Clustering consistency in neuroimaging data analysis

Clustering techniques have been applied to neuroscience data analysis for decades. New algorithms keep being developed and applied to address different problems. However, when it comes to the applications of clustering, it is often hard to select the appropriate algorithm and evaluate the quality of clustering results due to the unknown ground truth. It is also the case that conclusions might be biased based on only one specific algorithm because each algorithm has its own assumption of the structure of the data, which might not be the same as the real data. In this paper, we explore the benefits of integrating the clustering results from multiple clustering algorithms by a tunable consensus clustering strategy and demonstrate the importance and necessity of consistency in neuroimaging data analysis

Comparing the aesthetic experience of classic–romantic and contemporary classical music: An interview study (Online First)

Current models of aesthetic experience of music (AEM) have emerged in the recent years capitalizing on evidence from psychology and neuroscience research, thus modeling mainly cognitive and information processes in the brain. However, a large part of the empirical research on which these models are based is related to Western tonal music, while another style of Western music, namely, contemporary classical music (CCM), has been almost neglected. CCM is often dissonant and lacks a tonal hierarchical structure, as, for example, in serial musical pieces. The current study qualitatively explored aesthetic dimensions of a CCM experience by contrasting it to classic–romantic music (CM). To this end, 16 semi-structured interviews with experts of both CCM (n = 8) and CM (n = 8) were conducted. The interview guide consisted of questions relating to physiological, affective, and cognitive dimensions of music listening. We applied qualitative content analysis on the textual material and compared the emerging main and sub-themes between the groups. Our findings show that especially the categories of expectations, physiological and emotional responses, pleasurable aspects, and, lastly, existential relevance revealed striking differences which allow us to conclude that CM and CCM afford distinguishable types of AEM in listeners

Putting cells in motion: Advantages of endogenous boosting of BDNF production

Motor exercise, such as sport or musical activities, helps with a plethora of diseases by modulating brain functions in neocortical and subcortical regions, resulting in behavioural changes related to mood regulation, well-being, memory, and even cognitive preservation in aging and neurodegenerative diseases. Although evidence is accumulating on the systemic neural mechanisms mediating these brain effects, the specific mechanisms by which exercise acts upon the cellular level are still under investigation. This is particularly the case for music training, a much less studied instance of motor exercise than sport. With regards to sport, consistent neurobiological research has focused on the brain-derived neurotrophic factor (BDNF), an essential player in the central nervous system. BDNF stimulates the growth and differentiation of neurons and synapses. It thrives in the hippocampus, the cortex, and the basal forebrain, which are the areas vital for memory, learning, and higher cognitive functions. Animal models and neurocognitive experiments on human athletes converge in demonstrating that physical exercise reliably boosts BDNF levels. In this review, we highlight comparable early findings obtained with animal models and elderly humans exposed to musical stimulation, showing how perceptual exposure to music might affect BDNF release, similar to what has been observed for sport. We subsequently propose a novel hypothesis that relates the neuroplastic changes in the human brains after musical training to genetically-and exercise-driven BDNF levels

Towards tunable consensus clustering for studying functional brain connectivity during affective processing

In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package “UNCLES” available on http://cran.r-project.org/web/packages/UNCLES/index.html

Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non-musicians

Abstract Auditory prediction error responses elicited by surprising sounds can be reliably recorded with musical stimuli that are more complex and realistic than those typically employed in EEG or MEG oddball paradigms. However, these responses are reduced as the predictive uncertainty of the stimuli increases. In this study, we investigate whether this effect is modulated by musical expertise. Magnetic mismatch negativity (MMNm) responses were recorded from 26 musicians and 24 non-musicians while they listened to low-and high-uncertainty melodic sequences in a musical multi-feature paradigm that included pitch, slide, intensity, and timbre deviants. When compared to non-musicians, musically trained participants had significantly larger pitch and slide MMNm responses. However, both groups showed comparable reductions of pitch and slide MMNm amplitudes in the high-uncertainty condition compared to the low-uncertainty condition. In a separate, behavioral deviance detection experiment, musicians were more accurate and confident about their responses than non-musicians, but deviance detection in both groups was similarly affected by the uncertainty of the melodies. In both experiments, the interaction between uncertainty and expertise was not significant, suggesting that the effect is comparable in both groups. Consequently, our results replicate the modulatory effect of predictive uncertainty on prediction error; show that it is present across different types of listeners; and suggest that expertise-related and stimulus-driven modulations of predictive precision are dissociable and independent

Music with concurrent saliences of musical features elicits stronger brain responses

Brain responses are often studied under strictly experimental conditions in which elec-troencephalograms (EEGs) are recorded to reflect reactions to short and repetitive stimuli. However, in real life, aural stimuli are continuously mixed and cannot be found isolated, such as when listening to music. In this audio context, the acoustic features in music related to brightness, loudness, noise, and spectral flux, among others, change continuously; thus, significant values of these features can occur nearly simultaneously. Such situations are expected to give rise to increased brain reaction with respect to a case in which they would appear in isolation. In order to assert this, EEG signals recorded while listening to a tango piece were considered. The focus was on the amplitude and time of the negative deflation (N100) and positive deflation (P200) after the stimuli, which was defined on the basis of the selected music feature saliences, in order to perform a statistical analysis intended to test the initial hypothesis. Differences in brain reactions can be identified depending on the concurrence (or not) of such significant values of different features, proving that coterminous increments in several qualities of music influence and modulate the strength of brain responses

Musicianship and melodic predictability enhance neural gain in auditory cortex during pitch deviance detection

When listening to music, pitch deviations are more salient and elicit stronger prediction error responses when the melodic context is predictable and when the listener is a musician. Yet, the neuronal dynamics and changes in connectivity underlying such effects remain unclear. Here, we employed dynamic causal modeling (DCM) to investigate whether the magnetic mismatch negativity response (MMNm)—and its modulation by context predictability and musical expertise—are associated with enhanced neural gain of auditory areas, as a plausible mechanism for encoding precision-weighted prediction errors. Using Bayesian model comparison, we asked whether models with intrinsic connections within primary auditory cortex (A1) and superior temporal gyrus (STG)—typically related to gain control—or extrinsic connections between A1 and STG—typically related to propagation of prediction and error signals—better explained magnetoencephalography responses. We found that, compared to regular sounds, out-of-tune pitch deviations were associated with lower intrinsic (inhibitory) connectivity in A1 and STG, and lower backward (inhibitory) connectivity from STG to A1, consistent with disinhibition and enhanced neural gain in these auditory areas. More predictable melodies were associated with disinhibition in right A1, while musicianship was associated with disinhibition in left A1 and reduced connectivity from STG to left A1. These results indicate that musicianship and melodic predictability, as well as pitch deviations themselves, enhance neural gain in auditory cortex during deviance detection. Our findings are consistent with predictive processing theories suggesting that precise and informative error signals are selected by the brain for subsequent hierarchical processing

Auditory sensory memory and working memory skills : Association between frontal MMN and performance scores

Objective: Memory is the faculty responsible for encoding, storing and retrieving information, comprising several sub-systems such as sensory memory (SM) and working memory (WM). Some previous studies exclusively using clinical population revealed associations between these two memory systems. Here we aimed at investigating the relation between modality-general WM performance and auditory SM formation indexed by magnetic mismatch negativity (MMN) responses in a healthy population of young adults. Methods: Using magnetoencephalography (MEG), we recorded MMN amplitudes to changes related to six acoustic features (pitch, timbre, location, intensity, slide, and rhythm) inserted in a 4-tone sequence in 86 adult participants who were watching a silent movie. After the MEG recordings, participants were administered the WM primary subtests (Spatial Span and Letter Number Sequencing) of Wechsler Memory Scale (WMS). Results: We found significant correlations between frontal MMN amplitudes to intensity and slide deviants and WM performance. In case of intensity, the relation was revealed in all participants, while for slide only in individuals with a musical background. Conclusions: Automatic neural responses to auditory feature changes are increased in individuals with higher visual WM performance. Significance: Conscious WM abilities might be linked to pre-attentive sensory-specific neural skills of prediction and short-term storage of environmental regularities. (C) 2018 Elsevier B.V. All rights reserved.Peer reviewe

Exploration of distance metrics in consensus clustering analysis of FMRI data

Clustering techniques have gained great popularity in neuroscience data analysis especially in analysing data from complex experiment paradigm where it is hard to apply traditional model-based method. However, when employing clustering analysis, many clustering algorithms are available nowadays and even with an individual clustering algorithm, choices like parameter settings and distance metrics are very likely to have impacts on the final clustering results. In our previous work, we have demonstrated the benefits of integrating clustering results from multiple clustering algorithms, which provides more stable, reproducible, and complete clustering solutions. In this paper, we aim to further inspect the possible influences from the choices of distance metrics in clustering analysis