The emergence of new tools to image neurotransmitters,
neuromodulators,
and neuropeptides has transformed our understanding of the role of
neurochemistry in brain development and cognition, yet analysis of
this new dimension of neurobiological information remains challenging.
Here, we image dopamine modulation in striatal brain tissue slices
with near-infrared catecholamine nanosensors (nIRCat) and implement
machine learning to determine which features of dopamine modulation
are unique to changes in stimulation strength, and to different neuroanatomical
regions. We trained a support vector machine and a random forest classifier
to decide whether the recordings were made from the dorsolateral striatum
(DLS) versus the dorsomedial striatum (DMS) and find that machine
learning is able to accurately distinguish dopamine release that occurs
in DLS from that occurring in DMS in a manner unachievable with canonical
statistical analysis. Furthermore, our analysis determines that dopamine
modulatory signals including the number of unique dopamine release
sites and peak dopamine released per stimulation event are most predictive
of neuroanatomy. This is in light of integrated neuromodulator amount
being the conventional metric used to monitor neuromodulation in animal
studies. Lastly, our study finds that machine learning discrimination
of different stimulation strengths or neuroanatomical regions is only
possible in adult animals, suggesting a high degree of variability
in dopamine modulatory kinetics during animal development. Our study
highlights that machine learning could become a broadly utilized tool
to differentiate between neuroanatomical regions or between neurotypical
and disease states, with features not detectable by conventional statistical
analysis.