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Constant-Varlet C, Nakai T, Prado J. Intergenerational transmission of brain structure and function in humans: a narrative review of designs, methods, and findings. Brain Struct Funct 2024:10.1007/s00429-024-02804-5. [PMID: 38710874 DOI: 10.1007/s00429-024-02804-5] [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: 01/10/2024] [Accepted: 04/23/2024] [Indexed: 05/08/2024]
Abstract
Children often show cognitive and affective traits that are similar to their parents. Although this indicates a transmission of phenotypes from parents to children, little is known about the neural underpinnings of that transmission. Here, we provide a general overview of neuroimaging studies that explore the similarity between parents and children in terms of brain structure and function. We notably discuss the aims, designs, and methods of these so-called intergenerational neuroimaging studies, focusing on two main designs: the parent-child design and the multigenerational design. For each design, we also summarize the major findings, identify the sources of variability between studies, and highlight some limitations and future directions. We argue that the lack of consensus in defining the parent-child transmission of brain structure and function leads to measurement heterogeneity, which is a challenge for future studies. Additionally, multigenerational studies often use measures of family resemblance to estimate the proportion of variance attributed to genetic versus environmental factors, though this estimate is likely inflated given the frequent lack of control for shared environment. Nonetheless, intergenerational neuroimaging studies may still have both clinical and theoretical relevance, not because they currently inform about the etiology of neuromarkers, but rather because they may help identify neuromarkers and test hypotheses about neuromarkers coming from more standard neuroimaging designs.
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Affiliation(s)
- Charlotte Constant-Varlet
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM U1028 - CNRS UMR5292, Université de Lyon, Bron, France.
| | - Tomoya Nakai
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM U1028 - CNRS UMR5292, Université de Lyon, Bron, France
- Araya Inc., Tokyo, Japan
| | - Jérôme Prado
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM U1028 - CNRS UMR5292, Université de Lyon, Bron, France.
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2
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Demidenko MI, Mumford JA, Poldrack RA. Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585755. [PMID: 38562804 PMCID: PMC10983911 DOI: 10.1101/2024.03.19.585755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples ( N s: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho ) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the Neutral cue. This effect was largest for the Cue Model parameterization, however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.
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3
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Freund MC, Chen R, Chen G, Braver TS. Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.591032. [PMID: 38712215 PMCID: PMC11071497 DOI: 10.1101/2024.04.24.591032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Understanding individual differences in cognitive control is a central goal in psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures in cognitive neuroscience such as response times or task-based fMRI activity. While prior work has pinpointed the source of the issue - the vast amount of cross-trial variability within these measures - no study has rigorously evaluated potential solutions. Here, we do so with one potential way forward: an analytic framework that combines hierarchical Bayesian modeling with multivariate decoding of trial-level fMRI data. Using this framework and longitudinal data from the Dual Mechanisms of Cognitive Control project, we estimated individuals' neural responses associated with cognitive control within a color-word Stroop task, then assessed the reliability of these individuals' responses across a time interval of several months. We show that in many prefrontal and parietal brain regions, test-retest reliability was near maximal, and that only hierarchical models were able to reveal this state of affairs. Further, when compared to traditional univariate contrasts, multivariate decoding enabled individual-level correlations to be estimated with significantly greater precision. We specifically link these improvements in precision to the optimized suppression of cross-trial variability in decoding. Together, these findings not only indicate that cognitive control-related neural responses individuate people in a highly stable manner across time, but also suggest that integrating hierarchical and multivariate models provides a powerful approach for investigating individual differences in cognitive control, one that can effectively address the issue of high-variability measures.
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Affiliation(s)
- Michael C. Freund
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
- Department of Psychological & Brain Sciences, Washington University in St. Louis
| | - Ruiqi Chen
- Division of Biology and Biomedical Sciences, Washington University in St. Louis
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, MD, USA
| | - Todd S. Braver
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
- Department of Psychological & Brain Sciences, Washington University in St. Louis
- Department of Radiology, Washington University in St. Louis
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4
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Wen Z, Pace-Schott EF, Lazar SW, Rosén J, Åhs F, Phelps EA, LeDoux JE, Milad MR. Distributed neural representations of conditioned threat in the human brain. Nat Commun 2024; 15:2231. [PMID: 38472184 PMCID: PMC10933283 DOI: 10.1038/s41467-024-46508-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
Detecting and responding to threat engages several neural nodes including the amygdala, hippocampus, insular cortex, and medial prefrontal cortices. Recent propositions call for the integration of more distributed neural nodes that process sensory and cognitive facets related to threat. Integrative, sensitive, and reproducible distributed neural decoders for the detection and response to threat and safety have yet to be established. We combine functional MRI data across varying threat conditioning and negative affect paradigms from 1465 participants with multivariate pattern analysis to investigate distributed neural representations of threat and safety. The trained decoders sensitively and specifically distinguish between threat and safety cues across multiple datasets. We further show that many neural nodes dynamically shift representations between threat and safety. Our results establish reproducible decoders that integrate neural circuits, merging the well-characterized 'threat circuit' with sensory and cognitive nodes, discriminating threat from safety regardless of experimental designs or data acquisition parameters.
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Affiliation(s)
- Zhenfu Wen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
- Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Edward F Pace-Schott
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Sara W Lazar
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jörgen Rosén
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Åhs
- Department of Psychology and Social Work, Mid Sweden University, Östersund, Sweden
| | | | - Joseph E LeDoux
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
- Center for Neural Science and Department of Psychology, New York University, New York, NY, USA
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
| | - Mohammed R Milad
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA.
- Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA.
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
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5
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Li B, Tong L, Zhang C, Chen P, Wang L, Yan B. Prediction of image interpretation cognitive ability under different mental workloads: a task-state fMRI study. Cereb Cortex 2024; 34:bhae100. [PMID: 38494891 DOI: 10.1093/cercor/bhae100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/19/2024] Open
Abstract
Visual imaging experts play an important role in multiple fields, and studies have shown that the combination of functional magnetic resonance imaging and machine learning techniques can predict cognitive abilities, which provides a possible method for selecting individuals with excellent image interpretation skills. We recorded behavioral data and neural activity of 64 participants during image interpretation tasks under different workloads. Based on the comprehensive image interpretation ability, participants were divided into two groups. general linear model analysis showed that during image interpretation tasks, the high-ability group exhibited higher activation in middle frontal gyrus (MFG), fusiform gyrus, inferior occipital gyrus, superior parietal gyrus, inferior parietal gyrus, and insula compared to the low-ability group. The radial basis function Support Vector Machine (SVM) algorithm shows the most excellent performance in predicting participants' image interpretation abilities (Pearson correlation coefficient = 0.54, R2 = 0.31, MSE = 0.039, RMSE = 0.002). Variable importance analysis indicated that the activation features of the fusiform gyrus and MFG played an important role in predicting this ability. Our study revealed the neural basis related to image interpretation ability when exposed to different mental workloads. Additionally, our results demonstrated the efficacy of machine learning algorithms in extracting neural activation features to predict such ability.
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Affiliation(s)
- Bao Li
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
| | - Li Tong
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
| | - Chi Zhang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
| | - Panpan Chen
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
| | - Linyuan Wang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
| | - Bin Yan
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
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6
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Myerson J, Montelongo M, Rufino K, Patriquin M, Salas R, Oh H. Examination of reward processing dysfunctions in the left dorsal striatum and other brain regions among psychiatric inpatients with substance use. Drug Alcohol Depend 2024; 256:111097. [PMID: 38266574 PMCID: PMC10923081 DOI: 10.1016/j.drugalcdep.2024.111097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Substance misuse is a major public health issue and research has established attenuated reward responses to drug cues in those who misuse substances. Yet, little is known about whether the expectation of natural reinforcers engages distinct brain regions in substance misuse. METHODS Using functional magnetic resonance imaging (fMRI), we delivered juice at expected and unexpected times to examine reward processing dysfunctions. We focused on the responses within the left dorsal striatum (DS) in individuals with high-risk substance use (HRU, n = 65), low-risk substance use (psychiatric controls, PC, n = 65), and healthy controls (HC, n = 65). Additionally, we investigated whether the dysfunction in reward processing within the left DS is correlated with other common psychiatric symptoms. Finally, we conducted a comprehensive analysis of the whole brain to investigate other non-hypothesized brain regions. RESULTS Compared to HC, HRU displayed lower responses to juice delivery (i.e., reward) in the left DS (p <.05). The whole-brain analysis demonstrated that compared to HC, HRU displayed significantly lower responses to reward stimuli in various brain regions, including the bilateral caudate, temporal gyrus, left frontal gyrus, middle frontal gyrus, and right thalamus. LIMITATIONS Participants were individuals with polysubstance use; therefore, we were not able to examine the effects of individual substances. CONCLUSIONS Our findings suggest that HRU displays lower responses to reward stimuli within the left DS and other non-hypothesized brain regions. Our findings may help further elucidate reward processing dysfunctions related to substance misuse.
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Affiliation(s)
| | | | - Katrina Rufino
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA; The Menninger Clinic, Houston, TX, USA; The University of Houston Downtown, Houston, TX 77002, USA
| | - Michelle Patriquin
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA; The Menninger Clinic, Houston, TX, USA
| | - Ramiro Salas
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA; The Menninger Clinic, Houston, TX, USA; Center for Translational Research on Inflammatory Michael E. DeBakey VA Medical Center, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Hyuntaek Oh
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA; The Menninger Clinic, Houston, TX, USA.
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7
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Rasero J, Verstynen TD, DuPont CM, Kraynak TE, Barinas-Mitchell E, Scudder MR, Kamarck TW, Sentis AI, Leckie RL, Gianaros PJ. Stressor-evoked brain activity, cardiovascular reactivity, and subclinical atherosclerosis in midlife adults. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.05.24302236. [PMID: 38370849 PMCID: PMC10871357 DOI: 10.1101/2024.02.05.24302236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Background Cardiovascular responses to psychological stressors have been separately associated with preclinical atherosclerosis and hemodynamic brain activity patterns across different studies and cohorts; however, what has not been established is whether cardiovascular stress responses reliably link indicators of stressor-evoked brain activity and preclinical atherosclerosis that have been measured in the same individuals. Accordingly, the present study used cross-validation and predictive modeling to test for the first time whether stressor-evoked systolic blood pressure (SBP) responses statistically mediated the association between concurrently measured brain activity and a vascular marker of preclinical atherosclerosis in the carotid arteries. Methods 624 midlife adults (aged 28-56 years, 54.97% female) from two different cohorts underwent two information-conflict fMRI tasks, with concurrent SBP measures collected. Carotid artery intima-media thickness (CA-IMT) was measured by ultrasonography. A mediation framework that included harmonization, cross-validation, and penalized principal component regression was then employed, while significant areas in possible direct and indirect effects were identified through bootstrapping. Sensitivity analysis further tested the robustness of findings after accounting for prevailing levels of cardiovascular disease risk and brain imaging data quality control. Results Task-averaged patterns of hemodynamic brain responses exhibited a generalizable association with CA-IMT, which was mediated by an area-under-the-curve measure of aggregate SBP reactivity. Importantly, this effect held in sensitivity analyses. Implicated brain areas in this mediation included the ventromedial prefrontal cortex, anterior cingulate cortex, insula and amygdala. Conclusions These novel findings support a link between stressor-evoked brain activity and preclinical atherosclerosis accounted for by individual differences in corresponding levels of stressor-evoked cardiovascular reactivity.
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Affiliation(s)
- Javier Rasero
- Department of Psychology, Carnegie Mellon University, PA
- School of Data Science, University of Virginia, Charlottesville, VA
| | | | - Caitlin M DuPont
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA
| | - Thomas E Kraynak
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA
| | | | - Mark R Scudder
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA
| | - Thomas W Kamarck
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA
| | - Amy I Sentis
- School of Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Regina L Leckie
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA
| | - Peter J Gianaros
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA
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8
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Kowalczyk OS, Medina S, Tsivaka D, McMahon SB, Williams SCR, Brooks JCW, Lythgoe DJ, Howard MA. Spinal fMRI demonstrates segmental organisation of functionally connected networks in the cervical spinal cord: A test-retest reliability study. Hum Brain Mapp 2024; 45:e26600. [PMID: 38339896 PMCID: PMC10831202 DOI: 10.1002/hbm.26600] [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: 07/07/2023] [Revised: 12/21/2023] [Accepted: 01/04/2024] [Indexed: 02/12/2024] Open
Abstract
Resting functional magnetic resonance imaging (fMRI) studies have identified intrinsic spinal cord activity, which forms organised motor (ventral) and sensory (dorsal) resting-state networks. However, to facilitate the use of spinal fMRI in, for example, clinical studies, it is crucial to first assess the reliability of the method, particularly given the unique anatomical, physiological, and methodological challenges associated with acquiring the data. Here, we characterise functional connectivity relationships in the cervical cord and assess their between-session test-retest reliability in 23 young healthy volunteers. Resting-state networks were estimated in two ways (1) by estimating seed-to-voxel connectivity maps and (2) by calculating seed-to-seed correlations. Seed regions corresponded to the four grey matter horns (ventral/dorsal and left/right) of C5-C8 segmental levels. Test-retest reliability was assessed using the intraclass correlation coefficient. Spatial overlap of clusters derived from seed-to-voxel analysis between sessions was examined using Dice coefficients. Following seed-to-voxel analysis, we observed distinct unilateral dorsal and ventral organisation of cervical spinal resting-state networks that was largely confined in the rostro-caudal extent to each spinal segmental level, with more sparse connections observed between segments. Additionally, strongest correlations were observed between within-segment ipsilateral dorsal-ventral connections, followed by within-segment dorso-dorsal and ventro-ventral connections. Test-retest reliability of these networks was mixed. Reliability was poor when assessed on a voxelwise level, with more promising indications of reliability when examining the average signal within clusters. Reliability of correlation strength between seeds was highly variable, with the highest reliability achieved in ipsilateral dorsal-ventral and dorso-dorsal/ventro-ventral connectivity. However, the spatial overlap of networks between sessions was excellent. We demonstrate that while test-retest reliability of cervical spinal resting-state networks is mixed, their spatial extent is similar across sessions, suggesting that these networks are characterised by a consistent spatial representation over time.
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Affiliation(s)
- Olivia S. Kowalczyk
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
- The Wellcome Centre for Human Neuroimaging, Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Sonia Medina
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
| | - Dimitra Tsivaka
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
- Medical Physics Department, Medical SchoolUniversity of ThessalyLarisaGreece
| | | | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
| | | | - David J. Lythgoe
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
| | - Matthew A. Howard
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
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9
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Flournoy JC, Bryce NV, Dennison MJ, Rodman AM, McNeilly EA, Lurie LA, Bitran D, Reid-Russell A, Vidal Bustamante CM, Madhyastha T, McLaughlin KA. A precision neuroscience approach to estimating reliability of neural responses during emotion processing: Implications for task-fMRI. Neuroimage 2024; 285:120503. [PMID: 38141745 PMCID: PMC10872443 DOI: 10.1016/j.neuroimage.2023.120503] [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: 06/16/2023] [Revised: 12/13/2023] [Accepted: 12/15/2023] [Indexed: 12/25/2023] Open
Abstract
Recent work demonstrating low test-retest reliability of neural activation during fMRI tasks raises questions about the utility of task-based fMRI for the study of individual variation in brain function. Two possible sources of the instability in task-based BOLD signal over time are noise or measurement error in the instrument, and meaningful variation across time within-individuals in the construct itself-brain activation elicited during fMRI tasks. Examining the contribution of these two sources of test-retest unreliability in task-evoked brain activity has far-reaching implications for cognitive neuroscience. If test-retest reliability largely reflects measurement error, it suggests that task-based fMRI has little utility in the study of either inter- or intra-individual differences. On the other hand, if task-evoked BOLD signal varies meaningfully over time, it would suggest that this tool may yet be well suited to studying intraindividual variation. We parse these sources of variance in BOLD signal in response to emotional cues over time and within-individuals in a longitudinal sample with 10 monthly fMRI scans. Test-retest reliability was low, reflecting a lack of stability in between-person differences across scans. In contrast, within-person, within-session internal consistency of the BOLD signal was higher, and within-person fluctuations across sessions explained almost half the variance in voxel-level neural responses. Additionally, monthly fluctuations in neural response to emotional cues were associated with intraindividual variation in mood, sleep, and exposure to stressors. Rather than reflecting trait-like differences across people, neural responses to emotional cues may be more reflective of intraindividual variation over time. These patterns suggest that task-based fMRI may be able to contribute to the study of individual variation in brain function if more attention is given to within-individual variation approaches, psychometrics-beginning with improving reliability beyond the modest estimates observed here, and the validity of task fMRI beyond the suggestive associations reported here.
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Affiliation(s)
| | | | - Meg J Dennison
- Phoenix Australia-Centre for Posttraumatic Mental Health, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
| | | | | | - Lucy A Lurie
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
| | | | | | | | - Tara Madhyastha
- Rescale; Integrated Brain Imaging Center, University of Washington
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10
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Camp CC, Noble S, Scheinost D, Stringaris A, Nielson DM. Test-Retest Reliability of Functional Connectivity in Adolescents With Depression. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:21-29. [PMID: 37734478 PMCID: PMC10843837 DOI: 10.1016/j.bpsc.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 08/26/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023]
Abstract
BACKGROUND The test-retest reliability of functional magnetic resonance imaging is critical to identifying reproducible biomarkers for psychiatric illness. Recent work has shown how reliability limits the observable effect size of brain-behavior associations, hindering detection of these effects. However, while a fast-growing literature has explored both univariate and multivariate reliability in healthy individuals, relatively few studies have explored reliability in populations with psychiatric illnesses or how this interacts with age. METHODS Here, we investigated functional connectivity reliability over the course of 1 year in a longitudinal cohort of 88 adolescents (age at baseline = 15.63 ± 1.29 years; 64 female) with major depressive disorder (MDD) and without MDD (healthy volunteers [HVs]). We compared a univariate metric, intraclass correlation coefficient, and 2 multivariate metrics, fingerprinting and discriminability. RESULTS Adolescents with MDD had marginally higher mean intraclass correlation coefficient (μMDD = 0.34, 95% CI, 0.12-0.54; μHV = 0.27, 95% CI, 0.05-0.52), but both groups had poor average intraclass correlation coefficients (<0.4). Fingerprinting index was greater than chance and did not differ between groups (fingerprinting indexMDD = 0.75; fingerprinting indexHV = 0.91; Poisson tests p < .001). Discriminability indicated high multivariate reliability in both groups (discriminabilityMDD = 0.80; discriminabilityHV = 0.82; permutation tests p < .01). Neither univariate nor multivariate reliability was associated with symptom severity or edge-level effect size of group differences. CONCLUSIONS Overall, we found little evidence for a relationship between depression and reliability of functional connectivity during adolescence. These findings suggest that biomarker identification in depression is not limited due to reliability compared with healthy samples and support the shift toward multivariate analysis for improved power and reliability.
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Affiliation(s)
- Chris C Camp
- Interdepartmental Neuroscience Program, Yale School of Medicine, Yale University, New Haven, Connecticut.
| | - Stephanie Noble
- Department of Psychology, Northeastern University, Boston, Massachusetts; Department of Bioengineering, Northeastern University, Boston, Massachusetts; Center for Cognitive and Brain Health, Northeastern University, Boston, Massachusetts
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics & Data Science, Yale University, New Haven, Connecticut; Child Study Center, Yale School of Medicine, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut
| | - Argyris Stringaris
- Faculty of Brain Sciences, Division of Psychiatry and Psychology and Language Sciences, University College London, London, United Kingdom; 1st Department of Psychiatry, National and Kapodistrian University of Athens, Aiginition Hospital, Athens, Greece
| | - Dylan M Nielson
- Machine Learning Team, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
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11
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Dabbagh A, Horn U, Kaptan M, Mildner T, Müller R, Lepsien J, Weiskopf N, Brooks JCW, Finsterbusch J, Eippert F. Reliability of task-based fMRI in the dorsal horn of the human spinal cord. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.22.572825. [PMID: 38187724 PMCID: PMC10769329 DOI: 10.1101/2023.12.22.572825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The application of functional magnetic resonance imaging (fMRI) to the human spinal cord is still a relatively small field of research and faces many challenges. Here we aimed to probe the limitations of task-based spinal fMRI at 3T by investigating the reliability of spinal cord blood oxygen level dependent (BOLD) responses to repeated nociceptive stimulation across two consecutive days in 40 healthy volunteers. We assessed the test-retest reliability of subjective ratings, autonomic responses, and spinal cord BOLD responses to short heat pain stimuli (1s duration) using the intraclass correlation coefficient (ICC). At the group level, we observed robust autonomic responses as well as spatially specific spinal cord BOLD responses at the expected location, but no spatial overlap in BOLD response patterns across days. While autonomic indicators of pain processing showed good-to-excellent reliability, both β-estimates and z-scores of task-related BOLD responses showed poor reliability across days in the target region (gray matter of the ipsilateral dorsal horn). When taking into account the sensitivity of gradient-echo echo planar imaging (GE-EPI) to draining vein signals by including the venous plexus in the analysis, we observed BOLD responses with good reliability across days. Taken together, these results demonstrate that heat pain stimuli as short as one second are able to evoke a robust and spatially specific BOLD response, which is however strongly variable within participants across time, resulting in low reliability in the dorsal horn gray matter. Further improvements in data acquisition and analysis techniques are thus necessary before event-related spinal cord fMRI as used here can be reliably employed in longitudinal designs or clinical settings.
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Affiliation(s)
- Alice Dabbagh
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ulrike Horn
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Merve Kaptan
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, CA, USA
| | - Toralf Mildner
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Roland Müller
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jöran Lepsien
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, University of Leipzig, Leipzig, Germany
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Jonathan C W Brooks
- School of Psychology, University of East Anglia Wellcome Wolfson Brain Imaging Centre (UWWBIC), Norwich, United Kingdom
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Falk Eippert
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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12
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Clairis N, Lopez-Persem A. Debates on the dorsomedial prefrontal/dorsal anterior cingulate cortex: insights for future research. Brain 2023; 146:4826-4844. [PMID: 37530487 PMCID: PMC10690029 DOI: 10.1093/brain/awad263] [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: 02/24/2023] [Revised: 07/19/2023] [Accepted: 07/22/2023] [Indexed: 08/03/2023] Open
Abstract
The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) is a brain area subject to many theories and debates over its function(s). Even its precise anatomical borders are subject to much controversy. In the past decades, the dmPFC/dACC has been associated with more than 15 different cognitive processes, which sometimes appear quite unrelated (e.g. body perception, cognitive conflict). As a result, understanding what the dmPFC/dACC does has become a real challenge for many neuroscientists. Several theories of this brain area's function(s) have been developed, leading to successive and competitive publications bearing different models, which sometimes contradict each other. During the last two decades, the lively scientific exchanges around the dmPFC/dACC have promoted fruitful research in cognitive neuroscience. In this review, we provide an overview of the anatomy of the dmPFC/dACC, summarize the state of the art of functions that have been associated with this brain area and present the main theories aiming at explaining the dmPFC/dACC function(s). We explore the commonalities and the arguments between the different theories. Finally, we explain what can be learned from these debates for future investigations of the dmPFC/dACC and other brain regions' functions.
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Affiliation(s)
- Nicolas Clairis
- Laboratory of Behavioral Genetics (LGC)- Brain Mind Institute (BMI)- Sciences de la Vie (SV), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Alizée Lopez-Persem
- FrontLab, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Sorbonne University, AP HP, Hôpital de la Pitié Salpêtrière, 75013 Paris, France
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13
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Lengersdorff LL, Wagner IC, Mittmann G, Sastre-Yagüe D, Lüttig A, Olsson A, Petrovic P, Lamm C. Neuroimaging and behavioral evidence that violent video games exert no negative effect on human empathy for pain and emotional reactivity to violence. eLife 2023; 12:e84951. [PMID: 37975654 PMCID: PMC10791126 DOI: 10.7554/elife.84951] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 11/17/2023] [Indexed: 11/19/2023] Open
Abstract
Influential accounts claim that violent video games (VVGs) decrease players' emotional empathy by desensitizing them to both virtual and real-life violence. However, scientific evidence for this claim is inconclusive and controversially debated. To assess the causal effect of VVGs on the behavioral and neural correlates of empathy and emotional reactivity to violence, we conducted a prospective experimental study using functional magnetic resonance imaging (fMRI). We recruited 89 male participants without prior VVG experience. Over the course of two weeks, participants played either a highly violent video game or a non-violent version of the same game. Before and after this period, participants completed an fMRI experiment with paradigms measuring their empathy for pain and emotional reactivity to violent images. Applying a Bayesian analysis approach throughout enabled us to find substantial evidence for the absence of an effect of VVGs on the behavioral and neural correlates of empathy. Moreover, participants in the VVG group were not desensitized to images of real-world violence. These results imply that short and controlled exposure to VVGs does not numb empathy nor the responses to real-world violence. We discuss the implications of our findings regarding the potential and limitations of experimental research on the causal effects of VVGs. While VVGs might not have a discernible effect on the investigated subpopulation within our carefully controlled experimental setting, our results cannot preclude that effects could be found in settings with higher ecological validity, in vulnerable subpopulations, or after more extensive VVG play.
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Affiliation(s)
- Lukas Leopold Lengersdorff
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of ViennaViennaAustria
| | - Isabella C Wagner
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of ViennaViennaAustria
| | - Gloria Mittmann
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of ViennaViennaAustria
| | - David Sastre-Yagüe
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of ViennaViennaAustria
| | - Andre Lüttig
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of ViennaViennaAustria
| | - Andreas Olsson
- Department of Clinical Neuroscience, Division of Psychology, Karolinska InstituteStockholmSweden
| | - Pedrag Petrovic
- Department of Clinical Neuroscience, Division of Psychology, Karolinska InstituteStockholmSweden
| | - Claus Lamm
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of ViennaViennaAustria
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14
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Bajracharya A, Peelle JE. A systematic review of neuroimaging approaches to mapping language in individuals. JOURNAL OF NEUROLINGUISTICS 2023; 68:101163. [PMID: 37637379 PMCID: PMC10449384 DOI: 10.1016/j.jneuroling.2023.101163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Although researchers often rely on group-level fMRI results to draw conclusions about the neurobiology of language, doing so without accounting for the complexities of individual brains may reduce the validity of our findings. Furthermore, understanding brain organization in individuals is critically important for both basic science and clinical translation. To assess the state of single-subject language localization in the functional neuroimaging literature, we carried out a systematic review of studies published through April 2020. Out of 977 papers identified through our search, 121 met our inclusion criteria for reporting single-subject fMRI results (fMRI studies of language in adults that report task-based single-subject statistics). Of these, 20 papers reported using a single-subject test-retest analysis to assess reliability. Thus, we found that a relatively modest number of papers reporting single-subject results quantified single-subject reliability. These varied substantially in acquisition parameters, task design, and reliability measures, creating significant challenges for making comparisons across studies. Future endeavors to optimize the localization of language networks in individuals will benefit from the standardization and broader reporting of reliability metrics for different tasks and acquisition parameters.
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Affiliation(s)
| | - Jonathan E Peelle
- Center for Cognitive and Brain Health, Department of Communication Sciences and Disorders, and Department of Psychology, Northeastern University
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15
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Xing XX, Gao X, Jiang C. Individual Variability of Human Cortical Spontaneous Activity by 3T/7T fMRI. Neuroscience 2023; 528:117-128. [PMID: 37544577 DOI: 10.1016/j.neuroscience.2023.07.032] [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: 09/10/2022] [Revised: 07/29/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023]
Abstract
Mapping variability in cortical spontaneous activity (CSA) is an essential goal of understanding various sources of dark brain energy in human neuroscience. CSA was traditionally characterized using resting-state functional MRI (rfMRI) at 1.5T or 3T magnets while recently with 7T-rfMRI. However, the utility and interpretability of 7T-rfMRI must first be established for its variability. By leveraging rfMRI data from the Human Connectome Project (HCP), we derived CSA metrics with 3T-rfMRI and 7T-rfMRI for the same 84 healthy participants (52 females). The 7T-rfMRI produces different CSA metrics at multiple spatial-scales and their variability from the 3T-rfMRI. These differences were spatially dependent and varied according to specific cortical organization. For the amplitude metric, 7T-rfMRI enhanced its spatial contrasts in the anterior cortex but weakened it in the posterior cortex. An opposite pattern was observed for the connectivity metrics. The reliability changes of these metrics were scale dependent, indicating enhanced reliability for connectivity but weakened reliability for amplitude by 7T-rfMRI. These effects were primarily located in the high-order associate cortex, parsing the corresponding changes in individual differences with respect to 7T-rfMRI: (1) higher connectivity variability between participants and the lower connectivity variability within individual participants, and (2) lower amplitude variability between participants and higher amplitude variability within participants. Our work, for the first time, demonstrated the variability of the human CSA across space, rfMRI settings/platforms, and individuals. We discussed the statistical implications of our findings on CSA-based experimental designs and reproducible neuroscience as well as their translational value for personalized applications.
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Affiliation(s)
- Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing 100124, China.
| | - Xiao Gao
- School of Psychology, Capital Normal University, Beijing 100048, China
| | - Chao Jiang
- Faculty of Psychology, Southwest University, Chongqing 400715, China
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16
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Chen Y, Li CSR. Appetitive and aversive cue reactivities differentiate neural subtypes of alcohol drinkers. ADDICTION NEUROSCIENCE 2023; 7:100089. [PMID: 37483686 PMCID: PMC10358306 DOI: 10.1016/j.addicn.2023.100089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Craving reflects the subjective urge to use drugs and can be triggered by both positive and negative emotional states. No studies have systematically investigated the relative roles of these mechanisms in the pathophysiology of substance misuse. Here, we performed meta-analyses of drug cue-elicited reactivity and win and loss processing in the monetary incentive delay task to identify distinct neural correlates of appetitive and aversive responses to drug cues. We then characterized the appetitive and aversive cue responses in seventy-six alcohol drinkers performing a cue craving task during fMRI. Imaging data were processed according to published routines. The appetitive circuit involved medial cortical regions and the ventral striatum, and the aversive circuit involved the insula, caudate and mid-cingulate cortex. We observed a significant correlation of cue-elicited activity (β estimates) of the appetitive and aversive circuit. However, individuals varied in appetitive and aversive cue responses. From the regression of appetitive (y) vs. aversive (x) β, we identified participants in the top 1/3 each of those with positive and negative residuals as "approach" (n = 15) and "avoidance" (n = 11) and the others as the "mixed" (n = 50) subtype. In clinical characteristics, the avoidance subtype showed higher sensitivity to punishment and, in contrast, the approach subtype showed higher levels of sensation seeking and alcohol expectancy for social and physical pressure. The findings highlighted distinct neural underpinnings of appetitive and aversive components of cue-elicited reactivity and provided evidence for potential subtypes of alcohol drinkers.
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Affiliation(s)
- Yu Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Chiang-Shan R. Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA
- Inter-department Neuroscience Program, Yale University, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06520, USA
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17
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Nebe S, Reutter M, Baker DH, Bölte J, Domes G, Gamer M, Gärtner A, Gießing C, Gurr C, Hilger K, Jawinski P, Kulke L, Lischke A, Markett S, Meier M, Merz CJ, Popov T, Puhlmann LMC, Quintana DS, Schäfer T, Schubert AL, Sperl MFJ, Vehlen A, Lonsdorf TB, Feld GB. Enhancing precision in human neuroscience. eLife 2023; 12:e85980. [PMID: 37555830 PMCID: PMC10411974 DOI: 10.7554/elife.85980] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/23/2023] [Indexed: 08/10/2023] Open
Abstract
Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.
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Affiliation(s)
- Stephan Nebe
- Zurich Center for Neuroeconomics, Department of Economics, University of ZurichZurichSwitzerland
| | - Mario Reutter
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Daniel H Baker
- Department of Psychology and York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Jens Bölte
- Institute for Psychology, University of Münster, Otto-Creuzfeldt Center for Cognitive and Behavioral NeuroscienceMünsterGermany
| | - Gregor Domes
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
- Institute for Cognitive and Affective NeuroscienceTrierGermany
| | - Matthias Gamer
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Anne Gärtner
- Faculty of Psychology, Technische Universität DresdenDresdenGermany
| | - Carsten Gießing
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University of OldenburgOldenburgGermany
| | - Caroline Gurr
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | - Kirsten Hilger
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
- Department of Psychology, Psychological Diagnostics and Intervention, Catholic University of Eichstätt-IngolstadtEichstättGermany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Louisa Kulke
- Department of Developmental with Educational Psychology, University of BremenBremenGermany
| | - Alexander Lischke
- Department of Psychology, Medical School HamburgHamburgGermany
- Institute of Clinical Psychology and Psychotherapy, Medical School HamburgHamburgGermany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Maria Meier
- Department of Psychology, University of KonstanzKonstanzGermany
- University Psychiatric Hospitals, Child and Adolescent Psychiatric Research Department (UPKKJ), University of BaselBaselSwitzerland
| | - Christian J Merz
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University BochumBochumGermany
| | - Tzvetan Popov
- Department of Psychology, Methods of Plasticity Research, University of ZurichZurichSwitzerland
| | - Lara MC Puhlmann
- Leibniz Institute for Resilience ResearchMainzGermany
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Daniel S Quintana
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- NevSom, Department of Rare Disorders & Disabilities, Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of OsloOsloNorway
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | | | - Matthias FJ Sperl
- Department of Clinical Psychology and Psychotherapy, University of GiessenGiessenGermany
- Center for Mind, Brain and Behavior, Universities of Marburg and GiessenGiessenGermany
| | - Antonia Vehlen
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
| | - Tina B Lonsdorf
- Department of Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Psychology, Biological Psychology and Cognitive Neuroscience, University of BielefeldBielefeldGermany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychology, Heidelberg UniversityHeidelbergGermany
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
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18
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Kragel PA, Treadway MT, Admon R, Pizzagalli DA, Hahn EC. A mesocorticolimbic signature of pleasure in the human brain. Nat Hum Behav 2023; 7:1332-1343. [PMID: 37386105 DOI: 10.1038/s41562-023-01639-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 05/22/2023] [Indexed: 07/01/2023]
Abstract
Pleasure is a fundamental driver of human behaviour, yet its neural basis remains largely unknown. Rodent studies highlight opioidergic neural circuits connecting the nucleus accumbens, ventral pallidum, insula and orbitofrontal cortex as critical for the initiation and regulation of pleasure, and human neuroimaging studies exhibit some translational parity. However, whether activation in these regions conveys a generalizable representation of pleasure regulated by opioidergic mechanisms remains unclear. Here we use pattern recognition techniques to develop a human functional magnetic resonance imaging signature of mesocorticolimbic activity unique to states of pleasure. In independent validation tests, this signature is sensitive to pleasant tastes and affect evoked by humour. The signature is spatially co-extensive with mu-opioid receptor gene expression, and its response is attenuated by the opioid antagonist naloxone. These findings provide evidence for a basis of pleasure in humans that is distributed across brain systems.
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Affiliation(s)
- Philip A Kragel
- Department of Psychology, Emory University, Atlanta, GA, USA.
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA.
| | - Michael T Treadway
- Department of Psychology, Emory University, Atlanta, GA, USA
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Roee Admon
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA, USA
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA, USA
| | - Emma C Hahn
- Department of Psychology, Emory University, Atlanta, GA, USA
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19
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Haines N, Sullivan-Toole H, Olino T. From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:822-831. [PMID: 36997406 PMCID: PMC10333448 DOI: 10.1016/j.bpsc.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 01/13/2023]
Abstract
Advances in computational statistics and corresponding shifts in funding initiatives over the past few decades have led to a proliferation of neuroscientific measures being developed in the context of mental health research. Although such measures have undoubtedly deepened our understanding of neural mechanisms underlying cognitive, affective, and behavioral processes associated with various mental health conditions, the clinical utility of such measures remains underwhelming. Recent commentaries point toward the poor reliability of neuroscientific measures to partially explain this lack of clinical translation. Here, we provide a concise theoretical overview of how unreliability impedes clinical translation of neuroscientific measures; discuss how various modeling principles, including those from hierarchical and structural equation modeling frameworks, can help to improve reliability; and demonstrate how to combine principles of hierarchical and structural modeling within the generative modeling framework to achieve more reliable, generalizable measures of brain-behavior relationships for use in mental health research.
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Affiliation(s)
- Nathaniel Haines
- Department of Data Science, Bayesian Beginnings LLC, Columbus, Ohio.
| | | | - Thomas Olino
- Department of Psychology, Temple University, Philadelphia, Pennsylvania
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20
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Hardi FA, Goetschius LG, McLoyd V, Lopez‐Duran NL, Mitchell C, Hyde LW, Beltz AM, Monk CS. Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID-19 economic adversity. J Child Psychol Psychiatry 2023; 64:918-929. [PMID: 36579796 PMCID: PMC9880614 DOI: 10.1111/jcpp.13749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/10/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Stressful events, such as the COVID-19 pandemic, are major contributors to anxiety and depression, but only a subset of individuals develop psychopathology. In a population-based sample (N = 174) with a high representation of marginalized individuals, this study examined adolescent functional network connectivity as a marker of susceptibility to anxiety and depression in the context of adverse experiences. METHODS Data-driven network-based subgroups were identified using an unsupervised community detection algorithm within functional neural connectivity. Neuroimaging data collected during emotion processing (age 15) were extracted from a priori regions of interest linked to anxiety and depression. Symptoms were self-reported at ages 15, 17, and 21 (during COVID-19). During COVID-19, participants reported on pandemic-related economic adversity. Differences across subgroup networks were first examined, then subgroup membership and subgroup-adversity interaction were tested to predict change in symptoms over time. RESULTS Two subgroups were identified: Subgroup A, characterized by relatively greater neural network variation (i.e., heterogeneity) and density with more connections involving the amygdala, subgenual cingulate, and ventral striatum; and the more homogenous Subgroup B, with more connections involving the insula and dorsal anterior cingulate. Accounting for initial symptoms, subgroup A individuals had greater increases in symptoms across time (β = .138, p = .042), and this result remained after adjusting for additional covariates (β = .194, p = .023). Furthermore, there was a subgroup-adversity interaction: compared with Subgroup B, Subgroup A reported greater anxiety during the pandemic in response to reported economic adversity (β = .307, p = .006), and this remained after accounting for initial symptoms and many covariates (β = .237, p = .021). CONCLUSIONS A subgrouping algorithm identified young adults who were susceptible to adversity using their personalized functional network profiles derived from a priori brain regions. These results highlight potential prospective neural signatures involving heterogeneous emotion networks that predict individuals at the greatest risk for anxiety when experiencing adverse events.
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Affiliation(s)
| | | | - Vonnie McLoyd
- Department of PsychologyUniversity of MichiganAnn ArborMIUSA
| | | | - Colter Mitchell
- Survey Research Center of the Institute for Social ResearchUniversity of MichiganAnn ArborMIUSA
- Population Studies Center of the Institute for Social ResearchUniversity of MichiganAnn ArborMIUSA
| | - Luke W. Hyde
- Department of PsychologyUniversity of MichiganAnn ArborMIUSA
- Survey Research Center of the Institute for Social ResearchUniversity of MichiganAnn ArborMIUSA
| | | | - Christopher S. Monk
- Department of PsychologyUniversity of MichiganAnn ArborMIUSA
- Survey Research Center of the Institute for Social ResearchUniversity of MichiganAnn ArborMIUSA
- Neuroscience Graduate Program University of MichiganAnn ArborMIUSA
- Department of PsychiatryUniversity of MichiganAnn ArborMIUSA
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21
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Grogans SE, Bliss-Moreau E, Buss KA, Clark LA, Fox AS, Keltner D, Cowen AS, Kim JJ, Kragel PA, MacLeod C, Mobbs D, Naragon-Gainey K, Fullana MA, Shackman AJ. The Nature and Neurobiology of Fear and Anxiety: State of the Science and Opportunities for Accelerating Discovery. Neurosci Biobehav Rev 2023:105237. [PMID: 37209932 DOI: 10.1016/j.neubiorev.2023.105237] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/11/2023] [Accepted: 05/13/2023] [Indexed: 05/22/2023]
Abstract
Fear and anxiety play a central role in mammalian life, and there is considerable interest in clarifying their nature, identifying their biological underpinnings, and determining their consequences for health and disease. Here we provide a roundtable discussion on the nature and biological bases of fear- and anxiety-related states, traits, and disorders. The discussants include scientists familiar with a wide variety of populations and a broad spectrum of techniques. The goal of the roundtable was to take stock of the state of the science and provide a roadmap to the next generation of fear and anxiety research. Much of the discussion centered on the key challenges facing the field, the most fruitful avenues for future research, and emerging opportunities for accelerating discovery, with implications for scientists, funders, and other stakeholders. Understanding fear and anxiety is a matter of practical importance. Anxiety disorders are a leading burden on public health and existing treatments are far from curative, underscoring the urgency of developing a deeper understanding of the factors governing threat-related emotions.
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Affiliation(s)
| | - Eliza Bliss-Moreau
- Department of Psychology; California National Primate Research Center, University of California, Davis, CA 95616, USA
| | - Kristin A Buss
- Department of Psychology, The Pennsylvania State University, University Park, PA 16802 USA
| | - Lee Anna Clark
- Department of Psychology, University of Notre Dame, Notre Dame, IN 46556 USA
| | - Andrew S Fox
- Department of Psychology; California National Primate Research Center, University of California, Davis, CA 95616, USA
| | - Dacher Keltner
- Department of Psychology, University of California, Berkeley, Berkeley, CA 94720 USA
| | | | - Jeansok J Kim
- Department of Psychology, University of Washington, Seattle, WA 98195 USA
| | - Philip A Kragel
- Department of Psychology, Emory University, Atlanta, Georgia 30322 USA
| | - Colin MacLeod
- Centre for the Advancement of Research on Emotion, School of Psychological Science, The University of Western Australia, Perth, WA 6009, Australia
| | - Dean Mobbs
- Department of Humanities and Social Sciences; Computation and Neural Systems Program, California Institute of Technology, Pasadena, California 91125 USA
| | - Kristin Naragon-Gainey
- School of Psychological Science, University of Western Australia, Perth, WA 6009, Australia
| | - Miquel A Fullana
- Adult Psychiatry and Psychology Department, Institute of Neurosciences, Hospital Clinic, Barcelona, Spain; Imaging of Mood- and Anxiety-Related Disorders Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer, CIBERSAM, University of Barcelona, Barcelona, Spain
| | - Alexander J Shackman
- Department of Psychology; Neuroscience and Cognitive Science Program; Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742 USA.
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22
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Lopez KL, Monachino AD, Vincent KM, Peck FC, Gabard-Durnam LJ. Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities. Neuroimage 2023; 275:120116. [PMID: 37169118 DOI: 10.1016/j.neuroimage.2023.120116] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/27/2023] [Accepted: 04/13/2023] [Indexed: 05/13/2023] Open
Abstract
Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.
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Affiliation(s)
- K L Lopez
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - A D Monachino
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - K M Vincent
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - F C Peck
- University of California, Los Angeles, Los Angeles, CA, United States
| | - L J Gabard-Durnam
- Northeastern University, 360 Huntington Ave, Boston, MA, United States.
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23
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Tambini A, Miller J, Ehlert L, Kiyonaga A, D’Esposito M. Structured memory representations develop at multiple time scales in hippocampal-cortical networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.06.535935. [PMID: 37066263 PMCID: PMC10104124 DOI: 10.1101/2023.04.06.535935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Influential views of systems memory consolidation posit that the hippocampus rapidly forms representations of specific events, while neocortical networks extract regularities across events, forming the basis of schemas and semantic knowledge. Neocortical extraction of schematic memory representations is thought to occur on a protracted timescale of months, especially for information that is unrelated to prior knowledge. However, this theorized evolution of memory representations across extended timescales, and differences in the temporal dynamics of consolidation across brain regions, lack reliable empirical support. To examine the temporal dynamics of memory representations, we repeatedly exposed human participants to structured information via sequences of fractals, while undergoing longitudinal fMRI for three months. Sequence-specific activation patterns emerged in the hippocampus during the first 1-2 weeks of learning, followed one week later by high-level visual cortex, and subsequently the medial prefrontal and parietal cortices. Schematic, sequence-general representations emerged in the prefrontal cortex after 3 weeks of learning, followed by the medial temporal lobe and anterior temporal cortex. Moreover, hippocampal and most neocortical representations showed sustained rather than time-limited dynamics, suggesting that representations tend to persist across learning. These results show that specific hippocampal representations emerge early, followed by both specific and schematic representations at a gradient of timescales across hippocampal-cortical networks as learning unfolds. Thus, memory representations do not exist only in specific brain regions at a given point in time, but are simultaneously present at multiple levels of abstraction across hippocampal-cortical networks.
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Affiliation(s)
- Arielle Tambini
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY
| | - Jacob Miller
- Wu Tsai Institute, Department of Psychiatry, Yale University, New Haven, CT
| | - Luke Ehlert
- Department of Neurobiology and Behavior, University of California. Irvine, CA
| | - Anastasia Kiyonaga
- Department of Cognitive Science, University of California, San Diego, CA
| | - Mark D’Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA
- Department of Psychology, University of California, Berkeley, CA
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24
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Contreras-Huerta LS, Coll MP, Bird G, Yu H, Prosser A, Lockwood PL, Murphy J, Crockett MJ, Apps MAJ. Neural representations of vicarious rewards are linked to interoception and prosocial behaviour. Neuroimage 2023; 269:119881. [PMID: 36702212 DOI: 10.1016/j.neuroimage.2023.119881] [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: 08/31/2022] [Revised: 12/12/2022] [Accepted: 01/14/2023] [Indexed: 01/24/2023] Open
Abstract
Every day we constantly observe other people receiving rewards. Theoretical accounts posit that vicarious reward processing might be linked to people's sensitivity to internal body states (interoception) and facilitates a tendency to act prosocially. However, the neural processes underlying the links between vicarious reward processing, interoception, and prosocial behaviour are poorly understood. Previous research has linked vicarious reward processing to the anterior cingulate gyrus (ACCg) and the anterior insula (AI). Can we predict someone's propensity to be prosocial or to be aware of interoceptive signals from variability in how the ACCg and AI process rewards? Here, participants monitored rewards being delivered to themselves or a stranger during functional magnetic resonance imaging. Later, they performed a task measuring their willingness to exert effort to obtain rewards for others, and a task measuring their propensity to be aware and use interoceptive respiratory signals. Using multivariate similarity analysis, we show that people's willingness to be prosocial is predicted by greater similarity between self and other representations in the ACCg. Moreover, greater dissimilarity in self-other representations in the AI is linked to interoceptive propensity. These findings highlight that vicarious reward is linked to bodily signals in AI, and foster prosocial tendencies through the ACCg.
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Affiliation(s)
- Luis Sebastian Contreras-Huerta
- Department of Experimental Psychology, University of Oxford, Oxford OX1 3PH, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK; Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, UK; Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, UK; Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Viña del Mar, Chile.
| | - Michel-Pierre Coll
- School of Psychology and CIRRIS research center, Laval University, Quebec City QC G1V 0A6, Canada
| | - Geoffrey Bird
- Department of Experimental Psychology, University of Oxford, Oxford OX1 3PH, UK; School of Psychology, University of Birmingham, Birmingham B15 2TT, UK
| | - Hongbo Yu
- Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Annayah Prosser
- Department of Psychology, University of Bath, BA2 7AY, United Kingdom
| | - Patricia L Lockwood
- Department of Experimental Psychology, University of Oxford, Oxford OX1 3PH, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK; Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, UK; Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, UK; Christ Church, University of Oxford, Oxford OX1 1DP, UK
| | - Jennifer Murphy
- Department of Psychology, Royal Holloway, University of London, London TW20 0EY, UK
| | - M J Crockett
- Department of Experimental Psychology, University of Oxford, Oxford OX1 3PH, UK; Department of Psychology, Yale University, New Haven, CT 06511, USA; Department of Psychology and University Center for Human Values, Princeton University, Princeton, USA
| | - Matthew A J Apps
- Department of Experimental Psychology, University of Oxford, Oxford OX1 3PH, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK; Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, UK; Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, UK; Christ Church, University of Oxford, Oxford OX1 1DP, UK.
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25
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Pat N, Wang Y, Bartonicek A, Candia J, Stringaris A. Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition. Cereb Cortex 2023; 33:2682-2703. [PMID: 35697648 PMCID: PMC10016053 DOI: 10.1093/cercor/bhac235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 11/13/2022] Open
Abstract
Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman's H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.
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Affiliation(s)
- Narun Pat
- Corresponding author: Department of Psychology, University of Otago, William James Building, 275 Leith Walk, Dunedin 9016, New Zealand.
| | - Yue Wang
- Department of Psychology, University of Otago, William James Building, 275 Leith Walk, Dunedin 9016, New Zealand
| | - Adam Bartonicek
- Department of Psychology, University of Otago, William James Building, 275 Leith Walk, Dunedin 9016, New Zealand
| | - Julián Candia
- Longitudinal Studies Section, Translational Gerontology National Institute on Aging, National Institute of Health, Branch, 251 Bayview Boulevard, Rm 05B113A, Biomedical Research Center, Baltimore, MD 21224, USA
| | - Argyris Stringaris
- Division of Psychiatry and Department of Clinical, Educational – Health Psychology, University College London, 1-19 Torrington Pl, London WC1E 7HB, United Kingdom
- Department of Psychiatry, National and Kapodistrian University of Athens, Medical School, Mikras Asias 75, Athina 115 27, Greece
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26
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Hawco C, Steeves JKE, Voineskos AN, Blumberger DM, Daskalakis ZJ. Within-subject reliability of concurrent TMS-fMRI during a single session. Psychophysiology 2023:e14252. [PMID: 36694109 DOI: 10.1111/psyp.14252] [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: 10/19/2021] [Revised: 11/03/2022] [Accepted: 12/06/2022] [Indexed: 01/26/2023]
Abstract
Concurrent transcranial magnetic stimulation with functional MRI (concurrent TMS-fMRI) allows real-time causative probing of brain connectivity. However, technical challenges, safety, and tolerability may limit the number of trials employed during a concurrent TMS-fMRI experiment. We leveraged an existing data set with 100 trials of active TMS compared to a sub-threshold control condition to assess the reliability of the evoked BOLD response during concurrent TMS-fMRI. This data will permit an analysis of the minimum number of trials that should be employed in a concurrent TMS-fMRI protocol in order to achieve reliable spatial changes in activity. Single-subject maps of brain activity were created by splitting the trials within the same experimental session into groups of 50, 40, 30, 25, 20, 15, or 10 trials, correlations (R) between t-maps derived from paired subsets of trials within the same individual were calculated as reliability. R was moderate-high for 50 trials (mean R = .695) and decreased as the number of trials decreased. Consistent with previous findings of high individual variability in the spatial patterns of evoked neuronal changes following a TMS pulse, the spatial pattern of Rs differed across participants, but regional R was correlated with the magnitude of TMS-evoked activity. These results demonstrate concurrent TMS-fMRI produces a reliable pattern of activity at the individual level at higher trial numbers, particularly within localized regions. The spatial pattern of reliability is individually idiosyncratic and related to the individual pattern of evoked changes.
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Affiliation(s)
- Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Jennifer K E Steeves
- Centre for Vision Research and Department of Psychology, York University, Toronto, Ontario, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Daniel M Blumberger
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Temerty Centre for Therapeutic Brain Intervention, Toronto, Ontario, Canada
| | - Zafiris J Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Toronto, Ontario, Canada.,Department of Psychiatry, University of California, San Diego, California, USA
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27
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Carmichael O. The Role of fMRI in Drug Development: An Update. ADVANCES IN NEUROBIOLOGY 2023; 30:299-333. [PMID: 36928856 DOI: 10.1007/978-3-031-21054-9_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Functional magnetic resonance imaging (fMRI) of the brain is a technology that holds great potential for increasing the efficiency of drug development for the central nervous system (CNS). In preclinical studies and both early- and late-phase human trials, fMRI has the potential to improve cross-species translation of drug effects, help to de-risk compounds early in development, and contribute to the portfolio of evidence for a compound's efficacy and mechanism of action. However, to date, the utilization of fMRI in the CNS drug development process has been limited. The purpose of this chapter is to explore this mismatch between potential and utilization. This chapter provides introductory material related to fMRI and drug development, describes what is required of fMRI measurements for them to be useful in a drug development setting, lists current capabilities of fMRI in this setting and challenges faced in its utilization, and ends with directions for future development of capabilities in this arena. This chapter is the 5-year update of material from a previously published workshop summary (Carmichael et al., Drug DiscovToday 23(2):333-348, 2018).
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Affiliation(s)
- Owen Carmichael
- Pennington Biomedical Research Center, Baton Rouge, LA, USA.
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28
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Miller JA, Tambini A, Kiyonaga A, D'Esposito M. Long-term learning transforms prefrontal cortex representations during working memory. Neuron 2022; 110:3805-3819.e6. [PMID: 36240768 PMCID: PMC9768795 DOI: 10.1016/j.neuron.2022.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 06/28/2022] [Accepted: 09/14/2022] [Indexed: 11/06/2022]
Abstract
The role of the lateral prefrontal cortex (lPFC) in working memory (WM) is debated. Non-human primate (NHP) electrophysiology shows that the lPFC stores WM representations, but human neuroimaging suggests that the lPFC controls WM content in sensory cortices. These accounts are confounded by differences in task training and stimulus exposure. We tested whether long-term training alters lPFC function by densely sampling WM activity using functional MRI. Over 3 months, participants trained on both a WM and serial reaction time (SRT) task, wherein fractal stimuli were embedded within sequences. WM performance improved for trained (but not novel) fractals and, neurally, delay activity increased in distributed lPFC voxels across learning. Item-level WM representations became detectable within lPFC patterns, and lPFC activity reflected sequence relationships from the SRT task. These findings demonstrate that human lPFC develops stimulus-selective responses with learning, and WM representations are shaped by long-term experience, which could reconcile competing accounts of WM functioning.
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Affiliation(s)
- Jacob A Miller
- Wu Tsai Institute, Department of Psychiatry, Yale University, New Haven, CT, USA.
| | - Arielle Tambini
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Anastasia Kiyonaga
- Department of Cognitive Science, University of California, San Diego, CA, USA
| | - Mark D'Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA; Department of Psychology, University of California, Berkeley, CA, USA
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29
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Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
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30
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Capturing Brain-Cognition Relationship: Integrating Task-Based fMRI Across Tasks Markedly Boosts Prediction and Test-Retest Reliability. Neuroimage 2022; 263:119588. [PMID: 36057404 DOI: 10.1016/j.neuroimage.2022.119588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/13/2022] [Accepted: 08/23/2022] [Indexed: 11/22/2022] Open
Abstract
Capturing individual differences in cognition is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability can be improved by integrating MRI signals across the whole brain and across modalities, including task-based functional MRI (tfMRI) of different tasks along with other non-task MRI modalities, such as structural MRI, resting-state functional connectivity. Using the Human Connectome Project (n=873, 473 females, after quality control), we directly compared predictive models comprising different sets of MRI modalities (e.g., seven tasks vs. non-task modalities). We applied two approaches to integrate multimodal MRI, stacked vs. flat models, and implemented 16 combinations of machine-learning algorithms. The stacked model integrating all modalities via stacking Elastic Net provided the best prediction (r=.57), relatively to other models tested, as well as excellent test-retest reliability (ICC=∼.85) in capturing general cognitive abilities. Importantly, compared to the stacked model integrating across non-task modalities (r=.27), the stacked model integrating tfMRI across tasks led to significantly higher prediction (r=.56) while still providing excellent test-retest reliability (ICC=∼.83). The stacked model integrating tfMRI across tasks was driven by frontal and parietal areas and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results contradict the recently popular notion that tfMRI is not reliable enough to capture individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities.
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31
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Bloom PA, VanTieghem M, Gabard‐Durnam L, Gee DG, Flannery J, Caldera C, Goff B, Telzer EH, Humphreys KL, Fareri DS, Shapiro M, Algharazi S, Bolger N, Aly M, Tottenham N. Age-related change in task-evoked amygdala-prefrontal circuitry: A multiverse approach with an accelerated longitudinal cohort aged 4-22 years. Hum Brain Mapp 2022; 43:3221-3244. [PMID: 35393752 PMCID: PMC9188973 DOI: 10.1002/hbm.25847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/20/2022] [Accepted: 03/15/2022] [Indexed: 12/22/2022] Open
Abstract
The amygdala and its connections with medial prefrontal cortex (mPFC) play central roles in the development of emotional processes. While several studies have suggested that this circuitry exhibits functional changes across the first two decades of life, findings have been mixed - perhaps resulting from differences in analytic choices across studies. Here we used multiverse analyses to examine the robustness of task-based amygdala-mPFC function findings to analytic choices within the context of an accelerated longitudinal design (4-22 years-old; N = 98; 183 scans; 1-3 scans/participant). Participants recruited from the greater Los Angeles area completed an event-related emotional face (fear, neutral) task. Parallel analyses varying in preprocessing and modeling choices found that age-related change estimates for amygdala reactivity were more robust than task-evoked amygdala-mPFC functional connectivity to varied analytical choices. Specification curves indicated evidence for age-related decreases in amygdala reactivity to faces, though within-participant changes in amygdala reactivity could not be differentiated from between-participant differences. In contrast, amygdala-mPFC functional connectivity results varied across methods much more, and evidence for age-related change in amygdala-mPFC connectivity was not consistent. Generalized psychophysiological interaction (gPPI) measurements of connectivity were especially sensitive to whether a deconvolution step was applied. Our findings demonstrate the importance of assessing the robustness of findings to analysis choices, although the age-related changes in our current work cannot be overinterpreted given low test-retest reliability. Together, these findings highlight both the challenges in estimating developmental change in longitudinal cohorts and the value of multiverse approaches in developmental neuroimaging for assessing robustness of results.
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Affiliation(s)
| | | | | | - Dylan G. Gee
- Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | | | - Christina Caldera
- Department of PsychologyUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Bonnie Goff
- Department of PsychologyUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Eva H. Telzer
- University of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | | | | | | | - Sameah Algharazi
- Department of PsychologyCity College of New YorkNew YorkNew YorkUSA
| | - Niall Bolger
- Department of PsychologyColumbia UniversityNew YorkNew YorkUSA
| | - Mariam Aly
- Department of PsychologyColumbia UniversityNew YorkNew YorkUSA
| | - Nim Tottenham
- Department of PsychologyColumbia UniversityNew YorkNew YorkUSA
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32
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A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample. Neuroimage 2022; 258:119348. [PMID: 35659998 DOI: 10.1016/j.neuroimage.2022.119348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/26/2022] [Accepted: 05/31/2022] [Indexed: 11/23/2022] Open
Abstract
Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.
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33
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Gianaros PJ, Rasero J, DuPont CM, Kraynak TE, Gross JJ, McRae K, Wright AG, Verstynen TD, Barinas-Mitchell E. Multivariate Brain Activity while Viewing and Reappraising Affective Scenes Does Not Predict the Multiyear Progression of Preclinical Atherosclerosis in Otherwise Healthy Midlife Adults. AFFECTIVE SCIENCE 2022; 3:406-424. [PMID: 36046001 PMCID: PMC9382946 DOI: 10.1007/s42761-021-00098-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 12/17/2021] [Indexed: 06/03/2023]
Abstract
Cognitive reappraisal is an emotion regulation strategy that is postulated to reduce risk for atherosclerotic cardiovascular disease (CVD), particularly the risk due to negative affect. At present, however, the brain systems and vascular pathways that may link reappraisal to CVD risk remain unclear. This study thus tested whether brain activity evoked by using reappraisal to reduce negative affect would predict the multiyear progression of a vascular marker of preclinical atherosclerosis and CVD risk: carotid artery intima-media thickness (CA-IMT). Participants were 176 otherwise healthy adults (50.6% women; aged 30-51 years) who completed a functional magnetic resonance imaging task involving the reappraisal of unpleasant scenes from the International Affective Picture System. Ultrasonography was used to compute CA-IMT at baseline and a median of 2.78 (interquartile range, 2.67 to 2.98) years later among 146 participants. As expected, reappraisal engaged brain systems implicated in emotion regulation. Reappraisal also reduced self-reported negative affect. On average, CA-IMT progressed over the follow-up period. However, multivariate and cross-validated machine-learning models demonstrated that brain activity during reappraisal failed to predict CA-IMT progression. Contrary to hypotheses, brain activity during cognitive reappraisal to reduce negative affect does not appear to forecast the progression of a vascular marker of CVD risk. Supplementary Information The online version contains supplementary material available at 10.1007/s42761-021-00098-y.
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Affiliation(s)
- Peter J. Gianaros
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA USA
| | - Javier Rasero
- Department of Psychology, Carnegie Mellon University, 3131 Sennott Square, 210 S. Bouquet St, Pittsburgh, PA USA
| | - Caitlin M. DuPont
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA USA
| | - Thomas E. Kraynak
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA USA
| | - James J. Gross
- Department of Psychology, Stanford University, Stanford, CA USA
| | - Kateri McRae
- Department of Psychology, University of Denver, Denver, CO USA
| | - Aidan G.C. Wright
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA USA
| | - Timothy D. Verstynen
- Department of Psychology, Carnegie Mellon University, 3131 Sennott Square, 210 S. Bouquet St, Pittsburgh, PA USA
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Gruskin DC, Patel GH. Brain connectivity at rest predicts individual differences in normative activity during movie watching. Neuroimage 2022; 253:119100. [PMID: 35304263 PMCID: PMC9491116 DOI: 10.1016/j.neuroimage.2022.119100] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/11/2022] [Accepted: 03/14/2022] [Indexed: 12/19/2022] Open
Abstract
When exposed to the same sensory event, some individuals are bound to have less typical experiences than others. Previous research has investigated this phenomenon by showing that the typicality of one's sensory experience is associated with the typicality of their stimulus-evoked brain activity (as measured by intersubject correlation, or ISC). Individual differences in ISC have recently been attributed to variability in focal neural processing. However, the extent to which these differences reflect purely intra-regional variability versus variation in the brain's baseline ability to transmit information between regions has yet to be established. Here, we show that an individual's degree and spatial distribution of ISC are closely related to their brain's functional organization at rest. Using resting state and movie watching fMRI data from the Human Connectome Project, we reveal that resting state functional connectivity (RSFC) profiles can be used to predict cortex-wide ISC. Similar region-level analyses demonstrate that the levels of ISC exhibited by brain regions during movie watching are associated with their connectivity to other regions at rest, and that the nature of these connectivity-activity relationships varies as a function of regional roles in sensory information processing. Finally, we show that an individual's unique spatial distribution of ISC, independent of its magnitude, is also related to their RSFC profile. These findings contextualize reports of localized individual differences in ISC as potentially reflecting larger, network-level alterations in resting brain function and detail how the brain's ability to process complex sensory information is linked to its baseline functional organization.
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Affiliation(s)
- David C Gruskin
- Medical Scientist Training Program, Columbia University Irving Medical Center, NY 10032, USA.
| | - Gaurav H Patel
- New York State Psychiatric Institute, NY 10032, USA; Department of Psychiatry, Columbia University Irving Medical Center, NY 10032, USA.
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35
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Dennison JB, Sazhin D, Smith DV. Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2022; 13:e1589. [PMID: 35137549 PMCID: PMC9124684 DOI: 10.1002/wcs.1589] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 11/28/2021] [Accepted: 12/21/2021] [Indexed: 01/10/2023]
Abstract
In the past decade, decision neuroscience and neuroeconomics have developed many new insights in the study of decision making. This review provides an overarching update on how the field has advanced in this time period. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across economics and psychology. First, we review progress on topics including reward learning, explore-exploit decisions, risk and ambiguity, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choices and new exciting developments in the prediction and neuroforecasting of future decisions. Finally, we consider how trends in decision-neuroscience research reflect progress toward resolving past challenges, discuss new and exciting applications of recent research, and identify new challenges for the field. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Emotion and Motivation.
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Affiliation(s)
- Jeffrey B Dennison
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, USA
| | - Daniel Sazhin
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, USA
| | - David V Smith
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, USA
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36
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Rapuano KM, Conley MI, Juliano AC, Conan GM, Maza MT, Woodman K, Martinez SA, Earl E, Perrone A, Feczko E, Fair DA, Watts R, Casey BJ, Rosenberg MD. An open-access accelerated adult equivalent of the ABCD Study neuroimaging dataset (a-ABCD). Neuroimage 2022; 255:119215. [PMID: 35436615 DOI: 10.1016/j.neuroimage.2022.119215] [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: 12/18/2021] [Revised: 03/14/2022] [Accepted: 04/13/2022] [Indexed: 11/19/2022] Open
Abstract
As public access to longitudinal developmental datasets like the Adolescent Brain Cognitive Development StudySM (ABCD Study®) increases, so too does the need for resources to benchmark time-dependent effects. Scan-to-scan changes observed with repeated imaging may reflect development but may also reflect practice effects, day-to-day variability in psychological states, and/or measurement noise. Resources that allow disentangling these time-dependent effects will be useful in quantifying actual developmental change. We present an accelerated adult equivalent of the ABCD Study dataset (a-ABCD) using an identical imaging protocol to acquire magnetic resonance imaging (MRI) structural, diffusion-weighted, resting-state and task-based data from eight adults scanned five times over five weeks. We report on the task-based imaging data (n = 7). In-scanner stop-signal (SST), monetary incentive delay (MID), and emotional n-back (EN-back) task behavioral performance did not change across sessions. Post-scan recognition memory for emotional n-back stimuli, however, did improve as participants became more familiar with the stimuli. Functional MRI analyses revealed that patterns of task-based activation reflecting inhibitory control in the SST, reward success in the MID task, and working memory in the EN-back task were more similar within individuals across repeated scan sessions than between individuals. Within-subject, activity was more consistent across sessions during the EN-back task than in the SST and MID task, demonstrating differences in fMRI data reliability as a function of task. The a-ABCD dataset provides a unique testbed for characterizing the reliability of brain function, structure, and behavior across imaging modalities in adulthood and benchmarking neurodevelopmental change observed in the open-access ABCD Study.
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Affiliation(s)
| | | | | | - Gregory M Conan
- Masonic Institute for the Developing Brain, University of Minnesota Medical School
| | - Maria T Maza
- Department of Psychology, Yale University; Department of Psychology, University of North Carolina, Chapel Hill
| | - Kylie Woodman
- Department of Psychology, Yale University; Department of Communication, University of California, Santa Barbara
| | - Steven A Martinez
- Department of Psychology, Yale University; Department of Psychology, Temple University
| | - Eric Earl
- Department of Psychiatry, Oregon Health and Science University
| | - Anders Perrone
- Department of Psychiatry, Oregon Health and Science University; Masonic Institute for the Developing Brain, University of Minnesota Medical School
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota Medical School; Department of Pediatrics, University of Minnesota Medical School
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota Medical School
| | | | - B J Casey
- Department of Psychology, Yale University.
| | - Monica D Rosenberg
- Department of Psychology, Yale University; Department of Psychology, University of Chicago, United States.
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37
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Jabakhanji R, Vigotsky AD, Bielefeld J, Huang L, Baliki MN, Iannetti G, Apkarian AV. Limits of decoding mental states with fMRI. Cortex 2022; 149:101-122. [PMID: 35219121 PMCID: PMC9238276 DOI: 10.1016/j.cortex.2021.12.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/22/2021] [Accepted: 12/13/2021] [Indexed: 12/15/2022]
Abstract
A growing number of studies claim to decode mental states using multi-voxel decoders of brain activity. It has been proposed that the fixed, fine-grained, multi-voxel patterns in these decoders are necessary for discriminating between and identifying mental states. Here, we present evidence that the efficacy of these decoders might be overstated. Across various tasks, decoder patterns were spatially imprecise, as decoder performance was unaffected by spatial smoothing; 90% redundant, as selecting a random 10% of a decoder's constituent voxels recovered full decoder performance; and performed similarly to brain activity maps used as decoders. We distinguish decoder performance in discriminating between mental states from performance in identifying a given mental state, and show that even when discrimination performance is adequate, identification can be poor. Finally, we demonstrate that simple and intuitive similarity metrics explain 91% and 62% of discrimination performance within- and across-subjects, respectively. These findings indicate that currently used across-subject decoders of mental states are superfluous and inappropriate for decision-making.
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Affiliation(s)
- Rami Jabakhanji
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Andrew D Vigotsky
- Departments of Biomedical Engineering and Statistics, Northwestern University, Evanston, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Jannis Bielefeld
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Lejian Huang
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Marwan N Baliki
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, USA; Shirley Ryan AbilityLab, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Giandomenico Iannetti
- Division of Biosciences, University College London, London, UK; Neuroscience and Behaviour Laboratory, Italian Institute of Technology, Rome, Italy
| | - A Vania Apkarian
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, USA; Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA.
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38
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Ekhtiari H, Zare-Bidoky M, Sangchooli A, Janes AC, Kaufman MJ, Oliver JA, Prisciandaro JJ, Wüstenberg T, Anton RF, Bach P, Baldacchino A, Beck A, Bjork JM, Brewer J, Childress AR, Claus ED, Courtney KE, Ebrahimi M, Filbey FM, Ghahremani DG, Azbari PG, Goldstein RZ, Goudriaan AE, Grodin EN, Hamilton JP, Hanlon CA, Hassani-Abharian P, Heinz A, Joseph JE, Kiefer F, Zonoozi AK, Kober H, Kuplicki R, Li Q, London ED, McClernon J, Noori HR, Owens MM, Paulus MP, Perini I, Potenza M, Potvin S, Ray L, Schacht JP, Seo D, Sinha R, Smolka MN, Spanagel R, Steele VR, Stein EA, Steins-Loeber S, Tapert SF, Verdejo-Garcia A, Vollstädt-Klein S, Wetherill RR, Wilson SJ, Witkiewitz K, Yuan K, Zhang X, Zilverstand A. A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus. Nat Protoc 2022; 17:567-595. [PMID: 35121856 PMCID: PMC9063851 DOI: 10.1038/s41596-021-00649-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 10/21/2021] [Indexed: 12/23/2022]
Abstract
Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants' characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: 'Participants' Characteristics', 'General fMRI Information', 'General Task Information', 'Cue Information', 'Craving Assessment Inside Scanner', 'Craving Assessment Outside Scanner' and 'Pre- and Post-Scanning Considerations'. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the 'General fMRI Information' category were reported in 90.5% of the reviewed papers, items in the 'Pre- and Post-Scanning Considerations' category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.
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Affiliation(s)
- Hamed Ekhtiari
- Laureate Institute for Brain Research, Tulsa, OK, USA. .,Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
| | - Mehran Zare-Bidoky
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran.,Shahid-Sadoughi University of Medical Sciences, Yazd, Iran.,These authors contributed equally: Mehran Zare-Bidoky, Arshiya Sangchooli
| | - Arshiya Sangchooli
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran.,These authors contributed equally: Mehran Zare-Bidoky, Arshiya Sangchooli
| | - Amy C. Janes
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Marc J. Kaufman
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Jason A. Oliver
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.,TSET Health Promotion Research Center, Stephenson Cancer Center, Oklahoma City, OK, USA.,Department of Psychiatry & Behavioral Sciences, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA
| | - James J. Prisciandaro
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Torsten Wüstenberg
- Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Raymond F. Anton
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Patrick Bach
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Heidelberg University, Mannheim, Germany
| | - Alex Baldacchino
- Division of Population Studies and Behavioural Sciences, St Andrews University Medical School, University of St Andrews, Scotland, UK
| | - Anne Beck
- Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité–Universitätsmedizin Berlin, Berlin, Germany.,Faculty of Health, Health and Medical University, Campus Potsdam, Potsdam, Germany
| | - James M. Bjork
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Judson Brewer
- Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, RI, USA
| | - Anna Rose Childress
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eric D. Claus
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
| | - Kelly E. Courtney
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Mohsen Ebrahimi
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
| | - Francesca M. Filbey
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Dara G. Ghahremani
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Peyman Ghobadi Azbari
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran.,Department of Biomedical Engineering, Shahed University, Tehran, Iran
| | - Rita Z. Goldstein
- Departments of Psychiatry & Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anna E. Goudriaan
- Department of Psychiatry, Amsterdam University Medical Center, University of Amsterdam and Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Erica N. Grodin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - J. Paul Hamilton
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Colleen A. Hanlon
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | - Andreas Heinz
- Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Jane E. Joseph
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Falk Kiefer
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Heidelberg University, Mannheim, Germany
| | - Arash Khojasteh Zonoozi
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran.,Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hedy Kober
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Qiang Li
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi’an, China
| | - Edythe D. London
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joseph McClernon
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Hamid R. Noori
- International Center for Primate Brain Research, Center for Excellence in Brain Science and Intelligence Technology (CEBSIT)/Institute of Neuroscience (ION), Chinese Academy of Sciences, Shanghai, China.,McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Max M. Owens
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | | | - Irene Perini
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Marc Potenza
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.,Connecticut Mental Health Center, New Haven, CT, USA.,Connecticut Council on Problem Gambling, Wethersfield, CT, USA.,Department of Neuroscience, Child Study Center and Wu Tsai Institute, Yale School of Medicine, New Haven, CT, USA
| | - Stéphane Potvin
- Centre de recherche de l’Institut Universitaire en Santé Mentale de Montréal, University of Montreal, Montreal, Canada
| | - Lara Ray
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Dongju Seo
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Rajita Sinha
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Michael N. Smolka
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| | - Rainer Spanagel
- Institute of Psychopharmacology, Central Institute of Mental Health, Mannheim, Germany
| | - Vaughn R. Steele
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Elliot A. Stein
- Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA
| | - Sabine Steins-Loeber
- Department of Clinical Psychology and Psychotherapy, Otto-Friedrich-University of Bamberg, Bamberg, Germany
| | - Susan F. Tapert
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | | | - Sabine Vollstädt-Klein
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Heidelberg University, Mannheim, Germany
| | - Reagan R. Wetherill
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephen J. Wilson
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
| | - Katie Witkiewitz
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi’an, China
| | - Xiaochu Zhang
- Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Anhui, China.,Department of Radiology, First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Science at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Anhui, China
| | - Anna Zilverstand
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
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39
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Reliability and stability challenges in ABCD task fMRI data. Neuroimage 2022; 252:119046. [PMID: 35245674 DOI: 10.1016/j.neuroimage.2022.119046] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 02/25/2022] [Accepted: 02/28/2022] [Indexed: 01/23/2023] Open
Abstract
Trait stability of measures is an essential requirement for individual differences research. Functional MRI has been increasingly used in studies that rely on the assumption of trait stability, such as attempts to relate task related brain activation to individual differences in behavior and psychopathology. However, recent research using adult samples has questioned the trait stability of task-fMRI measures, as assessed by test-retest correlations. To date, little is known about trait stability of task fMRI in children. Here, we examined within-session reliability and long-term stability of individual differences in task-fMRI measures using fMRI measures of brain activation provided by the adolescent brain cognitive development (ABCD) Study Release v4.0 as an individual's average regional activity, using its tasks focused on reward processing, response inhibition, and working memory. We also evaluated the effects of factors potentially affecting reliability and stability. Reliability and stability (quantified as the ratio of non-scanner related stable variance to all variances) was poor in virtually all brain regions, with an average value of 0.088 and 0.072 for short term (within-session) reliability and long-term (between-session) stability, respectively, in regions of interest (ROIs) historically-recruited by the tasks. Only one reliability or stability value in ROIs exceeded the 'poor' cut-off of 0.4, and in fact rarely exceeded 0.2 (only 4.9%). Motion had a pronounced effect on estimated reliability/stability, with the lowest motion quartile of participants having a mean reliability/stability 2.5 times higher (albeit still 'poor') than the highest motion quartile. Poor reliability and stability of task-fMRI, particularly in children, diminishes potential utility of fMRI data due to a drastic reduction of effect sizes and, consequently, statistical power for the detection of brain-behavior associations. This essential issue urgently needs to be addressed through optimization of task design, scanning parameters, data acquisition protocols, preprocessing pipelines, and data denoising methods.
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40
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Whitehead JC, Armony JL. Intra-individual Reliability of Voice- and Music-elicited Responses and their Modulation by Expertise. Neuroscience 2022; 487:184-197. [PMID: 35182696 DOI: 10.1016/j.neuroscience.2022.02.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/19/2022] [Accepted: 02/10/2022] [Indexed: 10/19/2022]
Abstract
A growing number of functional neuroimaging studies have identified regions within the temporal lobe, particularly along the planum polare and planum temporale, that respond more strongly to music than other types of acoustic stimuli, including voice. This "music preferred" regions have been reported using a variety of stimulus sets, paradigms and analysis approaches and their consistency across studies confirmed through meta-analyses. However, the critical question of intra-subject reliability of these responses has received less attention. Here, we directly assessed this important issue by contrasting brain responses to musical vs. vocal stimuli in the same subjects across three consecutive fMRI runs, using different types of stimuli. Moreover, we investigated whether these music- and voice-preferred responses were reliably modulated by expertise. Results demonstrated that music-preferred activity previously reported in temporal regions, and its modulation by expertise, exhibits a high intra-subject reliability. However, we also found that activity in some extra-temporal regions, such as the precentral and middle frontal gyri, did depend on the particular stimuli employed, which may explain why these are less consistently reported in the literature. Taken together, our findings confirm and extend the notion that specific regions in the brain consistently respond more strongly to certain socially-relevant stimulus categories, such as faces, voices and music, but that some of these responses appear to depend, at least to some extent, on the specific features of the paradigm employed.
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Affiliation(s)
- Jocelyne C Whitehead
- Douglas Mental Health University Institute, Verdun, Canada; BRAMS Laboratory, Centre for Research on Brain, Language and Music, Montreal, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Canada.
| | - Jorge L Armony
- Douglas Mental Health University Institute, Verdun, Canada; BRAMS Laboratory, Centre for Research on Brain, Language and Music, Montreal, Canada; Department of Psychiatry, McGill University, Montreal, Canada
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41
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Han X, Ashar YK, Kragel P, Petre B, Schelkun V, Atlas LY, Chang LJ, Jepma M, Koban L, Losin EAR, Roy M, Woo CW, Wager TD. Effect sizes and test-retest reliability of the fMRI-based neurologic pain signature. Neuroimage 2022; 247:118844. [PMID: 34942367 PMCID: PMC8792330 DOI: 10.1016/j.neuroimage.2021.118844] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/13/2021] [Accepted: 12/19/2021] [Indexed: 01/28/2023] Open
Abstract
Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49). The NPS showed excellent short-term (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with ≥60 trials required for excellent test-retest reliability. Reliability was tested in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicate that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions.
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Affiliation(s)
- Xiaochun Han
- Faculty of Psychology, Beijing Normal University, Beijing, China; Dartmouth College, Hanover, NH, United States
| | - Yoni K Ashar
- Weill Cornell Medical College, New York, NY, United States
| | | | | | | | - Lauren Y Atlas
- National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, United States; National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States; National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | | | | | | | | | - Mathieu Roy
- Department of Psychology, McGill University, Montreal, Quebec, Canada
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Gyeonggi-do, South Korea
| | - Tor D Wager
- Dartmouth College, Hanover, NH, United States.
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42
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De Nadai AS, Hu Y, Thompson WK. Data Pollution in Neuropsychiatry-An Under-Recognized but Critical Barrier to Research Progress. JAMA Psychiatry 2022; 79:97-98. [PMID: 34851392 DOI: 10.1001/jamapsychiatry.2021.2812] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
| | - Yueqin Hu
- Department of Psychology, Beijing Normal University, Beijing, China
| | - Wesley K Thompson
- Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla
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43
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Beltz AM, Demidenko MI, Chaku N, Klump KL, Joseph JE. Intrauterine Device Use: A New Frontier for Behavioral Neuroendocrinology. Front Endocrinol (Lausanne) 2022; 13:853714. [PMID: 35937811 PMCID: PMC9352855 DOI: 10.3389/fendo.2022.853714] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 05/19/2022] [Indexed: 11/20/2022] Open
Abstract
Intrauterine devices (IUDs) are the most-used reversible contraceptive method for women in the world, but little is known about their potential modulation of brain function, cognition, and behavior. This is disconcerting because research on other hormonal contraceptives, especially oral contraceptives (OCs), increasingly shows that exogenous sex hormones have behavioral neuroendocrine consequences, especially for gendered cognition, including spatial skills. Effects are small and nuanced, however, partially reflecting heterogeneity. The goal of this paper is to introduce IUD use as a new frontier for basic and applied research, and to offer key considerations for studying it, emphasizing the importance of multimodal investigations and person-specific analyses. The feasibility and utility of studying IUD users is illustrated by: scanning women who completed a functional magnetic resonance imaging mental rotations task; taking an individualized approach to mapping functional connectivity during the task using network analyses containing connections common across participants and unique to individual women, focusing on brain regions in putative mental rotations and default mode networks; and linking metrics of brain connectivity from the individualized networks to both mental rotations task performance and circulating hormone levels. IUD users provide a promising natural experiment for the interplay between exogenous and endogenous sex hormones, and they are likely qualitatively different from OC users with whom they are often grouped in hormonal contraceptive research. This paper underscores how future research on IUD users can advance basic neuroendocrinological knowledge and women's health.
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Affiliation(s)
- Adriene M. Beltz
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States
- *Correspondence: Adriene M. Beltz,
| | | | - Natasha Chaku
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States
| | - Kelly L. Klump
- Department of Psychology, Michigan State University, East Lansing, MI, United States
| | - Jane E. Joseph
- Department of Neurosciences, Medical University of South Carolina, Charleston, SC, United States
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44
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Schöne M, Seidenbecher S, Kaufmann J, Antonucci LA, Frodl T, Koutsouleris N, Schiltz K, Bogerts B. Appetitive aggression is associated with lateralized activation in nucleus accumbens. Psychiatry Res Neuroimaging 2022; 319:111425. [PMID: 34891023 DOI: 10.1016/j.pscychresns.2021.111425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/14/2021] [Accepted: 12/02/2021] [Indexed: 12/01/2022]
Abstract
Aggression can have a hedonistic aspect in predisposed individuals labeled as appetitive aggression. The present study investigates the neurobiological correlates of this appetitive type of aggression in non-clinical samples from community. Applying functional magnet resonance imaging (fMRI), we tested whether 20 martial artists compared to 26 controls had a higher activation in the nucleus accumbens (NAcc), a central part of the dopaminergic, mesolimbic reward system. Subjects had to watch violent vs. neutral pictures representing appetitive aggression. The affinity towards hedonistic violence was assessed by the Appetitive and Facilitative Aggression Scale (AFAS). Furthermore, the subjects rated all the pictures with regard to how pleasant and violent they were. The martial artists reported a higher AFAS-score and a more positive perception of violent pictures. On the neural level, across all subjects, there was a significant positive correlation between the AFAS-score and the activation in the left NAcc and an inverse association with the activation of the right NAcc when watching violent compared to neutral pictures. This lateralization effect indicates a different processing of hedonistic aspects of aggression in the two hemispheres.
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Affiliation(s)
- Maria Schöne
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany; Salus Institute, Salus gGmbH, Magdeburg, Germany.
| | - Stephanie Seidenbecher
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany; Salus Institute, Salus gGmbH, Magdeburg, Germany
| | - Jörn Kaufmann
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | - Linda Antonella Antonucci
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany; Department of Education, Psychology, Communication, University of Bari Aldo Moro, Bari, Italy
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany; Department of Psychiatry and Institute of Neuroscience, Dublin, Ireland; Center for Behavioral Brain Sciences, Otto-von-Guericke University, Magdeburg, Germany; German Center for Neurodegenerative Diseases, Otto-von-Guericke University, Magdeburg, Germany; Department of Psychiatry, Psychotherapy, and Psychosomatic, RWTH-University, Aachen, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - Kolja Schiltz
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany; Department of Forensic Psychiatry, Mental Hospital of the Ludwig-Maximilians-University, Munich, Germany
| | - Bernhard Bogerts
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany; Salus Institute, Salus gGmbH, Magdeburg, Germany
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45
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Xing XX. Globally Aging Cortical Spontaneous Activity Revealed by Multiple Metrics and Frequency Bands Using Resting-State Functional MRI. Front Aging Neurosci 2021; 13:803436. [PMID: 35027890 PMCID: PMC8748263 DOI: 10.3389/fnagi.2021.803436] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/09/2021] [Indexed: 11/24/2022] Open
Abstract
Most existing aging studies using functional MRI (fMRI) are based on cross-sectional data but misinterpreted their findings (i.e., age-related differences) as longitudinal outcomes (i.e., aging-related changes). To delineate aging-related changes the of human cerebral cortex, we employed the resting-state fMRI (rsfMRI) data from 24 healthy elders in the PREVENT-AD cohort, obtaining five longitudinal scans per subject. Cortical spontaneous activity is measured globally with three rsfMRI metrics including its amplitude, homogeneity, and homotopy at three different frequency bands (slow-5: 0.02-0.03 Hz, slow-4: 0.03-0.08 Hz, and slow-3 band: 0.08-0.22 Hz). General additive mixed models revealed a universal pattern of the aging-related changes for the global cortical spontaneous activity, indicating increases of these rsfMRI metrics during aging. This aging pattern follows specific frequency and spatial profiles where higher slow bands show more non-linear curves and the amplitude exhibits more extensive and significant aging-related changes than the connectivity. These findings provide strong evidence that cortical spontaneous activity is aging globally, inspiring its clinical utility as neuroimaging markers for neruodegeneration disorders.
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Affiliation(s)
- Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing, China
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46
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Tian X, Hao X, Song Y, Liu J. Homogenization of face neural representation during development. Dev Cogn Neurosci 2021; 52:101040. [PMID: 34837875 PMCID: PMC8637318 DOI: 10.1016/j.dcn.2021.101040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/19/2021] [Accepted: 11/18/2021] [Indexed: 12/01/2022] Open
Abstract
Extensive studies have demonstrated that face processing ability develops gradually during development until adolescence. However, the underlying mechanism is unclear. One hypothesis is that children and adults represent faces in qualitatively different fashions with different group templates. An alternative hypothesis emphasizes the development as a quantitative change with a decrease of variation in representations. To test these hypotheses, we used between-participant correlation to measure activation pattern similarity both within and between late-childhood children and adults. We found that activation patterns for faces in the fusiform face area and occipital face area were less similar within the children group than within the adults group, indicating children had a greater variation in representing faces. Interestingly, the activation pattern similarity of children to their own group template was not significantly larger than that to adults' template, suggesting children and adults shared a template in representing faces. Further, the decrease in representation variance was likely a general principle in the ventral visual cortex, as a similar result was observed in a scene-selective region when perceiving scenes. Taken together, our study provides evidence that development of object representation may result from a homogenization process that shifts from greater variance in late-childhood to homogeneity in adults.
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Affiliation(s)
- Xue Tian
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Xin Hao
- Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan, China; School of Psychology, Central China Normal University, Wuhan, China
| | - Yiying Song
- Faculty of Psychology, Beijing Normal University, Beijing 100875, China.
| | - Jia Liu
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
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47
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Himmelberg MM, Kurzawski JW, Benson NC, Pelli DG, Carrasco M, Winawer J. Cross-dataset reproducibility of human retinotopic maps. Neuroimage 2021; 244:118609. [PMID: 34582948 PMCID: PMC8560578 DOI: 10.1016/j.neuroimage.2021.118609] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 09/16/2021] [Accepted: 09/20/2021] [Indexed: 11/11/2022] Open
Abstract
Population receptive field (pRF) models fit to fMRI data are used to non-invasively measure retinotopic maps in human visual cortex, and these maps are a fundamental component of visual neuroscience experiments. Here, we examined the reproducibility of retinotopic maps across two datasets: a newly acquired retinotopy dataset from New York University (NYU) (n = 44) and a public dataset from the Human Connectome Project (HCP) (n = 181). Our goal was to assess the degree to which pRF properties are similar across datasets, despite substantial differences in their experimental protocols. The two datasets simultaneously differ in their stimulus apertures, participant pool, fMRI protocol, MRI field strength, and preprocessing pipeline. We assessed the cross-dataset reproducibility of the two datasets in terms of the similarity of vertex-wise pRF estimates and in terms of large-scale polar angle asymmetries in cortical magnification. Within V1, V2, V3, and hV4, the group-median NYU and HCP vertex-wise polar angle estimates were nearly identical. Both eccentricity and pRF size estimates were also strongly correlated between the two datasets, but with a slope different from 1; the eccentricity and pRF size estimates were systematically greater in the NYU data. Next, to compare large-scale map properties, we quantified two polar angle asymmetries in V1 cortical magnification previously identified in the HCP data. The NYU dataset confirms earlier reports that more cortical surface area represents horizontal than vertical visual field meridian, and lower than upper vertical visual field meridian. Together, our findings show that the retinotopic properties of V1, V2, V3, and hV4 can be reliably measured across two datasets, despite numerous differences in their experimental design. fMRI-derived retinotopic maps are reproducible because they rely on an explicit computational model of the fMRI response. In the case of pRF mapping, the model is grounded in physiological evidence of how visual receptive fields are organized, allowing one to quantitatively characterize the BOLD signal in terms of stimulus properties (i.e., location and size). The new NYU Retinotopy Dataset will serve as a useful benchmark for testing hypotheses about the organization of visual areas and for comparison to the HCP 7T Retinotopy Dataset.
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Affiliation(s)
- Marc M Himmelberg
- Department of Psychology, New York University, New York 10003, NY, USA.
| | - Jan W Kurzawski
- Department of Psychology, New York University, New York 10003, NY, USA
| | - Noah C Benson
- eScience Institute, University of Washington, Seattle 98195, WA, USA
| | - Denis G Pelli
- Department of Psychology, New York University, New York 10003, NY, USA; Center for Neural Sciences, New York University, New York 10003, NY, USA
| | - Marisa Carrasco
- Department of Psychology, New York University, New York 10003, NY, USA; Center for Neural Sciences, New York University, New York 10003, NY, USA
| | - Jonathan Winawer
- Department of Psychology, New York University, New York 10003, NY, USA; Center for Neural Sciences, New York University, New York 10003, NY, USA
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48
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The Value of Hyperalignment to Unpack Neural Heterogeneity in the Precision Psychiatry Movement. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:935-936. [PMID: 33933400 DOI: 10.1016/j.bpsc.2021.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 02/16/2021] [Indexed: 11/21/2022]
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49
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Compère L, Siegle GJ, Young K. Importance of test-retest reliability for promoting fMRI based screening and interventions in major depressive disorder. Transl Psychiatry 2021; 11:387. [PMID: 34247184 PMCID: PMC8272717 DOI: 10.1038/s41398-021-01507-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/16/2021] [Accepted: 06/24/2021] [Indexed: 11/09/2022] Open
Abstract
Proponents of personalized medicine have promoted neuroimaging in three areas of clinical application for major depression: clinical prediction, outcome evaluation, and treatment, via neurofeedback. Whereas psychometric considerations such as test-retest reliability are basic precursors to clinical adoption for most clinical instruments, we show, in this article, that basic psychometrics have not been regularly attended to in fMRI of depression. For instance, no fMRI neurofeedback study has included measures of test-retest reliability, despite the implicit assumption that brain signals are stable enough to train. We consider several factors that could be useful to aid clinical translation, including (1) attending to how the BOLD response is parameterized, (2) identifying and promoting regions or voxels with stronger psychometric properties, (3) accounting for within-individual changes (e.g., in symptomatology) across time, and (4) focusing on tasks and clinical populations that are relevant for the intended clinical application. We apply these principles to published prognostic and neurofeedback data sets. The broad implication of this work is that attention to psychometrics is important for clinical adoption of mechanistic assessment, is feasible, and may improve the underlying science.
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Affiliation(s)
- Laurie Compère
- Department of Psychiatry, University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, Pittsburgh, PA, USA.
| | - Greg J Siegle
- Department of Psychiatry, University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, Pittsburgh, PA, USA
| | - Kymberly Young
- Department of Psychiatry, University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, Pittsburgh, PA, USA
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50
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McDermott TJ, Kirlic N, Akeman E, Touthang J, Clausen AN, Kuplicki R, Aupperle RL. Test-retest reliability of approach-avoidance conflict decision-making during functional magnetic resonance imaging in healthy adults. Hum Brain Mapp 2021; 42:2347-2361. [PMID: 33650761 PMCID: PMC8090786 DOI: 10.1002/hbm.25371] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/22/2021] [Accepted: 02/07/2021] [Indexed: 01/02/2023] Open
Abstract
Neural and behavioral mechanisms during approach-avoidance conflict decision-making are relevant across various psychiatric disorders, particularly anxiety disorders. Studies using approach-avoidance conflict paradigms in healthy adults have identified preliminary neural mechanisms, but findings must be replicated and demonstrated as reliable before further application. This study sought to replicate previous findings and examine test-retest reliability of behavioral (approach behavior, reaction time) and neural (regions of interest [ROIs]) responses during an approach-avoidance conflict task conducted during functional magnetic resonance imaging (fMRI). Thirty healthy adults completed an approach-avoidance conflict task during fMRI on two occasions (mean interval: 17 days; range: 11-32). Effects of task condition during three task phases (decision-making, affective outcome and monetary reward) and intraclass correlation coefficients (ICCs) were calculated across time points. Results replicated that approach behavior was modulated by conflict during decision-making. ROI activations were replicated such that dorsal anterior cingulate cortex (dACC) was modulated by conflict during decision-making, and dACC, striatum, and anterior insula were modulated by valence during affective outcomes (p's <.0083). Approach behavior during conflict demonstrated excellent reliability (ICCs ≥.77). Activation of dACC during conflict decision-making and anterior insula during negative outcomes demonstrated fair reliability (ICCs = .51 and .54), and dACC and striatum activation demonstrated good reliability during negative outcomes (ICCs = .63 and .69). Two additional ROIs (amygdala, left dorsolateral prefrontal cortex) showed good reliability during negative outcomes (ICCs ≥.60). These results characterize several specific behavioral and neuroimaging responses that are replicable and sufficiently reliable during approach-avoidance conflict decision-making to support future utility.
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Affiliation(s)
- Timothy J. McDermott
- Laureate Institute for Brain ResearchTulsaOklahomaUSA
- Department of PsychologyUniversity of TulsaTulsaOklahomaUSA
| | - Namik Kirlic
- Laureate Institute for Brain ResearchTulsaOklahomaUSA
| | | | | | - Ashley N. Clausen
- Laureate Institute for Brain ResearchTulsaOklahomaUSA
- Kansas City VA Medical CenterKansas CityMissouriUSA
| | | | - Robin L. Aupperle
- Laureate Institute for Brain ResearchTulsaOklahomaUSA
- Department of Community MedicineUniversity of TulsaTulsaOklahomaUSA
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