1
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Janet R, Smallwood J, Hutcherson CA, Plassmann H, Mckeown B, Tusche A. Body mass index-dependent shifts along large-scale gradients in human cortical organization explain dietary regulatory success. Proc Natl Acad Sci U S A 2024; 121:e2314224121. [PMID: 38648482 PMCID: PMC11067012 DOI: 10.1073/pnas.2314224121] [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: 08/19/2023] [Accepted: 03/14/2024] [Indexed: 04/25/2024] Open
Abstract
Making healthy dietary choices is essential for keeping weight within a normal range. Yet many people struggle with dietary self-control despite good intentions. What distinguishes neural processing in those who succeed or fail to implement healthy eating goals? Does this vary by weight status? To examine these questions, we utilized an analytical framework of gradients that characterize systematic spatial patterns of large-scale neural activity, which have the advantage of considering the entire suite of processes subserving self-control and potential regulatory tactics at the whole-brain level. Using an established laboratory food task capturing brain responses in natural and regulatory conditions (N = 123), we demonstrate that regulatory changes of dietary brain states in the gradient space predict individual differences in dietary success. Better regulators required smaller shifts in brain states to achieve larger goal-consistent changes in dietary behaviors, pointing toward efficient network organization. This pattern was most pronounced in individuals with lower weight status (low-BMI, body mass index) but absent in high-BMI individuals. Consistent with prior work, regulatory goals increased activity in frontoparietal brain circuits. However, this shift in brain states alone did not predict variance in dietary success. Instead, regulatory success emerged from combined changes along multiple gradients, showcasing the interplay of different large-scale brain networks subserving dietary control and possible regulatory strategies. Our results provide insights into how the brain might solve the problem of dietary control: Dietary success may be easier for people who adopt modes of large-scale brain activation that do not require significant reconfigurations across contexts and goals.
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Affiliation(s)
- Rémi Janet
- Department of Psychology, Queen’s University, Kingston, ONK7L 3N6, Canada
| | - Jonathan Smallwood
- Department of Psychology, Queen’s University, Kingston, ONK7L 3N6, Canada
| | - Cendri A. Hutcherson
- Department of Psychology, University of Toronto, Toronto, ONM5S 2E5, Canada
- Department of Marketing, Rotman School of Management, University of Toronto, Toronto, ONM5S 3E6, Canada
| | - Hilke Plassmann
- Marketing Area, INSEAD, FontainebleauF-77300, France
- Control-Interoception-Attention Team, Paris Brain Institute (ICM), Sorbonne University, Paris75013, France
| | - Bronte Mckeown
- Department of Psychology, Queen’s University, Kingston, ONK7L 3N6, Canada
| | - Anita Tusche
- Department of Psychology, Queen’s University, Kingston, ONK7L 3N6, Canada
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA91125
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2
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Gerosa M, Canessa N, Morawetz C, Mattavelli G. Cognitive reappraisal of food craving and emotions: a coordinate-based meta-analysis of fMRI studies. Soc Cogn Affect Neurosci 2024; 19:nsad077. [PMID: 38113382 PMCID: PMC10868133 DOI: 10.1093/scan/nsad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/02/2023] [Accepted: 12/18/2023] [Indexed: 12/21/2023] Open
Abstract
Growing evidence supports the effectiveness of cognitive reappraisal in down-regulating food desire. Still, the neural bases of food craving down-regulation via reappraisal, as well as their degree of overlap vs specificity compared with emotion down-regulation, remain unclear. We addressed this gap through activation likelihood estimation meta-analyses of neuroimaging studies on the neural bases of (i) food craving down-regulation and (ii) emotion down-regulation, alongside conjunction and subtraction analyses among the resulting maps. Exploratory meta-analyses on activations related to food viewing compared with active regulation and up-regulation of food craving have also been performed. Food and emotion down-regulation via reappraisal consistently engaged overlapping activations in dorsolateral and ventrolateral prefrontal, posterior parietal, pre-supplementary motor and lateral posterior temporal cortices, mainly in the left hemisphere. Its distinctive association with the right anterior/posterior insula and left inferior frontal gyrus suggests that food craving down-regulation entails a more extensive integration of interoceptive information about bodily states and greater inhibitory control over the appetitive urge towards food compared with emotion down-regulation. This evidence is suggestive of unique interoceptive and motivational components elicited by food craving reappraisal, associated with distinctive patterns of fronto-insular activity. These results might inform theoretical models of food craving regulation and prompt novel therapeutic interventions for obesity and eating disorders.
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Affiliation(s)
- Marta Gerosa
- IUSS Cognitive Neuroscience (ICON) Center, Scuola Universitaria Superiore IUSS, Pavia 27100, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia 27100, Italy
| | - Nicola Canessa
- IUSS Cognitive Neuroscience (ICON) Center, Scuola Universitaria Superiore IUSS, Pavia 27100, Italy
- Istituti Clinici Scientifici Maugeri IRCCS, Cognitive Neuroscience Laboratory of Pavia Institute, Pavia 27100, Italy
| | - Carmen Morawetz
- Department of Psychology, University of Innsbruck, Innsbruck 6020, Austria
| | - Giulia Mattavelli
- IUSS Cognitive Neuroscience (ICON) Center, Scuola Universitaria Superiore IUSS, Pavia 27100, Italy
- Istituti Clinici Scientifici Maugeri IRCCS, Cognitive Neuroscience Laboratory of Pavia Institute, Pavia 27100, Italy
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3
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Hayashi D, Edwards C, Emond JA, Gilbert-Diamond D, Butt M, Rigby A, Masterson TD. What Is Food Noise? A Conceptual Model of Food Cue Reactivity. Nutrients 2023; 15:4809. [PMID: 38004203 PMCID: PMC10674813 DOI: 10.3390/nu15224809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
As GLP-1 receptor agonists, like semaglutide, emerge as effective treatments for weight management, anecdotal reports from patients and clinicians alike point to a reduction in what has been colloquially termed "food noise", as patients report experiencing less rumination and obsessive preoccupation about food. In this narrative review, we discuss concepts used in studies to investigate human eating behavior that can help elucidate and define food noise, particularly food cue reactivity. We propose a conceptual model that summarizes the main factors that have been shown to determine the magnitude of the reactivity elicited by external and internal food cues and how these factors can affect short- and long-term behavioral and clinical outcomes. By integrating key research conducted in this field, the Cue-Influencer-Reactivity-Outcome (CIRO) model of food cue reactivity provides a framework that can be used in future research to design studies and interpret findings related to food noise and food cue reactivity.
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Affiliation(s)
- Daisuke Hayashi
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA 16801, USA (T.D.M.)
| | - Caitlyn Edwards
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA 16801, USA (T.D.M.)
| | - Jennifer A. Emond
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
| | - Diane Gilbert-Diamond
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
| | - Melissa Butt
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA
| | - Andrea Rigby
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA
- Penn State Health, Milton S. Hershey Medical Center, Hershey, PA 17033, USA
| | - Travis D. Masterson
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA 16801, USA (T.D.M.)
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4
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Cho H, Teoh YY, Cunningham WA, Hutcherson CA. Deliberative control is more than just reactive: Insights from sequential sampling models. Behav Brain Sci 2023; 46:e116. [PMID: 37462187 DOI: 10.1017/s0140525x22003120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Activating relevant responses is a key function of automatic processes in De Neys's model; however, what determines the order or magnitude of such activation is ambiguous. Focusing on recently developed sequential sampling models of choice, we argue that proactive control shapes response generation but does not cleanly fit into De Neys's automatic-deliberative distinction, highlighting the need for further model development.
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Affiliation(s)
- Hyuna Cho
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Yi Yang Teoh
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - William A Cunningham
- Department of Psychology, University of Toronto, Toronto, ON, Canada
- Department of Marketing, Rotman School of Management, University of Toronto, Toronto, ON, Canada
| | - Cendri A Hutcherson
- Department of Marketing, Rotman School of Management, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto Scarborough, Scarborough, ON, Canada ; https://torontodecisionneurolab.com
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5
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Guassi Moreira JF, Méndez Leal AS, Waizman YH, Tashjian SM, Galván A, Silvers JA. Value-based neural representations predict social decision preferences. Cereb Cortex 2023:7161774. [PMID: 37183179 DOI: 10.1093/cercor/bhad144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 05/16/2023] Open
Abstract
Social decision-making is omnipresent in everyday life, carrying the potential for both positive and negative consequences for the decision-maker and those closest to them. While evidence suggests that decision-makers use value-based heuristics to guide choice behavior, very little is known about how decision-makers' representations of other agents influence social choice behavior. We used multivariate pattern expression analyses on fMRI data to understand how value-based processes shape neural representations of those affected by one's social decisions and whether value-based encoding is associated with social decision preferences. We found that stronger value-based encoding of a given close other (e.g. parent) relative to a second close other (e.g. friend) was associated with a greater propensity to favor the former during subsequent social decision-making. These results are the first to our knowledge to explicitly show that value-based processes affect decision behavior via representations of close others.
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Affiliation(s)
| | | | - Yael H Waizman
- Department of Psychology, University of Southern California, Los Angeles, CA 90089, USA
| | - Sarah M Tashjian
- Division of the Humanities & Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Adriana Galván
- Department of Psychology, University of California, Los Angeles, CA 90095, USA
| | - Jennifer A Silvers
- Department of Psychology, University of California, Los Angeles, CA 90095, USA
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6
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Koban L, Wager TD, Kober H. A neuromarker for drug and food craving distinguishes drug users from non-users. Nat Neurosci 2023; 26:316-325. [PMID: 36536243 DOI: 10.1038/s41593-022-01228-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/01/2022] [Indexed: 12/24/2022]
Abstract
Craving is a core feature of substance use disorders. It is a strong predictor of substance use and relapse and is linked to overeating, gambling, and other maladaptive behaviors. Craving is measured via self-report, which is limited by introspective access and sociocultural contexts. Neurobiological markers of craving are both needed and lacking, and it remains unclear whether craving for drugs and food involve similar mechanisms. Across three functional magnetic resonance imaging studies (n = 99), we used machine learning to identify a cross-validated neuromarker that predicts self-reported intensity of cue-induced drug and food craving (P < 0.0002). This pattern, which we term the Neurobiological Craving Signature (NCS), includes ventromedial prefrontal and cingulate cortices, ventral striatum, temporal/parietal association areas, mediodorsal thalamus and cerebellum. Importantly, NCS responses to drug versus food cues discriminate drug users versus non-users with 82% accuracy. The NCS is also modulated by a self-regulation strategy. Transfer between separate neuromarkers for drug and food craving suggests shared neurobiological mechanisms. Future studies can assess the discriminant and convergent validity of the NCS and test whether it responds to clinical interventions and predicts long-term clinical outcomes.
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Affiliation(s)
- Leonie Koban
- Paris Brain Institute (ICM), Inserm, CNRS, Sorbonne Université, Paris, France.
- Centre de Recherche en Neurosciences de Lyon (CRNL), CNRS, INSERM, Université Claude Bernard Lyon 1, Bron, France.
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Hedy Kober
- Department of Psychiatry and Psychology, Yale University, New Haven, CT, USA.
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7
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The brain bases of regulation of eating behaviors: the role of reward, executive control, and valuation processes, and new paths to propel the field forward. Curr Opin Behav Sci 2022. [DOI: 10.1016/j.cobeha.2022.101214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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8
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Tsoi L, Burns SM, Falk EB, Tamir DI. The promises and pitfalls of functional magnetic resonance imaging hyperscanning for social interaction research. SOCIAL AND PERSONALITY PSYCHOLOGY COMPASS 2022; 16:e12707. [PMID: 36407123 PMCID: PMC9667901 DOI: 10.1111/spc3.12707] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/23/2022] [Accepted: 08/26/2022] [Indexed: 11/28/2022]
Abstract
Social neuroscience combines tools and perspectives from social psychology and neuroscience to understand how people interact with their social world. Here we discuss a relatively new method-hyperscanning-to study real-time, interactive social interactions using functional magnetic resonance imaging (fMRI). We highlight three contributions that fMRI hyperscanning makes to the study of the social mind: (1) Naturalism: it shifts the focus from tightly-controlled stimuli to more naturalistic social interactions; (2) Multi-person Dynamics: it shifts the focus from individuals as the unit of analysis to dyads and groups; and (3) Neural Resolution: fMRI hyperscanning captures high-resolution neural patterns and dynamics across the whole brain, unlike other neuroimaging hyperscanning methods (e.g., electroencephalogram, functional near-infrared spectroscopy). Finally, we describe the practical considerations and challenges that fMRI hyperscanning researchers must navigate. We hope researchers will harness this powerful new paradigm to address pressing questions in today's society.
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Affiliation(s)
- Lily Tsoi
- School of Psychology and CounselingCaldwell UniversityCaldwellNew JerseyUSA
| | - Shannon M. Burns
- Department of Psychological SciencePomona CollegeClaremontCaliforniaUSA,Department of NeurosciencePomona CollegeClaremontCaliforniaUSA
| | - Emily B. Falk
- Annenberg School for CommunicationUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA,Department of PsychologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA,Wharton Marketing DepartmentUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA,Operations, Information, and Decisions DepartmentUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Diana I. Tamir
- Department of PsychologyPrinceton UniversityPrincetonNew JerseyUSA,Princeton Neuroscience InstitutePrinceton UniversityPrincetonNew JerseyUSA
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9
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Semken C, Rossell D. Specification analysis for technology use and teenager well‐being: Statistical validity and a Bayesian proposal. J R Stat Soc Ser C Appl Stat 2022. [DOI: 10.1111/rssc.12578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Christoph Semken
- Universitat Pompeu Fabra BarcelonaSpain
- Barcelona School of Economics BarcelonaSpain
| | - David Rossell
- Universitat Pompeu Fabra BarcelonaSpain
- Barcelona School of Economics BarcelonaSpain
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10
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Dan O, Wertheimer E, Levy I. A Neuroeconomics Approach to Obesity. Biol Psychiatry 2022; 91:860-868. [PMID: 34861975 PMCID: PMC8960474 DOI: 10.1016/j.biopsych.2021.09.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 11/16/2022]
Abstract
Obesity is a heterogeneous condition that is affected by physiological, behavioral, and environmental factors. Value-based decision making is a useful framework for integrating these factors at the individual level. The disciplines of behavioral economics and reinforcement learning provide tools for identifying specific cognitive and motivational processes that may contribute to the development and maintenance of obesity. Neuroeconomics complements these disciplines by studying the neural mechanisms underlying these processes. We surveyed recent literature on individual decision characteristics that are most frequently implicated in obesity: discounting the value of future outcomes, attitudes toward uncertainty, and learning from rewards and punishments. Our survey highlighted both consistent and inconsistent behavioral findings. These findings underscore the need to examine multiple processes within individuals to identify unique behavioral profiles associated with obesity. Such individual characterization will inform future studies on the neurobiology of obesity as well as the design of effective interventions that are individually tailored.
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Affiliation(s)
- Ohad Dan
- Department of Comparative Medicine, Yale University,Wu-Tsai Institute, Yale University
| | - Emily Wertheimer
- Department of Comparative Medicine, Yale University,Wu-Tsai Institute, Yale University
| | - Ifat Levy
- Department of Comparative Medicine, Yale University, New Haven, Connecticut; Department of Neuroscience, Yale University, New Haven, Connecticut; Department of Psychology, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut.
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11
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Schubert E, Rosenblatt D, Eliby D, Kashima Y, Hogendoorn H, Bode S. Decoding explicit and implicit representations of health and taste attributes of foods in the human brain. Neuropsychologia 2021; 162:108045. [PMID: 34610343 DOI: 10.1016/j.neuropsychologia.2021.108045] [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: 07/12/2021] [Revised: 09/23/2021] [Accepted: 09/29/2021] [Indexed: 11/27/2022]
Abstract
Obesity has become a significant problem word-wide and is strongly linked to poor food choices. Even in healthy individuals, taste perceptions often drive dietary decisions more strongly than healthiness. This study tested whether health and taste representations can be directly decoded from brain activity, both when explicitly considered, and when implicitly processed for decision-making. We used multivariate support vector regression for event-related potentials (as measured by the electroencephalogram) to estimate a regression model predicting ratings of tastiness and healthiness for each participant, based on their neural activity occurring in the first second of food cue processing. In Experiment 1, 37 healthy participants viewed images of various foods and explicitly rated their tastiness and healthiness. In Experiment 2, 89 healthy participants completed a similar rating task, followed by an additional experimental phase, in which they indicated their desire to consume snack foods with no explicit instruction to consider tastiness or healthiness. In Experiment 1 both attributes could be decoded, with taste information being available earlier than health. In Experiment 2, both dimensions were also decodable, and their significant decoding preceded the decoding of decisions (i.e., desire to consume the food). However, in Experiment 2, health representations were decodable earlier than taste representations. These results suggest that health information is activated in the brain during the early stages of dietary decisions, which is promising for designing obesity interventions aimed at quickly activating health awareness.
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Affiliation(s)
- Elektra Schubert
- Melbourne School of Psychological Sciences, The University of Melbourne, Australia
| | - Daniel Rosenblatt
- Melbourne School of Psychological Sciences, The University of Melbourne, Australia
| | - Djamila Eliby
- Melbourne School of Psychological Sciences, The University of Melbourne, Australia
| | - Yoshihisa Kashima
- Melbourne School of Psychological Sciences, The University of Melbourne, Australia
| | - Hinze Hogendoorn
- Melbourne School of Psychological Sciences, The University of Melbourne, Australia
| | - Stefan Bode
- Melbourne School of Psychological Sciences, The University of Melbourne, Australia.
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12
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Kragel PA, Han X, Kraynak TE, Gianaros PJ, Wager TD. Functional MRI Can Be Highly Reliable, but It Depends on What You Measure: A Commentary on Elliott et al. (2020). Psychol Sci 2021; 32:622-626. [PMID: 33685310 PMCID: PMC8258303 DOI: 10.1177/0956797621989730] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 11/27/2020] [Indexed: 01/01/2023] Open
Affiliation(s)
| | - Xiaochun Han
- Department of Psychological and
Brain Sciences, Dartmouth College
| | - Thomas E. Kraynak
- Department of Psychology, Center
for the Neural Basis of Cognition, University of Pittsburgh
| | - Peter J. Gianaros
- Department of Psychology, Center
for the Neural Basis of Cognition, University of Pittsburgh
| | - Tor D. Wager
- Department of Psychological and
Brain Sciences, Dartmouth College
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13
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Klapwijk ET, van den Bos W, Tamnes CK, Raschle NM, Mills KL. Opportunities for increased reproducibility and replicability of developmental neuroimaging. Dev Cogn Neurosci 2021; 47:100902. [PMID: 33383554 PMCID: PMC7779745 DOI: 10.1016/j.dcn.2020.100902] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 11/19/2020] [Accepted: 12/08/2020] [Indexed: 01/08/2023] Open
Abstract
Many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience, in particular developmental neuroimaging. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies involve challenges such as choices about age groupings, lifespan modelling, analyses of longitudinal changes, and data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental neuroimaging, and how preregistration can improve methodological rigor. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.
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Affiliation(s)
- Eduard T Klapwijk
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
| | - Wouter van den Bos
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Max Planck Institute for Human Development, Center for Adaptive Rationality, Berlin, Germany
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
| | - Nora M Raschle
- Jacobs Center for Productive Youth Development at the University of Zurich, Zurich, Switzerland
| | - Kathryn L Mills
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychology, University of Oregon, Eugene, OR, USA
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14
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Cosme D, Lopez RB. Neural Indicators Of Food Cue Reactivity, Regulation, And Valuation And Their Associations With Body Composition And Daily Eating Behavior. Soc Cogn Affect Neurosci 2020; 18:nsaa155. [PMID: 33216123 PMCID: PMC10074773 DOI: 10.1093/scan/nsaa155] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 09/18/2020] [Accepted: 11/20/2020] [Indexed: 02/06/2023] Open
Abstract
Exposure to food cues activates the brain's reward system and undermines efforts to regulate impulses to eat. During explicit regulation, lateral prefrontal cortex activates and modulates activity in reward regions and decreases food cravings. However, it is unclear the extent to which between-person differences in recruitment of regions associated with reward processing, subjective valuation, and regulation during food cue exposure-absent instructions to regulate-predict body composition and daily eating behaviors. In this preregistered study, we pooled data from five fMRI samples (N = 262) to examine whether regions associated with reward, valuation, and regulation, as well as whole-brain pattern expression indexing these processes, were recruited during food cue exposure and associated with body composition and real-world eating behavior. Regression models for a single a priori analytic path indicated that univariate and multivariate measures of reward and valuation were associated with individual differences in BMI and enactment of daily food cravings. Specification curve analyses further revealed reliable associations between univariate and multivariate neural indicators of reactivity, regulation, and valuation, and all outcomes. These findings highlight the utility of these methods to elucidate brain-behavior associations and suggest that multiple processes are implicated in proximal and distal markers of eating behavior.
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Affiliation(s)
- Danielle Cosme
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Richard B Lopez
- Department of Psychology, Bard College, Annandale-on-Hudson, NY 12504, USA
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15
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Giuliani NR, Cosme D, Merchant JS, Dirks B, Berkman ET. Brain Activity Associated With Regulating Food Cravings Predicts Changes in Self-Reported Food Craving and Consumption Over Time. Front Hum Neurosci 2020; 14:577669. [PMID: 33281580 PMCID: PMC7689031 DOI: 10.3389/fnhum.2020.577669] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/28/2020] [Indexed: 01/10/2023] Open
Abstract
Neural patterns associated with viewing energy-dense foods can predict changes in eating-related outcomes. However, most research on this topic is limited to one follow-up time point, and single outcome measures. The present study seeks to add to that literature by employing a more refined assessment of food craving and consumption outcomes along with a more detailed neurobiological model of behavior change over several time points. Here, a community sample of 88 individuals (age: M = 39.17, SD = 3.47; baseline BMI: M = 31.5, SD = 3.9, range 24–42) with higher body mass index (BMI) performed a food craving reactivity and regulation task while undergoing functional magnetic resonance imaging. At that time—and 1, 3, and 6 months later—participants reported craving for and consumption of healthy and unhealthy foods via the Food Craving Inventory (FCI) and ASA24 (N at 6 months = 52–55 depending on the measure). A priori hypotheses that brain activity associated with both viewing and regulating personally desired unhealthy, energy-dense foods would be associated with self-reported craving for and consumption of unhealthy foods at baseline were not supported by the data. Instead, regression models controlling for age, sex, and BMI demonstrated that brain activity across several regions measured while individuals were regulating their desires for unhealthy food was associated with the self-reported craving for and consumption of healthy food. The hypothesis that vmPFC activity would predict patterns of healthier eating was also not supported. Instead, linear mixed models controlling for baseline age and sex, as well as changes in BMI, revealed that more regulation-related activity in the dlPFC, dACC, IFG, and vmPFC at baseline predicted decreases in the craving for and consumption of healthy foods over the course of 6 months.
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Affiliation(s)
- Nicole R Giuliani
- Department of Special Education and Clinical Sciences, Prevention Science Institute, University of Oregon, Eugene, OR, United States
| | - Danielle Cosme
- Communication Neuroscience Lab, Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, United States
| | - Junaid S Merchant
- Developmental Social Cognitive Neuroscience Lab, Neuroscience and Cognitive Science Program, Department of Psychology, University of Maryland, College Park, College Park, MD, United States
| | - Bryce Dirks
- Brain Connectivity and Cognition Lab, Department of Psychology, University of Miami, Miami, FL, United States
| | - Elliot T Berkman
- Social and Affective Neuroscience Lab, Department of Psychology, Center for Translational Neuroscience, University of Oregon, Eugene, OR, United States
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Inagaki TK. Health neuroscience 2.0: integration with social, cognitive and affective neuroscience. Soc Cogn Affect Neurosci 2020; 15:1017-1023. [PMID: 32888307 PMCID: PMC7657452 DOI: 10.1093/scan/nsaa123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 08/27/2020] [Accepted: 08/28/2020] [Indexed: 11/22/2022] Open
Affiliation(s)
- Tristen K Inagaki
- Department of Psychology, San Diego State University, San Diego, USA
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Flournoy JC, Vijayakumar N, Cheng TW, Cosme D, Flannery JE, Pfeifer JH. Improving practices and inferences in developmental cognitive neuroscience. Dev Cogn Neurosci 2020; 45:100807. [PMID: 32759026 PMCID: PMC7403881 DOI: 10.1016/j.dcn.2020.100807] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 06/13/2020] [Accepted: 06/19/2020] [Indexed: 01/19/2023] Open
Abstract
The past decade has seen growing concern about research practices in cognitive neuroscience, and psychology more broadly, that shake our confidence in many inferences in these fields. We consider how these issues affect developmental cognitive neuroscience, with the goal of progressing our field to support strong and defensible inferences from our neurobiological data. This manuscript focuses on the importance of distinguishing between confirmatory versus exploratory data analysis approaches in developmental cognitive neuroscience. Regarding confirmatory research, we discuss problems with analytic flexibility, appropriately instantiating hypotheses, and controlling the error rate given how we threshold data and correct for multiple comparisons. To counterbalance these concerns with confirmatory analyses, we present two complementary strategies. First, we discuss the advantages of working within an exploratory analysis framework, including estimating and reporting effect sizes, using parcellations, and conducting specification curve analyses. Second, we summarize defensible approaches for null hypothesis significance testing in confirmatory analyses, focusing on transparent and reproducible practices in our field. Specific recommendations are given, and templates, scripts, or other resources are hyperlinked, whenever possible.
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Affiliation(s)
- John C Flournoy
- Department of Psychology, University of Oregon, United States; Department of Psychology, Harvard University, United States
| | - Nandita Vijayakumar
- Department of Psychology, University of Oregon, United States; School of Psychology, Deakin University, Australia
| | - Theresa W Cheng
- Department of Psychology, University of Oregon, United States
| | - Danielle Cosme
- Department of Psychology, University of Oregon, United States; Annenberg School for Communication, University of Pennsylvania, United States
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Schmälzle R, Cooper N, O’Donnell MB, Tompson S, Lee S, Cantrell J, Vettel JM, Falk EB. The Effectiveness of Online Messages for Promoting Smoking Cessation Resources: Predicting Nationwide Campaign Effects From Neural Responses in the EX Campaign. Front Hum Neurosci 2020; 14:565772. [PMID: 33100997 PMCID: PMC7546826 DOI: 10.3389/fnhum.2020.565772] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 08/20/2020] [Indexed: 01/14/2023] Open
Abstract
What are the key ingredients that make some persuasive messages resonate with audiences and elicit action, while others fail? Billions of dollars per year are put towards changing human behavior, but it is difficult to know which messages will be the most persuasive in the field. By combining novel neuroimaging techniques and large-scale online data, we examine the role of key health communication variables relevant to motivating action at scale. We exposed a sample of smokers to anti-smoking web-banner messages from a real-world campaign while measuring message-evoked brain response patterns via fMRI, and we also obtained subjective evaluations of each banner. Neural indices were derived based on: (i) message-evoked activity in specific brain regions; and (ii) spatially distributed response patterns, both selected based on prior research and theoretical considerations. Next, we connected the neural and subjective data with an independent, objective outcome of message success, which is the per-banner click-through rate in the real-world campaign. Results show that messages evoking brain responses more similar to signatures of negative emotion and vividness had lower online click-through-rates. This strategy helps to connect and integrate the rapidly growing body of knowledge about brain function with formative research and outcome evaluation of health campaigns, and could ultimately further disease prevention efforts.
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Affiliation(s)
- Ralf Schmälzle
- Department of Communication, College of Communication Arts and Sciences, Michigan State University, East Lansing, MI, United States
| | - Nicole Cooper
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, United States
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Adelphi, MD, United States
| | - Matthew Brook O’Donnell
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, United States
| | - Steven Tompson
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Adelphi, MD, United States
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Sangil Lee
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States
| | - Jennifer Cantrell
- New York University School of Global Public Health, New York, NY, United States
| | - Jean M. Vettel
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Adelphi, MD, United States
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Emily B. Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, United States
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States
- Wharton Marketing Department, University of Pennsylvania, Philadelphia, PA, United States
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