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Sundermann B, Pfleiderer B, McLeod A, Mathys C. Seeing more than the Tip of the Iceberg: Approaches to Subthreshold Effects in Functional Magnetic Resonance Imaging of the Brain. Clin Neuroradiol 2024:10.1007/s00062-024-01422-2. [PMID: 38842737 DOI: 10.1007/s00062-024-01422-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 05/05/2024] [Indexed: 06/07/2024]
Abstract
Many functional magnetic resonance imaging (fMRI) studies and presurgical mapping applications rely on mass-univariate inference with subsequent multiple comparison correction. Statistical results are frequently visualized as thresholded statistical maps. This approach has inherent limitations including the risk of drawing overly-selective conclusions based only on selective results passing such thresholds. This article gives an overview of both established and newly emerging scientific approaches to supplement such conventional analyses by incorporating information about subthreshold effects with the aim to improve interpretation of findings or leverage a wider array of information. Topics covered include neuroimaging data visualization, p-value histogram analysis and the related Higher Criticism approach for detecting rare and weak effects. Further examples from multivariate analyses and dedicated Bayesian approaches are provided.
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Affiliation(s)
- Benedikt Sundermann
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus Oldenburg, Universitätsmedizin Oldenburg, Steinweg 13-17, 26122, Oldenburg, Germany.
- Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
- Clinic of Radiology, Medical Faculty, University of Münster, Münster, Germany.
| | - Bettina Pfleiderer
- Clinic of Radiology, Medical Faculty, University of Münster, Münster, Germany
| | - Anke McLeod
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus Oldenburg, Universitätsmedizin Oldenburg, Steinweg 13-17, 26122, Oldenburg, Germany
| | - Christian Mathys
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus Oldenburg, Universitätsmedizin Oldenburg, Steinweg 13-17, 26122, Oldenburg, Germany
- Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
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Taylor PA, Reynolds RC, Calhoun V, Gonzalez-Castillo J, Handwerker DA, Bandettini PA, Mejia AF, Chen G. Highlight results, don't hide them: Enhance interpretation, reduce biases and improve reproducibility. Neuroimage 2023; 274:120138. [PMID: 37116766 PMCID: PMC10233921 DOI: 10.1016/j.neuroimage.2023.120138] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 04/05/2023] [Accepted: 04/26/2023] [Indexed: 04/30/2023] Open
Abstract
Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples and extensions of this approach for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams' results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice-focusing on highlighting results, rather than hiding all but the strongest ones-can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses.
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Affiliation(s)
- Paul A Taylor
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, MD, USA.
| | | | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, and Emory University, Atlanta, GA, USA
| | | | | | - Peter A Bandettini
- Section on Functional Imaging Methods, NIMH, NIH, Bethesda, MD, USA; Functional MRI Core Facility, NIMH, NIH, Bethesda, MD, USA
| | | | - Gang Chen
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, MD, USA
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Ganesan S, Beyer E, Moffat B, Van Dam NT, Lorenzetti V, Zalesky A. Focused attention meditation in healthy adults: A systematic review and meta-analysis of cross-sectional functional MRI studies. Neurosci Biobehav Rev 2022; 141:104846. [PMID: 36067965 DOI: 10.1016/j.neubiorev.2022.104846] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/17/2022] [Accepted: 08/26/2022] [Indexed: 10/14/2022]
Abstract
Meditation trains the mind to focus attention towards an object or experience. Among different meditation techniques, focused attention meditation is considered foundational for more advanced practices. Despite renewed interest in its functional neural correlates, there is no unified neurocognitive model of focused attention meditation developed via quantitative synthesis of contemporary literature. Hence, we performed a quantitative systematic review and meta-analysis of all functional MRI studies examining focussed attention meditation. Following PRISMA guidelines, 28 studies were included in this review, of which 10 studies (200 participants) were amenable to activation likelihood estimation meta-analysis. We found that regions comprising three key functional brain networks i.e., Default-mode, Salience, and Executive Control, were consistently implicated in focused attention meditation. Furthermore, meditation expertise, mindfulness levels and attentional skills were found to significantly influence the magnitude, but not regional extent, of activation and functional connectivity in these networks. Aggregating all evidence, we present a unified neurocognitive brain-network model of focused attention meditation.
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Affiliation(s)
- Saampras Ganesan
- Melbourne Neuropsychiatry Centre, Carlton, Victoria 3053, Australia; Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia.
| | - Emillie Beyer
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Fitzroy, Victoria 3065, Australia.
| | - Bradford Moffat
- Melbourne Brain Centre Imaging Unit, Department of Radiology, The University of Melbourne, Parkville, Victoria 3052, Australia.
| | - Nicholas T Van Dam
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria 3010, Australia.
| | - Valentina Lorenzetti
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Fitzroy, Victoria 3065, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Carlton, Victoria 3053, Australia; Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia.
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
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