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Kliamovich DAKOTA, Miranda-Dominguez OSCAR, Byington NORA, Espinoza ABIGAILV, Lopez Flores ARTURO, Fair DAMIENA, Nagel BONNIEJ. Leveraging distributed brain signal at rest to predict internalizing symptoms in youth. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00215-5. [PMID: 39127423 DOI: 10.1016/j.bpsc.2024.07.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/30/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND The prevalence of internalizing psychopathology rises precipitously from early to mid-adolescence, yet the underlying neural phenotypes that give rise to depression and anxiety during this developmental period remain unclear. METHODS Youth from the Adolescent Brain and Cognitive DevelopmentSM Study (ages 9-10 years at baseline) with a resting-state fMRI scan and mental health data were eligible for inclusion. Internalizing subscale scores from the Brief Problem Monitor - Youth Form were combined across two years of follow-up to generate a cumulative measure of internalizing symptoms. The total sample (n = 6521) was split into a large discovery dataset and a smaller validation dataset. Brain-behavior associations of resting-state functional connectivity (RSFC) with internalizing symptoms were estimated in the discovery dataset. The weighted contributions of each functional connection were aggregated using multivariate statistics to generate a polyneuro risk score (PNRS). The predictive power of the PNRS was evaluated in the validation dataset. RESULTS The PNRS explained 10.73% of the observed variance in internalizing symptom scores in the validation dataset. Model performance peaked when the top 2% functional connections identified in the discovery dataset (ranked by absolute β-weight) were retained. The RSFC networks that were implicated most prominently were the default mode, dorsal attention, and cingulo-parietal networks. These findings were significant (p < 1*10-6) as accounted for by permutation testing (n = 7000). CONCLUSIONS These results suggest that the neural phenotype associated with internalizing symptoms during adolescence is functionally distributed. The PNRS approach is a novel method for capturing relationships between RSFC and behavior.
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Affiliation(s)
- D A K O T A Kliamovich
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR.
| | | | - N O R A Byington
- Department of Pediatrics, University of Minnesota, Minneapolis, MN
| | | | | | | | - B O N N I E J Nagel
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR; Department of Psychiatry, Oregon Health & Science University, Portland, OR
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Liu YT, Yang YT, Tang CX, Ma JQ, Kong X, Li JH, Li YM, Liu SY, Zhou CS, Wang YF, Zhang LJ. Aberrant cortical morphology patterns are associated with cognitive impairment in patients with chronic heart failure. Eur J Neurosci 2024; 60:3973-3983. [PMID: 38711292 DOI: 10.1111/ejn.16382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/17/2024] [Accepted: 04/23/2024] [Indexed: 05/08/2024]
Abstract
A mounting body of evidences suggests that patients with chronic heart failure (HF) frequently experience cognitive impairments, but the neuroanatomical mechanism underlying these impairments remains elusive. In this retrospective study, 49 chronic HF patients and 49 healthy controls (HCs) underwent brain structural MRI scans and cognitive assessments. Cortical morphology index (cortical thickness, complexity, sulcal depth and gyrification) were evaluated. Correlations between cortical morphology and cognitive scores and clinical variables were explored. Logistic regression analysis was employed to identify risk factors for predicting 3-year major adverse cardiovascular events. Compared with HCs, patients with chronic HF exhibited decreased cognitive scores (p < .001) and decreased cortical thickness, sulcal depth and gyrification in brain regions involved cognition, sensorimotor, autonomic nervous system (family-wise error correction, all p values <.05). Notably, HF duration and New York Heart Association (NYHA) demonstrated negative correlations with abnormal cortex morphology, particularly HF duration and thickness in left precentral gyrus (r = -.387, p = .006). Cortical morphology characteristics exhibited positive associations with global cognition, particularly cortical thickness in left pars opercularis (r = .476, p < .001). NYHA class is an independent risk factor for adverse outcome (p = .001). The observed correlation between abnormal cortical morphology and global cognition suggested that cortical morphology may serve as a promising imaging biomarker and provide insights into neuroanatomical underpinnings of cognitive impairment in patients with chronic HF.
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Affiliation(s)
- Yu Ting Liu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yu Ting Yang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Chun Xiang Tang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Jun Qing Ma
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xiang Kong
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Jian Hua Li
- Department of Cardiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yan Ming Li
- Department of Cardiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Shu Yu Liu
- Department of Cardiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Chang Sheng Zhou
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yun Fei Wang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Long Jiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
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Makowski C, Nichols TE, Dale AM. Quality over quantity: powering neuroimaging samples in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01893-4. [PMID: 38902353 DOI: 10.1038/s41386-024-01893-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 05/06/2024] [Accepted: 05/24/2024] [Indexed: 06/22/2024]
Abstract
Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.
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Affiliation(s)
- Carolina Makowski
- Department of Radiology, University of California San Diego, San Diego, CA, USA.
| | - Thomas E Nichols
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anders M Dale
- Departments of Radiology and Neurosciences, University of California San Diego, San Diego, CA, USA
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4
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Makowski C, Brown TT, Zhao W, Hagler Jr DJ, Parekh P, Garavan H, Nichols TE, Jernigan TL, Dale AM. Leveraging the adolescent brain cognitive development study to improve behavioral prediction from neuroimaging in smaller replication samples. Cereb Cortex 2024; 34:bhae223. [PMID: 38880786 PMCID: PMC11180541 DOI: 10.1093/cercor/bhae223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/08/2024] [Accepted: 05/14/2024] [Indexed: 06/18/2024] Open
Abstract
Neuroimaging is a popular method to map brain structural and functional patterns to complex human traits. Recently published observations cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional magnetic resonance imaging (MRI). We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM Study to inform the replication sample size required with univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~ 100 subjects for structural and resting state MRI. Even with 100 random re-samplings of 100 subjects in discovery, prediction can be adequately powered with 66 subjects in replication for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many research programs and grants.
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Affiliation(s)
- Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, United States
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Timothy T Brown
- Department of Neurosciences, University of California San Diego, La Jolla, CA,, United States
| | - Weiqi Zhao
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, United States
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, United States
| | - Donald J Hagler Jr
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, United States
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington, VT, United States
| | - Thomas E Nichols
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Terry L Jernigan
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, United States
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, United States
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
- Department of Neurosciences, University of California San Diego, La Jolla, CA,, United States
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5
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Makowski C, Brown TT, Zhao W, Hagler DJ, Parekh P, Garavan H, Nichols TE, Jernigan TL, Dale AM. Leveraging the Adolescent Brain Cognitive Development Study to improve behavioral prediction from neuroimaging in smaller replication samples. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.16.545340. [PMID: 37398195 PMCID: PMC10312746 DOI: 10.1101/2023.06.16.545340] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Magnetic resonance imaging (MRI) is a popular and useful non-invasive method to map patterns of brain structure and function to complex human traits. Recently published observations in multiple large scale studies cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional MRI, which seems to account for little behavioral variability. We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM (ABCD®) Study to inform the replication sample size required with both univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~100 subjects for structural MRI. Even with 100 random re-samplings of 50 subjects in the discovery sample, prediction can be adequately powered with 98 subjects in the replication sample for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many investigators' research programs and grants.
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Affiliation(s)
- Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, California, USA
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Timothy T Brown
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Weiqi Zhao
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, California, USA
- Department of Cognitive Science, University of California San Diego, La Jolla, California USA
| | - Donald J Hagler
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, California, USA
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington, Vermont, USA
| | - Thomas E Nichols
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU
| | - Terry L Jernigan
- Department of Cognitive Science, University of California San Diego, La Jolla, California USA
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, California, USA
- Department of Radiology, University of California San Diego, La Jolla, California, USA
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
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6
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Paulus MP, Zhao Y, Potenza MN, Aupperle RL, Bagot KS, Tapert SF. Screen media activity in youth: A critical review of mental health and neuroscience findings. JOURNAL OF MOOD AND ANXIETY DISORDERS 2023; 3:100018. [PMID: 37927536 PMCID: PMC10624397 DOI: 10.1016/j.xjmad.2023.100018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
This review has two primary objectives: (1) to offer a balanced examination of recent findings on the relationship between screen media activity (SMA) in young individuals and outcomes such as sleep patterns, mood disturbances, anxiety-related concerns, and cognitive processes; and (2) to introduce a novel multi-level system model that integrates these findings, resolves contradictions in the literature, and guides future studies in examining key covariates affecting the SMA-mental health relationship. Key findings include: (1) Several meta-analyses reveal a significant association between SMA and mental health issues, particularly anxiety and depression, including specific negative effects linked to prolonged screen time; (2) substantial evidence indicates that SMA has both immediate and long-term impacts on sleep duration and quality; (3) the relationship between SMA and cognitive functioning is complex, with mixed findings showing both positive and negative associations; and (4) the multifaceted relationship between SMA and various aspects of adolescent life is influenced by a wide range of environmental and contextual factors. SMA in youth is best understood within a complex system encompassing individual, caregiver, school, peer, and environmental factors, as framed by Bronfenbrenner's ecological systems theory, which identifies five interrelated systems (microsystem, mesosystem, exosystem, macrosystem, and chronosystem) that influence development across both proximal and distal levels of the environment. This model provides a framework for future research to examine these interactions, considering moderating factors, and to develop targeted interventions that can mitigate potential adverse effects of SMA on mental well-being.
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Affiliation(s)
- Martin P. Paulus
- Laureate Institute for Brain Research, 6655S. Yale Ave., Tulsa, OK 74136, USA
- School of Community Medicine, The University of Tulsa, 1215 South Boulder Ave. W., Tulsa, OK 74119, USA
| | - Yihong Zhao
- Columbia University School of Nursing, 560W 168th Street, Room 614, New York, NY 10032, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Marc N. Potenza
- Department of Psychiatry, Child Study Center, Department of Neuroscience, Yale University School of Medicine, 1 Church Street, Room 726, New Haven, CT 06510, USA
- Connecticut Mental Health Center, 1 Church Street, Room 726, New Haven, CT 06510, USA
- Connecticut Council on Problem Gambling, Wethersfield, 1 Church Street, Room 726, New Haven, CT 06510, USA
- Wu Tsai Institute, Yale University, 1 Church Street, Room 726, New Haven, CT 06510, USA
| | - Robin L. Aupperle
- Laureate Institute for Brain Research, 6655S. Yale Ave., Tulsa, OK 74136, USA
| | - Kara S. Bagot
- iIcahn School of Medicine at Mount Sinai, Departments of Psychiatry and Pediatrics, USA
| | - Susan F. Tapert
- Department of Psychiatry, UCSD Health Sciences, 9500 Gilman Drive, La Jolla, CA 92093, USA
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7
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Carrión-Castillo A, Paz-Alonso PM, Carreiras M. Brain structure, phenotypic and genetic correlates of reading performance. Nat Hum Behav 2023; 7:1120-1134. [PMID: 37037991 DOI: 10.1038/s41562-023-01583-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/08/2023] [Indexed: 04/12/2023]
Abstract
Reading is an evolutionarily recent development that recruits and tunes brain circuitry connecting primary- and language-processing regions. We investigated whether metrics of the brain's physical structure correlate with reading performance and whether genetic variants affect this relationship. To this aim, we used the Adolescent Brain Cognitive Development dataset (n = 9,013) of 9-10-year-olds and focused on 150 measures of cortical surface area (CSA) and thickness. Our results reveal that reading performance is associated with nine measures of brain structure including relevant regions of the reading network. Furthermore, we show that this relationship is partially mediated by genetic factors for two of these measures: the CSA of the entire left hemisphere and, specifically, of the left superior temporal gyrus CSA. These effects emphasize the complex and subtle interplay between genes, brain and reading, which is a partly heritable polygenic skill that relies on a distributed network.
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Affiliation(s)
| | - Pedro M Paz-Alonso
- Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastián, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Manuel Carreiras
- Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastián, Spain.
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
- University of the Basque Country, Bilbao, Spain.
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8
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Zhao W, Makowski C, Hagler DJ, Garavan HP, Thompson WK, Greene DJ, Jernigan TL, Dale AM. Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity. Neuroimage 2023; 270:119946. [PMID: 36801369 PMCID: PMC11037888 DOI: 10.1016/j.neuroimage.2023.119946] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 02/18/2023] Open
Abstract
Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.
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Affiliation(s)
- Weiqi Zhao
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA
| | - Carolina Makowski
- Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA
| | - Donald J Hagler
- University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA
| | | | | | - Deanna J Greene
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA
| | - Terry L Jernigan
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA; Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA; Department of Psychiatry, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA
| | - Anders M Dale
- Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA; Department of Neuroscience, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; Department of Psychiatry, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA.
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9
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Byington N, Grimsrud G, Mooney MA, Cordova M, Doyle O, Hermosillo RJM, Earl E, Houghton A, Conan G, Hendrickson TJ, Ragothaman A, Carrasco CM, Rueter A, Perrone A, Moore LA, Graham A, Nigg JT, Thompson WK, Nelson SM, Feczko E, Fair DA, Miranda-Dominguez O. Polyneuro risk scores capture widely distributed connectivity patterns of cognition. Dev Cogn Neurosci 2023; 60:101231. [PMID: 36934605 PMCID: PMC10031023 DOI: 10.1016/j.dcn.2023.101231] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/06/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework's ability to reliably capture brain-behavior relationships across 3 cognitive scores - general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.
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Affiliation(s)
- Nora Byington
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States.
| | - Gracie Grimsrud
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Michael A Mooney
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, United States; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, United States
| | - Michaela Cordova
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, CA 92120, United States
| | - Olivia Doyle
- Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, United States
| | - Robert J M Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Eric Earl
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD 20892, United States
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Gregory Conan
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | | | - Cristian Morales Carrasco
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Amanda Rueter
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Anders Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Alice Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, United States
| | - Joel T Nigg
- Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, United States; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, United States
| | - Wesley K Thompson
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK 74136, United States
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States; Department of Pediatrics, University of Minnesota, Minneapolis, MN 55414, United States
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States; Department of Pediatrics, University of Minnesota, Minneapolis, MN 55414, United States
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States; Department of Pediatrics, University of Minnesota, Minneapolis, MN 55414, United States; Institute of Child Development, University of Minnesota, Minneapolis, MN 55414, United States
| | - Oscar Miranda-Dominguez
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States; Department of Pediatrics, University of Minnesota, Minneapolis, MN 55414, United States
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10
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Makowski C, Wang H, Srinivasan A, Qi A, Qiu Y, van der Meer D, Frei O, Zou J, Visscher P, Yang J, Chen CH. Larger cerebral cortex is genetically correlated with greater frontal area and dorsal thickness. Proc Natl Acad Sci U S A 2023; 120:e2214834120. [PMID: 36893272 PMCID: PMC10089183 DOI: 10.1073/pnas.2214834120] [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: 08/30/2022] [Accepted: 01/18/2023] [Indexed: 03/11/2023] Open
Abstract
Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e., total surface area, mean cortical thickness) using a genetically informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, where 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recovered loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant genetic overlap of global and dorsolateral prefrontal modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.
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Affiliation(s)
- Carolina Makowski
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Hao Wang
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Anjali Srinivasan
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Anna Qi
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Yuqi Qiu
- School of Statistics, East China Normal University, Shanghai20050, China
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, University of Oslo, Oslo0450, Norway
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research Centre, Division of Mental Health and Addiction, University of Oslo, Oslo0450, Norway
| | - Jingjing Zou
- Division of Biostatistics and Bioinformatics, University of California San Diego, La Jolla, CA92093
| | - Peter M. Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD4072, Australia
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang310024, China
| | - Chi-Hua Chen
- Department of Radiology, University of California San Diego, La Jolla, CA92093
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11
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Loughnan R, Palmer CE, Makowski C, Thompson WK, Barch DM, Jernigan TL, Dale AM, Fan CC. Unique prediction of developmental psychopathology from genetic and familial risk. J Child Psychol Psychiatry 2022; 63:1631-1643. [PMID: 35764363 PMCID: PMC9691563 DOI: 10.1111/jcpp.13649] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/23/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Early detection is critical for easing the rising burden of psychiatric disorders. However, the specificity of psychopathological measurements and genetic predictors is unclear among youth. METHODS We measured associations between genetic risk for psychopathology (polygenic risk scores (PRS) and family history (FH) measures) and a wide range of behavioral measures in a large sample (n = 5,204) of early adolescent participants (9-11 years) from the Adolescent Brain and Cognitive Development StudySM . Associations were measured both with and without accounting for shared variance across measures of genetic risk. RESULTS When controlling for genetic risk for other psychiatric disorders, polygenic risk for problematic opioid use (POU) is uniquely associated with lower behavioral inhibition. Attention deficit hyperactivity disorder (ADHD), depression (DEP), and attempted suicide (SUIC) PRS shared many significant associations with externalizing, internalizing, and psychosis-related behaviors. However, when accounting for all measures of genetic and familial risk, these PRS also showed clear, unique patterns of association. Polygenic risk for ASD, BIP, and SCZ, and attempted suicide uniquely predicted variability in cognitive performance. FH accounted for unique variability in behavior above and beyond PRS and vice versa, with FH measures explaining a greater proportion of unique variability compared to the PRS. CONCLUSION Our results indicate that, among youth, many behaviors show shared genetic influences; however, there is also specificity in the profile of emerging psychopathologies for individuals with high genetic risk for particular disorders. This may be useful for quantifying early, differential risk for psychopathology in development.
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Affiliation(s)
- Robert Loughnan
- Population Neuroscience and Genetics, University of California San Diego, USA
| | - Clare E. Palmer
- Center for Human Development, University of California San Diego, USA
| | - Carolina Makowski
- Department of Radiology, University of California San Diego School of Medicine, USA
| | - Wesley K. Thompson
- Population Neuroscience and Genetics, University of California San Diego, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego USA
| | - Deanna M. Barch
- Psychological & Brain Sciences, Psychiatry and Radiology, Washington University in St. Louis, St. Louis USA
| | - Terry L. Jernigan
- Center for Human Development, University of California San Diego, USA
- Department of Radiology, University of California San Diego School of Medicine, USA
| | - Anders M. Dale
- Department of Radiology, University of California San Diego School of Medicine, USA
| | - Chun Chieh Fan
- Population Neuroscience and Genetics, University of California San Diego, USA
- Department of Radiology, University of California San Diego School of Medicine, USA
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12
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Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives. Transl Psychiatry 2022; 12:447. [PMID: 36241627 PMCID: PMC9568576 DOI: 10.1038/s41398-022-02193-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 09/06/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022] Open
Abstract
Cortical morphology is a key determinant of cognitive ability and mental health. Its development is a highly intricate process spanning decades, involving the coordinated, localized expression of thousands of genes. We are now beginning to unravel the genetic architecture of cortical morphology, thanks to the recent availability of large-scale neuroimaging and genomic data and the development of powerful biostatistical tools. Here, we review the progress made in this field, providing an overview of the lessons learned from genetic studies of cortical volume, thickness, surface area, and folding as captured by neuroimaging. It is now clear that morphology is shaped by thousands of genetic variants, with effects that are region- and time-dependent, thereby challenging conventional study approaches. The most recent genome-wide association studies have started discovering common genetic variants influencing cortical thickness and surface area, yet together these explain only a fraction of the high heritability of these measures. Further, the impact of rare variants and non-additive effects remains elusive. There are indications that the quickly increasing availability of data from whole-genome sequencing and large, deeply phenotyped population cohorts across the lifespan will enable us to uncover much of the missing heritability in the upcoming years. Novel approaches leveraging shared information across measures will accelerate this process by providing substantial increases in statistical power, together with more accurate mapping of genetic relationships. Important challenges remain, including better representation of understudied demographic groups, integration of other 'omics data, and mapping of effects from gene to brain to behavior across the lifespan.
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13
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Pfefferbaum A, Sullivan EV, Zahr NM, Pohl KM, Saranathan M. Multi-atlas thalamic nuclei segmentation on standard T1-weighed MRI with application to normal aging. Hum Brain Mapp 2022; 44:612-628. [PMID: 36181510 PMCID: PMC9842912 DOI: 10.1002/hbm.26088] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/15/2022] [Accepted: 09/01/2022] [Indexed: 01/25/2023] Open
Abstract
Specific thalamic nuclei are implicated in healthy aging and age-related neurodegenerative diseases. However, few methods are available for robust automated segmentation of thalamic nuclei. The threefold aims of this study were to validate the use of a modified thalamic nuclei segmentation method on standard T1 MRI data, to apply this method to quantify age-related volume declines, and to test functional meaningfulness by predicting performance on motor testing. A modified version of THalamus Optimized Multi-Atlas Segmentation (THOMAS) generated 22 unilateral thalamic nuclei. For validation, we compared nuclear volumes obtained from THOMAS parcellation of white-matter-nulled (WMn) MRI data to T1 MRI data in 45 participants. To examine the effects of age/sex on thalamic nuclear volumes, T1 MRI available from a second data set of 121 men and 117 women, ages 20-86 years, were segmented using THOMAS. To test for functional ramifications, composite regions and constituent nuclei were correlated with Grooved Pegboard test scores. THOMAS on standard T1 data showed significant quantitative agreement with THOMAS from WMn data, especially for larger nuclei. Sex differences revealing larger volumes in men than women were accounted for by adjustment with supratentorial intracranial volume (sICV). Significant sICV-adjusted correlations between age and thalamic nuclear volumes were detected in 20 of the 22 unilateral nuclei and whole thalamus. Composite Posterior and Ventral regions and Ventral Anterior/Pulvinar nuclei correlated selectively with higher scores from the eye-hand coordination task. These results support the use of THOMAS for standard T1-weighted data as adequately robust for thalamic nuclear parcellation.
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Affiliation(s)
- Adolf Pfefferbaum
- Center for Health SciencesSRI InternationalMenlo ParkCaliforniaUSA,Department of Psychiatry & Behavioral SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Edith V. Sullivan
- Department of Psychiatry & Behavioral SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Natalie M. Zahr
- Center for Health SciencesSRI InternationalMenlo ParkCaliforniaUSA,Department of Psychiatry & Behavioral SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Kilian M. Pohl
- Center for Health SciencesSRI InternationalMenlo ParkCaliforniaUSA,Department of Psychiatry & Behavioral SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Manojkumar Saranathan
- Department of RadiologyUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
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14
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Lawrence KE, Abaryan Z, Laltoo E, Hernandez LM, Gandal MJ, McCracken JT, Thompson PM. White matter microstructure shows sex differences in late childhood: Evidence from 6797 children. Hum Brain Mapp 2022; 44:535-548. [PMID: 36177528 PMCID: PMC9842921 DOI: 10.1002/hbm.26079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/29/2022] [Accepted: 08/19/2022] [Indexed: 02/01/2023] Open
Abstract
Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.
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Affiliation(s)
- Katherine E. Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Zvart Abaryan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Leanna M. Hernandez
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Michael J. Gandal
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA,Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA,Department of Human Genetics, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - James T. McCracken
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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