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Cajachagua-Torres KN, Quezada-Pinedo HG, Wu T, Trasande L, Ghassabian A. Exposure to Endocrine Disruptors in Early life and Neuroimaging Findings in Childhood and Adolescence: a Scoping Review. Curr Environ Health Rep 2024:10.1007/s40572-024-00457-4. [PMID: 39078539 DOI: 10.1007/s40572-024-00457-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2024] [Indexed: 07/31/2024]
Abstract
PURPOSE OF REVIEW: Evidence suggests neurotoxicity of endocrine disrupting chemicals (EDCs) during sensitive periods of development. We present an overview of pediatric population neuroimaging studies that examined brain influences of EDC exposure during prenatal period and childhood. RECENT FINDINGS: We found 46 studies that used magnetic resonance imaging (MRI) to examine brain influences of EDCs. These studies showed associations of prenatal exposure to phthalates, organophosphate pesticides (OPs), polyaromatic hydrocarbons and persistent organic pollutants with global and regional brain structural alterations. Few studies suggested alteration in functional MRI associated with prenatal OP exposure. However, studies on other groups of EDCs, such as bisphenols, and those that examined childhood exposure were less conclusive. These findings underscore the potential profound and lasting effects of prenatal EDC exposure on brain development, emphasizing the need for better regulation and strategies to reduce exposure and mitigate impacts. More studies are needed to examine the influence of postnatal exposure to EDC on brain imaging.
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Affiliation(s)
- Kim N Cajachagua-Torres
- Department of Pediatrics, NYU Grossman School of Medicine, 555 First Avenue, New York, NY, 10016, USA.
- Department of Pediatrics, Erasmus MC, Erasmus University Rotterdam, Rotterdam, The Netherlands.
| | - Hugo G Quezada-Pinedo
- Department of Pediatrics, Erasmus MC, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Tong Wu
- Department of Radiology and Nuclear Medicine, Erasmus MC, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Leonardo Trasande
- Department of Pediatrics, NYU Grossman School of Medicine, 555 First Avenue, New York, NY, 10016, USA
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Akhgar Ghassabian
- Department of Pediatrics, NYU Grossman School of Medicine, 555 First Avenue, New York, NY, 10016, USA
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
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Demidenko MI, Mumford JA, Poldrack RA. Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585755. [PMID: 38562804 PMCID: PMC10983911 DOI: 10.1101/2024.03.19.585755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (Ns: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the Neutral cue. This effect was largest for the Cue Model parameterization; however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.
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Tansey R, Graff K, Rai S, Merrikh D, Godfrey KJ, Vanderwal T, Bray S. Development of human visual cortical function: A scoping review of task- and naturalistic-fMRI studies through the interactive specialization and maturational frameworks. Neurosci Biobehav Rev 2024; 162:105729. [PMID: 38763178 DOI: 10.1016/j.neubiorev.2024.105729] [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: 03/07/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 05/21/2024]
Abstract
Overarching theories such as the interactive specialization and maturational frameworks have been proposed to describe human functional brain development. However, these frameworks have not yet been systematically examined across the fMRI literature. Visual processing is one of the most well-studied fields in neuroimaging, and research in this area has recently expanded to include naturalistic paradigms that facilitate study in younger age ranges, allowing for an in-depth critical appraisal of these frameworks across childhood. To this end, we conducted a scoping review of 94 developmental visual fMRI studies, including both traditional experimental task and naturalistic studies, across multiple sub-domains (early visual processing, category-specific higher order processing, naturalistic visual processing). We found that across domains, many studies reported progressive development, but few studies describe regressive or emergent changes necessary to fit the maturational or interactive specialization frameworks. Our findings suggest a need for the expansion of developmental frameworks and clearer reporting of both progressive and regressive changes, along with well-powered, longitudinal studies.
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Affiliation(s)
- Ryann Tansey
- Department of Psychiatry, Cumming School of Medicine, 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.
| | - Kirk Graff
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Shefali Rai
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Daria Merrikh
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Kate J Godfrey
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Tamara Vanderwal
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Signe Bray
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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4
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Churchill L, Chen YC, Lewis SJG, Matar E. Understanding REM Sleep Behavior Disorder through Functional MRI: A Systematic Review. Mov Disord 2024. [PMID: 38934216 DOI: 10.1002/mds.29898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/08/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Neuroimaging studies in rapid eye movement sleep behavior disorder (RBD) can inform fundamental questions about the pathogenesis of Parkinson's disease (PD). Across modalities, functional magnetic resonance imaging (fMRI) may be better suited to identify changes between neural networks in the earliest stages of Lewy body diseases when structural changes may be subtle or absent. This review synthesizes the findings from all fMRI studies of RBD to gain further insight into the pathophysiology and progression of Lewy body diseases. A total of 32 studies were identified using a systematic review conducted according to PRISMA guidelines between January 2000 to February 2024 for original fMRI studies in patients with either isolated RBD (iRBD) or RBD secondary to PD. Common functional alterations were detectable in iRBD patients compared with healthy controls across brainstem nuclei, basal ganglia, frontal and occipital lobes, and whole brain network measures. Patients with established PD and RBD demonstrated decreased functional connectivity across the whole brain and brainstem nuclei, but increased functional connectivity in the cerebellum and frontal lobe compared with those PD patients without RBD. Finally, longitudinal changes in resting state functional connectivity were found to track with disease progression. Currently, fMRI studies in RBD have demonstrated early signatures of neurodegeneration across both motor and non-motor pathways. Although more work is needed, such findings have the potential to inform our understanding of disease, help to distinguish between prodromal PD and prodromal dementia with Lewy bodies, and support the development of fMRI-based outcome measures of phenoconversion and progression in future disease modifying trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Lachlan Churchill
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Yu-Chi Chen
- Brain Dynamic Centre, Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Simon J G Lewis
- Macquarie Medical School and Macquarie University Centre for Parkinson's Disease Research, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Elie Matar
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
- Centre for Integrated Research and Understanding of Sleep (CIRUS), Woolcock Institute of Medical Research, Sydney, New South Wales, Australia
- Department of Neurology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
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5
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Eken A, Nassehi F, Eroğul O. Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review. Rev Neurosci 2024; 35:421-449. [PMID: 38308531 DOI: 10.1515/revneuro-2023-0117] [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: 09/23/2023] [Accepted: 01/12/2024] [Indexed: 02/04/2024]
Abstract
Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n = 11) and ΔHbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.
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Affiliation(s)
- Aykut Eken
- Department of Biomedical Engineering, Faculty of Engineering, TOBB University of Economics and Technology, Sogutozu, 06510, Ankara, Türkiye
| | - Farhad Nassehi
- Department of Biomedical Engineering, Faculty of Engineering, TOBB University of Economics and Technology, Sogutozu, 06510, Ankara, Türkiye
| | - Osman Eroğul
- Department of Biomedical Engineering, Faculty of Engineering, TOBB University of Economics and Technology, Sogutozu, 06510, Ankara, Türkiye
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Ferry RA, Shah VV, Jin J, Jarcho JM, Hajcak G, Nelson BD. Neural response to monetary and social rewards in adolescent girls and their parents. Neuroimage 2024; 297:120705. [PMID: 38914211 DOI: 10.1016/j.neuroimage.2024.120705] [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: 09/01/2023] [Revised: 06/14/2024] [Accepted: 06/21/2024] [Indexed: 06/26/2024] Open
Abstract
Functional magnetic resonance imaging (fMRI) studies have indicated that the mesocorticolimbic dopamine system is heavily involved in all stages of reward processing. However, the majority of research has been conducted using monetary rewards and it is unclear to what extent other types of rewards, such as social rewards, evoke similar or different neural activation. There have also been few investigations into potential differences or similarities between reward processing in parents and offspring. The present study examined fMRI neural activation in response to monetary and social reward in a sample of 14-22-year-old adolescent girls (N = 145) and a biological parent (N = 124) and compared activation across adolescent-parent dyads (N = 82). Across all participants, both monetary and social reward elicited bilateral striatal activation, which did not differ between reward types or between adolescents and their parents. Neural activation in response to the different reward types were positively correlated in the striatum among adolescents and in the mPFC and OFC among parents. Overall, the present study suggests that both monetary and social reward elicit striatal activation regardless of age and provides evidence that neural mechanisms underlying reward processing may converge differentially among youth and adults.
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Affiliation(s)
- Rachel A Ferry
- Department of Psychology, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794-2500, USA.
| | - Virja V Shah
- Department of Psychology, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794-2500, USA
| | - Jingwen Jin
- Department of Psychology, University of Hong Kong, The Jockey Club Tower, Centennial Campus, Pokfulam Road, Hong Kong
| | - Johanna M Jarcho
- Department of Psychology and Neuroscience, Temple University, 1701N 13th St, Philadelphia, PA 19122, USA
| | - Greg Hajcak
- School of Education and Counseling Psychology, Santa Clara University, 455 El Camino Real, Santa Clara, CA 95053, USA
| | - Brady D Nelson
- Department of Psychology, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794-2500, USA
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7
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Pinho AL, Richard H, Ponce AF, Eickenberg M, Amadon A, Dohmatob E, Denghien I, Torre JJ, Shankar S, Aggarwal H, Thual A, Chapalain T, Ginisty C, Becuwe-Desmidt S, Roger S, Lecomte Y, Berland V, Laurier L, Joly-Testault V, Médiouni-Cloarec G, Doublé C, Martins B, Varoquaux G, Dehaene S, Hertz-Pannier L, Thirion B. Individual Brain Charting dataset extension, third release for movie watching and retinotopy data. Sci Data 2024; 11:590. [PMID: 38839770 PMCID: PMC11153490 DOI: 10.1038/s41597-024-03390-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 05/20/2024] [Indexed: 06/07/2024] Open
Abstract
The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation. We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems. We also present the topographic organization of the visual system through retinotopy. In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.
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Affiliation(s)
- Ana Luísa Pinho
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France.
- Department of Computer Science, Western University, London, Ontario, Canada.
- Western Centre for Brain and Mind, Western University, London, Ontario, Canada.
| | - Hugo Richard
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Criteo AI Labs, Paris, France
- FAIRPLAY - IA coopérative: équité, vie privée, incitations, Paris, France
| | | | - Michael Eickenberg
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Flatiron Institute, New York, USA
| | - Alexis Amadon
- Université Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, 91191, Gif-sur-Yvette, France
| | - Elvis Dohmatob
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Meta FAIR, Paris, France
| | - Isabelle Denghien
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif-sur-Yvette, France
| | | | - Swetha Shankar
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
| | | | - Alexis Thual
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif-sur-Yvette, France
- Collège de France, Paris, France
| | | | | | | | | | - Yann Lecomte
- CEA Saclay/DRF/IFJ/NeuroSpin/UNIACT, Paris, France
| | | | | | | | | | | | | | - Gaël Varoquaux
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif-sur-Yvette, France
- Collège de France, Paris, France
| | - Lucie Hertz-Pannier
- CEA Saclay/DRF/IFJ/NeuroSpin/UNIACT, Paris, France
- UMR 1141, NeuroDiderot, Université de Paris, Paris, France
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Zhang S, Jung K, Langner R, Florin E, Eickhoff SB, Popovych OV. Impact of data processing varieties on DCM estimates of effective connectivity from task-fMRI. Hum Brain Mapp 2024; 45:e26751. [PMID: 38864293 PMCID: PMC11167406 DOI: 10.1002/hbm.26751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 01/05/2024] [Accepted: 05/22/2024] [Indexed: 06/13/2024] Open
Abstract
Effective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task-evoked EC has hardly ever been addressed. We therefore investigated how task-evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event-related design of the general linear model (GLM) used for the first-level task-evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group-averaged task-evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task-evoked EC between any two data processing conditions via between-group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group-averaged EC depending on the data processing choices. In particular, task-evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event-related GLM design appears to be more sensitive to task-evoked modulations of EC, but provides model parameters with lower certainty than the block-based design, while the latter is more sensitive to the type of activation contrast than is the event-related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task-evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes.
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Affiliation(s)
- Shufei Zhang
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute for Systems Neuroscience, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
| | - Kyesam Jung
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute for Systems Neuroscience, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
| | - Robert Langner
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute for Systems Neuroscience, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute for Systems Neuroscience, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
| | - Oleksandr V. Popovych
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute for Systems Neuroscience, Medical FacultyHeinrich‐Heine University DüsseldorfDüsseldorfGermany
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Lu B, Chen X, Xavier Castellanos F, Thompson PM, Zuo XN, Zang YF, Yan CG. The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration. Sci Bull (Beijing) 2024; 69:1536-1555. [PMID: 38519398 DOI: 10.1016/j.scib.2024.03.006] [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: 08/17/2023] [Revised: 12/12/2023] [Accepted: 02/27/2024] [Indexed: 03/24/2024]
Abstract
Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.
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Affiliation(s)
- Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York 10016, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg 10962, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles 90033, USA
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Basic Science Data Center, Beijing 100190, China
| | - Yu-Feng Zang
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310004, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 310030, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou 311121, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
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10
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Feurer C, Jimmy J, Uribe M, Shankman SA, Langenecker SA, Craske MG, Ajilore O, Phan KL, Klumpp H. Brain activity during reappraisal and associations with psychotherapy response in social anxiety and major depression: a randomized trial. Psychol Med 2024:1-11. [PMID: 38775085 DOI: 10.1017/s0033291724001120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
BACKGROUND Cognitive behavioral therapy (CBT) is an effective treatment for patients with social anxiety disorder (SAD) or major depressive disorder (MDD), yet there is variability in clinical improvement. Though prior research suggests pre-treatment engagement of brain regions supporting cognitive reappraisal (e.g. dorsolateral prefrontal cortex [dlPFC]) foretells CBT response in SAD, it remains unknown if this extends to MDD or is specific to CBT. The current study examined associations between pre-treatment neural activity during reappraisal and clinical improvement in patients with SAD or MDD following a trial of CBT or supportive therapy (ST), a common-factors comparator arm. METHODS Participants were 75 treatment-seeking patients with SAD (n = 34) or MDD (n = 41) randomized to CBT (n = 40) or ST (n = 35). Before randomization, patients completed a cognitive reappraisal task during functional magnetic resonance imaging. Additionally, patients completed clinician-administered symptom measures and a self-report cognitive reappraisal measure before treatment and every 2 weeks throughout treatment. RESULTS Results indicated that pre-treatment neural activity during reappraisal differentially predicted CBT and ST response. Specifically, greater trajectories of symptom improvement throughout treatment were associated with less ventrolateral prefrontal cortex (vlPFC) activity for CBT patients, but more vlPFC activity for ST patients. Also, less baseline dlPFC activity corresponded with greater trajectories of self-reported reappraisal improvement, regardless of treatment arm. CONCLUSIONS If replicated, findings suggest individual differences in brain response during reappraisal may be transdiagnostically associated with treatment-dependent improvement in symptom severity, but improvement in subjective reappraisal following psychotherapy, more broadly.
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Affiliation(s)
- Cope Feurer
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Jagan Jimmy
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Melissa Uribe
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - Stewart A Shankman
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA
| | - Scott A Langenecker
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Michelle G Craske
- Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California-Los Angeles, Los Angeles, CA, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - K Luan Phan
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Heide Klumpp
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
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Fennema D, Barker GJ, O'Daly O, Duan S, Godlewska BR, Goldsmith K, Young AH, Moll J, Zahn R. Neural responses to facial emotions and subsequent clinical outcomes in difficult-to-treat depression. Psychol Med 2024:1-9. [PMID: 38757184 DOI: 10.1017/s0033291724001144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
BACKGROUND Amygdala and dorsal anterior cingulate cortex responses to facial emotions have shown promise in predicting treatment response in medication-free major depressive disorder (MDD). Here, we examined their role in the pathophysiology of clinical outcomes in more chronic, difficult-to-treat forms of MDD. METHODS Forty-five people with current MDD who had not responded to ⩾2 serotonergic antidepressants (n = 42, meeting pre-defined fMRI minimum quality thresholds) were enrolled and followed up over four months of standard primary care. Prior to medication review, subliminal facial emotion fMRI was used to extract blood-oxygen level-dependent effects for sad v. happy faces from two pre-registered a priori defined regions: bilateral amygdala and dorsal/pregenual anterior cingulate cortex. Clinical outcome was the percentage change on the self-reported Quick Inventory of Depressive Symptomatology (16-item). RESULTS We corroborated our pre-registered hypothesis (NCT04342299) that lower bilateral amygdala activation for sad v. happy faces predicted favorable clinical outcomes (rs[38] = 0.40, p = 0.01). In contrast, there was no effect for dorsal/pregenual anterior cingulate cortex activation (rs[38] = 0.18, p = 0.29), nor when using voxel-based whole-brain analyses (voxel-based Family-Wise Error-corrected p < 0.05). Predictive effects were mainly driven by the right amygdala whose response to happy faces was reduced in patients with higher anxiety levels. CONCLUSIONS We confirmed the prediction that a lower amygdala response to negative v. positive facial expressions might be an adaptive neural signature, which predicts subsequent symptom improvement also in difficult-to-treat MDD. Anxiety reduced adaptive amygdala responses.
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Affiliation(s)
- Diede Fennema
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Suqian Duan
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
| | - Beata R Godlewska
- Psychopharmacology Research Unit, University Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Kimberley Goldsmith
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Allan H Young
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
- National Service for Affective Disorders, South London and Maudsley NHS Foundation Trust, London, UK
| | - Jorge Moll
- Cognitive and Behavioural Neuroscience Unit, D'Or Institute for Research and Education (IDOR), Pioneer Science Program, Rio de Janeiro, Brazil
| | - Roland Zahn
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
- National Service for Affective Disorders, South London and Maudsley NHS Foundation Trust, London, UK
- Cognitive and Behavioural Neuroscience Unit, D'Or Institute for Research and Education (IDOR), Pioneer Science Program, Rio de Janeiro, Brazil
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Kirk-Provencher KT, Sloan ME, Andereas K, Erickson CJ, Hakimi RH, Penner AE, Gowin JL. Neural responses to reward, threat, and emotion regulation and transition to hazardous alcohol use. Alcohol Alcohol 2024; 59:agae043. [PMID: 38953742 PMCID: PMC11217988 DOI: 10.1093/alcalc/agae043] [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: 01/11/2024] [Revised: 05/13/2024] [Accepted: 06/19/2024] [Indexed: 07/04/2024] Open
Abstract
AIMS Reward processing and regulation of emotions are thought to impact the development of addictive behaviors. In this study, we aimed to determine whether neural responses during reward anticipation, threat appraisal, emotion reactivity, and cognitive reappraisal predicted the transition from low-level to hazardous alcohol use over a 12-month period. METHODS Seventy-eight individuals aged 18-22 with low-level alcohol use [i.e. Alcohol Use Disorder Identification Test (AUDIT) score <7] at baseline were enrolled. They completed reward-based and emotion regulation tasks during magnetic resonance imaging to examine reward anticipation, emotional reactivity, cognitive reappraisal, and threat anticipation (in the nucleus accumbens, amygdala, superior frontal gyrus, and insula, respectively). Participants completed self-report measures at 3-, 6-, 9-, and 12-month follow-up time points to determine if they transitioned to hazardous use (as defined by AUDIT scores ≥8). RESULTS Of the 57 participants who completed follow-up, 14 (24.6%) transitioned to hazardous alcohol use. Higher baseline AUDIT scores were associated with greater odds of transitioning to hazardous use (odds ratio = 1.73, 95% confidence interval 1.13-2.66, P = .005). Brain activation to reward, threat, and emotion regulation was not associated with alcohol use. Of the neural variables, the amygdala response to negative imagery was numerically larger in young adults who transitioned to hazardous use (g = 0.31), but this effect was not significant. CONCLUSIONS Baseline drinking levels were significantly associated with the transition to hazardous alcohol use. Studies with larger samples and longer follow-up should test whether the amygdala response to negative emotional imagery can be used to indicate a future transition to hazardous alcohol use.
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Affiliation(s)
- Katelyn T Kirk-Provencher
- Department of Radiology, School of Medicine, University of Colorado Anschutz Medical Campus, 12700 E. 19th Avenue, Aurora, CO 80045, United States
| | - Matthew E Sloan
- Addictions Division, Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1H4, Canada
- Department of Pharmacology and Toxicology, University of Toronto, 1 King's College Circle Toronto, ON, M5S 1A8, Canada
- Division of Neurosciences and Clinical Translation, Department of Psychiatry, University of Toronto, 250 College St. Toronto, ON, M5T 1R8, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 620 University Ave. Toronto, ON, M5G 2C1, Canada
- Department of Psychological Clinical Science, University of Toronto Scarborough, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, 1 King's College Circle Toronto, ON, M5S 1A8, Canada
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, 479 Spadina Ave. Toronto, ON, M5S 2S1, Canada
| | - Keinada Andereas
- Department of Radiology, School of Medicine, University of Colorado Anschutz Medical Campus, 12700 E. 19th Avenue, Aurora, CO 80045, United States
| | - Cooper J Erickson
- Department of Radiology, School of Medicine, University of Colorado Anschutz Medical Campus, 12700 E. 19th Avenue, Aurora, CO 80045, United States
| | - Rosa H Hakimi
- Department of Radiology, School of Medicine, University of Colorado Anschutz Medical Campus, 12700 E. 19th Avenue, Aurora, CO 80045, United States
| | - Anne E Penner
- Department of Psychiatry, School of Medicine, University of Colorado Anschutz Medical Campus, 12700 E. 19th Avenue, Aurora, CO 80045, United States
| | - Joshua L Gowin
- Department of Radiology, School of Medicine, University of Colorado Anschutz Medical Campus, 12700 E. 19th Avenue, Aurora, CO 80045, United States
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13
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Fennema D, Barker GJ, O’Daly O, Duan S, Carr E, Goldsmith K, Young AH, Moll J, Zahn R. The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100308. [PMID: 38645404 PMCID: PMC11033067 DOI: 10.1016/j.bpsgos.2024.100308] [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: 08/18/2023] [Revised: 12/18/2023] [Accepted: 03/05/2024] [Indexed: 04/23/2024] Open
Abstract
Background A seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care. Methods Forty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology. Results We observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = -0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = -0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = -0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error-corrected p < .05). Conclusions Subgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.
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Affiliation(s)
- Diede Fennema
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Owen O’Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Suqian Duan
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
| | - Ewan Carr
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Kimberley Goldsmith
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Allan H. Young
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
- National Service for Affective Disorders, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Jorge Moll
- Cognitive and Behavioural Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Roland Zahn
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
- Cognitive and Behavioural Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil
- National Service for Affective Disorders, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
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14
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Mosquera FEC, Lizcano Martinez S, Liscano Y. Effectiveness of Psychobiotics in the Treatment of Psychiatric and Cognitive Disorders: A Systematic Review of Randomized Clinical Trials. Nutrients 2024; 16:1352. [PMID: 38732599 PMCID: PMC11085935 DOI: 10.3390/nu16091352] [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: 03/17/2024] [Revised: 04/23/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024] Open
Abstract
In this study, a systematic review of randomized clinical trials conducted from January 2000 to December 2023 was performed to examine the efficacy of psychobiotics-probiotics beneficial to mental health via the gut-brain axis-in adults with psychiatric and cognitive disorders. Out of the 51 studies involving 3353 patients where half received psychobiotics, there was a notably high measurement of effectiveness specifically in the treatment of depression symptoms. Most participants were older and female, with treatments commonly utilizing strains of Lactobacillus and Bifidobacteria over periods ranging from 4 to 24 weeks. Although there was a general agreement on the effectiveness of psychobiotics, the variability in treatment approaches and clinical presentations limits the comparability and generalization of the findings. This underscores the need for more personalized treatment optimization and a deeper investigation into the mechanisms through which psychobiotics act. The research corroborates the therapeutic potential of psychobiotics and represents progress in the management of psychiatric and cognitive disorders.
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Affiliation(s)
- Freiser Eceomo Cruz Mosquera
- Grupo de Investigación en Salud Integral (GISI), Departamento Facultad de Salud, Universidad Santiago de Cali, Cali 760035, Colombia
| | - Santiago Lizcano Martinez
- Área Servicio de Alimentación, Área Nutrición Clínica Hospitalización UCI Urgencias Y Equipo de Soporte nutricional, Clínica Nuestra, Cali 760041, Colombia;
| | - Yamil Liscano
- Grupo de Investigación en Salud Integral (GISI), Departamento Facultad de Salud, Universidad Santiago de Cali, Cali 760035, Colombia
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15
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Vaidya N, Marquand AF, Nees F, Siehl S, Schumann G. The impact of psychosocial adversity on brain and behaviour: an overview of existing knowledge and directions for future research. Mol Psychiatry 2024:10.1038/s41380-024-02556-y. [PMID: 38658773 DOI: 10.1038/s41380-024-02556-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
Environmental experiences play a critical role in shaping the structure and function of the brain. Its plasticity in response to different external stimuli has been the focus of research efforts for decades. In this review, we explore the effects of adversity on brain's structure and function and its implications for brain development, adaptation, and the emergence of mental health disorders. We are focusing on adverse events that emerge from the immediate surroundings of an individual, i.e., microenvironment. They include childhood maltreatment, peer victimisation, social isolation, affective loss, domestic conflict, and poverty. We also take into consideration exposure to environmental toxins. Converging evidence suggests that different types of adversity may share common underlying mechanisms while also exhibiting unique pathways. However, they are often studied in isolation, limiting our understanding of their combined effects and the interconnected nature of their impact. The integration of large, deep-phenotyping datasets and collaborative efforts can provide sufficient power to analyse high dimensional environmental profiles and advance the systematic mapping of neuronal mechanisms. This review provides a background for future research, highlighting the importance of understanding the cumulative impact of various adversities, through data-driven approaches and integrative multimodal analysis techniques.
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Affiliation(s)
- Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany.
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Frauke Nees
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | - Sebastian Siehl
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology of Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, China
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16
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Ma L, Braun SE, Steinberg JL, Bjork JM, Martin CE, Keen Ii LD, Moeller FG. Effect of scanning duration and sample size on reliability in resting state fMRI dynamic causal modeling analysis. Neuroimage 2024; 292:120604. [PMID: 38604537 DOI: 10.1016/j.neuroimage.2024.120604] [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: 01/18/2024] [Revised: 03/31/2024] [Accepted: 04/07/2024] [Indexed: 04/13/2024] Open
Abstract
Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.
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Affiliation(s)
- Liangsuo Ma
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA.
| | | | - Joel L Steinberg
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA
| | - James M Bjork
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA
| | - Caitlin E Martin
- Institute for Drug and Alcohol Studies, USA; Department of Obstetrics and Gynecology, USA
| | - Larry D Keen Ii
- Department of Psychology, Virginia State University, Petersburg, VA, USA
| | - F Gerard Moeller
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA; Department of Neurology, USA; Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA, USA
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17
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Watters H, Fazili A, Daley L, Belden A, LaGrow TJ, Bolt T, Loui P, Keilholz S. Creative tempo: Spatiotemporal dynamics of the default mode network in improvisational musicians. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.07.588391. [PMID: 38645080 PMCID: PMC11030431 DOI: 10.1101/2024.04.07.588391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The intrinsic dynamics of human brain activity display a recurring pattern of anti-correlated activity between the default mode network (DMN), associated with internal processing and mentation, and task positive regions, associated with externally directed attention. In human functional magnetic resonance imaging (fMRI) data, this anti-correlated pattern is detectable on the infraslow timescale (<0.1 Hz) as a quasi-periodic pattern (QPP). While the DMN is implicated in creativity and musicality in traditional time-averaged functional connectivity studies, no one has yet explored how creative training may alter dynamic spatiotemporal patterns involving the DMN such as QPPs. In the present study, we compare the outputs of two QPP detection approaches, sliding window algorithm and complex principal components analysis (cPCA). We apply both methods to an existing dataset of musicians captured with resting state fMRI, grouped as either classical, improvisational, or minimally trained non-musicians. The original time-averaged functional connectivity (FC) analysis of this dataset used improvisation as a proxy for creative thinking and found that the DMN and visual networks (VIS) display higher connectivity in improvisational musicians. We expand upon this dataset's original study and find that QPP analysis detects convergent results at the group level with both methods. In improvisational musicians, dynamic functional correlation in the group-averaged QPP was found to be increased between the DMN-VIS and DMN-FPN for both the QPP algorithm and complex principal components analysis (cPCA) methods. Additionally, we found an unexpected increase in FC in the group-averaged QPP between the dorsal attention network and amygdala in improvisational musicians; this result was not reported in the original seed-based study of this dataset. The current study represents a novel application of two dynamic FC detection methods with results that replicate and expand upon previous seed-based FC findings. The results show the robustness of both the QPP phenomenon and its detection methods. This study also demonstrates the value of dynamic FC methods in reproducing seed-based findings and their promise in detecting group-wise or individual differences that may be missed by traditional seed-based resting state fMRI studies.
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Affiliation(s)
| | | | - Lauren Daley
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | | | - T J LaGrow
- Georgia Institute of Technology School of Electrical and Computer Engineering
| | - Taylor Bolt
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | | | - Shella Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
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18
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Sangchooli A, Zare-Bidoky M, Fathi Jouzdani A, Schacht J, Bjork JM, Claus ED, Prisciandaro JJ, Wilson SJ, Wüstenberg T, Potvin S, Ahmadi P, Bach P, Baldacchino A, Beck A, Brady KT, Brewer JA, Childress AR, Courtney KE, Ebrahimi M, Filbey FM, Garavan H, Ghahremani DG, Goldstein RZ, Goudriaan AE, Grodin EN, Hanlon CA, Haugg A, Heilig M, Heinz A, Holczer A, Van Holst RJ, Joseph JE, Juliano AC, Kaufman MJ, Kiefer F, Khojasteh Zonoozi A, Kuplicki RT, Leyton M, London ED, Mackey S, McClernon FJ, Mellick WH, Morley K, Noori HR, Oghabian MA, Oliver JA, Owens M, Paulus MP, Perini I, Rafei P, Ray LA, Sinha R, Smolka MN, Soleimani G, Spanagel R, Steele VR, Tapert SF, Vollstädt-Klein S, Wetherill RR, Witkiewitz K, Yuan K, Zhang X, Verdejo-Garcia A, Potenza MN, Janes AC, Kober H, Zilverstand A, Ekhtiari H. Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity: A Systematic Review. JAMA Psychiatry 2024; 81:414-425. [PMID: 38324323 DOI: 10.1001/jamapsychiatry.2023.5483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Importance In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers. Objective To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts. Evidence Review The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders. Findings There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes. Conclusions and Relevance Based on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments.
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Affiliation(s)
- Arshiya Sangchooli
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia
| | - Mehran Zare-Bidoky
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Fathi Jouzdani
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
| | - Joseph Schacht
- Department of Psychiatry, University of Colorado School of Medicine, Aurora
| | - James M Bjork
- Institute for Drug and Alcohol Studies, Department of Psychiatry, Virginia Commonwealth University, Richmond
| | - Eric D Claus
- Department of Biobehavioral Health, The Pennsylvania State University, University Park
| | - James J Prisciandaro
- Addiction Sciences Division, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston
| | - Stephen J Wilson
- Department of Psychology, The Pennsylvania State University, State College
| | - Torsten Wüstenberg
- Field of Focus IV, Core Facility for Neuroscience of Self-Regulation (CNSR), Heidelberg University, Heidelberg, Germany
| | - Stéphane Potvin
- Department of Psychiatry and Addiction, Université de Montréal, Montréal, Quebec, Canada
| | - Pooria Ahmadi
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Patrick Bach
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Alex Baldacchino
- School of Medicine, University of St Andrews, St Andrews, Scotland
| | - Anne Beck
- Faculty of Health, Health and Medical University, Potsdam, Germany
- Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Kathleen T Brady
- Addiction Sciences Division, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston
| | - Judson A Brewer
- Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, Rhode Island
| | | | | | - Mohsen Ebrahimi
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
| | - Francesca M Filbey
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington
| | - Dara G Ghahremani
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
| | - Rita Z Goldstein
- Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Anneke E Goudriaan
- Department of Psychiatry, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Erica N Grodin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
| | - Colleen A Hanlon
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, North Carolina
- BrainsWay Inc, Winston-Salem, North Carolina
| | - Amelie Haugg
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Markus Heilig
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Andreas Heinz
- Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Adrienn Holczer
- Department of Neurology, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary
| | - Ruth J Van Holst
- Amsterdam Institute for Addiction Research, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Jane E Joseph
- Department of Neuroscience, Medical University of South Carolina, Charleston
| | | | - Marc J Kaufman
- McLean Hospital, Harvard Medical School, Belmont, Massachusetts
| | - Falk Kiefer
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Arash Khojasteh Zonoozi
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
| | | | - Marco Leyton
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Edythe D London
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
| | - Scott Mackey
- Department of Psychiatry, University of Vermont, Burlington
| | - F Joseph McClernon
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
| | - William H Mellick
- Addiction Sciences Division, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston
| | - Kirsten Morley
- Specialty of Addiction Medicine, Faculty of Medicine and Health, Sydney Medical School, University of Sydney, Sydney, Australia
| | - Hamid R Noori
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge
| | - Mohammad Ali Oghabian
- Neuroimaging and Analysis Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Jason A Oliver
- TSET Health Promotion Research Center, University of Oklahoma Health Sciences Center, Oklahoma City
| | - Max Owens
- Department of Psychiatry, University of Vermont, Burlington
| | | | - Irene Perini
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Parnian Rafei
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
| | - Lara A Ray
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
| | - Rajita Sinha
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Michael N Smolka
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| | - Ghazaleh Soleimani
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis
| | - Rainer Spanagel
- Institute of Psychopharmacology, Central Institute of Mental Health, Mannheim, Germany
| | - Vaughn R Steele
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Susan F Tapert
- Department of Psychiatry, University of California, San Diego
| | - Sabine Vollstädt-Klein
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | | | | | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Xiaochu Zhang
- Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Anhui, China
| | | | - Marc N Potenza
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| | - Amy C Janes
- Cognitive and Pharmacological Neuroimaging Unit, National Institute on Drug Abuse, Baltimore, Maryland
| | - Hedy Kober
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis
| | - Hamed Ekhtiari
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis
- Laureate Institute for Brain Research, Tulsa, Oklahoma
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Hamamoto Y, Oba K, Ishibashi R, Ding Y, Nouchi R, Sugiura M. Reduced body-image disturbance by body-image interventions is associated with neural-response changes in visual and social processing regions: a preliminary study. Front Psychiatry 2024; 15:1337776. [PMID: 38510808 PMCID: PMC10951070 DOI: 10.3389/fpsyt.2024.1337776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/15/2024] [Indexed: 03/22/2024] Open
Abstract
Introduction Body-image disturbance is a major factor in the development of eating disorders, especially among young women. There are two main components: perceptual disturbance, characterized by a discrepancy between perceived and actual body size, and affective disturbance, characterized by a discrepancy between perceived and ideal body size. Interventions targeting body-image disturbance ask individuals to describe their own body without using negative expressions when either viewing it in a mirror or imagining it. Despite the importance of reducing body-image disturbance, its neural mechanisms remain unclear. Here we investigated the changes in neural responses before and after an intervention. We hypothesized that neural responses correlated with the degree of body-image disturbance would also be related to its reduction, i.e., a reduction in perceptual and affective disturbances would be related to changes in attentional and socio-cognitive processing, respectively. Methods Twenty-eight young adult women without known psychiatric disorders underwent a single 40-min intervention. Participants completed tasks before and after the intervention, in which they estimated their perceived and ideal body sizes using distorted silhouette images to measure body-image disturbance. We analyzed the behavioral and neural responses of participants during the tasks. Results The intervention did not significantly reduce body-image disturbance. Analysis of individual differences showed distinct changes in neural responses for each type of disturbance. A decrease in perceptual disturbance was associated with bodily visuospatial processing: increased activation in the left superior parietal lobule, bilateral occipital gyri, and right cuneus. Reduced affective disturbance was associated with socio-cognitive processing; decreased activation in the right temporoparietal junction, and increased functional connectivity between the left extrastriate body area and the right precuneus. Discussion We identified distinct neural mechanisms (bodily visuospatial and socio-cognitive processing) associated with the reduction in each component of body-image disturbance. Our results imply that different neural mechanisms are related to reduced perceptual disturbance and the expression thereof, whereas similar neural mechanisms are related to the reduction and expression of affective disturbance. Considering the small sample size of this study, our results should be regarded as preliminary.
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Affiliation(s)
- Yumi Hamamoto
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Department of Psychology, Northumbria University, Newcastle upon Tyne, United Kingdom
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Kentaro Oba
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Ryo Ishibashi
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Yi Ding
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
- School of Medicine, Tohoku University, Sendai, Japan
| | - Rui Nouchi
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Motoaki Sugiura
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
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20
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Fischer H, Nilsson ME, Ebner NC. Why the Single-N Design Should Be the Default in Affective Neuroscience. AFFECTIVE SCIENCE 2024; 5:62-66. [PMID: 38495781 PMCID: PMC10942943 DOI: 10.1007/s42761-023-00182-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 02/07/2023] [Indexed: 03/19/2024]
Abstract
Many studies in affective neuroscience rely on statistical procedures designed to estimate population averages and base their main conclusions on group averages. However, the obvious unit of analysis in affective neuroscience is the individual, not the group, because emotions are individual phenomena that typically vary across individuals. Conclusions based on group averages may therefore be misleading or wrong, if interpreted as statements about emotions of an individual, or meaningless, if interpreted as statements about the group, which has no emotions. We therefore advocate the Single-N design as the default strategy in research on emotions, testing one or several individuals extensively with the primary purpose of obtaining results at the individual level. In neuroscience, the equivalent to the Single-N design is deep imaging, the emerging trend of extensive measurements of activity in single brains. Apart from the fact that individuals react differently to emotional stimuli, they also vary in shape and size of their brains. Group-based analysis of brain imaging data therefore refers to an "average brain" that was activated in a way that may not be representative of the physiology of any of the tested individual brains, nor of how these brains responded to the experimental stimuli. Deep imaging avoids such group-averaging artifacts by simply focusing on the individual brain. This methodological shift toward individual analysis has already opened new research areas in fields like vision science. Inspired by this, we call for a corresponding shift in affective neuroscience, away from group averages, and toward experimental designs targeting the individual.
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Affiliation(s)
- Håkan Fischer
- Department of Psychology, Stockholm University, 106 91 Stockholm, Sweden
- Stockholm University Brain Imaging Center (SUBIC), 106 91 Stockholm, Sweden
- Department of Psychology, University of Florida, Gainesville, FL 32611 USA
| | - Mats E. Nilsson
- Department of Psychology, Stockholm University, 106 91 Stockholm, Sweden
| | - Natalie C. Ebner
- Department of Psychology, University of Florida, Gainesville, FL 32611 USA
- Institute of Aging, University of Florida, Gainesville, FL 32611 USA
- Center for Cognitive Aging and Memory, Department of Clinical and Health Psychology, University of Florida, Gainesville, FL 32611 USA
- Florida Institute for Cybersecurity Research, University of Florida, Gainesville, FL 32610-0165 USA
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21
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Levitas D, Hayashi S, Vinci-Booher S, Heinsfeld A, Bhatia D, Lee N, Galassi A, Niso G, Pestilli F. ezBIDS: Guided standardization of neuroimaging data interoperable with major data archives and platforms. Sci Data 2024; 11:179. [PMID: 38332144 PMCID: PMC10853279 DOI: 10.1038/s41597-024-02959-0] [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: 04/11/2023] [Accepted: 01/12/2024] [Indexed: 02/10/2024] Open
Abstract
Data standardization promotes a common framework through which researchers can utilize others' data and is one of the leading methods neuroimaging researchers use to share and replicate findings. As of today, standardizing datasets requires technical expertise such as coding and knowledge of file formats. We present ezBIDS, a tool for converting neuroimaging data and associated metadata to the Brain Imaging Data Structure (BIDS) standard. ezBIDS contains four major features: (1) No installation or programming requirements. (2) Handling of both imaging and task events data and metadata. (3) Semi-automated inference and guidance for adherence to BIDS. (4) Multiple data management options: download BIDS data to local system, or transfer to OpenNeuro.org or to brainlife.io. In sum, ezBIDS requires neither coding proficiency nor knowledge of BIDS, and is the first BIDS tool to offer guided standardization, support for task events conversion, and interoperability with OpenNeuro.org and brainlife.io.
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Affiliation(s)
- Daniel Levitas
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Soichi Hayashi
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Sophia Vinci-Booher
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, TN, 37203, USA
| | - Anibal Heinsfeld
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Dheeraj Bhatia
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Nicholas Lee
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Anthony Galassi
- Center for Multimodal Neuroimaging, National Institute of Mental Health, Bethesda, MD, USA
| | | | - Franco Pestilli
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA.
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22
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Mazzotti DR. Multimodal integration of sleep electroencephalogram, brain imaging, and cognitive assessments: approaches using noisy clinical data. Sleep 2024; 47:zsad305. [PMID: 38019853 PMCID: PMC10851849 DOI: 10.1093/sleep/zsad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Indexed: 12/01/2023] Open
Affiliation(s)
- Diego R Mazzotti
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA
- Division of Pulmonary Critical Care and Sleep Medicine, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA
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23
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Hatzenbuehler ML, McLaughlin KA, Weissman DG, Cikara M. A research agenda for understanding how social inequality is linked to brain structure and function. Nat Hum Behav 2024; 8:20-31. [PMID: 38172629 PMCID: PMC11112523 DOI: 10.1038/s41562-023-01774-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 11/01/2023] [Indexed: 01/05/2024]
Abstract
Consistent evidence documents powerful effects of social inequality on health, well-being and academic achievement. Yet research on whether social inequality may also be linked to brain structure and function has, until recently, been rare. Here we describe three methodological approaches that can be used to study this question-single site, single study; multi-site, single study; and spatial meta-analysis. We review empirical work that, using these approaches, has observed associations between neural outcomes and structural measures of social inequality-including structural stigma, community-level prejudice, gender inequality, neighbourhood disadvantage and the generosity of the social safety net for low-income families. We evaluate the relative strengths and limitations of these approaches, discuss ethical considerations and outline directions for future research. In doing so, we advocate for a paradigm shift in cognitive neuroscience that explicitly incorporates upstream structural and contextual factors, which we argue holds promise for uncovering the neural correlates of social inequality.
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Affiliation(s)
| | | | - David G Weissman
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Mina Cikara
- Department of Psychology, Harvard University, Cambridge, MA, USA
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24
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Cao Z, McCabe M, Callas P, Cupertino RB, Ottino-González J, Murphy A, Pancholi D, Schwab N, Catherine O, Hutchison K, Cousijn J, Dagher A, Foxe JJ, Goudriaan AE, Hester R, Li CSR, Thompson WK, Morales AM, London ED, Lorenzetti V, Luijten M, Martin-Santos R, Momenan R, Paulus MP, Schmaal L, Sinha R, Solowij N, Stein DJ, Stein EA, Uhlmann A, van Holst RJ, Veltman DJ, Wiers RW, Yücel M, Zhang S, Conrod P, Mackey S, Garavan H. Recalibrating single-study effect sizes using hierarchical Bayesian models. FRONTIERS IN NEUROIMAGING 2023; 2:1138193. [PMID: 38179200 PMCID: PMC10764546 DOI: 10.3389/fnimg.2023.1138193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 11/27/2023] [Indexed: 01/06/2024]
Abstract
Introduction There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance. Methods We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method. Results The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. Discussion Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.
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Affiliation(s)
- Zhipeng Cao
- Shanghai Xuhui Mental Health Center, Shanghai, China
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States
| | - Matthew McCabe
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States
| | - Peter Callas
- Department of Mathematics and Statistics, University of Vermont College of Engineering and Mathematical Sciences, Burlington, VT, United States
| | - Renata B. Cupertino
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States
| | - Jonatan Ottino-González
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States
| | - Alistair Murphy
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States
| | - Devarshi Pancholi
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States
| | - Nathan Schwab
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States
| | - Orr Catherine
- Department of Psychological Sciences, School of Health Sciences, Swinburne University, Melbourne, VIC, Australia
| | - Kent Hutchison
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States
| | - Janna Cousijn
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Alain Dagher
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - John J. Foxe
- Department of Neuroscience, The Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States
| | - Anna E. Goudriaan
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Robert Hester
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Chiang-Shan R. Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | | | - Angelica M. Morales
- Department of Psychiatry at Oregon Health and Science University, Portland, OR, United States
| | - Edythe D. London
- David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, United States
| | - Valentina Lorenzetti
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural & Health Sciences, Faculty of Health Sciences, Australian Catholic University, Australia
| | - Maartje Luijten
- Behavioural Science Institute, Radboud University, Nijmegen, Netherlands
| | - Rocio Martin-Santos
- Department of Psychiatry and Psychology, University of Barcelona, Barcelona, Spain
| | - Reza Momenan
- Clinical NeuroImaging Research Core, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States
| | - Martin P. Paulus
- Laureate Institute for Brain Research, Tulsa, OK, United States
- VA San Diego Healthcare System and Department of Psychiatry, University of California San Diego, La Jolla, CA, United States
| | - Lianne Schmaal
- Orygen, Parkville, VIC, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Rajita Sinha
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Nadia Solowij
- School of Psychology and Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, NSW, Australia
| | - Dan J. Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Elliot A. Stein
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, United States
| | - Anne Uhlmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Ruth J. van Holst
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Dick J. Veltman
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Reinout W. Wiers
- Addiction Development and Psychopathology (ADAPT)-Lab, Department of Psychology and Center for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands
| | - Murat Yücel
- BrainPark, Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging Facility, Monash University, Melbourne, VIC, Australia
| | - Sheng Zhang
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Patricia Conrod
- Department of Psychiatry, Université de Montreal, CHU Ste Justine Hospital, Montreal, QC, Canada
| | - Scott Mackey
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, United States
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25
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Wang X, Chu Y, Wang Q, Cao L, Qiao L, Zhang L, Liu M. Unsupervised contrastive graph learning for resting-state functional MRI analysis and brain disorder detection. Hum Brain Mapp 2023; 44:5672-5692. [PMID: 37668327 PMCID: PMC10619386 DOI: 10.1002/hbm.26469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 07/08/2023] [Accepted: 08/11/2023] [Indexed: 09/06/2023] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) helps characterize regional interactions that occur in the human brain at a resting state. Existing research often attempts to explore fMRI biomarkers that best predict brain disease progression using machine/deep learning techniques. Previous fMRI studies have shown that learning-based methods usually require a large amount of labeled training data, limiting their utility in clinical practice where annotating data is often time-consuming and labor-intensive. To this end, we propose an unsupervised contrastive graph learning (UCGL) framework for fMRI-based brain disease analysis, in which a pretext model is designed to generate informative fMRI representations using unlabeled training data, followed by model fine-tuning to perform downstream disease identification tasks. Specifically, in the pretext model, we first design a bi-level fMRI augmentation strategy to increase the sample size by augmenting blood-oxygen-level-dependent (BOLD) signals, and then employ two parallel graph convolutional networks for fMRI feature extraction in an unsupervised contrastive learning manner. This pretext model can be optimized on large-scale fMRI datasets, without requiring labeled training data. This model is further fine-tuned on to-be-analyzed fMRI data for downstream disease detection in a task-oriented learning manner. We evaluate the proposed method on three rs-fMRI datasets for cross-site and cross-dataset learning tasks. Experimental results suggest that the UCGL outperforms several state-of-the-art approaches in automated diagnosis of three brain diseases (i.e., major depressive disorder, autism spectrum disorder, and Alzheimer's disease) with rs-fMRI data.
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Affiliation(s)
- Xiaochuan Wang
- The School of Mathematics ScienceLiaocheng UniversityLiaochengChina
| | - Ying Chu
- The School of Mathematics ScienceLiaocheng UniversityLiaochengChina
| | - Qianqian Wang
- The Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Liang Cao
- Taian Tumor Prevention and Treatment HospitalTaianChina
| | - Lishan Qiao
- The School of Mathematics ScienceLiaocheng UniversityLiaochengChina
| | - Limei Zhang
- School of Computer Science and TechnologyShandong Jianzhu UniversityJinanChina
| | - Mingxia Liu
- The Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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26
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Orlichenko A, Daly G, Zhou Z, Liu A, Shen H, Deng HW, Wang YP. ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound. NEUROIMAGE. REPORTS 2023; 3:100191. [PMID: 38125823 PMCID: PMC10732473 DOI: 10.1016/j.ynirp.2023.100191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Grant Daly
- College of Medicine, University of South Alabama, Mobile, AL, USA
| | - Ziyu Zhou
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Anqi Liu
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hui Shen
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hong-Wen Deng
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
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27
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Sijtsma M, Marjoram D, Gallagher HL, Grealy MA, Brennan D, Mathias C, Cavanagh J, Pollick FE. Major Depression and the Perception of Affective Instrumental and Expressive Gestures: An fMRI Investigation. Psychiatry Res Neuroimaging 2023; 336:111728. [PMID: 37939431 DOI: 10.1016/j.pscychresns.2023.111728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 09/24/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023]
Abstract
Major depressive disorder (MDD) is associated with biased perception of human movement. Gesture is important for communication and in this study we investigated neural correlates of gesture perception in MDD. We hypothesised different neural activity between individuals with MDD and typical individuals when viewing instrumental and expressive gestures that were negatively or positively valenced. Differences were expected in brain areas associated with gesture perception, including superior temporal, frontal, and emotion processing regions. We recruited 12 individuals with MDD and 12 typical controls matched on age, gender, and handedness. They viewed gestures displayed by stick figures while functional magnetic resonance imaging (fMRI) was performed. Results of a random effects three-way mixed ANOVA indicated that individuals with MDD had greater activity in the right claustrum compared to controls, regardless of gesture type or valence. Additionally, we observed main effects of gesture type and valence, regardless of group. Perceiving instrumental compared to expressive gestures was associated with greater activity in the left cuneus and left superior temporal gyrus, while perceiving negative compared to positive gestures was associated with greater activity in the right precuneus and right lingual gyrus. We also observed a two-way interaction between gesture type and valence in various brain regions.
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Affiliation(s)
- Mathilde Sijtsma
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Dominic Marjoram
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Helen L Gallagher
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
| | - Madeleine A Grealy
- Department of Psychological Science and Health, University of Strathclyde, Glasgow, UK
| | - David Brennan
- Department of MRI Physics, Imaging Centre of Excellence, Queen Elizabeth University Hospital, Glasgow, UK
| | | | - Jonathan Cavanagh
- School of Infection and Immunity, University of Glasgow, Glasgow, UK
| | - Frank E Pollick
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
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Burles F, Iaria G. Neurocognitive Adaptations for Spatial Orientation and Navigation in Astronauts. Brain Sci 2023; 13:1592. [PMID: 38002551 PMCID: PMC10669796 DOI: 10.3390/brainsci13111592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/04/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023] Open
Abstract
Astronauts often face orientation challenges while on orbit, which can lead to operator errors in demanding spatial tasks. In this study, we investigated the impact of long-duration spaceflight on the neural processes supporting astronauts' spatial orientation skills. Using functional magnetic resonance imaging (fMRI), we collected data from 16 astronauts six months before and two weeks after their International Space Station (ISS) missions while performing a spatial orientation task that requires generating a mental representation of one's surroundings. During this task, astronauts exhibited a general reduction in neural activity evoked from spatial-processing brain regions after spaceflight. The neural activity evoked in the precuneus was most saliently reduced following spaceflight, along with less powerful effects observed in the angular gyrus and retrosplenial regions of the brain. Importantly, the reduction in precuneus activity we identified was not accounted for by changes in behavioral performance or changes in grey matter concentration. These findings overall show less engagement of explicitly spatial neurological processes at postflight, suggesting astronauts make use of complementary strategies to perform some spatial tasks as an adaptation to spaceflight. These preliminary findings highlight the need for developing countermeasures or procedures that minimize the detrimental effects of spaceflight on spatial cognition, especially in light of planned long-distance future missions.
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Affiliation(s)
- Ford Burles
- Canadian Space Health Research Network, Department of Psychology, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada;
- NeuroLab, Department of Psychology, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Giuseppe Iaria
- Canadian Space Health Research Network, Department of Psychology, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada;
- NeuroLab, Department of Psychology, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
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29
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Haynes G, Muhammad F, Khan AF, Mohammadi E, Smith ZA, Ding L. The current state of spinal cord functional magnetic resonance imaging and its application in clinical research. J Neuroimaging 2023; 33:877-888. [PMID: 37740582 DOI: 10.1111/jon.13158] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/05/2023] [Accepted: 09/11/2023] [Indexed: 09/24/2023] Open
Abstract
Since its development, spinal cord functional magnetic resonance imaging (fMRI) has utilized various methodologies and stimulation protocols to develop a deeper understanding of a healthy human spinal cord that lays a foundation for its use in clinical research and practice. In this review, we conducted a comprehensive literature search on spinal cord fMRI studies and summarized the recent advancements and resulting scientific achievements of spinal cord fMRI in the following three aspects: the current state of spinal cord fMRI methodologies and stimulation protocols, knowledge about the healthy spinal cord's functions obtained via spinal cord fMRI, and fMRI's exemplary usage in spinal cord diseases and injuries. We conclude with a discussion that, while technical challenges exist, novel fMRI technologies for and new knowledge about the healthy human spinal cord have been established. Empowered by these developments, investigations of pathological and injury states within the spinal cord have become the next important direction of spinal cord fMRI. Recent clinical investigations into spinal cord pathologies, for example, fibromyalgia, multiple sclerosis, spinal cord injury, and cervical spondylotic myelopathy, have already provided deep insights into spinal cord impairments and the time course of impairment-caused changes. We expect that future spinal cord fMRI advancement and research development will further enhance our understanding of various spinal cord diseases and provide the foundation for evaluating existing and developing new treatment plans.
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Affiliation(s)
- Grace Haynes
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, USA
| | - Fauziyya Muhammad
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Ali F Khan
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Esmaeil Mohammadi
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Zachary A Smith
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Lei Ding
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, USA
- Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, Oklahoma, USA
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30
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Canna A, Cantone E, Roefs A, Franssen S, Prinster A, Formisano E, Di Salle F, Esposito F. Functional MRI activation of the nucleus tractus solitarius after taste stimuli at ultra-high field: a proof-of-concept single-subject study. Front Nutr 2023; 10:1173316. [PMID: 37955018 PMCID: PMC10637550 DOI: 10.3389/fnut.2023.1173316] [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: 02/24/2023] [Accepted: 09/15/2023] [Indexed: 11/14/2023] Open
Abstract
Using ultra-high field (7 Tesla) functional MRI (fMRI), we conducted the first in-vivo functional neuroimaging study of the normal human brainstem specifically designed to examine neural signals in the Nucleus Tractus Solitarius (NTS) in response to all basic taste stimuli. NTS represents the first relay station along the mammalian taste processing pathway which originates at the taste buds in the oral cavity and passes through the thalamus before reaching the primary taste cortex in the brain. In our proof-of-concept study, we acquired data from one adult volunteer using fMRI at 1.2 mm isotropic resolution and performed a univariate general linear model analysis. During fMRI acquisition, three shuffled injections of sweet, bitter, salty, sour, and umami solutions were administered following an event-related design. We observed a statistically significant blood oxygen level-dependent (BOLD) response in the anatomically predicted location of the NTS for all five basic tastes. The results of this study appear statistically robust, even though they were obtained from a single volunteer. The information derived from a similar experimental strategy may inspire novel research aimed at clarifying important details of central nervous system involvement in eating disorders, at designing and monitoring tailored therapeutic strategies.
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Affiliation(s)
- Antonietta Canna
- Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Baronissi, Italy
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - Elena Cantone
- Section of ENT, Department of Neuroscience, Reproductive and Odontostomatological Sciences, "Federico II" University, Napoli, Italy
| | - Anne Roefs
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Sieske Franssen
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Anna Prinster
- Biostructure and Bioimaging Institute, National Research Council, Napoli, Italy
| | - Elia Formisano
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Francesco Di Salle
- Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Baronissi, Italy
- Department of Diagnostic Imaging, University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Salerno, Italy
| | - Fabrizio Esposito
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli”, Napoli, Italy
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31
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Tsurugizawa T, Taki A, Zalesky A, Kasahara K. Increased interhemispheric functional connectivity during non-dominant hand movement in right-handed subjects. iScience 2023; 26:107592. [PMID: 37705959 PMCID: PMC10495657 DOI: 10.1016/j.isci.2023.107592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/15/2023] [Accepted: 08/07/2023] [Indexed: 09/15/2023] Open
Abstract
Hand preference is one of the behavioral expressions of lateralization in the brain. Previous fMRI studies showed the activation in several regions including the motor cortex and the cerebellum during single-hand movement. However, functional connectivity related to hand preference has not been investigated. Here, we used the generalized psychophysiological interaction (gPPI) approach to investigate the alteration of functional connectivity during single-hand movement from the resting state in right-hand subjects. The functional connectivity in interhemispheric motor-related regions including the supplementary motor area, the precentral gyrus, and the cerebellum was significantly increased during non-dominant hand movement, while functional connectivity was not increased during dominant hand movement. The general linear model (GLM) showed activation in contralateral supplementary motor area, contralateral precentral gyrus, and ipsilateral cerebellum during right- or left-hand movement. These results indicate that a combination of GLM and gPPI analysis can detect the lateralization of hand preference more clearly.
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Affiliation(s)
- Tomokazu Tsurugizawa
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, 1-1-1 Higashi, Tsukuba-City, Ibaraki 305-8568, Japan
- Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba 305-8573, Japan
| | - Ai Taki
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, 1-1-1 Higashi, Tsukuba-City, Ibaraki 305-8568, Japan
- Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba 305-8573, Japan
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Kazumi Kasahara
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, 1-1-1 Higashi, Tsukuba-City, Ibaraki 305-8568, Japan
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32
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Heinzel A, Mauler J, Herzog H, Boers F, Mottaghy FM, Langen KJ, Scheins J, Lerche C, Neumaier B, Northoff G, Shah NJ. GABA A receptor availability relates to emotion-induced BOLD responses in the medial prefrontal cortex: simultaneous fMRI/PET with [ 11C]flumazenil. Front Neurosci 2023; 17:1027697. [PMID: 37766785 PMCID: PMC10520870 DOI: 10.3389/fnins.2023.1027697] [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: 08/25/2022] [Accepted: 08/16/2023] [Indexed: 09/29/2023] Open
Abstract
Introduction The fMRI BOLD response to emotional stimuli highlighting the role of the medial prefrontal cortex (MPFC) has been thoroughly investigated. Recently, the relationship between emotion processing and GABA levels has been studied using MPFC proton magnetic resonance spectroscopy (1H-MRS). However, the role of GABAA receptors in the MPFC during emotion processing remains unexplored. Methods Using [11C]flumazenil PET, we investigated the relationship between the binding potential of GABAA receptors and emotion processing as measured using simultaneous fMRI BOLD. We hypothesized a correlation between the percent signal change in the BOLD signal and the binding potential of GABAA receptors in the MPFC. In a combined simultaneous fMRI and [11C]flumazenil-PET study, we analyzed the data from 15 healthy subjects using visual emotional stimuli. Our task comprised two types of emotional processing: passive viewing and appraisal. Following the administration of a bolus plus infusion protocol, PET and fMRI data were simultaneously acquired in a hybrid 3 T MR-BrainPET. Results We found a differential correlation of BOLD percent signal change with [11C]flumazenil binding potential in the MPFC. Specifically, [11C]flumazenil binding potential in the ventromedial prefrontal cortex (vMPFC) correlated with passive viewing of emotionally valenced pictures. In contrast, the [11C]flumazenil binding potential and the BOLD signal induced by picture appraisal did show a correlation in the paracingulate gyrus. Conclusion Our data deliver first evidence for a relationship between MPFC GABAA receptors and emotion processing in the same region. Moreover, we observed that GABAA receptors appear to play different roles in emotion processing in the vMPFC (passive viewing) and paracingulate gyrus (appraisal).
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Affiliation(s)
- Alexander Heinzel
- Institute of Neuroscience and Medicine – 4, Forschungszentrum Jülich, Jülich, Germany
- Department of Nuclear Medicine, Medical Faculty RWTH Aachen, Aachen, Germany
- Department of Nuclear medicine, University Hospital Halle, Halle (Saale), Germany
| | - Jörg Mauler
- Institute of Neuroscience and Medicine – 4, Forschungszentrum Jülich, Jülich, Germany
| | - Hans Herzog
- Institute of Neuroscience and Medicine – 4, Forschungszentrum Jülich, Jülich, Germany
| | - Frank Boers
- Institute of Neuroscience and Medicine – 4, Forschungszentrum Jülich, Jülich, Germany
| | - Felix M. Mottaghy
- Department of Nuclear Medicine, Medical Faculty RWTH Aachen, Aachen, Germany
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine – 4, Forschungszentrum Jülich, Jülich, Germany
- Department of Nuclear Medicine, Medical Faculty RWTH Aachen, Aachen, Germany
| | - Jürgen Scheins
- Institute of Neuroscience and Medicine – 4, Forschungszentrum Jülich, Jülich, Germany
| | - Christoph Lerche
- Institute of Neuroscience and Medicine – 4, Forschungszentrum Jülich, Jülich, Germany
| | - Bernd Neumaier
- Institute of Neuroscience and Medicine – 5, Forschungszentrum Jülich, Jülich, Germany
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, ON, Canada
| | - N. Jon Shah
- Institute of Neuroscience and Medicine – 4, Forschungszentrum Jülich, Jülich, Germany
- Institute of Neuroscience and Medicine – 11, Forschungszentrum Jülich, Jülich, Germany
- JARA – BRAIN – Translational Medicine, Aachen, Germany
- Department of Neurology, RWTH Aachen University, Aachen, Germany
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Dobbelaar S, Achterberg M, van Drunen L, van Duijvenvoorde AC, van IJzendoorn MH, Crone EA. Development of social feedback processing and responses in childhood: an fMRI test-replication design in two age cohorts. Soc Cogn Affect Neurosci 2023; 18:nsac039. [PMID: 35661224 PMCID: PMC10985675 DOI: 10.1093/scan/nsac039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 05/20/2022] [Accepted: 06/03/2022] [Indexed: 11/14/2022] Open
Abstract
This study investigated behavioral and neural correlates underlying social feedback processing and subsequent aggressive behaviors in childhood in two age cohorts (test sample: n = 509/n = 385 and replication sample: n = 354/n = 195, 7-9 years old). Using a previously validated Social Network Aggression Task, we showed that negative social feedback resulted in most behavioral aggression, followed by less aggression after neutral and least aggression after positive feedback. Receiving positive and negative social feedback was associated with increased activity in the insula, medial prefrontal cortex and ventrolateral prefrontal cortex. Responding to feedback was associated with additional activation in the dorsolateral prefrontal cortex (DLPFC) following positive feedback. This DLPFC activation correlated negatively with aggression. Furthermore, age analyses showed that older children showed larger reductions in aggression following positive feedback and more neural activation in the DLPFC when responding to positive feedback compared to younger children. To assess the robustness of our results, we examined these processes in two independent behavioral/functional magnetic resonance imaging samples using equivalence testing, thereby contributing to replicable reports. Together, these findings demonstrate an important role of social saliency and regulatory processes where regulation of aggression rapidly develops between the ages of 7 and 9 years.
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Affiliation(s)
- Simone Dobbelaar
- Leiden Consortium on Individual Development, Leiden University, Leiden 2333 AK, The Netherlands
- Developmental and Educational Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden 2333 AK, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden University, Leiden 2300 RC, The Netherlands
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam 3000 DR, The Netherlands
| | - Michelle Achterberg
- Leiden Consortium on Individual Development, Leiden University, Leiden 2333 AK, The Netherlands
- Developmental and Educational Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden 2333 AK, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden University, Leiden 2300 RC, The Netherlands
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam 3000 DR, The Netherlands
| | - Lina van Drunen
- Leiden Consortium on Individual Development, Leiden University, Leiden 2333 AK, The Netherlands
- Developmental and Educational Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden 2333 AK, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden University, Leiden 2300 RC, The Netherlands
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam 3000 DR, The Netherlands
| | - Anna c.k van Duijvenvoorde
- Leiden Consortium on Individual Development, Leiden University, Leiden 2333 AK, The Netherlands
- Developmental and Educational Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden 2333 AK, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden University, Leiden 2300 RC, The Netherlands
| | - Marinus H van IJzendoorn
- Leiden Consortium on Individual Development, Leiden University, Leiden 2333 AK, The Netherlands
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam 3000 DR, The Netherlands
- Research Department of Clinical, Education and Health Psychology, University College London, London WC1E 6BT, UK
| | - Eveline A Crone
- Leiden Consortium on Individual Development, Leiden University, Leiden 2333 AK, The Netherlands
- Developmental and Educational Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden 2333 AK, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden University, Leiden 2300 RC, The Netherlands
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam 3000 DR, The Netherlands
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Gil-Paterna P, Furmark T. Imaging the cerebellum in post-traumatic stress and anxiety disorders: a mini-review. Front Syst Neurosci 2023; 17:1197350. [PMID: 37645454 PMCID: PMC10460913 DOI: 10.3389/fnsys.2023.1197350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 07/24/2023] [Indexed: 08/31/2023] Open
Abstract
Post-traumatic stress disorder (PTSD) and anxiety disorders are among the most prevalent psychiatric conditions worldwide sharing many clinical manifestations and, most likely, neural mechanisms as suggested by neuroimaging research. While the so-called fear circuitry and traditional limbic structures of the brain, particularly the amygdala, have been extensively studied in sufferers of these disorders, the cerebellum has been relatively underexplored. The aim of this paper was to present a mini-review of functional (task-activity or resting-state connectivity) and structural (gray matter volume) results on the cerebellum as reported in magnetic resonance imaging studies of patients with PTSD or anxiety disorders (49 selected studies in 1,494 patients). While mixed results were noted overall, e.g., regarding the direction of effects and anatomical localization, cerebellar structures like the vermis seem to be highly involved. Still, the neurofunctional and structural alterations reported for the cerebellum in excessive anxiety and trauma are complex, and in need of further evaluation.
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León JJ, Fernández-Martin P, González-Rodríguez A, Rodríguez-Herrera R, García-Pinteño J, Pérez-Fernández C, Sánchez-Kuhn A, Amaya-Pascasio L, Soto-Ontoso M, Martínez-Sánchez P, Sánchez-Santed F, Flores P. Decision-making and frontoparietal resting-state functional connectivity among impulsive-compulsive diagnoses. Insights from a Bayesian approach. Addict Behav 2023; 143:107683. [PMID: 36963236 DOI: 10.1016/j.addbeh.2023.107683] [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: 10/14/2022] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 03/13/2023]
Abstract
The Iowa Gambling Task (IGT) is one of the most widely used paradigms for assessing decision-making. An impairment in this process may be linked to several psychopathological disorders, such as obsessive-compulsive disorder (OCD), substance abuse disorder (SUD) or attention-deficit/hyperactivity disorder (ADHD), which could make it a good candidate for being consider a transdiagnostic domain. Resting-state functional connectivity (rsFC) has been proposed as a promising biomarker of decision-making. In this study, we aimed to identify idiosyncratic decision-making profiles among healthy people and impulsive-compulsive spectrum patients during the IGT, and to investigate the role of frontoparietal network (FPN) rsFC as a possible biomarker of different decision-making patterns. Using functional near-infrared spectroscopy (fNIRS), rsFC of 114 adults (34 controls; 25 OCD; 41 SUD; 14 ADHD) was obtained. Then, they completed the IGT. Hybrid clustering methods based on individual deck choices yielded three decision-makers subgroups. Cluster 1 (n = 27) showed a long-term advantageous strategy. Cluster 2 (n = 25) presented a maladaptive decision-making strategy. Cluster 3 (n = 62) did not develop a preference for any deck during the task. Interestingly, the proportion of participants in each cluster was not different between diagnostic groups. A Bayesian general linear model showed no credible differences in the IGT performance between diagnostic groups nor credible evidence to support the role of FPN rsFC as a biomarker of decision-making under the IGT context. This study highlights the importance of exploring in depth the behavioral and neurophysiological variables that may drive decision-making in clinical and healthy populations.
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Affiliation(s)
- J J León
- Department of Psychology, Faculty of Psychology, University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain; Health Research Centre (CEINSA), University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain.
| | - P Fernández-Martin
- Department of Psychology, Faculty of Psychology, University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain; Health Research Centre (CEINSA), University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain.
| | - A González-Rodríguez
- Department of Psychology, Faculty of Psychology, University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain; Health Research Centre (CEINSA), University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain.
| | - R Rodríguez-Herrera
- Department of Psychology, Faculty of Psychology, University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain; Health Research Centre (CEINSA), University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain.
| | - J García-Pinteño
- Department of Psychology, Faculty of Psychology, University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain; Health Research Centre (CEINSA), University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain.
| | - C Pérez-Fernández
- Department of Psychology, Faculty of Psychology, University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain; Health Research Centre (CEINSA), University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain.
| | - A Sánchez-Kuhn
- Department of Psychology, Faculty of Psychology, University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain; Health Research Centre (CEINSA), University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain.
| | - L Amaya-Pascasio
- Department of Neurology and Stroke Centre. Torrecárdenas University Hospital, Spain.
| | - M Soto-Ontoso
- Mental Health Departament. Torrecárdenas University Hospital, Spain.
| | - P Martínez-Sánchez
- Department of Neurology and Stroke Centre. Torrecárdenas University Hospital, Spain.
| | - F Sánchez-Santed
- Department of Psychology, Faculty of Psychology, University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain; Health Research Centre (CEINSA), University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain.
| | - P Flores
- Department of Psychology, Faculty of Psychology, University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain; Health Research Centre (CEINSA), University of Almeria, Carretera de Sacramento S/N, 04120, La Cañada de San Urbano, Almeria, Spain.
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Javaheripour N, Colic L, Opel N, Li M, Maleki Balajoo S, Chand T, Van der Meer J, Krylova M, Izyurov I, Meller T, Goltermann J, Winter NR, Meinert S, Grotegerd D, Jansen A, Alexander N, Usemann P, Thomas-Odenthal F, Evermann U, Wroblewski A, Brosch K, Stein F, Hahn T, Straube B, Krug A, Nenadić I, Kircher T, Croy I, Dannlowski U, Wagner G, Walter M. Altered brain dynamic in major depressive disorder: state and trait features. Transl Psychiatry 2023; 13:261. [PMID: 37460460 DOI: 10.1038/s41398-023-02540-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/20/2023] Open
Abstract
Temporal neural synchrony disruption can be linked to a variety of symptoms of major depressive disorder (MDD), including mood rigidity and the inability to break the cycle of negative emotion or attention biases. This might imply that altered dynamic neural synchrony may play a role in the persistence and exacerbation of MDD symptoms. Our study aimed to investigate the changes in whole-brain dynamic patterns of the brain functional connectivity and activity related to depression using the hidden Markov model (HMM) on resting-state functional magnetic resonance imaging (rs-fMRI) data. We compared the patterns of brain functional dynamics in a large sample of 314 patients with MDD (65.9% female; age (mean ± standard deviation): 35.9 ± 13.4) and 498 healthy controls (59.4% female; age: 34.0 ± 12.8). The HMM model was used to explain variations in rs-fMRI functional connectivity and averaged functional activity across the whole-brain by using a set of six unique recurring states. This study compared the proportion of time spent in each state and the average duration of visits to each state to assess stability between different groups. Compared to healthy controls, patients with MDD showed significantly higher proportional time spent and temporal stability in a state characterized by weak functional connectivity within and between all brain networks and relatively strong averaged functional activity of regions located in the somatosensory motor (SMN), salience (SN), and dorsal attention (DAN) networks. Both proportional time spent and temporal stability of this brain state was significantly associated with depression severity. Healthy controls, in contrast to the MDD group, showed proportional time spent and temporal stability in a state with relatively strong functional connectivity within and between all brain networks but weak averaged functional activity across the whole brain. These findings suggest that disrupted brain functional synchrony across time is present in MDD and associated with current depression severity.
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Affiliation(s)
- Nooshin Javaheripour
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
| | - Lejla Colic
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- German Center for Mental Health (DZPG), Jena, Germany
| | - Nils Opel
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- German Center for Mental Health (DZPG), Jena, Germany
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany
| | - Somayeh Maleki Balajoo
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, 40225, Jülich, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, 52425, Jülich, Germany
| | - Tara Chand
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
- Department of Clinical Psychology, Friedrich Schiller University Jena, Am Steiger 3-1, 07743, Jena, Germany
| | - Johan Van der Meer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marina Krylova
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Institute for Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Igor Izyurov
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils R Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Ulrika Evermann
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
- Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Ilona Croy
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany
- Department of Clinical Psychology, Friedrich Schiller University Jena, Am Steiger 3-1, 07743, Jena, Germany
- Department of Psychotherapie and Psychosomatic Medicine, Carl Gustav Carus University Hospital Dresden, Fetscherstr. 74, 01307, Dresden, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Gerd Wagner
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany.
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany.
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany.
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany.
- German Center for Mental Health (DZPG), Jena, Germany.
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Jena, Germany.
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
- Leibniz Institute for Neurobiology, Magdeburg, Germany.
- Center for Behavioral Brain Sciences, Magdeburg, Germany.
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
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Shany O, Dunsky N, Gilam G, Greental A, Gilboa-Schechtman E, Hendler T. Self-evaluation of social-rank in socially anxious individuals associates with enhanced striatal reward function. Psychol Med 2023; 53:4569-4579. [PMID: 35698849 PMCID: PMC10388315 DOI: 10.1017/s0033291722001453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 04/21/2022] [Accepted: 05/04/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Negative self-views, especially in the domain of power (i.e. social-rank), characterize social anxiety (SA). Neuroimaging studies on self-evaluations in SA have mainly focused on subcortical threat processing systems. Yet, self-evaluation may concurrently invoke diverse affective processing, as motivational systems related to desired self-views may also be activated. To investigate the conflictual nature that may accompany self-evaluation of certain social domains in SA, we examined brain activity related to both threat and reward processing. METHODS Participants (N = 74) differing in self-reported SA-severity underwent fMRI while completing a self-evaluation task, wherein they judged the self-descriptiveness of high- v. low-intensity traits in the domains of power and affiliation (i.e. social connectedness). Participants also completed two auxiliary fMRI tasks designated to evoke reward- and threat-related activations in the ventral striatum (VS) and amygdala, respectively. We hypothesized that self-evaluations in SA, particularly in the domain of power, involve aberrant brain activity related to both threat and reward processing. RESULTS SA-severity was more negatively associated with power than with affiliation self-evaluations. During self-evaluative judgment of high-power (e.g. dominant), SA-severity associated with increased activity in the VS and ventromedial prefrontal cortex. Moreover, SA-severity correlated with higher similarity between brain activity patterns activated by high-power traits and patterns activated by incentive salience (i.e. reward anticipation) in the VS during the reward task. CONCLUSIONS Our findings indicate that self-evaluation of high-power in SA involves excessive striatal reward-related activation, and pinpoint the downregulation of VS-VMPFC activity within such self-evaluative context as a potential neural outcome for therapeutic interventions.
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Affiliation(s)
- Ofir Shany
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Netta Dunsky
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Gadi Gilam
- Faculty of Dental Medicine, The Institute of Biomedical and Oral Research, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ayam Greental
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Eva Gilboa-Schechtman
- Department of Psychology and the Gonda Brain Science Center, Bar-Ilan University, Ramat-Gan, Israel
| | - Talma Hendler
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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Taylor PA, Reynolds RC, Calhoun V, Gonzalez-Castillo J, Handwerker DA, Bandettini PA, Mejia AF, Chen G. Highlight results, don't hide them: Enhance interpretation, reduce biases and improve reproducibility. Neuroimage 2023; 274:120138. [PMID: 37116766 PMCID: PMC10233921 DOI: 10.1016/j.neuroimage.2023.120138] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 04/05/2023] [Accepted: 04/26/2023] [Indexed: 04/30/2023] Open
Abstract
Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples and extensions of this approach for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams' results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice-focusing on highlighting results, rather than hiding all but the strongest ones-can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses.
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Affiliation(s)
- Paul A Taylor
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, MD, USA.
| | | | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, and Emory University, Atlanta, GA, USA
| | | | | | - Peter A Bandettini
- Section on Functional Imaging Methods, NIMH, NIH, Bethesda, MD, USA; Functional MRI Core Facility, NIMH, NIH, Bethesda, MD, USA
| | | | - Gang Chen
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, MD, USA
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Gießing C. Identifying Reproducible Biomarkers of Autism Based on Functional Brain Connectivity. Biol Psychiatry 2023; 94:2-3. [PMID: 37316103 DOI: 10.1016/j.biopsych.2023.04.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 06/16/2023]
Affiliation(s)
- Carsten Gießing
- Biological Psychology Lab, Department of Psychology, School of Medicine and Health Sciences, Research Center Neurosensory Science and Systems, Carl von Ossietzky University Oldenburg, Oldenburg, Germany.
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40
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Fennema D, Barker GJ, O'Daly O, Duan S, Carr E, Goldsmith K, Young AH, Moll J, Zahn R. Self-blame-selective hyper-connectivity between anterior temporal and subgenual cortices predicts prognosis in major depressive disorder. Neuroimage Clin 2023; 39:103453. [PMID: 37352570 PMCID: PMC10336192 DOI: 10.1016/j.nicl.2023.103453] [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: 05/22/2023] [Revised: 06/12/2023] [Accepted: 06/13/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND Self-blame-related fMRI measures were shown to predict subsequent recurrence in remitted major depressive disorder (MDD). Their role in current MDD, however, is unknown. We hypothesised that these neural signatures reflect a highly recurrent but remitting course of MDD and therefore predict favourable outcomes over a four-month follow-up period in current MDD. METHODS Forty-five participants with current MDD and non-responders to at least two serotonergic antidepressants, were encouraged to optimise their medication and followed up after receiving four months of primary care treatment-as-usual. Prior to their medication review, participants completed an fMRI paradigm in which they viewed self- and other-blame emotion-evoking statements. Thirty-nine participants met pre-defined fMRI data minimum quality thresholds. Psychophysiological interaction analysis was used to determine baseline connectivity of the right superior anterior temporal lobe (RSATL), with an a priori BA25 region-of-interest for self-blaming vs other-blaming emotions, using Quick Inventory of Depressive Symptomatology (16-item) percentage change as a covariate. RESULTS We corroborated our pre-registered hypothesis that a favourable clinical outcome was associated with higher self-blame-selective RSATL-BA25 connectivity (Family-Wise Error-corrected p <.05 over the a priori BA25 region-of-interest; rs(34) = -0.47, p =.005). This generalised to the sample including participants with suboptimal fMRI quality (rs(39) = -0.32, p =.05). CONCLUSIONS This study shows that neural signatures of overgeneralised self-blame are relevant for prognostic stratification of current treatment-resistant MDD. Future studies need to confirm whether this neural signature indeed represents a trait-like feature of a fully remitting subtype of MDD, or whether it is also modulated by depressive state and related to treatment effects.
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Affiliation(s)
- Diede Fennema
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Suqian Duan
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
| | - Ewan Carr
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Kimberley Goldsmith
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Allan H Young
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK; National Service for Affective Disorders, South London and Maudsley NHS Foundation Trust, London, UK
| | - Jorge Moll
- Cognitive and Behavioural Neuroscience Unit, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Roland Zahn
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK; Cognitive and Behavioural Neuroscience Unit, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; National Service for Affective Disorders, South London and Maudsley NHS Foundation Trust, London, UK.
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Orlichenko A, Qu G, Zhang G, Patel B, Wilson TW, Stephen JM, Calhoun VD, Wang YP. Latent Similarity Identifies Important Functional Connections for Phenotype Prediction. IEEE Trans Biomed Eng 2023; 70:1979-1989. [PMID: 37015625 PMCID: PMC10284019 DOI: 10.1109/tbme.2022.3232964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects but high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size but high feature dimension datasets. METHODS LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method. RESULTS LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence. CONCLUSION We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks. SIGNIFICANCE We propose a novel prediction algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work can lead to new insights in both algorithm design and neuroscience research.
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Tully J, Sethi A, Griem J, Paloyelis Y, Craig MC, Williams SCR, Murphy D, Blair RJ, Blackwood N. Oxytocin normalizes the implicit processing of fearful faces in psychopathy: a randomized crossover study using fMRI. NATURE. MENTAL HEALTH 2023; 1:420-427. [PMID: 38665476 PMCID: PMC11041724 DOI: 10.1038/s44220-023-00067-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 04/14/2023] [Indexed: 04/28/2024]
Abstract
Adults with antisocial personality disorder with (ASPD + P) and without (ASPD - P) psychopathy commit the majority of violent crimes. Empathic processing abnormalities are particularly prominent in psychopathy, but effective pharmacological interventions have yet to be identified. Oxytocin modulates neural responses to fearful expressions in healthy populations. The current study investigates its effects in violent antisocial men. In a placebo-controlled, randomized crossover design, 34 violent offenders (19 ASPD + P; 15 ASPD - P) and 24 healthy non-offenders received 40 IU intranasal oxytocin or placebo and then completed an fMRI morphed faces task examining the implicit processing of fearful facial expressions. Increasing intensity of fearful facial expressions failed to appropriately modulate activity in the bilateral mid-cingulate cortex in violent offenders with ASPD + P, compared with those with ASPD - P. Oxytocin abolished these group differences. This represents evidence of neurochemical modulation of the empathic processing of others' distress in psychopathy.
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Affiliation(s)
- John Tully
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
- Academic Unit of Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Jubilee Campus, Nottingham, UK
| | - Arjun Sethi
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Julia Griem
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Yannis Paloyelis
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Michael C. Craig
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Steven C. R. Williams
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Declan Murphy
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Robert James Blair
- Child and Adolescent Mental Health Centre, Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark
| | - Nigel Blackwood
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
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Berger D, Matharoo GS, Levman J. Random matrix theory tools for the predictive analysis of functional magnetic resonance imaging examinations. J Med Imaging (Bellingham) 2023; 10:036003. [PMID: 37323123 PMCID: PMC10266090 DOI: 10.1117/1.jmi.10.3.036003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 06/17/2023] Open
Abstract
Purpose Random matrix theory (RMT) is an increasingly useful tool for understanding large, complex systems. Prior studies have examined functional magnetic resonance imaging (fMRI) scans using tools from RMT, with some success. However, RMT computations are highly sensitive to a number of analytic choices, and the robustness of findings involving RMT remains in question. We systematically investigate the usefulness of RMT on a wide variety of fMRI datasets using a rigorous predictive framework. Approach We develop open-source software to efficiently compute RMT features from fMRI images and examine the cross-validated predictive potential of eigenvalue and RMT-based features ("eigenfeatures") with classic machine-learning classifiers. We systematically vary pre-processing extent, normalization procedures, RMT unfolding procedures, and feature selection and compare the impact of these analytic choices on the distributions of cross-validated prediction performance for each combination of dataset binary classification task, classifier, and feature. To deal with class imbalance, we use the area under the receiver operating characteristic curve (AUROC) as the main performance metric. Results Across all classification tasks and analytic choices, we find RMT- and eigenvalue-based "eigenfeatures" to have predictive utility more often than not (82.4% of median AUROCs > 0.5 ; median AUROC range across classification tasks 0.47 to 0.64). Simple baseline reductions on source timeseries, by contrast, were less useful (58.8% of median AUROCs > 0.5 , median AUROC range across classification tasks 0.42 to 0.62). Additionally, eigenfeature AUROC distributions were overall more right-tailed than baseline features, suggesting greater predictive potential. However, performance distributions were wide and often significantly affected by analytic choices. Conclusions Eigenfeatures clearly have potential for understanding fMRI functional connectivity in a wide variety of scenarios. The utility of these features is strongly dependent on analytic decisions, suggesting caution when interpreting past and future studies applying RMT to fMRI. However, our study demonstrates that the inclusion of RMT statistics in fMRI investigations could improve prediction performances across a wide variety of phenomena.
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Affiliation(s)
- Derek Berger
- St. Francis Xavier University, Department of Computer Science, Antigonish, Nova Scotia, Canada
| | - Gurpreet S. Matharoo
- St. Francis Xavier University, ACENET, Antigonish, Nova Scotia, Canada
- St. Francis Xavier University, Department of Physics, Antigonish, Nova Scotia, Canada
| | - Jacob Levman
- St. Francis Xavier University, Department of Computer Science, Antigonish, Nova Scotia, Canada
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
- Nova Scotia Health Authority, Research Affiliate, Antigonish, Nova Scotia, Canada
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Lam CL, Wong CH, Junghöfer M, Roesmann K. Implicit threat learning involves the dorsolateral prefrontal cortex and the cerebellum. Int J Clin Health Psychol 2023; 23:100357. [DOI: 10.1016/j.ijchp.2022.100357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/14/2022] [Indexed: 11/25/2022] Open
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Yeung AWK, Robertson M, Uecker A, Fox PT, Eickhoff SB. Trends in the sample size, statistics, and contributions to the BrainMap database of activation likelihood estimation meta-analyses: An empirical study of 10-year data. Hum Brain Mapp 2023; 44:1876-1887. [PMID: 36479854 PMCID: PMC9980884 DOI: 10.1002/hbm.26177] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/18/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
The literature of neuroimaging meta-analysis has been thriving for over a decade. A majority of them were coordinate-based meta-analyses, particularly the activation likelihood estimation (ALE) approach. A meta-evaluation of these meta-analyses was performed to qualitatively evaluate their design and reporting standards. The publications listed from the BrainMap website were screened. Six hundred and three ALE papers published during 2010-2019 were included and analysed. For reporting standards, most of the ALE papers reported their total number of Papers involved and mentioned the inclusion/exclusion criteria on Paper selection. However, most papers did not describe how data redundancy was avoided when multiple related Experiments were reported within one paper. The most prevalent repeated-measures correction methods were voxel-level FDR (54.4%) and cluster-level FWE (33.8%), with the latter quickly replacing the former since 2016. For study characteristics, sample size in terms of number of Papers included per ALE paper and number of Experiments per analysis seemed to be stable over the decade. One-fifth of the surveyed ALE papers failed to meet the recommendation of having >17 Experiments per analysis. For data sharing, most of them did not provide input and output data. In conclusion, the field has matured well in terms of rising dominance of cluster-level FWE correction, and slightly improved reporting on elimination of data redundancy and providing input data. The provision of Data and Code availability statements and flow chart of literature screening process, as well as data submission to BrainMap, should be more encouraged.
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Affiliation(s)
- 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
| | - Michaela Robertson
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Angela Uecker
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA.,Department of Radiology, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
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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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Kampa M, Sebastian A, Tüscher O, Stark R, Klucken T. Refocus on stopping! Replication of reduced right amygdala reactivity to negative, visual primes during inhibition of motor responses. NEUROIMAGE: REPORTS 2023. [DOI: 10.1016/j.ynirp.2022.100151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Hranilovich JA, Legget KT, Dodd KC, Wylie KP, Tregellas JR. Functional magnetic resonance imaging of headache: Issues, best-practices, and new directions, a narrative review. Headache 2023; 63:309-321. [PMID: 36942411 PMCID: PMC10089616 DOI: 10.1111/head.14487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/26/2022] [Accepted: 01/20/2023] [Indexed: 03/23/2023]
Abstract
OBJECTIVE To ensure readers are informed consumers of functional magnetic resonance imaging (fMRI) research in headache, to outline ongoing challenges in this area of research, and to describe potential considerations when asked to collaborate on fMRI research in headache, as well as to suggest future directions for improvement in the field. BACKGROUND Functional MRI has played a key role in understanding headache pathophysiology, and mapping networks involved with headache-related brain activity have the potential to identify intervention targets. Some investigators have also begun to explore its use for diagnosis. METHODS/RESULTS The manuscript is a narrative review of the current best practices in fMRI in headache research, including guidelines on transparency and reproducibility. It also contains an outline of the fundamentals of MRI theory, task-related study design, resting-state functional connectivity, relevant statistics and power analysis, image preprocessing, and other considerations essential to the field. CONCLUSION Best practices to increase reproducibility include methods transparency, eliminating error, using a priori hypotheses and power calculations, using standardized instruments and diagnostic criteria, and developing large-scale, publicly available datasets.
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Affiliation(s)
- Jennifer A Hranilovich
- Division of Child Neurology, Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Kristina T Legget
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, Colorado, USA
- Research Service, Rocky Mountain Regional VA Medical Center, Aurora, Colorado, USA
| | - Keith C Dodd
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Korey P Wylie
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Jason R Tregellas
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, Colorado, USA
- Research Service, Rocky Mountain Regional VA Medical Center, Aurora, Colorado, USA
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Chadebec C, Thibeau-Sutre E, Burgos N, Allassonniere S. Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:2879-2896. [PMID: 35749321 DOI: 10.1109/tpami.2022.3185773] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In this paper, we propose a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based variational autoencoder (VAE). Our approach combines the proposal of 1) a new VAE model, the latent space of which is modeled as a Riemannian manifold and which combines both Riemannian metric learning and normalizing flows and 2) a new generation scheme which produces more meaningful samples especially in the context of small data sets. The method is tested through a wide experimental study where its robustness to data sets, classifiers and training samples size is stressed. It is also validated on a medical imaging classification task on the challenging ADNI database where a small number of 3D brain magnetic resonance images (MRIs) are considered and augmented using the proposed VAE framework. In each case, the proposed method allows for a significant and reliable gain in the classification metrics. For instance, balanced accuracy jumps from 66.3% to 74.3% for a state-of-the-art convolutional neural network classifier trained with 50 MRIs of cognitively normal (CN) and 50 Alzheimer disease (AD) patients and from 77.7% to 86.3% when trained with 243 CN and 210 AD while improving greatly sensitivity and specificity metrics.
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Marin-Marin L, Miró-Padilla A, Costumero V. Structural But Not Functional Connectivity Differences within Default Mode Network Indicate Conversion to Dementia. J Alzheimers Dis 2023; 91:1483-1494. [PMID: 36641666 DOI: 10.3233/jad-220603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
BACKGROUND Malfunctioning of the default mode network (DMN) has been consistently related to mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, evidence on differences in this network between MCI converters (MCI-c) and non-converters (MCI-nc), which could mark progression to AD, is still inconsistent. OBJECTIVE To multimodally investigate the DMN in the AD continuum. METHODS We measured gray matter (GM) volume, white matter (WM) integrity, and functional connectivity (FC) at rest in healthy elderly controls, MCI-c, MCI-nc, and AD patients, matched on sociodemographic variables. RESULTS Significant differences between AD patients and controls were found in the structure of most of the regions of the DMN. MCI-c only differed from MCI-nc in GM volume of the left parahippocampus and bilateral hippocampi and middle frontal gyri, as well as in WM integrity of the parahippocampal cingulum connecting the left hippocampus and precuneus. We found significant correlations between integrity in some of those regions and global neuropsychological status, as well as an excellent discrimination ability between converters and non-converters for the sum of GM volume of left parahippocampus, bilateral hippocampi, and middle frontal gyri, and WM integrity of left parahippocampal cingulum. However, we found no significant differences in FC. CONCLUSION These results further support the relationship between abnormalities in the DMN and AD, and suggest that structural measures could be more accurate than resting-state estimates as markers of conversion from MCI to AD.
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Affiliation(s)
- Lidón Marin-Marin
- Neuropsychology and Functional Neuroimaging Group, Department of Basic and Clinical Psychology and Psychobiology, University Jaume I, Castelló, Spain
| | - Anna Miró-Padilla
- Neuropsychology and Functional Neuroimaging Group, Department of Basic and Clinical Psychology and Psychobiology, University Jaume I, Castelló, Spain
| | - Víctor Costumero
- Neuropsychology and Functional Neuroimaging Group, Department of Basic and Clinical Psychology and Psychobiology, University Jaume I, Castelló, Spain
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