Fourier SPoC: A customised machine-learning analysis pipeline for auditory beat-based entrainment in the MEG

We propose here (the informed use) of a customised, data-driven machine-learning pipeline to analyse magnetoencephalography (MEG) in a theoretical source space, with respect to the processing of a regular beat. This hypothesis- and data-driven analysis pipeline allows us to extract the maximally relevant components in MEG source-space, with respect to the oscillatory power in the frequency band of interest and, most importantly, the beat-related modulation of that power. Our pipeline combines Spatio-Spectral Decomposition as a first step to seek activity in the frequency band of interest (SSD, [1]) with a Source Power Co-modulation analysis (SPoC; [2]), which extracts those components that maximally entrain their activity with the given target function, that is here with the periodicity of the beat in the frequency domain (hence, f-SPoC). MEG data (102 magnetometers) from 28 participants passively listening to a 5-min long regular tone sequence with a 400 ms beat period (the “target function” for SPoC) were segmented into epochs of two beat periods each to guarantee a sufficiently long time window. As a comparison pipeline to SSD and f-SpoC, we carried out a state-of-the-art cluster-based permutation analysis (CBPA, [3]). The time-frequency analysis (TFA) of the extracted activity showed clear regular patterns of periodically occurring peaks and troughs across the alpha and beta band (8-20 Hz) in the f-SPoC but not in the CBPA results, and both the depth and the specificity of modulation to the beat frequency yielded a significant advantage. Future applications of this pipeline will address target the relevance to behaviour and inform analogous analyses in the EEG, in order to finally work toward addressing dysfunctions in beat-based timing and their consequences.Author summaryWhen listening to a regular beat, oscillations in the brain have been shown to synchronise with the frequency of that given beat. This phenomenon is called entrainment and has in previous brain-imaging studies been shown in the form of one peak and trough per beat cycle in a range of frequency bands within 15-25 Hz (beta band). Using machine-learning techniques, we designed an analysis pipeline based on Source-Power Co-Modulation (SPoC) that enables us to extract spatial components in MEG recordings that show these synchronisation effects very clearly especially across 8-20 Hz. This approach requires no anatomical knowledge of the individual or even the average brain, it is purely data driven and can be applied in a hypothesis-driven fashion with respect to the “function” that we expect the brain to entrain with and the frequency band within which we expect to see this entrainment. We here apply our customised pipeline using “f-SPoC” to MEG recordings from 28 participants passively listening to a 5-min long tone sequence with a regular 2.5 Hz beat. In comparison to a cluster-based permutation analysis (CBPA) which finds sensors that show statistically significant power modulations across participants, our individually extracted f-SPoC components find a much stronger and clearer pattern of peaks and troughs within one beat cycle. In future work, this pipeline can be implemented to tackle more complex “target functions” like speech and music, and might pave the way toward rhythm-based rehabilitation strategies.

Auditory fear conditioning alters neural gain in the cochlear nucleus: a wireless neural recording study in freely behaving rats

Anxiety disorders involve distorted perception of the world including increased saliency of stress-associated cues. However, plasticity in the initial sensory regions of the brain following a fearful experience has never been examined. The cochlear nucleus (CN) is the first station in the central auditory system, with heterogeneous collections of neurons that not only project to but also receive projections from cortico-limbic regions, suggesting a potential for experience-dependent plasticity. Using wireless neural recordings in freely behaving rats, we demonstrate for the first time that neural gain in the CN is significantly altered by fear conditioning to auditory sequences. Specifically, the ventral subnuclei significantly increased firing rate to the conditioned tone sequence, while the dorsal subnuclei significantly decreased firing rate during the conditioning session overall. These findings suggest subregion-specific changes in the balance of inhibition and excitation in the CN as a result of conditioning experience. Heart rate was measured as the conditioned response (CR), which showed that while pre-conditioned stimulus (CS) responding did not change across baseline and conditioning sessions, significant changes in heart rate were observed to the tone sequence followed by shock. Heart-rate findings support acquisition of conditioned fear. Taken together, the present study presents first evidence for potential experience-dependent changes in auditory perception that involve novel plasticity within the first site of processing auditory information in the brain.

Statistical Learning of Melodic Patterns Influences the Brain's Response to Wrong Notes

The theory of statistical learning has been influential in providing a framework for how humans learn to segment patterns of regularities from continuous sensory inputs, such as speech and music. This form of learning is based on statistical cues and is thought to underlie the ability to learn to segment patterns of regularities from continuous sensory inputs, such as the transition probabilities in speech and music. However, the connection between statistical learning and brain measurements is not well understood. Here we focus on ERPs in the context of tone sequences that contain statistically cohesive melodic patterns. We hypothesized that implicit learning of statistical regularities would influence what was held in auditory working memory. We predicted that a wrong note occurring within a cohesive pattern (within-pattern deviant) would lead to a significantly larger brain signal than a wrong note occurring between cohesive patterns (between-pattern deviant), even though both deviant types were equally likely to occur with respect to the global tone sequence. We discuss this prediction within a simple Markov model framework that learns the transition probability regularities within the tone sequence. Results show that signal strength was stronger when cohesive patterns were violated and demonstrate that the transitional probability of the sequence influences the memory basis for melodic patterns. Our results thus characterize how informational units are stored in auditory memory trace for deviance detection and provide new evidence about how the brain organizes sequential sound input that is useful for perception.

Cues to Perceiving Tonal Stability in Music

Musical expectations may arise from short-term sensitivity to the statistics of the immediate context and from long-term knowledge acquired through previous listening experiences. Here we investigate the influence of two statistical structures on tonal expectations: the frequency with which individual pitches occur, and the occurrence of such pitches on strong or weak positions of the musical meter. We familiarized nonmusician adult listeners to a 2-min tone sequence in which some pitches occurred more frequently than others (Experiment 1) or some pitches occurred more frequently on strong than on weak metrical positions (Experiment 2). Participants then indicated which of two short test sequences matched the familiarization sequence (Experiments 1a and 2a), or they provided fit ratings for individual probe tones following short test sequences (Experiments 1b and 2b). In Experiments 1a and 2a, listeners correctly identified the test sequence that matched the familiarization. In Experiments 1b and 2b, we found that the statistics of the immediate context strongly influenced probe tone ratings. In Experiment 2b, but not Experiment 1b, prior familiarization also influenced participants’ ratings. Findings suggest that both frequency-of-occurrence and metrical position exert a short-term influence on perceived tonal stability, and metrical position also exerts a long-term influence.

Acquiring the pitch patterns of L2 Mandarin Chinese

This study examines the acquisition of utterance-level pitch patterns in Mandarin Chinese by American second language (L2) learners. It is an exploratory study with the goal of identifying the utterance-level prosody in L2 Mandarin Chinese. The focus of this study is not on the pitch patterns of individual learners but those of subject groups. The analysis shows that the pitch patterns between two syntactic structures for the same tone sequence vary with the tone sequence and the subject group. The biggest difference between first language (L1) and L2 Mandarin Chinese lies in the frequency of target undershoot in L2 speech. The infrequent tone target undershoot in L2 speech, especially among the intermediate learners, was attributed to the incomplete acquisition of L2 prosody. It was argued that the infrequent tone target undershoot may render L2 speech more staccato or robot-like, which contributes to the perception of a foreign accent in L2 Mandarin Chinese.