1
|
Wang Y, Yang J, Zhang H, Dong D, Yu D, Yuan K, Lei X. Altered morphometric similarity networks in insomnia disorder. Brain Struct Funct 2024; 229:1433-1445. [PMID: 38801538 DOI: 10.1007/s00429-024-02809-0] [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: 03/14/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024]
Abstract
Previous studies on structural covariance network (SCN) suggested that patients with insomnia disorder (ID) show abnormal structural connectivity, primarily affecting the somatomotor network (SMN) and default mode network (DMN). However, evaluating a single structural index in SCN can only reveal direct covariance relationship between two brain regions, failing to uncover synergistic changes in multiple structural features. To cover this research gap, the present study utilized novel morphometric similarity networks (MSN) to examine the morphometric similarity between cortical areas in terms of multiple sMRI parameters measured at each area. With seven T1-weighted imaging morphometric features from the Desikan-Killiany atlas, individual MSN was constructed for patients with ID (N = 87) and healthy control groups (HCs, N = 84). Two-sample t-test revealed differences in MSN between patients with ID and HCs. Correlation analyses examined associations between MSNs and sleep quality, insomnia symptom severity, and depressive symptoms severity in patients with ID. The right paracentral lobule (PCL) exhibited decreased morphometric similarity in patients with ID compared to HCs, mainly manifested by its de-differentiation (meaning loss of distinctiveness) with the SMN, DMN, and ventral attention network (VAN), as well as its decoupling with the visual network (VN). Greater PCL-based de-differentiation correlated with less severe insomnia and fewer depressive symptoms in the patients group. Additionally, patients with less depressive symptoms showed greater PCL de-differentiation from the SMN. As an important pilot step in revealing the underlying morphometric similarity alterations in insomnia disorder, the present study identified the right PCL as a hub region that is de-differentiated with other high-order networks. Our study also revealed that MSN has an important potential to capture clinical significance related to insomnia disorder.
Collapse
Affiliation(s)
- Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China.
- Key Laboratory of Cognition and Personality, 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.
| | - Jingqi Yang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Haobo Zhang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality, 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
| | - Dahua Yu
- Information Processing Laboratory, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 014010, China
| | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi'an, Shanxi, 710126, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China.
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, 400715, China.
| |
Collapse
|
2
|
Kraljević N, Langner R, Küppers V, Raimondo F, Patil KR, Eickhoff SB, Müller VI. Network and state specificity in connectivity-based predictions of individual behavior. Hum Brain Mapp 2024; 45:e26753. [PMID: 38864353 PMCID: PMC11167405 DOI: 10.1002/hbm.26753] [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: 05/16/2023] [Revised: 04/17/2024] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.
Collapse
Affiliation(s)
- Nevena Kraljević
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Robert Langner
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Vincent Küppers
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital CologneUniversity of CologneCologneGermany
| | - Federico Raimondo
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Kaustubh R. Patil
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Veronika I. Müller
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| |
Collapse
|
3
|
He N, Kou C. Predicting verbal and performance intelligence quotients from multimodal data in individuals with attention deficit/hyperactivity disorder. Int J Dev Neurosci 2024; 84:217-226. [PMID: 38387863 DOI: 10.1002/jdn.10320] [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: 10/27/2023] [Revised: 01/10/2024] [Accepted: 02/05/2024] [Indexed: 02/24/2024] Open
Abstract
Despite the importance of understanding how intelligence is ingrained in the function and structure of the brain in some neurological disorders, the alterations of intelligence-associated neurological factors in atypical neurodevelopmental disorders, such as attention deficit/hyperactivity disorder (ADHD), are limited. Therefore, we aimed to explore the relationship between the brain functional and morphological characteristics and the intellectual performance of 139 patients with ADHD. Resting-state functional and T1-weighted structural magnetic resonance imaging (MRI) data and intellectual-performance data of the patients were collected. The MRI data were preprocessed to extract four indicators characterizing the participants' brain features: fractional amplitude of low-frequency fluctuation, regional homogeneity, and gray and white matter volumes. Then, we used a two-layer feature-selection method with support vector regression models based on three kernel functions to predict the verbal and performance intelligent quotients of the patients, along with ten fold cross-validation to evaluate the models' predictive performance. All models showed good performance; the correlation coefficients between the predicted and observed values for each predictive phenotypic variable were >0.41, with statistical significance. The brain features that could best predict the intellectual performance of the patients were concentrated in the superior and inferior frontal gyrus of the prefrontal areas, the angular gyrus and precuneus of the parietal lobe, the inferior and middle temporal gyrus of the temporal lobe, and part of the cerebellar regions. Thus, the voxel-based brain-feature indicators could adequately predict the intellectual performance of patients with ADHD, providing a foundation for future neuroimaging studies of this disorder.
Collapse
Affiliation(s)
- Ningning He
- School of Mathematics and Statistics, Zhoukou Normal University, Zhoukou, People's Republic of China
| | - Chao Kou
- School of Foreign Languages, Zhoukou Normal University, Zhoukou, People's Republic of China
| |
Collapse
|
4
|
Holland CM, Alleyne K, Pierre-Louis A, Bansal R, Pollatou A, Barbato K, Cheng B, Hao X, Rosen TS, Peterson BS, Spann MN. Utilizing maternal prenatal cognition as a predictor of newborn brain measures of intellectual development. Child Neuropsychol 2024; 30:582-601. [PMID: 37489806 PMCID: PMC10808270 DOI: 10.1080/09297049.2023.2233155] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/28/2023] [Indexed: 07/26/2023]
Abstract
Identifying reliable indicators of cognitive functioning prior to age five has been challenging. Prior studies have shown that maternal cognition, as indexed by intellectual quotient (IQ) and years of education, predict child intelligence at school age. We examined whether maternal full scale IQ, education, and inhibitory control (index of executive function) are associated with newborn brain measures and toddler language outcomes to assess potential indicators of early cognition. We hypothesized that maternal indices of cognition would be associated with brain areas implicated in intelligence in school-age children and adults in the newborn period. Thirty-seven pregnant women and their newborns underwent an MRI scan. T2-weighted images and surface-based morphometric analysis were used to compute local brain volumes in newborn infants. Maternal cognition indices were associated with local brain volumes for infants in the anterior and posterior cingulate, occipital lobe, and pre/postcentral gyrus - regions associated with IQ, executive function, or sensori-motor functions in children and adults. Maternal education and executive function, but not maternal intelligence, were associated with toddler language scores at 12 and 24 months. Newborn brain volumes did not predict language scores. Overall, the pre/postcentral gyrus and occipital lobe may be unique indicators of early intellectual development in the newborn period. Given that maternal executive function as measured by inhibitory control has robust associations with the newborn brain and is objective, brief, and easy to administer, it may be a useful predictor of early developmental and cognitive capacity for young children.
Collapse
Affiliation(s)
- Cristin M. Holland
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY
| | - Kiarra Alleyne
- Department of Sociomedical Sciences, Columbia University Mailman School of Public Health, New York, NY
| | - Arline Pierre-Louis
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY
| | - Ravi Bansal
- Institute for the Developing Mind, Children’s Hospital Los Angeles, Los Angeles, CA
- Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Angeliki Pollatou
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY
| | - Kristiana Barbato
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY
| | - Bin Cheng
- Columbia University Mailman School of Public Health, New York, NY
| | - Xuejun Hao
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY
| | - Tove S Rosen
- Columbia University Mailman School of Public Health, New York, NY
| | - Bradley S. Peterson
- Institute for the Developing Mind, Children’s Hospital Los Angeles, Los Angeles, CA
- Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Marisa N. Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY
- New York State Psychiatric Institute, New York, NY
| |
Collapse
|
5
|
Jenkins LM, Heywood A, Gupta S, Kouchakidivkolaei M, Sridhar J, Rogalski E, Weintraub S, Popuri K, Rosen H, Wang L. Disinhibition in dementia related to reduced morphometric similarity of cognitive control network. Brain Commun 2024; 6:fcae124. [PMID: 38665960 PMCID: PMC11044061 DOI: 10.1093/braincomms/fcae124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 03/04/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Disinhibition is one of the most distressing and difficult to treat neuropsychiatric symptoms of dementia. It involves socially inappropriate behaviours, such as hypersexual comments, inappropriate approaching of strangers and excessive jocularity. Disinhibition occurs in multiple dementia syndromes, including behavioural variant frontotemporal dementia, and dementia of the Alzheimer's type. Morphometric similarity networks are a relatively new method for examining brain structure and can be used to calculate measures of network integrity on large scale brain networks and subnetworks such as the salience network and cognitive control network. In a cross-sectional study, we calculated morphometric similarity networks to determine whether disinhibition in behavioural variant frontotemporal dementia (n = 75) and dementia of the Alzheimer's type (n = 111) was associated with reduced integrity of these networks independent of diagnosis. We found that presence of disinhibition, measured by the Neuropsychiatric Inventory Questionnaire, was associated with reduced global efficiency of the cognitive control network in both dementia of the Alzheimer's type and behavioural variant frontotemporal dementia. Future research should replicate this transdiagnostic finding in other dementia diagnoses and imaging modalities, and investigate the potential for intervention at the level of the cognitive control network to target disinhibition.
Collapse
Affiliation(s)
- Lisanne M Jenkins
- Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL 60611, USA
| | - Ashley Heywood
- Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL 60611, USA
| | - Sonya Gupta
- Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL 60611, USA
| | | | - Jaiashre Sridhar
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University, Chicago, IL 60611, USA
| | - Emily Rogalski
- Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL 60611, USA
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University, Chicago, IL 60611, USA
| | - Sandra Weintraub
- Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL 60611, USA
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University, Chicago, IL 60611, USA
| | - Karteek Popuri
- Computer Science, Memorial University of Newfoundland, St. Johns, NL A1C 5S7, Canada
| | - Howard Rosen
- Neurology, University of California, San Francisco, CA 94143, USA
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State University, Columbus, OH 43210, USA
| |
Collapse
|
6
|
Dong D, Chen X, Li W, Gao X, Wang Y, Zhou F, Eickhoff SB, Chen H. Opposite changes in morphometric similarity of medial reward and lateral non-reward orbitofrontal cortex circuits in obesity. Neuroimage 2024; 290:120574. [PMID: 38467346 DOI: 10.1016/j.neuroimage.2024.120574] [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: 12/09/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 03/13/2024] Open
Abstract
Obesity has a profound impact on metabolic health thereby adversely affecting brain structure and function. However, the majority of previous studies used a single structural index to investigate the link between brain structure and body mass index (BMI), which hinders our understanding of structural covariance between regions in obesity. This study aimed to examine the relationship between macroscale cortical organization and BMI using novel morphometric similarity networks (MSNs). The individual MSNs were first constructed from individual eight multimodal cortical morphometric features between brain regions. Then the relationship between BMI and MSNs within the discovery sample of 434 participants was assessed. The key findings were further validated in an independent sample of 192 participants. We observed that the lateral non-reward orbitofrontal cortex (lOFC) exhibited decoupling (i.e., reduction in integration) in obesity, which was mainly manifested by its decoupling with the cognitive systems (i.e., DMN and FPN) while the medial reward orbitofrontal cortex (mOFC) showed de-differentiation (i.e., decrease in distinctiveness) in obesity, which was mainly represented by its de-differentiation with the cognitive and attention systems (i.e., DMN and VAN). Additionally, the lOFC showed de-differentiation with the visual system in obesity, while the mOFC showed decoupling with the visual system and hyper-coupling with the sensory-motor system in obesity. As an important first step in revealing the role of underlying structural covariance in body mass variability, the present study presents a novel mechanism that underlies the reward-control interaction imbalance in obesity, thus can inform future weight-management approaches.
Collapse
Affiliation(s)
- Debo Dong
- Key Laboratory of Cognition and Personality, 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, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Wei Li
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Yulin Wang
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China; Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Feng Zhou
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China; Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing 400715, China.
| |
Collapse
|
7
|
Kraljević N, Langner R, Küppers V, Raimondo F, Patil KR, Eickhoff SB, Müller VI. Network and State Specificity in Connectivity-Based Predictions of Individual Behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.11.540387. [PMID: 37215048 PMCID: PMC10197703 DOI: 10.1101/2023.05.11.540387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.
Collapse
Affiliation(s)
- Nevena Kraljević
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
| | - Robert Langner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
| | - Vincent Küppers
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Federico Raimondo
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
| | - Veronika I Müller
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
| |
Collapse
|
8
|
Griffiths-King D, Wood AG, Novak J. Predicting 'Brainage' in late childhood to adolescence (6-17yrs) using structural MRI, morphometric similarity, and machine learning. Sci Rep 2023; 13:15591. [PMID: 37730747 PMCID: PMC10511546 DOI: 10.1038/s41598-023-42414-5] [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: 02/13/2023] [Accepted: 09/10/2023] [Indexed: 09/22/2023] Open
Abstract
Brain development is regularly studied using structural MRI. Recently, studies have used a combination of statistical learning and large-scale imaging databases of healthy children to predict an individual's age from structural MRI. This data-driven, predicted 'Brainage' typically differs from the subjects chronological age, with this difference a potential measure of individual difference. Few studies have leveraged higher-order or connectomic representations of structural MRI data for this Brainage approach. We leveraged morphometric similarity as a network-level approach to structural MRI to generate predictive models of age. We benchmarked these novel Brainage approaches using morphometric similarity against more typical, single feature (i.e., cortical thickness) approaches. We showed that these novel methods did not outperform cortical thickness or cortical volume measures. All models were significantly biased by age, but robust to motion confounds. The main results show that, whilst morphometric similarity mapping may be a novel way to leverage additional information from a T1-weighted structural MRI beyond individual features, in the context of a Brainage framework, morphometric similarity does not provide more accurate predictions of age. Morphometric similarity as a network-level approach to structural MRI may be poorly positioned to study individual differences in brain development in healthy participants in this way.
Collapse
Affiliation(s)
- Daniel Griffiths-King
- Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK
| | - Amanda G Wood
- Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK
- School of Psychology, Faculty of Health, Melbourne Burwood Campus, Deakin University, Geelong, VIC, Australia
- Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Jan Novak
- Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK.
| |
Collapse
|
9
|
Wei L, Ding F, Gong M, Baeken C, Wu GR. The impact of sensation seeking personality trait on acute alcohol-induced disinhibition. Drug Alcohol Depend 2023; 250:110907. [PMID: 37523917 DOI: 10.1016/j.drugalcdep.2023.110907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 07/12/2023] [Accepted: 07/17/2023] [Indexed: 08/02/2023]
Abstract
BACKGROUND Acute alcohol-related behavioral disinhibition has been well studied. But whether individual differences in the personality trait sensation seeking affect alcohol-induced behavioral disinhibition remains uncertain. METHODS The present study used functional near-infrared spectroscopy (fNIRS) technique and a response inhibition task (i.e., Go/No-Go) to determine the impact of the sensation seeking trait on the relationship between acute alcohol administration and inhibitory control capacity, and further investigate the neural mechanisms underlying this behavioral effect. Twenty-five high-sensation seekers and twenty-six low-sensation seekers were enrolled in this study. These participants attended two sessions: once for alcohol intake (0.5g/kg) and once for placebo intake (0g/kg). RESULTS Our results showed that high-sensation seekers relative to low-sensation seekers showed a significant decrease in inhibition accuracy under alcohol versus the placebo condition. Moreover, reduced prefrontal activity following acute alcohol consumption was more pronounced in high-sensation seekers compared with low-sensation seekers. CONCLUSIONS These findings showed that alcohol-induced behavioral disinhibition was affected by the personality trait sensation seeking and that recruitment of the prefrontal cortex contributed to the observed behavioral effect.
Collapse
Affiliation(s)
- Luqing Wei
- School of Psychology, Jiangxi Normal University, Nanchang, China
| | - Fanxi Ding
- School of Psychology, Jiangxi Normal University, Nanchang, China
| | - Mingliang Gong
- School of Psychology, Jiangxi Normal University, Nanchang, China
| | - Chris Baeken
- Faculty of Medicine and Health Sciences, Department of Head and Skin, Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium; Department of Psychiatry, University Hospital (UZBrussel), Brussels, Belgium; Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, the Netherlands
| | - Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China; Faculty of Medicine and Health Sciences, Department of Head and Skin, Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium.
| |
Collapse
|
10
|
Li X, Hao H, Li Y, Au LWC, Du G, Gao X, Yan J, Tong RKY, Lou W. Menstrually-related migraine shapes the structural similarity network integration of brain. Cereb Cortex 2023; 33:9867-9876. [PMID: 37415071 DOI: 10.1093/cercor/bhad250] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023] Open
Abstract
Menstrually-related migraine (MM) is a primary migraine in women of reproductive age. The underlying neural mechanism of MM was still unclear. In this study, we aimed to reveal the case-control differences in network integration and segregation for the morphometric similarity network of MM. Thirty-six patients with MM and 29 healthy females were recruited and underwent MRI scanning. The morphometric features were extracted in each region to construct the single-subject interareal cortical connection using morphometric similarity. The network topology characteristics, in terms of integration and segregation, were analyzed. Our results revealed that, in the absence of morphology differences, disrupted cortical network integration was found in MM patients compared to controls. The patients with MM showed a decreased global efficiency and increased characteristic path length compared to healthy controls. Regional efficiency analysis revealed the decreased efficiency in the left precentral gyrus and bilateral superior temporal gyrus contributed to the decreased network integration. The increased nodal degree centrality in the right pars triangularis was positively associated with the attack frequency in MM. Our results suggested MM would reorganize the morphology in the pain-related brain regions and reduce the parallel information processing capacity of the brain.
Collapse
Affiliation(s)
- Xinyu Li
- Imaging Center, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Huifen Hao
- Imaging Center, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Yingying Li
- Imaging Center, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Lisa Wing-Chi Au
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Ganqin Du
- Department of Neurology, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Xiuju Gao
- Department of Neurology, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Junqiang Yan
- Department of Neurology, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China
| | - Raymond Kai-Yu Tong
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Wutao Lou
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| |
Collapse
|
11
|
Sun J, Zhao X, Zhou J, Dang X, Zhu S, Liu L, Zhou Z. Preliminary Analysis of Volume-Based Resting-State Functional MRI Characteristics of Successful Aging in China. J Alzheimers Dis 2023; 91:767-778. [PMID: 36502325 DOI: 10.3233/jad-220780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Resting-state function MRI (rs-fMRI) research on successful aging can provide insight into the mechanism of aging with a different perspective from aging-related disease. OBJECTIVE rs-fMRI research was used to analyze the brain function characteristics of successful aging. METHODS A total of 47 usual aging individuals and 26 successful aging (SA) individuals underwent rs-fMRI scans and neuropsychological tests. Volume-based rs-fMRI data analysis was performed with DPASF to obtain ALFF, ReHo, DC, and VMHC. RESULTS The SA group showed increased ALFF in right opercular part of inferior frontal gyrus (Frontal_Inf_Oper_R) and right supramarginal gyrus; increased ReHo in right middle temporal pole gyrus and decreased ReHo in left superior frontal gyrus and middle occipital gyrus; increased DC in right medial orbitofrontal gyrus and pulvinar part of thalamus; decreased DC in left fusiform gyrus and right medial frontal gyrus; increased VMHC in right medial orbitofrontal gyrus; and decreased VMHC in the right superior temporal gyrus, right and left middle temporal gyrus, right and left triangular part of inferior frontal gyrus. ALFF in Frontal_Inf_Oper_R were found to be significantly correlated with MMSE scores (r = 0.301, p = 0.014) and ages (r = -0.264, p = 0.032) in all subjects, which could be used to distinguish the SA (AUC = 0.733, 95% CI: 0.604-0.863) by ROC analysis. CONCLUSION The brain regions with altered fMRI characteristics in SA group were concentrated in frontal (6 brain regions) and temporal (4 brain regions) lobes. ALFF in Frontal_Inf_Oper_R was significantly correlated to cognitive function and ages, which might be used to distinguish the SA.
Collapse
Affiliation(s)
- Jiaojiao Sun
- Department of Geriatric Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, Jiangsu, China.,Department of General Psychiatry, Yangzhou Wutaishan Hospital, Yangzhou, Jiangsu, China
| | - Xingfu Zhao
- Department of Geriatric Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Jianbang Zhou
- Department of Psychiatry, Haidong First People's Hospital, Haidong, Qinghai, China
| | - Xinghong Dang
- Department of Psychiatry, Haidong First People's Hospital, Haidong, Qinghai, China
| | - Shenglong Zhu
- Department of Psychiatry, Haidong First People's Hospital, Haidong, Qinghai, China
| | - Liang Liu
- Department of Geriatric Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Zhenhe Zhou
- Department of Geriatric Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, Jiangsu, China
| |
Collapse
|
12
|
Meng D, Wang S, Wong PCM, Feng G. Generalizable predictive modeling of semantic processing ability from functional brain connectivity. Hum Brain Mapp 2022; 43:4274-4292. [PMID: 35611721 PMCID: PMC9435002 DOI: 10.1002/hbm.25953] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 03/11/2022] [Accepted: 05/06/2022] [Indexed: 11/08/2022] Open
Abstract
Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating conceptual and meaningful information. Neuroimaging studies of SP typically collapse data from many subjects, but its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural network organizations contribute to the individual differences in SP behaviors. We aim to identify the neural signatures underlying SP variabilities by analyzing functional connectivity (FC) patterns based on a large‐sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two‐stage predictive modeling approach to build an internally cross‐validated model and to test the model's generalizability with unseen data from different HCP samples and other out‐of‐sample datasets. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five SP‐related behavioral tests. This cross‐validated model can be used to predict unseen HCP data. The model generalizability was enhanced in the language task compared with other tasks used during scanning and was better for females than males. The model constructed from the HCP dataset can be partially generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out‐of‐sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education for healthy individuals and intervention for SP and language deficits patients.
Collapse
Affiliation(s)
- Danting Meng
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Suiping Wang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, China
| | - Patrick C M Wong
- Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong SAR, China.,Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Gangyi Feng
- Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong SAR, China.,Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
| |
Collapse
|
13
|
Pina V, Campello VM, Lekadir K, Seguí S, García-Santos JM, Fuentes LJ. Mathematical Abilities in School-Aged Children: A Structural Magnetic Resonance Imaging Analysis With Radiomics. Front Neurosci 2022; 16:819069. [PMID: 35495063 PMCID: PMC9047716 DOI: 10.3389/fnins.2022.819069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
Structural magnetic resonance imaging (sMRI) studies have shown that children that differ in some mathematical abilities show differences in gray matter volume mainly in parietal and frontal regions that are involved in number processing, attentional control, and memory. In the present study, a structural neuroimaging analysis based on radiomics and machine learning models is presented with the aim of identifying the brain areas that better predict children’s performance in a variety of mathematical tests. A sample of 77 school-aged children from third to sixth grade were administered four mathematical tests: Math fluency, Calculation, Applied problems and Quantitative concepts as well as a structural brain imaging scan. By extracting radiomics related to the shape, intensity, and texture of specific brain areas, we observed that areas from the frontal, parietal, temporal, and occipital lobes, basal ganglia, and limbic system, were differentially related to children’s performance in the mathematical tests. sMRI-based analyses in the context of mathematical performance have been mainly focused on volumetric measures. However, the results for radiomics-based analysis showed that for these areas, texture features were the most important for the regression models, while volume accounted for less than 15% of the shape importance. These findings highlight the potential of radiomics for more in-depth analysis of medical images for the identification of brain areas related to mathematical abilities.
Collapse
Affiliation(s)
- Violeta Pina
- Departamento de Psicología Evolutiva y de la Educación, Facultad de Educación, Economía y Tecnología de Ceuta, Universidad de Granada, Ceuta, Spain
| | - Víctor M. Campello
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Karim Lekadir
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Santi Seguí
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | | | - Luis J. Fuentes
- Departamento de Psicología Básica y Metodología, Universidad de Murcia, Murcia, Spain
- *Correspondence: Luis J. Fuentes,
| |
Collapse
|
14
|
Hahn S, Owens MM, Yuan D, Juliano AC, Potter A, Garavan H, Allgaier N. Performance scaling for structural MRI surface parcellations: a machine learning analysis in the ABCD Study. Cereb Cortex 2022; 33:176-194. [PMID: 35238352 PMCID: PMC9758581 DOI: 10.1093/cercor/bhac060] [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: 11/08/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 11/13/2022] Open
Abstract
The use of predefined parcellations on surface-based representations of the brain as a method for data reduction is common across neuroimaging studies. In particular, prediction-based studies typically employ parcellation-driven summaries of brain measures as input to predictive algorithms, but the choice of parcellation and its influence on performance is often ignored. Here we employed preprocessed structural magnetic resonance imaging (sMRI) data from the Adolescent Brain Cognitive Development Study® to examine the relationship between 220 parcellations and out-of-sample predictive performance across 45 phenotypic measures in a large sample of 9- to 10-year-old children (N = 9,432). Choice of machine learning (ML) pipeline and use of alternative multiple parcellation-based strategies were also assessed. Relative parcellation performance was dependent on the spatial resolution of the parcellation, with larger number of parcels (up to ~4,000) outperforming coarser parcellations, according to a power-law scaling of between 1/4 and 1/3. Performance was further influenced by the type of parcellation, ML pipeline, and general strategy, with existing literature-based parcellations, a support vector-based pipeline, and ensembling across multiple parcellations, respectively, as the highest performing. These findings highlight the choice of parcellation as an important influence on downstream predictive performance, showing in some cases that switching to a higher resolution parcellation can yield a relatively large boost to performance.
Collapse
Affiliation(s)
- Sage Hahn
- Corresponding author: Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, 100 South Prospect Street Burlington, Vermont 05401, United States.
| | - Max M Owens
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - DeKang Yuan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Anthony C Juliano
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Alexandra Potter
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Hugh Garavan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Nicholas Allgaier
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| |
Collapse
|
15
|
Nichols ES, Erez J, Stojanoski B, Lyons KM, Witt ST, Mace CA, Khalid S, Owen AM. Longitudinal white matter changes associated with cognitive training. Hum Brain Mapp 2021; 42:4722-4739. [PMID: 34268814 PMCID: PMC8410562 DOI: 10.1002/hbm.25580] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 06/11/2021] [Accepted: 06/22/2021] [Indexed: 12/30/2022] Open
Abstract
Improvements in behavior are known to be accompanied by both structural and functional changes in the brain. However, whether those changes lead to more general improvements, beyond the behavior being trained, remains a contentious issue. We investigated whether training on one of two cognitive tasks would lead to either near transfer (that is, improvements on a quantifiably similar task) or far transfer (that is, improvements on a quantifiably different task), and furthermore, if such changes did occur, what the underlying neural mechanisms might be. Healthy adults (n = 16, 15 females) trained on either a verbal inhibitory control task or a visuospatial working memory task for 4 weeks, over the course of which they received five diffusion tensor imaging scans. Two additional tasks served as measures of near and far transfer. Behaviorally, participants improved on the task that they trained on, but did not improve on cognitively similar tests (near transfer), nor cognitively dissimilar tests (far transfer). Extensive changes to white matter microstructure were observed, with verbal inhibitory control training leading to changes in a left-lateralized network of frontotemporal and occipitofrontal tracts, and visuospatial working memory training leading to changes in right-lateralized frontoparietal tracts. Very little overlap was observed in changes between the two training groups. On the basis of these results, we suggest that near and far transfer were not observed because the changes in white matter tracts associated with training on each task are almost entirely nonoverlapping with, and therefore afford no advantages for, the untrained tasks.
Collapse
Affiliation(s)
- Emily Sophia Nichols
- Faculty of Education, Western University, London, Ontario, Canada.,Brain and Mind Institute, Western University, London, Ontario, Canada
| | - Jonathan Erez
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | - Bobby Stojanoski
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | - Kathleen Michelle Lyons
- Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Psychology, Western University, London, Ontario, Canada
| | | | - Charlotte Anna Mace
- Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
| | - Sameera Khalid
- Neuroscience Program, Western University, London, Ontario, Canada
| | - Adrian Mark Owen
- Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Psychology, Western University, London, Ontario, Canada.,Department of Physiology and Pharmacology, Western University, London, Ontario, Canada
| |
Collapse
|
16
|
Sui J, Jiang R, Bustillo J, Calhoun V. Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises. Biol Psychiatry 2020; 88:818-828. [PMID: 32336400 PMCID: PMC7483317 DOI: 10.1016/j.biopsych.2020.02.016] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 02/13/2020] [Accepted: 02/17/2020] [Indexed: 01/08/2023]
Abstract
The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move toward a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously behavioral phenotype from neuroimaging data, opening up an exciting opportunity to describe the human brain at the single-subject level. In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade. We first review regression-based approaches and highlight connectome-based predictive modeling, which has grown in popularity in recent years. Next, we systematically describe recent representative studies using these tools in the context of cognitive function, symptom severity, personality traits, and emotion processing. Finally, we highlight a few challenges related to combining multimodal data, longitudinal prediction, external validations, and the employment of deep learning methods that have emerged from our review of the existing literature, as well as present some promising and challenging future directions.
Collapse
Affiliation(s)
- Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
| |
Collapse
|
17
|
Leroy A, Cheron G. EEG dynamics and neural generators of psychological flow during one tightrope performance. Sci Rep 2020; 10:12449. [PMID: 32709919 PMCID: PMC7381607 DOI: 10.1038/s41598-020-69448-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 06/19/2020] [Indexed: 12/13/2022] Open
Abstract
Psychological “flow” emerges from a goal requiring action, and a match between skills and challenge. Using high-density electroencephalographic (EEG) recording, we quantified the neural generators characterizing psychological “flow” compared to a mindful “stress” state during a professional tightrope performance. Applying swLORETA based on self-reported mental states revealed the right superior temporal gyrus (BA38), right globus pallidus, and putamen as generators of delta, alpha, and beta oscillations, respectively, when comparing “flow” versus “stress”. Comparison of “stress” versus “flow” identified the middle temporal gyrus (BA39) as the delta generator, and the medial frontal gyrus (BA10) as the alpha and beta generator. These results support that “flow” emergence required transient hypo-frontality. Applying swLORETA on the motor command represented by the tibialis anterior EMG burst identified the ipsilateral cerebellum and contralateral sensorimotor cortex in association with on-line control exerted during both “flow” and “stress”, while the basal ganglia was identified only during “flow”.
Collapse
Affiliation(s)
- A Leroy
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.,Haute Ecole Provinciale du Hainaut-Condorcet, Mons, Belgium
| | - G Cheron
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium. .,Laboratory of Electrophysiology, Université de Mons, Mons, Belgium.
| |
Collapse
|