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Wiersch L, Friedrich P, Hamdan S, Komeyer V, Hoffstaedter F, Patil KR, Eickhoff SB, Weis S. Sex classification from functional brain connectivity: Generalization to multiple datasets. Hum Brain Mapp 2024; 45:e26683. [PMID: 38647035 PMCID: PMC11034006 DOI: 10.1002/hbm.26683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 03/19/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or compound samples of two different sizes. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that the generalization performance of parcelwise classifiers (pwCs) trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwCs trained on the compound samples demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that both a large sample size and a heterogeneous data composition of a training sample have a central role in achieving generalizable results.
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Affiliation(s)
- Lisa Wiersch
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Patrick Friedrich
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Sami Hamdan
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Vera Komeyer
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
- Department of Biology, Faculty of Mathematics and Natural SciencesHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Felix Hoffstaedter
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Kaustubh R. Patil
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Simon B. Eickhoff
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Susanne Weis
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
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2
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Petersen M, Coenen M, DeCarli C, De Luca A, van der Lelij E, Barkhof F, Benke T, Chen CPLH, Dal-Bianco P, Dewenter A, Duering M, Enzinger C, Ewers M, Exalto LG, Fletcher EF, Franzmeier N, Hilal S, Hofer E, Koek HL, Maier AB, Maillard PM, McCreary CR, Papma JM, Pijnenburg YAL, Schmidt R, Smith EE, Steketee RME, van den Berg E, van der Flier WM, Venkatraghavan V, Venketasubramanian N, Vernooij MW, Wolters FJ, Xu X, Horn A, Patil KR, Eickhoff SB, Thomalla G, Biesbroek JM, Biessels GJ, Cheng B. Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients. medRxiv 2024:2024.03.28.24305007. [PMID: 38586023 PMCID: PMC10996741 DOI: 10.1101/2024.03.28.24305007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Introduction White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. Methods & results We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Conclusion Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.
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Affiliation(s)
- Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mirthe Coenen
- University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
| | | | - Alberto De Luca
- University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
- Image Sciences Institute, Division Imaging and Oncology, UMC Utrecht
| | | | | | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, the Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, UK
| | - Thomas Benke
- Clinic of Neurology, Medical University Innsbruck, Austria
| | - Christopher P. L. H. Chen
- Departments of Pharmacology and Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Memory, Aging and Cognition Center, National University Health System, Singapore
| | | | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, LMU Munich, Munich, Germany
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, LMU Munich, Munich, Germany
- Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Christian Enzinger
- Division of General Neurology, Department of Neurology, Medical University Graz, Austria
- Division of Neuroradiology, Interventional and Vascular Radiology, Department of Radiology, Medical University of Graz, Austria
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, LMU Munich, Munich, Germany
| | - Lieza G. Exalto
- University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
| | | | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, LMU Munich, Munich, Germany
| | - Saima Hilal
- Memory, Aging and Cognition Center, National University Health System, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Edith Hofer
- Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria
| | - Huiberdina L. Koek
- University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
- Department of Geriatric Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Andrea B. Maier
- Departments of Pharmacology and Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Memory, Aging and Cognition Center, National University Health System, Singapore
| | | | - Cheryl R. McCreary
- Department of Clinical Neurosciences and Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Janne M. Papma
- Alzheimer Center Erasmus MC, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Yolande A. L. Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Reinhold Schmidt
- Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria
| | - Eric E. Smith
- Department of Clinical Neurosciences and Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Rebecca M. E. Steketee
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Esther van den Berg
- Alzheimer Center Erasmus MC, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Vikram Venkatraghavan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Narayanaswamy Venketasubramanian
- Memory, Aging and Cognition Center, National University Health System, Singapore
- Raffles Neuroscience Center, Raffles Hospital, Singapore, Singapore
| | - Meike W. Vernooij
- Alzheimer Center Erasmus MC, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Frank J. Wolters
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Xin Xu
- Memory, Aging and Cognition Center, National University Health System, Singapore
- School of Public Health and the Second Affiliated Hospital of School of Medicine, Zhejiang University, China
| | - Andreas Horn
- Charité - Universitätsmedizin Berlin, Movement Disorders and Neuromodulation Unit, Department of Neurology with Experimental Neurology, 10117 Berlin, Germany
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA
| | - Kaustubh R. Patil
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Germany
| | - Simon B. Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - J. Matthijs Biesbroek
- University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
- Department of Neurology, Diakonessenhuis Hospital, Utrecht, The Netherlands
| | - Geert Jan Biessels
- University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Petersen M, Hoffstaedter F, Nägele FL, Mayer C, Schell M, Rimmele DL, Zyriax BC, Zeller T, Kühn S, Gallinat J, Fiehler J, Twerenbold R, Omidvarnia A, Patil KR, Eickhoff SB, Thomalla G, Cheng B. A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition. eLife 2024; 12:RP93246. [PMID: 38512127 PMCID: PMC10957178 DOI: 10.7554/elife.93246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024] Open
Abstract
The link between metabolic syndrome (MetS) and neurodegenerative as well as cerebrovascular conditions holds substantial implications for brain health in at-risk populations. This study elucidates the complex relationship between MetS and brain health by conducting a comprehensive examination of cardiometabolic risk factors, brain morphology, and cognitive function in 40,087 individuals. Multivariate, data-driven statistics identified a latent dimension linking more severe MetS to widespread brain morphological abnormalities, accounting for up to 71% of shared variance in the data. This dimension was replicable across sub-samples. In a mediation analysis, we could demonstrate that MetS-related brain morphological abnormalities mediated the link between MetS severity and cognitive performance in multiple domains. Employing imaging transcriptomics and connectomics, our results also suggest that MetS-related morphological abnormalities are linked to the regional cellular composition and macroscopic brain network organization. By leveraging extensive, multi-domain data combined with a dimensional stratification approach, our analysis provides profound insights into the association of MetS and brain health. These findings can inform effective therapeutic and risk mitigation strategies aimed at maintaining brain integrity.
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Affiliation(s)
- Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Felix Hoffstaedter
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Felix L Nägele
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Carola Mayer
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Maximilian Schell
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - D Leander Rimmele
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Birgit-Christiane Zyriax
- Midwifery Science-Health Services Research and Prevention, Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-EppendorfHamburgGermany
| | - Tanja Zeller
- Department of Cardiology, University Heart and Vascular CenterHamburgGermany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/LuebeckHamburgGermany
- University Center of Cardiovascular Science, University Heart and Vascular CenterHamburgGermany
| | - Simone Kühn
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Jürgen Gallinat
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Raphael Twerenbold
- Department of Cardiology, University Heart and Vascular CenterHamburgGermany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/LuebeckHamburgGermany
- University Center of Cardiovascular Science, University Heart and Vascular CenterHamburgGermany
- Epidemiological Study Center, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Amir Omidvarnia
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Kaustubh R Patil
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Goetz Thomalla
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
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Wiersch L, Friedrich P, Hamdan S, Komeyer V, Hoffstaedter F, Patil KR, Eickhoff SB, Weis S. Sex classification from functional brain connectivity: Generalization to multiple datasets Generalizability of sex classifiers. bioRxiv 2024:2023.08.30.555495. [PMID: 37693374 PMCID: PMC10491190 DOI: 10.1101/2023.08.30.555495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or a compound sample containing data from four different datasets. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that generalization performance of pwCs trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwC trained on the compound sample demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that a big and heterogenous training sample comprising data of multiple datasets is best suited to achieve generalizable results.
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Affiliation(s)
- Lisa Wiersch
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Patrick Friedrich
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Sami Hamdan
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Vera Komeyer
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Department of Biology, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Susanne Weis
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
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Hamdan S, More S, Sasse L, Komeyer V, Patil KR, Raimondo F. Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models. GigaByte 2024; 2024:gigabyte113. [PMID: 38496213 PMCID: PMC10940896 DOI: 10.46471/gigabyte.113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/27/2024] [Indexed: 03/19/2024] Open
Abstract
The fast-paced development of machine learning (ML) and its increasing adoption in research challenge researchers without extensive training in ML. In neuroscience, ML can help understand brain-behavior relationships, diagnose diseases and develop biomarkers using data from sources like magnetic resonance imaging and electroencephalography. Primarily, ML builds models to make accurate predictions on unseen data. Researchers evaluate models' performance and generalizability using techniques such as cross-validation (CV). However, choosing a CV scheme and evaluating an ML pipeline is challenging and, if done improperly, can lead to overestimated results and incorrect interpretations. Here, we created julearn, an open-source Python library allowing researchers to design and evaluate complex ML pipelines without encountering common pitfalls. We present the rationale behind julearn's design, its core features, and showcase three examples of previously-published research projects. Julearn simplifies the access to ML providing an easy-to-use environment. With its design, unique features, simple interface, and practical documentation, it poses as a useful Python-based library for research projects.
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Affiliation(s)
- Sami Hamdan
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany
| | - Shammi More
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany
| | - Leonard Sasse
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany
- Max Planck School of Cognition, Stephanstrasse 1a, Leipzig, Germany
| | - Vera Komeyer
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany
| | - Federico Raimondo
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany
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Wang X, Hoffstaedter F, Kasper J, Eickhoff SB, Patil KR, Dukart J. Lifetime Exposure to Depression and Neuroimaging Measures of Brain Structure and Function. JAMA Netw Open 2024; 7:e2356787. [PMID: 38372997 PMCID: PMC10877455 DOI: 10.1001/jamanetworkopen.2023.56787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/28/2023] [Indexed: 02/20/2024] Open
Abstract
Importance Despite decades of neuroimaging studies reporting brain structural and functional alterations in depression, discrepancies in findings across studies and limited convergence across meta-analyses have raised questions about the consistency and robustness of the observed brain phenotypes. Objective To investigate the associations between 6 operational criteria of lifetime exposure to depression and functional and structural neuroimaging measures. Design, Setting, and Participants This cross-sectional study analyzed data from a UK Biobank cohort of individuals aged 45 to 80 years who were enrolled between January 1, 2014, and December 31, 2018. Participants included individuals with a lifetime exposure to depression and matched healthy controls without indications of psychosis, mental illness, behavior disorder, and disease of the nervous system. Six operational criteria of lifetime exposure to depression were evaluated: help seeking for depression; self-reported depression; antidepressant use; depression definition by Smith et al; hospital International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis codes F32 and F33; and Composite International Diagnostic Interview Short Form score. Six increasingly restrictive depression definitions and groups were defined based on the 6 depression criteria, ranging from meeting only 1 criterion to meeting all 6 criteria. Data were analyzed between January and October 2022. Main Outcomes and Measures Functional measures were calculated using voxel-wise fractional amplitude of low-frequency fluctuation (fALFF), global correlation (GCOR), and local correlation (LCOR). Structural measures were calculated using gray matter volume (GMV). Results The study included 20 484 individuals with lifetime depression (12 645 females [61.7%]; mean [SD] age, 63.91 [7.60] years) and 25 462 healthy controls (14 078 males [55.3%]; mean [SD] age, 65.05 [7.8] years). Across all depression criteria, individuals with lifetime depression displayed regionally consistent decreases in fALFF, LCOR, and GCOR (Cohen d range, -0.53 [95% CI, -0.88 to -0.15] to -0.04 [95% CI, -0.07 to -0.01]) but not in GMV (Cohen d range, -0.47 [95 % CI, -0.75 to -0.12] to 0.26 [95% CI, 0.15-0.37]). Hospital ICD-10 diagnosis codes F32 and F33 (median [IQR] difference in effect sizes, -0.14 [-0.17 to -0.11]) and antidepressant use (median [IQR] difference in effect sizes, -0.12 [-0.16 to -0.10]) were criteria associated with the most pronounced alterations. Conclusions and Relevance Results of this cross-sectional study indicate that lifetime exposure to depression was associated with robust functional changes, with a more restrictive depression definition revealing more pronounced alterations. Different inclusion criteria for depression may be associated with the substantial variation in imaging findings reported in the literature.
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Affiliation(s)
- Xinyi Wang
- School of Biological Sciences and Medical Engineering, Child Development, and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing, China
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
| | - Jan Kasper
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
| | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
| | - Kaustubh R. Patil
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
| | - Juergen Dukart
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre Jülich, Jülich, Germany
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Petersen M, Hoffstaedter F, Nägele FL, Mayer C, Schell M, Rimmele DL, Zyriax BC, Zeller T, Kühn S, Gallinat J, Fiehler J, Twerenbold R, Omidvarnia A, Patil KR, Eickhoff SB, Thomalla G, Cheng B. A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition. bioRxiv 2023:2023.02.22.529531. [PMID: 36865285 PMCID: PMC9980040 DOI: 10.1101/2023.02.22.529531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
The link between metabolic syndrome (MetS) and neurodegenerative as well cerebrovascular conditions holds substantial implications for brain health in at-risk populations. This study elucidates the complex relationship between MetS and brain health by conducting a comprehensive examination of cardiometabolic risk factors, cortical morphology, and cognitive function in 40,087 individuals. Multivariate, data-driven statistics identified a latent dimension linking more severe MetS to widespread brain morphological abnormalities, accounting for up to 71% of shared variance in the data. This dimension was replicable across sub-samples. In a mediation analysis we could demonstrate that MetS-related brain morphological abnormalities mediated the link between MetS severity and cognitive performance in multiple domains. Employing imaging transcriptomics and connectomics, our results also suggest that MetS-related morphological abnormalities are linked to the regional cellular composition and macroscopic brain network organization. By leveraging extensive, multi-domain data combined with a dimensional stratification approach, our analysis provides profound insights into the association of MetS and brain health. These findings can inform effective therapeutic and risk mitigation strategies aimed at maintaining brain integrity.
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Affiliation(s)
- Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Felix Hoffstaedter
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Ju lich, Wilhelm-Johnen-Straße, 52425 Ju lich, Germany
| | - Felix L. Nägele
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Carola Mayer
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Maximilian Schell
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - D. Leander Rimmele
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Birgit-Christiane Zyriax
- Midwifery Science-Health Services Research and Prevention, Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Tanja Zeller
- Department of Cardiology, University Heart and Vascular Center, Martinistraße 52, 20251 Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/Luebeck, Martinistraße 52, 20251 Hamburg, Germany
- University Center of Cardiovascular Science, University Heart and Vascular Center, Martinistraße 52, 20251 Hamburg, Germany
| | - Simone Kühn
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Jürgen Gallinat
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Raphael Twerenbold
- Department of Cardiology, University Heart and Vascular Center, Martinistraße 52, 20251 Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/Luebeck, Martinistraße 52, 20251 Hamburg, Germany
- University Center of Cardiovascular Science, University Heart and Vascular Center, Martinistraße 52, 20251 Hamburg, Germany
- Epidemiological Study Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Amir Omidvarnia
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Ju lich, Wilhelm-Johnen-Straße, 52425 Ju lich, Germany
| | - Kaustubh R. Patil
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Ju lich, Wilhelm-Johnen-Straße, 52425 Ju lich, Germany
| | - Simon B. Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Ju lich, Wilhelm-Johnen-Straße, 52425 Ju lich, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
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Kraljević N, Langner R, Küppers V, Raimondo F, Patil KR, Eickhoff SB, Müller VI. Network and State Specificity in Connectivity-Based Predictions of Individual Behavior. bioRxiv 2023:2023.05.11.540387. [PMID: 37215048 PMCID: PMC10197703 DOI: 10.1101/2023.05.11.540387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.
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9
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Heckner MK, Cieslik EC, Paas Oliveros LK, Eickhoff SB, Patil KR, Langner R. Predicting executive functioning from brain networks: modality specificity and age effects. Cereb Cortex 2023; 33:10997-11009. [PMID: 37782935 PMCID: PMC10646699 DOI: 10.1093/cercor/bhad338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 10/04/2023] Open
Abstract
Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from the gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether the differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate-to-weak brain-behavior associations (R2 < 0.07, r < 0.28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.
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Affiliation(s)
- Marisa K Heckner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
| | - Edna C Cieslik
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
| | - Lya K Paas Oliveros
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
| | - Robert Langner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
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10
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Antonopoulos G, More S, Raimondo F, Eickhoff SB, Hoffstaedter F, Patil KR. A systematic comparison of VBM pipelines and their application to age prediction. Neuroimage 2023; 279:120292. [PMID: 37572766 PMCID: PMC10529438 DOI: 10.1016/j.neuroimage.2023.120292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 06/23/2023] [Accepted: 07/21/2023] [Indexed: 08/14/2023] Open
Abstract
Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n>500 each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better.
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Affiliation(s)
- Georgios Antonopoulos
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Shammi More
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Federico Raimondo
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
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11
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Weihs A, Frenzel S, Bi H, Schiel JE, Afshani M, Bülow R, Ewert R, Fietze I, Hoffstaedter F, Jahanshad N, Khazaie H, Riemann D, Rostampour M, Stubbe B, Thomopoulos SI, Thompson PM, Valk SL, Völzke H, Zarei M, Eickhoff SB, Grabe HJ, Patil KR, Spiegelhalder K, Tahmasian M. Lack of structural brain alterations associated with insomnia: findings from the ENIGMA-Sleep Working Group. J Sleep Res 2023; 32:e13884. [PMID: 36944539 DOI: 10.1111/jsr.13884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/19/2023] [Accepted: 03/02/2023] [Indexed: 03/23/2023]
Abstract
Existing neuroimaging studies have reported divergent structural alterations in insomnia disorder (ID). In the present study, we performed a large-scale coordinated meta-analysis by pooling structural brain measures from 1085 subjects (mean [SD] age 50.5 [13.9] years, 50.2% female, 17.4% with insomnia) across three international Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA)-Sleep cohorts. Two sites recruited patients with ID/controls: Freiburg (University of Freiburg Medical Center, Freiburg, Germany) 42/43 and KUMS (Kermanshah University of Medical Sciences, Kermanshah, Iran) 42/49, while the Study of Health in Pomerania (SHIP-Trend, University Medicine Greifswald, Greifswald, Germany) recruited population-based individuals with/without insomnia symptoms 75/662. The influence of insomnia on magnetic resonance imaging-based brain morphometry using an insomnia brain score was then assessed. Within each cohort, we used an ordinary least-squares linear regression to investigate the link between the individual regional cortical and subcortical volumes and the presence of insomnia symptoms. Then, we performed a fixed-effects meta-analysis across cohorts based on the first-level results. For the insomnia brain score, weighted logistic ridge regression was performed on one sample (Freiburg), which separated patients with ID from controls to train a model based on the segmentation measurements. Afterward, the insomnia brain scores were validated using the other two samples. The model was used to predict the log-odds of the subjects with insomnia given individual insomnia-related brain atrophy. After adjusting for multiple comparisons, we did not detect any significant associations between insomnia symptoms and cortical or subcortical volumes, nor could we identify a global insomnia-related brain atrophy pattern. Thus, we observed inconsistent brain morphology differences between individuals with and without insomnia across three independent cohorts. Further large-scale cross-sectional/longitudinal studies using both structural and functional neuroimaging are warranted to decipher the neurobiology of insomnia.
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Affiliation(s)
- Antoine Weihs
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Hanwen Bi
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian E Schiel
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Mortaza Afshani
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Ralf Ewert
- Internal Medicine B, Pneumology, University Medicine Greifswald, Greifswald, Germany
| | - Ingo Fietze
- Center for Sleep Medicine, Charité-University Hospital Berlin, Berlin, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Habibolah Khazaie
- Sleep Disorders Research Center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Dieter Riemann
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Masoumeh Rostampour
- Sleep Disorders Research Center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Beate Stubbe
- Internal Medicine B, Pneumology, University Medicine Greifswald, Greifswald, Germany
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Sofie L Valk
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Henry Völzke
- Institute for Community Medicine, Department Study of Health in Pomerania/Clinical Epidemiological Research, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
| | - Mojtaba Zarei
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
- Department of Neurology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Greifswald, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Kai Spiegelhalder
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Masoud Tahmasian
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
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12
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Wiersch L, Hamdan S, Hoffstaedter F, Votinov M, Habel U, Clemens B, Derntl B, Eickhoff SB, Patil KR, Weis S. Accurate sex prediction of cisgender and transgender individuals without brain size bias. Sci Rep 2023; 13:13868. [PMID: 37620339 PMCID: PMC10449927 DOI: 10.1038/s41598-023-37508-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 06/22/2023] [Indexed: 08/26/2023] Open
Abstract
The increasing use of machine learning approaches on neuroimaging data comes with the important concern of confounding variables which might lead to biased predictions and in turn spurious conclusions about the relationship between the features and the target. A prominent example is the brain size difference between women and men. This difference in total intracranial volume (TIV) can cause bias when employing machine learning approaches for the investigation of sex differences in brain morphology. A TIV-biased model will not capture qualitative sex differences in brain organization but rather learn to classify an individual's sex based on brain size differences, thus leading to spurious and misleading conclusions, for example when comparing brain morphology between cisgender- and transgender individuals. In this study, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for TIV either through featurewise confound removal or by matching the training samples for TIV. Our results provide strong evidence that models not biased by TIV can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modeling to avoid bias in automated decision making.
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Affiliation(s)
- Lisa Wiersch
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Sami Hamdan
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Mikhail Votinov
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Institute of Neuroscience and Medicine (INM-10: Brain Structure-Function Relationships), Research Centre Jülich, Jülich, Germany
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Institute of Neuroscience and Medicine (INM-10: Brain Structure-Function Relationships), Research Centre Jülich, Jülich, Germany
| | - Benjamin Clemens
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Institute of Neuroscience and Medicine (INM-10: Brain Structure-Function Relationships), Research Centre Jülich, Jülich, Germany
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- LEAD Graduate School and Research Network, University of Tübingen, Tübingen, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
| | - Susanne Weis
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
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13
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Sasse L, Larabi DI, Omidvarnia A, Jung K, Hoffstaedter F, Jocham G, Eickhoff SB, Patil KR. Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity. Commun Biol 2023; 6:705. [PMID: 37429937 DOI: 10.1038/s42003-023-05073-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 06/26/2023] [Indexed: 07/12/2023] Open
Abstract
Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of individual-level phenotypes.
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Affiliation(s)
- Leonard Sasse
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- Max Planck School of Cognition, Stephanstrasse 1a, Leipzig, Germany
| | - Daouia I Larabi
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Amir Omidvarnia
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Kyesam Jung
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Gerhard Jocham
- Institute for Experimental Psychology, Faculty of Mathematics and Natural Sciences, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
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14
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Heckner MK, Cieslik EC, Oliveros LKP, Eickhoff SB, Patil KR, Langner R. Predicting Executive Functioning from Brain Networks: Modality Specificity and Age Effects. bioRxiv 2023:2023.06.29.547036. [PMID: 37425780 PMCID: PMC10327061 DOI: 10.1101/2023.06.29.547036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate to weak brain-behavior associations (R2 < .07, r < .28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.
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Affiliation(s)
- Marisa K. Heckner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Edna C. Cieslik
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lya K. Paas Oliveros
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Robert Langner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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15
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Li X, Friedrich P, Patil KR, Eickhoff SB, Weis S. A topography-based predictive framework for naturalistic viewing fMRI. Neuroimage 2023:120245. [PMID: 37353099 DOI: 10.1016/j.neuroimage.2023.120245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 06/25/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) during naturalistic viewing (NV) provides exciting opportunities for studying brain functions in more ecologically valid settings. Understanding individual differences in brain functions during NV and their behavioural relevance has recently become an important goal. However, methods specifically designed for this purpose remain limited. Here, we propose a topography-based predictive framework (TOPF) to fill this methodological gap. TOPF identifies individual-specific evoked activity topographies in a data-driven manner and examines their behavioural relevance using a machine learning-based predictive framework. We validate TOPF on both NV and task-based fMRI data from multiple conditions. Our results show that TOPF effectively and stably captures individual differences in evoked brain activity and successfully predicts phenotypes across cognition, emotion and personality on unseen subjects from their activity topographies. Moreover, TOPF compares favourably with functional connectivity-based approaches in prediction performance, with the identified predictive brain regions being neurobiologically interpretable. Crucially, we highlight the importance of examining individual evoked brain activity topographies in advancing our understanding of the brain-behaviour relationship. We believe that the TOPF approach provides a simple but powerful tool for understanding brain-behaviour relationships on an individual level with a strong potential for clinical applications.
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Affiliation(s)
- Xuan Li
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany;; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.
| | - Patrick Friedrich
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany;; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany;; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany;; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany;; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
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16
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Heckner MK, Cieslik EC, Patil KR, Gell M, Eickhoff SB, Hoffstädter F, Langner R. Predicting executive functioning from functional brain connectivity: network specificity and age effects. Cereb Cortex 2023; 33:6495-6507. [PMID: 36635227 PMCID: PMC10233269 DOI: 10.1093/cercor/bhac520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 01/14/2023] Open
Abstract
Healthy aging is associated with altered executive functioning (EF). Earlier studies found age-related differences in EF performance to be partially accounted for by changes in resting-state functional connectivity (RSFC) within brain networks associated with EF. However, it remains unclear which role RSFC in EF-associated networks plays as a marker for individual differences in EF performance. Here, we investigated to what degree individual abilities across 3 different EF tasks can be predicted from RSFC within EF-related, perceptuo-motor, whole-brain, and random networks separately in young and old adults. Specifically, we were interested if (i) young and old adults differ in predictability depending on network or EF demand level (high vs. low), (ii) an EF-related network outperforms EF-unspecific networks when predicting EF abilities, and (iii) this pattern changes with demand level. Both our uni- and multivariate analysis frameworks analyzing interactions between age × demand level × networks revealed overall low prediction accuracies and a general lack of specificity regarding neurobiological networks for predicting EF abilities. This questions the idea of finding markers for individual EF performance in RSFC patterns and calls for future research replicating the current approach in different task states, brain modalities, different, larger samples, and with more comprehensive behavioral measures.
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Affiliation(s)
- Marisa K Heckner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Edna C Cieslik
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Martin Gell
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Felix Hoffstädter
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Robert Langner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
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17
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More S, Antonopoulos G, Hoffstaedter F, Caspers J, Eickhoff SB, Patil KR. Brain-age prediction: A systematic comparison of machine learning workflows. Neuroimage 2023; 270:119947. [PMID: 36801372 DOI: 10.1016/j.neuroimage.2023.119947] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 02/18/2023] Open
Abstract
The difference between age predicted using anatomical brain scans and chronological age, i.e., the brain-age delta, provides a proxy for atypical aging. Various data representations and machine learning (ML) algorithms have been used for brain-age estimation. However, how these choices compare on performance criteria important for real-world applications, such as; (1) within-dataset accuracy, (2) cross-dataset generalization, (3) test-retest reliability, and (4) longitudinal consistency, remains uncharacterized. We evaluated 128 workflows consisting of 16 feature representations derived from gray matter (GM) images and eight ML algorithms with diverse inductive biases. Using four large neuroimaging databases covering the adult lifespan (total N = 2953, 18-88 years), we followed a systematic model selection procedure by sequentially applying stringent criteria. The 128 workflows showed a within-dataset mean absolute error (MAE) between 4.73-8.38 years, from which 32 broadly sampled workflows showed a cross-dataset MAE between 5.23-8.98 years. The test-retest reliability and longitudinal consistency of the top 10 workflows were comparable. The choice of feature representation and the ML algorithm both affected the performance. Specifically, voxel-wise feature spaces (smoothed and resampled), with and without principal components analysis, with non-linear and kernel-based ML algorithms performed well. Strikingly, the correlation of brain-age delta with behavioral measures disagreed between within-dataset and cross-dataset predictions. Application of the best-performing workflow on the ADNI sample showed a significantly higher brain-age delta in Alzheimer's and mild cognitive impairment patients compared to healthy controls. However, in the presence of age bias, the delta estimates in the patients varied depending on the sample used for bias correction. Taken together, brain-age shows promise, but further evaluation and improvements are needed for its real-world application.
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Affiliation(s)
- Shammi More
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Georgios Antonopoulos
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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18
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Kröll JP, Friedrich P, Li X, Patil KR, Mochalski L, Waite L, Qian X, Chee MW, Zhou JH, Eickhoff S, Weis S. Naturalistic viewing increases individual identifiability based on connectivity within functional brain networks. Neuroimage 2023; 273:120083. [PMID: 37015270 DOI: 10.1016/j.neuroimage.2023.120083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 03/07/2023] [Accepted: 03/31/2023] [Indexed: 04/06/2023] Open
Abstract
Naturalistic viewing (NV) is currently considered a promising paradigm for studying individual differences in functional brain organization. While whole brain functional connectivity (FC) under NV has been relatively well characterized, so far little work has been done on a network level. Here, we extend current knowledge by characterizing the influence of NV on FC in fourteen meta-analytically derived brain networks considering three different movie stimuli in comparison to resting-state (RS). We show that NV increases identifiability of individuals over RS based on functional connectivity in certain, but not all networks. Furthermore, movie stimuli including a narrative appear more distinct from RS. In addition, we assess individual variability in network FC by comparing within- and between-subject similarity during NV and RS. We show that NV can evoke individually distinct NFC patterns by increasing inter-subject variability while retaining within-subject similarity. Crucially, our results highlight that this effect is not observable across all networks, but rather dependent on the network-stimulus combination. Our results confirm that NV can improve the detection of individual differences over RS and underline the importance of selecting the appropriate combination of movie and cognitive network for the research question at hand.
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Affiliation(s)
- Jean-Philippe Kröll
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52428, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Patrick Friedrich
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52428, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Xuan Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52428, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52428, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Lisa Mochalski
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52428, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Laura Waite
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52428, Germany
| | - Xing Qian
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore
| | - Michael Wl Chee
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - Juan Helen Zhou
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - Simon Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52428, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52428, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
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19
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Bajaj S, Blair KS, Dobbertin M, Patil KR, Tyler PM, Ringle JL, Bashford-Largo J, Mathur A, Elowsky J, Dominguez A, Schmaal L, Blair RJR. Machine learning based identification of structural brain alterations underlying suicide risk in adolescents. Discov Ment Health 2023; 3:6. [PMID: 37861863 PMCID: PMC10501026 DOI: 10.1007/s44192-023-00033-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 02/09/2023] [Indexed: 10/21/2023]
Abstract
Suicide is the third leading cause of death for individuals between 15 and 19 years of age. The high suicide mortality rate and limited prior success in identifying neuroimaging biomarkers indicate that it is crucial to improve the accuracy of clinical neural signatures underlying suicide risk. The current study implements machine-learning (ML) algorithms to examine structural brain alterations in adolescents that can discriminate individuals with suicide risk from typically developing (TD) adolescents at the individual level. Structural MRI data were collected from 79 adolescents who demonstrated clinical levels of suicide risk and 79 demographically matched TD adolescents. Region-specific cortical/subcortical volume (CV/SCV) was evaluated following whole-brain parcellation into 1000 cortical and 12 subcortical regions. CV/SCV parameters were used as inputs for feature selection and three ML algorithms (i.e., support vector machine [SVM], K-nearest neighbors, and ensemble) to classify adolescents at suicide risk from TD adolescents. The highest classification accuracy of 74.79% (with sensitivity = 75.90%, specificity = 74.07%, and area under the receiver operating characteristic curve = 87.18%) was obtained for CV/SCV data using the SVM classifier. Identified bilateral regions that contributed to the classification mainly included reduced CV within the frontal and temporal cortices but increased volume within the cuneus/precuneus for adolescents at suicide risk relative to TD adolescents. The current data demonstrate an unbiased region-specific ML framework to effectively assess the structural biomarkers of suicide risk. Future studies with larger sample sizes and the inclusion of clinical controls and independent validation data sets are needed to confirm our findings.
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Affiliation(s)
- Sahil Bajaj
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA.
| | - Karina S Blair
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Matthew Dobbertin
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
- Child and Adolescent Psychiatric Inpatient Center, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Patrick M Tyler
- Child and Family Translational Research Center, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Jay L Ringle
- Child and Family Translational Research Center, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Johannah Bashford-Largo
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Avantika Mathur
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Jaimie Elowsky
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Ahria Dominguez
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Lianne Schmaal
- Center for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, Australia
| | - R James R Blair
- Child and Adolescent Mental Health Centre, Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark
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20
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Dagaev N, Roads BD, Luo X, Barry DN, Patil KR, Love BC. A too-good-to-be-true prior to reduce shortcut reliance. Pattern Recognit Lett 2023; 166:164-171. [PMID: 37915616 PMCID: PMC10615835 DOI: 10.1016/j.patrec.2022.12.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 05/27/2022] [Accepted: 12/19/2022] [Indexed: 02/05/2023]
Abstract
Despite their impressive performance in object recognition and other tasks under standard testing conditions, deep networks often fail to generalize to out-of-distribution (o.o.d.) samples. One cause for this shortcoming is that modern architectures tend to rely on ǣshortcutsǥ superficial features that correlate with categories without capturing deeper invariants that hold across contexts. Real-world concepts often possess a complex structure that can vary superficially across contexts, which can make the most intuitive and promising solutions in one context not generalize to others. One potential way to improve o.o.d. generalization is to assume simple solutions are unlikely to be valid across contexts and avoid them, which we refer to as the too-good-to-be-true prior. A low-capacity network (LCN) with a shallow architecture should only be able to learn surface relationships, including shortcuts. We find that LCNs can serve as shortcut detectors. Furthermore, an LCN's predictions can be used in a two-stage approach to encourage a high-capacity network (HCN) to rely on deeper invariant features that should generalize broadly. In particular, items that the LCN can master are downweighted when training the HCN. Using a modified version of the CIFAR-10 dataset in which we introduced shortcuts, we found that the two-stage LCN-HCN approach reduced reliance on shortcuts and facilitated o.o.d. generalization.
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Affiliation(s)
- Nikolay Dagaev
- School of Psychology, HSE University, Moscow, Russia
- Department of Computer Science, University College London, London, United Kingdom
| | - Brett D. Roads
- Department of Experimental Psychology, University College London, London, United Kingdom
| | - Xiaoliang Luo
- Department of Experimental Psychology, University College London, London, United Kingdom
| | - Daniel N. Barry
- Department of Experimental Psychology, University College London, London, United Kingdom
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Bradley C. Love
- Department of Experimental Psychology, University College London, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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21
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Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research. Biol Psychiatry 2023; 93:18-28. [PMID: 36307328 DOI: 10.1016/j.biopsych.2022.07.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
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Affiliation(s)
- Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
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22
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Hamdan S, Love BC, von Polier GG, Weis S, Schwender H, Eickhoff SB, Patil KR. Confound-leakage: confound removal in machine learning leads to leakage. Gigascience 2022; 12:giad071. [PMID: 37776368 PMCID: PMC10541796 DOI: 10.1093/gigascience/giad071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 06/01/2023] [Accepted: 08/17/2023] [Indexed: 10/02/2023] Open
Abstract
BACKGROUND Machine learning (ML) approaches are a crucial component of modern data analysis in many fields, including epidemiology and medicine. Nonlinear ML methods often achieve accurate predictions, for instance, in personalized medicine, as they are capable of modeling complex relationships between features and the target. Problematically, ML models and their predictions can be biased by confounding information present in the features. To remove this spurious signal, researchers often employ featurewise linear confound regression (CR). While this is considered a standard approach for dealing with confounding, possible pitfalls of using CR in ML pipelines are not fully understood. RESULTS We provide new evidence that, contrary to general expectations, linear confound regression can increase the risk of confounding when combined with nonlinear ML approaches. Using a simple framework that uses the target as a confound, we show that information leaked via CR can increase null or moderate effects to near-perfect prediction. By shuffling the features, we provide evidence that this increase is indeed due to confound-leakage and not due to revealing of information. We then demonstrate the danger of confound-leakage in a real-world clinical application where the accuracy of predicting attention-deficit/hyperactivity disorder is overestimated using speech-derived features when using depression as a confound. CONCLUSIONS Mishandling or even amplifying confounding effects when building ML models due to confound-leakage, as shown, can lead to untrustworthy, biased, and unfair predictions. Our expose of the confound-leakage pitfall and provided guidelines for dealing with it can help create more robust and trustworthy ML models.
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Affiliation(s)
- Sami Hamdan
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Bradley C Love
- Department of Experimental Psychology, University College London, WC1H 0AP London, UK
- The Alan Turing Institute, London NW1 2DB, UK
- European Lab for Learning & Intelligent Systems (ELLIS), WC1E 6BT, London, UK
| | - Georg G von Polier
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, 60528 Frankfurt, Germany
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Holger Schwender
- Institute of Mathematics, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
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23
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Cheng B, Chen J, Königsberg A, Mayer C, Rimmele L, Patil KR, Gerloff C, Thomalla G, Eickhoff SB. Mapping the deficit dimension structure of the National Institutes of Health Stroke Scale. EBioMedicine 2022; 87:104425. [PMID: 36563488 PMCID: PMC9800288 DOI: 10.1016/j.ebiom.2022.104425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/04/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The National Institutes of Health Stroke Scale (NIHSS) is the most frequently applied clinical rating scale for standardized assessment of neurological deficits in acute stroke in both clinical and research settings. Notwithstanding this prominent role, important questions regarding its validity remain insufficiently addressed: Investigations of the underlying dimensional structure of the NIHSS yielded inconsistent results that are largely not generalizable across studies. Neurobiological validations by linking measured deficit dimensions to brain anatomy and function are missing. METHODS We, therefore, employ advanced machine learning to identify an optimal representation of the dimensional structure of the NIHSS across two independent and heterogeneous stroke datasets (N = 503 and N = 690). Associated lesion locations are identified by multivariate lesion-deficit mapping (LDM) and their functional relevance is profiled based on a-priori task activation meta-data analysis, to provide an independent link to the behavioural level. FINDINGS A five-factor structure of the NIHSS was identified as the most robust and generalizable representation of stroke deficit dimensions across study populations, settings, and clinical phenotypes. Specifically, the identified dimensions comprised NIHSS items for (F1) left motor deficits, (F2) right motor deficits, (F3) dysarthria and facial palsy, (F4) language, and (F5) deficits in spatial attention and gaze. LDM linked four of these factors to differentially localized, eloquent neuroanatomical areas. Functional characterization of LDM results aligned with detected deficit dimensions, revealing associations with motor functions, language processing, and various functions in the perception domain. INTERPRETATION By cross-validating machine learning in heterogeneous multi-site stroke cohorts, we report evidence on the validity of the NIHSS: We identified an overarching structure of the NISHS containing a five-dimensional representation of stroke deficits. We provide an anatomical map of the NIHSS that is of value for future applications of individualized stroke treatment and rehabilitation. FUNDING This research was supported by the National Key R&D Program of China (Grant No. 2021YFC2502200), the National Human Brain Project of China (Grant No. 2022ZD0214000)", the German Research Foundation (Deutsche Forschungsgemeinschaft), Project 178316478 (A1, C1, C2), and Project 454012190 of the SPP 2041, the Helmholtz Portfolio Theme "Supercomputing and Modelling for the Human Brain" and Helmholtz Imaging Platform grant NimRLS (ZT-I-PF-4-010).
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Affiliation(s)
- Bastian Cheng
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany,Corresponding author.
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Corresponding author.
| | - Alina Königsberg
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carola Mayer
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Leander Rimmele
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Gerloff
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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24
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Jung K, Florin E, Patil KR, Caspers J, Rubbert C, Eickhoff SB, Popovych OV. Whole-brain dynamical modelling for classification of Parkinson's disease. Brain Commun 2022; 5:fcac331. [PMID: 36601625 PMCID: PMC9798283 DOI: 10.1093/braincomms/fcac331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/29/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Simulated whole-brain connectomes demonstrate enhanced inter-individual variability depending on the data processing and modelling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson's disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioural data, such as subject classes, which we refer to as behavioural model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine learning reported in this study also demonstrated that the performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that the temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improved prediction performance. Based on our findings, we suggest that combining the simulation results with empirical data is effective for inter-individual research and its clinical application.
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Affiliation(s)
- Kyesam Jung
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52425 Jülich, Germany,Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52425 Jülich, Germany,Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, 40225 Düsseldorf, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, 40225 Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52425 Jülich, Germany,Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Oleksandr V Popovych
- Correspondence to: Oleksandr V. Popovych Institute of Neuroscience and Medicine Brain and Behaviour (INM-7) Research Centre Jülich, 52425 Jülich, Germany E-mail:
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25
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Vellani V, Garrett N, Gaule A, Patil KR, Sharot T. Quantifying the heritability of belief formation. Sci Rep 2022; 12:11833. [PMID: 35821231 PMCID: PMC9276818 DOI: 10.1038/s41598-022-15492-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 06/24/2022] [Indexed: 11/09/2022] Open
Abstract
Individual differences in behaviour, traits and mental-health are partially heritable. Traditionally, studies have focused on quantifying the heritability of high-order characteristics, such as happiness or education attainment. Here, we quantify the degree of heritability of lower-level mental processes that likely contribute to complex traits and behaviour. In particular, we quantify the degree of heritability of cognitive and affective factors that contribute to the generation of beliefs about risk, which drive behavior in domains ranging from finance to health. Monozygotic and dizygotic twin pairs completed a belief formation task. We first show that beliefs about risk are associated with vividness of imagination, affective evaluation and learning abilities. We then demonstrate that the genetic contribution to individual differences in these processes range between 13.5 and 39%, with affect evaluation showing a particular robust heritability component. These results provide clues to which mental factors may be driving the heritability component of beliefs formation, which in turn contribute to the heritability of complex traits.
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Affiliation(s)
- Valentina Vellani
- Affective Brain Lab, Department of Experimental Psychology, University College London, London, WC1H 0AP, UK. .,The Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, WC1B 5EH, UK.
| | - Neil Garrett
- School of Psychology, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
| | - Anne Gaule
- Developmental Risk and Resilience Unit, Department of Experimental Psychology, University College London, London, WC1H 0AP, UK
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Tali Sharot
- Affective Brain Lab, Department of Experimental Psychology, University College London, London, WC1H 0AP, UK. .,The Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, WC1B 5EH, UK. .,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
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26
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Li J, Bzdok D, Chen J, Tam A, Ooi LQR, Holmes AJ, Ge T, Patil KR, Jabbi M, Eickhoff SB, Yeo BTT, Genon S. Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. Sci Adv 2022; 8:eabj1812. [PMID: 35294251 PMCID: PMC8926333 DOI: 10.1126/sciadv.abj1812] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Algorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here, we assessed such bias in prediction models of behavioral phenotypes from brain functional magnetic resonance imaging. We examined the prediction bias using two independent datasets (preadolescent versus adult) of mixed ethnic/racial composition. When predictive models were trained on data dominated by white Americans (WA), out-of-sample prediction errors were generally higher for African Americans (AA) than for WA. This bias toward WA corresponds to more WA-like brain-behavior association patterns learned by the models. When models were trained on AA only, compared to training only on WA or an equal number of AA and WA participants, AA prediction accuracy improved but stayed below that for WA. Overall, the results point to the need for caution and further research regarding the application of current brain-behavior prediction models in minority populations.
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Affiliation(s)
- Jingwei Li
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal
Neurological Institute (MNI), McConnell Brain Imaging Institute (BIC), McGill
University, Montreal, QC, Canada
- Mila-Quebec Artificial Intelligence Institute,
Montreal, QC, Canada
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Leon Qi Rong Ooi
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Avram J. Holmes
- Departments of Psychology and Psychiatry, Yale
University, New Haven, CT, USA
- Psychiatric and Neurodevelopmental Genetics Unit,
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA,
USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit,
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA,
USA
- Stanley Center for Psychiatric Research, Broad
Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General
Hospital, Harvard Medical School, Boston, MA, USA
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Mbemba Jabbi
- Department of Psychiatry, Dell Medical School,
University of Texas at Austin, Austin, TX, USA
- The Mulva Clinic for Neurosciences, Dell Medical
School, University of Texas at Austin, Austin, TX, USA
- Institute of Neuroscience, University of Texas at
Austin, Austin, TX, USA
- Department of Psychology, University of Texas at
Austin, Austin, TX, USA
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme
(ISEP), National University of Singapore, Singapore, Singapore
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
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27
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Friedrich P, Patil KR, Mochalski LN, Li X, Camilleri JA, Kröll JP, Wiersch L, Eickhoff SB, Weis S. Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality. Brain Struct Funct 2021; 227:425-440. [PMID: 34882263 PMCID: PMC8844166 DOI: 10.1007/s00429-021-02418-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 10/18/2021] [Indexed: 11/09/2022]
Abstract
Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.
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Affiliation(s)
- Patrick Friedrich
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Lisa N Mochalski
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Xuan Li
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Julia A Camilleri
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Jean-Philippe Kröll
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Lisa Wiersch
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
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28
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Liu X, Eickhoff SB, Caspers S, Wu J, Genon S, Hoffstaedter F, Mars RB, Sommer IE, Eickhoff CR, Chen J, Jardri R, Reetz K, Dogan I, Aleman A, Kogler L, Gruber O, Caspers J, Mathys C, Patil KR. Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate. Neuroimage 2021; 235:118006. [PMID: 33819611 PMCID: PMC8214073 DOI: 10.1016/j.neuroimage.2021.118006] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 02/10/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022] Open
Abstract
A wide homology between human and macaque striatum is often assumed as in both the striatum is involved in cognition, emotion and executive functions. However, differences in functional and structural organization between human and macaque striatum may reveal evolutionary divergence and shed light on human vulnerability to neuropsychiatric diseases. For instance, dopaminergic dysfunction of the human striatum is considered to be a pathophysiological underpinning of different disorders, such as Parkinson's disease (PD) and schizophrenia (SCZ). Previous investigations have found a wide similarity in structural connectivity of the striatum between human and macaque, leaving the cross-species comparison of its functional organization unknown. In this study, resting-state functional connectivity (RSFC) derived striatal parcels were compared based on their homologous cortico-striatal connectivity. The goal here was to identify striatal parcels whose connectivity is human-specific compared to macaque parcels. Functional parcellation revealed that the human striatum was split into dorsal, dorsomedial, and rostral caudate and ventral, central, and caudal putamen, while the macaque striatum was divided into dorsal, and rostral caudate and rostral, and caudal putamen. Cross-species comparison indicated dissimilar cortico-striatal RSFC of the topographically similar dorsal caudate. We probed clinical relevance of the striatal clusters by examining differences in their cortico-striatal RSFC and gray matter (GM) volume between patients (with PD and SCZ) and healthy controls. We found abnormal RSFC not only between dorsal caudate, but also between rostral caudate, ventral, central and caudal putamen and widespread cortical regions for both PD and SCZ patients. Also, we observed significant structural atrophy in rostral caudate, ventral and central putamen for both PD and SCZ while atrophy in the dorsal caudate was specific to PD. Taken together, our cross-species comparative results revealed shared and human-specific RSFC of different striatal clusters reinforcing the complex organization and function of the striatum. In addition, we provided a testable hypothesis that abnormalities in a region with human-specific connectivity, i.e., dorsal caudate, might be associated with neuropsychiatric disorders.
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Affiliation(s)
- Xiaojin Liu
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jianxiao Wu
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, Netherlands
| | - Claudia R Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Ji Chen
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Renaud Jardri
- Division of Psychiatry, University of Lille, CNRS UMR9193, SCALab & CHU Lille, Fontan Hospital, CURE platform, Lille, France
| | - Kathrin Reetz
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, RWTH Aachen University, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - Imis Dogan
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, RWTH Aachen University, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - André Aleman
- Department of Neuroscience, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Lydia Kogler
- Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Germany
| | - Julian Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Christian Mathys
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany; Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, Oldenburg, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany.
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Chen J, Wensing T, Hoffstaedter F, Cieslik EC, Müller VI, Patil KR, Aleman A, Derntl B, Gruber O, Jardri R, Kogler L, Sommer IE, Eickhoff SB, Nickl-Jockschat T. Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling. Neuroimage Clin 2021; 30:102666. [PMID: 34215141 PMCID: PMC8105296 DOI: 10.1016/j.nicl.2021.102666] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 04/01/2021] [Accepted: 04/03/2021] [Indexed: 12/14/2022]
Abstract
Formal thought disorder (FTD) is a core symptom of schizophrenia, but its neurobiological substrates remain elusive. Resting-state functional connectivity (rsFC) of three meta-analytically defined seeds were correlated to positive and negative symptom dimensions of FTD. RsFC patterns allowed individual prediction of positive FTD symptom severity. These findings generalized to an independent data set. Our study has identified robust neurobiological correlates of positive FTD in schizophrenia.
Formal thought disorder (FTD) is a core symptom cluster of schizophrenia, but its neurobiological substrates remain poorly understood. Here we collected resting-state fMRI data from 276 subjects at seven sites and employed machine-learning to investigate the neurobiological correlates of FTD along positive and negative symptom dimensions in schizophrenia. Three a priori, meta-analytically defined FTD-related brain regions were used as seeds to generate whole-brain resting-state functional connectivity (rsFC) maps, which were then compared between schizophrenia patients and controls. A repeated cross-validation procedure was realized within the patient group to identify clusters whose rsFC patterns to the seeds were repeatedly observed as significantly associated with specific FTD dimensions. These repeatedly identified clusters (i.e., robust clusters) were functionally characterized and the rsFC patterns were used for predictive modeling to investigate predictive capacities for individual FTD dimensional-scores. Compared with controls, differential rsFC was found in patients in fronto-temporo-thalamic regions. Our cross-validation procedure revealed significant clusters only when assessing the seed-to-whole-brain rsFC patterns associated with positive-FTD. RsFC patterns of three fronto-temporal clusters, associated with higher-order cognitive processes (e.g., executive functions), specifically predicted individual positive-FTD scores (p = 0.005), but not other positive symptoms, and the PANSS general psychopathology subscale (p > 0.05). The prediction of positive-FTD was moreover generalized to an independent dataset (p = 0.013). Our study has identified neurobiological correlates of positive FTD in schizophrenia in a network associated with higher-order cognitive functions, suggesting a dysexecutive contribution to FTD in schizophrenia. We regard our findings as robust, as they allow a prediction of individual-level symptom severity.
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Affiliation(s)
- Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Tobias Wensing
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH, Aachen, Germany; JARA Translational Brain Medicine, Aachen, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Edna C Cieslik
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Veronika I Müller
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - André Aleman
- Department of Neuroscience, University of Groningen, University Medical Center Groningen, the Netherlands
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Germany
| | - Renaud Jardri
- Univ Lille, INSERM U1172, Lille Neuroscience & Cognition Centre, Plasticity &SubjectivitY Team & CHU Lille, Fontan Hospital, CURE Platform, Lille, France
| | - Lydia Kogler
- Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Germany
| | - Iris E Sommer
- Department of Biomedical Science of Cells and Systems, University of Groningen, University Medical Center Groningen, the Netherlands
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Thomas Nickl-Jockschat
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, United States; Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, United States.
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Wu J, Eickhoff SB, Hoffstaedter F, Patil KR, Schwender H, Yeo BTT, Genon S. A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies. Cereb Cortex 2021; 31:3732-3751. [PMID: 33884421 DOI: 10.1093/cercor/bhab044] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 02/09/2021] [Accepted: 02/11/2021] [Indexed: 01/01/2023] Open
Abstract
The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions' connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions' connectivity profiles. We first illustrate two main applications: 1) single brain region's predictive power for a range of psychometric variables and 2) single psychometric variable's predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships, as well as the unique insight into brain-behavior relationships offered by this approach.
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Affiliation(s)
- Jianxiao Wu
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Simon B Eickhoff
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Felix Hoffstaedter
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Kaustubh R Patil
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Holger Schwender
- Mathematical Institute, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore City 117575, Singapore.,Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore City 117597, Singapore.,N. 1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore City 117597, Singapore.,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore City 117575, Singapore.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Sarah Genon
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
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31
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Amunts J, Camilleri JA, Eickhoff SB, Patil KR, Heim S, von Polier GG, Weis S. Comprehensive verbal fluency features predict executive function performance. Sci Rep 2021; 11:6929. [PMID: 33767208 PMCID: PMC7994566 DOI: 10.1038/s41598-021-85981-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/09/2021] [Indexed: 02/07/2023] Open
Abstract
Semantic verbal fluency (sVF) tasks are commonly used in clinical diagnostic batteries as well as in a research context. When performing sVF tasks to assess executive functions (EFs) the sum of correctly produced words is the main measure. Although previous research indicates potentially better insights into EF performance by the use of finer grained sVF information, this has not yet been objectively evaluated. To investigate the potential of employing a finer grained sVF feature set to predict EF performance, healthy monolingual German speaking participants (n = 230) were tested with a comprehensive EF test battery and sVF tasks, from which features including sum scores, error types, speech breaks and semantic relatedness were extracted. A machine learning method was applied to predict EF scores from sVF features in previously unseen subjects. To investigate the predictive power of the advanced sVF feature set, we compared it to the commonly used sum score analysis. Results revealed that 8 / 14 EF tests were predicted significantly using the comprehensive sVF feature set, which outperformed sum scores particularly in predicting cognitive flexibility and inhibitory processes. These findings highlight the predictive potential of a comprehensive evaluation of sVF tasks which might be used as diagnostic screening of EFs.
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Affiliation(s)
- Julia Amunts
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str, 52428, Jülich, Germany.
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany.
| | - Julia A Camilleri
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - Stefan Heim
- Institute of Neuroscience and Medicine (INM-1 Structural and functional organization of the brain), Forschungszentrum Jülich, Wilhelm-Johnen-Str, 52428, Jülich, Germany
- Department of Psychiatry, Psychotherapy und Psychosomatics, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Georg G von Polier
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str, 52428, Jülich, Germany
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe-Universität Frankfurt am Main, Deutschordenstraße 50, 60528, Frankfurt am Main, Germany
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Neuenhofer Weg 21, 52074, Aachen, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Wilhelm-Johnen-Str, 52428, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich-Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany
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Friedrich P, Forkel SJ, Amiez C, Balsters JH, Coulon O, Fan L, Goulas A, Hadj-Bouziane F, Hecht EE, Heuer K, Jiang T, Latzman RD, Liu X, Loh KK, Patil KR, Lopez-Persem A, Procyk E, Sallet J, Toro R, Vickery S, Weis S, Wilson CRE, Xu T, Zerbi V, Eickoff SB, Margulies DS, Mars RB, Thiebaut de Schotten M. Imaging evolution of the primate brain: the next frontier? Neuroimage 2021; 228:117685. [PMID: 33359344 PMCID: PMC7116589 DOI: 10.1016/j.neuroimage.2020.117685] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 11/22/2022] Open
Abstract
Evolution, as we currently understand it, strikes a delicate balance between animals' ancestral history and adaptations to their current niche. Similarities between species are generally considered inherited from a common ancestor whereas observed differences are considered as more recent evolution. Hence comparing species can provide insights into the evolutionary history. Comparative neuroimaging has recently emerged as a novel subdiscipline, which uses magnetic resonance imaging (MRI) to identify similarities and differences in brain structure and function across species. Whereas invasive histological and molecular techniques are superior in spatial resolution, they are laborious, post-mortem, and oftentimes limited to specific species. Neuroimaging, by comparison, has the advantages of being applicable across species and allows for fast, whole-brain, repeatable, and multi-modal measurements of the structure and function in living brains and post-mortem tissue. In this review, we summarise the current state of the art in comparative anatomy and function of the brain and gather together the main scientific questions to be explored in the future of the fascinating new field of brain evolution derived from comparative neuroimaging.
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Affiliation(s)
- Patrick Friedrich
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany.
| | - Stephanie J Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Céline Amiez
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France
| | - Joshua H Balsters
- Department of Psychology, Royal Holloway University of London, United Kingdom
| | - Olivier Coulon
- Institut de Neurosciences de la Timone, Aix Marseille Univ, CNRS, UMR 7289, Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany
| | - Fadila Hadj-Bouziane
- Lyon Neuroscience Research Center, ImpAct Team, INSERM U1028, CNRS UMR5292, Université Lyon 1, Bron, France
| | - Erin E Hecht
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, United States
| | - Katja Heuer
- Center for Research and Interdisciplinarity (CRI), Université de Paris, Inserm, Paris 75004, France; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; The Queensland Brain Institute, University of Queensland, Brisbane QLD 4072, Australia
| | - Robert D Latzman
- Department of Psychology, Georgia State University, Atlanta, United States
| | - Xiaojin Liu
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Kep Kee Loh
- Institut de Neurosciences de la Timone, Aix Marseille Univ, CNRS, UMR 7289, Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Alizée Lopez-Persem
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Emmanuel Procyk
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France
| | - Jerome Sallet
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Roberto Toro
- Center for Research and Interdisciplinarity (CRI), Université de Paris, Inserm, Paris 75004, France; Neuroscience department, Institut Pasteur, UMR 3571, CNRS, Université de Paris, Paris 75015, France
| | - Sam Vickery
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Susanne Weis
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Charles R E Wilson
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France
| | - Ting Xu
- Child Mind Institute, New York, United States
| | - Valerio Zerbi
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Simon B Eickoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Daniel S Margulies
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique (CNRS) and Université de Paris, 75006, Paris, France
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
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Chen J, Müller VI, Dukart J, Hoffstaedter F, Baker JT, Holmes AJ, Vatansever D, Nickl-Jockschat T, Liu X, Derntl B, Kogler L, Jardri R, Gruber O, Aleman A, Sommer IE, Eickhoff SB, Patil KR. Intrinsic Connectivity Patterns of Task-Defined Brain Networks Allow Individual Prediction of Cognitive Symptom Dimension of Schizophrenia and Are Linked to Molecular Architecture. Biol Psychiatry 2021; 89:308-319. [PMID: 33357631 PMCID: PMC7770333 DOI: 10.1016/j.biopsych.2020.09.024] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/10/2020] [Accepted: 09/15/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND Despite the marked interindividual variability in the clinical presentation of schizophrenia, the extent to which individual dimensions of psychopathology relate to the functional variability in brain networks among patients remains unclear. Here, we address this question using network-based predictive modeling of individual psychopathology along 4 data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. METHODS We investigated resting-state functional magnetic resonance imaging data from 147 patients with schizophrenia recruited at 7 sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using a relevance vector machine based on functional connectivity within 17 meta-analytic task networks following repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of 9 receptors/transporters from prior molecular imaging in healthy populations. RESULTS Tenfold and leave-one-site-out analyses revealed 5 predictive network-symptom associations. Connectivity within theory of mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory of mind and socioaffective default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 receptor and serotonin reuptake transporter densities as well as dopamine synthesis capacity. CONCLUSIONS We revealed a robust association between intrinsic functional connectivity within networks for socioaffective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, this work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.
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Affiliation(s)
- Ji Chen
- Institute of Neuroscience and Medicine: Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| | - Veronika I. Müller
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Justin T. Baker
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA 02478,Department of Psychiatry, Harvard Medical School, Boston, MA 02114
| | - Avram J. Holmes
- Department of Psychology, Yale University, New Haven, CT 06520
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 200433, Shanghai, PR China
| | - Thomas Nickl-Jockschat
- Iowa Neuroscience Institute & Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Xiaojin Liu
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Germany
| | - Lydia Kogler
- Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Germany
| | - Renaud Jardri
- Univ Lille, INSERM U1172, Lille Neuroscience & Cognition Centre, Plasticity & SubjectivitY team & CHU Lille, Fontan Hospital, CURE platform, Lille, France
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Germany
| | - André Aleman
- Department of Neuroscience, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Iris E. Sommer
- Department of Biomedical Science of Cells and Systems, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany,Correspondence should be addressed to: Simon B. Eickhoff, Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany & Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany. Tel: +49 2461 61 1791; .; Ji Chen, Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany & Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany. Tel: +49 2461 61 85334;
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Machado D, Maistrenko OM, Andrejev S, Kim Y, Bork P, Patil KR, Patil KR. Polarization of microbial communities between competitive and cooperative metabolism. Nat Ecol Evol 2021; 5:195-203. [PMID: 33398106 PMCID: PMC7610595 DOI: 10.1038/s41559-020-01353-4] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 10/21/2020] [Indexed: 12/20/2022]
Abstract
Resource competition and metabolic cross-feeding are among the main drivers of microbial community assembly. Yet the degree to which these two conflicting forces are reflected in the composition of natural communities has not been systematically investigated. Here, we use genome-scale metabolic modelling to assess the potential for resource competition and metabolic cooperation in large co-occurring groups (up to 40 members) across thousands of habitats. Our analysis reveals two distinct community types, which are clustered at opposite ends of a spectrum in a trade-off between competition and cooperation. At one end are highly cooperative communities, characterized by smaller genomes and multiple auxotrophies. At the other end are highly competitive communities, which feature larger genomes and overlapping nutritional requirements, and harbour more genes related to antimicrobial activity. The latter are mainly present in soils, whereas the former are found in both free-living and host-associated habitats. Community-scale flux simulations show that, whereas competitive communities can better resist species invasion but not nutrient shift, cooperative communities are susceptible to species invasion but resilient to nutrient change. We also show, by analysing an additional data set, that colonization by probiotic species is positively associated with the presence of cooperative species in the recipient microbiome. Together, our results highlight the bifurcation between competitive and cooperative metabolism in the assembly of natural communities and its implications for community modulation.
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Affiliation(s)
- Daniel Machado
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.,Department of Biotechnology and Food Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | | | - Sergej Andrejev
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Yongkyu Kim
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Peer Bork
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kiran R Patil
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany. .,Medical Research Council (MRC) Toxicology Unit, University of Cambridge, Cambridge, UK.
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35
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Pläschke RN, Patil KR, Cieslik EC, Nostro AD, Varikuti DP, Plachti A, Lösche P, Hoffstaedter F, Kalenscher T, Langner R, Eickhoff SB. Age differences in predicting working memory performance from network-based functional connectivity. Cortex 2020; 132:441-459. [PMID: 33065515 PMCID: PMC7778730 DOI: 10.1016/j.cortex.2020.08.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 06/27/2020] [Accepted: 08/23/2020] [Indexed: 01/14/2023]
Abstract
Deterioration in working memory capacity (WMC) has been associated with normal aging, but it remains unknown how age affects the relationship between WMC and connectivity within functional brain networks. We therefore examined the predictability of WMC from fMRI-based resting-state functional connectivity (RSFC) within eight meta-analytically defined functional brain networks and the connectome in young and old adults using relevance vector machine in a robust cross-validation scheme. Particular brain networks have been associated with mental functions linked to WMC to a varying degree and are associated with age-related differences in performance. Comparing prediction performance between the young and old sample revealed age-specific effects: In young adults, we found a general unpredictability of WMC from RSFC in networks subserving WM, cognitive action control, vigilant attention, theory-of-mind cognition, and semantic memory, whereas in older adults each network significantly predicted WMC. Moreover, both WM-related and WM-unrelated networks were differently predictive in older adults with low versus high WMC. These results indicate that the within-network functional coupling during task-free states is specifically related to individual task performance in advanced age, suggesting neural-level reorganization. In particular, our findings support the notion of a decreased segregation of functional brain networks, deterioration of network integrity within different networks and/or compensation by reorganization as factors driving associations between individual WMC and within-network RSFC in older adults. Thus, using multivariate pattern regression provided novel insights into age-related brain reorganization by linking cognitive capacity to brain network integrity.
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Affiliation(s)
- Rachel N Pläschke
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Edna C Cieslik
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Alessandra D Nostro
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Deepthi P Varikuti
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Anna Plachti
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Patrick Lösche
- Leibniz Institute for International Educational Research (DIPF), Centre for Research on Human Development and Education, Frankfurt am Main, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Tobias Kalenscher
- Comparative Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Robert Langner
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
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36
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Liu X, Eickhoff SB, Hoffstaedter F, Genon S, Caspers S, Reetz K, Dogan I, Eickhoff CR, Chen J, Caspers J, Reuter N, Mathys C, Aleman A, Jardri R, Riedl V, Sommer IE, Patil KR. Joint Multi-modal Parcellation of the Human Striatum: Functions and Clinical Relevance. Neurosci Bull 2020; 36:1123-1136. [PMID: 32700142 DOI: 10.1007/s12264-020-00543-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 04/10/2020] [Indexed: 12/20/2022] Open
Abstract
The human striatum is essential for both low- and high-level functions and has been implicated in the pathophysiology of various prevalent disorders, including Parkinson's disease (PD) and schizophrenia (SCZ). It is known to consist of structurally and functionally divergent subdivisions. However, previous parcellations are based on a single neuroimaging modality, leaving the extent of the multi-modal organization of the striatum unknown. Here, we investigated the organization of the striatum across three modalities-resting-state functional connectivity, probabilistic diffusion tractography, and structural covariance-to provide a holistic convergent view of its structure and function. We found convergent clusters in the dorsal, dorsolateral, rostral, ventral, and caudal striatum. Functional characterization revealed the anterior striatum to be mainly associated with cognitive and emotional functions, while the caudal striatum was related to action execution. Interestingly, significant structural atrophy in the rostral and ventral striatum was common to both PD and SCZ, but atrophy in the dorsolateral striatum was specifically attributable to PD. Our study revealed a cross-modal convergent organization of the striatum, representing a fundamental topographical model that can be useful for investigating structural and functional variability in aging and in clinical conditions.
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Affiliation(s)
- Xiaojin Liu
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52428, Jülich, Germany.,Institute for Anatomy I, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Kathrin Reetz
- Department of Neurology, Rheinisch Westfällische Technische Hochschule (RWTH) Aachen University, 52074, Aachen, Germany
| | - Imis Dogan
- Jülich Aachen Research Alliance-BRAIN (JARA) Institute of Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, Rheinisch Westfällische Technische Hochschule (RWTH) Aachen University, 52074, Aachen, Germany.,Department of Neurology, Rheinisch Westfällische Technische Hochschule (RWTH) Aachen University, 52074, Aachen, Germany
| | - Claudia R Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, University of Düsseldorf, 40225, Düsseldorf, Germany
| | - Ji Chen
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52428, Jülich, Germany.,Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, 40225, Düsseldorf, Germany
| | - Niels Reuter
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Mathys
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, 40225, Düsseldorf, Germany.,Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, 26129, Oldenburg, Germany
| | - André Aleman
- Department of Neuroscience, University Medical Center Groningen, University of Groningen, 9713 AV, Groningen, The Netherlands
| | - Renaud Jardri
- SCALab (CNRS UMR9193) & CHU de Lille, Hôpital Fontan, Pôle de Psychiatrie (CURE), Université de Lille, 59037, Lille, France
| | - Valentin Riedl
- Departments of Neuroradiology, Nuclear Medicine and Neuroimaging Center, Technische Universität München, 80333, Munich, Germany
| | - Iris E Sommer
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, 26129, Oldenburg, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7, Brain and Behaviour), Research Centre Jülich, Jülich, Germany. .,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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37
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Botvinik-Nezer R, Holzmeister F, Camerer CF, Dreber A, Huber J, Johannesson M, Kirchler M, Iwanir R, Mumford JA, Adcock RA, Avesani P, Baczkowski BM, Bajracharya A, Bakst L, Ball S, Barilari M, Bault N, Beaton D, Beitner J, Benoit RG, Berkers RMWJ, Bhanji JP, Biswal BB, Bobadilla-Suarez S, Bortolini T, Bottenhorn KL, Bowring A, Braem S, Brooks HR, Brudner EG, Calderon CB, Camilleri JA, Castrellon JJ, Cecchetti L, Cieslik EC, Cole ZJ, Collignon O, Cox RW, Cunningham WA, Czoschke S, Dadi K, Davis CP, Luca AD, Delgado MR, Demetriou L, Dennison JB, Di X, Dickie EW, Dobryakova E, Donnat CL, Dukart J, Duncan NW, Durnez J, Eed A, Eickhoff SB, Erhart A, Fontanesi L, Fricke GM, Fu S, Galván A, Gau R, Genon S, Glatard T, Glerean E, Goeman JJ, Golowin SAE, González-García C, Gorgolewski KJ, Grady CL, Green MA, Guassi Moreira JF, Guest O, Hakimi S, Hamilton JP, Hancock R, Handjaras G, Harry BB, Hawco C, Herholz P, Herman G, Heunis S, Hoffstaedter F, Hogeveen J, Holmes S, Hu CP, Huettel SA, Hughes ME, Iacovella V, Iordan AD, Isager PM, Isik AI, Jahn A, Johnson MR, Johnstone T, Joseph MJE, Juliano AC, Kable JW, Kassinopoulos M, Koba C, Kong XZ, Koscik TR, Kucukboyaci NE, Kuhl BA, Kupek S, Laird AR, Lamm C, Langner R, Lauharatanahirun N, Lee H, Lee S, Leemans A, Leo A, Lesage E, Li F, Li MYC, Lim PC, Lintz EN, Liphardt SW, Losecaat Vermeer AB, Love BC, Mack ML, Malpica N, Marins T, Maumet C, McDonald K, McGuire JT, Melero H, Méndez Leal AS, Meyer B, Meyer KN, Mihai G, Mitsis GD, Moll J, Nielson DM, Nilsonne G, Notter MP, Olivetti E, Onicas AI, Papale P, Patil KR, Peelle JE, Pérez A, Pischedda D, Poline JB, Prystauka Y, Ray S, Reuter-Lorenz PA, Reynolds RC, Ricciardi E, Rieck JR, Rodriguez-Thompson AM, Romyn A, Salo T, Samanez-Larkin GR, Sanz-Morales E, Schlichting ML, Schultz DH, Shen Q, Sheridan MA, Silvers JA, Skagerlund K, Smith A, Smith DV, Sokol-Hessner P, Steinkamp SR, Tashjian SM, Thirion B, Thorp JN, Tinghög G, Tisdall L, Tompson SH, Toro-Serey C, Torre Tresols JJ, Tozzi L, Truong V, Turella L, van 't Veer AE, Verguts T, Vettel JM, Vijayarajah S, Vo K, Wall MB, Weeda WD, Weis S, White DJ, Wisniewski D, Xifra-Porxas A, Yearling EA, Yoon S, Yuan R, Yuen KSL, Zhang L, Zhang X, Zosky JE, Nichols TE, Poldrack RA, Schonberg T. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 2020; 582:84-88. [PMID: 32483374 PMCID: PMC7771346 DOI: 10.1038/s41586-020-2314-9] [Citation(s) in RCA: 423] [Impact Index Per Article: 105.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 04/07/2020] [Indexed: 01/13/2023]
Abstract
Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.
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Affiliation(s)
- Rotem Botvinik-Nezer
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Neurobiology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Felix Holzmeister
- Department of Banking and Finance, University of Innsbruck, Innsbruck, Austria
| | - Colin F Camerer
- HSS and CNS, California Institute of Technology, Pasadena, CA, USA
| | - Anna Dreber
- Department of Economics, Stockholm School of Economics, Stockholm, Sweden
- Department of Economics, University of Innsbruck, Innsbruck, Austria
| | - Juergen Huber
- Department of Banking and Finance, University of Innsbruck, Innsbruck, Austria
| | - Magnus Johannesson
- Department of Economics, Stockholm School of Economics, Stockholm, Sweden
| | - Michael Kirchler
- Department of Banking and Finance, University of Innsbruck, Innsbruck, Austria
| | - Roni Iwanir
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Neurobiology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Jeanette A Mumford
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - R Alison Adcock
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Paolo Avesani
- Neuroinformatics Laboratory, Fondazione Bruno Kessler, Trento, Italy
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
| | - Blazej M Baczkowski
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Aahana Bajracharya
- Department of Otolaryngology, Washington University in St. Louis, St. Louis, MO, USA
| | - Leah Bakst
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, USA
| | - Sheryl Ball
- Department of Economics, Virginia Tech, Blacksburg, VA, USA
- School of Neuroscience, Virginia Tech, Blacksburg, VA, USA
| | - Marco Barilari
- Crossmodal Perception and Plasticity Laboratory, Institutes for Research in Psychology (IPSY) and Neurosciences (IoNS), UCLouvain, Louvain-la-Neuve, Belgium
| | - Nadège Bault
- School of Psychology, University of Plymouth, Plymouth, UK
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences Centre, Toronto, Ontario, Canada
| | - Julia Beitner
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
- Department of Psychology, Goethe University, Frankfurt am Main, Germany
| | - Roland G Benoit
- Max Planck Research Group: Adaptive Memory, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ruud M W J Berkers
- Max Planck Research Group: Adaptive Memory, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jamil P Bhanji
- Department of Psychology, Rutgers University-Newark, Newark, NJ, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Tiago Bortolini
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | | | - Alexander Bowring
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Senne Braem
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
- Department of Psychology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Hayley R Brooks
- Department of Psychology, University of Denver, Denver, CO, USA
| | - Emily G Brudner
- Department of Psychology, Rutgers University-Newark, Newark, NJ, USA
| | | | - Julia A Camilleri
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jaime J Castrellon
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Luca Cecchetti
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Edna C Cieslik
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Zachary J Cole
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Olivier Collignon
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
- Crossmodal Perception and Plasticity Laboratory, Institutes for Research in Psychology (IPSY) and Neurosciences (IoNS), UCLouvain, Louvain-la-Neuve, Belgium
| | - Robert W Cox
- National Institute of Mental Health (NIMH), National Institutes of Health, Bethesda, MD, USA
| | | | - Stefan Czoschke
- Institute of Medical Psychology, Goethe University, Frankfurt am Main, Germany
| | | | - Charles P Davis
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
- Brain Imaging Research Center, University of Connecticut, Storrs, CT, USA
- Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, USA
| | - Alberto De Luca
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Lysia Demetriou
- Section of Endocrinology and Investigative Medicine, Faculty of Medicine, Imperial College London, London, UK
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | | | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Erin W Dickie
- Krembil Centre for Neuroinformatics, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Ekaterina Dobryakova
- Center for Traumatic Brain Injury Research, Kessler Foundation, East Hanover, NJ, USA
| | - Claire L Donnat
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Niall W Duncan
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
- Brain and Consciousness Research Centre, TMU-ShuangHo Hospital, New Taipei City, Taiwan
| | - Joke Durnez
- Department of Psychology and Stanford Center for Reproducible Neuroscience, Stanford University, Stanford, CA, USA
| | - Amr Eed
- Instituto de Neurociencias, CSIC-UMH, Alicante, Spain
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Andrew Erhart
- Department of Psychology, University of Denver, Denver, CO, USA
| | - Laura Fontanesi
- Faculty of Psychology, University of Basel, Basel, Switzerland
| | - G Matthew Fricke
- Computer Science Department, University of New Mexico, Albuquerque, NM, USA
| | - Shiguang Fu
- School of Management, Zhejiang University of Technology, Hangzhou, China
- Institute of Neuromanagement, Zhejiang University of Technology, Hangzhou, China
| | - Adriana Galván
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
| | - Remi Gau
- Crossmodal Perception and Plasticity Laboratory, Institutes for Research in Psychology (IPSY) and Neurosciences (IoNS), UCLouvain, Louvain-la-Neuve, Belgium
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Jelle J Goeman
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Sergej A E Golowin
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
| | | | | | - Cheryl L Grady
- Rotman Research Institute, Baycrest Health Sciences Centre, Toronto, Ontario, Canada
| | - Mikella A Green
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - João F Guassi Moreira
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
| | - Olivia Guest
- Department of Experimental Psychology, University College London, London, UK
- Research Centre on Interactive Media, Smart Systems and Emerging Technologies - RISE, Nicosia, Cyprus
| | - Shabnam Hakimi
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
| | - J Paul Hamilton
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Roeland Hancock
- Brain Imaging Research Center, University of Connecticut, Storrs, CT, USA
- Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, USA
| | - Giacomo Handjaras
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Bronson B Harry
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, New South Wales, Australia
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Peer Herholz
- McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Gabrielle Herman
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Stephan Heunis
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jeremy Hogeveen
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
- Psychology Clinical Neuroscience Center, University of New Mexico, Albuquerque, NM, USA
| | - Susan Holmes
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Chuan-Peng Hu
- Leibniz-Institut für Resilienzforschung (LIR), Mainz, Germany
| | - Scott A Huettel
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Matthew E Hughes
- School of Health Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Vittorio Iacovella
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
| | | | - Peder M Isager
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ayse I Isik
- Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Andrew Jahn
- fMRI Laboratory, University of Michigan, Ann Arbor, MI, USA
| | - Matthew R Johnson
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Tom Johnstone
- School of Health Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Michael J E Joseph
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Anthony C Juliano
- Center for Neuropsychology and Neuroscience Research, Kessler Foundation, East Hanover, NJ, USA
| | - Joseph W Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
- MindCORE, University of Pennsylvania, Philadelphia, PA, USA
| | - Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Cemal Koba
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Xiang-Zhen Kong
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Timothy R Koscik
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Nuri Erkut Kucukboyaci
- Center for Traumatic Brain Injury Research, Kessler Foundation, East Hanover, NJ, USA
- Department of Physical Medicine and Rehabilitation, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Brice A Kuhl
- Department of Psychology, University of Oregon, Eugene, OR, USA
| | - Sebastian Kupek
- Faculty of Economics and Statistics, University of Innsbruck, Innsbruck, Austria
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, Florida, USA
| | - Claus Lamm
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
- Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria
| | - Robert Langner
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Nina Lauharatanahirun
- US CCDC Army Research Laboratory, Human Research and Engineering Directorate, Aberdeen Proving Ground, MD, USA
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA
| | - Hongmi Lee
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Sangil Lee
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Andrea Leo
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Elise Lesage
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Flora Li
- Fralin Biomedical Research Institute, Roanoke, VA, USA
- Economics Experimental Lab, Nanjing Audit University, Nanjing, China
| | - Monica Y C Li
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
- Brain Imaging Research Center, University of Connecticut, Storrs, CT, USA
- Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, USA
- Haskins Laboratories, New Haven, CT, USA
| | - Phui Cheng Lim
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Evan N Lintz
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
| | | | - Annabel B Losecaat Vermeer
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Bradley C Love
- Department of Experimental Psychology, University College London, London, UK
- The Alan Turing Institute, London, UK
| | - Michael L Mack
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Norberto Malpica
- Laboratorio de Análisis de Imagen Médica y Biometría (LAIMBIO), Universidad Rey Juan Carlos, Madrid, Spain
| | - Theo Marins
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Kelsey McDonald
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Joseph T McGuire
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, USA
| | - Helena Melero
- Laboratorio de Análisis de Imagen Médica y Biometría (LAIMBIO), Universidad Rey Juan Carlos, Madrid, Spain
- Departamento de Psicobiología, División de Psicología, CES Cardenal Cisneros, Madrid, Spain
- Northeastern University Biomedical Imaging Center, Northeastern University, Boston, MA, USA
| | - Adriana S Méndez Leal
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin Meyer
- Leibniz-Institut für Resilienzforschung (LIR), Mainz, Germany
- Neuroimaging Center (NIC), Focus Program Translational Neurosciences (FTN), Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | - Kristin N Meyer
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Glad Mihai
- Max Planck Research Group: Neural Mechanisms of Human Communication, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Chair of Cognitive and Clinical Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, Quebec, Canada
| | - Jorge Moll
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Dylan M Nielson
- Data Science and Sharing Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Gustav Nilsonne
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Psychology, Stockholm University, Stockholm, Sweden
| | - Michael P Notter
- The Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology, University Hospital Center and University of Lausanne, Lausanne, Switzerland
| | - Emanuele Olivetti
- Neuroinformatics Laboratory, Fondazione Bruno Kessler, Trento, Italy
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
| | - Adrian I Onicas
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Paolo Papale
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jonathan E Peelle
- Department of Otolaryngology, Washington University in St. Louis, St. Louis, MO, USA
| | - Alexandre Pérez
- McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Doris Pischedda
- Bernstein Center for Computational Neuroscience and Berlin Center for Advanced Neuroimaging and Clinic for Neurology, Charité Universitätsmedizin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Cluster of Excellence Science of Intelligence, Technische Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- NeuroMI - Milan Center for Neuroscience, Milan, Italy
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Henry H. Wheeler, Jr. Brain Imaging Center, Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Yanina Prystauka
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
- Brain Imaging Research Center, University of Connecticut, Storrs, CT, USA
- Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, USA
| | - Shruti Ray
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | | | - Richard C Reynolds
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Emiliano Ricciardi
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Jenny R Rieck
- Rotman Research Institute, Baycrest Health Sciences Centre, Toronto, Ontario, Canada
| | - Anais M Rodriguez-Thompson
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Anthony Romyn
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Taylor Salo
- Department of Psychology, Florida International University, Miami, FL, USA
| | - Gregory R Samanez-Larkin
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Emilio Sanz-Morales
- Laboratorio de Análisis de Imagen Médica y Biometría (LAIMBIO), Universidad Rey Juan Carlos, Madrid, Spain
| | | | - Douglas H Schultz
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Qiang Shen
- School of Management, Zhejiang University of Technology, Hangzhou, China
- Institute of Neuromanagement, Zhejiang University of Technology, Hangzhou, China
| | - Margaret A Sheridan
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jennifer A Silvers
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
| | - Kenny Skagerlund
- Department of Behavioural Sciences and Learning, Linköping University, Linköping, Sweden
- Center for Social and Affective Neuroscience, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Alec Smith
- Department of Economics, Virginia Tech, Blacksburg, VA, USA
- School of Neuroscience, Virginia Tech, Blacksburg, VA, USA
| | - David V Smith
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | | | - Simon R Steinkamp
- Institute of Neuroscience and Medicine, Cognitive Neuroscience (INM-3), Research Centre Jülich, Jülich, Germany
| | - Sarah M Tashjian
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
| | | | - John N Thorp
- Department of Psychology, Columbia University, New York, NY, USA
| | - Gustav Tinghög
- Department of Management and Engineering, Linköping University, Linköping, Sweden
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Loreen Tisdall
- Department of Psychology, Stanford University, Stanford, CA, USA
- Center for Cognitive and Decision Sciences, University of Basel, Basel, Switzerland
| | - Steven H Tompson
- US CCDC Army Research Laboratory, Human Research and Engineering Directorate, Aberdeen Proving Ground, MD, USA
| | - Claudio Toro-Serey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, USA
| | | | - Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Vuong Truong
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
- Brain and Consciousness Research Centre, TMU-ShuangHo Hospital, New Taipei City, Taiwan
| | - Luca Turella
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
| | - Anna E van 't Veer
- Methodology and Statistics Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Tom Verguts
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Jean M Vettel
- US Combat Capabilities Development Command Army Research Laboratory, Aberdeen, MD, USA
- University of California Santa Barbara, Santa Barbara, CA, USA
- University of Pennsylvania, Philadelphia, PA, USA
| | - Sagana Vijayarajah
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Khoi Vo
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Matthew B Wall
- Invicro, London, UK
- Faculty of Medicine, Imperial College London, London, UK
- Clinical Psychopharmacology Unit, University College London, London, UK
| | - Wouter D Weeda
- Methodology and Statistics Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - David J White
- Centre for Human Psychopharmacology, Swinburne University, Hawthorn, Victoria, Australia
| | - David Wisniewski
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Alba Xifra-Porxas
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Emily A Yearling
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
- Brain Imaging Research Center, University of Connecticut, Storrs, CT, USA
- Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, USA
| | - Sangsuk Yoon
- Department of Management and Marketing, School of Business, University of Dayton, Dayton, OH, USA
| | - Rui Yuan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Kenneth S L Yuen
- Leibniz-Institut für Resilienzforschung (LIR), Mainz, Germany
- Neuroimaging Center (NIC), Focus Program Translational Neurosciences (FTN), Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | - Lei Zhang
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Xu Zhang
- Brain Imaging Research Center, University of Connecticut, Storrs, CT, USA
- Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, USA
- Biomedical Engineering Department, University of Connecticut, Storrs, CT, USA
| | - Joshua E Zosky
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Thomas E Nichols
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | | | - Tom Schonberg
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
- Department of Neurobiology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
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Weis S, Patil KR, Hoffstaedter F, Nostro A, Yeo BTT, Eickhoff SB. Sex Classification by Resting State Brain Connectivity. Cereb Cortex 2020; 30:824-835. [PMID: 31251328 PMCID: PMC7444737 DOI: 10.1093/cercor/bhz129] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 05/03/2019] [Accepted: 05/28/2019] [Indexed: 12/15/2022] Open
Abstract
A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants' sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.
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Affiliation(s)
- Susanne Weis
- 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
| | - Kaustubh R Patil
- 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
| | - Felix Hoffstaedter
- 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
| | - Alessandra Nostro
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Art and Sciences (KNAW), Amsterdam, the Netherlands
| | - B T Thomas Yeo
- ECE, CIRC, N.1, MNP and NGS, National University of Singapore, Singapore
| | - 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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Reuter N, Genon S, Kharabian Masouleh S, Hoffstaedter F, Liu X, Kalenscher T, Eickhoff SB, Patil KR. CBPtools: a Python package for regional connectivity-based parcellation. Brain Struct Funct 2020; 225:1261-1275. [PMID: 32144496 PMCID: PMC7271019 DOI: 10.1007/s00429-020-02046-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 02/08/2020] [Indexed: 02/02/2023]
Abstract
Regional connectivity-based parcellation (rCBP) is a widely used procedure for investigating the structural and functional differentiation within a region of interest (ROI) based on its long-range connectivity. No standardized software or guidelines currently exist for applying rCBP, making the method only accessible to those who develop their own tools. As such, there exists a discrepancy between the laboratories applying the procedure each with their own software solutions, making it difficult to compare and interpret the results. Here, we outline an rCBP procedure accompanied by an open source software package called CBPtools. CBPtools is a Python (version 3.5+) package that allows users to run an extensively evaluated rCBP analysis workflow on a given ROI. It currently supports two modalities: resting-state functional connectivity and structural connectivity based on diffusion-weighted imaging, along with support for custom connectivity matrices. Analysis parameters are customizable and the workflow can be scaled to a large number of subjects using a parallel processing environment. Parcellation results with corresponding validity metrics are provided as textual and graphical output. Thus, CBPtools provides a simple plug-and-play, yet customizable way to conduct rCBP analyses. By providing an open-source software we hope to promote reproducible and comparable rCBP analyses and, importantly, make the rCBP procedure readily available. Here, we demonstrate the utility of CBPtools using a voluminous data set on an average compute-cluster infrastructure by performing rCBP on three ROIs prominently featured in parcellation literature.
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Affiliation(s)
- Niels Reuter
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Sarah Genon
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Shahrzad Kharabian Masouleh
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Xiaojin Liu
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Tobias Kalenscher
- Comparative Psychology, Institute of Experimental Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany.
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
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Chen J, Patil KR, Weis S, Sim K, Nickl-Jockschat T, Zhou J, Aleman A, Sommer IE, Liemburg EJ, Hoffstaedter F, Habel U, Derntl B, Liu X, Fischer JM, Kogler L, Regenbogen C, Diwadkar VA, Stanley JA, Riedl V, Jardri R, Gruber O, Sotiras A, Davatzikos C, Eickhoff SB. Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study. Biol Psychiatry 2020; 87:282-293. [PMID: 31748126 PMCID: PMC6946875 DOI: 10.1016/j.biopsych.2019.08.031] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Revised: 07/22/2019] [Accepted: 08/31/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations. METHODS Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 ± 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns. RESULTS A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus. CONCLUSIONS Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.
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Affiliation(s)
- Ji Chen
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kang Sim
- Department of General Psychiatry, Institute of Mental Health, Singapore; Research Division, Institute of Mental Health, Singapore
| | - Thomas Nickl-Jockschat
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, Iowa; Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - Juan Zhou
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore
| | - André Aleman
- Department of Neuroscience, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Iris E Sommer
- Department of Neuroscience, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; BCN Neuroimaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Edith J Liemburg
- Rob Giel Research Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany; Jülich Aachen Research Alliance-Institute Brain Structure Function Relationship, Research Center Jülich, and RWTH Aachen University, Aachen, Germany
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Xiaojin Liu
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jona M Fischer
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lydia Kogler
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Christina Regenbogen
- Department of Psychiatry, Psychotherapy and Psychosomatics, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany; Jülich Aachen Research Alliance-Institute Brain Structure Function Relationship, Research Center Jülich, and RWTH Aachen University, Aachen, Germany
| | - Vaibhav A Diwadkar
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University, Detroit, Michigan
| | - Jeffrey A Stanley
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University, Detroit, Michigan
| | - Valentin Riedl
- Department of Neuroradiology, Rechts der Isar Hospital, Technical University of Munich, Munich, Germany
| | - Renaud Jardri
- University of Lille, National Centre for Scientific Research, UMR 9193, SCALab and CHU Lille, Fontan Hospital, CURE platform, Lille, France
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, Section of Biomedical Image Analysis, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Plachti A, Eickhoff SB, Hoffstaedter F, Patil KR, Laird AR, Fox PT, Amunts K, Genon S. Multimodal Parcellations and Extensive Behavioral Profiling Tackling the Hippocampus Gradient. Cereb Cortex 2019; 29:4595-4612. [PMID: 30721944 PMCID: PMC6917521 DOI: 10.1093/cercor/bhy336] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 03/12/2018] [Accepted: 12/11/2018] [Indexed: 12/16/2022] Open
Abstract
The hippocampus displays a complex organization and function that is perturbed in many neuropathologies. Histological work revealed a complex arrangement of subfields along the medial-lateral and the ventral-dorsal dimension, which contrasts with the anterior-posterior functional differentiation. The variety of maps has raised the need for an integrative multimodal view. We applied connectivity-based parcellation to 1) intrinsic connectivity 2) task-based connectivity, and 3) structural covariance, as complementary windows into structural and functional differentiation of the hippocampus. Strikingly, while functional properties (i.e., intrinsic and task-based) revealed similar partitions dominated by an anterior-posterior organization, structural covariance exhibited a hybrid pattern reflecting both functional and cytoarchitectonic subdivision. Capitalizing on the consistency of functional parcellations, we defined robust functional maps at different levels of partitions, which are openly available for the scientific community. Our functional maps demonstrated a head-body and tail partition, subdivided along the anterior-posterior and medial-lateral axis. Behavioral profiling of these fine partitions based on activation data indicated an emotion-cognition gradient along the anterior-posterior axis and additionally suggested a self-world-centric gradient supporting the role of the hippocampus in the construction of abstract representations for spatial navigation and episodic memory.
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Affiliation(s)
- Anna Plachti
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, TX, USA
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
- C. & O. Vogt Institute for Brain Research, Heinrich Heine University, Düsseldorf. Germany
| | - Sarah Genon
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
- GIGA-CRC In vivo Imaging, University of Liege, Liege, Belgium
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Joshi G, Arnold Anteraper S, Patil KR, Semwal M, Goldin RL, Furtak SL, Chai XJ, Saygin ZM, Gabrieli JDE, Biederman J, Whitfield-Gabrieli S. Integration and Segregation of Default Mode Network Resting-State Functional Connectivity in Transition-Age Males with High-Functioning Autism Spectrum Disorder: A Proof-of-Concept Study. Brain Connect 2018; 7:558-573. [PMID: 28942672 DOI: 10.1089/brain.2016.0483] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
The aim of this study is to assess the resting-state functional connectivity (RsFc) profile of the default mode network (DMN) in transition-age males with autism spectrum disorder (ASD). Resting-state blood oxygen level-dependent functional magnetic resonance imaging data were acquired from adolescent and young adult males with high-functioning ASD (n = 15) and from age-, sex-, and intelligence quotient-matched healthy controls (HCs; n = 16). The DMN was examined by assessing the positive and negative RsFc correlations of an average of the literature-based conceptualized major DMN nodes (medial prefrontal cortex [mPFC], posterior cingulate cortex, bilateral angular, and inferior temporal gyrus regions). RsFc data analysis was performed using a seed-driven approach. ASD was characterized by an altered pattern of RsFc in the DMN. The ASD group exhibited a weaker pattern of intra- and extra-DMN-positive and -negative RsFc correlations, respectively. In ASD, the strength of intra-DMN coupling was significantly reduced with the mPFC and the bilateral angular gyrus regions. In addition, the polarity of the extra-DMN correlation with the right hemispheric task-positive regions of fusiform gyrus and supramarginal gyrus was reversed from typically negative to positive in the ASD group. A wide variability was observed in the presentation of the RsFc profile of the DMN in both HC and ASD groups that revealed a distinct pattern of subgrouping using pattern recognition analyses. These findings imply that the functional architecture profile of the DMN is altered in ASD with weaker than expected integration and segregation of DMN RsFc. Future studies with larger sample sizes are warranted.
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Affiliation(s)
- Gagan Joshi
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
- 2 Department of Psychiatry, Harvard Medical School , Boston, Massachusetts
- 3 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - Sheeba Arnold Anteraper
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
- 3 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - Kaustubh R Patil
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
| | - Meha Semwal
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
| | - Rachel L Goldin
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
| | - Stephannie L Furtak
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
| | | | - Zeynep M Saygin
- 3 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - John D E Gabrieli
- 3 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, Massachusetts
- 5 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - Joseph Biederman
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
- 2 Department of Psychiatry, Harvard Medical School , Boston, Massachusetts
| | - Susan Whitfield-Gabrieli
- 3 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, Massachusetts
- 5 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology , Cambridge, Massachusetts
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Rubbert C, Patil KR, Beseoglu K, Mathys C, May R, Kaschner MG, Sigl B, Teichert NA, Boos J, Turowski B, Caspers J. Prediction of outcome after aneurysmal subarachnoid haemorrhage using data from patient admission. Eur Radiol 2018; 28:4949-4958. [PMID: 29948072 DOI: 10.1007/s00330-018-5505-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 03/23/2018] [Accepted: 04/19/2018] [Indexed: 01/02/2023]
Abstract
OBJECTIVES The pathogenesis leading to poor functional outcome after aneurysmal subarachnoid haemorrhage (aSAH) is multifactorial and not fully understood. We evaluated a machine learning approach based on easily determinable clinical and CT perfusion (CTP) features in the course of patient admission to predict the functional outcome 6 months after ictus. METHODS Out of 630 consecutive subarachnoid haemorrhage patients (2008-2015), 147 (mean age 54.3, 66.7% women) were retrospectively included (Inclusion: aSAH, admission within 24 h of ictus, CTP within 24 h of admission, documented modified Rankin scale (mRS) grades after 6 months. Exclusion: occlusive therapy before first CTP, previous aSAH, CTP not evaluable). A random forests model with conditional inference trees was optimised and trained on sex, age, World Federation of Neurosurgical Societies (WFNS) and modified Fisher grades, aneurysm in anterior vs. posterior circulation, early external ventricular drainage (EVD), as well as MTT and Tmax maximum, mean, standard deviation (SD), range, 75th quartile and interquartile range to predict dichotomised mRS (≤ 2; > 2). Performance was assessed using the balanced accuracy over the training and validation folds using 20 repeats of 10-fold cross-validation. RESULTS In the final model, using 200 trees and the synthetic minority oversampling technique, median balanced accuracy was 84.4% (SD 0.7) over the training folds and 70.9% (SD 1.2) over the validation folds. The five most important features were the modified Fisher grade, age, MTT range, WFNS and early EVD. CONCLUSIONS A random forests model trained on easily determinable features in the course of patient admission can predict the functional outcome 6 months after aSAH with considerable accuracy. KEY POINTS • Features determinable in the course of admission of a patient with aneurysmal subarachnoid haemorrhage (aSAH) can predict the functional outcome 6 months after the occurrence of aSAH. • The top five predictive features were the modified Fisher grade, age, the mean transit time (MTT) range from computed tomography perfusion (CTP), the WFNS grade and the early necessity for an external ventricular drainage (EVD). • The range between the minimum and the maximum MTT may prove to be a valuable biomarker for detrimental functional outcome.
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Affiliation(s)
- Christian Rubbert
- University Düsseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Moorenstr. 5, D-40225, Düsseldorf, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, D-52425, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, D-40225, Düsseldorf, Germany
| | - Kerim Beseoglu
- Department of Neurosurgery, Medical Faculty, Heinrich-Heine-University, D-40225, Düsseldorf, Germany
| | - Christian Mathys
- University Düsseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Moorenstr. 5, D-40225, Düsseldorf, Germany
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, D-26122, Oldenburg, Germany
| | - Rebecca May
- University Düsseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Moorenstr. 5, D-40225, Düsseldorf, Germany
| | - Marius G Kaschner
- University Düsseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Moorenstr. 5, D-40225, Düsseldorf, Germany
| | - Benjamin Sigl
- University Düsseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Moorenstr. 5, D-40225, Düsseldorf, Germany
| | - Nikolas A Teichert
- University Düsseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Moorenstr. 5, D-40225, Düsseldorf, Germany
| | - Johannes Boos
- University Düsseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Moorenstr. 5, D-40225, Düsseldorf, Germany
| | - Bernd Turowski
- University Düsseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Moorenstr. 5, D-40225, Düsseldorf, Germany
| | - Julian Caspers
- University Düsseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Moorenstr. 5, D-40225, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, D-52425, Jülich, Germany
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Nostro AD, Müller VI, Varikuti DP, Pläschke RN, Hoffstaedter F, Langner R, Patil KR, Eickhoff SB. Predicting personality from network-based resting-state functional connectivity. Brain Struct Funct 2018; 223:2699-2719. [PMID: 29572625 DOI: 10.1007/s00429-018-1651-z] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 03/12/2018] [Indexed: 12/20/2022]
Abstract
Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC-personality relations should not be considered independently of gender.
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Affiliation(s)
- Alessandra D Nostro
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany. .,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany. .,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany.
| | - Veronika I Müller
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Deepthi P Varikuti
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Rachel N Pläschke
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Robert Langner
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
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Abstract
Class imbalance presents a major hurdle in the application of classification methods. A commonly taken approach is to learn ensembles of classifiers using rebalanced data. Examples include bootstrap averaging (bagging) combined with either undersampling or oversampling of the minority class examples. However, rebalancing methods entail asymmetric changes to the examples of different classes, which in turn can introduce their own biases. Furthermore, these methods often require specifying the performance measure of interest a priori, i.e., before learning. An alternative is to employ the threshold moving technique, which applies a threshold to the continuous output of a model, offering the possibility to adapt to a performance measure a posteriori, i.e., a plug-in method. Surprisingly, little attention has been paid to this combination of a bagging ensemble and threshold-moving. In this paper, we study this combination and demonstrate its competitiveness. Contrary to the other resampling methods, we preserve the natural class distribution of the data resulting in well-calibrated posterior probabilities. Additionally, we extend the proposed method to handle multiclass data. We validated our method on binary and multiclass benchmark data sets by using both, decision trees and neural networks as base classifiers. We perform analyses that provide insights into the proposed method.
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Affiliation(s)
- Guillem Collell
- MIT Sloan Neuroeconomics Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Computer Science Department, KU Leuven, Heverlee 3001, Belgium
| | - Drazen Prelec
- MIT Sloan Neuroeconomics Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Economics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Kaustubh R Patil
- MIT Sloan Neuroeconomics Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich 52425, Germany
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Patil KR, Goyal SN, Sharma C, Patil CR, Ojha S. Phytocannabinoids for Cancer Therapeutics: Recent Updates and Future Prospects. Curr Med Chem 2016; 22:3472-501. [PMID: 26179998 DOI: 10.2174/0929867322666150716115057] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 06/24/2015] [Accepted: 07/13/2015] [Indexed: 11/22/2022]
Abstract
Phytocannabinoids (pCBs) are lipid-soluble phytochemicals present in the plant, Cannabis sativa L. and non-cannabis plants which have a long history in recreation and traditional medicine. The plant and the constituents isolated were central in the discovery of the endocannabinoid system (ECS), the most new target for drug discovery. The ECS includes two G-protein-coupled receptors; the cannabinoid receptors-1 and -2 (CB1 and CB2) for marijuana's psychoactive principle Δ(9)-tetrahydrocannabinol (Δ(9)-THC), their endogenous small lipid ligands; namely anandamide (AEA) and 2-arachidonoylglycerol (2-AG), also known as endocannabinoids and the enzymes for endocannabinoid biosynthesis and degradation such as fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL). The ECS has been suggested as a pro-homeostatic and pleiotropic signaling system activated in a time- and tissue-specific way during pathological conditions including cancer. Targeting the CB1 receptors becomes a concern because of adverse psychotropic reactions. Hence, targeting the CB2 receptors or the endocannabinoid metabolizing enzymes by pCBs obtained from plants lacking psychotropic adverse reactions has garnered interest in drug discovery. These pCBs derived from plants appear safe and effective with a wider access and availability. In the recent years, several pCBs derived other than non-cannabinoid plants have been reported to bind to and functionally interact with cannabinoid receptors and appear promising candidate for drug development including cancer therapeutics. Several of them also targets the endocannabinoid metabolizing enzymes that control endocannabinoid levels. In this article, we summarize and critically discuss the updates and future prospects of the pCBs as novel and promising candidates for cancer therapeutics.
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Affiliation(s)
| | | | | | | | - S Ojha
- Department of Pharmacology and Therapeutics, College of Medicine and Health Sciences United Arab Emirates University, Al Ain, United Arab Emirates, UAE.
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Abstract
Understanding the evolutionary relationships between organisms is vital for their in-depth study. Gene-based methods are often used to infer such relationships, which are not without drawbacks. One can now attempt to use genome-scale information, because of the ever increasing number of genomes available. This opportunity also presents a challenge in terms of computational efficiency. Two fundamentally different methods are often employed for sequence comparisons, namely alignment-based and alignment-free methods. Alignment-free methods rely on the genome signature concept and provide a computationally efficient way that is also applicable to nonhomologous sequences. The genome signature contains evolutionary signal as it is more similar for closely related organisms than for distantly related ones. We used genome-scale sequence information to infer taxonomic distances between organisms without additional information such as gene annotations. We propose a method to improve genome tree inference by learning specific distance metrics over the genome signature for groups of organisms with similar phylogenetic, genomic, or ecological properties. Specifically, our method learns a Mahalanobis metric for a set of genomes and a reference taxonomy to guide the learning process. By applying this method to more than a thousand prokaryotic genomes, we showed that, indeed, better distance metrics could be learned for most of the 18 groups of organisms tested here. Once a group-specific metric is available, it can be used to estimate the taxonomic distances for other sequenced organisms from the group. This study also presents a large scale comparison between 10 methods--9 alignment-free and 1 alignment-based.
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Affiliation(s)
- Kaustubh R Patil
- Max-Planck Research Group for Computational Genomics and Epidemiology, Max-Planck Institute for Informatics, Saarbrücken, Germany.
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Patil KR, Haider P, Pope PB, Turnbaugh PJ, Morrison M, Scheffer T, McHardy AC. Taxonomic metagenome sequence assignment with structured output models. Nat Methods 2011. [PMID: 21358620 DOI: 10.1038/nmeth0311-191.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Patil KR, Sathaye SD, Hawaldar R, Sathe BR, Mandale AB, Mitra A. Copper phthalocyanine films deposited by liquid–liquid interface recrystallization technique (LLIRCT). J Colloid Interface Sci 2007; 315:747-52. [PMID: 17707393 DOI: 10.1016/j.jcis.2007.07.031] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2006] [Revised: 05/23/2007] [Accepted: 07/14/2007] [Indexed: 11/19/2022]
Abstract
The simple recrystallization process is innovatively used to obtain the nanoparticles of copper phthalocyanine by a simple method. Liquid-liquid interface recrystallization technique (LLIRCT) has been employed successfully to produce small sized copper phthalocyanine nanoparticles with diameter between 3-5 nm. The TEM-SAED studies revealed the formation of 3-5 nm sized with beta-phase dominated mixture of alpha and beta copper phthalocyanine nanoparticles. The XRD, SEM, and the UV-vis studies were further carried out to confirm the formation of copper phthalocyanine thin films. The cyclic voltametry (CV) studies conclude that redox reaction is totally reversible one electron transfer process. The process is attributed to Cu(II)/Cu(I) redox reaction.
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Affiliation(s)
- K R Patil
- Center for Materials Characterization, National Chemical Laboratory, Pune 411008, India.
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50
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Abstract
In this article, we present a graph theoretic method to visualize and analyze the system behavior under different operating conditions. The system attributes (or variables) are the nodes in the graphs, and partial correlation between a pair of attributes defines the distance between corresponding nodes, resulting in a fully connected graph. Then, the redundant links are reduced using Pathfinder Network Scaling technique to uncover the latent network structure. We use a simulated biological reactor dataset in normal and faulty operation to validate our method. The method is general and can be used to analyze several different systems.
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