Brain networks can be naturally divided into clusters or communities where the cluster’s nodes dynamics have similar trajectories in phase space. This process is known as synchronization, and represents characteristics of intragroup features and not between groups. Fractional calculus represents a generalization of ordinary differentiation and integration to arbitrary non-integer order, and can be thought of as a smooth interpolation between different orders of differentiation/integration, providing the ability to probe the system from many different viewpoints of the dynamics. Fractional calculus has been explored as an excellent tool for the description of memory in many processes and may be more accurate for modeling brain processes than traditional integer-order ones. We apply the concept of cluster synchronization in fractional-order structural brain networks ranging from healthy controls to Alzheimer’s disease subjects and determine whether cluster synchronization can be achieved in these networks. We observe the existence of a hypersynchronization only in AD structural networks and consider that this could represent an excellent non-invasive biomarker for tracking the disease evolution and decide upon therapeutic interventions.
Brain connectivity is usually analyzed based on graph theory and pinning control theory. Previous studies suggested that the topological properties of structural and functional networks for brain networks may be altered in association with neurodegnerative diseases. To better understand and characterize these alterations, we introduce a new approach - robustness of network controllability to evaluate network robustness, and identify the critical nodes, whose removals maximally destroys the network’s functionality. These alterations are due to external or internal changes in the network. Understanding and describing these interactions at the level of large-scale brain circuitry may be a significant step towards unraveling dementia disease evolution. In this study, we analyze structural and functional brain networks for healthy controls, MCI and AD patients such that we reveal the connection between network robustness and architecture and the differences between patients’ groups. We determine the critical and driver nodes of these networks as the key components for robustness of network controllability. Our results suggest that healthy controls for both functional and structural connectivity have more critical nodes than AD and MCI networks, and that these critical nodes appear clustered in almost all networks. Our findings provide useful information for determining disease evolution in dementia under the aspects of controllability and robustness.
Significance: Hyperspectral and multispectral imaging (HMSI) in medical applications provides information about the physiology, morphology, and composition of tissues and organs. The use of these technologies enables the evaluation of biological objects and can potentially be applied as an objective assessment tool for medical professionals.
Aim: Our study investigates HMSI systems for their usability in medical applications.
Approach: Four HMSI systems (one hyperspectral pushbroom camera and three multispectral snapshot cameras) were examined and a spectrometer was used as a reference system, which was initially validated with a standardized color chart. The spectral accuracy of the cameras reproducing chemical properties of different biological objects (porcine blood, physiological porcine tissue, and pathological porcine tissue) was analyzed using the Pearson correlation coefficient.
Results: All the HMSI cameras examined were able to provide the characteristic spectral properties of blood and tissues. A pushbroom camera and two snapshot systems achieve Pearson coefficients of at least 0.97 compared to the ground truth, indicating a very high positive correlation. Only one snapshot camera performs moderately to high positive correlation (0.59 to 0.85).
Conclusion: The knowledge of the suitability of HMSI cameras for accurate measurement of chemical properties of biological objects offers a good opportunity for the selection of the optimal imaging tool for specific medical applications, such as organ transplantation.
Imaging photoplethysmography (iPPG) has attracted much attention over the last years. The vast majority of works focuses on methods to reliably extract the heart rate from videos. Only a few works addressed iPPGs ability to exploit spatio-temporal perfusion pattern to derive further diagnostic statements.
This work directs at the spatio-temporal analysis of blood perfusion from videos. We present a novel algorithm that bases on the two-dimensional representation of the blood pulsation (perfusion map). The basic idea behind the proposed algorithm consists of a pairwise estimation of time delays between photoplethysmographic signals of spatially separated regions. The probabilistic approach yields a parameter denoted as perfusion speed. We compare the perfusion speed versus two parameters, which assess the strength of blood pulsation (perfusion strength and signal to noise ratio).
Preliminary results using video data with different physiological stimuli (cold pressure test, cold face test) show that all measures are influenced by those stimuli (some of them with statistical certainty). The perfusion speed turned out to be more sensitive than the other measures in some cases. However, our results also show that the intraindividual stability and interindividual comparability of all used measures remain critical points.
This work proves the general feasibility of employing the perfusion speed as novel iPPG quantity. Future studies will address open points like the handling of ballistocardiographic effects and will try to deepen the understanding of the predominant physiological mechanisms and their relation to the algorithmic performance.
KEYWORDS: Vital signs, Signal to noise ratio, Independent component analysis, Principal component analysis, Heart, RGB color model, Signal processing, Beam propagation method, Cameras, Video
Blind source separation (BSS) aims at separating useful signal content from distortions. In the contactless acquisition of vital signs by means of the camera-based photoplethysmogram (cbPPG), BSS has evolved the most widely used approach to extract the cardiac pulse. Despite its frequent application, there is no consensus about the optimal usage of BSS and its general benefit. This contribution investigates the performance of BSS to enhance the cardiac pulse from cbPPGs in dependency to varying input data characteristics. The BSS input conditions are controlled by an automated spatial preselection routine of regions of interest. Input data of different characteristics (wavelength, dominant frequency, and signal quality) from 18 postoperative cardiovascular patients are processed with standard BSS techniques, namely principal component analysis (PCA) and independent component analysis (ICA). The effect of BSS is assessed by the spectral signal-to-noise ratio (SNR) of the cardiac pulse. The preselection of cbPPGs, appears beneficial providing higher SNR compared to standard cbPPGs. Both, PCA and ICA yielded better outcomes by using monochrome inputs (green wavelength) instead of inputs of different wavelengths. PCA outperforms ICA for more homogeneous input signals. Moreover, for high input SNR, the application of ICA using standard contrast is likely to decrease the SNR.
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