Metabolic connectivity in Alzheimer’s diseases

Altered metabolic connectivity between the amygdala and default mode network is related to pain perception in patients with cancer

We investigated the neural correlates for chronic cancer pain conditions by retrospectively analyzing whole brain regions on 18F-fluoro-2-deoxyglucose-positron emission tomography images acquired from 80 patients with head and neck squamous cell carcinoma and esophageal cancer. The patients were divided into three groups according to perceived pain severity and type of analgesic treatment, namely patients not under analgesic treatment because of no or minor pain, patients with good pain control under analgesic treatment, and patients with poor pain control despite analgesic treatment. Uncontrollable cancer pain enhanced the activity of the hippocampus, amygdala, inferior temporal gyrus, and temporal pole. Metabolic connectivity analysis further showed that amygdala co-activation with the hippocampus was reduced in the group with poor pain control and preserved in the groups with no or minor pain and good pain control. The increased although imbalanced activity of the medial temporal regions may represent poor pain control in patients with cancer. The number of patients who used anxiolytics was higher in the group with poor pain control, whereas the usage rates were comparable between the other two groups. Therefore, further studies should investigate the relationship between psychological conditions and pain in patients with cancer and analyze the resultant brain activity.Trial registration: This study was registered at clinicaltrials.gov on 9/3/20 (NCT04537845).

Aging-Related Modular Architectural Reorganization of the Metabolic Brain Network

Background: Modules in brain network represent groups of brain regions that are collectively involved in one or more cognitive domains. Exploring aging-related reorganization of the brain modular architecture using metabolic brain network could further our understanding about aging-related neuromechanism and neurodegenerations. Materials and Methods: In this study, 432 subjects who performed ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) were enrolled and divided into young and old adult groups, as well as female and male groups. The modular architecture was detected, and the connector and hub nodes were identified to explore the topological role of the brain regions based on the metabolic brain network. Results: This study revealed that human metabolic brain network was modular and could be clustered into three modules. The modular architecture was reorganized from young to old ages with regions related to sensorimotor function clustered into the same module; and the number of connector nodes was reduced and most connector nodes were localized in temporo-occipital areas related to visual and auditory functions in old ages. The major gender difference is that the metabolic brain network was delineated into four modules in old female group with the nodes related to sensorimotor function split into two modules. Discussion: Those findings suggest aging is associated with reorganized brain modular architecture. Clinical Trial Registration number: ChiCTR2000036842.

Understanding the Immunologic Characteristics of Neurologic Manifestations of SARS-CoV-2 and Potential Immunological Mechanisms

Similar to its predecessors, coronavirus disease 2019 (COVID-19) exhibits neurotrophic properties, which lead to progression of neurologic sequelae. Besides direct viral invasion to the central nervous system (CNS), indirect CNS involvement through viral-mediated immune response is plausible. Aberrant immune pathways such as extreme release of cytokines (cytokine storm), autoimmunity mediated by cross-reactivity between CNS components and viral particles, and microglial activation propagate CNS damage in these patients. Here, we review the currently available evidence to discuss the plausible immunologic pathways that may contribute to the development of COVID-19 neurological complications, namely Alzheimer’s disease, Parkinson’s disease, stroke, multiple sclerosis, Guillain-Barre syndrome, seizure, and brainstem involvement.