Multi-task machine learning reveals the neuroanatomy fingerprint of mental processing

Abstract

AbstractMental processing delineates the functions of the human mind encompassing a wide range of motor, sensory, emotional, and cognitive processes, each of which is underlain by the neuroanatomical substrates. Identifying accurate representation of neuroanatomical substrates of mental processing could inform understanding of its neural mechanism. The challenge is that it is unclear whether a specific mental process possesses a ’neuroanatomy fingerprint’, i.e., a unique pattern of neuroanatomy that determines the mental process. We used multi-task deep learning to disentangle the neuroanatomy fingerprint of a mental process from other multiple mental processes. The neuroanatomy fingerprint is a unique set of functional activity strength of whole-brain regions with high discrimination ability (AUC = 0.99) among different mental processes. The functional activity strength distribution of neuroanatomy fingerprint unveils both common and distinct brain regions involved in different mental processes. The neuroanatomy fingerprint also addresses a long-standing hypothesis: At least in Chinese and US populations, there consistently exists a neuroanatomy fingerprint to underlie the associated mental process.One-Sentence SummaryEach mental process has a neuroanatomy fingerprint, which is represented as a unique pattern of functional activity strength of whole-brain regions, to differentiate from other different mental processes.