Neuro-Immune Cross-Talk in the Striatum: From Basal Ganglia Physiology to Circuit Dysfunction
Bibliographic record
Abstract
The basal ganglia network is represented by an interconnected group of subcortical nuclei traditionally thought to play a crucial role in motor learning and movement execution. During the last decades, knowledge about basal ganglia physiology significantly evolved and this network is now considered as a key regulator of important cognitive and emotional processes. Accordingly, the disruption of basal ganglia network dynamics represents a crucial pathogenic factor in many neurological and psychiatric disorders. The striatum is the input station of the circuit. Thanks to the synaptic properties of striatal medium spiny neurons (MSNs) and their ability to express synaptic plasticity, the striatum exerts a fundamental integrative and filtering role in the basal ganglia network, influencing the functional output of the whole circuit. Although it is currently established that the immune system is able to regulate neuronal transmission and plasticity in specific cortical areas, the role played by immune molecules and immune/glial cells in the modulation of intra-striatal connections and basal ganglia activity still needs to be clarified. In this manuscript, we review the available evidence of immune-based regulation of synaptic activity in the striatum, also discussing how an abnormal immune activation in this region could be involved in the pathogenesis of inflammatory and degenerative central nervous system (CNS) diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".