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Shi L, Ren Z, Feng Q, Qiu J. Individualized prediction of online shopping addiction from whole-brain functional connectivity. Neuropsychologia 2024:108967. [PMID: 39103090 DOI: 10.1016/j.neuropsychologia.2024.108967] [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: 02/21/2024] [Revised: 06/30/2024] [Accepted: 08/02/2024] [Indexed: 08/07/2024]
Abstract
Online shopping addiction (OSA) is defined as a behavioral addiction where an individual exhibits an unhealthy and excessive attachment to shopping on the Internet. Since the OSA shown its adverse impacts on individuals' daily life and social functions, it is important to examine the neurobiological underpinnings of OSA that could be used in clinical practice to identify individuals with OSA. The present study addressed this question by employing a connectome-based prediction model approach to predict the OSA tendency of healthy subjects from whole-brain resting-state functional connectivity. The OSA connectome - a set of connections across multiple brain networks that contributed to predict individuals' OSA tendency was identified, including the functional connectivity between the frontal-parietal network (FPN) and cingulo-opercular network (CON) (i.e., positive network), as well as the functional connectivity within default mode network (DMN) and that between FPN and DMN (i.e., negative network). Key nodes that contributed to the prediction model included the middle frontal gyrus, inferior frontal gyrus, anterior cingulate cortex, and inferior temporal gyrus, which have been associated with impulsivity and emotional processing. Notably, this connectome has shown its specific role in predicting OSA by controlling for the influence of general Internet addiction. Moreover, the strength of the negative network mediated the relationship between OSA and impulsivity, highlighting that the negative network underlies the impulsivity characteristic of OSA. Together, these findings advanced our understanding of the neural correlates of OSA and provided a promising framework for diagnosing OSA.
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Affiliation(s)
- Liang Shi
- School of Psychology, Nanjing Normal University, Nanjing, China.
| | - Zhiting Ren
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Faculty of Psychology, Southwest University, Chongqing, China
| | - Qiuyang Feng
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Faculty of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Faculty of Psychology, Southwest University, Chongqing, China.
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2
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Lu J, Yan T, Yang L, Zhang X, Li J, Li D, Xiang J, Wang B. Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability. Neuroimage 2024; 295:120651. [PMID: 38788914 DOI: 10.1016/j.neuroimage.2024.120651] [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: 03/19/2024] [Revised: 05/16/2024] [Accepted: 05/22/2024] [Indexed: 05/26/2024] Open
Abstract
The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.
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Affiliation(s)
- Jiayu Lu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, 100081, China
| | - Lan Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xi Zhang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jiaxin Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China.
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Mandino F, Horien C, Shen X, Desrosiers-Gregoire G, Luo W, Markicevic M, Constable RX, Papademetris X, Chakravarty MM, Betzel RF, Lake EMR. Multimodal identification of the mouse brain using simultaneous Ca 2+ imaging and fMRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.594620. [PMID: 38826324 PMCID: PMC11142213 DOI: 10.1101/2024.05.24.594620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome- based identification to be successful and explored various features of these data.
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4
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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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5
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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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6
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Mellema CJ, Montillo AA. Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI. J Neural Eng 2023; 20:066023. [PMID: 37963396 DOI: 10.1088/1741-2552/ad0c5f] [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: 07/06/2023] [Accepted: 11/14/2023] [Indexed: 11/16/2023]
Abstract
Objective.New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of FC (ML.FC) which efficiently captures linear and nonlinear aspects.Approach.To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity (ML.EC), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality (SP.GC) adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms ofreproducibilityand theability to predict individual traitsin order to demonstrate these measures' internal validity. We use four repeat scans of the same individuals from the Human Connectome Project and measure the ability of the measures to predict individual subject physiologic and cognitive traits.Main results.The proposed new FC measure ofML.FCattains high reproducibility (mean intra-subjectR2of 0.44), while the proposed EC measure ofSP.GCattains the highest predictive power (meanR2across prediction tasks of 0.66).Significance.The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.
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Affiliation(s)
- Cooper J Mellema
- Lyda Hill Department of Bioinformatics, Dallas, TX, United States of America
- Biomedical Engineering Department, Dallas, TX, United States of America
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, United States of America
| | - Albert A Montillo
- Lyda Hill Department of Bioinformatics, Dallas, TX, United States of America
- Biomedical Engineering Department, Dallas, TX, United States of America
- Advanced Imaging Research Center, Dallas, TX, United States of America
- Radiology Department, Dallas, TX, United States of America
- University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, United States of America
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7
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Liu X, Yang L. Individual differences in the language task-evoked and resting-state functional networks. Front Hum Neurosci 2023; 17:1283069. [PMID: 38021226 PMCID: PMC10656779 DOI: 10.3389/fnhum.2023.1283069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 09/29/2023] [Indexed: 12/01/2023] Open
Abstract
The resting state functional network is highly variable across individuals. However, inter-individual differences in functional networks evoked by language tasks and their comparison with resting state are still unclear. To address these two questions, we used T1 anatomical data and functional brain imaging data of resting state and a story comprehension task from the Human Connectome Project (HCP) to characterize functional network variability and investigate the uniqueness of the functional network in both task and resting states. We first demonstrated that intrinsic and task-induced functional networks exhibited remarkable differences across individuals, and language tasks can constrain inter-individual variability in the functional brain network. Furthermore, we found that the inter-individual variability of functional networks in two states was broadly consistent and spatially heterogeneous, with high-level association areas manifesting more significant variability than primary visual processing areas. Our results suggested that the functional network underlying language comprehension is unique at the individual level, and the inter-individual variability architecture of the functional network is broadly consistent in language task and resting state.
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Affiliation(s)
- Xin Liu
- Air Force Medical Center, Air Force Medical University, Beijing, China
| | - Liu Yang
- Air Force Medical Center, Air Force Medical University, Beijing, China
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8
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Rabini G, Pierotti E, Meli C, Dodich A, Papagno C, Turella L. Connectome-based fingerprint of motor impairment is stable along the course of Parkinson's disease. Cereb Cortex 2023; 33:9896-9907. [PMID: 37455441 DOI: 10.1093/cercor/bhad252] [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: 04/21/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023] Open
Abstract
Functional alterations in brain connectivity have previously been described in Parkinson's disease, but it is not clear whether individual differences in connectivity profiles might be also linked to severity of motor-symptom manifestation. Here we investigated the relevance of individual functional connectivity patterns measured with resting-state fMRI with respect to motor-symptom severity in Parkinson's disease, through a whole-brain, data-driven approach (connectome-based predictive modeling). Neuroimaging and clinical data of Parkinson's disease patients from the Parkinson's Progression Markers Initiative were derived at baseline (session 1, n = 81) and at follow-up (session 2, n = 53). Connectome-based predictive modeling protocol was implemented to predict levels of motor impairment from individual connectivity profiles. The resulting predictive model comprised a network mainly involving functional connections between regions located in the cerebellum, and in the motor and frontoparietal networks. The predictive power of the model was stable along disease progression, as the connectivity within the same network could predict levels of motor impairment, even at a later stage of the disease. Finally, connectivity profiles within this network could be identified at the individual level, suggesting the presence of individual fingerprints within resting-state fMRI connectivity associated with motor manifestations in Parkinson's disease.
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Affiliation(s)
- Giuseppe Rabini
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Enrica Pierotti
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Claudia Meli
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Alessandra Dodich
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Costanza Papagno
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Luca Turella
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
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9
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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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10
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Horien C, Greene AS, Shen X, Fortes D, Brennan-Wydra E, Banarjee C, Foster R, Donthireddy V, Butler M, Powell K, Vernetti A, Mandino F, O’Connor D, Lake EMR, McPartland JC, Volkmar FR, Chun M, Chawarska K, Rosenberg MD, Scheinost D, Constable RT. A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth. Cereb Cortex 2023; 33:6320-6334. [PMID: 36573438 PMCID: PMC10183743 DOI: 10.1093/cercor/bhac506] [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: 03/09/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 12/29/2022] Open
Abstract
Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- MD-PhD Program, Yale School of Medicine, New Haven, CT, United States
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- MD-PhD Program, Yale School of Medicine, New Haven, CT, United States
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Diogo Fortes
- Yale Child Study Center, New Haven, CT, United States
| | | | | | - Rachel Foster
- Yale Child Study Center, New Haven, CT, United States
| | | | | | - Kelly Powell
- Yale Child Study Center, New Haven, CT, United States
| | | | - Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - David O’Connor
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - James C McPartland
- Yale Child Study Center, New Haven, CT, United States
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Fred R Volkmar
- Yale Child Study Center, New Haven, CT, United States
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Marvin Chun
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Katarzyna Chawarska
- Yale Child Study Center, New Haven, CT, United States
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, United States
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL, United States
- Neuroscience Institute, University of Chicago, Chicago, IL, United States
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
- Yale Child Study Center, New Haven, CT, United States
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, United States
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11
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Shinn M, Hu A, Turner L, Noble S, Preller KH, Ji JL, Moujaes F, Achard S, Scheinost D, Constable RT, Krystal JH, Vollenweider FX, Lee D, Anticevic A, Bullmore ET, Murray JD. Functional brain networks reflect spatial and temporal autocorrelation. Nat Neurosci 2023; 26:867-878. [PMID: 37095399 DOI: 10.1038/s41593-023-01299-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 03/14/2023] [Indexed: 04/26/2023]
Abstract
High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.
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Affiliation(s)
- Maxwell Shinn
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Amber Hu
- Yale College, Yale University, New Haven, CT, USA
| | | | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Katrin H Preller
- Department of Psychiatry, Yale University, New Haven, CT, USA
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, Zurich, Switzerland
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Flora Moujaes
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, Zurich, Switzerland
| | - Sophie Achard
- University of Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Franz X Vollenweider
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry, Zurich, Switzerland
| | - Daeyeol Lee
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA
- Kavli Discovery Neuroscience Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - Alan Anticevic
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | | | - John D Murray
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.
- Department of Psychiatry, Yale University, New Haven, CT, USA.
- Department of Physics, Yale University, New Haven, CT, USA.
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12
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Helwegen K, Libedinsky I, van den Heuvel MP. Statistical power in network neuroscience. Trends Cogn Sci 2023; 27:282-301. [PMID: 36725422 DOI: 10.1016/j.tics.2022.12.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/31/2023]
Abstract
Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies.
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Affiliation(s)
- Koen Helwegen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ilan Libedinsky
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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13
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Cutts SA, Faskowitz J, Betzel RF, Sporns O. Uncovering individual differences in fine-scale dynamics of functional connectivity. Cereb Cortex 2023; 33:2375-2394. [PMID: 35690591 DOI: 10.1093/cercor/bhac214] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/07/2022] [Accepted: 05/08/2022] [Indexed: 01/01/2023] Open
Abstract
Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.
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Affiliation(s)
- Sarah A Cutts
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States.,Network Science Institute, Indiana University, Bloomington, IN 47408, United States.,Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States.,Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States.,Network Science Institute, Indiana University, Bloomington, IN 47408, United States.,Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
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14
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Kroemer NB, Opel N, Teckentrup V, Li M, Grotegerd D, Meinert S, Lemke H, Kircher T, Nenadić I, Krug A, Jansen A, Sommer J, Steinsträter O, Small DM, Dannlowski U, Walter M. Functional Connectivity of the Nucleus Accumbens and Changes in Appetite in Patients With Depression. JAMA Psychiatry 2022; 79:993-1003. [PMID: 36001327 PMCID: PMC9403857 DOI: 10.1001/jamapsychiatry.2022.2464] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/14/2022] [Indexed: 11/14/2022]
Abstract
Importance Major depressive disorder (MDD) is characterized by a substantial burden on health, including changes in appetite and body weight. Heterogeneity of depressive symptoms has hampered the identification of biomarkers that robustly generalize to most patients, thus calling for symptom-based mapping. Objective To define the functional architecture of the reward circuit subserving increases vs decreases in appetite and body weight in patients with MDD by specifying their contributions and influence on disease biomarkers using resting-state functional connectivity (FC). Design, Setting, and Participants In this case-control study, functional magnetic resonance imaging (fMRI) data were taken from the Marburg-Münster FOR 2107 Affective Disorder Cohort Study (MACS), collected between September 2014 and November 2016. Cross-sectional data of patients with MDD (n = 407) and healthy control participants (n = 400) were analyzed from March 2018 to June 2022. Main Outcomes and Measures Changes in appetite during the depressive episode and their association with FC were examined using fMRI. By taking the nucleus accumbens (NAcc) as seed of the reward circuit, associations with opposing changes in appetite were mapped, and a sparse symptom-specific elastic-net model was built with 10-fold cross-validation. Results Among 407 patients with MDD, 249 (61.2%) were women, and the mean (SD) age was 36.79 (13.4) years. Reduced NAcc-based FC to the ventromedial prefrontal cortex (vmPFC) and the hippocampus was associated with reduced appetite (vmPFC: bootstrap r = 0.13; 95% CI, 0.02-0.23; hippocampus: bootstrap r = 0.15; 95% CI, 0.05-0.26). In contrast, reduced NAcc-based FC to the insular ingestive cortex was associated with increased appetite (bootstrap r = -0.14; 95% CI, -0.24 to -0.04). Critically, the cross-validated elastic-net model reflected changes in appetite based on NAcc FC and explained variance increased with increasing symptom severity (all patients: bootstrap r = 0.24; 95% CI, 0.16-0.31; patients with Beck Depression Inventory score of 28 or greater: bootstrap r = 0.42; 95% CI, 0.25-0.58). In contrast, NAcc FC did not classify diagnosis (MDD vs healthy control). Conclusions and Relevance In this study, NAcc-based FC reflected important individual differences in appetite and body weight in patients with depression that can be leveraged for personalized prediction. However, classification of diagnosis using NAcc-based FC did not exceed chance levels. Such symptom-specific associations emphasize the need to map biomarkers onto more confined facets of psychopathology to improve the classification and treatment of MDD.
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Affiliation(s)
- Nils B. Kroemer
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Nils Opel
- Department of Psychiatry and Psychotherapy, University Hospital Jena, Jena, Germany
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Vanessa Teckentrup
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University Hospital Jena, Jena, Germany
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Jens Sommer
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Olaf Steinsträter
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Dana M. Small
- Departments of Psychiatry and Psychology, Yale University, New Haven, Connecticut
- Modern Diet and Physiology Research Center, Yale University, New Haven, Connecticut
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University Hospital Jena, Jena, Germany
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
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15
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Huang Y, Deng Y, Jiang X, Chen Y, Mao T, Xu Y, Jiang C, Rao H. Resting-state occipito-frontal alpha connectome is linked to differential word learning ability in adult learners. Front Neurosci 2022; 16:953315. [PMID: 36188469 PMCID: PMC9521374 DOI: 10.3389/fnins.2022.953315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/18/2022] [Indexed: 11/19/2022] Open
Abstract
Adult language learners show distinct abilities in acquiring a new language, yet the underlying neural mechanisms remain elusive. Previous studies suggested that resting-state brain connectome may contribute to individual differences in learning ability. Here, we recorded electroencephalography (EEG) in a large cohort of 106 healthy young adults (50 males) and examined the associations between resting-state alpha band (8–12 Hz) connectome and individual learning ability during novel word learning, a key component of new language acquisition. Behavioral data revealed robust individual differences in the performance of the novel word learning task, which correlated with their performance in the language aptitude test. EEG data showed that individual resting-state alpha band coherence between occipital and frontal regions positively correlated with differential word learning performance (p = 0.001). The significant positive correlations between resting-state occipito-frontal alpha connectome and differential world learning ability were replicated in an independent cohort of 35 healthy adults. These findings support the key role of occipito-frontal network in novel word learning and suggest that resting-state EEG connectome may be a reliable marker for individual ability during new language learning.
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Affiliation(s)
- Yan Huang
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
- School of Foreign Languages, East China University of Science and Technology, Shanghai, China
- Institute of Linguistics, Shanghai International Studies University, Shanghai, China
| | - Yao Deng
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
| | - Xiaoming Jiang
- Institute of Linguistics, Shanghai International Studies University, Shanghai, China
| | - Yiyuan Chen
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
- Institute of Linguistics, Shanghai International Studies University, Shanghai, China
| | - Tianxin Mao
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
| | - Yong Xu
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
| | - Caihong Jiang
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
| | - Hengyi Rao
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
- Institute of Linguistics, Shanghai International Studies University, Shanghai, China
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- *Correspondence: Hengyi Rao,
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16
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Reliability and subject specificity of personalized whole-brain dynamical models. Neuroimage 2022; 257:119321. [PMID: 35580807 DOI: 10.1016/j.neuroimage.2022.119321] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/06/2022] [Accepted: 05/12/2022] [Indexed: 11/23/2022] Open
Abstract
Dynamical whole-brain models were developed to link structural (SC) and functional connectivity (FC) together into one framework. Nowadays, they are used to investigate the dynamical regimes of the brain and how these relate to behavioral, clinical and demographic traits. However, there is no comprehensive investigation on how reliable and subject specific the modeling results are given the variability of the empirical FC. In this study, we show that the parameters of these models can be fitted with a "poor" to "good" reliability depending on the exact implementation of the modeling paradigm. We find, as a general rule of thumb, that enhanced model personalization leads to increasingly reliable model parameters. In addition, we observe no clear effect of the model complexity evaluated by separately sampling results for linear, phase oscillator and neural mass network models. In fact, the most complex neural mass model often yields modeling results with "poor" reliability comparable to the simple linear model, but demonstrates an enhanced subject specificity of the model similarity maps. Subsequently, we show that the FC simulated by these models can outperform the empirical FC in terms of both reliability and subject specificity. For the structure-function relationship, simulated FC of individual subjects may be identified from the correlations with the empirical SC with an accuracy up to 70%, but not vice versa for non-linear models. We sample all our findings for 8 distinct brain parcellations and 6 modeling conditions and show that the parcellation-induced effect is much more pronounced for the modeling results than for the empirical data. In sum, this study provides an exploratory account on the reliability and subject specificity of dynamical whole-brain models and may be relevant for their further development and application. In particular, our findings suggest that the application of the dynamical whole-brain modeling should be tightly connected with an estimate of the reliability of the results.
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17
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Kühnel A, Czisch M, Sämann PG, Binder EB, Kroemer NB. Spatiotemporal Dynamics of Stress-Induced Network Reconfigurations Reflect Negative Affectivity. Biol Psychiatry 2022; 92:158-169. [PMID: 35260225 DOI: 10.1016/j.biopsych.2022.01.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 01/09/2022] [Accepted: 01/13/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND Maladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk. METHODS Using an established psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines. RESULTS We identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy = 70%, pperm < .001) and increases in heart rate (R2 = 0.075, pperm < .001). Critically, individual spatiotemporal trajectories of changes across networks also predicted negative affectivity (ΔR2 = 0.075, pperm = .030) but not the presence or absence of a mood and anxiety disorder. CONCLUSIONS Spatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.
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Affiliation(s)
- Anne Kühnel
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany; International Max Planck Research School for Translational Psychiatry, Munich, Germany.
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- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.
| | - Nils B Kroemer
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
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18
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Graff K, Tansey R, Rai S, Ip A, Rohr C, Dimond D, Dewey D, Bray S. Functional connectomes become more longitudinally self-stable, but not more distinct from others, across early childhood. Neuroimage 2022; 258:119367. [PMID: 35716841 DOI: 10.1016/j.neuroimage.2022.119367] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 06/06/2022] [Accepted: 06/08/2022] [Indexed: 11/17/2022] Open
Abstract
Functional connectomes, as measured with functional magnetic resonance imaging (fMRI), are highly individualized, and evidence suggests this individualization may increase across childhood. A connectome can become more individualized either by increasing self-stability or decreasing between-subject-similarity. Here we used a longitudinal early childhood dataset to investigate age associations with connectome self-stability, between-subject-similarity, and developmental individualization, defined as an individual's self-stability across a 12-month interval relative to their between-subject-similarity. fMRI data were collected during an 18-minute passive viewing scan from 73 typically developing children aged 4-7 years, at baseline and 12-month follow-up. We found that young children had highly individualized connectomes, with sufficient self-stability across 12-months for 98% identification accuracy. Linear models showed a significant relationship between age and developmental individualization across the whole brain and in most networks. This association appeared to be largely driven by an increase in self-stability with age, with only weak evidence for relationships between age and similarity across participants. Together our findings suggest that children's connectomes become more individualized across early childhood, and that this effect is driven by increasing self-stability rather than decreasing between-subject-similarity.
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Affiliation(s)
- Kirk Graff
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Neuroscience, University of Calgary, Calgary, AB, Canada.
| | - Ryann Tansey
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Neuroscience, University of Calgary, Calgary, AB, Canada
| | - Shefali Rai
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Neuroscience, University of Calgary, Calgary, AB, Canada
| | - Amanda Ip
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Neuroscience, University of Calgary, Calgary, AB, Canada
| | - Christiane Rohr
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Neuroscience, University of Calgary, Calgary, AB, Canada
| | - Dennis Dimond
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Neuroscience, University of Calgary, Calgary, AB, Canada
| | - Deborah Dewey
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, AB, Canada; Department of Pediatrics, University of Calgary, Calgary, AB, Canada; Community Health Science, University of Calgary, Calgary, AB, Canada
| | - Signe Bray
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada
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19
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Jwa AS, Poldrack RA. The spectrum of data sharing policies in neuroimaging data repositories. Hum Brain Mapp 2022; 43:2707-2721. [PMID: 35142409 PMCID: PMC9057092 DOI: 10.1002/hbm.25803] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 01/19/2022] [Accepted: 01/28/2022] [Indexed: 02/05/2023] Open
Abstract
Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of public investment and research resources. Considering these benefits, data sharing has been widely promoted in diverse fields and neuroscience has been no exception to this movement. For all its promise, however, the sharing of human neuroimaging data raises critical ethical and legal issues, such as data privacy. Recently, the heightened risks to data privacy posed by the rapid advances in artificial intelligence and machine learning techniques have made data sharing more challenging; the regulatory landscape around data sharing has also been evolving rapidly. Here we present an in-depth ethical and regulatory analysis that examines how neuroimaging data are currently shared against the backdrop of the relevant regulations and policies in the United States and how advanced software tools and algorithms might undermine subjects' privacy in neuroimaging data sharing. The implications of these novel technological threats to privacy in neuroimaging data sharing practices and policies will also be discussed. We then conclude with a proposal for a legal prohibition against malicious use of neuroscience data as a regulatory mechanism to address privacy risks associated with the data while maximizing the benefits of data sharing and open science practice in the field of neuroscience.
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Affiliation(s)
- Anita S Jwa
- Department of Psychology, Stanford University, Stanford, California, USA
| | - Russell A Poldrack
- Department of Psychology, Stanford University, Stanford, California, USA
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20
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Griffa A, Amico E, Liégeois R, Ville DVD, Preti MG. Brain structure-function coupling provides signatures for task decoding and individual fingerprinting. Neuroimage 2022; 250:118970. [DOI: 10.1016/j.neuroimage.2022.118970] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 01/12/2022] [Accepted: 02/03/2022] [Indexed: 11/17/2022] Open
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21
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Similarity and stability of face network across populations and throughout adolescence and adulthood. Neuroimage 2021; 244:118587. [PMID: 34560271 DOI: 10.1016/j.neuroimage.2021.118587] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 11/20/2022] Open
Abstract
The ability to extract cues from faces is fundamental for social animals, including humans. An individual's profile of functional connectivity across a face network can be shaped by common organizing principles, stable individual traits, and time-varying mental states. In the present study, we used data obtained with functional magnetic resonance imaging in two cohorts, IMAGEN (N = 534) and ALSPAC (N = 465), to investigate - both at group and individual levels - the consistency of the regional profile of functional connectivity across populations (IMAGEN, ALSPAC) and time (Visits 1 to 3 in IMAGEN; age 14 to 22 years). At the group level, we found a robust canonical profile of connectivity both across populations and time. At the individual level, connectivity profiles deviated from the canonical profile, and the magnitude of this deviation related to the presence of psychopathology. These findings suggest that the brain processes faces in a highly stereotypical manner, and that the deviations from this normative pattern may be related to the risk of mental illness.
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22
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Ribeiro FL, Dos Santos FRC, Sato JR, Pinaya WHL, Biazoli CE. Inferring the heritability of large-scale functional networks with a multivariate ACE modeling approach. Netw Neurosci 2021; 5:527-548. [PMID: 34189376 PMCID: PMC8233119 DOI: 10.1162/netn_a_00189] [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: 09/24/2020] [Accepted: 02/10/2021] [Indexed: 11/06/2022] Open
Abstract
Recent evidence suggests that the human functional connectome is stable at different timescales and is unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of additive genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. Twin pairs were identified above chance level using connectome fingerprinting, with monozygotic twin identification accuracy equal to 57.2% on average for whole-brain connectome. Additionally, we found that a visual (0.37), the medial frontal (0.31), and the motor (0.30) functional networks were the most influenced by additive genetic factors. Our findings suggest that genetic factors not only partially determine intersubject variability of the functional connectome, such that twins can be identified using connectome fingerprinting, but also differentially influence connectivity strength in large-scale functional networks. The functional connectome is a unique representation of the functional organization of the human brain. As such, it has been extensively used as an individual marker, a “fingerprint,” because of its high intersubject variability. Here, we sought to investigate the influence of genetic factors on intersubject variability of functional networks. Therefore, we extended the connectome fingerprinting analysis to the identification of twin pairs, and we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. We found that genetic factors not only partially determine intersubject variability of the functional connectome, such that monozygotic twin identification accuracy achieved 57.2% on average using whole-brain connectome in the fingerprinting analysis, but also differentially influence connectivity strength in large-scale functional networks.
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Affiliation(s)
- Fernanda L Ribeiro
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | | | - João R Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Walter H L Pinaya
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Claudinei E Biazoli
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
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23
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Finn ES, Rosenberg MD. Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes. Neuroimage 2021; 239:118254. [PMID: 34118397 DOI: 10.1016/j.neuroimage.2021.118254] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/25/2021] [Accepted: 06/07/2021] [Indexed: 12/20/2022] Open
Abstract
Recent years have seen a surge of research on variability in functional brain connectivity within and between individuals, with encouraging progress toward understanding the consequences of this variability for cognition and behavior. At the same time, well-founded concerns over rigor and reproducibility in psychology and neuroscience have led many to question whether functional connectivity is sufficiently reliable, and call for methods to improve its reliability. The thesis of this opinion piece is that when studying variability in functional connectivity-both across individuals and within individuals over time-we should use behavior prediction as our benchmark rather than optimize reliability for its own sake. We discuss theoretical and empirical evidence to compel this perspective, both when the goal is to study stable, trait-level differences between people, as well as when the goal is to study state-related changes within individuals. We hope that this piece will be useful to the neuroimaging community as we continue efforts to characterize inter- and intra-subject variability in brain function and build predictive models with an eye toward eventual real-world applications.
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Affiliation(s)
- Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, United States.
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, United States; Neuroscience Institute, University of Chicago, United States.
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24
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Li K, Wisner K, Atluri G. Feature selection framework for functional connectome fingerprinting. Hum Brain Mapp 2021; 42:3717-3732. [PMID: 34076306 PMCID: PMC8288098 DOI: 10.1002/hbm.25379] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 12/03/2020] [Accepted: 02/09/2021] [Indexed: 12/03/2022] Open
Abstract
The ability to uniquely characterize individual subjects based on their functional connectome (FC) is a key requirement for progress toward precision psychiatry. FC fingerprinting is increasingly studied in the neuroimaging community for this purpose, where a variety of approaches have been developed for effective FC fingerprinting. Recent independent studies showed that fingerprinting accuracy suffers at large sample sizes and when coarser parcellations are used for computing the FC. Quantifying this problem and understanding the reasons these factors impact fingerprinting accuracy is crucial to develop more accurate fingerprinting methods for large sample sizes. Part of the challenge in fingerprinting is that FC captures both generic and subject‐specific information. A systematic approach for identifying subject‐specific FC information is crucial for making progress in addressing the fingerprinting problem. In this study, we addressed three gaps in our understanding of the FC fingerprinting problem. First, we studied the joint effect of sample size and parcellation granularity. Second, we explained the reason for reduced fingerprinting accuracy with increased sample size and reduced parcellation granularity. To this end, we used a clustering quality metric from the data mining community. Third, we developed a general feature selection framework for systematically identifying resting‐state functional connectivity (RSFC) elements that capture information to uniquely identify subjects. In sum, we evaluated six different approaches from this framework by quantifying both subject‐specific fingerprinting accuracy and the decrease in accuracy with an increase in sample size to identify which approach improved quality metrics the most.
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Affiliation(s)
- Kendrick Li
- Department of EECS, University of Cincinnati, Cincinnati, Ohio, USA
| | - Krista Wisner
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA
| | - Gowtham Atluri
- Department of EECS, University of Cincinnati, Cincinnati, Ohio, USA
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25
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Wang Z, Sair HI, Crainiceanu C, Lindquist M, Landman BA, Resnick S, Vogelstein JT, Caffo B. On statistical tests of functional connectome fingerprinting. CAN J STAT 2021. [DOI: 10.1002/cjs.11591] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Zeyi Wang
- Department of Biostatistics Johns Hopkins University Baltimore MD U.S.A
| | - Haris I. Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Neuroradiology Johns Hopkins University Baltimore MD U.S.A
| | | | - Martin Lindquist
- Department of Biostatistics Johns Hopkins University Baltimore MD U.S.A
| | - Bennett A. Landman
- Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN U.S.A
| | - Susan Resnick
- Laboratory of Behavioral Neuroscience National Institute on Aging, National Institutes of Health Baltimore MD U.S.A
| | - Joshua T. Vogelstein
- Department of Biomedical Engineering Institute of Computational Medicine, JHU Baltimore Maryland U.S.A
| | - Brian Caffo
- Department of Biostatistics Johns Hopkins University Baltimore MD U.S.A
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26
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Parkes L, Satterthwaite TD, Bassett DS. Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment. Curr Opin Neurobiol 2020; 65:120-128. [PMID: 33242721 PMCID: PMC7770086 DOI: 10.1016/j.conb.2020.10.016] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 10/11/2020] [Accepted: 10/14/2020] [Indexed: 02/01/2023]
Abstract
Searching for biomarkers has been a chief pursuit of the field of psychiatry. Toward this end, studies have catalogued candidate resting-state biomarkers in nearly all forms of mental disorder. However, it is becoming increasingly clear that these biomarkers lack specificity, limiting their capacity to yield clinical impact. We discuss three avenues of research that are overcoming this limitation: (i) the adoption of transdiagnostic research designs, which involve studying and explicitly comparing multiple disorders from distinct diagnostic axes of psychiatry; (ii) dimensional models of psychopathology that map the full spectrum of symptomatology and that cut across traditional disorder boundaries; and (iii) modeling individuals' unique functional connectomes throughout development. We provide a framework for tying these subfields together that draws on tools from machine learning and network science.
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA.
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27
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Ren Z, Daker RJ, Shi L, Sun J, Beaty RE, Wu X, Chen Q, Yang W, Lyons IM, Green AE, Qiu J. Connectome-Based Predictive Modeling of Creativity Anxiety. Neuroimage 2020; 225:117469. [PMID: 33099006 DOI: 10.1016/j.neuroimage.2020.117469] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 08/04/2020] [Accepted: 10/12/2020] [Indexed: 12/16/2022] Open
Abstract
While a recent upsurge in the application of neuroimaging methods to creative cognition has yielded encouraging progress toward understanding the neural underpinnings of creativity, the neural basis of barriers to creativity are as yet unexplored. Here, we report the first investigation into the neural correlates of one such recently identified barrier to creativity: anxiety specific to creative thinking, or creativity anxiety (Daker et al., 2019). We employed a machine-learning technique for exploring relations between functional connectivity and behavior (connectome-based predictive modeling; CPM) to investigate the functional connections underlying creativity anxiety. Using whole-brain resting-state functional connectivity data, we identified a network of connections or "edges" that predicted individual differences in creativity anxiety, largely comprising connections within and between regions of the executive and default networks and the limbic system. We then found that the edges related to creativity anxiety identified in one sample generalize to predict creativity anxiety in an independent sample. We additionally found evidence that the network of edges related to creativity anxiety were largely distinct from those found in previous work to be related to divergent creative ability (Beaty et al., 2018). In addition to being the first work on the neural correlates of creativity anxiety, this research also included the development of a new Chinese-language version of the Creativity Anxiety Scale, and demonstrated that key behavioral findings from the initial work on creativity anxiety are replicable across cultures and languages.
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Affiliation(s)
- Zhiting Ren
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China
| | - Richard J Daker
- Department of Psychology, Georgetown University, Washington, DC, 20057, USA
| | - Liang Shi
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China
| | - Roger E Beaty
- Department of Psychology, Pennsylvania State University, State College, PA, 16801, USA
| | - Xinran Wu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China
| | - Ian M Lyons
- Department of Psychology, Georgetown University, Washington, DC, 20057, USA
| | - Adam E Green
- Department of Psychology, Georgetown University, Washington, DC, 20057, USA.
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China.
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28
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Jalbrzikowski M, Liu F, Foran W, Klei L, Calabro FJ, Roeder K, Devlin B, Luna B. Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5-years apart. Hum Brain Mapp 2020; 41:4187-4199. [PMID: 32652852 PMCID: PMC7502841 DOI: 10.1002/hbm.25118] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/06/2020] [Accepted: 04/12/2020] [Indexed: 12/28/2022] Open
Abstract
Pioneering studies have shown that individual correlation measures from resting‐state functional magnetic resonance imaging studies can identify another scan from that same individual. This method is known as “connectotyping” or functional connectome “fingerprinting.” We analyzed a unique dataset of 12–30 years old (N = 140) individuals who had two distinct resting state scans on the same day and again 12–18 months later to assess the sensitivity and specificity of fingerprinting accuracy across different time scales (same day, ~1.5 years apart) and developmental periods (youths, adults). Sensitivity and specificity to identify one's own scan was high (average AUC = 0.94), although it was significantly higher in the same day (average AUC = 0.97) than 1.5‐years later (average AUC = 0.91). Accuracy in youths (average AUC = 0.93) was not significantly different from adults (average AUC = 0.96). Multiple statistical methods revealed select connections from the Frontoparietal, Default, and Dorsal Attention networks enhanced the ability to identify an individual. Identification of these features generalized across datasets and improved fingerprinting accuracy in a longitudinal replication data set (N = 208). These results provide a framework for understanding the sensitivity and specificity of fingerprinting accuracy in adolescents and adults at multiple time scales. Importantly, distinct features of one's “fingerprint” contribute to one's uniqueness, suggesting that cognitive and default networks play a primary role in the individualization of one's connectome.
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Affiliation(s)
| | - Fuchen Liu
- Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - William Foran
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Finnegan J Calabro
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Kathryn Roeder
- Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.,Department of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Bernie Devlin
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Pediatrics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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29
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Lin YC, Baete SH, Wang X, Boada FE. Mapping brain-behavior networks using functional and structural connectome fingerprinting in the HCP dataset. Brain Behav 2020; 10:e01647. [PMID: 32351025 PMCID: PMC7303390 DOI: 10.1002/brb3.1647] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 03/12/2020] [Accepted: 03/20/2020] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION Connectome analysis of the human brain's structural and functional architecture provides a unique opportunity to understand the organization of the brain's functional architecture. In previous studies, connectome fingerprinting using brain functional connectivity profiles as an individualized trait was able to predict an individual's neurocognitive performance from the Human Connectome Project (HCP) neurocognitive datasets. MATERIALS AND METHODS In the present study, we extend connectome fingerprinting from functional connectivity (FC) to structural connectivity (SC), identifying multiple relationships between behavioral traits and brain connectivity. Higher-order neurocognitive tasks were found to have a weaker association with structural connectivity than its functional connectivity counterparts. RESULTS Neurocognitive tasks with a higher sensory footprint were, however, found to have a stronger association with structural connectivity than their functional connectivity counterparts. Language behavioral measurements had a particularly stronger correlation, especially between performance on the picture language test (Pic Vocab) and both FC (r = .28, p < .003) and SC (r = 0.27, p < .00077). CONCLUSIONS At the neural level, we found that the pattern of structural brain connectivity related to high-level language performance is consistent with the language white matter regions identified in presurgical mapping. We illustrate how this approach can be used to generalize the connectome fingerprinting framework to structural connectivity and how this can help understand the connections between cognitive behavior and the white matter connectome of the brain.
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Affiliation(s)
- Ying-Chia Lin
- Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, USA.,Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
| | - Steven H Baete
- Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, USA.,Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
| | - Xiuyuan Wang
- Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, USA.,Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
| | - Fernando E Boada
- Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, USA.,Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
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30
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Lin C, Yeung AWK. What do we learn from brain imaging?—A primer for the dentists who want to know more about the association between the brain and human stomatognathic functions. J Oral Rehabil 2020; 47:659-671. [DOI: 10.1111/joor.12935] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 12/10/2019] [Accepted: 01/05/2020] [Indexed: 12/12/2022]
Affiliation(s)
- Chia‐shu Lin
- Department of Dentistry School of Dentistry National Yang‐Ming University Taipei Taiwan
- Institute of Brain Science School of Medicine National Yang‐Ming University Taipei Taiwan
- Brain Research Center National Yang‐Ming University Taipei Taiwan
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology Applied Oral Sciences and Community Dental Care Faculty of Dentistry The University of Hong Kong Hong Kong China
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31
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Eickhoff SB, Langner R. Neuroimaging-based prediction of mental traits: Road to utopia or Orwell? PLoS Biol 2019; 17:e3000497. [PMID: 31725713 PMCID: PMC6879158 DOI: 10.1371/journal.pbio.3000497] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 11/26/2019] [Indexed: 11/19/2022] Open
Abstract
Predicting individual mental traits and behavioral dispositions from brain imaging data through machine-learning approaches is becoming a rapidly evolving field in neuroscience. Beyond scientific and clinical applications, such approaches also hold the potential to gain substantial influence in fields such as human resource management, education, or criminal law. Although several challenges render real-life applications of such tools difficult, future conflicts of individual, economic, and public interests are preprogrammed, given the prospect of improved personalized predictions across many domains. In this Perspective paper, we thus argue for the need to engage in a discussion on the ethical, legal, and societal implications of the emergent possibilities for brain-based predictions and outline some of the aspects for this discourse.
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Affiliation(s)
- Simon B. Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- * E-mail: (SBE); (RL)
| | - Robert Langner
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- * E-mail: (SBE); (RL)
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32
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Uncovering multi-site identifiability based on resting-state functional connectomes. Neuroimage 2019; 202:115967. [DOI: 10.1016/j.neuroimage.2019.06.045] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 04/18/2019] [Accepted: 06/19/2019] [Indexed: 01/21/2023] Open
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33
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Zhang R, Kranz GS, Lee TM. Functional Connectome from Phase Synchrony at Resting State is a Neural Fingerprint. Brain Connect 2019; 9:519-528. [DOI: 10.1089/brain.2018.0657] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Ruibin Zhang
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
- Laboratory of Neuropsychology, Department of Psychology, The University of Hong Kong, Hong Kong, China
- Laboratory of Cognitive Affective Neuroscience, The University of Hong Kong, Hong Kong, China
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
- Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Georg S. Kranz
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Tatia M.C. Lee
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
- Laboratory of Neuropsychology, Department of Psychology, The University of Hong Kong, Hong Kong, China
- Institute of Clinical Neuropsychology, The University of Hong Kong, Hong Kong, China
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34
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Gaut G, Turner B, Lu ZL, Li X, Cunningham WA, Steyvers M. Predicting Task and Subject Differences with Functional Connectivity and Blood-Oxygen-Level-Dependent Variability. Brain Connect 2019; 9:451-463. [DOI: 10.1089/brain.2018.0632] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Garren Gaut
- Department of Cognitive Sciences, University of California Irvine, Irvine, California
| | - Brandon Turner
- Department of Psychology, The Ohio State University, Columbus, Ohio
| | - Zhong-Lin Lu
- Department of Psychology, The Ohio State University, Columbus, Ohio
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio
| | - Xiangrui Li
- Department of Psychology, The Ohio State University, Columbus, Ohio
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio
| | | | - Mark Steyvers
- Department of Cognitive Sciences, University of California Irvine, Irvine, California
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35
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Ji G, Chen X, Bai T, Wang L, Wei Q, Gao Y, Tao L, He K, Li D, Dong Y, Hu P, Yu F, Zhu C, Tian Y, Yu Y, Wang K. Classification of schizophrenia by intersubject correlation in functional connectome. Hum Brain Mapp 2019; 40:2347-2357. [PMID: 30663853 PMCID: PMC6865403 DOI: 10.1002/hbm.24527] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 12/07/2018] [Accepted: 01/08/2019] [Indexed: 01/16/2023] Open
Abstract
Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting-state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH-SCH) than different groups (i.e., HC-SCH). These findings suggest that the average connectome within group (termed as group-specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75-77%) and area under curve (81-86%) were found in discriminating SCH from HC or depression patients by GFC-based leave-one-out cross-validation. Cross-center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group-specific biological features and may be helpful in clinical diagnosis for schizophrenia.
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Affiliation(s)
- Gong‐Jun Ji
- Department of Medical PsychologyChaohu Clinical Medical College, Anhui Medical UniversityHefeiChina
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Xingui Chen
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Tongjian Bai
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Lu Wang
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Qiang Wei
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Yaxiang Gao
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Longxiang Tao
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Kongliang He
- Anhui Mental Health CenterHefeiChina
- The Fourth People's Hospital of HefeiHefeiChina
| | - Dandan Li
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Yi Dong
- Anhui Mental Health CenterHefeiChina
- The Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Panpan Hu
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Fengqiong Yu
- Department of Medical PsychologyChaohu Clinical Medical College, Anhui Medical UniversityHefeiChina
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Chunyan Zhu
- Department of Medical PsychologyChaohu Clinical Medical College, Anhui Medical UniversityHefeiChina
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Yanghua Tian
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Yongqiang Yu
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Kai Wang
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
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de Souza Rodrigues J, Ribeiro FL, Sato JR, Mesquita RC, Júnior CEB. Identifying individuals using fNIRS-based cortical connectomes. BIOMEDICAL OPTICS EXPRESS 2019; 10:2889-2897. [PMID: 31259059 PMCID: PMC6583329 DOI: 10.1364/boe.10.002889] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 05/10/2019] [Accepted: 05/12/2019] [Indexed: 05/29/2023]
Abstract
The fMRI-based functional connectome was shown to be sufficiently unique to allow individual identification (fingerprinting). We aimed to test whether a fNIRS-based connectome could also be used to identify individuals. Forty-four participants performed experimental protocols that consisted of two periods of resting-state interleaved by a cognitive task period. Connectome identification was performed for all possible pairwise combinations of the three periods. The influence of hemodynamic global variation was tested using global signal regression and principal component analysis. High identification accuracies well-above chance level (2.3%) were observed overall, being particularly high (93%) to the oxyhemoglobin signal between resting conditions. Our results suggest that fNIRS is a suitable technique to assess connectome fingerprints.
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Affiliation(s)
- Júlia de Souza Rodrigues
- Center for Mathematics, Computation and Cognition, University of ABC, São Bernardo do Campo, SP, 09606-045, Brazil
| | - Fernanda Lenita Ribeiro
- Center for Mathematics, Computation and Cognition, University of ABC, São Bernardo do Campo, SP, 09606-045, Brazil
- School of Psychology, The University of Queensland, Brisbane, QLD 407, Australia
| | - João Ricardo Sato
- Center for Mathematics, Computation and Cognition, University of ABC, São Bernardo do Campo, SP, 09606-045, Brazil
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37
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38
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The individual functional connectome is unique and stable over months to years. Neuroimage 2019; 189:676-687. [PMID: 30721751 DOI: 10.1016/j.neuroimage.2019.02.002] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 01/30/2019] [Accepted: 02/01/2019] [Indexed: 12/30/2022] Open
Abstract
Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across 3 months to 1-2 years (and even detectable at above-chance levels across 3 years). Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that edges contributing the most to a successful ID tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and that high ID rates are not an idiosyncratic aspect of a specific dataset, but rather reflect stable individual differences in the functional connectivity of the brain.
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Byrge L, Kennedy DP. High-accuracy individual identification using a "thin slice" of the functional connectome. Netw Neurosci 2019; 3:363-383. [PMID: 30793087 PMCID: PMC6370471 DOI: 10.1162/netn_a_00068] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 08/22/2018] [Indexed: 01/17/2023] Open
Abstract
Connectome fingerprinting-a method that uses many thousands of functional connections in aggregate to identify individuals-holds promise for individualized neuroimaging. A better characterization of the features underlying successful fingerprinting performance-how many and which functional connections are necessary and/or sufficient for high accuracy-will further inform our understanding of uniqueness in brain functioning. Thus, here we examine the limits of high-accuracy individual identification from functional connectomes. Using ∼3,300 scans from the Human Connectome Project in a split-half design and an independent replication sample, we find that a remarkably small "thin slice" of the connectome-as few as 40 out of 64,620 functional connections-was sufficient to uniquely identify individuals. Yet, we find that no specific connections or even specific networks were necessary for identification, as even small random samples of the connectome were sufficient. These results have important conceptual and practical implications for the manifestation and detection of uniqueness in the brain.
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Affiliation(s)
- Lisa Byrge
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Daniel P. Kennedy
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
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40
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Zimmermann J, Griffiths J, Schirner M, Ritter P, McIntosh AR. Subject specificity of the correlation between large-scale structural and functional connectivity. Netw Neurosci 2018; 3:90-106. [PMID: 30793075 PMCID: PMC6326745 DOI: 10.1162/netn_a_00055] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 04/04/2018] [Indexed: 12/25/2022] Open
Abstract
Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal coactivations, are known to be tightly linked. However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individual's SC and FC is not subject specific for five of the datasets. Subject specificity of SC-FC fit is achieved only for one of the six datasets, the multimodal Glasser Human Connectome Project (HCP) parcellated dataset. We show that subject specificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in SC compared with the larger variability in FC.
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Affiliation(s)
- J. Zimmermann
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario, Canada
| | - J. Griffiths
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario, Canada
| | - M. Schirner
- Department of Neurology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universitët Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
- Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - P. Ritter
- Department of Neurology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universitët Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health
- Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany
- Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt University, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - A. R. McIntosh
- Baycrest Health Sciences, Rotman Research Institute, Toronto, Ontario, Canada
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Horien C, Noble S, Finn ES, Shen X, Scheinost D, Constable RT. Considering factors affecting the connectome-based identification process: Comment on Waller et al. Neuroimage 2017; 169:172-175. [PMID: 29253655 DOI: 10.1016/j.neuroimage.2017.12.045] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 12/11/2017] [Accepted: 12/14/2017] [Indexed: 12/11/2022] Open
Abstract
A recent study by Waller and colleagues evaluated the reliability, specificity, and generalizability of using functional connectivity data to identify individuals from a group. The authors note they were able to replicate identification rates in a larger version of the original Human Connectome Project (HCP) dataset. However, they also report lower identification accuracies when using historical neuroimaging acquisitions with low spatial and temporal resolution. The authors suggest that their results indicate connectomes derived from historical imaging data may be similar across individuals, to the extent that this connectome-based approach may be inappropriate for precision psychiatry and the goal of drawing inferences based on subject-level data. Here we note that the authors did not take into account factors affecting data quality and hence identification rates, independent of whether a low spatiotemporal resolution acquisition or a high spatiotemporal resolution acquisition is used. Specifically, we show here that the amount of data collected per subject and in-scanner motion are the predominant factors influencing identification rates, not the spatiotemporal resolution of the acquisition. To do this, we investigated identification rates in the HCP dataset as a function of the amount of data and motion. Using a dataset from the Consortium for Reliability and Reproducibility (CoRR), we investigated the impact of multiband versus non-multiband imaging parameters; that is, high spatiotemporal resolution versus low spatiotemporal resolution acquisitions. We show scan length and motion affect identification, whereas the imaging protocol does not affect these rates. Our results suggest that motion and amount of data per subject are the primary factors impacting individual connectivity profiles, but that within these constraints, individual differences in the connectome are readily observable.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA.
| | - Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
| | - Emily S Finn
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA; Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA; The Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA; Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA
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