Cognitive Neurodynamics

We are concerned about action selection in the basal ganglia (BG). We quantitatively analyze functions of direct pathway (DP) and indirect pathway (IP) for action selection in a spiking neural network with 3 competing channels. For such quantitative analysis, in each channel, we obtain the competition degree [C]d, given by the ratio of strength of DP ([S]DP) to strength of IP ([S]IP) (i.e., [C]d = [S]DP /[S]IP). Then, a desired action is selected in the channel with the largest [C]d. Desired action selection is made mainly due to strong focused inhibitory projection to the output nucleus, SNr (substantia nigra pars reticulata) via the DP in the corresponding channel. Unlike the case of DP, there are two types of IPs; intra-channel IP and inter-channel IP, due to widespread diffusive excitation from the STN (subthalamic nucleus). The intra-channel IP serves a function of brake to suppress the desired action selection. In contrast, the inter-channel IP to the SNr in the neighboring channels suppresses competing actions, leading to highlight the desired action selection. In this way, function of the inter-channel IP is opposite to that of the intra-channel IP. However, to the best of our knowledge, no quantitative analysis for such functions of the DP and the two IPs was made. Here, through direct calculations of the DP and the intra- and the inter-channel IP presynaptic currents into the SNr in each channel, we obtain the competition degree of each channel to determine a desired action, and then functions of the DP and the intra- and inter-channel IPs are quantitatively made clear. PACS numbers87.19.lj, 87.19.lu, 87.19.rs

We are concerned about the main encoding granule cells (GCs) in the hippocampal dentate gyrus (DG). Young immature GCs (imGCs) appear through adult neurogenesis. In comparison to the mature GCs (mGCs) (born during development), the imGCs show high activation due to lower firing threshold. On the other hand, they receive low excitatory drive from the entorhinal cortex via perforant paths and from the hilar mossy cells with lower connection probability pc (= 20 x %) (x : synaptic connectivity fraction; 0 [≤] x [≤] 1) than the mGCs with the connection probability pc (= 20 %). Thus, the effect of low excitatory innervation (reducing activation degree) for the imGCs counteracts the effect of their high excitability. We consider a spiking neural network for the DG, incorporating both the mGCs and the imGCs. With decreasing x from 1 to 0, we investigate the effect of young adult-born imGCs on the sparsely synchronized rhythms (SSRs) of the GCs (mGCs, imGC, and whole GCs). For each x, population and individual firing behaviors in the SSRs are characterized in terms of the amplitude measure [Formula] (X = m, im, w for the mGCs, the imGCs, and the whole GCs, respectively) (representing the population synchronization degree) and the random phase-locking degree [Formula] (characterizing the regularity of individual single-cell discharges), respectively. We also note that, for 0 [≤] x [≤] 1, the mGCs and the imGCs exhibit pattern separation (i.e., a process of transforming similar input patterns into less similar output patterns) and pattern integration (making association between patterns), respectively. Quantitative relationship between SSRs and pattern separation and integration is also discussed. PACS numbers87.19.lj, 87.19.lm, 87.19.lv

Dopamine (DA) signals play critical roles in reward-related behavior, decision making, and learning. Yet the mainstream notion that DA signals are encoded by the temporal dynamics of individual DA cell activity is increasingly contested with data supporting that DA signals prefer to be encoded by the spatial organization of DA neuron populations. However, how distributed and parallel excitatory afferent inputs simultaneously induce burst synchronization (BS) is unclear. Our previous work implies that the burst could presumably transition from an integrator to a resonator if the excitatory inputs increase further. Here the responses of networked DA neurons to different intensity of excitatory inputs are investigated. It is found that as NMDA conductance increases, the network will transition from resting state to burst asynchronization (BA) state and then to BS state, showing a bounded BA and BS region in the NMDA conductance space. Furthermore, it is found that as muscarinic receptors modulated Ca2+ dependent cationic (CAN) conductance increases, both boundaries between resting and BA, and between BA and BS gradually decrease. Phase plane analysis on DA reduced model unveils that the burst transition to a resonator underpins the changes in the network dynamics. Slow-fast dissection analysis on DA full model uncovers that the underlying mechanism of the roles and synergy of NMDA and muscarinic receptors in inducing the burst transition emerge from the enlargement of nonlinear positive feedback relationship between more Ca2+ influx provided by additional NMDA current and more ICAN modulated by added muscarinic receptors. Moreover, the lag in DA volume transmission has no effect on excitatory inputs-elicited resonator BS except for requiring more excitatory inputs. These findings shed new lights on understanding the collective behavior of DA cells population regulated by the distributed excitatory inputs, and might provide a new perspective for understanding the abnormal DA release in pathological states. Author summaryThe importance of DA signals is beyond doubt, so their encoding mechanism has very important biological significance and draws widespread attention. Yet the mainstream notion that DA cells individual provide a uniform, broadly distributed signal is increasingly contested with data supporting both homogeneity across dopamine cell activity and diversity in DA signals in target regions. Our article proposes that diverse distributed and parallel excitatory inputs can not only regulate the temporal dynamics of individual DA cell activity, but also simultaneously and synergistically regulate the network dynamics of DA cell populations by changing the local dynamics of DA cells, namely the burst transition from integrators to resonators. According to our perspective, many data that are difficult to interpret by the notion of the DA neuron individual coding can be well explained, such as burst asynchronization coding DA ramping signals, the scale of burst synchronization coding the amplitude of phase DA release, inhibitory DA autoreceptors facilitating resonator burst synchronization by postinhibitory rebound, etc. This study aims to elucidate the working mechanism of the DA system in physiological states such as positive reinforcement, and then to provide a new research perspective and foundation for understanding the abnormal DA release in pathological states.

Primates and rodents are able to continually acquire, adapt, and transfer knowledge and skill, and lead to goal-directed behavior during their lifespan. For the case when context switches slowly, animals learn via slow processes. For the case when context switches rapidly, animals learn via fast processes. We build a biologically realistic model with modules similar to a distributed computing system. Specifically, we are emphasizing the role of thalamocortical learning on a slow time scale between the prefrontal cortex (PFC) and medial dorsal thalamus (MD). Previous work [1] has already shown experimental evidence supporting classification of cell ensembles in the medial dorsal thalamus, where each class encodes a different context. However, the mechanism by which such classification is learned is not clear. In this work, we show that such learning can be self-organizing in the manner of an automaton (a distributed computing system), via a combination of Hebbian learning and homeostatic synaptic scaling. We show that in the simple case of two contexts, the network with hierarchical structure can do context-based decision making and smooth switching between different contexts. Our learning rule creates synaptic competition [2] between the thalamic cells to create winner-take-all activity. Our theory shows that the capacity of such a learning process depends on the total number of task-related hidden variables, and such a capacity is limited by system size N. We also theoretically derived the effective functional connectivity as a function of an order parameter dependent on the thalamo-cortical coupling structure. Significance StatementAnimals need to adapt to dynamically changing environments and make decisions based on changing contexts. Here we propose a combination of neural circuit structure with learning mechanisms to account for such behaviors. Specifically, we built a reservoir computing network improved by a Hebbian learning rule together with a synaptic scaling learning mechanism between the prefrontal cortex and the medial-dorsal (MD) thalamus. This model shows that MD thalamus is crucial in such context-based decision making. I also make use of dynamical mean field theory to predict the effective neural circuit. Furthermore, theoretical analysis provides a prediction that the capacity of such a network increases with the network size and the total number of tasks-related latent variables.

Information experiences complex transformation processes in the brain, involving various errors. A daunting and critical challenge in neuroscience is to understand the origin of these errors and their effects on neural information processing. While previous efforts have made substantial progresses in studying the information errors in bounded, unreliable and noisy transformation cases, it still remains elusive whether the neural system is inherently error-free under an ideal and noise-free condition. This work brings the controversy to an end with a negative answer. We propose a novel neural information confusion theory, indicating the widespread presence of information confusion phenomenon after the end of transmission process, which originates from innate neuron characteristics rather than external noises. Then, we reformulate the definition of zero-error capacity under the context of neuroscience, presenting an optimal upper bound of the zero-error transformation rates determined by the tuning properties of neurons. By applying this theory to neural coding analysis, we unveil the multi-dimensional impacts of information confusion on neural coding. Although it reduces the variability of neural responses and limits mutual information, it controls the stimulus-irrelevant neural activities and improves the interpretability of neural responses based on stimuli. Together, the present study discovers an inherent and ubiquitous precision limitation of neural information transformation, which shapes the coding process by neural ensembles. These discoveries reveal that the neural system is intrinsically error-prone in information processing even in the most ideal cases. Author summaryOne of the most central challenges in neuroscience is to understand the information processing capacity of the neural system. Decades of efforts have identified various errors in nonideal neural information processing cases, indicating that the neural system is not optimal in information processing because of the widespread presences of external noises and limitations. These incredible progresses, however, can not address the problem about whether the neural system is essentially error-free and optimal under ideal information processing conditions, leading to extensive controversies in neuroscience. Our work brings this well-known controversy to an end with a negative answer. We demonstrate that the neural system is intrinsically error-prone in information processing even in the most ideal cases, challenging the conventional ideas about the superior neural information processing capacity. We further indicate that the neural coding process is shaped by this innate limit, revealing how the characteristics of neural information functions and further cognitive functions are determined by the inherent limitation of the neural system.

Metastability in the brain is thought to be a mechanism involved in dynamic organization of cognitive and behavioral functions across multiple spatiotemporal scales. However, it is not clear how such organization is realized in underlying neural oscillations in a high-dimensional state space. It was shown that macroscopic oscillations often form phase-phase coupling (PPC) and phase-amplitude coupling (PAC) which result in synchronization and amplitude modulation, respectively, even without external stimuli. These oscillations can also make spontaneous transitions across synchronous states at rest. Using resting-state electroencephalographic signals and the autism-spectrum quotient scores acquired from healthy humans, we show experimental evidence that the PAC combined with PPC allows amplitude modulation to be transient, and that the metastable dynamics with this transient modulation is associated with autistic-like traits. In individuals with a longer attention span, such dynamics tended to show fewer transitions between states by forming delta-alpha PAC. We identified these states as two-dimensional metastable states that could share consistent patterns across individuals. Our findings suggest that the human brain dynamically organizes inter-individual differences in a hierarchy of macroscopic oscillations with multiple timescales by utilizing metastability. Author SummaryThe human brain organizes cognitive and behavioral functions dynamically. For decades, the dynamic organization of underlying neural oscillations has been a fundamental topic in neuroscience research. Even without external stimuli, macroscopic oscillations often form phase-phase coupling and phase-amplitude coupling (PAC) that result in synchronization and amplitude modulation, respectively, and can make spontaneous transitions across synchronous states at rest. Using resting-state electroencephalography signals acquired from healthy humans, we show evidence that these two neural couplings enable amplitude modulation to be transient, and that this transient modulation can be viewed as the transition among oscillatory states with different PAC strengths. We also demonstrate that such transition dynamics are associated with the ability to maintain attention to detail and to switch attention, as measured by autism-spectrum quotient scores. These individual dynamics were visualized as a trajectory among states with attracting tendencies, and involved consistent brain states across individuals. Our findings have significant implications for unraveling variability in the individual brains showing typical and atypical development.

Inspired by heterogeneity in biological neural networks, we explore a heterogeneous network consisting of receipt, transmission and computation layers. It reconciles the dilemma that the data analysis scheme for empirical records yields non-power laws when applied to microscopic simulation of critical neural dynamics. Detailed analysis shows that the reconciliation is due to synchronization effect of the feedforward connectivity. The network favours avalanches with denser activity in the first half of life, and the result is consistent with the experimental observation. This heterogeneous structure facilitates robust criticality against external stimuli, which implies the inappropriateness of interpreting the subcritcality signature as an indication of subcrtical dynamics. These results propose the network heterogeneity as an essential piece for understanding the brain criticality.

Complex spiral traveling waves observed experimentally occur across the cortex. The underlying mechanisms responsible for generating such mesoscopic activity are not well understood. Understanding how local cortical neuronal populations interact to produce emergent spiral dynamics during cognitive processing remains unknown. Therefore, to bridge this gap, a spatiotemporal cortical field rate model of local cortical circuits, composed of excitatory and three distinct time-scale inhibitory populations, is proposed. This model is extended to a two-dimensional cortical sheet, consisting of both nonlinear local interactions and diffusive global coupling, with distance-dependent axonal delays. Simulation results indicate mixed-mode oscillations occur in the local circuits, which may represent the coexistence of multiple rhythms and show the emergence of complex dynamics, such as rotating spirals with annihilation events. Spiral waves differentially respond to the strength of the grating input stimulus and exhibit working memory-like characteristics. Also hypothesize that local patterns, such as planar, source, sink, or concentric across the cortex, might be an inherent integral part of the spiral state dynamics.

Respiration and emotional stimuli modulate cognitive ability and the reaction time to generate bodily movement. To understand mechanisms for emotion-respiration-cognition coupling, first, we considered a schematic feed-forward neural network, in which neurons was biased by respiratory-relevant sensory input and the activation function of a neuron was modulated by a neuromodulator, such as norepinephrine (NE). Furthermore, we assumed that the neural model received a stimulus input and generated a response action upon the activity of the output neuron exceeding a certain threshold. Time-to-respond (TTR) was equivalently modulated by the intensity of the input bias and the neuromodulator strength for small action execution threshold; however, it was dominantly modulated by only the neuromodulator for high threshold. Second, we implemented a comprehensive model comprising a cardio-respiration relevant neuromechanical-gas system, a respiratory central pattern generator (CPG), NE dynamics to modulate neurocognitive dynamics, and a locus coeruleus (LC) circuit, which was the primary nucleus for controlling NE. The LC neurons received pCO2 or synaptic current from an inspiratory neurons, which resulted in shortened TTR by a stimulus input during inhalation. By contrast, upon receiving pulmonary stretch information, the TTR was shortened by a stimulus input during exhalation. In humans, TTR is shortened when a fear-related stimulus is presented during inhalation, and likewise, TTR is weakly-shortened when surprise-related stimulus is presented during exhalation. Hence, we conclude that emotional stimuli in humans may switch the gating strategies of information and the inflow to LC to change the attention or behavior strategy.

Perceptual alternation in human binocular rivalry is more likely to occur during certain respiratory phases. In this paper, we show that the respiration dependence of perceptual alternations can be reproduced by a randomly connected recurrent neural network coupled with respiration relevant information via a neuromodulator of norepinephrine (NA). We considered two models of NA modulations; NA increases or decreases the nonlinearity of the activation function of neurons, and we found that the shape of the likelihood function of perceptual alternation depends only on respiratory phase, regardless of whether NA increases or decreases neural nonlinearity. Our results suggest that periodic neuromodulation facilitates the switching of competing neural states in specific phases and that this effect is independent of the excitatory or inhibitory effect of NA.

The brain is believed to process information efficiently in a different manner from deep learning-based artificial intelligence (AI). Brain-like next-generation AI is gaining attention owing to its potential to perform human-like, highly adaptive, robust, and power-efficient computation. To realize such AI, one crucial approach is the bottom-up implementation of the neuronal systems, capturing their electrophysiological characteristics in electronic circuits. However, this neuromorphic approach generally focuses on simplified neuronal models that do not refer to many biological findings. Developing closer-to-brain models is a natural direction that serve as a fundamental computing model for next-generation AI. One of the constraints of neuromorphic circuits is the bit resolution of synaptic efficacy memory, as the memory footprint scales with it precision. Although low-resolution synaptic efficacy is essential for minimizing memory circuit footprint and energy consumption, it generally leads to performance degradation in many tasks such as the spatio-temporal spike pattern detection. This study proposed a closer-to-brain learning rule that incorporates heterosynaptic plasticity (HP) induced by glutamate spillover. It is demonstrated that our model mitigates the performance degradation associated with low-bit resolution synaptic efficacy, achieving the pattern detection success rate with 3-bit resolution synaptic efficacy, which is comparable to 64-bit floating-point precision. Furthermore, the findings of the study indicate that HP based model accelerates the convergence of the synaptic effcacy and effectively potentiates the synapses relevant to the pattern detection while suppressing irrelevant ones, thereby promoting a bimodal distribution of synaptic efficacies. These findings may provide a basic framework for constructing an energy-efficient, brain-like next-generation AI that maintains high performance under hardware constraints.

Humans and now computers can derive subjective valuations from sensory events although such transformation process is essentially unknown. In this study, we elucidated unknown neural mechanisms by comparing convolutional neural networks (CNNs) to their corresponding representations in humans. Specifically, we optimized CNNs to predict aesthetic valuations of paintings and examined the relationship between the CNN representations and brain activity via multivoxel pattern analysis. Primary visual cortex and higher association cortex activities were similar to computations in shallow CNN layers and deeper layers, respectively. The vision-to-value transformation is hence proved to be a hierarchical process which is consistent with the principal gradient that connects unimodal to transmodal brain regions (i.e. default mode network). The activity of the frontal and parietal cortices was approximated by goal-driven CNN. Consequently, representations of the hidden layers of CNNs can be understood and visualized by their correspondence with brain activity-facilitating parallels between artificial intelligence and neuroscience.

The brain criticality hypothesis has been a central research topic in theoretical neuroscience for two decades. This hypothesis suggests that the brain operates near the critical point at the boundary between order and disorder, where it acquires its information-processing capabilities. The mechanism that maintains this critical state has been proposed as a feedback system known as self-organized criticality (SOC); brain parameters, such as synaptic plasticity, are regulated internally without external adjustment. Therefore, clarifying how SOC occurs can help us to understand the mechanisms that maintain brain function and cause brain disorders. From the standpoint of neural network structures, the topology of neural circuits also plays a crucial role in information processing, with healthy neural networks exhibiting small-world, scale-free, and modular characteristics. However, how these network structures affect SOC remains poorly understood. In this study, we numerically investigated the possibility that the structure of neural networks contributes to the brains critical state and dysfunction using a mathematical model. Our results reveal that the time scales at which synaptic plasticity operates to achieve a critical state differ depending on the network structure. Additionally, we observed Dragon king phenomena associated with abnormal neural activity, depending on the network structure and synaptic plasticity time scales. Notably, Dragon king was observed over a wide range of synaptic plasticity time scales in scale-free networks with high-degree hub nodes. This study emphasizes the importance of neural network topology in neuroscience from the perspective of SOC.

Decoding brain states of the underlying cognitive processes via learning discriminative feature representations has recently gained a lot of interest in brain imaging studies. Particularly, there has been an impetus to encode the dynamics of brain functioning by analyzing temporal information available in the fMRI data. Long-short term memory (LSTM), a class of machine learning model possessing a "memory" component, is increasingly being observed to perform well in various applications with dynamic temporal behavior, including brain state decoding. Because of the dynamics and inherent latency in fMRI BOLD responses, future temporal context is crucial. However, it is neither encoded nor captured by the conventional LSTM model. This paper performs robust brain state decoding via information encapsulation from both the past and future instances of fMRI data via bi-directional LSTM. This allows for explicitly modeling the dynamics of BOLD response without any delay adjustment. The two hidden activations of forward and reverse directions in bi-LSTM are collated to build the "memory" of the model and are used to robustly predict the brain states at every time instance. Working memory data from the Human Connectome Project (HCP) is utilized for validation and was observed to perform 18% better than its unidirectional counterpart in terms of accuracy in predicting the brain states.

The hippocampus is key to memory encoding, consolidation, and retrieval. Previous work shows that neurons in the hippocampus fire in sequence to encode spatial information. The same group of cells will replay in memory consolidation, coupled with ripples, spindle, and slow waves. As for episodic memory, engram cells have been proposed to explain the encoding and transformation of episodic memory. Another universe theory about memory is the cognitive map theory. Here I use cognitive map theory as the bridge to overcome the gap between spatial and episodic memory. I believe spatial memory is a highly compressed case of episodic memory. In addition, I propose the hypothesis that engram can trigger sharp-wave ripples. I use a spike neural network-based computational model to verify this hypothesis. In conclusion, I believe engram cells and sharp-wave ripples are two different phenomena under a unified memory system.

Continuous persist activity of the competitive network is related to many functions, such as working memory, oculomotor integrator and decision making. Many competition models with mutual inhibition structures achieve activity maintenance via positive feedback, which requires meticulous fine tuning of the network parameters strictly. Negative derivative feedback, according to recent research, might represent a novel mechanism for sustaining neural activity that is more resistant to multiple neural perturbations than positive feedback. Many classic models with only mutual inhibition structure are not capable of providing negative derivative feedback because double-inhibition acts as a positive feedback loop, and lack of negative feedback loop that is indispensable for negative derivative feedback. Here in the proposal, we aim to derive a new competition network with negative derivative feedback. The network is made up of two symmetric pairs of EI populations that the four population are completely connected. We conclude that the negative derivative occurs in two circumstances, in which one the activity of the two sides is synchronous but push-pull-like in the other, as well as the switch of two conditions in mathematical analysis and numerical simulation.

Cognitive reasoning, also known as mental programming, is fundamental to our intelligence. A central function in mental programming is working memory (WM), involving both temporarily maintaining the information and manipulating it based on rules. While the neural mechanisms of WM maintenance have been extensively studied, those governing WM manipulation remain largely unknown. To bridge this gap, the present study focuses on an elementary operation in WM manipulation: re-ordering items in WM sequences. We propose a functional, biologically plausible neural circuit model that consists of two interconnected modules: a memory module composed of continuous attractor-based memory slots that store item features, and a control module sending gain-modulating commands to orchestrate specific operations in the memory module. The model successfully implements two-item swapping in a WM sequence, generating neuronal responses similar to recent primate experiments of WM sequence manipulation. By incorporating principles from the algebraic permutation group, we generalize the circuit model to accommodate more complex sequence manipulations. This math foundation reveals how arbitrary permutations can be decomposed into sequences of elementary swapping operations, which can be generated by a hierarchical tree-structured control circuit module. And the mutual inhibition within the control tree ensures that only one program is being executed at the same time. Our study establishes overarching connections among mental programming neural circuit models, neuro-science experiments, and abstract algebraic structure. These insights enhance our understanding of the neural underpinnings of cognitive reasoning and inspire the design of artificial cognitive systems.

The primary objective of system neuroscience is to understand the functional mapping and its causation in the dynamics of the brain network. Some experimental and methodological studies suggest that functional modularity and its hierarchical information processing in the brain network are crucial to understanding the functional role of task-specific or state-specific information flow in the brain. However, because most of the established techniques for detecting effective network structures in the neuroscience research field are strongly based on the "Granger causality" perspective, existing causal discovery methods specified for brain network analysis cannot identify the causal hierarchy in the modular network in the brain due to spurious correlation issues and indistinguishability of causal direction under the Gaussianity of observational noise in a linear system. To address the issues, we developed a causal discovery method for synchronous neural dynamics, called the Jacobian-informed linear non-Gaussian acyclic model, "j-VAR-LiNGAM", by incorporating the information of the Jacobian matrix determined from a phase-coupled oscillator model estimated from observed neural data into the VAR-LiNGAM algorithms. The method was validated by showing that it could extract causal ordering in both synthetic data and empirical neural observed data. Moreover, by analyzing the observed neural oscillatory signals obtained from mice and humans, we confirmed that our method identified causally hierarchical structures in the brain, which aligned with the neurophysiological interpretations. These findings suggested that our proposed method can reveal the neural basis of hierarchical information processing in the brain network.

Humans modulate the behavior flexibly after timely receiving and processing information from the environment. To better understand and measure human behavior in the driving process, we integrate humans and the environment as a system. The eye-movement methodologies are used to provide a bridge between humans and environment. Thus, we conduct a goal-directed task in virtual driving to investigate the law of eye-movement that could characterize the humans (internal) and environmental (external) state measured by fixation distribution and optical flows distribution. The analysis of eye-movement data combined with the information-theoretic tool, transfer entropy, active information storage, quantify the humans cognitive effort and receiving information, and in fact, there is a balance (optimal) range between two, because of the mutual synergy and inhibition, whose quantified value is named balance of information processing. Subsequently, we update a system-level model, finding that those information measurements, transfer entropy, active information storage, and balance of information processing, all are included. This information set is information flow, which is quantified by the square root of Jensen-Shannon divergence (SRJSD), named information flow gain. Whats more, results also demonstrate that the influence of system-level information flow correlated with behavioral performance stronger than the separate measurements. In conclusion, we research humans eye-movement based on information theory to analyze behavioral performance. Besides driving, these measurements may be a predictor for other behaviors such as walking, running, etc. Still, the limitation is that the information flow may be a proxy of determinants of behavior.

The emotion research with artificial stimuli does not represent the dynamic processing of emotions in real-life situations. The lack of data on emotion with the ecologically valid naturalistic paradigm hinders the knowledge of emotion mechanism in a real-world interaction. To this aim, we collected the emotional multimedia clips, validated them with the university students, recorded the neuro-physiological activities and self-assessment ratings for these stimuli. Participants localized their emotional feelings (in time) and were free to choose the best emotion for describing their feelings with minimum distractions and cognitive load. The obtained electrophysiological and self-assessment responses were analyzed with functional connectivity, machine learning and source localization techniques. We observed that the connectivity patterns in the theta and beta band could differentiate emotions better. Using machine learning, we observed that the classification of affective self-assessment features, namely dominance, familiarity, and self-relevance, involves midline brain regions responsible for mentalization and event construction activity compared to valence and arousal, which were mainly associated with lateral brain regions. This finding advocates the need for more than two dimensions for emotion representation. In addition, the channels with high predictability were source localized to the brain regions in default-mode, sensorimotor and salience networks. Hence, in this naturalistic study, we find that the domain-general systems contribute to emotion construction.