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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; 34:531-539. [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] [MESH Headings] [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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Peraza JA, Salo T, Riedel MC, Bottenhorn KL, Poline JB, Dockès J, Kent JD, Bartley JE, Flannery JS, Hill-Bowen LD, Lobo RP, Poudel R, Ray KL, Robinson JL, Laird RW, Sutherland MT, de la Vega A, Laird AR. Methods for decoding cortical gradients of functional connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.01.551505. [PMID: 37577598 PMCID: PMC10418206 DOI: 10.1101/2023.08.01.551505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network and representative of abstract functioning at the other. The functional significance and interpretation of macroscale gradients remains a central topic of discussion in the neuroimaging community, with some studies demonstrating that gradients may be described using meta-analytic functional decoding techniques. However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance. Here, we conducted a comprehensive series of analyses to investigate and improve the framework of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient segmentation and functional decoding. We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding functional connectivity gradients. Finally, we proposed a method for decoding additional components of the gradient decomposition. The current work aims to provide recommendations on best practices and flexible methods for gradient-based functional decoding of fMRI data.
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Affiliation(s)
- Julio A. Peraza
- Department of Physics, Florida International University, Miami, FL, USA
| | - Taylor Salo
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Katherine L. Bottenhorn
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Jean-Baptiste Poline
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jérôme Dockès
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - James D. Kent
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | | | - Jessica S. Flannery
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | | | | | - Ranjita Poudel
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, USA
| | - Kimberly L. Ray
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | | | - Robert W. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | | | | | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
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Smith DD, Meca A, Bottenhorn KL, Bartley JE, Riedel MC, Salo T, Peraza JA, Laird RW, Pruden SM, Sutherland MT, Brewe E, Laird AR. Task-based attentional and default mode connectivity associated with science and math anxiety profiles among university physics students. Trends Neurosci Educ 2023; 32:100204. [PMID: 37689430 PMCID: PMC10501206 DOI: 10.1016/j.tine.2023.100204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 05/15/2023] [Accepted: 05/24/2023] [Indexed: 09/11/2023]
Abstract
PURPOSE Attentional control theory (ACT) posits that elevated anxiety increases the probability of re-allocating cognitive resources needed to complete a task to processing anxiety-related stimuli. This process impairs processing efficiency and can lead to reduced performance effectiveness. Science, technology, engineering, and math (STEM) students frequently experience anxiety about their coursework, which can interfere with learning and performance and negatively impact student retention and graduation rates. The objective of this study was to extend the ACT framework to investigate the neurobiological associations between science and math anxiety and cognitive performance among 123 physics undergraduate students. PROCEDURES Latent profile analysis (LPA) identified four profiles of science and math anxiety among STEM students, including two profiles that represented the majority of the sample (Low Science and Math Anxiety; 59.3% and High Math Anxiety; 21.9%) and two additional profiles that were not well represented (High Science and Math Anxiety; 6.5% and High Science Anxiety; 4.1%). Students underwent a functional magnetic resonance imaging (fMRI) session in which they performed two tasks involving physics cognition: the Force Concept Inventory (FCI) task and the Physics Knowledge (PK) task. FINDINGS No significant differences were observed in FCI or PK task performance between High Math Anxiety and Low Science and Math Anxiety students. During the three phases of the FCI task, we found no significant brain connectivity differences during scenario and question presentation, yet we observed significant differences during answer selection within and between the dorsal attention network (DAN), ventral attention network (VAN), and default mode network (DMN). Further, we found significant group differences during the PK task were limited to the DAN, including DAN-VAN and within-DAN connectivity. CONCLUSIONS These results highlight the different cognitive processes required for physics conceptual reasoning compared to physics knowledge retrieval, provide new insight into the underlying brain dynamics associated with anxiety and physics cognition, and confirm the relevance of ACT theory for science and math anxiety.
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Affiliation(s)
- Donisha D Smith
- Department of Psychology, Florida International University, Miami, FL, United States of America.
| | - Alan Meca
- Department of Psychology, University of Texas San Antonio, San Antonio, United States of America
| | - Katherine L Bottenhorn
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Jessica E Bartley
- Department of Physics, Florida International University, Miami, FL, United States of America
| | - Michael C Riedel
- Department of Physics, Florida International University, Miami, FL, United States of America
| | - Taylor Salo
- Department of Psychology, Florida International University, Miami, FL, United States of America
| | - Julio A Peraza
- Department of Physics, Florida International University, Miami, FL, United States of America
| | - Robert W Laird
- Department of Physics, Florida International University, Miami, FL, United States of America
| | - Shannon M Pruden
- Department of Psychology, Florida International University, Miami, FL, United States of America
| | - Matthew T Sutherland
- Department of Psychology, Florida International University, Miami, FL, United States of America
| | - Eric Brewe
- Department of Physics, Drexel University, Philadelphia, PA, United States of America
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, United States of America
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Clark CAC, Helikar T, Dauer J. Simulating a Computational Biological Model, Rather Than Reading, Elicits Changes in Brain Activity during Biological Reasoning. CBE LIFE SCIENCES EDUCATION 2020; 19:ar45. [PMID: 32870080 PMCID: PMC8711807 DOI: 10.1187/cbe.19-11-0237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 06/30/2020] [Accepted: 07/09/2020] [Indexed: 06/08/2023]
Abstract
The creation and analysis of models is integral to all scientific disciplines, and modeling is considered a core competency in undergraduate biology education. There remains a gap in understanding how modeling activities may support changes in students' neural representations. The aim of this study was to evaluate the effects of simulating a model on undergraduates' behavioral accuracy and neural response patterns when reasoning about biological systems. During brief tutorials, students (n = 30) either simulated a computer model or read expert analysis of a gene regulatory system. Subsequently, students underwent functional magnetic resonance imaging while responding to system-specific questions and system-general questions about modeling concepts. Although groups showed similar behavioral accuracy, the Simulate group showed higher levels of activation than the Read group in right cuneal and postcentral regions during the system-specific task and in the posterior insula and cingulate gyrus during the system-general task. Students' behavioral accuracy during the system-specific task correlated with lateral prefrontal brain activity independent of instruction group. Findings highlight the sensitivity of neuroimaging methods for identifying changes in representations that may not be evident at the behavioral level. This work provides a foundation for research on how distinct pedagogical approaches may affect the neural networks students engage when reasoning about biological phenomena.
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Affiliation(s)
- Caron A. C. Clark
- Department of Educational Psychology, University of Nebraska–Lincoln, Lincoln, NE 68503
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, 68583
| | - Joseph Dauer
- School of Natural Resources, University of Nebraska–Lincoln, Lincoln, NE 68503
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