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Coppersmith DDL, Kleiman EM, Millner AJ, Wang SB, Arizmendi C, Bentley KH, DeMarco D, Fortgang RG, Zuromski KL, Maimone JS, Haim A, Onnela JP, Bird SA, Smoller JW, Mair P, Nock MK. Heterogeneity in suicide risk: Evidence from personalized dynamic models. Behav Res Ther 2024; 180:104574. [PMID: 38838615 PMCID: PMC11323201 DOI: 10.1016/j.brat.2024.104574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 05/09/2024] [Accepted: 05/22/2024] [Indexed: 06/07/2024]
Abstract
Most theories of suicide propose within-person changes in psychological states cause suicidal thoughts/behaviors; however, most studies use between-person analyses. Thus, there are little empirical data exploring current theories in the way they are hypothesized to occur. We used a form of statistical modeling called group iterative multiple model estimation (GIMME) to explore one theory of suicide: The Interpersonal Theory of Suicide (IPTS). GIMME estimates personalized statistical models for each individual and associations shared across individuals. Data were from a real-time monitoring study of individuals with a history of suicidal thoughts/behavior (adult sample: participants = 111, observations = 25,242; adolescent sample: participants = 145, observations = 26,182). Across both samples, none of theorized IPTS effects (i.e., contemporaneous effect from hopeless to suicidal thinking) were shared at the group level. There was significant heterogeneity in the personalized models, suggesting there are different pathways through which different people come to experience suicidal thoughts/behaviors. These findings highlight the complexity of suicide risk and the need for more personalized approaches to assessment and prediction.
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Affiliation(s)
| | - Evan M Kleiman
- Rutgers, The State University of New Jersey, Department of Psychology, USA
| | - Alexander J Millner
- Harvard University, Department of Psychology, USA; Franciscan Children's, Mental Health Research, USA
| | | | - Cara Arizmendi
- Duke University School of Medicine, Department of Population Health Sciences, USA
| | - Kate H Bentley
- Harvard University, Department of Psychology, USA; Massachusetts General Hospital, Department of Psychiatry, USA
| | | | - Rebecca G Fortgang
- Harvard University, Department of Psychology, USA; Massachusetts General Hospital, Department of Psychiatry, USA
| | | | | | - Adam Haim
- National Institute of Mental Health, USA
| | - Jukka-Pekka Onnela
- Harvard T. H. Chan School of Public Health, Department of Biostatistics, USA
| | - Suzanne A Bird
- Massachusetts General Hospital, Department of Psychiatry, USA
| | | | - Patrick Mair
- Harvard University, Department of Psychology, USA
| | - Matthew K Nock
- Harvard University, Department of Psychology, USA; Franciscan Children's, Mental Health Research, USA; Massachusetts General Hospital, Department of Psychiatry, USA
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2
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Ulusoy I, Geduk S. Improved brain effective connectivity modelling by dynamic Bayesian networks. J Neurosci Methods 2024; 409:110211. [PMID: 38968975 DOI: 10.1016/j.jneumeth.2024.110211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 06/10/2024] [Accepted: 06/28/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND If brain effective connectivity network modelling (ECN) could be accurately achieved, early diagnosis of neurodegenerative diseases would be possible. It has been observed in the literature that Dynamic Bayesian Network (DBN) based methods are more successful than others. However, DBNs have not been applied easily and tested much due to computational complexity problems in structure learning. NEW METHOD This study introduces an advanced method for modelling brain ECNs using improved discrete DBN (Improved- dDBN) which addresses the computational challenges previously limiting DBN application, offering solutions that enable accurate and fast structure modelling. RESULTS The practical data and prior sizes needed for the convergence to the globally correct network structure are proved to be much smaller than the theoretical ones using simulated dDBN data. Besides, Hill Climbing is shown to converge to the true structure at a reasonable iteration step size when the appropriate data and prior sizes are used. Finally, importance of data quantization methods are analysed. COMPARISON WITH EXISTING METHODS The Improved-dDBN method performs better and robust, when compared to the existing methods for realistic scenarios such as varying graph complexity, various input conditions, noise cases and non-stationary connections. The data used in these tests is the simulated fMRI BOLD time series proposed in the literature. CONCLUSIONS Improved-dDBN is a good candidate to be used on real datasets to accelerate developments in brain ECN modelling and neuroscience. Appropriate data and prior sizes can be identified based on the approach proposed in this study for global and fast convergence.
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Affiliation(s)
- Ilkay Ulusoy
- Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey.
| | - Salih Geduk
- Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey.
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3
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Ye A, Bollen KA. Path and Direction Discovery in Individual Dynamic Factor Models: A Regularized Hybrid Unified Structural Equation Modeling with Latent Variable. MULTIVARIATE BEHAVIORAL RESEARCH 2024:1-24. [PMID: 39058418 DOI: 10.1080/00273171.2024.2354232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/28/2024]
Abstract
There has been an increasing call to model multivariate time series data with measurement error. The combination of latent factors with a vector autoregressive (VAR) model leads to the dynamic factor model (DFM), in which dynamic relations are derived within factor series, among factors and observed time series, or both. However, a few limitations exist in the current DFM representatives and estimation: (1) the dynamic component contains either directed or undirected contemporaneous relations, but not both, (2) selecting the optimal model in exploratory DFM is a challenge, (3) the consequences of structural misspecifications from model selection is barely studied. Our paper serves to advance DFM with a hybrid VAR representations and the utilization of LASSO regularization to select dynamic implied instrumental variable, two-stage least squares (MIIV-2SLS) estimation. Our proposed method highlights the flexibility in modeling the directions of dynamic relations with a robust estimation. We aim to offer researchers guidance on model selection and estimation in person-centered dynamic assessments.
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Affiliation(s)
- Ai Ye
- Lehrstuhl für Psychologische Methodenlehre & Diagnostik, Department Psychologie, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Kenneth A Bollen
- Department of Psychology and Neuroscience, Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Heshmati S, Westhoff M, Hofmann SG. Novel Approaches Toward Studying Change: Implications for Understanding and Treating Psychopathology. Psychiatr Clin North Am 2024; 47:287-300. [PMID: 38724120 DOI: 10.1016/j.psc.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
In this article, the authors critically evaluate contemporary models of psychopathology and therapies, underscoring the limitations of traditional symptom-based classification approaches in mental health. The authors introduce a paradigm shift in the field, toward a process-oriented and dynamic systems approach to psychotherapy that offers deeper insights into the complex interplay of symptoms and individual experiences in psychopathology. These approaches offer a more personalized and effective understanding and treatment of mental health issues, moving beyond static and 1-dimensional views. The authors discuss the implications for clinical practice, emphasizing improved assessment, diagnosis, and tailored treatment strategies.
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Affiliation(s)
- Saida Heshmati
- Department of Psychology, Claremont Graduate University, 150 E. 10th Street, Claremont, CA 91711, USA.
| | - Marlon Westhoff
- Department of Psychology, Philipps-University of Marburg, Translational Clinical Psychology Group, Schulstraße 12, Marburg D-35032, Germany
| | - Stefan G Hofmann
- Department of Psychology, Philipps-University of Marburg, Translational Clinical Psychology Group, Schulstraße 12, Marburg D-35032, Germany
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5
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Park JJ, Chow SM, Epskamp S, Molenaar PCM. Subgrouping with Chain Graphical VAR Models. MULTIVARIATE BEHAVIORAL RESEARCH 2024; 59:543-565. [PMID: 38351547 PMCID: PMC11187704 DOI: 10.1080/00273171.2023.2289058] [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] [Indexed: 06/19/2024]
Abstract
Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.
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Affiliation(s)
- Jonathan J. Park
- Department of Human Development and Family Studies, The Pennsylvania State University
| | - Sy-Miin Chow
- Department of Human Development and Family Studies, The Pennsylvania State University
| | - Sacha Epskamp
- Department of Psychology, National University of Singapore
| | - Peter C. M. Molenaar
- Department of Human Development and Family Studies, The Pennsylvania State University
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6
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Guo T, Wang X, Wu J, Schwieter WJ, Liu H. Effects of contextualized emotional conflict control on domain-general conflict control: fMRI evidence of neural network reconfiguration. Soc Cogn Affect Neurosci 2024; 19:nsae001. [PMID: 38174430 PMCID: PMC10868129 DOI: 10.1093/scan/nsae001] [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: 03/24/2023] [Revised: 10/24/2023] [Accepted: 01/03/2024] [Indexed: 01/05/2024] Open
Abstract
Domain-general conflict control refers to the cognitive process in which individuals suppress task-irrelevant information and extract task-relevant information. It supports both effective implementation of cognitive conflict control and emotional conflict control. The present study employed functional magnetic resonance imaging and adopted an emotional valence conflict task and the arrow version of the flanker task to induce contextualized emotional conflicts and cognitive conflicts, respectively. The results from the conjunction analysis showed that the multitasking-related activity in the pre-supplementary motor area, bilateral dorsal premotor cortices, the left posterior intraparietal sulcus (IPS), the left anterior IPS and the right inferior occipital gyrus represents common subprocesses for emotional and cognitive conflict control, either in parallel or in close succession. These brain regions were used as nodes in the domain-general conflict control network. The results from the analyses on the brain network connectivity patterns revealed that emotional conflict control reconfigures the domain-general conflict control network in a connective way as evidenced by different communication and stronger connectivity among the domain-general conflict control network. Together, these findings offer the first empirical-based elaboration on the brain network underpinning emotional conflict control and how it reconfigures the domain-general conflict control network in interactive ways.
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Affiliation(s)
- Tingting Guo
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China
- Key Laboratory of Brain and Cognitive Neuroscience, Dalian, Liaoning Province 116029, China
| | - Xiyuan Wang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China
- Key Laboratory of Brain and Cognitive Neuroscience, Dalian, Liaoning Province 116029, China
| | - Junjie Wu
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Tianjin Normal University, Tianjin 300382, China
| | - W. John Schwieter
- Language Acquisition, Multilingualism, and Cognition Laboratory/Bilingualism Matters, Wilfrid Laurier University, Waterloo N2L3C5, Canada
- Department of Linguistics and Languages, McMaster University, Hamilton L8S4L8, Canada
| | - Huanhuan Liu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China
- Key Laboratory of Brain and Cognitive Neuroscience, Dalian, Liaoning Province 116029, China
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Caudle MM, Spadoni AD, Schiehser DM, Simmons AN, Bomyea J. Neural activity and network analysis for understanding reasoning using the matrix reasoning task. Cogn Process 2023; 24:585-594. [PMID: 37597116 PMCID: PMC10533635 DOI: 10.1007/s10339-023-01152-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/18/2023] [Indexed: 08/21/2023]
Abstract
Reasoning requires the ability to manipulate mental representations and understand relationships between objects. There is a paucity of research regarding the functional connections between multiple brain areas that may interact during commonly used reasoning tasks. The present study aimed to examine functional activation and connectivity of frontoparietal regions during a Matrix Decision Making Task, completed by twenty-one right-handed healthy participants while undergoing fMRI. Voxel-wise whole brain analysis of neural response to the task revealed activation spanning dorsal and lateral prefrontal, occipital, and parietal regions. Utilizing Group Iterative Multiple Model Estimation, a data-driven approach that estimates the presence and direction of connectivity between specific ROIs, connectivity between prefrontal and sensory processing regions were revealed. Moreover, the magnitude of connectivity strength between the left precentral gyrus and left dorsal cingulate (dACC) was positively correlated with MR behavioral performance. Taken together, results are consistent with earlier work demonstrating involvement of regions comprising the central executive network in relational reasoning. These data expand existing knowledge regarding communication of key brain regions during the task and demonstrate that understanding how key brain regions are interconnected can effectively predict the quality of behavioral output.
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Affiliation(s)
- M M Caudle
- Department of Psychiatry, University of California, 9500 Gilman Dr, La Jolla, CA, 92093, USA
- Joint Doctoral Program in Clinical Psychology, San Diego State University, University of California San Diego, 6363 Alvarado Court, Suite 103, San Diego, CA, 92120, USA
- Research Service, VA San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA, 92161, USA
| | - A D Spadoni
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA, 92161, USA
- Department of Psychiatry, University of California, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - D M Schiehser
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA, 92161, USA
- Department of Psychiatry, University of California, 9500 Gilman Dr, La Jolla, CA, 92093, USA
- Research Service, VA San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA, 92161, USA
| | - A N Simmons
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA, 92161, USA
- Department of Psychiatry, University of California, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - J Bomyea
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, 3350 La Jolla Village Dr, San Diego, CA, 92161, USA.
- Department of Psychiatry, University of California, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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8
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Kraus B, Zinbarg R, Braga RM, Nusslock R, Mittal VA, Gratton C. Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry. Neurosci Biobehav Rev 2023; 152:105259. [PMID: 37268180 PMCID: PMC10527506 DOI: 10.1016/j.neubiorev.2023.105259] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/22/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023]
Abstract
A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.
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Affiliation(s)
- Brian Kraus
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Richard Zinbarg
- Department of Psychology, Northwestern University, Evanston, IL, USA; The Family Institute at Northwestern University, Evanston, IL, USA
| | - Rodrigo M Braga
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA; Institute for Innovations in Developmental Sciences (DevSci), Northwestern University, Chicago, IL, USA; Northwestern University, Department of Psychiatry, Chicago, IL, USA; Northwestern University, Medical Social Sciences, Chicago, IL, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA; Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Psychology, Florida State University, Tallahassee, FL, USA; Program in Neuroscience, Florida State University, Tallahassee, FL, USA
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9
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Blain SD, Taylor SF, Rutherford SE, Lasagna CA, Yao B, Angstadt M, Green MF, Johnson TD, Peltier S, Diwadkar VA, Tso IF. Neurobehavioral indices of gaze perception are associated with social cognition across schizophrenia patients and healthy controls. JOURNAL OF PSYCHOPATHOLOGY AND CLINICAL SCIENCE 2023; 132:733-748. [PMID: 37384487 PMCID: PMC10513759 DOI: 10.1037/abn0000846] [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] [Indexed: 07/01/2023]
Abstract
BACKGROUND Gaze perception is a basic building block of social cognition, which is impaired in schizophrenia (SZ) and contributes to functional outcomes. Few studies, however, have investigated neural underpinnings of gaze perception and their relation to social cognition. We address this gap. METHOD We recruited 77 SZ patients and 71 healthy controls, who completed various social-cognition tasks. During functional magnetic resonance imaging, participants (62 SZ, 54 controls) completed a gaze-perception task, where they judged whether faces with varying gaze angles were self-directed or averted; as a control condition, participants identified stimulus gender. Activation estimates were extracted based on (a) task versus baseline, (b) gaze-perception versus gender-identification, (c) parametric modulation by perception of stimuli as self-directed versus averted, and (d) parametric modulation by stimulus gaze angle. We used latent variable analysis to test associations among diagnostic group, brain activation, gaze perception, and social cognition. RESULTS Preferential activation to gaze perception was observed throughout dorsomedial prefrontal cortex, superior temporal sulcus, and insula. Activation was modulated by stimulus gaze angle and perception of stimuli as self-directed versus averted. More precise gaze perception and higher task-related activation were associated with better social cognition. Patients with SZ showed hyperactivation within left pre-/postcentral gyrus, which was associated with more precise gaze perception and fewer symptoms and thus may be a compensatory mechanism. CONCLUSIONS Neural and behavioral indices of gaze perception were related to social cognition, across patients and controls. This suggests gaze perception is an important perceptual building block for more complex social cognition. Results are discussed in the context of dimensional psychopathology and clinical heterogeneity. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Scott D. Blain
- Department of Psychiatry, University of Michigan, Ann Arbor, MI
- Department of Psychiatry & Behavioral Health, The Ohio State University, Columbus, OH
| | - Stephan F. Taylor
- Department of Psychiatry, University of Michigan, Ann Arbor, MI
- Department of Psychology, University of Michigan, Ann Arbor, MI
| | - Saige E. Rutherford
- Department of Psychiatry, University of Michigan, Ann Arbor, MI
- Donders Center for Medical Neuroscience, Nijmegen, Netherlands
| | | | - Beier Yao
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA; Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Mike Angstadt
- Functional MRI Lab, University of Michigan, Ann Arbor, MI
| | - Michael F. Green
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, CA
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA
| | | | - Scott Peltier
- Functional MRI Lab, University of Michigan, Ann Arbor, MI
| | - Vaibhav A. Diwadkar
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University, Detroit, MI
| | - Ivy F. Tso
- Department of Psychiatry, University of Michigan, Ann Arbor, MI
- Department of Psychiatry & Behavioral Health, The Ohio State University, Columbus, OH
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10
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Zhang Z, Zhang Z, Ji J, Liu J. Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data. Brain Sci 2023; 13:995. [PMID: 37508927 PMCID: PMC10376969 DOI: 10.3390/brainsci13070995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 07/30/2023] Open
Abstract
Using machine learning methods to estimate brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data has garnered significant attention in the fields of neuroinformatics and bioinformatics. However, existing methods usually require retraining the model for each subject, which ignores the knowledge shared across subjects. In this paper, we propose a novel framework for estimating effective connectivity based on an amortization transformer, named AT-EC. In detail, AT-EC first employs an amortization transformer to model the dynamics of fMRI time series and infer brain effective connectivity across different subjects, which can train an amortized model that leverages the shared knowledge from different subjects. Then, an assisted learning mechanism based on functional connectivity is designed to assist the estimation of the brain effective connectivity network. Experimental results on both simulated and real-world data demonstrate the efficacy of our method.
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Affiliation(s)
- Zuozhen Zhang
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Ziqi Zhang
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Junzhong Ji
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jinduo Liu
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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11
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Webb CA, Murray L, Tierney AO, Gates KM. Dynamic processes in behavioral activation therapy for anhedonic adolescents: Modeling common and patient-specific relations. J Consult Clin Psychol 2023:2023-78506-001. [PMID: 37276084 PMCID: PMC10696134 DOI: 10.1037/ccp0000830] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
OBJECTIVE Behavioral activation (BA) is a brief intervention for depression encouraging gradual and systematic re-engagement with rewarding activities and behaviors. Given this treatment focus, BA may be particularly beneficial for adolescents with prominent anhedonia, a predictor of poor treatment response and common residual symptom. We applied group iterative multiple model estimation (GIMME) to ecological momentary assessment (EMA) treatment data to investigate common and person-specific processes during BA for anhedonic adolescents. METHOD Thirty-nine adolescents (Mage = 15.7 years old, 67% female, 81% White) with elevated anhedonia (Snaith-Hamilton Pleasure Scale) were enrolled in a 12-week BA trial, with weekly anhedonia assessments. EMA surveys were triggered every other week (2-3 surveys per day) throughout treatment assessing current positive affect (PA) and negative affect (NA), engagement in pleasurable activities and social interactions, anticipatory pleasure, rumination, and recent pleasurable and stressful experiences. RESULTS A multilevel model revealed significant decreases in anhedonia, t(25.5) = -4.76, p < .001, over the 12-week trial. GIMME results indicated substantial heterogeneity in variable networks across patients. PA was the variable with the greatest number (22% of all paths vs. 11% for NA) of predictive paths to other symptoms (i.e., highest out-degree). Higher PA (but not NA) out-degree was associated with greater anhedonia improvement, t(25.8) = -2.22, p = .035. CONCLUSIONS Results revealed substantial heterogeneity in variable relations across patients, which may obscure the search for common processes of change in BA. PA may be a particularly important treatment target for anhedonic adolescents in BA. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Christian A. Webb
- Harvard Medical School, Department of Psychiatry, Boston, MA
- McLean Hospital, Center for Depression, Anxiety & Stress Research, Belmont, MA
| | - Laura Murray
- Harvard Medical School, Department of Psychiatry, Boston, MA
- McLean Hospital, Center for Depression, Anxiety & Stress Research, Belmont, MA
| | - Anna O. Tierney
- McLean Hospital, Center for Depression, Anxiety & Stress Research, Belmont, MA
| | - Kathleen M. Gates
- University of North Carolina at Chapel Hill, Department of Psychology, Chapel Hill, NC
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12
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Tiego J, Martin EA, DeYoung CG, Hagan K, Cooper SE, Pasion R, Satchell L, Shackman AJ, Bellgrove MA, Fornito A. Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology. NATURE MENTAL HEALTH 2023; 1:304-315. [PMID: 37251494 PMCID: PMC10210256 DOI: 10.1038/s44220-023-00057-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 03/24/2023] [Indexed: 05/31/2023]
Abstract
Our capacity to measure diverse aspects of human biology has developed rapidly in the past decades, but the rate at which these techniques have generated insights into the biological correlates of psychopathology has lagged far behind. The slow progress is partly due to the poor sensitivity, specificity and replicability of many findings in the literature, which have in turn been attributed to small effect sizes, small sample sizes and inadequate statistical power. A commonly proposed solution is to focus on large, consortia-sized samples. Yet it is abundantly clear that increasing sample sizes will have a limited impact unless a more fundamental issue is addressed: the precision with which target behavioral phenotypes are measured. Here, we discuss challenges, outline several ways forward and provide worked examples to demonstrate key problems and potential solutions. A precision phenotyping approach can enhance the discovery and replicability of associations between biology and psychopathology.
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Affiliation(s)
- Jeggan Tiego
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Elizabeth A. Martin
- Department of Psychological Science, University of California, Irvine, CA, USA
| | - Colin G. DeYoung
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Kelsey Hagan
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Samuel E. Cooper
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA
| | - Rita Pasion
- HEI-LAB, Lusófona University, Lisbon, Portugal
| | - Liam Satchell
- Department of Psychology, University of Winchester, Winchester, UK
| | | | - Mark A. Bellgrove
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
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Song J, Tong G, Chao J, Chung J, Zhang M, Lin W, Zhang T, Bentler PM, Zhu W. Data driven pathway analysis and forecast of global warming and sea level rise. Sci Rep 2023; 13:5536. [PMID: 37015939 PMCID: PMC10073234 DOI: 10.1038/s41598-023-30789-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 03/01/2023] [Indexed: 04/06/2023] Open
Abstract
Climate change is a critical issue of our time, and its causes, pathways, and forecasts remain a topic of broader discussion. In this paper, we present a novel data driven pathway analysis framework to identify the key processes behind mean global temperature and sea level rise, and to forecast the magnitude of their increase from the present to 2100. Based on historical data and dynamic statistical modeling alone, we have established the causal pathways that connect increasing greenhouse gas emissions to increasing global mean temperature and sea level, with its intermediate links encompassing humidity, sea ice coverage, and glacier mass, but not for sunspot numbers. Our results indicate that if no action is taken to curb anthropogenic greenhouse gas emissions, the global average temperature would rise to an estimated 3.28 °C (2.46-4.10 °C) above its pre-industrial level while the global sea level would be an estimated 573 mm (474-671 mm) above its 2021 mean by 2100. However, if countries adhere to the greenhouse gas emission regulations outlined in the 2021 United Nations Conference on Climate Change (COP26), the rise in global temperature would lessen to an average increase of 1.88 °C (1.43-2.33 °C) above its pre-industrial level, albeit still higher than the targeted 1.5 °C, while the sea level increase would reduce to 449 mm (389-509 mm) above its 2021 mean by 2100.
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Affiliation(s)
- Jiecheng Song
- Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY, 11794-3600, USA.
| | - Guanchao Tong
- Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY, 11794-3600, USA
| | - Jiayou Chao
- Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY, 11794-3600, USA
| | - Jean Chung
- Duke University, 2080 Duke University Road, Durham, NC, 27708, USA
| | - Minghua Zhang
- School of Marine and Atmospheric Sciences, State University of New York at Stony Brook, Stony Brook, NY, 11794-5000, USA
| | - Wuyin Lin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973-5000, USA
| | - Tao Zhang
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973-5000, USA
| | - Peter M Bentler
- Department of Statistics, University of California, Los Angeles, Los Angeles, CA, 90095-1554, USA
| | - Wei Zhu
- Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY, 11794-3600, USA.
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14
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Ji J, Zou A, Liu J, Yang C, Zhang X, Song Y. A Survey on Brain Effective Connectivity Network Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:1879-1899. [PMID: 34469315 DOI: 10.1109/tnnls.2021.3106299] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of the pathological mechanism associated with neuropsychiatric diseases and facilitate finding new brain network imaging markers for the early diagnosis and evaluation for the treatment of cerebral diseases. A deeper understanding of brain ECNs also greatly promotes brain-inspired artificial intelligence (AI) research in the context of brain-like neural networks and machine learning. Thus, how to picture and grasp deeper features of brain ECNs from functional magnetic resonance imaging (fMRI) data is currently an important and active research area of the human brain connectome. In this survey, we first show some typical applications and analyze existing challenging problems in learning brain ECNs from fMRI data. Second, we give a taxonomy of ECN learning methods from the perspective of computational science and describe some representative methods in each category. Third, we summarize commonly used evaluation metrics and conduct a performance comparison of several typical algorithms both on simulated and real datasets. Finally, we present the prospects and references for researchers engaged in learning ECNs.
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15
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Weigard A, Lane S, Gates K, Beltz A. The influence of autoregressive relation strength and search strategy on directionality recovery in group iterative multiple model estimation. Psychol Methods 2023; 28:379-400. [PMID: 34941327 PMCID: PMC9897594 DOI: 10.1037/met0000460] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Unified structural equation modeling (uSEM) implemented in the group iterative multiple model estimation (GIMME) framework has recently been widely used for characterizing within-person network dynamics of behavioral and functional neuroimaging variables. Previous studies have established that GIMME accurately recovers the presence of relations between variables. However, recovery of relation directionality is less consistent, which is concerning given the importance of directionality estimates for many research questions. There is evidence that strong autoregressive relations may aid directionality recovery and indirect evidence that a novel version of GIMME allowing for multiple solutions could improve recovery when such relations are weak, but it remains unclear how these strategies perform under a range of study conditions. Using comprehensive simulations that varied the strength of autoregressive relations among other factors, this study evaluated the directionality recovery of two GIMME search strategies: (a) estimating autoregressive relations by default in the null model (GIMME-AR) and (b) generating multiple solution paths (GIMME-MS). Both strategies recovered directionality best-and were roughly equivalent in performance-when autoregressive relations were strong (e.g., β = .60). When they were weak (β ≤ .10), GIMME-MS displayed an advantage, although overall directionality recovery was modest. Analyses of empirical data in which autoregressive relations were characteristically strong (resting state functional MRI) versus weak (daily diary) mirrored simulation results and confirmed that these strategies can disagree on directionality when autoregressive relations are weak. Findings have important implications for psychological and neuroimaging applications of uSEM/GIMME and suggest specific scenarios in which researchers might or might not be confident in directionality results. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Alexander Weigard
- Department of Psychology, University of Michigan
- Department of Psychiatry, University of Michigan
| | - Stephanie Lane
- Department of Psychology and Neuroscience, University of
North Carolina at Chapel Hill
| | - Kathleen Gates
- Department of Psychology and Neuroscience, University of
North Carolina at Chapel Hill
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16
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Brinberg M, Lydon-Staley DM. Conceptualizing and Examining Change in Communication Research. COMMUNICATION METHODS AND MEASURES 2023; 17:59-82. [PMID: 37122497 PMCID: PMC10139745 DOI: 10.1080/19312458.2023.2167197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Communication research often focuses on processes of communication, such as how messages impact individuals over time or how interpersonal relationships develop and change. Despite their importance, these change processes are often implicit in much theoretical and empirical work in communication. Intensive longitudinal data are becoming increasingly feasible to collect and, when coupled with appropriate analytic frameworks, enable researchers to better explore and articulate the types of change underlying communication processes. To facilitate the study of change processes, we (a) describe advances in data collection and analytic methods that allow researchers to articulate complex change processes of phenomena in communication research, (b) provide an overview of change processes and how they may be captured with intensive longitudinal methods, and (c) discuss considerations of capturing change when designing and implementing studies. We are excited about the future of studying processes of change in communication research, and we look forward to the iterations between empirical tests and theory revision that will occur as researchers delve into studying change within communication processes.
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17
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Mill RD, Hamilton JL, Winfield EC, Lalta N, Chen RH, Cole MW. Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior. PLoS Biol 2022; 20:e3001686. [PMID: 35980898 PMCID: PMC9387855 DOI: 10.1371/journal.pbio.3001686] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/24/2022] [Indexed: 11/21/2022] Open
Abstract
How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the "where and when") and then allow for empirical testing of alternative network models of brain function that link information to behavior (the "how"). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach-dynamic activity flow modeling-then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory-motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.
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Affiliation(s)
- Ravi D. Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Julia L. Hamilton
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Emily C. Winfield
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Nicole Lalta
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Richard H. Chen
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
- Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, New Jersey, United States of America
| | - Michael W. Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
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18
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Gunther KE, Petrie D, Pearce AL, Fuchs BA, Pérez-Edgar K, Keller KL, Geier C. Heterogeneity in PFC-amygdala connectivity in middle childhood, and concurrent interrelations with inhibitory control and anxiety symptoms. Neuropsychologia 2022; 174:108313. [PMID: 35798067 DOI: 10.1016/j.neuropsychologia.2022.108313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 06/27/2022] [Accepted: 06/29/2022] [Indexed: 10/17/2022]
Abstract
The prefrontal cortex (PFC) is a key brain area in considering adaptive regulatory behaviors. This includes regulatory projections to regions of the limbic system such as the amygdala, where the nature of functional connections may confer lower risk for anxiety disorders. The PFC is also associated with behaviors like executive functioning. Inhibitory control is a behavior encompassed by executive functioning and is generally viewed favorably for adaptive socioemotional development. Yet, some research suggests that high levels of inhibitory control may actually be a risk factor for some maladaptive developmental outcomes, like anxiety disorders. In a sample of 51 children ranging from 7 to 9 years old, we examined resting state functional connectivity between regions of the PFC and the amygdala. We used Subgrouping Group Iterative Multiple Model Estimation (S-GIMME) to identify and characterize data-driven subgroups of individuals with similar networks of connectivity between these brain regions. Generated subgroups were collapsed into children characterized by the presence or absence of recovered connections between the PFC and amygdala. For subsets of children with available data (N = 38-44), we then tested whether inhibitory control, as measured by a stop signal task, moderated the relation between these subgroups and child-reported anxiety symptoms. We found an inverse relation between stop-signal reaction times and reported count of anxiety symptoms when covarying for connectivity group, suggesting that greater inhibitory control was actually related to greater anxiety symptoms, but only when accounting for patterns of PFC-amygdala connectivity. These data suggest that there is a great deal of heterogeneity in the nature of functional connections between the PFC and amygdala during this stage of development. The findings also provide support for the notion of high levels of inhibitory control as a risk factor for anxiety, but trait-level biopsychosocial factors may be important to consider in assessing the nature of risk.
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19
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Sanford BT, Ciarrochi J, Hofmann SG, Chin F, Gates KM, Hayes SC. Toward empirical process-based case conceptualization: An idionomic network examination of the process-based assessment tool. JOURNAL OF CONTEXTUAL BEHAVIORAL SCIENCE 2022. [DOI: 10.1016/j.jcbs.2022.05.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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20
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Doyle CM, Lane ST, Brooks JA, Wilkins RW, Gates KM, Lindquist KA. Unsupervised classification reveals consistency and degeneracy in neural network patterns of emotion. Soc Cogn Affect Neurosci 2022; 17:995-1006. [PMID: 35445241 PMCID: PMC9629478 DOI: 10.1093/scan/nsac028] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 02/24/2022] [Accepted: 04/19/2022] [Indexed: 01/12/2023] Open
Abstract
In the present study, we used an unsupervised classification algorithm to reveal both consistency and degeneracy in neural network connectivity during anger and anxiety. Degeneracy refers to the ability of different biological pathways to produce the same outcomes. Previous research is suggestive of degeneracy in emotion, but little research has explicitly examined whether degenerate functional connectivity patterns exist for emotion categories such as anger and anxiety. Twenty-four subjects underwent functional magnetic resonance imaging (fMRI) while listening to unpleasant music and self-generating experiences of anger and anxiety. A data-driven model building algorithm with unsupervised classification (subgrouping Group Iterative Multiple Model Estimation) identified patterns of connectivity among 11 intrinsic networks that were associated with anger vs anxiety. As predicted, degenerate functional connectivity patterns existed within these overarching consistent patterns. Degenerate patterns were not attributable to differences in emotional experience or other individual-level factors. These findings are consistent with the constructionist account that emotions emerge from flexible functional neuronal assemblies and that emotion categories such as anger and anxiety each describe populations of highly variable instances.
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Affiliation(s)
- Cameron M Doyle
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Stephanie T Lane
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Jeffrey A Brooks
- Department of Psychology, University of California, Berkeley, CA 84720, USA,Hume AI, New York, NY 10010, USA
| | - Robin W Wilkins
- Gateway University of North Carolina Greensboro MRI Center, Greensboro, NC 27412, USA
| | - Kathleen M Gates
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Kristen A Lindquist
- Correspondence should be addressed to Kristen A. Lindquist, Department of Psychology and Neuroscience, University of North Carolina, CB #3270, 230 E. Cameron Avenue, Chapel Hill, NC 27599, USA. E-mail:
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21
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Abstract
Global sea-level rise has been drawing increasingly greater attention in recent years, as it directly impacts the livelihood and sustainable development of humankind. Our research focuses on identifying causal factors and pathways on sea level changes (both global and regional) and subsequently predicting the magnitude of such changes. To this end, we have designed a novel analysis pipeline including three sequential steps: (1) a dynamic structural equation model (dSEM) to identify pathways between the global mean sea level (GMSL) and various predictors, (2) a vector autoregression model (VAR) to quantify the GMSL changes due to the significant relations identified in the first step, and (3) a generalized additive model (GAM) to model the relationship between regional sea level and GMSL. Historical records of GMSL and other variables from 1992 to 2020 were used to calibrate the analysis pipeline. Our results indicate that greenhouse gases, water, and air temperatures, change in Antarctic and Greenland Ice Sheet mass, sea ice, and historical sea level all play a significant role in future sea-level rise. The resulting 95% upper bound of the sea-level projections was combined with a threshold for extreme flooding to map out the extent of sea-level rise in coastal communities using a digital coastal tracker.
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22
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Chahal R, Weissman DG, Hallquist MN, Robins RW, Hastings PD, Guyer AE. Neural connectivity biotypes: associations with internalizing problems throughout adolescence. Psychol Med 2021; 51:2835-2845. [PMID: 32466823 PMCID: PMC7845761 DOI: 10.1017/s003329172000149x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND Neurophysiological patterns may distinguish which youth are at risk for the well-documented increase in internalizing symptoms during adolescence. Adolescents with internalizing problems exhibit altered resting-state functional connectivity (RSFC) of brain regions involved in socio-affective processing. Whether connectivity-based biotypes differentiate adolescents' levels of internalizing problems remains unknown. METHOD Sixty-eight adolescents (37 females) reported on their internalizing problems at ages 14, 16, and 18 years. A resting-state functional neuroimaging scan was collected at age 16. Time-series data of 15 internalizing-relevant brain regions were entered into the Subgroup-Group Iterative Multi-Model Estimation program to identify subgroups based on RSFC maps. Associations between internalizing problems and connectivity-based biotypes were tested with regression analyses. RESULTS Two connectivity-based biotypes were found: a Diffusely-connected biotype (N = 46), with long-range fronto-parietal paths, and a Hyper-connected biotype (N = 22), with paths between subcortical and medial frontal areas (e.g. affective and default-mode network regions). Higher levels of past (age 14) internalizing problems predicted a greater likelihood of belonging to the Hyper-connected biotype at age 16. The Hyper-connected biotype showed higher levels of concurrent problems (age 16) and future (age 18) internalizing problems. CONCLUSIONS Differential patterns of RSFC among socio-affective brain regions were predicted by earlier internalizing problems and predicted future internalizing problems in adolescence. Measuring connectivity-based biotypes in adolescence may offer insight into which youth face an elevated risk for internalizing disorders during this critical developmental period.
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Affiliation(s)
- Rajpreet Chahal
- Department of Human Ecology, University of California, Davis, One Shields Avenue, Davis, CA 95618
- Center for Mind and Brain, University of California, Davis, 267 Cousteau Place, Davis, CA 95616
| | | | - Michael N. Hallquist
- Department of Psychology, Pennsylvania State University, 309 Moore Building, University Park, PA 16802
| | - Richard W. Robins
- Department of Psychology, University of California, Davis, One Shields Avenue, Davis, CA 95618
| | - Paul D. Hastings
- Center for Mind and Brain, University of California, Davis, 267 Cousteau Place, Davis, CA 95616
- Department of Psychology, University of California, Davis, One Shields Avenue, Davis, CA 95618
| | - Amanda E. Guyer
- Department of Human Ecology, University of California, Davis, One Shields Avenue, Davis, CA 95618
- Center for Mind and Brain, University of California, Davis, 267 Cousteau Place, Davis, CA 95616
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23
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Woody ML, Panny B, Degutis M, Griffo A, Price RB. Resting state functional connectivity subtypes predict discrete patterns of cognitive-affective functioning across levels of analysis among patients with treatment-resistant depression. Behav Res Ther 2021; 146:103960. [PMID: 34488187 PMCID: PMC8653528 DOI: 10.1016/j.brat.2021.103960] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 07/22/2021] [Accepted: 09/01/2021] [Indexed: 01/02/2023]
Abstract
Resting state functional connectivity (RSFC) in ventral affective (VAN), default mode (DMN) and cognitive control (CCN) networks may partially underlie heterogeneity in depression. The current study used data-driven parsing of RSFC to identify subgroups of patients with treatment-resistant depression (TRD; n = 70) and determine if subgroups generalized to transdiagnostic measures of cognitive-affective functioning relevant to depression (indexed across self-report, behavioral, and molecular levels of analysis). RSFC paths within key networks were characterized using Subgroup-Group Iterative Multiple Model Estimation. Three connectivity-based subgroups emerged: Subgroup A, the largest subset and containing the fewest pathways; Subgroup B, containing unique bidirectional VAN/DMN negative feedback; and Subgroup C, containing the most pathways. Compared to other subgroups, subgroup B was characterized by lower self-reported positive affect and subgroup C by higher self-reported positive affect, greater variability in induced positive affect, worse response inhibition, and reduced striatal tissue iron concentration. RSFC-based categorization revealed three TRD subtypes associated with discrete aberrations in transdiagnostic cognitive-affective functioning that were largely unified across levels of analysis and were maintained after accounting for the variability captured by a disorder-specific measure of depressive symptoms. Findings advance understanding of transdiagnostic brain-behavior heterogeneity in TRD and may inform novel treatment targets for this population.
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Affiliation(s)
- Mary L Woody
- Department of Psychiatry, University of Pittsburgh School of Medicine, USA.
| | - Benjamin Panny
- Department of Psychiatry, University of Pittsburgh School of Medicine, USA
| | - Michelle Degutis
- Heinz College of Information Systems and Public Policy, Carnegie Mellon University, USA
| | - Angela Griffo
- Department of Psychiatry, University of Pittsburgh School of Medicine, USA
| | - Rebecca B Price
- Department of Psychiatry, University of Pittsburgh School of Medicine, USA; Department of Psychology, University of Pittsburgh, USA
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Huie JR, Mondello S, Lindsell CJ, Antiga L, Yuh EL, Zanier ER, Masson S, Rosario BL, Ferguson AR. Biomarkers for Traumatic Brain Injury: Data Standards and Statistical Considerations. J Neurotrauma 2021; 38:2514-2529. [PMID: 32046588 PMCID: PMC8403188 DOI: 10.1089/neu.2019.6762] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Recent biomarker innovations hold potential for transforming diagnosis, prognostic modeling, and precision therapeutic targeting of traumatic brain injury (TBI). However, many biomarkers, including brain imaging, genomics, and proteomics, involve vast quantities of high-throughput and high-content data. Management, curation, analysis, and evidence synthesis of these data are not trivial tasks. In this review, we discuss data management concepts and statistical and data sharing strategies when dealing with biomarker data in the context of TBI research. We propose that application of biomarkers involves three distinct steps-discovery, evaluation, and evidence synthesis. First, complex/big data has to be reduced to useful data elements at the stage of biomarker discovery. Second, inferential statistical approaches must be applied to these biomarker data elements for assessment of biomarker clinical utility and validity. Last, synthesis of relevant research is required to support practice guidelines and enable health decisions informed by the highest quality, up-to-date evidence available. We focus our discussion around recent experiences from the International Traumatic Brain Injury Research (InTBIR) initiative, with a specific focus on four major clinical projects (Transforming Research and Clinical Knowledge in TBI, Collaborative European NeuroTrauma Effectiveness Research in TBI, Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe, and Approaches and Decisions in Acute Pediatric TBI Trial), which are currently enrolling subjects in North America and Europe. We discuss common data elements, data collection efforts, data-sharing opportunities, and challenges, as well as examine the statistical techniques required to realize successful adoption and use of biomarkers in the clinic as a foundation for precision medicine in TBI.
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Affiliation(s)
- J. Russell Huie
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Stefania Mondello
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
| | - Christopher J. Lindsell
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Esther L. Yuh
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Elisa R. Zanier
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Serge Masson
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Bedda L. Rosario
- Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Adam R. Ferguson
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- San Francisco Veterans Affairs Medical Center (SFVAMC), San Francisco, California, USA
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25
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Arizmendi C, Gates K, Fredrickson B, Wright A. Specifying exogeneity and bilinear effects in data-driven model searches. Behav Res Methods 2021; 53:1276-1288. [PMID: 33037600 PMCID: PMC8032821 DOI: 10.3758/s13428-020-01469-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Data-driven model searches provide the opportunity to quantify person-specific processes using ambulatory assessment data. Here, the search space typically includes all potential relations among variables, meaning that all variables can potentially explain variability in all other variables. Oftentimes, this is unrealistic. For example, weather is unlikely to be predicted by someone's emotional state, whereas the reverse might be true. Allowing for specification of exogenous variables, or variables that are not predicted within the system, permits more realistic models and allows the researcher to model contextual change processes via the use of moderation variables. We use two sets of daily diary data to demonstrate the capabilities of allowing for the specification of exogenous variables in GIMME (Group Iterative Multiple Model Estimation), a model search algorithm that allows for models with idiographic, individual-level as well as subgroup- and group-level processes with intensive longitudinal data. First, using data collected from individuals diagnosed with personality disorders, we show results where weather-related and temporal basis variables are specified as exogenous, and reports on affect and behavior are endogenous. Next, we demonstrate the modeling of treatment effects in an intervention study, looking at data from a 6-week meditation workshop in midlife adults. Finally, we use the meditation intervention data to demonstrate modeling moderation effects, where relationships between two endogenous variables are dependent on the current stage of the study for a given participant (i.e., currently attending meditation classes or not). We end by presenting adaptive LASSO as a method for probing results obtained from GIMME.
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Affiliation(s)
- Cara Arizmendi
- The University of North Carolina Chapel Hill, CB #3270, Davie Hall, Chapel Hill, NC, 27599-3270, USA.
| | - Kathleen Gates
- The University of North Carolina Chapel Hill, CB #3270, Davie Hall, Chapel Hill, NC, 27599-3270, USA
| | - Barbara Fredrickson
- The University of North Carolina Chapel Hill, CB #3270, Davie Hall, Chapel Hill, NC, 27599-3270, USA
| | - Aidan Wright
- The University of Pittsburgh, Pittsburgh, PA, 15260, USA
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26
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Ye A, Gates KM, Henry TR, Luo L. Path and Directionality Discovery in Individual Dynamic Models: A Regularized Unified Structural Equation Modeling Approach for Hybrid Vector Autoregression. PSYCHOMETRIKA 2021; 86:404-441. [PMID: 33840003 DOI: 10.1007/s11336-021-09753-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 01/29/2021] [Accepted: 02/25/2021] [Indexed: 06/12/2023]
Abstract
There recently has been growing interest in the study of psychological and neurological processes at an individual level. One goal in such endeavors is to construct person-specific dynamic assessments using time series techniques such as Vector Autoregressive (VAR) models. However, two problems exist with current VAR specifications: (1) VAR models are restricted in that contemporaneous relations are typically modeled either as undirected relations among residuals or directed relations among observed variables, but not both; (2) current estimation frameworks are limited by the reliance on stepwise model building procedures. This study adopts a new modeling approach. We first extended the current unified SEM (uSEM) framework, a widely used structural VAR model, to a hybrid representation (i.e., "huSEM") to include both undirected and directed contemporaneous effects, and then replaced the stepwise modeling with a LASSO-type regularization for a global search of the optimal sparse model. Our simulation study showed that regularized huSEM performed uniformly the best over alternative VAR representations and/or modeling approaches, with respect to accurately recovering the presence and directionality of hybrid relations and reliably removing false relations when the data are generated to have two types of contemporaneous relations. The present study to our knowledge is the first application of the recently developed regularized SEM technique to the estimation of huSEM, which points to a promising future for statistical learning in psychometric models.
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Affiliation(s)
- Ai Ye
- L. L. Thurstone Psychometric Lab, Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, 235 E. Cameron Avenue, Campus Box 3270, Chapel Hill, NC, 27599, USA.
| | - Kathleen M Gates
- L. L. Thurstone Psychometric Lab, Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, 235 E. Cameron Avenue, Campus Box 3270, Chapel Hill, NC, 27599, USA
| | - Teague Rhine Henry
- L. L. Thurstone Psychometric Lab, Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, 235 E. Cameron Avenue, Campus Box 3270, Chapel Hill, NC, 27599, USA
| | - Lan Luo
- L. L. Thurstone Psychometric Lab, Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, 235 E. Cameron Avenue, Campus Box 3270, Chapel Hill, NC, 27599, USA
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Duffy KA, Fisher ZF, Arizmendi CA, Molenaar PCM, Hopfinger J, Cohen JR, Beltz AM, Lindquist MA, Hallquist MN, Gates KM. Detecting Task-Dependent Functional Connectivity in Group Iterative Multiple Model Estimation with Person-Specific Hemodynamic Response Functions. Brain Connect 2021; 11:418-429. [PMID: 33478367 DOI: 10.1089/brain.2020.0864] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction: Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at functional connectivity maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This article introduces HRF-GIMME, an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on functional magnetic resonance imaging data collected by using event-related designs. Critically, hemodynamic response function (HRF)-GIMME incorporates person-specific modeling of the HRF to accommodate known variability in onset delay and shape. Methods: After an introduction of the technical aspects of HRF-GIMME, the performance of HRF-GIMME is evaluated via both a simulation study and application to empirical data. The simulation study assesses the sensitivity and specificity of HRF-GIMME by using data simulated from one slow and two rapid event-related designs, and HRF-GIMME is then applied to two empirical data sets from similar designs to evaluate performance in recovering known neural circuitry. Results: HRF-GIMME showed high sensitivity and specificity across all simulated conditions, and it performed well in the recovery of expected relations between convolved task vectors and brain regions in both simulated and empirical data, particularly for the slow event-related design. Conclusion: Results from simulated and empirical data indicate that HRF-GIMME is a powerful new tool for obtaining directed functional connectivity maps of intrinsic and task-related connections that is able to uncover what is common across the sample as well as crucial individual-level path connections and estimates. Impact statement Group iterative multiple model estimation (GIMME) is a reliable method for creating functional connectivity maps of the connections between brain regions across time, and it is able to detect what is common across the sample and what is shared between subsets of participants, as well as individual-level path estimates. However, historically, GIMME does not model task-related effects. The novel HRF-GIMME algorithm enables the modeling of direct and modulatory task effects through individual-level estimation of the hemodynamic response function (HRF), presenting a powerful new tool for assessing task effects on functional connectivity networks in functional magnetic resonance imaging data.
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Affiliation(s)
- Kelly A Duffy
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Zachary F Fisher
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Cara A Arizmendi
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Peter C M Molenaar
- Human Development and Family Studies, The Pennsylvania State University at State College, University Park, Pennsylvania, USA
| | - Joseph Hopfinger
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Adriene M Beltz
- Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA
| | - Martin A Lindquist
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Michael N Hallquist
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kathleen M Gates
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Zhang Z, Yuan Q, Liu Z, Zhang M, Wu J, Lu C, Ding G, Guo T. The cortical organization of writing sequence: evidence from observing Chinese characters in motion. Brain Struct Funct 2021; 226:1627-1639. [DOI: 10.1007/s00429-021-02276-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 04/09/2021] [Indexed: 12/27/2022]
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Chen L, Goucha T, Männel C, Friederici AD, Zaccarella E. Hierarchical syntactic processing is beyond mere associating: Functional magnetic resonance imaging evidence from a novel artificial grammar. Hum Brain Mapp 2021; 42:3253-3268. [PMID: 33822433 PMCID: PMC8193521 DOI: 10.1002/hbm.25432] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/02/2021] [Accepted: 03/24/2021] [Indexed: 01/31/2023] Open
Abstract
Grammar is central to any natural language. In the past decades, the artificial grammar of the AnBn type in which a pair of associated elements can be nested in the other pair was considered as a desirable model to mimic human language syntax without semantic interference. However, such a grammar relies on mere associating mechanisms, thus insufficient to reflect the hierarchical nature of human syntax. Here, we test how the brain imposes syntactic hierarchies according to the category relations on linearized sequences by designing a novel artificial “Hierarchical syntactic structure‐building Grammar” (HG), and compare this to the AnBn grammar as a “Nested associating Grammar” (NG) based on multilevel associations. Thirty‐six healthy German native speakers were randomly assigned to one of the two grammars. Both groups performed a grammaticality judgment task on auditorily presented word sequences generated by the corresponding grammar in the scanner after a successful explicit behavioral learning session. Compared to the NG group, we found that the HG group showed a (a) significantly higher involvement of Brodmann area (BA) 44 in Broca's area and the posterior superior temporal gyrus (pSTG); and (b) qualitatively distinct connectivity between the two regions. Thus, the present study demonstrates that the build‐up process of syntactic hierarchies on the basis of category relations critically relies on a distinctive left‐hemispheric syntactic network involving BA 44 and pSTG. This indicates that our novel artificial grammar can constitute a suitable experimental tool to investigate syntax‐specific processes in the human brain.
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Affiliation(s)
- Luyao Chen
- College of Chinese Language and Culture, Beijing Normal University, Beijing.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Tomás Goucha
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Claudia Männel
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Department of Audiology and Phoniatrics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Angela D Friederici
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Emiliano Zaccarella
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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30
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Nestler S, Humberg S. Gimme’s ability to recover group-level path coefficients and individual-level path coefficients. METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES 2021. [DOI: 10.5964/meth.2863] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The growing availability of intensive longitudinal data has increased psychological researchers' interest in ideographic-statistical methods that, for example, reveal the contemporaneous or lagged associations between different variables for a specific individual. However, when researchers assess several individuals, the results of such models are difficult to generalize across individuals. Researchers recently suggested an algorithm called GIMME, which allows for the identification of coefficients that exist across all individuals (group-level coefficients) or are specific to one or a subgroup of individuals (individual-level coefficients). In three simulation studies we investigated GIMME's performance in recovering group-level and individual-level coefficients. For the former, we found that GIMME performed well when the magnitude of the parameters was moderate to high and when the number of measurements was sufficiently large. However, GIMME had problems detecting individual-level coefficients or coefficients that occurred for a subset of individuals from the whole sample.
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Chiang FL, Feng M, Romero RS, Price L, Franklin CG, Deng S, Gray JP, Yu FF, Tantiwongkosi B, Huang SY, Fox PT. Disruption of the Atrophy-based Functional Network in Multiple Sclerosis Is Associated with Clinical Disability: Validation of a Meta-Analytic Model in Resting-State Functional MRI. Radiology 2021; 299:159-166. [PMID: 33529135 DOI: 10.1148/radiol.2021203414] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Background In multiple sclerosis (MS), gray matter (GM) atrophy exhibits a specific pattern, which correlates strongly with clinical disability. However, the mechanism of regional specificity in GM atrophy remains largely unknown. Recently, the network degeneration hypothesis (NDH) was quantitatively defined (using coordinate-based meta-analysis) as the atrophy-based functional network (AFN) model, which posits that localized GM atrophy in MS is mediated by functional networks. Purpose To test the NDH in MS in a data-driven manner using the AFN model to direct analyses in an independent test sample. Materials and Methods Model fit testing was conducted with structural equation modeling, which is based on the computation of semipartial correlations. Model verification was performed in coordinate-based data of healthy control participants from the BrainMap database (https://www.brainmap.org). Model validation was conducted in prospectively acquired resting-state functional MRI in participants with relapsing-remitting MS who were recruited between September 2018 and January 2019. Correlation analyses of model fit indices and volumetric measures with Expanded Disability Status Scale (EDSS) scores and disease duration were performed. Results Model verification of healthy control participants included 80 194 coordinates from 9035 experiments. Model verification in healthy control data resulted in excellent model fit (root mean square error of approximation, 0.037; 90% CI: 0.036, 0.039). Twenty participants (mean age, 36 years ± 9 [standard deviation]; 12 women) with relapsing-remitting MS were evaluated. Model validation in resting-state functional MRI in participants with MS resulted in deviation from optimal model fit (root mean square error of approximation, 0.071; 90% CI: 0.070, 0.072), which correlated with EDSS scores (r = 0.68; P = .002). Conclusion The atrophy-based functional network model predicts functional network disruption in multiple sclerosis (MS), thereby supporting the network degeneration hypothesis. On resting-state functional MRI scans, reduced functional network integrity in participants with MS had a strong positive correlation with clinical disability. © RSNA, 2021 Online supplemental material is available for this article.
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Affiliation(s)
- Florence L Chiang
- From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.)
| | - Max Feng
- From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.)
| | - Rebecca S Romero
- From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.)
| | - Larry Price
- From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.)
| | - Crystal G Franklin
- From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.)
| | - Shengwen Deng
- From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.)
| | - Jodie P Gray
- From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.)
| | - Fang F Yu
- From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.)
| | - Bundhit Tantiwongkosi
- From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.)
| | - Susie Y Huang
- From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.)
| | - Peter T Fox
- From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.)
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Zilcha-Mano S, Webb CA. Disentangling Trait-Like Between-Individual vs. State-Like Within-Individual Effects in Studying the Mechanisms of Change in CBT. Front Psychiatry 2021; 11:609585. [PMID: 33551873 PMCID: PMC7859252 DOI: 10.3389/fpsyt.2020.609585] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 12/11/2020] [Indexed: 12/14/2022] Open
Abstract
Hofmann et al. argued that "[w]hile the clinical field has produced a dizzying number of treatment models and treatment protocols for virtually every psychiatric and psychological problem imaginable, increases in understanding of the processes of change in psychotherapy has been slow to arrive." We propose that one of the reasons for the slow progress is that prior psychotherapy research conflates trait-like and state-like components of mechanisms of change. Trait-like components can serve as prescriptive or prognostic variables, whereas state-like components reflect within-client processes of change, and may highlight active ingredients of successful treatment. Distinguishing between the two is essential for clarifying the underlying processes of change in psychotherapy, and ultimately identifying empirically-derived individualized treatment targets. We review studies that implement methodological and statistical approaches for disentangling the two. These studies clarified particular mechanisms of change that may operate in a given treatment, highlighted differences in the processes of change between different treatments, and explored the within-individual interplay between different mechanisms of change during treatment. Examples include studies investigating the therapeutic role of behavioral, cognitive, and interpersonal skills, as well as emotional processing. We conclude with suggestions for future research, including attention to diversity, improved measurement to facilitate a reliable and valid estimation of trait-like and state-like components, the use of appropriate statistical approaches to adequately disentangle the two components, integration of theory-driven and data-driven methods of analysis, and the need to experimentally manipulate the state-like changes in a given mechanism of change to strengthen causal inferences.
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Affiliation(s)
| | - Christian A. Webb
- McLean Hospital and Harvard Medical School, Boston, MA, United States
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Haslbeck JMB, Bringmann LF, Waldorp LJ. A Tutorial on Estimating Time-Varying Vector Autoregressive Models. MULTIVARIATE BEHAVIORAL RESEARCH 2021; 56:120-149. [PMID: 32324066 DOI: 10.1080/00273171.2020.1743630] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid the notorious problem of making within-subjects inferences from between-subjects data, and naturally address heterogeneity between subjects. A popular model for these data is the Vector Autoregressive (VAR) model, in which each variable is predicted by a linear function of all variables at previous time points. A key assumption of this model is that its parameters are constant (or stationary) across time. However, in many areas of psychological research time-varying parameters are plausible or even the subject of study. In this tutorial paper, we introduce methods to estimate time-varying VAR models based on splines and kernel-smoothing with/without regularization. We use simulations to evaluate the relative performance of all methods in scenarios typical in applied research, and discuss their strengths and weaknesses. Finally, we provide a step-by-step tutorial showing how to apply the discussed methods to an openly available time series of mood-related measurements.
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Yuan Q, Ma F, Zhang M, Chen M, Zhang Z, Wu J, Lu C, Guo T. Neural interaction between language control and cognitive control: Evidence from cross-task adaptation. Behav Brain Res 2020; 401:113086. [PMID: 33359369 DOI: 10.1016/j.bbr.2020.113086] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 12/13/2020] [Accepted: 12/16/2020] [Indexed: 01/28/2023]
Abstract
It has been documented that conflict adaptation (conflict resolution in a task enhanced by that in a previous task) exists not only in the same domain but also across different domains with shared cognitive control mechanisms. For the first time, the present study adopted a cross-task adaptive blocked design to examine the relationship between bilingual language control and cognitive control from the perspective of the immediately adjacent, mutual influence on the neural connectivity level. The results showed that the conflict setting induced by previous tasks changed the nodal degrees of the anterior cingulate cortex/presupplementary motor area and the right thalamus, and connectivity strength of shared links between adjacent language and cognitive control tasks. In addition, pre-activation of the cognitive control network affected the transitivity of the successive use of the language control network. These findings not only indicate a cross-task adaptation effect on the neural connectivity level, but also provide evidence for similarities in conflict detection and inhibition control between language-specific control and domain-general cognitive control. In addition, our results also suggest that there is only partial overlap between bilingual language control and domain-general cognitive control.
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Affiliation(s)
- Qiming Yuan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Fengyang Ma
- School of Education, University of Cincinnati, Cincinnati, USA
| | - Man Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Mo Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhaoqi Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Junjie Wu
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Taomei Guo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
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Xu N, Doerschuk PC, Keilholz SD, Spreng RN. Spatiotemporal functional interactivity among large-scale brain networks. Neuroimage 2020; 227:117628. [PMID: 33316394 DOI: 10.1016/j.neuroimage.2020.117628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 11/21/2020] [Accepted: 12/03/2020] [Indexed: 02/06/2023] Open
Abstract
The macro-scale intrinsic functional network architecture of the human brain has been well characterized. Early studies revealed robust and enduring patterns of static connectivity, while more recent work has begun to explore the temporal dynamics of these large-scale brain networks. Little work to date has investigated directed connectivity within and between these networks, or the temporal patterns of afferent (input) and efferent (output) connections between network nodes. Leveraging a novel analytic approach, prediction correlation, we investigated the causal interactions within and between large-scale networks of the brain using resting state fMRI. This technique allows us to characterize information transfer between brain regions in both the spatial (direction) and temporal (duration) scales. Using data from the Human Connectome Project (N = 200) we applied prediction correlation techniques to four resting-state fMRI scans (each scan has TRs = 1200). Three central observations emerged. First, the strongest and longest duration connections were observed within the somatomotor, visual, and dorsal attention networks. Second, the short duration connections were observed for high-degree nodes in the visual and default networks, as well as in the hippocampus. Specifically, the connectivity profile of the highest-degree nodes was dominated by efferent connections to multiple cortical areas. Moderate high-degree nodes, particularly in hippocampal regions, showed an afferent connectivity profile. Finally, multimodal association nodes in lateral prefrontal brain regions demonstrated a short duration, bidirectional connectivity profile, consistent with this region's role in integrative and modulatory processing. These results provide novel insights into the spatiotemporal dynamics of human brain function.
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Affiliation(s)
- Nan Xu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.
| | - Peter C Doerschuk
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, United States; Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States.
| | - Shella D Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.
| | - R Nathan Spreng
- Laboratory of Brain and Cognition, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada; Douglas Hospital Research Centre, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
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Dotterer HL, Beltz AM, Foster KT, Simms LJ, Wright AGC. Personalized models of personality disorders: using a temporal network method to understand symptomatology and daily functioning in a clinical sample. Psychol Med 2020; 50:2397-2405. [PMID: 31597579 DOI: 10.1017/s0033291719002563] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND An ongoing challenge in understanding and treating personality disorders (PDs) is a significant heterogeneity in disorder expression, stemming from variability in underlying dynamic processes. These processes are commonly discussed in clinical settings, but are rarely empirically studied due to their personalized, temporal nature. The goal of the current study was to combine intensive longitudinal data collection with person-specific temporal network models to produce individualized symptom-level structures of personality pathology. These structures were then linked to traditional PD diagnoses and stress (to index daily functioning). METHODS Using about 100 daily assessments of internalizing and externalizing domains underlying PDs (i.e. negative affect, detachment, impulsivity, hostility), a temporal network mapping approach (i.e. group iterative multiple model estimation) was used to create person-specific networks of the temporal relations among domains for 91 individuals (62.6% female) with a PD. Network characteristics were then associated with traditional PD symptomatology (controlling for mean domain levels) and with daily variation in clinically-relevant phenomena (i.e. stress). RESULTS Features of the person-specific networks predicted paranoid, borderline, narcissistic, and obsessive-PD symptom counts above average levels of the domains, in ways that align with clinical conceptualizations. They also predicted between-person variation in stress across days. CONCLUSIONS Relations among behavioral domains thought to underlie heterogeneity in PDs were indeed associated with traditional diagnostic constructs and with daily functioning (i.e. stress) in person-specific networks. Findings highlight the importance of leveraging data and models that capture person-specific, dynamic processes, and suggest that person-specific networks may have implications for precision medicine.
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Affiliation(s)
| | - Adriene M Beltz
- Department of Psychology, University of Michigan, Ann Arbor, USA
| | | | - Leonard J Simms
- Department of Psychology, University at Buffalo, Buffalo, USA
| | - Aidan G C Wright
- Department of Psychology, University of Pittsburgh, Pittsburgh, USA
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Jordan DG, Winer ES, Salem T. The current status of temporal network analysis for clinical science: Considerations as the paradigm shifts? J Clin Psychol 2020; 76:1591-1612. [PMID: 32386334 DOI: 10.1002/jclp.22957] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 04/21/2020] [Accepted: 04/25/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Network analysis in psychology has ushered in a potentially revolutionary way of analyzing clinical data. One novel methodology is in the construction of temporal networks, models that examine directionality between symptoms over time. This paper provides context for how these models are applied to clinically-relevant longitudinal data. METHODS We provide a survey of statistical and methodological issues involved in temporal network analysis, providing a description of available estimation tools and applications for conducting such analyses. Further, we provide supplemental R code and discuss simulations examining temporal networks that vary in sample size, number of variables, and number of time points. RESULTS The following packages and software are reviewed: graphicalVAR, mlVAR, gimme, SparseTSCGM, mgm, psychonetrics, and the Mplus dynamic structural equation modeling module. We discuss the utility each procedure has for specific design considerations. CONCLUSION We conclude with notes on resources for estimating these models, emphasizing how temporal networks best approximate network theory.
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Affiliation(s)
- D Gage Jordan
- Department of Psychology, Mississippi State University, Starkville, Mississippi
| | - E Samuel Winer
- Department of Psychology, Mississippi State University, Starkville, Mississippi
| | - Taban Salem
- Harding Hospital, The Ohio State University Wexner Medical Center, Columbus, Ohio
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Price RB, Beltz AM, Woody ML, Cummings L, Gilchrist D, Siegle GJ. Neural Connectivity Subtypes Predict Discrete Attentional Bias Profiles Among Heterogeneous Anxiety Patients. Clin Psychol Sci 2020; 8:491-505. [PMID: 33758682 PMCID: PMC7983837 DOI: 10.1177/2167702620906149] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
On average, anxious patients show altered attention to threat-including early vigilance towards threat and later avoidance of threat-accompanied by altered functional connectivity across brain regions. However, substantial heterogeneity within clinical, neural, and attentional features of anxiety is overlooked in typical group-level comparisons. We used a well-validated method for data-driven parsing of neural connectivity to reveal connectivity-based subgroups among 60 adults with transdiagnostic anxiety. Subgroups were externally compared on attentional patterns derived from independent behavioral measures. Two subgroups emerged. Subgroup A (68% of patients) showed stronger executive network influences on sensory processing regions and a paradigmatic "vigilance-avoidance" pattern on external behavioral measures. Subgroup B was defined by a larger number of limbic influences on sensory regions and exhibited a more atypical and inconsistent attentional profile. Neural connectivity-based categorization revealed an atypical, limbic-driven pattern of connectivity in a subset of anxious patients that generalized to atypical patterns of selective attention.
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Affiliation(s)
- Rebecca B. Price
- Department of Psychiatry, University of Pittsburgh School of Medicine
| | | | - Mary L. Woody
- Department of Psychiatry, University of Pittsburgh School of Medicine
| | - Logan Cummings
- Department of Psychology, Florida International University
| | | | - Greg J. Siegle
- Department of Psychiatry, University of Pittsburgh School of Medicine
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Woods WC, Arizmendi C, Gates KM, Stepp SD, Pilkonis PA, Wright AGC. Personalized models of psychopathology as contextualized dynamic processes: An example from individuals with borderline personality disorder. J Consult Clin Psychol 2020; 88:240-254. [PMID: 32068425 PMCID: PMC7034576 DOI: 10.1037/ccp0000472] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
OBJECTIVE Psychopathology research has relied on discrete diagnoses, which neglects the unique manifestations of each individual's pathology. Borderline personality disorder combines interpersonal, affective, and behavioral regulation impairments making it particularly ill-suited to a "one size fits all" diagnosis. Clinical assessment and case formulation involve understanding and developing a personalized model for each patient's contextualized dynamic processes, and research would benefit from a similar focus on the individual. METHOD We use group iterative multiple model estimation, which estimates a model for each individual and identifies general or shared features across individuals, in both a mixed-diagnosis sample (N = 78) and a subsample with a single diagnosis (n = 24). RESULTS We found that individuals vary widely in their dynamic processes in affective and interpersonal domains both within and across diagnoses. However, there was some evidence that dynamic patterns relate to transdiagnostic baseline measures. We conclude with descriptions of 2 person-specific models as an example of the heterogeneity of dynamic processes. CONCLUSIONS The idiographic models presented here join a growing literature showing that the individuals differ dramatically in the total patterning of these processes, even as key processes are shared across individuals. We argue that these processes are best estimated in the context of person-specific models, and that so doing may advance our understanding of the contextualized dynamic processes that could identify maintenance mechanisms and treatment targets. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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Affiliation(s)
| | - Cara Arizmendi
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
| | - Kathleen M Gates
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
| | - Stephanie D Stepp
- Department of Psychiatry, University of Pittsburgh School of Medicine
| | - Paul A Pilkonis
- Department of Psychiatry, University of Pittsburgh School of Medicine
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Abstract
The personalized approach to psychopathology conceptualizes mental disorder as a complex system of contextualized dynamic processes that is nontrivially specific to each individual, and it seeks to develop formal idiographic statistical models to represent these individual processes. Although the personalized approach draws on long-standing influences in clinical psychology, there has been an explosion of research in recent years following the development of intensive longitudinal data capture and statistical techniques that facilitate modeling of the dynamic processes of each individual's pathology. Advances are also making idiographic analyses scalable and generalizable. We review emerging research using the personalized approach in descriptive psychopathology, precision assessment, and treatment selection and tailoring, and we identify future challenges and areas in need of additional research. The personalized approach to psychopathology holds promise to resolve thorny diagnostic issues, generate novel insights, and improve the timing and efficacy of interventions.
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Affiliation(s)
- Aidan G C Wright
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA; ,
| | - William C Woods
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA; ,
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Thompson AM, Wiedermann W, Herman KC, Reinke WM. Effect of Daily Teacher Feedback on Subsequent Motivation and Mental Health Outcomes in Fifth Grade Students: a Person-Centered Analysis. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2020; 22:775-785. [DOI: 10.1007/s11121-020-01097-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Dajani DR, Burrows CA, Nebel MB, Mostofsky SH, Gates KM, Uddin LQ. Parsing Heterogeneity in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder with Individual Connectome Mapping. Brain Connect 2019; 9:673-691. [PMID: 31631690 PMCID: PMC6862970 DOI: 10.1089/brain.2019.0669] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Traditional diagnostic systems for neurodevelopmental disorders define diagnostic categories that are heterogeneous in behavior and underlying neurobiological alterations. The goal of this study was to parse heterogeneity in a core executive function (EF), cognitive flexibility, in children with a range of abilities (N = 132; children with autism spectrum disorder, attention-deficit/hyperactivity disorder [ADHD], and typically developing children) using directed functional connectivity profiles derived from resting-state functional magnetic resonance imaging data. Brain regions activated in response to a cognitive flexibility task in adults were used to guide region-of-interest selection to estimate individual connectivity profiles in this study. We expected to find subgroups of children who differed in their network connectivity metrics and symptom measures. Unexpectedly, we did not find a stable or valid subgrouping solution, which suggests that categorical models of the neural substrates of cognitive flexibility in children may be invalid. Exploratory analyses revealed dimensional associations between network connectivity metrics and ADHD symptomatology and EF ability across the entire sample. Results shed light on the validity of conceptualizing the neural substrates of cognitive flexibility categorically in children. Ultimately, this work may provide a foundation for the development of a revised nosology focused on neurobiological substrates as an alternative to traditional symptom-based classification systems.
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Affiliation(s)
- Dina R. Dajani
- Department of Psychology, University of Miami, Coral Gables, Florida
| | - Catherine A. Burrows
- Institute on Community Integration, University of Minnesota, Minneapolis, Minnesota
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Stewart H. Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Kathleen M. Gates
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, North Carolina
| | - Lucina Q. Uddin
- Department of Psychology, University of Miami, Coral Gables, Florida
- Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida
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43
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Wu J, Yang J, Chen M, Li S, Zhang Z, Kang C, Ding G, Guo T. Brain network reconfiguration for language and domain-general cognitive control in bilinguals. Neuroimage 2019; 199:454-465. [DOI: 10.1016/j.neuroimage.2019.06.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 06/09/2019] [Accepted: 06/10/2019] [Indexed: 10/26/2022] Open
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Piccirillo ML, Rodebaugh TL. Foundations of idiographic methods in psychology and applications for psychotherapy. Clin Psychol Rev 2019; 71:90-100. [PMID: 30665765 PMCID: PMC11130566 DOI: 10.1016/j.cpr.2019.01.002] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 01/08/2019] [Accepted: 01/12/2019] [Indexed: 01/30/2023]
Abstract
Researchers have long called for greater recognition and use of longitudinal, individual-level research in the study of psychopathology and psychotherapy. Much of our current research attempts to indirectly investigate individual-level, or idiographic, psychological processes via group-based, or nomothetic, designs. However, results from nomothetic research do not necessarily translate to the individual-level. In this review, we discuss how idiographic analyses can be integrated into psychotherapy and psychotherapy research. We examine and review key statistical methods for conducting idiographic analyses. These methods include factor-based and vector autoregressive approaches using longitudinal data. The theoretical framework behind each approach is reviewed and critically evaluated. Empirical examples of each approach are discussed, with the aim of helping interested readers consider how they may use idiographic methods to analyze longitudinal data and psychological processes. Finally, we conclude by citing key limitations of the idiographic approach, calling for greater development of these analyses to ease their successful integration into clinical settings.
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Affiliation(s)
- Marilyn L Piccirillo
- Department of Psychological and Brain Sciences, Washington University in St. Louis, USA.
| | - Thomas L Rodebaugh
- Department of Psychological and Brain Sciences, Washington University in St. Louis, USA
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45
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Wittenborn AK, Liu T, Ridenour TA, Lachmar EM, Mitchell EA, Seedall RB. Randomized controlled trial of emotionally focused couple therapy compared to treatment as usual for depression: Outcomes and mechanisms of change. JOURNAL OF MARITAL AND FAMILY THERAPY 2019; 45:395-409. [PMID: 30105808 DOI: 10.1111/jmft.12350] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This randomized controlled trial examined the effectiveness of Emotionally Focused Therapy (EFT) for depression and relationship satisfaction versus usual care (i.e., couple therapy other than EFT), and explored mechanisms of change. Mixed model trajectory analyses of 16 couples indicated EFT was associated with greater improvement in relationship satisfaction among men and women. Men receiving EFT reported greater improvements in depressive symptoms compared to usual care. Unified structural equation modeling revealed changes in relationship satisfaction preceded changes in depressive symptoms in one cluster of partners, while changes in depression preceded changes in relationship satisfaction in a second cluster. Two other clusters reported simultaneous changes in satisfaction and depression. This study provides encouraging results on the effectiveness of EFT for depression, and insight into mechanisms of change.
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Affiliation(s)
| | - Ting Liu
- Philadelphia Center for Emotionally Focused Therapy
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46
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Yang J, Li P. Mechanisms for Auditory Perception: A Neurocognitive Study of Second Language Learning of Mandarin Chinese. Brain Sci 2019; 9:brainsci9060139. [PMID: 31212921 PMCID: PMC6627958 DOI: 10.3390/brainsci9060139] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Revised: 06/05/2019] [Accepted: 06/11/2019] [Indexed: 01/30/2023] Open
Abstract
Speech perception is an important early skill for language learning. This study uses functional magnetic resonance imaging (fMRI) to examine the relationship between auditory perception abilities and second language (L2) vocabulary learning in an effort to explore behavior-brain correlations. Twenty-one English monolinguals learned 48 auditory Chinese pseudowords over six weeks. Their pre-training abilities in non-linguistic pitch and linguistic tone perception significantly and positively predicted their novel word-learning performance, which correlated with their brain response patterns in the left Heschl’s gyrus. Analyses of regions of interest (ROIs) showed coactivation of the frontal and temporal regions during novel lexical retrieval, and the non-linguistic pitch perception ability modulated brain activations in these regions. Effective connectivity analyses further indicated a collaboration of a ventral stream for speech perception and a dorsal stream for sensory-motor mapping in the L2 network. The ventral stream, compared with the dorsal stream, played a more dominant role in auditory word learning as the L2 proficiency increased. Better pitch and tone perception abilities strengthened the ventral pathways and decreased the reliance on frontal regions. These findings are discussed in light of current models of speech processing and L2 learning.
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Affiliation(s)
- Jing Yang
- Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies, Guangzhou 510420, China.
- Bilingual Cognition and Development Lab, Guangdong University of Foreign Studies, Guangzhou 510420, China.
- Department of Psychology and Center for Brain, Behavior, and Cognition, Pennsylvania State University, University Park, PA 16802, USA.
| | - Ping Li
- Department of Psychology and Center for Brain, Behavior, and Cognition, Pennsylvania State University, University Park, PA 16802, USA.
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Smallwood RF, Price LR, Campbell JL, Garrett AS, Atalla SW, Monroe TB, Aytur SA, Potter JS, Robin DA. Network Alterations in Comorbid Chronic Pain and Opioid Addiction: An Exploratory Approach. Front Hum Neurosci 2019; 13:174. [PMID: 31191279 PMCID: PMC6548857 DOI: 10.3389/fnhum.2019.00174] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 05/13/2019] [Indexed: 12/17/2022] Open
Abstract
The comorbidity of chronic pain and opioid addiction is a serious problem that has been growing with the practice of prescribing opioids for chronic pain. Neuroimaging research has shown that chronic pain and opioid dependence both affect brain structure and function, but this is the first study to evaluate the neurophysiological alterations in patients with comorbid chronic pain and addiction. Eighteen participants with chronic low back pain and opioid addiction were compared with eighteen age- and sex-matched healthy individuals in a pain-induction fMRI task. Unified structural equation modeling (SEM) with Lagrange multiplier (LM) testing yielded a network model of pain processing for patient and control groups based on 19 a priori defined regions. Tests of differences between groups on specific regression parameters were determined on a path-by-path basis using z-tests corrected for the number of comparisons. Patients with the chronic pain and addiction comorbidity had increased connection strengths; many of these connections were interhemispheric and spanned regions involved in sensory, affective, and cognitive processes. The affected regions included those that are commonly altered in chronic pain or addiction alone, indicating that this comorbidity manifests with neurological symptoms of both disorders. Understanding the neural mechanisms involved in the comorbidity is crucial to finding a comprehensive treatment, rather than treating the symptoms individually.
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Affiliation(s)
- Rachel F Smallwood
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Larry R Price
- Metholology, Measurement and Statistical Analysis, Texas State University, San Marcos, TX, United States
| | - Jenna L Campbell
- Department of Communication Sciences and Disorders, University of New Hampshire, Durham, NH, United States
| | - Amy S Garrett
- Department of Psychiatry, University of Texas Health Science Center San Antonio, San Antonio, TX, United States
| | - Sebastian W Atalla
- College of Nursing, The Ohio State University, Columbus, OH, United States
| | - Todd B Monroe
- College of Nursing, The Ohio State University, Columbus, OH, United States
| | - Semra A Aytur
- Department of Health Management and Policy, University of New Hampshire, Durham, NH, United States
| | - Jennifer S Potter
- Department of Psychiatry, University of Texas Health Science Center San Antonio, San Antonio, TX, United States
| | - Donald A Robin
- Department of Communication Sciences and Disorders, University of New Hampshire, Durham, NH, United States.,Interdisciplinary Program in Neuroscience and Behavior, University of New Hampshire, Durham, NH, United States
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Johnson EB, Gregory S. Huntington's disease: Brain imaging in Huntington's disease. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 165:321-369. [PMID: 31481169 DOI: 10.1016/bs.pmbts.2019.04.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Huntington's disease (HD) gene-carriers show prominent neuronal loss by end-stage disease, and the use of magnetic resonance imaging (MRI) has been increasingly used to quantify brain changes during earlier stages of the disease. MRI offers an in vivo method of measuring structural and functional brain change. The images collected via MRI are processed to measure different anatomical features, such as brain volume, macro- and microstructural changes within white matter and functional brain activity. Structural imaging has demonstrated significant volume loss across multiple white and gray matter regions in HD, particularly within subcortical structures. There also appears to be increasing disorganization of white matter tracts and between-region connectivity with increasing disease progression. Finally, functional changes are thought to represent changes in brain activity underlying compensatory mechanisms in HD. This chapter will provide an overview of the principles of MRI and practicalities associated with using MRI in HD studies, and summarize findings from MRI studies investigating brain structure and function in HD.
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Affiliation(s)
- Eileanoir B Johnson
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sarah Gregory
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
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Henry TR, Feczko E, Cordova M, Earl E, Williams S, Nigg JT, Fair DA, Gates KM. Comparing directed functional connectivity between groups with confirmatory subgrouping GIMME. Neuroimage 2019; 188:642-653. [PMID: 30583065 PMCID: PMC6901282 DOI: 10.1016/j.neuroimage.2018.12.040] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 11/29/2018] [Accepted: 12/18/2018] [Indexed: 01/17/2023] Open
Abstract
Connectivity modeling in functional neuroimaging has become widely used method of analysis for understanding functional architecture. One method for deriving directed connectivity models is Group Iterative Multiple Model Estimation (GIMME; Gates and Molenaar, 2012). GIMME looks for commonalities across the sample to detect signal from noise and arrive at edges that exist across the majority in the group ("group-level edges") and individual-level edges. In this way, GIMME obtains generalizable results via the group-level edges while also allowing for between subject heterogeneity in connectivity, moving the field closer to obtaining reliable personalized connectivity maps. In this article, we present a novel extension of GIMME, confirmatory subgrouping GIMME, which estimates subgroup-level edges for a priori known groups (e.g. typically developing controls vs. clinical group). Detecting edges that consistently exist for individuals within predefined subgroups aids in interpretation of the heterogeneity in connectivity maps and allows for subgroup-specific inferences. We describe this algorithm, as well as several methods to examine the results. We present an empirical example that finds similarities and differences in resting state functional connectivity among four groups of children: typically developing controls (TDC), children with autism spectrum disorder (ASD), children with Inattentive (ADHD-I) and Combined (ADHD-C) Type ADHD. Findings from this study suggest common involvement of the left Broca's area in all the clinical groups, as well as several unique patterns of functional connectivity specific to a given disorder. Overall, the current approach and proof of principle findings highlight a novel and reliable tool for capturing heterogeneity in complex mental health disorders.
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Affiliation(s)
- Teague Rhine Henry
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Eric Feczko
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Michaela Cordova
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - Eric Earl
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - Sandra Williams
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joel T Nigg
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA; Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Kathleen M Gates
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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50
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Cooper SR, Jackson JJ, Barch DM, Braver TS. Neuroimaging of individual differences: A latent variable modeling perspective. Neurosci Biobehav Rev 2019; 98:29-46. [PMID: 30611798 PMCID: PMC6980382 DOI: 10.1016/j.neubiorev.2018.12.022] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 11/16/2018] [Accepted: 12/18/2018] [Indexed: 12/31/2022]
Abstract
Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.
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Affiliation(s)
- Shelly R Cooper
- Washington University in St. Louis, Psychological and Brain Sciences, St. Louis, Missouri, United States.
| | - Joshua J Jackson
- Washington University in St. Louis, Psychological and Brain Sciences, St. Louis, Missouri, United States
| | - Deanna M Barch
- Washington University in St. Louis, Psychological and Brain Sciences, St. Louis, Missouri, United States
| | - Todd S Braver
- Washington University in St. Louis, Psychological and Brain Sciences, St. Louis, Missouri, United States
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