High-throughput automated methods for classical and operant conditioning of Drosophila larvae.

Learning which stimuli (classical conditioning) or which actions (operant conditioning) predict rewards or punishments can improve chances of survival. However, the circuit mechanisms that underlie distinct types of associative learning are still not fully understood. Automated, high-throughput paradigms for studying different types of associative learning, combined with manipulation of specific neurons in freely behaving animals, can help advance this field. The Drosophila melanogaster larva is a tractable model system for studying the circuit basis of behaviour, but many forms of associative learning have not yet been demonstrated in this animal. Here, we developed a high-throughput (i.e. multi-larva) training system that combines real-time behaviour detection of freely moving larvae with targeted opto- and thermogenetic stimulation of tracked animals. Both stimuli are controlled in either open- or closed-loop, and delivered with high temporal and spatial precision. Using this tracker, we show for the first time that Drosophila larvae can perform classical conditioning with no overlap between sensory stimuli (i.e. trace conditioning). We also demonstrate that larvae are capable of operant conditioning by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Our results extend the known associative learning capacities of Drosophila larvae. Our automated training rig will facilitate the study of many different forms of associative learning and the identification of the neural circuits that underpin them.

Examining the relationships between cortical maturation and white matter myelination throughout early childhood.

Cortical development and white matter myelination are hallmark processes of infant and child neurodevelopment, and play a central role in the evolution of cognitive and behavioral functioning. Non-invasive magnetic resonance imaging (MRI) has been used to independently track these microstructural and morphological changes in vivo, however few studies have investigated the relationship between them despite their concurrency in the developing brain. Further, because measures of cortical morphology rely on underlying gray-white matter tissue contrast, which itself is a function of white matter myelination, it is unclear if contrast-based measures of cortical development accurately reflect cortical architecture, or if they merely represent adjacent white matter maturation. This may be particularly true in young children, in whom brain structure is rapidly maturing. Here for the first time, we investigate the dynamic relationship between cortical and white matter development across early childhood, from 1 to 6years. We present measurements of cortical thickness with respect to cortical and adjacent myelin water fraction (MWF) in 33 bilateral cortical regions. Significant results in only 14 of 66 (21%) cortical regions suggest that cortical thickness measures are not heavily driven by changes in adjacent white matter, and that brain imaging studies of cortical and white matter maturation reflect distinct, but complimentary, neurodevelopmental processes.