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Li Y, Grabell AS, Perlman SB. Irritability Moderates the Association between Cognitive Flexibility Task Performance and Related Prefrontal Cortex Activation in Young Children. Brain Sci 2023; 13:882. [PMID: 37371362 PMCID: PMC10296206 DOI: 10.3390/brainsci13060882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/24/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
The association between cognitive flexibility and related neural functioning has been inconsistent. This is particularly true in young children, where previous studies have found heterogenous results linking behavior and neural function, raising the possibility of unexplored moderators. The current study explored the moderating role of dimensional irritability in the association between cognitive flexibility task performance and prefrontal activation in young children. A total of 106 3- to 7-year-old children were recruited to complete a custom-designed, child-adapted, cognitive flexibility task, and 98 of them were included in the data analysis. The children's dorsolateral prefrontal cortex activation was monitored using functional near-infrared spectroscopy, and their levels of irritability were reported by parents using the MAP-DB Temper Loss subscale. Results indicated that the mean reaction time of the cognitive flexibility task was negatively correlated with concurrent prefrontal activation. No evidence was found for the association between task accuracy and prefrontal activation. Moreover, irritability moderated the association between the mean reaction time and prefrontal activation. Children high in irritability exhibited a stronger negative association between the mean reaction time and related prefrontal activation than children low in irritability. The moderating model suggested a novel affective-cognitive interaction to investigate the associations between cognitive task performance and their neural underpinnings.
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Affiliation(s)
- Yanwei Li
- College of Early Childhood Education, Nanjing Xiaozhuang University, Nanjing 210017, China
| | - Adam S. Grabell
- Department of Psychological and Brain Sciences, University of Massachusetts, Amherst, MA 01003, USA
| | - Susan B. Perlman
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63130, USA
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Guo L, Zhang Y, Liu Q, Guo K, Wang Z. Multi-band network fusion for Alzheimer's disease identification with functional MRI. Front Psychiatry 2022; 13:1070198. [PMID: 36590604 PMCID: PMC9798220 DOI: 10.3389/fpsyt.2022.1070198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION The analysis of functional brain networks (FBNs) has become a promising and powerful tool for auxiliary diagnosis of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Previous studies usually estimate FBNs using full band Blood Oxygen Level Dependent (BOLD) signal. However, a single band is not sufficient to capture the diagnostic and prognostic information contained in multiple frequency bands. METHOD To address this issue, we propose a novel multi-band network fusion framework (MBNF) to combine the various information (e.g., the diversification of structural features) of multi-band FBNs. We first decompose the BOLD signal adaptively into two frequency bands named high-frequency band and low-frequency band by the ensemble empirical mode decomposition (EEMD). Then the similarity network fusion (SNF) is performed to blend two networks constructed by two frequency bands together into a multi-band fusion network. In addition, we extract the features of the fused network towards a better classification performance. RESULT To verify the validity of the scheme, we conduct our MBNF method on the public ADNI database for identifying subjects with AD/MCI from normal controls. DISCUSSION Experimental results demonstrate that the proposed scheme extracts rich multi-band network features and biomarker information, and also achieves better classification accuracy.
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Affiliation(s)
- Lingyun Guo
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Yangyang Zhang
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Qinghua Liu
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Kaiyu Guo
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Zhengxia Wang
- School of Computer Science and Technology, Hainan University, Haikou, China
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Nenadić I. Narcissistic Traits and Executive Functions. Front Psychol 2021; 12:707887. [PMID: 34790143 PMCID: PMC8591048 DOI: 10.3389/fpsyg.2021.707887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 09/01/2021] [Indexed: 11/13/2022] Open
Abstract
Several personality disorders have been associated with cognitive impairment, including executive functions like working memory. Yet, it is unclear whether subclinical expression in non-clinical persons is associated with cognitive functioning. Recent studies indicate that non-clinical subjects might, in fact, perform better with increasing moderate to mild expressions of narcissistic features. We tested working memory performance in a cohort of n=70 psychiatrically and neurologically healthy subjects using Wechsler Adult Intelligence Scale (WAIS/WIE) subtests Arithmetic, Digit Span and Letter-Number Sequencing, and assessed narcissistic features using three different inventories: the widely used Narcissistic Personality Inventory (NPI), as well as two clinically used measures of narcissistic traits and states, respectively, derived from schema-focused therapy, i.e., the Young Schema Questionnaire (YSQ) entitlement/grandiosity subscale and the Schema Mode Inventory (SMI) self-aggrandizer subscale. In accordance with our hypothesis, we found nominally significant positive correlations of WIE Arithmetic performance with NPI total score (Spearman's rho=0.208; p=0.043) and SMI self-aggrandizer scale (Spearman's rho=0.231; p=0.027), but findings did not survive false discovery rate (FDR) adjustment for multiple comparisons (pFDR=0.189 and pFDR=0.243, respectively). While our findings add to recent studies on cognitive performance in subclinical narcissism, they fail to demonstrate an association of cognitive performance with narcissistic traits across multiple working memory tests, indicating the need for additional study, including complementary executive functions in larger cohorts and ranges of phenotype expression.
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Affiliation(s)
- Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Marburg, Germany
- Department of Psychology, Goethe-Universität Frankfurt, Frankfurt, Germany
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
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Wanniarachchi H, Lang Y, Wang X, Pruitt T, Nerur S, Chen KY, Liu H. Alterations of Cerebral Hemodynamics and Network Properties Induced by Newsvendor Problem in the Human Prefrontal Cortex. Front Hum Neurosci 2021; 14:598502. [PMID: 33519401 PMCID: PMC7843457 DOI: 10.3389/fnhum.2020.598502] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 12/14/2020] [Indexed: 01/21/2023] Open
Abstract
While many publications have reported brain hemodynamic responses to decision-making under various conditions of risk, no inventory management scenarios, such as the newsvendor problem (NP), have been investigated in conjunction with neuroimaging. In this study, we hypothesized (I) that NP stimulates the dorsolateral prefrontal cortex (DLPFC) and the orbitofrontal cortex (OFC) joined with frontal polar area (FPA) significantly in the human brain, and (II) that local brain network properties are increased when a person transits from rest to the NP decision-making phase. A 77-channel functional near infrared spectroscopy (fNIRS) system with wide field-of-view (FOV) was employed to measure frontal cerebral hemodynamics in response to NP in 27 healthy human subjects. NP-induced changes in oxy-hemoglobin concentration, Δ[HbO], were investigated using a general linear model (GLM) and graph theory analysis (GTA). Significant activation induced by NP was shown in both DLPFC and OFC+FPA across all subjects. Specifically, higher risk NP with low-profit margins (LM) activated left-DLPFC but deactivated right-DLPFC in 14 subjects, while lower risk NP with high-profit margins (HM) stimulated both DLPFC and OFC+FPA in 13 subjects. The local efficiency, clustering coefficient, and path length of the network metrics were significantly enhanced under NP decision making. In summary, multi-channel fNIRS enabled us to identify DLPFC and OFC+FPA as key cortical regions of brain activations when subjects were making inventory-management risk decisions. We demonstrated that challenging NP resulted in the deactivation within right-DLPFC due to higher levels of stress. Also, local brain network properties were increased when a person transitioned from the rest phase to the NP decision-making phase.
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Affiliation(s)
- Hashini Wanniarachchi
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Yan Lang
- Department of Information Systems and Operations Management, University of Texas at Arlington, Arlington, TX, United States
| | - Xinlong Wang
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Tyrell Pruitt
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Sridhar Nerur
- Department of Information Systems and Operations Management, University of Texas at Arlington, Arlington, TX, United States
| | - Kay-Yut Chen
- Department of Information Systems and Operations Management, University of Texas at Arlington, Arlington, TX, United States
| | - Hanli Liu
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
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Wang M, Hu Z, Liu L, Li H, Qian Q, Niu H. Disrupted functional brain connectivity networks in children with attention-deficit/hyperactivity disorder: evidence from resting-state functional near-infrared spectroscopy. NEUROPHOTONICS 2020; 7:015012. [PMID: 32206679 PMCID: PMC7064804 DOI: 10.1117/1.nph.7.1.015012] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Accepted: 02/20/2020] [Indexed: 05/19/2023]
Abstract
Significance: Attention-deficit/hyperactivity disorder (ADHD) is the most common psychological disease in childhood. Currently, widely used neuroimaging techniques require complete body confinement and motionlessness and thus are extremely hard for brain scanning of ADHD children. Aim: We present resting-state functional near-infrared spectroscopy (fNIRS) as an imaging technique to record spontaneous brain activity in children with ADHD. Approach: The brain functional connectivity was calculated, and the graph theoretical analysis was further applied to investigate alterations in the global and regional properties of the brain network in the patients. In addition, the relationship between brain network features and core symptoms was examined. Results: ADHD patients exhibited significant decreases in both functional connectivity and global network efficiency. Meanwhile, the nodal efficiency in children with ADHD was also found to be altered, e.g., increase in the visual and dorsal attention networks and decrease in somatomotor and default mode networks, compared to the healthy controls. More importantly, the disrupted functional connectivity and nodal efficiency significantly correlated with dimensional ADHD scores. Conclusions: We clearly demonstrate the feasibility and potential of fNIRS-based connectome technique in ADHD or other neurological diseases in the future.
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Affiliation(s)
- Mengjing Wang
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
| | - Zhishan Hu
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
| | - Lu Liu
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, China
- Peking University Sixth Hospital, National Clinical Research Center for Mental Disorders, Beijing, China
- Peking University, National Health Commission Key Laboratory of Mental Health, Beijing, China
| | - Haimei Li
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, China
- Peking University Sixth Hospital, National Clinical Research Center for Mental Disorders, Beijing, China
- Peking University, National Health Commission Key Laboratory of Mental Health, Beijing, China
| | - Qiujin Qian
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, China
- Peking University Sixth Hospital, National Clinical Research Center for Mental Disorders, Beijing, China
- Peking University, National Health Commission Key Laboratory of Mental Health, Beijing, China
| | - Haijing Niu
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
- Beijing Normal University, Center of Social Welfare Studies, Beijing, China
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