### Abstract

Fractals - signals that display scale-invariant behaviour - are ubiquitous in nature including a wide variety of physiological processes. Fractal analysis of blood oxygen level dependent (BOLD) time-series of fMRI acquisitions from the brain can be achieved by decomposing the data Into a hierarchy of temporal scales so that although the signal may well be irregular and contain singularities, the properties of these singularities are constant in time and the entire series can be characterised by a single scaling exponent: the Hurst exponent, H. The observation that a signal has a noninteger fractal dimension suggests that the generating system is complex and has the potential to adapt to a wide variety of challenges. In contrast, the emergence of white noise or, alternatively, signal periodicity can be seen as degradation of fractal complexity and hence, maladaptivity. We tested the hypothesis that exogenous stimuli affects fractal signal properties In the context of brain function by manipulating the cognitive demand of a working memory task and using H as a summary measure of signal complexity. We show that this stimulus has a significant effect on H estimated from resting data acquired immediately before and after the task, and that the degree of change is related to cognitive load.

Original language | English |
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Title of host publication | 2008 International Joint Conference on Neural Networks, IJCNN 2008 |

Pages | 3762-3765 |

Number of pages | 4 |

DOIs | |

Publication status | Published - 24 Nov 2008 |

Event | 2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China Duration: 1 Jun 2008 → 8 Jun 2008 |

### Publication series

Name | Proceedings of the International Joint Conference on Neural Networks |
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### Conference

Conference | 2008 International Joint Conference on Neural Networks, IJCNN 2008 |
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Country | China |

City | Hong Kong |

Period | 01/06/2008 → 08/06/2008 |

### Cite this

*2008 International Joint Conference on Neural Networks, IJCNN 2008*(pp. 3762-3765). [4634338] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2008.4634338

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*2008 International Joint Conference on Neural Networks, IJCNN 2008.*, 4634338, Proceedings of the International Joint Conference on Neural Networks, pp. 3762-3765, 2008 International Joint Conference on Neural Networks, IJCNN 2008, Hong Kong, China, 01/06/2008. https://doi.org/10.1109/IJCNN.2008.4634338

**Modulation of the fractal properties of low frequency endogenous brain oscillations in functional MRI by a working memory task.** / Barnes, Anna; Honey, Garry; Wink, Alle Meije; Bullmore, Edward T.; Suckling, John.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Modulation of the fractal properties of low frequency endogenous brain oscillations in functional MRI by a working memory task

AU - Barnes, Anna

AU - Honey, Garry

AU - Wink, Alle Meije

AU - Bullmore, Edward T.

AU - Suckling, John

PY - 2008/11/24

Y1 - 2008/11/24

N2 - Fractals - signals that display scale-invariant behaviour - are ubiquitous in nature including a wide variety of physiological processes. Fractal analysis of blood oxygen level dependent (BOLD) time-series of fMRI acquisitions from the brain can be achieved by decomposing the data Into a hierarchy of temporal scales so that although the signal may well be irregular and contain singularities, the properties of these singularities are constant in time and the entire series can be characterised by a single scaling exponent: the Hurst exponent, H. The observation that a signal has a noninteger fractal dimension suggests that the generating system is complex and has the potential to adapt to a wide variety of challenges. In contrast, the emergence of white noise or, alternatively, signal periodicity can be seen as degradation of fractal complexity and hence, maladaptivity. We tested the hypothesis that exogenous stimuli affects fractal signal properties In the context of brain function by manipulating the cognitive demand of a working memory task and using H as a summary measure of signal complexity. We show that this stimulus has a significant effect on H estimated from resting data acquired immediately before and after the task, and that the degree of change is related to cognitive load.

AB - Fractals - signals that display scale-invariant behaviour - are ubiquitous in nature including a wide variety of physiological processes. Fractal analysis of blood oxygen level dependent (BOLD) time-series of fMRI acquisitions from the brain can be achieved by decomposing the data Into a hierarchy of temporal scales so that although the signal may well be irregular and contain singularities, the properties of these singularities are constant in time and the entire series can be characterised by a single scaling exponent: the Hurst exponent, H. The observation that a signal has a noninteger fractal dimension suggests that the generating system is complex and has the potential to adapt to a wide variety of challenges. In contrast, the emergence of white noise or, alternatively, signal periodicity can be seen as degradation of fractal complexity and hence, maladaptivity. We tested the hypothesis that exogenous stimuli affects fractal signal properties In the context of brain function by manipulating the cognitive demand of a working memory task and using H as a summary measure of signal complexity. We show that this stimulus has a significant effect on H estimated from resting data acquired immediately before and after the task, and that the degree of change is related to cognitive load.

UR - http://www.scopus.com/inward/record.url?scp=56349158724&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2008.4634338

DO - 10.1109/IJCNN.2008.4634338

M3 - Conference contribution

SN - 9781424418213

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 3762

EP - 3765

BT - 2008 International Joint Conference on Neural Networks, IJCNN 2008

ER -