Functional magnetic resonance imaging of auditory cortical fields in awake marmosets

Abstract

The primate auditory cortex is organized into a network of anatomically and functionally distinct processing fields. Because of its tonotopic properties, the auditory core has been the main target of neurophysiological studies ranging from sensory encoding to perceptual decision-making. By comparison, the auditory belt has been less extensively studied, in part due to the fact that neurons in the belt areas prefer more complex stimuli and integrate over a wider frequency range than neurons in the core, which prefer pure tones of a single frequency. Complementary approaches, such as functional magnetic resonance imaging (fMRI), allow the anatomical identification of both the auditory core and belt and facilitate their functional characterization by rapidly testing a range of stimuli across multiple brain areas simultaneously that can be used to guide subsequent neural recordings. Bridging these technologies in primates will serve to further expand our understanding of primate audition. Here, we developed a novel preparation to test whether different areas of the auditory cortex could be identified using fMRI in common marmosets (Callithrix jacchus), a powerful model of the primate auditory system. We used two types of stimulation, band pass noise and pure tones, to parse apart the auditory core from surrounding secondary belt fields. In contrast to most auditory fMRI experiments in primates, we employed a continuous sampling paradigm to rapidly collect data with little deleterious effects. Here we found robust bilateral auditory cortex activation in two marmosets and unilateral activation in a third utilizing this preparation. Furthermore, we confirmed results previously reported in electrophysiology experiments, such as the tonotopic organization of the auditory core and regions activating preferentially to complex over simple stimuli. Overall, these data establish a key preparation for future research to investigate various functional properties of marmoset auditory cortex.

Constructing the hierarchy of predictive auditory sequences in the marmoset brain

“…However, the lissencephalic brain of the marmoset provides convenience to study primary and high-order auditory cortices ( Okano and Mitra, 2015 ). While most of the previous marmoset studies were limited to electrophysiological techniques and mainly focused on auditory calls and pitch processing ( Bendor and Wang, 2005 ; Bendor and Wang, 2008 ; Toarmino et al, 2017 ; Zhou and Wang, 2012 ), the present study has begun to examine the auditory sequences at the whole-brain level. In particular, we revealed that both prediction-error and prediction-updating signals are located in the core and belt regions of the auditory cortex.…”

Common principles in the lateralization of auditory cortex structure and function for vocal communication in primates and rodents

“…Chimpanzees show rightward lateralization for at least some communication calls (Taglialatela et al, 2009), and macaques have dedicated voice selective cells and regions primarily in the right anterior temporal lobe (‘voice patches’, Perrodin et al, 2011; Petkov et al, 2008). Although these higher order regions may be in different locations with respect to lower level AC in different primate species, the same principle of abstraction of incoming stimuli along the auditory processing hierarchy holds in non‐human primates (Belin et al, 2018; Romanski & Averbeck, 2009; Sadagopan & Wang, 2009; Toarmino et al, 2017; Zhu et al, 2019b) and humans (Humphries et al, 2014; Latinus et al, 2013; Pernet et al, 2015; Tremblay et al, 2013).…”

Beyond MRI: on the scientific value of combining non-human primate neuroimaging with metadata

“…Due to the difficulty of scanning NHP subjects while they are awake, and the requirement that subjects remain still during the scanning session, task-related fMRI in NHP is rare and most studies have focused on perceptual tasks in two or three subjects (e.g. Poirier et al., 2017 ; Toarmino et al., 2017 ; Clery et al., 2018 ). To understand inter-subject variability, the most promising approach is to correlate inter-individual differences in brain structure and function at rest with behaviour measured outside the scanner.…”