Testing Stationarity of Brain Functional Connectivity Using Change-Point Detection in fMRI Data

2016 ◽  
Author(s):  
Mengyu Dai ◽  
Zhengwu Zhang ◽  
Anuj Srivastava
Keyword(s):  
Functional Connectivity ◽  
Change Point ◽  
Change Point Detection ◽  
Fmri Data ◽  
Point Detection ◽  
Brain Functional Connectivity

scholarly journals Network change point detection in resting-state functional connectivity dynamics of mild cognitive impairment patients

2020 ◽  
Vol 20 (3) ◽  
pp. 200-212
Author(s):  
Núria Mancho-Fora ◽  
Marc Montalà-Flaquer ◽  
Laia Farràs-Permanyer ◽  
Daniel Zarabozo-Hurtado ◽  
Geisa Bearitz Gallardo-Moreno ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Resting State ◽  
Change Point ◽  
Change Point Detection ◽  
Resting State Functional Connectivity ◽  
Network Change ◽  
Point Detection

Resting-State Functional Connectivity Dynamics in Healthy Aging: An Approach Through Network Change Point Detection

2020 ◽  
Vol 10 (3) ◽  
pp. 134-142
Author(s):  
Núria Mancho-Fora ◽  
Marc Montalà-Flaquer ◽  
Laia Farràs-Permanyer ◽  
David Bartrés-Faz ◽  
Lídia Vaqué-Alcázar ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Change Point ◽  
Healthy Aging ◽  
Change Point Detection ◽  
Resting State Functional Connectivity ◽  
Network Change ◽  
Point Detection

scholarly journals Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity

2020 ◽  
Author(s):  
Andreas Anastasiou ◽  
Ivor Cribben ◽  
Piotr Fryzlewicz
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Change Point ◽  
Sliding Window ◽  
Resting State Fmri ◽  
Change Point Detection ◽  
Change Points ◽  
Fmri Data ◽  
Cross Covariance ◽  
Window Approach

Evidence of the non stationary behavior of functional connectivity (FC) networks has been observed in task based functional magnetic resonance imaging (fMRI) experiments and even prominently in resting state fMRI data. This has led to the development of several new statistical methods for estimating this time-varying connectivity, with the majority of the methods utilizing a sliding window approach. While computationally feasible, the sliding window approach has several limitations. In this paper, we circumvent the sliding window, by introducing a statistical method that finds change-points in FC networks where the number and location of change-points are unknown a priori. The new method, called cross-covariance isolate detect (CCID), detects multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. CCID allows for change-point detection in the presence of frequent changes of possibly small magnitudes, can assign change-points to one or multiple brain regions, and is computationally fast. In addition, CCID is particularly suited to task based data, where the subject alternates between task and rest, as it firstly attempts isolation of each of the change-points within subintervals, and secondly their detection therein. Furthermore, we also propose a new information criterion for CCID to identify the change-points. We apply CCID to several simulated data sets and to task based and resting state fMRI data and compare it to recent change-point methods. CCID is also applicable to electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. Similar to other biological networks, understanding the complex network organization and functional dynamics of the brain can lead to profound clinical implications. Finally, the R package ccid implementing the method from the paper is available from GitHub.

scholarly journals Dynamic Functional Connectivity Change-Point Detection With Random Matrix Theory Inference

2021 ◽  
Vol 15 ◽  
Author(s):  
Jaehee Kim ◽  
Woorim Jeong ◽  
Chun Kee Chung
Keyword(s):  
Functional Connectivity ◽  
Random Matrix Theory ◽  
Random Matrix ◽  
Change Point ◽  
Matrix Theory ◽  
Change Point Detection ◽  
Change Points ◽  
Point Problem ◽  
Change Point Problem ◽  
Point Detection

To study the dynamic nature of brain activity, functional magnetic resonance imaging (fMRI) data is useful including some temporal dependencies between the corresponding neural activity estimates. Recent studies have shown that the functional connectivity (FC) varies according to time and location which should be incorporated into the model. Modeling this dynamic FC (DFC) requires time-varying measures of spatial region of interest (ROI) sets. To know about the DFC, change-point detection in FC is of particular interest. In this paper, we propose a method of detecting a change-point based on the maximum of eigenvalues via random matrix theory (RMT). From covariance matrices for FC of all ROI's, the temporal change-point of FC is decided by an RMT approach. Simulation results show that our proposed method can detect meaningful FC change-points. We also illustrate the effectiveness of our FC detection approach by applying our method to epilepsy data where change-points detected are explained by the changes in memory capacity. Our study shows the possibility of RMT based approach in DFC change-point problem and in studying the complex dynamic pattern of functional brain interactions.

Understanding the CoVID-19 pandemic Curve through statistical approach (Preprint)

2020 ◽  
Author(s):  
Ibrar Ul Hassan Akhtar
Keyword(s):  
Probability Density ◽  
Change Point ◽  
Statistical Approach ◽  
Sudden Change ◽  
Global Scale ◽  
Change Point Detection ◽  
Segment Length ◽  
Infected Population ◽  
The Mean ◽  
Point Detection

UNSTRUCTURED Current research is an attempt to understand the CoVID-19 pandemic curve through statistical approach of probability density function with associated skewness and kurtosis measures, change point detection and polynomial fitting to estimate infected population along with 30 days projection. The pandemic curve has been explored for above average affected countries, six regions and global scale during 64 days of 22nd January to 24th March, 2020. The global cases infection as well as recovery rate curves remained in the ranged of 0 ‒ 9.89 and 0 ‒ 8.89%, respectively. The confirmed cases probability density curve is high positive skewed and leptokurtic with mean global infected daily population of 6620. The recovered cases showed bimodal positive skewed curve of leptokurtic type with daily recovery of 1708. The change point detection helped to understand the CoVID-19 curve in term of sudden change in term of mean or mean with variance. This pointed out disease curve is consist of three phases and last segment that varies in term of day lengths. The mean with variance based change detection is better in differentiating phases and associated segment length as compared to mean. Global infected population might rise in the range of 0.750 to 4.680 million by 24th April 2020, depending upon the pandemic curve progress beyond 24th March, 2020. Expected most affected countries will be USA, Italy, China, Spain, Germany, France, Switzerland, Iran and UK with at least infected population of over 0.100 million. Infected population polynomial projection errors remained in the range of -78.8 to 49.0%.

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