101
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Nie Z, Xie X, Kang L, Wang W, Xu S, Chen M, Yao L, Gong Q, Zhou E, Li M, Wang H, Bu L, Liu Z. A Cross-Sectional Study: Structural and Related Functional Connectivity Changes in the Brain: Stigmata of Adverse Parenting in Patients with Major Depressive Disorder? Brain Sci 2023; 13:brainsci13040694. [PMID: 37190659 DOI: 10.3390/brainsci13040694] [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: 03/20/2023] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
Background: There is a high correlation between the risk of major depressive disorder (MDD) and adverse childhood experiences (ACEs) such as adverse parenting (AP). While there appears to be an association between ACEs and changes in brain structure and function, there have yet to be multimodal neuroimaging studies of associations between parenting style and brain developmental changes in MDD patients. To explore the effect of AP on brain structure and function. Methods: In this cross-sectional study, 125 MDD outpatients were included in the study and divided into the AP group and the optimal parenting (OP) group. Participants completed self-rating scales to assess depressive severity, symptoms, and their parents' styles. They also completed magnetic resonance imaging within one week of filling out the instruments. The differences between groups of gender, educational level, and medications were analyzed using the chi-squared test and those of age, duration of illness, and scores on scales using the independent samples t-test. Differences in gray matter volume (GMV) and resting-state functional connectivity (RS-FC) were assessed between groups. Results: AP was associated with a significant increase in GMV in the right superior parietal lobule (SPL) and FC between the right SPL and the bilateral medial superior frontal cortex in MDD patients. Limitations: The cross-cultural characteristics of AP will result in the lack of generalizability of the findings. Conclusions: The results support the hypothesis that AP during childhood may imprint the brain and affect depressive symptoms in adulthood. Parents should pay attention to the parenting style and avoid a style that lacks warmth.
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Affiliation(s)
- Zhaowen Nie
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Xinhui Xie
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lijun Kang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Wei Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Shuxian Xu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Mianmian Chen
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lihua Yao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Qian Gong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Enqi Zhou
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Meng Li
- PET/CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lihong Bu
- PET/CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhongchun Liu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430072, China
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102
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Kim H, Kwak S, Baek EC, Oh N, Baldina E, Youm Y, Chey J. Brain connectivity during social exclusion differs depending on the closeness within a triad among older adults living in a village. Soc Cogn Affect Neurosci 2023; 18:7135903. [PMID: 37084399 PMCID: PMC10121205 DOI: 10.1093/scan/nsad015] [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: 09/18/2021] [Revised: 12/06/2022] [Accepted: 03/19/2023] [Indexed: 04/23/2023] Open
Abstract
Social exclusion occurs in various types of social relationships, from anonymous others to close friends. However, the role that social relationships play in social exclusion is less well known because most paradigms investigating social exclusion have been done in laboratory contexts, without considering the features of individuals' real-world social relationships. Here, we addressed this gap by examining how pre-existing social relationships with rejecters may influence the brain response of individuals experiencing social exclusion. Eighty-eight older adults living in a rural village visited the laboratory with two other participants living in the same village and played Cyberball in an Magnetic Resonance Imaging scanner. Utilizing whole-brain connectome-based predictive modeling, we analyzed functional connectivity (FC) data obtained during the social exclusion task. First, we found that the level of self-reported distress during social exclusion was significantly related to sparsity, i.e. lack of closeness, within a triad. Furthermore, the sparsity was significantly predicted by the FC model, demonstrating that a sparse triadic relationship was associated with stronger connectivity patterns in brain regions previously implicated in social pain and mentalizing during Cyberball. These findings extend our understanding of how real-world social intimacy and relationships with excluders affect neural and emotional responses to social exclusion.
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Affiliation(s)
- Hairin Kim
- Department of Psychology, Seoul National University, Seoul 08826, South Korea
| | - Seyul Kwak
- Department of Psychology, Pusan National University, Busan 46241, South Korea
| | - Elisa C Baek
- Department of Psychology, University of California, Los Angeles, CA 90095, USA
| | - Naeun Oh
- Department of Psychology, Seoul National University, Seoul 08826, South Korea
| | - Ekaterina Baldina
- Department of Sociology, Yonsei University, Seoul 03722, South Korea
| | - Yoosik Youm
- Department of Sociology, Yonsei University, Seoul 03722, South Korea
| | - Jeanyung Chey
- Department of Psychology, Seoul National University, Seoul 08826, South Korea
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103
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Dhamala E, Yeo BTT, Holmes AJ. One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry. Biol Psychiatry 2023; 93:717-728. [PMID: 36577634 DOI: 10.1016/j.biopsych.2022.09.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/07/2022] [Accepted: 09/23/2022] [Indexed: 12/30/2022]
Abstract
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.
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Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut.
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104
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Zhao W, Makowski C, Hagler DJ, Garavan HP, Thompson WK, Greene DJ, Jernigan TL, Dale AM. Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity. Neuroimage 2023; 270:119946. [PMID: 36801369 PMCID: PMC11037888 DOI: 10.1016/j.neuroimage.2023.119946] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 02/18/2023] Open
Abstract
Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.
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Affiliation(s)
- Weiqi Zhao
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA
| | - Carolina Makowski
- Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA
| | - Donald J Hagler
- University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA
| | | | | | - Deanna J Greene
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA
| | - Terry L Jernigan
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA; Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA; Department of Psychiatry, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA
| | - Anders M Dale
- Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA; Department of Neuroscience, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; Department of Psychiatry, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA.
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105
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Rolls ET, Feng R, Feng J. Lifestyle risks associated with brain functional connectivity and structure. Hum Brain Mapp 2023; 44:2479-2492. [PMID: 36799566 PMCID: PMC10028639 DOI: 10.1002/hbm.26225] [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: 10/05/2022] [Revised: 01/16/2023] [Accepted: 01/23/2023] [Indexed: 02/18/2023] Open
Abstract
Some lifestyle factors are related to health and brain function and structure, but the brain systems involved are incompletely understood. A general linear model was used to test the associations of the combined and separate lifestyle risk measures of alcohol use, smoking, diet, amounts of physical activity, leisure activity, and mobile phone use, with brain functional connectivity with the high resolution Human Connectome Project (HCP) atlas in 19,415 participants aged 45-78 from the UK Biobank, with replication with HCP data. Higher combined lifestyle risk scores were associated with lower functional connectivity across the whole brain, but especially of three brain systems. Low physical, and leisure and social, activity were associated with low connectivities of the somatosensory/motor cortical regions and of hippocampal memory-related regions. Low mobile phone use, perhaps indicative of poor social communication channels, was associated with low functional connectivity of brain regions in and related to the superior temporal sulcus that are involved in social behavior and face processing. Smoking was associated with lower functional connectivity of especially frontal regions involved in attention. Lower cortical thickness in some of these regions, and also lower subcortical volume of the hippocampus, amygdala, and globus pallidus, were also associated with the sum of the poor lifestyle scores. This very large scale analysis emphasizes how the lifestyle of humans relates to their brain structure and function, and provides a foundation for understanding the causalities that relate to the differences found here in the brains of different individuals.
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Affiliation(s)
- Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry, UK
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Ruiqing Feng
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, UK
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
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106
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Ovando-Tellez M, Kenett YN, Benedek M, Bernard M, Belo J, Beranger B, Bieth T, Volle E. Brain Connectivity-Based Prediction of Combining Remote Semantic Associates for Creative Thinking. CREATIVITY RESEARCH JOURNAL 2023. [DOI: 10.1080/10400419.2023.2192563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Affiliation(s)
- Marcela Ovando-Tellez
- Sorbonne University, FrontLab at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
| | - Yoed N. Kenett
- Faculty of Data and Decision Sciences, Technion – Israel Institute of Technology,Haifa Israel
| | | | - Matthieu Bernard
- Sorbonne University, FrontLab at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
| | - Joan Belo
- Sorbonne University, FrontLab at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
| | - Benoit Beranger
- Sorbonne University, CENIR at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
| | - Theophile Bieth
- Sorbonne University, FrontLab at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
- Neurology department, Pitié-Salpêtrière hospital, AP-HP, Paris, France
| | - Emmanuelle Volle
- Sorbonne University, FrontLab at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
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107
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Blanchett R, Chen Y, Aguate F, Xia K, Cornea E, Burt SA, de Los Campos G, Gao W, Gilmore JH, Knickmeyer RC. Genetic and environmental factors influencing neonatal resting-state functional connectivity. Cereb Cortex 2023; 33:4829-4843. [PMID: 36190430 PMCID: PMC10110449 DOI: 10.1093/cercor/bhac383] [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: 06/01/2021] [Revised: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 11/14/2022] Open
Abstract
Functional magnetic resonance imaging has been used to identify complex brain networks by examining the correlation of blood-oxygen-level-dependent signals between brain regions during the resting state. Many of the brain networks identified in adults are detectable at birth, but genetic and environmental influences governing connectivity within and between these networks in early infancy have yet to be explored. We investigated genetic influences on neonatal resting-state connectivity phenotypes by generating intraclass correlations and performing mixed effects modeling to estimate narrow-sense heritability on measures of within network and between-network connectivity in a large cohort of neonate twins. We also used backwards elimination regression and mixed linear modeling to identify specific demographic and medical history variables influencing within and between network connectivity in a large cohort of typically developing twins and singletons. Of the 36 connectivity phenotypes examined, only 6 showed narrow-sense heritability estimates greater than 0.10, with none being statistically significant. Demographic and obstetric history variables contributed to between- and within-network connectivity. Our results suggest that in early infancy, genetic factors minimally influence brain connectivity. However, specific demographic and medical history variables, such as gestational age at birth and maternal psychiatric history, may influence resting-state connectivity measures.
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Affiliation(s)
- Reid Blanchett
- Genetics and Genome Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Yuanyuan Chen
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Fernando Aguate
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - S Alexandra Burt
- Department of Psychology, Michigan State University, East Lansing, MI 48824, USA
| | - Gustavo de Los Campos
- Departments of Epidemiology and Biostatistics and Statistics and Probability, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Wei Gao
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Rebecca C Knickmeyer
- Department of Pediatrics and Human Development, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI 48824, USA
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108
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Corriveau A, Yoo K, Kwon YH, Chun MM, Rosenberg MD. Functional connectome stability and optimality are markers of cognitive performance. Cereb Cortex 2023; 33:5025-5041. [PMID: 36408606 PMCID: PMC10110430 DOI: 10.1093/cercor/bhac396] [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: 04/22/2022] [Revised: 09/02/2022] [Accepted: 09/03/2022] [Indexed: 11/22/2022] Open
Abstract
Patterns of whole-brain fMRI functional connectivity, or connectomes, are unique to individuals. Previous work has identified subsets of functional connections within these patterns whose strength predicts aspects of attention and cognition. However, overall features of these connectomes, such as how stable they are over time and how similar they are to a group-average (typical) or high-performance (optimal) connectivity pattern, may also reflect cognitive and attentional abilities. Here, we test whether individuals who express more stable, typical, optimal, and distinctive patterns of functional connectivity perform better on cognitive tasks using data from three independent samples. We find that individuals with more stable task-based functional connectivity patterns perform better on attention and working memory tasks, even when controlling for behavioral performance stability. Additionally, we find initial evidence that individuals with more typical and optimal patterns of functional connectivity also perform better on these tasks. These results demonstrate that functional connectome stability within individuals and similarity across individuals predicts individual differences in cognition.
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Affiliation(s)
- Anna Corriveau
- Department of Psychology, The University of Chicago, Chicago, IL 60637, USA
| | - Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT 06520, USA
| | - Young Hye Kwon
- Department of Psychology, Yale University, New Haven, CT 06520, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT 06520, USA
| | - Monica D Rosenberg
- Department of Psychology, The University of Chicago, Chicago, IL 60637, USA
- Neuroscience Institute, The University of Chicago, Chicago, IL 60637, USA
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109
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Ren Z, Sun J, Liu C, Li X, Li X, Li X, Liu Z, Bi T, Qiu J. Individualized prediction of trait self-control from whole-brain functional connectivity. Psychophysiology 2023; 60:e14209. [PMID: 36325626 DOI: 10.1111/psyp.14209] [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: 09/18/2021] [Revised: 09/24/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022]
Abstract
Self-control is a core psychological construct for human beings and it plays a crucial role in the adaptation to society and achievement of success and happiness for individuals. Although progress has been made in behavioral studies examining self-control, its neural mechanisms remain unclear. In this study, we employed a machine-learning approach-relevance vector regression (RVR) to explore the potential predictive power of intrinsic functional connections to trait self-control in a large sample (N = 390). We used resting-state functional MRI (fMRI) to explore whole-brain functional connectivity patterns characteristic of 390 healthy adults and to confirm the effectiveness of RVR in predicting individual trait self-control scores. A set of connections across multiple neural networks that significantly predicted individual differences were identified, including the classic control network (e.g., fronto-parietal network (FPN), salience network (SAL)), the sensorimotor network (Mot), and the medial frontal network (MF). Key nodes that contributed to the predictive model included the dorsolateral prefrontal cortex (dlPFC), middle frontal gyrus (MFG), anterior cingulate and paracingulate gyri, inferior temporal gyrus (ITG) that have been associated with trait self-control. Our findings further assert that self-control is a multidimensional construct rooted in the interactions between multiple neural networks.
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Affiliation(s)
- Zhiting Ren
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
- College of International Studies, Southwest University, Chongqing, China
| | - Cheng Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Xinyue Li
- Department of Psychology, University of Washington, Seattle, Washington, USA
| | - Xianrui Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Xinyi Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Zeqing Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Taiyong Bi
- Centre for Mental Health Research in School of Management, Zunyi Medical University, Zunyi, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
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110
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Sassenberg TA, Burton PC, Mwilambwe-Tshilobo L, Jung RE, Rustichini A, Spreng RN, DeYoung CG. Conscientiousness associated with efficiency of the salience/ventral attention network: Replication in three samples using individualized parcellation. Neuroimage 2023; 272:120081. [PMID: 37011715 PMCID: PMC10132286 DOI: 10.1016/j.neuroimage.2023.120081] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 03/21/2023] [Accepted: 03/31/2023] [Indexed: 04/03/2023] Open
Abstract
Conscientiousness, and related constructs impulsivity and self-control, have been related to structural and functional properties of regions in the prefrontal cortex (PFC) and anterior insula. Network-based conceptions of brain function suggest that these regions belong to a single large-scale network, labeled the salience/ventral attention network (SVAN). The current study tested associations between conscientiousness and resting-state functional connectivity in this network using two community samples (N's = 244 and 239) and data from the Human Connectome Project (N = 1000). Individualized parcellation was used to improve functional localization accuracy and facilitate replication. Functional connectivity was measured using an index of network efficiency, a graph theoretical measure quantifying the capacity for parallel information transfer within a network. Efficiency of a set of parcels in the SVAN was significantly associated with conscientiousness in all samples. Findings are consistent with a theory of conscientiousness as a function of variation in neural networks underlying effective prioritization of goals.
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111
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Jiang J, Bruss J, Lee WT, Tranel D, Boes AD. White matter disconnection of left multiple demand network is associated with post-lesion deficits in cognitive control. Nat Commun 2023; 14:1740. [PMID: 36990985 PMCID: PMC10060223 DOI: 10.1038/s41467-023-37330-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 03/13/2023] [Indexed: 03/31/2023] Open
Abstract
Cognitive control modulates other cognitive functions to achieve internal goals and is important for adaptive behavior. Cognitive control is enabled by the neural computations distributed over cortical and subcortical areas. However, due to technical challenges in recording neural activity from the white matter, little is known about the anatomy of white matter tracts that coordinate the distributed neural computations that support cognitive control. Here, we leverage a large sample of human patients with focal brain lesions (n = 643) and investigate how lesion location and connectivity profiles account for variance in cognitive control performance. We find that lesions in white matter connecting left frontoparietal regions of the multiple demand network reliably predict deficits in cognitive control performance. These findings advance our understanding of the white matter correlates of cognitive control and provide an approach for incorporating network disconnection to predict deficits following lesions.
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Affiliation(s)
- Jiefeng Jiang
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, 52242, USA.
- Cognitive Control Collaborative, University of Iowa, Iowa City, IA, 52242, USA.
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, 52242, USA.
| | - Joel Bruss
- Department of Neurology (Division of Neuropsychology and Cognitive Neuroscience), Carver College of Medicine, Iowa City, IA, 52242, USA
- Department of Psychiatry, Carver College of Medicine, Iowa City, IA, 52242, USA
| | - Woo-Tek Lee
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, 52242, USA
- Cognitive Control Collaborative, University of Iowa, Iowa City, IA, 52242, USA
- Behavioral-biomedical Interface Training Program, University of Iowa, Iowa City, IA, 52242, USA
| | - Daniel Tranel
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, 52242, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, 52242, USA
- Department of Neurology (Division of Neuropsychology and Cognitive Neuroscience), Carver College of Medicine, Iowa City, IA, 52242, USA
| | - Aaron D Boes
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, 52242, USA.
- Department of Neurology (Division of Neuropsychology and Cognitive Neuroscience), Carver College of Medicine, Iowa City, IA, 52242, USA.
- Department of Psychiatry, Carver College of Medicine, Iowa City, IA, 52242, USA.
- Department of Pediatrics, Carver College of Medicine, Iowa City, IA, 52242, USA.
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Chen HF, Sheng XN, Yang ZY, Shao PF, Xu HH, Qin RM, Zhao H, Bai F. Multi-networks connectivity at baseline predicts the clinical efficacy of left angular gyrus-navigated rTMS in the spectrum of Alzheimer's disease: A sham-controlled study. CNS Neurosci Ther 2023. [PMID: 36942495 DOI: 10.1111/cns.14177] [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: 07/26/2022] [Revised: 02/07/2023] [Accepted: 03/01/2023] [Indexed: 03/23/2023] Open
Abstract
INTRODUCTION Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) is effective in alleviating cognitive deficits in Alzheimer's disease (AD). However, the strategy for target determination and the mechanisms for cognitive improvement remain unclear. METHODS One hundred and thirteen elderly subjects were recruited in this study, including both cross-sectional (n = 79) and longitudinal experiments (the rTMS group: n = 24; the sham group: n = 10). The cross-sectional experiment explored the precise intervention target based on the cortical-hippocampal network. The longitudinal experiment investigated the clinical efficacy of neuro-navigated rTMS treatment over a four-week period and explored its underlying neural mechanism using seed-based and network-based analysis. Finally, we applied connectome-based predictive modeling to predict the rTMS response using these functional features at baseline. RESULTS RTMS at a targeted site of the left angular gyrus (MNI: -45, -67, 38) significantly induced cognitive improvement in memory and language function (p < 0.001). The improved cognition correlated with the default mode network (DMN) subsystems. Furthermore, the connectivity patterns of DMN subsystems (r = 0.52, p = 0.01) or large-scale networks (r = 0.85, p = 0.001) at baseline significantly predicted the Δ language cognition after the rTMS treatment. The connectivity patterns of DMN subsystems (r = 0.47, p = 0.019) or large-scale networks (r = 0.80, p = 0.001) at baseline could predict the Δ memory cognition after the rTMS treatment. CONCLUSION These findings suggest that neuro-navigated rTMS targeting the left angular gyrus could improve cognitive function in AD patients. Importantly, dynamic regulation of the intra- and inter-DMN at baseline may represent a potential predictor for favorable rTMS treatment response in patients with cognitive impairment.
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Affiliation(s)
- Hai-Feng Chen
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Xiao-Ning Sheng
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Zhi-Yuan Yang
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Peng-Fei Shao
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Heng-Heng Xu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Ruo-Meng Qin
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
- Geriatric Medicine Center, Affiliated Taikang Xianlin Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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113
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Ju S, Horien C, Shen X, Abuwarda H, Trainer A, Constable RT, Fredericks CA. Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance. FRONTIERS IN DEMENTIA 2023; 2:1126016. [PMID: 39082002 PMCID: PMC11285565 DOI: 10.3389/frdem.2023.1126016] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/28/2023] [Indexed: 08/02/2024]
Abstract
Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (n = 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD.
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Affiliation(s)
- Suyeon Ju
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Hamid Abuwarda
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - Anne Trainer
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - R. Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
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Izakson L, Gal S, Shahar M, Tavor I, Levy DJ. Similar functional networks predict performance in both perceptual and value-based decision tasks. Cereb Cortex 2023; 33:2669-2681. [PMID: 35724432 DOI: 10.1093/cercor/bhac234] [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: 03/21/2022] [Revised: 05/16/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
There are numerous commonalities between perceptual and preferential decision processes. For instance, previous studies have shown that both of these decision types are influenced by context. Also, the same computational models can explain both. However, the neural processes and functional connections that underlie these similarities between perceptual and value-based decisions are still unclear. Hence, in the current study, we examine whether perceptual and preferential processes can be explained by similar functional networks utilizing data from the Human Connectome Project. We used resting-state functional magnetic resonance imaging data to predict performance of 2 different decision-making tasks: a value-related task (the delay discounting task) and a perceptual task (the flanker task). We then examined the existence of shared predictive-network features across these 2 decision tasks. Interestingly, we found a significant positive correlation between the functional networks, which predicted the value-based and perceptual tasks. In addition, a larger functional connectivity between visual and frontal decision brain areas was a critical feature in the prediction of both tasks. These results demonstrate that functional connections between perceptual and value-related areas in the brain are inherently related to decision-making processes across domains.
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Affiliation(s)
- Liz Izakson
- Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
- Coller School of Management, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
| | - Shachar Gal
- Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
| | - Moni Shahar
- Center of AI and Data Science, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
| | - Ido Tavor
- Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
- Strauss Center for Computational Neuroimaging, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
| | - Dino J Levy
- Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
- Coller School of Management, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
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115
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Zhang D, Zhang R, Zhou L, Zhou K, Chang C. The brain network underlying attentional blink predicts symptoms of attention deficit hyperactivity disorder in children. Cereb Cortex 2023; 33:2761-2773. [PMID: 35699600 DOI: 10.1093/cercor/bhac240] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/20/2022] [Accepted: 05/21/2022] [Indexed: 12/30/2022] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is a chronic neuropsychiatric disease that can markedly impair educational, social, and occupational function throughout life. Behavioral deficits may provide clues to the underlying neurological impairments. Children with ADHD exhibit a larger attentional blink (AB) deficit in rapid serial visual presentation (RSVP) tasks than typically developing children, so we examined whether brain connectivity in the neural network associated with AB can predict ADHD symptoms and thus serve as potential biomarkers of the underlying neuropathology. We first employed a connectome-based predictive model analysis of adult resting-state functional magnetic resonance imaging data to identify a distributed brain network for AB. The summed functional connectivity (FC) strength within the AB network reliably predicted individual differences in AB magnitude measured by a classical dual-target RSVP task. Furthermore, the summed FC strength within the AB network predicted individual differences in ADHD Rating Scale scores from an independent dataset of pediatric patients. Our findings suggest that the individual AB network could serve as an applicable neuroimaging-based biomarker of AB deficit and ADHD symptoms.
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Affiliation(s)
- Dai Zhang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, No. 1066, Xueyuan Street, Nanshan District, Shenzhen 518073, China
| | - Ruotong Zhang
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Liqin Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Ke Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Chunqi Chang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, No. 1066, Xueyuan Street, Nanshan District, Shenzhen 518073, China
- Peng Cheng Laboratory, No. 2, Xingke Street, Nanshan District, Shenzhen 518055, China
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116
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Porter A, Nielsen A, Dorn M, Dworetsky A, Edmonds D, Gratton C. Masked features of task states found in individual brain networks. Cereb Cortex 2023; 33:2879-2900. [PMID: 35802477 PMCID: PMC10016040 DOI: 10.1093/cercor/bhac247] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 11/14/2022] Open
Abstract
Completing complex tasks requires that we flexibly integrate information across brain areas. While studies have shown how functional networks are altered during different tasks, this work has generally focused on a cross-subject approach, emphasizing features that are common across people. Here we used extended sampling "precision" fMRI data to test the extent to which task states generalize across people or are individually specific. We trained classifiers to decode state using functional network data in single-person datasets across 5 diverse task states. Classifiers were then tested on either independent data from the same person or new individuals. Individualized classifiers were able to generalize to new participants. However, classification performance was significantly higher within a person, a pattern consistent across model types, people, tasks, feature subsets, and even for decoding very similar task conditions. Notably, these findings also replicated in a new independent dataset. These results suggest that individual-focused approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individual-focused approaches have the potential to deepen our understanding of brain interactions during complex cognition.
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Affiliation(s)
- Alexis Porter
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Ashley Nielsen
- Department of Neurology, Washington University in St. Louis, 1 Brookings Dr, St. Louis, MO 63130, United States
| | - Megan Dorn
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Ally Dworetsky
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Donnisa Edmonds
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Caterina Gratton
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
- Department of Neurology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
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117
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Sankar A, Shen X, Colic L, Goldman DA, Villa LM, Kim JA, Pittman B, Scheinost D, Constable RT, Blumberg HP. Predicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes. Psychol Med 2023; 53:1-10. [PMID: 36891769 PMCID: PMC10491744 DOI: 10.1017/s003329172300003x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 12/07/2022] [Accepted: 01/03/2023] [Indexed: 03/10/2023]
Abstract
BACKGROUND The study is aimed to identify brain functional connectomes predictive of depressed and elevated mood symptomatology in individuals with bipolar disorder (BD) using the machine learning approach Connectome-based Predictive Modeling (CPM). METHODS Functional magnetic resonance imaging data were obtained from 81 adults with BD while they performed an emotion processing task. CPM with 5000 permutations of leave-one-out cross-validation was applied to identify functional connectomes predictive of depressed and elevated mood symptom scores on the Hamilton Depression and Young Mania rating scales. The predictive ability of the identified connectomes was tested in an independent sample of 43 adults with BD. RESULTS CPM predicted the severity of depressed [concordance between actual and predicted values (r = 0.23, pperm (permutation test) = 0.031) and elevated (r = 0.27, pperm = 0.01) mood. Functional connectivity of left dorsolateral prefrontal cortex and supplementary motor area nodes, with inter- and intra-hemispheric connections to other anterior and posterior cortical, limbic, motor, and cerebellar regions, predicted depressed mood severity. Connectivity of left fusiform and right visual association area nodes with inter- and intra-hemispheric connections to the motor, insular, limbic, and posterior cortices predicted elevated mood severity. These networks were predictive of mood symptomatology in the independent sample (r ⩾ 0.45, p = 0.002). CONCLUSIONS This study identified distributed functional connectomes predictive of depressed and elevated mood severity in BD. Connectomes subserving emotional, cognitive, and psychomotor control predicted depressed mood severity, while those subserving emotional and social perceptual functions predicted elevated mood severity. Identification of these connectome networks may help inform the development of targeted treatments for mood symptoms.
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Affiliation(s)
- Anjali Sankar
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Lejla Colic
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- German Center for Mental Health, Halle-Jena-Magdeburg, Magdeburg, Germany
| | - Danielle A. Goldman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
| | - Luca M. Villa
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Jihoon A. Kim
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Brian Pittman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R. Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Hilary P. Blumberg
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
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118
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Harry BB, Margulies DS, Falkiewicz M, Keller PE. Brain networks for temporal adaptation, anticipation, and sensory-motor integration in rhythmic human behavior. Neuropsychologia 2023; 183:108524. [PMID: 36868500 DOI: 10.1016/j.neuropsychologia.2023.108524] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 01/21/2023] [Accepted: 02/22/2023] [Indexed: 03/05/2023]
Abstract
Human interaction often requires the precise yet flexible interpersonal coordination of rhythmic behavior, as in group music making. The present fMRI study investigates the functional brain networks that may facilitate such behavior by enabling temporal adaptation (error correction), prediction, and the monitoring and integration of information about 'self' and the external environment. Participants were required to synchronize finger taps with computer-controlled auditory sequences that were presented either at a globally steady tempo with local adaptations to the participants' tap timing (Virtual Partner task) or with gradual tempo accelerations and decelerations but without adaptation (Tempo Change task). Connectome-based predictive modelling was used to examine patterns of brain functional connectivity related to individual differences in behavioral performance and parameter estimates from the adaptation and anticipation model (ADAM) of sensorimotor synchronization for these two tasks under conditions of varying cognitive load. Results revealed distinct but overlapping brain networks associated with ADAM-derived estimates of temporal adaptation, anticipation, and the integration of self-controlled and externally controlled processes across task conditions. The partial overlap between ADAM networks suggests common hub regions that modulate functional connectivity within and between the brain's resting-state networks and additional sensory-motor regions and subcortical structures in a manner reflecting coordination skill. Such network reconfiguration might facilitate sensorimotor synchronization by enabling shifts in focus on internal and external information, and, in social contexts requiring interpersonal coordination, variations in the degree of simultaneous integration and segregation of these information sources in internal models that support self, other, and joint action planning and prediction.
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Affiliation(s)
- Bronson B Harry
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia.
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France; Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Marcel Falkiewicz
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Peter E Keller
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark.
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119
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Shi D, Ren Z, Zhang H, Wang G, Guo Q, Wang S, Ding J, Yao X, Li Y, Ren K. Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease. Heliyon 2023; 9:e14325. [PMID: 36950566 PMCID: PMC10025115 DOI: 10.1016/j.heliyon.2023.e14325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 01/18/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.
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Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Zhendong Ren
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Haoran Zhang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Guangsong Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Qiu Guo
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Siyuan Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Jie Ding
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xiang Yao
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yanfei Li
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Key Laboratory for Endocrine-Related Cancer Precision Medicine, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Corresponding author. Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
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Wang X, Zwosta K, Wolfensteller U, Ruge H. Changes in global functional network properties predict individual differences in habit formation. Hum Brain Mapp 2023; 44:1565-1578. [PMID: 36413054 PMCID: PMC9921330 DOI: 10.1002/hbm.26158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 11/23/2022] Open
Abstract
Prior evidence suggests that sensorimotor regions play a crucial role in habit formation. Yet, whether and how their global functional network properties might contribute to a more comprehensive characterization of habit formation still remains unclear. Capitalizing on advances in Elastic Net regression and predictive modeling, we examined whether learning-related functional connectivity alterations distributed across the whole brain could predict individual habit strength. Using the leave-one-subject-out cross-validation strategy, we found that the habit strength score of the novel unseen subjects could be successfully predicted. We further characterized the contribution of both, individual large-scale networks and individual brain regions by calculating their predictive weights. This highlighted the pivotal role of functional connectivity changes involving the sensorimotor network and the cingulo-opercular network in subject-specific habit strength prediction. These results contribute to the understanding the neural basis of human habit formation by demonstrating the importance of global functional network properties especially also for predicting the observable behavioral expression of habits.
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Affiliation(s)
- Xiaoyu Wang
- Fakultät Psychologie, Technische Universität Dresden, Dresden, Germany
| | - Katharina Zwosta
- Fakultät Psychologie, Technische Universität Dresden, Dresden, Germany
| | - Uta Wolfensteller
- Fakultät Psychologie, Technische Universität Dresden, Dresden, Germany
| | - Hannes Ruge
- Fakultät Psychologie, Technische Universität Dresden, Dresden, Germany
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Koban L, Lee S, Schelski DS, Simon MC, Lerman C, Weber B, Kable JW, Plassmann H. An fMRI-Based Brain Marker of Individual Differences in Delay Discounting. J Neurosci 2023; 43:1600-1613. [PMID: 36657973 PMCID: PMC10008056 DOI: 10.1523/jneurosci.1343-22.2022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/02/2022] [Accepted: 12/07/2022] [Indexed: 01/20/2023] Open
Abstract
Individual differences in delay discounting-how much we discount future compared to immediate rewards-are associated with general life outcomes, psychopathology, and obesity. Here, we use machine learning on fMRI activity during an intertemporal choice task to develop a functional brain marker of these individual differences in human adults. Training and cross-validating the marker in one dataset (Study 1, N = 110 male adults) resulted in a significant prediction-outcome correlation (r = 0.49), generalized to predict individual differences in a completely independent dataset (Study 2: N = 145 male and female adults, r = 0.45), and predicted discounting several weeks later. Out-of-sample responses of the functional brain marker, but not discounting behavior itself, differed significantly between overweight and lean individuals in both studies, and predicted fasting-state blood levels of insulin, c-peptide, and leptin in Study 1. Significant predictive weights of the marker were found in cingulate, insula, and frontoparietal areas, among others, suggesting an interplay among regions associated with valuation, conflict processing, and cognitive control. This new functional brain marker is a step toward a generalizable brain model of individual differences in delay discounting. Future studies can evaluate it as a potential transdiagnostic marker of altered decision-making in different clinical and developmental populations.SIGNIFICANCE STATEMENT People differ substantially in how much they prefer smaller sooner rewards or larger later rewards such as spending money now versus saving it for retirement. These individual differences are generally stable over time and have been related to differences in mental and bodily health. What is their neurobiological basis? We applied machine learning to brain-imaging data to identify a novel brain activity pattern that accurately predicts how much people prefer sooner versus later rewards, and which can be used as a new brain-based measure of intertemporal decision-making in future studies. The resulting functional brain marker also predicts overweight and metabolism-related blood markers, providing new insight into the possible links between metabolism and the cognitive and brain processes involved in intertemporal decision-making.
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Affiliation(s)
- Leonie Koban
- Marketing Area, INSEAD, F-77300 Fontainebleau, France
- Control-Interoception-Attention Team, Paris Brain Institute (ICM), INSERM U1127, CNRS UMR7225, Sorbonne University, 75013 Paris, France
- CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, Université Claude Bernard Lyon 1, 69500 Bron, France
| | - Sangil Lee
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6018
| | - Daniela S Schelski
- Center for Economics and Neuroscience, University of Bonn, 53113 Bonn, Germany
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, 53113 Bonn, Germany
| | - Marie-Christine Simon
- Institute for Nutrition and Food Science, Nutrition and Microbiota, University of Bonn, 53113 Bonn, Germany
| | - Caryn Lerman
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California 90033
| | - Bernd Weber
- Center for Economics and Neuroscience, University of Bonn, 53113 Bonn, Germany
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, 53113 Bonn, Germany
| | - Joseph W Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6018
| | - Hilke Plassmann
- Marketing Area, INSEAD, F-77300 Fontainebleau, France
- Control-Interoception-Attention Team, Paris Brain Institute (ICM), INSERM U1127, CNRS UMR7225, Sorbonne University, 75013 Paris, France
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122
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Li J, Qiu J, Li H. Connectome-based predictive modeling of trait forgiveness. Soc Cogn Affect Neurosci 2023; 18:7003410. [PMID: 36695429 PMCID: PMC9972814 DOI: 10.1093/scan/nsad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 12/29/2022] [Accepted: 01/24/2023] [Indexed: 01/26/2023] Open
Abstract
Forgiveness is a positive, prosocial manner of reacting to transgressions and is strongly associated with mental health and well-being. Despite recent studies exploring the neural mechanisms underlying forgiveness, a model capable of predicting trait forgiveness at the individual level has not been developed. Herein, we applied a machine-learning approach, connectome-based predictive modeling (CPM), with whole-brain resting-state functional connectivity (rsFC) to predict individual differences in trait forgiveness in a training set (dataset 1, N = 100, 35 men, 17-24 years). As a result, CPM successfully predicted individual trait forgiveness based on whole-brain rsFC, especially via the functional connectivity of the limbic, prefrontal and temporal areas, which are key contributors to the prediction model comprising regions previously implicated in forgiveness. These regions include the retrosplenial cortex, temporal pole, dorsolateral prefrontal cortex (PFC), dorsal anterior cingulate cortex, precuneus and dorsal posterior cingulate cortex. Importantly, this predictive model could be successfully generalized to an independent sample (dataset 2, N = 71, 17 men, 16-25 years). These findings highlight the important roles of the limbic system, PFC and temporal region in trait forgiveness prediction and represent the initial steps toward establishing an individualized prediction model of forgiveness.
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Affiliation(s)
- Jingyu Li
- Department of Psychology, Shanghai Normal University, Shanghai 200234, China.,The Research Base of Online Education for Shanghai Middle and Primary Schools, Shanghai 200234, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Haijiang Li
- Department of Psychology, Shanghai Normal University, Shanghai 200234, China.,The Research Base of Online Education for Shanghai Middle and Primary Schools, Shanghai 200234, China
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123
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Feng P, Jiang R, Wei L, Calhoun VD, Jing B, Li H, Sui J. Determining four confounding factors in individual cognitive traits prediction with functional connectivity: an exploratory study. Cereb Cortex 2023; 33:2011-2020. [PMID: 35567795 PMCID: PMC9977351 DOI: 10.1093/cercor/bhac189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 11/14/2022] Open
Abstract
Resting-state functional connectivity (RSFC) has been widely adopted for individualized trait prediction. However, multiple confounding factors may impact the predicted brain-behavior relationships. In this study, we investigated the impact of 4 confounding factors including time series length, functional connectivity (FC) type, brain parcellation choice, and variance of the predicted target. The data from Human Connectome Project including 1,206 healthy subjects were employed, with 3 cognitive traits including fluid intelligence, working memory, and picture vocabulary ability as the prediction targets. We compared the prediction performance under different settings of these 4 factors using partial least square regression. Results demonstrated appropriate time series length (300 time points) and brain parcellation (independent component analysis, ICA100/200) can achieve better prediction performance without too much time consumption. FC calculated by Pearson, Spearman, and Partial correlation achieves higher accuracy and lower time cost than mutual information and coherence. Cognitive traits with larger variance among subjects can be better predicted due to the well elaboration of individual variability. In addition, the beneficial effects of increasing scan duration to prediction partially arise from the improved test-retest reliability of RSFC. Taken together, the study highlights the importance of determining these factors in RSFC-based prediction, which can facilitate standardization of RSFC-based prediction pipelines going forward.
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Affiliation(s)
- Pujie Feng
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 300 Cedar Street, New Haven, 06510 CT, United States
| | - Lijiang Wei
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekou Outer Street, Haidian District, 100875 Beijing, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, 30303, GA, United States
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Haiyun Li
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekou Outer Street, Haidian District, 100875 Beijing, China.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, 30303, GA, United States
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124
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Cutts SA, Faskowitz J, Betzel RF, Sporns O. Uncovering individual differences in fine-scale dynamics of functional connectivity. Cereb Cortex 2023; 33:2375-2394. [PMID: 35690591 DOI: 10.1093/cercor/bhac214] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/07/2022] [Accepted: 05/08/2022] [Indexed: 01/01/2023] Open
Abstract
Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.
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Affiliation(s)
- Sarah A Cutts
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States.,Network Science Institute, Indiana University, Bloomington, IN 47408, United States.,Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States.,Network Science Institute, Indiana University, Bloomington, IN 47408, United States.,Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
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125
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Wu X, Yang Q, Xu C, Huo H, Seger CA, Peng Z, Chen Q. Connectome-based predictive modeling of compulsion in obsessive-compulsive disorder. Cereb Cortex 2023; 33:1412-1425. [PMID: 35443038 DOI: 10.1093/cercor/bhac145] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/13/2022] Open
Abstract
Compulsion is one of core symptoms of obsessive-compulsive disorder (OCD). Although many studies have investigated the neural mechanism of compulsion, no study has used brain-based measures to predict compulsion. Here, we used connectome-based predictive modeling (CPM) to identify networks that could predict the levels of compulsion based on whole-brain functional connectivity in 57 OCD patients. We then applied a computational lesion version of CPM to examine the importance of specific brain areas. We also compared the predictive network strength in OCD with unaffected first-degree relatives (UFDR) of patients and healthy controls. CPM successfully predicted individual level of compulsion and identified networks positively (primarily subcortical areas of the striatum and limbic regions of the hippocampus) and negatively (primarily frontoparietal regions) correlated with compulsion. The prediction power of the negative model significantly decreased when simulating lesions to the prefrontal cortex and cerebellum, supporting the importance of these regions for compulsion prediction. We found a similar pattern of network strength in the negative predictive network for OCD patients and their UFDR, demonstrating the potential of CPM to identify vulnerability markers for psychopathology.
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Affiliation(s)
- Xiangshu Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Qiong Yang
- Affiliated Brain Hospital of Guangzhou Medical University, 510370 Guangzhou, China
| | - Chuanyong Xu
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen 518047, China
| | - Hangfeng Huo
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Carol A Seger
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.,Department of Psychology, Colorado State University, Fort Collins, CO 80523, United States
| | - Ziwen Peng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.,Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen University School of Medicine, Shenzhen 518061, China
| | - Qi Chen
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
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126
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Rolls ET, Feng R, Cheng W, Feng J. Orbitofrontal cortex connectivity is associated with food reward and body weight in humans. Soc Cogn Affect Neurosci 2023; 18:nsab083. [PMID: 34189586 PMCID: PMC10498940 DOI: 10.1093/scan/nsab083] [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: 04/15/2021] [Revised: 06/10/2021] [Accepted: 06/29/2021] [Indexed: 11/12/2022] Open
Abstract
The aim was to investigate with very large-scale analyses whether there are underlying functional connectivity differences between humans that relate to food reward and whether these in turn are associated with being overweight. In 37 286 humans from the UK Biobank, resting-state functional connectivities of the orbitofrontal cortex (OFC), especially with the anterior cingulate cortex, were positively correlated with the liking for sweet foods (False Discovery Rate (FDR) P < 0.05). They were also positively correlated with the body mass index (BMI) (FDR P < 0.05). Moreover, in a sample of 502 492 people, the 'liking for sweet foods' was correlated with their BMI (r = 0.06, P < 10-125). In a cross-validation with 545 participants from the Human Connectome Project, a higher functional connectivity involving the OFC relative to other brain areas was associated with a high BMI (≥30) compared to a mid-BMI group (22-25; P = 6 × 10-5), and low OFC functional connectivity was associated with a low BMI (≤20.5; P < 0.024). It is proposed that a high BMI relates to increased efficacy of OFC food reward systems and a low BMI to decreased efficacy. This was found with no stimulation by food, so may be an underlying individual difference in brain connectivity that is related to food reward and BMI.
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Affiliation(s)
- Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Ruiqing Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
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127
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Keane BP, Krekelberg B, Mill RD, Silverstein SM, Thompson JL, Serody MR, Barch DM, Cole MW. Dorsal attention network activity during perceptual organization is distinct in schizophrenia and predictive of cognitive disorganization. Eur J Neurosci 2023; 57:458-478. [PMID: 36504464 DOI: 10.1111/ejn.15889] [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: 07/09/2022] [Revised: 12/02/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022]
Abstract
Visual shape completion is a canonical perceptual organization process that integrates spatially distributed edge information into unified representations of objects. People with schizophrenia show difficulty in discriminating completed shapes, but the brain networks and functional connections underlying this perceptual difference remain poorly understood. Also unclear is whether brain network differences in schizophrenia occur in related illnesses or vary with illness features transdiagnostically. To address these topics, we scanned (functional magnetic resonance imaging, fMRI) people with schizophrenia, bipolar disorder, or no psychiatric illness during rest and during a task in which they discriminated configurations that formed or failed to form completed shapes (illusory and fragmented condition, respectively). Multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping was used to evaluate the likely involvement of resting-state connections for shape completion. Illusory/fragmented task activation differences ('modulations') in the dorsal attention network (DAN) could distinguish people with schizophrenia from the other groups (AUCs > .85) and could transdiagnostically predict cognitive disorganization severity. Activity flow over functional connections from the DAN could predict secondary visual network modulations in each group, except in schizophrenia. The secondary visual network was strongly and similarly modulated in each group. Task modulations were dispersed over more networks in patients compared to controls. In summary, DAN activity during visual perceptual organization is distinct in schizophrenia, symptomatically relevant, and potentially related to improper attention-related feedback into secondary visual areas.
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Affiliation(s)
- Brian P Keane
- University Behavioral Health Care, Department of Psychiatry, and Center for Cognitive Science, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
| | - Bart Krekelberg
- Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
| | - Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
| | - Steven M Silverstein
- University Behavioral Health Care, Department of Psychiatry, and Center for Cognitive Science, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, New York, USA
| | - Judy L Thompson
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
- Department of Psychiatric Rehabilitation and Counseling Professions, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Megan R Serody
- University Behavioral Health Care, Department of Psychiatry, and Center for Cognitive Science, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
| | - Deanna M Barch
- Departments of Psychological & Brain Sciences, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
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128
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Functional connectivity based brain signatures of behavioral regulation in children with ADHD, DCD, and ADHD-DCD. Dev Psychopathol 2023; 35:85-94. [PMID: 34937602 DOI: 10.1017/s0954579421001449] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Behavioral regulation problems have been associated with daily-life and mental health challenges in children with neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD) and developmental coordination disorder (DCD). Here, we investigated transdiagnostic brain signatures associated with behavioral regulation. Resting-state fMRI data were collected from 115 children (31 typically developing (TD), 35 ADHD, 21 DCD, 28 ADHD-DCD) aged 7-17 years. Behavioral regulation was measured using the Behavior Rating Inventory of Executive Function and was found to differ between children with ADHD (i.e., children with ADHD and ADHD-DCD) and without ADHD (i.e., TD children and children with DCD). Functional connectivity (FC) maps were computed for 10 regions of interest and FC maps were tested for correlations with behavioral regulation scores. Across the entire sample, greater behavioral regulation problems were associated with stronger negative FC within prefrontal pathways and visual reward pathways, as well as with weaker positive FC in frontostriatal reward pathways. These findings significantly increase our knowledge on FC in children with and without ADHD and highlight the potential of FC as brain-based signatures of behavioral regulation across children with differing neurodevelopmental conditions.
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129
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Koban L, Wager TD, Kober H. A neuromarker for drug and food craving distinguishes drug users from non-users. Nat Neurosci 2023; 26:316-325. [PMID: 36536243 DOI: 10.1038/s41593-022-01228-w] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/01/2022] [Indexed: 12/24/2022]
Abstract
Craving is a core feature of substance use disorders. It is a strong predictor of substance use and relapse and is linked to overeating, gambling, and other maladaptive behaviors. Craving is measured via self-report, which is limited by introspective access and sociocultural contexts. Neurobiological markers of craving are both needed and lacking, and it remains unclear whether craving for drugs and food involve similar mechanisms. Across three functional magnetic resonance imaging studies (n = 99), we used machine learning to identify a cross-validated neuromarker that predicts self-reported intensity of cue-induced drug and food craving (P < 0.0002). This pattern, which we term the Neurobiological Craving Signature (NCS), includes ventromedial prefrontal and cingulate cortices, ventral striatum, temporal/parietal association areas, mediodorsal thalamus and cerebellum. Importantly, NCS responses to drug versus food cues discriminate drug users versus non-users with 82% accuracy. The NCS is also modulated by a self-regulation strategy. Transfer between separate neuromarkers for drug and food craving suggests shared neurobiological mechanisms. Future studies can assess the discriminant and convergent validity of the NCS and test whether it responds to clinical interventions and predicts long-term clinical outcomes.
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Affiliation(s)
- Leonie Koban
- Paris Brain Institute (ICM), Inserm, CNRS, Sorbonne Université, Paris, France.
- Centre de Recherche en Neurosciences de Lyon (CRNL), CNRS, INSERM, Université Claude Bernard Lyon 1, Bron, France.
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Hedy Kober
- Department of Psychiatry and Psychology, Yale University, New Haven, CT, USA.
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130
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Boyle R, Connaughton M, McGlinchey E, Knight SP, De Looze C, Carey D, Stern Y, Robertson IH, Kenny RA, Whelan R. Connectome-based predictive modelling of cognitive reserve using task-based functional connectivity. Eur J Neurosci 2023; 57:490-510. [PMID: 36512321 PMCID: PMC10107737 DOI: 10.1111/ejn.15896] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 11/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022]
Abstract
Cognitive reserve supports cognitive function in the presence of pathology or atrophy. Functional neuroimaging may enable direct and accurate measurement of cognitive reserve which could have considerable clinical potential. The present study aimed to develop and validate a measure of cognitive reserve using task-based fMRI data that could then be applied to independent resting-state data. Connectome-based predictive modelling with leave-one-out cross-validation was applied to predict a residual measure of cognitive reserve using task-based functional connectivity from the Cognitive Reserve/Reference Ability Neural Network studies (n = 220, mean age = 51.91 years, SD = 17.04 years). This model generated summary measures of connectivity strength that accurately predicted a residual measure of cognitive reserve in unseen participants. The theoretical validity of these measures was established via a positive correlation with a socio-behavioural proxy of cognitive reserve (verbal intelligence) and a positive correlation with global cognition, independent of brain structure. This fitted model was then applied to external test data: resting-state functional connectivity data from The Irish Longitudinal Study on Ageing (TILDA, n = 294, mean age = 68.3 years, SD = 7.18 years). The network-strength predicted measures were not positively associated with a residual measure of cognitive reserve nor with measures of verbal intelligence and global cognition. The present study demonstrated that task-based functional connectivity data can be used to generate theoretically valid measures of cognitive reserve. Further work is needed to establish if, and how, measures of cognitive reserve derived from task-based functional connectivity can be applied to independent resting-state data.
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Affiliation(s)
- Rory Boyle
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
| | - Michael Connaughton
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
- Department of Psychiatry, School of MedicineTrinity College DublinDublinIreland
| | - Eimear McGlinchey
- School of Nursing and MidwiferyTrinity College DublinDublinIreland
- Global Brain Health InstituteTrinity College DublinDublinIreland
| | - Silvin P. Knight
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
| | - Céline De Looze
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
| | - Daniel Carey
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of NeurologyColumbia UniversityNew York CityNew YorkUSA
| | - Ian H. Robertson
- Global Brain Health InstituteTrinity College DublinDublinIreland
| | - Rose Anne Kenny
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
- Mercer's Institute for Successful AgeingSt. James's HospitalDublinIreland
| | - Robert Whelan
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
- Global Brain Health InstituteTrinity College DublinDublinIreland
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131
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Global Functional Connectivity at Rest Is Associated with Attention: An Arterial Spin Labeling Study. Brain Sci 2023; 13:brainsci13020228. [PMID: 36831771 PMCID: PMC9954008 DOI: 10.3390/brainsci13020228] [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: 01/07/2023] [Revised: 01/25/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Neural markers of attention, including those frequently linked to the event-related potential P3 (P300) or P3b component, vary widely within and across participants. Understanding the neural mechanisms of attention that contribute to the P3 is crucial for better understanding attention-related brain disorders. All ten participants were scanned twice with a resting-state PCASL perfusion MRI and an ERP with a visual oddball task to measure brain resting-state functional connectivity (rsFC) and P3 parameters (P3 amplitudes and P3 latencies). Global rsFC (average rsFC across the entire brain) was associated with both P3 amplitudes (r = 0.57, p = 0.011) and P3 onset latencies (r = -0.56, p = 0.012). The observed P3 parameters were correlated with predicted P3 amplitude from the global rsFC (amplitude: r = +0.48, p = 0.037; latency: r = +0.40, p = 0.088) but not correlated with the rsFC over the most significant individual edge. P3 onset latency was primarily related to long-range connections between the prefrontal and parietal/limbic regions, while P3 amplitudes were related to connections between prefrontal and parietal/occipital, between sensorimotor and subcortical, and between limbic/subcortical and parietal/occipital regions. These results demonstrated the power of resting-state PCASL and P3 correlation with brain global functional connectivity.
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Zhang D, Yu L, Chen Y, Shen J, Du L, Lin L, Wu J. Connectome-based predictive modeling predicts paranoid ideation in young men with paranoid personality disorder: a resting-state functional magnetic resonance imaging study. Cereb Cortex 2023:6992943. [PMID: 36657794 DOI: 10.1093/cercor/bhac531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 01/21/2023] Open
Abstract
Paranoid personality disorder (PPD), a mental disorder that affects interpersonal relationships and work, is frequently neglected during diagnosis and evaluation at the individual-level. This preliminary study aimed to investigate whether connectome-based predictive modeling (CPM) can predict paranoia scores of young men with PPD using whole-brain resting-state functional connectivity (rs-FC). College students with paranoid tendencies were screened using paranoia scores ≥60 derived from the Minnesota Multiphasic Personality Inventory; 18 participants were ultimately diagnosed with PPD according to the Diagnostic and Statistical Manual of Mental Disorders and subsequently underwent resting-state functional magnetic resonance imaging. Whole-brain rs-FC was constructed, and the ability of this rs-FC to predict paranoia scores was evaluated using CPM. The significance of the models was assessed using permutation tests. The model constructed based on the negative prediction network involving the limbic system-temporal lobe was observed to have significant predictive ability for paranoia scores, whereas the model constructed using the positive and combined prediction network had no significant predictive ability. In conclusion, using CPM, whole-brain rs-FC predicted the paranoia score of patients with PPD. The limbic system-temporal lobe FC pattern is expected to become an important neurological marker for evaluating paranoid ideation.
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Affiliation(s)
- Die Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China.,Department of Radiology, Shenzhen Third People's Hospital, Shenzhen 518000, China
| | - Lan Yu
- Department of Radiology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou 211166,China
| | - Yingying Chen
- Department of Radiology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen 518172, China
| | - Jing Shen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Lina Du
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Lin Lin
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
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Schwarzlose RF, Tillman R, Hoyniak CP, Luby JL, Barch DM. Sensory Over-responsivity: A Feature of Childhood Psychiatric Illness Associated With Altered Functional Connectivity of Sensory Networks. Biol Psychiatry 2023; 93:92-101. [PMID: 36357217 PMCID: PMC10308431 DOI: 10.1016/j.biopsych.2022.09.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 08/20/2022] [Accepted: 09/21/2022] [Indexed: 11/02/2022]
Abstract
BACKGROUND Sensory over-responsivity (SOR) is recognized as a common feature of autism spectrum disorder. However, SOR is also common among typically developing children, in whom it is associated with elevated levels of psychiatric symptoms. The clinical significance and neurocognitive bases of SOR in these children remain poorly understood and actively debated. METHODS This study used linear mixed-effects models to identify psychiatric symptoms and network-level functional connectivity (FC) differences associated with parent-reported SOR in the Adolescent Brain Cognitive Development (ABCD) Study, a large community sample (9 to 12 years of age) (N = 11,210). RESULTS Children with SOR constituted 18% of the overall sample but comprised more than half of the children with internalizing or externalizing scores in the clinical range. Controlling for autistic traits, both mild and severe SOR were associated with greater concurrent symptoms of depression, anxiety, obsessive-compulsive disorder, and attention-deficit/hyperactivity disorder. Controlling for psychiatric symptoms and autistic traits, SOR predicted increased anxiety, attention-deficit/hyperactivity disorder, and prodromal psychosis symptoms 1 year later and was associated with FC differences in brain networks supporting sensory and salience processing in datasets collected 2 years apart. Differences included reduced FC within and between sensorimotor networks, enhanced sensorimotor-salience FC, and altered FC between sensory networks and bilateral hippocampi. CONCLUSIONS SOR is a common, clinically relevant feature of childhood psychiatric illness that provides unique predictive information about risk. It is associated with differences in brain networks that subserve tactile processing, implicating a neural basis for sensory differences in affected children.
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Affiliation(s)
- Rebecca F Schwarzlose
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri.
| | - Rebecca Tillman
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Caroline P Hoyniak
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Joan L Luby
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri
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Abrupt vs. gradual visual onsets in go/no-go sustained attention tasks. Atten Percept Psychophys 2023; 85:9-22. [PMID: 36307747 DOI: 10.3758/s13414-022-02574-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/08/2022] [Indexed: 01/10/2023]
Abstract
Two experiments compared both average performance and changes in performance across time in abrupt- and gradual-onset sustained attention tasks. Experiment 1 compared abrupt- and gradual-onset digits. In conditions where the digits onset and offset abruptly and appeared only briefly, similar to typical conditions in the Sustained Attention to Response Task (SART), participants committed more errors on no-go trials and responded faster overall, indicative of a shift in the speed/accuracy tradeoff toward speed. When the digits abruptly onset but remained on-screen for a longer period of time, there were no differences in no-go error rates, hit rates, or reaction time (RT) variability, but participants still emitted faster RTs overall. Experiment 2 compared abrupt- and gradual-onset images. Similar to Experiment 1, abrupt-onset, short-duration images induced more no-go errors and faster RTs, but also more RT variability and reduced hit rates. In the abrupt-onset, long-duration condition, again the only performance difference was a decrease in average RTs. We discuss implications for using these two types of tasks in sustained attention research.
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135
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Tomasi D, Volkow ND. Brain motion networks predict head motion during rest- and task-fMRI. Front Neurosci 2023; 17:1096232. [PMID: 37113158 PMCID: PMC10126373 DOI: 10.3389/fnins.2023.1096232] [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: 11/11/2022] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
Introduction The capacity to stay still during scanning, which is necessary to avoid motion confounds while imaging, varies markedly between people. Methods Here we investigated the effect of head motion on functional connectivity using connectome-based predictive modeling (CPM) and publicly available brain functional magnetic resonance imaging (fMRI) data from 414 individuals with low frame-to-frame motion (Δd < 0.18 mm). Leave-one-out was used for internal cross-validation of head motion prediction in 207 participants, and twofold cross-validation was used in an independent sample (n = 207). Results and Discussion Parametric testing, as well as CPM-based permutations for null hypothesis testing, revealed strong linear associations between observed and predicted values of head motion. Motion prediction accuracy was higher for task- than for rest-fMRI, and for absolute head motion (d) than for Δd. Denoising attenuated the predictability of head motion, but stricter framewise displacement threshold (FD = 0.2 mm) for motion censoring did not alter the accuracy of the predictions obtained with lenient censoring (FD = 0.5 mm). For rest-fMRI, prediction accuracy was lower for individuals with low motion (mean Δd < 0.02 mm; n = 200) than for those with moderate motion (Δd < 0.04 mm; n = 414). The cerebellum and default-mode network (DMN) regions that forecasted individual differences in d and Δd during six different tasks- and two rest-fMRI sessions were consistently prone to the deleterious effect of head motion. However, these findings generalized to a novel group of 1,422 individuals but not to simulated datasets without neurobiological contributions, suggesting that cerebellar and DMN connectivity could partially reflect functional signals pertaining to inhibitory motor control during fMRI.
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Affiliation(s)
- Dardo Tomasi
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States
- *Correspondence: Dardo Tomasi,
| | - Nora D. Volkow
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States
- National Institute on Drug Abuse, Bethesda, MD, United States
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Krämer C, Stumme J, da Costa Campos L, Rubbert C, Caspers J, Caspers S, Jockwitz C. Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach. Netw Neurosci 2023; 7:122-147. [PMID: 37339286 PMCID: PMC10270720 DOI: 10.1162/netn_a_00275] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/22/2022] [Indexed: 09/22/2023] Open
Abstract
Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.
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Affiliation(s)
- Camilla Krämer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lucas da Costa Campos
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Kong Z, Zhu X, Chang S, Bao Y, Ma Y, Yu W, Zhu R, Sun Q, Sun W, Deng J, Sun H. Somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and its neuroimaging mechanisms. BMC Psychiatry 2022; 22:835. [PMID: 36581819 PMCID: PMC9798660 DOI: 10.1186/s12888-022-04488-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 12/20/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Subclinical anxiety, depressive and somatic symptoms appear closely related. However, it remains unclear whether somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and what the underlying neuroimaging mechanisms are for the mediating effect. METHODS Data of healthy participants (n = 466) and participants in remission of major depressive disorder (n = 53) were obtained from the Human Connectome Project. The Achenbach Adult Self-Report was adopted to assess anxiety, depressive and somatic symptoms. All participants completed four runs of resting-state functional magnetic resonance imaging. Mediation analyses were utilized to explore the interactions among these symptoms and their neuroimaging mechanisms. RESULTS Somatic symptoms partially mediated the association between subclinical anxiety and depressive symptoms in healthy participants (anxiety→somatic→depression: effect: 0.2785, Boot 95% CI: 0.0958-0.3729; depression→somatic→anxiety: effect: 0.0753, Boot 95% CI: 0.0232-0.1314) and participants in remission of MDD (anxiety→somatic→depression: effect: 0.2948, Boot 95% CI: 0.0357-0.7382; depression→somatic→anxiety: effect: 0.0984, Boot 95% CI: 0.0007-0.2438). Resting-state functional connectivity (FC) between the right medial superior frontal gyrus and the left thalamus and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in healthy participants (effect: 0.0020, Boot 95% CI: 0.0003-0.0043). The mean strength of common FCs of subclinical depressive and somatic symptoms, somatic symptoms, and the mean strength of common FCs of subclinical anxiety and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in remission of MDD (effect: 0.0437, Boot 95% CI: 0.0024-0.1190). These common FCs mainly involved the insula, precentral gyri, postcentral gyri and cingulate gyri. Furthermore, FC between the triangular part of the left inferior frontal gyrus and the left postcentral gyrus was positively associated with subclinical anxiety, depressive and somatic symptoms in remission of MDD (FDR-corrected p < 0.01). CONCLUSIONS Somatic symptoms partially mediate the interaction between subclinical anxiety and depressive symptoms. FCs involving the right medial superior frontal gyrus, left thalamus, triangular part of left inferior frontal gyrus, bilateral insula, precentral gyri, postcentral gyri and cingulate gyri maybe underlie the mediating effect of somatic symptoms.
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Affiliation(s)
- Zhifei Kong
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Ximei Zhu
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Suhua Chang
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Yanping Bao
- grid.11135.370000 0001 2256 9319National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, 100191 China ,grid.11135.370000 0001 2256 9319School of Public Health, Peking University, Beijing, 100191 China
| | - Yundong Ma
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Wenwen Yu
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Ran Zhu
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Qiqing Sun
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Wei Sun
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Jiahui Deng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
| | - Hongqiang Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
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Fan L, Zhuang K, Wang X, Zhang J, Liu C, Gu J, Qiu J. Exploring the behavioral and neural correlates of semantic distance in creative writing. Psychophysiology 2022; 60:e14239. [PMID: 36537015 DOI: 10.1111/psyp.14239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/26/2022] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Creativity is critical to economic growth and societal progress. However, assessing creativity using objective approaches remains a challenge. To address this, we employ three objective indicators based on semantic distance to quantify the originality and appropriateness of creativity by analyzing long texts in a story-writing experiment. Global and local distances were generated separately by computing the mean distance of the whole text and the distance between adjacent sentences, and they were positively correlated with story originality in writing. Global cohesion was positively correlated with story rationality in writing, as generated by computing the semantic coherence between the text and story context. At the behavioral level, three semantic indicators were used to measure originality and appropriateness of creativity and reflected individual differences, including creative achievement and creative personality. At the neural level, global distance was best predicted by the features of the salience and default networks, whereas global cohesion corresponded to the control and salience networks. These findings point to a stable neural basis for semantic indicators and verify the idea of separating different dimensions of creativity. Taken together, our results demonstrate the significance of semantic indicators in assessing creativity and provide insights into analyzing long texts in natural paradigm.
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Affiliation(s)
- Li Fan
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Xueyang Wang
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Jingyi Zhang
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Cheng Liu
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Jing Gu
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University Chongqing China
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Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis. Heliyon 2022; 8:e12276. [PMID: 36582679 PMCID: PMC9793282 DOI: 10.1016/j.heliyon.2022.e12276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/19/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics. Radiomics features were extracted from DC and VMHC metrics generated via resting-state functional magnetic resonance imaging, and significant features were selected and dimensionality was reduced using t-tests and least absolute shrinkage and selection operator. Subsequently, we built our model using a support vector machine classifier. We observed that our method obtained great classification performance (area under the curve, 0.808; accuracy, 74.02%), and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somatomotor, limbic, and default mode networks. Our findings showed that the radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of SZ more comprehensively and contribute to the accurate diagnosis of patients with SZ.
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Liu B, Zhang Q, Xue L, Song PXK, Kang J. Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis. J Am Stat Assoc 2022; 119:715-729. [PMID: 38818252 PMCID: PMC11136478 DOI: 10.1080/01621459.2022.2142590] [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: 11/14/2019] [Accepted: 10/18/2022] [Indexed: 11/06/2022]
Abstract
It is important to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible heavy tails and outliers in real-world applications such as imaging data analyses. We propose a new robust high-dimensional regression with coefficient thresholding, in which an efficient nonconvex estimation procedure is proposed through a thresholding function and the robust Huber loss. The proposed regularization method accounts for complex dependence structures in predictors and is robust against heavy tails and outliers in outcomes. Theoretically, we rigorously analyze the landscape of the population and empirical risk functions for the proposed method. The fine landscape enables us to establish both statistical consistency and computational convergence under the high-dimensional setting. We also present an extension to incorporate spatial information into the proposed method. Finite-sample properties of the proposed methods are examined by extensive simulation studies. An application concerns a scalar-on-image regression analysis for an association of psychiatric disorder measured by the general factor of psychopathology with features extracted from the task functional MRI data in the Adolescent Brain Cognitive Development (ABCD) study.
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Affiliation(s)
| | - Qi Zhang
- The Pennsylvania State University
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141
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Zhang T, Zhang X, Zhu W, Lu Z, Wang Y, Zhang Y. Study on the diversity of mental states and neuroplasticity of the brain during human-machine interaction. Front Neurosci 2022; 16:921058. [PMID: 36570838 PMCID: PMC9768214 DOI: 10.3389/fnins.2022.921058] [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: 04/15/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
Introduction With the increasing demand for human-machine collaboration systems, more and more attention has been paid to the influence of human factors on the performance and security of the entire system. Especially in high-risk, high-precision, and difficult special tasks (such as space station maintenance tasks, anti-terrorist EOD tasks, surgical robot teleoperation tasks, etc.), there are higher requirements for the operator's perception and cognitive level. However, as the human brain is a complex and open giant system, the perception ability and cognitive level of the human are dynamically variable, so that it will seriously affect the performance and security of the whole system. Methods The method proposed in this paper innovatively explained this phenomenon from two dimensions of brain space and time and attributed the dynamic changes of perception, cognitive level, and operational skills to the mental state diversity and the brain neuroplasticity. In terms of the mental state diversity, the mental states evoked paradigm and the functional brain network analysis method during work were proposed. In terms of neuroplasticity, the cognitive training intervention paradigm and the functional brain network analysis method were proposed. Twenty-six subjects participated in the mental state evoked experiment and the cognitive training intervention experiment. Results The results showed that (1) the mental state of the subjects during work had the characteristics of dynamic change, and due to the influence of stimulus conditions and task patterns, the mental state showed diversity. There were significant differences between functional brain networks in different mental states, the information processing efficiency and the mechanism of brain area response had changed significantly. (2) The small-world attributes of the functional brain network of the subjects before and after the cognitive training experiment were significantly different. The brain had adjusted the distribution of information flow and resources, reducing costs and increasing efficiency as a whole. It was demonstrated that the global topology of the cortical connectivity network was reconfigured and neuroplasticity was altered through cognitive training intervention. Discussion In summary, this paper revealed that mental state and neuroplasticity could change the information processing efficiency and the response mechanism of brain area, thus causing the change of perception, cognitive level and operational skills, which provided a theoretical basis for studying the relationship between neural information processing and behavior.
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Affiliation(s)
- Teng Zhang
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Xiaodong Zhang
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China,Shaanxi Key Laboratory of Intelligent Robot, Xi’an Jiaotong University, Xi’an, China,*Correspondence: Xiaodong Zhang,
| | - Wenjing Zhu
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Zhufeng Lu
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Yu Wang
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Yingjie Zhang
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
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Kardan O, Stier AJ, Cardenas-Iniguez C, Schertz KE, Pruin JC, Deng Y, Chamberlain T, Meredith WJ, Zhang X, Bowman JE, Lakhtakia T, Tindel L, Avery EW, Lin Q, Yoo K, Chun MM, Berman MG, Rosenberg MD. Differences in the functional brain architecture of sustained attention and working memory in youth and adults. PLoS Biol 2022; 20:e3001938. [PMID: 36542658 DOI: 10.1371/journal.pbio.3001938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 01/05/2023] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Sustained attention (SA) and working memory (WM) are critical processes, but the brain networks supporting these abilities in development are unknown. We characterized the functional brain architecture of SA and WM in 9- to 11-year-old children and adults. First, we found that adult network predictors of SA generalized to predict individual differences and fluctuations in SA in youth. A WM model predicted WM performance both across and within children-and captured individual differences in later recognition memory-but underperformed in youth relative to adults. We next characterized functional connections differentially related to SA and WM in youth compared to adults. Results revealed 2 network configurations: a dominant architecture predicting performance in both age groups and a secondary architecture, more prominent for WM than SA, predicting performance in each age group differently. Thus, functional connectivity (FC) predicts SA and WM in youth, with networks predicting WM performance differing more between youths and adults than those predicting SA.
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Affiliation(s)
- Omid Kardan
- University of Chicago, Chicago, Illinois, United States of America
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Andrew J Stier
- University of Chicago, Chicago, Illinois, United States of America
| | | | | | - Julia C Pruin
- University of Chicago, Chicago, Illinois, United States of America
| | - Yuting Deng
- University of Chicago, Chicago, Illinois, United States of America
| | | | - Wesley J Meredith
- University of California, Los Angeles, California, United States of America
| | - Xihan Zhang
- University of Chicago, Chicago, Illinois, United States of America
- Yale University, New Haven, Connecticut, United States of America
| | - Jillian E Bowman
- University of Chicago, Chicago, Illinois, United States of America
| | - Tanvi Lakhtakia
- University of Chicago, Chicago, Illinois, United States of America
| | - Lucy Tindel
- University of Chicago, Chicago, Illinois, United States of America
| | - Emily W Avery
- Yale University, New Haven, Connecticut, United States of America
| | - Qi Lin
- Yale University, New Haven, Connecticut, United States of America
| | - Kwangsun Yoo
- Yale University, New Haven, Connecticut, United States of America
| | - Marvin M Chun
- Yale University, New Haven, Connecticut, United States of America
| | - Marc G Berman
- University of Chicago, Chicago, Illinois, United States of America
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143
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Ma SS, Zhang JT, Song KR, Zhao R, Fang RH, Wang LB, Yao ST, Hu YF, Jiang XY, Potenza MN, Fang XY. Connectome-based prediction of marital quality in husbands' processing of spousal interactions. Soc Cogn Affect Neurosci 2022; 17:1055-1067. [PMID: 35560211 PMCID: PMC9714425 DOI: 10.1093/scan/nsac034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 04/12/2022] [Accepted: 05/13/2022] [Indexed: 01/12/2023] Open
Abstract
Marital quality may decrease during the early years of marriage. Establishing models predicting individualized marital quality may help develop timely and effective interventions to maintain or improve marital quality. Given that marital interactions have an important impact on marital well-being cross-sectionally and prospectively, neural responses during marital interactions may provide insight into neural bases underlying marital well-being. The current study applies connectome-based predictive modeling, a recently developed machine-learning approach, to functional magnetic resonance imaging (fMRI) data from both partners of 25 early-stage Chinese couples to examine whether an individual's unique pattern of brain functional connectivity (FC) when responding to spousal interactive behaviors can reliably predict their own and their partners' marital quality after 13 months. Results revealed that husbands' FC involving multiple large networks, when responding to their spousal interactive behaviors, significantly predicted their own and their wives' marital quality, and this predictability showed gender specificity. Brain connectivity patterns responding to general emotional stimuli and during the resting state were not significantly predictive. This study demonstrates that husbands' differences in large-scale neural networks during marital interactions may contribute to their variability in marital quality and highlights gender-related differences. The findings lay a foundation for identifying reliable neuroimaging biomarkers for developing interventions for marital quality early in marriages.
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Affiliation(s)
- Shan-Shan Ma
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Jin-Tao Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Kun-Ru Song
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Rui Zhao
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Ren-Hui Fang
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Luo-Bin Wang
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Shu-Ting Yao
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Yi-Fan Hu
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA
| | - Xin-Ying Jiang
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Marc N Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
- Child Study Center, Yale University School of Medicine, New Haven, CT 06519, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT 06109, USA
- Connecticut Mental Health Center, New Haven, CT 06519, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Xiao-Yi Fang
- Correspondence should be addressed to Xiao-Yi Fang, Institute of Developmental Psychology, Beijing Normal University, No. 19, Xinjiekou Wai Street, Haidian District, Beijing 100875, China. E-mail:
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144
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Wang XH, Zhao B, Li L. Mapping white matter structural covariance connectivity for single subject using wavelet transform with T1-weighted anatomical brain MRI. Front Neurosci 2022; 16:1038514. [PMID: 36507319 PMCID: PMC9727234 DOI: 10.3389/fnins.2022.1038514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/08/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction Current studies of structural covariance networks were focused on the gray matter in the human brain. The structural covariance connectivity in the white matter remains largely unexplored. This paper aimed to build novel metrics that can infer white matter structural covariance connectivity, and to explore the predictive power of the proposed features. Methods To this end, a cohort of 315 adult subjects with the anatomical brain MRI datasets were obtained from the publicly available Dallas Lifespan Brain Study (DLBS) project. The 3D wavelet transform was applied on the individual voxel-based morphology (VBM) volume to obtain the white matter structural covariance connectivity. The predictive models for cognitive functions were built using support vector regression (SVR). Results The predictive models exhibited comparable performance with previous studies. The novel features successfully predicted the individual ability of digit comparison (DC) (r = 0.41 ± 0.01, p < 0.01) and digit symbol (DSYM) (r = 0.5 ± 0.01, p < 0.01). The sensorimotor-related white matter system exhibited as the most predictive network node. Furthermore, the node strengths of sensorimotor mode were significantly correlated to cognitive scores. Discussion The results suggested that the white matter structural covariance connectivity was informative and had potential for predictive tasks of brain-behavior research.
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145
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Chamberlain TA, Rosenberg MD. Propofol selectively modulates functional connectivity signatures of sustained attention during rest and narrative listening. Cereb Cortex 2022; 32:5362-5375. [PMID: 35285485 DOI: 10.1093/cercor/bhac020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/06/2022] [Accepted: 01/08/2022] [Indexed: 12/27/2022] Open
Abstract
Sustained attention is a critical cognitive function reflected in an individual's whole-brain pattern of functional magnetic resonance imaging functional connectivity. However, sustained attention is not a purely static trait. Rather, attention waxes and wanes over time. Do functional brain networks that underlie individual differences in sustained attention also underlie changes in attentional state? To investigate, we replicate the finding that a validated connectome-based model of individual differences in sustained attention tracks pharmacologically induced changes in attentional state. Specifically, preregistered analyses revealed that participants exhibited functional connectivity signatures of stronger attention when awake than when under deep sedation with the anesthetic agent propofol. Furthermore, this effect was relatively selective to the predefined sustained attention networks: propofol administration modulated strength of the sustained attention networks more than it modulated strength of canonical resting-state networks and a network defined to predict fluid intelligence, and the functional connections most affected by propofol sedation overlapped with the sustained attention networks. Thus, propofol modulates functional connectivity signatures of sustained attention within individuals. More broadly, these findings underscore the utility of pharmacological intervention in testing both the generalizability and specificity of network-based models of cognitive function.
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Affiliation(s)
- Taylor A Chamberlain
- Department of Psychology, The University of Chicago, 5848 S University Ave, IL 60637, Chicago
| | - Monica D Rosenberg
- Department of Psychology, The University of Chicago, 5848 S University Ave, IL 60637, Chicago.,Neuroscience Institute, The University of Chicago, 5812 South Ellis Ave., MC 0912, Suite P-400, IL 60637, Chicago
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146
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Wu J, Li J, Eickhoff SB, Hoffstaedter F, Hanke M, Yeo BTT, Genon S. Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns. Neuroimage 2022; 262:119569. [PMID: 35985618 PMCID: PMC9611632 DOI: 10.1016/j.neuroimage.2022.119569] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/04/2022] [Accepted: 08/15/2022] [Indexed: 11/20/2022] Open
Abstract
An increasing number of studies have investigated the relationships between inter-individual variability in brain regions' connectivity and behavioral phenotypes, making use of large population neuroimaging datasets. However, the replicability of brain-behavior associations identified by these approaches remains an open question. In this study, we examined the cross-dataset replicability of brain-behavior association patterns for fluid cognition and openness predictions using a previously developed region-wise approach, as well as using a standard whole-brain approach. Overall, we found moderate similarity in patterns for fluid cognition predictions across cohorts, especially in the Human Connectome Project Young Adult, Human Connectome Project Aging, and Enhanced Nathan Kline Institute Rockland Sample cohorts, but low similarity in patterns for openness predictions. In addition, we assessed the generalizability of prediction models in cross-dataset predictions, by training the model in one dataset and testing in another. Making use of the region-wise prediction approach, we showed that first, a moderate extent of generalizability could be achieved with fluid cognition prediction, and that, second, a set of common brain regions related to fluid cognition across cohorts could be identified. Nevertheless, the moderate replicability and generalizability could only be achieved in specific contexts. Thus, we argue that replicability and generalizability in connectivity-based prediction remain limited and deserve greater attention in future studies.
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Affiliation(s)
- Jianxiao Wu
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany.
| | - Jingwei Li
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Michael Hanke
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore City, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore City, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore City, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore City, Singapore; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Sarah Genon
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany.
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147
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Wang Y, Dong D, Chen X, Gao X, Liu Y, Xiao M, Guo C, Chen H. Individualized morphometric similarity predicts body mass index and food approach behavior in school-age children. Cereb Cortex 2022; 33:4794-4805. [PMID: 36300597 DOI: 10.1093/cercor/bhac380] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 11/15/2022] Open
Abstract
Abstract
Childhood obesity is associated with alterations in brain structure. Previous studies generally used a single structural index to characterize the relationship between body mass index(BMI) and brain structure, which could not describe the alterations of structural covariance between brain regions. To cover this research gap, this study utilized two independent datasets with brain structure profiles and BMI of 155 school-aged children. Connectome-based predictive modeling(CPM) was used to explore whether children’s BMI is reliably predictable by the novel individualized morphometric similarity network(MSN). We revealed the MSN can predict the BMI in school-age children with good generalizability to unseen dataset. Moreover, these revealed significant brain structure covariant networks can further predict children’s food approach behavior. The positive predictive networks mainly incorporated connections between the frontoparietal network(FPN) and the visual network(VN), between the FPN and the limbic network(LN), between the default mode network(DMN) and the LN. The negative predictive network primarily incorporated connections between the FPN and DMN. These results suggested that the incomplete integration of the high-order brain networks and the decreased dedifferentiation of the high-order networks to the primary reward networks can be considered as a core structural basis of the imbalance between inhibitory control and reward processing in childhood obesity.
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Affiliation(s)
- Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University , Chongqing, 400715, China
- Key Laboratory of Cognition and Personality of Ministry of Education , Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality of Ministry of Education , Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7) , Research Centre Jülich, Jülich, Germany
| | - Ximei Chen
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology , Southwest University, Chongqing, 400715, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University , Chongqing, 400715, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University , Chongqing, 400715, China
| | - Mingyue Xiao
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University , Chongqing, 400715, China
| | - Cheng Guo
- Research Center of Mental Health Education, Faculty of Psychology, Southwest University , Chongqing, 400715, Germany
| | - Hong Chen
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology , Southwest University, Chongqing, 400715, China
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148
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Shi Y, Zeng W. The integrative functional connectivity analysis between seafarer’s brain networks using functional magnetic resonance imaging data of different states. Front Neurosci 2022; 16:1008652. [DOI: 10.3389/fnins.2022.1008652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/30/2022] [Indexed: 11/13/2022] Open
Abstract
The particularity of seafarers’ occupation makes their brain functional activities vulnerable to the influence of working environments, which leads to abnormal functional connectivities (FCs) between brain networks. To further investigate the influences of maritime environments on the seafarers’ functional brain networks, the functional magnetic resonance imaging (fMRI) datasets of 33 seafarers before and after sailing were used to study FCs among the functional brain networks in this paper. On the basis of making full use of the intrinsic prior information from fMRI data, six resting-state brain functional networks of seafarers before and after sailing were obtained by using group independent component analysis with intrinsic reference, and then the differences between the static and dynamic FCs among these six brain networks of seafarers before and after sailing were, respectively, analyzed from both group and individual levels. Subsequently, the potential dynamic functional connectivity states of seafarers before and after sailing were extracted by using the affine propagation clustering algorithm and the probabilities of state transition between them were obtained simultaneously. The results show that the dynamic FCs among large-scale brain networks have significant difference seafarers before and after sailing both at the group level and individual level, while the static FCs between them varies only at the individual level. This suggests that the maritime environments can indeed affect the brain functional activity of seafarers in real time, and the degree of influence is different for different subjects, which is of a great significance to explore the neural changes of seafarer’s brain functional network.
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149
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Horien C, Floris DL, Greene AS, Noble S, Rolison M, Tejavibulya L, O'Connor D, McPartland JC, Scheinost D, Chawarska K, Lake EMR, Constable RT. Functional Connectome-Based Predictive Modeling in Autism. Biol Psychiatry 2022; 92:626-642. [PMID: 35690495 PMCID: PMC10948028 DOI: 10.1016/j.biopsych.2022.04.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 04/14/2022] [Accepted: 04/17/2022] [Indexed: 01/08/2023]
Abstract
Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut.
| | - Dorothea L Floris
- Methods of Plasticity Research, Department of Psychology, University of Zürich, Zurich, Switzerland; Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Max Rolison
- Yale Child Study Center, New Haven, Connecticut
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut
| | - David O'Connor
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - James C McPartland
- Department of Psychology, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Katarzyna Chawarska
- Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut.
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150
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White matter connectivity in brain networks supporting social and affective processing predicts real-world social network characteristics. Commun Biol 2022; 5:1048. [PMID: 36192629 PMCID: PMC9529948 DOI: 10.1038/s42003-022-03655-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 05/20/2022] [Indexed: 01/10/2023] Open
Abstract
Human behavior is embedded in social networks. Certain characteristics of the positions that people occupy within these networks appear to be stable within individuals. Such traits likely stem in part from individual differences in how people tend to think and behave, which may be driven by individual differences in the neuroanatomy supporting socio-affective processing. To investigate this possibility, we reconstructed the full social networks of three graduate student cohorts (N = 275; N = 279; N = 285), a subset of whom (N = 112) underwent diffusion magnetic resonance imaging. Although no single tract in isolation appears to be necessary or sufficient to predict social network characteristics, distributed patterns of white matter microstructural integrity in brain networks supporting social and affective processing predict eigenvector centrality (how well-connected someone is to well-connected others) and brokerage (how much one connects otherwise unconnected others). Thus, where individuals sit in their real-world social networks is reflected in their structural brain networks. More broadly, these results suggest that the application of data-driven methods to neuroimaging data can be a promising approach to investigate how brains shape and are shaped by individuals' positions in their real-world social networks.
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