Pathological repetitive behaviours are a common feature of various neuropsychiatric disorders, including compulsions in obsessive–compulsive disorder or tics in Gilles de la Tourette syndrome. Clinical research suggests that compulsive-like symptoms are related to associative cortico-striatal dysfunctions, and tic-like symptoms to sensorimotor cortico-striatal dysfunctions. The Sapap3 knockout mouse (Sapap3-KO), the current reference model to study such repetitive behaviours, presents both associative as well as sensorimotor cortico-striatal dysfunctions. Previous findings point to deficits in both macro-, as well as micro-circuitry, both of which can be affected by neuronal structural changes. However, to date, structural connectivity has not been analysed. Hence, in the present study, we conducted a comprehensive structural characterisation of both associative and sensorimotor striatum as well as major cortical areas connecting onto these regions. Besides a thorough immunofluorescence study on oligodendrocytes, we applied AxonDeepSeg, an open source software, to automatically segment and characterise myelin thickness and axon area. We found that axon calibre, the main contributor to changes in conduction speed, is specifically reduced in the associative striatum of the Sapap3-KO mouse; myelination per se seems unaffected in associative and sensorimotor cortico-striatal circuits.
During cortico-basal ganglia dependent learning, relevant environmental information is associated with certain outcomes; such learning is essential to generate adaptive behaviour in a continuously changing environment. Through repetitive trial-and-error experiences, actions are optimized and cognitive associative load can be relieved through consolidation and automatization. Although the molecular basis of learning is well studied, region-specific genome-wide expression profiles of the striatum, the major input region of cortico-basal ganglia circuits, during learning are lacking. Here we combined an automated operant conditioning paradigm with an efficient RNA-sequencing protocol to compare expression profiles among three learning stages in three striatal regions per hemisphere in a total of 240 striatal biopsies. Notably, the inclusion of matched yoked controls allowed reliably identifying learning-related expression changes. With 593 differently expressed genes (3.3% of all detected genes), we find the strongest effect of learning at an early, goal-directed stage across all three striatal region and identify a total of 921 learning-related expression changes. Our dataset provides a unique resource to study molecular markers of striatal learning.
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