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ScienceDirect Physics of Life Reviews ••• (••••) •••–••• www.elsevier.com/locate/plrev
Comment
Neural murmurations Comment on “Understanding brain networks and brain organization” by Luiz Pessoa Paul J. Laurienti Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, United States Received 12 May 2014; accepted 13 May 2014
Communicated by L. Perlovsky
If not the last frontier, understanding the human brain is certainly one of the last. Over the past decade there has been a shift in the focus of neuroscience. The concept of the brain as a network is gaining traction and is rapidly becoming a dominant perspective [1]. In the target article [2], Luiz Pessoa discusses major conceptual shifts that must accompany the methodological changes associated with network science applications to the brain. The software, algorithms, and computational power needed to perform network analyses are now at the fingertips of all neuroscientists. But, this places us at a fork in the road. Will these tools be used to substantiate what has already been discovered, or will we seek a totally new and improved understanding of the brain? In this thorough review, Pessoa highlights several concepts that are central to understanding the brain as a complex network. Included are key issues, such as the importance of subcortical structures, network decomposition, multi-scale investigation, and anatomic versus functional connectivity. While each of these is important, I think that Pessoa opens and closes his work with what is probably the most essential issue. “. . .understanding the structure–function mapping at the level of brain regions is unproductive because regions are not a meaningful unit. . .” This one sentence contradicts most past neuroscience research and many of the ongoing studies that use networks (see [3]). The traditional structure/function view of the brain is like a patchwork quilt (Fig. 1). The brain is broken into discrete regions and each has a specific function. Pessoa argues that a single brain region, however that may be defined, can serve many functions, can participate in quite disparate computations, and can belong to virtually an unlimited number of subnetworks. In fact, functional fingerprinting [4] has convincingly shown that a brain area can participate, to a greater or lesser degree, in a plethora of neural processes. For example, the anterior insula has a substantial role in neural processes that include emotion, executive function, language, sensory processing, motor control, and memory. If you believe the network perspective, the importance of an area is supplanted by the importance of the circuit. There are no actual boundaries between so-called brain areas. We must remember that borders, boundaries, and circumscribed areas are all human fabrications, no matter the context [5]. I contend that the whole notion of functional DOI of original article: http://dx.doi.org/10.1016/j.plrev.2014.03.005. http://dx.doi.org/10.1016/j.plrev.2014.05.001 1571-0645/© 2014 Elsevier B.V. All rights reserved.
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Fig. 1. Traditional view of the brain with discrete areas (left) resembling a patchwork quilt (right, [7]). In this view, the anatomical organization of each area drives the specific function.
Fig. 2. Network view of the brain (left) showing inter-relationships between brain areas. The system is highly dynamic like a flock of starlings (right, [8]). In this view, specific neural processes are associated with the various states of the network not with predefined brain regions.
areas should be abandoned [6]. Rather than the brain being a fixed collection of functional areas, it is much more like a continuously changing flock of starlings (Fig. 2). At any given point in time it is possible to identify clusters in a flock of birds. However, the clusters rapidly change size, shape, and location. In addition, the individual birds constantly change with whom they are closely flocking. This is analogous to the many-to-many mapping concept presented by Pessoa. A brain “area” can be involved in many computational processes, and a single process can be achieved through multiple different realizations of a brain circuit. Swapping one “area” from a circuit and replacing it with another “area” may quantitatively change a given process, but the overall process could still be qualitatively the same. Similarly, swapping a single starling in the flock could change the flight pattern, but the flock will still unmistakably be a flock. I believe that this review should be required reading for anyone planning to use network methodologies in neuroscience. Pessoa should have us all asking the real purpose of novel neuroscience methodologies. If the use of new methods is restricted by the dogma of what is “known,” it is likely that studies will serve only to reaffirm current neuroscience concepts. If we allow the methods to explore untested hypotheses without prior biases, we may come away with a fundamentally new understanding of the brain and one closer to the truth. References [1] Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009;10(3):186–98. [2] Pessoa L. Understanding brain networks and brain organization. Phys Life Rev 2014. http://dx.doi.org/10.1016/j.plrev.2014.03.005 [in this issue]. [3] Turner R, Geyer S. Introduction to the NeuroImage Special Issue: “In vivo Brodmann mapping of the human brain”. NeuroImage 2014;93 Pt 2:155–6. [4] Passingham RE, Stephan KE, Kotter R. The anatomical basis of functional localization in the cortex. Nat Rev Neurosci 2002;3(8):606–16. [5] Meadows DH. Thinking in systems: a primer. White River Junction, VT: Chelsea Green Publishing; 2008. p. 240.
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[6] Stanley ML, Moussa MN, Paolini BM, Lyday RG, Burdette JH, Laurienti PJ. Defining nodes in complex brain networks. Front Comput Neurosci 2013;7:169. [7] Bidwell A. Patchwork quilt – take two. http://blueisbleu.blogspot.com/2011/09/custom-patchwork-quilt-take-two.html, 2011. [8] Thirlaway J. Faster than the speed of light 0902. https://www.flickr.com/photos/after-the-rain/11221968115/in/set-72157636978556803, 2013.
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Available online at www.sciencedirect.com
ScienceDirect Physics of Life Reviews ••• (••••) •••–••• www.elsevier.com/locate/plrev
Comment
Neural murmurations Comment on “Understanding brain networks and brain organization” by Luiz Pessoa Paul J. Laurienti Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, United States Received 12 May 2014; accepted 13 May 2014
Communicated by L. Perlovsky
If not the last frontier, understanding the human brain is certainly one of the last. Over the past decade there has been a shift in the focus of neuroscience. The concept of the brain as a network is gaining traction and is rapidly becoming a dominant perspective [1]. In the target article [2], Luiz Pessoa discusses major conceptual shifts that must accompany the methodological changes associated with network science applications to the brain. The software, algorithms, and computational power needed to perform network analyses are now at the fingertips of all neuroscientists. But, this places us at a fork in the road. Will these tools be used to substantiate what has already been discovered, or will we seek a totally new and improved understanding of the brain? In this thorough review, Pessoa highlights several concepts that are central to understanding the brain as a complex network. Included are key issues, such as the importance of subcortical structures, network decomposition, multi-scale investigation, and anatomic versus functional connectivity. While each of these is important, I think that Pessoa opens and closes his work with what is probably the most essential issue. “. . .understanding the structure–function mapping at the level of brain regions is unproductive because regions are not a meaningful unit. . .” This one sentence contradicts most past neuroscience research and many of the ongoing studies that use networks (see [3]). The traditional structure/function view of the brain is like a patchwork quilt (Fig. 1). The brain is broken into discrete regions and each has a specific function. Pessoa argues that a single brain region, however that may be defined, can serve many functions, can participate in quite disparate computations, and can belong to virtually an unlimited number of subnetworks. In fact, functional fingerprinting [4] has convincingly shown that a brain area can participate, to a greater or lesser degree, in a plethora of neural processes. For example, the anterior insula has a substantial role in neural processes that include emotion, executive function, language, sensory processing, motor control, and memory. If you believe the network perspective, the importance of an area is supplanted by the importance of the circuit. There are no actual boundaries between so-called brain areas. We must remember that borders, boundaries, and circumscribed areas are all human fabrications, no matter the context [5]. I contend that the whole notion of functional DOI of original article: http://dx.doi.org/10.1016/j.plrev.2014.03.005. http://dx.doi.org/10.1016/j.plrev.2014.05.001 1571-0645/© 2014 Elsevier B.V. All rights reserved.
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P.J. Laurienti / Physics of Life Reviews ••• (••••) •••–•••
Fig. 1. Traditional view of the brain with discrete areas (left) resembling a patchwork quilt (right, [7]). In this view, the anatomical organization of each area drives the specific function.
Fig. 2. Network view of the brain (left) showing inter-relationships between brain areas. The system is highly dynamic like a flock of starlings (right, [8]). In this view, specific neural processes are associated with the various states of the network not with predefined brain regions.
areas should be abandoned [6]. Rather than the brain being a fixed collection of functional areas, it is much more like a continuously changing flock of starlings (Fig. 2). At any given point in time it is possible to identify clusters in a flock of birds. However, the clusters rapidly change size, shape, and location. In addition, the individual birds constantly change with whom they are closely flocking. This is analogous to the many-to-many mapping concept presented by Pessoa. A brain “area” can be involved in many computational processes, and a single process can be achieved through multiple different realizations of a brain circuit. Swapping one “area” from a circuit and replacing it with another “area” may quantitatively change a given process, but the overall process could still be qualitatively the same. Similarly, swapping a single starling in the flock could change the flight pattern, but the flock will still unmistakably be a flock. I believe that this review should be required reading for anyone planning to use network methodologies in neuroscience. Pessoa should have us all asking the real purpose of novel neuroscience methodologies. If the use of new methods is restricted by the dogma of what is “known,” it is likely that studies will serve only to reaffirm current neuroscience concepts. If we allow the methods to explore untested hypotheses without prior biases, we may come away with a fundamentally new understanding of the brain and one closer to the truth. References [1] Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009;10(3):186–98. [2] Pessoa L. Understanding brain networks and brain organization. Phys Life Rev 2014. http://dx.doi.org/10.1016/j.plrev.2014.03.005 [in this issue]. [3] Turner R, Geyer S. Introduction to the NeuroImage Special Issue: “In vivo Brodmann mapping of the human brain”. NeuroImage 2014;93 Pt 2:155–6. [4] Passingham RE, Stephan KE, Kotter R. The anatomical basis of functional localization in the cortex. Nat Rev Neurosci 2002;3(8):606–16. [5] Meadows DH. Thinking in systems: a primer. White River Junction, VT: Chelsea Green Publishing; 2008. p. 240.
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[6] Stanley ML, Moussa MN, Paolini BM, Lyday RG, Burdette JH, Laurienti PJ. Defining nodes in complex brain networks. Front Comput Neurosci 2013;7:169. [7] Bidwell A. Patchwork quilt – take two. http://blueisbleu.blogspot.com/2011/09/custom-patchwork-quilt-take-two.html, 2011. [8] Thirlaway J. Faster than the speed of light 0902. https://www.flickr.com/photos/after-the-rain/11221968115/in/set-72157636978556803, 2013.
