A 7T Longitudinal study investigating the effect of sleep on processing of naturalistic stimuli
Poster No:
2096
Submission Type:
Abstract Submission
Authors:
Joshita Majumdar1, Nguyen Huynh1, Gopikrishna Deshpande1,2,3,4,5,6
Institutions:
1Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn, AL, 2Department of Psychological Sciences, Auburn University, Auburn, AL, 3Alabama Advanced Imaging Consortium, Birmingham, AL, 4Center for Neuroscience, Auburn University, Auburn, AL, 5Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India, 6Department of Heritage Science and Technology, Indian Institute of Technology, Hyderabad, India
First Author:
Joshita Majumdar
Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering
Auburn, AL
Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering
Auburn, AL
Co-Author(s):
Nguyen Huynh
Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering
Auburn, AL
Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering
Auburn, AL
Gopikrishna Deshpande
Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering|Department of Psychological Sciences, Auburn University|Alabama Advanced Imaging Consortium|Center for Neuroscience, Auburn University|Department of Psychiatry, National Institute of Mental Health and Neurosciences|Department of Heritage Science and Technology, Indian Institute of Technology
Auburn, AL|Auburn, AL|Birmingham, AL|Auburn, AL|Bangalore, India|Hyderabad, India
Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering|Department of Psychological Sciences, Auburn University|Alabama Advanced Imaging Consortium|Center for Neuroscience, Auburn University|Department of Psychiatry, National Institute of Mental Health and Neurosciences|Department of Heritage Science and Technology, Indian Institute of Technology
Auburn, AL|Auburn, AL|Birmingham, AL|Auburn, AL|Bangalore, India|Hyderabad, India
Introduction:
Sleep homeostasis plays a key role in brain function that affects mental health, cognitive functioning, and overall health of humans. The relationship between sleep and brain connectivity has been studied using cross-sectional designs employing sleep deprivation or using subjective sleep scores. In this study, we address these limitations by acquiring functional data and passively measuring sleep patterns in a single individual over nine months. We utilize a naturalistic longitudinal framework to examine the association of sleep quality with processing of naturalistic stimuli.
Methods:
MRI were acquired from a single participant (author J.M.), using a Siemens 7T Terra.X scanner . The scan sessions (Tuesdays each week) occurred in the evenings between 2:30 pm and 7:30 pm. A total of 28 scans were obtained over 41 weeks. During functional data acquisition, the participant watched a 4-minute music video (family and friends themed). Whole brain anatomical images were acquired using the following parameters: MPRAGE sequence with 0.6 mm isotropic voxels, TR/TE: 4000/3.45 ms. Functional data were acquired using a multiband EPI sequence (0.8 mm isotropic voxels, TR/TE: 1500/23 ms). The participant wore an Oura Ring during sleep (OURA, n.d.), and the data from the previous night was used for analysis. The functional data was processed using standard pipeline. The mean timeseries from each ROI of the 272 Power Atlas (Power, 2011) were extracted and Pearson's correlation between those time series were calculated to obtain a whole brain functional connectivity matrix for each naturalistic viewing scan. The connectivity matrix was thresholded at p<0.05 (FDR corrected) (Benjamini & Hochberg, 1995) and input into a partial least-squares regression (PLSR) (Krishnan, 2011). PLSR was conducted between significant brain connections and sleep data variables. Significant multivariate correlation (Tran, 2014) identified key PLSR loadings, which were plotted on the brain using BrainNetViewer (Xia, 2013).
·Longitudinal functional and sleep data (Oura Ring) acquisition timeline. The green dots: start (left) and stop (right) days. The elevated lines with the miniature human represent successful scan days.
Results:
The latent variable (LV) representing functional connections (FC) demonstrated a strong positive correlation (R=0.9195) with the LV representing physiological sleep parameters and autonomic variables (p = 4.8 × 10 -12 ). A total of 68 FC (19 positive and 49 negative loadings) and 11 sleep parameters (3 positive and 8 negative loadings) were significant. From the loadings of the LVs (Fig.2b), we can deduce that the Oura ring sleep LV represented quality of sleep while the brain connectivity LV tended to increase with long range (LR) connections majorly involving the frontal cortex and decrease with short range connections (SRC) involving the sensory and unimodal cortices. Thereby, we interpret that the imaging LV may represent higher cognitive function and abstract thinking that is supported by LR frontal connections (Friedman & Robbins, 2022). The results indicate that better sleep the previous night engages more frontal LR connections associated with higher cognitive function while viewing naturalistic videos. On the contrary, worse sleep engages more SRC in the posterior brain networks involving sensory and unimodal cortices which may support basic understanding of the sensory input but may preclude more abstract thinking about them. These results are notable as they reflect natural sleep patterns observed in a single individual through deep phenotyping over nine months, without sleep deprivation.
·2a. Correlation plot between imaging and non imaging variables in the latent space. 2b. Brain connections for PLS1
Conclusions:
We investigated how the previous night's sleep impacts an individual's functional brain network. Our results suggest that sleep quality does influence how the brain responds to the rich, dynamic stimuli environment very similar to daily life.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 1
Keywords:
Sleep
Other - connectivity, naturalistic paradigm, deep phenotyping, longitudinal design
1|2Indicates the priority used for review
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2. Friedman, N. P., & Robbins, T. W. (2022). The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology, 47 (1), 72–89.
3. Krishnan, A., Williams, L. J., McIntosh, A. R., & Abdi, H. (2011). Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage, 56 (2), 455–475.
4. Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., . . . others (2011). Functional network organization of the human brain. Neuron, 72 (4), 665–678.
5. Tran, T. N., Afanador, N. L., Buydens, L. M., & Blanchet, L. (2014). Interpretation of variable importance in partial least squares with significance multivariate correlation (sMC). Chemometrics and Intelligent Laboratory Systems, 138 , 153–160.
6. Xia, M., Wang, J., & He, Y. (2013). BrainNet Viewer: a network visualization tool for human brain connectomics. PloS one, 8 (7), e68910.
7. OURA. (n.d.). ¯ Introducing the new oura ring generation 3. Retrieved from https://ouraring.com/ ([Cited 2024 December 17]).