scholarly journals Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

2021 ◽  
Author(s):  
Wenya. Liu ◽  
Xiulin. Wang ◽  
Jing. Xu ◽  
Yi. Chang ◽  
Timo. Hämäläinen ◽  
...  
Keyword(s):  
Major Depression ◽  
Music Perception ◽  
Permutation Test ◽  
Tensor Decomposition ◽  
Brain Regions ◽  
Music Listening ◽  
Time Frequency ◽  
Oscillatory Networks ◽  
Coupled Constraints ◽  
The Individual

AbstractPrevious researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Cortical electroencephalography (EEG) oscillations act as coordinators to connect different brain regions, and various assemblies of oscillations can form different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of EEG oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time × frequency × connectivity × subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillation-modulated networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.

scholarly journals Serotonin 2A Receptor Signaling Underlies LSD-induced Alteration of the Neural Response to Dynamic Changes in Music

2017 ◽  
Vol 28 (11) ◽  
pp. 3939-3950 ◽  
Author(s):  
Frederick S Barrett ◽  
Katrin H Preller ◽  
Marcus Herdener ◽  
Petr Janata ◽  
Franz X Vollenweider
Keyword(s):  
Music Perception ◽  
Critical Role ◽  
Brain Regions ◽  
Neural Response ◽  
Music Listening ◽  
Receptor Signaling ◽  
Tonal Structure ◽  
5Ht2a Receptor ◽  
Serotonin 2A

AbstractClassic psychedelic drugs (serotonin 2A, or 5HT2A, receptor agonists) have notable effects on music listening. In the current report, blood oxygen level-dependent (BOLD) signal was collected during music listening in 25 healthy adults after administration of placebo, lysergic acid diethylamide (LSD), and LSD pretreated with the 5HT2A antagonist ketanserin, to investigate the role of 5HT2A receptor signaling in the neural response to the time-varying tonal structure of music. Tonality-tracking analysis of BOLD data revealed that 5HT2A receptor signaling alters the neural response to music in brain regions supporting basic and higher-level musical and auditory processing, and areas involved in memory, emotion, and self-referential processing. This suggests a critical role of 5HT2A receptor signaling in supporting the neural tracking of dynamic tonal structure in music, as well as in supporting the associated increases in emotionality, connectedness, and meaningfulness in response to music that are commonly observed after the administration of LSD and other psychedelics. Together, these findings inform the neuropsychopharmacology of music perception and cognition, meaningful music listening experiences, and altered perception of music during psychedelic experiences.

scholarly journals Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition

2019 ◽  
Author(s):  
Yaqub Jonmohamadi ◽  
Suresh Muthukumaraswamy ◽  
Joseph Chen ◽  
Jonathan Roberts ◽  
Ross Crawford ◽  
...  
Keyword(s):  
Memory Task ◽  
Tensor Decomposition ◽  
Space Time ◽  
Fmri Data ◽  
Tensor Space ◽  
Time Frequency ◽  
Neuroscience Research ◽  
Neuroimaging Data ◽  
Source Level ◽  
The Individual

AbstractThe fusion of simultaneously recorded EEG and fMRI data is of great value to neuroscience research due to the complementary properties of the individual modalities. Traditionally, techniques such as PCA and ICA, which rely on strong strong non-physiological assumptions such as orthogonality and statistical independence, have been used for this purpose. Recently, tensor decomposition techniques such as parallel factor analysis have gained more popularity in neuroimaging applications as they are able to inherently contain the multidimensionality of neuroimaging data and achieve uniqueness in decomposition without imposing strong assumptions. Previously, the coupled matrix-tensor decomposition (CMTD) has been applied for the fusion of the EEG and fMRI. Only recently the coupled tensor-tensor decomposition (CTTD) has been proposed. Here for the first time, we propose the use of CTTD of a 4th order EEG tensor (space, time, frequency, and participant) and 3rd order fMRI tensor (space, time, participant), coupled partially in time and participant domains, for the extraction of the task related features in both modalities. We used both the sensor-level and source-level EEG for the coupling. The phase shifted paradigm signals were incorporated as the temporal initializers of the CTTD to extract the task related features. The validation of the approach is demonstrated on simultaneous EEG-fMRI recordings from six participants performing an N-Back memory task. The EEG and fMRI tensors were coupled in 9 components out of which 7 components had a high correlation (more than 0.85) with the task. The result of the fusion recapitulates the well-known attention network as being positively, and the default mode network working negatively time-locked to the memory task.

scholarly journals Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiulin Wang ◽  
Wenya Liu ◽  
Xiaoyu Wang ◽  
Zhen Mu ◽  
Jing Xu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Major Depression ◽  
Music Perception ◽  
Spectral Feature ◽  
Current Data ◽  
Music Listening ◽  
Tensor Factorization ◽  
Eeg Signals ◽  
Spatio Temporal ◽  
Music Information

Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients.

Evaluation of Functionality in Families with Depressed Patient by Using the FFS - Poster Prezentation

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
T. Maria-Silvia
Keyword(s):  
Major Depression ◽  
Depressive Disorder ◽  
Positive Affect ◽  
Psychiatric Patients ◽  
Normal Families ◽  
Control Group ◽  
World Population ◽  
Dsm Iv ◽  
The Individual ◽  
Family Functionality

Depression is a disorder of representation and regulation of mood and emotion; it affects 5% of world population, in a year. Unlike normal loss and sadness feelings, major depression is persistant and it interferes significantly with thoughts, behaviour, emotions, activity and health of the individual. If untreated, depression can lead to suicide. Using family therapy in treating psychiatric patients is a must due to the significance that a family holds in individual and society life.Objective:Assesing family functionality in families with a member diagnosed according to DSM IV TR with depressive disorder; depression intensity was assesed with HDRS.Methods:A sample of 3o families (71 members); FFS assesses the most important and consistent five functioning areas: positive affect, comunication, conflicts, worries and rituals.Results:Values obtained in each of the 40 questions of the scale can give information on variables affecting the increase or decrease in subscales values. Positive affect 35,07, communication 37, conflicts 15,11, worries 40,77, rituals 45,03. The reuslts were compared to those obtained by assessin normal families from a control group of 132 families (323 members).Conclusions:Differences were noticed. Values obtained in our study represent the standard of functioning of families with a depressed member.

scholarly journals Evaluation of An Interactive Music Awareness Program for Cochlear Implant Recipients

2016 ◽  
Vol 33 (4) ◽  
pp. 493-508 ◽  
Author(s):  
Rachel M. van Besouw ◽  
Benjamin R. Oliver ◽  
Mary L. Grasmeder ◽  
Sarah M. Hodkinson ◽  
Heidi Solheim
Keyword(s):  
Cochlear Implant ◽  
Music Perception ◽  
Positive Impact ◽  
Music Listening ◽  
Interactive Music ◽  
Awareness Program ◽  
Instrument Recognition ◽  
Phase Group ◽  
Group 2

The objective of this study was to evaluate the efficacy of a prototype interactive music awareness program (IMAP) for adult cochlear implant (CI) users. An unblinded, randomized crossover design was used. Twenty-one CI users were recruited and allocated to two groups. Group 1 received the IMAP first, followed by a retention of learning phase. Group 2 were given the IMAP after 12 weeks. Participants were instructed to undertake two half-hour sessions per week at home over 12 weeks. Both groups attended appointments at the start, halfway through, and at the end of the trial. At each appointment participants completed tests of speech perception, melodic contour identification, and instrument recognition, rated the sound quality of music, and indicated their music listening habits. Sixteen participants completed the study. Following training both groups showed improved instrument recognition abilities and feedback suggests further positive impact on participants’ lives. The findings suggest that the IMAP is beneficial for music perception and in particular, improved instrument recognition.

scholarly journals Multivariate Predictors of Music Perception and Appraisal by Adult Cochlear Implant Users

2008 ◽  
Vol 19 (02) ◽  
pp. 120-134 ◽  
Author(s):  
Kate Gfeller ◽  
Jacob Oleson ◽  
John F. Knutson ◽  
Patrick Breheny ◽  
Virginia Driscoll ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Cochlear Implant ◽  
Music Perception ◽  
Life Experience ◽  
Recognition Performance ◽  
Strong Predictor ◽  
Music Listening ◽  
Linear Regression Models ◽  
Hearing Aid Use ◽  
Music Appraisal

The research examined whether performance by adult cochlear implant recipients on a variety of recognition and appraisal tests derived from real-world music could be predicted from technological, demographic, and life experience variables, as well as speech recognition scores. A representative sample of 209 adults implanted between 1985 and 2006 participated. Using multiple linear regression models and generalized linear mixed models, sets of optimal predictor variables were selected that effectively predicted performance on a test battery that assessed different aspects of music listening. These analyses established the importance of distinguishing between the accuracy of music perception and the appraisal of musical stimuli when using music listening as an index of implant success. Importantly, neither device type nor processing strategy predicted music perception or music appraisal. Speech recognition performance was not a strong predictor of music perception, and primarily predicted music perception when the test stimuli included lyrics. Additionally, limitations in the utility of speech perception in predicting musical perception and appraisal underscore the utility of music perception as an alternative outcome measure for evaluating implant outcomes. Music listening background, residual hearing (i.e., hearing aid use), cognitive factors, and some demographic factors predicted several indices of perceptual accuracy or appraisal of music. La investigación examinó si el desempeño, por parte de adultos receptores de un implante coclear, sobre una variedad de pruebas de reconocimiento y evaluación derivadas de la música del mundo real, podrían predecirse a partir de variables tecnológicas, demográficas y de experiencias de vida, así como de puntajes de reconocimiento del lenguaje. Participó una muestra representativa de 209 adultos implantados entre 1965 y el 2006. Usando múltiples modelos de regresión lineal y modelos mixtos lineales generalizados, se seleccionaron grupos de variables óptimas de predicción, que pudieran predecir efectivamente el desempeño por medio de una batería de pruebas que permitiera evaluar diferentes aspectos de la apreciación musical. Estos análisis establecieron la importancia de distinguir entre la exactitud en la percepción musical y la evaluación de estímulos musicales cuando se utiliza la apreciación musical como un índice de éxito en la implantación. Importantemente, ningún tipo de dispositivo o estrategia de procesamiento predijo la percepción o la evaluación musical. El desempeño en el reconocimiento del lenguaje no fue un elemento fuerte de predicción, y llegó a predecir primariamente la percepción musical cuando los estímulos de prueba incluyeron las letras. Adicionalmente, las limitaciones en la utilidad de la percepción del lenguaje a la hora de predecir la percepción y la evaluación musical, subrayan la utilidad de la percepción de la música como una medida alternativa de resultado para evaluar la implantación coclear. La música de fondo, la audición residual (p.e., el uso de auxiliares auditivos), los factores cognitivos, y algunos factores demográficos predijeron varios índices de exactitud y evaluación perceptual de la música.

scholarly journals Identification and Research of Adhd and Healthy Controls using Fmri

2019 ◽  
Vol 8 (6S) ◽  
pp. 370-372
Keyword(s):  
Brain Regions ◽  
Healthy Controls ◽  
Hyperactivity Disorder ◽  
Network Activation ◽  
Healthy Control ◽  
Temporal And Spatial ◽  
The Individual ◽  
The Brain ◽  
Coronal View

Analyzing the brain regions for different activations corresponding to the activation input for an experimental setup of task functional MRI or a resting state functional Magnetic Resonance Imaging(fMRI) for a diagnosed or healthy control is a challenging issue as the processing data is voluminous 4D data with nearly 1,51,552 voxels for a single volume of 261 scans fMRI. The data considered for analysis consists of 10 healthy controls and 10 Attention Deficit Hyperactivity Disorder(ADHD) fMRI. The workflow starts with preprocessing the individual scan for realignment, coregistration and Normalisation to Montreal Neurological Institute (MNI) space. Single site scan visit consists of 64x64x37 voxels. Seventy independent components are obtained from processed data by data reduction, Independent Component Analysis (ICA) calculation, Back reconstruction and Component Calibration. ICA performs satisfactorily well on temporal and spatial localization. Visual medial network activation is pronounced in ADHD Controls than in healthy people. Sagittal, Axial and Coronal view of ADHD controls is obtained as component number 42.The analysis is further used for the automatic classification of healthy controls and ADHD people.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

What kind of conscious activity is ‘listening to music’? A contribution from Theodor Adorno by way of psychoanalysis

2019 ◽  
pp. 215-230
Author(s):  
Shierry Weber Nicholsen
Keyword(s):  
Collaborative Work ◽  
Free Association ◽  
Musical Composition ◽  
Conscious Awareness ◽  
Music Listening ◽  
False Consciousness ◽  
Second Viennese School ◽  
Theodor Adorno ◽  
The Individual

This chapter elaborates Theodor Adorno’s notion of genuine music listening and the role of consciousness within it by analogy with the psychoanalytic conceptualization of listening in the analytic dialogue as described in Freud’s model of the free-association process. Crucial in both models of listening is the simultaneous restraining of conventional expectations and the reception of what is new in what is being heard. For both, listening is collaborative work (between patient and analyst, or between listener and the musical composition), engaging the interaction of consciousness and the unconscious by confronting resistances and bringing new meaning into conscious awareness. Implicit in Adorno’s conception of music listening, as part of his critical theory of society, is a socio-historical dimension: the collaboration between genuinely advanced music like that of the Second Viennese School and the individual engaged in genuine listening works against false consciousness to further an authentic subjecthood..

Neurocircuitries of Emotion Processing Affected in Major Depressive Disorder

2019 ◽  
pp. 101-114
Author(s):  
Anjali Sankar ◽  
Cynthia H.Y. Fu
Keyword(s):  
Major Depression ◽  
Predictive Accuracy ◽  
Emotion Processing ◽  
Affective State ◽  
Brain Regions ◽  
First Episode ◽  
Neuroimaging Data ◽  
Multivariate Pattern ◽  
Future Work ◽  
Hallmark Feature

Impairments in processing emotions are a hallmark feature of depression. Advances in neuroimaging techniques have rapidly improved our understanding of the pathophysiology underlying major depression. In this chapter, we provide an overview of influential neural models of emotion perception and regulation and discuss the neurocircuitries of emotion processing that are affected. Major depression is characterized by impairments in widespread brain regions that are evident in the first episode. Models have sought to distinguish the neural circuitry associated with recognition of the emotion, integration of somatic responses, and monitoring of the affective state. In particular, there has been a preponderance of research on the neurocircuitries affected during processing of mood-congruent negative emotional stimuli in depression. While neuroimaging correlates have been investigated and models proposed, these findings have had limited clinical applicability to date. Novel methods such as multivariate pattern recognition applied to neuroimaging data might enable identification of reliable, valid, and robust biomarkers with high predictive accuracy that can be applied to an individual. Last, we discuss avenues for extension and future work.

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