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Bednarek L, Glover S, Ma X, Pittenger C, Pushkarskaya H. Externally orienting cues improve cognitive control in OCD. J Behav Ther Exp Psychiatry 2024; 84:101959. [PMID: 38531125 PMCID: PMC11192454 DOI: 10.1016/j.jbtep.2024.101959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/31/2023] [Accepted: 03/01/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND AND OBJECTIVES An executive overload model of obsessive-compulsive disorder (OCD) posits that broad difficulties with executive functioning in OCD result from an overload on the executive system by obsessive thoughts. It implies that, if individuals with OCD "snap out" of their obsessive thoughts, their performance on neurocognitive tasks will improve. METHODS We test this prediction using the revised Attention Network Test, ANT-R, and distinct subsamples of data from unmedicated OCD and healthy controls (HC). ANT-R includes Simon and Flanker tasks; in both, incongruent trials take longer to resolve ('conflict costs'). On some trials, a warning cue helps participants to respond faster ('alerting benefits'). In OCD (N = 34) and HC (N = 46), matched on age, IQ, and sex, we tested (1) the effect of OCD on alerting benefits, and (2) the effect of OCD on warning cue related reductions in conflict costs. In a distinct subsample of OCD (N = 32) and HC (N = 51), we assessed whether alerting benefits and cue-related reductions in conflict costs are associated differently with different OCD symptoms. RESULTS A warning cue can help individuals with OCD more than HC to improve performance on Simon and Flanker tasks. This effect is positively associated with severity of contamination symptoms. LIMITATIONS This study did not directly assess how distracted participants are by obsessive thoughts. It relied on the ANT-R subtraction measures. Symptom severity was assessed using self-report measures. CONCLUSIONS Difficulties in resolving conflict during decision-making in OCD can be modulated by a warning cue presented immediately before an attentional task.
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Affiliation(s)
- Lora Bednarek
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States
| | - Stephanie Glover
- PGSP-Stanford PsyD Consortium, Palo Alto University, Palo Alto, CA, United States
| | - Xiao Ma
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States
| | - Christopher Pittenger
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States; Department of Psychology, Yale University, New Haven, CT, United States; Yale Child Study Center, Yale School of Medicine, New Haven, CT, United States; Wu Tsai Institute, Yale University, New Haven, CT, United States; Yale Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT, United States
| | - Helen Pushkarskaya
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States.
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Xiao X, Hammond C, Salmeron BJ, Wang D, Gu H, Zhai T, Nguyen H, Lu H, Ross TJ, Yang Y. Brain Functional Connectome Defines a Transdiagnostic Dimension Shared by Cognitive Function and Psychopathology in Preadolescents. Biol Psychiatry 2024; 95:1081-1090. [PMID: 37769982 PMCID: PMC10963340 DOI: 10.1016/j.biopsych.2023.08.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 07/27/2023] [Accepted: 08/30/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND Cognitive function and general psychopathology are two important classes of human behavior dimensions that are individually related to mental disorders across diagnostic categories. However, whether these two transdiagnostic dimensions are linked to common or distinct brain networks that convey resilience or risk for the development of psychiatric disorders remains unclear. METHODS The current study is a longitudinal investigation with 11,875 youths from the Adolescent Brain Cognitive Development (ABCD) Study at ages 9 to 10 years at the onset of the study. A machine learning approach based on canonical correlation analysis was used to identify latent dimensional associations of the resting-state functional connectome with multidomain behavioral assessments including cognitive functions and psychopathological measures. For the latent resting-state functional connectivity factor showing a robust behavioral association, its ability to predict psychiatric disorders was assessed using 2-year follow-up data, and its genetic association was evaluated using twin data from the same cohort. RESULTS A latent functional connectome pattern was identified that showed a strong and generalizable association with the multidomain behavioral assessments (5-fold cross-validation: ρ = 0.68-0.73 for the training set [n = 5096]; ρ = 0.56-0.58 for the test set [n = 1476]). This functional connectome pattern was highly heritable (h2 = 74.42%, 95% CI: 56.76%-85.42%), exhibited a dose-response relationship with the cumulative number of psychiatric disorders assessed concurrently and at 2 years post-magnetic resonance imaging scan, and predicted the transition of diagnosis across disorders over the 2-year follow-up period. CONCLUSIONS These findings provide preliminary evidence for a transdiagnostic connectome-based measure that underlies individual differences in the development of psychiatric disorders during early adolescence.
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Affiliation(s)
- Xiang Xiao
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Christopher Hammond
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Betty Jo Salmeron
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Danni Wang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Hong Gu
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Tianye Zhai
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Hieu Nguyen
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Hanbing Lu
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Thomas J Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland.
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3
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Xu Y, Liao X, Lei T, Cao M, Zhao J, Zhang J, Zhao T, Li Q, Jeon T, Ouyang M, Chalak L, Rollins N, Huang H, He Y. Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2. Cereb Cortex 2024; 34:bhae204. [PMID: 38771241 DOI: 10.1093/cercor/bhae204] [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: 12/15/2023] [Revised: 04/23/2024] [Accepted: 05/01/2024] [Indexed: 05/22/2024] Open
Abstract
The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2 years of age. Our findings highlight network-level neural substrates underlying early cognitive development.
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Affiliation(s)
- Yuehua Xu
- School of Systems Science, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Miao Cao
- Institution of Science and Technology for Brain-Inspired Intelligence, Fudan University, No. 220 Handan Road, Shanghai 200433, China
| | - Jianlong Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Jiaying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tina Jeon
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States
| | - Nancy Rollins
- Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Chinese Institute for Brain Research, No. 26 Kexueyuan Road, Beijing 102206, China
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Flinkenflügel K, Gruber M, Meinert S, Thiel K, Winter A, Goltermann J, Usemann P, Brosch K, Stein F, Thomas-Odenthal F, Wroblewski A, Pfarr JK, David FS, Beins EC, Grotegerd D, Hahn T, Leehr EJ, Dohm K, Bauer J, Forstner AJ, Nöthen MM, Jamalabadi H, Straube B, Alexander N, Jansen A, Witt SH, Rietschel M, Nenadić I, van den Heuvel MP, Kircher T, Repple J, Dannlowski U. The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression. Mol Psychiatry 2024:10.1038/s41380-024-02577-7. [PMID: 38693319 DOI: 10.1038/s41380-024-02577-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 04/15/2024] [Accepted: 04/19/2024] [Indexed: 05/03/2024]
Abstract
Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Processing speed performance of n = 284 acutely depressed, n = 177 partially and n = 198 fully remitted patients, and n = 743 healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. We investigated whether network connectivity mediates the association between TNF-α PGS and processing speed. We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD. The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.
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Grants
- DA1151/5-1, DA1151/5-2, DA1151/11‑1 DA1151/6-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
- HA7070/2-2, HA7070/3, HA7070/4 Deutsche Forschungsgemeinschaft (German Research Foundation)
- NO 246/10-1, NO 246/10-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- STR 1146/18-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
- JA 1890/7-1, JA 1890/7-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- WI 3439/3-1, WI 3439/3-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- RI 908/11-1, RI 908/11-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- KI 588/14-1, KI 588/14-2, KI 588/22-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
- ERC-COG 101001062, VIDI-452-16-015 Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)
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Affiliation(s)
- Kira Flinkenflügel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Friederike S David
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Eva C Beins
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Radiology, University of Münster, Münster, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Martijn P van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Child Psychiatry, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
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Popp JL, Thiele JA, Faskowitz J, Seguin C, Sporns O, Hilger K. Structural-functional brain network coupling predicts human cognitive ability. Neuroimage 2024; 290:120563. [PMID: 38492685 DOI: 10.1016/j.neuroimage.2024.120563] [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: 08/01/2023] [Revised: 10/14/2023] [Accepted: 03/01/2024] [Indexed: 03/18/2024] Open
Abstract
Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.
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Affiliation(s)
- Johanna L Popp
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
| | - Jonas A Thiele
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Kirsten Hilger
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
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Desvaux T, Danna J, Velay JL, Frey A. From gifted to high potential and twice exceptional: A state-of-the-art meta-review. APPLIED NEUROPSYCHOLOGY. CHILD 2024; 13:165-179. [PMID: 37665678 DOI: 10.1080/21622965.2023.2252950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Despite the abundant literature on intelligence and high potential individuals, there is still a lack of international consensus on the terminology and clinical characteristics associated to this population. It has been argued that unstandardized use of diagnosis tools and research methods make comparisons and interpretations of scientific and epidemiological evidence difficult in this field. If multiple cognitive and psychological models have attempted to explain the mechanisms underlying high potentiality, there is a need to confront new scientific evidence with the old, to uproot a global understanding of what constitutes the neurocognitive profile of high-potential in gifted individuals. Another particularly relevant aspect of applied research on high potentiality concerns the challenges faced by individuals referred to as "twice exceptional" in the field of education and in their socio-affective life. Some individuals have demonstrated high forms of intelligence together with learning, affective or neurodevelopmental disorders posing the question as to whether compensating or exacerbating psycho-cognitive mechanisms might underlie their observed behavior. Elucidating same will prove relevant to questions concerning the possible need for differential diagnosis tools, specialized educational and clinical support. A meta-review of the latest findings from neuroscience to developmental psychology, might help in the conception and reviewing of intervention strategies.
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Affiliation(s)
- Tatiana Desvaux
- CNRS, Laboratoire de Neurosciences Cognitives, Aix-Marseille University, UMR 7291, Marseille, France
| | - J Danna
- CLLE, Université de Toulouse, CNRS, Toulouse, France
| | - J-L Velay
- CNRS, Laboratoire de Neurosciences Cognitives, Aix-Marseille University, UMR 7291, Marseille, France
| | - A Frey
- CNRS, Laboratoire de Neurosciences Cognitives, Aix-Marseille University, UMR 7291, Marseille, France
- INSPE of Aix-Marseille University, Marseille, France
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7
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Guichet C, Banjac S, Achard S, Mermillod M, Baciu M. Modeling the neurocognitive dynamics of language across the lifespan. Hum Brain Mapp 2024; 45:e26650. [PMID: 38553863 PMCID: PMC10980845 DOI: 10.1002/hbm.26650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed between language comprehension and language production (LP). Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this variability. At the cognitive level, our findings suggest that LP is not an isolated function but is modulated throughout the lifespan by the extent of inter-cognitive synergy between semantic and domain-general processes. At the cerebral level, we show that default mode network (DMN) suppression coupled with fronto-parietal network (FPN) integration is the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related decline in LP. Relatedly, reduced DMN suppression in midlife could compromise the ability to manage the cost of FPN integration. This may prompt older adults to adopt a more cost-efficient compensatory strategy that maintains global homeostasis at the expense of LP performances. Taken together, we propose that midlife represents a critical neurocognitive juncture that signifies the onset of LP decline, as older adults gradually lose control over semantic representations. We summarize our findings in a novel synergistic, economical, nonlinear, emergent, cognitive aging model, integrating connectomic and cognitive dimensions within a complex system perspective.
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Affiliation(s)
| | - Sonja Banjac
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
| | - Sophie Achard
- LJK, UMR CNRS 5224, Université Grenoble AlpesGrenobleFrance
| | | | - Monica Baciu
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
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Chinichian N, Lindner M, Yanchuk S, Schwalger T, Schöll E, Berner R. Modeling brain network flexibility in networks of coupled oscillators: a feasibility study. Sci Rep 2024; 14:5713. [PMID: 38459077 PMCID: PMC10923875 DOI: 10.1038/s41598-024-55753-8] [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: 09/19/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024] Open
Abstract
Modeling the functionality of the human brain is a major goal in neuroscience for which many powerful methodologies have been developed over the last decade. The impact of working memory and the associated brain regions on the brain dynamics is of particular interest due to their connection with many functions and malfunctions in the brain. In this context, the concept of brain flexibility has been developed for the characterization of brain functionality. We discuss emergence of brain flexibility that is commonly measured by the identification of changes in the cluster structure of co-active brain regions. We provide evidence that brain flexibility can be modeled by a system of coupled FitzHugh-Nagumo oscillators where the network structure is obtained from human brain Diffusion Tensor Imaging (DTI). Additionally, we propose a straightforward and computationally efficient alternative macroscopic measure, which is derived from the Pearson distance of functional brain matrices. This metric exhibits similarities to the established patterns of brain template flexibility that have been observed in prior investigations. Furthermore, we explore the significance of the brain's network structure and the strength of connections between network nodes or brain regions associated with working memory in the observation of patterns in networks flexibility. This work enriches our understanding of the interplay between the structure and function of dynamic brain networks and proposes a modeling strategy to study brain flexibility.
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Affiliation(s)
- Narges Chinichian
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany.
- Psychiatry Department, Charité-Universitätsmedizin, Berlin, Germany.
- Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Michael Lindner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Serhiy Yanchuk
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
- Institute of Mathematics, Humboldt Universität zu Berlin, Berlin, Germany
- School of Mathematical Sciences, University College Cork, Cork, Ireland
| | - Tilo Schwalger
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Institute of Mathematics, Technische Universität Berlin, Berlin, Germany
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Rico Berner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Department of Physics, Humboldt Universität zu Berlin, Berlin, Germany
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9
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Ran H, Chen G, Ran C, He Y, Xie Y, Yu Q, Liu J, Hu J, Zhang T. Altered White-Matter Functional Network in Children with Idiopathic Generalized Epilepsy. Acad Radiol 2024:S1076-6332(23)00722-5. [PMID: 38350813 DOI: 10.1016/j.acra.2023.12.043] [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: 11/02/2023] [Revised: 12/27/2023] [Accepted: 12/30/2023] [Indexed: 02/15/2024]
Abstract
RATIONALE AND OBJECTIVES The white matter (WM) functional network changes offers insights into the potential pathological mechanisms of certain diseases, the alterations of WM functional network in idiopathic generalized epilepsy (IGE) remain unclear. We aimed to explore the topological characteristics changes of WM functional network in childhood IGE using resting-state functional Magnetic resonance imaging (MRI) and T1-weighted images. METHODS A total of 84 children (42 IGE and 42 matched healthy controls) were included in this study. Functional and structural MRI data were acquired to construct a WM functional network. Group differences in the global and regional topological characteristics were assessed by graph theory and the correlations with clinical and neuropsychological scores were analyzed. A support vector machine algorithm model was employed to classify individuals with IGE using WM functional connectivity as features, and the model's accuracy was evaluated using leave-one-out cross-validation. RESULTS In IGE group, at the network level, the WM functional network exhibited increased assortativity; at the nodal level, 17 nodes presented nodal disturbances in WM functional network, and nodal disturbances of 11 nodes were correlated with cognitive performance scores, disease duration and age of onset. The classification model achieved the 72.6% accuracy, 0.746 area under the curve, 69.1% sensitivity, 76.2% specificity. CONCLUSION Our study demonstrated that the WM functional network topological properties changes in childhood IGE, which were associated with cognitive function, and WM functional network may help clinical classification for childhood IGE. These findings provide novel information for understanding the pathogenesis of IGE and suggest that the WM function network might be qualified as potential biomarkers.
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Affiliation(s)
- Haifeng Ran
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563003, China
| | - Guiqin Chen
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563003, China
| | - Chunyan Ran
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563003, China
| | - Yulun He
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563003, China
| | - Yuxin Xie
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563003, China
| | - Qiane Yu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563003, China
| | - Junwei Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563003, China
| | - Jie Hu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563003, China; Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tijiang Zhang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563003, China.
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10
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Zhang L, Feng J, Liu C, Hu H, Zhou Y, Yang G, Peng X, Li T, Chen C, Xue G. Improved estimation of general cognitive ability and its neural correlates with a large battery of cognitive tasks. Cereb Cortex 2024; 34:bhad510. [PMID: 38183183 DOI: 10.1093/cercor/bhad510] [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: 10/21/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 01/07/2024] Open
Abstract
Elucidating the neural mechanisms of general cognitive ability (GCA) is an important mission of cognitive neuroscience. Recent large-sample cohort studies measured GCA through multiple cognitive tasks and explored its neural basis, but they did not investigate how task number, factor models, and neural data type affect the estimation of GCA and its neural correlates. To address these issues, we tested 1,605 Chinese young adults with 19 cognitive tasks and Raven's Advanced Progressive Matrices (RAPM) and collected resting state and n-back task fMRI data from a subsample of 683 individuals. Results showed that GCA could be reliably estimated by multiple tasks. Increasing task number enhances both reliability and validity of GCA estimates and reliably strengthens their correlations with brain data. The Spearman model and hierarchical bifactor model yield similar GCA estimates. The bifactor model has better model fit and stronger correlation with RAPM but explains less variance and shows weaker correlations with brain data than does the Spearman model. Notably, the n-back task-based functional connectivity patterns outperform resting-state fMRI in predicting GCA. These results suggest that GCA derived from a multitude of cognitive tasks serves as a valid measure of general intelligence and that its neural correlates could be better characterized by task fMRI than resting-state fMRI data.
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Affiliation(s)
- Liang Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Junjiao Feng
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Chuqi Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Huinan Hu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Yu Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Gangyao Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Xiaojing Peng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Tong Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Chuansheng Chen
- Department of Psychological Science, University of California, Irvine, CA 92697, USA
| | - Gui Xue
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
- Chinese Institute for Brain Research, Beijing 102206, PR China
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11
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Metzen D, Stammen C, Fraenz C, Schlüter C, Johnson W, Güntürkün O, DeYoung CG, Genç E. Investigating robust associations between functional connectivity based on graph theory and general intelligence. Sci Rep 2024; 14:1368. [PMID: 38228689 DOI: 10.1038/s41598-024-51333-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/29/2023] [Indexed: 01/18/2024] Open
Abstract
Previous research investigating relations between general intelligence and graph-theoretical properties of the brain's intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.
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Affiliation(s)
- Dorothea Metzen
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801, Bochum, Germany.
- Institute of Psychology, Department of Educational Sciences and Psychology, TU Dortmund University, 44227, Dortmund, Germany.
| | - Christina Stammen
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139, Dortmund, Germany
| | - Christoph Fraenz
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139, Dortmund, Germany
| | - Caroline Schlüter
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801, Bochum, Germany
| | - Wendy Johnson
- Department of Psychology, University of Edinburgh, EH8 9JZ, Edinburgh, UK
| | - Onur Güntürkün
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801, Bochum, Germany
| | - Colin G DeYoung
- Department of Psychology, University of Minnesota, 55455, Minneapolis, MN, USA
| | - Erhan Genç
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139, Dortmund, Germany
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12
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Michalik I, Kuder KJ. Machine Learning Methods in Protein-Protein Docking. Methods Mol Biol 2024; 2780:107-126. [PMID: 38987466 DOI: 10.1007/978-1-0716-3985-6_7] [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] [Indexed: 07/12/2024]
Abstract
An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is vast-from understanding fundamental cellular processes to discovering new drugs and breakthrough therapies. Computational studies of protein-protein interactions, crucial for understanding the operation of biological systems, are no exception in this field. However, despite the rapid development of technology and the progress in developing new approaches, many aspects remain challenging to solve, such as predicting conformational changes in proteins, or more "trivial" issues as high-quality data in huge quantities.Therefore, this chapter focuses on a short introduction to various AI approaches to study protein-protein interactions, followed by a description of the most up-to-date algorithms and programs used for this purpose. Yet, given the considerable pace of development in this hot area of computational science, at the time you read this chapter, the development of the algorithms described, or the emergence of new (and better) ones should come as no surprise.
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Affiliation(s)
- Ilona Michalik
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland
| | - Kamil J Kuder
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland.
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13
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Hemmatian B, Varshney LR, Pi F, Barbey AK. The utilitarian brain: Moving beyond the Free Energy Principle. Cortex 2024; 170:69-79. [PMID: 38135613 DOI: 10.1016/j.cortex.2023.11.013] [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: 10/17/2023] [Revised: 11/28/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
The Free Energy Principle (FEP) is a normative computational framework for iterative reduction of prediction error and uncertainty through perception-intervention cycles that has been presented as a potential unifying theory of all brain functions (Friston, 2006). Any theory hoping to unify the brain sciences must be able to explain the mechanisms of decision-making, an important cognitive faculty, without the addition of independent, irreducible notions. This challenge has been accepted by several proponents of the FEP (Friston, 2010; Gershman, 2019). We evaluate attempts to reduce decision-making to the FEP, using Lucas' (2005) meta-theory of the brain's contextual constraints as a guidepost. We find reductive variants of the FEP for decision-making unable to explain behavior in certain types of diagnostic, predictive, and multi-armed bandit tasks. We trace the shortcomings to the core theory's lack of an adequate notion of subjective preference or "utility", a concept central to decision-making and grounded in the brain's biological reality. We argue that any attempts to fully reduce utility to the FEP would require unrealistic assumptions, making the principle an unlikely candidate for unifying brain science. We suggest that researchers instead attempt to identify contexts in which either informational or independent reward constraints predominate, delimiting the FEP's area of applicability. To encourage this type of research, we propose a two-factor formal framework that can subsume any FEP model and allows experimenters to compare the contributions of informational versus reward constraints to behavior.
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Affiliation(s)
- Babak Hemmatian
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, USA
| | - Lav R Varshney
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, USA; Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, USA
| | - Frederick Pi
- Department of Cognitive Science, University of California San Diego, USA
| | - Aron K Barbey
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, USA; Center for Brain, Biology and Behavior, University of Nebraska Lincoln, USA.
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14
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Zhuang K, Zeitlen DC, Beaty RE, Vatansever D, Chen Q, Qiu J. Diverse functional interaction driven by control-default network hubs supports creative thinking. Cereb Cortex 2023; 33:11206-11224. [PMID: 37823346 DOI: 10.1093/cercor/bhad356] [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: 06/30/2023] [Revised: 09/06/2023] [Accepted: 09/07/2023] [Indexed: 10/13/2023] Open
Abstract
Complex cognitive processes, like creative thinking, rely on interactions among multiple neurocognitive processes to generate effective and innovative behaviors on demand, for which the brain's connector hubs play a crucial role. However, the unique contribution of specific hub sets to creative thinking is unknown. Employing three functional magnetic resonance imaging datasets (total N = 1,911), we demonstrate that connector hub sets are organized in a hierarchical manner based on diversity, with "control-default hubs"-which combine regions from the frontoparietal control and default mode networks-positioned at the apex. Specifically, control-default hubs exhibit the most diverse resting-state connectivity profiles and play the most substantial role in facilitating interactions between regions with dissimilar neurocognitive functions, a phenomenon we refer to as "diverse functional interaction". Critically, we found that the involvement of control-default hubs in facilitating diverse functional interaction robustly relates to creativity, explaining both task-induced functional connectivity changes and individual creative performance. Our findings suggest that control-default hubs drive diverse functional interaction in the brain, enabling complex cognition, including creative thinking. We thus uncover a biologically plausible explanation that further elucidates the widely reported contributions of certain frontoparietal control and default mode network regions in creativity studies.
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Affiliation(s)
- Kaixiang Zhuang
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Daniel C Zeitlen
- Department of Psychology, Pennsylvania State University, University Park, Pennsylvania 16801, United States
| | - Roger E Beaty
- Department of Psychology, Pennsylvania State University, University Park, Pennsylvania 16801, United States
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Qunlin Chen
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Jiang Qiu
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
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15
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Tang X, Zhang J, Liu L, Yang M, Li S, Chen J, Ma Y, Zhang J, Liu H, Lu C, Ding G. Distinct brain state dynamics of native and second language processing during narrative listening in late bilinguals. Neuroimage 2023; 280:120359. [PMID: 37661079 DOI: 10.1016/j.neuroimage.2023.120359] [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: 02/23/2023] [Revised: 07/01/2023] [Accepted: 08/31/2023] [Indexed: 09/05/2023] Open
Abstract
The process of complex cognition, which includes language processing, is dynamic in nature and involves various network modes or cognitive modes. This dynamic process can be manifested by a set of brain states and transitions between them. Previous neuroimaging studies have shed light on how bilingual brains support native language (L1) and second language (L2) through a shared network. However, the mechanism through which this shared brain network enables L1 and L2 processing remains unknown. This study examined this issue by testing the hypothesis that L1 and L2 processing is associated with distinct brain state dynamics in terms of brain state integration and transition flexibility. A group of late Chinese-English bilinguals was scanned using functional magnetic resonance imaging (fMRI) while listening to eight short narratives in Chinese (L1) and English (L2). Brain state dynamics were modeled using the leading eigenvector dynamic analysis framework. The results show that L1 processing involves more integrated states and frequent transitions between integrated and segregated states, while L2 processing involves more segregated states and fewer transitions. Our work provides insight into the dynamic process of narrative listening comprehension in late bilinguals and sheds new light on the neural representation of language processing and related disorders.
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Affiliation(s)
- Xiangrong Tang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Juan Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Beijing Institute of Graphic Communication, Beijing 102600, China
| | - Lanfang Liu
- Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University at Zhuhai, Zhuhai 519087, China; Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai 519087, China.
| | - Menghan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Department of Psychological and Brain Sciences, Dartmouth College, Hanover NH 03755, USA
| | - Shijie Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Yumeng Ma
- Department of Neuroscience, Physiology and Pharmacology, University College London, London WC1E 6BT, UK; Department of Psychology, Emory University, Atlanta GA 30322, USA
| | - Jia Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; School of Psychology, Beijing Language and Culture University, Beijing 100083, China
| | - Haiyi Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Guosheng Ding
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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16
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Kristanto D, Hildebrandt A, Sommer W, Zhou C. Cognitive abilities are associated with specific conjunctions of structural and functional neural subnetworks. Neuroimage 2023; 279:120304. [PMID: 37536528 DOI: 10.1016/j.neuroimage.2023.120304] [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: 11/09/2022] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 08/05/2023] Open
Abstract
Cognitive neuroscience assumes that different mental abilities correspond to at least partly separable brain subnetworks and strives to understand their relationships. However, single-task approaches typically revealed multiple brain subnetworks to be involved in performance. Here, we chose a bottom-up approach of investigating the association between structural and functional brain subnetworks, on the one hand, and domain-specific cognitive abilities, on the other. Structural network was identified using machine-learning graph neural network by clustering anatomical brain properties measured in 838 individuals enroled in the WU-Minn Young Adult Human Connectome Project. Functional network was adapted from seven Resting State Networks (7-RSN). We then analyzed the results of 15 cognitive tasks and estimated five latent abilities: fluid reasoning (Gf), crystallized intelligence (Gc), memory (Mem), executive functions (EF), and processing speed (Gs). In a final step we determined linear associations between these independently identified ability and brain entities. We found no one-to-one mapping between latent abilities and brain subnetworks. Analyses revealed that abilities are associated with properties of particular combinations of brain subnetworks. While some abilities are more strongly associated to within-subnetwork connections, others are related with connections between multiple subnetworks. Importantly, domain-specific abilities commonly rely on node(s) as hub(s) to connect with other subnetworks. To test the robustness of our findings, we ran the analyses through several defensible analytical decisions. Together, the present findings allow a novel perspective on the distinct nature of domain-specific cognitive abilities building upon unique combinations of associated brain subnetworks.
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Affiliation(s)
- Daniel Kristanto
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China; Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany
| | - Werner Sommer
- Department of Psychology, Humboldt University at Berlin, Berlin, Germany; Department of Psychology, Zhejiang Normal University, Jin Hua, China; Department of Physics, Hong Kong Baptist University, Hong Kong, China.
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China; Life Science Imaging Centre, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China; Department of Physics, Zhejiang University, Hangzhou 310000, China.
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17
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Wilcox RR, Barbey AK. Connectome-based predictive modeling of fluid intelligence: evidence for a global system of functionally integrated brain networks. Cereb Cortex 2023; 33:10322-10331. [PMID: 37526284 DOI: 10.1093/cercor/bhad284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 06/21/2023] [Accepted: 07/16/2023] [Indexed: 08/02/2023] Open
Abstract
Cognitive neuroscience continues to advance our understanding of the neural foundations of human intelligence, with significant progress elucidating the role of the frontoparietal network in cognitive control mechanisms for flexible, intelligent behavior. Recent evidence in network neuroscience further suggests that this finding may represent the tip of the iceberg and that fluid intelligence may depend on the collective interaction of multiple brain networks. However, the global brain mechanisms underlying fluid intelligence and the nature of multi-network interactions remain to be well established. We therefore conducted a large-scale Connectome-based Predictive Modeling study, administering resting-state fMRI to 159 healthy college students and examining the contributions of seven intrinsic connectivity networks to the prediction of fluid intelligence, as measured by a state-of-the-art cognitive task (the Bochum Matrices Test). Specifically, we aimed to: (i) identify whether fluid intelligence relies on a primary brain network or instead engages multiple brain networks; and (ii) elucidate the nature of brain network interactions by assessing network allegiance (within- versus between-network connections) and network topology (strong versus weak connections) in the prediction of fluid intelligence. Our results demonstrate that whole-brain predictive models account for a large and significant proportion of variance in fluid intelligence (18%) and illustrate that the contribution of individual networks is relatively modest by comparison. In addition, we provide novel evidence that the global architecture of fluid intelligence prioritizes between-network connections and flexibility through weak ties. Our findings support a network neuroscience approach to understanding the collective role of brain networks in fluid intelligence and elucidate the system-wide network mechanisms from which flexible, adaptive behavior is constructed.
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Affiliation(s)
- Ramsey R Wilcox
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, NE 68501, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Illinois, Urbana, IL 61801, United States
| | - Aron K Barbey
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, NE 68501, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Illinois, Urbana, IL 61801, United States
- Department of Bioengineering, University of Illinois, Urbana, IL 61801, United States
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18
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Xing XX, Gao X, Jiang C. Individual Variability of Human Cortical Spontaneous Activity by 3T/7T fMRI. Neuroscience 2023; 528:117-128. [PMID: 37544577 DOI: 10.1016/j.neuroscience.2023.07.032] [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: 09/10/2022] [Revised: 07/29/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023]
Abstract
Mapping variability in cortical spontaneous activity (CSA) is an essential goal of understanding various sources of dark brain energy in human neuroscience. CSA was traditionally characterized using resting-state functional MRI (rfMRI) at 1.5T or 3T magnets while recently with 7T-rfMRI. However, the utility and interpretability of 7T-rfMRI must first be established for its variability. By leveraging rfMRI data from the Human Connectome Project (HCP), we derived CSA metrics with 3T-rfMRI and 7T-rfMRI for the same 84 healthy participants (52 females). The 7T-rfMRI produces different CSA metrics at multiple spatial-scales and their variability from the 3T-rfMRI. These differences were spatially dependent and varied according to specific cortical organization. For the amplitude metric, 7T-rfMRI enhanced its spatial contrasts in the anterior cortex but weakened it in the posterior cortex. An opposite pattern was observed for the connectivity metrics. The reliability changes of these metrics were scale dependent, indicating enhanced reliability for connectivity but weakened reliability for amplitude by 7T-rfMRI. These effects were primarily located in the high-order associate cortex, parsing the corresponding changes in individual differences with respect to 7T-rfMRI: (1) higher connectivity variability between participants and the lower connectivity variability within individual participants, and (2) lower amplitude variability between participants and higher amplitude variability within participants. Our work, for the first time, demonstrated the variability of the human CSA across space, rfMRI settings/platforms, and individuals. We discussed the statistical implications of our findings on CSA-based experimental designs and reproducible neuroscience as well as their translational value for personalized applications.
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Affiliation(s)
- Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing 100124, China.
| | - Xiao Gao
- School of Psychology, Capital Normal University, Beijing 100048, China
| | - Chao Jiang
- Faculty of Psychology, Southwest University, Chongqing 400715, China
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19
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Leite S, Mota B, Silva AR, Commons ML, Miller PM, Rodrigues PP. Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity. PLoS One 2023; 18:e0290743. [PMID: 37651418 PMCID: PMC10470958 DOI: 10.1371/journal.pone.0290743] [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: 04/21/2023] [Accepted: 08/14/2023] [Indexed: 09/02/2023] Open
Abstract
Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the complexity of the neural structure of the brain. After segregating the input set, minimal neural network models were trained separately for each input subset, and adjacent complexity models were analyzed sequentially to observe whether there was a structural progression. Results show that three different network structural progressions were found, performing with similar accuracy, pointing towards self-organization. Also, more complex structures could be built out of less complex ones without substituting them, successfully addressing catastrophic forgetting and leveraging performance of previous models in the literature. Furthermore, the model structures trained on the two highest complexity subsets performed better than simulations of the balance beam present in the literature. As a major contribution, this work was successful in addressing hierarchical complexity structural growth in neural networks, and is the first that segregates inputs by Order of Hierarchical Complexity. Since this measure can be applied to all domains of data, the present method can be applied to future simulations, systematizing the simulation of developmental and evolutionary structural growth in neural networks.
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Affiliation(s)
- Sofia Leite
- CINTESIS – Center for Health Technology and Services Research, Porto, Portugal
- Dare Association, Inc. Boston, Massachusetts, United States of America
| | - Bruno Mota
- Laboratory of Experimental Mathematics and Theoretical Biology, Physics Institute, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil
| | - António Ramos Silva
- Department of Mechanical Engineering, Faculty of Engineering University of Porto, Porto, Portugal
- INEGI Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Michael Lamport Commons
- Dare Association, Inc. Boston, Massachusetts, United States of America
- Beth Israel Deaconess Medical Center, Harvard Medical School, Cambridge, Massachusetts, United States of America
| | - Patrice Marie Miller
- Dare Association, Inc. Boston, Massachusetts, United States of America
- Department of Psychology, Salem State University, Salem, Massachusetts, United States of America
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Ping L, Sun S, Zhou C, Que J, You Z, Xu X, Cheng Y. Altered topology of individual brain structural covariance networks in major depressive disorder. Psychol Med 2023:1-12. [PMID: 37427670 DOI: 10.1017/s003329172300168x] [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] [Indexed: 07/11/2023]
Abstract
BACKGROUND The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks. METHODS We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics. RESULTS Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex. CONCLUSIONS The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.
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Affiliation(s)
- Liangliang Ping
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Shan Sun
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Cong Zhou
- School of Mental Health, Jining Medical University, Jining, China
| | - Jianyu Que
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Zhiyi You
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
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21
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Wong OWH, Barzilay R, Lam AMW, Chan S, Calkins ME, Gur RE, Gur RC. Executive function as a generalized determinant of psychopathology and functional outcome in school-aged autism spectrum disorder: a case-control study. Psychol Med 2023; 53:4788-4798. [PMID: 35912846 PMCID: PMC10388326 DOI: 10.1017/s0033291722001787] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/24/2022] [Accepted: 05/26/2022] [Indexed: 01/30/2023]
Abstract
BACKGROUND Individuals with autism spectrum disorder (ASD) are challenged not only by the defining features of social-communication deficits and restricted repetitive behaviors, but also by a myriad of psychopathology varying in severity. Different cognitive deficits underpin these psychopathologies, which could be subjected to intervention to alter the course of the disorder. Understanding domain-specific mediating effects of cognition is essential for developing targeted intervention strategies. However, the high degree of inter-correlation among different cognitive functions hinders elucidation of individual effects. METHODS In the Philadelphia Neurodevelopmental Cohort, 218 individuals with ASD were matched with 872 non-ASD controls on sex, age, race, and socioeconomic status. Participants of this cohort were deeply and broadly phenotyped on neurocognitive abilities and dimensional psychopathology. Using structural equation modeling, inter-correlation among cognitive domains were adjusted before mediation analysis on outcomes of multi-domain psychopathology and functional level. RESULTS While social cognition, complex cognition, and memory each had a unique pattern of mediating effect on psychopathology domains in ASD, none had significant effects on the functional level. In contrast, executive function was the only cognitive domain that exerted a generalized negative impact on every psychopathology domain (p factor, anxious-misery, psychosis, fear, and externalizing), as well as functional level. CONCLUSIONS Executive function has a unique association with the severity of comorbid psychopathology in ASD, and could be a target of interventions. As executive dysfunction occurs variably in ASD, our result also supports the clinical utility of assessing executive function for prognostic purposes.
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Affiliation(s)
- Oscar W. H. Wong
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong
| | - Ran Barzilay
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, Pennsylvania, USA
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, Pennsylvania, USA
| | - Angela M. W. Lam
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong
| | - Sandra Chan
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong
| | - Monica E. Calkins
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, Pennsylvania, USA
| | - Raquel E. Gur
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, Pennsylvania, USA
| | - Ruben C. Gur
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, Pennsylvania, USA
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22
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Wehrheim MH, Faskowitz J, Sporns O, Fiebach CJ, Kaschube M, Hilger K. Few temporally distributed brain connectivity states predict human cognitive abilities. Neuroimage 2023:120246. [PMID: 37364742 DOI: 10.1016/j.neuroimage.2023.120246] [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: 02/18/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 06/28/2023] Open
Abstract
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities - which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
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Affiliation(s)
- Maren H Wehrheim
- Department of Psychology, Goethe University Frankfurt, D-60323 Frankfurt am Main, Germany; Department of Computer Science, Goethe University Frankfurt, D-60325 Frankfurt am Main, Germany.
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405.
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405.
| | - Christian J Fiebach
- Department of Psychology, Goethe University Frankfurt, D-60323 Frankfurt am Main, Germany; Brain Imaging Center, Goethe University, D-60528 Frankfurt am Main, Germany.
| | - Matthias Kaschube
- Department of Computer Science, Goethe University Frankfurt, D-60325 Frankfurt am Main, Germany; Frankfurt Institute for Advanced Studies, D-60438 Frankfurt am Main, Germany.
| | - Kirsten Hilger
- Department of Psychology, Goethe University Frankfurt, D-60323 Frankfurt am Main, Germany; Department of Psychology I, Julius Maximilian University, D-97070 Würzburg, Germany.
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23
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Genç E, Metzen D, Fraenz C, Schlüter C, Voelkle MC, Arning L, Streit F, Nguyen HP, Güntürkün O, Ocklenburg S, Kumsta R. Structural architecture and brain network efficiency link polygenic scores to intelligence. Hum Brain Mapp 2023; 44:3359-3376. [PMID: 37013679 PMCID: PMC10171514 DOI: 10.1002/hbm.26286] [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: 07/27/2022] [Revised: 02/15/2023] [Accepted: 03/01/2023] [Indexed: 04/05/2023] Open
Abstract
Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.
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Affiliation(s)
- Erhan Genç
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Dorothea Metzen
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Christoph Fraenz
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Caroline Schlüter
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Manuel C Voelkle
- Psychological Research Methods Department of Psychology, Humboldt University, Berlin, Germany
| | - Larissa Arning
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Fabian Streit
- Department Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Huu Phuc Nguyen
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Onur Güntürkün
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Sebastian Ocklenburg
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Psychology, Medical School Hamburg, Hamburg, Germany
- ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Hamburg, Germany
| | - Robert Kumsta
- Genetic Psychology, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Behavioural and Cognitive Sciences, Laboratory for Stress and Gene-Environment Interplay, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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Khodaei M, Laurienti PJ, Dagenbach D, Simpson SL. Brain working memory network indices as landmarks of intelligence. NEUROIMAGE. REPORTS 2023; 3:100165. [PMID: 37425210 PMCID: PMC10327823 DOI: 10.1016/j.ynirp.2023.100165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Identifying the neural correlates of intelligence has long been a goal in neuroscience. Recently, the field of network neuroscience has attracted researchers' attention as a means for answering this question. In network neuroscience, the brain is considered as an integrated system whose systematic properties provide profound insights into health and behavioral outcomes. However, most network studies of intelligence have used univariate methods to investigate topological network measures, with their focus limited to a few measures. Furthermore, most studies have focused on resting state networks despite the fact that brain activation during working memory tasks has been linked to intelligence. Finally, the literature is still missing an investigation of the association between network assortativity and intelligence. To address these issues, here we employ a recently developed mixed-modeling framework for analyzing multi-task brain networks to elucidate the most critical working memory task network topological properties corresponding to individuals' intelligence differences. We used a data set of 379 subjects (22-35 y/o) from the Human Connectome Project (HCP). Each subject's data included composite intelligence scores, and fMRI during resting state and a 2-back working memory task. Following comprehensive quality control and preprocessing of the minimally preprocessed fMRI data, we extracted a set of the main topological network features, including global efficiency, degree, leverage centrality, modularity, and clustering coefficient. The estimated network features and subject's confounders were then incorporated into the multi-task mixed-modeling framework to investigate how brain network changes between working memory and resting state relate to intelligence score. Our results indicate that the general intelligence score (cognitive composite score) is associated with a change in the relationship between connection strength and multiple network topological properties, including global efficiency, leverage centrality, and degree difference during working memory as it is compared to resting state. More specifically, we observed a higher increase in the positive association between global efficiency and connection strength for the high intelligence group when they switch from resting state to working memory. The strong connections might form superhighways for a more efficient global flow of information through the brain network. Furthermore, we found an increase in the negative association between degree difference and leverage centrality with connection strength during working memory tasks for the high intelligence group. These indicate higher network resilience and assortativity along with higher circuit-specific information flow during working memory for those with a higher intelligence score. Although the exact neurobiological implications of our results are speculative at this point, our results provide evidence for the significant association of intelligence with hallmark properties of brain networks during working memory.
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Affiliation(s)
- Mohammadreza Khodaei
- Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Paul J. Laurienti
- Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Dale Dagenbach
- Department of Psychology, Wake Forest University, Winston-Salem, NC, USA
| | - Sean L. Simpson
- Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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25
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Xu H, Xu C, Yang Z, Bai G, Yin B. Two sides of the same coin: distinct neuroanatomical patterns predict crystallized and fluid intelligence in adults. Front Neurosci 2023; 17:1199106. [PMID: 37304014 PMCID: PMC10249781 DOI: 10.3389/fnins.2023.1199106] [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: 04/02/2023] [Accepted: 05/08/2023] [Indexed: 06/13/2023] Open
Abstract
Background Crystallized intelligence (Gc) and fluid intelligence (Gf) are regarded as distinct intelligence components that statistically correlate with each other. However, the distinct neuroanatomical signatures of Gc and Gf in adults remain contentious. Methods Machine learning cross-validated elastic net regression models were performed on the Human Connectome Project Young Adult dataset (N = 1089) to characterize the neuroanatomical patterns of structural magnetic resonance imaging variables that are associated with Gc and Gf. The observed relationships were further examined by linear mixed-effects models. Finally, intraclass correlations were computed to examine the similarity of the neuroanatomical correlates between Gc and Gf. Results The results revealed distinct multi-region neuroanatomical patterns predicted Gc and Gf, respectively, which were robust in a held-out test set (R2 = 2.40, 1.97%, respectively). The relationship of these regions with Gc and Gf was further supported by the univariate linear mixed effects models. Besides that, Gc and Gf displayed poor neuroanatomical similarity. Conclusion These findings provided evidence that distinct machine learning-derived neuroanatomical patterns could predict Gc and Gf in healthy adults, highlighting differential neuroanatomical signatures of different aspects of intelligence.
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Affiliation(s)
- Hui Xu
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Peter Boris Centre for Addictions Research, St. Joseph’s Healthcare Hamilton, McMaster University, Hamilton, ON, Canada
| | - Cheng Xu
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Zhenliang Yang
- Faculty of Psychology, Tianjin Normal University, Tianjin, China
| | - Guanghui Bai
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Bo Yin
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
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26
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Posner MI, Rothbart MK. Fifty Years Integrating Neurobiology and Psychology to Study Attention. Biol Psychol 2023; 180:108574. [PMID: 37148960 DOI: 10.1016/j.biopsycho.2023.108574] [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: 02/09/2023] [Revised: 05/03/2023] [Accepted: 05/03/2023] [Indexed: 05/08/2023]
Abstract
At the time of the start of Biological Psychology cognitive studies had developed approaches to measuring cognitive processes. However, linking these to the underlying biology in the typical human brain had hardly begun. A critical step came in 1988 when methods for imaging the human brain in cognitive tasks began. By 1990 it was possible to describe three brain networks that carried out the hypothesized cognitive functions outlined 20 years before. Their development was traced in infancy, first using age-appropriate tasks and later through resting state imaging. Imaging was applied to both voluntary and involuntary cued shifts of visual orienting in humans and primates, and a summary was presented in 2002. By 2008 these new imaging findings were used to test hypotheses about the genes involved in each network. Recently, studies of mice using optogenetics to control populations of neurons have brought us closer to a synthesis of how attention and memory networks operate together in human learning. Perhaps the coming years will bring us to an integrated theory of aspects of attention using data from all the levels that can illuminate these issues, thus fulfilling a key goal of the Journal.
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27
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Anderson ED, Barbey AK. Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach. Hum Brain Mapp 2023; 44:1647-1665. [PMID: 36537816 PMCID: PMC9921238 DOI: 10.1002/hbm.26164] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/18/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022] Open
Abstract
Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large-scale connectome-based predictive modeling study (N = 297), administering resting-state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto-Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole-brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information-processing architecture, suggesting future directions for theory-driven research on system-wide network mechanisms underlying general intelligence.
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Affiliation(s)
- Evan D Anderson
- Decision Neuroscience Laboratory, Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, Illinois, USA.,Neuroscience Program, University of Illinois, Urbana, Illinois, USA.,Ball Aerospace and Technologies Corp, Broomfield, Colorado, USA.,Air Force Research Laboratory, Wright-Patterson AFB, Ohio, USA
| | - Aron K Barbey
- Decision Neuroscience Laboratory, Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, Illinois, USA.,Neuroscience Program, University of Illinois, Urbana, Illinois, USA.,Department of Psychology, University of Illinois, Urbana, Illinois, USA.,Department of Bioengineering, University of Illinois, Urbana, Illinois, USA
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28
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de Souza EA, Silva SA, Vieira BH, Salmon CEG. fMRI functional connectivity is a better predictor of general intelligence than cortical morphometric features and ICA parcellation order affects predictive performance. INTELLIGENCE 2023. [DOI: 10.1016/j.intell.2023.101727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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29
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Wittmann MK, Scheuplein M, Gibbons SG, Noonan MP. Local and global reward learning in the lateral frontal cortex show differential development during human adolescence. PLoS Biol 2023; 21:e3002010. [PMID: 36862726 PMCID: PMC10013901 DOI: 10.1371/journal.pbio.3002010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 03/14/2023] [Accepted: 01/20/2023] [Indexed: 03/03/2023] Open
Abstract
Reward-guided choice is fundamental for adaptive behaviour and depends on several component processes supported by prefrontal cortex. Here, across three studies, we show that two such component processes, linking reward to specific choices and estimating the global reward state, develop during human adolescence and are linked to the lateral portions of the prefrontal cortex. These processes reflect the assignment of rewards contingently to local choices, or noncontingently, to choices that make up the global reward history. Using matched experimental tasks and analysis platforms, we show the influence of both mechanisms increase during adolescence (study 1) and that lesions to lateral frontal cortex (that included and/or disconnected both orbitofrontal and insula cortex) in human adult patients (study 2) and macaque monkeys (study 3) impair both local and global reward learning. Developmental effects were distinguishable from the influence of a decision bias on choice behaviour, known to depend on medial prefrontal cortex. Differences in local and global assignments of reward to choices across adolescence, in the context of delayed grey matter maturation of the lateral orbitofrontal and anterior insula cortex, may underlie changes in adaptive behaviour.
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Affiliation(s)
- Marco K. Wittmann
- Department of Experimental Psychology, University of Oxford, Radcliffe Observatory, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Headington, Oxford, United Kingdom
- Department of Experimental Psychology, University College London, London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, United Kingdom
| | - Maximilian Scheuplein
- Department of Experimental Psychology, University of Oxford, Radcliffe Observatory, Oxford, United Kingdom
- Institute of Education and Child Studies, Leiden University, Leiden, the Netherlands
| | - Sophie G. Gibbons
- Department of Experimental Psychology, University of Oxford, Radcliffe Observatory, Oxford, United Kingdom
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - MaryAnn P. Noonan
- Department of Experimental Psychology, University of Oxford, Radcliffe Observatory, Oxford, United Kingdom
- Department of Psychology, University of York, York, United Kingdom
- * E-mail:
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30
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He H, Lin W, Yang J, Chen Y, Tan S, Guan Q. Age-related intrinsic functional connectivity underlying emotion utilization. Cereb Cortex 2023:7033308. [PMID: 36758953 DOI: 10.1093/cercor/bhad023] [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: 11/02/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 02/11/2023] Open
Abstract
Previous studies investigated the age-related positivity effect in terms of emotion perception and management, whereas little is known about whether the positivity effect is shown in emotion utilization (EU). If yes, the EU-related intrinsic functional connectivity and its age-associated alterations remain to be elucidated. In this study, we collected resting-state functional magnetic resonance imaging data from 62 healthy older adults and 72 undergraduates as well as their self-ratings of EU. By using the connectome-based predictive modeling (CPM) method, we constructed a predictive model of the positive relationship between EU self-ratings and resting-state functional connectivity. Lesion simulation analyses revealed that the medial-frontal network, default mode network, frontoparietal network, and subcortical regions played key roles in the EU-related CPM. Older subjects showed significantly higher EU self-ratings than undergraduates, which was associated with strengthened connectivity between the left dorsolateral prefrontal cortex and bilateral frontal poles, and between the left frontal pole and thalamus. A mediation analysis indicated that the age-related EU network mediated the age effect on EU self-ratings. Our findings extend previous research on the age-related "positivity effect" to the EU domain, suggesting that the positivity effect on the self-evaluation of EU is probably associated with emotion knowledge which accumulates with age.
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Affiliation(s)
- Hao He
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Wenyi Lin
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China
| | - Jiawang Yang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China
| | - Yiqi Chen
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Department of Psychology, University of Mannheim, Mannheim, Germany
| | - Siping Tan
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
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31
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Carroll's Three-Stratum (3S) Cognitive Ability Theory at 30 Years: Impact, 3S-CHC Theory Clarification, Structural Replication, and Cognitive-Achievement Psychometric Network Analysis Extension. J Intell 2023; 11:jintelligence11020032. [PMID: 36826930 PMCID: PMC9959556 DOI: 10.3390/jintelligence11020032] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/18/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
Carroll's treatise on the structure of human cognitive abilities is a milestone in psychometric intelligence research. Thirty years later, Carroll's work continues to influence research on intelligence theories and the development and interpretation of intelligence tests. A historical review of the relations between the 3S and CHC theories necessitates the recommendation that the theories of Cattell, Horn, and Carroll be reframed as a family of obliquely correlated CHC theories-not a single CHC theory. Next, a previously unpublished Carroll exploratory factor analysis of 46 cognitive and achievement tests is presented. A complimentary bifactor analysis is presented that reinforces Carroll's conclusion that his 3S model more accurately represents the structure of human intelligence than two prominent alternative models. Finally, a Carroll-recommended higher-stratum psychometric network analysis (PNA) of CHC cognitive, reading, and math variables is presented. The PNA results demonstrate how PNA can complement factor analysis and serve as a framework for identifying and empirically evaluating cognitive-achievement causal relations and mechanisms (e.g., developmental cascade and investment theories), with an eye toward improved cognitive-achievement intervention research. It is believed that Carroll, given his long-standing interest in school learning, would welcome the integration of theory-driven factor and PNA research.
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32
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Stammen C, Fraenz C, Grazioplene RG, Schlüter C, Merhof V, Johnson W, Güntürkün O, DeYoung CG, Genç E. Robust associations between white matter microstructure and general intelligence. Cereb Cortex 2023:6994402. [PMID: 36682883 DOI: 10.1093/cercor/bhac538] [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: 04/29/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 01/24/2023] Open
Abstract
Few tract-based spatial statistics (TBSS) studies have investigated the relations between intelligence and white matter microstructure in healthy (young) adults, and those have yielded mixed observations, yet white matter is fundamental for efficient and accurate information transfer throughout the human brain. We used a multicenter approach to identify white matter regions that show replicable structure-function associations, employing data from 4 independent samples comprising over 2000 healthy participants. TBSS indicated 188 voxels exhibited significant positive associations between g factor scores and fractional anisotropy (FA) in all 4 data sets. Replicable voxels formed 3 clusters, located around the left-hemispheric forceps minor, superior longitudinal fasciculus, and cingulum-cingulate gyrus with extensions into their surrounding areas (anterior thalamic radiation, inferior fronto-occipital fasciculus). Our results suggested that individual differences in general intelligence are robustly associated with white matter FA in specific fiber bundles distributed across the brain, consistent with the Parieto-Frontal Integration Theory of intelligence. Three possible reasons higher FA values might create links with higher g are faster information processing due to greater myelination, more direct information processing due to parallel, homogenous fiber orientation distributions, or more parallel information processing due to greater axon density.
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Affiliation(s)
- Christina Stammen
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139 Dortmund, Germany
| | - Christoph Fraenz
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139 Dortmund, Germany
| | | | - Caroline Schlüter
- Department of Biopsychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, 44801 Bochum, Germany
| | - Viola Merhof
- Chair of Research Methods and Psychological Assessment, University of Mannheim, 68161 Mannheim, Germany
| | - Wendy Johnson
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - Onur Güntürkün
- Department of Biopsychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, 44801 Bochum, Germany
| | - Colin G DeYoung
- Department of Psychology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Erhan Genç
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139 Dortmund, Germany
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33
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Neural Contributions to Reduced Fluid Intelligence across the Adult Lifespan. J Neurosci 2023; 43:293-307. [PMID: 36639907 PMCID: PMC9838706 DOI: 10.1523/jneurosci.0148-22.2022] [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: 01/20/2022] [Revised: 07/27/2022] [Accepted: 10/19/2022] [Indexed: 12/12/2022] Open
Abstract
Fluid intelligence, the ability to solve novel, complex problems, declines steeply during healthy human aging. Using fMRI, fluid intelligence has been repeatedly associated with activation of a frontoparietal brain network, and impairment following focal damage to these regions suggests that fluid intelligence depends on their integrity. It is therefore possible that age-related functional differences in frontoparietal activity contribute to the reduction in fluid intelligence. This paper reports on analysis of the Cambridge Center for Ageing and Neuroscience data, a large, population-based cohort of healthy males and females across the adult lifespan. The data support a model in which age-related differences in fluid intelligence are partially mediated by the responsiveness of frontoparietal regions to novel problem-solving. We first replicate a prior finding of such mediation using an independent sample. We then precisely localize the mediating brain regions, and show that mediation is specifically associated with voxels most activated by cognitive demand, but not with voxels suppressed by cognitive demand. We quantify the robustness of this result to potential unmodeled confounders, and estimate the causal direction of the effects. Finally, exploratory analyses suggest that neural mediation of age-related differences in fluid intelligence is moderated by the variety of regular physical activities, more reliably than by their frequency or duration. An additional moderating role of the variety of nonphysical activities emerged when controlling for head motion. A better understanding of the mechanisms that link healthy aging with lower fluid intelligence may suggest strategies for mitigating such decline.SIGNIFICANCE STATEMENT Global populations are living longer, driving urgency to understand age-related cognitive declines. Fluid intelligence is of prime importance because it reflects performance across many domains, and declines especially steeply during healthy aging. Despite consensus that fluid intelligence is associated with particular frontoparietal brain regions, little research has investigated suggestions that under-responsiveness of these regions mediates age-related decline. We replicate a recent demonstration of such mediation, showing specific association with brain regions most activated by cognitive demand, and robustness to moderate confounding by unmodeled variables. By showing that this mediation model is moderated by the variety of regular physical activities, more reliably than by their frequency or duration, we identify a potential modifiable lifestyle factor that may help promote successful aging.
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34
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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
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35
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Jastrzębski J, Ociepka M, Chuderski A. Graph Mapping: A novel and simple test to validly assess fluid reasoning. Behav Res Methods 2023; 55:448-460. [PMID: 35441361 PMCID: PMC9918571 DOI: 10.3758/s13428-022-01846-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/21/2022] [Indexed: 11/08/2022]
Abstract
We present Graph Mapping - a simple and effective computerized test of fluid intelligence (reasoning ability). The test requires structure mapping - a key component of the reasoning process. Participants are asked to map a pair of corresponding nodes across two mathematically isomorphic but visually different graphs. The test difficulty can be easily manipulated - the more complex structurally and dissimilar visually the graphs, the higher response error rate. Graph Mapping offers high flexibility in item generation, ranging from trivial to extremally difficult items, supporting progressive item sequences suitable for correlational studies. It also allows multiple item instances (clones) at a fixed difficulty level as well as full item randomization, both particularly suitable for within-subject experimental designs, longitudinal studies, and adaptive testing. The test has short administration times and is unfamiliar to participants, yielding practical advantages. Graph Mapping has excellent psychometric properties: Its convergent validity and reliability is comparable to the three leading traditional fluid reasoning tests. The convenient software allows a researcher to design the optimal test variant for a given study and sample. Graph Mapping can be downloaded from: https://osf.io/wh7zv/.
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Affiliation(s)
- Jan Jastrzębski
- Institute of Philosophy, Jagiellonian University, Grodzka 52, 31-044 Krakow, Poland
| | - Michał Ociepka
- Institute of Psychology, Jagiellonian University, Ingardena 6, 30-060 Krakow, Poland
| | - Adam Chuderski
- Institute of Philosophy, Jagiellonian University, Grodzka 52, 31-044 Krakow, Poland
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36
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Henry TR, Fogleman ND, Nugiel T, Cohen JR. Effect of methylphenidate on functional controllability: a preliminary study in medication-naïve children with ADHD. Transl Psychiatry 2022; 12:518. [PMID: 36528602 PMCID: PMC9759578 DOI: 10.1038/s41398-022-02283-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 11/18/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
Methylphenidate (MPH) is the recommended first-line treatment for attention-deficit/hyperactivity disorder (ADHD). While MPH's mechanism of action as a dopamine and noradrenaline transporter blocker is well known, how this translates to ADHD-related symptom mitigation is still unclear. As functional connectivity is reliably altered in ADHD, with recent literature indicating dysfunctional connectivity dynamics as well, one possible mechanism is through altering brain network dynamics. In a double-blind, placebo-controlled MPH crossover trial, 19 medication-naïve children with ADHD underwent two functional MRI scanning sessions (one on MPH and one on placebo) that included a resting state scan and two inhibitory control tasks; 27 typically developing (TD) children completed the same protocol without medication. Network control theory, which quantifies how brain activity reacts to system inputs based on underlying connectivity, was used to assess differences in average and modal functional controllability during rest and both tasks between TD children and children with ADHD (on and off MPH) and between children with ADHD on and off MPH. Children with ADHD on placebo exhibited higher average controllability and lower modal controllability of attention, reward, and somatomotor networks than TD children. Children with ADHD on MPH were statistically indistinguishable from TD children on almost all controllability metrics. These findings suggest that MPH may stabilize functional network dynamics in children with ADHD, both reducing reactivity of brain organization and making it easier to achieve brain states necessary for cognitively demanding tasks.
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Affiliation(s)
- Teague R Henry
- Department of Psychology and School of Data Science, University of Virginia, Charlottesville, VA, USA.
| | - Nicholas D Fogleman
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tehila Nugiel
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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37
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Sofologi M, Papantoniou G, Avgita T, Lyraki A, Thomaidou C, Zaragas H, Ntritsos G, Varsamis P, Staikopoulos K, Kougioumtzis G, Papantoniou A, Moraitou D. The Gifted Rating Scales-Preschool/Kindergarten Form (GRS-P): A Preliminary Examination of Their Psychometric Properties in Two Greek Samples. Diagnostics (Basel) 2022; 12:diagnostics12112809. [PMID: 36428869 PMCID: PMC9689534 DOI: 10.3390/diagnostics12112809] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/04/2022] [Accepted: 11/11/2022] [Indexed: 11/18/2022] Open
Abstract
The present paper is based on data of two samples concerning the Gifted Rating Scales-Preschool/Kindergarten Form (GRS-P) that aimed to gain insight into the psychometric properties (internal consistency reliability, structural and convergent validity) of the Greek version of the GRS-P. In both studies, teachers estimated their students' giftedness with the GRS-P and executive functions with the Childhood Executive Functioning Inventory (Study 1). In Study 2, kindergarteners were examined in cognitive measurements which included the colored progressive matrices, the children category test, the Athena test, and the mini-mental state examination. Statistical analyses (EFA, CFA, Cronbach's α, and Pearson's r coefficients) revealed the excellent internal consistency of the scales as well as their good factorial and convergent/discriminant validity. In relation to the children's cognitive ability measures, it emphasized the fact that the GRS-P is a reliable and valid tool for teachers to assess their gifted students in a Greek cultural context.
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Affiliation(s)
- Maria Sofologi
- Laboratory of Psychology, Department of Early Childhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece
- Institute of Humanities and Social Sciences, University Research Centre of Ioannina (U.R.C.I.), 45110 Ioannina, Greece
- Laboratory of Art, Motor Expression and Didactic Application, Department of Early Chidlhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece
- Correspondence:
| | - Georgia Papantoniou
- Laboratory of Psychology, Department of Early Childhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece
- Institute of Humanities and Social Sciences, University Research Centre of Ioannina (U.R.C.I.), 45110 Ioannina, Greece
- Laboratory of Art, Motor Expression and Didactic Application, Department of Early Chidlhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece
- Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI—AUTH) Balkan Center, Buildings A & B, Aristotle University of Thessaloniki, 57001 Thessaloniki, Greece
| | - Theodora Avgita
- Laboratory of Psychology, Department of Early Childhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece
| | - Aikaterina Lyraki
- Laboratory of Psychology, Department of Early Childhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece
| | - Chrysoula Thomaidou
- Laboratory of Psychology, Department of Early Childhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece
| | - Harilaos Zaragas
- Laboratory of Art, Motor Expression and Didactic Application, Department of Early Chidlhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece
| | - Georgios Ntritsos
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, 47150 Arta, Greece
- Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, 45110 Ioannina, Greece
| | - Panagiotis Varsamis
- Department of Educational and Social Policy, University of Macedonia, 54636 Thessaloniki, Greece
| | - Konstantinos Staikopoulos
- Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece
| | - Georgios Kougioumtzis
- Department of Turkish Studies, National and Kapodistrian University, 10559 Athens, Greece
| | - Aphrodite Papantoniou
- Laboratory of Psychology, Department of Early Childhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece
| | - Despina Moraitou
- Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI—AUTH) Balkan Center, Buildings A & B, Aristotle University of Thessaloniki, 57001 Thessaloniki, Greece
- Laboratory of Psychology, Section of Experimental and Cognitive Psychology, School of Psychology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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38
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Miskowiak KW, Yalin N, Seeberg I, Burdick KE, Balanzá‐Martínez V, Bonnin CDM, Bowie CR, Carvalho AF, Dols A, Douglas K, Gallagher P, Hasler G, Kessing LV, Lafer B, Lewandowski KE, López‐Jaramillo C, Martinez‐Aran A, McIntyre RS, Porter RJ, Purdon SE, Schaffer A, Sumiyoshi T, Torres IJ, Van Rheenen TE, Yatham LN, Young AH, Vieta E, Stokes PRA. Can magnetic resonance imaging enhance the assessment of potential new treatments for cognitive impairment in mood disorders? A systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force. Bipolar Disord 2022; 24:615-636. [PMID: 35950925 PMCID: PMC9826389 DOI: 10.1111/bdi.13247] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
BACKGROUND Developing treatments for cognitive impairment is key to improving the functioning of people with mood disorders. Neuroimaging may assist in identifying brain-based efficacy markers. This systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force examines the evidence from neuroimaging studies of pro-cognitive interventions. METHODS We included magnetic resonance imaging (MRI) studies of candidate interventions in people with mood disorders or healthy individuals, following the procedures of the Preferred Reporting Items for Systematic reviews and Meta-Analysis 2020 statement. Searches were conducted on PubMed/MEDLINE, PsycInfo, EMBASE, Cochrane Library, and Clinicaltrials.gov from inception to 30th April 2021. Two independent authors reviewed the studies using the National Heart, Lung, Blood Institutes of Health Quality Assessment Tool for Controlled Intervention Studies and the quality of neuroimaging methodology assessment checklist. RESULTS We identified 26 studies (N = 702). Six investigated cognitive remediation or pharmacological treatments in mood disorders (N = 190). In healthy individuals, 14 studies investigated pharmacological interventions (N = 319), 2 cognitive training (N = 73) and 4 neuromodulatory treatments (N = 120). Methodologies were mostly rated as 'fair'. 77% of studies investigated effects with task-based fMRI. Findings varied but most consistently involved treatment-associated cognitive control network (CCN) activity increases with cognitive improvements, or CCN activity decreases with no cognitive change, and increased functional connectivity. In mood disorders, treatment-related default mode network suppression occurred. CONCLUSIONS Modulation of CCN and DMN activity is a putative efficacy biomarker. Methodological recommendations are to pre-declare intended analyses and use task-based fMRI, paradigms probing the CCN, longitudinal assessments, mock scanning, and out-of-scanner tests.
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Affiliation(s)
- Kamilla W. Miskowiak
- Copenhagen Affective disorder Research Centre (CADIC), Psychiatric Centre CopenhagenCopenhagen University HospitalCopenhagenDenmark,Department of PsychologyUniversity of CopenhagenCopenhagenDenmark
| | - Nefize Yalin
- Department of Psychological MedicineInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Ida Seeberg
- Copenhagen Affective disorder Research Centre (CADIC), Psychiatric Centre CopenhagenCopenhagen University HospitalCopenhagenDenmark
| | - Katherine E. Burdick
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA,Department of PsychiatryBrigham and Women's HospitalBostonMassachusettsUSA
| | - Vicent Balanzá‐Martínez
- Teaching Unit of Psychiatry and Psychological Medicine, Department of MedicineUniversity of Valencia, CIBERSAMValenciaSpain
| | - Caterina del Mar Bonnin
- Clinical Institute of Neuroscience, Hospital ClinicUniversity of Barcelona, IDIBAPS, CIBERSAMBarcelonaSpain
| | | | - Andre F. Carvalho
- IMPACT Strategic Research Centre (Innovation in Mental and Physical Health and Clinical Treatment)Deakin UniversityGeelongVictoriaAustralia
| | - Annemieke Dols
- Department of Old Age Psychiatry, GGZ in Geest, Amsterdam UMC, location VUmc, Amsterdam NeuroscienceAmsterdam Public Health research instituteAmsterdamThe Netherlands
| | - Katie Douglas
- Department of Psychological MedicineUniversity of OtagoChristchurchNew Zealand
| | - Peter Gallagher
- Translational and Clinical Research Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle‐upon‐TyneUK
| | - Gregor Hasler
- Psychiatry Research UnitUniversity of FribourgFribourgSwitzerland
| | - Lars V. Kessing
- Copenhagen Affective disorder Research Centre (CADIC), Psychiatric Centre CopenhagenCopenhagen University HospitalCopenhagenDenmark,Department of Clinical MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Beny Lafer
- Bipolar Disorder Research Program, Institute of Psychiatry, Hospital das Clinicas, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Kathryn E. Lewandowski
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA,McLean HospitalSchizophrenia and Bipolar Disorder ProgramBelmontMassachusettsUSA
| | - Carlos López‐Jaramillo
- Research Group in Psychiatry, Department of PsychiatryUniversidad de AntioquiaMedellínColombia
| | - Anabel Martinez‐Aran
- Clinical Institute of Neuroscience, Hospital ClinicUniversity of Barcelona, IDIBAPS, CIBERSAMBarcelonaSpain
| | - Roger S. McIntyre
- Mood Disorders Psychopharmacology Unit, Brain and Cognition Discovery FoundationUniversity of TorontoTorontoCanada
| | - Richard J. Porter
- Department of Psychological MedicineUniversity of OtagoChristchurchNew Zealand
| | - Scot E. Purdon
- Department of PsychiatryUniversity of AlbertaEdmontonCanada
| | - Ayal Schaffer
- Department of PsychiatryUniversity of TorontoTorontoCanada
| | - Tomiki Sumiyoshi
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental HealthNational Center of Neurology and PsychiatryTokyoJapan
| | - Ivan J. Torres
- Department of PsychiatryUniversity of British ColumbiaVancouverCanada
| | - Tamsyn E. Van Rheenen
- Melbourne Neuropsychiatry Centre, Department of PsychiatryUniversity of MelbourneCarltonAustralia,Centre for Mental Health, Faculty of Health, Arts and DesignSwinburne UniversityHawthornAustralia
| | - Lakshmi N. Yatham
- Department of PsychiatryUniversity of British ColumbiaVancouverCanada
| | - Allan H. Young
- Department of Psychological MedicineInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Eduard Vieta
- Clinical Institute of Neuroscience, Hospital ClinicUniversity of Barcelona, IDIBAPS, CIBERSAMBarcelonaSpain
| | - Paul R. A. Stokes
- Department of Psychological MedicineInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
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39
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Chuderski A. Fluid Intelligence Emerges from Representing Relations. J Intell 2022; 10:jintelligence10030051. [PMID: 35997406 PMCID: PMC9396997 DOI: 10.3390/jintelligence10030051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/21/2022] [Accepted: 07/27/2022] [Indexed: 02/04/2023] Open
Abstract
Based on recent findings in cognitive neuroscience and psychology as well as computational models of working memory and reasoning, I argue that fluid intelligence (fluid reasoning) can amount to representing in the mind the key relation(s) for the task at hand. Effective representation of relations allows for enormous flexibility of thinking but depends on the validity and robustness of the dynamic patterns of argument–object (role–filler) bindings, which encode relations in the brain. Such a reconceptualization of the fluid intelligence construct allows for the simplification and purification of its models, tests, and potential brain mechanisms.
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Affiliation(s)
- Adam Chuderski
- Cognitive Science Department, Institute of Philosophy, Jagiellonian Univeristy in Krakow, PL-31007 Kraków, Poland
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40
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Kristanto D, Liu X, Sommer W, Hildebrandt A, Zhou C. What do neuroanatomical networks reveal about the ontology of human cognitive abilities? iScience 2022; 25:104706. [PMID: 35865139 PMCID: PMC9293763 DOI: 10.1016/j.isci.2022.104706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/15/2022] [Accepted: 06/28/2022] [Indexed: 11/18/2022] Open
Abstract
Over the last decades, cognitive psychology has come to a fair consensus about the human intelligence ontological structure. However, it remains an open question whether anatomical properties of the brain support the same ontology. The present study explored the ontological structure derived from neuroanatomical networks associated with performance on 15 cognitive tasks indicating various abilities. Results suggest that the brain-derived (neurometric) ontology partly agrees with the cognitive performance-derived (psychometric) ontology complemented with interpretable differences. Moreover, the cortical areas associated with different inferred abilities are segregated, with little or no overlap. Nevertheless, these spatially segregated cortical areas are integrated via denser white matter structural connections as compared with the general brain connectome. The integration of ability-related cortical networks constitutes a neural counterpart to the psychometric construct of general intelligence, while the consistency and difference between psychometric and neurometric ontologies represent crucial pieces of knowledge for theory building, clinical diagnostics, and treatment. Psychometric and neurometric cognitive ontologies are partly equivalent Ability-related brain areas are ontologically segregated with little to no overlap However, ability-related brain areas are densely interconnected by fiber tracts
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Lima Dias Pinto I, Rungratsameetaweemana N, Flaherty K, Periyannan A, Meghdadi A, Richard C, Berka C, Bansal K, Garcia JO. Intermittent brain network reconfigurations and the resistance to social media influence. Netw Neurosci 2022; 6:870-896. [PMID: 36605415 PMCID: PMC9810364 DOI: 10.1162/netn_a_00255] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 05/10/2022] [Indexed: 01/09/2023] Open
Abstract
Since its development, social media has grown as a source of information and has a significant impact on opinion formation. Individuals interact with others and content via social media platforms in a variety of ways, but it remains unclear how decision-making and associated neural processes are impacted by the online sharing of informational content, from factual to fabricated. Here, we use EEG to estimate dynamic reconfigurations of brain networks and probe the neural changes underlying opinion change (or formation) within individuals interacting with a simulated social media platform. Our findings indicate that the individuals who changed their opinions are characterized by less frequent network reconfigurations while those who did not change their opinions tend to have more flexible brain networks with frequent reconfigurations. The nature of these frequent network configurations suggests a fundamentally different thought process between intervals in which individuals are easily influenced by social media and those in which they are not. We also show that these reconfigurations are distinct to the brain dynamics during an in-person discussion with strangers on the same content. Together, these findings suggest that brain network reconfigurations may not only be diagnostic to the informational context but also the underlying opinion formation.
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Affiliation(s)
| | | | - Kristen Flaherty
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Cornell Tech, New York, NY, USA
| | - Aditi Periyannan
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Tufts University, Medford, MA, USA
| | | | | | - Chris Berka
- Advanced Brain Monitoring, Carlsbad, CA, USA
| | - Kanika Bansal
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Department of Biomedical Engineering, Columbia University, New York, NY, USA,* Corresponding Authors: ;
| | - Javier Omar Garcia
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,* Corresponding Authors: ;
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Vieira BH, Pamplona GSP, Fachinello K, Silva AK, Foss MP, Salmon CEG. On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting. INTELLIGENCE 2022. [DOI: 10.1016/j.intell.2022.101654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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43
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Sexual dimorphism in the relationship between brain complexity, volume and general intelligence (g): a cross-cohort study. Sci Rep 2022; 12:11025. [PMID: 35773463 PMCID: PMC9247090 DOI: 10.1038/s41598-022-15208-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 06/20/2022] [Indexed: 01/20/2023] Open
Abstract
Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences.
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Wang R, Fan Y, Wu Y, Zang YF, Zhou C. Lifespan associations of resting-state brain functional networks with ADHD symptoms. iScience 2022; 25:104673. [PMID: 35832890 PMCID: PMC9272385 DOI: 10.1016/j.isci.2022.104673] [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: 01/11/2022] [Revised: 04/26/2022] [Accepted: 06/21/2022] [Indexed: 12/04/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in both children and adults, but the neural mechanisms that underlie its distinct symptoms and whether children and adults share the same mechanism remain poorly understood. Here, we used a nested-spectral partition approach to study resting-state brain networks of ADHD patients (n = 97) and healthy controls (HCs, n = 97) across the lifespan (7–50 years). Compared to the linear lifespan associations of brain segregation and integration with age in HCs, ADHD patients have a quadratic association in the whole-brain and in most functional systems, whereas the limbic system dominantly affected by ADHD has a linear association. Furthermore, the limbic system better predicts hyperactivity, and the salient attention system better predicts inattention. These predictions are shared in children and adults with ADHD. Our findings reveal a lifespan association of brain networks with ADHD and provide potential shared neural bases of distinct ADHD symptoms in children and adults. ADHD patients have a quadratic association with age in brain functional networks Limbic system better predicts hyperactivity across the lifespan Salient attention system better predicts the inattention across the lifespan The predictions on symptom scores are shared in ADHD children and adults
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46
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Raja R, Na X, Moore A, Otoo R, Glasier CM, Badger TM, Ou X. Associations Between White Matter Microstructures and Cognitive Functioning in 8-Year-Old Children: A Track-Weighted Imaging Study. J Child Neurol 2022; 37:471-490. [PMID: 35254148 PMCID: PMC9149064 DOI: 10.1177/08830738221083487] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE Quantitative tractography using diffusion-weighted magnetic resonance imaging data is widely used in characterizing white matter microstructure throughout childhood, but more studies are still needed to investigate comprehensive brain-behavior relationships between tract-specific white matter measures and multiple cognitive functions in children. METHODS In this study, we analyzed diffusion-weighted MRI data of 71 healthy 8-year-old children utilizing white matter tract-specific quantitative measures derived from diffusion-weighted MRI tractography based on a novel track-weighted imaging approach. Track density imaging, average path length map and 4 track-weighted diffusion tensor imaging measures including: mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were computed for 63 white matter tracts. The track-weighted imaging measures were then correlated with a comprehensive set of neuropsychological test scores in different cognitive domains including intelligence, language, memory, academic skills, and executive functions to identify tract-specific brain-behavior relationships. RESULTS Significant correlations (P < .05, false discovery rate corrected; r = 0.27-0.57) were found in multiple white matter tracts, with a total of 40 correlations identified between various track-weighted imaging measures including average path length map, track-weighted imaging-fractional anisotropy, and neuropsychological test scores and subscales. Specifically, track-weighted imaging measures indicative of better white matter connectivity and/or microstructural development significantly correlated with higher IQ and better language abilities. CONCLUSION Our findings demonstrate the ability of track-weighted imaging measures in establishing associations between white matter and cognitive functioning in healthy children and can serve as a reference for normal brain/cognition relationships in young school-age children and further aid in identifying imaging biomarkers predictive of adverse neurodevelopmental outcomes.
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Affiliation(s)
- Rajikha Raja
- Department of Radiology, University of Arkansas for Medical Sciences
| | - Xiaoxu Na
- Department of Radiology, University of Arkansas for Medical Sciences
| | - Alexandra Moore
- College of Medicine, University of Arkansas for Medical Sciences
| | - Raymond Otoo
- College of Medicine, University of Arkansas for Medical Sciences
| | - Charles M. Glasier
- Department of Radiology, University of Arkansas for Medical Sciences,Department of Pediatrics, University of Arkansas for Medical Sciences
| | - Thomas M. Badger
- Department of Pediatrics, University of Arkansas for Medical Sciences,Arkansas Children’s Nutrition Center
| | - Xiawei Ou
- Department of Radiology, University of Arkansas for Medical Sciences,Department of Pediatrics, University of Arkansas for Medical Sciences
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Hausman HK, Hardcastle C, Albizu A, Kraft JN, Evangelista ND, Boutzoukas EM, Langer K, O'Shea A, Van Etten EJ, Bharadwaj PK, Song H, Smith SG, Porges E, DeKosky ST, Hishaw GA, Wu S, Marsiske M, Cohen R, Alexander GE, Woods AJ. Cingulo-opercular and frontoparietal control network connectivity and executive functioning in older adults. GeroScience 2022; 44:847-866. [PMID: 34950997 PMCID: PMC9135913 DOI: 10.1007/s11357-021-00503-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/15/2021] [Indexed: 10/19/2022] Open
Abstract
Executive function is a cognitive domain that typically declines in non-pathological aging. Two cognitive control networks that are vulnerable to aging-the cingulo-opercular (CON) and fronto-parietal control (FPCN) networks-play a role in various aspects of executive functioning. However, it is unclear how communication within these networks at rest relates to executive function subcomponents in older adults. This study examines the associations between CON and FPCN connectivity and executive function performance in 274 older adults across working memory, inhibition, and set-shifting tasks. Average CON connectivity was associated with better working memory, inhibition, and set-shifting performance, while average FPCN connectivity was associated solely with working memory. CON region of interest analyses revealed significant connections with classical hub regions (i.e., anterior cingulate and anterior insula) for each task, language regions for verbal working memory, right hemisphere dominance for inhibitory control, and widespread network connections for set-shifting. FPCN region of interest analyses revealed largely right hemisphere fronto-parietal connections important for working memory and a few temporal lobe connections for set-shifting. These findings characterize differential brain-behavior relationships between cognitive control networks and executive function in aging. Future research should target these networks for intervention to potentially attenuate executive function decline in older adults.
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Affiliation(s)
- Hanna K Hausman
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Cheshire Hardcastle
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Alejandro Albizu
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Jessica N Kraft
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Nicole D Evangelista
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Emanuel M Boutzoukas
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Kailey Langer
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Andrew O'Shea
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Emily J Van Etten
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Pradyumna K Bharadwaj
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Hyun Song
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Samantha G Smith
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Eric Porges
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Steven T DeKosky
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Neurology, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Georg A Hishaw
- Department of Psychiatry, Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs, and BIO5 Institute, University of Arizona and Arizona Alzheimer's Disease Consortium, Tucson, AZ, USA
| | - Samuel Wu
- Department of Biostatistics, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Michael Marsiske
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Ronald Cohen
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Gene E Alexander
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
- Department of Psychiatry, Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs, and BIO5 Institute, University of Arizona and Arizona Alzheimer's Disease Consortium, Tucson, AZ, USA
| | - Adam J Woods
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
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A framework for rigorous evaluation of human performance in human and machine learning comparison studies. Sci Rep 2022; 12:5444. [PMID: 35361786 PMCID: PMC8971503 DOI: 10.1038/s41598-022-08078-3] [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: 06/25/2021] [Accepted: 01/13/2022] [Indexed: 11/29/2022] Open
Abstract
Rigorous comparisons of human and machine learning algorithm performance on the same task help to support accurate claims about algorithm success rates and advances understanding of their performance relative to that of human performers. In turn, these comparisons are critical for supporting advances in artificial intelligence. However, the machine learning community has lacked a standardized, consensus framework for performing the evaluations of human performance necessary for comparison. We demonstrate common pitfalls in a designing the human performance evaluation and propose a framework for the evaluation of human performance, illustrating guiding principles for a successful comparison. These principles are first, to design the human evaluation with an understanding of the differences between human and algorithm cognition; second, to match trials between human participants and the algorithm evaluation, and third, to employ best practices for psychology research studies, such as the collection and analysis of supplementary and subjective data and adhering to ethical review protocols. We demonstrate our framework’s utility for designing a study to evaluate human performance on a one-shot learning task. Adoption of this common framework may provide a standard approach to evaluate algorithm performance and aid in the reproducibility of comparisons between human and machine learning algorithm performance.
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49
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Gu S, Fotiadis P, Parkes L, Xia CH, Gur RC, Gur RE, Roalf DR, Satterthwaite TD, Bassett DS. Network controllability mediates the relationship between rigid structure and flexible dynamics. Netw Neurosci 2022; 6:275-297. [PMID: 36605890 PMCID: PMC9810281 DOI: 10.1162/netn_a_00225] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 12/15/2021] [Indexed: 01/07/2023] Open
Abstract
Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid interareal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain's structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region's functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes' boundary controllability, suggesting that a region's strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity.
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Affiliation(s)
- Shi Gu
- Brain and Intelligence Group, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Cedric H. Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
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50
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A survey of brain network analysis by electroencephalographic signals. Cogn Neurodyn 2022; 16:17-41. [PMID: 35126769 PMCID: PMC8807775 DOI: 10.1007/s11571-021-09689-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/25/2021] [Accepted: 05/31/2021] [Indexed: 02/03/2023] Open
Abstract
Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.
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