Epilepsy Connectome Project: Resting-State Connectivity Dynamics in Temporal Lobe Epilepsy
Stand-By Time
Wednesday, June 28, 2017: 12:45 PM - 2:45 PMSubmission No:
3130
Submission Type:
Abstract Submission
On Display:
Wednesday, June 28 & Thursday, June 29
Authors:
Gengyan Zhao1, Jed Mathis2, VEENA NAIR1, Andrew Nencka2, Gyujoon Hwang1, Megan Rozman2, Taylor McMillan1, Dace Almane1, Ferdaus Kawsar2, Mohsen Mazrooyisebdani1, Elizabeth Felton1, Aaron Struck1, Rama Maganti1, Lisa Conant2, Colin Humphries2, Bruce Hermann1, Manoj Raghavan2, Edgar DeYoe2, Vivek Prabhakaran1, Jeffrey Binder2, Beth Meyerand1, Rasmus Birn1
Institutions:
1University of Wisconsin-Madison, Madison, WI, 2Medical College of Wisconsin, Milwaukee, WI
First Author:
Introduction:
Patients with temporal lobe epilepsy (TLE) compose a large proportion of medically refractory epilepsy patients, and are at high risk for cognitive and psychosocial co-morbidity.1 TLE often originates from a circumscribed structural or functional abnormality, which evolves over time to include abnormal connectivity across a network of regions, and studies have increasingly viewed it as a network-level phenomenon.2 Recent studies have also shown that some types of epilepsy (e.g. idiopathic generalized epilepsy) are associated with significant alterations in dynamic functional connectivity (dFC), a technique that has shown great promise in elucidating differences in a range of disorders.3,4 However, it is unclear whether similar alterations are present in TLE. In this study, the resting-state connectivity dynamics are analyzed for 21 patients with TLE and 28 healthy controls as a preliminary sample study of a larger-scale Epilepsy Connectome Project (ECP).
Methods:
Resting-state fMRI data were acquired on a GE 3T MR750 scanner using a Simultaneous Multi-Slice (SMS) sequence (2mm voxel size, TR=0.802s), and processed using the minimum preprocessing pipeline of the Human Connectome Project (HCP).5 dFC was computed by following the steps from Allen et al. (2012).3 First, group independent component analysis (gICA) was performed to extract the resting-state networks (RSNs).6 Next, sliding window correlation with 40.1s window size was calculated to generate the dFC matrices. Finally, k-means clustering was carried out to group the dFC matrices into 6 discrete states to identify patterns of dFC reoccurring in time and across subjects.7 For each subject, the rfMRI from 2 separate imaging sessions were analyzed. A 2x2 ANOVA was carried out to assess significant differences across groups (Epilepsy, Control) and sessions.
Results:
8 RSNs were identified from the gICA: subcortical network (SC), auditory network (AUD), somatomotor network (SM), visual network (VIS), cognitive control network (CC), default mode network (DM), frontal parietal network (FP) and cerebellum (CB). Fig. 1 shows the spectral center of mass (COM) and standard deviation (STD) of the dFC matrices (across the sliding windows) compared across groups and sessions. In across-session comparison, the mean spectral COM and dFC STD are very consistent. In across-group comparison, the patient group's spectral COM reflects that its dFC is dominant at a much lower frequency, and the dFC STD illustrates that several networks in the patient group have a much larger variation in correlation through time. The 2x2 ANOVA shows that connectivity dynamics of several connections are significantly different between groups but not sessions. There were no significant group differences in head motion (p=0.0933). Additionally, the difference in spectral COM appears to be significant mostly in between-network connectivity, while the difference in dFC STD is significant in between-network connectivity as well as the within-network connectivity of CC and DM. Fig. 2 shows the percent time appearance and mean dwell time of each state, compared across groups and sessions. Again, these metrics are very consistent across sessions. The mean percent time appearance and the mean dwell time are higher for patients in the 5th state, which shows strong connectivity in the VIS and SM.
Conclusions:
Resting-state functional connectivity dynamics show significantly greater variability in TLE compared to control subjects. This finding provides additional information about the extent of the network perturbations in TLE, and may provide additional means to localize the source of these perturbations.
Disorders of the Nervous System:
Epilepsy 1
Imaging Methods:
BOLD fMRI
Informatics:
Databasing and Data Sharing
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis
Keywords:
Epilepsy
Other - Resting-state fMRI; Functional connectivity dynamics
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2. Maillard L, Vignal JP, Gavaret M, Guye M, Biraben A, McGonigal A, et al. (2004). Semiologic and electrophysiologic correlations in temporal lobe seizure subtypes. Epilepsia 45: 1590-1599.
3. Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2012). Tracking whole-brain connectivity dynamics in the resting state. Cerebral cortex, bhs352.
4. Liu, F., Wang, Y., Li, M., Wang, W., Li, R., Zhang, Z., ... & Chen, H. (2016). Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic–clonic seizure. Human Brain Mapping.
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7. Lloyd, S. (1982). Least squares quantization in PCM. IEEE transactions on information theory, 28(2), 129-137.