scholarly journals Extracting Reproducible Time-Resolved Resting State Networks using Dynamic Mode Decomposition

2018 ◽  
Author(s):  
James M. Kunert-Graf ◽  
Kristian M. Eschenburg ◽  
David J. Galas ◽  
J. Nathan Kutz ◽  
Swati D. Rane ◽  
...  
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Regions ◽  
Dynamic Mode Decomposition ◽  
Single Subject ◽  
Dynamic Mode ◽  
Resting State Networks ◽  
Time Resolved ◽  
Mode Decomposition ◽  
Human Connectome Project

AbstractResting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5–15 minutes. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. We demonstrate this method on data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.

scholarly journals Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition

2019 ◽  
Vol 13 ◽  
Author(s):  
James M. Kunert-Graf ◽  
Kristian M. Eschenburg ◽  
David J. Galas ◽  
J. Nathan Kutz ◽  
Swati D. Rane ◽  
...  
Keyword(s):  
Resting State ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Resting State Networks ◽  
Time Resolved ◽  
Mode Decomposition

scholarly journals Medial Temporal Lobe Contributions to Resting-State Networks

2021 ◽  
Author(s):  
Sara Seoane ◽  
Cristián Modroño ◽  
José Luis González Mora ◽  
Niels Janssen
Keyword(s):  
Temporal Lobe ◽  
Resting State ◽  
Medial Temporal Lobe ◽  
Brain Regions ◽  
Resting State Networks ◽  
Attention Networks ◽  
Default Mode ◽  
Parahippocampal Cortex ◽  
Human Connectome Project ◽  
Dorsal Attention Network

Abstract The medial temporal lobe (MTL) is a set of interconnected brain regions that have been shown to play a central role in behavior as well as in neurological disease. Recent studies using resting-state functional Magnetic Resonance Imaging (rsfMRI) have attempted to understand the MTL in terms of its functional connectivity with the rest of the brain. However, the exact characterization of the whole-brain networks that co-activate with the MTL as well as how the various sub-regions of the MTL are associated with these networks remains poorly understood. Here we attempted to advance these issues by exploiting the high spatial-resolution 7T rsfMRI dataset from the Human Connectome Project with a data-driven analysis approach that relied on Independent Component Analysis (ICA) restricted to the MTL. We found that four different well-known resting-state networks co-activated with a unique configuration of MTL subcomponents. Specifically, we found that different sections of the parahippocampal cortex were involved in the default mode, visual and dorsal attention networks, sections of the hippocampus in the somatomotor and default mode networks, and the lateral entorhinal cortex in the dorsal attention network. We replicated this set of results in a validation sample. These results provide new insight into how the MTL and its subcomponents contribute to known resting-state networks. The participation of the MTL in an expanded range of resting-state networks requires a rethink of its presumed role in behavior and disease.

scholarly journals Time-resolved denoising using model order reduction, dynamic mode decomposition, and kalman filter and smoother

2020 ◽  
Vol 7 (2) ◽  
pp. 469-487
Author(s):  
Mojtaba F. Fathi ◽  
◽  
Ahmadreza Baghaie ◽  
Ali Bakhshinejad ◽  
Raphael H. Sacho ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Model Order ◽  
Time Resolved ◽  
Mode Decomposition

scholarly journals Dynamic mode decomposition of resting-state and task fMRI

NeuroImage ◽  
2019 ◽  
Vol 194 ◽  
pp. 42-54 ◽  
Author(s):  
Jeremy Casorso ◽  
Xiaolu Kong ◽  
Wang Chi ◽  
Dimitri Van De Ville ◽  
B.T. Thomas Yeo ◽  
...  
Keyword(s):  
Resting State ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Mode Decomposition ◽  
And Task

Dynamic Mode Decomposition for Extracting Cycle-to-Cycle Variation of SIDI Engine In-Cylinder Flow Under Motoring Condition

2020 ◽  
Author(s):  
Mengqi Liu ◽  
Fengnian Zhao ◽  
Xuesong Li ◽  
Min Xu ◽  
David L. S. Hung
Keyword(s):  
Flow Field ◽  
Direct Injection ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Field Variation ◽  
Dynamic Features ◽  
Time Resolved ◽  
Cycle Variation ◽  
Mode Decomposition ◽  
Cylinder Flow

Abstract Cycle-to-cycle variation (CCV) of in-cylinder flow strongly affects the performance and efficiency of spark ignition direct injection (SIDI) engines. In order to achieve a precise flow control inside the engine, the underlying dynamic features of flow field CCV must be thoroughly investigated. In this work, large-eddy simulations (LES) with 50 consecutive cycles are employed for high fidelity numerical realizations of engine flow under motoring condition. To supplement the numerical analysis, time-resolved particle image velocimetry (PIV) measurements are also conducted in several cutting planes. Although the velocity root mean square (RMS) is calculated to quantify the cyclic variation intensity of simulation and experiment results, some important dynamic characteristics cannot be observed directly from velocity data. Therefore, dynamic mode decompositions (DMD), which is a widely used modal decomposition algorithm on fluid study, is used to decompose flow fields into modes with specific frequencies and provide growth rates of corresponding flow structures. This spectral information of in-cylinder flow field is ponderable for uncovering dynamic features of engine CCV. In this study, DMD algorithm is applied on both LES and PIV datasets. The frequency and growth rate differences are employed to elucidate the CCV feature deviations captured by LES and PIV. This research provides a guideline for extracting engine flow field cyclic variability feature using DMD algorithm. Based on the discussion for spectral features and potential sources of flow field variation, the capability of LES to capture CCV features is evaluated. The DMD spectrum differences between PIV and LES can guide the boundary condition perturbations used for simulation fidelity improvements.

Analysis of synthetic jet PIV experiment based on dynamic mode decomposition

2020 ◽  
Vol 34 (14n16) ◽  
pp. 2040103
Author(s):  
Zhi-Xian Ye ◽  
Yi-Yang Jiang ◽  
Ze-Nan Tian ◽  
Shao-Chang Mo ◽  
Yuan-Qi Fang ◽  
...  
Keyword(s):  
Flow Field ◽  
Field Research ◽  
Input Voltage ◽  
Synthetic Jet ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Vibration Displacement ◽  
Time Resolved ◽  
Mode Decomposition ◽  
Flow Field Characteristics

Synthetic Jet Actuator (SJA) works cyclically with the directionally transportation of fluid near the exit, and the paper presents the performance of a loudspeaker driven SJA in static flow field. Time-Resolved Particle Image Velocimetry (TR-PIV) system is used to measure the flow field characteristics near the SJA slot exit with input voltage changing, and the flow field snapshots obtained by TR-PIV are modality analyzed by Dynamic Mode Decomposition (DMD) method. The PIV experiments show that by varying input voltage at fixed oscillating frequency, the loudspeaker diaphragm vibration displacement is the parameter that affects the jet velocity and the performance of the SJA. The traveling vortex vanishes at high voltage due to the interaction between vortex structures and the synthetic main jet. In DMD method, the first three-order modes can characterize the main information of the original flow field with reverting the flow field snapshot sequence. It indicates that the DMD method is applicable in the SJA flow field research and the reduced order model can effectively simplify the analysis of flow field.

scholarly journals Predicting behavior through dynamic modes in resting-state fMRI data

2021 ◽  
Author(s):  
Shigeyuki Ikeda ◽  
Koki Kawano ◽  
Soichi Watanabe ◽  
Okito Yamashita ◽  
Yoshinobu Kawahara
Keyword(s):  
Resting State ◽  
Permutation Test ◽  
Dynamic Properties ◽  
Methodological Approach ◽  
Dynamic Mode Decomposition ◽  
Fmri Data ◽  
Gram Matrix ◽  
Dynamic Mode ◽  
Behavioral Measures ◽  
Frequency Bands

Dynamic properties of resting-state functional connectivity (FC) provide rich information on brain-behavior relationships. Dynamic mode decomposition (DMD) has been used as a method to characterize FC dynamics. However, it remains unclear whether dynamic modes (DMs), spatial-temporal coherent patterns computed by DMD, provide information about individual behavioral differences. This study established a methodological approach to predict individual differences in behavior using DMs. Furthermore, we investigated the contribution of DMs within each of seven specific frequency bands (0–0.1,...,0.6–0.7 Hz) for prediction. To validate our approach, we confirmed whether each of 59 behavioral measures could be predicted by performing multivariate pattern analysis on a gram matrix, which was created using subject-specific DMs computed from resting-state functional magnetic resonance imaging (rs-fMRI) data of individuals. The prediction was successful, and DMD outperformed temporal independent component analysis, a conventional data decomposition method for extracting spatial activity patterns. Most of the behavioral measures that showed significant prediction accuracies in a permutation test were cognitive-behavioral measures. Our results suggested that DMs within frequency bands <0.2 Hz primarily contributed to prediction. In addition, we found that DMs <0.2 Hz had spatial structures similar to several common resting-state networks. We demonstrated the effectiveness of DMs, indicating that DMD is a key approach for extracting spatiotemporal features from rs-fMRI data.

scholarly journals Discordant attributes of structural and functional connectivity in a two-layer multiplex network

2018 ◽  
Author(s):  
Sol Lim ◽  
Filippo Radicchi ◽  
Martijn P van den Heuvel ◽  
Olaf Sporns
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Resting State Networks ◽  
Structural Failure ◽  
Resonance Imaging ◽  
Multiplex Network ◽  
Human Connectome Project ◽  
Magnetic Resonance Imaging Mri

AbstractSeveral studies have suggested that functional connectivity (FC) is constrained by the underlying structural connectivity (SC) and mutually correlated. However, not many studies have focused on differences in the network organization of SC and FC, and on how these differences may inform us about their mutual interaction. To explore this issue, we adopt a multi-layer framework, with SC and FC, constructed using Magnetic Resonance Imaging (MRI) data from the Human Connectome Project, forming a two-layer multiplex network. In particular, we examine whether node strength assortativity within and between the SC and FC layer may confer increased robustness against structural failure. We find that, in general, SC is organized assortatively, indicating brain regions are on average connected to other brain regions with similar node strengths. On the other hand, FC shows disassortative mixing. This discrepancy is apparent also among individual resting-state networks within SC and FC. In addition, these patterns show lateralization, with disassortative mixing within FC subnetworks mainly driven from the left hemisphere. We discuss our findings in the context of robustness to structural failure, and we suggest that discordant and lateralized patterns of associativity in SC and FC may explain laterality of some neurological dysfunctions and recovery.

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