scholarly journals Frequency-phase analysis of resting-state functional MRI

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Gadi Goelman ◽  
Rotem Dan ◽  
Filip Růžička ◽  
Ondrej Bezdicek ◽  
Evžen Růžička ◽  
...  
Keyword(s):  
Time Series ◽  
Functional Mri ◽  
Correlation Functions ◽  
Resting State ◽  
Cross Correlation ◽  
Time Lag ◽  
Unified Framework ◽  
Functional Connections ◽  
Original Time ◽  
Phase Differences

Abstract We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience.

scholarly journals Seasonality of Influenza‐Like‐Illness and Acute Cardiovascular Events Are Related Regardless of Vaccine Effectiveness

2020 ◽  
Vol 9 (20) ◽  
Author(s):  
Erin R. Kulick ◽  
Michelle Canning ◽  
Neal S. Parikh ◽  
Mitchell S. V. Elkind ◽  
Amelia K. Boehme
Keyword(s):  
New York ◽  
Time Series ◽  
Influenza Vaccination ◽  
Correlation Functions ◽  
Cardiovascular Events ◽  
Cross Correlation ◽  
Moving Average ◽  
International Classification Of Diseases ◽  
Time Series Regression ◽  
Influenza Like Illness

Background Influenza has been identified as a trigger for stroke and myocardial infarction (MI) with prior studies demonstrating that influenza vaccination may decrease risk of stroke and MI. Methods and Results We used data from the New York Department of Health Statewide Planning and Research Cooperative System to evaluate whether annual variability in influenza vaccination effectiveness (VE) would be associated with cardiovascular events. Daily and monthly counts of outpatient and inpatient visits for influenza‐like illness (ILI), stroke, and MI were identified using International Classification of Diseases, Ninth Revision ( ICD‐9 ) codes; VE data for each year are publicly available. We identified pertinent lags between ILI, stroke, and MI using prewhitening cross‐correlation functions and applied them to autoregressive integrated moving average time series regression models. Time series forecasting systems assessed correlations among ILI, stroke, and MI, and the effect of VE on these relationships. Cross‐correlation functions indicated stroke events increased 1 month after increases in ILI rates; MIs increased immediately. Accounting for seasonality and lag, peaks in ILI rates were significantly related to peaks in stroke ( P =0.04) and MI ( P =0.01). Time forecasting analyses indicated no relationship between VE and cardiovascular events. Conclusions We identified that seasonality of cardiovascular events may be associated with seasonality in ILI, though VE did not modify this relationship.

scholarly journals Sex classification using long‐range temporal dependence of resting‐state functional MRI time series

2020 ◽  
Vol 41 (13) ◽  
pp. 3567-3579 ◽  
Author(s):  
Elvisha Dhamala ◽  
Keith W. Jamison ◽  
Mert R. Sabuncu ◽  
Amy Kuceyeski
Keyword(s):  
Time Series ◽  
Functional Mri ◽  
Long Range ◽  
Resting State ◽  
Temporal Dependence

Detrended Correlogram Method for Non-Stationary Time-Series Analysis

2021 ◽  
pp. 2250012
Author(s):  
G. F. Zebende ◽  
E. F. Guedes
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Correlation Coefficient ◽  
Memory Effect ◽  
Cross Correlation ◽  
Time Lag ◽  
Stationary Time Series ◽  
Time Lags ◽  
Statistical Tool ◽  
Auto Correlation

A correlogram is a statistical tool that is used to check time-series memory by computing the auto-correlation coefficient as a function of the time lag. If the time-series has no memory, then the auto-correlation must be close to zero for any time lag, otherwise if there is a memory, then the auto-correlations must be significantly different from zero. Therefore, based on the robust detrended cross-correlation coefficient, [Formula: see text], we propose the detrended correlogram method in this paper, which will be tested for some time-series (simulated and empirical). This new statistical tool is able to visualize a complete map of the auto-correlation for many time lags and time-scales, and can therefore analyze the memory effect for any time-series.

scholarly journals GRACE-Derived Time Lag of Mekong Estuarine Freshwater Transport in the Western South China Sea Validated by Isotopic Tracer Age

2021 ◽  
Vol 13 (6) ◽  
pp. 1193
Author(s):  
Zhongtian Ma ◽  
Hok Sum Fok ◽  
Linghao Zhou
Keyword(s):  
Time Series ◽  
South China Sea ◽  
South China ◽  
Cross Correlation ◽  
Time Lag ◽  
Time Lags ◽  
Substantial Impact ◽  
China Sea ◽  
Freshwater Transport ◽  
Western South China Sea

Estuarine freshwater transport has a substantial impact on the near-shore ecosystem and coastal ocean environment away from the estuary. This paper introduces two independent methods to track the Mekong freshwater-induced mass transport by calculating the time lag (or equivalently, the phase) between in situ Mekong basin runoff and the equivalent water height (EWH) time series over the western South China Sea from a gravity recovery and climate experiment (GRACE). The first method is the harmonic analysis that determines the phase difference between annual components of the two time series (called the P-method), and the other is the cross-correlation analysis that directly obtains the time lag by shifting the lagged time series forward to attain the highest cross-correlation between the two time series (called the C-method). Using a three-year rolling window, the time lag variations in three versions of GRACE between 2005 and 2012 are computed for demonstrating the consistency of the results. We found that the time lag derived from the P-method is, on average, slightly larger and more variable than that from the C-method. A comparison of our gridded time lag against the age determined via radium isotopes in September, 2007 by Chen et al. (2010) revealed that our gridded time lag results were in good agreement with most isotope-derived ages, with the largest difference less than 6 days. Among the three versions of the GRACE time series, CSR Release 05 performed the best. The lowest standard deviation of time lag was ~1.6 days, calculated by the C-method, whereas the mean difference for all the time lags from the isotope-derived ages is ~1 day by P-method. This study demonstrates the potential of monitoring Mekong estuarine freshwater transport over the western South China Sea by GRACE.

LONG RANGE TIME SERIES FORECASTING BY UPSAMPLING AND USING CROSS-CORRELATION BASED SELECTION OF NEAREST NEIGHBOR

2006 ◽  
Vol 20 (08) ◽  
pp. 1261-1278 ◽  
Author(s):  
SYED RAHAT ABBAS ◽  
MUHAMMAD ARIF
Keyword(s):  
Time Series ◽  
Long Range ◽  
Euclidean Distance ◽  
Cross Correlation ◽  
Nearest Neighbor ◽  
Time Series Forecasting ◽  
Embedding Dimension ◽  
Auto Correlation Function ◽  
Original Time ◽  
Selection Of

Long range or multistep-ahead time series forecasting is an important issue in various fields of business, science and technology. In this paper, we have proposed a modified nearest neighbor based algorithm that can be used for long range time series forecasting. In the original time series, optimal selection of embedding dimension that can unfold the dynamics of the system is improved by using upsampling of the time series. Zeroth order cross-correlation and Euclidian distance criterion are used to select the nearest neighbor from up-sampled time series. Embedding dimension size and number of candidate vectors for nearest neighbor selection play an important role in forecasting. The size of embedding is optimized by using auto-correlation function (ACF) plot of the time series. It is observed that proposed algorithm outperforms the standard nearest neighbor algorithm. The cross-correlation based criteria shows better performance than Euclidean distance criteria.

scholarly journals A Time-Series Analysis of Clostridium difficile and Its Seasonal Association with Influenza

2010 ◽  
Vol 31 (4) ◽  
pp. 382-387 ◽  
Author(s):  
Philip M. Polgreen ◽  
Ming Yang ◽  
Lucas C. Bohnett ◽  
Joseph E. Cavanaugh
Keyword(s):  
Time Series ◽  
Clostridium Difficile ◽  
Time Series Analysis ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Linear Trend ◽  
International Classification Of Diseases ◽  
Series Data ◽  
Series Analysis ◽  
Classification Of Diseases

Objective.To characterize the temporal progression of the monthly incidence of Clostridium difficile infections (CDIs) and to determine whether the incidence of CDI is related to the incidence of seasonal influenza.Design.A retrospective study of patients in the Nationwide Inpatient Sample during the period from 1998 through 2005.Methods.We identified all hospitalizations with a primary or secondary diagnosis of CDI with use of International Classification of Diseases, 9th Revision, Clinical Modification codes, and we did the same for influenza. The incidence of CDI was modeled as an autoregression about a linear trend. To investigate the association of CDI with influenza, we compared national and regional CDI and influenza series data and calculated cross-correlation functions with data that had been prewhitened (filtered to remove temporal patterns common to both series). To estimate the burden of seasonal CDI, we developed a proportional measure of seasonal CDI.Results.Time-series analysis of the monthly number of CDI cases reveals a distinct positive linear trend and a clear pattern of seasonal variation (R2 = 0.98). The cross-correlation functions indicate that influenza activity precedes CDI activity on both a national and regional basis. The average burden of seasonal (ie, winter) CDI is 23%.Conclusions.The epidemiologic characteristics of CDI follow a pattern that is seasonal and associated with influenza, which is likely due to antimicrobial use during influenza seasons. Approximately 23% of average monthly CDI during the peak 3 winter months could be eliminated if CDI remained at summer levels.

scholarly journals Temporal Stacking of Cross-Correlation for Glacier Offset Tracking

2020 ◽  
Author(s):  
Shiyi Li ◽  
Philipp Bernhard ◽  
Irena Hajnsek ◽  
Silvan Leinss
Keyword(s):  
Time Series ◽  
Flow Velocity ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Series Data ◽  
Sar Images ◽  
Spatial Coverage ◽  
Image Pairs ◽  
Offset Tracking ◽  
Image Templates

<p>Offset tracking is one of the most widely applied methods for measuring glacier flow velocities using remote sensing data. It uses the pair-wise cross-correlation of images acquired at two different times to detect offsets between image templates of a certain size. Despite the simplicity and reliability of the method, accurate estimations of glacier velocities are limited by the accountability of features and the noise, e.g. radar speckles in synthetic aperture radar (SAR) images. One way of gaining robust estimations is to increase the size of image templates, but the resolution of obtained velocity field is inevitably depreciate. Furthermore, for templates that only contain extremely weak features with respect to the noise, increasing the size of templates is not helpful as the noise is boosted more than the features.</p><p>To overcome these issues, we propose a temporal stacking algorithm that first averages a time series of local cross-correlation functions calculated from a series of consecutive image pairs, and then estimates the averaged velocity from the stacked cross-correlation functions. Assuming the flow velocity of a glacier is constant during a certain time span (e.g. a season), the offsets between consecutive image pairs in the time series ought to be equal. Therefore, the cross-correlation functions can be considered as a time series of signals that record the identical offsets and thus are temporally coherent. Hence, we can temporally stack the signals to enhance the signal-to-noise ratio (SNR) of cross-correlation functions and better estimate offsets from the stacked cross-correlation functions. </p><p>The proposed algorithm is assessed by mapping the flow velocity of the Aletsch Glacier using a time series of about 10 SAR images acquired by TanDEM-X in 2017 with constant revisit time of 11 days. The results show that temporal stacking of cross-correlation functions significantly enhances the spatial coverage and resolution of the obtained velocity fields compared to standard offset tracking using only pair-wise cross-correlation functions. This algorithm promotes the ability of mapping glacier velocities to a new extent with larger spatial coverage and higher spatial resolution, and provides a new perspective of measuring glacier velocities through exploiting the emerging time series data from recent high resolution space-born imaging sensors.</p>

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