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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dietrich, Stout; Thierry, Chaminade;

    The appearance of the first intentionally modified stone tools over 2.5 million years ago marked a watershed in human evolutionary history, expanding the human adaptive niche and initiating a trend of technological elaboration that continues to the present day. However, the cognitive foundations of this behavioral revolution remain controversial, as do its implications for the nature and evolution of modern human technological abilities. Here we shed new light on the neural and evolutionary foundations of human tool making skill by presenting functional brain imaging data from six inexperienced subjects learning to make stone tools of the kind found in the earliest archaeological record. Functional imaging of this complex, naturalistic task was accomplished through positron emission tomography with the slowly decaying radiological tracer (18)flouro-2-deoxyglucose. Results show that simple stone tool making is supported by a mosaic of primitive and derived parietofrontal perceptual-motor systems, including recently identified human specializations for representation of the central visual field and perception of three-dimensional form from motion. In the naive tool makers reported here, no activation was observed in prefrontal executive cortices associated with strategic action planning or in inferior parietal cortex thought to play a role in the representation of everyday tool use skills. We conclude that uniquely human capacities for sensorimotor adaptation and affordance perception, rather than abstract conceptualization and planning, were central factors in the initial stages of human technological evolution. The appearance of the first intentionally modified stone tools over 2.5 million years ago marked a watershed in human evolutionary history, expanding the human adaptive niche and initiating a trend of technological elaboration that continues to the present day. However, the cognitive foundations of this behavioral revolution remain controversial, as do its implications for the nature and evolution of modern human technological abilities. Here we shed new light on the neural and evolutionary foundations of human tool making skill by presenting functional brain imaging data from six inexperienced subjects learning to make stone tools of the kind found in the earliest archaeological record. Functional imaging of this complex, naturalistic task was accomplished through positron emission tomography with the slowly decaying radiological tracer (18)flouro-2-deoxyglucose. Results show that simple stone tool making is supported by a mosaic of primitive and derived parietofrontal perceptual-motor systems, including recently identified human specializations for representation of the central visual field and perception of three-dimensional form from motion. In the naive tool makers reported here, no activation was observed in prefrontal executive cortices associated with strategic action planning or in inferior parietal cortex thought to play a role in the representation of everyday tool use skills. We conclude that uniquely human capacities for sensorimotor adaptation and affordance perception, rather than abstract conceptualization and planning, were central factors in the initial stages of human technological evolution.

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    UCL Discovery
    Article . 2007
    Data sources: UCL Discovery
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    Neuropsychologia
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Neuropsychologia
    Article . 2007 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
    Neuropsychologia
    Article . 2006
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ UCL Discoveryarrow_drop_down
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      UCL Discovery
      Article . 2007
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      Neuropsychologia
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Neuropsychologia
      Article . 2007 . Peer-reviewed
      License: Elsevier TDM
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      Neuropsychologia
      Article . 2006
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    Authors: Rongtao Jiang; Dustin Scheinost; Nianming Zuo; Jing Wu; +9 Authors

    AbstractCognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in the brain in terms of neuroimaging‐based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19–89 years, n = 567), it is shown that both cognitive function (domains spanning executive function, emotion processing, motor function, and memory) and human age can be reliably predicted from unique patterns of functional connectivity, with models generalizable in two external datasets (n = 533 and n = 453). Results show that cognitive decline and normal aging both manifest decrease within‐network connections (especially default mode and ventral attention networks) and increase between‐network connections (somatomotor network). Whereas dorsal attention network is an exception, which is highly predictive on cognitive ability but is weakly correlated with aging. Further, the positively weighted connections in predicting fluid intelligence significantly mediate its association with age. Together, these findings offer insights into why normal aging is often associated with cognitive decline in terms of brain network organization, indicating a process of neural dedifferentiation and compensational theory.

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    Advanced Science
    Article . 2022 . Peer-reviewed
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    Advanced Science
    Article . 2022
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      Advanced Science
      Article . 2022 . Peer-reviewed
      License: CC BY
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      Advanced Science
      Article . 2022
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sonja M C de Zwarte; Rachel M. Brouwer; Ingrid Agartz; Martin Alda; +87 Authors

    The researchers and studies included in this Article were supported by the Research Council of Norway (Grant No. 223273), National Institutes of Health (NIH) (Grant No. R01 MH117601 [to NJ], Grant Nos. R01 MH116147, R01 MH111671, and P41 EB015922 [to PMT], Grant Nos. 5T32MH073526 and U54EB020403 [to CRKC], and Grant No. R03 MH105808 [to CEB and SCF]) and National Institute on Aging (NIA) (Grant No. T32AG058507 [to CRKC]).; C-SFS: This work was supported by Canadian Institutes of Health Research.; Cardiff: This work was supported by the National Centre for Mental Health, Bipolar Disorder Research Network, 2010 National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award (Grant No. 17319).; DEU: This work was supported by Dokuz Eylul University Department of Scientific Research Projects Funding (Grant No. 2012. KB. SAG. 062). This report represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR, or Department of Health.; EGEU: This work was supported by the Ege University School of Medicine Research Foundation (Grant No. 2009-D-00017).; EHRS: The Edinburgh High Risk Study was supported by the Medical Research Council.; GROUP: The infrastructure for the GROUP study was supported by the Geestkracht program of the Netherlands Organisation for Health Research and Development (Grant No. 10-000-1002).; ENBD_UT/BPO_FLB: This work was supported by the National Institute of Mental Health (Grant No. R01 MH 085667).; HHR/PHHR: This work was supported by the Canadian Institutes of Health Research (Grant Nos. 103703, 106469, and 341717), Nova Scotia Health Research Foundation, Dalhousie Clinical Research Scholarship (to TH), 2007 Brain and Behavior Research Foundation Young Investigator Award (to TH), and Ministry of Health of the Czech Republic (Grant Nos. NR8786 and NT13891).; HUBIN: This work was supported by the Swedish Research Council (Grant Nos. K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3), regional agreement on medical training and clinical research between Stockholm County Council and the Karolinska Institutet, Knut and Alice Wallenberg Foundation, and HUBIN project.; IDIBAPS: This work was supported by the Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III (Grant Nos. PI070066, PI1100683, and PI1500467) and Fundacio Marato TV3 (Grant No. 091630), co-financed by ERDF Funds from the European Commission A Way of Making Europe"), Brain and Behaviour Research Foundation (NARSAD Young Investigator Award), and Alicia Koplowitz Foundation.; IoP-BD: The Maudsley Bipolar Twin Study was supported by the Stanley Medical Research Institute and NARSAD.; IoP-SZ: This work was supported by a Wellcome Trust Research Training Fellowship (Grant No. 064971 to MMP), NARSAD Young Investigator Award (to TT), and European Community's Sixth Framework Programme through a Marie Curie Training Network called the European Twin Study Network on Schizophrenia.; Lieber Institute for Brain Development (LIBD): This work was supported by the NIMH Intramural Research Program (to DRW's laboratory). LIBD is a nonprofit research institute located in Baltimore, MD. The work performed at LIBD was performed in accordance with an NIMH material transfer agreement with LIBD.; MFS: The Maudsley Family Study cohort collection was supported by the Wellcome Trust (Grant Nos. 085475/B/08/Z and 085475/Z/08/Z), NIHR Biomedical Research Centre at University College London Hospital, Medical Research Council (Grant No. G0901310), and British Medical Association Margaret Temple Fellowship 2016.; MooDS: This work was supported by the German Federal Ministry for Education and Research grants NGFNplus MooDS (Systematic Investigation of the Molecular Causes of Major Mood Disorders and Schizophrenia) and Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders) under the auspices of the e: Med program (Grant Nos. O1ZX1314B and O1ZX1314G) and Deutsche Forschungsgemeinschaft (Grant No. 1617 [to AH]).; MSSM: This work was supported by NIMH (Grant Nos. R01 MH116147 and R01 MH113619).; NU: This work was supported by NIH (Grant Nos. U01 MH097435, R01 MH084803, and R01 EB020062) and National Science Foundation (Grant Nos. 1636893 and 1734853).; OLIN: This work was supported by NIH (Grant No. R01 MH080912).; STAR: This work was supported by NIH (Grant No. R01 MH052857).; SydneyBipolarGroup: The Australian cohort collection was supported by the Australian National Health and Medical Research Council Program Grants (Grant No. 510135 [to PBM] and Grant No. 1037196 [to PBM and PRS]) and Project Grants (Grant No. 1063960 [to JMF and PRS] and Grant No. 1066177 [to JMF]).; UMCU: This work was supported by NARSAD (Grant No. 20244 [to MHJH]), ZonMw (Grant No. 908-02-123 [to HEHP]), VIDI (Grant No. 452-11-014 [to NEMvH] and Grant No. 917-46-370 [to HEHP]), and Stanley Medical Research Institute.; CliNG: We thank Anna Fanelli, Kathrin Jakob, and Maria Keil for help with data acquisition.; All authors have contributed to and approved the contents of this manuscript.; GS has received research and travel support from Janssen Pharmaceutica and Otsuka Pharmaceutical and honoraria from Adamed Pharma. NY has been an investigator in clinical studies conducted together with Janssen-Cilag, Corcept Therapeutics, and COMPASS Pathways in the last 3 years. AM-L has received consultant fees from Boehringer Ingelheim, BrainsWay, Elsevier, Lundbeck International Neuroscience Foundation, and Science Advances. CRKC has received partial research support from Biogen, Inc. (Boston, MA) for work unrelated to the topic of this manuscript. The remaining authors report no biomedical financial interests or potential conflicts of interest. Research Council of NorwayResearch Council of Norway [223273]; National Institutes of Health (NIH)United States Department of Health ; Human ServicesNational Institutes of Health (NIH) - USA [R01 MH117601, R01 MH116147, R01 MH111671, P41 EB015922, 5T32MH073526, U54EB020403, R03 MH105808]; National Institute on Aging (NIA)United States Department of Health ; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [T32AG058507]; Canadian Institutes of Health ResearchCanadian Institutes of Health Research (CIHR) [103703, 106469, 341717]; National Centre for Mental Health; 2010 National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award [17319]; Dokuz Eylul University Department of Scientific Research Projects FundingDokuz Eylul University [2012.KB.SAG.062]; National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King's College London; Ege University School of Medicine Research Foundation [2009-D-00017]; Medical Research CouncilMedical Research Council UK (MRC) [G0901310]; Geestkracht program of the Netherlands Organisation for Health Research and Development [10-000-1002]; National Institute of Mental HealthUnited States Department of Health ; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH 085667]; Nova Scotia Health Research Foundation; Dalhousie Clinical Research Scholarship; 2007 Brain and Behavior Research Foundation Young Investigator Award; Ministry of Health of the Czech RepublicMinistry of Health, Czech Republic [NR8786, NT13891]; Swedish Research CouncilSwedish Research Council [K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3]; Stockholm County CouncilStockholm County Council; Karolinska InstitutetKarolinska Institutet; Knut and Alice Wallenberg FoundationKnut ; Alice Wallenberg Foundation; HUBIN project; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III [PI070066, PI1100683, PI1500467]; Fundacio Marato TV3 [091630]; ERDF Funds from the European Commission A Way of Making Europe"); Brain and Behaviour Research Foundation (NARSAD Young Investigator Award); Alicia Koplowitz Foundation; Stanley Medical Research Institute; NARSADNARSAD [20244]; Wellcome TrustWellcome Trust [064971, 085475/B/08/Z, 085475/Z/08/Z]; NARSAD Young Investigator AwardNARSAD; European CommunityEuropean Community (EC); NIMH Intramural Research ProgramUnited States Department of Health ; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH); NIHR Biomedical Research Centre at University College London Hospital; British Medical Association Margaret Temple Fellowship 2016; German Federal Ministry for Education and ResearchFederal Ministry of Education ; Research (BMBF) [O1ZX1314B, O1ZX1314G]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [1617]; NIMHUnited States Department of Health ; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH116147, R01 MH113619]; NIHUnited States Department of Health ; Human ServicesNational Institutes of Health (NIH) - USA [U01 MH097435, R01 MH084803, R01 EB020062, R01 MH080912, R01 MH052857]; National Science FoundationNational Science Foundation (NSF) [1636893, 1734853]; Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [510135, 1037196, 1063960, 1066177]; ZonMwNetherlands Organization for Health Research and Development [908-02-123]; VIDINetherlands Organization for Scientific Research (NWO) [452-11-014, 917-46-370]; Janssen PharmaceuticaJohnson ; Johnson USAJanssen Biotech Inc; Otsuka PharmaceuticalOtsuka Pharmaceutical; Bipolar Disorder Research Network BACKGROUND: Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects. METHODS: We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects. RESULTS: FDRs-BD had significantly larger ICV (d = +10.16, q .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d -0.09, q .05 corrected); and third ventricle was larger (d = +0.15, q .05 corrected). The findings were not explained by psychopathology in the relatives or control subjects. CONCLUSIONS: Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct. WOS: 000485217300010 PubMed ID: 31443932

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    NARCIS
    Article . 2019
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    Europe PubMed Central
    Article . 2019
    Data sources: PubMed Central
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    Article . 2019
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    Article . 2019
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    Biological Psychiatry
    Article . 2019
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    Biological Psychiatry
    Article . 2019
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    Article . 2019
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    Biological Psychiatry
    Article . 2019
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    Biological Psychiatry
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    Biological Psychiatry
    Article . 2019
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      Europe PubMed Central
      Article . 2019
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      Article . 2019
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      Biological Psychiatry
      Article . 2019
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      Biological Psychiatry
      Article . 2019
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      Biological Psychiatry
      Article . 2019
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      Biological Psychiatry
      Article
      License: CC BY NC ND
      Data sources: UnpayWall
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      Biological Psychiatry
      Article . 2019
      Data sources: NARCIS
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    Authors: Yuhui Du; Yuhui Du; Dongdong Lin; Qingbao Yu; +7 Authors

    Spatial group independent component analysis (GICA) methods decompose multiple-subject functional magnetic resonance imaging (fMRI) data into a linear mixture of spatially independent components (ICs), some of which are subsequently characterized as brain functional networks. Group information guided independent component analysis (GIG-ICA) as a variant of GICA has been proposed to improve the accuracy of the subject-specific ICs estimation by optimizing their independence. Independent vector analysis (IVA) is another method which optimizes the independence among each subject's components and the dependence among corresponding components of different subjects. Both methods are promising in neuroimaging study and showed a better performance than the traditional GICA. However, the difference between IVA and GIG-ICA has not been well studied. A detailed comparison between them is demanded to provide guidance for functional network analyses. In this work, we employed multiple simulations to evaluate the performances of the two approaches in estimating subject-specific components and time courses under conditions of different data quality and quantity, varied number of sources generated and inaccurate number of components used in computation, as well as the presence of spatially subject-unique sources. We also compared the two methods using healthy subjects' test-retest resting-state fMRI data in terms of spatial functional networks and functional network connectivity (FNC). Results from simulations support that GIG-ICA showed better recovery accuracy of both components and time courses than IVA for those subject-common sources, and IVA outperformed GIG-ICA in component and time course estimation for the subject-unique sources. Results from real fMRI data suggest that GIG-ICA resulted in more reliable spatial functional networks and yielded higher and more robust modularity property of FNC, compared to IVA. Taken together, GIG-ICA is appropriate for estimating networks which are consistent across subjects, while IVA is able to estimate networks with great inter-subject variability or subject-unique property.

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    Europe PubMed Central
    Article . 2017
    Data sources: PubMed Central
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    Frontiers in Neuroscience
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    DOAJ-Articles
    Article . 2017
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    Frontiers in Neuroscience
    Article . 2017 . Peer-reviewed
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      Europe PubMed Central
      Article . 2017
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      Frontiers in Neuroscience
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      Frontiers in Neuroscience
      Article . 2017 . Peer-reviewed
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    Authors: Gang Qu; Wenxing Hu; Li Xiao; Junqi Wang; +4 Authors

    OBJECTIVE: Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and complex relationships between different brain regions. METHOD: In this work, a graph convolutional network (GCN) based framework is proposed by exploiting the information from both region-to-region connectivities of the brain and subject-subject relationships. We first construct an affinity subject-subject graph followed by GCN analysis. A Laplacian regularization term is introduced in our model to tackle the overfitting problem. We apply and validate the proposed model to the Philadelphia Neurodevelopmental Cohort for the brain cognition study. RESULTS: Experimental analysis shows that our proposed framework outperforms other competing models in classifying groups with low and high Wide Range Achievement Test (WRAT) scores. Moreover, to examine each brain region’s contribution to cognitive function, we use the occlusion sensitivity analysis method to identify cognition-related brain functional networks. The results are consistent with previous research yet yield new findings. CONCLUSION AND SIGNIFICANCE: Our study demonstrates that GCN incorporating prior knowledge about brain networks offers a powerful way to detect important brain networks and regions associated with cognitive functions.

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    Europe PubMed Central
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    IEEE Transactions on Biomedical Engineering
    Article . 2022 . Peer-reviewed
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      IEEE Transactions on Biomedical Engineering
      Article . 2022 . Peer-reviewed
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    Authors: Wenxing Hu; Xiang-He Meng; Yuntong Bai; Aiying Zhang; +7 Authors

    The combination of multimodal imaging and genomics provides a more comprehensive way for the study of mental illnesses and brain functions. Deep network-based data fusion models have been developed to capture their complex associations, resulting in improved diagnosis of diseases. However, deep learning models are often difficult to interpret, bringing about challenges for uncovering biological mechanisms using these models. In this work, we develop an interpretable multimodal fusion model to perform automated diagnosis and result interpretation simultaneously. We name it Grad-CAM guided convolutional collaborative learning (gCAM-CCL), which is achieved by combining intermediate feature maps with gradient-based weights. The gCAM-CCL model can generate interpretable activation maps to quantify pixel-level contributions of the input features. Moreover, the estimated activation maps are class-specific, which can therefore facilitate the identification of biomarkers underlying different groups. We validate the gCAM-CCL model on a brain imaging-genetic study, and demonstrate its applications to both the classification of cognitive function groups and the discovery of underlying biological mechanisms. Specifically, our analysis results suggest that during task-fMRI scans, several object recognition related regions of interests (ROIs) are activated followed by several downstream encoding ROIs. In addition, the high cognitive group may have stronger neurotransmission signaling while the low cognitive group may have problems in brain/neuron development due to genetic variations.

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    IEEE Transactions on Medical Imaging
    Article . 2021 . Peer-reviewed
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      IEEE Transactions on Medical Imaging
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    Authors: Kuang, Li-Dan; Lin, Qiu-Hua; Gong, Xiao-Feng; Cong, Fengyu; +2 Authors

    Abstract Background Component splitting at higher model orders is a widely accepted finding for independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. However, our recent study found that intact components occurred with subcomponents at higher model orders. New method This study investigated model order effects on ICA of resting-state complex-valued fMRI data from 82 subjects, which included 40 healthy controls (HCs) and 42 schizophrenia patients. In addition, we explored underlying causes for distinct component splitting between complex-valued data and magnitude-only data by examining model order effects on ICA of phase fMRI data. A best run selection method was proposed to combine subject averaging and a one-sample t-test. We selected the default mode network (DMN)-, visual-, and sensorimotor-related components from the best run of ICA at varying model orders from 10 to 140. Results Results show that component integration occurred in complex-valued and phase analyses, whereas component splitting emerged in magnitude-only analysis with increasing model order. Incorporation of phase data appears to play a complementary role in preserving integrity of brain networks. Comparison with existing method(s) When compared with magnitude-only analysis, the intact DMN component obtained in complex-valued analysis at higher model orders exhibited highly significant subject-level differences between HCs and patients with schizophrenia. We detected significantly higher activity and variation in anterior areas for HCs and in posterior areas for patients with schizophrenia. Conclusions These results demonstrate the potential of complex-valued fMRI data to contribute generally and specifically to brain network analysis in identification of schizophrenia-related changes.

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    Journal of Neuroscience Methods
    Article . 2018
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    Journal of Neuroscience Methods
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    Journal of Neuroscience Methods
    Article . 2018 . Peer-reviewed
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      Journal of Neuroscience Methods
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      Journal of Neuroscience Methods
      Article . 2018 . Peer-reviewed
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    Authors: Yu, Mou-Chuan; Lin, Qiu-Hua; Kuang, Li-Dan; Gong, Xiao-Feng; +2 Authors

    Background ICA of complex-valued fMRI data is challenging because of the ambiguous and noisy nature of the phase. A typical solution is to remove noisy regions from fMRI data prior to ICA. However, it may be more optimal to carry out ICA of full complex-valued fMRI data, since any filtering or voxel-based processing may disrupt information that can be useful to ICA. New method We enable ICA of the full complex-valued fMRI data by utilizing phase information of estimated spatial maps (SMs). The SM phases are first adjusted to properly represent spatial phase changes of all voxels based on estimated time courses (TCs), and then these are used to segment the voxels into BOLD-related and unwanted voxels based on a criterion of TC real-part power maximization. Single-subject and group phase masks are finally constructed to remove the unwanted voxels from the individual and group SM estimates. Results Our method efficiently estimated not only the task-related component but also the non-task-related component DMN. Comparison with existing method(s) Our method extracted 139–331% more contiguous and reasonable activations than magnitude-only infomax for the task-related component and DMN at |Z| > 2.5, and detected more BOLD-related voxels, but eliminated more unwanted voxels than ICA of complex-valued fMRI data with pre-ICA de-noising. Our TC-based phase de-ambiguity exhibited higher accuracy and robustness than the SM-based method. Conclusions The TC-based phase de-ambiguity is essential to prepare the SM phases. The SM phases provide a new post-ICA index for reliably identifying and suppressing the unwanted voxels.

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    Authors: Deepika Dasari; Guofa Shou; Lei Ding; Lei Ding;

    Electroencephalograph (EEG) has been increasingly studied to identify distinct mental factors when persons perform cognitively demanding tasks. However, most of these studies examined EEG correlates at channel domain, which suffers the limitation that EEG signals are the mixture of multiple underlying neuronal sources due to the volume conduction effect. Moreover, few studies have been conducted in real-world tasks. To precisely probe EEG correlates with specific neural substrates to mental factors in real-world tasks, the present study examined EEG correlates to three mental factors, i.e., mental fatigue (also known as time-on-task (TOT) effect), workload and effort, in EEG component signals, which were obtained using an independent component analysis (ICA) on high-density EEG data. EEG data were recorded when subjects performed a realistically simulated air traffic control (ATC) task for two hours. Five EEG independent component (IC) signals that were associated with specific neural substrates (i.e., the frontal, central medial, motor, parietal, occipital areas) were identified. Their spectral powers at their corresponding dominant bands, i.e., the theta power of the frontal IC and the alpha power of the other four ICs, were detected to be correlated to mental workload and effort levels, measured by behavioral metrics. Meanwhile, a linear regression analysis indicated that spectral powers at five ICs significantly increased with TOT. These findings indicated that different levels of mental factors can be sensitively reflected in EEG signals associated with various brain functions, including visual perception, cognitive processing, and motor outputs, in real-world tasks. These results can potentially aid in the development of efficient operational interfaces to ensure productivity and safety in ATC and beyond.

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    Europe PubMed Central
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    Frontiers in Neuroscience
    Article . 2017 . Peer-reviewed
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    Frontiers in Neuroscience
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    Article . 2017
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      Frontiers in Neuroscience
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    Authors: Aaron Kucyi; Amy L. Daitch; Omri Raccah; Baotian Zhao; +7 Authors

    Neuroimaging evidence suggests that the default mode network (DMN) exhibits antagonistic activity with dorsal attention (DAN) and salience (SN) networks. Here we use human intracranial electroencephalography to investigate the behavioral relevance of fine-grained dynamics within and between these networks. The three networks show dissociable profiles of task-evoked electrophysiological activity, best captured in the high-frequency broadband (HFB; 70–170 Hz) range. On the order of hundreds of milliseconds, HFB responses peak fastest in the DAN, at intermediate speed in the SN, and slowest in the DMN. Lapses of attention (behavioral errors) are marked by distinguishable patterns of both pre- and post-stimulus HFB activity within each network. Moreover, the magnitude of temporally lagged, negative HFB coupling between the DAN and DMN (but not SN and DMN) is associated with greater sustained attention performance and is reduced during wakeful rest. These findings underscore the behavioral relevance of temporally delayed coordination between antagonistic brain networks. Brain imaging studies suggest that specific, large-scale, cortical networks show antagonistic activity with one another. Here, the authors studied the dynamics of these networks using implanted electrodes in the human brain, revealing that the coordination of inter-network dynamics on fast time scales relates to fluctuations in attention.

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    Nature Communications
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    Authors: Dietrich, Stout; Thierry, Chaminade;