MusicMindHealth

Here we present an extension to the studyforrest dataset – a versatile resource for studying the behavior of the human brain in situations of real-life complexity (http://studyforrest.org). This release adds more high-resolution, ultra high-field (7 Tesla) functional magnetic resonance imaging (fMRI) data from the same individuals. The twenty participants were repeatedly stimulated with a total of 25 music clips, with and without speech content, from five different genres using a slow event-related paradigm.

We investigated the perception of music in a cognitive musicology study, employing behavioral methods to examine general associative patterns--i.e. the propensity for subjects to recruit associations when listening to music, reminiscent of synaesthetic cross-wiring (Cytowic, 2009). Although non-Synaesthetic associations to music are less explored, experiments such as Köhler’s (1929) linguistic “Kiki, Boulba” study, demonstrated associations in non-synaesthetes, supporting the hypothesis that general listeners engage cross-sensorial connections.

Our research explores theories based upon past behavioural studies and FMRI scans with Synaesthetes and general listeners. FMRI experiments have revealed that the cross-modal associations to sounds in Synaesthetes are less pronounced, yet still present in the general population. The results of our psycho-musicology study with 40 Synaesthetes and 40 non-Synaesthetes reveal a quasi-Synaesthetic [Nikolic, 2014] spectrum extending to general listeners, similar to culturally founded Synaesthesia [Kohler].

This paper investigates perceptual relationships between art in the auditory and visual domains. First, we conducted a behavioral experiment asking subjects to assess similarity between 10 musical recordings and 10 works of abstract art. We found a significant degree of agreement across subjects as to which i mages correspond to which audio, even though neither the audio nor the images possessed semantic content. Secondly, we sought to find the relationship between audio and images within a defined feature space that correlated with the subjective similarity judgments.

Much of music neuroscience research has focused on finding functionally specific brain re-gions, often employing highly controlled stimuli. Recent results in computational neuroscience suggest that auditory information is represented in distributed, overlapping patterns in the brain [4] and that natural sounds may be optimal for studying the functional architecture of higher order auditory areas [3]. With this in mind, the goal of the present work was to decode musical informa-tion from brain activity collected during naturalistic music listening.

Abstract. The ability to reconstruct audio-visual stimuli from human brain activity is an important step towards creating intelligent brain-computer interfaces and also serves as a valuable tool for cognitive neu-roscience research. We propose a general method for stimulus reconstruc-tion that simultaneously learns from multiple sources of brain activity and multiple stimulus representations.

Our previous work (Casey, Thompson, Kang, Raizada, and Wheatley 2012) investigated decoding hemodynamic brain activity in the feed-forward pathways involved in music listening with rich stimuli. Our current work investigates top-down music processing via auditory imagery with an imagined music task. Most previous work on auditory imagery (e.g. Zatorre 2000; Zatorre, Halpern, and Bouffard 2010) used familiar tunes, such as nursery rhymes, that have associated lyrics which elicit activation of language areas in the brain.

A new branch of systematic musicology, “normative musicology,” is proposed and its practice demonstrated. Normative musicology is the study of optimal (“norma-tive”) expectations about future musical signals, given some corpus of past signals. It is a formalization of many “statistical learning” approaches (e.g. [1]) and may be considered a computational counterpart to empirical musicology.

The cognitive representations that support our experience of pitch perception and imagery are not well understood and they generally focus on tonotopic organization of neural columns in the brain (place-based coding of absolute frequency). From prior behavioural studies, we understand musical pitch space to be relative to a reference key, and hierarchically organized. Our current study uses a new between-subject common representation of spatio-temporal multivariate population codes to identify the representational space of musical pitch.