The Brain Without the Body? Virtual Reality, Neuroscience and the Living Flesh 1 The Brain Without the Body? Virtual Reality, Neuroscience and the Living Flesh Marion Roussel Introduction 1 “The Brain Without the Body” can strike one as a curious title. It reminds us of the concept of the “Body without Organs” developed by French philosophers Gilles Deleuze and Félix Guattari (1980). I am using this expression to refer to a virtual reality environment project named AlloBrain@AlloSphere that was conducted between 2005 and 2009 by architect and artist Marcos Novak. I experienced it myself in March 2014 at the University of California, Santa Barbara. 2 AlloBrain is an immersive environment modelled from Novak’s brain MRIs and then extruded in the form of a three-dimensional volume. Its aim is to plunge inside the architect’s head, in his cerebral space.
    [Show full text]
  • Systems Processes and Pathologies: Creating an Integrated Framework for Systems Science
    IS13-SysProc&Path-Troncale.docx Page 1 of 24 Systems Processes and Pathologies: Creating An Integrated Framework for Systems Science Dr. Len Troncale Professor Emeritus and Past Chair, Biological Sciences Director, Institute for Advanced Systems Science California State Polytechnic University, Pomona, California, 91711 [email protected] Copyright © 2013 by Len Troncale. Published and used by INCOSE with permission Abstract. Among the several official projects of the INCOSE Systems Science Working Group, one focuses on integrating the plethora of systems theories, sources, approaches, and tools developed over the past half-century with the purpose of enabling a new and unified “science” of systems as a fundamental basis for SE. Another seeks to develop a much more SE-usable Systems Pathology also grounded in a “science” of systems. This paper introduces the wider SE community to the current status of this unique knowledge base produced over the past three years by an INCOSE-ISSS alliance summarizing the current output of 7 Workshops, 12 Papers, >24 Presentations or Webinars, and 5 Reports. It describes the need for integration of systems knowledge by demonstrating the extensive fragmentation of numerous contributing fields. It presents the current 12-step “protocol” used by the current group to guide its efforts at synthesis across systems domains, disciplines, tools, and scales asking for feedback to improve the approach. It introduces 15 Working Assumptions or Hypotheses that form the foundation for this attempt at unification citing why these could be used as working principles but why it may be undesirable to call them “principles” as others often do. The paper presents working frameworks for integration and criteria used to judge whether results are a “science” of systems or not with reminders that these early guidelines are being subjected to constant testing and revision.
    [Show full text]
  • SYSTEMS NEUROSCIENCE (FALL 2018) [Neurobio 208 and Anat 210]
    SYSTEMS NEUROSCIENCE (FALL 2018) [Neurobio 208 and Anat 210] Neurbio 208 is required for 1st year graduate students in Neurobiology and Behavior and serves as “S” area core courses for the INP. Anat 210 is open to all graduate students in Anatomy and Neurobiology. Graduate students from other departments may enroll in either Neurbio 208 or Anat 210 with permission from the course director, Dr. Ron Frostig. Time/place: 9:00-10:20AM, MWF in MH 2246 Text: Neuroscience, 6th Edition, Purves, D. et al. (Eds.) Sinauer, 2017. The instructors will distribute other readings. Exams and grading: The final grade will be based on performance on the midterm exams. The instructor for that section will announce the format of each exam. Exams will be predominately essay. There will be no cumulative final exam, and grades will be normalized to the number of lectures leading to the midterm. Final grade will be based on averaging of all midterms. Participating Faculty: Neurobiology & Behavior Anatomy & Neurobiology Ron Frostig, director Steve Cramer Steve Mahler Fall 2018 Date Topic Instructor Readings (in text unless noted) Fri Principles of Mahler https://my.rocketmix.com/enrollcourse.aspx?courseid=3087 9/28 Brain --Please enroll in this and look at it BEFORE CLASS Organization Other helpful resources: http://zoomablebrain.bio.uci.edu/ http://www.exploratorium.edu/memory/braindissection/ Mon Neuroanatomy- Mahler William James, 1890, chapter 2 (http://psychclassics.yorku.ca/ 10/1 Dissection James/Principles/prin2.htm) Wed Introduction to Frostig 10/3 sensory systems Fri The eye: Frostig Ch.11 10/5 structure and function Mon The eye: Frostig Ch.11 10/8 structure and function Wed Central visual Frostig Ch.12 10/10 pathways I Fri Central visual Frostig Ch.
    [Show full text]
  • What Is Systems Theory?
    What is Systems Theory? Systems theory is an interdisciplinary theory about the nature of complex systems in nature, society, and science, and is a framework by which one can investigate and/or describe any group of objects that work together to produce some result. This could be a single organism, any organization or society, or any electro-mechanical or informational artifact. As a technical and general academic area of study it predominantly refers to the science of systems that resulted from Bertalanffy's General System Theory (GST), among others, in initiating what became a project of systems research and practice. Systems theoretical approaches were later appropriated in other fields, such as in the structural functionalist sociology of Talcott Parsons and Niklas Luhmann . Contents - 1 Overview - 2 History - 3 Developments in system theories - 3.1 General systems research and systems inquiry - 3.2 Cybernetics - 3.3 Complex adaptive systems - 4 Applications of system theories - 4.1 Living systems theory - 4.2 Organizational theory - 4.3 Software and computing - 4.4 Sociology and Sociocybernetics - 4.5 System dynamics - 4.6 Systems engineering - 4.7 Systems psychology - 5 See also - 6 References - 7 Further reading - 8 External links - 9 Organisations // Overview 1 / 20 What is Systems Theory? Margaret Mead was an influential figure in systems theory. Contemporary ideas from systems theory have grown with diversified areas, exemplified by the work of Béla H. Bánáthy, ecological systems with Howard T. Odum, Eugene Odum and Fritj of Capra , organizational theory and management with individuals such as Peter Senge , interdisciplinary study with areas like Human Resource Development from the work of Richard A.
    [Show full text]
  • Systems Neuroscience of Natural Behaviors in Rodents
    The Journal of Neuroscience, February 3, 2021 • 41(5):911–919 • 911 Symposium/Mini-Symposium Systems Neuroscience of Natural Behaviors in Rodents Emily Jane Dennis,1 Ahmed El Hady,1 Angie Michaiel,2 Ann Clemens,3 Dougal R. Gowan Tervo,4 Jakob Voigts,5 and Sandeep Robert Datta6 1Princeton University and Howard Hughes Medical Institute, Princeton, New Jersey, 08540, 2University of Oregon, Eugene, Oregon, 97403-1254, 3University of Edinburgh, Edinburgh, Scotland, EH8 9JZ, 4Janelia Research Campus, Ashburn, Virginia, 20147, 5Massachusetts Institute of Technology, Cambridge, Massachusets, 02139, and 6Harvard University, Cambridge, Massachusets, 02115 Animals evolved in complex environments, producing a wide range of behaviors, including navigation, foraging, prey capture, and conspecific interactions, which vary over timescales ranging from milliseconds to days. Historically, these behaviors have been the focus of study for ecology and ethology, while systems neuroscience has largely focused on short timescale behaviors that can be repeated thousands of times and occur in highly artificial environments. Thanks to recent advances in machine learning, miniaturization, and computation, it is newly possible to study freely moving animals in more natural conditions while applying systems techniques: performing temporally specific perturbations, modeling behavioral strategies, and record- ing from large numbers of neurons while animals are freely moving. The authors of this review are a group of scientists with deep appreciation for the common aims of systems neuroscience, ecology, and ethology. We believe it is an extremely exciting time to be a neuroscientist, as we have an opportunity to grow as a field, to embrace interdisciplinary, open, collaborative research to provide new insights and allow researchers to link knowledge across disciplines, species, and scales.
    [Show full text]
  • Systems Neuroscience of Natural Behaviors in Rodents
    Systems Neuroscience of Natural Behaviors in Rodents 1,2 1,2 3 4 Emily Jane Dennis* ,​ Ahmed El Hady ,​ Angie Michaiel ,​ Ann Clemens ,​ Dougal R. Gowan 5 ​ 6 ​ 3 ​ ​ Tervo ,​ Jakob Voigts ,​ and Sandeep Robert Datta ​ ​ ​ Abstract Animals evolved in complex environments, producing a wide range of behaviors, including navigation, foraging, prey capture, and conspecific interactions, which vary over ​ timescales ranging from milliseconds to days. Historically, these behaviors have been the focus of study for ecology and ethology, while systems neuroscience has largely focused on short timescale behaviors that can be repeated thousands of times and occur in highly artificial environments.Thanks to recent advances in machine learning, miniaturization, and computation, it is newly possible to study freely-moving animals in more natural conditions while applying systems techniques: performing temporally-specific perturbations, modeling behavioral strategies, and recording from large numbers of neurons while animals are freely moving. The authors of this review are a group of scientists with deep appreciation for the common aims of systems neuroscience, ecology, and ethology. We believe it is an extremely exciting time to be a neuroscientist, as we have an opportunity to grow as a field, to embrace interdisciplinary, open, collaborative research to provide new insights and allow researchers to link knowledge across disciplines, species, and scales. Here we discuss the origins of ethology, ecology, and systems neuroscience in the context of our own work and highlight how combining approaches across these fields has provided fresh insights into our research. We hope this review facilitates some of these interactions and alliances and helps us all do even better science, together.
    [Show full text]