Perivascular spaces and the two steps to neuroinflammation

Immune cells enter the central nervous system (CNS) from the circulation under normal conditions for immunosurveillance and in inflammatory neurologic diseases. This review describes the distinct anatomic features of the CNS vasculature that permit it to maintain parenchymal homeostasis and which necessitate specific mechanisms for neuroinflammation to occur. We review the historical evolution of the concept of the blood-brain barrier and discuss distinctions between diffusion/transport of solutes and migration of cells from the blood to CNS parenchyma. The former is regulated at the level of capillaries, whereas the latter takes place in postcapillary venules. We summarize evidence that entry of immune cells into the CNS parenchyma in inflammatory conditions involves 2 differently regulated steps: transmigration of the vascular wall into the perivascular space and progression across the glia limitans into the parenchyma.

Macrophage-independent T cell infiltration to the site of injury-induced brain inflammation

We have addressed the role of macrophages in glial response and T cell entry to the CNS after axonal injury, by using intravenous injection of clodronate-loaded mannosylated liposomes, in C57BL6 mice. As expected, clodronate-liposome treatment resulted in depletion of peripheral macrophages which was confirmed by F4/80- and MOMA-1(-) stainings in spleen. Sequential clodronate-liposome treatment 4, 2 and 0 days before axotomy resulted in significant reduction of infiltrating CD45(high) CD11b+ macrophages in the hippocampus at 1, 2 and 3 days post-lesion, measured by flow cytometry. There was a slight delay in the expansion of CD45(dim) CD11+ microglia in clodronate-liposome treated mice, but macrophage depletion had no effect on the percentage of infiltrating T cells in the lesion-reactive hippocampus. Lesion-induced TNFalpha mRNA expression was not affected by macrophage depletion, suggesting that activated glial cells are the primary source of this cytokine in the axonal injury-reactive brain. This identifies a potentially important distinction from inflammatory autoimmune infiltration in EAE, where macrophages are a prominent source of TNFalpha and their depletion prevents parenchymal T cell infiltration and disease.

General distress, hopelessness—suicidal ideation and worrying in adolescence: Concurrent and predictive validity of a symptom-level bifactor model for clinical diagnoses

BACKGROUND: Clinical disorders often share common symptoms and aetiological factors. Bifactor models acknowledge the role of an underlying general distress component and more specific sub-domains of psychopathology which specify the unique components of disorders over and above a general factor. METHODS: A bifactor model jointly calibrated data on subjective distress from The Mood and Feelings Questionnaire and the Revised Children's Manifest Anxiety Scale. The bifactor model encompassed a general distress factor, and specific factors for (a) hopelessness-suicidal ideation, (b) generalised worrying and (c) restlessness-fatigue at age 14 which were related to lifetime clinical diagnoses established by interviews at ages 14 (concurrent validity) and current diagnoses at 17 years (predictive validity) in a British population sample of 1159 adolescents. RESULTS: Diagnostic interviews confirmed the validity of a symptom-level bifactor model. The underlying general distress factor was a powerful but non-specific predictor of affective, anxiety and behaviour disorders. The specific factors for hopelessness-suicidal ideation and generalised worrying contributed to predictive specificity. Hopelessness-suicidal ideation predicted concurrent and future affective disorder; generalised worrying predicted concurrent and future anxiety, specifically concurrent generalised anxiety disorders. Generalised worrying was negatively associated with behaviour disorders. LIMITATIONS: The analyses of gender differences and the prediction of specific disorders was limited due to a low frequency of disorders other than depression. CONCLUSIONS: The bifactor model was able to differentiate concurrent and predict future clinical diagnoses. This can inform the development of targeted as well as non-specific interventions for prevention and treatment of different disorders.