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1.
PLoS Comput Biol ; 16(9): e1008192, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32946433

RESUMO

Balanced excitation and inhibition is widely observed in cortex. How does this balance shape neural computations and stimulus representations? This question is often studied using computational models of neuronal networks in a dynamically balanced state. But balanced network models predict a linear relationship between stimuli and population responses. So how do cortical circuits implement nonlinear representations and computations? We show that every balanced network architecture admits stimuli that break the balanced state and these breaks in balance push the network into a "semi-balanced state" characterized by excess inhibition to some neurons, but an absence of excess excitation. The semi-balanced state produces nonlinear stimulus representations and nonlinear computations, is unavoidable in networks driven by multiple stimuli, is consistent with cortical recordings, and has a direct mathematical relationship to artificial neural networks.


Assuntos
Modelos Neurológicos , Rede Nervosa/fisiologia , Neurônios/fisiologia , Dinâmica não Linear , Animais , Córtex Cerebral/fisiologia , Biologia Computacional , Redes Neurais de Computação , Sinapses/fisiologia
2.
Nat Commun ; 11(1): 4590, 2020 09 14.
Artigo em Inglês | MEDLINE | ID: mdl-32929067

RESUMO

An adaptive memory system rarely learns information tabula rasa, but rather builds on prior knowledge to facilitate learning. How prior knowledge influences the neural representation of novel associations remains unknown. Here, participants associated pairs of faces in two conditions: a famous, highly familiar face with a novel face or two novel faces while undergoing fMRI. We examine multivoxel activity patterns corresponding to individual faces before and after learning. The activity patterns representing members of famous-novel pairs becomes separated in the hippocampus, that is, more distinct from one another through learning, in striking contrast to paired novel faces that become similar. In the left inferior frontal gyrus, however, prior knowledge leads to integration, and in a specific direction: the representation of the novel face becomes similar to that of the famous face after learning, suggesting assimilation of new into old memories. We propose that hippocampal separation might resolve interference between existing and newly learned information, allowing cortical assimilation. Thus, associative learning with versus without prior knowledge relies on radically different computations.


Assuntos
Giro do Cíngulo/fisiologia , Hipocampo/fisiologia , Córtex Pré-Frontal/fisiologia , Adulto , Assimetria Facial/fisiopatologia , Feminino , Humanos , Masculino , Memória , Rede Nervosa/fisiologia , Adulto Jovem
3.
Nat Commun ; 11(1): 4560, 2020 09 11.
Artigo em Inglês | MEDLINE | ID: mdl-32917899

RESUMO

The rhesus macaque is an important model species in several branches of science, including neuroscience, psychology, ethology, and medicine. The utility of the macaque model would be greatly enhanced by the ability to precisely measure behavior in freely moving conditions. Existing approaches do not provide sufficient tracking. Here, we describe OpenMonkeyStudio, a deep learning-based markerless motion capture system for estimating 3D pose in freely moving macaques in large unconstrained environments. Our system makes use of 62 machine vision cameras that encircle an open 2.45 m × 2.45 m × 2.75 m enclosure. The resulting multiview image streams allow for data augmentation via 3D-reconstruction of annotated images to train a robust view-invariant deep neural network. This view invariance represents an important advance over previous markerless 2D tracking approaches, and allows fully automatic pose inference on unconstrained natural motion. We show that OpenMonkeyStudio can be used to accurately recognize actions and track social interactions.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Macaca mulatta/fisiologia , Movimento (Física) , Algoritmos , Animais , Fenômenos Biomecânicos , Aprendizado Profundo , Masculino , Modelos Animais , Movimento , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiologia , Redes Neurais de Computação
4.
Phys Rev Lett ; 125(8): 088103, 2020 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-32909804

RESUMO

The ability of humans and animals to quickly adapt to novel tasks is difficult to reconcile with the standard paradigm of learning by slow synaptic weight modification. Here, we show that fixed-weight neural networks can learn to generate required dynamics by imitation. After appropriate weight pretraining, the networks quickly and dynamically adapt to learn new tasks and thereafter continue to achieve them without further teacher feedback. We explain this ability and illustrate it with a variety of target dynamics, ranging from oscillatory trajectories to driven and chaotic dynamical systems.


Assuntos
Aprendizagem/fisiologia , Modelos Neurológicos , Neurônios/fisiologia , Animais , Comunicação Celular/fisiologia , Humanos , Rede Nervosa/citologia , Rede Nervosa/fisiologia , Neurônios/citologia
5.
Epidemiol Psychiatr Sci ; 29: e166, 2020 Sep 08.
Artigo em Inglês | MEDLINE | ID: mdl-32895076

RESUMO

Since its discovery in 1997, the default mode network (DMN) and its components have been extensively studied in both healthy individuals and psychiatric patients. Several studies have investigated possible DMN alterations in specific mental conditions such as bipolar disorder (BD). In this review, we describe current evidence from resting-state functional magnetic resonance imaging studies with the aim to understand possible changes in the functioning of the DMN in BD. Overall, several types of analyses including seed-based and independent component have been conducted on heterogeneous groups of patients highlighting different results. Despite the differences, findings seem to indicate that BD is associated with alterations in both frontal and posterior DMN structures, mainly in the prefrontal, posterior cingulate and inferior parietal cortices. We conclude this review by suggesting possible future research directions.


Assuntos
Transtorno Bipolar/diagnóstico por imagem , Mapeamento Encefálico/métodos , Encéfalo/diagnóstico por imagem , Imagem por Ressonância Magnética/métodos , Rede Nervosa/fisiologia , Vias Neurais/diagnóstico por imagem , Transtorno Bipolar/fisiopatologia , Humanos
6.
PLoS Comput Biol ; 16(9): e1008165, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32941457

RESUMO

Combining information from multiple sources is a fundamental operation performed by networks of neurons in the brain, whose general principles are still largely unknown. Experimental evidence suggests that combination of inputs in cortex relies on nonlinear summation. Such nonlinearities are thought to be fundamental to perform complex computations. However, these non-linearities are inconsistent with the balanced-state model, one of the most popular models of cortical dynamics, which predicts networks have a linear response. This linearity is obtained in the limit of very large recurrent coupling strength. We investigate the stationary response of networks of spiking neurons as a function of coupling strength. We show that, while a linear transfer function emerges at strong coupling, nonlinearities are prominent at finite coupling, both at response onset and close to saturation. We derive a general framework to classify nonlinear responses in these networks and discuss which of them can be captured by rate models. This framework could help to understand the diversity of non-linearities observed in cortical networks.


Assuntos
Potenciais de Ação/fisiologia , Modelos Neurológicos , Rede Nervosa/citologia , Rede Nervosa/fisiologia , Neurônios/fisiologia , Animais , Encéfalo/citologia , Encéfalo/fisiologia , Biologia Computacional , Haplorrinos , Camundongos , Dinâmica não Linear
8.
PLoS Biol ; 18(8): e3000800, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32776945

RESUMO

Studies of neural processes underlying delay of gratification usually focus on prefrontal networks related to curbing affective impulses. Here, we provide evidence for an alternative mechanism that facilitates delaying gratification by mental orientation towards the future. Combining continuous theta-burst stimulation (cTBS) with functional neuroimaging, we tested how the right temporoparietal junction (rTPJ) facilitates processing of future events and thereby promotes delay of gratification. Participants performed an intertemporal decision task and a mental time-travel task in the MRI scanner before and after receiving cTBS over the rTPJ or the vertex (control site). rTPJ cTBS led to both stronger temporal discounting for longer delays and reduced processing of future relative to past events in the mental time-travel task. This finding suggests that the rTPJ contributes to the ability to delay gratification by facilitating mental representation of outcomes in the future. On the neural level, rTPJ cTBS led to a reduction in the extent to which connectivity of rTPJ with striatum reflected the value of delayed rewards, indicating a role of rTPJ-striatum connectivity in constructing neural representations of future rewards. Together, our findings provide evidence that the rTPJ is an integral part of a brain network that promotes delay of gratification by facilitating mental orientation to future rewards.


Assuntos
Corpo Estriado/fisiologia , Tomada de Decisões/fisiologia , Desvalorização pelo Atraso/fisiologia , Rede Nervosa/fisiologia , Lobo Parietal/fisiologia , Lobo Temporal/fisiologia , Adulto , Mapeamento Encefálico , Corpo Estriado/anatomia & histologia , Corpo Estriado/diagnóstico por imagem , Feminino , Neuroimagem Funcional , Humanos , Comportamento Impulsivo/fisiologia , Masculino , Rede Nervosa/anatomia & histologia , Rede Nervosa/diagnóstico por imagem , Lobo Parietal/anatomia & histologia , Lobo Parietal/diagnóstico por imagem , Recompensa , Lobo Temporal/anatomia & histologia , Lobo Temporal/diagnóstico por imagem , Estimulação Magnética Transcraniana
9.
Nat Commun ; 11(1): 3933, 2020 08 07.
Artigo em Inglês | MEDLINE | ID: mdl-32770038

RESUMO

Humans and animals can effortlessly coordinate their movements with external stimuli. This capacity indicates that sensory inputs can rapidly and flexibly reconfigure the ongoing dynamics in the neural circuits that control movements. Here, we develop a circuit-level model that coordinates movement times with expected and unexpected temporal events. The model consists of two interacting modules, a motor planning module that controls movement times and a sensory anticipation module that anticipates external events. Both modules harbor a reservoir of latent dynamics, and their interaction forms a control system whose output is adjusted adaptively to minimize timing errors. We show that the model's output matches human behavior in a range of tasks including time interval production, periodic production, synchronization/continuation, and Bayesian time interval reproduction. These results demonstrate how recurrent interactions in a simple and modular neural circuit could create the dynamics needed to control timing behavior.


Assuntos
Retroalimentação Sensorial/fisiologia , Modelos Neurológicos , Movimento/fisiologia , Rede Nervosa/fisiologia , Percepção do Tempo/fisiologia , Teorema de Bayes , Simulação por Computador , Humanos
10.
Nat Commun ; 11(1): 4014, 2020 08 11.
Artigo em Inglês | MEDLINE | ID: mdl-32782303

RESUMO

Perception reflects not only sensory inputs, but also the endogenous state when these inputs enter the brain. Prior studies show that endogenous neural states influence stimulus processing through non-specific, global mechanisms, such as spontaneous fluctuations of arousal. It is unclear if endogenous activity influences circuit and stimulus-specific processing and behavior as well. Here we use intracranial recordings from 30 pre-surgical epilepsy patients to show that patterns of endogenous activity are related to the strength of trial-by-trial neural tuning in different visual category-selective neural circuits. The same aspects of the endogenous activity that relate to tuning in a particular neural circuit also correlate to behavioral reaction times only for stimuli from the category that circuit is selective for. These results suggest that endogenous activity can modulate neural tuning and influence behavior in a circuit- and stimulus-specific manner, reflecting a potential mechanism by which endogenous neural states facilitate and bias perception.


Assuntos
Rede Nervosa/fisiologia , Córtex Visual/fisiologia , Percepção Visual/fisiologia , Adulto , Eletrocorticografia , Epilepsia/fisiopatologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Modelos Neurológicos , Reconhecimento Visual de Modelos/fisiologia , Estimulação Luminosa , Tempo de Reação/fisiologia
11.
PLoS Comput Biol ; 16(8): e1007983, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32745096

RESUMO

Many large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.


Assuntos
Encéfalo/fisiologia , Rede Nervosa/fisiologia , Potenciais de Ação/fisiologia , Adulto , Animais , Encéfalo/diagnóstico por imagem , Feminino , Humanos , Macaca mulatta , Imagem por Ressonância Magnética , Masculino , Rede Nervosa/diagnóstico por imagem , Neurônios/fisiologia , Análise e Desempenho de Tarefas , Adulto Jovem
12.
PLoS Comput Biol ; 16(8): e1008080, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32745134

RESUMO

Neural computation is determined by neurons' dynamics and circuit connectivity. Uncertain and dynamic environments may require neural hardware to adapt to different computational tasks, each requiring different connectivity configurations. At the same time, connectivity is subject to a variety of constraints, placing limits on the possible computations a given neural circuit can perform. Here we examine the hypothesis that the organization of neural circuitry favors computational flexibility: that it makes many computational solutions available, given physiological constraints. From this hypothesis, we develop models of connectivity degree distributions based on constraints on a neuron's total synaptic weight. To test these models, we examine reconstructions of the mushroom bodies from the first instar larva and adult Drosophila melanogaster. We perform a Bayesian model comparison for two constraint models and a random wiring null model. Overall, we find that flexibility under a homeostatically fixed total synaptic weight describes Kenyon cell connectivity better than other models, suggesting a principle shaping the apparently random structure of Kenyon cell wiring. Furthermore, we find evidence that larval Kenyon cells are more flexible earlier in development, suggesting a mechanism whereby neural circuits begin as flexible systems that develop into specialized computational circuits.


Assuntos
Modelos Neurológicos , Rede Nervosa , Sinapses/fisiologia , Animais , Drosophila melanogaster , Larva/citologia , Larva/fisiologia , Corpos Pedunculados/citologia , Corpos Pedunculados/fisiologia , Rede Nervosa/citologia , Rede Nervosa/fisiologia , Neurônios/citologia , Neurônios/fisiologia
13.
PLoS Comput Biol ; 16(8): e1007659, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32764745

RESUMO

The brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable computations might be achieved despite biological complexity. Crucially, our analysis is not limited to analyzing the stability of fixed geometric objects in state space (e.g points, lines, planes), but rather the stability of state trajectories which may be complex and time-varying.


Assuntos
Modelos Neurológicos , Rede Nervosa/fisiologia , Plasticidade Neuronal/fisiologia , Algoritmos , Animais , Encéfalo/fisiologia , Biologia Computacional , Simulação por Computador , Humanos
14.
PLoS Comput Biol ; 16(8): e1008033, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32776924

RESUMO

Transient oscillations in network activity upon sensory stimulation have been reported in different sensory areas of the brain. These evoked oscillations are the generic response of networks of excitatory and inhibitory neurons (EI-networks) to a transient external input. Recently, it has been shown that this resonance property of EI-networks can be exploited for communication in modular neuronal networks by enabling the transmission of sequences of synchronous spike volleys ('pulse packets'), despite the sparse and weak connectivity between the modules. The condition for successful transmission is that the pulse packet (PP) intervals match the period of the modules' resonance frequency. Hence, the mechanism was termed communication through resonance (CTR). This mechanism has three severe constraints, though. First, it needs periodic trains of PPs, whereas single PPs fail to propagate. Second, the inter-PP interval needs to match the network resonance. Third, transmission is very slow, because in each module, the network resonance needs to build up over multiple oscillation cycles. Here, we show that, by adding appropriate feedback connections to the network, the CTR mechanism can be improved and the aforementioned constraints relaxed. Specifically, we show that adding feedback connections between two upstream modules, called the resonance pair, in an otherwise feedforward modular network can support successful propagation of a single PP throughout the entire network. The key condition for successful transmission is that the sum of the forward and backward delays in the resonance pair matches the resonance frequency of the network modules. The transmission is much faster, by more than a factor of two, than in the original CTR mechanism. Moreover, it distinctly lowers the threshold for successful communication by synchronous spiking in modular networks of weakly coupled networks. Thus, our results suggest a new functional role of bidirectional connectivity for the communication in cortical area networks.


Assuntos
Potenciais de Ação/fisiologia , Retroalimentação Fisiológica/fisiologia , Modelos Neurológicos , Neurônios/fisiologia , Animais , Encéfalo/fisiologia , Biologia Computacional , Rede Nervosa/fisiologia
15.
PLoS Comput Biol ; 16(8): e1007566, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32804971

RESUMO

Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precision and robustness to noise. To validate the proposed algorithm, we performed an extensive comparison against the classical Kalman filter, in both realistic surrogate networks and real electroencephalography (EEG) data. In both simulations and real data, we show that the STOK filter estimates time-frequency patterns of directed connectivity with significantly superior performance. The advantages of the STOK filter were even clearer in real EEG data, where the algorithm recovered latent structures of dynamic connectivity from epicranial EEG recordings in rats and human visual evoked potentials, in excellent agreement with known physiology. These results establish the STOK filter as a powerful tool for modeling dynamic network structures in biological systems, with the potential to yield new insights into the rapid evolution of network states from which brain functions emerge.


Assuntos
Algoritmos , Encéfalo/fisiologia , Modelos Neurológicos , Rede Nervosa/fisiologia , Adulto , Animais , Mapeamento Encefálico , Biologia Computacional , Eletroencefalografia , Humanos , Masculino , Ratos , Processamento de Sinais Assistido por Computador , Adulto Jovem
16.
Nat Commun ; 11(1): 3689, 2020 07 23.
Artigo em Inglês | MEDLINE | ID: mdl-32704144

RESUMO

While neurons principally mediate brain function, astrocytes are emerging as cells with important neuromodulatory actions in brain physiology. In addition to homeostatic roles, astrocytes respond to neurotransmitters with calcium transients stimulating the release of gliotransmitters that regulate synaptic and neuronal functions. We investigated astrocyte-neuronal network interactions in vivo by combining two-photon microscopy to monitor astrocyte calcium and electrocorticogram to record neuronal network activity in the somatosensory cortex during sensory stimulation. We found astrocytes respond to sensory stimuli in a stimulus-dependent manner. Sensory stimuli elicit a surge of neuronal network activity in the gamma range (30-50 Hz) followed by a delayed astrocyte activity that dampens the steady-state gamma activity. This sensory-evoked gamma activity increase is enhanced in transgenic mice with impaired astrocyte calcium signaling and is decreased by pharmacogenetic stimulation of astrocytes. Therefore, cortical astrocytes respond to sensory inputs and regulate sensory-evoked neuronal network activity maximizing its dynamic range.


Assuntos
Astrócitos/metabolismo , Rede Nervosa/fisiologia , Células Receptoras Sensoriais/fisiologia , Animais , Cálcio/metabolismo , Estimulação Elétrica , Feminino , Ritmo Gama/fisiologia , Masculino , Camundongos , Córtex Somatossensorial/citologia
17.
Phys Rev Lett ; 125(2): 028101, 2020 Jul 10.
Artigo em Inglês | MEDLINE | ID: mdl-32701351

RESUMO

We propose an analytically tractable neural connectivity model with power-law distributed synaptic strengths. When threshold neurons with biologically plausible number of incoming connections are considered, our model features a continuous transition to chaos and can reproduce biologically relevant low activity levels and scale-free avalanches, i.e., bursts of activity with power-law distributions of sizes and lifetimes. In contrast, the Gaussian counterpart exhibits a discontinuous transition to chaos and thus cannot be poised near the edge of chaos. We validate our predictions in simulations of networks of binary as well as leaky integrate-and-fire neurons. Our results suggest that heavy-tailed synaptic distribution may form a weakly informative sparse-connectivity prior that can be useful in biological and artificial adaptive systems.


Assuntos
Modelos Neurológicos , Rede Nervosa/fisiologia , Sinapses/fisiologia , Animais , Encéfalo/anatomia & histologia , Encéfalo/fisiologia , Simulação por Computador , Rede Nervosa/anatomia & histologia , Vias Neurais/anatomia & histologia , Vias Neurais/fisiologia , Neurônios/citologia , Neurônios/fisiologia , Dinâmica não Linear
18.
Nat Commun ; 11(1): 3625, 2020 07 17.
Artigo em Inglês | MEDLINE | ID: mdl-32681001

RESUMO

Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. Yet in spite of extensive research, how they can learn through synaptic plasticity to carry out complex network computations remains unclear. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A mathematical result tells us how these pieces need to be combined to enable biologically plausible online network learning through gradient descent, in particular deep reinforcement learning. This learning method-called e-prop-approaches the performance of backpropagation through time (BPTT), the best-known method for training recurrent neural networks in machine learning. In addition, it suggests a method for powerful on-chip learning in energy-efficient spike-based hardware for artificial intelligence.


Assuntos
Encéfalo/fisiologia , Modelos Neurológicos , Rede Nervosa/fisiologia , Neurônios/fisiologia , Recompensa , Potenciais de Ação/fisiologia , Animais , Encéfalo/citologia , Aprendizado Profundo , Humanos , Camundongos , Plasticidade Neuronal/fisiologia
19.
Proc Natl Acad Sci U S A ; 117(29): 17308-17319, 2020 07 21.
Artigo em Inglês | MEDLINE | ID: mdl-32632019

RESUMO

The human brain is organized into large-scale networks identifiable using resting-state functional connectivity (RSFC). These functional networks correspond with broad cognitive domains; for example, the Default-mode network (DMN) is engaged during internally oriented cognition. However, functional networks may contain hierarchical substructures corresponding with more specific cognitive functions. Here, we used individual-specific precision RSFC to test whether network substructures could be identified in 10 healthy human brains. Across all subjects and networks, individualized network subdivisions were more valid-more internally homogeneous and better matching spatial patterns of task activation-than canonical networks. These measures of validity were maximized at a hierarchical scale that contained ∼83 subnetworks across the brain. At this scale, nine DMN subnetworks exhibited topographical similarity across subjects, suggesting that this approach identifies homologous neurobiological circuits across individuals. Some DMN subnetworks matched known features of brain organization corresponding with cognitive functions. Other subnetworks represented separate streams by which DMN couples with other canonical large-scale networks, including language and control networks. Together, this work provides a detailed organizational framework for studying the DMN in individual humans.


Assuntos
Encéfalo/fisiologia , Idioma , Rede Nervosa/fisiologia , Adulto , Mapeamento Encefálico , Cognição , Feminino , Humanos , Imagem por Ressonância Magnética , Masculino , Adulto Jovem
20.
PLoS One ; 15(7): e0235770, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32639987

RESUMO

In real music, the original melody may appear intact, with little elaboration only, or significantly modified. Since a melody is most easily perceived in music, hearing significantly modified melody may change a brain connectivity. Mozart KV 265 is comprised of a theme with an original melody of "Twinkle Twinkle Little Star" and its significant variations. We studied whether effective connectivity changes with significantly modified melody, between bilateral inferior frontal gyri (IFGs) and Heschl's gyri (HGs) using magnetoencephalography (MEG). Among the 12 connectivities, the connectivity from the left IFG to the right HG was consistently increased with significantly modified melody compared to the original melody in 2 separate sets of the same rhythmic pattern with different melody (p = 0.005 and 0.034, Bonferroni corrected). Our findings show that the modification of an original melody in a real music changes the brain connectivity.


Assuntos
Córtex Auditivo/fisiologia , Percepção Auditiva , Conectoma , Música , Córtex Pré-Frontal/fisiologia , Adulto , Encéfalo/fisiologia , Feminino , Humanos , Magnetoencefalografia , Masculino , Rede Nervosa/fisiologia , Adulto Jovem
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