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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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Liu J, Supekar K, El-Said D, de los Angeles C, Zhang Y, Chang H, Menon V. Neuroanatomical, transcriptomic, and molecular correlates of math ability and their prognostic value for predicting learning outcomes. SCIENCE ADVANCES 2024; 10:eadk7220. [PMID: 38820151 PMCID: PMC11141625 DOI: 10.1126/sciadv.adk7220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 04/29/2024] [Indexed: 06/02/2024]
Abstract
Foundational mathematical abilities, acquired in early childhood, are essential for success in our technology-driven society. Yet, the neurobiological mechanisms underlying individual differences in children's mathematical abilities and learning outcomes remain largely unexplored. Leveraging one of the largest multicohort datasets from children at a pivotal stage of knowledge acquisition, we first establish a replicable mathematical ability-related imaging phenotype (MAIP). We then show that brain gene expression profiles enriched for candidate math ability-related genes, neuronal signaling, synaptic transmission, and voltage-gated potassium channel activity contributed to the MAIP. Furthermore, the similarity between MAIP gene expression signatures and brain structure, acquired before intervention, predicted learning outcomes in two independent math tutoring cohorts. These findings advance our knowledge of the interplay between neuroanatomical, transcriptomic, and molecular mechanisms underlying mathematical ability and reveal predictive biomarkers of learning. Our findings have implications for the development of personalized education and interventions.
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Affiliation(s)
- Jin Liu
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kaustubh Supekar
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Dawlat El-Said
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Carlo de los Angeles
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yuan Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Hyesang Chang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
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Freund MC, Chen R, Chen G, Braver TS. Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.591032. [PMID: 38712215 PMCID: PMC11071497 DOI: 10.1101/2024.04.24.591032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Understanding individual differences in cognitive control is a central goal in psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures in cognitive neuroscience such as response times or task-based fMRI activity. While prior work has pinpointed the source of the issue - the vast amount of cross-trial variability within these measures - no study has rigorously evaluated potential solutions. Here, we do so with one potential way forward: an analytic framework that combines hierarchical Bayesian modeling with multivariate decoding of trial-level fMRI data. Using this framework and longitudinal data from the Dual Mechanisms of Cognitive Control project, we estimated individuals' neural responses associated with cognitive control within a color-word Stroop task, then assessed the reliability of these individuals' responses across a time interval of several months. We show that in many prefrontal and parietal brain regions, test-retest reliability was near maximal, and that only hierarchical models were able to reveal this state of affairs. Further, when compared to traditional univariate contrasts, multivariate decoding enabled individual-level correlations to be estimated with significantly greater precision. We specifically link these improvements in precision to the optimized suppression of cross-trial variability in decoding. Together, these findings not only indicate that cognitive control-related neural responses individuate people in a highly stable manner across time, but also suggest that integrating hierarchical and multivariate models provides a powerful approach for investigating individual differences in cognitive control, one that can effectively address the issue of high-variability measures.
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Affiliation(s)
- Michael C. Freund
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
- Department of Psychological & Brain Sciences, Washington University in St. Louis
| | - Ruiqi Chen
- Division of Biology and Biomedical Sciences, Washington University in St. Louis
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, MD, USA
| | - Todd S. Braver
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
- Department of Psychological & Brain Sciences, Washington University in St. Louis
- Department of Radiology, Washington University in St. Louis
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Cao Z, Xiao X, Xie C, Wei L, Yang Y, Zhu C. Personalized connectivity-based network targeting model of transcranial magnetic stimulation for treatment of psychiatric disorders: computational feasibility and reproducibility. Front Psychiatry 2024; 15:1341908. [PMID: 38419897 PMCID: PMC10899497 DOI: 10.3389/fpsyt.2024.1341908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) holds promise for treating psychiatric disorders; however, the variability in treatment efficacy among individuals underscores the need for further improvement. Growing evidence has shown that TMS induces a broad network modulatory effect, and its effectiveness may rely on accurate modulation of the pathological network specific to each disorder. Therefore, determining the optimal TMS coil setting that will engage the functional pathway delivering the stimulation is crucial. Compared to group-averaged functional connectivity (FC), individual FC provides specific information about a person's brain functional architecture, offering the potential for more accurate network targeting for personalized TMS. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC. To overcome this challenge, the proposed solutions include increasing the scan duration and employing the cluster method to enhance the stability of FC. This study aimed to evaluate the stability of a personalized FC-based network targeting model in individuals with major depressive disorder or schizophrenia with auditory verbal hallucinations. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we assessed the model's stability. We employed longer scan durations and cluster methodologies to improve the precision in identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the cluster method achieved stable identification of individual sites, as evidenced by the intraindividual distance falling below the ~1cm spatial resolution of TMS. The current model provides a feasible approach to obtaining stable personalized TMS targets from the scalp, offering a more accurate method of TMS targeting in clinical applications.
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Affiliation(s)
- Zhengcao Cao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- School of Arts and Communication, Beijing Normal University, Beijing, China
| | - Xiang Xiao
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Cong Xie
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Lijiang Wei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Chaozhe Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
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Fiorenzato E, Moaveninejad S, Weis L, Biundo R, Antonini A, Porcaro C. Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease. Mov Disord 2024; 39:305-317. [PMID: 38054573 DOI: 10.1002/mds.29678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/13/2023] [Accepted: 11/17/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline. OBJECTIVES The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features. METHODS Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models. RESULTS FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity. CONCLUSIONS Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Eleonora Fiorenzato
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
| | - Sadaf Moaveninejad
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Luca Weis
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- IRCCS, San Camillo Hospital, Venice, Italy
| | - Roberta Biundo
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- Department of Neuroscience, Center for Neurodegenerative Disease Research (CESNE), University of Padova, Padova, Italy
- Department of General Psychology, University of Padua, Padua, Italy
| | - Angelo Antonini
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of Neuroscience, Center for Neurodegenerative Disease Research (CESNE), University of Padova, Padova, Italy
| | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Institute of Cognitive Sciences and Technologies-National Research Council, Rome, Italy
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
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Wengler K, Baker SC, Velikovskaya A, Fogelson A, Girgis RR, Reyes-Madrigal F, Lee S, de la Fuente-Sandoval C, Ojeil N, Horga G. Generalizability and Out-of-Sample Predictive Ability of Associations Between Neuromelanin-Sensitive Magnetic Resonance Imaging and Psychosis in Antipsychotic-Free Individuals. JAMA Psychiatry 2024; 81:198-208. [PMID: 37938847 PMCID: PMC10633403 DOI: 10.1001/jamapsychiatry.2023.4305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/08/2023] [Indexed: 11/10/2023]
Abstract
Importance The link between psychosis and dopaminergic dysfunction is established, but no generalizable biomarkers with clear potential for clinical adoption exist. Objective To replicate previous findings relating neuromelanin-sensitive magnetic resonance imaging (NM-MRI), a proxy measure of dopamine function, to psychosis severity in antipsychotic-free individuals in the psychosis spectrum and to evaluate the out-of-sample predictive ability of NM-MRI for psychosis severity. Design, Setting, and Participants This cross-sectional study recruited participants from 2019 to 2023 in the New York City area (main samples) and Mexico City area (external validation sample). The main samples consisted of 42 antipsychotic-free patients with schizophrenia, 53 antipsychotic-free individuals at clinical high risk for psychosis (CHR), and 52 matched healthy controls. An external validation sample consisted of 16 antipsychotic-naive patients with schizophrenia. Main Outcomes and Measures NM-MRI contrast within a subregion of the substantia nigra previously linked to psychosis severity (a priori psychosis region of interest [ROI]) and psychosis severity measured using the Positive and Negative Syndrome Scale (PANSS) in schizophrenia and the Structured Interview for Psychosis-Risk Syndromes (SIPS) in CHR. The cross-validated performance of linear support vector regression to predict psychosis severity across schizophrenia and CHR was assessed, and a final trained model was tested on the external validation sample. Results Of the 163 included participants, 76 (46.6%) were female, and the mean (SD) age was 29.2 (10.4) years. In the schizophrenia sample, higher PANSS positive total scores correlated with higher mean NM-MRI contrast in the psychosis ROI (t37 = 2.24, P = .03; partial r = 0.35; 95% CI, 0.05 to 0.55). In the CHR sample, no significant association was found between higher SIPS positive total score and NM-MRI contrast in the psychosis ROI (t48 = -0.55, P = .68; partial r = -0.08; 95% CI, -0.36 to 0.23). The 10-fold cross-validated prediction accuracy of psychosis severity was above chance in held-out test data (mean r = 0.305, P = .01; mean root-mean-square error [RMSE] = 1.001, P = .005). External validation prediction accuracy was also above chance (r = 0.422, P = .046; RMSE = 0.882, P = .047). Conclusions and Relevance This study provided a direct ROI-based replication of the in-sample association between NM-MRI contrast and psychosis severity in antipsychotic-free patients with schizophrenia. In turn, it failed to replicate such association in CHR individuals. Most critically, cross-validated machine-learning analyses provided a proof-of-concept demonstration that NM-MRI patterns can be used to predict psychosis severity in new data, suggesting potential for developing clinically useful tools.
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Affiliation(s)
- Kenneth Wengler
- Department of Psychiatry, Columbia University, New York, New York
- New York State Psychiatric Institute, New York
| | - Seth C. Baker
- New York State Psychiatric Institute, New York
- University at Buffalo Jacobs School of Medicine and Biological Sciences, Buffalo, New York
| | | | | | - Ragy R. Girgis
- Department of Psychiatry, Columbia University, New York, New York
- New York State Psychiatric Institute, New York
| | - Francisco Reyes-Madrigal
- Laboratory of Experimental Psychiatry & Neuropsychiatry Department, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Seonjoo Lee
- Department of Psychiatry, Columbia University, New York, New York
- New York State Psychiatric Institute, New York
- Department of Biostatistics, Columbia University, New York, New York
| | - Camilo de la Fuente-Sandoval
- Laboratory of Experimental Psychiatry & Neuropsychiatry Department, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | | | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, New York
- New York State Psychiatric Institute, New York
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Jiang Y, Gong G. Common and distinct patterns underlying different linguistic tasks: multivariate disconnectome symptom mapping in poststroke patients. Cereb Cortex 2024; 34:bhae008. [PMID: 38265297 DOI: 10.1093/cercor/bhae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 01/25/2024] Open
Abstract
Numerous studies have been devoted to neural mechanisms of a variety of linguistic tasks (e.g. speech comprehension and production). To date, however, whether and how the neural patterns underlying different linguistic tasks are similar or differ remains elusive. In this study, we compared the neural patterns underlying 3 linguistic tasks mainly concerning speech comprehension and production. To address this, multivariate regression approaches with lesion/disconnection symptom mapping were applied to data from 216 stroke patients with damage to the left hemisphere. The results showed that lesion/disconnection patterns could predict both poststroke scores of speech comprehension and production tasks; these patterns exhibited shared regions on the temporal pole of the left hemisphere as well as unique regions contributing to the prediction for each domain. Lower scores in speech comprehension tasks were associated with lesions/abnormalities in the superior temporal gyrus and middle temporal gyrus, while lower scores in speech production tasks were associated with lesions/abnormalities in the left inferior parietal lobe and frontal lobe. These results suggested an important role of the ventral and dorsal stream pathways in speech comprehension and production (i.e. supporting the dual stream model) and highlighted the applicability of the novel multivariate disconnectome-based symptom mapping in cognitive neuroscience research.
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Affiliation(s)
- Yaya Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 102206, China
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8
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Liu X, Hu Y, Hao Y, Yang L. Individual differences in the neural architecture in semantic processing. Sci Rep 2024; 14:170. [PMID: 38168133 PMCID: PMC10761854 DOI: 10.1038/s41598-023-49538-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 12/09/2023] [Indexed: 01/05/2024] Open
Abstract
Neural mechanisms underlying semantic processing have been extensively studied by using functional magnetic resonance imaging, nevertheless, the individual differences of it are yet to be unveiled. To further our understanding of functional and anatomical brain organization underlying semantic processing to the level of individual humans, we used out-of-scanner language behavioral data, T1, resting-state, and story comprehension task-evoked functional image data in the Human Connectome Project, to investigate individual variability in the task-evoked semantic processing network, and attempted to predict individuals' language skills based on task and intrinsic functional connectivity of highly variable regions, by employing a machine-learning framework. Our findings first confirmed that individual variability in both functional and anatomical markers were heterogeneously distributed throughout the semantic processing network, and that the variability increased towards higher levels in the processing hierarchy. Furthermore, intrinsic functional connectivities among these highly variable regions were found to contribute to predict individual reading decoding abilities. The contributing nodes in the overall network were distributed in the left superior, inferior frontal, and temporo-parietal cortices. Our results suggested that the individual differences of neurobiological markers were heterogeneously distributed in the semantic processing network, and that neurobiological markers of highly variable areas are not only linked to individual variability in language skills, but can predict language skills at the individual level.
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Affiliation(s)
- Xin Liu
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China.
| | - Yiwen Hu
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
| | - Yaokun Hao
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
| | - Liu Yang
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
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Camp CC, Noble S, Scheinost D, Stringaris A, Nielson DM. Test-Retest Reliability of Functional Connectivity in Adolescents With Depression. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:21-29. [PMID: 37734478 PMCID: PMC10843837 DOI: 10.1016/j.bpsc.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 08/26/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023]
Abstract
BACKGROUND The test-retest reliability of functional magnetic resonance imaging is critical to identifying reproducible biomarkers for psychiatric illness. Recent work has shown how reliability limits the observable effect size of brain-behavior associations, hindering detection of these effects. However, while a fast-growing literature has explored both univariate and multivariate reliability in healthy individuals, relatively few studies have explored reliability in populations with psychiatric illnesses or how this interacts with age. METHODS Here, we investigated functional connectivity reliability over the course of 1 year in a longitudinal cohort of 88 adolescents (age at baseline = 15.63 ± 1.29 years; 64 female) with major depressive disorder (MDD) and without MDD (healthy volunteers [HVs]). We compared a univariate metric, intraclass correlation coefficient, and 2 multivariate metrics, fingerprinting and discriminability. RESULTS Adolescents with MDD had marginally higher mean intraclass correlation coefficient (μMDD = 0.34, 95% CI, 0.12-0.54; μHV = 0.27, 95% CI, 0.05-0.52), but both groups had poor average intraclass correlation coefficients (<0.4). Fingerprinting index was greater than chance and did not differ between groups (fingerprinting indexMDD = 0.75; fingerprinting indexHV = 0.91; Poisson tests p < .001). Discriminability indicated high multivariate reliability in both groups (discriminabilityMDD = 0.80; discriminabilityHV = 0.82; permutation tests p < .01). Neither univariate nor multivariate reliability was associated with symptom severity or edge-level effect size of group differences. CONCLUSIONS Overall, we found little evidence for a relationship between depression and reliability of functional connectivity during adolescence. These findings suggest that biomarker identification in depression is not limited due to reliability compared with healthy samples and support the shift toward multivariate analysis for improved power and reliability.
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Affiliation(s)
- Chris C Camp
- Interdepartmental Neuroscience Program, Yale School of Medicine, Yale University, New Haven, Connecticut.
| | - Stephanie Noble
- Department of Psychology, Northeastern University, Boston, Massachusetts; Department of Bioengineering, Northeastern University, Boston, Massachusetts; Center for Cognitive and Brain Health, Northeastern University, Boston, Massachusetts
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics & Data Science, Yale University, New Haven, Connecticut; Child Study Center, Yale School of Medicine, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut
| | - Argyris Stringaris
- Faculty of Brain Sciences, Division of Psychiatry and Psychology and Language Sciences, University College London, London, United Kingdom; 1st Department of Psychiatry, National and Kapodistrian University of Athens, Aiginition Hospital, Athens, Greece
| | - Dylan M Nielson
- Machine Learning Team, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
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Knodt AR, Elliott ML, Whitman ET, Winn A, Addae A, Ireland D, Poulton R, Ramrakha S, Caspi A, Moffitt TE, Hariri AR. Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient. Hum Brain Mapp 2023; 44:6399-6417. [PMID: 37851700 PMCID: PMC10681655 DOI: 10.1002/hbm.26517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/23/2023] [Accepted: 09/30/2023] [Indexed: 10/20/2023] Open
Abstract
Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.
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Affiliation(s)
- Annchen R. Knodt
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Maxwell L. Elliott
- Department of Psychology, Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
| | - Ethan T. Whitman
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Alex Winn
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Angela Addae
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Avshalom Caspi
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- Institute of Psychiatry, Psychology, and NeuroscienceKing's College LondonLondonUK
| | - Terrie E. Moffitt
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- Institute of Psychiatry, Psychology, and NeuroscienceKing's College LondonLondonUK
| | - Ahmad R. Hariri
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
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11
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Zhang H, Chen K, Bao J, Wu H. Oxytocin enhances the triangular association among behavior, resting-state, and task-state functional connectivity. Hum Brain Mapp 2023; 44:6074-6089. [PMID: 37771300 PMCID: PMC10619367 DOI: 10.1002/hbm.26498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 09/12/2023] [Accepted: 09/15/2023] [Indexed: 09/30/2023] Open
Abstract
Considerable advances in the role of oxytocin (OT) effect on behavior and the brain network have been made, but the effect of OT on the association between inter-individual differences in functional connectivity (FC) and behavior is elusive. Here, by using a face-perception task and multiple connectome-based predictive models, we aimed to (1) determine whether OT could enhance the association among behavioral performance, resting-state FC (rsFC), and task-state FC (tsFC) and (2) if so, explore the role of OT in enhancing this triangular association. We found that in the OT group, the prediction performance of using rsFC or tsFC to predict task behavior was higher than that of the PL group. Additionally, the correlation coefficient between rsFC and tsFC was substantially higher in the OT group than in the PL group. The strength of these associations could be partly explained by OT altering the brain's FCs related to social cognition and face perception in both the resting and task states, mainly in brain regions such as the limbic system, prefrontal cortex, temporal poles, and temporoparietal junction. Taken together, these results provide novel evidence and a corresponding mechanism for how neuropeptides cause increased associations among inter-individual differences across different levels (e.g., behavior and large-scale brain networks in both resting and task-state), and may inspire future research on the role of neuropeptides in the cross levels association of both clinical and nonclinical use.
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Affiliation(s)
- Haoming Zhang
- Centre for Cognitive and Brain Sciences and Department of PsychologyUniversity of MacauMacauChina
| | - Kun Chen
- Centre for Cognitive and Brain Sciences and Department of PsychologyUniversity of MacauMacauChina
| | - Jin Bao
- Shenzhen Neher Neural Plasticity Laboratory, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences (CAS)ShenzhenChina
- Shenzhen‐Hong Kong Institute of Brain Science‐Shenzhen Fundamental Research InstitutionsShenzhenChina
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences and Department of PsychologyUniversity of MacauMacauChina
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12
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Song L, Wang P, Li H, Weiss PH, Fink GR, Zhou X, Chen Q. Increased functional connectivity between the auditory cortex and the frontoparietal network compensates for impaired visuomotor transformation after early auditory deprivation. Cereb Cortex 2023; 33:11126-11145. [PMID: 37814363 DOI: 10.1093/cercor/bhad351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 10/11/2023] Open
Abstract
Early auditory deprivation leads to a reorganization of large-scale brain networks involving and extending beyond the auditory system. It has been documented that visuomotor transformation is impaired after early deafness, associated with a hyper-crosstalk between the task-critical frontoparietal network and the default-mode network. However, it remains unknown whether and how the reorganized large-scale brain networks involving the auditory cortex contribute to impaired visuomotor transformation after early deafness. Here, we asked deaf and early hard of hearing participants and normal hearing controls to judge the spatial location of a visual target. Compared with normal hearing controls, the superior temporal gyrus showed significantly increased functional connectivity with the frontoparietal network and the default-mode network in deaf and early hard of hearing participants, specifically during egocentric judgments. However, increased superior temporal gyrus-frontoparietal network and superior temporal gyrus-default-mode network coupling showed antagonistic effects on egocentric judgments. In deaf and early hard of hearing participants, increased superior temporal gyrus-frontoparietal network connectivity was associated with improved egocentric judgments, whereas increased superior temporal gyrus-default-mode network connectivity was associated with deteriorated performance in the egocentric task. Therefore, the data suggest that the auditory cortex exhibits compensatory neuroplasticity (i.e. increased functional connectivity with the task-critical frontoparietal network) to mitigate impaired visuomotor transformation after early auditory deprivation.
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Affiliation(s)
- Li Song
- Center for Studies of Psychological Application and School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Pengfei Wang
- Center for Studies of Psychological Application and School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Hui Li
- Center for Studies of Psychological Application and School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Peter H Weiss
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Wilhelm-Johnen-Strasse, Jülich 52428, Germany
- Department of Neurology, University Hospital Cologne, Cologne University, Cologne 509737, Germany
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Wilhelm-Johnen-Strasse, Jülich 52428, Germany
- Department of Neurology, University Hospital Cologne, Cologne University, Cologne 509737, Germany
| | - Xiaolin Zhou
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Qi Chen
- Center for Studies of Psychological Application and School of Psychology, South China Normal University, Guangzhou 510631, China
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Wilhelm-Johnen-Strasse, Jülich 52428, Germany
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13
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Norton SA, Baranger DAA, Young ES, Voss M, Hansen I, Bondy E, Rodrigues M, Paul SE, Edershile E, Hill PL, Oltmanns TF, Simpson J, Bogdan R. Reliability of diurnal salivary cortisol metrics: A meta-analysis and investigation in two independent samples. COMPREHENSIVE PSYCHONEUROENDOCRINOLOGY 2023; 16:100191. [PMID: 37635863 PMCID: PMC10458689 DOI: 10.1016/j.cpnec.2023.100191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 07/06/2023] [Indexed: 08/29/2023] Open
Abstract
Stress-induced dysregulation of diurnal cortisol is a cornerstone of stress-disease theories; however, observed associations between cortisol, stress, and health have been inconsistent. The reliability of diurnal cortisol features may contribute to these equivocal findings. Our meta-analysis (5 diurnal features from 11 studies; total participant n = 3307) and investigation (15 diurnal cortisol features) in 2 independent studies (St. Louis Personality and Aging Network [SPAN] Study, n = 147, ages 61-73; Minnesota Longitudinal Study of Risk and Adaptation [MLSRA] Study, n = 90, age 37) revealed large variability in the day-to-day test-retest reliability of diurnal features derived from salivary cortisol data (i.e., ICC = 0.00-0.75). Collectively, these data indicate that some commonly used diurnal cortisol features have poor reliability that is insufficient for individual differences research (e.g., cortisol awakening response) while others (e.g., area under the curve with respect to ground) have fair-to-good reliability that could support reliable identification of associations in well-powered studies.
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Affiliation(s)
- Sara A. Norton
- Washington University in St. Louis, Department of Psychological & Brain Sciences, USA
| | - David AA. Baranger
- Washington University in St. Louis, Department of Psychological & Brain Sciences, USA
| | - Ethan S. Young
- Utrecht University, Department of Psychology, the Netherlands
| | - Michaela Voss
- Washington University in St. Louis, Department of Psychological & Brain Sciences, USA
| | - Isabella Hansen
- Washington University in St. Louis, Department of Psychological & Brain Sciences, USA
| | - Erin Bondy
- Washington University in St. Louis, Department of Psychological & Brain Sciences, USA
| | - Merlyn Rodrigues
- Washington University in St. Louis, Department of Psychological & Brain Sciences, USA
| | - Sarah E. Paul
- Washington University in St. Louis, Department of Psychological & Brain Sciences, USA
| | | | - Patrick L. Hill
- Washington University in St. Louis, Department of Psychological & Brain Sciences, USA
| | - Thomas F. Oltmanns
- Washington University in St. Louis, Department of Psychological & Brain Sciences, USA
| | | | - Ryan Bogdan
- Washington University in St. Louis, Department of Psychological & Brain Sciences, USA
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Xu L, Ma H, Guan Y, Liu J, Huang H, Zhang Y, Tian L. A Siamese Network With Node Convolution for Individualized Predictions Based on Connectivity Maps Extracted From Resting-State fMRI Data. IEEE J Biomed Health Inform 2023; 27:5418-5429. [PMID: 37578917 DOI: 10.1109/jbhi.2023.3304974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Deep learning has demonstrated great potential for objective diagnosis of neuropsychiatric disorders based on neuroimaging data, which includes the promising resting-state functional magnetic resonance imaging (RS-fMRI). However, the insufficient sample size has long been a bottleneck for deep model training for the purpose. In this study, we proposed a Siamese network with node convolution (SNNC) for individualized predictions based on RS-fMRI data. With the involvement of Siamese network, which uses sample pair (rather than a single sample) as input, the problem of insufficient sample size can largely be alleviated. To adapt to connectivity maps extracted from RS-fMRI data, we applied node convolution to each of the two branches of the Siamese network. For regression purposes, we replaced the contrastive loss in classic Siamese network with the mean square error loss and thus enabled Siamese network to quantitatively predict label differences. The label of a test sample can be predicted based on any of the training samples, by adding the label of the training sample to the predicted label difference between them. The final prediction for a test sample in this study was made by averaging the predictions based on each of the training samples. The performance of the proposed SNNC was evaluated with age and IQ predictions based on a public dataset (Cam-CAN). The results indicated that SNNC can make effective predictions even with a sample size of as small as 40, and SNNC achieved state-of-the-art accuracy among a variety of deep models and standard machine learning approaches.
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15
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Chang SE, Lenartowicz A, Hellemann GS, Uddin LQ, Bearden CE. Variability in Cognitive Task Performance in Early Adolescence Is Associated With Stronger Between-Network Anticorrelation and Future Attention Problems. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:948-957. [PMID: 37881561 PMCID: PMC10593900 DOI: 10.1016/j.bpsgos.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/22/2022] [Accepted: 11/09/2022] [Indexed: 11/28/2022] Open
Abstract
Background Intraindividual variability (IIV) during cognitive task performance is a key behavioral index of attention and a consistent marker of attention-deficit/hyperactivity disorder. In adults, lower IIV has been associated with anticorrelation between the default mode network (DMN) and dorsal attention network (DAN)-thought to underlie effective allocation of attention. However, whether these behavioral and neural markers of attention are 1) associated with each other and 2) can predict future attention-related deficits has not been examined in a developmental, population-based cohort. Methods We examined relationships at the baseline visit between IIV on 3 cognitive tasks, DMN-DAN anticorrelation, and parent-reported attention problems using data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 11,878 participants, ages 9 to 10 years, female = 47.8%). We also investigated whether behavioral and neural markers of attention at baseline predicted attention problems 1, 2, and 3 years later. Results At baseline, greater DMN-DAN anticorrelation was associated with lower IIV across all 3 cognitive tasks (B = 0.22 to 0.25). Older age at baseline was associated with stronger DMN-DAN anticorrelation and lower IIV (B = -0.005 to -0.0004). Weaker DMN-DAN anticorrelation and IIV were cross-sectionally associated with attention problems (B = 1.41 to 7.63). Longitudinally, lower IIV at baseline was associated with less severe attention problems 1 to 3 years later, after accounting for baseline attention problems (B = 0.288 to 0.77). Conclusions The results suggest that IIV in early adolescence is associated with worsening attention problems in a representative cohort of U.S. youth. Attention deficits in early adolescence may be important for understanding and predicting future cognitive and clinical outcomes.
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Affiliation(s)
- Sarah E. Chang
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
| | - Agatha Lenartowicz
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
| | - Gerhard S. Hellemann
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
- Department of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
| | - Carrie E. Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
- Department of Psychology, University of California, Los Angeles, Los Angeles, California
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16
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Morriss J. Distinguishing different parameters of uncertainty under threat in the human brain. Neurosci Biobehav Rev 2023; 153:105385. [PMID: 37677992 DOI: 10.1016/j.neubiorev.2023.105385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/18/2023] [Accepted: 09/04/2023] [Indexed: 09/09/2023]
Affiliation(s)
- Jayne Morriss
- School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK.
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17
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Cao H, Barber AD, Rubio JM, Argyelan M, Gallego JA, Lencz T, Malhotra AK. Effects of phase encoding direction on test-retest reliability of human functional connectome. Neuroimage 2023; 277:120238. [PMID: 37364743 PMCID: PMC10529794 DOI: 10.1016/j.neuroimage.2023.120238] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/23/2023] [Accepted: 06/18/2023] [Indexed: 06/28/2023] Open
Abstract
The majority of human connectome studies in the literature based on functional magnetic resonance imaging (fMRI) data use either an anterior-to-posterior (AP) or a posterior-to-anterior (PA) phase encoding direction (PED). However, whether and how PED would affect test-retest reliability of functional connectome is unclear. Here, in a sample of healthy subjects with two sessions of fMRI scans separated by 12 weeks (two runs per session, one with AP, the other with PA), we tested the influence of PED on global, nodal, and edge connectivity in the constructed brain networks. All data underwent the state-of-the-art Human Connectome Project (HCP) pipeline to correct for phase-encoding-related distortions before entering analysis. We found that at the global level, the PA scans showed significantly higher intraclass correlation coefficients (ICCs) for global connectivity compared with AP scans, which was particularly prominent when using the Seitzman-300 atlas (versus the CAB-NP-718 atlas). At the nodal level, regions most strongly affected by PED were consistently mapped to the cingulate cortex, temporal lobe, sensorimotor areas, and visual areas, with significantly higher ICCs during PA scans compared with AP scans, regardless of atlas. Better ICCs were also observed during PA scans at the edge level, in particular when global signal regression (GSR) was not performed. Further, we demonstrated that the observed reliability differences between PEDs may relate to a similar effect on the reliability of temporal signal-to-noise ratio (tSNR) in the same regions (that PA scans were associated with higher reliability of tSNR than AP scans). Averaging the connectivity outcome from the AP and PA scans could increase median ICCs, especially at the nodal and edge levels. Similar results at the global and nodal levels were replicated in an independent, public dataset from the HCP-Early Psychosis (HCP-EP) study with a similar design but a much shorter scan session interval. Our findings suggest that PED has significant effects on the reliability of connectomic estimates in fMRI studies. We urge that these effects need to be carefully considered in future neuroimaging designs, especially in longitudinal studies such as those related to neurodevelopment or clinical intervention.
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Affiliation(s)
- Hengyi Cao
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States.
| | - Anita D Barber
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Jose M Rubio
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Miklos Argyelan
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Juan A Gallego
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Todd Lencz
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Anil K Malhotra
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, United States; Division of Psychiatry Research, Zucker Hillside Hospital, 265-16 74th Avenue, Glen Oaks, NY 11004, United States; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
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18
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Mantwill M, Asseyer S, Chien C, Kuchling J, Schmitz-Hübsch T, Brandt AU, Haynes JD, Paul F, Finke C. Functional connectome fingerprinting and stability in multiple sclerosis. Mult Scler J Exp Transl Clin 2023; 9:20552173231195879. [PMID: 37641618 PMCID: PMC10460476 DOI: 10.1177/20552173231195879] [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: 03/06/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023] Open
Abstract
Background Functional connectome fingerprinting can identify individuals based on their functional connectome. Previous studies relied mostly on short intervals between fMRI acquisitions. Objective This cohort study aimed to determine the stability of connectome-based identification and their underlying signatures in patients with multiple sclerosis and healthy individuals with long follow-up intervals. Methods We acquired resting-state fMRI in 70 patients with multiple sclerosis and 273 healthy individuals with long follow-up times (up to 4 and 9 years, respectively). Using functional connectome fingerprinting, we examined the stability of the connectome and additionally investigated which regions, connections and networks supported individual identification. Finally, we predicted cognitive and behavioural outcome based on functional connectivity. Results Multiple sclerosis patients showed connectome stability and identification accuracies similar to healthy individuals, with longer time delays between imaging sessions being associated with accuracies dropping from 89% to 76%. Lesion load, brain atrophy or cognitive impairment did not affect identification accuracies within the range of disease severity studied. Connections from the fronto-parietal and default mode network were consistently most distinctive, i.e., informative of identity. The functional connectivity also allowed the prediction of individual cognitive performances. Conclusion Our results demonstrate that discriminatory signatures in the functional connectome are stable over extended periods of time in multiple sclerosis, resulting in similar identification accuracies and distinctive long-lasting functional connectome fingerprinting signatures in patients and healthy individuals.
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Affiliation(s)
- Maron Mantwill
- Maron Mantwill, Hertzbergstraße 12, 12055 Berlin, Germany.
| | - Susanna Asseyer
- A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité-Universitätsmedizin, Experimental and Clinical Research Center, Berlin, Germany
- Neuroscience Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Claudia Chien
- A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité-Universitätsmedizin, Experimental and Clinical Research Center, Berlin, Germany
- Neuroscience Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Charitéplatz, Berlin, Germany
| | - Joseph Kuchling
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité-Universitätsmedizin, Experimental and Clinical Research Center, Berlin, Germany
- Neuroscience Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Tanja Schmitz-Hübsch
- A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité-Universitätsmedizin, Experimental and Clinical Research Center, Berlin, Germany
- Neuroscience Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Alexander U Brandt
- A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité-Universitätsmedizin, Experimental and Clinical Research Center, Berlin, Germany
- Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Department of Neurology, University of California, Irvine, CA, USA
| | - John-Dylan Haynes
- Faculty of Philosophy, Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin Berlin, Berlin, Germany
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Khodaei M, Laurienti PJ, Dagenbach D, Simpson SL. Brain working memory network indices as landmarks of intelligence. NEUROIMAGE. REPORTS 2023; 3:100165. [PMID: 37425210 PMCID: PMC10327823 DOI: 10.1016/j.ynirp.2023.100165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Identifying the neural correlates of intelligence has long been a goal in neuroscience. Recently, the field of network neuroscience has attracted researchers' attention as a means for answering this question. In network neuroscience, the brain is considered as an integrated system whose systematic properties provide profound insights into health and behavioral outcomes. However, most network studies of intelligence have used univariate methods to investigate topological network measures, with their focus limited to a few measures. Furthermore, most studies have focused on resting state networks despite the fact that brain activation during working memory tasks has been linked to intelligence. Finally, the literature is still missing an investigation of the association between network assortativity and intelligence. To address these issues, here we employ a recently developed mixed-modeling framework for analyzing multi-task brain networks to elucidate the most critical working memory task network topological properties corresponding to individuals' intelligence differences. We used a data set of 379 subjects (22-35 y/o) from the Human Connectome Project (HCP). Each subject's data included composite intelligence scores, and fMRI during resting state and a 2-back working memory task. Following comprehensive quality control and preprocessing of the minimally preprocessed fMRI data, we extracted a set of the main topological network features, including global efficiency, degree, leverage centrality, modularity, and clustering coefficient. The estimated network features and subject's confounders were then incorporated into the multi-task mixed-modeling framework to investigate how brain network changes between working memory and resting state relate to intelligence score. Our results indicate that the general intelligence score (cognitive composite score) is associated with a change in the relationship between connection strength and multiple network topological properties, including global efficiency, leverage centrality, and degree difference during working memory as it is compared to resting state. More specifically, we observed a higher increase in the positive association between global efficiency and connection strength for the high intelligence group when they switch from resting state to working memory. The strong connections might form superhighways for a more efficient global flow of information through the brain network. Furthermore, we found an increase in the negative association between degree difference and leverage centrality with connection strength during working memory tasks for the high intelligence group. These indicate higher network resilience and assortativity along with higher circuit-specific information flow during working memory for those with a higher intelligence score. Although the exact neurobiological implications of our results are speculative at this point, our results provide evidence for the significant association of intelligence with hallmark properties of brain networks during working memory.
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Affiliation(s)
- Mohammadreza Khodaei
- Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Paul J. Laurienti
- Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Dale Dagenbach
- Department of Psychology, Wake Forest University, Winston-Salem, NC, USA
| | - Sean L. Simpson
- Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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20
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He H, Lin W, Yang J, Chen Y, Tan S, Guan Q. Age-related intrinsic functional connectivity underlying emotion utilization. Cereb Cortex 2023:7033308. [PMID: 36758953 DOI: 10.1093/cercor/bhad023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 02/11/2023] Open
Abstract
Previous studies investigated the age-related positivity effect in terms of emotion perception and management, whereas little is known about whether the positivity effect is shown in emotion utilization (EU). If yes, the EU-related intrinsic functional connectivity and its age-associated alterations remain to be elucidated. In this study, we collected resting-state functional magnetic resonance imaging data from 62 healthy older adults and 72 undergraduates as well as their self-ratings of EU. By using the connectome-based predictive modeling (CPM) method, we constructed a predictive model of the positive relationship between EU self-ratings and resting-state functional connectivity. Lesion simulation analyses revealed that the medial-frontal network, default mode network, frontoparietal network, and subcortical regions played key roles in the EU-related CPM. Older subjects showed significantly higher EU self-ratings than undergraduates, which was associated with strengthened connectivity between the left dorsolateral prefrontal cortex and bilateral frontal poles, and between the left frontal pole and thalamus. A mediation analysis indicated that the age-related EU network mediated the age effect on EU self-ratings. Our findings extend previous research on the age-related "positivity effect" to the EU domain, suggesting that the positivity effect on the self-evaluation of EU is probably associated with emotion knowledge which accumulates with age.
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Affiliation(s)
- Hao He
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Wenyi Lin
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China
| | - Jiawang Yang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China
| | - Yiqi Chen
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Department of Psychology, University of Mannheim, Mannheim, Germany
| | - Siping Tan
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
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21
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Pae C, Kim MJ, Chang WS, Jung HH, Chang KW, Eo J, Park HJ, Chang JW. Differences in intrinsic functional networks in patients with essential tremor who had good and poor long-term responses after thalamotomy performed using MR-guided ultrasound. J Neurosurg 2023; 138:318-328. [PMID: 35901685 DOI: 10.3171/2022.5.jns22324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/24/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Thalamotomy at the nucleus ventralis intermedius using MR-guided focused ultrasound has been an effective treatment method for essential tremor (ET). However, this is not true for all cases, even for successful ablation. How the brain differs in patients with ET between those with long-term good and poor outcomes is not clear. To analyze the functional connectivity difference between patients in whom thalamotomy was effective and those in whom thalamotomy was ineffective and its prognostic role in ET treatment, the authors evaluated preoperative resting-state functional MRI in thalamotomy-treated patients. METHODS Preoperative resting-state functional MRI data in 85 patients with ET, who were experiencing tremor relief at the time of treatment and were followed up for a minimum of 6 months after the procedure, were collected for the study. The authors conducted a graph independent component analysis of the functional connectivity matrices of tremor-related networks. The patients were divided into thalamotomy-effective and thalamotomy-ineffective groups (thalamotomy-effective group, ≥ 50% motor symptom reduction; thalamotomy-ineffective group, < 50% motor symptom reduction at 6 months after treatment) and the authors compared network components between groups. RESULTS Seventy-two (84.7%) of the 85 patients showed ≥ 50% tremor reduction from baseline at 6 months after thalamotomy. The network analysis shows significant suppression of functional network components with connections between the areas of the cerebellum and the basal ganglia and thalamus, but enhancement of those between the premotor cortex and supplementary motor area in the noneffective group compared to the effective group. CONCLUSIONS The present study demonstrates that patients in the noneffective group have suppressed functional subnetworks in the cerebellum and subcortex regions and have enhanced functional subnetworks among motor-sensory cortical networks compared to the thalamotomy-effective group. Therefore, the authors suggest that the functional connectivity pattern might be a possible predictive factor for outcomes of MR-guided focused ultrasound thalamotomy.
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Affiliation(s)
- Chongwon Pae
- 1Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul.,2Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul.,8Department of Psychiatry, Bundang CHA Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Myung Ji Kim
- 3Department of Neurosurgery, Korea University College of Medicine, Korea University Medical Center, Ansan Hospital, Gyeonggi-do
| | - Won Seok Chang
- 4Department of Neurosurgery, Yonsei University College of Medicine, Seoul.,5Center for Innovative Functional Neurosurgery, Brain Research Institute, Seoul
| | - Hyun Ho Jung
- 4Department of Neurosurgery, Yonsei University College of Medicine, Seoul.,5Center for Innovative Functional Neurosurgery, Brain Research Institute, Seoul
| | - Kyung Won Chang
- 4Department of Neurosurgery, Yonsei University College of Medicine, Seoul
| | - Jinseok Eo
- 1Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul.,2Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul.,6Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul
| | - Hae-Jeong Park
- 1Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul.,2Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul.,6Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul.,7Department of Cognitive Science, Yonsei University, Seoul; and
| | - Jin Woo Chang
- 4Department of Neurosurgery, Yonsei University College of Medicine, Seoul.,5Center for Innovative Functional Neurosurgery, Brain Research Institute, Seoul
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22
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Boyle R, Connaughton M, McGlinchey E, Knight SP, De Looze C, Carey D, Stern Y, Robertson IH, Kenny RA, Whelan R. Connectome-based predictive modelling of cognitive reserve using task-based functional connectivity. Eur J Neurosci 2023; 57:490-510. [PMID: 36512321 PMCID: PMC10107737 DOI: 10.1111/ejn.15896] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 11/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022]
Abstract
Cognitive reserve supports cognitive function in the presence of pathology or atrophy. Functional neuroimaging may enable direct and accurate measurement of cognitive reserve which could have considerable clinical potential. The present study aimed to develop and validate a measure of cognitive reserve using task-based fMRI data that could then be applied to independent resting-state data. Connectome-based predictive modelling with leave-one-out cross-validation was applied to predict a residual measure of cognitive reserve using task-based functional connectivity from the Cognitive Reserve/Reference Ability Neural Network studies (n = 220, mean age = 51.91 years, SD = 17.04 years). This model generated summary measures of connectivity strength that accurately predicted a residual measure of cognitive reserve in unseen participants. The theoretical validity of these measures was established via a positive correlation with a socio-behavioural proxy of cognitive reserve (verbal intelligence) and a positive correlation with global cognition, independent of brain structure. This fitted model was then applied to external test data: resting-state functional connectivity data from The Irish Longitudinal Study on Ageing (TILDA, n = 294, mean age = 68.3 years, SD = 7.18 years). The network-strength predicted measures were not positively associated with a residual measure of cognitive reserve nor with measures of verbal intelligence and global cognition. The present study demonstrated that task-based functional connectivity data can be used to generate theoretically valid measures of cognitive reserve. Further work is needed to establish if, and how, measures of cognitive reserve derived from task-based functional connectivity can be applied to independent resting-state data.
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Affiliation(s)
- Rory Boyle
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
| | - Michael Connaughton
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
- Department of Psychiatry, School of MedicineTrinity College DublinDublinIreland
| | - Eimear McGlinchey
- School of Nursing and MidwiferyTrinity College DublinDublinIreland
- Global Brain Health InstituteTrinity College DublinDublinIreland
| | - Silvin P. Knight
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
| | - Céline De Looze
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
| | - Daniel Carey
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of NeurologyColumbia UniversityNew York CityNew YorkUSA
| | - Ian H. Robertson
- Global Brain Health InstituteTrinity College DublinDublinIreland
| | - Rose Anne Kenny
- The Irish Longitudinal Study on Aging (TILDA), School of MedicineTrinity College DublinDublinIreland
- Mercer's Institute for Successful AgeingSt. James's HospitalDublinIreland
| | - Robert Whelan
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
- Global Brain Health InstituteTrinity College DublinDublinIreland
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23
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Wanger TJ, Janes AC, Frederick BB. Spatial variation of changes in test-retest reliability of functional connectivity after global signal regression: The effect of considering hemodynamic delay. Hum Brain Mapp 2022; 44:668-678. [PMID: 36214198 PMCID: PMC9842913 DOI: 10.1002/hbm.26091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 08/24/2022] [Accepted: 09/07/2022] [Indexed: 01/25/2023] Open
Abstract
Global signal regression (GSR) is a controversial analysis method, since its removal of signal has been observed to reduce the reliability of functional connectivity estimates. Here, we used test-retest reliability to characterize potential differences in spatial patterns between conventional, static GSR (sGSR) and a novel dynamic form of GSR (dGSR). In contrast with sGSR, dGSR models the global signal at a time delay to correct for blood arrival time. Thus, dGSR accounts for greater variation in global signal, removes blood-flow-related nuisance signal, and leaves higher quality neuronal signal remaining. We used intraclass correlation coefficients (ICCs) to estimate the reliability of functional connectivity in 462 healthy controls from the Human Connectome Project. We tested across two factors: denoising method used (control, sGSR, and dGSR), and interacquisition interval (between days, or within session while varying phase encoding direction). Reliability was estimated regionally to identify topographic patterns for each condition. sGSR and dGSR provided global reductions in reliability compared with the non-GSR control. Test-retest reliability was highest in the frontoparietal and default mode regions, and lowest in sensorimotor cortex for all conditions. dGSR provides more effective denoising in regions where both strategies greatly reduce reliability. Both GSR methods substantially reduced test-retest reliability, which was most evident in brain regions that had low reliability prior to denoising. These findings suggest that reliability of interregional correlation is likely inflated by the global signal, which is thought to primarily reflect dynamic blood flow.
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Affiliation(s)
- Timothy J. Wanger
- McLean Imaging CenterMcLean HospitalBelmontMassachusettsUSA,Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Amy C. Janes
- Neuroimaging Research Branch, National Institute on Drug Abuse (NIDA)Intramural Research Program, National Institutes of HealthBaltimoreMarylandUSA
| | - Blaise B. Frederick
- McLean Imaging CenterMcLean HospitalBelmontMassachusettsUSA,Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
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24
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Xiao L, Cai B, Qu G, Zhang G, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies. IEEE Trans Biomed Eng 2022; 69:3039-3050. [PMID: 35316180 PMCID: PMC9594860 DOI: 10.1109/tbme.2022.3160447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity (FC) patterns have been extensively used to delineate global functional organization of the human brain in healthy development and neuropsychiatric disorders. In this paper, we investigate how FC in males and females differs in an age prediction framework. METHODS We first estimate FC between regions-of-interest (ROIs) using distance correlation instead of Pearson's correlation. Distance correlation, as a multivariate statistical method, explores spatial relations of voxel-wise time courses within individual ROIs and measures both linear and nonlinear dependence, capturing more complex between-ROI interactions. Then, we propose a novel non-convex multi-task learning (NC-MTL) model to study age-related gender differences in FC, where age prediction for each gender group is viewed as one task, and a composite regularizer with a combination of the non-convex l2,1-2 and l1-2 terms is introduced for selecting both common and task-specific features. RESULTS AND CONCLUSION We validate the effectiveness of our NC-MTL model with distance correlation-based FC derived from rs-fMRI for predicting ages of both genders. The experimental results on the Philadelphia Neurodevelopmental Cohort demonstrate that our NC-MTL model outperforms several other competing MTL models in age prediction. We also compare the age prediction performance of our NC-MTL model using FC estimated by Pearson's correlation and distance correlation, which shows that distance correlation-based FC is more discriminative for age prediction than Pearson's correlation-based FC. SIGNIFICANCE This paper presents a novel framework for functional connectome developmental studies, characterizing developmental gender differences in FC patterns.
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25
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Michon KJ, Khammash D, Simmonite M, Hamlin AM, Polk TA. Person-specific and precision neuroimaging: Current methods and future directions. Neuroimage 2022; 263:119589. [PMID: 36030062 DOI: 10.1016/j.neuroimage.2022.119589] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/13/2022] [Accepted: 08/23/2022] [Indexed: 10/31/2022] Open
Abstract
Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.
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Affiliation(s)
| | - Dalia Khammash
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Molly Simmonite
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Abbey M Hamlin
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Thad A Polk
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
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26
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Yoo K, Rosenberg MD, Kwon YH, Scheinost D, Constable RT, Chun MM. A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome. Neuroimage 2022; 257:119279. [PMID: 35577026 PMCID: PMC9307138 DOI: 10.1016/j.neuroimage.2022.119279] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 04/11/2022] [Accepted: 05/02/2022] [Indexed: 11/07/2022] Open
Abstract
The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model context-dependent changes. Here, we present a novel connectome-to-connectome (C2C) transformation framework that enables us to model the brain's functional reorganization from one connectome state to another in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-related connectomes from their task-free (resting-state) connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions with increased power, achieving similar performance with just a third of the subjects needed when relying on resting-state data alone. Finally, the C2C model reveals how the brain reorganizes between cognitive states. Our observations support the existence of reliable state-specific subsystems in the brain and demonstrate that we can quantitatively model how the connectome reconfigures to different cognitive states, enabling more accurate predictions of behavior with fewer subjects.
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Affiliation(s)
- Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America.
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America,Department of Psychology, University of Chicago, Chicago, IL 60637, United States of America
| | - Young Hye Kwon
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, United States of America
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, United States of America,Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, United States of America,Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06510, United States of America
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT 06520, United States of America,Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, United States of America,Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, United States of America
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27
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Vieira BH, Pamplona GSP, Fachinello K, Silva AK, Foss MP, Salmon CEG. On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting. INTELLIGENCE 2022. [DOI: 10.1016/j.intell.2022.101654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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28
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Yoo K, Rosenberg MD, Kwon YH, Lin Q, Avery EW, Sheinost D, Constable RT, Chun MM. A brain-based general measure of attention. Nat Hum Behav 2022; 6:782-795. [PMID: 35241793 PMCID: PMC9232838 DOI: 10.1038/s41562-022-01301-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 01/14/2022] [Indexed: 11/15/2022]
Abstract
Attention is central to many aspects of cognition, but there is no singular neural measure of a person's overall attentional functioning across tasks. Here, using original data from 92 participants performing three different attention-demanding tasks during functional magnetic resonance imaging, we constructed a suite of whole-brain models that can predict a profile of multiple attentional components (sustained attention, divided attention and tracking, and working memory capacity) for novel individuals. Multiple brain regions across the salience, subcortical and frontoparietal networks drove accurate predictions, supporting a common (general) attention factor across tasks, distinguished from task-specific ones. Furthermore, connectome-to-connectome transformation modelling generated an individual's task-related connectomes from rest functional magnetic resonance imaging, substantially improving predictive power. Finally, combining the connectome transformation and general attention factor, we built a standardized measure that shows superior generalization across four independent datasets (total N = 495) of various attentional measures, suggesting broad utility for research and clinical applications.
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Affiliation(s)
- Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT, USA.
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT, USA,Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Young Hye Kwon
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Qi Lin
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Emily W Avery
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Dustin Sheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA,Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT, USA. .,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA. .,Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA. .,Wu Tsai Institute, Yale University, New Haven, CT, USA.
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29
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Blair RJR, Mathur A, Haines N, Bajaj S. Future directions for cognitive neuroscience in psychiatry: recommendations for biomarker design based on recent test re-test reliability work. Curr Opin Behav Sci 2022. [DOI: 10.1016/j.cobeha.2022.101102] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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30
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Han X, Ashar YK, Kragel P, Petre B, Schelkun V, Atlas LY, Chang LJ, Jepma M, Koban L, Losin EAR, Roy M, Woo CW, Wager TD. Effect sizes and test-retest reliability of the fMRI-based neurologic pain signature. Neuroimage 2022; 247:118844. [PMID: 34942367 PMCID: PMC8792330 DOI: 10.1016/j.neuroimage.2021.118844] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/13/2021] [Accepted: 12/19/2021] [Indexed: 01/28/2023] Open
Abstract
Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49). The NPS showed excellent short-term (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with ≥60 trials required for excellent test-retest reliability. Reliability was tested in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicate that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions.
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Affiliation(s)
- Xiaochun Han
- Faculty of Psychology, Beijing Normal University, Beijing, China; Dartmouth College, Hanover, NH, United States
| | - Yoni K Ashar
- Weill Cornell Medical College, New York, NY, United States
| | | | | | | | - Lauren Y Atlas
- National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, United States; National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States; National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | | | | | | | | | - Mathieu Roy
- Department of Psychology, McGill University, Montreal, Quebec, Canada
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Gyeonggi-do, South Korea
| | - Tor D Wager
- Dartmouth College, Hanover, NH, United States.
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31
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Räty S, Ruuth R, Silvennoinen K, Sabel BA, Tatlisumak T, Vanni S. Resting-state Functional Connectivity After Occipital Stroke. Neurorehabil Neural Repair 2021; 36:151-163. [PMID: 34949135 DOI: 10.1177/15459683211062897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Resting-state functional magnetic resonance imaging (rsfMRI) reflects spontaneous activation of cortical networks. After stroke, these networks reorganize, both due to structural lesion and reorganization of functional connectivity (FC). OBJECTIVE We studied FC in chronic phase occipital stroke patients with homonymous visual field defects before and after repetitive transorbital alternating current stimulation (rtACS). METHODS This spin-off study, embedded in the randomized, sham-controlled REVIS trial, comprised 16 chronic occipital stroke patients with visual field defect and 12 healthy control subjects. The patients underwent rsfMRI at baseline, after two weeks of rtACS or sham treatment, and after two months of treatment-free follow-up, whereas the control subjects were measured once. We used a multivariate regression connectivity model to determine mutual prediction accuracy between 74 cortical regions of interest. Additionally, the model parameters were included into a graph to analyze average path length, centrality eigenvector, centrality degree, and clustering of the network. The patients and controls at baseline and the two treatment groups were compared with multilevel modeling. RESULTS Before treatment, the patients and controls had similar whole-network prediction accuracy and network parameters, whereas centrality eigenvector differed in perilesional regions, indicating local modification in connectivity. In line with behavioral results, neither prediction accuracy nor any network parameter changed systematically as a result of rtACS rehabilitation compared to sham. CONCLUSIONS Whole-network FC showed no difference between occipital stroke patients and healthy population, congruent with the peripheral location of the visual network in relation to the high-density cortical core. rtACS treatment in the given setting did not affect FC.
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Affiliation(s)
- Silja Räty
- Department of Neurology, 3836Helsinki University Hospital and University of Helsinki, Helsinki, Finland.,Advanced Magnetic Imaging Centre, 174277Aalto University, Espoo, Finland
| | - Riikka Ruuth
- HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Katri Silvennoinen
- Department of Neurology, 3836Helsinki University Hospital and University of Helsinki, Helsinki, Finland.,Department of Clinical and Experimental Epilepsy, 61554UCL Queen Square Institute of Neurology, London, UK
| | - Bernhard A Sabel
- Institute of Medical Psychology, Medical Faculty, Otto-v, -Guericke University of Magdeburg, Magdeburg, Germany
| | - Turgut Tatlisumak
- Department of Clinical Neurosciences/Neurology, 70712Institute of Neurosciences and Physiology, Sahlgrenska Academy at University of Gothenburg and Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Simo Vanni
- Department of Neurology, 3836Helsinki University Hospital and University of Helsinki, Helsinki, Finland
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32
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Predicting multilingual effects on executive function and individual connectomes in children: An ABCD study. Proc Natl Acad Sci U S A 2021; 118:2110811118. [PMID: 34845019 DOI: 10.1073/pnas.2110811118] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2021] [Indexed: 11/18/2022] Open
Abstract
While there is a substantial amount of work studying multilingualism's effect on cognitive functions, little is known about how the multilingual experience modulates the brain as a whole. In this study, we analyzed data of over 1,000 children from the Adolescent Brain Cognitive Development (ABCD) Study to examine whether monolinguals and multilinguals differ in executive function, functional brain connectivity, and brain-behavior associations. We observed significantly better performance from multilingual children than monolinguals in working-memory tasks. In one finding, we were able to classify multilinguals from monolinguals using only their whole-brain functional connectome at rest and during an emotional n-back task. Compared to monolinguals, the multilingual group had different functional connectivity mainly in the occipital lobe and subcortical areas during the emotional n-back task and in the occipital lobe and prefrontal cortex at rest. In contrast, we did not find any differences in behavioral performance and functional connectivity when performing a stop-signal task. As a second finding, we investigated the degree to which behavior is reflected in the brain by implementing a connectome-based behavior prediction approach. The multilingual group showed a significant correlation between observed and connectome-predicted individual working-memory performance scores, while the monolingual group did not show any correlations. Overall, our observations suggest that multilingualism enhances executive function and reliably modulates the corresponding brain functional connectome, distinguishing multilinguals from monolinguals even at the developmental stage.
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Lee D, Quattrocki Knight E, Song H, Lee S, Pae C, Yoo S, Park HJ. Differential structure-function network coupling in the inattentive and combined types of attention deficit hyperactivity disorder. PLoS One 2021; 16:e0260295. [PMID: 34851976 PMCID: PMC8635373 DOI: 10.1371/journal.pone.0260295] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 11/05/2021] [Indexed: 11/19/2022] Open
Abstract
The heterogeneous presentation of inattentive and hyperactive-impulsive core symptoms in attention deficit hyperactivity disorder (ADHD) warrants further investigation into brain network connectivity as a basis for subtype divisions in this prevalent disorder. With diffusion and resting-state functional magnetic resonance imaging data from the Healthy Brain Network database, we analyzed both structural and functional network efficiency and structure-functional network (SC-FC) coupling at the default mode (DMN), executive control (ECN), and salience (SAN) intrinsic networks in 201 children diagnosed with the inattentive subtype (ADHD-I), the combined subtype (ADHD-C), and typically developing children (TDC) to characterize ADHD symptoms relative to TDC and to test differences between ADHD subtypes. Relative to TDC, children with ADHD had lower structural connectivity and network efficiency in the DMN, without significant group differences in functional networks. Children with ADHD-C had higher SC-FC coupling, a finding consistent with diminished cognitive flexibility, for all subnetworks compared to TDC. The ADHD-C group also demonstrated increased SC-FC coupling in the DMN compared to the ADHD-I group. The correlation between SC-FC coupling and hyperactivity scores was negative in the ADHD-I, but not in the ADHD-C group. The current study suggests that ADHD-C and ADHD-I may differ with respect to their underlying neuronal connectivity and that the added dimensionality of hyperactivity may not explain this distinction.
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Affiliation(s)
- Dongha Lee
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, Republic of Korea
| | - Elizabeth Quattrocki Knight
- Department of Psychiatry, Harvard Medical School, McLean Hospital, Belmont, Massachusetts, United States of America
| | - Hyunjoo Song
- Department of Educational Psychology, Seoul Women’s University, Seoul, Republic of Korea
| | - Saebyul Lee
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chongwon Pae
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sol Yoo
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
- Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
- * E-mail:
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Luo Q, Liu W, Jin L, Chang C, Peng Z. Classification of Obsessive-Compulsive Disorder Using Distance Correlation on Resting-State Functional MRI Images. Front Neuroinform 2021; 15:676491. [PMID: 34744676 PMCID: PMC8564498 DOI: 10.3389/fninf.2021.676491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022] Open
Abstract
Both the Pearson correlation and partial correlation methods have been widely used in the resting-state functional MRI (rs-fMRI) studies. However, they can only measure linear relationship, although partial correlation excludes some indirect effects. Recent distance correlation can discover both the linear and non-linear dependencies. Our goal was to use the multivariate pattern analysis to compare the ability of such three correlation methods to distinguish between the patients with obsessive-compulsive disorder (OCD) and healthy control subjects (HCSs), so as to find optimal correlation method. The main process includes four steps. First, the regions of interest are defined by automated anatomical labeling (AAL). Second, functional connectivity (FC) matrices are constructed by the three correlation methods. Third, the best discriminative features are selected by support vector machine recursive feature elimination (SVM-RFE) with a stratified N-fold cross-validation strategy. Finally, these discriminative features are used to train a classifier. We had a total of 128 subjects out of which 61 subjects had OCD and 67 subjects were normal. All the three correlation methods with SVM have achieved good results, among which distance correlation is the best [accuracy = 93.01%, specificity = 89.71%, sensitivity = 95.08%, and area under the receiver-operating characteristic curve (AUC) = 0.94], followed by Pearson correlation and partial correlation is the last. The most discriminative regions of the brain for distance correlation are right dorsolateral superior frontal gyrus, orbital part of left superior frontal gyrus, orbital part of right middle frontal gyrus, right anterior cingulate and paracingulate gyri, left the supplementary motor area, and right precuneus, which are the promising biomarkers of OCD.
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Affiliation(s)
- Qian Luo
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China.,National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China
| | - Weixiang Liu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China.,National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China
| | - Lili Jin
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.,Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China
| | - Chunqi Chang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China.,National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China.,Peng Cheng Laboratory, Shenzhen, China
| | - Ziwen Peng
- Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China.,Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen University School of Medicine, Shenzhen, China
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Weigard AS, Brislin SJ, Cope LM, Hardee JE, Martz ME, Ly A, Zucker RA, Sripada C, Heitzeg MM. Evidence accumulation and associated error-related brain activity as computationally-informed prospective predictors of substance use in emerging adulthood. Psychopharmacology (Berl) 2021; 238:2629-2644. [PMID: 34173032 PMCID: PMC8452274 DOI: 10.1007/s00213-021-05885-w] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 05/27/2021] [Indexed: 01/05/2023]
Abstract
RATIONALE Substance use peaks during the developmental period known as emerging adulthood (ages 18-25), but not every individual who uses substances during this period engages in frequent or problematic use. Although individual differences in neurocognition appear to predict use severity, mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have yet to be identified. Here, we aim to do so with an approach rooted in computational psychiatry, an emerging field in which formal models are used to identify candidate biobehavioral dimensions that confer risk for psychopathology. OBJECTIVES We test whether lower efficiency of evidence accumulation (EEA), a computationally characterized individual difference variable that drives performance on the go/no-go and other neurocognitive tasks, is a risk factor for substance use in emerging adults. METHODS AND RESULTS In an fMRI substudy within a sociobehavioral longitudinal study (n = 106), we find that lower EEA and reductions in a robust neural-level correlate of EEA (error-related activations in salience network structures) measured at ages 18-21 are both prospectively related to greater substance use during ages 22-26, even after adjusting for other well-known risk factors. Results from Bayesian model comparisons corroborated inferences from conventional hypothesis testing and provided evidence that both EEA and its neuroimaging correlates contain unique predictive information about substance use involvement. CONCLUSIONS These findings highlight EEA as a computationally characterized neurocognitive risk factor for substance use during a critical developmental period, with clear links to both neuroimaging measures and well-established formal theories of brain function.
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Affiliation(s)
- Alexander S Weigard
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.
| | - Sarah J Brislin
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Lora M Cope
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Jillian E Hardee
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Meghan E Martz
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Alexander Ly
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
- Machine Learning Group, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
| | - Robert A Zucker
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Mary M Heitzeg
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
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Stark GF, Avery EW, Rosenberg MD, Greene AS, Gao S, Scheinost D, Todd Constable R, Chun MM, Yoo K. Using functional connectivity models to characterize relationships between working and episodic memory. Brain Behav 2021; 11:e02105. [PMID: 34142458 PMCID: PMC8413720 DOI: 10.1002/brb3.2105] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 01/26/2021] [Accepted: 02/18/2021] [Indexed: 02/02/2023] Open
Abstract
INTRODUCTION Working memory is a critical cognitive ability that affects our daily functioning and relates to many cognitive processes and clinical conditions. Episodic memory is vital because it enables individuals to form and maintain their self-identities. Our study analyzes the extent to which whole-brain functional connectivity observed during completion of an N-back memory task, a common measure of working memory, can predict both working memory and episodic memory. METHODS We used connectome-based predictive models (CPMs) to predict 502 Human Connectome Project (HCP) participants' in-scanner 2-back memory test scores and out-of-scanner working memory test (List Sorting) and episodic memory test (Picture Sequence and Penn Word) scores based on functional magnetic resonance imaging (fMRI) data collected both during rest and N-back task performance. We also analyzed the functional brain connections that contributed to prediction for each of these models. RESULTS Functional connectivity observed during N-back task performance predicted out-of-scanner List Sorting scores and to a lesser extent out-of-scanner Picture Sequence scores, but did not predict out-of-scanner Penn Word scores. Additionally, the functional connections predicting 2-back scores overlapped to a greater degree with those predicting List Sorting scores than with those predicting Picture Sequence or Penn Word scores. Functional connections with the insula, including connections between insular and parietal regions, predicted scores across the 2-back, List Sorting, and Picture Sequence tasks. CONCLUSIONS Our findings validate functional connectivity observed during the N-back task as a measure of working memory, which generalizes to predict episodic memory to a lesser extent. By building on our understanding of the predictive power of N-back task functional connectivity, this work enhances our knowledge of relationships between working memory and episodic memory.
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Affiliation(s)
- Gigi F Stark
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Emily W Avery
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT, USA.,Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Diagnostic Radiology, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.,Department of Diagnostic Radiology, Yale School of Medicine, New Haven, CT, USA.,Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT, USA.,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.,Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT, USA
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Jo Y, Faskowitz J, Esfahlani FZ, Sporns O, Betzel RF. Subject identification using edge-centric functional connectivity. Neuroimage 2021; 238:118204. [PMID: 34087363 DOI: 10.1016/j.neuroimage.2021.118204] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 05/23/2021] [Accepted: 05/24/2021] [Indexed: 12/11/2022] Open
Abstract
Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed 'fingerprinting' analyses on functional connectivity to identify subjects' idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterize subjects' identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.
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Affiliation(s)
- Youngheun Jo
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
| | - Farnaz Zamani Esfahlani
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA.
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Zhao Y, Caffo BS, Wang B, Li CSR, Luo X. A whole-brain modeling approach to identify individual and group variations in functional connectivity. Brain Behav 2021; 11:e01942. [PMID: 33210469 PMCID: PMC7821576 DOI: 10.1002/brb3.1942] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 12/28/2022] Open
Abstract
Resting-state functional connectivity is an important and widely used measure of individual and group differences. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a modeling approach that regresses whole-brain functional connectivity on covariates. Our approach is a mesoscale approach that enables identification of brain subnetworks. These subnetworks are composite of spatially independent components discovered by a dimension reduction approach (such as whole-brain group ICA) and covariate-related projections determined by the covariate-assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results suggest that the approach may improve statistical power in detecting interaction effects of gender and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bingkai Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA.,Department of Neuroscience, Yale School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA
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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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Abdallah CG. Brain Networks Associated With COVID-19 Risk: Data From 3662 Participants. CHRONIC STRESS 2021; 5:24705470211066770. [PMID: 34993375 PMCID: PMC8725219 DOI: 10.1177/24705470211066770] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 11/24/2021] [Accepted: 11/29/2021] [Indexed: 11/18/2022]
Abstract
Background Our behavioral traits, and subsequent actions, could affect the risk of exposure to the
coronavirus disease of 2019 (COVID-19). The current study aimed to determine whether
unique brain networks are associated with the COVID-19 infection risk. Methods This research was conducted using the UK Biobank Resource. Functional magnetic
resonance imaging scans in a cohort of general population (n = 3662) were used to
compute the whole-brain functional connectomes. A network-informed machine learning
approach was used to identify connectome and nodal fingerprints that are associated with
positive COVID-19 status during the pandemic up to February fourth, 2021. Results The predictive models successfully identified 6 fingerprints that were associated with
COVID-19 positive, compared to negative status (all p values <
0.005). Overall, lower integration across the brain modules and increased segregation,
as reflected by internal within module connectivity, were associated with higher
infection rates. More specifically, COVID-19 positive status was associated with 1)
reduced connectivity between the central executive and ventral salience, as well as
between the dorsal salience and default mode networks; 2) increased internal
connectivity within the default mode, ventral salience, subcortical and sensorimotor
networks; and 3) increased connectivity between the ventral salience, subcortical and
sensorimotor networks. Conclusion Individuals are at increased risk of COVID-19 infections if their brain connectome is
consistent with reduced connectivity in the top-down attention and executive networks,
along with increased internal connectivity in the introspective and instinctive
networks. These identified risk networks could be investigated as target for treatment
of illnesses with impulse control deficits.
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Affiliation(s)
- Chadi G. Abdallah
- VA Medical Center, Houston, TX, USA
- Baylor College of Medicine, Houston, TX, USA
- West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
- Chadi G. Abdallah, Menninger Department of
Psychiatry, Baylor College of Medicine; 1977 Butler Blvd, E4187, Houston, TX, 77030 USA.
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He T, Kong R, Holmes AJ, Nguyen M, Sabuncu MR, Eickhoff SB, Bzdok D, Feng J, Yeo BTT. Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. Neuroimage 2020; 206:116276. [PMID: 31610298 PMCID: PMC6984975 DOI: 10.1016/j.neuroimage.2019.116276] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 09/16/2019] [Accepted: 10/10/2019] [Indexed: 12/14/2022] Open
Abstract
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.
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Affiliation(s)
- Tong He
- Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Ru Kong
- Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Avram J Holmes
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - Minh Nguyen
- Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Danilo Bzdok
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, Germany; Parietal Team, INRIA, Neurospin, Bat 145, CEA Saclay, 91191, Gif-sur-Yvette, France
| | - Jiashi Feng
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - B T Thomas Yeo
- Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore.
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Noble S, Scheinost D, Constable RT. A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis. Neuroimage 2019; 203:116157. [PMID: 31494250 PMCID: PMC6907736 DOI: 10.1016/j.neuroimage.2019.116157] [Citation(s) in RCA: 262] [Impact Index Per Article: 52.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 08/30/2019] [Accepted: 09/02/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Once considered mere noise, fMRI-based functional connectivity has become a major neuroscience tool in part due to early studies demonstrating its reliability. These fundamental studies revealed only the tip of the iceberg; over the past decade, many test-retest reliability studies have continued to add nuance to our understanding of this complex topic. A summary of these diverse and at times contradictory perspectives is needed. OBJECTIVES We aimed to summarize the existing knowledge regarding test-retest reliability of functional connectivity at the most basic unit of analysis: the individual edge level. This entailed (1) a meta-analytic estimate of reliability and (2) a review of factors influencing reliability. METHODS A search of Scopus was conducted to identify studies that estimated edge-level test-retest reliability. To facilitate comparisons across studies, eligibility was restricted to studies measuring reliability via the intraclass correlation coefficient (ICC). The meta-analysis included a random effects pooled estimate of mean edge-level ICC, with studies nested within datasets. The review included a narrative summary of factors influencing edge-level ICC. RESULTS From an initial pool of 212 studies, 44 studies were identified for the qualitative review and 25 studies for quantitative meta-analysis. On average, individual edges exhibited a "poor" ICC of 0.29 (95% CI = 0.23 to 0.36). The most reliable measurements tended to involve: (1) stronger, within-network, cortical edges, (2) eyes open, awake, and active recordings, (3) more within-subject data, (4) shorter test-retest intervals, (5) no artifact correction (likely due in part to reliable artifact), and (6) full correlation-based connectivity with shrinkage. CONCLUSION This study represents the first meta-analysis and systematic review investigating test-retest reliability of edge-level functional connectivity. Key findings suggest there is room for improvement, but care should be taken to avoid promoting reliability at the expense of validity. By pooling existing knowledge regarding this key facet of accuracy, this study supports broader efforts to improve inferences in the field.
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Affiliation(s)
- Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University, USA.
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Statistics and Data Science, Yale University, USA; Child Study Center, Yale School of Medicine, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA
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Nielsen AN, Barch DM, Petersen SE, Schlaggar BL, Greene DJ. Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 5:791-798. [PMID: 31982357 DOI: 10.1016/j.bpsc.2019.11.007] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 10/29/2019] [Accepted: 11/17/2019] [Indexed: 01/08/2023]
Abstract
Psychiatric disorders are complex, involving heterogeneous symptomatology and neurobiology that rarely involves the disruption of single, isolated brain structures. In an attempt to better describe and understand the complexities of psychiatric disorders, investigators have increasingly applied multivariate pattern classification approaches to neuroimaging data and in particular supervised machine learning methods. However, supervised machine learning approaches also come with unique challenges and trade-offs, requiring additional study design and interpretation considerations. The goal of this review is to provide a set of best practices for evaluating machine learning applications to psychiatric disorders. We discuss how to evaluate two common efforts: 1) making predictions that have the potential to aid in diagnosis, prognosis, and treatment and 2) interrogating the complex neurophysiological mechanisms underlying psychopathology. We focus here on machine learning as applied to functional connectivity with magnetic resonance imaging, as an example to ground discussion. We argue that for machine learning classification to have translational utility for individual-level predictions, investigators must ensure that the classification is clinically informative, independent of confounding variables, and appropriately assessed for both performance and generalizability. We contend that shedding light on the complex mechanisms underlying psychiatric disorders will require consideration of the unique utility, interpretability, and reliability of the neuroimaging features (e.g., regions, networks, connections) identified from machine learning approaches. Finally, we discuss how the rise of large, multisite, publicly available datasets may contribute to the utility of machine learning approaches in psychiatry.
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Affiliation(s)
- Ashley N Nielsen
- Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, Illinois; Department of Medical Social Sciences, Northwestern University, Chicago, Illinois.
| | - Deanna M Barch
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Steven E Petersen
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri; Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Bradley L Schlaggar
- Kennedy Krieger Institute, Baltimore, Maryland; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Deanna J Greene
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
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Lu Z, Huang Y, Lu Q, Feng L, Nguchu BA, Wang Y, Wang H, Li G, Zhou Y, Qiu B, Zhou J, Wang X. Abnormal intra-network architecture in extra-striate cortices in amblyopia: a resting state fMRI study. EYE AND VISION 2019; 6:20. [PMID: 31334295 PMCID: PMC6615160 DOI: 10.1186/s40662-019-0145-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 06/13/2019] [Indexed: 11/18/2022]
Abstract
Background Amblyopia (lazy eye) is one of the most common causes of monocular visual impairment. Intensive investigation has shown that amblyopes suffer from a range of deficits not only in the primary visual cortex but also the extra-striate visual cortex. However, amblyopic brain processing deficits in large-scale information networks especially in the visual network remain unclear. Methods Through resting state functional magnetic resonance imaging (rs-fMRI), we studied the functional connectivity and efficiency of the brain visual processing networks in 18 anisometropic amblyopic patients and 18 healthy controls (HCs). Results We found a loss of functional correlation within the higher visual network (HVN) and the visuospatial network (VSN) in amblyopes. Additionally, compared with HCs, amblyopic patients exhibited disruptions in local efficiency in the V3v (third visual cortex, ventral part) and V4 (fourth visual cortex) of the HVN, as well as in the PFt, hIP3 (human intraparietal area 3), and BA7p (Brodmann area 7 posterior) of the VSN. No significant alterations were found in the primary visual network (PVN). Conclusion Our results indicate that amblyopia results in an intrinsic decrease of both network functional correlations and local efficiencies in the extra-striate visual networks.
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Affiliation(s)
- Zhuo Lu
- 1Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China
| | - Yufeng Huang
- 2Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China.,3CAS Key Laboratory of Brain Function and Diseases and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China.,4Shenyang Aerospace University, Shenyang, Liaoning 110136 People's Republic of China
| | - Qilin Lu
- 2Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China.,3CAS Key Laboratory of Brain Function and Diseases and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China
| | - Lixia Feng
- 5Department of Ophthalmology, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui People's Republic of China
| | - Benedictor Alexander Nguchu
- 1Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China
| | - Yanming Wang
- 1Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China
| | - Huijuan Wang
- 1Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China
| | - Geng Li
- Asia Pediatric Ophthalmologist Association, Rm 2006, CC Wu Bldg., 302-308 Hennessy Rd., Wanchai, Hong Kong, People's Republic of China
| | - Yifeng Zhou
- 2Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China.,3CAS Key Laboratory of Brain Function and Diseases and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China
| | - Bensheng Qiu
- 1Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China
| | - Jiawei Zhou
- 7School of Ophthalmology and Optometry and Eye hospital, Wenzhou Medical University, Wenzhou, Zhejiang, 325003 People's Republic of China
| | - Xiaoxiao Wang
- 1Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China.,2Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China.,3CAS Key Laboratory of Brain Function and Diseases and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230027 People's Republic of China
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