Vigilance Effects in Resting-State fMRI

Liu, Thomas T.; Falahpour, Maryam

doi:10.3389/fnins.2020.00321

Cited by 43 publications

(42 citation statements)

References 99 publications

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“…Temporal variation in spontaneous fMRI signals and connectivity have also been found to be altered in neuropsychiatric disorders as well as in aging ( Yang et al, 2014 ; Nomi et al, 2017 ; Calhoun et al, 2014 ). However, relatively few fMRI studies have examined the specific role of dynamically fluctuating arousal states in shaping ongoing neural or behavioral variability ( Dinstein et al, 2015 ; Liu and Falahpour, 2020 ). One barrier stems from practical challenges linked with gathering established measures of arousal (such as pupillometry and EEG) during routine fMRI scans – including the need for additional setup time, MRI-compatible hardware, and post-processing for removal of MR-related artifacts.…”

Section: Introductionmentioning

confidence: 99%

fMRI-based detection of alertness predicts behavioral response variability

Goodale

Ahmed

Zhao

et al. 2021

eLife

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Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.

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Section: Introductionmentioning

confidence: 99%

fMRI-based detection of alertness predicts behavioral response variability

Goodale

Ahmed

Zhao

et al. 2021

eLife

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“…Such concurrent acquisitions are not common practice in resting-state research, and the effects of fluctuations in wakefulness (or subject’s lack of compliance with keeping eyes open) are often ignored. Prior research has demonstrated that subjects have a propensity to fall asleep during resting-state scans (Allen et al, 2014; Tagliazucchi and Laufs, 2014), and that fluctuations in vigilance are to be expected (Chang et al, 2016; Liu and Falahpour, 2020). It is important to find ways to determine the occurrence of these events when no concurrent measures are available.…”

Section: Introductionmentioning

confidence: 99%

Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness

Gonzalez‐Castillo

Fernandez

Handwerker

et al. 2021

Preprint

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Vigilance and wakefulness modulate estimates of functional connectivity, and, if unaccounted for, they can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project. Our results confirm the presence of those fluctuations in the HCP sample despite this data having relatively small inflow weighting. Moreover, we show that fluctuations of a similar frequency appear in large portions of grey matter with different temporal delays, and that they can substantially influence estimates of functional connectivity. Finally, we demonstrate that the temporal evolution of this signal cannot only help us reproduce previously reported overall sleep patterns in resting-state data, but also predict individual periods of eye closure with 70% accuracy, matching predictions attainable using the amplitude of the global signal (a common fMRI marker of arousal). In summary, our results demonstrate the ubiquitous presence of this signal in a large, publicly available, fMRI sample, its value as a marker of arousal in absence of a better metric, its relationship to the global signal, and its potential nuisance effects on functional connectivity estimates when ignored.

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“…Although substantial variations in functional connectivity can occur on short time scales (i.e. minutes to hours) due to factors such as temporal fluctuations in subject arousal and vigilance (8, 9), our results indicate that high performance can be obtained over a 1-day interval even in the presence of these factors. Future large-scale studies with a greater time interval between training and test data will be needed to assess whether high identification accuracy can be obtained over longer intervals (i.e.…”

Section: Discussionmentioning

confidence: 62%

Functional Connectome Fingerprinting Using Shallow Feedforward Neural Networks

Sarar

Rao

Liu

2020

Preprint

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Although individual subjects can be identified with high accuracy using correlation matrices computed from resting-state functional magnetic resonance imaging (rsfMRI) data, the performance significantly degrades as the scan duration is decreased. Recurrent neural networks can achieve high accuracy with short duration (72s) data segments but are designed to use temporal features not present in the correlation matrices. Here we show that shallow feedforward neural networks that rely solely on the information in rsfMRI correlation matrices can achieve state-of-the-art identification accuracies ( ≥ 99.5\%) with data segments as short as 20s and across a range of input data-size combinations when the total number of data points (# regions × # time points) is on the order of 10,000.

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Vigilance Effects in Resting-State fMRI

Cited by 43 publications

References 99 publications

fMRI-based detection of alertness predicts behavioral response variability

fMRI-based detection of alertness predicts behavioral response variability

Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness

Functional Connectome Fingerprinting Using Shallow Feedforward Neural Networks

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