Aberrant multimodal brain networks in patients with anti‐NMDA receptor encephalitis

Abstract

Aims To explore large‐scale brain network alterations and examine their clinical and neuropsychological relevance in patients with anti‐N‐methyl‐D‐aspartate receptor (NMDAR) encephalitis. Methods Twenty‐four patients with anti‐NMDAR encephalitis and 26 matched healthy controls (HCs) were enrolled in our study. Based on the multimodal MRI dataset, individual morphological, structural, and functional brain networks were constructed and compared between the two groups at multiple levels. The associations with clinical/neuropsychological variables and the discriminant ability of significant alterations were further studied. Results Multimodal network analysis revealed that anti‐NMDAR encephalitis mainly affected morphological and structural networks, but subtle alterations were observed in functional networks. Intriguingly, decreased network local efficiency was observed for both morphological and structural networks and increased nodal centrality in the lateral orbital gyrus was convergently observed among the three types of networks in the patients. Moreover, the alterations, particularly those from structural networks, accounted largely for cognitive deficits of the patients and could distinguish the diseased individuals from the HCs with excellent performance (area under the curve =0.933). Conclusions The current study provides a comprehensive view of characteristic multimodal network dysfunction in anti‐NMDAR encephalitis, which is crucial to establish new diagnostic biomarkers and promising therapeutic targets for the disease.

Linking brain networks to cognition with magnetoencephalography in paediatric autoimmune encephalitis

“…Adult AE studies have found associations between fMRI connectivity in regionally defined networks with working memory and processing speed 18, 91 . Previous fMRI studies in NMDARE observed a higher connectivity to neighbouring nodes in the right lateral orbital gyrus compared to controls 66 , and lower clustering coefficient and local efficiency in the left insula 65 , which suggests differing local areas within a network could be identified in future studies. The present study lacked statistical power to investigate specific regions of interest and was focused on global and average measures of network efficiency.…”

“…Measures of efficiency and modularity, that reflect how well brain networks integrate and segregate distinct modules to efficiently transmit neuronal information, were chosen. These specific measures were investigated as they were applied in previous AE fMRI research 65, 66 .…”

Diffusion kurtosis imaging and diffusion tensor imaging parameters applied to white matter and gray matter of patients with anti-N-methyl-D-aspartate receptor encephalitis

“…However, some of these studies focused on the superficial white matter areas rather than the whole-brain including white-gray matter and deep gray matter areas of recovered and partially recovered patients (Phillips et al, 2018), while others used Tract-Based Spatial Statistic (TBSS) analysis methods that were inconsistent with ours (Finke et al, 2013;Yang et al, 2022). Recently, Wang et al applied multimodal brain network analysis to show that changes in structural networks were mainly in fiber number and fiber length, and very few significant changes in FA in anti-NMDAR encephalitis (Wang et al, 2021), which is consistent with our findings. While in our research, we didn't found changes of DTI parameters in white matter.…”

Cerebral Metabolic Network in Patients With Anti-N-Methyl-D-Aspartate Receptor Encephalitis on 18F-FDG PET Imaging

“…The pathological changes might involve NMDAR-Ab, which changes intrinsic cortical connections and neuronal population dynamics (Rosch et al, 2018;Dalmau et al, 2019). To the best of our knowledge, most studies in the literature have focused on changes in brain networks using magnetic resonance imaging (MRI) and electroencephalography (EEG) in patients with encephalitis (Peer et al, 2017;Xu et al, 2017;Heine et al, 2018;Symmonds et al, 2018;Bauer et al, 2020;Li et al, 2021;Wang et al, 2021), while the use of 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) has not been reported before, which is earlier for recognition and more sensitive than MRI (Novy et al, 2016;Yuan et al, 2016;Wei et al, 2020;Seniaray et al, 2021). Metabolic brain network analysis based on graph theory has proved useful in analyzing brain activity changes and is widely used in neuropsychological imaging (Huang et al, 2020;Rajagopalan and Pioro, 2020).…”