Article
Clinical Neurology
Bunmi O. Olatunji
Summary: The study found that emotionally arousing images as distractors significantly reduced accuracy for control participants at lag 2, but not for those with OCD. OCD symptoms were significantly correlated with difficulty disengaging from emotionally arousing images. These findings suggest inefficient engagement and disengagement of attentional networks in OCD.
JOURNAL OF AFFECTIVE DISORDERS
(2021)
Article
Psychology, Multidisciplinary
Yura Loscalzo, Marco Giannini
Summary: Studyholism is a new potential clinical condition characterized by high levels of obsessive-compulsive symptoms and high study engagement. A Studyholism Inventory (SI-10) has been developed to evaluate Studyholism and Study Engagement. In this study, a 15-item version of SI-10, called SI-15, was proposed and validated through factor analysis on 1047 Italian university students. SI-15 showed good fit, internal consistency, and convergent and divergent validity, indicating its usefulness in further research on Studyholism.
CURRENT PSYCHOLOGY
(2022)
Article
Pediatrics
Jesus Ferrandez-Mas, Beatriz Moreno-Amador, Juan C. Marzo, Raquel Falco, Jonatan Molina-Torres, Matti Cervin, Jose A. Piqueras
Summary: This study investigates the relationship between cognitive strategies for emotion regulation and specific dimensions of obsessive-compulsive symptoms in adolescents. The results show that emotion regulation strategies and gender account for a significant amount of the variation in overall obsessive-compulsive symptoms. The study also reveals that certain cognitive strategies are uniquely linked to specific symptom dimensions.
Article
Behavioral Sciences
Jais Adam-Troian, Jocelyn Belanger
Summary: This study presents a model that links obsessive passion and obsessive-compulsive disorder (OCD) to radicalization. The research finds a direct and indirect effect of OCD symptom severity on radical intentions, suggesting that OCD plays a significant role in the formation of violent ideological intentions. These findings open up new possibilities for the prevention and treatment of violent extremism.
AGGRESSIVE BEHAVIOR
(2023)
Article
Computer Science, Artificial Intelligence
Fang Chen, Ziwei Shi, Zhongliang Yang, Yongfeng Huang
Summary: This study proposes a recurrent synchronization network to explicitly model the interaction among different tasks in emotion-cause pair extraction, effectively collaborating in the analysis of emotions, causes, and emotion-cause pairs. Experimental results demonstrate the accuracy and superiority of the proposed model.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Thanh-Son Nguyen, Zhengxuan Wu, Desmond C. Ong
Summary: The paper investigates the capability of attention mechanisms in deep neural network models to focus on semantically meaningful words. Through experiments, it is found that words receiving higher attention weights tend to have greater emotional semantic meaning in the model. Experimental results using pre-trained word embeddings suggest that models achieving human-level performance may learn to place greater attention on words with semantic meaning to the task at hand.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
Rui Song, Fausto Giunchiglia, Lida Shi, Qiang Shen, Hao Xu
Summary: Emotion Recognition in Conversations (ERC) is used to capture emotional changes in speakers during multiple rounds of conversation and has various applications. Graph Neural Networks have been used in ERC tasks due to their ability to capture complex non-Euclidean spatial features. However, there is still a need to explore how to easily and effectively model conversations to improve the effectiveness of ERC in complex interaction modes.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Hanjie Liu, Jinren Zhang, Qingshan Liu, Jinde Cao
Summary: This paper presents a novel method to capture the distinct minimum spanning tree (MST) topology underlying different emotions in emotion recognition. Extensive experiments on the DEAP dataset demonstrate the superior performance of the model in emotion classification, highlighting the importance of multi-frequency interaction in emotion processing.
Article
Computer Science, Information Systems
Yongqiang Zheng, Jie Ding, Feng Liu, Dongqing Wang
Summary: In this study, an adaptive neural decision tree is investigated for the recognition of electroencephalogram (EEG) emotion signal. The method intelligently selects network structure and overcomes the lack of position information in the input signal by converting it into a two-dimensional matrix signal with added channel position information. The use of adaptive moment estimation algorithm and exploration-exploitation trade-off reinforcement learning method enables the algorithm to automatically search for optimized parameters and explore tree architectures for global optimal network structure. Experimental results on DEAP datasets demonstrate the effectiveness of the proposed method compared to the traditional decision tree method.
INFORMATION SCIENCES
(2023)
Review
Neurosciences
Florence Steiner, Natalia Fernandez, Joris Dietziker, Philipp Stampfli, Erich Seifritz, Anton Rey, Sascha Fruhholz
Summary: Affective speech communication involves decoding of affect information in the cortico-limbic brain systems. Previous research neglected the social nature of affective communication and underestimated its real-time adaptive dynamics. Using real-time neuroimaging, we found that live adaptive affective speech is acoustically distinct, adaptive, and individualized, and makes more efficient use of neural affect decoding mechanisms.
PROGRESS IN NEUROBIOLOGY
(2022)
Article
Computer Science, Information Systems
Carlos de la Fuente, Francisco J. Castellanos, Jose J. Valero-Mas, Jorge Calvo-Zaragoza
Summary: This research presents a new approach to detect frustration in game-play scenarios by automatically extracting meaningful descriptors from individual audio and video sources of information using Deep Neural Networks (DNN). The multimodal proposals introduced in this study outperform other state-of-the-art approaches, achieving error rate improvements of between 40% and 90%.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ismail Shahin, Noor Hindawi, Ali Bou Nassif, Adi Alhudhaif, Kemal Polat
Summary: Recent research in speech emotion recognition has shown significant advancements by using MFCC's spectrogram features and novel classifier algorithms such as CapsNet. The proposed DC-LSTM COMP-CapsNet algorithm achieves a higher accuracy in emotion recognition compared to other known methods and classical classifiers, with an average accuracy of 89.3% in recognizing Arabic Emirati-accented speech.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Binqiang Wang, Gang Dong, Yaqian Zhao, Rengang Li, Qichun Cao, Kekun Hu, Dongdong Jiang
Summary: Accurate emotion recognition enables robots to understand human affection intentions and deliver emotional responses. This paper proposes a novel Hierarchically Stacked Graph Convolution Framework (HSGCF) that extracts emotional discriminative features using a hierarchical structure. Experimental results show a 4.12% improvement in accuracy and a 4.80% improvement in F1 score compared to the baseline method.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Biology
Xiangkai Qiu, Shenglin Wang, Ruqing Wang, Yiling Zhang, Liya Huang
Summary: This paper introduces a novel multi-head residual graph convolutional neural network (MRGCN) model that incorporates complex brain networks and graph convolution networks for extracting EEG emotional features. The decomposition of multi-band differential entropy (DE) features exposes the temporal intricacy of emotion-linked brain activity, and the combination of short and long-distance brain networks can explore complex topological characteristics. Moreover, the residual-based architecture enhances performance and classification stability across subjects. The visualization of brain network connectivity offers a practical technique for investigating emotional regulation mechanisms. The MRGCN model exhibits high classification accuracies of 95.8% and 98.9% for the DEAP and SEED datasets, respectively, highlighting its excellent performance and robustness.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Chemistry, Analytical
Ming-Che Lee, Sheng-Cheng Yeh, Jia-Wei Chang, Zhen-Yi Chen
Summary: This research developed a Chinese speech emotion recognition system using voice emotion recognition technology, trained the model with acoustic features, proposed multiple audio adjustment methods, and achieved high accuracy in the Chinese sentiment corpus.
Article
Neurosciences
Edmund T. Rolls, Gustavo Deco, Chu-Chung Huang, Jianfeng Feng
Summary: This study measured the effective connectivity between 21 regions in the human posterior parietal cortex and 360 cortical regions, revealing functional characteristics and interconnections among these regions.
Article
Neurosciences
Edmund T. Rolls, Gustavo Deco, Chu-Chung Huang, Jianfeng Feng
Summary: Using the HCP-MMP atlas, the effective connectivity between visual cortical regions in human brain was investigated. Different visual streams were found to be involved in object recognition, scene representations, language systems, and social behavior. These streams are connected hierarchically and interact with reward systems and memory systems.
Article
Neurosciences
Edmund T. Rolls, Gustavo Deco, Chu-Chung Huang, Jianfeng Feng
Summary: This study used the multimodal parcellation atlas of the Human Connectome Project (HCP) to measure the effective connectivity, functional connectivity, and tractography between 57 cortical frontal and somatosensory regions and the 360 cortical regions. The results showed that the ventral somatosensory stream and the dorsal action somatosensory stream have different connections with other brain regions, indicating their involvement in different cognitive functions.
Review
Neurosciences
Edmund T. Rolls
Summary: Hippocampal and parahippocampal gyrus spatial view neurons in primates respond to spatial location and have relatively invariant responses in terms of retinal position and eye position. These spatial view cells are formed by self-organized combinations of feature inputs. They play a key role in episodic memory and navigation.
Article
Neurosciences
Edmund T. Rolls, Sylvia Wirth, Gustavo Deco, Chu-Chung Huang, Jianfeng Feng
Summary: The functional connectivity of different parts of the human posterior cingulate cortex with other brain regions was investigated, revealing their involvement in memory and navigation.
HUMAN BRAIN MAPPING
(2023)
Article
Neurosciences
Manel Vila-Vidal, Mariam Khawaja, Mar Carreno, Pedro Roldan, Jordi Rumia, Antonio Donaire, Gustavo Deco, Adria Tauste Campo
Summary: A novel analytical framework was developed to explore the relationship between local brain activity and global connectivity fluctuations during a face-recognition task. The framework was applied to data from patients with drug-resistant epilepsy and the findings suggested that similar connectivity fluctuations across patients were mainly correlated with the local activity of brain regions involved in face information processing. These global measures may serve as a new signature of functional brain activity reorganization during task processing.
HUMAN BRAIN MAPPING
(2023)
Article
Neurosciences
Josephine Cruzat, Ruben Herzog, Pavel Prado, Yonatan Sanz-Perl, Raul Gonzalez-Gomez, Sebastian Moguilner, Morten L. Kringelbach, Gustavo Deco, Enzo Tagliazucchi, Agustin Ibanez
Summary: Healthy brain dynamics are characterized by a complex system that is far from thermodynamic equilibrium. However, Alzheimer's disease (AD) disrupts the time-reversal symmetry of brain activity, moving it towards equilibrium dynamics. Through the analysis of brain data from AD patients and healthy control subjects, it was found that AD is associated with a decrease in temporal irreversibility at global, local, and network levels, affecting multiple frequency bands. Specifically, frontal and temporoparietal regions were most affected at the local level, while limbic, frontoparietal, default mode, and salience networks were most compromised at the network level. Temporal reversibility was related to cognitive decline in AD and gray matter volume in healthy control subjects.
JOURNAL OF NEUROSCIENCE
(2023)
Article
Neurosciences
Yonatan Sanz Perl, Gorka Zamora-Lopez, Ernest Montbrio, Marti Monge-Asensio, Jakub Vohryzek, Sol Fittipaldi, Cecilia Gonzalez Campo, Sebastian Moguilner, Agustin Ibanez, Enzo Tagliazucchi, B. T. Thomas Yeo, Morten L. Kringelbach, Gustavo Deco
Summary: The role of heterogeneities in brain dynamics, specifically in synchronous behavior, is explored using computational models. Models with oscillations better reproduce empirical properties when considering both structural and functional regional heterogeneities.
NETWORK NEUROSCIENCE
(2023)
Article
Neurosciences
Ludovica Mana, Manel Vila-Vidal, Charlotte Kockeritz, Kevin Aquino, Alex Fornito, Morten L. Kringelbach, Gustavo Deco
Summary: Through analyzing resting-state fMRI data from 47 schizophrenia patients and 118 age-matched healthy controls, this study found that brain dynamics in schizophrenia patients were characterized by an increased probability of globally coherent states and reduced recurrence of a substate dominated by coupled activity in the default mode and limbic networks. By using in silico perturbation of a whole-brain model, critical areas involved in the disease were identified. Perturbing temporo-parietal sensory and associative areas in a healthy brain model reproduced global pathological dynamics, while perturbing medial fronto-temporal and cingulate regions restored healthy brain dynamics in the model of pathology.
Article
Multidisciplinary Sciences
Michael Schirner, Gustavo Deco, Petra Ritter
Summary: A better understanding of the trade-off between decision-making speed and accuracy is crucial for applying biological intelligence to machines. The authors propose a brain-inspired learning algorithm that can uncover dependencies in fMRI networks and predict individual differences in decision-making. By developing personalized brain network models for 650 participants, the study reveals links between intelligence scores, processing speed, functional connectivity, and brain synchrony, shedding light on the relationship between brain structure and behavior.
NATURE COMMUNICATIONS
(2023)
Article
Multidisciplinary Sciences
Raphael Bergoin, Alessandro Torcini, Gustavo Deco, Mathias Quoy, Gorka Zamora-Lopez
Summary: Brain circuits display modular architecture at different scales. This study investigates the role of inhibition in structuring new neural assemblies driven by synchronization to various inputs. The presence of inhibitory neurons is crucial for the emergence and consolidation of modular structures in the neural network.
SCIENTIFIC REPORTS
(2023)
Article
Neurosciences
Edmund T. Rolls, Gustavo Deco, Yi Zhang, Jianfeng Feng
Summary: The hierarchical organization between ventral stream visual cortical regions and cortical regions was measured using magnetoencephalography and the Human Connectome Project Multimodal Parcellation atlas. The study revealed a hierarchical organization based on generative effective connectivity, showing that V1-V4 formed a group of interconnected regions, with V4 having connectivity to ventrolateral visual stream regions and these regions further connecting to inferior temporal cortex regions. In addition, a ventromedial visual stream was identified with connectivity from V1-V4 to ventromedial regions, which then connected to the medial parahippocampal gyrus and the hippocampal system regions.
Article
Neurosciences
Gustavo Deco, Yonatan Sanz Perl, Adrian Ponce-Alvarez, Enzo Tagliazucchi, Peter C. Whybrow, Joaquin Fuster, Morten L. Kringelbach
Summary: Surviving and thriving in a complex world requires balancing higher brain functions with essential survival-related behaviors. The prefrontal cortex (PFC) plays key roles in cognitive and emotional tasks. This study discovered the driving brain regions at the top of the hierarchy in the PFC, responsible for orchestrating higher brain function. Lesioning these regions demonstrated their importance in brain dynamics and cognitive tasks.
PROGRESS IN NEUROBIOLOGY
(2023)