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Structural and functional neural correlates of emotional responses to music

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The sophisticated signal processing techniques developed during last years for structural and functional imaging methods allow us to detect abnormalities of brain connectivity in brain disorders with unprecedented detail. Interestingly, recent works shed light on both functional and structural underpinnings of musical anhedonia (i.e., the individual's incapacity to enjoy listening to music). On the other hand, computational models based on brain simulation tools are being used more and more for mapping the functional consequences of structural abnormalities. The latter could help to better understand the mechanism that is impaired in people unable to derive pleasure from music, and formulate hypotheses on how music acquired reward value. The presentation gives an overview of today's studies and proposes a possible simulation pipeline to reproduce such scenario.

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Structural and functional neural correlates of emotional responses to music

  1. 1. Structural and Functional Neural Correlates of Emotional Responses to Music Gianluca Susi UPM/UCM Laboratory of Cognitive and Computational Neuroscience Centro de Tecnologia Biomedica Madrid C3GI 2017
  2. 2. Connectomics
  3. 3. Connectomics (2005) [Hagmann 2005; Sporns et al., 2005]: field of neuroscience concerned with the mapping and analysis of connectomes. • Connectome: wiring diagram of the brain. - Structural (or anatomical) connectome - Functional connectome • Made possible by the convergence between technological evolution (tract tracing) and avancements in complex networks science. • Graphs allows us to model the human brain connectomes, using graph theory to abstractly define a nervous system as a set of nodes (denoting anatomical regions) and interconnecting edges (denoting structural or functional connections) [Bullmore and Bassett, 2011] • Connectomics of brain disorders: possibility to identify participants at risk. Connectomics
  4. 4. Brain signals ROI1 . . . . . . ROI2 ROIk ROIn Functional connectivity (FC) . . . . . . FC refers to the interaction between the signals from couples of sensors or brain regions. Both in resting state or during task execution. Sub-band filtering β [12,30]Hz α [8,12]Hz θ [3, 8]Hz δ [1, 3]Hz γ [30,45]Hz + + + + FC indices, evaluating interaction (same band, different ROIs) simm. # ROI #ROI . . . . . . simm. simm. simm. simm. β α θ δ γ
  5. 5. FC analysis: Phase synchronization indices PS FC indices: phases of two coupled “oscillators” synchronize, even though their amplitudes may remain uncorrected x(t) y(t) ~ 1 ~ 1 ~0 φx(t) φy(t) 0 2π 0 2π ~ 1 Example: Phase Locking Value (PLV) [0,1] : how the phase difference between two signals is preserved during the time course? [Lachaux et al.1999, Niso et al. 2013]
  6. 6. FC analysis: Amplitude correlation indices PS FC indices: based on the similarity of the envelopes of a couple of signals. ~ +1 ~ -1 x(t) y(t) Example: Amplitude envelope correlation (AEC) [-1, +1]: measures the linear correlation between the envelopes of two signals x(t) and y(t) 2 ))(yH(x),(HCorr(y))H),(x(HCorr AEC rmmmrm
  7. 7. Neuroimaging techniques
  8. 8. Functional neuroimaging • fMRI (functional Magnetic Resonance Imaging). – Blood Oxygen Level Dependent – BOLD (indirect measure). • PET (Positron Emission Tomography). – use of a radiotracer (invasive) • M/EEG (Magneto-/Electro-EncephaloGraphy): - real time - non invasive - direct measure of brain activity. • iEEG/ECoG (intracranial ElectroEncephaloGraphy, ElectroCorticoGraphy) – Intracranial (very invasive) Spatial resolution (mm) Temporal Resolution (s) 20 15 10 5 1 10-3 10-2 10-1 10 0 10 1 10 2 10 3 Invasivity Low Medium High Very high iEEG / ECoG
  9. 9. MEG - signal genesis (I) • Cerebral cortex is the brain’s outer layer of neural tissue in mammals. • Cerebral cortex is dramatically folded (sulci, gyri). Gyri Sulci Cortical gray matter Inner gray- white matter • In cerebral cortex neurons are: - connected vertically, and dendrites are typically oriented outward (apical dendrites of pyramidal neurons); - organized into 6 main layers; - arranged in columnar structures (hierarchic organization of micro-macro columns).
  10. 10. MEG – signal genesis (II) • MEG – Magnetic fields are a consequence of postsynaptic currents generated mainly by pyramidal neurons. They are arranged in the form of a palisade, with their main axes parallel to each other, and perpendicular to the cortex. – Sensor or source space? sensor space source space Inverse problem
  11. 11. Structural neuroimaging Structural methods: • sMRI (structural Magnetic Resonance Imaging): non- invasive technique to qualitatively and quantitatively describe the shape, size, and integrity of gray and white matter structures in the brain – DTI: MRI-based technique to map white matter links in the brain, then provide models of brain structural connectivity Structural links (MN, ML, etc.)
  12. 12. Musical anhedonia
  13. 13. Musical anhedonia (MA) • Musical Anhedonia, or “specific musical anhedonia” indicates the individual's incapacity to enjoy listening to music. • Physical anhedonia scale (PAS): self report scale of general anhedonia. • Barcelona Music Reward Questionnaire (BMRQ): self report scale to assess musical anhedonia. The BMRQ examines five main facets that characterize musical reward experience in individuals: musical seeking, emotion evocation, mood regulation, social reward and sensory-motor.
  14. 14. Functional correlates of MA - Musically-induced pleasure arise from the interaction between auditory cortical networks and mesolimbic reward networks (expecially the nucleus accumbens), as well as other areas involved in evaluation [Salimpoor, 2013]. - Altered interactions between auditory cortices and limbic regions, reducing the reward and pleasure induced by music (reduced liking experience) [Mas- Herrero et al., 2014] - Musical anhedonia is associated with reductions in the interactions within these two networks [Martinez-Molina et al., 2016] Coronal sectionLateral views
  15. 15. Structural correlates of MA - Structural connectivity between auditory and reward systems reflect individual differences in perceiving reward from music, in a large population.[Loui, 2017]. An extreme case of musical anhedonia presents decreased white-matter volume between left superior temporal gyrus and left Nucleus accumbens.
  16. 16. MA: recap Why it is important to study the underpinnings of musical anhedonia: - a way to understand individual variability in the way the general reward system works; - this mechanism might help in understanding some disorders involving the reward system, such as addiction and food disorders, or general anhedonia; - development of therapies for treatment of reward-related disorders, including apathy, depression, and addiction [Zatorre]; - a better understanding of the SC and FC underpinnings of music reward is useful to characterize a correlate of wellbeing in brain structure and function. Hypotheses validation: - Optimal metastability in pleasure systems would be linked to optimal flow of information and connected emotion processing networks, then could represent the key ingredient in enabling wellbeing [Kringelbach & Berridge, 2017]. metastability: variability of the states of phase configurations as a function of time, that is, how the synchronization between the different regions fluctuates across time [Cabral, Kringelbach, et al., 2014]
  17. 17. Dynamic models of large-scale brain activity in the connectomics era
  18. 18. Simulation issues (I) 1 - Reproduction of brain structure: • Links represent axonal pathways or tracts (white matter); • Nodes represent groups of densely interconnected neurons (gray matter). Starting point: predict FC from SC, in resting state.
  19. 19. Fine-tuning (varying non- observable parameters) RSFC comparison Simulation issues (II) Real brainReal brain (Brain) analysis Real brain functional c. Rough structural data Rough functional data Real brain structural c. Simulated brain functional c. (Model) synthesis Real brain structural c. Local dyn. SimulatedSimulated brainbrain sMRI REGIONS DTI SENSORS SOURCES EEG, MEG CORTICAL ATLAS ADD. INFOADD. INFO (vol, n.type…)(vol, n.type…) REGIONS
  20. 20. Simulated brain functional c. (Model) synthesis Real brain structural c. Local dyn. SimulatedSimulated brainbrain In current models RSFC still doesn’t match, but a satisfying correlation degree has been reached. Simulation issues (III) Real brainReal brain Real brain functional c. Rough structural data Rough functional data Real brain structural c. sMRI REGIONS DTI SENSORS SOURCES EEG, MEG CORTICAL ATLAS ADD. INFOADD. INFO (vol, n.type…)(vol, n.type…) REGIONS (Brain) analysis
  21. 21. Future directions (I): • Large-scale whole brain simulation introducing multi-level diversity: - Atlas-based node cardinality (region volumes); - Set of neuron parameters for each node (spiking threshold, spike latency, refractory time, etc.) and edge (length and weight distributions).
  22. 22. Conclusion • Lack of MEG studies of musical anhedonia. MEG can provide an extended view with respect to MRI. • A spiking network model of MA subject as test-bench: – to study functional regimes (metastability) in reward system with respect to the modulaton of the structural parameter identified from recent literature; – to shed light on of function and dysfunction of the reward system [Zatorre] : characterize a correlate of wellbeing in brain structure and function.
  23. 23. Research group(s) Laboratory of Cognitive and Computational Neuroscience, CTB. Universidad Politecnica/Universidad Complutense, Madrid ELTlab group. University of Rome, “Tor Vergata”
  24. 24. Thanks for your attention! VERTEBRALVERTEBRAL ARTERYARTERY FRONTALFRONTAL LOBELOBE SKULLSKULL LATERALLATERAL SULCUSSULCUS
  25. 25. Bibliography • Barardi A. et al., 2014.“Phase-coherence transitions and communication in the gamma range between delay-coupled neuronal populations”. Plos comput. Biol. • Bullmore, E.T. and Bassett, D.S., 2011: Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol. • Cristini, A. et al., 2015."A continuous-time spiking neural network paradigm," Advances in Neural Networks: Computational and Theoretical Issues, New York: Springer. • Gollo, L. et al., 2016. Diversity improves performances in excitable networks”. PeerJ. • Gollo, L., et al. 2014. Mechanisms of Zero-Lag Synchronization in Cortical Motifs. Plos comp biol. • Hagmann, P., 2005. From diffusion mri to brain connectomics. PhD Dissertation (EPFL, Lausanne). • Mazzoni A. et al., 2008.Encoding of naturalistic stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons. Plos one. • Nakagawa, T. et al., 2014. How delay matters in an oscillatory whole-brain spiking neuron network model for MEG alpha rithms at rest. Neuroimage 87. • Salerno M., Susi G., Cristini A., Sanfelice Y., D’Annessa A., "Spiking neural networks as continuous-time dynamical systems: fundamentals, elementary structures and simple applications," ACEEE Int. J. on Information Technology, Vol. 3 - No. 1, March 2013. ACEEE, USA. ISSN 2158-012X (print); ISSN 2158-0138 (online) • Sporns O.et al., 2005. The Human Connectome: A Structural Description of the Human Brain. Plos Computational biology, 1.4. • Susi G., "Bio-inspired temporal-decoding network topologies for the accurate recognition of spike patterns," Transactions on Machine Learning and Artificial Intelligence, vol. 3 - No. 4, 2015. Society for Science and Education, UK. ISSN: 2054-7390 • Susi,G. et al., 2016. "Path multimodality in a feedforward SNN module, using LIF with latency model," Neural Network World, vol. 26, n.4. • Vicente, R. et al., 2008. Dynamical relaying can yield zero time lag neuronal synchrony despite long conduction delays. PNAS 105.44. • Zhu Z. et a.l, 2009. The relationship between magnetic and electrophysiological responses to complex tactile stimuli. BMC Neurosci, 15.10.

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