scholarly journals Template-based prediction of vigilance fluctuations in resting-state fMRI

2017 ◽  
Author(s):  
Maryam Falahpour ◽  
Catie Chang ◽  
Chi Wah Wong ◽  
Thomas T. Liu
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Eyes Closed ◽  
Vigilance State ◽  
Global Signal ◽  
Significant Difference ◽  
Eyes Open ◽  
Positive Correlations

AbstractChanges in vigilance or alertness during a typical resting state fMRI scan are inevitable and have been found to affect measures of functional brain connectivity. Since it is not often feasible to monitor vigilance with EEG during fMRI scans, it would be of great value to have methods for estimating vigilance levels from fMRI data alone. A recent study, conducted in macaque monkeys, proposed a template-based approach for fMRI-based estimation of vigilance fluctuations. Here, we use simultaneously acquired EEG/fMRI data to investigate whether the same template-based approach can be employed to estimate vigilance fluctuations of awake humans across different resting-state conditions. We first demonstrate that the spatial pattern of correlations between EEG-defined vigilance and fMRI in our data is consistent with the previous literature. Notably, however, we observed a significant difference between the eyes-closed (EC) and eyes-open (EO) conditions finding stronger negative correlations with vigilance in regions forming the default mode network and higher positive correlations in thalamus and insula in the EC condition when compared to the EO condition. Taking these correlation maps as “templates” for vigilance estimation, we found that the template-based approach produced fMRI-based vigilance estimates that were significantly correlated with EEG-based vigilance measures, indicating its generalizability from macaques to humans. We also demonstrate that the performance of this method was related to the overall amount of variability in a subject’s vigilance state, and that the template-based approach outperformed the use of the global signal as a vigilance estimator. In addition, we show that the template-based approach can be used to estimate the variability across scans in the amplitude of the vigilance fluctuations. We discuss the benefits and tradeoffs of using the template-based approach in future fMRI studies.

scholarly journals Resting state connectivity during total sleep deprivation: A functional magnetic resonance study

2020 ◽  
Author(s):  
Vivianne Jakobsson
Keyword(s):  
Magnetic Resonance ◽  
Sleep Deprivation ◽  
Resting State ◽  
Resting State Fmri ◽  
Head Motion ◽  
Functional Magnetic Resonance ◽  
Global Signal ◽  
Significant Difference ◽  
Before And After ◽  
Eyes Open

Introduction: Sleep deprivation is a common problem that may have serious consequences. In this study, functional magnetic resonance imaging (fMRI), a technique frequently used to study networks in the brain, was used to investigate the resting state of the sleep deprived brain, in order to discover whether this state affects the intrinsic connectivity and the global signal variability (GSV). Aims: To investigate whether GSV increases with sleep deprivation. Material and Methods: In this cross over study 18 healthy participants, age 20 – 30, underwent in randomized order resting-state fMRI for 20min before and after 24h sleep deprivation. We extracted the global signal, calculated the standard deviation per participant, and analysed it with respect to sleep depraved yes/no, head motion, eyes open/closed and self-evaluation of sleepiness using Karolinska Sleepiness Score (KSS). Results: We found that GSV was higher during sleep deprivation (0.3362 ± 0.0241, p<0.0001) without KSS data. With KSS, sleep deprivation was not significant (0.0619 ± 0.1145, p=0.5889). High KSS rating had a significant effect on GSV (0.1497 ± 0.0409, p=0.0003), as had head motion (1.7974 ± 0.1539, p<0.0001). There was no significant difference between having eyes open or closed (0.0126 ± 0.0578, p=0.8278), and no significant increase for each time period of 20s in the scanner (0.0065 ± 0.0021, p=0.0029). Conclusions: We found that the global signal variation is increased by sleep deprivation and sleepiness. More specific conclusions cannot be made from our data so far.

Eyes-Open/Eyes-Closed Dataset Sharing for Reproducibility Evaluation of Resting State fMRI Data Analysis Methods

2013 ◽  
Vol 11 (4) ◽  
pp. 469-476 ◽  
Author(s):  
Dongqiang Liu ◽  
Zhangye Dong ◽  
Xinian Zuo ◽  
Jue Wang ◽  
Yufeng Zang
Keyword(s):  
Data Analysis ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Eyes Closed ◽  
Analysis Methods ◽  
Fmri Data Analysis ◽  
Eyes Open ◽  
Data Analysis Methods

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals Modular and state-relevant connectivity in high-frequency resting-state BOLD fMRI data: An independent component analysis

2020 ◽  
Author(s):  
Thomas DeRamus ◽  
Ashkan Faghiri ◽  
Armin Iraji ◽  
Oktay Agcaoglu ◽  
Victor Vergara ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Bold Signal ◽  
Drift Motion ◽  
Eyes Closed

AbstractResting-state fMRI (rs-fMRI) data are typically filtered at different frequency bins between 0.008∼0.2 Hz (varies across the literature) prior to analysis to mitigate nuisance variables (e.g., drift, motion, cardiac, and respiratory) and maximize the sensitivity to neuronal-mediated BOLD signal. However, multiple lines of evidence suggest meaningful BOLD signal may also be parsed at higher frequencies. To test this notion, a functional network connectivity (FNC) analysis based on a spatially informed independent component analysis (ICA) was performed at seven different bandpass frequency bins to examine FNC matrices across spectra. Further, eyes open (EO) vs. eyes closed (EC) resting-state acquisitions from the same participants were compared across frequency bins to examine if EO vs. EC FNC matrices and randomness estimations of FNC matrices are distinguishable at different frequencies.Results show that FNCs in higher-frequency bins display modular FNC similar to the lowest frequency bin, while r-to-z FNC and FNC-based measures indicating matrix non-randomness were highest in the 0.31-0.46 Hz range relative to all frequency bins above and below this range. As such, the FNC within this range appears to be the most temporally correlated, but the mechanisms facilitating this coherence require further analyses. Compared to EO, EC displayed greater FNC (involved in visual, cognitive control, somatomotor, and auditory domains) and randomness values at lower frequency bins, but this phenomenon flipped (EO > EC) at frequency bins greater than 0.46 Hz, particularly within visual regions.While the effect sizes range from small to large specific to frequency range and resting state (EO vs. EC), with little influence from common artifacts. These differences indicate that unique information can be derived from FNC between BOLD signals at different frequencies relative to a given restingstate acquisition and support the hypothesis meaningful BOLD signal is present at higher frequency ranges.

scholarly journals Prediction of Brain Connectivity Map in Resting-State Fmri Data Using Shrinkage Estimator

2018 ◽  
Author(s):  
Atiye Nazari ◽  
Hamid Alavimajd ◽  
Nezhat Shakeri ◽  
Mohsen Bakhshandeh ◽  
Elham Faghihzadeh ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Shrinkage Estimator ◽  
Fmri Data ◽  
Connectivity Map

PHASE SYNCHRONIZATION IN BRAIN NETWORKS DERIVED FROM CORRELATION BETWEEN PROBABILITIES OF RECURRENCES IN FUNCTIONAL MRI DATA

2013 ◽  
Vol 23 (02) ◽  
pp. 1350003 ◽  
Author(s):  
D. RANGAPRAKASH ◽  
XIAOPING HU ◽  
GOPIKRISHNA DESHPANDE
Keyword(s):  
Linear Correlation ◽  
Resting State ◽  
Brain Function ◽  
Phase Synchronization ◽  
Human Subjects ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fmri Data

It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.

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