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The Fallacy of Univariate Solutions to Complex Systems Problems
Complex biological systems, by definition, are composed of multiple components that interact non-linearly. The human brain constitutes, arguably, the most complex biological system known. Yet most investigation of the brain and its function is carried out using assumptions appropriate for simple sys...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4896944/ https://www.ncbi.nlm.nih.gov/pubmed/27375425 http://dx.doi.org/10.3389/fnins.2016.00267 |
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author | Lessov-Schlaggar, Christina N. Rubin, Joshua B. Schlaggar, Bradley L. |
author_facet | Lessov-Schlaggar, Christina N. Rubin, Joshua B. Schlaggar, Bradley L. |
author_sort | Lessov-Schlaggar, Christina N. |
collection | PubMed |
description | Complex biological systems, by definition, are composed of multiple components that interact non-linearly. The human brain constitutes, arguably, the most complex biological system known. Yet most investigation of the brain and its function is carried out using assumptions appropriate for simple systems—univariate design and linear statistical approaches. This heuristic must change before we can hope to discover and test interventions to improve the lives of individuals with complex disorders of brain development and function. Indeed, a movement away from simplistic models of biological systems will benefit essentially all domains of biology and medicine. The present brief essay lays the foundation for this argument. |
format | Online Article Text |
id | pubmed-4896944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-48969442016-07-01 The Fallacy of Univariate Solutions to Complex Systems Problems Lessov-Schlaggar, Christina N. Rubin, Joshua B. Schlaggar, Bradley L. Front Neurosci Psychiatry Complex biological systems, by definition, are composed of multiple components that interact non-linearly. The human brain constitutes, arguably, the most complex biological system known. Yet most investigation of the brain and its function is carried out using assumptions appropriate for simple systems—univariate design and linear statistical approaches. This heuristic must change before we can hope to discover and test interventions to improve the lives of individuals with complex disorders of brain development and function. Indeed, a movement away from simplistic models of biological systems will benefit essentially all domains of biology and medicine. The present brief essay lays the foundation for this argument. Frontiers Media S.A. 2016-06-08 /pmc/articles/PMC4896944/ /pubmed/27375425 http://dx.doi.org/10.3389/fnins.2016.00267 Text en Copyright © 2016 Lessov-Schlaggar, Rubin and Schlaggar. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Lessov-Schlaggar, Christina N. Rubin, Joshua B. Schlaggar, Bradley L. The Fallacy of Univariate Solutions to Complex Systems Problems |
title | The Fallacy of Univariate Solutions to Complex Systems Problems |
title_full | The Fallacy of Univariate Solutions to Complex Systems Problems |
title_fullStr | The Fallacy of Univariate Solutions to Complex Systems Problems |
title_full_unstemmed | The Fallacy of Univariate Solutions to Complex Systems Problems |
title_short | The Fallacy of Univariate Solutions to Complex Systems Problems |
title_sort | fallacy of univariate solutions to complex systems problems |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4896944/ https://www.ncbi.nlm.nih.gov/pubmed/27375425 http://dx.doi.org/10.3389/fnins.2016.00267 |
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