Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study
Anticorrelations among brain areas observed in fMRI acquisitions under resting state are not endowed with a well-defined set of characters. Some evidence points to a possible physiological role for them, and simulation models showed that it is appropriate to explore such an issue. A large-scale brai...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2018/6815040 |
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| _version_ | 1832548115094175744 |
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| author | Fabrizio Parente Alfredo Colosimo |
| author_facet | Fabrizio Parente Alfredo Colosimo |
| author_sort | Fabrizio Parente |
| collection | DOAJ |
| description | Anticorrelations among brain areas observed in fMRI acquisitions under resting state are not endowed with a well-defined set of characters. Some evidence points to a possible physiological role for them, and simulation models showed that it is appropriate to explore such an issue. A large-scale brain representation was considered, implementing an agent-based brain-inspired model (ABBM) incorporating the SER (susceptible-excited-refractory) cyclic mechanism of state change. The experimental data used for validation included 30 selected functional images of healthy controls from the 1000 Functional Connectomes Classic collection. To study how different fractions of positive and negative connectivities could modulate the model efficiency, the correlation coefficient was systematically used to check the goodness-of-fit of empirical data by simulations under different combinations of parameters. The results show that a small fraction of positive connectivity is necessary to match at best the empirical data. Similarly, a goodness-of-fit improvement was observed upon addition of negative links to an initial pattern of only-positive connections, indicating a significant information intrinsic to negative links. As a general conclusion, anticorrelations showed that it is crucial to improve the performance of our simulation and, since these cannot be assimilated to noise, should be always considered in order to refine any brain functional model. |
| format | Article |
| id | doaj-art-4ee9dbaf4d9a4804bcb369b21ccff7d4 |
| institution | Kabale University |
| issn | 2090-5904 1687-5443 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Neural Plasticity |
| spelling | doaj-art-4ee9dbaf4d9a4804bcb369b21ccff7d42025-02-03T06:42:11ZengWileyNeural Plasticity2090-59041687-54432018-01-01201810.1155/2018/68150406815040Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation StudyFabrizio Parente0Alfredo Colosimo1Deparment of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University of Rome, Rome, ItalyDeparment of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University of Rome, Rome, ItalyAnticorrelations among brain areas observed in fMRI acquisitions under resting state are not endowed with a well-defined set of characters. Some evidence points to a possible physiological role for them, and simulation models showed that it is appropriate to explore such an issue. A large-scale brain representation was considered, implementing an agent-based brain-inspired model (ABBM) incorporating the SER (susceptible-excited-refractory) cyclic mechanism of state change. The experimental data used for validation included 30 selected functional images of healthy controls from the 1000 Functional Connectomes Classic collection. To study how different fractions of positive and negative connectivities could modulate the model efficiency, the correlation coefficient was systematically used to check the goodness-of-fit of empirical data by simulations under different combinations of parameters. The results show that a small fraction of positive connectivity is necessary to match at best the empirical data. Similarly, a goodness-of-fit improvement was observed upon addition of negative links to an initial pattern of only-positive connections, indicating a significant information intrinsic to negative links. As a general conclusion, anticorrelations showed that it is crucial to improve the performance of our simulation and, since these cannot be assimilated to noise, should be always considered in order to refine any brain functional model.http://dx.doi.org/10.1155/2018/6815040 |
| spellingShingle | Fabrizio Parente Alfredo Colosimo Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study Neural Plasticity |
| title | Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study |
| title_full | Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study |
| title_fullStr | Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study |
| title_full_unstemmed | Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study |
| title_short | Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study |
| title_sort | anticorrelations between active brain regions an agent based model simulation study |
| url | http://dx.doi.org/10.1155/2018/6815040 |
| work_keys_str_mv | AT fabrizioparente anticorrelationsbetweenactivebrainregionsanagentbasedmodelsimulationstudy AT alfredocolosimo anticorrelationsbetweenactivebrainregionsanagentbasedmodelsimulationstudy |