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Feng Q, Ren Z, Wei D, Liu C, Wang X, Li X, Tie B, Tang S, Qiu J. Connectome-based predictive modeling of Internet addiction symptomatology. Soc Cogn Affect Neurosci 2024; 19:nsae007. [PMID: 38334691 PMCID: PMC10878364 DOI: 10.1093/scan/nsae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 12/14/2023] [Accepted: 02/06/2024] [Indexed: 02/10/2024] Open
Abstract
Internet addiction symptomatology (IAS) is characterized by persistent and involuntary patterns of compulsive Internet use, leading to significant impairments in both physical and mental well-being. Here, a connectome-based predictive modeling approach was applied to decode IAS from whole-brain resting-state functional connectivity in healthy population. The findings showed that IAS could be predicted by the functional connectivity between prefrontal cortex with the cerebellum and limbic lobe and connections of the occipital lobe with the limbic lobe and insula lobe. The identified edges associated with IAS exhibit generalizability in predicting IAS within an independent sample. Furthermore, we found that the unique contributing network, which predicted IAS in contrast to the prediction networks of alcohol use disorder symptomatology (the range of symptoms and behaviors associated with alcohol use disorder), prominently comprised connections involving the occipital lobe and other lobes. The current data-driven approach provides the first evidence of the predictive brain features of IAS based on the organization of intrinsic brain networks, thus advancing our understanding of the neurobiological basis of Internet addiction disorder (IAD) susceptibility, and may have implications for the timely intervention of people potentially at risk of IAD.
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Affiliation(s)
- Qiuyang Feng
- Center for Studies of Education and Psychology of Ethnic Minorities in Southwest China, Southwest University (SWU), Chongqing 400715, China
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
| | - Zhiting Ren
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Cheng Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Xueyang Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Xianrui Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Bijie Tie
- Center for Studies of Education and Psychology of Ethnic Minorities in Southwest China, Southwest University (SWU), Chongqing 400715, China
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
| | - Shuang Tang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing 100000, China
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Lang J, Yang LZ, Li H. TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network. Front Neurosci 2023; 17:1288882. [PMID: 38188031 PMCID: PMC10768162 DOI: 10.3389/fnins.2023.1288882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/01/2023] [Indexed: 01/09/2024] Open
Abstract
Neuropsychiatric disorder (ND) is often accompanied by abnormal functional connectivity (FC) patterns in specific task contexts. The distinctive task-specific FC patterns can provide valuable features for ND classification models using deep learning. However, most previous studies rely solely on the whole-brain FC matrix without considering the prior knowledge of task-specific FC patterns. Insight by the decoding studies on brain-behavior relationship, we develop TSP-GNN, which extracts task-specific prior (TSP) connectome patterns and employs graph neural network (GNN) for disease classification. TSP-GNN was validated using publicly available datasets. Our results demonstrate that different ND types show distinct task-specific connectivity patterns. Compared with the whole-brain node characteristics, utilizing task-specific nodes enhances the accuracy of ND classification. TSP-GNN comprises the first attempt to incorporate prior task-specific connectome patterns and the power of deep learning. This study elucidates the association between brain dysfunction and specific cognitive processes, offering valuable insights into the cognitive mechanism of neuropsychiatric disease.
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Affiliation(s)
- Jinwei Lang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- University of Science and Technology of China, Hefei, China
| | - Li-Zhuang Yang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
| | - Hai Li
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
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Tokimoto S, Tokimoto N. Time course of effective connectivity associated with perspective taking in utterance comprehension. Front Hum Neurosci 2023; 17:1179230. [PMID: 38021233 PMCID: PMC10658713 DOI: 10.3389/fnhum.2023.1179230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
This study discusses the effective connectivity in the brain and its time course in realizing perspective taking in verbal communication through electroencephalogram (EEG) associated with the understanding of Japanese utterances. We manipulated perspective taking in a sentence with the Japanese subsidiary verbs -ageru and -kureru, which mean "to give". We measured the EEG during the auditory presentation of the sentences with a multichannel electroencephalograph, and the partial directed coherence and its temporal variations were analyzed using the source localization method to examine causal interactions between nineteen regions of interest in the brain. Three different processing stages were recognized on the basis of the connectivity hubs, direction of information flow, increase or decrease in flow, and temporal variation. We suggest that perspective taking in speech comprehension is realized by interactions between the mentalizing network, mirror neuron network, and executive control network. Furthermore, we found that individual differences in the sociality of typically developing adult speakers were systematically related to effective connectivity. In particular, attention switching was deeply concerned with perspective taking in real time, and the precuneus played a crucial role in implementing individual differences.
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Affiliation(s)
- Shingo Tokimoto
- Department of English Language Studies, Mejiro University, Tokyo, Japan
| | - Naoko Tokimoto
- Department of Performing Arts, Shobi University, Saitama, Japan
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Wilcox RR, Barbey AK. Connectome-based predictive modeling of fluid intelligence: evidence for a global system of functionally integrated brain networks. Cereb Cortex 2023; 33:10322-10331. [PMID: 37526284 DOI: 10.1093/cercor/bhad284] [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: 01/14/2023] [Revised: 06/21/2023] [Accepted: 07/16/2023] [Indexed: 08/02/2023] Open
Abstract
Cognitive neuroscience continues to advance our understanding of the neural foundations of human intelligence, with significant progress elucidating the role of the frontoparietal network in cognitive control mechanisms for flexible, intelligent behavior. Recent evidence in network neuroscience further suggests that this finding may represent the tip of the iceberg and that fluid intelligence may depend on the collective interaction of multiple brain networks. However, the global brain mechanisms underlying fluid intelligence and the nature of multi-network interactions remain to be well established. We therefore conducted a large-scale Connectome-based Predictive Modeling study, administering resting-state fMRI to 159 healthy college students and examining the contributions of seven intrinsic connectivity networks to the prediction of fluid intelligence, as measured by a state-of-the-art cognitive task (the Bochum Matrices Test). Specifically, we aimed to: (i) identify whether fluid intelligence relies on a primary brain network or instead engages multiple brain networks; and (ii) elucidate the nature of brain network interactions by assessing network allegiance (within- versus between-network connections) and network topology (strong versus weak connections) in the prediction of fluid intelligence. Our results demonstrate that whole-brain predictive models account for a large and significant proportion of variance in fluid intelligence (18%) and illustrate that the contribution of individual networks is relatively modest by comparison. In addition, we provide novel evidence that the global architecture of fluid intelligence prioritizes between-network connections and flexibility through weak ties. Our findings support a network neuroscience approach to understanding the collective role of brain networks in fluid intelligence and elucidate the system-wide network mechanisms from which flexible, adaptive behavior is constructed.
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Affiliation(s)
- Ramsey R Wilcox
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, NE 68501, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Illinois, Urbana, IL 61801, United States
| | - Aron K Barbey
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, NE 68501, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Illinois, Urbana, IL 61801, United States
- Department of Bioengineering, University of Illinois, Urbana, IL 61801, United States
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Anderson ED, Talukdar T, Goodwin G, Di Pietro V, Yakoub KM, Zwilling CE, Davies D, Belli A, Barbey AK. Assessing blood oxygen level-dependent signal variability as a biomarker of brain injury in sport-related concussion. Brain Commun 2023; 5:fcad215. [PMID: 37649639 PMCID: PMC10465085 DOI: 10.1093/braincomms/fcad215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/02/2023] [Accepted: 08/10/2023] [Indexed: 09/01/2023] Open
Abstract
Mild traumatic brain injury is a complex neurological disorder of significant concern among athletes who play contact sports. Athletes who sustain sport-related concussion typically undergo physical examination and neurocognitive evaluation to determine injury severity and return-to-play status. However, traumatic disruption to neurometabolic processes can occur with minimal detectable anatomic pathology or neurocognitive alteration, increasing the risk that athletes may be cleared for return-to-play during a vulnerable period and receive a repetitive injury. This underscores the need for sensitive functional neuroimaging methods to detect altered cerebral physiology in concussed athletes. The present study compared the efficacy of Immediate Post-concussion Assessment and Cognitive Testing composite scores and whole-brain measures of blood oxygen level-dependent signal variability for classifying concussion status and predicting concussion symptomatology in healthy, concussed and repetitively concussed athletes, assessing blood oxygen level-dependent signal variability as a potential diagnostic tool for characterizing functional alterations to cerebral physiology and assisting in the detection of sport-related concussion. We observed significant differences in regional blood oxygen level-dependent signal variability measures for concussed athletes but did not observe significant differences in Immediate Post-concussion Assessment and Cognitive Testing scores of concussed athletes. We further demonstrate that incorporating measures of functional brain alteration alongside Immediate Post-concussion Assessment and Cognitive Testing scores enhances the sensitivity and specificity of supervised random forest machine learning methods when classifying and predicting concussion status and post-concussion symptoms, suggesting that alterations to cerebrovascular status characterize unique variance that may aid in the detection of sport-related concussion and repetitive mild traumatic brain injury. These results indicate that altered blood oxygen level-dependent variability holds promise as a novel neurobiological marker for detecting alterations in cerebral perfusion and neuronal functioning in sport-related concussion, motivating future research to establish and validate clinical assessment protocols that can incorporate advanced neuroimaging methods to characterize altered cerebral physiology following mild traumatic brain injury.
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Affiliation(s)
- Evan D Anderson
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Air Force Research Laboratory, Wright-Patterson AFB, OH 45433, USA
| | - Tanveer Talukdar
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Grace Goodwin
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Psychology, University of Nevada, Las Vegas, NV 89557, USA
| | - Valentina Di Pietro
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Neurotrauma and Ophthalmology Research Group, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TH, UK
| | - Kamal M Yakoub
- Neurotrauma and Ophthalmology Research Group, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TH, UK
| | - Christopher E Zwilling
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - David Davies
- Neurotrauma and Ophthalmology Research Group, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TH, UK
| | - Antonio Belli
- Neurotrauma and Ophthalmology Research Group, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TH, UK
| | - Aron K Barbey
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Psychology, University of Illinois, Urbana, IL 61801, USA
- Department of Bioengineering, University of Illinois, Urbana, IL 61801, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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