Dataset Information

Frequency Modulated Mobius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences.

ABSTRACT: Motivated by applications in physical and biological sciences, we developed a Frequency Modulated Möbius (FMM) model to describe rhythmic patterns in oscillatory systems. Unlike standard symmetric sinusoidal models, FMM is a flexible parametric model that allows deformations to sinusoidal shape to accommodate commonly seen asymmetries in applications. FMM model parameters are easy to estimate and the model is easy to interpret complex rhythmic data. We illustrate FMM model in three disparate applications, namely, circadian clock gene expression, corticoptropin levels in depressed patients and the temporal light intensity patterns of distant stars. In each case, FMM model is demonstrated to be flexible, scientifically plausible and easy to interpret. Analysis of synthetic data derived from patterns of real data, suggest that FMM model fits the data very well both visually as well as in terms of the goodness of fit measure total mean squared error. An R language based software for implementing FMM model is available.

SUBMITTER: Rueda C

PROVIDER: S-EPMC6904729 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Frequency Modulated Möbius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences.

Rueda Cristina C Larriba Yolanda Y Peddada Shyamal D SD

Scientific reports 20191210 1

Motivated by applications in physical and biological sciences, we developed a Frequency Modulated Möbius (FMM) model to describe rhythmic patterns in oscillatory systems. Unlike standard symmetric sinusoidal models, FMM is a flexible parametric model that allows deformations to sinusoidal shape to accommodate commonly seen asymmetries in applications. FMM model parameters are easy to estimate and the model is easy to interpret complex rhythmic data. We illustrate FMM model in three disparate app ...[more]

PMID: 31822685

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Project description:"Asymptomatic" carotid artery stenosis (aCAS) patients usually have cognitive impairment in the domains of executive, psychomotor speed, and memory function. However, the pathophysiology of this impairment in aCAS patients is still unclear. In this study, amplitude of low-frequency fluctuation (ALFF) method was used based on resting-state blood oxygenation level dependent (BOLD) signals, to investigate local brain activity in 19 aCAS patients and 24 healthy controls, aimed to explore this pathophysiology mechanism. We analyzed this intrinsic activity in four individual frequency bands: Slow-2 (0.198-0.25 Hz), Slow-3 (0.073-0.198 Hz), Slow-4 (0.027-0.073 Hz), and Slow-5 (0.01-0.027 Hz). The aCAS-related ALFF changes were mainly distributed in (1) cortical midline structure, including bilateral dorsomedial prefrontal (dmPFC), cingulate cortex (CC) and precuneus (PCu); (2) hippocampus and its adjacent structures, including bilateral hippocampus, thalamus and medial temporal regions. We found these spatial patterns were frequency-dependent. Significant interaction between frequency and group was found distributed in left putamen, triangle part of inferior temporal and bilateral precentral/postcentral gyrus when Slow-4 and Slow-5 were considered. The delay recall ability of aCAS patient was significantly positive correlated to the mean ALFF in dmPFC within Slow-4 band and the mean ALFF in the bilateral hippocampus within Slow-3 band, respectively. We also found the Montreal Cognitive Assessme score of aCAS patient was significantly positive correlated to the mean ALFF in right fusiform and parahippocampus within Slow-3 band. Furthermore, we built the automatic diagnosis and prediction models based on support vector machine (SVM) and back propagation neural network (BPNN), respectively. Both two types of models could achieve relatively competent performance, which meant the frequency-dependent changes in ALFF could not only reveal the pathophysiology mechanism of cognitive impairment of aCAS, but also could be used as neuroimaging marker in the analysis of cognition impairment for aCAS patients.

Dataset Information

Frequency Modulated Mobius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences.

Publications

Frequency Modulated Möbius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences.

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