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Li R, Shen F, Sun X, Zou T, Li L, Wang X, Deng C, Duan X, He Z, Yang M, Li Z, Chen H. Dissociable salience and default mode network modulation in generalized anxiety disorder: a connectome-wide association study. Cereb Cortex 2023; 33:6354-6365. [PMID: 36627243 DOI: 10.1093/cercor/bhac509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 01/12/2023] Open
Abstract
Generalized anxiety disorder (GAD) is a common anxiety disorder experiencing psychological and somatic symptoms. Here, we explored the link between the individual variation in functional connectome and anxiety symptoms, especially psychological and somatic dimensions, which remains unknown. In a sample of 118 GAD patients and matched 85 healthy controls (HCs), we used multivariate distance-based matrix regression to examine the relationship between resting-state functional connectivity (FC) and the severity of anxiety. We identified multiple hub regions belonging to salience network (SN) and default mode network (DMN) where dysconnectivity associated with anxiety symptoms (P < 0.05, false discovery rate [FDR]-corrected). Follow-up analyses revealed that patient's psychological anxiety was dominated by the hyper-connectivity within DMN, whereas the somatic anxiety could be modulated by hyper-connectivity within SN and DMN. Moreover, hypo-connectivity between SN and DMN were related to both anxiety dimensions. Furthermore, GAD patients showed significant network-level FC changes compared with HCs (P < 0.01, FDR-corrected). Finally, we found the connectivity of DMN could predict the individual psychological symptom in an independent GAD sample. Together, our work emphasizes the potential dissociable roles of SN and DMN in the pathophysiology of GAD's anxiety symptoms, which may be crucial in providing a promising neuroimaging biomarker for novel personalized treatment strategies.
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Affiliation(s)
- Rong Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Fei Shen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Xiyue Sun
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Ting Zou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Liyuan Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Xuyang Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Chijun Deng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Mi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
| | - Zezhi Li
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, P.R. China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
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Abstract
Considerable research has focused on how people derive information about others' social category memberships from their faces. Theoretical models posit that early extraction of task-relevant information from a face should determine the efficiency with which that face is categorized, but evidence supporting this idea has been elusive. Here, we used a novel trial-level data analytic approach to examine the relationship between two event-related potential components-the P2, indexing early attention to category-relevant information, and the P3, indexing stimulus evaluation-and the speed of overt categorization judgments. As predicted, a larger face-elicited P2 on a particular trial was associated with faster overt race or gender categorization of that face. Moreover, this association was mediated by P3 latency, indicating that extraction of more category-relevant information early in processing facilitated stimulus evaluation. These findings support continuous flow models of information processing and the long-theorized functional significance of face-elicited neurophysiological responses for social categorization.
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Affiliation(s)
| | - Bruce D. Bartholow
- Department of Psychological Sciences, University of Missouri, Columbia, MO 65211, USA
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Wu J, Eickhoff SB, Hoffstaedter F, Patil KR, Schwender H, Yeo BTT, Genon S. A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies. Cereb Cortex 2021; 31:3732-3751. [PMID: 33884421 DOI: 10.1093/cercor/bhab044] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 02/09/2021] [Accepted: 02/11/2021] [Indexed: 01/01/2023] Open
Abstract
The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions' connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions' connectivity profiles. We first illustrate two main applications: 1) single brain region's predictive power for a range of psychometric variables and 2) single psychometric variable's predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships, as well as the unique insight into brain-behavior relationships offered by this approach.
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Affiliation(s)
- Jianxiao Wu
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Simon B Eickhoff
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Felix Hoffstaedter
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Kaustubh R Patil
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Holger Schwender
- Mathematical Institute, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore City 117575, Singapore.,Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore City 117597, Singapore.,N. 1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore City 117597, Singapore.,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore City 117575, Singapore.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Sarah Genon
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
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Hessen E, Hokkanen L, Ponsford J, van Zandvoort M, Watts A, Evans J, Haaland KY. Core competencies in clinical neuropsychology training across the world. Clin Neuropsychol 2017; 32:642-656. [PMID: 29214891 DOI: 10.1080/13854046.2017.1413210] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
OBJECTIVE This work aimed to review main competency requirements from training models in countries with well-established specialties in clinical neuropsychology and to extract core competencies that likely will apply to clinical neuropsychologists regardless of regional and cultural context. METHOD We reviewed standards for post-graduate training in clinical neuropsychology from countries in Europe, Australia, and North America based on existing literature, presentations at international conferences, and from description of the training models from national psychological or neuropsychological associations. RESULTS Despite differences, the reviewed models share similar core competencies considered necessary for a specialty in clinical neuropsychology: (1) In-depth knowledge of general psychology including clinical psychology (post-graduate level), ethical, and legal standards. (2) Expert knowledge about clinically relevant brain-behavioral relationships. (3) Comprehensive knowledge about, and skills in, related clinical disciplines. (4) In-depth knowledge about and skills in neuropsychological assessment, including decision-making and diagnostic competency according to current classification of diseases. (5) Competencies in the area of diversity and culture in relation to clinical neuropsychology. (6) Communication competency of neuropsychological findings and test results to relevant and diverse audiences. (7) Knowledge about and skills in psychological and neuropsychological intervention, including treatment and rehabilitation. CONCLUSIONS All the models have undergone years of development in accordance with requirements of national health care systems in different parts of the world. Despite differences, the common core competency requirements across different regions of the world suggest generalizability of these competencies. We hope this summary can be useful as countries with less established neuropsychology training programs develop their models.
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Affiliation(s)
- Erik Hessen
- a Department of Psychology , University of Oslo , Oslo , Norway
| | - Laura Hokkanen
- b Faculty of Medicine, Department of Psychology and Logopedics , University of Helsinki , Helsinki , Finland
| | - Jennie Ponsford
- c School of Psychological Sciences , Monash University , Melbourne , Australia
| | | | - Ann Watts
- e Entabeni Hospital , Durban , South Africa
| | - Jonathan Evans
- f Institute of Health & Wellbeing , University of Glasgow , Glasgow , UK
| | - Kathleen Y Haaland
- g Department of Psychiatry and Behavioral Sciences , University of New Mexico , Albuquerque , NM , USA
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Wylie DR, Gutiérrez-Ibáñez C, Iwaniuk AN. Integrating brain, behavior, and phylogeny to understand the evolution of sensory systems in birds. Front Neurosci 2015; 9:281. [PMID: 26321905 PMCID: PMC4531248 DOI: 10.3389/fnins.2015.00281] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Accepted: 07/28/2015] [Indexed: 12/29/2022] Open
Abstract
The comparative anatomy of sensory systems has played a major role in developing theories and principles central to evolutionary neuroscience. This includes the central tenet of many comparative studies, the principle of proper mass, which states that the size of a neural structure reflects its processing capacity. The size of structures within the sensory system is not, however, the only salient variable in sensory evolution. Further, the evolution of the brain and behavior are intimately tied to phylogenetic history, requiring studies to integrate neuroanatomy with behavior and phylogeny to gain a more holistic view of brain evolution. Birds have proven to be a useful group for these studies because of widespread interest in their phylogenetic relationships and a wealth of information on the functional organization of most of their sensory pathways. In this review, we examine the principle of proper mass in relation differences in the sensory capabilities among birds. We discuss how neuroanatomy, behavior, and phylogeny can be integrated to understand the evolution of sensory systems in birds providing evidence from visual, auditory, and somatosensory systems. We also consider the concept of a "trade-off," whereby one sensory system (or subpathway within a sensory system), may be expanded in size, at the expense of others, which are reduced in size.
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Affiliation(s)
- Douglas R. Wylie
- Neurosciences and Mental Health Institute, University of AlbertaEdmonton, AB, Canada
| | | | - Andrew N. Iwaniuk
- Department of Neuroscience, University of LethbridgeLethbridge, AB, Canada
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Ciccia AH, Meulenbroek P, Turkstra LS. Adolescent Brain and Cognitive Developments: Implications for Clinical Assessment in Traumatic Brain Injury. Top Lang Disord 2009; 29:249-265. [PMID: 30220763 PMCID: PMC6135107 DOI: 10.1097/tld.0b013e3181b53211] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Adolescence is a time of significant physical, social, and emotional developments, accompanied by changes in cognitive and language skills. Underlying these are significant developments in brain structures and functions including changes in cortical and subcortical gray matter and white matter tracts. Among the brain regions that develop during adolescence are areas that are commonly damaged as a result of a traumatic brain injury (TBI). This paper summarizes major brain changes during adolescence and evidence linking maturation of these cognitive and language functions to brain development, placing consideration of both areas of development in the context of rehabilitation for adolescents with TBI.
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Affiliation(s)
- Angela Hein Ciccia
- Department of Communication Sciences, Case Western Reserve University, Cleveland, Ohio (Dr Ciccia); and Department of Communicative Disorders, University of Wisconsin-Madison (Mr Meulenbroek and Dr Turkstra)
| | - Peter Meulenbroek
- Department of Communication Sciences, Case Western Reserve University, Cleveland, Ohio (Dr Ciccia); and Department of Communicative Disorders, University of Wisconsin-Madison (Mr Meulenbroek and Dr Turkstra)
| | - Lyn S Turkstra
- Department of Communication Sciences, Case Western Reserve University, Cleveland, Ohio (Dr Ciccia); and Department of Communicative Disorders, University of Wisconsin-Madison (Mr Meulenbroek and Dr Turkstra)
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