scholarly journals Test–Retest Reliability of Synchrony and Metastability in Resting State fMRI

2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Lan Yang ◽  
Jing Wei ◽  
Ying Li ◽  
Bin Wang ◽  
Hao Guo ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Resting State Networks ◽  
Functional Magnetic Resonance ◽  
Intra Class Correlation ◽  
Retest Reliability ◽  
Brain Signals ◽  
Human Connectome Project ◽  
Test Retest Reliability ◽  
The Brain

In recent years, interest has been growing in dynamic characteristic of brain signals from resting-state functional magnetic resonance imaging (rs-fMRI). Synchrony and metastability, as neurodynamic indexes, are considered as one of methods for analyzing dynamic characteristics. Although much research has studied the analysis of neurodynamic indices, few have investigated its reliability. In this paper, the datasets from the Human Connectome Project have been used to explore the test–retest reliabilities of synchrony and metastability from multiple angles through intra-class correlation (ICC). The results showed that both of these indexes had fair test–retest reliability, but they are strongly affected by the field strength, the spatial resolution, and scanning interval, less affected by the temporal resolution. Denoising processing can help improve their ICC values. In addition, the reliability of neurodynamic indexes was affected by the node definition strategy, but these effects were not apparent. In particular, by comparing the test–retest reliability of different resting-state networks, we found that synchrony of different networks was basically stable, but the metastability varied considerably. Among these, DMN and LIM had a relatively higher test–retest reliability of metastability than other networks. This paper provides a methodological reference for exploring the brain dynamic neural activity by using synchrony and metastability in fMRI signals.

scholarly journals Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

2020 ◽  
Author(s):  
Arun S. Mahadevan ◽  
Ursula A. Tooley ◽  
Maxwell A. Bertolero ◽  
Allyson P. Mackey ◽  
Danielle S. Bassett
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Partial Correlation ◽  
Motion Artifact ◽  
Resting State Fmri ◽  
Alternative Methods ◽  
Fmri Data ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Test Retest Reliability

AbstractFunctional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

The Effect of General Anesthesia On Test-Retest Reliability of Resting-State FMRI Metrics And Optimization of Scan Length

2021 ◽  
Author(s):  
Faezeh Vedaei ◽  
Mahdi Alizadeh ◽  
Victor M Romo ◽  
Feroze B. Mohamed ◽  
Chengyuan Wu
Keyword(s):  
Resting State ◽  
Intraclass Correlation ◽  
Resting State Fmri ◽  
Frequency Fluctuation ◽  
Retest Reliability ◽  
Factors Affecting ◽  
Fractional Frequency ◽  
Drug Resistant Epilepsy ◽  
Test Retest Reliability ◽  
Scan Length

Abstract Resting-state functional magnetic resonance imaging (rs-fMRI) has been known as a powerful tool in neuroscience. However, exploring the test-retest reliability of the metrics derived from rs-fMRI BOLD signal is essential particularly in the studies of patients with neurological development. Two factors affecting reliability of rs-fMRI measurements including the effect of anesthesia and scan length have been estimated in this study. A total of 9 patients with drug-resistant epilepsy (DRE) of requiring interstitial thermal therapy (LITT) were scanned in two states of awake and under anesthesia. At each state, two rs-fMRI sessions were obtained that each one lasted 15 minutes, and the effect of scan length was evaluated. Voxel-wise rs-fMRI metrics including amplitude of low fractional frequency fluctuation (ALFF), amplitude of low fractional frequency fluctuation (fALFF), functional connectivity (FC), and regional homogeneity (ReHo) were measured. Intraclass correlation coefficient (ICC) was calculated to estimate the reliability between two sessions of scanning for both states. Overall, our finding revealed that reliability improves under anesthesia as well as by increasing the scanning length of the scanning sessions. Furthermore, we showed that the optimal scan length to achieve reliable rs-fMRI measurements was 3.1 – 7.5 minutes shorter in an anesthetized, compared to wakeful state.

Identification of Brain Functional Networks Using a Model-Based Approach

2019 ◽  
Vol 34 (08) ◽  
pp. 2057004
Author(s):  
Vangelis P. Oikonomou ◽  
Konstantinos Blekas ◽  
Loukas Astrakas
Keyword(s):  
Time Series ◽  
Resting State ◽  
Probabilistic Method ◽  
Resting State Fmri ◽  
Model Parameters ◽  
Resting State Networks ◽  
Model Based ◽  
Functional Brain Networks ◽  
Regression Mixture ◽  
The Brain

Functional MRI (fMRI) is a valuable brain imaging technique. A significant problem, when analyzing fMRI time series, is the finding of functional brain networks when the brain is at rest, i.e. no external stimulus is applied to the subject. In this work, we present a probabilistic method to estimate the Resting State Networks (RSNs) using a model-based approach. More specifically, RSNs are assumed to be the result of a clustering procedure. In order to perform the clustering, a mixture of regression models are used. Furthermore, special care has been given in order to incorporate important functionalities, such as spatial and embedded sparsity enforcing properties, through the use of informative priors over the model parameters. Another interesting feature of the proposed scheme is the flexibility to handle all the brain time series at once, allowing more robust solutions. We provide comparative experimental results, using an artificial fMRI dataset and two real resting state fMRI datasets, that empirically illustrate the efficiency of the proposed regression mixture model.

scholarly journals High spatial correlation in brain connectivity between micturition and resting states within bladder-related networks using 7 T MRI in multiple sclerosis women with voiding dysfunction

2021 ◽  
Author(s):  
Zhaoyue Shi ◽  
Khue Tran ◽  
Christof Karmonik ◽  
Timothy Boone ◽  
Rose Khavari
Keyword(s):  
Multiple Sclerosis ◽  
Resting State ◽  
Voiding Dysfunction ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Functional Magnetic Resonance ◽  
Non Invasive ◽  
Potential Alternative ◽  
The Brain

Abstract Background Several studies have reported brain activations and functional connectivity (FC) during micturition using functional magnetic resonance imaging (fMRI) and concurrent urodynamics (UDS) testing. However, due to the invasive nature of UDS procedure, non-invasive resting-state fMRI is being explored as a potential alternative. The purpose of this study is to evaluate the feasibility of utilizing resting states as a non-invasive alternative for investigating the bladder-related networks in the brain. Methods We quantitatively compared FC in brain regions belonging to the bladder-related network during the following states: ‘strong desire to void’, ‘voiding initiation (or attempt at voiding initiation)’, and ‘voiding (or continued attempt of voiding)’ with FC during rest in nine multiple sclerosis women with voiding dysfunction using fMRI data acquired at 7 T and 3 T. Results The inter-subject correlation analysis showed that voiding (or continued attempt of voiding) is achieved through similar network connections in all subjects. The task-based bladder-related network closely resembles the resting-state intrinsic network only during voiding (or continued attempt of voiding) process but not at other states. Conclusion Resting states fMRI can be potentially utilized to accurately reflect the voiding (or continued attempt of voiding) network. Concurrent UDS testing is still necessary for studying the effects of strong desire to void and initiation of voiding (or attempt at initiation of voiding).

scholarly journals Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

2019 ◽  
Author(s):  
John Fallon ◽  
Phil Ward ◽  
Linden Parkes ◽  
Stuart Oldham ◽  
Aurina Arnatkevic̆iūtė ◽  
...  
Keyword(s):  
Resting State ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Information Storage ◽  
Brain Organization ◽  
Temporal Properties ◽  
Temporal Features ◽  
Human Connectome Project ◽  
The Brain ◽  
Connectivity Strength

AbstractIntrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain’s underlying structural-connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural-connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale-related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6000 time-series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

scholarly journals Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling

2019 ◽  
Author(s):  
Aman Taxali ◽  
Mike Angstadt ◽  
Saige Rutherford ◽  
Chandra Sripada
Keyword(s):  
Predictive Modeling ◽  
Predictive Models ◽  
Resting State ◽  
Imaging Features ◽  
Retest Reliability ◽  
Modeling Methods ◽  
Aggregate Information ◽  
Human Connectome Project ◽  
Test Retest Reliability ◽  
Resting State Connectivity

AbstractRecent studies found low test-retest reliability in fMRI, raising serious concerns among researchers, but these studies mostly focused on reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply ten predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared to mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all ten modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume-versus surface-based processing). For the most reliable methods, reliability of predicted outcomes was mostly, though not exclusively, in the “good” range (above 0.60).Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.

scholarly journals Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

2020 ◽  
Vol 4 (3) ◽  
pp. 788-806 ◽  
Author(s):  
John Fallon ◽  
Phillip G. D. Ward ◽  
Linden Parkes ◽  
Stuart Oldham ◽  
Aurina Arnatkevičiūtė ◽  
...  
Keyword(s):  
Resting State ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Information Storage ◽  
Brain Organization ◽  
Temporal Properties ◽  
Temporal Features ◽  
Human Connectome Project ◽  
The Brain ◽  
Connectivity Strength

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain’s underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

scholarly journals Lag threads organize the brain’s intrinsic activity

2015 ◽  
Vol 112 (17) ◽  
pp. E2235-E2244 ◽  
Author(s):  
Anish Mitra ◽  
Abraham Z. Snyder ◽  
Tyler Blazey ◽  
Marcus E. Raichle
Keyword(s):  
Spontaneous Activity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Intrinsic Activity ◽  
Resting State Networks ◽  
Slow Activity ◽  
Intrinsic Brain Activity ◽  
Temporal Sequences ◽  
The Brain

It has been widely reported that intrinsic brain activity, in a variety of animals including humans, is spatiotemporally structured. Specifically, propagated slow activity has been repeatedly demonstrated in animals. In human resting-state fMRI, spontaneous activity has been understood predominantly in terms of zero-lag temporal synchrony within widely distributed functional systems (resting-state networks). Here, we use resting-state fMRI from 1,376 normal, young adults to demonstrate that multiple, highly reproducible, temporal sequences of propagated activity, which we term “lag threads,” are present in the brain. Moreover, this propagated activity is largely unidirectional within conventionally understood resting-state networks. Modeling experiments show that resting-state networks naturally emerge as a consequence of shared patterns of propagation. An implication of these results is that common physiologic mechanisms may underlie spontaneous activity as imaged with fMRI in humans and slowly propagated activity as studied in animals.

scholarly journals Mapping of Whole-Brain Resting-State Networks with Half-Millimetre Resolution

2021 ◽  
Author(s):  
Seong Dae Yun ◽  
Patricia Pais-Roldán ◽  
Nicola Palomero-Gallagher ◽  
N. Jon Shah
Keyword(s):  
Spatial Resolution ◽  
Resting State ◽  
Granular Layer ◽  
Resting State Fmri ◽  
Resting State Networks ◽  
Whole Brain ◽  
Anatomical Structures ◽  
Functional Image ◽  
Fmri Session ◽  
The Brain

AbstractResting-state fMRI has been used in numerous studies to map networks in the brain that employ spatially disparate regions. However, attempts to map networks with high spatial resolution have been hampered by conflicting technical demands and associated problems. Results from recent fMRI studies have shown that spatial resolution remains around 0.7 × 0.7 × 0.7 mm3, with only partial brain coverage. This work presents a novel fMRI method, TR-external EPI with keyhole (TR-external EPIK), which can provide a nominal spatial resolution of 0.51 × 0.51 × 1.00 mm3 (0.26 mm3 voxel) with whole-brain coverage. TR-external EPIK enabled the identification of various resting-state networks distributed throughout the brain from a single fMRI session, with mapping fidelity onto the grey matter at 7T. The high-resolution functional image further revealed mesoscale anatomical structures, such as small cerebral vessels and the internal granular layer of the cortex within the postcentral gyrus.

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