White Matter and Subcortical Grey Matter Network Connectivity Analysis in Temporal Lobe Epilepsy
Poster No:
1676
Submission Type:
Abstract Submission
Authors:
Sukesh Das1, George B Hanna1, Bharat Biswal1, Hai Sun2
Institutions:
1New Jersey Institute of Technology, Newark, NJ, 2Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ
First Author:
Co-Author(s):
Introduction:
Temporal lobe epilepsy (TLE) is a common type of epilepsy in adults, with seizures primarily originating in the deep temporal lobe [1,2]. This condition results in changes in connectivity across cortical gray matter (GM), subcortical GM, and white matter (WM) regions. This altered connectivity categorizes TLE as a network disease, thus making the investigation of resting state functional network connectivity (FNC) in these areas necessary for understanding TLE[6,7, 8, 9]. Functional networks (FNs) are typically derived from clustering static functional connectivity (sFC) to study WM networks [3, 4, 5, 6, 7,8, 9]. Dynamic functional connectivity (dFC), which measures time-varying correlations between two or multiple regions of interest, offers valuable insights about functional states, with dFC-driven clusters highlighting networks where connectivity among voxels or regions behaves in a similar fashion.
Methods:
In this study, we included a total of 103 subjects from the Epilepsy Connectome Project (ECP), comprising 51 healthy controls, 34 subjects with left TLE, and 18 subjects with right TLE. Data preprocessing included the following steps: discarding the initial 10 volumes, realignment, exclusion of subjects based on frame-wise displacement (>2mm), segmentation, nuisance signal regression (24 motion parameters and CSF), temporal filtering, smoothing (4mm), and normalization.
We obtained static functional networks (sFNs) and dynamic functional networks (dFNs) using K-means clustering on ROI-based average sFC and dFC (Standard deviation as a statistic), respectively(Eve atlas, Type III WM parcellation map)[10]. In K-means clustering, the distance metric was used as the correlation, and the optimal number of clusters was achieved based on 8 fold cross-validation using adjacency matrices [4]. Finally, K means clustering was performed using the optimal number of K, and clusters (FNs) were obtained.
An average of the time series of all voxels within every WM-FN was computed. For each pair of network time courses, a sFC/dFC value is obtained using Pearson's correlation. The FCs were Fisher's Z-transformed, and sex and age effects were regressed out for all subjects. Lastly, a static/dynamic FNC (sFNC/dFNC) matrix (KxK) was obtained. Alteration in FC between FNs was assessed using two-sample t-tests between the FNCs of two groups (HC and TLE) to identify significant connections.
We obtained static functional networks (sFNs) and dynamic functional networks (dFNs) using K-means clustering on ROI-based average sFC and dFC (Standard deviation as a statistic), respectively(Eve atlas, Type III WM parcellation map)[10]. In K-means clustering, the distance metric was used as the correlation, and the optimal number of clusters was achieved based on 8 fold cross-validation using adjacency matrices [4]. Finally, K means clustering was performed using the optimal number of K, and clusters (FNs) were obtained.
An average of the time series of all voxels within every WM-FN was computed. For each pair of network time courses, a sFC/dFC value is obtained using Pearson's correlation. The FCs were Fisher's Z-transformed, and sex and age effects were regressed out for all subjects. Lastly, a static/dynamic FNC (sFNC/dFNC) matrix (KxK) was obtained. Alteration in FC between FNs was assessed using two-sample t-tests between the FNCs of two groups (HC and TLE) to identify significant connections.
·Procedure for clustering static and dynamic functional connectivity (sFC and dFC) to obtain static and dynamic functional networks (sFNs and dFNs) in WM
Results:
The static and dynamic FNs are demonstrated in Fig. A and B respectively. The static and dynamic FNCs and corresponding p values are illustrated in the Fig.C. The static FNC was significantly decreased between the Forceps minor-Anterior corona radiata - genu and left inferior longitudinal fasciculus (sWM2 - sWM11) in TLE. Dynamic FNC significantly decreased between the corpus callosum (body) - superior corona radiata - right superior longitudinal fasciculus network and the Forceps minor - anterior corona radiata - medial frontal gyrus network (dWM4 - dWM5) in patients with TLE. This result implies that this WM connection changes diversely with lower variability in TLE. On the other hand, the dynamic connections between the left temporal sub gyral - left thalamus - left pallidus - left hippocampus and right thalamus - right putamen - right temporal sub gyral - right pallidus network (dWM 2 - dWM 8) and the connections between the cingulum(hippocampus) network and right thalamus - right putamen - right temporal sub gyral - right pallidus network (dWM6 - dWM8) significantly increased. These results indicate that these two GM subcortical connections change diversely with higher variability in TLE.
·A. Clusters (sFNs) obtained from K-means clustering of the sFC in WM. B. dFNs obtained from K-means clustering of the dFC in WM. C. Averaged sFNCs and dFNCs, T and p values from 2 sample t-test.
Conclusions:
This methodology could serve as a framework to identify patterns of altered connectivity for further studies on other neurological disorders. Our study presented the altered FNC (both dynamic and static) in WM regions. Our results indicate that the dynamic characteristics in WM and subcortical GM matter can reveal alterations in cohorts with TLE.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis 1
Keywords:
Epilepsy
FUNCTIONAL MRI
Sub-Cortical
White Matter
1|2Indicates the priority used for review
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