![]() ![]() Indeed, previous Functional Magnetic Resonance Imaging (fMRI), MagnetoEncephaloGraphic (MEG), ElectroCorticoGraphic (ECoG), and Electroencephalographic (EEG) studies have evidenced direct links between cognitive performance and human brain signal variability (for a review see ). įollowing this idea, processes underlying brain functions can be characterized by evaluating their complexity using non-linear measures –. Indeed, fractal nature of a signal has often been used to quantify the topological and functional complexity of processes generating that signal. In the light of this evidence, the time-variability properties of the signals, often interpreted as “noise”, could instead have an organized complex structure, reflecting non-linear characteristics of the system that are deeply linked to the underlying organ functioning. The power spectrum of these signals, plotted as log- power over log-frequency, follows a descendent straight line (power-law distribution, ). This relationship is quantified by the fractal dimension. the same features of small time scales emerge in large ones. A signal is fractal if the scaling properties fit a scale-free behavior, i.e. Also a time series can display fractal properties, if statistical similarity emerges at different time scales of its dynamics. In particular, fractal properties have been described for these signals.Ī structure exhibits fractal properties if similar details are observed on different scales. Rather they can reveal a temporal organization over multiple time scales. Differently from this view, a wide bulk of data is now providing evidence that complex fluctuations, observed in several physiological time signals such as heartbeat, , respiration, gait rhythm, ,, dynamics of neurotransmitter release, electromyography, brain activity –, are not purely random. Thus, active controls act to damper fluctuations due to random noise that shift actual operating conditions from the preset states. The traditional model of control assumes that healthy human physiological systems preserve steadiness by self-regulating their activity reducing fluctuations around a homeostatic equilibrium point. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: The research leading to these results has received funding from: 1. Received: FebruAccepted: Published: June 26, 2014Ĭopyright: © 2014 Zappasodi et al. PLoS ONE 9(6):Įditor: Helmut Ahammer, Medical University of Graz, Austria FDasy result highlights the functional relevance of the balance between homologous brain structures’ activities in stroke recovery.Ĭitation: Zappasodi F, Olejarczyk E, Marzetti L, Assenza G, Pizzella V, Tecchio F (2014) Fractal Dimension of EEG Activity Senses Neuronal Impairment in Acute Stroke. This picture is coherent with neuronal activity complexity decrease paired to a reduced repertoire of functional abilities. This decrease seems to reveal the intimate nature of structure-function unity, where the regional neural multi-scale self-similar activity is impaired by the anatomical lesion. FD in our patients captured the loss of complexity reflecting the global system dysfunction resulting from the structural damage. Larger FDasy in acute phase was paired to a worse clinical recovery at six months. FD decrease was associated to alpha increase and beta decrease of oscillatory activity power. FD was smaller in patients than in controls (1.447☐.092 vs 1.525☐.105) and its reduction was paired to a worse acute clinical status. Highuchi FD, its inter-hemispheric asymmetry (FDasy) and spectral band powers were calculated for EEG signals. National Health Institute Stroke Scale (NIHss) was collected at T0 and 6 months later. Resting EEG was collected in 36 patients 4–10 days after a unilateral ischemic stroke in the middle cerebral artery territory and 19 healthy controls. Under the hypothesis that the fractal dimension (FD) of the electroencephalographic signal (EEG) is optimally sensitive to the neuronal dysfunction secondary to a brain lesion, we tested the FD’s ability in assessing two key processes in acute stroke: the clinical impairment and the recovery prognosis. Thus, the complexity of its dynamics, associated to efficient processing and functional advantages, is expected to be captured by a measure of its scale-free (fractal) properties. The brain is a self-organizing system which displays self-similarities at different spatial and temporal scales. ![]()
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