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Luo Y, Yu Q, Wu S, Luo YJ. Distinct neural bases of visual art- and music-induced aesthetic experiences. Neuroimage 2025; 305:120962. [PMID: 39638082 DOI: 10.1016/j.neuroimage.2024.120962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 11/25/2024] [Accepted: 12/02/2024] [Indexed: 12/07/2024] Open
Abstract
Aesthetic experiences are characterized by a conscious, emotionally and hedonically rewarding perceptions of a stimulus's aesthetic qualities and are thought to arise from a unique combination of cognitive and affective processes. To pinpoint neural correlates of aesthetic experiences, in the present study, we performed a series of meta-analyses based on the existing functional Magnetic Resonance Imaging (fMRI) studies of art appreciation in visual art (34 experiments, 692 participants) and music (34 experiments, 718 participants). The Activation Likelihood Estimation (ALE) analyses showed that the frontal pole (FP), ventromedial prefrontal cortex (vmPFC), and inferior frontal gyrus (IFG) were commonly activated in visual-art-induced aesthetic experiences, whilst bilateral superior temporal gyrus (STG) and striatal areas were commonly activated in music appreciation. Additionally, task-independent Resting-state Functional Connectivity (RSFC), task-dependent Meta-analytical Connectivity Modelling (MACM) analyses, as well as Activation Network Modeling (ANM) further showed that visual art and music engaged quite distinct brain networks. Our findings support the domain-specific view of aesthetic appreciation and challenge the notion that there is a general "common neural currency" for aesthetic experiences across domains.
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Affiliation(s)
- Youjing Luo
- School of Psychology, Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen 518060, China; Department of Psychology, New York University, New York 10003, NY, USA; Department of Psychology, New York University Abu Dhabi, Abu Dhabi, UAE.
| | - Qianqian Yu
- School of Psychology, Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen 518060, China; Cognitive and Brain Function Laboratory, Shenzhen Mental Health Center/Shenzhen Kangning Hospital, Shenzhen, 518060, China
| | - Shuyi Wu
- School of Hotel and Tourism Management, The Hong Kong Polytechnic University, 17 Science Museum Road 818, TST East, Kowloon, Hong Kong SAR, PR China
| | - Yue-Jia Luo
- School of Psychology, Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen 518060, China; The State Key Lab of Cognitive and Learning, Faculty of Psychology, Beijing Normal University, Beijing 100875, China; Institute for Neuropsychological Rehabilitation, University of Health and Rehabilitation Sciences, Qingdao 266114, China.
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Krohn S, von Schwanenflug N, Waschke L, Romanello A, Gell M, Garrett DD, Finke C. A spatiotemporal complexity architecture of human brain activity. SCIENCE ADVANCES 2023; 9:eabq3851. [PMID: 36724223 PMCID: PMC9891702 DOI: 10.1126/sciadv.abq3851] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 01/04/2023] [Indexed: 05/07/2023]
Abstract
The human brain operates in large-scale functional networks. These networks are an expression of temporally correlated activity across brain regions, but how global network properties relate to the neural dynamics of individual regions remains incompletely understood. Here, we show that the brain's network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous "complexity drops" in functional magnetic resonance imaging signals. These episodes closely explain functional connectivity strength between regions, subserve the propagation of neural activity patterns, and reflect interindividual differences in age and behavior. Furthermore, complexity drops define neural activity states that dynamically shape the connectivity strength, topological configuration, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a principled complexity architecture of neural activity-a human "complexome" that underpins the brain's functional network organization.
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Affiliation(s)
- Stephan Krohn
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Nina von Schwanenflug
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Leonhard Waschke
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Amy Romanello
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Martin Gell
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, RWTH Aachen University, Aachen, Germany
| | - Douglas D. Garrett
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Carsten Finke
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
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Xu H, Wang L, Zuo C, Jiang J. Brain network analysis between Parkinson's Disease and Health Control based on edge functional connectivity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4805-4808. [PMID: 36085832 DOI: 10.1109/embc48229.2022.9871613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Parkinson's Disease (PD) is the second largest neurodegenerative disease. Brain functional connectivity (FC) studies for PD were useful. In this study, we employed a novel brain network construction method, edge functional connectivity (eFC), to explore FC differences between healthy control (HC) subjects and PD patients. The data used in this study included 34 HCs and 47 PDs from Huashan Hospital, Fudan University, China. Resting state functional magnetic resonance imaging (rsfMRI) and clinical information were selected. Firstly, we constructed eFC brain network and calculated network matrix for the HC and PD groups. Then, we compared brain network matrix between eFC and the traditional nodal functional connectivity (nFC) method. Receiver operating characteristic curve (ROC) analysis was applied to validate the efficiency of the eFC brain network. The results showed that both nFC and eFC brain networks could identify significantly different characteristics between the HC and PD groups. Important hubs were mainly concentrated in visual network, sensorimotor network, subcortex and cerebellum. In addition, new hubs in basal ganglia and cerebellum regions were found in eFC. Furthermore, eFC achieved better classification results (AUC=0.985) than nFC (AUC=0.861) in discriminating PD from CN subjects.
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Missing links: The functional unification of language and memory (L∪M). Neurosci Biobehav Rev 2021; 133:104489. [PMID: 34929226 DOI: 10.1016/j.neubiorev.2021.12.012] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 11/14/2021] [Accepted: 12/07/2021] [Indexed: 10/19/2022]
Abstract
The field of neurocognition is currently undergoing a significant change of perspective. Traditional neurocognitive models evolved into an integrative and dynamic vision of cognitive functioning. Dynamic integration assumes an interaction between cognitive domains traditionally considered to be distinct. Language and declarative memory are regarded as separate functions supported by different neural systems. However, they also share anatomical structures (notably, the inferior frontal gyrus, the supplementary motor area, the superior and middle temporal gyrus, and the hippocampal complex) and cognitive processes (such as semantic and working memory) that merge to endorse our quintessential daily lives. We propose a new model, "L∪M" (i.e., Language/union/Memory), that considers these two functions interactively. We fractionated language and declarative memory into three fundamental dimensions or systems ("Receiver-Transmitter", "Controller-Manager" and "Transformer-Associative" Systems), that communicate reciprocally. We formalized their interactions at the brain level with a connectivity-based approach. This new taxonomy overcomes the modular view of cognitive functioning and reconciles functional specialization with plasticity in neurological disorders.
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The diversity and multiplexity of edge communities within and between brain systems. Cell Rep 2021; 37:110032. [PMID: 34788617 DOI: 10.1016/j.celrep.2021.110032] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/08/2021] [Accepted: 10/28/2021] [Indexed: 11/24/2022] Open
Abstract
The human brain is composed of functionally specialized systems that support cognition. Recently, we proposed an edge-centric model for detecting overlapping communities. It remains unclear how these communities and brain systems are related. Here, we address this question using data from the Midnight Scan Club and show that all brain systems are linked via at least two edge communities. We then examine the diversity of edge communities within each system, finding that heteromodal systems are more diverse than sensory systems. Next, we cluster the entire cortex to reveal it according to the regions' edge-community profiles. We find that regions in heteromodal systems are more likely to form their own clusters. Finally, we show that edge communities are personalized. Our work reveals the pervasive overlap of edge communities across the cortex and their relationship with brain systems. Our work provides pathways for future research using edge-centric brain networks.
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Jo Y, Faskowitz J, Esfahlani FZ, Sporns O, Betzel RF. Subject identification using edge-centric functional connectivity. Neuroimage 2021; 238:118204. [PMID: 34087363 DOI: 10.1016/j.neuroimage.2021.118204] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 05/23/2021] [Accepted: 05/24/2021] [Indexed: 12/11/2022] Open
Abstract
Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed 'fingerprinting' analyses on functional connectivity to identify subjects' idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterize subjects' identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.
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Affiliation(s)
- Youngheun Jo
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
| | - Farnaz Zamani Esfahlani
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA.
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