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Kühnel A, Hagenberg J, Knauer-Arloth J, Ködel M, Czisch M, Sämann PG, Binder EB, Kroemer NB. Stress-induced brain responses are associated with BMI in women. Commun Biol 2023; 6:1031. [PMID: 37821711 PMCID: PMC10567923 DOI: 10.1038/s42003-023-05396-8] [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: 03/18/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
Overweight and obesity are associated with altered stress reactivity and increased inflammation. However, it is not known whether stress-induced changes in brain function scale with BMI and if such associations are driven by peripheral cytokines. Here, we investigate multimodal stress responses in a large transdiagnostic sample using predictive modeling based on spatio-temporal profiles of stress-induced changes in activation and functional connectivity. BMI is associated with increased brain responses as well as greater negative affect after stress and individual response profiles are associated with BMI in females (pperm < 0.001), but not males. Although stress-induced changes reflecting BMI are associated with baseline cortisol, there is no robust association with peripheral cytokines. To conclude, alterations in body weight and energy metabolism might scale acute brain responses to stress more strongly in females compared to males, echoing observational studies. Our findings highlight sex-dependent associations of stress with differences in endocrine markers, largely independent of peripheral inflammation.
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Affiliation(s)
- Anne Kühnel
- Section of Medical Psychology, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Bonn, Bonn, Germany.
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany.
| | - Jonas Hagenberg
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
- Institute of Computational Biology, Helmholtz Zentrum Munich, Neuherberg, Germany
| | - Janine Knauer-Arloth
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Computational Biology, Helmholtz Zentrum Munich, Neuherberg, Germany
| | - Maik Ködel
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | | | | | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.
- German Center for Mental Health, Tübingen, Germany.
| | - Nils B Kroemer
- Section of Medical Psychology, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Bonn, Bonn, Germany
- German Center for Mental Health, Tübingen, Germany
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TüCMH), University of Tübingen, Tübingen, Germany
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2
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Hodges CB, Steinberg JL, Zuniga EA, Ma L, Bjork JM, Moeller FG. Chronic Cocaine Use and White Matter Coherence: A Diffusion Tensor Imaging Study. J Stud Alcohol Drugs 2023; 84:585-597. [PMID: 36971714 PMCID: PMC10488304 DOI: 10.15288/jsad.21-00410] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 01/09/2023] [Indexed: 08/29/2023] Open
Abstract
OBJECTIVE Chronic substance use and its effects on brain function and structure has long been of interest to clinicians and researchers. Prior cross-sectional comparisons of diffusion tensor imaging (DTI) metrics have suggested deleterious effects of chronic substance use (i.e., cocaine use) on white matter coherence. However, it is unclear how these effects may replicate across geographic regions when examined with similar technologies. In this study, we sought to conduct a replication of previous work in this area and determine whether there are any patterns of persistent differences in white matter microstructure between individuals with a history of cocaine use disorder (CocUD, according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition) and healthy controls. METHOD A total of 46 participants (21 healthy controls, 25 chronic cocaine users) were recruited from the Richmond, Virginia metropolitan area. Information regarding past and current substance use was collected from all participants. Participants also completed structural and DTI scans. RESULTS Consistent with previous DTI studies, significant differences were found between fractional anisotropy (FA) and axial diffusivity (AD) CocUD and controls, with CocUD showing lower FA and AD in the right inferior and superior longitudinal fasciculus, the genu, body, and splenium of the corpus callosum, and the anterior, posterior, and superior corona radiata, among several other regions. These differences were not significant for other diffusivity metrics. Lifetime alcohol consumption was greater in the CocUD group, but lifetime alcohol consumption did not show a significant linear relationship with any of the DTI metrics in within-group regression analyses. CONCLUSIONS These data align with previously reported declines in white matter coherence in chronic cocaine users. However, it is less clear whether comorbid alcohol consumption results in an additive deleterious effect on white matter microstructure.
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Affiliation(s)
- Cooper B. Hodges
- Department of Physical Medicine and Rehabilitation, School of Medicine, Virginia Commonwealth University, Richmond, Virginia
| | - Joel L. Steinberg
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, Virginia
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
- C. Kenneth and Dianne Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, Virginia
| | - Edward A. Zuniga
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, Virginia
| | - Liangsuo Ma
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, Virginia
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - James M. Bjork
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, Virginia
| | - F. Gerard Moeller
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
- C. Kenneth and Dianne Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, Virginia
- Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, Virginia
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3
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Steinhäuser JL, Teed AR, Al-Zoubi O, Hurlemann R, Chen G, Khalsa SS. Reduced vmPFC-insula functional connectivity in generalized anxiety disorder: a Bayesian confirmation study. Sci Rep 2023; 13:9626. [PMID: 37316518 DOI: 10.1038/s41598-023-35939-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/26/2023] [Indexed: 06/16/2023] Open
Abstract
Differences in the correlated activity of networked brain regions have been reported in individuals with generalized anxiety disorder (GAD) but an overreliance on null-hypothesis significance testing (NHST) limits the identification of disorder-relevant relationships. In this preregistered study, we applied both a Bayesian statistical framework and NHST to the analysis of resting-state fMRI scans from females with GAD and matched healthy comparison females. Eleven a-priori hypotheses about functional connectivity (FC) were evaluated using Bayesian (multilevel model) and frequentist (t-test) inference. Reduced FC between the ventromedial prefrontal cortex (vmPFC) and the posterior-mid insula (PMI) was confirmed by both statistical approaches and was associated with anxiety sensitivity. FC between the vmPFC-anterior insula, the amygdala-PMI, and the amygdala-dorsolateral prefrontal cortex (dlPFC) region pairs did not survive multiple comparison correction using the frequentist approach. However, the Bayesian model provided evidence for these region pairs having decreased FC in the GAD group. Leveraging Bayesian modeling, we demonstrate decreased FC of the vmPFC, insula, amygdala, and dlPFC in females with GAD. Exploiting the Bayesian framework revealed FC abnormalities between region pairs excluded by the frequentist analysis and other previously undescribed regions in GAD, demonstrating the value of applying this approach to resting-state FC data in clinical investigations.
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Affiliation(s)
- Jonas L Steinhäuser
- Laureate Institute for Brain Research, Tulsa, OK, USA.
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
| | - Adam R Teed
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Obada Al-Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, USA
| | - René Hurlemann
- Department of Psychiatry, School of Medicine & Health Sciences, University of Oldenburg, Oldenburg, Germany
- Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
| | - Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, MD, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, Tulsa, OK, USA.
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA.
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4
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Pollak C, Kügler D, Breteler MMB, Reuter M. Quantifying MR Head Motion in the Rhineland Study - A Robust Method for Population Cohorts. Neuroimage 2023; 275:120176. [PMID: 37209757 DOI: 10.1016/j.neuroimage.2023.120176] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/22/2023] [Accepted: 05/15/2023] [Indexed: 05/22/2023] Open
Abstract
Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.
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Affiliation(s)
- Clemens Pollak
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - David Kügler
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Martin Reuter
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
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Forthman KL, Kuplicki R, Yeh HW, Khalsa SS, Paulus MP, Guinjoan SM. Transdiagnostic behavioral and genetic contributors to repetitive negative thinking: A machine learning approach. J Psychiatr Res 2023; 162:207-213. [PMID: 37178517 DOI: 10.1016/j.jpsychires.2023.05.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 04/25/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Repetitive negative thinking (RNT) is a symptom that can negatively impact the treatment and course of common psychiatric disorders such as depression and anxiety. We aimed to characterize behavioral and genetic correlates of RNT to infer potential contributors to its genesis and maintenance. METHODS We applied a machine learning (ML) ensemble method to define the contribution of fear, interoceptive, reward, and cognitive variables to RNT, along with polygenic risk scores (PRS) for neuroticism, obsessive compulsive disorder (OCD), worry, insomnia, and headaches. We used the PRS and 20 principal components of the behavioral and cognitive variables to predict intensity of RNT. We employed the Tulsa-1000 study, a large database of deeply phenotyped individuals recruited between 2015 and 2018. RESULTS PRS for neuroticism was the main predictor of RNT intensity (R2=0.027,p<0.001). Behavioral variables indicative of faulty fear learning and processing, as well as aberrant interoceptive aversiveness, were significant contributors to RNT severity. Unexpectedly, we observed no contribution of reward behavior and diverse cognitive function variables. LIMITATIONS This study is an exploratory approach that must be validated with a second, independent cohort. Furthermore, this is an association study, limiting causal inference. CONCLUSIONS RNT is highly determined by genetic risk for neuroticism, a behavioral construct that confers risk to a variety of internalizing disorders, and by emotional processing and learning features, including interoceptive aversiveness. These results suggest that targeting emotional and interoceptive processing areas, which involve central autonomic network structures, could be useful in the modulation of RNT intensity.
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Affiliation(s)
- Katherine L Forthman
- Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK, 74136, USA
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK, 74136, USA
| | - Hung-Wen Yeh
- Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK, 74136, USA; Health Services & Outcomes Research, Children's Mercy Research Institute, 2401 Gilham Road, Kansas City, MO, 64108, USA; School of Medicine, University of Missouri-Kansas City, 2411 Holmes St, Kansas City, MO, 64108, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK, 74136, USA; Oxley College of Health Sciences, University of Tulsa, 1215 South Boulder Ave W, Tulsa, OK, 74119, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK, 74136, USA; Oxley College of Health Sciences, University of Tulsa, 1215 South Boulder Ave W, Tulsa, OK, 74119, USA
| | - Salvador M Guinjoan
- Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK, 74136, USA; Department of Psychiatry, Oklahoma University Health Sciences Center, The University of Oklahoma-Tulsa, Schusterman Center, 4502 E. 41st Street, Tulsa, OK, 74135, USA.
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Smith R, Lavalley CA, Taylor S, Stewart JL, Khalsa SS, Berg H, Ironside M, Paulus MP, Aupperle R. Elevated decision uncertainty and reduced avoidance drives in depression, anxiety and substance use disorders during approach-avoidance conflict: a replication study. J Psychiatry Neurosci 2023; 48:E217-E231. [PMID: 37339816 PMCID: PMC10281720 DOI: 10.1503/jpn.220226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND Decision-making under approach-avoidance conflict (AAC; e.g., sacrificing quality of life to avoid feared outcomes) may be affected in multiple psychiatric disorders. Recently, we used a computational (active inference) model to characterize information processing differences during AAC in individuals with depression, anxiety and/or substance use disorders. Individuals with psychiatric disorders exhibited increased decision uncertainty (DU) and reduced sensitivity to unpleasant stimuli. This preregistered study aimed to determine the replicability of this processing dysfunction. METHODS A new sample of participants completed the AAC task. Individual-level computational parameter estimates, reflecting decision uncertainty and sensitivity to unpleasant stimuli ("emotion conflict"; EC), were obtained and compared between groups. Subsequent analyses combining the prior and current samples allowed assessment of narrower disorder categories. RESULTS The sample in the present study included 480 participants: 97 healthy controls, 175 individuals with substance use disorders and 208 individuals with depression and/or anxiety disorders. Individuals with substance use disorders showed higher DU and lower EC values than healthy controls. The EC values were lower in females, but not males, with depression and/or anxiety disorders than in healthy controls. However, the previously observed difference in DU between participants with depression and/or anxiety disorders and healthy controls did not replicate. Analyses of specific disorders in the combined samples indicated that effects were common across different substance use disorders and affective disorders. LIMITATIONS There were differences, although with small effect size, in age and baseline intellectual functioning between the previous and current sample, which may have affected replication of DU differences in participants with depression and/or anxiety disorders. CONCLUSION The now robust evidence base for these clinical group differences motivates specific questions that should be addressed in future research: can DU and EC become behavioural treatment targets, and can we identify neural substrates of DU and EC that could be used to measure severity of dysfunction or as neuromodulatory treatment targets?
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Affiliation(s)
- Ryan Smith
- From the Laureate Institute for Brain Research, Tulsa, Okla., USA
| | | | - Samuel Taylor
- From the Laureate Institute for Brain Research, Tulsa, Okla., USA
| | | | - Sahib S Khalsa
- From the Laureate Institute for Brain Research, Tulsa, Okla., USA
| | - Hannah Berg
- From the Laureate Institute for Brain Research, Tulsa, Okla., USA
| | - Maria Ironside
- From the Laureate Institute for Brain Research, Tulsa, Okla., USA
| | - Martin P Paulus
- From the Laureate Institute for Brain Research, Tulsa, Okla., USA
| | - Robin Aupperle
- From the Laureate Institute for Brain Research, Tulsa, Okla., USA
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7
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Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Ment Health 2023; 10:e42045. [PMID: 36729567 PMCID: PMC9936371 DOI: 10.2196/42045] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/02/2022] [Accepted: 11/20/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. OBJECTIVE This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. METHODS A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. RESULTS A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). CONCLUSIONS These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.
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Affiliation(s)
- Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Ledia Lazeri
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
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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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9
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Logan NE, Westfall DR, Raine LB, Anteraper SA, Chaddock-Heyman L, Whitfield-Gabrieli S, Kramer AF, Hillman CH. The Differential Effects of Adiposity and Fitness on Functional Connectivity in Preadolescent Children. Med Sci Sports Exerc 2022; 54:1702-1713. [PMID: 35763600 PMCID: PMC9481684 DOI: 10.1249/mss.0000000000002964] [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] [Indexed: 11/21/2022]
Abstract
PURPOSE Childhood obesity is a global health concern, with >340 million youth considered overweight or obese. In addition to contributing greatly to health care costs, excess adiposity associated with obesity is considered a major risk factor for premature mortality from cardiovascular and metabolic diseases and is also negatively associated with cognitive and brain health. A complementary line of research highlights the importance of cardiorespiratory fitness, a by-product of engaging in physical activity, on an abundance of health factors, including cognitive and brain health. METHODS This study investigated the relationship among excess adiposity (visceral adipose tissue [VAT], subcutaneous abdominal adipose tissue), total abdominal adipose tissue, whole-body percent fat [WB%FAT], body mass index (BMI), and fat-free cardiorespiratory fitness (FF-V̇O 2max ) on resting-state functional connectivity (RSFC) in 121 ( f = 68) children (7-11 yr) using a data-driven whole-brain multivoxel pattern analysis. RESULTS Multivoxel pattern analysis revealed brain regions that were significantly associated with VAT, BMI, WB%FAT, and FF-V̇O 2 measures. Yeo's (2011) RSFC-based seven-network cerebral cortical parcellation was used for labeling the results . Post hoc seed-to-voxel analyses found robust negative correlations of VAT and BMI with areas involved in the visual, somatosensory, dorsal attention, ventral attention, limbic, frontoparietal, and default mode networks. Further, positive correlations of FF-V̇O 2 were observed with areas involved in the ventral attention and frontoparietal networks. These novel findings indicate that negative health factors in childhood may be selectively and negatively associated with the 7 Yeo-defined functional networks, yet positive health factors (FF-V̇O 2 ) may be positively associated with these networks. CONCLUSIONS These novel results extend the current literature to suggest that BMI and adiposity are negatively associated with, and cardiorespiratory fitness (corrected for fat-free mass) is positively associated with, RSFC networks in children.
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Affiliation(s)
- Nicole E. Logan
- Department of Psychology, Northeastern University, Boston, MA
| | | | - Lauren B. Raine
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, MA
| | - Sheeba A. Anteraper
- Carle Illinois Advanced Imaging Center (CIAIC), The University of Illinois Urbana-Champaign, Urbana, IL
| | - Laura Chaddock-Heyman
- Department of Psychology, Northeastern University, Boston, MA
- Beckman Institute, University of Illinois Urbana-Champaign, Urbana, IL
| | | | - Arthur F. Kramer
- Department of Psychology, Northeastern University, Boston, MA
- Beckman Institute, University of Illinois Urbana-Champaign, Urbana, IL
| | - Charles H. Hillman
- Department of Psychology, Northeastern University, Boston, MA
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, MA
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10
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Figueroa-Hall LK, Xu B, Kuplicki R, Ford BN, Burrows K, Teague TK, Sen S, Yeh HW, Irwin MR, Savitz J, Paulus MP. Psychiatric symptoms are not associated with circulating CRP concentrations after controlling for medical, social, and demographic factors. Transl Psychiatry 2022; 12:279. [PMID: 35821205 PMCID: PMC9276683 DOI: 10.1038/s41398-022-02049-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 06/26/2022] [Accepted: 06/30/2022] [Indexed: 01/08/2023] Open
Abstract
Elevated serum concentrations (>3 mg/L) of the acute-phase protein, C-reactive protein (CRP), is used as a clinical marker of inflammation and is reported to be a strong risk factor for cardiovascular disease. In psychiatric populations, CRP concentration is reported to be higher in depressed versus healthy individuals. Positive associations between CRP and depression have been established in both clinical and community samples, but effect sizes are attenuated after controlling for confounding variables. Similarly, emerging research has begun to draw a link between inflammation, symptoms of anxiety, and substance abuse. Given the high level of comorbid anxiety and substance use disorders in many depressed populations, this study examined whether depression (Patient Health Questionnaire 9 [PHQ-9]) and substance use-related (Drug Abuse Screening Test [DAST]) symptoms were associated with CRP concentrations in the blood after adjusting for relevant medical, social, and demographic covariates in a large sample undergoing screening for several transdiagnostic psychiatric research studies. A total of 1,724 participants were analyzed for association of CRP with variables using multivariate linear regression. An unadjusted model with no covariates showed that PHQ-9 was significantly associated with CRP in All (β = 0.125), Female (β = 0.091), and Male (β = 0.154) participants, but DAST was significantly associated with CRP in males only (β = 0.120). For the adjusted model, in both males and females, mood-stabilizer treatment (β = 0.630), opioid medication (β = 0.360), body mass index (β = 0.244), percent body fat (β = 0.289), nicotine use (β = 0.063), and self-reported sleep disturbance (β = 0.061) were significantly associated with increased CRP concentrations. In females, oral contraceptive use (β = 0.576), and waist-to-hip ratio (β = 0.086), and in males, non-steroidal anti-inflammatory drug use (β = 0.367) were also associated with increased CRP concentrations. There was no significant association between CRP and individual depressive, anxiety, or substance use-related symptoms when covariates were included in the regression models. These results suggest that associations between circulating CRP and the severity of psychiatric symptoms are dependent on the type of covariates controlled for in statistical analyses.
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Affiliation(s)
| | - Bohan Xu
- Laureate Institute for Brain Research, Tulsa, OK, 74136, USA
- Department of Computer Science, Tandy School of Computer Science, The University of Tulsa, Tulsa, OK, 74104, USA
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, Tulsa, OK, 74136, USA
| | - Bart N Ford
- Department of Pharmacology & Physiology, Oklahoma State University, Center for Health Sciences, Tulsa, OK, 74107, USA
| | - Kaiping Burrows
- Laureate Institute for Brain Research, Tulsa, OK, 74136, USA
| | - T Kent Teague
- Department of Surgery and Department of Psychiatry, University of Oklahoma-School of Community Medicine, Tulsa, OK, 74135, USA
| | - Sandip Sen
- Department of Computer Science, Tandy School of Computer Science, The University of Tulsa, Tulsa, OK, 74104, USA
| | - Hung-Wen Yeh
- Division of Health Services & Outcomes Research, Children's Mercy Kansas City, Kansas City, MO, 64108, USA
| | - Michael R Irwin
- Department of Psychiatry and Behavioral Sciences, UCLA Geffen School of Medicine, Los Angeles, CA, 90095, USA
| | - Jonathan Savitz
- Laureate Institute for Brain Research, Tulsa, OK, 74136, USA
- Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK, 74199, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, 74136, USA
- Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK, 74199, USA
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11
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Poudel R, Tobia MJ, Riedel MC, Salo T, Flannery JS, Hill-Bowen LD, Dick AS, Laird AR, Parra CM, Sutherland MT. Risky decision-making strategies mediate the relationship between amygdala activity and real-world financial savings among individuals from lower income households: A pilot study. Behav Brain Res 2022; 428:113867. [PMID: 35385783 PMCID: PMC10739684 DOI: 10.1016/j.bbr.2022.113867] [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: 07/25/2021] [Revised: 03/06/2022] [Accepted: 03/28/2022] [Indexed: 11/19/2022]
Abstract
Lower financial savings among individuals experiencing adverse social determinants of health (SDoH) increases vulnerabilities during times of crisis. SDoH including low socioeconomic status (low-SES) influence cognitive abilities as well as health and life outcomes that may perpetuate poverty and disparities. Despite evidence suggesting a role for financial growth in minimizing SDoH-related disparities and vulnerabilities, neurobiological mechanisms linked with financial behavior remain to be elucidated. As such, we examined the relationships between brain activity during decision-making (DM), laboratory-based task performance, and money savings behavior. Participants (N = 24, 14 females) from low-SES households (income<$20,000/year) underwent fMRI scanning while performing the Balloon Analogue Risk Task (BART), a DM paradigm probing risky- and strategic-DM processes. Participants also completed self-report instruments characterizing relevant personality characteristics and then engaged in a community outreach financial program where amount of money saved was tracked over a 6-month period. Regarding BART-related brain activity, we observed expected activity in regions implicated in reward and emotional processing including the amygdala. Regarding brain-behavior relationships, we found that laboratory-based BART performance mediated the impact of amygdala activity on real-world behavior. That is, elevated amygdala activity was linked with BART strategic-DM which, in turn, was linked with more money saved after 6 months. In exploratory analyses, this mediation was moderated by emotion-related personality characteristics such that, only individuals reporting lower alexithymia demonstrated a relationship between amygdala activity and savings. These outcomes suggest that DM-related amygdala activity and/or emotion-related personality characteristics may provide utility as an endophenotypic marker of individual's financial savings behavior.
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Affiliation(s)
- Ranjita Poudel
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Michael J Tobia
- Department of Physics, Florida International University, Miami, FL, United States
| | - Michael C Riedel
- Department of Physics, Florida International University, Miami, FL, United States
| | - Taylor Salo
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Jessica S Flannery
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Lauren D Hill-Bowen
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Anthony S Dick
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, United States
| | - Carlos M Parra
- College of Business, Florida International University, Miami, FL, United States
| | - Matthew T Sutherland
- Department of Psychology, Florida International University, Miami, FL, United States.
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12
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Limits to the generalizability of resting-state functional magnetic resonance imaging studies of youth: An examination of ABCD Study® baseline data. Brain Imaging Behav 2022; 16:1919-1925. [PMID: 35552993 DOI: 10.1007/s11682-022-00665-2] [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] [Accepted: 03/18/2022] [Indexed: 11/02/2022]
Abstract
This study examined how resting-state functional magnetic resonance imaging (rs-fMRI) data quality and availability relate to clinical and sociodemographic variables within the Adolescent Brain Cognitive Development Study. A sample of participants with an adequate sample of quality baseline rs-fMRI data containing low average motion (framewise displacement ≤ 0.15; low-noise; n = 4,356) was compared to a sample of participants without an adequate sample of quality data and/or containing high average motion (higher-noise; n = 7,437) using Chi-squared analyses and t-tests. A linear mixed model examined relationships between clinical and sociodemographic characteristics and average head motion in the sample with low-noise data. Relative to the sample with higher-noise data, the low-noise sample included more females, youth identified by parents as non-Hispanic white, and youth with married parents, higher parent education, and greater household incomes (ORs = 1.32-1.42). Youth in the low-noise sample were also older and had higher neurocognitive skills, lower BMIs, and fewer externalizing and neurodevelopmental problems (ds = 0.12-0.30). Within the low-noise sample, several clinical and demographic characteristics related to motion. Thus, participants with low-noise rs-fMRI data may be less representative of the general population and motion may remain a confound in this sample. Future rs-fMRI studies of youth should consider these limitations in the design and analysis stages in order to optimize the representativeness and clinical relevance of analyses and results.
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13
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Langenecker SA, Westlund Schreiner M, Thomas LR, Bessette KL, DelDonno SR, Jenkins LM, Easter RE, Stange JP, Pocius SL, Dillahunt A, Love TM, Phan KL, Koppelmans V, Paulus M, Lindquist MA, Caffo B, Mickey BJ, Welsh RC. Using Network Parcels and Resting-State Networks to Estimate Correlates of Mood Disorder and Related Research Domain Criteria Constructs of Reward Responsiveness and Inhibitory Control. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:76-84. [PMID: 34271215 PMCID: PMC8748287 DOI: 10.1016/j.bpsc.2021.06.014] [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: 01/07/2021] [Revised: 05/14/2021] [Accepted: 06/13/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND Resting-state graph-based network edges can be powerful tools for identification of mood disorders. We address whether these edges can be integrated with Research Domain Criteria (RDoC) constructs for accurate identification of mood disorder-related markers, while minimizing active symptoms of disease. METHODS We compared 132 individuals with currently remitted or euthymic mood disorder with 65 healthy comparison participants, ages 18-30 years. Subsets of smaller brain parcels, combined into three prominent networks and one network of parcels overlapping across these networks, were used to compare edge differences between groups. Consistent with the RDoC framework, we evaluated individual differences with performance measure regressors of inhibitory control and reward responsivity. Within an omnibus regression model, we predicted edges related to diagnostic group membership, performance within both RDoC domains, and relevant interactions. RESULTS There were several edges of mood disorder group, predominantly of greater connectivity across networks, different than those related to individual differences in inhibitory control and reward responsivity. Edges related to diagnosis and inhibitory control did not align well with prior literature, whereas edges in relation to reward responsivity constructs showed greater alignment with prior literature. Those edges in interaction between RDoC constructs and diagnosis showed a divergence for inhibitory control (negative interactions in default mode) relative to reward (positive interactions with salience and emotion network). CONCLUSIONS In conclusion, there is evidence that prior simple network models of mood disorders are currently of insufficient biological or diagnostic clarity or that parcel-based edges may be insufficiently sensitive for these purposes.
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Affiliation(s)
| | | | - Leah R Thomas
- Department of Psychiatry, University of Utah, Salt Lake City, Utah; Department of Psychology, University of Utah, Salt Lake City, Utah
| | - Katie L Bessette
- Department of Psychiatry, University of Utah, Salt Lake City, Utah; Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois
| | - Sophia R DelDonno
- Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois
| | - Lisanne M Jenkins
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Evanston, Illinois
| | - Rebecca E Easter
- Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois
| | - Jonathan P Stange
- Department of Psychiatry & Psychology, University of Illinois at Chicago, Chicago, Illinois; Department of Psychology, University of Southern California, Los Angeles, California
| | | | - Alina Dillahunt
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
| | - Tiffany M Love
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
| | - K Luan Phan
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, Ohio
| | | | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma
| | | | - Brian Caffo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Brian J Mickey
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
| | - Robert C Welsh
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
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14
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Daoust J, Schaffer J, Zeighami Y, Dagher A, García-García I, Michaud A. White matter integrity differences in obesity: A meta-analysis of diffusion tensor imaging studies. Neurosci Biobehav Rev 2021; 129:133-141. [PMID: 34284063 DOI: 10.1016/j.neubiorev.2021.07.020] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 06/07/2021] [Accepted: 07/15/2021] [Indexed: 01/17/2023]
Abstract
Some Diffusion Tensor Imaging studies have shown a loss of white matter (WM) integrity linked to impaired cognitive function in obese individuals. However, inconsistent WM integrity changes have been reported. We aimed to identify which WM tracts show consistent changes with obesity. We conducted a systematic search to find studies examining the association between obesity-related measures and Fractional Anisotropy (FA) or Mean Diffusivity. We performed a meta-analysis with FA datasets using Anisotropic Effect Size-Signed Differential Mapping software. The meta-analysis showed that increased obesity measurements were related to reduced FA in the genu of the corpus callosum. We validated our findings using an independent sample from the Human Connectome Project dataset, which supports lower FA in this region in individuals with obesity compared to those with normal weight (p = 0.028). Our findings provide evidence that obesity is associated with reduced WM integrity in the genu of the corpus callosum, a tract linking frontal areas involved in executive function. Future studies are needed on the mechanisms linking obesity with loss of WM integrity.
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Affiliation(s)
- Justine Daoust
- Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, 2725 chemin Sainte-Foy, Québec, Québec, G1V 4G5, Canada; School of Nutrition, Université Laval, 2325 rue de l'Université, Québec, Québec, G1V 0A6, Canada
| | - Joelle Schaffer
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, 3801 University Street, Montreal, Québec, H3A 2B4, Canada
| | - Yashar Zeighami
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, 3801 University Street, Montreal, Québec, H3A 2B4, Canada
| | - Alain Dagher
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, 3801 University Street, Montreal, Québec, H3A 2B4, Canada
| | - Isabel García-García
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Gran Via de les Corts Catalanes, 585, 08007, Barcelona, Spain
| | - Andréanne Michaud
- Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, 2725 chemin Sainte-Foy, Québec, Québec, G1V 4G5, Canada; School of Nutrition, Université Laval, 2325 rue de l'Université, Québec, Québec, G1V 0A6, Canada.
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15
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Kuplicki R, Touthang J, Al Zoubi O, Mayeli A, Misaki M, Aupperle RL, Teague TK, McKinney BA, Paulus MP, Bodurka J. Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies. Front Psychiatry 2021; 12:682495. [PMID: 34220587 PMCID: PMC8247461 DOI: 10.3389/fpsyt.2021.682495] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 05/19/2021] [Indexed: 01/16/2023] Open
Abstract
Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.
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Affiliation(s)
- Rayus Kuplicki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - James Touthang
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - NeuroMAP-Investigators
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Community Medicine, Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States
| | - Robin L. Aupperle
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Community Medicine, Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States
| | - T. Kent Teague
- Department of Surgery, University of Oklahoma School of Community Medicine, Tulsa, OK, United States
- Department of Psychiatry, University of Oklahoma School of Community Medicine, Tulsa, OK, United States
- Department of Biochemistry and Microbiology, Oklahoma State University Center for Health Sciences, Tulsa, OK, United States
| | - Brett A. McKinney
- Department of Mathematics, University of Tulsa, Tulsa, OK, United States
- Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States
| | | | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States
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16
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Smith R, Kirlic N, Stewart JL, Touthang J, Kuplicki R, McDermott TJ, Taylor S, Khalsa SS, Paulus MP, Aupperle RL. Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample. Sci Rep 2021; 11:11783. [PMID: 34083701 PMCID: PMC8175390 DOI: 10.1038/s41598-021-91308-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 05/17/2021] [Indexed: 11/16/2022] Open
Abstract
Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N = 48), SUDs (N = 29), and DEP/ANX (N = 121). We also assessed 2-3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps < .001) and self-reported anxiety (r = .30, p < .001) and decision difficulty (r = .44, p < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA.
| | - Namik Kirlic
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Jennifer L Stewart
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - James Touthang
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Timothy J McDermott
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Samuel Taylor
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Robin L Aupperle
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
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17
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Moia S, Termenon M, Uruñuela E, Chen G, Stickland RC, Bright MG, Caballero-Gaudes C. ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI. Neuroimage 2021; 233:117914. [PMID: 33684602 PMCID: PMC8351526 DOI: 10.1016/j.neuroimage.2021.117914] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 01/25/2021] [Accepted: 02/22/2021] [Indexed: 12/19/2022] Open
Abstract
Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.
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Affiliation(s)
- Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Spain; University of the Basque Country UPV/EHU, Donostia, Spain.
| | - Maite Termenon
- Basque Center on Cognition, Brain and Language, Donostia, Spain
| | - Eneko Uruñuela
- Basque Center on Cognition, Brain and Language, Donostia, Spain; University of the Basque Country UPV/EHU, Donostia, Spain
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH/NIH/HHS, Bethesda, MD, United States
| | - Rachael C Stickland
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Molly G Bright
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
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18
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Smith R, Feinstein JS, Kuplicki R, Forthman KL, Stewart JL, Paulus MP, Khalsa SS. Perceptual insensitivity to the modulation of interoceptive signals in depression, anxiety, and substance use disorders. Sci Rep 2021; 11:2108. [PMID: 33483527 PMCID: PMC7822872 DOI: 10.1038/s41598-021-81307-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 12/28/2020] [Indexed: 01/18/2023] Open
Abstract
This study employed a series of heartbeat perception tasks to assess the hypothesis that cardiac interoceptive processing in individuals with depression/anxiety (N = 221), and substance use disorders (N = 136) is less flexible than that of healthy individuals (N = 53) in the context of physiological perturbation. Cardiac interoception was assessed via heartbeat tapping when: (1) guessing was allowed; (2) guessing was not allowed; and (3) experiencing an interoceptive perturbation (inspiratory breath hold) expected to amplify cardiac sensation. Healthy participants showed performance improvements across the three conditions, whereas those with depression/anxiety and/or substance use disorder showed minimal improvement. Machine learning analyses suggested that individual differences in these improvements were negatively related to anxiety sensitivity, but explained relatively little variance in performance. These results reveal a perceptual insensitivity to the modulation of interoceptive signals that was evident across several common psychiatric disorders, suggesting that interoceptive deficits in the realm of psychopathology manifest most prominently during states of homeostatic perturbation.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Justin S Feinstein
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | | | - Jennifer L Stewart
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA.
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA.
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19
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Smith R, Kirlic N, Stewart JL, Touthang J, Kuplicki R, Khalsa SS, Feinstein J, Paulus MP, Aupperle RL. Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach. J Psychiatry Neurosci 2021. [PMID: 33119490 DOI: 10.31234/osf.io/t2dhn] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict). METHODS A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence. RESULTS The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002). LIMITATIONS This study was limited by heterogeneity of the clinical sample and an inability to examine learning. CONCLUSION These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.
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Affiliation(s)
- Ryan Smith
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Namik Kirlic
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Jennifer L Stewart
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - James Touthang
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Rayus Kuplicki
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Sahib S Khalsa
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Justin Feinstein
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Martin P Paulus
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Robin L Aupperle
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
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20
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Smith R, Kirlic N, Stewart JL, Touthang J, Kuplicki R, Khalsa SS, Feinstein J, Paulus MP, Aupperle RL. Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach. J Psychiatry Neurosci 2021; 46:E74-E87. [PMID: 33119490 PMCID: PMC7955838 DOI: 10.1503/jpn.200032] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict). METHODS A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence. RESULTS The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002). LIMITATIONS This study was limited by heterogeneity of the clinical sample and an inability to examine learning. CONCLUSION These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.
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Affiliation(s)
- Ryan Smith
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Namik Kirlic
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Jennifer L Stewart
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - James Touthang
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Rayus Kuplicki
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Sahib S Khalsa
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Justin Feinstein
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Martin P Paulus
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
| | - Robin L Aupperle
- From the Laureate Institute for Brain Research, Tulsa, OK, USA (Smith, Kirlic, Stewart, Touthang, Kuplicki, Khalsa, Feinstein, Paulus, Aupperle); and the Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA (Stewart, Khalsa, Paulus, Aupperle)
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21
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Beyer F, Prehn K, Wüsten KA, Villringer A, Ordemann J, Flöel A, Witte AV. Weight loss reduces head motion: Revisiting a major confound in neuroimaging. Hum Brain Mapp 2020; 41:2490-2494. [PMID: 32239733 PMCID: PMC7267971 DOI: 10.1002/hbm.24959] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/17/2020] [Accepted: 02/11/2020] [Indexed: 01/09/2023] Open
Abstract
Head motion during magnetic resonance imaging (MRI) induces image artifacts that affect virtually every brain measure. In parallel, cross‐sectional observations indicate a correlation of head motion with age, psychiatric disease status and obesity, raising the possibility of a systematic artifact‐induced bias in neuroimaging outcomes in these conditions, due to the differences in head motion. Yet, a causal link between obesity and head motion has not been tested in an experimental design. Here, we show that a change in body mass index (BMI) (i.e., weight loss after bariatric surgery) systematically decreases head motion during MRI. In this setting, reduced imaging artifacts due to lower head motion might result in biased estimates of neural differences induced by changes in BMI. Overall, our finding urges the need to rigorously control for head motion during MRI to enable valid results of neuroimaging outcomes in populations that differ in head motion due to obesity or other conditions.
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Affiliation(s)
- Frauke Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Subproject A1, CRC 1052 "Obesity Mechanisms", University of Leipzig, Leipzig, Germany
| | - Kristin Prehn
- Department of Neurology & NeuroCure Clinical Research Center, Charité University Medicine, Berlin, Germany.,Department of Psychology, Medical School Hamburg, Hamburg, Germany
| | - Katharina A Wüsten
- Department of Neurology, University of Greifswald, Greifswald, Germany.,German Center for Neurodegenerative Diseases, Standort Rostock/Greifswald, Greifswald, Germany
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Subproject A1, CRC 1052 "Obesity Mechanisms", University of Leipzig, Leipzig, Germany
| | - Jürgen Ordemann
- Center for Bariatric and Metabolic Surgery, Charité University Medicine, Berlin, Germany.,Zentrum für Adipositas und Metabolische Chirurgie, Vivantes Klinikum Spandau, Berlin, Germany
| | - Agnes Flöel
- Department of Neurology & NeuroCure Clinical Research Center, Charité University Medicine, Berlin, Germany.,Department of Neurology, University of Greifswald, Greifswald, Germany.,German Center for Neurodegenerative Diseases, Standort Rostock/Greifswald, Greifswald, Germany.,Center for Stroke Research, Charité University Medicine, Berlin, Germany
| | - A Veronica Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Subproject A1, CRC 1052 "Obesity Mechanisms", University of Leipzig, Leipzig, Germany
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22
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Xu B, Moradi M, Kuplicki R, Stewart JL, McKinney B, Sen S, Paulus MP. Machine Learning Analysis of Electronic Nose in a Transdiagnostic Community Sample With a Streamlined Data Collection Approach: No Links Between Volatile Organic Compounds and Psychiatric Symptoms. Front Psychiatry 2020; 11:503248. [PMID: 33192639 PMCID: PMC7524957 DOI: 10.3389/fpsyt.2020.503248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 08/24/2020] [Indexed: 11/25/2022] Open
Abstract
Non-intrusive, easy-to-use and pragmatic collection of biological processes is warranted to evaluate potential biomarkers of psychiatric symptoms. Prior work with relatively modest sample sizes suggests that under highly-controlled sampling conditions, volatile organic compounds extracted from the human breath (exhalome), often measured by an electronic nose ("e-nose"), may be related to physical and mental health. The present study utilized a streamlined data collection approach and attempted to replicate and extend prior e-nose links to mental health in a standard research setting within large transdiagnostic community dataset (N = 1207; 746 females; 18-61 years) who completed a screening visit at the Laureate Institute for Brain Research between 07/2016 and 05/2018. Factor analysis was used to obtain latent exhalome variables, and machine learning approaches were employed using these latent variables to predict three types of symptoms independent of each other (depression, anxiety, and substance use disorder) within separate training and a test sets. After adjusting for age, gender, body mass index, and smoking status, the best fitting algorithm produced by the training set accounted for nearly 0% of the test set's variance. In each case the standard error included the zero line, indicating that models were not predictive of clinical symptoms. Although some sample variance was predicted, findings did not generalize to out-of-sample data. Based on these findings, we conclude that the exhalome, as measured by the e-nose within a less-controlled environment than previously reported, is not able to provide clinically useful assessments of current depression, anxiety or substance use severity.
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Affiliation(s)
- Bohan Xu
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Computer Science, Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States
| | - Mahdi Moradi
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Computer Science, Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Jennifer L Stewart
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Community Medicine, Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States
| | - Brett McKinney
- Department of Computer Science, Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States.,Department of Mathematics, College of Engineering & Natural Sciences, University of Tulsa, Tulsa, OK, United States
| | - Sandip Sen
- Department of Computer Science, Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Community Medicine, Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States.,Department of Psychiatry, School of Medicine, University of California San Diego, San Diego, CA, United States
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23
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Bolton TAW, Kebets V, Glerean E, Zöller D, Li J, Yeo BTT, Caballero-Gaudes C, Van De Ville D. Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI. Neuroimage 2019; 209:116433. [PMID: 31841680 DOI: 10.1016/j.neuroimage.2019.116433] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 11/11/2019] [Accepted: 12/02/2019] [Indexed: 12/21/2022] Open
Abstract
The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-of-the-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise displacement (FD) score, to regress out models of nuisance variables, and to include average FD as a covariate in group-level analyses. Here, we studied individual motion time courses at time points typically retained in fMRI analyses. We observed that even in this set of putatively clean time points, motion exhibited a very clear spatio-temporal structure, so that we could distinguish subjects into separate groups of movers with varying characteristics. Then, we showed that this spatio-temporal motion cartography tightly relates to a broad array of anthropometric and cognitive factors. Convergent results were obtained from two different analytical perspectives: univariate assessment of behavioural differences across mover subgroups unraveled defining markers, while subsequent multivariate analysis broadened the range of involved factors and clarified that multiple motion/behaviour modes of covariance overlap in the data. Our results demonstrate that even the smaller episodes of motion typically retained in fMRI analyses carry structured, behaviourally relevant information. They call for further examinations of possible biases in current regression-based motion correction strategies.
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Affiliation(s)
- Thomas A W Bolton
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
| | - Valeria Kebets
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland; Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland
| | - Daniela Zöller
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland; Developmental Imaging and Psychopathology Laboratory, Office Médico-Pédagogique, Department of Psychiatry, University of Geneva (UNIGE), Geneva, Switzerland
| | - Jingwei Li
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | | | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
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24
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Clausen AN, Aupperle RL, Yeh HW, Waller D, Payne J, Kuplicki R, Akeman E, Paulus M. Machine Learning Analysis of the Relationships Between Gray Matter Volume and Childhood Trauma in a Transdiagnostic Community-Based Sample. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:734-742. [PMID: 31053534 DOI: 10.1016/j.bpsc.2019.03.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 03/01/2019] [Accepted: 03/01/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND Childhood trauma is a significant risk factor for adult psychopathology. Previous investigations have implicated childhood trauma-related structural changes in anterior cingulate, dorsolateral prefrontal and orbitofrontal cortex, and hippocampus. Using a large transdiagnostic community sample, the goal of this investigation was to differentially associate regional gray matter (GM) volume with childhood trauma severity specifically, distinct from adult psychopathology. METHODS A total of 577 non-treatment-seeking adults (n = 207 men) completed diagnostic, childhood trauma, and structural magnetic resonance imaging assessments with regional GM volume estimated using FreeSurfer. Elastic net analysis was conducted in a nested cross-validation framework, with GM volumes, adult psychopathology, age, education, sex, and magnetic resonance imaging coil type as potential predictors for childhood trauma severity. RESULTS Elastic net identified age, education, sex, medical condition, adult psychopathology, and 13 GM regions as predictors of childhood trauma severity. GM regions identified included right caudate; left pallidum; bilateral insula and cingulate sulcus; left superior, inferior, and orbital frontal regions; and regions within temporal and parietal lobes and cerebellum. CONCLUSIONS Results from this large, transdiagnostic sample implicate GM volume in regions central to current neurobiological theories of trauma (e.g., prefrontal cortex) as well as additional regions involved in reward, interoceptive, attentional, and sensory processing (e.g., striatal, insula, and parietal/occipital cortices). Future longitudinal studies examining the functional impact of structural changes in this broader network of regions are needed to clarify the role each may play in longer-term outcomes following trauma.
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Affiliation(s)
- Ashley N Clausen
- Laureate Institute for Brain Research, Tulsa, Oklahoma; VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham VA Healthcare System, Durham, North Carolina; Duke Brain Imaging and Analysis Center, Duke Medical University, Durham, North Carolina.
| | - Robin L Aupperle
- Laureate Institute for Brain Research, Tulsa, Oklahoma; Department of Community Medicine, University of Tulsa, Tulsa, Oklahoma; Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma
| | - Hung-Wen Yeh
- Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Darcy Waller
- Department of Psychology, University of Iowa, Iowa City, Iowa
| | - Janelle Payne
- Laureate Institute for Brain Research, Tulsa, Oklahoma
| | | | | | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma; Department of Community Medicine, University of Tulsa, Tulsa, Oklahoma; Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma
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