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Kruper J, Richie-Halford A, Benson NC, Caffarra S, Owen J, Wu Y, Egan C, Lee AY, Lee CS, Yeatman JD, Rokem A. Convolutional neural network-based classification of glaucoma using optic radiation tissue properties. Commun Med (Lond) 2024; 4:72. [PMID: 38605245 PMCID: PMC11009254 DOI: 10.1038/s43856-024-00496-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 03/28/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections. METHODS We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models. RESULTS We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration. CONCLUSIONS Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Adam Richie-Halford
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - Noah C Benson
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Sendy Caffarra
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
- University of Modena and Reggio Emilia, Modena, Italy
| | - Julia Owen
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | | | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Jason D Yeatman
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, USA.
- eScience Institute, University of Washington, Seattle, WA, USA.
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Caffarra S, Kanopka K, Kruper J, Richie-Halford A, Roy E, Rokem A, Yeatman JD. Development of the Alpha Rhythm Is Linked to Visual White Matter Pathways and Visual Detection Performance. J Neurosci 2024; 44:e0684232023. [PMID: 38124006 PMCID: PMC11059423 DOI: 10.1523/jneurosci.0684-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 11/21/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023] Open
Abstract
Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI n = 2,747, EEG n = 2,561) of children and adolescents of either sex (age range, 5-21 years old) and showed that maturation of the optic radiation specifically accounts for developmental changes of alpha frequency. Behavioral analyses also confirmed that variations of alpha frequency are related to maturational changes in visual perception. The present findings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.
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Affiliation(s)
- Sendy Caffarra
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford 94305, California
- Stanford University Graduate School of Education, Stanford 94305, California
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Klint Kanopka
- Stanford University Graduate School of Education, Stanford 94305, California
| | - John Kruper
- Department of Psychology, University of Washington, Seattle 91905, Washington
- eScience Institute, University of Washington, Seattle 98195-1570, Washington
| | - Adam Richie-Halford
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford 94305, California
- Stanford University Graduate School of Education, Stanford 94305, California
| | - Ethan Roy
- Stanford University Graduate School of Education, Stanford 94305, California
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle 91905, Washington
- eScience Institute, University of Washington, Seattle 98195-1570, Washington
| | - Jason D Yeatman
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford 94305, California
- Stanford University Graduate School of Education, Stanford 94305, California
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Roy E, Richie-Halford A, Kruper J, Narayan M, Bloom D, Nedelec P, Rauschecker AM, Sugrue LP, Brown TT, Jernigan TL, McCandliss BD, Rokem A, Yeatman JD. White matter and literacy: A dynamic system in flux. Dev Cogn Neurosci 2024; 65:101341. [PMID: 38219709 PMCID: PMC10825614 DOI: 10.1016/j.dcn.2024.101341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 08/24/2023] [Accepted: 01/03/2024] [Indexed: 01/16/2024] Open
Abstract
Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.
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Affiliation(s)
- Ethan Roy
- Graduate School of Education, Stanford University, Stanford, CA, USA.
| | - Adam Richie-Halford
- Graduate School of Education, Stanford University, Stanford, CA, USA; Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - John Kruper
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Manjari Narayan
- Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - David Bloom
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Pierre Nedelec
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Andreas M Rauschecker
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Leo P Sugrue
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Timothy T Brown
- School of Medicine, University of California San Diego, San Diego, CA, USA
| | - Terry L Jernigan
- Center for Human Development, University of California San Diego, San Diego, CA, USA
| | | | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
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4
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Kruper J, Richie-Halford A, Benson N, Caffarra S, Owen J, Wu Y, Lee A, Lee C, Yeatman J, Rokem A. Contributed Session I: Specific and non-linear effects of glaucoma on optic radiation tissue properties. J Vis 2023; 23:73. [PMID: 38109575 DOI: 10.1167/jov.23.15.73] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023] Open
Abstract
Changes in sensory input with aging and disease affect brain tissue properties. To establish the link between glaucoma, the most prevalent cause of irreversible blindness, and changes in major brain connections, we characterized white matter tissue properties in diffusion MRI measurements in a large sample of subjects with glaucoma (N=905; age 49-80) and healthy controls (N=5,292; age 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. A convolutional neural network (CNN) accurately classified whether a subject has glaucoma using information from the primary visual connection to cortex (the optic radiations, OR), but not from non-visual brain connections. On the other hand, regularized linear regression could not classify glaucoma, and the CNN did not generalize to classification of age-group or of age-related macular degeneration. This suggests a unique non-linear signature of glaucoma in OR tissue properties.
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Grotheer M, Bloom D, Kruper J, Richie-Halford A, Zika S, Aguilera González VA, Yeatman JD, Grill-Spector K, Rokem A. Human white matter myelinates faster in utero than ex utero. Proc Natl Acad Sci U S A 2023; 120:e2303491120. [PMID: 37549280 PMCID: PMC10438384 DOI: 10.1073/pnas.2303491120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/27/2023] [Indexed: 08/09/2023] Open
Abstract
The formation of myelin, the fatty sheath that insulates nerve fibers, is critical for healthy brain function. A fundamental open question is what impact being born has on myelin growth. To address this, we evaluated a large (n = 300) cross-sectional sample of newborns from the Developing Human Connectome Project (dHCP). First, we developed software for the automated identification of 20 white matter bundles in individual newborns that is well suited for large samples. Next, we fit linear models that quantify how T1w/T2w (a myelin-sensitive imaging contrast) changes over time at each point along the bundles. We found faster growth of T1w/T2w along the lengths of all bundles before birth than right after birth. Further, in a separate longitudinal sample of preterm infants (N = 34), we found lower T1w/T2w than in full-term peers measured at the same age. By applying the linear models fit on the cross-section sample to the longitudinal sample of preterm infants, we find that their delay in T1w/T2w growth is well explained by the amount of time they spent developing in utero and ex utero. These results suggest that white matter myelinates faster in utero than ex utero. The reduced rate of myelin growth after birth, in turn, explains lower myelin content in individuals born preterm and could account for long-term cognitive, neurological, and developmental consequences of preterm birth. We hypothesize that closely matching the environment of infants born preterm to what they would have experienced in the womb may reduce delays in myelin growth and hence improve developmental outcomes.
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Affiliation(s)
- Mareike Grotheer
- Department of Psychology, Philipps-Universität Marburg, Marburg35039, Germany
- Center for Mind, Brain and Behavior, Philipps-Universität Marburg and Justus-Liebig-Universität Giessen, Marburg35039, Germany
| | - David Bloom
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
| | - John Kruper
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
| | - Adam Richie-Halford
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
| | - Stephanie Zika
- Department of Psychology, Philipps-Universität Marburg, Marburg35039, Germany
- Center for Mind, Brain and Behavior, Philipps-Universität Marburg and Justus-Liebig-Universität Giessen, Marburg35039, Germany
| | - Vicente A. Aguilera González
- Department of Psychology, Philipps-Universität Marburg, Marburg35039, Germany
- Center for Mind, Brain and Behavior, Philipps-Universität Marburg and Justus-Liebig-Universität Giessen, Marburg35039, Germany
| | - Jason D. Yeatman
- Department of Psychology, Stanford University, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA94305
- Graduate School of Education, Stanford University, Stanford, CA94305
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA94305
| | - Kalanit Grill-Spector
- Department of Psychology, Stanford University, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA94305
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
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Kruper J, Benson NC, Caffarra S, Owen J, Wu Y, Lee AY, Lee CS, Yeatman JD, Rokem A. Optic radiations representing different eccentricities age differently. Hum Brain Mapp 2023; 44:3123-3135. [PMID: 36896869 PMCID: PMC10171550 DOI: 10.1002/hbm.26267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 01/10/2023] [Accepted: 02/16/2023] [Indexed: 03/11/2023] Open
Abstract
The neural pathways that carry information from the foveal, macular, and peripheral visual fields have distinct biological properties. The optic radiations (OR) carry foveal and peripheral information from the thalamus to the primary visual cortex (V1) through adjacent but separate pathways in the white matter. Here, we perform white matter tractometry using pyAFQ on a large sample of diffusion MRI (dMRI) data from subjects with healthy vision in the U.K. Biobank dataset (UKBB; N = 5382; age 45-81). We use pyAFQ to characterize white matter tissue properties in parts of the OR that transmit information about the foveal, macular, and peripheral visual fields, and to characterize the changes in these tissue properties with age. We find that (1) independent of age there is higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal and macular OR than in peripheral OR, consistent with denser, more organized nerve fiber populations in foveal/parafoveal pathways, and (2) age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with aging. However, anisotropy in foveal OR decreases faster with age than in peripheral OR, while diffusivity increases faster in peripheral OR, suggesting foveal/peri-foveal OR and peripheral OR differ in how they age.
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Affiliation(s)
- John Kruper
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
| | - Noah C. Benson
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
| | - Sendy Caffarra
- Graduate School of Education, Stanford University and Division of Developmental‐Behavioral Pediatrics, Stanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Julia Owen
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Yue Wu
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Aaron Y. Lee
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Cecilia S. Lee
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Jason D. Yeatman
- Graduate School of Education, Stanford University and Division of Developmental‐Behavioral Pediatrics, Stanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
| | - Ariel Rokem
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
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7
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Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Satterthwaite TD, Rokem A. Author Correction: An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2023; 10:247. [PMID: 37117243 PMCID: PMC10147723 DOI: 10.1038/s41597-023-02137-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023] Open
Affiliation(s)
- Adam Richie-Halford
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA.
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA.
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
| | - Lei Ai
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
| | - Sendy Caffarra
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- University of Modena and Reggio Emilia, Department of Biomedical, Metabolic and Neural Sciences, 41125, Modena, Italy
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Alexandre R Franco
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Iliana I Karipidis
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
- University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - John Kruper
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
| | - Michael Milham
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Bárbara Avelar-Pereira
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
| | - Ethan Roy
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Jason D Yeatman
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ariel Rokem
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
- University of Washington, eScience Institute, Seattle, Washington, 98195, USA
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8
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Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Abbott NJ, Anderson JAE, Gagana B, Bleile M, Bloomfield PS, Bottom V, Bourque J, Boyle R, Brynildsen JK, Calarco N, Castrellon JJ, Chaku N, Chen B, Chopra S, Coffey EBJ, Colenbier N, Cox DJ, Crippen JE, Crouse JJ, David S, Leener BD, Delap G, Deng ZD, Dugre JR, Eklund A, Ellis K, Ered A, Farmer H, Faskowitz J, Finch JE, Flandin G, Flounders MW, Fonville L, Frandsen SB, Garic D, Garrido-Vásquez P, Gonzalez-Escamilla G, Grogans SE, Grotheer M, Gruskin DC, Guberman GI, Haggerty EB, Hahn Y, Hall EH, Hanson JL, Harel Y, Vieira BH, Hettwer MD, Hobday H, Horien C, Huang F, Huque ZM, James AR, Kahhale I, Kamhout SLH, Keller AS, Khera HS, Kiar G, Kirk PA, Kohl SH, Korenic SA, Korponay C, Kozlowski AK, Kraljevic N, Lazari A, Leavitt MJ, Li Z, Liberati G, Lorenc ES, Lossin AJ, Lotter LD, Lydon-Staley DM, Madan CR, Magielse N, Marusak HA, Mayor J, McGowan AL, Mehta KP, Meisler SL, Michael C, Mitchell ME, Morand-Beaulieu S, Newman BT, Nielsen JA, O’Mara SM, Ojha A, Omary A, Özarslan E, Parkes L, Peterson M, Pines AR, Pisanu C, Rich RR, Sahoo AK, Samara A, Sayed F, Schneider JT, Shaffer LS, Shatalina E, Sims SA, Sinclair S, Song JW, Hogrogian GS, Tamnes CK, Tooley UA, Tripathi V, Turker HB, Valk SL, Wall MB, Walther CK, Wang Y, Wegmann B, Welton T, Wiesman AI, Wiesman AG, Wiesman M, Winters DE, Yuan R, Zacharek SJ, Zajner C, Zakharov I, Zammarchi G, Zhou D, Zimmerman B, Zoner K, Satterthwaite TD, Rokem A. Author Correction: An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2022; 9:709. [PMID: 36396653 PMCID: PMC9671885 DOI: 10.1038/s41597-022-01816-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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9
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Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Satterthwaite TD, Rokem A. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2022; 9:616. [PMID: 36224186 PMCID: PMC9556519 DOI: 10.1038/s41597-022-01695-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022] Open
Abstract
We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.
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Affiliation(s)
- Adam Richie-Halford
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA.
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA.
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
| | - Lei Ai
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
| | - Sendy Caffarra
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- University of Modena and Reggio Emilia, Department of Biomedical, Metabolic and Neural Sciences, 41125, Modena, Italy
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Alexandre R Franco
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Iliana I Karipidis
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
- University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - John Kruper
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
| | - Michael Milham
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Bárbara Avelar-Pereira
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
| | - Ethan Roy
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Jason D Yeatman
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ariel Rokem
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
- University of Washington, eScience Institute, Seattle, Washington, 98195, USA
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10
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Kruper J, Benson NC, Caffarra S, Owen J, Wu Y, Lee A. Tissue properties of optic radiations representing the foveal and peripheral visual fields. J Vis 2022. [PMID: 35120245 DOI: 10.1167/jov.22.3.15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The biological properties of the foveal and peripheral visual pathways differ substantially. Here, we compared tissue properties of optic radiations (OR) carrying foveal and peripheral information to primary visual cortex (V1), measured with diffusion MRI (dMRI). We analyzed dMRI in two datasets: the Human Connectome Project (HCP; n=180; age 22-35) and the UK Biobank (UKB; n=7,088; age 45-81). In the HCP, OR was delineated using a three-dimensional atlas; parts of OR representing fovea and periphery were divided based on V1 fMRI responses close to OR endpoints. (2) In the UKB, OR was delineated using landmarks and divided using anatomically-based estimates of V1 responses close to OR endpoints. The dMRI signal was modeled using a kurtosis model, which provides information about tissue microstructure. Despite differences in data collection, population characteristics, and analysis methods, both datasets revealed higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal OR than in peripheral OR, consistent with denser nerve fiber populations in foveal pathways. In further analysis of the UKB, we found that age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with aging. However, anisotropy in fovea decreases faster with age than in periphery, while diffusivity increases faster in periphery, suggesting foveal and peripheral OR differ in terms of aging.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, USA eScience Institute, University of Washington, USA
| | | | - Sendy Caffarra
- Graduate School of Education and Department of Pediatrics, Stanford, USA
| | - Julia Owen
- Department of Ophthalmology, University of Washington, USA
| | - Yue Wu
- Department of Ophthalmology, University of Washington, USA
| | - Aaron Lee
- Department of Ophthalmology, University of Washington, USA
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11
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Caffarra S, Joo SJ, Bloom D, Kruper J, Rokem A, Yeatman JD. Development of the visual white matter pathways mediates development of electrophysiological responses in visual cortex. Hum Brain Mapp 2021; 42:5785-5797. [PMID: 34487405 PMCID: PMC8559498 DOI: 10.1002/hbm.25654] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/23/2021] [Accepted: 08/27/2021] [Indexed: 12/24/2022] Open
Abstract
The latency of neural responses in the visual cortex changes systematically across the lifespan. Here, we test the hypothesis that development of visual white matter pathways mediates maturational changes in the latency of visual signals. Thirty-eight children participated in a cross-sectional study including diffusion magnetic resonance imaging (MRI) and magnetoencephalography (MEG) sessions. During the MEG acquisition, participants performed a lexical decision and a fixation task on words presented at varying levels of contrast and noise. For all stimuli and tasks, early evoked fields were observed around 100 ms after stimulus onset (M100), with slower and lower amplitude responses for low as compared to high contrast stimuli. The optic radiations and optic tracts were identified in each individual's brain based on diffusion MRI tractography. The diffusion properties of the optic radiations predicted M100 responses, especially for high contrast stimuli. Higher optic radiation fractional anisotropy (FA) values were associated with faster and larger M100 responses. Over this developmental window, the M100 responses to high contrast stimuli became faster with age and the optic radiation FA mediated this effect. These findings suggest that the maturation of the optic radiations over childhood accounts for individual variations observed in the developmental trajectory of visual cortex responses.
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Affiliation(s)
- Sendy Caffarra
- Division of Developmental‐Behavioral PediatricsStanford University School of MedicineStanfordCalifornia
- Stanford University Graduate School of EducationStanfordCalifornia
- Basque Center on Cognition Brain and LanguageSan SebastianSpain
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Sung Jun Joo
- Department of PsychologyPusan National UniversityPusanRepublic of Korea
| | - David Bloom
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - John Kruper
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - Ariel Rokem
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - Jason D. Yeatman
- Division of Developmental‐Behavioral PediatricsStanford University School of MedicineStanfordCalifornia
- Stanford University Graduate School of EducationStanfordCalifornia
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12
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Caffarra S, Joo SJ, Bloom D, Kruper J, Rokem A, Yeatman JD. Development of the visual pathways predicts changes in electrophysiological responses in visual cortex. J Vis 2021. [DOI: 10.1167/jov.21.9.2127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Sendy Caffarra
- Stanford University School of Medicine, Division of Developmental-Behavioral Pediatrics, Stanford, CA, USA
- Stanford University Graduate School of Education, Stanford, CA, USA
- Basque Center on Cognition, Brain and Language, San Sebastian, Spain
| | - Sung Jun Joo
- Department of Psychology, Pusan National University, Busan, Republic of Korea
| | - David Bloom
- Department of Psychology, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - John Kruper
- Department of Psychology, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Jason D. Yeatman
- Stanford University School of Medicine, Division of Developmental-Behavioral Pediatrics, Stanford, CA, USA
- Stanford University Graduate School of Education, Stanford, CA, USA
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13
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Henriques RN, Correia MM, Marrale M, Huber E, Kruper J, Koudoro S, Yeatman JD, Garyfallidis E, Rokem A. Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project. Front Hum Neurosci 2021; 15:675433. [PMID: 34349631 PMCID: PMC8327208 DOI: 10.3389/fnhum.2021.675433] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/17/2021] [Indexed: 12/28/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project-a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.
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Affiliation(s)
| | - Marta M. Correia
- Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Maurizio Marrale
- Department of Physics and Chemistry “Emilio Segrè”, University of Palermo, Palermo, Italy
- National Institute for Nuclear Physics (INFN), Catania Division, Catania, Italy
| | - Elizabeth Huber
- Department of Speech and Hearing, Institute for Learning and Brain Science, University of Washington, Seattle, WA, United States
| | - John Kruper
- Department of Psychology and eScience Institute, The University of Washington, Seattle, WA, United States
| | - Serge Koudoro
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computer Science and Engineering, Indiana University, Bloomington, IN, United States
| | - Jason D. Yeatman
- Department of Speech and Hearing, Institute for Learning and Brain Science, University of Washington, Seattle, WA, United States
- Department of Pediatrics, Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computer Science and Engineering, Indiana University, Bloomington, IN, United States
| | - Ariel Rokem
- Department of Psychology and eScience Institute, The University of Washington, Seattle, WA, United States
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14
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Kruper J, Yeatman JD, Richie-Halford A, Bloom D, Grotheer M, Caffarra S, Kiar G, Karipidis II, Roy E, Chandio BQ, Garyfallidis E, Rokem A. Evaluating the Reliability of Human Brain White Matter Tractometry. Apert Neuro 2021; 1:10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669. [PMID: 35079748 PMCID: PMC8785971 DOI: 10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on the reliability of analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability, and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to establish the reliability of computational analysis pipelines in neuroimaging.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - David Bloom
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Mareike Grotheer
- Center for Mind, Brain and Behavior - CMBB, Hans-Meerwein-Straße 6, Marburg 35032, Germany
- Department of Psychology, University of Marburg, Marburg 35039, Germany
| | - Sendy Caffarra
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Basque Center on Cognition, Brain and Language, BCBL, 20009, Spain
| | - Gregory Kiar
- Department of Biomedical Engineering, McGill University, Montreal, H3A 0E9, Canada
| | - Iliana I Karipidis
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine,Stanford, CA, 94305, USA
| | - Ethan Roy
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
| | - Bramsh Q Chandio
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
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Stern JJ, Cudillo CA, Kruper J. Ventromedial hypothalamus and short-term feeding suppression by caerulein in male rats. J Comp Physiol Psychol 1976; 90:484-90. [PMID: 977823 DOI: 10.1037/h0077218] [Citation(s) in RCA: 97] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
A series of six experiments examined the neural loci responsible for caerulein's suppression of eating. Caerulein is a decapeptide chemically and physiologically similar to cholecystokinin, a naturally occurring gut hormone in rats. Rats with lesions in the ventromedial hypothalamus (VMH) showed reduced sensitivity to caerulein (1 mug/kg); rats with lateral hypothalamic (LH) destruction showed heightened sensitivity. Microinjections of caerulein into the VMH, but not into the LH, limited feeding. Finally, tritiated caerulein was selectively bound to tissue in the VMH. The results are discussed in terms of the hypothesis that the VMH manages postprandial inhibition in the rat.
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Gutin B, Stewart K, Lewis S, Kruper J. Oxygen consumption in the first stages of strenuous work as a function of prior exercise. J Sports Med Phys Fitness 1976; 16:60-5. [PMID: 1263478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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