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Portugal LCL, Ramos TC, Fernandes O, Bastos AF, Campos B, Mendlowicz MV, da Luz M, Portella C, Berger W, Volchan E, David IA, Erthal F, Pereira MG, de Oliveira L. Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms. BMC Psychiatry 2023; 23:719. [PMID: 37798693 PMCID: PMC10552290 DOI: 10.1186/s12888-023-05220-x] [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: 05/12/2023] [Accepted: 09/25/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND The present study aimed to apply multivariate pattern recognition methods to predict posttraumatic stress symptoms from whole-brain activation patterns during two contexts where the aversiveness of unpleasant pictures was manipulated by the presence or absence of safety cues. METHODS Trauma-exposed participants were presented with neutral and mutilation pictures during functional magnetic resonance imaging (fMRI) collection. Before the presentation of pictures, a text informed the subjects that the pictures were fictitious ("safe context") or real-life scenes ("real context"). We trained machine learning regression models (Gaussian process regression (GPR)) to predict PTSD symptoms in real and safe contexts. RESULTS The GPR model could predict PTSD symptoms from brain responses to mutilation pictures in the real context but not in the safe context. The brain regions with the highest contribution to the model were the occipito-parietal regions, including the superior parietal gyrus, inferior parietal gyrus, and supramarginal gyrus. Additional analysis showed that GPR regression models accurately predicted clusters of PTSD symptoms, nominal intrusion, avoidance, and alterations in cognition. As expected, we obtained very similar results as those obtained in a model predicting PTSD total symptoms. CONCLUSION This study is the first to show that machine learning applied to fMRI data collected in an aversive context can predict not only PTSD total symptoms but also clusters of PTSD symptoms in a more aversive context. Furthermore, this approach was able to identify potential biomarkers for PTSD, especially in occipitoparietal regions.
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Affiliation(s)
- Liana Catarina Lima Portugal
- Neurophysiology Laboratory, Department of Physiological Sciences, Roberto Alcantara Gomes Biology Institute, Biomedical Center, Universidade do Estado do Rio de Janeiro, Boulevard 28 de Setembro, 87 - Vila Isabel, Rio de Janeiro, RJ, 20551-030, Brazil
- Laboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Universidade Federal Fluminense, R. Prof. Hernani Pires de Mello, 101, São Domingos, Niterói, RJ, 24210-130, Brazil
| | - Taiane Coelho Ramos
- Laboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Universidade Federal Fluminense, R. Prof. Hernani Pires de Mello, 101, São Domingos, Niterói, RJ, 24210-130, Brazil
- Mídiacom Lab, Institute of Computing, Universidade Federal Fluminense, Av. Gal. Milton Tavares de Souza, s/n, São Domingos, Niterói, RJ, 24210-310, Brazil
| | - Orlando Fernandes
- Laboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Universidade Federal Fluminense, R. Prof. Hernani Pires de Mello, 101, São Domingos, Niterói, RJ, 24210-130, Brazil
| | - Aline Furtado Bastos
- Laboratório de Neurobiologia, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, 373 - Cidade Universitária da Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, 21941-902, Brazil
| | - Bruna Campos
- Laboratório de Neurobiologia, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, 373 - Cidade Universitária da Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, 21941-902, Brazil
| | - Mauro Vitor Mendlowicz
- Linpes, Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Av. Venceslau Brás, 71 - Botafogo, Rio de Janeiro, RJ, 22290-140, Brazil
| | - Mariana da Luz
- Linpes, Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Av. Venceslau Brás, 71 - Botafogo, Rio de Janeiro, RJ, 22290-140, Brazil
| | - Carla Portella
- Linpes, Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Av. Venceslau Brás, 71 - Botafogo, Rio de Janeiro, RJ, 22290-140, Brazil
| | - William Berger
- Linpes, Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Av. Venceslau Brás, 71 - Botafogo, Rio de Janeiro, RJ, 22290-140, Brazil
| | - Eliane Volchan
- Laboratório de Neurobiologia, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, 373 - Cidade Universitária da Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, 21941-902, Brazil
- Linpes, Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Av. Venceslau Brás, 71 - Botafogo, Rio de Janeiro, RJ, 22290-140, Brazil
| | - Isabel Antunes David
- Laboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Universidade Federal Fluminense, R. Prof. Hernani Pires de Mello, 101, São Domingos, Niterói, RJ, 24210-130, Brazil
| | - Fátima Erthal
- Laboratório de Neurobiologia, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, 373 - Cidade Universitária da Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, 21941-902, Brazil
- Linpes, Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Av. Venceslau Brás, 71 - Botafogo, Rio de Janeiro, RJ, 22290-140, Brazil
| | - Mirtes Garcia Pereira
- Laboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Universidade Federal Fluminense, R. Prof. Hernani Pires de Mello, 101, São Domingos, Niterói, RJ, 24210-130, Brazil
| | - Leticia de Oliveira
- Laboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Universidade Federal Fluminense, R. Prof. Hernani Pires de Mello, 101, São Domingos, Niterói, RJ, 24210-130, Brazil.
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Lv Q, Zeljic K, Zhao S, Zhang J, Zhang J, Wang Z. Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning. Neurosci Bull 2023; 39:1309-1326. [PMID: 37093448 PMCID: PMC10387015 DOI: 10.1007/s12264-023-01057-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 02/17/2023] [Indexed: 04/25/2023] Open
Abstract
Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.
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Affiliation(s)
- Qian Lv
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
| | - Kristina Zeljic
- School of Health and Psychological Sciences, City, University of London, London, EC1V 0HB, UK
| | - Shaoling Zhao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Jiangtao Zhang
- Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China
| | - Jianmin Zhang
- Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China.
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Balters S, Schlichting MR, Foland-Ross L, Brigadoi S, Miller JG, Kochenderfer MJ, Garrett AS, Reiss AL. Towards assessing subcortical "deep brain" biomarkers of PTSD with functional near-infrared spectroscopy. Cereb Cortex 2023; 33:3969-3984. [PMID: 36066436 PMCID: PMC10068291 DOI: 10.1093/cercor/bhac320] [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: 06/01/2022] [Revised: 07/21/2022] [Accepted: 07/23/2022] [Indexed: 11/13/2022] Open
Abstract
Assessment of brain function with functional near-infrared spectroscopy (fNIRS) is limited to the outer regions of the cortex. Previously, we demonstrated the feasibility of inferring activity in subcortical "deep brain" regions using cortical functional magnetic resonance imaging (fMRI) and fNIRS activity in healthy adults. Access to subcortical regions subserving emotion and arousal using affordable and portable fNIRS is likely to be transformative for clinical diagnostic and treatment planning. Here, we validate the feasibility of inferring activity in subcortical regions that are central to the pathophysiology of posttraumatic stress disorder (PTSD; i.e. amygdala and hippocampus) using cortical fMRI and simulated fNIRS activity in a sample of adolescents diagnosed with PTSD (N = 20, mean age = 15.3 ± 1.9 years) and age-matched healthy controls (N = 20, mean age = 14.5 ± 2.0 years) as they performed a facial expression task. We tested different prediction models, including linear regression, a multilayer perceptron neural network, and a k-nearest neighbors model. Inference of subcortical fMRI activity with cortical fMRI showed high prediction performance for the amygdala (r > 0.91) and hippocampus (r > 0.95) in both groups. Using fNIRS simulated data, relatively high prediction performance for deep brain regions was maintained in healthy controls (r > 0.79), as well as in youths with PTSD (r > 0.75). The linear regression and neural network models provided the best predictions.
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Affiliation(s)
- Stephanie Balters
- Department of Psychiatry and Behavioral Sciences, Stanford University, 94305 Stanford, CA, USA
| | - Marc R Schlichting
- Department of Aeronautics and Astronautics, Stanford University, 94305 Stanford, CA, USA
| | - Lara Foland-Ross
- Department of Psychiatry and Behavioral Sciences, Stanford University, 94305 Stanford, CA, USA
| | - Sabrina Brigadoi
- Department of Developmental Psychology and Socialisation, University of Padova, 35122 Padova PD, Italy
| | - Jonas G Miller
- Department of Psychology, Stanford University, 94305 Stanford, CA, USA
| | - Mykel J Kochenderfer
- Department of Aeronautics and Astronautics, Stanford University, 94305 Stanford, CA, USA
| | - Amy S Garrett
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at San Antonio, 78229 San Antonio, TX, USA
| | - Allan L Reiss
- Department of Psychiatry and Behavioral Sciences, Stanford University, 94305 Stanford, CA, USA
- Department of Radiology, Stanford University, 94304 Palo Alto, CA, USA
- Department of Pediatrics, Stanford University, 94304 Palo Alto, CA, USA
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Kumar U, Arya A, Agarwal V. Altered functional connectivity in children with ADHD while performing cognitive control task. Psychiatry Res Neuroimaging 2022; 326:111531. [PMID: 36055037 DOI: 10.1016/j.pscychresns.2022.111531] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 07/05/2022] [Accepted: 07/27/2022] [Indexed: 10/15/2022]
Abstract
Response inhibition is one of the crucial cognitive domains that exhibit deficit in children with ADHD. To further elucidate it, this study examines the task-based functional-connectivity in children with attention deficit hyperactive disorder (ADHD). We acquired the fMRI data of 16 unmedicated children with ADHD and 16 typically developing (TD) children who performed the flanker task. MVPA and seed-based connectivity analysis was performed to identify the abnormal connectivity pattern across the whole brain. MVPA revealed that six important regions, namely the right IFG, right SMA, bilateral precentral gyrus, left DLPFC, and left cerebellum, had abnormal connectivity in children with ADHD while they performed the cognitive control task. Out of these six regions, four were further used for whole-brain seed-based functional connectivity analyses, which revealed patterns of significantly altered connectivity across multiple regions. Signal intensities changes were also extracted to perform BOLD- reaction time (RT) correlation analysis, that suggest positive correlation between left DLPFC and right IFG. Overall, the results suggest that children with ADHD are unable to endure high cognitive control demand. Our findings highlight the utility of analyzing brain connectivity data in identifying the abnormal connectivity in children with ADHD.
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Affiliation(s)
- Uttam Kumar
- Centre of Bio-Medical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow India.
| | - Amit Arya
- Department of Psychiatry, King George Medical University, Lucknow India
| | - Vivek Agarwal
- Department of Psychiatry, King George Medical University, Lucknow India
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Fitzgerald JM, Webb EK, Weis CN, Huggins AA, Bennett KP, Miskovich TA, Krukowski JL, deRoon-Cassini TA, Larson CL. Hippocampal Resting-State Functional Connectivity Forecasts Individual Posttraumatic Stress Disorder Symptoms: A Data-Driven Approach. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:139-149. [PMID: 34478884 PMCID: PMC8825698 DOI: 10.1016/j.bpsc.2021.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 07/18/2021] [Accepted: 08/22/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) is a debilitating disorder, and there is no current accurate prediction of who develops it after trauma. Neurobiologically, individuals with chronic PTSD exhibit aberrant resting-state functional connectivity (rsFC) between the hippocampus and other brain regions (e.g., amygdala, prefrontal cortex, posterior cingulate), and these aberrations correlate with severity of illness. Previous small-scale research (n < 25) has also shown that hippocampal rsFC measured acutely after trauma is predictive of future severity using a region-of-interest-based approach. While this is a promising biomarker, to date, no study has used a data-driven approach to test whole-brain hippocampal FC patterns in forecasting the development of PTSD symptoms. METHODS A total of 98 adults at risk of PTSD were recruited from the emergency department after traumatic injury and completed resting-state functional magnetic resonance imaging (8 min) within 1 month; 6 months later, they completed the Clinician-Administered PTSD Scale for DSM-5 for assessment of PTSD symptom severity. Whole-brain rsFC values with bilateral hippocampi were extracted (using CONN) and used in a machine learning kernel ridge regression analysis (PRoNTo); a k-folds (k = 10) and 70/30 testing versus training split approach were used for cross-validation (1000 iterations to bootstrap confidence intervals for significance values). RESULTS Acute hippocampal rsFC significantly predicted Clinician-Administered PTSD Scale for DSM-5 scores at 6 months (r = 0.30, p = .006; mean squared error = 120.58, p = .006; R2 = 0.09, p = .025). In post hoc analyses, hippocampal rsFC remained significant after controlling for demographics, PTSD symptoms at baseline, and depression, anxiety, and stress severity at 6 months (B = 0.59, SE = 0.20, p = .003). CONCLUSIONS Findings suggest that functional connectivity of the hippocampus across the brain acutely after traumatic injury is associated with prospective PTSD symptom severity.
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Affiliation(s)
| | - Elisabeth Kate Webb
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
| | - Carissa N. Weis
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
| | - Ashley A. Huggins
- Medical University of South Carolina, Department of Psychiatry, Charleston, SC, USA
| | | | | | | | - Terri A. deRoon-Cassini
- Medical College of Wisconsin, Department of Surgery, Division of Trauma & Acute Care Surgery, Milwaukee, WI, USA
| | - Christine L. Larson
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
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Young KA. Matthew J. Friedman and the VA National PTSD Brain Bank: New Transcriptomic Insight into PTSD Pathophysiology. Psychiatry 2022; 85:171-182. [PMID: 35588482 DOI: 10.1080/00332747.2022.2068932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Balters S, Li R, Espil FM, Piccirilli A, Liu N, Gundran A, Carrion VG, Weems CF, Cohen JA, Reiss AL. Functional near-infrared spectroscopy brain imaging predicts symptom severity in youth exposed to traumatic stress. J Psychiatr Res 2021; 144:494-502. [PMID: 34768071 DOI: 10.1016/j.jpsychires.2021.10.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 09/11/2021] [Accepted: 10/18/2021] [Indexed: 10/19/2022]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique with the potential to enable the assessment of posttraumatic stress disorder (PTSD) brain biomarkers in an affordable and portable manner. Consistent with biological models of PTSD, functional magnetic resonance imaging (fMRI) and fNIRS studies of adults with trauma exposure and PTSD symptoms suggest increased activation in the dorsolateral prefrontal cortex (dlPFC) and ventrolateral PFC (vlPFC) in response to negative emotion stimuli. We tested this theory with fNIRS assessment among youth exposed to traumatic stress and experiencing PTSD symptoms (PTSS). A portable fNIRS system collected hemodynamic responses from (N = 57) youth with PTSS when engaging in a classic emotion expression task that included fearful and neutral faces stimuli. The General Linear Model was applied to identify cortical activations associated with the facial stimuli. Subsequently, a prediction model was established via a Support Vector Regression to determine whether PTSS severity could be predicted based on fNIRS-derived cortical response measures and individual demographic information. Results were consistent with findings from adult fMRI and fNIRS studies of PTSS showing increased activation in the dlPFC and vlPFC in response to negative emotion stimuli. Subsequent prediction analysis revealed ten features (i.e., cortical responses from eight frontocortical fNIRS channels, age and sex) strongly correlated with PTSS severity (r = 0.65, p < .001). Our findings suggest the potential utility of fNIRS as a portable tool for the detection of putative PTSS brain biomarkers.
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Affiliation(s)
- Stephanie Balters
- Department of Psychiatry and Behavioral Sciences, Stanford University, United States.
| | - Rihui Li
- Department of Psychiatry and Behavioral Sciences, Stanford University, United States.
| | - Flint M Espil
- Department of Psychiatry and Behavioral Sciences, Stanford University, United States
| | - Aaron Piccirilli
- Department of Psychiatry and Behavioral Sciences, Stanford University, United States
| | - Ning Liu
- Department of Psychiatry and Behavioral Sciences, Stanford University, United States
| | - Andrew Gundran
- Department of Psychiatry and Behavioral Sciences, Stanford University, United States
| | - Victor G Carrion
- Department of Psychiatry and Behavioral Sciences, Stanford University, United States
| | - Carl F Weems
- Department of Human Development and Family Studies, Iowa State University, United States
| | - Judith A Cohen
- Allegheny Health Network, Drexel University College of Medicine, United States
| | - Allan L Reiss
- Department of Psychiatry and Behavioral Sciences, Stanford University, United States; Department of Radiology, Stanford University, United States; Department of Pediatrics, Stanford University, United States
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Fitzgerald JM, Belleau EL, Miskovich TA, Pedersen WS, Larson CL. Multi-voxel pattern analysis of amygdala functional connectivity at rest predicts variability in posttraumatic stress severity. Brain Behav 2020; 10:e01707. [PMID: 32525273 PMCID: PMC7428479 DOI: 10.1002/brb3.1707] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 04/16/2020] [Accepted: 05/15/2020] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION Resting state functional magnetic resonance imaging (rsfMRI) studies demonstrate that individuals with posttraumatic stress disorder (PTSD) exhibit atypical functional connectivity (FC) between the amygdala, involved in the generation of emotion, and regions responsible for emotional appraisal (e.g., insula, orbitofrontal cortex [OFC]) and regulation (prefrontal cortex [PFC], anterior cingulate cortex). Consequently, atypical amygdala FC within an emotional processing and regulation network may be a defining feature of PTSD, although altered FC does not seem constrained to one brain region. Instead, altered amygdala FC involves a large, distributed brain network in those with PTSD. The present study used a machine-learning data-driven approach, multi-voxel pattern analysis (MVPA), to predict PTSD severity based on whole-brain patterns of amygdala FC. METHODS Trauma-exposed adults (N = 90) completed the PTSD Checklist-Civilian Version to assess symptoms and a 5-min rsfMRI. Whole-brain FC values to bilateral amygdala were extracted and used in a relevance vector regression analysis with a leave-one-out approach for cross-validation with permutation testing (1,000) to obtain significance values. RESULTS Results demonstrated that amygdala FC predicted PCL-C scores with statistically significant accuracy (r = .46, p = .001; mean sum of squares = 130.46, p = .001; R2 = 0.21, p = .001). Prediction was based on whole-brain amygdala FC, although regions that informed prediction (top 10%) included the OFC, amygdala, and dorsolateral PFC. CONCLUSION Findings demonstrate the utility of MVPA based on amygdala FC to predict individual severity of PTSD symptoms and that amygdala FC within a fear acquisition and regulation network contributed to accurate prediction.
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Affiliation(s)
| | - Emily L Belleau
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA.,Harvard Medical School, Boston, MA, USA
| | | | - Walker S Pedersen
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - Christine L Larson
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
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