Predicting behavior through dynamic modes in resting-state fMRI data

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.

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

10.1101/343061 ◽

2018 ◽

Cited By ~ 3

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.

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Design and Decomposition Analysis of Mixing Zone Structures on Flame Dynamics for a Swirl Burner

Energies ◽

10.3390/en13246744 ◽

2020 ◽

Vol 13 (24) ◽

pp. 6744

Author(s):

Yang Yang ◽

Zhijian Yu

Keyword(s):

Transfer Functions ◽

Decomposition Analysis ◽

Dynamic Mode Decomposition ◽

Dynamic Mode ◽

Streamwise Vortices ◽

Frequency Bands ◽

Time Dynamic ◽

Burner Exit ◽

Decomposition Procedure ◽

Flame Structures

The recirculation zone and the swirl flame behavior can be influenced by the burner exit shape, and few studies have been made into this structure. Large eddy simulation was carried out on 16 cases to distinguish critical geometry factors. The time series of the heat release rate were decomposed using seasonal-trend decomposition procedure to exclude the effect of short physical time. Dynamic mode decomposition (DMD) was performed to separate flame structures. The frequency characteristics extracted from the DMD modes were compared with those from the flame transfer functions. Results show that the flame cases can be categorized into three types, all of which are controlled by a specific geometric parameter. Except one type of flame, they show nonstationary behavior by the Kwiatkowski–Phillips–Schmidt–Shin test. The frequency bands corresponding to the coherent structures are identified. The flame transfer function indicates that the flame can respond to external excitation in the frequency range 100–300 Hz. The DMD modes capture the detailed flame structures. The higher frequency bands can be interpolated as the streamwise vortices and shedding vortices. The DMD modes, which correspond to the bands of flame transfer functions, can be estimated as streamwise vortices at the edges.

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Multivariate empirical mode decomposition based sub-frequency bands analysis of the default mode network: a resting-state fMRI data study

Applied Informatics ◽

10.1186/s40535-014-0005-z ◽

2015 ◽

Vol 2 (1) ◽

Cited By ~ 8

Author(s):

Tao Zhang ◽

Peng Xu ◽

Lanjin Guo ◽

Rui Chen ◽

Rui Zhang ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Default Mode Network ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

Default Mode ◽

Frequency Bands ◽

Multivariate Empirical Mode Decomposition ◽

Mode Decomposition

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Dynamic mode decomposition of resting-state and task fMRI

NeuroImage ◽

10.1016/j.neuroimage.2019.03.019 ◽

2019 ◽

Vol 194 ◽

pp. 42-54 ◽

Cited By ~ 12

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

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

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2019.00075 ◽

2019 ◽

Vol 13 ◽

Cited By ~ 2

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

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Altered Functional Connectivity of the Primary Visual Cortex in Patients With Iridocyclitis and Assessment of Its Predictive Value Using Machine Learning

Frontiers in Immunology ◽

10.3389/fimmu.2021.660554 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yan Tong ◽

Xin Huang ◽

Chen-Xing Qi ◽

Yin Shen

Keyword(s):

Machine Learning ◽

Visual Cortex ◽

Functional Connectivity ◽

Resting State ◽

Permutation Test ◽

Resting State Fmri ◽

Fmri Data ◽

Svm Classifier ◽

Postcentral Gyrus ◽

The Right

PurposeTo explore the intrinsic functional connectivity (FC) alteration of the primary visual cortex (V1) between individuals with iridocyclitis and healthy controls (HCs) by the resting-state functional magnetic resonance imaging (fMRI) technique, and to investigate whether FC findings be used to differentiate patients with iridocyclitis from HCs.MethodsTwenty-six patients with iridocyclitis and twenty-eight well-matched HCs were recruited in our study and underwent resting-state fMRI examinations. The fMRI data were analyzed by Statistical Parametric Mapping (SPM12), Data Processing and Analysis for Brain Imaging (DPABI), and Resting State fMRI Data Analysis Toolkit (REST) software. Differences in FC signal values of the V1 between the individuals with iridocyclitis and HCs were compared using independent two-sample t-tests. Significant differences in FC between two groups were chosen as classification features for distinguishing individuals with iridocyclitis from HCs using a support vector machine (SVM) classifier that involved machine learning. Classifier performance was evaluated using permutation test analysis.ResultsCompared with HCs, patients with iridocyclitis displayed significantly increased FC between the left V1 and left cerebellum crus1, left cerebellum 10, bilateral inferior temporal gyrus, right hippocampus, and left superior occipital gyrus. Moreover, patients with iridocyclitis displayed significantly lower FC between the left V1 and both the bilateral calcarine and bilateral postcentral gyrus. Patients with iridocyclitis also exhibited significantly higher FC values between the right V1 and left cerebellum crus1, bilateral thalamus, and left middle temporal gyrus; while they displayed significantly lower FC between the right V1 and both the bilateral calcarine and bilateral postcentral gyrus (voxel-level P<0.01, Gaussian random field correction, cluster-level P<0.05). Our results showed that 63.46% of the participants were correctly classified using the leave-one-out cross-validation technique with an SVM classifier based on the FC of the left V1; and 67.31% of the participants were correctly classified based on the FC of the right V1 (P<0.001, non-parametric permutation test).ConclusionPatients with iridocyclitis displayed significantly disturbed FC between the V1 and various brain regions, including vision-related, somatosensory, and cognition-related regions. The FC variability could distinguish patients with iridocyclitis from HCs with substantial accuracy. These findings may aid in identifying the potential neurological mechanisms of impaired visual function in individuals with iridocyclitis.

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Experimental Measurement of In-Plane Rolling Nonpneumatic Tire Vibrations Using High-Speed Imaging

Tire Science and Technology ◽

10.2346/tire.18.470101 ◽

2019 ◽

Vol 47 (3) ◽

pp. 196-210

Author(s):

Meghashyam Panyam ◽

Beshah Ayalew ◽

Timothy Rhyne ◽

Steve Cron ◽

John Adcox

Keyword(s):

High Speed ◽

Image Correlation ◽

Dynamic Mode Decomposition ◽

Dynamic Mode ◽

High Speed Imaging ◽

Correlation Algorithm ◽

Mode Decomposition ◽

Series Of Experiments ◽

Plane Deformations ◽

Dominant Frequencies

ABSTRACT This article presents a novel experimental technique for measuring in-plane deformations and vibration modes of a rotating nonpneumatic tire subjected to obstacle impacts. The tire was mounted on a modified quarter-car test rig, which was built around one of the drums of a 500-horse power chassis dynamometer at Clemson University's International Center for Automotive Research. A series of experiments were conducted using a high-speed camera to capture the event of the rotating tire coming into contact with a cleat attached to the surface of the drum. The resulting video was processed using a two-dimensional digital image correlation algorithm to obtain in-plane radial and tangential deformation fields of the tire. The dynamic mode decomposition algorithm was implemented on the deformation fields to extract the dominant frequencies that were excited in the tire upon contact with the cleat. It was observed that the deformations and the modal frequencies estimated using this method were within a reasonable range of expected values. In general, the results indicate that the method used in this study can be a useful tool in measuring in-plane deformations of rolling tires without the need for additional sensors and wiring.

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