Fractal scaling of C. elegans behavior is shaped by insulin signaling

Fractal scaling governs the complex behavior of various animal species and, in humans, can be altered by neurodegenerative diseases and aging1. However, the mechanism underlying fractal scaling remains unknown. Here, we videorecorded C. elegans that had been cultured in a microfluidic device for 3 days and analyzed temporal patterns of C. elegans actions by fractal analyses. The residence-time distribution of C. elegans shared a common feature with those of human and mice2–4. Specifically, the residence-time power-law distribution of the active state changed to an exponential-like decline at a longer time scale, whereas this change did not occur in the inactive state. The exponential-like decline disappeared in starved C. elegans but was restored by culturing animals with glucose. The exponential-like decline similarly disappeared in insulin-signaling daf-2 and daf-16 mutants. Therefore, we conclude that insulin signaling regulates fractal scaling of C. elegans behavior. Our findings indicate that neurosensory modulation of C. elegans behavior by insulin signaling is achieved by regulation of fractal scaling. In humans, diabetes mellitus is associated with depression, bipolar disorder, and anxiety disorder5, which affect daily behavioral activities. We hypothesize that comorbid behavioral defects in patients with diabetes may be attributed to altered fractal scaling of human behavior.

Fractal scaling of C. elegans behavior is shaped by insulin signaling

Fractal scaling governs the complex behavior of various animal species and, in humans, can be altered by neurodegenerative diseases and aging1. However, the mechanism underlying fractal scaling remains unknown. Here, we videorecorded C. elegans that had been cultured in a microfluidic device for 3 days and analyzed temporal patterns of C. elegans actions by fractal analyses. The residence-time distribution of C. elegans shared a common feature with those of human and mice2–4. Specifically, the residence-time power-law distribution of the active state changed to an exponential-like decline at a longer time scale, whereas this change did not occur in the inactive state. The exponential-like decline disappeared in starved C. elegans but was restored by culturing animals with glucose. The exponential-like decline similarly disappeared in insulin-signaling daf-2 and daf-16 mutants. Therefore, we conclude that insulin signaling regulates fractal scaling of C. elegans behavior. Our findings indicate that neurosensory modulation of C. elegans behavior by insulin signaling is achieved by regulation of fractal scaling. In humans, diabetes mellitus is associated with depression, bipolar disorder, and anxiety disorder5, which affect daily behavioral activities. We hypothesize that comorbid behavioral defects in patients with diabetes may be attributed to altered fractal scaling of human behavior.

To draw or not to draw: understanding the temporal organization of drawing behaviour using fractal analyses

Studies on drawing often focused on spatial aspects of the finished products. Here, the drawing behaviour was studied by analysing its intermittent process, between drawing (i.e. marking a surface) and interruption (i.e. a pause in the marking gesture). To assess how this intermittence develops with age, we collected finger-drawings on a touchscreen by 185 individuals (children and adults). We measured the temporal structure of each drawing sequence to determine its complexity. To do this, we applied temporal fractal estimators to each drawing time series before combining them in a Principal Component Analysis procedure. The youngest children (3 years-old) drew in a more stereotypical way with long-range dependence detected in their alternations between states. Among older children and adults, the complexity of drawing sequences increased showing a less predictable behaviour as their drawings become more detailed and figurative. This study improves our understanding of the temporal aspects of drawing behaviour, and contributes to an objective understanding of its ontogeny.

A Scale-Free Gradient of Cognitive Resource Disruptions in Childhood Psychopathology

The Hurst exponent (H) isolated in fractal analyses of neuroimaging time-series is implicated broadly in cognition. The connection between H and the mathematics of criticality makes it a candidate measure of individual differences in cognitive resource allocation. Relationships between H and multiple mental disorders have been detected, suggesting that H is transdiagnostically associated with psychopathology. Here, we demonstrate a gradient of decreased H with increased general psychopathology and attention-deficit/hyperactivity extracted factor scores during a working memory task which predicts concurrent and future working memory performance in 1,839 children. This gradient defines psychological and functional axes which indicate that psychopathology is associated with an imbalance in resource allocation between fronto-parietal and sensory-motor regions, driven by reduced resource allocation to fonto-parietal regions. This suggests the hypothesis that impaired cognitive function associated with psychopathology follows from a reduced cognitive resource pool and a reduction in resources allocated to the task at hand.

Identification of Electroencephalogram Signals in Alzheimer's Disease by Multifractal and Multiscale Entropy Analysis

Alzheimer's disease (AD) is the most common form of dementia and is a progressive neurodegenerative disease that primarily develops in old age. In recent years, it has been reported that early diagnosis of AD and early intervention significantly delays disease progression. Hence, early diagnosis and intervention are emphasized. As a diagnostic index for AD patients, evaluating the complexity of the dependence of the electroencephalography (EEG) signal on the temporal scale of Alzheimer's disease (AD) patients is effective. Multiscale entropy analysis and multifractal analysis have been performed individually, and their usefulness as diagnostic indicators has been confirmed, but the complemental relationship between these analyses, which may enhance diagnostic accuracy, has not been investigated. We hypothesize that combining multiscale entropy and fractal analyses may add another dimension to understanding the alteration of EEG dynamics in AD. In this study, we performed both multiscale entropy and multifractal analyses on EEGs from AD patients and healthy subjects. We found that the classification accuracy was improved using both techniques. These findings suggest that the use of multiscale entropy analysis and multifractal analysis may lead to the development of AD diagnostic tools.

Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study

Estimates suggest that more than 70% of the world’s rangelands are degraded. The Normalized Difference Vegetation Index (NDVI) is commonly used by ecologists and agriculturalists to monitor vegetation and contribute to more sustainable rangeland management. This paper aims to explore the scaling character of NDVI and NDVI anomaly (NDVIa) time series by applying three fractal analyses: generalized structure function (GSF), multifractal detrended fluctuation analysis (MF-DFA), and Hurst index (HI). The study was conducted in four study areas in Southeastern Spain. Results suggest a multifractal character influenced by different land uses and spatial diversity. MF-DFA indicated an antipersistent character in study areas, while GSF and HI results indicated a persistent character. Different behaviors of generalized Hurst and scaling exponents were found between herbaceous and tree dominated areas. MF-DFA and surrogate and shuffle series allow us to study multifractal sources, reflecting the importance of long-range correlations in these areas. Two types of long-range correlation appear to be in place due to short-term memory reflecting seasonality and longer-term memory based on a time scale of a year or longer. The comparison of these series also provides us with a differentiating profile to distinguish among our four study areas that can improve land use and risk management in arid rangelands.

Micro- and Nanoscale Pore Structure Characterization of Carbonates from the Xiaoerbulake Formation in the Tarim Basin, Northwest China

The evaluation of pore structure is an essential part in the assessment of carbonate reservoirs. The structures (geometry and connectivity) of nm to μm-scale pore networks in outcrop samples of carbonates from Xiaoerbulake Formation in Tarim Basin of China were studied by using optical microscopy, field emission-scanning electron microscopy (FE-SEM), as well as mercury intrusion porosimetry (MIP) with fractal analyses of the data, and spontaneous imbibition tests (distilled water). The results demonstrate that the lithologies are micritic dolomites, fine-to-medium-to-coarse crystalline dolomites, microbial dolomites, and dolarenite. At micro- to nanoscales in size, pore types are dominated by intergranular, intercrystalline, and intragranular (e.g., dissolution) pores. These pore networks have pore-throat diameters from 0.01 to >10 μm. Compared with a nanoscale pore network, the μm-scale pore networks are relatively well connected and serve as the most important permeability pathways. Although the pore volume accounts for most of the total porosity, the permeability of nanoscale pore networks is low. The existence of micro-nano-fractures could improve connectivity, especially for the nanoscale pore networks, by linking the intragranular (dissolution) pores which are mostly in the range of nm-scale.