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Moser J, Koirala S, Madison T, Labonte AK, Carrasco CM, Feczko E, Moore LA, Ahmed W, Myers MJ, Yacoub E, Trevo-Clemmens B, Larsen B, Laumann TO, Nelson SM, Vizioli L, Sylvester CM, Fair DA. Multi-echo Acquisition and Thermal Denoising Advances Infant Precision Functional Imaging. bioRxiv 2023:2023.10.27.564416. [PMID: 37961636 PMCID: PMC10634909 DOI: 10.1101/2023.10.27.564416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development, but precise characterization of systems organization during periods of high plasticity might be most influential towards discoveries promoting lifelong health. Collecting and analyzing precision fMRI data during early development has unique challenges and emphasizes the importance of novel methods to improve data acquisition, processing, and analysis strategies in infant samples. Here, we investigate the applicability of two such methods from adult MRI research, multi-echo (ME) data acquisition and thermal noise removal with Noise reduction with distribution corrected principal component analysis (NORDIC), in precision fMRI data from three newborn infants. Compared to an adult example subject, T2* relaxation times calculated from ME data in infants were longer and more variable across the brain, pointing towards ME acquisition being a promising tool for optimizing developmental fMRI. The application of thermal denoising via NORDIC increased tSNR and the overall strength of functional connections as well as the split-half reliability of functional connectivity matrices in infant ME data. While our findings related to NORDIC denoising are coherent with the adult literature and ME data acquisition showed high promise, its application in developmental samples needs further investigation. The present work reveals gaps in our understanding of the best techniques for developmental brain imaging and highlights the need for further developmentally-specific methodological advances and optimizations, towards precision functional imaging in infants.
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Affiliation(s)
- Julia Moser
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Sanju Koirala
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Thomas Madison
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Weli Ahmed
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Brenden Trevo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Bart Larsen
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Luca Vizioli
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Taylor Family Institute for Innovative Research, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
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Alexander-Bloch AF, Sood R, Shinohara RT, Moore TM, Calkins ME, Chertavian C, Wolf DH, Gur RC, Satterthwaite TD, Gur RE, Barzilay R. Connectome-wide Functional Connectivity Abnormalities in Youth With Obsessive-Compulsive Symptoms. Biol Psychiatry Cogn Neurosci Neuroimaging 2022; 7:1068-1077. [PMID: 34375730 PMCID: PMC8821731 DOI: 10.1016/j.bpsc.2021.07.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 07/16/2021] [Accepted: 07/29/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND Obsessive-compulsive symptomatology (OCS) is common in adolescence but usually does not meet the diagnostic threshold for obsessive-compulsive disorder. Nevertheless, both obsessive-compulsive disorder and subthreshold OCS are associated with increased likelihood of experiencing other serious psychiatric conditions, including depression and suicidal ideation. Unfortunately, there is limited information on the neurobiology of OCS. METHODS Here, we undertook one of the first brain imaging studies of OCS in a large adolescent sample (analyzed n = 832) from the Philadelphia Neurodevelopmental Cohort. We investigated resting-state functional magnetic resonance imaging functional connectivity using complementary analytic approaches that focus on different neuroanatomical scales, from known functional systems to connectome-wide tests. RESULTS We found a robust pattern of connectome-wide, OCS-related differences, as well as evidence of specific abnormalities involving known functional systems, including dorsal and ventral attention, frontoparietal, and default mode systems. Analysis of cerebral perfusion imaging and high-resolution structural imaging did not show OCS-related differences, consistent with domain specificity to functional connectivity. CONCLUSIONS The brain connectomic associations with OCS reported here, together with early studies of its clinical relevance, support the potential for OCS as an early marker of psychiatric risk that may enhance our understanding of mechanisms underlying the onset of adolescent psychopathology.
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Affiliation(s)
- Aaron F Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; CHOP/Penn Lifespan Brain Institute, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Rahul Sood
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tyler M Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; CHOP/Penn Lifespan Brain Institute, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Monica E Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; CHOP/Penn Lifespan Brain Institute, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Casey Chertavian
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; CHOP/Penn Lifespan Brain Institute, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; CHOP/Penn Lifespan Brain Institute, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; CHOP/Penn Lifespan Brain Institute, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ran Barzilay
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; CHOP/Penn Lifespan Brain Institute, University of Pennsylvania, Philadelphia, Pennsylvania
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Smyser CD, Dosenbach NUF, Smyser TA, Snyder AZ, Rogers CE, Inder TE, Schlaggar BL, Neil JJ. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 2016; 136:1-9. [PMID: 27179605 DOI: 10.1016/j.neuroimage.2016.05.029] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Revised: 05/05/2016] [Accepted: 05/08/2016] [Indexed: 12/24/2022] Open
Abstract
Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p<0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants.
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Affiliation(s)
- Christopher D Smyser
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Tara A Smyser
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Terrie E Inder
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
| | - Bradley L Schlaggar
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Neurobiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Jeffrey J Neil
- Department of Neurology, Boston Children's Hospital, 333 Longwood Avenue, Boston, MA 02115, USA.
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Graham AM, Pfeifer JH, Fisher PA, Lin W, Gao W, Fair DA. The potential of infant fMRI research and the study of early life stress as a promising exemplar. Dev Cogn Neurosci 2014; 12:12-39. [PMID: 25459874 PMCID: PMC4385461 DOI: 10.1016/j.dcn.2014.09.005] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Revised: 09/24/2014] [Accepted: 09/29/2014] [Indexed: 01/09/2023] Open
Abstract
fMRI research with infants and toddlers has increased rapidly over the past decade. Infant fMRI has provided unique insight into early functional brain development. Complex methodological challenges associated with infant fMRI warrant careful consideration and ongoing research. Infant fMRI has potential to contribute to multiple fields of study. The study of early life stress is a prime example of a field that is ripe to benefit from this technique.
Functional magnetic resonance imaging (fMRI) research with infants and toddlers has increased rapidly over the past decade, and provided a unique window into early brain development. In the current report, we review the state of the literature, which has established the feasibility and utility of task-based fMRI and resting state functional connectivity MRI (rs-fcMRI) during early periods of brain maturation. These methodologies have been successfully applied beginning in the neonatal period to increase understanding of how the brain both responds to environmental stimuli, and becomes organized into large-scale functional systems that support complex behaviors. We discuss the methodological challenges posed by this promising area of research. We also highlight that despite these challenges, early work indicates a strong potential for these methods to influence multiple research domains. As an example, we focus on the study of early life stress and its influence on brain development and mental health outcomes. We illustrate the promise of these methodologies for building on, and making important contributions to, the existing literature in this field.
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Affiliation(s)
- Alice M Graham
- Department of Behavioral Neuroscience, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, United States.
| | - Jennifer H Pfeifer
- Department of Psychology, University of Oregon, 1715 Franklin Boulevard, Eugene, OR 97403, United States
| | - Philip A Fisher
- Department of Psychology, University of Oregon, 1715 Franklin Boulevard, Eugene, OR 97403, United States
| | - Weili Lin
- Departments of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Wei Gao
- Departments of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Damien A Fair
- Department of Psychology, University of Oregon, 1715 Franklin Boulevard, Eugene, OR 97403, United States; Department of Psychiatry, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, United States; Advanced Imaging Research Center, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, United States
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Abstract
In this combined structural and functional MRI developmental study, we tested 48 participants aged 7-37 years on 3 simple face-processing tasks (identity, expression, and gaze task), which were designed to yield very similar performance levels across the entire age range. The same participants then carried out 3 more difficult out-of-scanner tasks, which provided in-depth measures of changes in performance. For our analysis we adopted a novel, systematic approach that allowed us to differentiate age- from performance-related changes in the BOLD response in the 3 tasks, and compared these effects to concomitant changes in brain structure. The processing of all face aspects activated the core face-network across the age range, as well as additional and partially separable regions. Small task-specific activations in posterior regions were found to increase with age and were distinct from more widespread activations that varied as a function of individual task performance (but not of age). Our results demonstrate that activity during face-processing changes with age, and these effects are still observed when controlling for changes associated with differences in task performance. Moreover, we found that changes in white and gray matter volume were associated with changes in activation with age and performance in the out-of-scanner tasks.
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Affiliation(s)
- Kathrin Cohen Kadosh
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AR, UK.
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