Abnormal, distinct biases in normative/non-normative intrinsic systems across epilepsy syndromes
Poster No:
1904
Submission Type:
Abstract Submission
Authors:
Qirui Zhang1, Sam Javidi1, Zhiqiang Zhang2, Michael Sperling1, Joseph Tracy3
Institutions:
1Thomas Jefferson University, Philadelphia, PA, 2Nanjing University School of Medicine, Nanjing, Jiangsu, 3Thomas Jefferson University/Farber Institute for Neuroscience, Philadelphia, PA
First Author:
Co-Author(s):
Joseph Tracy, PHD/ABPP-CN
Thomas Jefferson University/Farber Institute for Neuroscience
Philadelphia, PA
Thomas Jefferson University/Farber Institute for Neuroscience
Philadelphia, PA
Introduction:
The degree to which the multiple epilepsy syndromes display a shared versus highly unique set of brain macroscale organizational characteristics remains unknown. In this study, we compared the common childhood and adult epilepsy syndromes for the relative presence and integrity of typical, normative versus atypical, highly individual intrinsic connectivity networks (ICN).
Methods:
We retrospectively investigated data from 1128 epilepsy patients and matched 979 healthy participants (HPs) with resting-state functional MRI (rsfMRI) from Thomas Jefferson University (TJU, 2 scanners) and Jinling Hospital(JLH, 1 scanner). Patients were divided into the following subgroups: left temporal lobe epilepsy (TLEL), right TLE (TLER), TLE (laterality unclear, TLEO), extratemporal lobe epilepsy (EXE), genetic generalized epilepsy (GGE), rolandic epilepsy (RE) and absence epilepsy (AE) (see Table 1 for sample details). RsfMRI data were decomposed using independent component analysis to obtain individualized ICNs. The degree of match between an individual's ICNs and the canonical ICNs (Yeo et.al 17 resting-state networks plus thalamus, striatum, and cerebellum) was determined, with matching versus non-matching ICNs labeled normative and non-normative, respectively. To quantify syndrome-specific connectome characteristics of the normative/non-normative networks (e.g., strength of match, number of networks), we utilized W-scores that accounted for the influence of age, sex, scanner type, and head motion. One-way ANOVA analyses (with Tukey's correction) tested for differences between the epilepsy subgroups.
·Table 1
Results:
TLEL displayed the largest decrease in normative ICN number compared to HPs, followed by significant decreases in TLER, TLEO, and EXE subgroups. GGE, RE, and AE subgroups showed no reliable difference from HPs (see Figure 1a). Conversely, TLEL displayed the largest increase in non-normative ICN number versus HPs, followed by significant increases in TLER, TLEO, and EXE, no GGE/HP difference, and a substantive decrease observed in RE and AE (see Figure 1b). The level of match data (i.e., strength of match to canonical ICNs), revealed that TLEL patients showed extensive network undermatching, mainly involving the temporal parietal, default mode, and control networks. The TLER and TLEO groups also showed undermatching to these networks. EXE displayed a weak match to the sensorimotor and dorsal attention networks. GGE showed only minor network match discrepancies compared to HPs. The two childhood-onset epilepsies (AE and RE) displayed a match pattern distinct from the adult epilepsies, generally showing an overmatch relative to HPs (control and default networks undermatched but sensorimotor, thalamic, and cerebellar and other networks overmatched). In contrast, the adult syndromes consistently undermatched the canonical ICNs. In AE, the thalamic network showed an unusual overmatch, suggesting a strong presence for the thalamic network, consistent with the known thalamic epileptogenicity in that disorder (see Figure 1c).
·Figure 1
Conclusions:
We provided the first comparison across epilepsy syndromes of macroscale network organization biases toward either normative/canonical, or non-normative/idiosyncratic intrinsic connectivity systems. We demonstrated that adult and childhood epilepsies possessed distinct network biases with the former displaying the most severe abnormalities, involving both normative network undermatching and a stronger presence of non-normative ICN systems. Childhood epilepsies generally displayed an abnormal overmatch to the canonical ICNs.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
Epilepsy
FUNCTIONAL MRI
1|2Indicates the priority used for review
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