Available online at www.sciencedirect.com
ScienceDirect Physics of Life Reviews 11 (2014) 440–441 www.elsevier.com/locate/plrev
Comment
Brain networks: The next steps Comment on: “Understanding brain networks and brain organization” by Luiz Pessoa Vince D. Calhoun a,b,∗ a The Mind Research Network, Albuquerque, NM 87131, United States b Dept. of ECE, University of New Mexico, Albuquerque, NM, United States
Received 10 June 2014; accepted 11 June 2014 Available online 16 June 2014 Communicated by L. Perlovsky
The study of brain function from the perspective of whole brain networks has been a focus within the brain imaging community for many years, but has not yet overtaken the traditional approach of focusing on a specific region or set of regions. Pessoa [1] provides a very nice summary of the many reasons why network-based approaches should be used more commonly while also outlining the open questions and challenges, many of which also exist for the predominant region-based approach. One important point to frame the problem well, however, is to define carefully what is meant by the term network, which can be used in many different ways. Pessoa’s definition is consistent with that used in the network science field, that is, a graph theoretical perspective based on nodes and edges, though other (useful) definitions are also quite widely used in the brain imaging community and should not be discounted [2]. The concept of networks is a very powerful tool for studying the brain, and also for potentially pointing us to regions that are at high-risk or potentially especially important to protect (or the oft undervalued weak but wide-spread connections as Pessoa points out). Nodes & edges: A key point that Pessoa makes is that we should also be looking at definitions of nodes that allow for overlap. This is one of the main advantages of node-defining approaches like independent component analysis, for example [3]. Indeed, the combination of methods that can parcellate the brain into subnetworks while moving beyond second order statistics and pair-wise relationship may lead to new information about the brain [4]. Related approaches such as deep-learning are also showing considerable promise in identifying hidden, but important, relationships in noisy high-dimensional data [5,6]. Dynamics: Another important aspect to consider, especially for the brain at rest, is that the brain is highly dynamic (not just in its activity, but in its connectivity). EEG microstates have been studied for many years [7] and current approaches for estimating dynamics states from fMRI are growing rapidly [8]. The intersection of time-varying graph theory-based approaches with time-varying brain networks provides a promising approach [9]. Multimodal considerations: Another important point, also mentioned by Pessoa is the incorporation of multimodal information into the network description. Incorporating diffusion information with fMRI is likely the best DOI of original article: http://dx.doi.org/10.1016/j.plrev.2014.03.005. * Correspondence to: The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87131, United States. Tel.: +1 860 833 5511.
E-mail address: [email protected]. http://dx.doi.org/10.1016/j.plrev.2014.06.014 1571-0645/© 2014 Elsevier B.V. All rights reserved.
V.D. Calhoun / Physics of Life Reviews 11 (2014) 440–441
441
place to start as this provides (hardware) connectivity information. Today’s imaging devices provide us powerful information about electrical, magnetic, and hemodynamic brain function as well as spectroscopic imaging, diffusion information, and morphometric changes. Ideally we should be integrating all of this information in order to improve inferences, there are many current approaches to this challenging problem [10]. It is quite common to find somewhat different conclusions even just using fMRI and EEG/MEG data for example [11]. Future directions: In summary, the description of the brain in the context of graphical networks is an extremely powerful paradigm to study both health and disease and much more work is needed in this area, both through knowledge transfer from other fields, definition of terms, and also development of new techniques which are motivated by specific challenges in the brain imaging field. Acknowledgements This work was funded in part by NIH grants P20GM103472, R01EB006841 & R01EB005846. References [1] Pessoa L. Understanding brain networks and brain organization. Phys Life Rev 2014;11:400–35 [in this issue]. [2] Erhardt E, Allen E, Damaraju E, Calhoun VD. On network derivation, classification, and visualization: a response to Habeck and Moeller. Brain Connect 2011;1:1–19 [PMC pending #304235]. [3] Yu Q, Sui J, Kiehl KA, Pearlson G, Calhoun VD. State-related functional integration and functional segregation brain networks in schizophrenia. Schizophr Res 2013;150:450–8 [PMC Journal – in process]. [4] Plis SM, Sui J, Lane T, Roy S, Clark V, Potluru V, et al. High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia. NeuroImage 2014 [in press, PMC3896503]. [5] Plis SM, Hjelm D, Salakhutdinov RR, Calhoun VD. Deep learning for neuroimaging: a validation study. In: International conference on learning representation (ICLR). Banff, Canada. 2014. [6] Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput Jul 2006;18:1527–54. [7] Pascual-Marqui RD, Michel CM, Lehmann D. Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans Biomed Eng Jul 1995;42:658–65. [8] Hutchison RM, Womelsdorf T, Allen EA, Bandettini P, Calhoun VD, Corbetta M, et al. Dynamic functional connectivity: promises, issues, and interpretations. NeuroImage 2013;80:360–78 [PMC Journal – in process]. [9] Tang J, Scellato S, Musolesi M, Mascolo C, Latora V. Small-world behavior in time-varying graphs. Phys Rev E, Stat Nonlinear Soft Matter Phys May 2010;81:055101. [10] Sui J, Adalı T, Yu Q, Calhoun VD. A review of multivariate methods for multimodal fusion of brain imaging data. J Neurosci Methods 2012;204:68–81 [PMC3690333]. [11] Plis SM, Weisend MP, Damaraju E, Eichele T, Mayer A, Clark VP, et al. Effective connectivity analysis of fMRI and MEG data collected under identical paradigms. Comput Biol Med 2011;41:1156–65 [PMC pending #292265].
ScienceDirect Physics of Life Reviews 11 (2014) 440–441 www.elsevier.com/locate/plrev
Comment
Brain networks: The next steps Comment on: “Understanding brain networks and brain organization” by Luiz Pessoa Vince D. Calhoun a,b,∗ a The Mind Research Network, Albuquerque, NM 87131, United States b Dept. of ECE, University of New Mexico, Albuquerque, NM, United States
Received 10 June 2014; accepted 11 June 2014 Available online 16 June 2014 Communicated by L. Perlovsky
The study of brain function from the perspective of whole brain networks has been a focus within the brain imaging community for many years, but has not yet overtaken the traditional approach of focusing on a specific region or set of regions. Pessoa [1] provides a very nice summary of the many reasons why network-based approaches should be used more commonly while also outlining the open questions and challenges, many of which also exist for the predominant region-based approach. One important point to frame the problem well, however, is to define carefully what is meant by the term network, which can be used in many different ways. Pessoa’s definition is consistent with that used in the network science field, that is, a graph theoretical perspective based on nodes and edges, though other (useful) definitions are also quite widely used in the brain imaging community and should not be discounted [2]. The concept of networks is a very powerful tool for studying the brain, and also for potentially pointing us to regions that are at high-risk or potentially especially important to protect (or the oft undervalued weak but wide-spread connections as Pessoa points out). Nodes & edges: A key point that Pessoa makes is that we should also be looking at definitions of nodes that allow for overlap. This is one of the main advantages of node-defining approaches like independent component analysis, for example [3]. Indeed, the combination of methods that can parcellate the brain into subnetworks while moving beyond second order statistics and pair-wise relationship may lead to new information about the brain [4]. Related approaches such as deep-learning are also showing considerable promise in identifying hidden, but important, relationships in noisy high-dimensional data [5,6]. Dynamics: Another important aspect to consider, especially for the brain at rest, is that the brain is highly dynamic (not just in its activity, but in its connectivity). EEG microstates have been studied for many years [7] and current approaches for estimating dynamics states from fMRI are growing rapidly [8]. The intersection of time-varying graph theory-based approaches with time-varying brain networks provides a promising approach [9]. Multimodal considerations: Another important point, also mentioned by Pessoa is the incorporation of multimodal information into the network description. Incorporating diffusion information with fMRI is likely the best DOI of original article: http://dx.doi.org/10.1016/j.plrev.2014.03.005. * Correspondence to: The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87131, United States. Tel.: +1 860 833 5511.
E-mail address: [email protected]. http://dx.doi.org/10.1016/j.plrev.2014.06.014 1571-0645/© 2014 Elsevier B.V. All rights reserved.
V.D. Calhoun / Physics of Life Reviews 11 (2014) 440–441
441
place to start as this provides (hardware) connectivity information. Today’s imaging devices provide us powerful information about electrical, magnetic, and hemodynamic brain function as well as spectroscopic imaging, diffusion information, and morphometric changes. Ideally we should be integrating all of this information in order to improve inferences, there are many current approaches to this challenging problem [10]. It is quite common to find somewhat different conclusions even just using fMRI and EEG/MEG data for example [11]. Future directions: In summary, the description of the brain in the context of graphical networks is an extremely powerful paradigm to study both health and disease and much more work is needed in this area, both through knowledge transfer from other fields, definition of terms, and also development of new techniques which are motivated by specific challenges in the brain imaging field. Acknowledgements This work was funded in part by NIH grants P20GM103472, R01EB006841 & R01EB005846. References [1] Pessoa L. Understanding brain networks and brain organization. Phys Life Rev 2014;11:400–35 [in this issue]. [2] Erhardt E, Allen E, Damaraju E, Calhoun VD. On network derivation, classification, and visualization: a response to Habeck and Moeller. Brain Connect 2011;1:1–19 [PMC pending #304235]. [3] Yu Q, Sui J, Kiehl KA, Pearlson G, Calhoun VD. State-related functional integration and functional segregation brain networks in schizophrenia. Schizophr Res 2013;150:450–8 [PMC Journal – in process]. [4] Plis SM, Sui J, Lane T, Roy S, Clark V, Potluru V, et al. High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia. NeuroImage 2014 [in press, PMC3896503]. [5] Plis SM, Hjelm D, Salakhutdinov RR, Calhoun VD. Deep learning for neuroimaging: a validation study. In: International conference on learning representation (ICLR). Banff, Canada. 2014. [6] Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput Jul 2006;18:1527–54. [7] Pascual-Marqui RD, Michel CM, Lehmann D. Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans Biomed Eng Jul 1995;42:658–65. [8] Hutchison RM, Womelsdorf T, Allen EA, Bandettini P, Calhoun VD, Corbetta M, et al. Dynamic functional connectivity: promises, issues, and interpretations. NeuroImage 2013;80:360–78 [PMC Journal – in process]. [9] Tang J, Scellato S, Musolesi M, Mascolo C, Latora V. Small-world behavior in time-varying graphs. Phys Rev E, Stat Nonlinear Soft Matter Phys May 2010;81:055101. [10] Sui J, Adalı T, Yu Q, Calhoun VD. A review of multivariate methods for multimodal fusion of brain imaging data. J Neurosci Methods 2012;204:68–81 [PMC3690333]. [11] Plis SM, Weisend MP, Damaraju E, Eichele T, Mayer A, Clark VP, et al. Effective connectivity analysis of fMRI and MEG data collected under identical paradigms. Comput Biol Med 2011;41:1156–65 [PMC pending #292265].
