Correspondence of functional brain states at rest and during a memory task
Poster No:
1418
Submission Type:
Abstract Submission
Authors:
Xiaowei Zhuang1, Zhengshi Yang1, Mark Lowe2, Dietmar Cordes1
Institutions:
1Cleveland Clinic, Las Vegas, NV, 2The Cleveland Clinic, Cleveland, OH
First Author:
Co-Author(s):
Introduction:
Coactivation pattern (CAP) analysis tracks temporal variations in fMRI data within each individual time frame. The underlying premise is that critical information about a functional network is expressed by discrete time points where the fMRI signal is large in regions strongly associated with the network (i.e., seed region)(Liu et al., 2013; Liu and Duyn, 2013). Here we utilized the CAP method to determine functional brain states during rest and an object lure memory task in older participants with normal cognition (NC) and early amnestic mild cognitive impairment (aMCI).
Methods:
MRI data were acquired from 43 NC (70.67±3.96 years old, 17 Males/26 Females) and 21 aMCI (70.19±5.68 years old, 10 Males/11 Females) participants on a 7-Tesla Siemens scanner, including one resting-state and three object lure task(Stark et al., 2013) fMRI runs (TR=1.53s, iPAT=2, SMS=3, 1.5mm isotropic, 510 timeframes). During the task, a set of everyday objects were first presented in the encoding phase. The same, similar and new objects were later presented in the recognition phase, during which participants were asked to respond whether the objects were the same, different, or new. All four fMRI runs were realigned and unwrapped together with a voxel-displacement map computed from the GRE field mapping sequences using SPM12. Advanced Normalization Tools (ANTs) was then utilized to register fMRI data to subject anatomical space and normalize to the MNI152-2mm template. A 4mm full-width-half-maximum (FWHM) Gaussian filter was used to further smooth the time-series. CAP analysis. Hippocampal subfields were segmented for each participant. CAP analyses were carried out for NC and aMCI with CA1 or CA3DG as a seed, separately. Timeframes with the average seed signal intensity amongst the top 10% (51 frames) of each subject were extracted and temporally concatenated. Dominant CAPs (dCAP) were determined following previously published pipelines(Chen et al., 2015; Zhuang et al., 2018). DCAPs were determined for NC and aMCI participants together, and for resting-state and task fMRI, separately. Switching probability (SR) among all dCAPs, temporal fraction (TF) and spatial consistency (SC) for each dCAP were computed to quantify brain temporal dynamics at rest and during object lure tasks.
Results:
For CA1 seed, two dCAPs were determined for NC and aMCI at rest (Fig. 1 top row) and during object lure tasks (Fig. 1 bottom row). Spatially, dCAP-rest1 was highly similar to dCAP-task1 and dCAP-task2 in both NC (spatial correlation (r)=0.25 and 0.23) and aMCI groups (r=0.29 and 0.33). Besides dCAP-rest1, dCAP-task1 was spatially similar to dCAP-rest2 in both NC (r=0.21) and aMCI (r=0.24) groups.
It is interesting that a higher TF during dCAP-task 1 or a lower TF during dCAP-rest1 were both correlated with better RAVLT performances (Fig. 2, 1st column). A higher SC of the 1st dCAP in aMCIs, either at rest or during task, was additionally correlated with better RAVLT performances (Fig. 2, 2nd column). On the contrary, a higher SC of the dCAP-task2 indicated worse memory scores. Lastly, a higher switching probability between rest dCAPs also correlated with better RAVLT performances in NCs (Fig. 2 last column).
It is interesting that a higher TF during dCAP-task 1 or a lower TF during dCAP-rest1 were both correlated with better RAVLT performances (Fig. 2, 1st column). A higher SC of the 1st dCAP in aMCIs, either at rest or during task, was additionally correlated with better RAVLT performances (Fig. 2, 2nd column). On the contrary, a higher SC of the dCAP-task2 indicated worse memory scores. Lastly, a higher switching probability between rest dCAPs also correlated with better RAVLT performances in NCs (Fig. 2 last column).
·Figure 1. CA1-seeded dominant CAPs (dCAPs) obtained for NC and MCI participants at rest or during object lure tasks. Their spatial similarities were indicated in black boxes.
·Figure 2. Associations between dCAP-quantitative measures and RAVLT memory scores.
Conclusions:
Our results indicate that dCAP-task1 and dCAP-rest2 could be a more memory performance related state, as higher TF in these states correlates with better memory performances. In addition, dCAP-task2 tend to be less stable, as SC for this state is on average less than r=0.1. More importantly, both dCAP-rest1 and dCAP-rest2 are spatially similar to dCAP-task1, indicating functional states at rest could also be task relevant. Furthermore, temporal dynamics between these states at rest could be a key to better memory performances.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Learning and Memory:
Long-Term Memory (Episodic and Semantic) 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Keywords:
FUNCTIONAL MRI
Memory
Other - coactivation pattern analysis
1|2Indicates the priority used for review
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Provide references using APA citation style.
Liu, X., Chang, C., Duyn, J.H., 2013. Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns. Front Syst Neurosci 7. https://doi.org/10.3389/fnsys.2013.00101
Liu, X., Duyn, J.H., 2013. Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proceedings of the National Academy of Sciences 110, 4392–4397. https://doi.org/10.1073/pnas.1216856110
Stark, S.M., Yassa, M.A., Lacy, J.W., Stark, C.E.L., 2013. A task to assess behavioral pattern separation (BPS) in humans: Data from healthy aging and mild cognitive impairment. Neuropsychologia 51, 2442–2449. https://doi.org/10.1016/j.neuropsychologia.2012.12.014
Zhuang, X., Walsh, R.R., Sreenivasan, K., Yang, Z., Mishra, V., Cordes, D., 2018. Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: An application to Parkinson’s disease. Neuroimage 172, 64–84. https://doi.org/10.1016/j.neuroimage.2018.01.019