Fractal analysis of spontaneous fluctuations of the BOLD signal in the human brain networks

Li, Yi-Chia; Huang, Yun‐An

doi:10.1002/jmri.24274

Cited by 2 publications

(2 citation statements)

References 55 publications

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“…In this regard, Hofman was one of the first researchers who has shown that the cerebral cortex manifests fractality 16 , and this property was confirmed by other studies, including the works by Free et al and Kiselev et al who demonstrated, using different approaches, that the cerebral cortex presents statistical self-similarity 5,13 . More recently, the fractal analysis has also been effectively applied to time series of brain signals, e.g., in functional MRI [18][19][20] .…”

Section: Toward a More Reliable Characterization Of Fractal Propertiementioning

confidence: 99%

Toward a more reliable characterization of fractal properties of the cerebral cortex of healthy subjects during the lifespan

Marzi

Giannelli

Tessa

et al. 2020

Sci Rep

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The cerebral cortex manifests an inherent structural complexity of folding. The fractal geometry describes the complexity of structures which show self-similarity in a proper interval of spatial scales. In this study, we aimed at evaluating in-vivo the effect of different criteria for selecting the interval of spatial scales in the estimation of the fractal dimension (FD) of the cerebral cortex in T1-weighted magnetic resonance imaging (MRI). We compared four different strategies, including two a priori selections of the interval of spatial scales, an automated selection of the spatial scales within which the cerebral cortex manifests the highest statistical self-similarity, and an improved approach, based on the search of the interval of spatial scales which presents the highest rounded R2adj coefficient and, in case of equal rounded R2adj coefficient, preferring the widest interval in the log–log plot. We employed two public and international datasets of in-vivo MRI scans for a total of 159 healthy subjects (age range 6–85 years). The improved approach showed strong associations of FD with age and yielded the most accurate machine learning models for individual age prediction in both datasets. Our results indicate that the selection of the interval of spatial scales of the cerebral cortex is thus critical in the estimation of FD.

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Section: Toward a More Reliable Characterization Of Fractal Propertiementioning

confidence: 99%

Toward a more reliable characterization of fractal properties of the cerebral cortex of healthy subjects during the lifespan

Marzi

Giannelli

Tessa

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Indeed, fMRI time series have been analyzed by means of fractal analysis, demonstrating the ability of such an analysis to quantify the dysregulation of dynamic interactions between different limbic system regions in healthy adults in states of increased anxiety (Olejarczyk 2007). Fractal properties and applications of fractal analysis to fMRI can be found in the literature (Bullmore and others 2004; Li and Huang 2013; Rubin and others 2013).…”

Section: Fractal Analysis In Neuroimagingmentioning

confidence: 99%

Fractals in the Neurosciences, Part II

et al. 2013

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It has been ascertained that the human brain is a complex system studied at multiple scales, from neurons and microcircuits to macronetworks. The brain is characterized by a hierarchical organization that gives rise to its highly topological and functional complexity. Over the last decades, fractal geometry has been shown as a universal tool for the analysis and quantification of the geometric complexity of natural objects, including the brain. The fractal dimension has been identified as a quantitative parameter for the evaluation of the roughness of neural structures, the estimation of time series, and the description of patterns, thus able to discriminate different states of the brain in its entire physiopathological spectrum. Fractal-based computational analyses have been applied to the neurosciences, particularly in the field of clinical neurosciences including neuroimaging and neuroradiology, neurology and neurosurgery, psychiatry and psychology, and neuro-oncology and neuropathology. After a review of the basic concepts of fractal analysis and its main applications to the basic neurosciences in part I of this series, here, we review the main applications of fractals to the clinical neurosciences for a holistic approach towards a fractal geometry model of the brain.

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Fractal analysis of spontaneous fluctuations of the BOLD signal in the human brain networks

Cited by 2 publications

References 55 publications

Toward a more reliable characterization of fractal properties of the cerebral cortex of healthy subjects during the lifespan

Toward a more reliable characterization of fractal properties of the cerebral cortex of healthy subjects during the lifespan

Fractals in the Neurosciences, Part II

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