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Kozak S, Dezachyo O, Stanford W, Bar-Haim Y, Censor N, Dayan E. Elevated integration within the reward network underlies vulnerability to distress. Cereb Cortex 2022; 33:5797-5807. [PMID: 36453462 DOI: 10.1093/cercor/bhac460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/26/2022] [Accepted: 10/31/2022] [Indexed: 12/05/2022] Open
Abstract
Abstract
Distress tolerance (DT), the capability to persist under negative circumstances, underlies a range of psychopathologies. It has been proposed that DT may originate from the activity and connectivity in diverse neural networks integrated by the reward system. To test this hypothesis, we examined the link between DT and integration and segregation in the reward network as derived from resting-state functional connectivity data. DT was measured in 147 participants from a large community sample using the Behavioral Indicator of Resiliency to Distress task. Prior to DT evaluation, participants underwent a resting-state functional magnetic resonance imaging scan. For each participant, we constructed a whole-brain functional connectivity network and calculated the degree of reward network integration and segregation based on the extent to which reward network nodes showed functional connections within and outside their network. We found that distress-intolerant participants demonstrated heightened reward network integration relative to the distress-tolerant participants. In addition, these differences in integration were higher relative to the rest of the brain and, more specifically, the somatomotor network, which has been implicated in impulsive behavior. These findings support the notion that increased integration in large-scale brain networks may constitute a risk for distress intolerance and its psychopathological correlates.
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Affiliation(s)
- Stas Kozak
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - Or Dezachyo
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
- Sagol School of Neuroscience, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - William Stanford
- Biological & Biomedical Sciences Program, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599 , United States
| | - Yair Bar-Haim
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
- Sagol School of Neuroscience, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - Nitzan Censor
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
- Sagol School of Neuroscience, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - Eran Dayan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599 , United States
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Kung YC, Li CW, Hsiao FC, Tsai PJ, Chen S, Li MK, Lee HC, Chang CY, Wu CW, Lin CP. Cross-Scale Dynamicity of Entropy and Connectivity in the Sleeping Brain. Brain Connect 2022; 12:835-845. [PMID: 35343241 PMCID: PMC9839343 DOI: 10.1089/brain.2021.0174] [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] [Indexed: 01/22/2023] Open
Abstract
Introduction: The concept of local sleep refers to the phenomenon of local brain activity that modifies neural networks during unresponsive global sleep. Such network rewiring may differ across spatial scales; however, the global and local alterations in brain systems remain elusive in human sleep. Materials and Methods: We examined cross-scale changes of brain networks in sleep. Functional magnetic resonance imaging data were acquired from 28 healthy participants during nocturnal sleep. We adopted both metrics of connectivity (functional connectivity [FC] and regional homogeneity [ReHo]) and complexity (multiscale entropy) to explore the global and local functionality of the neural assembly across nonrapid eye movement sleep stages. Results: Long-range FC decreased with sleep depth, whereas local ReHo peaked at the N2 stage and reached its lowest level at the N3 stage. Entropy exhibited a general decline at the local scale (Scale 1) as sleep deepened, whereas the coarse-scale entropy (Scale 3) was consistent across stages. Discussion: The negative correlation between Scale-1 entropy and ReHo reflects the enhanced signal regularity and synchronization in sleep, identifying the information exchange at the local scale. The N2 stage showed a distinctive pattern toward local information processing with scrambled long-distance information exchange, indicating a specific time window for network reorganization. Collectively, the multidimensional metrics indicated an imbalanced global-local relationship among brain functional networks across sleep-wake stages.
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Affiliation(s)
- Yi-Chia Kung
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chia-Wei Li
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Fan-Chi Hsiao
- Department of Counseling and Industrial/Organizational Psychology, Ming Chuan University, Taoyuan, Taiwan
| | - Pei-Jung Tsai
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, Maryland, USA
| | - Shuo Chen
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Ming-Kang Li
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan
| | - Chun-Yen Chang
- Science Education Center, National Taiwan Normal University, Taipei, Taiwan
| | - Changwei W. Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
- Brain and Consciousness Research Center, Shuang-Ho Hospital,Taipei Medical University, New Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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53
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Strain JF, Brier MR, Tanenbaum A, Gordon BA, McCarthy JE, Dincer A, Marcus DS, Chhatwal JP, Graff-Radford NR, Day GS, la Fougère C, Perrin RJ, Salloway S, Schofield PR, Yakushev I, Ikeuchi T, Vöglein J, Morris JC, Benzinger TLS, Bateman RJ, Ances BM, Snyder AZ. Covariance-based vs. correlation-based functional connectivity dissociates healthy aging from Alzheimer disease. Neuroimage 2022; 261:119511. [PMID: 35914670 PMCID: PMC9750733 DOI: 10.1016/j.neuroimage.2022.119511] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 07/04/2022] [Accepted: 07/22/2022] [Indexed: 01/05/2023] Open
Abstract
Prior studies of aging and Alzheimer disease have evaluated resting state functional connectivity (FC) using either seed-based correlation (SBC) or independent component analysis (ICA), with a focus on particular functional systems. SBC and ICA both are insensitive to differences in signal amplitude. At the same time, accumulating evidence indicates that the amplitude of spontaneous BOLD signal fluctuations is physiologically meaningful. We systematically compared covariance-based FC, which is sensitive to amplitude, vs. correlation-based FC, which is not, in affected individuals and controls drawn from two cohorts of participants including autosomal dominant Alzheimer disease (ADAD), late onset Alzheimer disease (LOAD), and age-matched controls. Functional connectivity was computed over 222 regions of interest and group differences were evaluated in terms of components projected onto a space of lower dimension. Our principal observations are: (1) Aging is associated with global loss of resting state fMRI signal amplitude that is approximately uniform across resting state networks. (2) Thus, covariance FC measures decrease with age whereas correlation FC is relatively preserved in healthy aging. (3) In contrast, symptomatic ADAD and LOAD both lead to loss of spontaneous activity amplitude as well as severely degraded correlation structure. These results demonstrate a double dissociation between age vs. Alzheimer disease and the amplitude vs. correlation structure of resting state BOLD signals. Modeling results suggest that the AD-associated loss of correlation structure is attributable to a relative increase in the fraction of locally restricted as opposed to widely shared variance.
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Affiliation(s)
- Jeremy F Strain
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO 63110, USA
| | - Matthew R Brier
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO 63110, USA
| | - Aaron Tanenbaum
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO 63110, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in Saint Louis, Box 8225, 660 South Euclid Ave, St. Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO 63110, USA; Department of Psychological & Brain Sciences, Washington University, St. Louis, MO, USA
| | - John E McCarthy
- Department of Mathematics and Statistics, Washington University, St. Louis, MO 63130, USA
| | - Aylin Dincer
- Department of Radiology, Washington University in Saint Louis, Box 8225, 660 South Euclid Ave, St. Louis, MO 63110, USA
| | - Daniel S Marcus
- Department of Radiology, Washington University in Saint Louis, Box 8225, 660 South Euclid Ave, St. Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Jasmeer P Chhatwal
- Martinos Center, Massachusetts General Hospital, 149 13th St Room 2662, Charlestown, MA 02129, USA
| | - Neill R Graff-Radford
- Department of Neurology, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, Fl 32224, USA
| | - Gregory S Day
- Department of Neurology, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, Fl 32224, USA
| | - Christian la Fougère
- Department of Nuclear Medicine and Clinical Molecular Imaging, Universityhospital Tübingen, Tübingen, Germany; German Center for Neurodegenerative Diseases (DZNE) Tübingen, Germany
| | - Richard J Perrin
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO 63110, USA; Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO 63110, USA; Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Stephen Salloway
- Alpert Medical School of Brown University, 345 Blackstone Boulevard, Providence, RI 02906, USA
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, NSW 2131, Australia; School of Medical Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Igor Yakushev
- Department of Nuclear Medicine, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich 81675, Germany
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Japan
| | - Jonathan Vöglein
- Department of Neurology, Ludwig-Maximilians-Universität Munich, Germany
| | - John C Morris
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University in Saint Louis, Box 8225, 660 South Euclid Ave, St. Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO 63110, USA; Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO 63110, USA; Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Beau M Ances
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO 63110, USA; Department of Radiology, Washington University in Saint Louis, Box 8225, 660 South Euclid Ave, St. Louis, MO 63110, USA; Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Abraham Z Snyder
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO 63110, USA; Department of Radiology, Washington University in Saint Louis, Box 8225, 660 South Euclid Ave, St. Louis, MO 63110, USA.
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Vieira BH, Liem F, Dadi K, Engemann DA, Gramfort A, Bellec P, Craddock RC, Damoiseaux JS, Steele CJ, Yarkoni T, Langer N, Margulies DS, Varoquaux G. Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging. Neurobiol Aging 2022; 118:55-65. [PMID: 35878565 PMCID: PMC9853405 DOI: 10.1016/j.neurobiolaging.2022.06.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 06/21/2022] [Accepted: 06/23/2022] [Indexed: 01/24/2023]
Abstract
Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Nonbrain data (demographics, clinical, and neuropsychological scores), structural MRI, and functional connectivity data from OASIS-3 (N = 662; age = 46-96 years) were entered into cross-validated multitarget random forest models to predict future cognitive decline (measured by CDR and MMSE), on average 5.8 years into the future. The analysis was preregistered, and all analysis code is publicly available. Combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42). Cognitive performance, daily functioning, and subcortical volume drove the performance of our model. Including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective individualized cognitive, pharmacological, and behavioral interventions.
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Affiliation(s)
- Bruno Hebling Vieira
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland,Neuroscience Center Zurich (ZNZ), University of Zurich & ETH Zurich, Zurich, Switzerland,Corresponding author. (B. Hebling Vieira)
| | - Franziskus Liem
- University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | | | - Denis A. Engemann
- UniversitéParis-Saclay, Inria, CEA, Palaiseau, France,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - Pierre Bellec
- Functional Neuroimaging Unit, Geriatric Institute, University of Montreal, Montreal, Quebec, Canada
| | | | - Jessica S. Damoiseaux
- Institute of Gerontology and the Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Tal Yarkoni
- Department of Psychology, The University of Texas, Austin, TX, USA
| | - Nicolas Langer
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland,Neuroscience Center Zurich (ZNZ), University of Zurich & ETH Zurich, Zurich, Switzerland,University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | - Daniel S. Margulies
- Cognitive Neuroanatomy Lab, Institut du Cerveau et de la Moelle épinière, Paris, France
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Associations of lifetime concussion history and repetitive head impact exposure with resting-state functional connectivity in former collegiate American football players: An NCAA 15-year follow-up study. PLoS One 2022; 17:e0273918. [PMID: 36084077 PMCID: PMC9462826 DOI: 10.1371/journal.pone.0273918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 08/17/2022] [Indexed: 11/19/2022] Open
Abstract
The objective of this study was to examine associations of lifetime concussion history (CHx) and an advanced metric of lifetime repetitive head impact exposure with resting-state functional connectivity (rsFC) across the whole-brain and among large-scale functional networks (Default Mode; Dorsal Attention; and Frontoparietal Control) in former collegiate football players. Individuals who completed at least one year of varsity collegiate football were eligible to participate in this observational cohort study (n = 48; aged 36–41 years; 79.2% white/Caucasian; 12.5±4.4 years of football played; all men). Individuals were excluded if they reported history/suspicion of psychotic disorder with active symptoms, contraindications to participation in study procedures (e.g., MRI safety concern), or inability to travel. Each participant provided concussion and football playing histories. Self-reported concussion history was analyzed in two different ways based on prior research: dichotomous “High” (≥3 concussions; n = 28) versus “Low” (<3 concussions; n = 20); and four ordinal categories (0–1 concussion [n = 19]; 2–4 concussions [n = 8]; 5–7 concussions [n = 9]; and ≥8 concussions [n = 12]). The Head Impact Exposure Estimate (HIEE) was calculated from football playing history captured via structured interview. Resting-state fMRI and T1-weighted MRI were acquired and preprocessed using established pipelines. Next, rsFC was calculated using the Seitzman et al., (2020) 300-ROI functional atlas. Whole-brain, within-network, and between-network rsFC were calculated using all ROIs and network-specific ROIs, respectively. Effects of CHx and HIEE on rsFC values were examined using separate multivariable linear regression models, with a-priori α set to 0.05. We observed no statistically significant associations between rsFC outcomes and either CHx or HIEE (ps ≥ .12). Neither CHx nor HIEE were associated with neural signatures that have been observed in studies of typical and pathological aging. While CHx and repetitive head impacts have been associated with changes in brain health in older former athletes, our preliminary results suggest that associations with rsFC may not be present in early midlife former football players.
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Girault JB, Donovan K, Hawks Z, Talovic M, Forsen E, Elison JT, Shen MD, Swanson MR, Wolff JJ, Kim SH, Nishino T, Davis S, Snyder AZ, Botteron KN, Estes AM, Dager SR, Hazlett HC, Gerig G, McKinstry R, Pandey J, Schultz RT, St John T, Zwaigenbaum L, Todorov A, Truong Y, Styner M, Pruett JR, Constantino JN, Piven J. Infant Visual Brain Development and Inherited Genetic Liability in Autism. Am J Psychiatry 2022; 179:573-585. [PMID: 35615814 PMCID: PMC9356977 DOI: 10.1176/appi.ajp.21101002] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Autism spectrum disorder (ASD) is heritable, and younger siblings of ASD probands are at higher likelihood of developing ASD themselves. Prospective MRI studies of siblings report that atypical brain development precedes ASD diagnosis, although the link between brain maturation and genetic factors is unclear. Given that familial recurrence of ASD is predicted by higher levels of ASD traits in the proband, the authors investigated associations between proband ASD traits and brain development among younger siblings. METHODS In a sample of 384 proband-sibling pairs (89 pairs concordant for ASD), the authors examined associations between proband ASD traits and sibling brain development at 6, 12, and 24 months in key MRI phenotypes: total cerebral volume, cortical surface area, extra-axial cerebrospinal fluid, occipital cortical surface area, and splenium white matter microstructure. Results from primary analyses led the authors to implement a data-driven approach using functional connectivity MRI at 6 months. RESULTS Greater levels of proband ASD traits were associated with larger total cerebral volume and surface area and larger surface area and reduced white matter integrity in components of the visual system in siblings who developed ASD. This aligned with weaker functional connectivity between several networks and the visual system among all siblings during infancy. CONCLUSIONS The findings provide evidence that specific early brain MRI phenotypes of ASD reflect quantitative variation in familial ASD traits. Multimodal anatomical and functional convergence on cortical regions, fiber pathways, and functional networks involved in visual processing suggest that inherited liability has a role in shaping the prodromal development of visual circuitry in ASD.
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Affiliation(s)
- Jessica B Girault
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Educational Psychology (Wolff), University of Minnesota, Minneapolis;Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Kevin Donovan
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Zoë Hawks
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Muhamed Talovic
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Elizabeth Forsen
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Jed T Elison
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Mark D Shen
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Meghan R Swanson
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Jason J Wolff
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Sun Hyung Kim
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Tomoyuki Nishino
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Savannah Davis
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Abraham Z Snyder
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Kelly N Botteron
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Annette M Estes
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Stephen R Dager
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Heather C Hazlett
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Guido Gerig
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Robert McKinstry
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Juhi Pandey
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Robert T Schultz
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Tanya St John
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Lonnie Zwaigenbaum
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Alexandre Todorov
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Young Truong
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Martin Styner
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - John R Pruett
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - John N Constantino
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | - Joseph Piven
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
| | -
- Carolina Institute for Developmental Disabilities (Girault, Forsen, Shen, Hazlett, Piven), Department of Psychiatry (Girault, Shen, Kim, Hazlett, Styner, Piven), Department of Biostatistics (Donovan, Truong), and ; Department of Psychological and Brain Sciences (Hawks) and Department of Psychiatry (Talovic, Nishino, Davis, Botteron, Todorov, Pruett, Constantino), Washington University School of Medicine in St. Louis; Institute of Child Development (Elison) and Department of Psychology, School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, Tex. (Swanson); Department of Radiology, Washington University in St. Louis (Snyder, McKinstry); Department of Speech and Hearing Science, University of Washington, Seattle (Estes, St. John); Department of Radiology, University of Washington Medical Center, Seattle (Dager); Tandon School of Engineering, New York University, New York (Gerig); Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia (Pandey, Schultz); Department of Pediatrics, University of Alberta, Edmonton, Canada (Zwaigenbaum)
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Millar PR, Luckett PH, Gordon BA, Benzinger TLS, Schindler SE, Fagan AM, Cruchaga C, Bateman RJ, Allegri R, Jucker M, Lee JH, Mori H, Salloway SP, Yakushev I, Morris JC, Ances BM. Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease. Neuroimage 2022; 256:119228. [PMID: 35452806 PMCID: PMC9178744 DOI: 10.1016/j.neuroimage.2022.119228] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/28/2022] [Accepted: 04/19/2022] [Indexed: 12/29/2022] Open
Abstract
"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.
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Affiliation(s)
- Peter R Millar
- Department of Neurology, Washington University, St. Louis, MO 63110, USA.
| | - Patrick H Luckett
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Tammie LS Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Ricardo Allegri
- Department of Cognitive Neurology, Institute for Neurological Research Fleni, Buenos Aires, Argentina
| | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Jae-Hong Lee
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Hiroshi Mori
- Department of Clinical Neuroscience, Osaka City University Medical School, Abenoku, Osaka, 545-8585, Japan, Nagaoka Sutoku University
| | | | - Igor Yakushev
- Department of Nuclear Medicine, Technical University of Munich, Munich, Germany
| | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
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58
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Dataset of brain functional connectome and its maturation in adolescents. Data Brief 2022; 43:108454. [PMID: 35864878 PMCID: PMC9294043 DOI: 10.1016/j.dib.2022.108454] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 06/30/2022] [Accepted: 07/04/2022] [Indexed: 11/23/2022] Open
Abstract
We provided the dataset of brain connectome matrices, their similarities measures to self and others longitudinally, and Kessler's psychological distress scales (K10) including the response to each question. The dataset can be used to replicate the results of the manuscript titled “A longitudinal study of functional connectome uniqueness and its association with psychological distress in adolescence”. The functional connectome (whole-brain and 13 networks) matrices were calculated from the resting-state functional MRIs (rs-fMRIs). We collected rs-fMRI and Kessler's psychological distress scale (K10) in 77 adolescents longitudinally up to 9 times from 12 years of age every four months. After removal of data with excessive motion, 262 functional connectome matrices were provided with this paper. The 300 regions of interest (ROIs) were defined using the Greene lab brain atlas. The functional connectome matrices were calculated as correlations between time series from any pair of ROIs extracted from pre-processed fMRIs. This dataset could be potentially used toUnderstand developmental changes in the functional brain connectivity, As a normal control database of functional connectome matrices, Develop and validate connectome and network-related analysing methods.
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59
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Humphries JB, Mattos DJS, Rutlin J, Daniel AGS, Rybczynski K, Notestine T, Shimony JS, Burton H, Carter A, Leuthardt EC. Motor Network Reorganization Induced in Chronic Stroke Patients with the Use of a Contralesionally-Controlled Brain Computer Interface. BRAIN-COMPUTER INTERFACES 2022. [DOI: 10.1080/2326263x.2022.2057757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Joseph B. Humphries
- Departments of Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Jerrel Rutlin
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Andy G. S. Daniel
- Departments of Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Theresa Notestine
- Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA
| | - Joshua S. Shimony
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Harold Burton
- Neuroscience, Washington University in St. Louis, St. Louis, MO, USA
| | - Alexandre Carter
- Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Eric C. Leuthardt
- Departments of Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA
- Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA
- Neuroscience, Washington University in St. Louis, St. Louis, MO, USA
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, USA
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Wei YC, Kung YC, Huang WY, Lin C, Chen YL, Chen CK, Shyu YC, Lin CP. Functional Connectivity Dynamics Altered of the Resting Brain in Subjective Cognitive Decline. Front Aging Neurosci 2022; 14:817137. [PMID: 35813944 PMCID: PMC9263398 DOI: 10.3389/fnagi.2022.817137] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 05/19/2022] [Indexed: 12/05/2022] Open
Abstract
Background Subjective cognitive decline (SCD) appears in the preclinical stage of the Alzheimer's disease continuum. In this stage, dynamic features are more sensitive than static features to reflect early subtle changes in functional brain connectivity. Therefore, we studied local and extended dynamic connectivity of the resting brain of people with SCD to determine their intrinsic brain changes. Methods We enrolled cognitively normal older adults from the communities and divided them into SCD and normal control (NC) groups. We used mean dynamic amplitude of low-frequency fluctuation (mdALFF) to evaluate region of interest (ROI)-wise local dynamic connectivity of resting-state functional MRI. The dynamic functional connectivity (dFC) between ROIs was tested by whole-brain-based statistics. Results When comparing SCD (N = 40) with NC (N = 45), mdALFFmean decreased at right inferior parietal lobule (IPL) of the frontoparietal network (FPN). Still, it increased at the right middle temporal gyrus (MTG) of the ventral attention network (VAN) and right calcarine of the visual network (VIS). Also, the mdALFFvar (variance) increased at the left superior temporal gyrus of AUD, right MTG of VAN, right globus pallidum of the cingulo-opercular network (CON), and right lingual gyrus of VIS. Furthermore, mdALFFmean at right IPL of FPN are correlated negatively with subjective complaints and positively with objective cognitive performance. In the dFC seeded from the ROIs with local mdALFF group differences, SCD showed a generally lower dFCmean and higher dFCvar (variance) to other regions of the brain. These weakened and unstable functional connectivity appeared among FPN, CON, the default mode network, and the salience network, the large-scale networks of the triple network model for organizing neural resource allocations. Conclusion The local dynamic connectivity of SCD decreased in brain regions of cognitive executive control. Meanwhile, compensatory visual efforts and bottom-up attention rose. Mixed decrease and compensatory increase of dynamics of intrinsic brain activity suggest the transitional nature of SCD. The FPN local dynamics balance subjective and objective cognition and maintain cognitive preservation in preclinical dementia. Aberrant triple network model features the dFC alternations of SCD. Finally, the right lateralization phenomenon emerged early in the dementia continuum and affected local dynamic connectivity.
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Affiliation(s)
- Yi-Chia Wei
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Taiwan
- Department of Neurology, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Yi-Chia Kung
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wen-Yi Huang
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Taiwan
- Department of Neurology, Chang Gung Memorial Hospital, Keelung, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chemin Lin
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Psychiatry, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Yao-Liang Chen
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
- Department of Radiology, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Chih-Ken Chen
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Psychiatry, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Yu-Chiau Shyu
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Taiwan
- Department of Nursing, Chang Gung University of Science and Technology, Taoyuan, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
- *Correspondence: Ching-Po Lin
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A longitudinal study of functional connectome uniqueness and its association with psychological distress in adolescence. Neuroimage 2022; 258:119358. [PMID: 35700948 DOI: 10.1016/j.neuroimage.2022.119358] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/31/2022] [Accepted: 06/02/2022] [Indexed: 11/21/2022] Open
Abstract
Each human brain has a unique functional synchronisation pattern (functional connectome) analogous to a fingerprint that underpins brain functions and related behaviours. Here we examine functional connectome (whole-brain and 13 networks) maturation by measuring its uniqueness in adolescents who underwent brain scans longitudinally from 12 years of age every four months. The uniqueness of a functional connectome is defined as its ratio of self-similarity (from the same subject at a different time point) to the maximal similarity-to-others (from a given subject and any others at a different time point). We found that the unique whole brain connectome exists in 12 years old adolescents, with 92% individuals having a whole brain uniqueness value greater than one. The cingulo-opercular network (CON; a long-acting 'brain control network' configuring information processing) demonstrated marginal uniqueness in early adolescence with 56% of individuals showing uniqueness greater than one (i.e., more similar to her/his own CON four months later than those from any other subjects) and this increased longitudinally. Notably, the low uniqueness of the CON correlates (β = -18.6, FDR-Q < < 0.001) with K10 levels at the subsequent time point. This association suggests that the individualisation of CON network is related to psychological distress levels. Our findings highlight the potential of longitudinal neuroimaging to capture mental health problems in young people who are undergoing profound neuroplasticity and environment sensitivity period.
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Neural substrates of brand equity: applying a quantitative meta-analytical method for neuroimage studies. Heliyon 2022; 8:e09702. [PMID: 35734557 PMCID: PMC9207674 DOI: 10.1016/j.heliyon.2022.e09702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 01/29/2022] [Accepted: 06/06/2022] [Indexed: 11/24/2022] Open
Abstract
Although the concept of brand equity has been investigated using various approaches, a comprehensive neural basis for brand equity remains unclear. The default mode network (DMN) as a mental process might influence brand equity related consumers' decision-making, as reported in the marketing literature. While studies on the overlapping regions between the DMN and value-based decision-making related brain regions have been reported in neuroscience literature, relationships between the DMN and a neural mechanism of brand equity have not been clarified. The aim of our study is to identify neural substrates of brand equity and examine brand equity-related mental processes by comparing them to the DMN. To determine the neural substrates of brand equity, we first carried out the activation likelihood estimation (ALE) meta-analysis. We examined 26 studies using branded objects as experimental stimuli for the ALE. Next, we set the output regions from ALE as the region of interest for meta-analytic connectivity modeling (MACM). Further, we compared the brand equity-related brain network (BE-RBN) revealed by the MACM with the DMN. We confirmed that the BE-RBN brain regions overlap with the medial temporal lobule (MTL) sub-system, a module composed of the DMN but excluding the retrosplenial cortex. Further, we discovered that several brain regions apart from the DMN are also distinctive BE-RBN brain regions (i.e., the insula, the inferior frontal gyrus, amygdala, ventral striatum, parietal region). We decoded the BE-RBN brain regions using the BrandMap module. The decoded results revealed that the brand equity-related mental processes are complex constructs integrated via multiple mental processes such as self-referential, reward, emotional, memory, and sensorimotor processing. Our study demonstrated that the DMN alone is insufficient to engage in brand equity-related mental processes. Therefore, marketers are required to make strategic plans to integrate the five consumer's multiple mental processes while building brand equity.
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Clark SV, Satterthwaite TD, King TZ, Morris RD, Zendehrouh E, Turner JA. Cerebellum-cingulo-opercular network connectivity strengthens in adolescence and supports attention efficiency only in childhood. Dev Cogn Neurosci 2022; 56:101129. [PMID: 35820341 PMCID: PMC9284395 DOI: 10.1016/j.dcn.2022.101129] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 06/10/2022] [Accepted: 06/23/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Sarah V Clark
- VA Palo Alto Health Care System, Psychology Service, United States.
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, United States
| | - Tricia Z King
- Georgia State University, Department of Psychology, United States; Georgia State University, Neuroscience Institute, United States
| | - Robin D Morris
- Georgia State University, Department of Psychology, United States
| | - Elaheh Zendehrouh
- Georgia State University, Department of Computer Science, United States
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, The Ohio State University College of Medicine, United States
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Heilicher M, Crombie KM, Cisler JM. Test-retest reliability of fMRI during an emotion processing task: Investigating the impact of analytical approaches on ICC values. FRONTIERS IN NEUROIMAGING 2022; 1:859792. [PMID: 35782991 PMCID: PMC9245148 DOI: 10.3389/fnimg.2022.859792] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Test-retest reliability of fMRI is often assessed using the intraclass correlation coefficient (ICC), a numerical representation of reliability. Reports of low reliability at the individual level may be attributed to analytical approaches and inherent bias/error in the measures used to calculate ICC. It is unclear whether low reliability at the individual level is related to methodological decisions or if fMRI is inherently unreliable. The purpose of this study was to investigate methodological considerations when calculating ICC to improve understanding of fMRI reliability. fMRI data were collected from adolescent females (N=23) at pre- and post-cognitive behavioral therapy. Participants completed an emotion processing task during fMRI. We calculated ICC values using contrasts and β coefficients separately from voxelwise and network (ICA) analyses of the task-based fMRI data. For both voxelwise analysis and ICA, ICC values were higher when calculated using β coefficients. This work provides support for the use of β coefficients over contrasts when assessing reliability of fMRI, and the use of contrasts may underlie low reliability estimates reported in the existing literature. Continued research in this area is warranted to establish fMRI as a reliable measure to draw conclusions and utilize fMRI in clinical settings.
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Affiliation(s)
- Mickela Heilicher
- Mental Health and Incarceration Laboratory, University of
Wisconsin-Madison, School of Medicine and Public Health, Psychiatry Department,
Madison, WI, USA
| | - Kevin M. Crombie
- Neurocircuitry of Trauma and PTSD Laboratory, The
University of Texas at Austin, Dell Medical School, Department of Psychiatry and
Behavioral Sciences, Austin, TX, USA
| | - Josh M. Cisler
- Neurocircuitry of Trauma and PTSD Laboratory, The
University of Texas at Austin, Dell Medical School, Department of Psychiatry and
Behavioral Sciences, Austin, TX, USA
- Institute for Early Life Adversity Research, The University
of Texas at Austin, Dell Medical School, Department of Psychiatry and Behavioral
Sciences, Austin, TX, USA
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65
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Yang F, Jiang J, Alberts I, Wang M, Li T, Sun X, Rominger A, Zuo C, Shi K. Combining PET with MRI to improve predictions of progression from mild cognitive impairment to Alzheimer's disease: an exploratory radiomic analysis study. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:513. [PMID: 35928737 PMCID: PMC9347042 DOI: 10.21037/atm-21-4349] [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] [Received: 08/18/2021] [Accepted: 12/19/2021] [Indexed: 11/28/2022]
Abstract
Background This study aimed to explore the potential of a combination of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and magnetic resonance imaging (MRI) to improve predictions of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). The predictive performances and specific associated biomarkers of these imaging techniques used alone (single-modality imaging) and in combination (dual-modality imaging) were compared. Methods This study enrolled 377 patients with MCI and 94 healthy control participants from 2 medical centers. Enrolment was based on the patients' brain MRI and PET images. Radiomic analysis was performed to evaluate the predictive performance of dual-modality 18F-FDG PET and MRI scans. Regions of interest (ROIs) were determined using an a priori brain atlas. Radiomic features in these ROIs were extracted from the MRI and 18F-FDG PET scan data. These features were either concatenated or used separately to select features and construct Cox regression models for prediction in each modality. Harrell's concordance index (C-index) was then used to assess the predictive accuracies of the resulting models, and correlations between the MRI and 18F-FDG PET features were evaluated. Results The C-indices for the two test datasets were 0.77 and 0.80 for dual-modality 18F-FDG PET/MRI, 0.75 and 0.73 for single-modality 18F-FDG PET, and 0.74 and 0.76 for single-modality MRI. In addition, there was a significant correlation between the crucial image signatures of the different modalities. Conclusions These results indicate the value of imaging features in monitoring the progress of MCI in populations at high risk of developing AD. However, the incremental benefit of combining 18F-FDG PET and MRI is limited, and radiomic analysis of a single modality may yield acceptable predictive results.
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Affiliation(s)
- Fan Yang
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China
| | - Ian Alberts
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
| | - Min Wang
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Taoran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xiaoming Sun
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Axel Rominger
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Kuangyu Shi
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
- Department of Informatics, Technische Universität München, Munich, Germany
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66
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Naturalistic driving measures of route selection associate with resting state networks in older adults. Sci Rep 2022; 12:6486. [PMID: 35443765 PMCID: PMC9021301 DOI: 10.1038/s41598-022-09919-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 03/01/2022] [Indexed: 11/16/2022] Open
Abstract
Our objective was to identify functional brain changes that associate with driving behaviors in older adults. Within a cohort of 64 cognitively normal adults (age 60+), we compared naturalistic driving behavior with resting state functional connectivity using machine learning. Functional networks associated with the ability to interpret and respond to external sensory stimuli and the ability to multi-task were associated with measures of route selection. Maintenance of these networks may be important for continued preservation of driving abilities.
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67
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Areshenkoff C, Gale DJ, Standage D, Nashed JY, Flanagan JR, Gallivan JP. Neural excursions from manifold structure explain patterns of learning during human sensorimotor adaptation. eLife 2022; 11:e74591. [PMID: 35438633 PMCID: PMC9018069 DOI: 10.7554/elife.74591] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 03/04/2022] [Indexed: 11/24/2022] Open
Abstract
Humans vary greatly in their motor learning abilities, yet little is known about the neural mechanisms that underlie this variability. Recent neuroimaging and electrophysiological studies demonstrate that large-scale neural dynamics inhabit a low-dimensional subspace or manifold, and that learning is constrained by this intrinsic manifold architecture. Here, we asked, using functional MRI, whether subject-level differences in neural excursion from manifold structure can explain differences in learning across participants. We had subjects perform a sensorimotor adaptation task in the MRI scanner on 2 consecutive days, allowing us to assess their learning performance across days, as well as continuously measure brain activity. We find that the overall neural excursion from manifold activity in both cognitive and sensorimotor brain networks is associated with differences in subjects' patterns of learning and relearning across days. These findings suggest that off-manifold activity provides an index of the relative engagement of different neural systems during learning, and that subject differences in patterns of learning and relearning are related to reconfiguration processes occurring in cognitive and sensorimotor networks.
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Affiliation(s)
- Corson Areshenkoff
- Centre for Neuroscience Studies, Queen's UniversityKingstonCanada
- Department of Psychology, Queen's UniversityKingstonCanada
| | - Daniel J Gale
- Centre for Neuroscience Studies, Queen's UniversityKingstonCanada
| | - Dominic Standage
- School of Psychology, Centre for Computational Neuroscience and Cognitive Robotics, University of BirminghamBirminghamUnited Kingdom
| | - Joseph Y Nashed
- Centre for Neuroscience Studies, Queen's UniversityKingstonCanada
| | - J Randall Flanagan
- Centre for Neuroscience Studies, Queen's UniversityKingstonCanada
- Department of Psychology, Queen's UniversityKingstonCanada
| | - Jason P Gallivan
- Centre for Neuroscience Studies, Queen's UniversityKingstonCanada
- Department of Psychology, Queen's UniversityKingstonCanada
- Department of Biomedical and Molecular Sciences, Queen's UniversityKingstonCanada
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68
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Li H, Yuan Q, Luo YJ, Tao W. A new perspective for understanding the contributions of the cerebellum to reading: The cerebro-cerebellar mapping hypothesis. Neuropsychologia 2022; 170:108231. [DOI: 10.1016/j.neuropsychologia.2022.108231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 03/28/2022] [Accepted: 03/29/2022] [Indexed: 02/07/2023]
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69
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Yang FN, Hassanzadeh-Behbahani S, Kumar P, Moore DJ, Ellis RJ, Jiang X. The impacts of HIV infection, age, and education on functional brain networks in adults with HIV. J Neurovirol 2022; 28:265-273. [PMID: 35044643 PMCID: PMC9584140 DOI: 10.1007/s13365-021-01039-y] [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: 07/19/2021] [Revised: 12/07/2021] [Accepted: 12/09/2021] [Indexed: 10/19/2022]
Abstract
HIV-associated neurocognitive disorders (HAND) remain highly prevalent in people with HIV (PWH). Studies suggested that certain sociodemographic factors are associated with the risk of HAND in PWH. Here we investigated the impact of HIV infection and demographics on functional brain networks. One run of 8.5 min resting state functional MRI (fMRI) data was collected from 101 PWH (41-70 years old) and 40 demographically comparable controls. Functional connectivity (FC) was calculated using average wavelet coherence. The impact of demographic factors on FCs was investigated using canonical correlation analysis (CCA). Wavelet coherence analysis revealed a reduced within-network connectivity in the dorsal somatomotor network (dSMN), along with a reduced between-network connectivity between dSMN and medial temporal lobe (MTL) in PWH (compared to controls). Across all participants, CCA revealed that older age and HIV infection had negative impacts on network connectivity measures (mainly reduced within- and between-network FCs), whereas education had an opposite effect. In addition, being female at birth or a member of a minority ethnic/racial group was also associated with network disruptions. Our data suggested that advanced age and HIV infection are risk factors for functional brain network disruptions, whereas higher educational attainment was linked to better preserved functional network connectivity.
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Affiliation(s)
- Fan Nils Yang
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, 20007, USA
| | | | - Princy Kumar
- Department of Medicine, Georgetown University Medical Center, Washington, DC, 20057, USA
| | - David J Moore
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Ronald J Ellis
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Xiong Jiang
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, 20007, USA.
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McKay NS, Dincer A, Mehrotra V, Aschenbrenner AJ, Balota D, Hornbeck RC, Hassenstab J, Morris JC, Benzinger TLS, Gordon BA. Beta-amyloid moderates the relationship between cortical thickness and attentional control in middle- and older-aged adults. Neurobiol Aging 2022; 112:181-190. [PMID: 35227946 PMCID: PMC9208719 DOI: 10.1016/j.neurobiolaging.2021.12.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 12/13/2021] [Accepted: 12/28/2021] [Indexed: 12/13/2022]
Abstract
Although often unmeasured in studies of cognition, many older adults possess Alzheimer disease (AD) pathologies such as beta-amyloid (Aβ) deposition, despite being asymptomatic. We were interested in examining whether the behavior-structure relationship observed in later life was altered by the presence of preclinical AD pathology. A total of 511 cognitively unimpaired adults completed magnetic resonance imaging and three attentional control tasks; a subset (n = 396) also underwent Aβ-positron emissions tomography. A vertex-wise model was conducted to spatially represent the relationship between cortical thickness and average attentional control accuracy, while moderation analysis examined whether Aβ deposition impacted this relationship. First, we found that reduced cortical thickness in temporal, medial- and lateral-parietal, and dorsolateral prefrontal cortex, predicted worse performance on the attention task composite. Subsequent moderation analyses observed that levels of Aβ significantly influence the relationship between cortical thickness and attentional control. Our results support the hypothesis that preclinical AD, as measured by Aβ deposition, is partially driving what would otherwise be considered general aging in a cognitively normal adult population.
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Affiliation(s)
- Nicole S McKay
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO.
| | - Aylin Dincer
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO
| | | | - Andrew J Aschenbrenner
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO; Department of Neurology, Washington School of Medicine, St. Louis, MO
| | - David Balota
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO; Department of Psychological and Brain Sciences, Washington University in St. Louis, MO
| | - Russ C Hornbeck
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO
| | - Jason Hassenstab
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO; Department of Neurology, Washington School of Medicine, St. Louis, MO; Department of Psychological and Brain Sciences, Washington University in St. Louis, MO
| | - John C Morris
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO; Department of Neurology, Washington School of Medicine, St. Louis, MO
| | - Tammie L S Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO
| | - Brian A Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO; Department of Psychological and Brain Sciences, Washington University in St. Louis, MO
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Chambers T, Escott-Price V, Legge S, Baker E, Singh KD, Walters JTR, Caseras X, Anney RJL. Genetic common variants associated with cerebellar volume and their overlap with mental disorders: a study on 33,265 individuals from the UK-Biobank. Mol Psychiatry 2022; 27:2282-2290. [PMID: 35079123 PMCID: PMC9126806 DOI: 10.1038/s41380-022-01443-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 12/16/2021] [Accepted: 01/11/2022] [Indexed: 12/13/2022]
Abstract
Interest in the cerebellum is expanding given evidence of its contributions to cognition and emotion, and dysfunction in various psychopathologies. However, research into its genetic architecture and shared influences with liability for mental disorders is lacking. We conducted a genome-wide association study (GWAS) of total cerebellar volume and underlying cerebellar lobe volumes in 33,265 UK-Biobank participants. Total cerebellar volume was heritable (h2SNP = 50.6%), showing moderate genetic homogeneity across lobes (h2SNP from 35.4% to 57.1%; mean genetic correlation between lobes rg ≈ 0.44). We identified 33 GWAS signals associated with total cerebellar volume, of which 6 are known to alter protein-coding gene structure, while a further five mapped to genomic regions known to alter cerebellar tissue gene expression. Use of summary data-based Mendelian randomisation further prioritised genes whose change in expression appears to mediate the SNP-trait association. In total, we highlight 21 unique genes of greatest interest for follow-up analyses. Using LD-regression, we report significant genetic correlations between total cerebellar volume and brainstem, pallidum and thalamus volumes. While the same approach did not result in significant correlations with psychiatric phenotypes, we report enrichment of schizophrenia, bipolar disorder and autism spectrum disorder associated signals within total cerebellar GWAS results via conditional and conjunctional-FDR analysis. Via these methods and GWAS catalogue, we identify which of our cerebellar genomic regions also associate with psychiatric traits. Our results provide important insights into the common allele architecture of cerebellar volume and its overlap with other brain volumes and psychiatric phenotypes.
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Affiliation(s)
- Tom Chambers
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Valentina Escott-Price
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Sophie Legge
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Emily Baker
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - James T R Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
| | - Richard J L Anney
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
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Snyder AZ, Nishino T, Shimony JS, Lenze EJ, Wetherell JL, Voegtle M, Miller JP, Yingling MD, Marcus D, Gurney J, Rutlin J, Scott D, Eyler L, Barch D. Covariance and Correlation Analysis of Resting State Functional Magnetic Resonance Imaging Data Acquired in a Clinical Trial of Mindfulness-Based Stress Reduction and Exercise in Older Individuals. Front Neurosci 2022; 16:825547. [PMID: 35368291 PMCID: PMC8971902 DOI: 10.3389/fnins.2022.825547] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/22/2022] [Indexed: 11/13/2022] Open
Abstract
We describe and apply novel methodology for whole-brain analysis of resting state fMRI functional connectivity data, combining conventional multi-channel Pearson correlation with covariance analysis. Unlike correlation, covariance analysis preserves signal amplitude information, which feature of fMRI time series may carry physiological significance. Additionally, we demonstrate that dimensionality reduction of the fMRI data offers several computational advantages including projection onto a space of manageable dimension, enabling linear operations on functional connectivity measures and exclusion of variance unrelated to resting state network structure. We show that group-averaged, dimensionality reduced, covariance and correlation matrices are related, to reasonable approximation, by a single scalar factor. We apply this methodology to the analysis of a large, resting state fMRI data set acquired in a prospective, controlled study of mindfulness training and exercise in older, sedentary participants at risk for developing cognitive decline. Results show marginally significant effects of both mindfulness training and exercise in both covariance and correlation measures of functional connectivity.
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Affiliation(s)
- Abraham Z. Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Tomoyuki Nishino
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Joshua S. Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Eric J. Lenze
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Julie Loebach Wetherell
- VA San Diego Healthcare System, San Diego, CA, United States
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Michelle Voegtle
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - J. Philip Miller
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Michael D. Yingling
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Daniel Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Jenny Gurney
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Jerrel Rutlin
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Drew Scott
- Master of Social Welfare Program, University of California, Berkeley, Berkeley, CA, United States
| | - Lisa Eyler
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Deanna Barch
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Department of Psychological and Brain Sciences, Washington University, St. Louis, MO, United States
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73
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Mori K, Haruno M. Resting functional connectivity of the left inferior frontal gyrus with the dorsomedial prefrontal cortex and temporoparietal junction reflects the social network size for active interactions. Hum Brain Mapp 2022; 43:2869-2879. [PMID: 35261111 PMCID: PMC9120559 DOI: 10.1002/hbm.25822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/03/2022] [Accepted: 02/16/2022] [Indexed: 11/08/2022] Open
Abstract
The size of an individual active social network is a key parameter of human social behavior and is correlated with subjective well-being. However, it remains unknown how the social network size of active interactions is represented in the brain. Here, we examined whether resting-state functional magnetic resonance imaging (fMRI) connectivity is associated with the social network size of active interactions using behavioral data of a large sample (N = 222) on Twitter. Region of interest (ROI)-to-ROI analysis, graph theory analysis, seed-based analysis, and decoding analysis together provided compelling evidence that people who have a large social network size of active interactions, as measured by "reply," show higher fMRI connectivity of the left inferior frontal gyrus with the dorsomedial prefrontal cortex and temporoparietal junction, which represents the core of the theory of mind network. These results demonstrated that people who have a large social network size of active interactions maintain activity of the identified functional connectivity in daily life, possibly providing a mechanism for efficient information transmission between the brain networks related to language and theory-of-mind.
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Affiliation(s)
- Kazuma Mori
- Center for Information and Neural Networks, National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan.,Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, Japan
| | - Masahiko Haruno
- Center for Information and Neural Networks, National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan.,Grauduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan
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74
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Gao Z, Cheng L, Li J, Chen Q, Hao N. The dark side of creativity: Neural correlates of malevolent creative idea generation. Neuropsychologia 2022; 167:108164. [DOI: 10.1016/j.neuropsychologia.2022.108164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 10/19/2022]
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75
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Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP, Hatoum AS, Donohue MR, Foran W, Miller RL, Hendrickson TJ, Malone SM, Kandala S, Feczko E, Miranda-Dominguez O, Graham AM, Earl EA, Perrone AJ, Cordova M, Doyle O, Moore LA, Conan GM, Uriarte J, Snider K, Lynch BJ, Wilgenbusch JC, Pengo T, Tam A, Chen J, Newbold DJ, Zheng A, Seider NA, Van AN, Metoki A, Chauvin RJ, Laumann TO, Greene DJ, Petersen SE, Garavan H, Thompson WK, Nichols TE, Yeo BTT, Barch DM, Luna B, Fair DA, Dosenbach NUF. Reproducible brain-wide association studies require thousands of individuals. Nature 2022; 603:654-660. [PMID: 35296861 PMCID: PMC8991999 DOI: 10.1038/s41586-022-04492-9] [Citation(s) in RCA: 757] [Impact Index Per Article: 378.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 01/31/2022] [Indexed: 02/01/2023]
Abstract
Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1-3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.
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Affiliation(s)
- Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA.
| | - Brenden Tervo-Clemmens
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Finnegan J Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - David F Montez
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Meghan Rose Donohue
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - William Foran
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ryland L Miller
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Timothy J Hendrickson
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Stephen M Malone
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Sridhar Kandala
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Oscar Miranda-Dominguez
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Eric A Earl
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Anders J Perrone
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Michaela Cordova
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Olivia Doyle
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Lucille A Moore
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Gregory M Conan
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Johnny Uriarte
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Kathy Snider
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Benjamin J Lynch
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - James C Wilgenbusch
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Thomas Pengo
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Angela Tam
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health, Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health, Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Nicole A Seider
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Roselyne J Chauvin
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Steven E Petersen
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurological Surgery, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Wesley K Thompson
- Division of Biostatistics, University of California San Diego, La Jolla, CA, USA
| | - Thomas E Nichols
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health, Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA
| | - Beatriz Luna
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA.
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA.
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN, USA.
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA.
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA.
- Department of Radiology, Washington University School of Medicine, St Louis, MO, USA.
- Program in Occupational Therapy, Washington University School of Medicine, St Louis, MO, USA.
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA.
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76
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Chen Z, Zhang R, Huo H, Liu P, Zhang C, Feng T. Functional connectome of human cerebellum. Neuroimage 2022; 251:119015. [DOI: 10.1016/j.neuroimage.2022.119015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 01/26/2022] [Accepted: 02/17/2022] [Indexed: 10/19/2022] Open
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77
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An exploration of dimensions of early adversity and the development of functional brain network connectivity during adolescence: Implications for trajectories of internalizing symptoms. Dev Psychopathol 2022; 34:557-571. [PMID: 35094729 DOI: 10.1017/s0954579421001814] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Different dimensions of adversity may affect mental health through distinct neurobiological mechanisms, though current supporting evidence consists largely of cross-sectional associations between threat or deprivation and fronto-limbic circuitry. In this exploratory three-wave longitudinal study spanning ages 9-19 years, we examined the associations between experiences of unpredictability, threat, and deprivation with the development of functional connectivity within and between three brain networks implicated in psychopathology: the salience (SAL), default mode (DMN), and fronto-parietal (FPN) networks, and tested whether network trajectories moderated associations between adversity and changes in internalizing symptoms. Connectivity decreased with age on average; these changes differed by dimension of adversity. Whereas family-level deprivation was associated with lower initial levels and more stability across most networks, unpredictability was associated with stability only in SAL connectivity, and threat was associated with stability in FPN and DMN-SAL connectivity. In youth exposed to higher levels of any adversity, lower initial levels and more stability in connectivity were related to smaller increases in internalizing symptoms. Our findings suggest that whereas deprivation is associated with widespread neurodevelopmental differences in cognitive and emotion processing networks, unpredictability is related selectively to salience detection circuitry. Studies with wider developmental windows should examine whether these neurodevelopmental alterations are adaptive or serve to maintain internalizing symptoms.
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78
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Age Estimation from fMRI Data Using Recurrent Neural Network. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12020749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Finding a biomarker that indicates the subject’s age is one of the most important topics in biology. Several recent studies tried to extract a biomarker from brain imaging data including fMRI data. However, most of them focused on MRI data, which do not provide dynamics and lack attempts to apply recently proposed deep learning models. We propose a deep neural network model that estimates the age of a subject from fMRI images using a recurrent neural network (RNN), more precisely, a gated recurrent unit (GRU). However, applying neural networks is not trivial due to the high dimensional nature of fMRI data. In this work, we propose a novel preprocessing technique using the Automated Anatomical Labeling (AAL) atlas, which significantly reduces the input dimension. The proposed dimension reduction technique allows us to train our model with 640 training and validation samples from different projects under mean squared error (MSE). Finally, we obtain the correlation value of 0.905 between the predicted age and the actual age on 155 test samples. The proposed model estimates the age within the range of ±12 on most of the test samples. Our model is written in Python and is freely available for download.
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79
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Bernard JA. Understanding cerebellar function through network perspectives: A review of resting-state connectivity of the cerebellum. PSYCHOLOGY OF LEARNING AND MOTIVATION 2022. [DOI: 10.1016/bs.plm.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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80
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Votinov M, Myznikov A, Zheltyakova M, Masharipov R, Korotkov A, Cherednichenko D, Habel U, Kireev M. The Interaction Between Caudate Nucleus and Regions Within the Theory of Mind Network as a Neural Basis for Social Intelligence. Front Neural Circuits 2021; 15:727960. [PMID: 34720887 PMCID: PMC8552029 DOI: 10.3389/fncir.2021.727960] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 09/27/2021] [Indexed: 12/04/2022] Open
Abstract
The organization of socio-cognitive processes is a multifaceted problem for which many sophisticated concepts have been proposed. One of these concepts is social intelligence (SI), i.e., the set of abilities that allow successful interaction with other people. The theory of mind (ToM) human brain network is a good candidate for the neural substrate underlying SI since it is involved in inferring the mental states of others and ourselves and predicting or explaining others’ actions. However, the relationship of ToM to SI remains poorly explored. Our recent research revealed an association between the gray matter volume of the caudate nucleus and the degree of SI as measured by the Guilford-Sullivan test. It led us to question whether this structural peculiarity is reflected in changes to the integration of the caudate with other areas of the brain associated with socio-cognitive processes, including the ToM system. We conducted seed-based functional connectivity (FC) analysis of resting-state fMRI data for 42 subjects with the caudate as a region of interest. We found that the scores of the Guilford-Sullivan test were positively correlated with the FC between seeds in the right caudate head and two clusters located within the right superior temporal gyrus and bilateral precuneus. Both regions are known to be nodes of the ToM network. Thus, the current study demonstrates that the SI level is associated with the degree of functional integration between the ToM network and the caudate nuclei.
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Affiliation(s)
- Mikhail Votinov
- N.P. Bechtereva Institute of Human Brain, Russian Academy of Science, Saint Petersburg, Russia.,Institute of Neuroscience and Medicine, Research Centre Jülich, Jülich, Germany.,Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Artem Myznikov
- N.P. Bechtereva Institute of Human Brain, Russian Academy of Science, Saint Petersburg, Russia
| | - Maya Zheltyakova
- N.P. Bechtereva Institute of Human Brain, Russian Academy of Science, Saint Petersburg, Russia
| | - Ruslan Masharipov
- N.P. Bechtereva Institute of Human Brain, Russian Academy of Science, Saint Petersburg, Russia
| | - Alexander Korotkov
- N.P. Bechtereva Institute of Human Brain, Russian Academy of Science, Saint Petersburg, Russia
| | - Denis Cherednichenko
- N.P. Bechtereva Institute of Human Brain, Russian Academy of Science, Saint Petersburg, Russia
| | - Ute Habel
- Institute of Neuroscience and Medicine, Research Centre Jülich, Jülich, Germany.,Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Maxim Kireev
- N.P. Bechtereva Institute of Human Brain, Russian Academy of Science, Saint Petersburg, Russia.,Institute for Cognitive Studies, Saint Petersburg State University, Saint Petersburg, Russia
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81
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Kassinopoulos M, Mitsis GD. A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity. Magn Reson Imaging 2021; 85:228-250. [PMID: 34715292 DOI: 10.1016/j.mri.2021.10.028] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 09/17/2021] [Accepted: 10/17/2021] [Indexed: 12/17/2022]
Abstract
It is well established that head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This is partly due to the fact that the quality control (QC) metrics used to evaluate differences in performance across pipelines have often yielded contradictory results. Furthermore, preprocessing techniques based on physiological recordings or data decomposition techniques (e.g. aCompCor) have not been comprehensively examined. Here, to address the aforementioned issues, we propose a framework that summarizes the scores from eight previously proposed and novel QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using this framework, we evaluate the performance of three commonly used practices on the quality of data: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. Using resting-state fMRI data from the Human Connectome Project, we show that the scores of the examined QC metrics improve the most when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. Finally, we observe a small further improvement with low-pass filtering at 0.20 Hz and milder variants of WM denoising, but not with scrubbing.
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Affiliation(s)
- Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada
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82
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Langella S, Mucha PJ, Giovanello KS, Dayan E. The association between hippocampal volume and memory in pathological aging is mediated by functional redundancy. Neurobiol Aging 2021; 108:179-188. [PMID: 34614422 DOI: 10.1016/j.neurobiolaging.2021.09.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 08/16/2021] [Accepted: 09/01/2021] [Indexed: 10/20/2022]
Abstract
Hippocampal neurodegeneration, a primary component of Alzheimer's disease pathology, relates to poor cognition; however, the mechanisms underlying this relationship are not well understood. Using a sample of cognitively normal older adults and individuals with mild cognitive impairment, this study aims to determine the topological properties of functional networks accompanying hippocampal atrophy in aging, along with their association to cognition and clinical progression. We considered two conceptually differing topological properties: redundancy (the existence of alternative channels of functional commutation) and local efficiency (the efficiency of local information exchange). Hippocampal redundancy, but not local efficiency, mediated the association between low hippocampal volume and low memory in both the whole sample and in ß-amyloid positive participants. Additionally, participants with high hippocampal volume, redundancy, and memory clustered separately from those with low values on all three measures, with the latter group showing higher conversion rates to dementia within three years. Together, these results demonstrate that reduced hippocampal redundancy is one mechanism through which hippocampal atrophy associates with memory impairment in healthy and pathological aging.
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Affiliation(s)
- Stephanie Langella
- Department of Psychology & Neuroscience, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, NH 03755, USA
| | - Kelly S Giovanello
- Department of Psychology & Neuroscience, University of North Carolina at Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27514, USA
| | - Eran Dayan
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27514, USA; Department of Radiology, University of North Carolina at Chapel Hill, NC 27599, USA.
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83
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Tong TT, Vaidya JG, Kramer JR, Kuperman S, Langbehn DR, O’Leary DS. Impact of binge drinking during college on resting state functional connectivity. Drug Alcohol Depend 2021; 227:108935. [PMID: 34388578 PMCID: PMC8464531 DOI: 10.1016/j.drugalcdep.2021.108935] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 06/05/2021] [Accepted: 06/21/2021] [Indexed: 10/20/2022]
Abstract
AIM The current study examined the longitudinal effects of standard binge drinking (4+/5+ drinks for females/males in 2 hours) and extreme binge drinking (8+/10+ drinks for females/males in 2 hours) on resting-state functional connectivity. METHOD 119 college students (61 males) were recruited in groups of distinct bingeing patterns at baseline: non-bingeing controls, standard and extreme bingers. Resting-state scans were first obtained when participants were freshmen/sophomores and again approximately two years later. Associations between longitudinal bingeing (reported during this two-year gap) and network connectivity were examined. Network connectivity was calculated by aggregating all edges affiliated with the same network (an edge is a functional connection between two brain regions). The relationship between longitudinal bingeing and connectivity edges was also studied using connectome-based predictive modeling (CPM). RESULTS Greater standard bingeing was negatively associated with change in connectivity between Default Mode Network and Ventral Attention Network (DMN-VAN; False Discovery Rate corrected), controlling for initial binge groups, longitudinal network changes, motions, scanner, SES, sex, and age. The correlations between change in DMN-VAN connectivity and change in cognitive performance (Stroop, Digit Span, Letter Fluency, and Trail Making) were also tested, but the results were not significant. Lastly, CPM failed to identify a generalizable predictive model of longitudinal bingeing from change in connectivity edges. CONCLUSIONS Binge drinking is associated with abnormality in networks implicated in attention and self-focused processes, which, in turn, have been implicated in rumination, craving, and relapse. More extensive alterations in functional connectivity might be observed with heavier or longer binge drinking pattern.
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Affiliation(s)
- Tien T. Tong
- Interdisciplinary Graduate Program in Neuroscience, The University of Iowa, 200 Hawkins Dr., Iowa City, IA 52242, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1399 Park Ave., New York, NY 10029, USA
| | - Jatin G. Vaidya
- Department of Psychiatry, Carver College of Medicine, The University of Iowa, 200 Hawkins Dr., Iowa City, IA 52242, USA
| | - John R. Kramer
- Department of Psychiatry, Carver College of Medicine, The University of Iowa, 200 Hawkins Dr., Iowa City, IA 52242, USA
| | - Samuel Kuperman
- Department of Psychiatry, Carver College of Medicine, The University of Iowa, 200 Hawkins Dr., Iowa City, IA, 52242, USA.
| | - Douglas R. Langbehn
- Department of Psychiatry, Carver College of Medicine, The University of Iowa, 200 Hawkins Dr., Iowa City, IA 52242, USA
| | - Daniel S. O’Leary
- Department of Psychiatry, Carver College of Medicine, The University of Iowa, 200 Hawkins Dr., Iowa City, IA 52242, USA
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84
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Cascone AD, Langella S, Sklerov M, Dayan E. Frontoparietal network resilience is associated with protection against cognitive decline in Parkinson's disease. Commun Biol 2021; 4:1021. [PMID: 34471211 PMCID: PMC8410800 DOI: 10.1038/s42003-021-02478-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 07/22/2021] [Indexed: 02/07/2023] Open
Abstract
Though Parkinson's disease is primarily defined as a movement disorder, it is also characterized by a range of non-motor symptoms, including cognitive decline. The onset and progression of cognitive decline in individuals with Parkinson's disease is variable, and the neurobiological mechanisms that contribute to, or protect against, cognitive decline in Parkinson's disease are poorly understood. Using resting-state functional magnetic resonance imaging data collected from individuals with Parkinson's disease with and without cognitive decline, we examined the relationship between topological brain-network resilience and cognition in Parkinson's disease. By leveraging network attack analyses, we demonstrate that relative to individuals with Parkinson's disease experiencing cognitive decline, the frontoparietal network in cognitively stable individuals with Parkinson's disease is significantly more resilient to network perturbation. Our findings suggest that the topological robustness of the frontoparietal network is associated with the absence of cognitive decline in individuals with Parkinson's disease.
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Affiliation(s)
- Arianna D Cascone
- Neuroscience Curriculum, University of North at Chapel Hill, Chapel Hill, NC, United States
| | - Stephanie Langella
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Miriam Sklerov
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Eran Dayan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
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Cernasov P, Walsh EC, Kinard JL, Kelley L, Phillips R, Pisoni A, Eisenlohr-Moul TA, Arnold M, Lowery SC, Ammirato M, Truong K, Nagy GA, Oliver JA, Haworth K, Smoski M, Dichter GS. Multilevel growth curve analyses of behavioral activation for anhedonia (BATA) and mindfulness-based cognitive therapy effects on anhedonia and resting-state functional connectivity: Interim results of a randomized trial ✰. J Affect Disord 2021; 292:161-171. [PMID: 34126308 PMCID: PMC8282772 DOI: 10.1016/j.jad.2021.05.054] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 05/03/2021] [Accepted: 05/23/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND The neural mechanisms associated with anhedonia treatment response are poorly understood. Additionally, no study has investigated changes in resting-state functional connectivity (rsFC) accompanying psychosocial treatment for anhedonia. METHODS We evaluated a novel psychotherapy, Behavioral Activation Therapy for Anhedonia (BATA, n = 38) relative to Mindfulness-Based Cognitive Therapy (MBCT, n = 35) in a medication-free, transdiagnostic, anhedonic sample in a parallel randomized controlled trial. Participants completed up to 15 sessions of therapy and up to four 7T MRI scans before, during, and after treatment (n = 185 scans). Growth curve models estimated change over time in anhedonia and in rsFC using average region-of-interest (ROI)-to-ROI connectivity within the default mode network (DMN), frontoparietal network (FPN), salience network, and reward network. Changes in rsFC from pre- to post-treatment were further evaluated using whole-network seed-to-voxel and ROI-to-ROI edgewise analyses. RESULTS Growth curve models showed significant reductions in anhedonia symptoms and in average rsFC within the DMN and FPN over time, across BATA and MBCT. There were no differences in anhedonia reductions between treatments. Within-person, changes in average rsFC were unrelated to changes in anhedonia. Between-person, higher than average FPN rsFC was related to less anhedonia across timepoints. Seed-to-voxel and edgewise rsFC analyses corroborated reductions within the DMN and between the DMN and FPN over time, across the sample. CONCLUSIONS Reductions in rsFC within the DMN, FPN, and between these networks co-occurred with anhedonia improvement across two psychosocial treatments for anhedonia. Future anhedonia clinical trials with a waitlist control group should disambiguate treatment versus time-related effects on rsFC.
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Affiliation(s)
- Paul Cernasov
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Erin C Walsh
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC 57514, USA
| | - Jessica L Kinard
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27510, USA; Division of Speech and Hearing Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Lisalynn Kelley
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA
| | - Rachel Phillips
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Angela Pisoni
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27505, USA
| | - Tory A Eisenlohr-Moul
- Department of Psychiatry, University of Illinois at Chicago, Neuropsychiatry Institute, Chicago, IL 60612, USA
| | - Macey Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA
| | - Sarah C Lowery
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Marcy Ammirato
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA
| | - Kinh Truong
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Gabriela A Nagy
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA; Duke University School of Nursing, 307 Trent Drive, Durham, NC 27710, USA
| | - Jason A Oliver
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA; Division of Cancer Control and Population Sciences, Duke Cancer Institute, Durham, NC 27705, USA
| | - Kevin Haworth
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA
| | - Moria Smoski
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27705, USA; Department of Psychology and Neuroscience, Duke University, Durham, NC 27505, USA
| | - Gabriel S Dichter
- Department of Psychology and Neuroscience, University of North Carolina-Chapel Hill, Chapel Hill, NC 27514, USA; Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC 57514, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27510, USA.
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86
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Gilbert KE, Wheelock MD, Kandala S, Eggebrecht AT, Luby JL, Barch DM. Associations of observed preschool performance monitoring with brain functional connectivity in adolescence. Cortex 2021; 142:15-27. [PMID: 34174721 PMCID: PMC8405590 DOI: 10.1016/j.cortex.2021.05.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 04/12/2021] [Accepted: 05/18/2021] [Indexed: 10/21/2022]
Abstract
Monitoring one's performance helps detect errors and adapt to prevent future mistakes. However, elevated performance monitoring is associated with increased checking behaviors and perfectionism and is characteristic of multiple psychiatric disorders. Understanding how heightened performance monitoring in early childhood relates to subsequent brain connectivity may elucidate mechanistic risk factors that influence brain and psychiatric outcomes. The aim of this study was to examine the association between performance monitoring in preschool-aged children and functional connectivity during adolescence. In the current prospective longitudinal study, we performed seed-based functional connectivity analysis using a dorsal anterior cingulate cortex (dACC) seed to assess brain-behavior relationships between observationally coded performance monitoring in preschool-aged children and adolescent functional connectivity (n = 79). We also utilized enrichment analysis to investigate network-level connectome-wide associations. Seed-based analysis revealed negative correlations between preschool performance monitoring and adolescent fc between dACC and orbitofrontal and dorsolateral prefrontal cortex while a positive correlation was observed between dACC-occipital cortex connectivity. Enrichment analysis revealed a negative correlation between preschool performance monitoring and connectivity between motor (MOT) - cingulo-opercular (CO) and salience (SN) - Reward (REW) and a positive correlation with MOT-DMN, and cerebellum (CB) - motor connectivity. Elevated performance monitoring in early childhood is associated with functional connectivity during adolescence in regions and networks associated with cognitive control, sensorimotor processing and cortico-striatal-thalamic-cortico (CTSC) aberrations. These regions and networks are implicated in psychiatric disorders characterized by elevated performance monitoring. Findings shed light on a mechanistic risk factor in early childhood with long-term associations with neural functioning.
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Affiliation(s)
- Kirsten E Gilbert
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
| | - Muriah D Wheelock
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Sridhar Kandala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Adam T Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
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87
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Dezachyo O, Kozak S, Bar-Haim Y, Censor N, Dayan E. Intrinsic Functional Connectivity of the Anterior Cingulate Cortex Is Associated with Tolerance to Distress. eNeuro 2021; 8:ENEURO.0277-21.2021. [PMID: 34593518 PMCID: PMC8577043 DOI: 10.1523/eneuro.0277-21.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 08/26/2021] [Accepted: 09/27/2021] [Indexed: 11/21/2022] Open
Abstract
The ability to adapt under significant adversity, defined as psychological resilience, is instrumental in preventing stress-related disorders. An important aspect of resilience is the capacity to endure affective distress when in pursuit of goals, also known as distress tolerance. Evidence that links intrinsic baseline interactions within large-scale functional networks with performance under distress remains missing. We hypothesized that the anterior cingulate cortex (ACC) may engage in distress tolerance because of its involvement in attention and emotion regulation. Accordingly, we tested whether behavioral performance under distress is associated with baseline resting-state ACC functional connectivity (FC). Distress tolerance was measured in 97 participants using the behavioral indicator of resiliency to distress (BIRD) task. Analyses contrasted participants who quit the task before its designated termination (n = 51) with those who persisted throughout it (n = 46). Seed-based FC analysis indicated greater connectivity between the ACC and dorsolateral prefrontal cortex (DLPFC) in subjects who persisted throughout the task, along with greater FC between the ACC and the precentral gyrus in those who quit before its termination. The results shed light on the mechanisms underlying interindividual differences in the ability to handle distress.
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Affiliation(s)
- Or Dezachyo
- School of Psychological Sciences and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Stas Kozak
- School of Psychological Sciences and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Yair Bar-Haim
- School of Psychological Sciences and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Nitzan Censor
- School of Psychological Sciences and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Dayan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
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88
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Paredes O, López JB, Covantes-Osuna C, Ocegueda-Hernández V, Romo-Vázquez R, Morales JA. A Transcriptome Community-and-Module Approach of the Human Mesoconnectome. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1031. [PMID: 34441171 PMCID: PMC8393183 DOI: 10.3390/e23081031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 12/15/2022]
Abstract
Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network.
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Affiliation(s)
| | | | | | | | - Rebeca Romo-Vázquez
- Computer Sciences Department, Exact Sciences and Engineering University Centre, Universidad de Guadalajara, Guadalajara 44430, Mexico; (O.P.); (J.B.L.); (C.C.-O.); (V.O.-H.)
| | - J. Alejandro Morales
- Computer Sciences Department, Exact Sciences and Engineering University Centre, Universidad de Guadalajara, Guadalajara 44430, Mexico; (O.P.); (J.B.L.); (C.C.-O.); (V.O.-H.)
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89
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Xifra-Porxas A, Kassinopoulos M, Mitsis GD. Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability. eLife 2021; 10:e62324. [PMID: 34342582 PMCID: PMC8378847 DOI: 10.7554/elife.62324] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 08/02/2021] [Indexed: 02/06/2023] Open
Abstract
Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.
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Affiliation(s)
- Alba Xifra-Porxas
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada
| | - Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada
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90
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Horin AP, Myers PS, Pickett KA, Earhart GM, Campbell MC. Resting-state functional connectivity associated with gait characteristics in people with Parkinson's disease. Behav Brain Res 2021; 411:113398. [PMID: 34087255 DOI: 10.1016/j.bbr.2021.113398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 05/27/2021] [Accepted: 05/30/2021] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Parkinson's disease (PD) is a movement disorder caused by dysfunction in the basal ganglia (BG). Clinically relevant gait deficits, such as decreased velocity and increased variability, may be caused by underlying neural dysfunction. Reductions in resting-state functional connectivity (rs-FC) between networks have been identified in PD compared to controls; however, the association between gait characteristics and rs-FC of brain networks in people with PD has not yet been explored. The present study aimed to investigate these associations. METHODS Gait characteristics and rs-FC MRI data were collected for participants with PD (N = 50). Brain networks were identified from a set of seeds representing cortical, subcortical, and cerebellar regions. Gait outcomes were correlated with the strength of rs-FC within and between networks of interest. A stepwise regression analysis was also conducted to determine whether the rs-FC strength of brain networks, along with clinical motor scores, were predictive of gait characteristics. RESULTS Gait velocity was associated with rs-FC within the visual network and between motor and cognitive networks, most notably BG-thalamus internetwork rs-FC. The stepwise regression analysis showed strength of BG-thalamus internetwork rs-FC and clinical motor scores were predictive of gait velocity. CONCLUSION The results of the present study demonstrate gait characteristics are associated with functional organization of the brain at the network level, providing insight into the neural mechanisms of clinically relevant gait characteristics. This knowledge could be used to optimize the design of gait rehabilitation interventions for people with neurological conditions.
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Affiliation(s)
- Adam P Horin
- Program in Physical Therapy, Washington University School of Medicine, St Louis, MO, United States
| | - Peter S Myers
- Department of Neurology, Washington University School of Medicine, St Louis, MO, United States
| | - Kristen A Pickett
- Occupational Therapy Program, Department of Kinesiology, University of Wisconsin-Madison, Madison, United States; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Gammon M Earhart
- Program in Physical Therapy, Washington University School of Medicine, St Louis, MO, United States; Department of Neurology, Washington University School of Medicine, St Louis, MO, United States; Department of Neuroscience, Washington University School of Medicine, St Louis, MO, United States
| | - Meghan C Campbell
- Department of Neurology, Washington University School of Medicine, St Louis, MO, United States; Department of Radiology, Washington University School of Medicine, St Louis, MO, United States.
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91
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Barnett AJ, Reilly W, Dimsdale-Zucker HR, Mizrak E, Reagh Z, Ranganath C. Intrinsic connectivity reveals functionally distinct cortico-hippocampal networks in the human brain. PLoS Biol 2021; 19:e3001275. [PMID: 34077415 PMCID: PMC8202937 DOI: 10.1371/journal.pbio.3001275] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 06/14/2021] [Accepted: 05/07/2021] [Indexed: 12/13/2022] Open
Abstract
Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. Here, using data-driven analyses of resting-state functional magnetic resonance imaging (fMRI) data, we identified the networks that interact with the hippocampus-the default mode network (DMN) and a "medial temporal network" (MTN) that included regions in the medial temporal lobe (MTL) and precuneus. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into 3 subnetworks: a "posterior medial" (PM) subnetwork comprised of posterior cingulate and lateral parietal cortices; an "anterior temporal" (AT) subnetwork comprised of regions in the temporopolar and dorsomedial prefrontal cortex; and a "medial prefrontal" (MP) subnetwork comprised of regions primarily in the medial prefrontal cortex (mPFC). These networks vary in their functional connectivity (FC) along the hippocampal long axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory.
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Affiliation(s)
- Alexander J. Barnett
- Center for Neuroscience, University of California at Davis, Davis, California, United States of America
| | - Walter Reilly
- Center for Neuroscience, University of California at Davis, Davis, California, United States of America
| | | | - Eda Mizrak
- Center for Neuroscience, University of California at Davis, Davis, California, United States of America
- Department of Psychology, University of Zurich, Zürich, Switzerland
| | - Zachariah Reagh
- Center for Neuroscience, University of California at Davis, Davis, California, United States of America
- Department of Neurology, University of California at Davis, Sacramento, California, United States of America
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Charan Ranganath
- Center for Neuroscience, University of California at Davis, Davis, California, United States of America
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92
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Probabilistic mapping of human functional brain networks identifies regions of high group consensus. Neuroimage 2021; 237:118164. [PMID: 34000397 PMCID: PMC8296467 DOI: 10.1016/j.neuroimage.2021.118164] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 05/11/2021] [Indexed: 11/21/2022] Open
Abstract
Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain's large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show "core" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.
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93
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Yang FN, Hassanzadeh-Behbahani S, Bronshteyn M, Dawson M, Kumar P, Moore DJ, Ellis RJ, Jiang X. Connectome-based prediction of global cognitive performance in people with HIV. NEUROIMAGE-CLINICAL 2021; 30:102677. [PMID: 34215148 PMCID: PMC8102633 DOI: 10.1016/j.nicl.2021.102677] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/16/2021] [Accepted: 04/12/2021] [Indexed: 11/26/2022]
Abstract
Networks strengths predicted global cognitive performance in PWH. Model generalized to data from an independent PWH sample. Network strengths in PWH with HAND were different from either controls or PWH without HAND. Network strengths may serve as a potential biomarker to assist HAND diagnosis.
Global cognitive performance plays an important role in the diagnosis of HIV-associated neurocognitive disorders (HAND), yet to date, there is no simple way to measure global cognitive performance in people with HIV (PWH). Here, we performed connectome-based predictive modeling (CPM) to pursue a neural biomarker of global cognitive performance in PWH based on whole-brain resting-state functional connectivity. We built a CPM model that successfully predicted individual differences in global cognitive performance in the training set of 67 PWH by using leave-one-out cross-validation. This model generalized to both 33 novel PWH in the testing set and a subset of 39 PWH who completed a follow-up visit two years later. Furthermore, network strengths identified by the CPM model were significantly different between PWH with HAND and without HAND. Together, these results demonstrate that whole-brain functional network strengths could serve as a potential neural biomarker of global cognitive performance in PWH.
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Affiliation(s)
- Fan Nils Yang
- Departments of Neuroscience, Georgetown University Medical Center, Washington, DC 20057, United States.
| | | | - Margarita Bronshteyn
- Departments of Neuroscience, Georgetown University Medical Center, Washington, DC 20057, United States
| | - Matthew Dawson
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, United States
| | - Princy Kumar
- Department of Medicine, Georgetown University Medical Center, Washington, DC 20057, United States
| | - David J Moore
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, United States
| | - Ronald J Ellis
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, United States; Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, United States
| | - Xiong Jiang
- Departments of Neuroscience, Georgetown University Medical Center, Washington, DC 20057, United States
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94
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McCormick EM, Peters S, Crone EA, Telzer EH. Longitudinal network re-organization across learning and development. Neuroimage 2021; 229:117784. [PMID: 33503482 PMCID: PMC7994295 DOI: 10.1016/j.neuroimage.2021.117784] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/07/2021] [Accepted: 01/11/2021] [Indexed: 12/15/2022] Open
Abstract
While it is well understood that the brain experiences changes across short-term experience/learning and long-term development, it is unclear how these two mechanisms interact to produce developmental outcomes. Here we test an interactive model of learning and development where certain learning-related changes are constrained by developmental changes in the brain against an alternative development-as-practice model where outcomes are determined primarily by the accumulation of experience regardless of age. Participants (8-29 years) participated in a three-wave, accelerated longitudinal study during which they completed a feedback learning task during an fMRI scan. Adopting a novel longitudinal modeling approach, we probed the unique and moderated effects of learning, experience, and development simultaneously on behavioral performance and network modularity during the task. We found nonlinear patterns of development for both behavior and brain, and that greater experience supported increased learning and network modularity relative to naïve subjects. We also found changing brain-behavior relationships across adolescent development, where heightened network modularity predicted improved learning, but only following the transition from adolescence to young adulthood. These results present compelling support for an interactive view of experience and development, where changes in the brain impact behavior in context-specific fashion based on developmental goals.
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Affiliation(s)
- Ethan M McCormick
- Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC 27599, United States.
| | - Sabine Peters
- Department of Developmental and Educational Psychology, Leiden University, 2333AK Leiden, the Netherlands; Leiden Institute for Brain and Cognition, 2333ZA Leiden, the Netherlands
| | - Eveline A Crone
- Department of Developmental and Educational Psychology, Leiden University, 2333AK Leiden, the Netherlands; Leiden Institute for Brain and Cognition, 2333ZA Leiden, the Netherlands; School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Eva H Telzer
- Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC 27599, United States
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95
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Siegel JS, Palanca BJA, Ances BM, Kharasch ED, Schweiger JA, Yingling MD, Snyder AZ, Nicol GE, Lenze EJ, Farber NB. Prolonged ketamine infusion modulates limbic connectivity and induces sustained remission of treatment-resistant depression. Psychopharmacology (Berl) 2021; 238:1157-1169. [PMID: 33483802 PMCID: PMC7969576 DOI: 10.1007/s00213-021-05762-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 01/06/2021] [Indexed: 12/11/2022]
Abstract
Ketamine produces a rapid antidepressant response in over 50% of adults with treatment-resistant depression. A long infusion of ketamine may provide durable remission of depressive symptoms, but the safety, efficacy, and neurobiological correlates are unknown. In this open-label, proof-of-principle study, adults with treatment-resistant depression (N = 23) underwent a 96-h infusion of intravenous ketamine (0.15 mg/kg/h titrated toward 0.6 mg/kg/h). Clonidine was co-administered to reduce psychotomimetic effects. We measured clinical response for 8 weeks post-infusion. Resting-state functional magnetic resonance imaging was used to assess functional connectivity in patients pre- and 2 weeks post-infusion and in matched non-depressed controls (N = 27). We hypothesized that responders to therapy would demonstrate response-dependent connectivity changes while all subjects would show treatment-dependent connectivity changes. Most participants completed infusion (21/23; mean final dose 0.54 mg/kg/h, SD 0.13). The infusion was well tolerated with minimal cognitive and psychotomimetic side effects. Depressive symptoms were markedly reduced (MADRS 29 ± 4 at baseline to 9 ± 8 one day post-infusion), which was sustained at 2 weeks (13 ± 8) and 8 weeks (15 ± 8). Imaging demonstrated a response-dependent decrease in hyperconnectivity of the subgenual anterior cingulate cortex to the default mode network, and a treatment-dependent decrease in hyperconnectivity within the limbic system (hippocampus, amygdala, medial thalamus, nucleus accumbens). In exploratory analyses, connectivity was increased between the limbic system and frontal areas, and smaller right hippocampus volume at baseline predicted larger MADRS change. A single prolonged infusion of ketamine provides a tolerated, rapid, and sustained response in treatment-resistant depression and normalizes depression-related hyperconnectivity in the limbic system and frontal lobe. ClinicalTrials.gov : Treatment Resistant Depression (Pilot), NCT01179009.
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Affiliation(s)
- Joshua S Siegel
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid, Box 8134, St. Louis, MO, 63110, USA.
| | - Ben J A Palanca
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Beau M Ances
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Julie A Schweiger
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid, Box 8134, St. Louis, MO, 63110, USA
| | - Michael D Yingling
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid, Box 8134, St. Louis, MO, 63110, USA
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ginger E Nicol
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid, Box 8134, St. Louis, MO, 63110, USA
| | - Eric J Lenze
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid, Box 8134, St. Louis, MO, 63110, USA
| | - Nuri B Farber
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid, Box 8134, St. Louis, MO, 63110, USA
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96
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Editorial: The Centrality of Both Hyper- and Hypo-thalamocortical Connectivity in Psychosis. J Am Acad Child Adolesc Psychiatry 2021; 60:438-440. [PMID: 33545306 DOI: 10.1016/j.jaac.2021.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 01/27/2021] [Indexed: 11/21/2022]
Abstract
Long-standing hypotheses about schizophrenia as a "dysconnection" syndrome are consistent with the idea that mental illness arises in part from brain circuit disruptions, with impairments in cognition and behavior occurring because of a failure of coordinated action across multiple brain regions. One such theory, put forth by Andreasen and colleagues, suggested that schizophrenia involves a disruption in the integration of cortical-striatal-thalamic-cerebellar circuits.1 Anatomical work in primates has shown that the thalamus is topographically organized into parallel pathways connecting specific thalamic nuclei to different regions of cortex. The medial dorsal and anterior nuclei of the thalamus project to the dorsolateral prefrontal cortex (dlPFC), whereas the lateral nuclei project more to sensorimotor regions, with similar findings in functional brain connectivity studies in humans. A large body of evidence has shown reduced connectivity from bilateral thalamic regions, medial dorsal, and anterior nuclei in particular, to the bilateral dlPFC, dorsal anterior cingulate, parts of the striatum, and bilateral cerebellum in schizophrenia.2 This is often coupled increased connectivity between the thalamus, lateral nuclei in particular, and motor, visual, and/or auditory sensory regions.2.
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97
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Rawls E, Kummerfeld E, Zilverstand A. An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis. Commun Biol 2021; 4:435. [PMID: 33790384 PMCID: PMC8012376 DOI: 10.1038/s42003-021-01955-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 02/24/2021] [Indexed: 12/24/2022] Open
Abstract
Alcohol use disorder (AUD) has high prevalence and adverse societal impacts, but our understanding of the factors driving AUD is hampered by a lack of studies that describe the complex neurobehavioral mechanisms driving AUD. We analyzed causal pathways to AUD severity using Causal Discovery Analysis (CDA) with data from the Human Connectome Project (HCP; n = 926 [54% female], 22% AUD [37% female]). We applied exploratory factor analysis to parse the wide HCP phenotypic space (100 measures) into 18 underlying domains, and we assessed functional connectivity within 12 resting-state brain networks. We then employed data-driven CDA to generate a causal model relating phenotypic factors, fMRI network connectivity, and AUD symptom severity, which highlighted a limited set of causes of AUD. The model proposed a hierarchy with causal influence propagating from brain connectivity to cognition (fluid/crystalized cognition, language/math ability, & working memory) to social (agreeableness/social support) to affective/psychiatric function (negative affect, low conscientiousness/attention, externalizing symptoms) and ultimately AUD severity. Our data-driven model confirmed hypothesized influences of cognitive and affective factors on AUD, while underscoring that addiction models need to be expanded to highlight the importance of social factors, amongst others.
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Affiliation(s)
- Eric Rawls
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
| | - Erich Kummerfeld
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
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98
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Abdallah M, Zahr NM, Saranathan M, Honnorat N, Farrugia N, Pfefferbaum A, Sullivan EV, Chanraud S. Altered Cerebro-Cerebellar Dynamic Functional Connectivity in Alcohol Use Disorder: a Resting-State fMRI Study. THE CEREBELLUM 2021; 20:823-835. [PMID: 33655376 PMCID: PMC8413394 DOI: 10.1007/s12311-021-01241-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/31/2021] [Indexed: 12/28/2022]
Abstract
Alcohol use disorder (AUD) is widely associated with cerebellar dysfunction and altered cerebro-cerebellar functional connectivity (FC) that lead to cognitive impairments. Evidence for this association comes from resting-state functional magnetic resonance imaging (rsfMRI) studies that assess time-averaged measures of FC across the duration of a typical scan. This approach, however, precludes the assessment of potentially FC dynamics happening at faster timescales. In this study, using rsfMRI data, we aim at exploring cerebro-cerebellar FC dynamics in AUD patients (N = 18) and age- and sex-matched controls (N = 18). In particular, we quantified group-level differences in the temporal variability of FC between the posterior cerebellum and large-scale cognitive systems, and we investigated the role of the cerebellum in large-scale brain dynamics in terms of the temporal flexibility and integration of its regions. We found that, relative to controls, the AUD group exhibited significantly greater FC variability between the cerebellum and both the frontoparietal executive control (F1,31 = 7.01, p(FDR) = 0.028) and ventral attention (F1,31 = 7.35, p(FDR) = 0.028) networks. Moreover, the AUD group exhibited significantly less flexibility (F1,31 = 8.61, p(FDR) = 0.028) and greater integration (F1,31 = 9.11, p(FDR) = 0.028) in the cerebellum. Finally, in an exploratory analysis, we found distributed changes in the dynamics of canonical large-scale networks in AUD. Overall, this study brings evidence of AUD-related alterations in dynamic FC within major cerebro-cerebellar networks. This pattern has implications for explaining the development and maintenance of this disorder and improving our understating of the cerebellum's involvement in addiction.
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Affiliation(s)
- Majd Abdallah
- Aquitaine Institute of Cognitive and Integrative Neuroscience, UMR CNRS 5287, University of Bordeaux, Bordeaux, France
| | - Natalie M Zahr
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305-5723, USA.,Center for Health Sciences, SRI International, Menlo Park, CA, 94025, USA
| | | | - Nicolas Honnorat
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305-5723, USA.,Center for Health Sciences, SRI International, Menlo Park, CA, 94025, USA
| | | | - Adolf Pfefferbaum
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305-5723, USA.,Center for Health Sciences, SRI International, Menlo Park, CA, 94025, USA
| | - Edith V Sullivan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305-5723, USA.,Center for Health Sciences, SRI International, Menlo Park, CA, 94025, USA
| | - Sandra Chanraud
- Aquitaine Institute of Cognitive and Integrative Neuroscience, UMR CNRS 5287, University of Bordeaux, Bordeaux, France. .,Laboratory of Neuroimaging and Daily Life, EPHE, PSL, Research University, Bordeaux, France.
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99
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Lamichhane B, Daniel AGS, Lee JJ, Marcus DS, Shimony JS, Leuthardt EC. Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients. Front Neurol 2021; 12:642241. [PMID: 33692747 PMCID: PMC7937731 DOI: 10.3389/fneur.2021.642241] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 02/03/2021] [Indexed: 12/27/2022] Open
Abstract
Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.
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Affiliation(s)
- Bidhan Lamichhane
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Andy G S Daniel
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
| | - John J Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Eric C Leuthardt
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States.,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States.,Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States.,Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, United States.,Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO, United States.,Brain Laser Center, Washington University School of Medicine, St. Louis, MO, United States
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100
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Millar PR, Ances BM, Gordon BA, Benzinger TLS, Morris JC, Balota DA. Evaluating Cognitive Relationships with Resting-State and Task-driven Blood Oxygen Level-Dependent Variability. J Cogn Neurosci 2021; 33:279-302. [PMID: 33135966 PMCID: PMC7877897 DOI: 10.1162/jocn_a_01645] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Recent functional magnetic resonance imaging studies have reported that moment-to-moment variability in the blood oxygen level-dependent (BOLD) signal is positively associated with task performance and, thus, may reflect a behaviorally sensitive signal. However, it is not clear whether estimates of resting-state and task-driven BOLD variability are differentially related to cognition, as they may be driven by distinct sources of variance in the BOLD signal. Moreover, other studies have suggested that age differences in resting-state BOLD variability may be particularly sensitive to individual differences in cardiovascular, rather than neural, factors. In this study, we tested relationships between measures of behavioral task performance and BOLD variability during both resting-state and task-driven runs of a Stroop and an animacy judgment task in a large, well-characterized sample of cognitively normal middle-aged to older adults. Resting-state BOLD variability was related to composite measures of global cognition and attentional control, but these relationships were eliminated after correction for age or cardiovascular estimates. In contrast, task-driven BOLD variability was related to attentional control measured both inside and outside the scanner, and importantly, these relationships persisted after correction for age and cardiovascular measures. Overall, these results suggest that BOLD variability is a behaviorally sensitive signal. However, resting-state and task-driven estimates of BOLD variability may differ in the degree to which they are sensitive to age-related, cardiovascular, and neural mechanisms.
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Affiliation(s)
- Peter R Millar
- Department of Psychological & Brain Sciences, Washington University in St. Louis,Department of Neurology, Washington University in St. Louis,Peter R Millar, , 314-935-6524, One Brookings Drive, Campus Box 1125, St. Louis, MO 63130
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis,Department of Radiology, Washington University in St. Louis
| | - Brian A Gordon
- Department of Psychological & Brain Sciences, Washington University in St. Louis,Department of Radiology, Washington University in St. Louis
| | | | - John C Morris
- Department of Neurology, Washington University in St. Louis
| | - David A Balota
- Department of Psychological & Brain Sciences, Washington University in St. Louis,Department of Neurology, Washington University in St. Louis
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