51
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Cormier DC, Bulut O, McGrew KS, Kennedy K. Linguistic Influences on Cognitive Test Performance: Examinee Characteristics Are More Important than Test Characteristics. J Intell 2022; 10:jintelligence10010008. [PMID: 35225924 PMCID: PMC8883963 DOI: 10.3390/jintelligence10010008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 01/17/2022] [Accepted: 01/24/2022] [Indexed: 01/27/2023] Open
Abstract
Consideration of the influence of English language skills during testing is an understandable requirement for fair and valid cognitive test interpretation. Several professional standards and expert recommendations exist to guide psychologists as they attempt to engage in best practices when assessing English learners (ELs). Nonetheless, relatively few evidence-based recommendations for practice have been specified for psychologists. To address this issue, we used a mixed-effects modeling approach to examine the influences of test characteristics (i.e., test directions) and examinee characteristics (i.e., expressive and receptive language abilities) on cognitive test performance. Our results suggest that language abilities appear to have a significant influence on cognitive test performance, whereas test characteristics do not influence performance, after accounting for language abilities. Implications for practice include the assessment of expressive and receptive language abilities of EL students prior to administering, scoring, and interpreting cognitive test scores.
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Affiliation(s)
- Damien C. Cormier
- Department of Educational Psychology, University of Alberta, Edmonton, AB T6G 2G5, Canada;
- Correspondence: (D.C.C.); (O.B.)
| | - Okan Bulut
- Centre for Research in Applied Measurement and Evaluation, University of Alberta, Edmonton, AB T6G 2G5, Canada
- Correspondence: (D.C.C.); (O.B.)
| | - Kevin S. McGrew
- Institute for Community Integration, University of Minnesota and Institute for Applied Psychometrics, St. Joseph, MN 56374, USA;
| | - Kathleen Kennedy
- Department of Educational Psychology, University of Alberta, Edmonton, AB T6G 2G5, Canada;
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52
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Maliske L, Kanske P. The Social Connectome - Moving Toward Complexity in the Study of Brain Networks and Their Interactions in Social Cognitive and Affective Neuroscience. Front Psychiatry 2022; 13:845492. [PMID: 35449570 PMCID: PMC9016142 DOI: 10.3389/fpsyt.2022.845492] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Over the past 150 years of neuroscientific research, the field has undergone a tremendous evolution. Starting out with lesion-based inference of brain function, functional neuroimaging, introduced in the late 1980s, and increasingly fine-grained and sophisticated methods and analyses now allow us to study the live neural correlates of complex behaviors in individuals and multiple agents simultaneously. Classically, brain-behavior coupling has been studied as an association of a specific area in the brain and a certain behavioral outcome. This has been a crucial first step in understanding brain organization. Social cognitive processes, as well as their neural correlates, have typically been regarded and studied as isolated functions and blobs of neural activation. However, as our understanding of the social brain as an inherently dynamic organ grows, research in the field of social neuroscience is slowly undergoing the necessary evolution from studying individual elements to how these elements interact and their embedding within the overall brain architecture. In this article, we review recent studies that investigate the neural representation of social cognition as interacting, complex, and flexible networks. We discuss studies that identify individual brain networks associated with social affect and cognition, interaction of these networks, and their relevance for disorders of social affect and cognition. This perspective on social cognitive neuroscience can highlight how a more fine-grained understanding of complex network (re-)configurations could improve our understanding of social cognitive deficits in mental disorders such as autism spectrum disorder and schizophrenia, thereby providing new impulses for methods of interventions.
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Affiliation(s)
- Lara Maliske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Philipp Kanske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
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53
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DeYoung CG, Beaty RE, Genç E, Latzman RD, Passamonti L, Servaas MN, Shackman AJ, Smillie LD, Spreng RN, Viding E, Wacker J. Personality Neuroscience: An Emerging Field with Bright Prospects. PERSONALITY SCIENCE 2022; 3:e7269. [PMID: 36250039 PMCID: PMC9561792 DOI: 10.5964/ps.7269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Personality neuroscience is the study of persistent psychological individual differences, typically in the general population, using neuroscientific methods. It has the potential to shed light on the neurobiological mechanisms underlying individual differences and their manifestation in ongoing behavior and experience. The field was inaugurated many decades ago, yet has only really gained momentum in the last two, as suitable technologies have become widely available. Personality neuroscience employs a broad range of methods, including molecular genetics, pharmacological assays or manipulations, electroencephalography, and various neuroimaging modalities, such as magnetic resonance imaging and positron emission tomography. Although exciting progress is being made in this young field, much remains unknown. In this brief review, we discuss discoveries that have been made, methodological challenges and advances, and important questions that remain to be answered. We also discuss best practices for personality neuroscience research and promising future directions for the field.
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Affiliation(s)
- Colin G. DeYoung
- University of Minnesota, Minneapolis, USA,Address correspondence to: Colin DeYoung, Department of Psychology, 75 East River Rd., Minneapolis, MN USA.
| | | | - Erhan Genç
- Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany
| | | | - Luca Passamonti
- University of Cambridge, Cambridge, UK,Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Michelle N. Servaas
- University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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54
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Hebling Vieira B, Dubois J, Calhoun VD, Garrido Salmon CE. A deep learning based approach identifies regions more relevant than resting-state networks to the prediction of general intelligence from resting-state fMRI. Hum Brain Mapp 2021; 42:5873-5887. [PMID: 34587333 PMCID: PMC8596958 DOI: 10.1002/hbm.25656] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 11/30/2022] Open
Abstract
Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that specific domain, being agnostic to time‐distributed features and dynamics. We used an ensemble of recurrent neural networks to circumvent this limitation, bypassing feature extraction, to predict general intelligence from resting‐state functional magnetic resonance imaging regional signals of a large sample (n = 873) of Human Connectome Project adult subjects. Ablating common resting‐state networks (RSNs) and measuring degradation in performance, we show that model reliance can be mostly explained by network size. Using our approach based on the temporal variance of saliencies, that is, gradients of outputs with regards to inputs, we identify a candidate set of networks that more reliably affect performance in the prediction of general intelligence than similarly sized RSNs. Our approach allows us to further test the effect of local alterations on data and the expected changes in derived metrics such as functional connectivity and instantaneous innovations.
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Affiliation(s)
- Bruno Hebling Vieira
- InBrain Lab, Departamento de Física, Universidade de São Paulo, Ribeirão Preto, Brazil.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Julien Dubois
- Cedars-Sinai Medical Center, Los Angeles, California, USA.,Caltech, Pasadena, California, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,The Mind Research Network, Albuquerque, New Mexico, USA.,School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
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55
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Hakim N, Awh E, Vogel EK, Rosenberg MD. Inter-electrode correlations measured with EEG predict individual differences in cognitive ability. Curr Biol 2021; 31:4998-5008.e6. [PMID: 34637747 DOI: 10.1016/j.cub.2021.09.036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/07/2021] [Accepted: 09/15/2021] [Indexed: 02/07/2023]
Abstract
Human brains share a broadly similar functional organization with consequential individual variation. This duality in brain function has primarily been observed when using techniques that consider the spatial organization of the brain, such as MRI. Here, we ask whether these common and unique signals of cognition are also present in temporally sensitive but spatially insensitive neural signals. To address this question, we compiled electroencephalogram (EEG) data from individuals of both sexes while they performed multiple working memory tasks at two different data-collection sites (n = 171 and 165). Results revealed that trial-averaged EEG activity exhibited inter-electrode correlations that were stable within individuals and unique across individuals. Furthermore, models based on these inter-electrode correlations generalized across datasets to predict participants' working memory capacity and general fluid intelligence. Thus, inter-electrode correlation patterns measured with EEG provide a signature of working memory and fluid intelligence in humans and a new framework for characterizing individual differences in cognitive abilities.
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Affiliation(s)
- Nicole Hakim
- Department of Psychology, University of Chicago, Chicago, IL 60637, USA; Institute for Mind and Biology, University of Chicago, Chicago, IL 60637, USA.
| | - Edward Awh
- Department of Psychology, University of Chicago, Chicago, IL 60637, USA; Institute for Mind and Biology, University of Chicago, Chicago, IL 60637, USA; Neuroscience Institute, University of Chicago, Chicago, IL 60637, USA
| | - Edward K Vogel
- Department of Psychology, University of Chicago, Chicago, IL 60637, USA; Institute for Mind and Biology, University of Chicago, Chicago, IL 60637, USA; Neuroscience Institute, University of Chicago, Chicago, IL 60637, USA
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL 60637, USA; Neuroscience Institute, University of Chicago, Chicago, IL 60637, USA
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56
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Sripada C, Angstadt M, Taxali A, Clark DA, Greathouse T, Rutherford S, Dickens JR, Shedden K, Gard AM, Hyde LW, Weigard A, Heitzeg M. Brain-wide functional connectivity patterns support general cognitive ability and mediate effects of socioeconomic status in youth. Transl Psychiatry 2021; 11:571. [PMID: 34750359 PMCID: PMC8575890 DOI: 10.1038/s41398-021-01704-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/18/2021] [Accepted: 10/25/2021] [Indexed: 12/14/2022] Open
Abstract
General cognitive ability (GCA) is an individual difference dimension linked to important academic, occupational, and health-related outcomes and its development is strongly linked to differences in socioeconomic status (SES). Complex abilities of the human brain are realized through interconnections among distributed brain regions, but brain-wide connectivity patterns associated with GCA in youth, and the influence of SES on these connectivity patterns, are poorly understood. The present study examined functional connectomes from 5937 9- and 10-year-olds in the Adolescent Brain Cognitive Development (ABCD) multi-site study. Using multivariate predictive modeling methods, we identified whole-brain functional connectivity patterns linked to GCA. In leave-one-site-out cross-validation, we found these connectivity patterns exhibited strong and statistically reliable generalization at 19 out of 19 held-out sites accounting for 18.0% of the variance in GCA scores (cross-validated partial η2). GCA-related connections were remarkably dispersed across brain networks: across 120 sets of connections linking pairs of large-scale networks, significantly elevated GCA-related connectivity was found in 110 of them, and differences in levels of GCA-related connectivity across brain networks were notably modest. Consistent with prior work, socioeconomic status was a strong predictor of GCA in this sample, and we found that distributed GCA-related brain connectivity patterns significantly statistically mediated this relationship (mean proportion mediated: 15.6%, p < 2 × 10-16). These results demonstrate that socioeconomic status and GCA are related to broad and diffuse differences in functional connectivity architecture during early adolescence, potentially suggesting a mechanism through which socioeconomic status influences cognitive development.
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Affiliation(s)
- Chandra Sripada
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
| | - Mike Angstadt
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - Aman Taxali
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - D. Angus Clark
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - Tristan Greathouse
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - Saige Rutherford
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - Joseph R. Dickens
- grid.214458.e0000000086837370Department of Statistics, University of Michigan, Ann Arbor, MI USA
| | - Kerby Shedden
- grid.214458.e0000000086837370Department of Statistics, University of Michigan, Ann Arbor, MI USA
| | - Arianna M. Gard
- grid.164295.d0000 0001 0941 7177Department of Psychology and Neuroscience and Cognitive Neuroscience Program, University of Maryland, College Park, MD USA
| | - Luke W. Hyde
- grid.214458.e0000000086837370Department of Psychology and Survey Research Center at the Institute for Social Research, University of Michigan, Ann Arbor, MI USA
| | - Alexander Weigard
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - Mary Heitzeg
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
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57
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Testing the structure of human cognitive ability using evidence obtained from the impact of brain lesions over abilities. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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58
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Krupnik R, Yovel Y, Assaf Y. Inner Hemispheric and Interhemispheric Connectivity Balance in the Human Brain. J Neurosci 2021; 41:8351-8361. [PMID: 34465598 PMCID: PMC8496194 DOI: 10.1523/jneurosci.1074-21.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/25/2021] [Accepted: 08/08/2021] [Indexed: 11/21/2022] Open
Abstract
The connectome of the brain has a great impact on the function of the brain as the structure of the connectome affects the speed and efficiency of information transfer. As a highly energy-consuming organ, an efficient network structure is essential. A previous study has shown consistent overall brain connectivity across a large variety of species. This connectivity conservation was explained by a balance between interhemispheric and intrahemispheric connections; that is, spices with highly connected hemispheres appear to have weaker interhemisphere connections. This study examines this connectivity trade-off in the human brain using diffusion-based tractography and network analysis in the Human Connectome Project (970 subjects, 527 female). We explore the biological origins of this phenomenon, heritability, and the effect on cognitive measures.The proportion of commissural fibers in the brain had a negative correlation to hemispheric efficiency, pointing to a trade-off between inner hemispheric and interhemispheric connectivity. Network hubs including anterior and middle cingulate cortex, superior frontal areas, medial occipital areas, the parahippocampal gyrus, post- and precentral gyri, and the precuneus had the strongest contribution to this phenomenon. Other results show a high heritability as well as a strong connection to crystalized intelligence. This work presents cohort-based network analysis research, spanning a large variety of samples and exploring the overall architecture of the human connectome. Our results show a connectivity conservation phenomenon at the base of the overall brain network architecture. This network structure may explain much of the functional, behavioral, and cognitive variability among different brains.SIGNIFICANCE STATEMENT The network structure of the brain is at the basis of every brain function as it dictates the characteristics of information transfer. Understanding the patterns and mechanisms that guide the connectome structure is crucial to understanding the brain itself. Here we unravel the mechanism at the base of the connectivity conservation phenomenon by exploring the interaction between hemispheric and commissural connectivity in a large-scale cohort-based connectivity study. We describe the trade-off between the two components and examine the origins of the trade-off and observe the effect on cognitive abilities and behavior.
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Affiliation(s)
- Ronnie Krupnik
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yossi Yovel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- School of Zoology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
- Steinhardt Museum of Natural History, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yaniv Assaf
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- School of Neurobiology, Biochemistry and Biophysics, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
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59
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McGrew KS. Is the Intellectual Functioning Component of AAIDD's 12th Manual Satisficing? INTELLECTUAL AND DEVELOPMENTAL DISABILITIES 2021; 59:369-375. [PMID: 34551097 DOI: 10.1352/1934-9556-59.5.369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 04/02/2020] [Indexed: 06/13/2023]
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60
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López-Arango G, Deguire F, Côté V, Barlaam F, Agbogba K, Knoth IS, Lippé S. Infant repetition effects and change detection: Are they related to adaptive skills? Eur J Neurosci 2021; 54:7193-7213. [PMID: 34585451 DOI: 10.1111/ejn.15475] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 09/16/2021] [Accepted: 09/21/2021] [Indexed: 11/29/2022]
Abstract
Repetition effects and change detection response have been proposed as neuro-electrophysiological correlates of fundamental learning processes. As such, they could be a good predictor of brain maturation and cognitive development. We recorded high density EEG in 71 healthy infants (32 females) aged between 3 and 9 months, while they listened to vowel sequences (standard /a/a/a/i/ [80%] and deviant /a/a/a/a/ [20%]). Adaptive skills, a surrogate of cognitive development, were measured via the parent form of the Adaptive Behavior Assessment System Second Edition (ABAS-II). Cortical auditory-evoked potentials (CAEPs) analyses, time-frequency analyses and a statistical approach using linear mixed models (LMMs) and linear regression models were performed. Age and adaptive skills were tested as predictors. Age modulation of repetition effects and change detection response was observed in theta (3-5 Hz), alpha (5-10 Hz) and high gamma (80-90 Hz) oscillations and in all CAEPs. Moreover, adaptive skills modulation of repetition effects was evidenced in theta (3-5 Hz), high gamma oscillations (80-90 Hz), N250/P350 peak-to-peak amplitude and P350 latency. Finally, adaptive skills modulation of change detection response was observed in the N250/P350 peak-to-peak amplitude. Our results confirm that repetition effects and change detection response evolve with age. Moreover, our results suggest that repetition effects and change detection response vary according to adaptive skills displayed by infants during the first year of life, demonstrating their predictive value for neurodevelopment.
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Affiliation(s)
- Gabriela López-Arango
- Neurosciences Department, Montreal University, Montreal, Quebec, Canada.,Research Center, Sainte-Justine Hospital, Montreal University, Montreal, Quebec, Canada
| | - Florence Deguire
- Psychology Department, Montreal University, Montreal, Quebec, Canada.,Research Center, Sainte-Justine Hospital, Montreal University, Montreal, Quebec, Canada
| | - Valérie Côté
- Psychology Department, Montreal University, Montreal, Quebec, Canada.,Research Center, Sainte-Justine Hospital, Montreal University, Montreal, Quebec, Canada
| | - Fanny Barlaam
- Research Center, Sainte-Justine Hospital, Montreal University, Montreal, Quebec, Canada
| | - Kristian Agbogba
- Research Center, Sainte-Justine Hospital, Montreal University, Montreal, Quebec, Canada
| | - Inga S Knoth
- Research Center, Sainte-Justine Hospital, Montreal University, Montreal, Quebec, Canada
| | - Sarah Lippé
- Psychology Department, Montreal University, Montreal, Quebec, Canada.,Research Center, Sainte-Justine Hospital, Montreal University, Montreal, Quebec, Canada
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61
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62
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The relationship between cognitive ability and BOLD activation across sleep-wake states. Brain Imaging Behav 2021; 16:305-315. [PMID: 34432229 DOI: 10.1007/s11682-021-00504-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/07/2021] [Indexed: 10/20/2022]
Abstract
The sleep spindle, a waxing and waning oscillation in the sigma frequency range, has been shown to correlate with fluid intelligence; i.e. the ability to use logic, learn novel rules/patterns, and solve problems. Using simultaneous EEG and fMRI, we previously identified the neural correlates of this relationship, including activation of the thalamus, bilateral putamen, medial frontal gyrus, middle cingulate cortex, and precuneus. However, research to date has focussed primarily on non-rapid eye movement (NREM) sleep, and spindles per se, thus overlooking the possibility that brain activity that occurs in other sleep-wake states might also be related to cognitive abilities. In our current study, we sought to investigate whether brain activity across sleep/wake states is also related to human intelligence in N = 29 participants. During NREM sleep, positive correlations were observed between fluid intelligence and blood oxygen level dependent (BOLD) activations in the bilateral putamen and the paracentral lobule/precuneus, as well as between short-term memory (STM) abilities and activity in the medial frontal cortex and inferior frontal gyrus. During wake, activity in bilateral postcentral gyri and occipital lobe was positively correlated with short-term memory abilities. In participants who experienced REM sleep in the scanner, fluid intelligence was positively associated with midbrain activation, and verbal intelligence was associated with right postcentral gyrus activation. These findings provide evidence that the relationship between sleep and intellectual abilities exists beyond sleep spindles.
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63
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Ohi K, Nishizawa D, Sugiyama S, Takai K, Kuramitsu A, Hasegawa J, Soda M, Kitaichi K, Hashimoto R, Ikeda K, Shioiri T. Polygenic Risk Scores Differentiating Schizophrenia From Bipolar Disorder Are Associated With Premorbid Intelligence in Schizophrenia Patients and Healthy Subjects. Int J Neuropsychopharmacol 2021; 24:562-569. [PMID: 33738471 PMCID: PMC8299820 DOI: 10.1093/ijnp/pyab014] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 02/25/2021] [Accepted: 03/17/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Impairments in intelligence are more severe in patients with schizophrenia (SCZ) than in patients with bipolar disorder (BD) despite clinical and genetic similarities between the disorders. Genetic loci differentiating SCZ from BD, that is, SCZ-specific risk, have been identified. Polygenetic [risk] scores (PGSs) for SCZ-specific risk are higher in SCZ patients than in healthy controls (HCs). However, the influence of genetic risk on impaired intelligence is poorly understood. Here, we investigated whether SCZ-specific risk could predict impairments in intelligence in SCZ patients and HCs. METHODS Large-scale genome-wide association study datasets related to SCZ vs BD, childhood intelligence (CHI), and adulthood intelligence (n = 12 441-282 014) were utilized to compute PGSs. PGSs derived from the genome-wide association studies were calculated for 130 patients with SCZ and 146 HCs. Premorbid and current intelligence and the decline were measured in SCZ patients and HCs. Correlations between PGSs and intelligence functions were investigated. RESULTS High PGSs for SCZ-specific risk were correlated with low premorbid intelligence in SCZ patients and HCs (β = -0.17, P = 4.12 × 10-3). The correlation was still significant after adjusting for diagnostic status (β = -0.13, P = .024). There were no significant correlations between PGSs for SCZ-specific risk and current intelligence or intelligence decline (P > .05). PGSs for CHI were lower in SCZ patients than in HCs (R2 = 0.025, P = .025), while the PGSs for CHI were not significantly correlated with premorbid and current intelligence, the decline, or the PGSs for SCZ-specific risk (P > .05). CONCLUSIONS These findings suggest that genetic factors differentiating SCZ from BD might affect the pathogenesis of SCZ and/or pathological differences between SCZ and BD via the impairment of premorbid intelligence, that is, crystallized intelligence, while genetic factors for CHI might affect the pathogenesis of SCZ but not via impairments in intelligence.
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Affiliation(s)
- Kazutaka Ohi
- Department of Psychiatry and Psychotherapy, Gifu University Graduate School of Medicine, Gifu, Japan
- Department of General Internal Medicine, Kanazawa Medical University, Ishikawa, Japan
| | - Daisuke Nishizawa
- Addictive Substance Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Shunsuke Sugiyama
- Department of Psychiatry and Psychotherapy, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Kentaro Takai
- Department of Psychiatry and Psychotherapy, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Ayumi Kuramitsu
- Department of Psychiatry and Psychotherapy, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Junko Hasegawa
- Addictive Substance Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Midori Soda
- Department of Biomedical Pharmaceutics, Gifu Pharmaceutical University, Gifu, Japan
| | - Kiyoyuki Kitaichi
- Department of Biomedical Pharmaceutics, Gifu Pharmaceutical University, Gifu, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Kazutaka Ikeda
- Addictive Substance Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Toshiki Shioiri
- Department of Psychiatry and Psychotherapy, Gifu University Graduate School of Medicine, Gifu, Japan
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64
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The effects of positive or negative self-talk on the alteration of brain functional connectivity by performing cognitive tasks. Sci Rep 2021; 11:14873. [PMID: 34290300 PMCID: PMC8295361 DOI: 10.1038/s41598-021-94328-9] [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: 03/05/2021] [Accepted: 07/09/2021] [Indexed: 02/06/2023] Open
Abstract
Self-talk can improve cognitive performance, but the underlying mechanism of such improvement has not been investigated. This study aimed to elucidate the effects of self-talks on functional connectivity associated with cognitive performance. We used the short form of Progressive Matrices Test (sRPM) to measure differences in performance improvements between self-respect and self-criticism. Participants were scanned using functional magnetic resonance imaging in the following order: baseline, during-sRPM1, post-sRPM1, self-respect or self-criticism, during-sRPM2, and post-sRPM2. Analysis was conducted to identify the self-talks' modulatory effects on the reward-motivation, default mode, and central-executive networks. Increase in sRPM2 score compared to sRPM1 score was observed only after self-criticism. The self-talk-by-repetition interaction effect was not found for during-sRPM, but found for post-sRPM; decreased nucleus accumbens-based connectivity was shown after self-criticism compared with self-respect. However, the significant correlations between the connectivity change and performance change appeared only in the self-respect group. Our findings showed that positive self-talk and negative self-talk differently modulate brain states concerning cognitive performance. Self-respect may have both positive and negative effects due to enhanced executive functions and inaccurate confidence, respectively, whereas self-criticism may positively affect cognitive performance by inducing a less confident state that increases internal motivation and attention.
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65
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The future of intelligence: The central meaning-making unit of intelligence in the mind, the brain, and artificial intelligence. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101562] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Kwak S, Kim H, Kim H, Youm Y, Chey J. Distributed functional connectivity predicts neuropsychological test performance among older adults. Hum Brain Mapp 2021; 42:3305-3325. [PMID: 33960591 PMCID: PMC8193511 DOI: 10.1002/hbm.25436] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 01/30/2023] Open
Abstract
Neuropsychological test is an essential tool in assessing cognitive and functional changes associated with late-life neurocognitive disorders. Despite the utility of the neuropsychological test, the brain-wide neural basis of the test performance remains unclear. Using the predictive modeling approach, we aimed to identify the optimal combination of functional connectivities that predicts neuropsychological test scores of novel individuals. Resting-state functional connectivity and neuropsychological tests included in the OASIS-3 dataset (n = 428) were used to train the predictive models, and the identified models were iteratively applied to the holdout internal test set (n = 216) and external test set (KSHAP, n = 151). We found that the connectivity-based predicted score tracked the actual behavioral test scores (r = 0.08-0.44). The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed. Our results suggest that late-life neuropsychological test performance can be formally characterized with distributed connectome-based predictive models, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models.
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Affiliation(s)
- Seyul Kwak
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
| | - Hairin Kim
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
| | - Hoyoung Kim
- Department of PsychologyChonbuk National UniversityJeonjuRepublic of Korea
| | - Yoosik Youm
- Department of SociologyYonsei UniversitySeoulRepublic of Korea
| | - Jeanyung Chey
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
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Simpson-Kent IL, Fried EI, Akarca D, Mareva S, Bullmore ET, Kievit RA. Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners. J Intell 2021; 9:32. [PMID: 34204009 PMCID: PMC8293355 DOI: 10.3390/jintelligence9020032] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/26/2021] [Accepted: 06/02/2021] [Indexed: 12/24/2022] Open
Abstract
Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role 'between' brain and behavior. We discuss implications and possible avenues for future studies.
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Affiliation(s)
- Ivan L. Simpson-Kent
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Eiko I. Fried
- Department of Clinical Psychology, Leiden University, 2300 RA Leiden, The Netherlands;
| | - Danyal Akarca
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Silvana Mareva
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Edward T. Bullmore
- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, Cambridgeshire CB2 0SP, UK;
| | - the CALM Team
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Rogier A. Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
- Cognitive Neuroscience Department, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
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69
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Wang R, Liu M, Cheng X, Wu Y, Hildebrandt A, Zhou C. Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proc Natl Acad Sci U S A 2021; 118:e2022288118. [PMID: 34074762 PMCID: PMC8201916 DOI: 10.1073/pnas.2022288118] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains an open question how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here we use an eigenmode-based approach to identify hierarchical modules in functional brain networks and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n = 991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations and highly flexible switching between them. Furthermore, we employ structural equation modeling to estimate general and domain-specific cognitive phenotypes from nine tasks and demonstrate that network segregation, integration, and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and an individual's tendency toward balance supports better memory. Our findings provide a comprehensive and deep understanding of the brain's functioning principles in supporting diverse functional demands and cognitive abilities and advance modern network neuroscience theories of human cognition.
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Affiliation(s)
- Rong Wang
- College of Science, Xi'an University of Science and Technology, Xi'an 710054, China
- Department of Physics, Centre for Nonlinear Studies, Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong
- School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi'an Jiaotong University, Xi'an 710049, China
| | - Mianxin Liu
- Department of Physics, Centre for Nonlinear Studies, Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Xinhong Cheng
- College of Science, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Ying Wu
- School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi'an Jiaotong University, Xi'an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi'an Jiaotong University, Xi'an 710049, China
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany;
- Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies, Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong;
- Department of Physics, Zhejiang University, Hangzhou 310027, China
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Petrican R, Graham KS, Lawrence AD. Brain-environment alignment during movie watching predicts fluid intelligence and affective function in adulthood. Neuroimage 2021; 238:118177. [PMID: 34020016 PMCID: PMC8350144 DOI: 10.1016/j.neuroimage.2021.118177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/11/2021] [Accepted: 05/14/2021] [Indexed: 11/29/2022] Open
Abstract
Functional brain connectivity (FC) patterns vary with changes in the environment. Adult FC variability is linked to age-specific network communication profiles. Across adulthood, the younger network interaction profile predicts higher fluid IQ. Yoked FC-concrete environmental changes predict poorer fluid IQ and anxiety. Brain areas linked to episodic memory underpin FC changes at multiple timescales.
BOLD fMRI studies have provided compelling evidence that the human brain demonstrates substantial moment-to-moment fluctuations in both activity and functional connectivity (FC) patterns. While the role of brain signal variability in fostering cognitive adaptation to ongoing environmental demands is well-documented, the relevance of moment-to-moment changes in FC patterns is still debated. Here, we adopt a graph theoretical approach in order to shed light on the cognitive-affective implications of FC variability and associated profiles of functional network communication in adulthood. Our goal is to identify brain communication pathways underlying FC reconfiguration at multiple timescales, thereby improving understanding of how faster perceptually bound versus slower conceptual processes shape neural tuning to the dynamics of the external world and, thus, indirectly, mold affective and cognitive responding to the environment. To this end, we used neuroimaging and behavioural data collected during movie watching by the Cambridge Center for Ageing and Neuroscience (N = 642, 326 women) and the Human Connectome Project (N = 176, 106 women). FC variability evoked by changes to both the concrete perceptual and the more abstract conceptual representation of an ongoing situation increased from young to older adulthood. However, coupling between variability in FC patterns and concrete environmental features was stronger at younger ages. FC variability (both moment-to-moment/concrete featural and abstract conceptual boundary-evoked) was associated with age-distinct profiles of network communication, specifically, greater functional integration of the default mode network in older adulthood, but greater informational flow across neural networks implicated in environmentally driven attention and control (cingulo-opercular, salience, ventral attention) in younger adulthood. Whole-brain communication pathways anchored in default mode regions relevant to episodic and semantic context creation (i.e., angular and middle temporal gyri) supported FC reconfiguration in response to changes in the conceptual representation of an ongoing situation (i.e., narrative event boundaries), as well as stronger coupling between moment-to-moment fluctuations in FC and concrete environmental features. Fluid intelligence/abstract reasoning was directly linked to levels of brain-environment alignment, but only indirectly associated with levels of FC variability. Specifically, stronger coupling between moment-to-moment FC variability and concrete environmental features predicted poorer fluid intelligence and greater affectively driven environmental vigilance. Complementarily, across the adult lifespan, higher fluid (but not crystallised) intelligence was related to stronger expression of the network communication profile underlying momentary and event boundary-based FC variability during youth. Our results indicate that the adaptiveness of dynamic FC reconfiguration during naturalistic information processing changes across the lifespan due to the associated network communication profiles. Moreover, our findings on brain-environment alignment complement the existing literature on the beneficial consequences of modulating brain signal variability in response to environmental complexity. Specifically, they imply that coupling between moment-to-moment FC variability and concrete environmental features may index a bias towards perceptually-bound, rather than conceptual processing, which hinders affective functioning and strategic cognitive engagement with the external environment.
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Affiliation(s)
- Raluca Petrican
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom.
| | - Kim S Graham
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom
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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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72
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Mitochondrial Functioning and the Relations among Health, Cognition, and Aging: Where Cell Biology Meets Cognitive Science. Int J Mol Sci 2021; 22:ijms22073562. [PMID: 33808109 PMCID: PMC8037956 DOI: 10.3390/ijms22073562] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/26/2021] [Accepted: 03/28/2021] [Indexed: 12/12/2022] Open
Abstract
Cognitive scientists have determined that there is a set of mechanisms common to all sensory, perceptual, and cognitive abilities and correlated with age- and disease-related declines in cognition. These mechanisms also contribute to the development and functional coherence of the large-scale brain networks that support complex forms of cognition. At the same time, these brain and cognitive patterns are correlated with myriad health outcomes, indicating that at least some of the underlying mechanisms are common to all biological systems. Mitochondrial functions, including cellular energy production and control of oxidative stress, among others, are well situated to explain the relations among the brain, cognition, and health. Here, I provide an overview of the relations among cognitive abilities, associated brain networks, and the importance of mitochondrial energy production for their functioning. These are then linked to the relations between cognition, health, and aging. The discussion closes with implications for better integrating research in cognitive science and cell biology in the context of developing more sensitive measures of age- and disease-related declines in cognition.
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73
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Hammill C, Lerch JP, Taylor MJ, Ameis SH, Chakravarty MM, Szatmari P, Anagnostou E, Lai MC. Quantitative and Qualitative Sex Modulations in the Brain Anatomy of Autism. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:898-909. [PMID: 33713843 DOI: 10.1016/j.bpsc.2021.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 02/27/2021] [Accepted: 03/01/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Sex-based neurobiological heterogeneity in autism is poorly understood. Research is disproportionately biased to males, leading to an unwarranted presumption that autism neurobiology is the same across sexes. Previous neuroimaging studies using amalgamated multicenter datasets to increase autistic female samples are characterized by large statistical noise. METHODS We used a better-powered dataset of 1183 scans of 839 individuals-299 (467 scans) autistic males, 74 (102 scans) autistic females, 240 (334 scans) control males, and 226 (280 scans) control females-to test two whole-brain models of overall/global sex modulations on autism neuroanatomy, by summary measures computed across the brain: the local magnitude model, in which the same brain regions/circuitries are involved across sexes but effect sizes are larger in females, indicating quantitative sex modulation; and spatial dissimilarity model, in which the neuroanatomy differs spatially between sexes, indicating qualitative sex modulation. The male and female autism groups were matched on age, IQ, and autism symptoms. Autism brain features were defined by comparisons with same-sex control individuals. RESULTS Across five metrics (cortical thickness, surface area, volume, mean absolute curvature, and subcortical volume), we found no evidence supporting the local magnitude model. We found indicators supporting the spatial dissimilarity model on cortical mean absolute curvature and subcortical volume, but not on other metrics. CONCLUSIONS The overall/global autism neuroanatomy in females and males does not simply differ quantitatively in the same brain regions/circuitries. They may differ qualitatively in spatial involvement in cortical curvature and subcortical volume. The neuroanatomy of autism may be partly sex specific. Sex stratification to inform autism preclinical/clinical research is needed to identify sex-informed neurodevelopmental targets.
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Affiliation(s)
| | - Jason P Lerch
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Margot J Taylor
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada
| | - Stephanie H Ameis
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Peter Szatmari
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Evdokia Anagnostou
- Holland Bloorview Kids Rehabilitation Hospital and Department of Paediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Meng-Chuan Lai
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
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75
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Anagnostopoulou A, Styliadis C, Kartsidis P, Romanopoulou E, Zilidou V, Karali C, Karagianni M, Klados M, Paraskevopoulos E, Bamidis PD. Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down syndrome: A network science EEG study. Netw Neurosci 2021; 5:274-294. [PMID: 33688615 PMCID: PMC7935030 DOI: 10.1162/netn_a_00177] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 12/01/2020] [Indexed: 01/31/2023] Open
Abstract
Understanding the neuroplastic capacity of people with Down syndrome (PwDS) can potentially reveal the causal relationship between aberrant brain organization and phenotypic characteristics. We used resting-state EEG recordings to identify how a neuroplasticity-triggering training protocol relates to changes in the functional connectivity of the brain's intrinsic cortical networks. Brain activity of 12 PwDS before and after a 10-week protocol of combined physical and cognitive training was statistically compared to quantify changes in directed functional connectivity in conjunction with psychosomatometric assessments. PwDS showed increased connectivity within the left hemisphere and from left-to-right hemisphere, as well as increased physical and cognitive performance. Our findings reveal a strong adaptive neuroplastic reorganization as a result of the training that leads to a less-random network with a more pronounced hierarchical organization. Our results go beyond previous findings by indicating a transition to a healthier, more efficient, and flexible network architecture, with improved integration and segregation abilities in the brain of PwDS. Resting-state electrophysiological brain activity is used here for the first time to display meaningful relationships to underlying Down syndrome processes and outcomes of importance in a translational inquiry. This trial is registered with ClinicalTrials.gov Identifier NCT04390321.
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Affiliation(s)
- Alexandra Anagnostopoulou
- Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Charis Styliadis
- Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Panagiotis Kartsidis
- Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Evangelia Romanopoulou
- Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Vasiliki Zilidou
- Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Chrysi Karali
- School of Biology, Faculty of Science, Aristotle University of Thessaloniki, Greece
| | - Maria Karagianni
- Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Manousos Klados
- Department of Psychology, The University of Sheffield International Faculty, City College, Thessaloniki, Greece
| | - Evangelos Paraskevopoulos
- Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
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76
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Hubbard NA, Turner MP, Sitek KR, West KL, Kaczmarzyk JR, Himes L, Thomas BP, Lu H, Rypma B. Resting cerebral oxygen metabolism exhibits archetypal network features. Hum Brain Mapp 2021; 42:1952-1968. [PMID: 33544446 PMCID: PMC8046048 DOI: 10.1002/hbm.25352] [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: 07/28/2020] [Revised: 12/04/2020] [Accepted: 01/12/2021] [Indexed: 12/23/2022] Open
Abstract
Standard magnetic resonance imaging approaches offer high‐resolution but indirect measures of neural activity, limiting understanding of the physiological processes associated with imaging findings. Here, we used calibrated functional magnetic resonance imaging during the resting state to recover low‐frequency fluctuations of the cerebral metabolic rate of oxygen (CMRO2). We tested whether functional connections derived from these fluctuations exhibited organization properties similar to those established by previous standard functional and anatomical connectivity studies. Seventeen participants underwent 20 min of resting imaging during dual‐echo, pseudocontinuous arterial spin labeling, and blood‐oxygen‐level dependent (BOLD) signal acquisition. Participants also underwent a 10 min normocapnic and hypercapnic procedure. Brain‐wide, CMRO2 low‐frequency fluctuations were subjected to graph‐based and voxel‐wise functional connectivity analyses. Results demonstrated that connections derived from resting CMRO2 fluctuations exhibited complex, small‐world topological properties (i.e., high integration and segregation, cost efficiency) consistent with those observed in previous studies using functional and anatomical connectivity approaches. Voxel‐wise CMRO2 connectivity also exhibited spatial patterns consistent with four targeted resting‐state subnetworks: two association (i.e., frontoparietal and default mode) and two perceptual (i.e., auditory and occipital‐visual). These are the first findings to support the use of calibration‐derived CMRO2 low‐frequency fluctuations for detecting brain‐wide organizational properties typical of healthy participants. We discuss interpretations, advantages, and challenges in using calibration‐derived oxygen metabolism signals for examining the intrinsic organization of the human brain.
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Affiliation(s)
- Nicholas A Hubbard
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Monroe P Turner
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Kevin R Sitek
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Division of Medical Sciences, Harvard Medical School, Boston, Massachusetts, USA
| | - Kathryn L West
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Jakub R Kaczmarzyk
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Lyndahl Himes
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Binu P Thomas
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Texas, USA.,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Hanzhang Lu
- Department of Radiology, John's Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Bart Rypma
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Texas, USA.,Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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Nguchu BA, Zhao J, Wang Y, Li Y, Wei Y, Uwisengeyimana JDD, Wang X, Qiu B, Li H. Atypical Resting-State Functional Connectivity Dynamics Correlate With Early Cognitive Dysfunction in HIV Infection. Front Neurol 2021; 11:606592. [PMID: 33519683 PMCID: PMC7841016 DOI: 10.3389/fneur.2020.606592] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 12/01/2020] [Indexed: 01/20/2023] Open
Abstract
Purpose: Previous studies have shown that HIV affects striato-cortical regions, leading to persisting cognitive impairment in 30-70% of the infected individuals despite combination antiretroviral therapy. This study aimed to investigate brain functional dynamics whose deficits might link to early cognitive decline or immunologic deterioration. Methods: We applied sliding windows and K-means clustering to fMRI data (HIV patients with asymptomatic neurocognitive impairment and controls) to construct dynamic resting-state functional connectivity (RSFC) maps and identify states of their reoccurrences. The average and variability of dynamic RSFC, and the dwelling time and state transitioning of each state were evaluated. Results: HIV patients demonstrated greater variability in RSFC between the left pallidum and regions of right pre-central and post-central gyri, and between the right supramarginal gyrus and regions of the right putamen and left pallidum. Greater variability was also found in the frontal RSFC of pars orbitalis of the left inferior frontal gyrus and right superior frontal gyrus (medial). While deficits in learning and memory recall of HIV patients related to greater striato-sensorimotor variability, deficits in attention and working memory were associated with greater frontal variability. Greater striato-parietal variability presented a strong link with immunologic function (CD4+/CD8+ ratio). Furthermore, HIV-infected patients exhibited longer time and reduced transitioning in states typified by weaker connectivity in specific networks. CD4+T-cell counts of the HIV-patients were related to reduced state transitioning. Conclusion: Our findings suggest that HIV alters brain functional connectivity dynamics, which may underlie early cognitive impairment. These findings provide novel insights into our understanding of HIV pathology, complementing the existing knowledge.
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Affiliation(s)
- Benedictor Alexander Nguchu
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Jing Zhao
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yanming Wang
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Yu Li
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Yarui Wei
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Jean de Dieu Uwisengeyimana
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Xiaoxiao Wang
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Bensheng Qiu
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Hongjun Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
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78
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Feklicheva I, Zakharov I, Chipeeva N, Maslennikova E, Korobova S, Adamovich T, Ismatullina V, Malykh S. Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity. Brain Sci 2021; 11:94. [PMID: 33450902 PMCID: PMC7828310 DOI: 10.3390/brainsci11010094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/04/2021] [Accepted: 01/11/2021] [Indexed: 11/17/2022] Open
Abstract
The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven's total scores and E series), and the ability to operate with verbal information (the "Conclusions" verbal subtest). At the same time, individual differences in non-verbal intelligence (Raven's total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance.
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Affiliation(s)
- Inna Feklicheva
- Laboratory of Molecular Genetic Research of Human Health and Development, Scientific and Educational Center “Biomedical Technologies”, Higher Medical and Biological School, South Ural State University, 454080 Chelyabinsk, Russia; (N.C.); (S.K.)
| | - Ilya Zakharov
- Developmental Behavioral Genetics Lab, Psychological Institute of Russian Academy of Education, 125009 Moscow, Russia; (I.Z.); (T.A.); (V.I.); (S.M.)
| | - Nadezda Chipeeva
- Laboratory of Molecular Genetic Research of Human Health and Development, Scientific and Educational Center “Biomedical Technologies”, Higher Medical and Biological School, South Ural State University, 454080 Chelyabinsk, Russia; (N.C.); (S.K.)
| | - Ekaterina Maslennikova
- Center of Interdisciplinary Research in Education, Russian Academy of Education, 199121 Moscow, Russia;
| | - Svetlana Korobova
- Laboratory of Molecular Genetic Research of Human Health and Development, Scientific and Educational Center “Biomedical Technologies”, Higher Medical and Biological School, South Ural State University, 454080 Chelyabinsk, Russia; (N.C.); (S.K.)
| | - Timofey Adamovich
- Developmental Behavioral Genetics Lab, Psychological Institute of Russian Academy of Education, 125009 Moscow, Russia; (I.Z.); (T.A.); (V.I.); (S.M.)
| | - Victoria Ismatullina
- Developmental Behavioral Genetics Lab, Psychological Institute of Russian Academy of Education, 125009 Moscow, Russia; (I.Z.); (T.A.); (V.I.); (S.M.)
| | - Sergey Malykh
- Developmental Behavioral Genetics Lab, Psychological Institute of Russian Academy of Education, 125009 Moscow, Russia; (I.Z.); (T.A.); (V.I.); (S.M.)
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79
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Kim K, Duc NT, Choi M, Lee B. EEG microstate features according to performance on a mental arithmetic task. Sci Rep 2021; 11:343. [PMID: 33431963 PMCID: PMC7801706 DOI: 10.1038/s41598-020-79423-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 11/30/2020] [Indexed: 11/16/2022] Open
Abstract
In this study, we hypothesized that task performance could be evaluated applying EEG microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of microstates as novel features to discriminate task performance. Thirty-six subjects were divided into good and poor performers, depending on how well they performed the task. Microstate features were derived from EEG recordings during resting and task states. In the good performers, there was a decrease in type C and an increase in type D features during the task compared to the resting state. Mean duration and occurrence decreased and increased, respectively. In the poor performers, occurrence of type D feature, mean duration and occurrence showed greater changes. We investigated whether microstate features were suitable for task performance classification and eleven features including four archetypes were selected by recursive feature elimination (RFE). The model that implemented them showed the highest classification performance for differentiating between groups. Our pilot findings showed that the highest mean Area Under Curve (AUC) was 0.831. This study is the first to apply EEG microstate features to specific cognitive tasks in healthy subjects, suggesting that EEG microstate features can reflect task achievement.
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Affiliation(s)
- Kyungwon Kim
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, South Korea
| | - Nguyen Thanh Duc
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, South Korea
| | - Min Choi
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, South Korea
| | - Boreom Lee
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, South Korea.
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80
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Tricklebank MD, Robbins TW, Simmons C, Wong EHF. Time to re-engage psychiatric drug discovery by strengthening confidence in preclinical psychopharmacology. Psychopharmacology (Berl) 2021; 238:1417-1436. [PMID: 33694032 PMCID: PMC7945970 DOI: 10.1007/s00213-021-05787-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 02/04/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND There is urgent need for new medications for psychiatric disorders. Mental illness is expected to become the leading cause of disability worldwide by 2030. Yet, the last two decades have seen the pharmaceutical industry withdraw from psychiatric drug discovery after costly late-stage trial failures in which clinical efficacy predicted pre-clinically has not materialised, leading to a crisis in confidence in preclinical psychopharmacology. METHODS Based on a review of the relevant literature, we formulated some principles for improving investment in translational neuroscience aimed at psychiatric drug discovery. RESULTS We propose the following 8 principles that could be used, in various combinations, to enhance CNS drug discovery: (1) consider incorporating the NIMH Research Domain Criteria (RDoC) approach; (2) engage the power of translational and systems neuroscience approaches; (3) use disease-relevant experimental perturbations; (4) identify molecular targets via genomic analysis and patient-derived pluripotent stem cells; (5) embrace holistic neuroscience: a partnership with psychoneuroimmunology; (6) use translational measures of neuronal activation; (7) validate the reproducibility of findings by independent collaboration; and (8) learn and reflect. We provide recent examples of promising animal-to-human translation of drug discovery projects and highlight some that present re-purposing opportunities. CONCLUSIONS We hope that this review will re-awaken the pharma industry and mental health advocates to the opportunities for improving psychiatric pharmacotherapy and so restore confidence and justify re-investment in the field.
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Affiliation(s)
- Mark David Tricklebank
- Centre for Neuroimaging Sciences, Institute of Psychiatry Psychology and Neuroscience, King's College, London, UK.
| | - Trevor W. Robbins
- Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, CB23EB, Cambridge, UK
| | - Camilla Simmons
- Centre for Neuroimaging Sciences, Institute of Psychiatry Psychology and Neuroscience, King’s College, London, UK
| | - Erik H. F. Wong
- Department of Psychiatry, University of British Columbia, Vancouver, Canada
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81
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de Kieviet JF, Lustenhouwer R, Königs M, van Elburg RM, Pouwels PJW, Oosterlaan J. Altered structural connectome and motor problems of very preterm born children at school-age. Early Hum Dev 2021; 152:105274. [PMID: 33227634 DOI: 10.1016/j.earlhumdev.2020.105274] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/05/2020] [Accepted: 11/11/2020] [Indexed: 01/01/2023]
Abstract
Infants born very preterm (<32 weeks of gestation) show distinct cognitive and motor problems throughout childhood. This study aims 1) to investigate differences in the structural connectome between very preterm born children and term born controls at school-age, and 2) to examine the relationship of the structural connectome with cognitive and motor problems. This study included 29 very preterm (12 males, mean age 8.6 years) and 52 term born peers (25 males, mean age 8.7 years). Wechsler Intelligence Scale for Children and Movement Assessment Battery for Children were used. Brain network measures of smallworldness, clustering coefficient and shortest path length based on fiber density of white matter tracts were determined from Diffusion Tensor Imaging data using probabilistic tractography. Smallworldness (F(1,79) = -2.09, p = .04, d = 0.52) and clustering coefficient (F(1,79) = -2.63, p = .01, d = 0.64) were significantly higher for very preterm children as compared to term peers. For Total Motor Impairment score and Manual Dexterity, there was a significant interaction between group and smallworldness (Beta = -10.81, p = .03 and Beta = -2.99, p = .004, respectively). Greater Total Motor Impairment and poorer Manual Dexterity were only significantly related to higher smallworldness in term controls (r = 0.35, p = .01 and r = 0.27, p = .04, respectively). Poorer Ball Skills were significantly related to higher smallworldness in both groups (Beta = -0.30, p = .03). This study clearly shows a more segregated network organization in very preterm children as compared to term peers. Importantly, motor problems go together with altered organization of the structural connectome in term born children, whereas this potential compensational process is only found for Ball Skills for very preterm children.
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Affiliation(s)
- Jorrit F de Kieviet
- Amsterdam UMC, Department of Rehabilitation Medicine, Amsterdam, the Netherlands.
| | - Renee Lustenhouwer
- Radboud UMC, Department of Rehabilitation, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands.
| | - Marsh Königs
- Amsterdam UMC Emma Children's Hospital, Emma Neuroscience Group, Department of Paediatrics, Amsterdam, the Netherlands.
| | - Ruurd M van Elburg
- Amsterdam UMC Emma Children's Hospital, Emma Neuroscience Group, Department of Paediatrics, Amsterdam, the Netherlands.
| | - Petra J W Pouwels
- Amsterdam UMC, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands.
| | - Jaap Oosterlaan
- Amsterdam UMC Emma Children's Hospital, Emma Neuroscience Group, Department of Paediatrics, Amsterdam, the Netherlands.
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82
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Satary Dizaji A, Vieira BH, Khodaei MR, Ashrafi M, Parham E, Hosseinzadeh GA, Salmon CEG, Soltanianzadeh H. Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data. Basic Clin Neurosci 2021; 12:1-28. [PMID: 33995924 PMCID: PMC8114859 DOI: 10.32598/bcn.12.1.574.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 05/10/2020] [Accepted: 10/28/2020] [Indexed: 11/20/2022] Open
Abstract
Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman's general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and restingstate fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.
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Affiliation(s)
- Aslan Satary Dizaji
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Bruno Hebling Vieira
- Inbrain Lab, Department of Physics, FFCLRP, University of São Paulo, Ribeirao Preto, Brazil
| | - Mohmmad Reza Khodaei
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mahnaz Ashrafi
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Elahe Parham
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Gholam Ali Hosseinzadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | | | - Hamid Soltanianzadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Radiology Image Analysis Laboratory, Henry Ford Health System, Detroit, USA
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83
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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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84
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Charting brain growth in tandem with brain templates at school age. Sci Bull (Beijing) 2020; 65:1924-1934. [PMID: 36738058 DOI: 10.1016/j.scib.2020.07.027] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/30/2020] [Accepted: 06/09/2020] [Indexed: 02/07/2023]
Abstract
Brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental trajectories. The present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging protocol. Two accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and China. Anatomical magnetic resonance imaging (MRI) of typically developing school-age children (TDC) was obtained up to three times at nominal intervals of 1.25 years. The protocol generated and compared population- and age-specific brain templates and growth charts, respectively. A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American TDC. Population- and age-specific brain templates were used to quantify warp cost, the differences between individual brains and brain templates. Volumetric growth charts for labeled brain network areas were generated. Shape analyses of cost functions supported the necessity of age-specific and ethnicity-matched brain templates, which was confirmed by growth chart analyses. These analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas, regions which are most variable across individuals in regard to their structure and function. Age- and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts, indicating the necessity of the brain charts and brain templates generated in tandem. These templates and growth charts as well as related codes have been made freely available to the public for open neuroscience (https://github.com/zuoxinian/CCS/tree/master/H3/GrowthCharts).
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85
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White matter microarchitecture and structural network integrity correlate with children intelligence quotient. Sci Rep 2020; 10:20722. [PMID: 33244043 PMCID: PMC7691327 DOI: 10.1038/s41598-020-76528-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 10/26/2020] [Indexed: 11/17/2022] Open
Abstract
The neural substrate of high intelligence performances remains not well understood. Based on diffusion tensor imaging (DTI) which provides microstructural information of white matter fibers, we proposed in this work to investigate the relationship between structural brain connectivity and intelligence quotient (IQ) scores. Fifty-seven children (8–12 y.o.) underwent a MRI examination, including conventional T1-weighted and DTI sequences, and neuropsychological testing using the fourth edition of Wechsler Intelligence Scale for Children (WISC-IV), providing an estimation of the Full-Scale Intelligence Quotient (FSIQ) based on four subscales: verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), and processing speed index (PSI). Correlations between the IQ scores and both graphs and diffusivity metrics were explored. First, we found significant correlations between the increased integrity of WM fiber-bundles and high intelligence scores. Second, the graph theory analysis showed that integration and segregation graph metrics were positively and negatively correlated with WISC-IV scores, respectively. These results were mainly driven by significant correlations between FSIQ, VCI, and PRI and graph metrics in the temporal and parietal lobes. In conclusion, these findings demonstrated that intelligence performances are related to the integrity of WM fiber-bundles as well as the density and homogeneity of WM brain networks.
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86
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Li J, Chen H, Fan F, Qiu J, Du L, Xiao J, Duan X, Chen H, Liao W. White-matter functional topology: a neuromarker for classification and prediction in unmedicated depression. Transl Psychiatry 2020; 10:365. [PMID: 33127899 PMCID: PMC7603321 DOI: 10.1038/s41398-020-01053-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 09/28/2020] [Accepted: 10/09/2020] [Indexed: 02/03/2023] Open
Abstract
Aberrant topological organization of brain connectomes underlies pathological mechanisms in major depressive disorder (MDD). However, accumulating evidence has only focused on functional organization in brain gray-matter, ignoring functional information in white-matter (WM) that has been confirmed to have reliable and stable topological organizations. The present study aimed to characterize the functional pattern disruptions of MDD from a new perspective-WM functional connectome topological organization. A case-control, cross-sectional resting-state functional magnetic resonance imaging study was conducted on both discovery [91 unmedicated MDD patients, and 225 healthy controls (HCs)], and replication samples (34 unmedicated MDD patients, and 25 HCs). The WM functional networks were constructed in 128 anatomical regions, and their global topological properties (e.g., small-worldness) were analyzed using graph theory-based approaches. At the system-level, ubiquitous small-worldness architecture and local information-processing capacity were detectable in unmedicated MDD patients but were less salient than in HCs, implying a shift toward randomization in MDD WM functional connectomes. Consistent results were replicated in an independent sample. For clinical applications, small-world topology of WM functional connectome showed a predictive effect on disease severity (Hamilton Depression Rating Scale) in discovery sample (r = 0.34, p = 0.001). Furthermore, the topologically-based classification model could be generalized to discriminate MDD patients from HCs in replication sample (accuracy, 76%; sensitivity, 74%; specificity, 80%). Our results highlight a reproducible topologically shifted WM functional connectome structure and provide possible clinical applications involving an optimal small-world topology as a potential neuromarker for the classification and prediction of MDD patients.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Heng Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- School of Medicine, Guizhou University, Guiyang, 550025, People's Republic of China
| | - Feiyang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, 400715, People's Republic of China
| | - Lian Du
- Department of PsyCiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People's Republic of China
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
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87
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Duncan J, Assem M, Shashidhara S. Integrated Intelligence from Distributed Brain Activity. Trends Cogn Sci 2020; 24:838-852. [PMID: 32771330 PMCID: PMC7116395 DOI: 10.1016/j.tics.2020.06.012] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 01/21/2023]
Abstract
How does organized cognition arise from distributed brain activity? Recent analyses of fluid intelligence suggest a core process of cognitive focus and integration, organizing the components of a cognitive operation into the required computational structure. A cortical 'multiple-demand' (MD) system is closely linked to fluid intelligence, and recent imaging data define nine specific MD patches distributed across frontal, parietal, and occipitotemporal cortex. Wide cortical distribution, relative functional specialization, and strong connectivity suggest a basis for cognitive integration, matching electrophysiological evidence for binding of cognitive operations to their contents. Though still only in broad outline, these data suggest how distributed brain activity can build complex, organized cognition.
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Affiliation(s)
- John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK; Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, UK.
| | - Moataz Assem
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Sneha Shashidhara
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
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Effects of incidental physical activity on morphosyntactic processing in aging. PLoS One 2020; 15:e0239727. [PMID: 32991617 PMCID: PMC7523944 DOI: 10.1371/journal.pone.0239727] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 09/11/2020] [Indexed: 11/19/2022] Open
Abstract
Older adults have difficulties in sentence comprehension when working memory (WM) load increases (e.g., multiple embedded clauses). Structured physical activity has been related to improvements in cognition; however, incidental physical activity (PA, i.e., unstructured daily physical activities), particularly incidental vigorous activity has been poorly studied in relation to its effects on behavior. Furthermore, no positive effect on language has been reported in either form of physical activity. The aim of this study was to evaluate how two levels of PA (high or low) affect WM processing and how this, in turn, may affect morphosyntactic processing in older adults. Individuals with high PA (n = 18) had a higher WM load effect than those with low PA (n = 18), both behaviorally (greater differences between high and low WM loads in correct responses) and in terms of event-related potentials (only subjects with high PA showed LAN and P600b amplitude differences between high and low WM loads). These findings suggest that PA promotes cognitive strategies to face WM loads and morphosyntactic processing.
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89
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Steyvers M, Schafer RJ. Inferring latent learning factors in large-scale cognitive training data. Nat Hum Behav 2020; 4:1145-1155. [PMID: 32868884 DOI: 10.1038/s41562-020-00935-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 07/15/2020] [Indexed: 12/27/2022]
Abstract
The flexibility to learn diverse tasks is a hallmark of human cognition. To improve our understanding of individual differences and dynamics of learning across tasks, we analyse the latent structure of learning trajectories from 36,297 individuals as they learned 51 different tasks on the Lumosity online cognitive training platform. Through a data-driven modelling approach using probabilistic dimensionality reduction, we investigate covariation across learning trajectories with few assumptions about learning curve form or relationships between tasks. Modelling results show substantial covariation across tasks, such that an entirely unobserved learning trajectory can be predicted by observing trajectories on other tasks. The latent learning factors from the model include a general ability factor that is expressed mostly at later stages of practice and additional task-specific factors that carry information capable of accounting for manually defined task features and task domains such as attention, spatial processing, language and math.
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Affiliation(s)
- Mark Steyvers
- Department of Cognitive Sciences, University of California, Irvine, CA, USA.
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90
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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: 19] [Impact Index Per Article: 4.8] [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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91
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Cross-network interactions in social cognition: A review of findings on task related brain activation and connectivity. Cortex 2020; 130:142-157. [DOI: 10.1016/j.cortex.2020.05.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 04/06/2020] [Accepted: 05/13/2020] [Indexed: 01/17/2023]
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92
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Abstract
While adult brains are known to be functionally flexible, the emergence of a functionally flexible brain during early infancy is largely uncharted due the lack of approaches to assess neural flexibility in infants. Using recent advances of multilayer network approaches and a cohort of typically developing children who underwent longitudinal MRI during the first 2 y of life, we investigated the developmental characteristics of brain neural flexibility. The temporal and spatial emergence of a functionally flexible brain was revealed. Brain regions with high neural flexibility appear consistent with the core regions supporting cognitive flexibility processing in adults, whereas brain regions governing basic brain functions exhibit lower neural flexibility, demonstrating the emergence of functionally flexible brain during early infancy. Adult brains are functionally flexible, a unique characteristic that is thought to contribute to cognitive flexibility. While tools to assess cognitive flexibility during early infancy are lacking, we aimed to assess the spatiotemporal developmental features of “neural flexibility” during the first 2 y of life. Fifty-two typically developing children 0 to 2 y old were longitudinally imaged up to seven times during natural sleep using resting-state functional MRI. Using a sliding window approach, MR-derived neural flexibility, a quantitative measure of the frequency at which brain regions change their allegiance from one functional module to another during a given time period, was used to evaluate the temporal emergence of neural flexibility during early infancy. Results showed that neural flexibility of whole brain, motor, and high-order brain functional networks/regions increased significantly with age, while visual regions exhibited a temporally stable pattern, suggesting spatially and temporally nonuniform developmental features of neural flexibility. Additionally, the neural flexibility of the primary visual network at 3 mo of age was significantly and negatively associated with cognitive ability evaluated at 5/6 y of age. The “flexible club,” comprising brain regions with neural flexibility significantly higher than whole-brain neural flexibility, were consistent with brain regions known to govern cognitive flexibility in adults and exhibited unique characteristics when compared to the functional hub and diverse club regions. Thus, MR-derived neural flexibility has the potential to reveal the underlying neural substrates for developing a cognitively flexible brain during early infancy.
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93
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Opposing associations between sedentary time and decision-making competence in young adults revealed by functional connectivity in the dorsal attention network. Sci Rep 2020; 10:13993. [PMID: 32814816 PMCID: PMC7438333 DOI: 10.1038/s41598-020-70679-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 07/21/2020] [Indexed: 12/21/2022] Open
Abstract
How daily physical activity and sedentary time relate to human judgement and functional connectivity (FC) patterns that support them remains underexplored. We investigated the relationships between accelerometer-measured moderate-to-vigorous physical activity (MVPA) and sedentary time to decision-making competence (DMC) in young adults using a comprehensive Adult-Decision Making Competence battery. We applied graph theory measures of global and local efficiency to test the mediating effects of FC in cognitively salient brain networks (fronto-parietal; dorsal attention, DAN; ventral attention; and default mode), assessed from the resting-state fMRI. Sedentary time was related to lower susceptibility to a framing bias. However, once global and local efficiency of the DAN were considered we observed (1) higher susceptibility to framing with more sedentary time, mediated through lower local and global efficiency in the DAN, and (2) lower susceptibility to framing with more sedentary time. MVPA was not related to DMC or graph theory measures. These results suggest that remaining sedentary may reduce neurofunctional readiness for top-down control and decrease engagement of deliberate thought, required to ignore irrelevant aspects of a problem. The positive effect suggests that the relationship between sedentary time and DMC may be moderated by unmeasured factors such as the type of sedentary behavior.
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94
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Alderson TH, Bokde ALW, Kelso JAS, Maguire L, Coyle D. Metastable neural dynamics underlies cognitive performance across multiple behavioural paradigms. Hum Brain Mapp 2020; 41:3212-3234. [PMID: 32301561 PMCID: PMC7375112 DOI: 10.1002/hbm.25009] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 01/20/2020] [Accepted: 03/31/2020] [Indexed: 12/24/2022] Open
Abstract
Despite resting state networks being associated with a variety of cognitive abilities, it remains unclear how these local areas act in concert to express particular cognitive operations. Theoretical and empirical accounts indicate that large-scale resting state networks reconcile dual tendencies towards integration and segregation by operating in a metastable regime of their coordination dynamics. Metastability may confer important behavioural qualities by binding distributed local areas into large-scale neurocognitive networks. We tested this hypothesis by analysing fMRI data in a large cohort of healthy individuals (N = 566) and comparing the metastability of the brain's large-scale resting network architecture at rest and during the performance of several tasks. Metastability was estimated using a well-defined collective variable capturing the level of 'phase-locking' between large-scale networks over time. Task-based reasoning was principally characterised by high metastability in cognitive control networks and low metastability in sensory processing areas. Although metastability between resting state networks increased during task performance, cognitive ability was more closely linked to spontaneous activity. High metastability in the intrinsic connectivity of cognitive control networks was linked to novel problem solving or fluid intelligence, but was less important in tasks relying on previous experience or crystallised intelligence. Crucially, subjects with resting architectures similar or 'pre-configured' to a task-general arrangement demonstrated superior cognitive performance. Taken together, our findings support a key linkage between the spontaneous metastability of large-scale networks in the cerebral cortex and cognition.
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Affiliation(s)
- Thomas H. Alderson
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana‐ChampaignUrbanaIllinoisUnited States
| | - Arun L. W. Bokde
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of MedicineTrinity College DublinDublinIreland
| | - J. A. Scott Kelso
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
- Center for Complex Systems and Brain SciencesFlorida Atlantic UniversityBoca RatonFloridaUnited States
| | - Liam Maguire
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
| | - Damien Coyle
- Intelligent Systems Research CentreUlster UniversityAntrimUnited Kingdom
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95
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Sen B, Parhi KK. Predicting Biological Gender and Intelligence From fMRI via Dynamic Functional Connectivity. IEEE Trans Biomed Eng 2020; 68:815-825. [PMID: 32746070 DOI: 10.1109/tbme.2020.3011363] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE This paper explores the predictive capability of dynamic functional connectivity extracted from functional magnetic resonance imaging (fMRI) of the human brain, in contrast to static connectivity used in past research. METHODS Several state-of-the-art features extracted from static functional connectivity of the brain are employed to predict biological gender and intelligence using publicly available Human Connectome Project (HCP) database. Next, a novel tensor parallel factor (PARAFAC) decomposition model is proposed to decompose sequence of dynamic connectivity matrices into common connectivity components that are orthonormal to each other, common time-courses, and corresponding distinct subject-wise weights. The subject-wise loading of the components are employed to predict biological gender and intelligence using a random forest classifier (respectively, regressor) using 5-fold cross-validation. RESULTS The results demonstrate that dynamic functional connectivity can indeed classify biological gender with a high accuracy (0.94, where male identification accuracy was 0.87 and female identification accuracy was 0.97). It can also predict intelligence with less normalized mean square error (0.139 for fluid intelligence and 0.031 for fluid ability metrics) compared with other functional connectivity measures (the nearest mean square error were 0.147 and 0.037 for fluid intelligence and fluid ability metrics, respectively, using static connectivity approaches). CONCLUSION Our work is an important milestone for the understanding of non-stationary behavior of hemodynamic blood-oxygen level dependent (BOLD) signal in brain and how they are associated with biological gender and intelligence. SIGNIFICANCE The paper demonstrates that dynamic behavior of brain can contribute substantially towards forming a fingerprint of biological gender and intelligence.
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96
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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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97
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Jiang W, Zhao Z, Wu Q, Wang L, Zhou L, Li D, He L, Tan Y. Study on brain structure network of patients with delayed encephalopathy after carbon monoxide poisoning: based on diffusion tensor imaging. Radiol Med 2020; 126:133-141. [PMID: 32557108 DOI: 10.1007/s11547-020-01222-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 04/27/2020] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To analyze the network alteration characteristics of brain structure network in patients with delayed encephalopathy after CO poisoning (DEACMP) based on diffusion tensor imaging (DTI), and to explore the structural correlation neuroimaging mechanism of DEACMP cognitive impairment. METHODS DTI scanning was performed in 33 patients with DEACMP and 25 healthy controls (HCs) who were matched in age and sex. The whole brain was divided into 90 regions by automated anatomical marker templates. The continuous tracing method was used to reconstruct the brain fiber bundle connection and construct the brain structure weighted network. The global and regional properties were computed by graph theoretical analysis. To compare the brain network regional properties between the DEACMP group and the HCs group, two-sample t test (false discovery rate correction, P < 0.05) was utilized. The correlations between the brain structural network properties and clinical parameters were further analyzed. RESULTS Both of the two groups were found to follow the efficient small-world characteristics. The shortest path length of the DEACMP group increased (Lp = 0.86 ± 0.05), whereas global efficiency (Eglob = 9.60 ± 2.65) and local efficiency (Eloc = 17.98 ± 3.89) decreased. Moreover, the core nodes of the DEACMP group's default network, highlighting network, central execution network, and visual area, were decreased (P < 0.05, FDR correction). The left amygdala node degree of DEACMP group was positively correlated with MMSE and MoCA scores of the clinical scale (r = 0.863, P = 0.001, r = 0.525, P = 0.021). The node degree value of the left lingual gyrus was positively correlated with MoCA score (r = 0.406, P = 0.019) and negatively correlated with CDR score (r = -0.563, P = 0.016). The efficiency value of the right dorsolateral superior frontal gyrus in the DEACMP group was negatively correlated with the CDR score (r = -0.377, P = 0.031). CONCLUSION By comparing the differences and changes in the topological properties and nodes of the brain structure network between DEACMP group and HCs group, the degree of related brain regions, especially the damage of higher brain functions in DEACMP patients, was verified, which was helpful to understand the cognitive damage caused by CO poisoning and to predict the efficacy of late remodeling. Small-worldness is a dynamic reorganization of the small-world topology and its community structure from the brain network to provide system-wide flexibility and adaptability (Barbey in Trends Cogn Sci 22(1):8-20, 2018). The combination with DTI is helpful for the accurate localization of brain structural damage, especially in DEACMP patients.
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Affiliation(s)
- Wenqian Jiang
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Yongwaizheng Street 17, Nanchang, 330006, Jiangxi Province, China
| | - Ziru Zhao
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Yongwaizheng Street 17, Nanchang, 330006, Jiangxi Province, China
| | - Qingyu Wu
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Yongwaizheng Street 17, Nanchang, 330006, Jiangxi Province, China
| | - Liang Wang
- Rehabilitation Department, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
| | - Lu Zhou
- Rehabilitation Department, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
| | - Dan Li
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Yongwaizheng Street 17, Nanchang, 330006, Jiangxi Province, China
| | - Laichang He
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Yongwaizheng Street 17, Nanchang, 330006, Jiangxi Province, China
| | - Yongming Tan
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Yongwaizheng Street 17, Nanchang, 330006, Jiangxi Province, China.
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98
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Li J, Biswal BB, Meng Y, Yang S, Duan X, Cui Q, Chen H, Liao W. A neuromarker of individual general fluid intelligence from the white-matter functional connectome. Transl Psychiatry 2020; 10:147. [PMID: 32404889 PMCID: PMC7220913 DOI: 10.1038/s41398-020-0829-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 04/20/2020] [Accepted: 04/28/2020] [Indexed: 12/13/2022] Open
Abstract
Neuroimaging studies have uncovered the neural roots of individual differences in human general fluid intelligence (Gf). Gf is characterized by the function of specific neural circuits in brain gray-matter; however, the association between Gf and neural function in brain white-matter (WM) remains unclear. Given reliable detection of blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI) signals in WM, we used a functional, rather than an anatomical, neuromarker in WM to identify individual Gf. We collected longitudinal BOLD-fMRI data (in total three times, ~11 months between time 1 and time 2, and ~29 months between time 1 and time 3) in normal volunteers at rest, and identified WM functional connectomes that predicted the individual Gf at time 1 (n = 326). From internal validation analyses, we demonstrated that the constructed predictive model at time 1 predicted an individual's Gf from WM functional connectomes at time 2 (time 1 ∩ time 2: n = 105) and further at time 3 (time 1 ∩ time 3: n = 83). From external validation analyses, we demonstrated that the predictive model from time 1 was generalized to unseen individuals from another center (n = 53). From anatomical aspects, WM functional connectivity showing high predictive power predominantly included the superior longitudinal fasciculus system, deep frontal WM, and ventral frontoparietal tracts. These results thus demonstrated that WM functional connectomes offer a novel applicable neuromarker of Gf and supplement the gray-matter connectomes to explore brain-behavior relationships.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Qian Cui
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- School of Public Administration, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
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99
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Mitochondrial Functioning ≠ General Intelligence. J Intell 2020; 8:jintelligence8020020. [PMID: 32375211 PMCID: PMC7712998 DOI: 10.3390/jintelligence8020020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/20/2020] [Accepted: 04/24/2020] [Indexed: 11/17/2022] Open
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100
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Spanoudis G, Demetriou A. Mapping Mind-Brain Development: Towards a Comprehensive Theory. J Intell 2020; 8:E19. [PMID: 32357452 PMCID: PMC7713015 DOI: 10.3390/jintelligence8020019] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 04/13/2020] [Accepted: 04/20/2020] [Indexed: 12/12/2022] Open
Abstract
The relations between the developing mind and developing brain are explored. We outline a theory of intellectual development postulating that the mind comprises four systems of processes (domain-specific, attention and working memory, reasoning, and cognizance) developing in four cycles (episodic, realistic, rule-based, and principle-based representations, emerging at birth, 2, 6, and 11 years, respectively), with two phases in each. Changes in reasoning relate to processing efficiency in the first phase and working memory in the second phase. Awareness of mental processes is recycled with the changes in each cycle and drives their integration into the representational unit of the next cycle. Brain research shows that each type of processes is served by specialized brain networks. Domain-specific processes are rooted in sensory cortices; working memory processes are mainly rooted in hippocampal, parietal, and prefrontal cortices; abstraction and alignment processes are rooted in parietal, frontal, and prefrontal and medial cortices. Information entering these networks is available to awareness processes. Brain networks change along the four cycles, in precision, connectivity, and brain rhythms. Principles of mind-brain interaction are discussed.
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Affiliation(s)
- George Spanoudis
- Psychology Department, University of Cyprus, 1678 Nicosia, Cyprus
| | - Andreas Demetriou
- Department of Psychology, University of Nicosia, 1700 Nicosia, Cyprus;
- Cyprus Academy of Science, Letters, and Arts, 1700 Nicosia, Cyprus
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