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Kristanto D, Burkhardt M, Thiel C, Debener S, Gießing C, Hildebrandt A. The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis. Neurosci Biobehav Rev 2024; 165:105846. [PMID: 39117132 DOI: 10.1016/j.neubiorev.2024.105846] [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: 01/22/2024] [Revised: 04/04/2024] [Accepted: 08/04/2024] [Indexed: 08/10/2024]
Abstract
The large number of different analytical choices used by researchers is partly responsible for the challenge of replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge of the full space of analytical options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable parameter choices. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the parameter settings within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) parameter tuning within steps, and 3) distinct sequencing of steps. We have developed a decision support application with high educational value called METEOR to facilitate access to the data in order to design well-informed robustness (multiverse) analysis.
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Affiliation(s)
- Daniel Kristanto
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany.
| | - Micha Burkhardt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany
| | - Christiane Thiel
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany; Cluster of Excellence "Hearing4All", Carl von Ossietzky Universität Oldenburg, Germany
| | - Stefan Debener
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany; Cluster of Excellence "Hearing4All", Carl von Ossietzky Universität Oldenburg, Germany
| | - Carsten Gießing
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany.
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany; Cluster of Excellence "Hearing4All", Carl von Ossietzky Universität Oldenburg, Germany.
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2
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Yi H, Xiao M, Chen X, Yan Q, Yang Y, Liu Y, Song S, Gao X, Chen H. Resting-state functional network connectivity underlying conscientiousness in school-aged children. Child Neuropsychol 2024; 30:486-502. [PMID: 37278282 DOI: 10.1080/09297049.2023.2221757] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 05/31/2023] [Indexed: 06/07/2023]
Abstract
Conscientiousness is a personality trait that matures from early childhood to late adolescence, yet little is known about its underlying brain mechanisms during this period. To investigate this, our study examined the resting-state functional network connectivity (rsFNC) of 69 school-aged children (mean age = 10.12 years, range = 9-12) using a whole-brain region-of-interest (ROI) based analysis, based on functional magnetic resonance imaging (fMRI). The results indicated a positive association between conscientiousness and the rsFNC between the fronto-parietal network (FPN) and two brain networks: the somatosensory motor-hand network (SMHN) and the auditory network (AN). However, conscientiousness was negatively associated with the rsFNC between FPN and two other networks: the salience network (SN); the default mode network (DMN). Moreover, our results suggest that the FPN may play a hub role in the neural performance of children's conscientiousness. Intrinsic brain networks, particularly those involved in higher-order cognitive functions, impact children's conscientiousness. Therefore, FPN plays an important role in the development of children's personality, providing insight into the neural mechanisms underlying children's personality.
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Affiliation(s)
- Haijing Yi
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Mingyue Xiao
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Ximei Chen
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Qiaoling Yan
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yue Yang
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Shiqing Song
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
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3
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Litwińczuk MC, Muhlert N, Trujillo‐Barreto N, Woollams A. Impact of brain parcellation on prediction performance in models of cognition and demographics. Hum Brain Mapp 2024; 45:e26592. [PMID: 38339892 PMCID: PMC10831203 DOI: 10.1002/hbm.26592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 12/18/2023] [Accepted: 12/31/2023] [Indexed: 02/12/2024] Open
Abstract
Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with five different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, executive function, self-regulation, language, encoding and sequence processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme showed a superior predictive performance across all cognitive domains and demographics. In addition, although parcellation schemes impacted the graph theory measures of each connectivity type (structural, functional and combined), these differences did not account for the out-of-sample predictive performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by the higher resolution of the parcellation likely disrupts model generalisability.
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Affiliation(s)
| | - Nils Muhlert
- School of Health SciencesUniversity of ManchesterManchesterUK
| | | | - Anna Woollams
- School of Health SciencesUniversity of ManchesterManchesterUK
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4
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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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5
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Zhang D, Li L, Sripada C, Kang J. Image response regression via deep neural networks. J R Stat Soc Series B Stat Methodol 2023; 85:1589-1614. [PMID: 38584801 PMCID: PMC10994199 DOI: 10.1093/jrsssb/qkad073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 06/22/2023] [Accepted: 06/28/2023] [Indexed: 04/09/2024]
Abstract
Delineating associations between images and covariates is a central aim of imaging studies. To tackle this problem, we propose a novel non-parametric approach in the framework of spatially varying coefficient models, where the spatially varying functions are estimated through deep neural networks. Our method incorporates spatial smoothness, handles subject heterogeneity, and provides straightforward interpretations. It is also highly flexible and accurate, making it ideal for capturing complex association patterns. We establish estimation and selection consistency and derive asymptotic error bounds. We demonstrate the method's advantages through intensive simulations and analyses of two functional magnetic resonance imaging data sets.
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Affiliation(s)
- Daiwei Zhang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Lexin Li
- Department of Biostatistics and Epidemiology, University of California, Berkeley, CA, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
- Department of Philosophy, University of Michigan, Ann Arbor, MI, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
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6
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Wang J, Li H, Qu G, Cecil KM, Dillman JR, Parikh NA, He L. Dynamic weighted hypergraph convolutional network for brain functional connectome analysis. Med Image Anal 2023; 87:102828. [PMID: 37130507 PMCID: PMC10247416 DOI: 10.1016/j.media.2023.102828] [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: 10/14/2022] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/04/2023]
Abstract
The hypergraph structure has been utilized to characterize the brain functional connectome (FC) by capturing the high order relationships among multiple brain regions of interest (ROIs) compared with a simple graph. Accordingly, hypergraph neural network (HGNN) models have emerged and provided efficient tools for hypergraph embedding learning. However, most existing HGNN models can only be applied to pre-constructed hypergraphs with a static structure during model training, which might not be a sufficient representation of the complex brain networks. In this study, we propose a dynamic weighted hypergraph convolutional network (dwHGCN) framework to consider a dynamic hypergraph with learnable hyperedge weights. Specifically, we generate hyperedges based on sparse representation and calculate the hyper similarity as node features. The hypergraph and node features are fed into a neural network model, where the hyperedge weights are updated adaptively during training. The dwHGCN facilitates the learning of brain FC features by assigning larger weights to hyperedges with higher discriminative power. The weighting strategy also improves the interpretability of the model by identifying the highly active interactions among ROIs shared by a common hyperedge. We validate the performance of the proposed model on two classification tasks with three paradigms functional magnetic resonance imaging (fMRI) data from Philadelphia Neurodevelopmental Cohort. Experimental results demonstrate the superiority of our proposed method over existing hypergraph neural networks. We believe our model can be applied to other applications in neuroimaging for its strength in representation learning and interpretation.
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Affiliation(s)
- Junqi Wang
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Gang Qu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Kim M Cecil
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
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7
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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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8
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Wang X, Zhou H, Hu Y. Altered neural associations with cognitive and emotional functions in cannabis dependence. Cereb Cortex 2023; 33:8724-8733. [PMID: 37143177 PMCID: PMC10505425 DOI: 10.1093/cercor/bhad153] [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: 11/01/2022] [Revised: 04/14/2023] [Accepted: 04/16/2023] [Indexed: 05/06/2023] Open
Abstract
Negative emotional state has been found to correlate with poor cognitive performance in cannabis-dependent (CD) individuals, but not healthy controls (HCs). To examine the neural substrates underlying such unusual emotion-cognition coupling, we analyzed the behavioral and resting state fMRI data from the Human Connectome Project and found opposite brain-behavior associations in the CD and HC groups: (i) although the cognitive performance was positively correlated with the within-network functional connectivity strength and segregation (i.e. clustering coefficient and local efficiency) of the cognitive network in HCs, these correlations were inversed in CDs; (ii) although the cognitive performance was positively correlated with the within-network Granger effective connectivity strength and integration (i.e. characteristic path length) of the cognitive network in CDs, such associations were not significant in HCs. In addition, we also found that the effective connectivity strength within cognition network mediated the behavioral coupling between emotional state and cognitive performance. These results indicate a disorganization of the cognition network in CDs, and may help improve our understanding of substance use disorder.
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Affiliation(s)
- Xinying Wang
- Department of Psychology and Behavioral Sciences, Zhejiang University, Zijingang Campus, 866 Yuhangtang Road, Hangzhou, Zhejiang Province 310058, China
| | - Hui Zhou
- Department of Psychology and Behavioral Sciences, Zhejiang University, Zijingang Campus, 866 Yuhangtang Road, Hangzhou, Zhejiang Province 310058, China
| | - Yuzheng Hu
- Department of Psychology and Behavioral Sciences, Zhejiang University, Zijingang Campus, 866 Yuhangtang Road, Hangzhou, Zhejiang Province 310058, China
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9
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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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10
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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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11
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Cermakova P, Chlapečka A, Csajbók Z, Andrýsková L, Brázdil M, Marečková K. Parental education, cognition and functional connectivity of the salience network. Sci Rep 2023; 13:2761. [PMID: 36797291 PMCID: PMC9935859 DOI: 10.1038/s41598-023-29508-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/06/2023] [Indexed: 02/18/2023] Open
Abstract
The aim was to investigate the association of parental education at birth with cognitive ability in childhood and young adulthood and determine, whether functional connectivity of the salience network underlies this association. We studied participants of the Czech arm of the European Longitudinal Study of Pregnancy and Childhood who underwent assessment of their cognitive ability at age 8 (Wechsler Intelligence Scale for Children) and 28/29 years (Wechsler Adult Intelligence Scale) and measurement with resting state functional MRI at age 23/24. We estimated the associations of parental education with cognitive ability and functional connectivity between the seeds in the salience network and other voxels in the brain. We found that lower education of both mothers and fathers was associated with lower verbal IQ, performance IQ and full-scale IQ of the offspring at age 8. Only mother´s education was associated with performance IQ at age 28/29. Lower mother´s education correlated with greater functional connectivity between the right rostral prefrontal cortex and a cluster of voxels in the occipital cortex, which, in turn, was associated with lower performance IQ at age 28/29. We conclude that the impact of parental education, particularly father´s, on offspring´s cognitive ability weakens during the lifecourse. Functional connectivity between the right rostral prefrontal cortex and occipital cortex may be a biomarker underlying the transmission of mother´s education on performance IQ of their offspring.
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Affiliation(s)
- Pavla Cermakova
- Second Faculty of Medicine, Charles University Prague, 150 06, Prague 5, Czech Republic. .,National Institute of Mental Health, 250 67, Klecany, Czech Republic.
| | - Adam Chlapečka
- grid.4491.80000 0004 1937 116XThird Faculty of Medicine, Charles University Prague, 100 00 Prague 10, Czech Republic ,grid.4491.80000 0004 1937 116XCentre of Clinical Neuroscience, Department of Neurology, First Faculty of Medicine, General University Hospital, Charles University in Prague, 128 21 Prague 2, Czech Republic
| | - Zsófia Csajbók
- grid.4491.80000 0004 1937 116XFaculty of Humanities, Charles University Prague, 182 00 Prague 8, Czech Republic
| | - Lenka Andrýsková
- grid.10267.320000 0001 2194 0956RECETOX, Faculty of Science, Masaryk University, 611 37 Brno, Czech Republic
| | - Milan Brázdil
- grid.10267.320000 0001 2194 0956Brain and Mind Research, Central European Institute of Technology, Masaryk University, 625 00 Brno, Czech Republic
| | - Klára Marečková
- grid.10267.320000 0001 2194 0956Brain and Mind Research, Central European Institute of Technology, Masaryk University, 625 00 Brno, Czech Republic
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12
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Schubert AL, Löffler C, Hagemann D, Sadus K. How robust is the relationship between neural processing speed and cognitive abilities? Psychophysiology 2023; 60:e14165. [PMID: 35995756 DOI: 10.1111/psyp.14165] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/08/2022] [Accepted: 07/31/2022] [Indexed: 01/04/2023]
Abstract
Individual differences in processing speed are consistently related to individual differences in cognitive abilities, but the mechanisms through which a higher processing speed facilitates reasoning remain largely unknown. To identify these mechanisms, researchers have been using latencies of the event-related potential (ERP) to study how the speed of cognitive processes associated with specific ERP components is related to cognitive abilities. Although there is some evidence that latencies of ERP components associated with higher-order cognitive processes are related to intelligence, results are overall quite inconsistent. These inconsistencies likely result from variations in analytic procedures and little consideration of the psychometric properties of ERP latencies in relatively small sample studies. Here we used a multiverse approach to evaluate how different analytical choices regarding references, low-pass filter cutoffs, and latency measures affect the psychometric properties of P2, N2, and P3 latencies and their relations with cognitive abilities in a sample of 148 participants. Latent correlations between neural processing speed and cognitive abilities ranged from -.49 to -.78. ERP latency measures contained about equal parts of measurement error variance and systematic variance, and only about half of the systematic variance was related to cognitive abilities, whereas the other half reflected nuisance factors. We recommend addressing these problematic psychometric properties by recording EEG data from multiple tasks and modeling relations between ERP latencies and covariates in latent variable models. All in all, our results indicate that there is a substantial and robust relationship between neural processing speed and cognitive abilities when those issues are addressed.
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Affiliation(s)
| | - Christoph Löffler
- Department of Psychology, University of Mainz, Mainz, Germany.,Institute of Psychology, Heidelberg University, Heidelberg, Germany
| | - Dirk Hagemann
- Institute of Psychology, Heidelberg University, Heidelberg, Germany
| | - Kathrin Sadus
- Institute of Psychology, Heidelberg University, Heidelberg, Germany
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13
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Wang L, Sheng A, Chang L, Zhou R. Improving fluid intelligence of children through working memory training: The role of inhibition control. Front Psychol 2022; 13:1025036. [PMID: 36507034 PMCID: PMC9732572 DOI: 10.3389/fpsyg.2022.1025036] [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: 08/22/2022] [Accepted: 11/03/2022] [Indexed: 11/27/2022] Open
Abstract
Intelligence is strongly associated with working memory. Working memory training can improve fluid intelligence, but the underlying mechanism requires further investigation. Because inhibition control may play a key role in working memory training, this study investigated this process from an electrophysiological perspective. In total, 40 children aged 9 to 11 years were enrolled and randomly divided into a training group (n = 20) and a control group (n = 20). The training group received 20 days of working memory training, whereas the control group did not receive any training. Before and after the training period, all participants were tested using Raven's Standard Progressive Matrices (RSPM), and electrophysiological indicators were recorded while they performed go/no-go and Stroop tasks. The results revealed that relative to the control group, the training group had significantly improved RSPM scores in the test conducted after their training. For the go/no-go tasks, the training group exhibited a significant decrease in N2 amplitude, a significant increase in P3 amplitude, a significant decrease in theta band energy, and an improvement in response inhibition ability. No significant change was observed for the Stroop task. Correlation analysis revealed that an improvement in individual response inhibition can positively predict an improvement in fluid intelligence. These results suggest that working memory training enhances the fluid intelligence of children by enhancing their response inhibition ability.
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Affiliation(s)
- Lei Wang
- Department of Psychology, Nanjing University, Nanjing, Jiangsu, China
| | - Ang Sheng
- Department of Psychology, Nanjing University, Nanjing, Jiangsu, China
| | - Lei Chang
- Department of Psychology, Faculty of Social Sciences, University of Macau, Taipa, Macao SAR, China
| | - Renlai Zhou
- Department of Psychology, Nanjing University, Nanjing, Jiangsu, China,State Key Laboratory of Media Convergence Production Technology and Systems, Beijing, China,*Correspondence: Renlai Zhou,
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14
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Shi M, Li Y, Sun J, Li X, Han Y, Liu Z, Qiu J. Intelligence Correlates with the Temporal Variability of Brain Networks. Neuroscience 2022; 504:56-62. [PMID: 35964835 DOI: 10.1016/j.neuroscience.2022.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 11/29/2022]
Abstract
Intelligence is the ability to recognize and understand objective things, and use knowledge and experience to solve problems. Highly intelligent people show the ability to switch between different thought patterns and shift their mental focus. This suggests a link between intelligence and the dynamic interaction of brain networks. Thus, we investigated the relationships between resting-state dynamic brain network remodeling (temporal variability) and scores on the Wechsler Adult Intelligent Scale using a large dataset comprising 606 individuals. We found that performance intelligence was associated with greater temporal variability in the functional connectivity patterns of the dorsal attention network. High variability in these areas indicates flexible connectivity patterns, which may contribute to cognitive processes such as attention selection. In addition, performance intelligence was related to greater temporal variability in the functional connectivity patterns of the salience network. Thus, this study revealed a close relationship between performance intelligence and high variability in brain networks involved in attentional choice, spatial orientation, and cognitive control.
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Affiliation(s)
- Manqing Shi
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Yu Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Xinyi Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Yurong Han
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Zeqing Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China.
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15
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Farahani FV, Karwowski W, D’Esposito M, Betzel RF, Douglas PK, Sobczak AM, Bohaterewicz B, Marek T, Fafrowicz M. Diurnal variations of resting-state fMRI data: A graph-based analysis. Neuroimage 2022; 256:119246. [PMID: 35477020 PMCID: PMC9799965 DOI: 10.1016/j.neuroimage.2022.119246] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 02/18/2022] [Accepted: 04/22/2022] [Indexed: 12/31/2022] Open
Abstract
Circadian rhythms (lasting approximately 24 h) control and entrain various physiological processes, ranging from neural activity and hormone secretion to sleep cycles and eating habits. Several studies have shown that time of day (TOD) is associated with human cognition and brain functions. In this study, utilizing a chronotype-based paradigm, we applied a graph theory approach on resting-state functional MRI (rs-fMRI) data to compare whole-brain functional network topology between morning and evening sessions and between morning-type (MT) and evening-type (ET) participants. Sixty-two individuals (31 MT and 31 ET) underwent two fMRI sessions, approximately 1 hour (morning) and 10 h (evening) after their wake-up time, according to their declared habitual sleep-wake pattern on a regular working day. In the global analysis, the findings revealed the effect of TOD on functional connectivity (FC) patterns, including increased small-worldness, assortativity, and synchronization across the day. However, we identified no significant differences based on chronotype categories. The study of the modular structure of the brain at mesoscale showed that functional networks tended to be more integrated with one another in the evening session than in the morning session. Local/regional changes were affected by both factors (i.e., TOD and chronotype), mostly in areas associated with somatomotor, attention, frontoparietal, and default networks. Furthermore, connectivity and hub analyses revealed that the somatomotor, ventral attention, and visual networks covered the most highly connected areas in the morning and evening sessions: the latter two were more active in the morning sessions, and the first was identified as being more active in the evening. Finally, we performed a correlation analysis to determine whether global and nodal measures were associated with subjective assessments across participants. Collectively, these findings contribute to an increased understanding of diurnal fluctuations in resting brain activity and highlight the role of TOD in future studies on brain function and the design of fMRI experiments.
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Affiliation(s)
- Farzad V. Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA,Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, USA,Corresponding author: Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA. (F.V. Farahani)
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, USA
| | - Mark D’Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA,Department of Psychology, University of California, Berkeley, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Pamela K. Douglas
- Institute for Simulation and Training, University of Central Florida, Orlando, FL, USA,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Anna Maria Sobczak
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland
| | - Bartosz Bohaterewicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland,Department of Psychology of Individual Differences, Psychological Diagnosis, and Psychometrics, Institute of Psychology, University of Social Sciences and Humanities, Warsaw, Poland
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland
| | - Magdalena Fafrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland,Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland,Corresponding author. Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Krakow, Poland. (M. Fafrowicz)
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16
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Kolskår KK, Ulrichsen KM, Richard G, Dørum ES, de Schotten MT, Rokicki J, Monereo-Sánchez J, Engvig A, Hansen HI, Nordvik JE, Westlye LT, Alnaes D. Structural disconnectome mapping of cognitive function in poststroke patients. Brain Behav 2022; 12:e2707. [PMID: 35861657 PMCID: PMC9392540 DOI: 10.1002/brb3.2707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 06/19/2022] [Accepted: 06/25/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND AND PURPOSE Sequalae following stroke represents a significant challenge in current rehabilitation. The location and size of focal lesions are only moderately predictive of the diverse cognitive outcome after stroke. One explanation building on recent work on brain networks proposes that the cognitive consequences of focal lesions are caused by damages to anatomically distributed brain networks supporting cognition rather than specific lesion locations. METHODS To investigate the association between poststroke structural disconnectivity and cognitive performance, we estimated individual level whole-brain disconnectivity probability maps based on lesion maps from 102 stroke patients using normative data from healthy controls. Cognitive performance was assessed in the whole sample using Montreal Cognitive Assessment, and a more comprehensive computerized test protocol was performed on a subset (n = 82). RESULTS Multivariate analysis using Partial Least Squares on the disconnectome maps revealed that higher disconnectivity in right insular and frontal operculum, superior temporal gyrus and putamen was associated with poorer MoCA performance, indicating that lesions in regions connected with these brain regions are more likely to cause cognitive impairment. Furthermore, our results indicated that disconnectivity within these clusters was associated with poorer performance across multiple cognitive domains. CONCLUSIONS These findings demonstrate that the extent and distribution of structural disconnectivity following stroke are sensitive to cognitive deficits and may provide important clinical information predicting poststroke cognitive sequalae.
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Affiliation(s)
- Knut K Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Kristine M Ulrichsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Genevieve Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Erlend S Dørum
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
| | - Jaroslav Rokicki
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Jennifer Monereo-Sánchez
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, the Netherlands
| | - Andreas Engvig
- Department of Nephrology, Oslo University Hospital, Ullevål, Norway.,Department of Medicine, Diakonhjemmet Hospital, Oslo, Norway
| | | | - Jan Egil Nordvik
- CatoSenteret Rehabilitation Center, Son, Norway.,Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Dag Alnaes
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Bjørknes College, Oslo, Norway
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17
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Frischkorn GT, Hilger K, Kretzschmar A, Schubert AL. Intelligenzdiagnostik der Zukunft. PSYCHOLOGISCHE RUNDSCHAU 2022. [DOI: 10.1026/0033-3042/a000598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Zusammenfassung. Die menschliche Intelligenz ist eines der am besten erforschten und validierten Konstrukte innerhalb der Psychologie. Dennoch wird die Validität von Intelligenztests im gruppen- und insbesondere kulturvergleichenden Kontext regelmäßig und berechtigterweise kritisch hinterfragt. Obwohl verschiedene Alternativen und Weiterentwicklungen der Intelligenzdiagnostik vorgeschlagen wurden (z. B. kulturfaire Tests), sind fundamentale Probleme in der vergleichenden Intelligenzdiagnostik noch immer ungelöst und die Validitäten entsprechender Verfahren unklar. In dem vorliegenden Positionspapier wird diese Thematik aus der Perspektive der Kognitionspsychologie und der kognitiven Neurowissenschaften beleuchtet und eine prozessorientierte und biologisch inspirierte Form der Intelligenzdiagnostik als potentieller Lösungsansatz vorgeschlagen. Wir zeigen die Bedeutung elementarer kognitiver Prozesse auf (insbesondere Arbeitsgedächtniskapazität, Aufmerksamkeit, Verarbeitungsgeschwindigkeit), die individuellen Leistungsunterschieden zu Grunde liegen, und betonen, dass der Unterscheidung zwischen Inhalten und Prozessen eine zentrale, jedoch oft vernachlässigte Rolle in der Diagnostik allgemeiner kognitiver Leistungsunterschiede zukommt. Während aus kognitions- und neuropsychologischer Sicht davon ausgegangen werden kann, dass sich insbesondere Prozesse für interkulturelle Vergleiche eignen, sollten Inhalte als stärker kulturspezifisch verstanden werden. Darauf aufbauend diskutieren wir drei verschiedene Ansätze zur Verbesserung interkultureller Vergleichbarkeit der Intelligenzdiagnostik sowie deren Grenzen. Wir postulieren, dass sich die Intelligenzforschung im Austausch mit verschiedenen Disziplinen stärker auf die Identifikation von generellen kognitiven Prozessen fokussieren sollte und diskutieren das Potenzial zukünftiger Forschung hin zu einer prozessorientierten und biologisch inspirierten Intelligenzdiagnostik. Schließlich zeigen wir derzeitige Möglichkeiten auf, gehen aber auch auf etwaige Herausforderungen ein und beleuchten Implikationen für die zukünftige Intelligenzdiagnostik und -forschung.
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Affiliation(s)
| | - Kirsten Hilger
- Institut für Psychologie, Universität Würzburg, Deutschland
| | | | - Anna-Lena Schubert
- Psychologisches Institut, Universität Heidelberg, Deutschland
- Psychologisches Institut, Universität Mainz, Deutschland
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18
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19
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Transcriptome Profiling of the Dorsomedial Prefrontal Cortex in Suicide Victims. Int J Mol Sci 2022; 23:ijms23137067. [PMID: 35806070 PMCID: PMC9266666 DOI: 10.3390/ijms23137067] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 01/27/2023] Open
Abstract
The default mode network (DMN) plays an outstanding role in psychiatric disorders. Still, gene expressional changes in its major component, the dorsomedial prefrontal cortex (DMPFC), have not been characterized. We used RNA sequencing in postmortem DMPFC samples to investigate suicide victims compared to control subjects. 1400 genes differed using log2FC > ±1 and adjusted p-value < 0.05 criteria between groups. Genes associated with depressive disorder, schizophrenia and impaired cognition were strongly overexpressed in top differentially expressed genes. Protein−protein interaction and co-expressional networks coupled with gene set enrichment analysis revealed that pathways related to cytokine receptor signaling were enriched in downregulated, while glutamatergic synaptic signaling upregulated genes in suicidal individuals. A validated differentially expressed gene, which is known to be associated with mGluR5, was the N-terminal EF-hand calcium-binding protein 2 (NECAB2). In situ hybridization histochemistry and immunohistochemistry proved that NECAB2 is expressed in two different types of inhibitory neurons located in layers II-IV and VI, respectively. Our results imply extensive gene expressional alterations in the DMPFC related to suicidal behavior. Some of these genes may contribute to the altered mental state and behavior of suicide victims.
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20
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Fuentealba-Villarroel FJ, Renner J, Hilbig A, Bruton OJ, Rasia-Filho AA. Spindle-Shaped Neurons in the Human Posteromedial (Precuneus) Cortex. Front Synaptic Neurosci 2022; 13:769228. [PMID: 35087390 PMCID: PMC8787311 DOI: 10.3389/fnsyn.2021.769228] [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: 09/01/2021] [Accepted: 11/29/2021] [Indexed: 01/24/2023] Open
Abstract
The human posteromedial cortex (PMC), which includes the precuneus (PC), represents a multimodal brain area implicated in emotion, conscious awareness, spatial cognition, and social behavior. Here, we describe the presence of Nissl-stained elongated spindle-shaped neurons (suggestive of von Economo neurons, VENs) in the cortical layer V of the anterior and central PC of adult humans. The adapted "single-section" Golgi method for postmortem tissue was used to study these neurons close to pyramidal ones in layer V until merging with layer VI polymorphic cells. From three-dimensional (3D) reconstructed images, we describe the cell body, two main longitudinally oriented ascending and descending dendrites as well as the occurrence of spines from proximal to distal segments. The primary dendritic shafts give rise to thin collateral branches with a radial orientation, and pleomorphic spines were observed with a sparse to moderate density along the dendritic length. Other spindle-shaped cells were observed with straight dendritic shafts and rare branches or with an axon emerging from the soma. We discuss the morphology of these cells and those considered VENs in cortical areas forming integrated brain networks for higher-order activities. The presence of spindle-shaped neurons and the current discussion on the morphology of putative VENs address the need for an in-depth neurochemical and transcriptomic characterization of the PC cytoarchitecture. These findings would include these spindle-shaped cells in the synaptic and information processing by the default mode network and for general intelligence in healthy individuals and in neuropsychiatric disorders involving the PC in the context of the PMC functioning.
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Affiliation(s)
- Francisco Javier Fuentealba-Villarroel
- Department of Basic Sciences/Physiology, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil.,Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Josué Renner
- Department of Basic Sciences/Physiology, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil.,Graduate Program in Biosciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
| | - Arlete Hilbig
- Department of Medical Clinics/Neurology, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
| | - Oliver J Bruton
- Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
| | - Alberto A Rasia-Filho
- Department of Basic Sciences/Physiology, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil.,Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Graduate Program in Biosciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
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21
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Varriale V, De Pascalis V, van der Molen MW. Post-error slowing is associated with intelligence. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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22
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Conflict-Related Brain Activity after Individualized Cognitive Training in Preschoolers from Poor Homes. JOURNAL OF COGNITIVE ENHANCEMENT 2021. [DOI: 10.1007/s41465-021-00223-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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23
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Recent developments, current challenges, and future directions in electrophysiological approaches to studying intelligence. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101569] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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24
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Fraenz C, Schlüter C, Friedrich P, Jung RE, Güntürkün O, Genç E. Interindividual differences in matrix reasoning are linked to functional connectivity between brain regions nominated by Parieto-Frontal Integration Theory. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2022]
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25
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Is there a “g-neuron”? Establishing a systematic link between general intelligence (g) and the von Economo neuron. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101540] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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26
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Exploring communication between the thalamus and cognitive control-related functional networks in the cerebral cortex. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2021; 21:656-677. [PMID: 33864195 DOI: 10.3758/s13415-021-00892-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/15/2021] [Indexed: 11/08/2022]
Abstract
It has been suggested by multiple studies (postmortem studies, invasive animal studies, and diffusion tensor imaging in the human brain) that the thalamus is important for communication among cortical regions. Many functional magnetic resonance imaging (fMRI) studies, including noninvasive and whole-brain studies, have reported thalamic co-activation with several cognitive control-related cortical systems. This forms a complex network that may be important for advanced cognitive control-related processes, such as working memory and attention. Nevertheless, how the thalamus communicates with the cognitive control-related network in the intact human brain is an essential question and needs further investigation. To address this question, we conducted a study using dynamic functional connectivity analysis and effective connectivity analysis based on fMRI data from young, healthy adult participants. The results showed that the middle thalamus exhibited both high in- and out-degree regarding the complex network related to cognitive control during both rest and task conditions. Furthermore, intrinsic communication via the middle thalamic regions showed dynamically co-varying patterns, and the thalamic regions showed high flexibility in dynamic community analysis. These results indicated that the mid-thalamic region is an important station for communication between nodes in cognitive control-related networks.
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28
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Li C, Qiao K, Mu Y, Jiang L. Large-Scale Morphological Network Efficiency of Human Brain: Cognitive Intelligence and Emotional Intelligence. Front Aging Neurosci 2021; 13:605158. [PMID: 33732136 PMCID: PMC7959829 DOI: 10.3389/fnagi.2021.605158] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 02/01/2021] [Indexed: 12/13/2022] Open
Abstract
Network efficiency characterizes how information flows within a network, and it has been used to study the neural basis of cognitive intelligence in adolescence, young adults, and elderly adults, in terms of the white matter in the human brain and functional connectivity networks. However, there were few studies investigating whether the human brain at different ages exhibited different underpins of cognitive and emotional intelligence (EI) from young adults to the middle-aged group, especially in terms of the morphological similarity networks in the human brain. In this study, we used 65 datasets (aging 18–64), including sMRI and behavioral measurements, to study the associations of network efficiency with cognitive intelligence and EI in young adults and the middle-aged group. We proposed a new method of defining the human brain morphological networks using the morphological distribution similarity (including cortical volume, surface area, and thickness). Our results showed inverted age × network efficiency interactions in the relationship of surface-area network efficiency with cognitive intelligence and EI: a negative age × global efficiency (nodal efficiency) interaction in cognitive intelligence, while a positive age × global efficiency (nodal efficiency) interaction in EI. In summary, this study not only proposed a new method of morphological similarity network but also emphasized the developmental effects on the brain mechanisms of intelligence from young adult to middle-aged groups and may promote mental health study on the middle-aged group in the future.
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Affiliation(s)
- Chunlin Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Kaini Qiao
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yan Mu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Lili Jiang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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Dreszer J, Grochowski M, Lewandowska M, Nikadon J, Gorgol J, Bałaj B, Finc K, Duch W, Kałamała P, Chuderski A, Piotrowski T. Spatiotemporal complexity patterns of resting-state bioelectrical activity explain fluid intelligence: Sex matters. Hum Brain Mapp 2020; 41:4846-4865. [PMID: 32808732 PMCID: PMC7643359 DOI: 10.1002/hbm.25162] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Revised: 07/12/2020] [Accepted: 07/27/2020] [Indexed: 11/11/2022] Open
Abstract
Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting-state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6-min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo-parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro-temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing.
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Affiliation(s)
- Joanna Dreszer
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Faculty of Philosophy and Social SciencesInstitute of Psychology, Nicolaus Copernicus UniversityToruńPoland
| | - Marek Grochowski
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Department of Informatics, Faculty of Physics, Astronomy, and InformaticsNicolaus Copernicus UniversityToruńPoland
| | - Monika Lewandowska
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Faculty of Philosophy and Social SciencesInstitute of Psychology, Nicolaus Copernicus UniversityToruńPoland
| | - Jan Nikadon
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
| | - Joanna Gorgol
- Faculty of PsychologyUniversity of WarsawWarsawPoland
| | - Bibianna Bałaj
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Faculty of Philosophy and Social SciencesInstitute of Psychology, Nicolaus Copernicus UniversityToruńPoland
| | - Karolina Finc
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
| | - Włodzisław Duch
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Department of Informatics, Faculty of Physics, Astronomy, and InformaticsNicolaus Copernicus UniversityToruńPoland
| | - Patrycja Kałamała
- Department of Cognitive ScienceInstitute of Philosophy, Jagiellonian UniversityKrakowPoland
| | - Adam Chuderski
- Department of Cognitive ScienceInstitute of Philosophy, Jagiellonian UniversityKrakowPoland
| | - Tomasz Piotrowski
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Department of Informatics, Faculty of Physics, Astronomy, and InformaticsNicolaus Copernicus UniversityToruńPoland
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Li Y, Xue YZ, Zhao WT, Li SS, Li J, Xu Y. Correlates of intelligence via resting-state functional connectivity of the amygdala in healthy adults. Brain Res 2020; 1751:147176. [PMID: 33121922 DOI: 10.1016/j.brainres.2020.147176] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 10/19/2020] [Accepted: 10/21/2020] [Indexed: 11/24/2022]
Abstract
Intelligence is a form of advanced cognition that includes reasoning, problem solving, pattern recognition, and establishing relationships among items. The amygdala plays an important role in cognitive processing, but the relationship between amygdalar function and intelligence has rarely been explored directly. Here, we investigated the relationship between resting-state functional connectivity (RSFC) of the amygdala and intelligence test performance in a large sample of healthy adults (N = 197). We found that two pairs of RSFCs were significantly increased in the high IQ group compared with that of the general IQ group. One of these RSFCs consisted of the right amygdala and the right superior parietal lobule, whereas the other RSFC consisted of the right amygdala and the left middle cingulum. In addition, we found that the brain regions in which the strength of RSFC significantly correlated with full IQ (FIQ) were mainly distributed in the parietal and limbic lobes. What's more, a further mediation analysis indicated that the functional connectivity of the right amygdala and the right superior parietal lobule significantly mediated the correlation between comprehension and object assembly, whereas the functional connectivity of the right amygdala and the left middle cingulum mediated the association between similarities and digit symbol. These findings suggest that amygdalar RSFC may reflect individual differences in intelligence and mediate specific relationships among different intellectual abilities.
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Affiliation(s)
- Yue Li
- Department of Psychiatry, First Hospital /First Clinical Medical College of Shanxi Medical University, Taiyuan, China; Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yun-Zhen Xue
- Department of Humanities and Social Science, Shanxi Medical University, Taiyuan, China
| | - Wen-Tao Zhao
- Department of Psychiatry, First Hospital /First Clinical Medical College of Shanxi Medical University, Taiyuan, China; Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Sha-Sha Li
- Department of Humanities and Social Science, Shanxi Medical University, Taiyuan, China
| | - Jing Li
- Department of Psychiatry, First Hospital /First Clinical Medical College of Shanxi Medical University, Taiyuan, China; Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yong Xu
- Department of Psychiatry, First Hospital /First Clinical Medical College of Shanxi Medical University, Taiyuan, China; Department of Humanities and Social Science, Shanxi Medical University, Taiyuan, China; MDT Center for Cognitive Impairment and Sleep Disorders, First Hospital of Shanxi Medical University, Taiyuan, China; Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.
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Hilger K, Winter NR, Leenings R, Sassenhagen J, Hahn T, Basten U, Fiebach CJ. Predicting intelligence from brain gray matter volume. Brain Struct Funct 2020; 225:2111-2129. [PMID: 32696074 PMCID: PMC7473979 DOI: 10.1007/s00429-020-02113-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 07/04/2020] [Indexed: 12/21/2022]
Abstract
A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume. Features were derived from structural magnetic resonance imaging data (N = 308) using (a) a purely data-driven method (principal component analysis) and (b) a domain knowledge-based approach (atlas parcellation). When using relative gray matter (corrected for total brain size), only the atlas-based approach provided significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all significant predictions, the absolute error was relatively high, i.e., greater than ten IQ points, and in the atlas-based models, the predicted IQ scores varied closely around the sample mean. This renders the practical value even of statistically significant prediction results questionable. Analyses based on the gray matter of functional brain networks yielded significant predictions for the fronto-parietal network and the cerebellum. However, the mean absolute errors were not reduced in contrast to the global models, suggesting that general intelligence may be related more to global than region-specific differences in gray matter volume. More generally, our study highlights the importance of predictive statistical analysis approaches for clarifying the neurobiological bases of intelligence and provides important suggestions for future research using predictive modeling.
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Affiliation(s)
- Kirsten Hilger
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany.
- Department of Psychology, Julius Maximilian University Würzburg, Würzburg, Germany.
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany.
- Department of Psychology I, University Wuerzburg, Marcusstr. 9-11, 97070, Würzburg, Germany.
| | - Nils R Winter
- Institute of Translational Psychiatry, University Hospital Münster, Münster, Germany
| | - Ramona Leenings
- Institute of Translational Psychiatry, University Hospital Münster, Münster, Germany
| | - Jona Sassenhagen
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Tim Hahn
- Institute of Translational Psychiatry, University Hospital Münster, Münster, Germany
| | - Ulrike Basten
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Christian J Fiebach
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
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The Evidence for Geary's Theory on the Role of Mitochondrial Functioning in Human Intelligence Is Not Entirely Convincing. J Intell 2020; 8:jintelligence8030029. [PMID: 32698405 PMCID: PMC7555447 DOI: 10.3390/jintelligence8030029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 07/01/2020] [Accepted: 07/16/2020] [Indexed: 11/24/2022] Open
Abstract
Geary (2018, 2019) suggested that heritable and environmentally caused differences in mitochondrial functioning affect the integrity and efficiency of neurons and supporting glia cells and may thus contribute to individual differences in higher-order cognitive functioning and physical health. In our comment, we want to pose three questions aimed at different aspects of Geary’s theory that critically evaluate his theory in the light of evidence from neurocognitive, cognitive enhancement, and behavioral genetics research. We question (1) if Geary’s theory explains why certain cognitive processes show a stronger age-related decline than others; (2) if intervention studies in healthy younger adults support the claim that variation in mitochondrial functioning underlies variation in human intelligence; and (3) if predictions arising from the matrilineal heredity of mitochondrial DNA are supported by behavioral genetics research. We come to the conclusion that there are likely many more biological and social factors contributing to variation in human intelligence than mitochondrial functioning.
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Information Integration and Mesoscopic Cortical Connectivity during Propofol Anesthesia. Anesthesiology 2020; 132:504-524. [PMID: 31714269 DOI: 10.1097/aln.0000000000003015] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Abstract
Background
The neurophysiologic mechanisms of propofol-induced loss of consciousness have been studied in detail at the macro (scalp electroencephalogram) and micro (spiking or local field potential) scales. However, the changes in information integration and cortical connectivity during propofol anesthesia at the mesoscopic level (the cortical scale) are less clear.
Methods
The authors analyzed electrocorticogram data recorded from surgical patients during propofol-induced unconsciousness (n = 9). A new information measure, genuine permutation cross mutual information, was used to analyze how electrocorticogram cross-electrode coupling changed with electrode-distances in different brain areas (within the frontal, parietal, and temporal regions, as well as between the temporal and parietal regions). The changes in cortical networks during anesthesia—at nodal and global levels—were investigated using clustering coefficient, path length, and nodal efficiency measures.
Results
In all cortical regions, and in both wakeful and unconscious states (early and late), the genuine permutation cross mutual information and the percentage of genuine connections decreased with increasing distance, especially up to about 3 cm. The nodal cortical network metrics (the nodal clustering coefficients and nodal efficiency) decreased from wakefulness to unconscious state in the cortical regions we analyzed. In contrast, the global cortical network metrics slightly increased in the early unconscious state (the time span from loss of consciousness to 200 s after loss of consciousness), as compared with wakefulness (normalized average clustering coefficient: 1.05 ± 0.01 vs. 1.06 ± 0.03, P = 0.037; normalized average path length: 1.02 ± 0.01 vs. 1.04 ± 0.01, P = 0.021).
Conclusions
The genuine permutation cross mutual information reflected propofol-induced coupling changes measured at a cortical scale. Loss of consciousness was associated with a redistribution of the pattern of information integration; losing efficient global information transmission capacity but increasing local functional segregation in the cortical network.
Editor’s Perspective
What We Already Know about This Topic
What This Article Tells Us That Is New
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Jaušovec N. The neural code of intelligence: From correlation to causation. Phys Life Rev 2019; 31:171-187. [PMID: 31706924 DOI: 10.1016/j.plrev.2019.10.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 10/18/2019] [Indexed: 01/03/2023]
Abstract
Research into the neural underpinning of intelligence has mainly adopted a construct perspective: trying to find structural and functional brain characteristics that would accommodate the psychological concept of g. Few attempts have been made to explain intelligence exclusively based on brain characteristics - the brain perspective. From a methodological viewpoint the brain intelligence relation has been studied by means of correlational and interventional studies. The later providing a causal elucidation of the brain - intelligence relation. The best neuro-anatomical predictor of intelligence is brain volume showing a modest positive correlation with g, explaining between 9 to 16% of variance. The most likely explanation was that larger brains, containing more neurons, have a greater computational power and in that way allow more complex cognitive processing. Correlations with brain surface, thickness, convolution and callosal shape showed less consistent patterns. The development of diffusion tensor imaging has allowed researchers to look also into the microstructure of brain tissue. Consistently observed was a positively correlation between white matter integrity and intelligence, supporting the idea that efficient information transfer between hemispheres and brain areas is crucial for higher intellectual competence. Based on functional studies of the brain intelligence relationship three theories have been put forward: the neural efficiency, the P-FIT and the multi demand (MD) system theory. On the other hand, The Network Neuroscience Theory of g, based on methods from mathematics, physics, and computer science, is an example for the brain perspective on neurobiological underpinning of intelligence. In this framework network flexibility and dynamics provide the foundation for general intelligence. With respect to intervention studies the most promising results have been achieved with noninvasive brain stimulation and behavioral training providing tentative support for findings put forward by the correlational approach. To date the best consensus based on the diversity of results reported would be that g is predominantly determined by lateral prefrontal attentional control of structured sensory episodes in posterior brain areas. The capacity of flexible transitions between these network states represents the essence of intelligence - g.
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Suprano I, Delon-Martin C, Kocevar G, Stamile C, Hannoun S, Achard S, Badhwar A, Fourneret P, Revol O, Nusbaum F, Sappey-Marinier D. Topological Modification of Brain Networks Organization in Children With High Intelligence Quotient: A Resting-State fMRI Study. Front Hum Neurosci 2019; 13:241. [PMID: 31354458 PMCID: PMC6639736 DOI: 10.3389/fnhum.2019.00241] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 06/28/2019] [Indexed: 11/13/2022] Open
Abstract
The idea that intelligence is embedded not only in a single brain network, but instead in a complex, well-optimized system of complementary networks, has led to the development of whole brain network analysis. Using graph theory to analyze resting-state functional MRI data, we investigated the brain graph networks (or brain networks) of high intelligence quotient (HIQ) children. To this end, we computed the "hub disruption index κ," an index sensitive to graph network modifications. We found significant topological differences in the integration and segregation properties of brain networks in HIQ compared to standard IQ children, not only for the whole brain graph, but also for each hemispheric graph, and for the homotopic connectivity. Moreover, two profiles of HIQ children, homogenous and heterogeneous, based on the differences between the two main IQ subscales [verbal comprehension index (VCI) and perceptual reasoning index (PRI)], were compared. Brain network changes were more pronounced in the heterogeneous than in the homogeneous HIQ subgroups. Finally, we found significant correlations between the graph networks' changes and the full-scale IQ (FSIQ), as well as the subscales VCI and PRI. Specifically, the higher the FSIQ the greater was the brain organization modification in the whole brain, the left hemisphere, and the homotopic connectivity. These results shed new light on the relation between functional connectivity topology and high intelligence, as well as on different intelligence profiles.
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Affiliation(s)
- Ilaria Suprano
- Univ. Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
| | - Chantal Delon-Martin
- Univ. Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Gabriel Kocevar
- Univ. Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
| | - Claudio Stamile
- Univ. Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
| | - Salem Hannoun
- Nehme and Therese Tohme Multiple Sclerosis Center, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Sophie Achard
- GIPSA-Lab, UMR CNRS 5216, Université Grenoble Alpes, Grenoble, France
| | - Amanpreet Badhwar
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Université de Montréal, Montreal, QC, Canada
| | - Pierre Fourneret
- Service de Psychopathologie du Développement de l’Enfant et de l’Adolescent, Hospices Civils de Lyon, Lyon, France
| | - Olivier Revol
- Service de Psychopathologie du Développement de l’Enfant et de l’Adolescent, Hospices Civils de Lyon, Lyon, France
| | - Fanny Nusbaum
- Laboratoire Parcours Santé Systémique (Equipe d’Accueil 4129), Université de Lyon, Université Claude Bernard-Lyon 1, Lyon, France
- Centre PSYRENE, Lyon, France
| | - Dominique Sappey-Marinier
- Univ. Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France
- CERMEP – Imagerie du Vivant, Université de Lyon, Lyon, France
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Frischkorn GT, Schubert AL, Hagemann D. Processing speed, working memory, and executive functions: Independent or inter-related predictors of general intelligence. INTELLIGENCE 2019. [DOI: 10.1016/j.intell.2019.05.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Distributed neural efficiency: Intelligence and age modulate adaptive allocation of resources in the brain. Trends Neurosci Educ 2019; 15:48-61. [PMID: 31176471 DOI: 10.1016/j.tine.2019.02.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 01/18/2019] [Accepted: 02/27/2019] [Indexed: 11/23/2022]
Abstract
Whether superior intelligence is associated with global lower resource consumption in the brain remains unresolved. In order to tap fluid intelligence "Gf" cortical networks, 50 neurologically healthy adults were functionally neuro-imaged while solving a modified version of the Raven Advanced Progressive Matrices. "Gf" predicted increased activation of key components of the dorsal attention network (DAN); and age predicted extent of simultaneous deactivation in key components of the default mode network (DMN) during problem-solving. However, there was no significant difference in mean levels of whole brain activation even when cognitively taxed. This suggests that the brain may dynamically switch resource consumption between these anti-correlated DAN and DMN networks, concentrating processing power in regions critical to enhanced cognitive performance. We term this mechanism of activation increase and decrease of these anti-correlated DAN/DMN systems, modulated by "Gf" and age, as "distributed neural efficiency". This may achieve local cost-efficiency trade-offs, while maintaining global energy homeostasis.
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Farahani FV, Karwowski W, Lighthall NR. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review. Front Neurosci 2019; 13:585. [PMID: 31249501 PMCID: PMC6582769 DOI: 10.3389/fnins.2019.00585] [Citation(s) in RCA: 281] [Impact Index Per Article: 56.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 05/23/2019] [Indexed: 12/20/2022] Open
Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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Affiliation(s)
- Farzad V Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Nichole R Lighthall
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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Girn M, Mills C, Christoff K. Linking brain network reconfiguration and intelligence: Are we there yet? Trends Neurosci Educ 2019; 15:62-70. [PMID: 31176472 DOI: 10.1016/j.tine.2019.04.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 02/22/2019] [Accepted: 04/04/2019] [Indexed: 01/08/2023]
Abstract
Recent applications of dynamic network analyses to functional neuroimaging data have revealed relationships between a number of cognition conditions and the dynamic reconfiguration of brain networks. Here we critically review such applications of network neuroscience to intelligence. After providing an overview of network neuroscience, we center our discussion around the recently proposed Network Neuroscience Theory of Intelligence (Barbey, 2017). We evaluate and review existing empirical support for the theses made by this theory and argue that while studies strongly suggest their plausibility, evidence to date has largely been indirect. We propose avenues for future research to directly evaluate these theses by overcoming the methodological and analytical shortcomings of previous studies. In doing so, our goal is to stimulate future empirical investigations and present valuable ways forward in the network neuroscience of intelligence.
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Affiliation(s)
- Manesh Girn
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Department of Psychology, University of British Columbia, Vancouver, British Columbia.
| | - Caitlin Mills
- Department of Psychology, University of New Hampshire, Durham, New Hampshire
| | - Kalina Christoff
- Department of Psychology, University of British Columbia, Vancouver, British Columbia; Centre for Brain Health, University of British Columbia, Vancouver, British Columbia
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Cornblath EJ, Lydon-Staley DM, Bassett DS. Harnessing networks and machine learning in neuropsychiatric care. Curr Opin Neurobiol 2019; 55:32-39. [PMID: 30641443 PMCID: PMC6839408 DOI: 10.1016/j.conb.2018.12.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 12/10/2018] [Accepted: 12/19/2018] [Indexed: 12/27/2022]
Abstract
The development of next-generation therapies for neuropsychiatric illness will likely rely on a precise and accurate understanding of human brain dynamics. Toward this end, researchers have focused on collecting large quantities of neuroimaging data. For simplicity, we will refer to large cross-sectional neuroimaging studies as broad studies and to intensive longitudinal studies as deep studies. Recent progress in identifying illness subtypes and predicting treatment response in neuropsychiatry has been supported by these study designs, along with methods bridging machine learning and network science. Such methods combine analytic power, interpretability, and direct connection to underlying theory in cognitive neuroscience. Ultimately, we propose a general framework for the treatment of neuropsychiatric illness relying on the findings from broad and deep studies combined with basic cognitive and physiologic measurements.
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Affiliation(s)
- Eli J Cornblath
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David M Lydon-Staley
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Hilger K, Fiebach CJ. ADHD symptoms are associated with the modular structure of intrinsic brain networks in a representative sample of healthy adults. Netw Neurosci 2019; 3:567-588. [PMID: 31089485 PMCID: PMC6497005 DOI: 10.1162/netn_a_00083] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 03/06/2019] [Indexed: 12/13/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with significant and often lifelong effects on social, emotional, and cognitive functioning. Influential neurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. Here, we investigate whether network-based theories of ADHD can be generalized to understanding variations in ADHD-related behaviors within the normal (i.e., clinically unaffected) adult population. In a large and representative sample, self-rated presence of ADHD symptoms varied widely; only 8 out of 291 participants scored in the clinical range. Subject-specific brain network graphs were modeled from functional MRI resting-state data and revealed significant associations between (nonclinical) ADHD symptoms and region-specific profiles of between-module and within-module connectivity. Effects were located in brain regions associated with multiple neuronal systems including the default-mode network, the salience network, and the central executive system. Our results are consistent with network perspectives of ADHD and provide further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions. More generally, our findings support a dimensional conceptualization of ADHD and contribute to a growing understanding of cognition as an emerging property of functional brain networks.
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Affiliation(s)
- Kirsten Hilger
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany
| | - Christian J. Fiebach
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
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Farahani FV, Karwowski W, Lighthall NR. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review. Front Neurosci 2019. [PMID: 31249501 DOI: 10.3389/fnins.2019.00585/bibtex] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023] Open
Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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Affiliation(s)
- Farzad V Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Nichole R Lighthall
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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Zhang X, Yang Y, Liu R, Zhong N. Route Adjustment of Functional Brain Network in Mental Arithmetic Using Task-Evoked FMRI. Brain Inform 2019. [DOI: 10.1007/978-3-030-37078-7_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Schubert AL, Hagemann D, Frischkorn GT, Herpertz SC. Faster, but not smarter: An experimental analysis of the relationship between mental speed and mental abilities. INTELLIGENCE 2018. [DOI: 10.1016/j.intell.2018.10.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Pozuelos JP, Combita LM, Abundis A, Paz‐Alonso PM, Conejero Á, Guerra S, Rueda MR. Metacognitive scaffolding boosts cognitive and neural benefits following executive attention training in children. Dev Sci 2018; 22:e12756. [DOI: 10.1111/desc.12756] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 09/20/2018] [Indexed: 11/29/2022]
Affiliation(s)
- Joan Paul Pozuelos
- Department of Experimental Psychology and Center for Research on Mind, Brain and Behavior (CIMCYC)Universidad de Granada Granada Spain
| | - Lina M. Combita
- Department of Experimental Psychology and Center for Research on Mind, Brain and Behavior (CIMCYC)Universidad de Granada Granada Spain
| | - Alicia Abundis
- Department of Experimental Psychology and Center for Research on Mind, Brain and Behavior (CIMCYC)Universidad de Granada Granada Spain
| | | | - Ángela Conejero
- Department of Experimental Psychology and Center for Research on Mind, Brain and Behavior (CIMCYC)Universidad de Granada Granada Spain
| | - Sonia Guerra
- Department of Experimental Psychology and Center for Research on Mind, Brain and Behavior (CIMCYC)Universidad de Granada Granada Spain
| | - M. Rosario Rueda
- Department of Experimental Psychology and Center for Research on Mind, Brain and Behavior (CIMCYC)Universidad de Granada Granada Spain
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Altered resting-state functional connectivity in children and adolescents born very preterm short title. NEUROIMAGE-CLINICAL 2018; 20:1148-1156. [PMID: 30388598 PMCID: PMC6214877 DOI: 10.1016/j.nicl.2018.10.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 08/26/2018] [Accepted: 10/02/2018] [Indexed: 01/25/2023]
Abstract
The formation of resting-state functional networks in infancy has been reported to be strongly impacted by very preterm birth. Studies in childhood and adolescence have largely focused on language processing networks and identified both decreased and increased functional connectivity. It is unclear, however, whether functional connectivity strength is altered globally in children and adolescents born very preterm and whether these alterations are related to the frequently occurring cognitive deficits. Here, resting-state functional MRI was assessed in a group of 32 school-aged children and adolescents born very preterm with normal intellectual and motor abilities and 39 healthy term-born peers. Functional connectivity within and between a comprehensive set of well-established resting-state networks was compared between the groups. IQ and executive function abilities were tested with standardized tasks and potential associations with connectivity strength were explored. Functional connectivity was weaker in the very preterm compared to the term-born group between the sensorimotor network and the visual and dorsal attention network, within the sensorimotor network and within the central executive network. In contrast, functional connectivity was stronger in the very preterm group between the sensorimotor network and parts of the salience and the central executive network. Little evidence was found that these alterations underlie lower IQ or poorer executive function abilities. This study provides evidence for a long-lasting impact of very preterm birth on the organization of resting-state networks. The potential consequence of these alterations for other neurodevelopmental domains than the ones investigated in the current study warrants further investigation.
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Dubois J, Galdi P, Paul LK, Adolphs R. A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philos Trans R Soc Lond B Biol Sci 2018; 373:20170284. [PMID: 30104429 PMCID: PMC6107566 DOI: 10.1098/rstb.2017.0284] [Citation(s) in RCA: 149] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2018] [Indexed: 02/04/2023] Open
Abstract
Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here, we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N = 884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.This article is part of the theme issue 'Causes and consequences of individual differences in cognitive abilities'.
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Affiliation(s)
- Julien Dubois
- Division of Humanities and Social Sciences, Pasadena, CA 91125, USA
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Paola Galdi
- Department of Management and Innovation Systems, University of Salerno, Fisciano Salerno, Italy
- MRC Centre for Reproductive Health, University of Edinburgh, EH16 4TJ, UK
| | - Lynn K Paul
- Division of Humanities and Social Sciences, Pasadena, CA 91125, USA
- Chen Neuroscience Institute, California Institute of Technology, Pasadena, CA 91125, USA
| | - Ralph Adolphs
- Division of Humanities and Social Sciences, Pasadena, CA 91125, USA
- Division of Biology and Biological Engineering, Pasadena, CA 91125, USA
- Chen Neuroscience Institute, California Institute of Technology, Pasadena, CA 91125, USA
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Network Neuroscience and Personality. PERSONALITY NEUROSCIENCE 2018; 1:e14. [PMID: 32435733 PMCID: PMC7219685 DOI: 10.1017/pen.2018.12] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 03/28/2018] [Accepted: 04/14/2018] [Indexed: 12/11/2022]
Abstract
Personality and individual differences originate from the brain. Despite major advances in the affective and cognitive neurosciences, however, it is still not well understood how personality and single personality traits are represented within the brain. Most research on brain-personality correlates has focused either on morphological aspects of the brain such as increases or decreases in local gray matter volume, or has investigated how personality traits can account for individual differences in activation differences in various tasks. Here, we propose that personality neuroscience can be advanced by adding a network perspective on brain structure and function, an endeavor that we label personality network neuroscience. With the rise of resting-state functional magnetic resonance imaging (MRI), the establishment of connectomics as a theoretical framework for structural and functional connectivity modeling, and recent advancements in the application of mathematical graph theory to brain connectivity data, several new tools and techniques are readily available to be applied in personality neuroscience. The present contribution introduces these concepts, reviews recent progress in their application to the study of individual differences, and explores their potential to advance our understanding of the neural implementation of personality. Trait theorists have long argued that personality traits are biophysical entities that are not mere abstractions of and metaphors for human behavior. Traits are thought to actually exist in the brain, presumably in the form of conceptual nervous systems. A conceptual nervous system refers to the attempt to describe parts of the central nervous system in functional terms with relevance to psychology and behavior. We contend that personality network neuroscience can characterize these conceptual nervous systems on a functional and anatomical level and has the potential do link dispositional neural correlates to actual behavior.
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General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set. Neuroimage 2018; 171:323-331. [DOI: 10.1016/j.neuroimage.2018.01.018] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 01/05/2018] [Accepted: 01/09/2018] [Indexed: 01/30/2023] Open
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