Neural Networks of Beat 1 Investigating a Beat Network Using Passive Listening and Functional Connectivity

Previous literature has shown a network of activation between motor and auditory cortices when listening to rhythms that elicited a strong perception of a beat (Chen et al. 2008; Grahn & Rowe, 2009). However, no research to this date has investigated these networks using a resting-state paradigm. The purpose of this thesis project is assess the functional connectivity between motor and auditory regions during the perception of a beat, with the goal of mapping a unique network of structures that are co-active during beat perception. To do this, we will administer a functional magnetic resonance imaging (fMRI) scan to 5 healthy participants while they passively listen to a metric simple rhythm or silence. Each condition will be 5-minutes in duration. To determine the functional connectivity between cortical regions 30 hypothesis driven structurally defined ROIs will be correlated and then compared. It was shown that motor and auditory regions showed high magnitude of connectivity with themselves when compared to regions in control networks (visual, default mode). However, the similar trend was observed in the silent condition, therefore we are unable to infer that this network of auditory and motor regions is related to the perception of a beat. Neural Networks of Beat 3 Neural Networks Associated in the Perception of a Beat The ability to synchronize motor movements to a musical rhythm is a very complex task, as it requires the perceiver to simultaneously decipher audio information and coordinate muscle movements accordingly. However, this task seems to come easy to humans, shown by our ability to tap our feet to the rhythms of a musical piece with profound accuracy (Large, 2000). Moreover, the ability to accurately synchronize motor movements to music seems to be a uniquely human ability, as apes, our closest genetic ancestors do not show this capacity (Zacro, 2009). What Guides Our Perception of a Beat? Music can be broken down into components such as rhythm, tempo and pitch. These components work together to provide the listener with a complete experience of music. This thesis project is focused on music’s rhythmic structure. The best description of rhythm comes from a quote from the famous jazz musician Miles Davis who says, “Music is the space between the notes. It’s not the notes you play; it’s the notes you don’t play”. Rhythm is defined as the arrangement of time intervals across the span of an auditory sequence (Grahn, 2009). In other words, what signifies a rhythm is the sequence of spaces between the notes of a musical piece. Listening to a rhythm usually leaves the listener with a feeling of an underlying pulse. This pulse is what is defined as the beat. The beat of a musical piece is a property of its rhythm and is subjectively perceived by the listener (Large & Palmer, 2002). Our perception of a beat is affected by the metrical structure of rhythm. The metrical structure of a rhythm is described as the order in which notes are organized across the sequence of a rhythm. The metrical structure of a rhythm can be quantified as the amount of notes that fall in a position of where the beat could be found, or rather the quantity of notes that Neural Networks of Beat 4 fall on the position of a persistent pulse. Operationally we define these different metric structures as being either metric simple, or metric complex. The structure of a metric simple rhythm will be organized in a way that the majority of beat positions are occupied with note (see Figure 1.). Metric simple rhythms have been shown to readily elicit a feeling of a beat (Grahn & Brett, 2007). Conversely, metric complex rhythms are organized as to leave many beat positions unoccupied by notes, thus making it much harder to perceive a feeling of a beat. It is crucial to point out that the perception of a beat is something that is inferred onto a musical rhythm. Furthermore, as shown by the variety of metrical structures, notes do not always fall in line with the beat, therefore the perception of a beat must be is formed internally, to some extent. As a result, two observers may perceive completely different beats when listening to the same rhythm (Repp, 2007). Thus, the metricality of a rhythm only affects the ease in which someone is able to perceive a beat, it does not dictate the specific beat that is perceived. This is determined by the observer. The Neural Substrates of Beat Patel and Iversen (2014), propose the theory of “action simulation for auditory prediction” (ASAP). Their theory suggests that musical beat perception is based on the integration of auditory and motor planning regions of the brain. Moreover, their theory suggests that our perception of a beat is based on our ability to make predictions of when subsequent notes will fall. These predictive assessments of a rhythm rely on the interaction between cortical regions; such the basal ganglia (Grahn & Brett, 2007; Grahn & Rowe 2009), associated in timing and regions responsible for audition. This theory is given support by research that has shown the activation of motor system brain during the passive listening of music (Zattore, Chen, & Penhune, 2007; Cameron et al., 2012). Furthermore, this theory is supported anecdotally by the Neural Networks of Beat 5 ease in our ability to tap along to the beat of a musical stimulus with extreme precision, suggesting that we are wired to integrate motor movements with auditory stimuli. Figure 1. This figure depicts the arrangement of the concatenated macaque calls used in the simple rhythm condition. The X-axis represents a hypothetical measurement of time. The pink lines are the equally spaced points that mark the beat and the black notches represent are the sounds. As shown every pink line contained a sound eliciting a strong feeling of a beat. A study by Chen et al. (2008), investigated the activation of cortical regions responsible for beat perception. In this experiment they manipulated the metrical parameters of rhythm, by exposing musicians and non-musicians to either metric simple or metric complex rhythms. They used functional magnetic resonance imaging (fMRI) to determine the regional activity during different metrical rhythms, and in between scans they had participants complete a beat tapping task that measured the ability to synchronize to a rhythm. Performance on the task indexed the ability to perceive a beat. The result of the tapping task showed that synchrony of tapping increased in conditions with a metric simple structure. It was also observed that higher performance predicted a higher levels of activity in regions of the motor cortex responsible for motor planning (premotor cortex, cerebellum & supplementary motor area) during the exposure to metric simple rhythms. This suggests that an increased ability to perceive a beat is related to increased recruitment of the motor cortex in beat perception. These results align with the hypothesis that motor planning is related to our ability to perceive a beat. Further work conducted by Grahn & Rowe (2009) investigated the neural substrates that are related to the metrical structure of rhythm. In their study they aimed to determine what Metric simple