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Neuroimage. 2019 Apr 1;189:241-247. doi: 10.1016/j.neuroimage.2019.01.021. Epub 2019 Jan 11.

Univariate and multivariate analyses of functional networks in absolute pitch.

Author information

1
Division Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland. Electronic address: christian.brauchli@uzh.ch.
2
Division Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland. Electronic address: simon.leipold@uzh.ch.
3
Division Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland; University Research Priority Program (URPP) "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland; Department of Special Education, King Abdulaziz University, Jeddah, Saudi Arabia. Electronic address: lutz.jaencke@uzh.ch.

Abstract

Absolute pitch (AP) refers to the rare ability to identify the pitch of any given tone without an external reference tone. Previous studies have shown that during auditory processing, AP musicians activate the auditory cortex (AC), the prefrontal cortex (PFC), and parietal areas of the brain. Therefore, it has been hypothesized that AP is sustained by a widespread functional network. In the present functional magnetic resonance imaging (fMRI) study, we tested this hypothesis by employing a mass-univariate analysis of resting-state functional connectivity within the AC, the PFC, and parietal areas in a large sample of musicians with and without AP (N = 100). AP musicians showed increased functional connectivity in the left middle frontal gyrus (MFG), left intraparietal sulcus (IPS), and right superior parietal lobule (SPL). These results provide the first evidence for an AP-specific network characterized by increased functional connections in higher-order cognitive areas. Interestingly, AP was not associated with increases in functional connectivity of the AC, but AP was successfully decoded from functional connectivity patterns in the left AC using multi-voxel pattern analysis (MVPA, also known as multivariate pattern analysis), with group classification accuracy being highest for the left Heschl's gyrus (HG). MVPA can capture fine-grained patterns in the brain connectivity profile of AP musicians, whilst a mass-univariate analysis is sensitive to macroscopic trends in the data. The successful differentiation of AP musicians by MVPA but not by a mass-univariate analysis of connectivity in the AC thus indicates that AP musicians differ in the fine-grained rather than the macroscopic AC function. Based on our findings, and in light of current literature, we propose pitch-label associations, tonal working memory, pitch categorization, and multimodal integration as potential mechanisms underlying the AP ability. This set of psychological functions is controlled by a distributed functional network and a particular AC connectivity pattern only present in AP musicians.

KEYWORDS:

Absolute pitch; Functional connectivity; MVPA; Machine learning; Resting-state fMRI

[Indexed for MEDLINE]

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