Laura Lewis1,2, Giorgio Bonmassar3, Kawin Setsompop3, Robert Stickgold4, Bruce Rosen3, and Jonathan Polimeni3
1Boston University, Boston, MA, United States, 2Massachusetts General Hospital, Boston, MA, United States, 3Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States, 4BIDMC/Harvard Medical School, Boston, MA, United States
Synopsis
The thalamus plays an important role
in regulating brain states, but remains poorly understood due to the technical
challenges in imaging small brain structures with simultaneous
electrophysiology. We implemented simultaneous fast fMRI and EEG at 7 Tesla to
achieve high-SNR imaging of thalamic dynamics during human sleep. We found that
we could detect selective activity within a focal set of thalamic nuclei that
preceded the moment of awakening. These results identify potential network
mechanisms engaged in regulating brain states, and demonstrate the potential
for multimodal 7T imaging to identify new roles for deep brain structures in
regulating cortical function and cognition.
Introduction
Brain arousal states are dynamically
modulated throughout the day, and can shift within hundreds of milliseconds –
for example, at the transition between sleep and wake. Animal studies have
suggested an important role for the thalamus in controlling brain states and
cortical dynamics, but human studies have been limited, due to the
technological challenges in measuring activity in small, deep brain structures
in humans. EEG tracks fast changes in cortical dynamics, but cannot detect
neural activity in deep structures such as the thalamus. Furthermore, thalamus
is made up of functionally diverse nuclei, and the roles of these substructures
in modulating brain states remains poorly understood. Ultra-high field fMRI
could enable detection of activity within these small thalamic nuclei, but recording
EEG at 7 Tesla is technically challenging due to the severe artifacts induced
by the static magnetic field. We developed a simultaneous EEG and fast fMRI paradigm
at 7 Tesla to track dynamics within individual thalamic nuclei, and examined
their relationship to cortical electrophysiology and arousal.Methods
Five
subjects gave informed consent and were scanned on a 7T Siemens whole-body
scanner with a custom 32-channel head coil, as they slept inside the scanner
for up to two hours. EEG was simultaneously acquired using a polymer thick
film-based InkNet designed for use at 7T 1. Artifacts in the EEG signal were removed
using a time-varying reference-based cleaning approach adapted from prior
methods 2. Sessions began with a 750-micron
resolution T1-MEMPRAGE, and then resting-state BOLD fMRI data were acquired with a
single-shot gradient echo (TR=247 ms, SMS=8, 2.5 mm isotropic resolution). Data
were motion corrected, slice timing corrected, and physiological noise was
removed through a sliding window adaptation of RETROICOR to accommodate long
duration scans. For the visual validation, 12 Hz EEG power was extracted and
convolved with a standard hemodynamic response function. Statistical maps were
computed in FSL. For the sleep study, three thalamic nuclei with relatively
large volumes and were automatically segmented from the T1 anatomical data 3: lateral geniculate nucleus (LGN, primary
visual); pulvinar (PUL, higher-order visual); mediodorsal (MD, higher-order
frontal attentional). Spontaneous awakenings were identified manually using the
alpha (~10 Hz) power of the EEG data, and then the mean signal in each nucleus was
computed, locked to the time of awakening.Results
We first validated that our 7T
EEG-fMRI approach yielded sufficiently high-quality data to resolve EEG oscillations
and local thalamic fMRI activity, using a visual experiment. We correlated the
fMRI signal with the envelope of the stimulus-induced 12 Hz EEG flicker
(convolved with an HRF), and observed clear activation both throughout visual
cortex, and specifically within the LGN of the thalamus (Fig. 1).
We next analyzed fMRI dynamics
around the time of spontaneous awakening from sleep, defined through the change
in cortical electrophysiology detected in the EEG (Fig. 2a). We observed clear
thalamic fMRI activations at each arousal from sleep (Fig. 2b). To determine
whether distinct dynamics in thalamic nuclei were associated with this
transition, we examined the mean timeseries in ROIs relative to the moment of
awakening (Fig. 3). Awakening was not associated with a strong increase in
cortical activity (Fig. 4a,b). However, we observed distinct increases in selective
activity within the MD and PUL thalamic nuclei upon awakening. In contrast, LGN
activity was suppressed after awakening from sleep, potentially reflecting that
the eyes remained closed, while sleep-related activity ceased. The timescale of
the MD activation showed an increase within a second of awakening – due to the
~4-5 second hemodynamic lag of fMRI signals, this suggested that these focal
thalamic activations preceded the change in cortical electrophysiological
state.
Discussion
We find that 7T fast fMRI-EEG detects
increased activity within individual thalamic nuclei that occurs immediately
prior to awakening from sleep. Previous studies have shown that deactivations
throughout the thalamus during low arousal states 4; our work is consistent with these
results, and further demonstrates that within the thalamus, specific subnuclei exhibit
distinct dynamics locked to the transitions between arousal states. These
results suggest that the mediodorsal nucleus may be selectively engaged at the transition
between sleep and wake, and could potentially drive subsequent changes in
cortical oscillatory signals. Our results also demonstrate the potential for
EEG-fMRI at 7T to dissect the large-scale network mechanisms that underlie
regulation of cortical dynamics and brain state. Acknowledgements
This
work was supported in part by NIH grants K99-MH111748, R01-EB019437, S10-RR023043 and S10-RR019371 ,
and by the MGH/HST Athinoula A. Martinos Center for Biomedical Imaging.References
1. Poulsen,
C. et al. Polymer thick film technology for improved simultaneous
dEEG/MRI recording: Safety and MRI data quality. Magn. Reson. Med. 77,
895–903 (2017).
2. Luo,
Q., Huang, X. & Glover, G. H. Ballistocardiogram artifact removal with a
reference layer and standard EEG cap. Journal of Neuroscience Methods 233,
137–149 (2014).
3. Iglesias,
J. E. et al. A probabilistic atlas of the human thalamic nuclei
combining ex vivo MRI and histology. Neuroimage 183, 314–326
(2018).
4. Chang,
C. et al. Tracking brain arousal fluctuations with fMRI. Proc Natl
Acad Sci USA 113, 4518–4523 (2016).