What Does Immunology Have to Do With Normal Brain Development and the Pathophysiology Underlying Tourette Syndrome and Related Neuropsychiatric Disorders?
Bibliographic record
Abstract
Objective: The goal of this article is to review the past decade’s literature and provide a critical commentary on the involvement of immunological mechanisms in normal brain development, as well as its role in the pathophysiology of Tourette syndrome, other Chronic tic disorders (CTD), and related neuropsychiatric disorders including Obsessive-compulsive disorder (OCD) and Attention deficit hyperactivity disorder (ADHD). Methods: We conducted a literature search using the Medline/PubMed and EMBASE electronic databases to locate relevant articles and abstracts published between 2009 and 2020, using a comprehensive list of search terms related to immune mechanisms and the diseases of interest, including both clinical and animal model studies. Results: The cellular and molecular processes that constitute our ‘immune system’ are crucial to normal brain development and the formation and maintenance of neural circuits. It is also increasingly evident that innate and adaptive systemic immune pathways, as well as neuroinflammatory mechanisms, play an important role in the pathobiology of at least a subset of individuals with Tourette syndrome and related neuropsychiatric disorders In the conceptual framework of the holobiont theory, emerging evidence points also to the importance of the ‘microbiota-gut-brain axis’ in the pathobiology of these neurodevelopmental disorders. Conclusions: Neural development is an enormously complex and dynamic process. Immunological pathways are implicated in several early neurodevelopmental processes including the formation and refinement of neural circuits. Hyper-reactivity of systemic immune pathways and neuroinflammation may contribute to the natural fluctuations of the core behavioral features of CTD, OCD and ADHD. There is still limited knowledge of the efficacy of direct and indirect (i.e. through environmental modifications) immune-modulatory interventions in the treatment of these disorders. Future research also needs to focus on the key molecular pathways through which dysbiosis of different tissue microbiota influence neuroimmune interactions in these disorders, and how microbiota modification could modify their natural history. It is also possible that valid biomarkers will emerge that will guide a more personalized approach to the treatment of these disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".