Acute TNFα levels predict cognitive impairment 6-9 months after COVID-19 infection.

BACKGROUND: A neurocognitive phenotype of post-COVID-19 infection has recently been described that is characterized by a lack of awareness of memory impairment (i.e., anosognosia), altered functional connectivity in the brain's default mode and limbic networks, and an elevated monocyte count. However, the relationship between these cognitive and brain functional connectivity alterations in the chronic phase with the level of cytokines during the acute phase has yet to be identified. AIM: Determine whether acute cytokine type and levels is associated with anosognosia and functional patterns of brain connectivity 6-9 months after infection. METHODS: We analyzed the predictive value of the concentration of acute cytokines (IL-1RA, IL-1ß, IL-6, IL-8, IFNγ, G-CSF, GM-CSF) (cytokine panel by multiplex immunoassay) in the plasma of 39 patients (mean age 59 yrs, 38-78) in relation to their anosognosia scores for memory deficits via stepwise linear regression. Then, associations between the different cytokines and brain functional connectivity patterns were analyzed by MRI and multivariate partial least squares correlations for the whole group. RESULTS: Stepwise regression modeling allowed us to show that acute TNFα levels predicted (R2 = 0.145; ß = -0.38; p = .017) and were associated (r = -0.587; p < .001) with scores of anosognosia for memory deficits observed 6-9 months post-infection. Finally, high TNFα levels were associated with hippocampal, temporal pole, accumbens nucleus, amygdala, and cerebellum connectivity. CONCLUSION: Increased plasma TNFα levels in the acute phase of COVID-19 predict the presence of long-term anosognosia scores and changes in limbic system functional connectivity.

Modulation of epileptic networks by transient interictal epileptic activity: A dynamic approach to simultaneous EEG-fMRI.

Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to define brain hemodynamic changes, measured by the Blood Oxygen Level Dependent (BOLD) signal, associated to the interictal epileptic discharges (IED), which together with ictal events constitute a signature of epileptic disease. Given the highly time-varying nature of epileptic activity, a dynamic functional connectivity (dFC) analysis of EEG-fMRI data appears particularly suitable, having the potential to identify transitory features of specific connections in epileptic networks. In the present study, we propose a novel method, defined dFC-EEG, that integrates dFC assessed by fMRI with the information recorded by simultaneous scalp EEG, in order to identify the connections characterised by a dynamic profile correlated with the occurrence of IED, forming the dynamic epileptic subnetwork. Ten patients with drug-resistant focal epilepsy were included, with different aetiology and showing a widespread (or multilobar) BOLD activation, defined as involving at least two distinct clusters, located in two different lobes and/or extended to the hemisphere contralateral to the epileptic focus. The epileptic focus was defined from the IED-related BOLD map. Regions involved in the occurrence of interictal epileptic activity; i.e., forming the epileptic network, were identified by a general linear model considering the timecourse of the fMRI-defined focus as main regressor. dFC between these regions was assessed with a sliding-window approach. dFC timecourses were then correlated with the sliding-window variance of the IED signal (VarIED), to identify connections whose dynamics related to the epileptic activity; i.e., the dynamic epileptic subnetwork. As expected, given the very different clinical picture of each individual, the extent of this subnetwork was highly variable across patients, but was but was reduced of at least 30% with respect to the initially identified epileptic network in 9/10 patients. The connections of the dynamic subnetwork were most commonly close to the epileptic focus, as reflected by the laterality index of the subnetwork connections, reported higher than the one within the original epileptic network. Moreover, the correlation between dFC timecourses and VarIED was predominantly positive, suggesting a strengthening of the dynamic subnetwork associated to the occurrence of IED. The integration of dFC and scalp IED offers a more specific description of the epileptic network, identifying connections strongly influenced by IED. These findings could be relevant in the pre-surgical evaluation for the resection or disconnection of the epileptogenic zone and help in reaching a better post-surgical outcome. This would be particularly important for patients characterised by a widespread pathological brain activity which challenges the surgical intervention.