Cross-domain effect of pitch processing in lexical and melodic contexts reflected by non-musicians: An MEG study

The pitch processing of language and music is generally considered engaging overlapped neural correlates. Previous studies on musicians showed that the ability of pitch processing in music could be transferred to language. It is known that music training can facilitate neural processing of speech, however, the underlying neural mechanisms of pitch processing of language and music are not fully understood, especially in non-musicians. Using magnetoencephalography, we presented a pitch anomaly paradigm which consists of language/music phrases ending in either congruous or incongruous tones/pitches to non-musicians. We found the distinctive brain activity patterns in two groups with high and low musical pitch perceptual abilities. The brain-behavior results showed a positive correlation between the performance of musical pitch tasks and the activation of the left frontotemporal cortical regions elicited by lexical tones. Our results suggested that the cross-domain effect of language and music could be generalized to people without formal music training.

Download Full-text

Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice

10.1101/2020.05.22.111021 ◽

2020 ◽

Cited By ~ 1

Author(s):

Navvab Afrashteh ◽

Samsoon Inayat ◽

Edgar Bermudez Contreras ◽

Artur Luczak ◽

Bruce L. McNaughton ◽

...

Keyword(s):

Spontaneous Activity ◽

Brain Activity ◽

Activity Patterns ◽

Sensory Stimulation ◽

Wide Field ◽

Evoked Responses ◽

Evoked Activity ◽

Cortical Regions ◽

The One ◽

Sensory Evoked

AbstractBrain activity propagates across the cortex in diverse spatiotemporal patterns, both as a response to sensory stimulation and during spontaneous activity. Despite been extensively studied, the relationship between the characteristics of such patterns during spontaneous and evoked activity is not completely understood. To investigate this relationship, we compared visual, auditory, and tactile evoked activity patterns elicited with different stimulus strengths and spontaneous activity motifs in lightly anesthetized and awake mice using mesoscale wide-field voltage-sensitive dye and glutamate imaging respectively. The characteristics of cortical activity that we compared include amplitude, speed, direction, and complexity of propagation trajectories in spontaneous and evoked activity patterns. We found that the complexity of the propagation trajectories of spontaneous activity, quantified as their fractal dimension, is higher than the one from sensory evoked responses. Moreover, the speed and direction of propagation, are modulated by the amplitude during both, spontaneous and evoked activity. Finally, we found that spontaneous activity had similar amplitude and speed when compared to evoked activity elicited with low stimulus strengths. However, this similarity gradually decreased when the strength of stimuli eliciting evoked responses increased. Altogether, these findings are consistent with the fact that even primary sensory areas receive widespread inputs from other cortical regions, and that, during rest, the cortex tends to reactivate traces of complex, multi-sensory experiences that may have occurred in a range of different behavioural contexts.

Download Full-text

Stimulus set meaningfulness and neurophysiological differentiation: a functional magnetic resonance imaging study

10.1101/013078 ◽

2014 ◽

Author(s):

Melanie Boly ◽

Shuntaro Sasai ◽

Olivia Gosseries ◽

Masafumi Oizumi ◽

Adenauer Casali ◽

...

Keyword(s):

Information Integration ◽

Brain Activity ◽

Activity Patterns ◽

Imaging Study ◽

Cortical Activation ◽

Neural Responses ◽

Activation Patterns ◽

Stimulus Set ◽

The Subject ◽

Cortical Regions

A meaningful set of stimuli, such as a sequence of frames from a movie, triggers a set of different experiences. By contrast, a meaningless set of stimuli, such as a sequence of 'TV noise' frames, triggers always the same experience – of seeing 'TV noise' – even though the stimuli themselves are as different from each other as the movie frames. We reasoned that the differentiation of cortical responses underlying the subject's experiences, as measured by Lempel-Ziv complexity (incompressibility) of functional MRI images, should reflect the overall meaningfulness of a set of stimuli for the subject, rather than differences among the stimuli. We tested this hypothesis by quantifying the differentiation of brain activity patterns in response to a movie sequence, to the same movie scrambled in time, and to 'TV noise', where the pixels from each movie frame were scrambled in space. While overall cortical activation was strong and widespread in all conditions, the differentiation (Lempel-Ziv complexity) of brain activation patterns was correlated with the meaningfulness of the stimulus set, being highest in the movie condition, intermediate in the scrambled movie condition, and minimal for 'TV noise'. Stimulus set meaningfulness was also associated with higher information integration among cortical regions. These results suggest that the differentiation of neural responses can be used to assess the meaningfulness of a given set of stimuli for a given subject, without the need to identify the features and categories that are relevant to the subject, nor the precise location of selective neural responses.

Download Full-text

Behavior in the Brain

Journal of Psychophysiology ◽

10.1027/0269-8803/a000016 ◽

2010 ◽

Vol 24 (2) ◽

pp. 76-82 ◽

Cited By ~ 15

Author(s):

Martin M. Monti ◽

Adrian M. Owen

Keyword(s):

Motor Behavior ◽

Functional Neuroimaging ◽

Brain Activity ◽

Motor Output ◽

The Unconscious ◽

Ethical Implications ◽

Brain Responses ◽

Minimally Conscious ◽

Brain Insults ◽

Brain Behavior

Recent evidence has suggested that functional neuroimaging may play a crucial role in assessing residual cognition and awareness in brain injury survivors. In particular, brain insults that compromise the patient’s ability to produce motor output may render standard clinical testing ineffective. Indeed, if patients were aware but unable to signal so via motor behavior, they would be impossible to distinguish, at the bedside, from vegetative patients. Considering the alarming rate with which minimally conscious patients are misdiagnosed as vegetative, and the severe medical, legal, and ethical implications of such decisions, novel tools are urgently required to complement current clinical-assessment protocols. Functional neuroimaging may be particularly suited to this aim by providing a window on brain function without requiring patients to produce any motor output. Specifically, the possibility of detecting signs of willful behavior by directly observing brain activity (i.e., “brain behavior”), rather than motoric output, allows this approach to reach beyond what is observable at the bedside with standard clinical assessments. In addition, several neuroimaging studies have already highlighted neuroimaging protocols that can distinguish automatic brain responses from willful brain activity, making it possible to employ willful brain activations as an index of awareness. Certainly, neuroimaging in patient populations faces some theoretical and experimental difficulties, but willful, task-dependent, brain activation may be the only way to discriminate the conscious, but immobile, patient from the unconscious one.

Download Full-text

Brain Activity Patterns in Anxiety-Prone People Suggest Deficits in Handling Fear

PsycEXTRA Dataset ◽

10.1037/e614992011-001 ◽

2011 ◽

Keyword(s):

Brain Activity ◽

Activity Patterns

Download Full-text

Neural Mechanisms of Music and Language

The Oxford Handbook of Neurolinguistics ◽

10.1093/oxfordhb/9780190672027.013.35 ◽

2019 ◽

pp. 906-952

Author(s):

Mattson Ogg ◽

L. Robert Slevc

Keyword(s):

Auditory Cortex ◽

Language Processing ◽

Auditory Processing ◽

Cortical Plasticity ◽

Speaker Identification ◽

Auditory Pathway ◽

Cognitive Mechanisms ◽

Cross Domain ◽

Music And Language ◽

Cortical Regions

Music and language are uniquely human forms of communication. What neural structures facilitate these abilities? This chapter conducts a review of music and language processing that follows these acoustic signals as they ascend the auditory pathway from the brainstem to auditory cortex and on to more specialized cortical regions. Acoustic, neural, and cognitive mechanisms are identified where processing demands from both domains might overlap, with an eye to examples of experience-dependent cortical plasticity, which are taken as strong evidence for common neural substrates. Following an introduction describing how understanding musical processing informs linguistic or auditory processing more generally, findings regarding the major components (and parallels) of music and language research are reviewed: pitch perception, syntax and harmonic structural processing, semantics, timbre and speaker identification, attending in auditory scenes, and rhythm. Overall, the strongest evidence that currently exists for neural overlap (and cross-domain, experience-dependent plasticity) is in the brainstem, followed by auditory cortex, with evidence and the potential for overlap becoming less apparent as the mechanisms involved in music and speech perception become more specialized and distinct at higher levels of processing.

Download Full-text

Neural alignment predicts learning outcomes in students taking an introduction to computer science course

Nature Communications ◽

10.1038/s41467-021-22202-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Meir Meshulam ◽

Liat Hasenfratz ◽

Hanna Hillman ◽

Yun-Fei Liu ◽

Mai Nguyen ◽

...

Keyword(s):

Computer Science ◽

Learning Outcomes ◽

Brain Activity ◽

Activity Patterns ◽

Real Life ◽

Final Exam ◽

Neural Representations ◽

Real Life Setting ◽

The Right ◽

Mri Study

AbstractDespite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.

Download Full-text

A Preliminary Study of Abnormal Centrality of Cortical Regions and Subsystems in Whole Brain Functional Connectivity of Autism Spectrum Disorder Boys

Clinical EEG and Neuroscience ◽

10.1177/15500594211026282 ◽

2021 ◽

pp. 155005942110262

Author(s):

Bo Chen

Keyword(s):

Autism Spectrum Disorder ◽

Activity Patterns ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Eigenvector Centrality ◽

Information Process ◽

Whole Brain ◽

Functional Connections ◽

Cortical Regions ◽

Prefrontal Lobe

The abnormal cortices of autism spectrum disorder (ASD) brains are uncertain. However, the pathological alterations of ASD brains are distributed throughout interconnected cortical systems. Functional connections (FCs) methodology identifies cooperation and separation characteristics of information process in macroscopic cortical activity patterns under the context of network neuroscience. Embracing the graph theory concepts, this paper introduces eigenvector centrality index (EC score) ground on the FCs, and further develops a new framework for researching the dysfunctional cortex of ASD in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in EC-scores of 26 ASD boys and 28 matched healthy controls (HCs). For whole brain regional EC scores of ASD boys, orbitofrontal superior medial cortex, insula R, posterior cingulate gyrus L, and cerebellum 9 L are endowed with different EC scores significantly. In the brain subsystems level, EC scores of DMN, prefrontal lobe, and cerebellum are aberrant in the ASD boys. Generally, the EC scores display widespread distribution of diseased regions in ASD brains. Meanwhile, the discovered regions and subsystems, such as MPFC, AMYG, INS, prefrontal lobe, and DMN, are engaged in social processing. Meanwhile, the CBCL externalizing problem scores are associated with EC scores.

Download Full-text

Using Brain Activity Patterns to Differentiate Real and Virtual Attended Targets during Augmented Reality Scenarios

Information ◽

10.3390/info12060226 ◽

2021 ◽

Vol 12 (6) ◽

pp. 226

Author(s):

Lisa-Marie Vortmann ◽

Leonid Schwenke ◽

Felix Putze

Keyword(s):

Augmented Reality ◽

Eye Tracking ◽

Brain Activity ◽

Activity Patterns ◽

Training Data ◽

Machine Learning Techniques ◽

Neural Net ◽

Dependent Manner ◽

Tracking Data ◽

Average Accuracy

Augmented reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target that is either real or virtual. In this study, we investigated whether this target is real or virtual by using machine learning techniques to classify electroencephalographic (EEG) and eye tracking data collected in augmented reality scenarios. A shallow convolutional neural net classified 3 second EEG data windows from 20 participants in a person-dependent manner with an average accuracy above 70% if the testing data and training data came from different trials. This accuracy could be significantly increased to 77% using a multimodal late fusion approach that included the recorded eye tracking data. Person-independent EEG classification was possible above chance level for 6 out of 20 participants. Thus, the reliability of such a brain–computer interface is high enough for it to be treated as a useful input mechanism for augmented reality applications.

Download Full-text