Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness
Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, 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 show...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922005419 |
| _version_ | 1818520859131772928 |
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| author | Javier Gonzalez-Castillo Isabel S. Fernandez Daniel A. Handwerker Peter A. Bandettini |
| author_facet | Javier Gonzalez-Castillo Isabel S. Fernandez Daniel A. Handwerker Peter A. Bandettini |
| author_sort | Javier Gonzalez-Castillo |
| collection | DOAJ |
| description | Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, 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, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations (∼0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy. |
| first_indexed | 2024-12-11T01:43:16Z |
| format | Article |
| id | doaj.art-51538d1e5e564ded892ea7c0bad5a115 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-11T01:43:16Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-51538d1e5e564ded892ea7c0bad5a1152022-12-22T01:24:58ZengElsevierNeuroImage1095-95722022-10-01259119424Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsinessJavier Gonzalez-Castillo0Isabel S. Fernandez1Daniel A. Handwerker2Peter A. Bandettini3Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD; Corresponding author.Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MDSection on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MDSection on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD; Functional MRI Core, National Institutes of Health, Bethesda, MDWakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, 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, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations (∼0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.http://www.sciencedirect.com/science/article/pii/S1053811922005419Functional MRIWakefulnessSleepCSFResting-stateFourth ventricle |
| spellingShingle | Javier Gonzalez-Castillo Isabel S. Fernandez Daniel A. Handwerker Peter A. Bandettini Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness NeuroImage Functional MRI Wakefulness Sleep CSF Resting-state Fourth ventricle |
| title | Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness |
| title_full | Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness |
| title_fullStr | Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness |
| title_full_unstemmed | Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness |
| title_short | Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness |
| title_sort | ultra slow fmri fluctuations in the fourth ventricle as a marker of drowsiness |
| topic | Functional MRI Wakefulness Sleep CSF Resting-state Fourth ventricle |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922005419 |
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