Improving the Test-Retest Reliability of Resting State fMRI by Removing the Impact of Sleep

Wang, Jiahui; Han, Junwei; Nguyen, Vinh T.; Guo, Christine

doi:10.3389/fnins.2017.00249

Cited by 34 publications

(31 citation statements)

References 100 publications

(180 reference statements)

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“…The variability in the EC condition may be greater as a result of participants being in a more drowsy state or to the fact that its less controlled nature may lead to more mind wandering. Of note, Wang, Han, Nguyen, Guo, and Guo () reached higher reliability of FC by removing volumes with high sleepiness, and Patriat et al (); Zou et al () reported higher test–retest reliability in EO or EO‐F conditions compared to EC. In addition to that, (Allen et al, ) reported that according to EEG data; subjects are more likely to get into drowsy/early‐sleep states during EC comparing to EO condition.…”

Section: Discussionmentioning

confidence: 95%

Resting state connectivity differences in eyes open versus eyes closed conditions

Agcaoglu

Wilson

Wang

et al. 2019

Human Brain Mapping

140

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Functional magnetic resonance imaging data are commonly collected during the resting state. Resting state functional magnetic resonance imaging (rs‐fMRI) is very practical and applicable for a wide range of study populations. Rs‐fMRI is usually collected in at least one of three different conditions/tasks, eyes closed (EC), eyes open (EO), or eyes fixated on an object (EO‐F). Several studies have shown that there are significant condition‐related differences in the acquired data. In this study, we compared the functional network connectivity (FNC) differences assessed via group independent component analysis on a large rs‐fMRI dataset collected in both EC and EO‐F conditions, and also investigated the effect of covariates (e.g., age, gender, and social status score). Our results indicated that task condition significantly affected a wide range of networks; connectivity of visual networks to themselves and other networks was increased during EO‐F, while EC was associated with increased connectivity of auditory and sensorimotor networks to other networks. In addition, the association of FNC with age, gender, and social status was observed to be significant only in the EO‐F condition (though limited as well). However, statistical analysis did not reveal any significant effect of interaction between eyes status and covariates. These results indicate that resting‐state condition is an important variable that may limit the generalizability of clinical findings using rs‐fMRI.

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Section: Discussionmentioning

confidence: 95%

Resting state connectivity differences in eyes open versus eyes closed conditions

Agcaoglu

Wilson

Wang

et al. 2019

Human Brain Mapping

140

View full text Add to dashboard Cite

show abstract

“…Finally, we note another important factor that limits the reliability of rsfMRI-guided target is the temporally varying cognitive states which leads to dynamic FC maps [38, 39, 40]. A suitable approach to remove the influence of different cognitive states could potentially improve the reliability of FC maps [41]. …”

Section: Discussionmentioning

confidence: 99%

Limits and reproducibility of resting-state functional MRI definition of DLPFC targets for neuromodulation

et al. 2019

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Background: Transcranial magnetic stimulation (TMS) is a noninvasive neuromodulation technique with therapeutic applications for the treatment of major depressive disorder (MDD). The standard protocol uses high frequency stimulation over the left dorsolateral prefrontal cortex (DLPFC) identified in a heuristic manner leading to moderate clinical efficacy. A proposed strategy to increase the anatomical precision in targeting, based on resting-state functional MRI (rsfMRI), identifies the subregion within the DLPFC having the strongest anticorrelated functional connectivity with the subgenual cortex (SGC) for each individual subject. Objective: In this work, we comprehensively test the reliability and reproducibility of this targeting method for different scan lengths on 100 subjects from the Human Connectome Project (HCP) where each subject had a four 15-minute rsfMRI scan on 2 different days. Methods: We quantified the inter-scan and inter-day distance between the rsfMRI-guided DLPFC targets for each subject controlling for a number of expected sources of noise using volumetric as well as surface analyses. Results: Our results show that the average inter-day distance (with fMRI scans lasting 30 minutes on each day) is 25% less variable than the inter-scan distance, which uses 50% less data. Specifically, the inter-scan distance was more than 37 mm, while for the longer-scan, the inter-day distance had lower variability at 25 mm. Finally, we tested the same rsfMRI strategy using the nucleus accumbens (NAc) as a control region relevant to MDD but less susceptible to artifacts, using both volume and surface rsfMRI data. The results showed similar variability to the SGC-DLPFC functional connectivity. Moreover, our results suggest that a smoothing kernel with 12 mm full-width half maximum (FWHM) lead to more stable and reliable target estimates. Conclusion: Our work provides a quantitative assessment of the topographic precision of this targeting method, describing an anatomical variability that may surpass the spatial resolution of some forms of focal TMS as it is commonly applied, and provides recommendations for improved accuracy.

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“…However, such solutions do not work for studies of passive states (e.g., resting state fMRI, naturalistic viewing fMRI), as there are no such responses to monitor. The resting state fMRI literature has struggled with this issue due to the increased likelihood of individuals falling asleep (Tagliazucchi and Laufs 2014;Wang et al 2017), driving most to require participants to remain with their eyes open during scans -a requirement that can be tracked through direct observation by a technician or via video recording. The requirements of conducting fMRI movie viewing experiments are greater, as one needs to know that an individual is actually paying attention to specific information on the screen.…”

Section: Introductionmentioning

confidence: 99%

Evaluating fMRI-Based Estimation of Eye Gaze during Naturalistic Viewing

et al. 2018

Preprint

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The collection of eye movement information during functional magnetic resonance imaging (fMRI) is a valuable, though commonly overlooked component of monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from fMRI data that simply adds a 1.5-minute calibration scan to any protocol. Here, we provide a large-scale assessment of PEER for inferring eye fixations on a TR-by-TR basis during movie viewing using a subset of data (n=448) from the Child Mind Institute Healthy Brain Network Biobank. Consistent with prior work, we demonstrate the ability of PEER to provide accurate estimates of fixation location throughout the course of fMRI scans and we establish head motion as the primary determinant of model accuracy. Minimum data requirement analyses suggest model estimation can be carried out with less than half the data obtained in the 1.5-minute calibration scan. We demonstrate the ability to predict the movie an individual is watching (i.e., Despicable Me , The Present ) based on the PEER time series. Out-of-scanner eye tracker-based measurements obtained during a repeat viewing of the movie The Present was used to further validate the time series obtained using PEER. Consistent with prior findings in the eye tracking literature, the fixation sequences showed a high consistency across participants, reducing the ability to identify an individual based on their fixation sequence. Finally, examination of neural activations associated with the PEER time series replicated prior findings regarding the neural correlates of eye movements. In summary, we demonstrate that PEER is an inexpensive, easy-to-use tool for researchers to determine eye fixations from naturalistic viewing data that overcomes the cost and burdens of in-scanner eye tracking.

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Improving the Test-Retest Reliability of Resting State fMRI by Removing the Impact of Sleep

Cited by 34 publications

References 100 publications

Resting state connectivity differences in eyes open versus eyes closed conditions

Resting state connectivity differences in eyes open versus eyes closed conditions

Limits and reproducibility of resting-state functional MRI definition of DLPFC targets for neuromodulation

Evaluating fMRI-Based Estimation of Eye Gaze during Naturalistic Viewing

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