scholarly journals Unpredicted Pitch Modulates Beta Oscillatory Power during Rhythmic Entrainment to a Tone Sequence

2016 ◽  
Vol 7 ◽  
Author(s):  
Andrew Chang ◽  
Dan J. Bosnyak ◽  
Laurel J. Trainor
Keyword(s):  
Tone Sequence ◽  
Oscillatory Power

Auditory Cortical Activity Related to Perception of Regularity in Tone Sequence

2015 ◽  
Vol 135 (9) ◽  
pp. 1106-1111
Author(s):  
Tomoki Amemiya ◽  
Takahiro Noda ◽  
Tomoyo I. Shiramatsu ◽  
Ryohei Kanzaki ◽  
Hirokazu Takahashi
Keyword(s):  
Cortical Activity ◽  
Tone Sequence

Emotion-Related Responses to Audio News with Rising versus Falling Background Tone Sequences

2003 ◽  
Vol 7 (1_suppl) ◽  
pp. 85-110 ◽  
Author(s):  
Kari Kallinen ◽  
Niklas Ravaja
Keyword(s):  
Electrodermal Activity ◽  
Cardiac Activity ◽  
Tone Sequence ◽  
Music Listening ◽  
Minor Mode ◽  
Age Related ◽  
Major Mode ◽  
Valence And Arousal ◽  
Listening Task ◽  
Addition Level

We examined the emotional effects of (a) a rising versus a falling chromatic tone sequence in the background of audio news and (b) foreground versus background diatonic and chromatic tone sequences. In experiment one, 26 participants rated audio news messages with rising and falling chromatic background tone sequences on the valence and arousal dimensions. Cardiac activity, electrodermal activity (EDA), and facial muscle activity were also recorded continuously. In experiment two, 24 participants rated six plain tone sequences ( i.e., rising and falling chromatic, major, and minor) and six news messages with the aforementioned tone sequences mixed in the background on the valence and arousal dimensions. In experiment 1, both self-reported arousal and physiological arousal as measured by EDA were higher during the news with a rising-tone sequence compared to those with a falling-tone sequence. In experiment 2, rising-tone sequences prompted both higher arousal and pleasantness ratings. However, the responses were moderated by the type of listening task: foreground listening prompted responses related to musical connotations ( i.e., major tone sequences were rated as most pleasant and minor as most unpleasant), whereas background listening prompted responses dependent on the emotional congruence between the news messages and tone sequences ( i.e., the minor mode versions were rated as most pleasant and the major mode versions as most unpleasant). In addition, level of education-, music listening frequency-, and age-related differences in the responses were found and are discussed.

scholarly journals Fronto-medial theta coordinates posterior maintenance of working memory content

2021 ◽  
Author(s):  
Oliver Ratcliffe ◽  
Kimron Shapiro ◽  
Bernhard P. Staresina
Keyword(s):  
Working Memory ◽  
Computational Models ◽  
Medial Cortex ◽  
Duty Cycles ◽  
Machine Learning Approach ◽  
Theta Frequency ◽  
Oscillatory Power ◽  
Posterior Parietal ◽  
Memory Content ◽  
Memory Contents

AbstractHow does the human brain manage multiple bits of information to guide goal-directed behaviour? Successful working memory (WM) functioning has consistently been linked to oscillatory power in the theta frequency band (4-8 Hz) over fronto-medial cortex (fronto-medial theta, FMT). Specifically, FMT is thought to reflect the mechanism of an executive sub-system that coordinates maintenance of memory contents in posterior regions. However, direct evidence for the role of FMT in controlling specific WM content is lacking. Here we collected high-density Electroencephalography (EEG) data whilst participants engaged in load-varying WM tasks and then used multivariate decoding methods to examine WM content during the maintenance period. Higher WM load elicited a focal increase in FMT. Importantly, decoding of WM content was driven by posterior/parietal sites, which in turn showed load-induced functional theta coupling with fronto-medial cortex. Finally, we observed a significant slowing of FMT frequency with increasing WM load, consistent with the hypothesised broadening of a theta ‘duty cycle’ to accommodate additional WM items. Together these findings demonstrate that frontal theta orchestrates posterior maintenance of WM content. Moreover, the observed frequency slowing elucidates the function of FMT oscillations by specifically supporting phase-coding accounts of WM.Significance StatementHow does the brain juggle the maintenance of multiple items in working memory (WM)? Here we show that increased WM demands increase theta power (4-8 Hz) in fronto-medial cortex. Interestingly, using a machine learning approach, we found that the content held in WM could be read out not from frontal, but from posterior areas. These areas were in turn functionally coupled with fronto-medial cortex, consistent with the idea that frontal cortex orchestrates WM representations in posterior regions. Finally, we observed that holding an additional item in WM leads to significant slowing of the frontal theta rhythm, supporting computational models that postulate longer ‘duty cycles’ to accommodate additional WM demands.

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

2021 ◽  
Author(s):  
Stephanie Brandl ◽  
Niels Trusbak Haumann ◽  
Simjon Radloff ◽  
Sven Dähne ◽  
Leonardo Bonetti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Frequency Band ◽  
Target Function ◽  
Tone Sequence ◽  
Data Driven ◽  
Beta Band ◽  
Analysis Pipeline ◽  
Source Power ◽  
The Brain ◽  
Permutation Analysis

AbstractWe 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.

scholarly journals Depressive rumination is correlated with brain responses during self-related processing

2021 ◽  
Author(s):  
Tzu-Yu Hsu ◽  
Tzu-Ling Liu ◽  
Paul Z. Cheng ◽  
Hsin-Chien Lee ◽  
Timothy J. Lane ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Low Frequency ◽  
Event Related Potentials ◽  
Unipolar Depression ◽  
Brain Responses ◽  
Late Event ◽  
Oscillatory Power ◽  
Related Aspect ◽  
Related Potentials ◽  
Central Symptom

AbstractBackgroundRumination, a tendency to focus on negative self-related thoughts, is a central symptom of depression. Studying the self-related aspect of such symptoms is challenging due to the need to distinguish self effects per se from the emotional content of task stimuli. This study employs an emotionally neutral self-related paradigm to investigate possible altered self processing in depression and its link to rumination.MethodsPeople with unipolar depression (MDD; n = 25) and controls (n = 25) underwent task-based EEG recording. Late event-related potentials were studied along with low frequency oscillatory power. EEG metrics were compared between groups and correlated with depressive symptoms and reported rumination.ResultsThe MDD group displayed a difference in late potentials across fronto-central electrodes between self-related and non-self-related conditions. No such difference was seen in controls. The magnitude of this difference was positively related with depressive symptoms and reported rumination. MDD also had elevated theta oscillation power at central electrodes in self-related conditions, which was not seen in controls.ConclusionsRumination appears linked to altered self-related processing in depression, independently of stimuli-related emotional confounds. This connection between self-related processing and depression may point to self-disorder being a core component of the condition.

scholarly journals Imaging of neural oscillations with embedded inferential and group prevalence statistics

2017 ◽  
Author(s):  
Peter W. Donhauser ◽  
Esther Florin ◽  
Sylvain Baillet
Keyword(s):  
Brain Activity ◽  
Signal Strength ◽  
Brain Regions ◽  
Oscillatory Activity ◽  
Neural Oscillations ◽  
Source Reconstruction ◽  
Signal To Noise ◽  
Source Imaging ◽  
Methodological Challenges ◽  
Oscillatory Power

AbstractMagnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. Imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges1. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be adjusted to many experimental questions in systems neuroscience.Author summaryThe oscillatory activity of the brain produces a repertoire of signal dynamics that is rich and complex. Noninvasive recording techniques such as scalp magnetoencephalography and electroencephalography (MEG, EEG) are key methods to advance our comprehension of the role played by neural oscillations in brain functions and dysfunctions. Yet, there are methodological challenges in mapping these elusive components of brain activity that have remained unresolved. We introduce a new mapping technique, called imaging with embedded statistics (iES), which alleviates these difficulties. With iES, signal detection is constrained explicitly to the operational hypotheses of the study design. We show, in a variety of experimental contexts, how iES emphasizes the oscillatory components of brain activity, if any, that match the experimental hypotheses, even in deeper brain regions where signal strength is expected to be weak in MEG. Overall, the proposed method is a new imaging tool to respond to a wide range of neuroscience questions concerning the scaffolding of brain dynamics via anatomically-distributed neural oscillations.

Harmonic Factors in the Perception of Tonal Melodies

2002 ◽  
Vol 20 (1) ◽  
pp. 51-85 ◽  
Author(s):  
Dirk-Jan Povel ◽  
Erik Jansen
Keyword(s):  
Mental Representation ◽  
Tone Sequence ◽  
Minor Role ◽  
Pitch Height ◽  
Common Assumption ◽  
Harmonic Progression ◽  
A Minor

By common assumption, the first step in processing a tonal melody consists in setting up the appropriate metrical and harmonic frames required for the mental representation of the sequence of tones. Focusing on the generation of a harmonic frame, this study aims (a) to discover the factors that facilitate or interfere with the development of a harmonic interpretation, and (b) to test the hypothesis that goodness ratings of tone sequences largely depend on whether the listener succeeds in creating a suitable harmonic interpretation. In two experiments, listeners rated the melodic goodness of selected sequences of 10 and 13 tones and indicated which individual tones seemed not to fit. Results indicate that goodness ratings (a) are higher the more common the induced harmonic progression, (b) are strongly affected by the occurrence and position of nonchord tones: sequences without nonchord tones were rated highest, sequences with anchoring nonchord tones intermediately, and nonanchoring nonchord tones lowest. The explanation offered is compared with predictions derived from other theories, which leads to the conclusion that when a tone sequence is perceived as a melody, it is represented in terms of its underlying harmony, in which exact pitch-height characteristics play a minor role.

scholarly journals Solar feature tracking in both spatial and temporal domains

2007 ◽  
Vol 3 (S247) ◽  
pp. 288-295
Author(s):  
D. B. Jess ◽  
M. Mathioudakis ◽  
R. Erdélyi ◽  
G. Verth ◽  
R. T. J. McAteer ◽  
...  
Keyword(s):  
Coronal Loop ◽  
Feature Tracking ◽  
New Method ◽  
Kink Mode ◽  
Spatial Periodicity ◽  
Mode Oscillation ◽  
Oscillatory Power ◽  
Solar Feature

AbstractA new method for automated coronal loop tracking, in both spatial and temporal domains, is presented. The reliability of this technique was tested with TRACE 171 Å observations. The application of this technique to a flare-induced kink-mode oscillation, revealed a 3500 km spatial periodicity which occur along the loop edge. We establish a reduction in oscillatory power, for these spatial periodicities, of 45% over a 322 s interval. We relate the reduction in oscillatory power to the physical damping of these loop-top oscillations.

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