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Michael J. Mason

Bio: Michael J. Mason is an academic researcher from University of Tennessee. The author has contributed to research in topics: Substance abuse & Poison control. The author has an hindex of 27, co-authored 101 publications receiving 2207 citations. Previous affiliations of Michael J. Mason include Villanova University & Georgetown University.


Papers
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Journal ArticleDOI
Donald J. Hagler1, Sean N. Hatton1, M. Daniela Cornejo1, Carolina Makowski2, Damien A. Fair3, Anthony Steven Dick4, Matthew T. Sutherland4, B. J. Casey5, M Deanna6, Michael P. Harms6, Richard Watts5, James M. Bjork7, Hugh Garavan8, Laura Hilmer1, Christopher J. Pung1, Chelsea S. Sicat1, Joshua M. Kuperman1, Hauke Bartsch1, Feng Xue1, Mary M. Heitzeg9, Angela R. Laird4, Thanh T. Trinh1, Raul Gonzalez4, Susan F. Tapert1, Michael C. Riedel4, Lindsay M. Squeglia10, Luke W. Hyde9, Monica D. Rosenberg5, Eric Earl3, Katia D. Howlett11, Fiona C. Baker12, Mary E. Soules9, Jazmin Diaz1, Octavio Ruiz de Leon1, Wesley K. Thompson1, Michael C. Neale7, Megan M. Herting13, Elizabeth R. Sowell13, Ruben P. Alvarez11, Samuel W. Hawes4, Mariana Sanchez4, Jerzy Bodurka14, Florence J. Breslin14, Amanda Sheffield Morris14, Martin P. Paulus14, W. Kyle Simmons14, Jonathan R. Polimeni15, Andre van der Kouwe15, Andrew S. Nencka16, Kevin M. Gray10, Carlo Pierpaoli11, John A. Matochik11, Antonio Noronha11, Will M. Aklin11, Kevin P. Conway11, Meyer D. Glantz11, Elizabeth Hoffman11, Roger Little11, Marsha F. Lopez11, Vani Pariyadath11, Susan R.B. Weiss11, Dana L. Wolff-Hughes, Rebecca DelCarmen-Wiggins, Sarah W. Feldstein Ewing3, Oscar Miranda-Dominguez3, Bonnie J. Nagel3, Anders Perrone3, Darrick Sturgeon3, Aimee Goldstone12, Adolf Pfefferbaum12, Kilian M. Pohl12, Devin Prouty12, Kristina A. Uban17, Susan Y. Bookheimer18, Mirella Dapretto18, Adriana Galván18, Kara Bagot1, Jay N. Giedd1, M. Alejandra Infante1, Joanna Jacobus1, Kevin Patrick1, Paul D. Shilling1, Rahul S. Desikan19, Yi Li19, Leo P. Sugrue19, Marie T. Banich20, Naomi P. Friedman20, John K. Hewitt20, Christian J. Hopfer20, Joseph T. Sakai20, Jody Tanabe20, Linda B. Cottler21, Sara Jo Nixon21, Linda Chang22, Christine C. Cloak22, Thomas Ernst22, Gloria Reeves22, David N. Kennedy23, Steve Heeringa9, Scott Peltier9, John E. Schulenberg9, Chandra Sripada9, Robert A. Zucker9, William G. Iacono24, Monica Luciana24, Finnegan J. Calabro25, Duncan B. Clark25, David A. Lewis25, Beatriz Luna25, Claudiu Schirda25, Tufikameni Brima26, John J. Foxe26, Edward G. Freedman26, Daniel W. Mruzek26, Michael J. Mason27, Rebekah S. Huber28, Erin McGlade28, Andrew P. Prescot28, Perry F. Renshaw28, Deborah A. Yurgelun-Todd28, Nicholas Allgaier8, Julie A. Dumas8, Masha Y. Ivanova8, Alexandra Potter8, Paul Florsheim29, Christine L. Larson29, Krista M. Lisdahl29, Michael E. Charness30, Michael E. Charness31, Michael E. Charness15, Bernard F. Fuemmeler7, John M. Hettema7, Hermine H. Maes7, Joel L. Steinberg7, Andrey P. Anokhin6, Paul E.A. Glaser6, Andrew C. Heath6, Pamela A. F. Madden6, Arielle R. Baskin-Sommers5, R. Todd Constable5, Steven Grant11, Gayathri J. Dowling11, Sandra A. Brown1, Terry L. Jernigan1, Anders M. Dale1 
TL;DR: The baseline neuroimaging processing and subject-level analysis methods used by the Adolescent Brain Cognitive Development Study are described to be a resource of unprecedented scale and depth for studying typical and atypical development.

431 citations

Posted ContentDOI
Donald J. Hagler1, Sean N. Hatton1, Carolina Makowski2, M. Daniela Cornejo3, Damien A. Fair3, Anthony Steven Dick4, Matthew T. Sutherland4, B. J. Casey5, M Deanna6, Michael P. Harms6, Richard Watts5, James M. Bjork7, Hugh Garavan8, Laura Hilmer1, Christopher J. Pung1, Chelsea S. Sicat1, Joshua M. Kuperman1, Hauke Bartsch1, Feng Xue1, Mary M. Heitzeg9, Angela R. Laird4, Thanh T. Trinh1, Raul Gonzalez4, Susan F. Tapert1, Michael C. Riedel4, Lindsay M. Squeglia10, Luke W. Hyde9, Monica D. Rosenberg5, Eric Earl3, Katia D. Howlett11, Fiona C. Baker12, Mary E. Soules9, Jazmin Diaz1, Octavio Ruiz de Leon1, Wesley K. Thompson1, Michael C. Neale7, Megan M. Herting13, Elizabeth R. Sowell13, Ruben P. Alvarez14, Samuel W. Hawes4, Mariana Sanchez4, Jerzy Bodurka15, Florence J. Breslin15, Amanda Sheffield Morris15, Martin P. Paulus15, W. Kyle Simmons15, Jonathan R. Polimeni16, Andre van der Kouwe16, Andrew S. Nencka17, Kevin M. Gray10, Carlo Pierpaoli14, John A. Matochik14, Antonio Noronha14, Will M. Aklin11, Kevin P. Conway11, Meyer D. Glantz11, Elizabeth Hoffman11, Roger Little11, Marsha F. Lopez11, Vani Pariyadath11, Susan R.B. Weiss11, Dana L. Wolff-Hughes, Rebecca DelCarmen-Wiggins, Sarah W. Feldstein Ewing3, Oscar Miranda-Dominguez3, Bonnie J. Nagel3, Anders Perrone3, Darrick Sturgeon3, Aimee Goldstone12, Adolf Pfefferbaum12, Kilian M. Pohl12, Devin Prouty12, Kristina A. Uban1, Susan Y. Bookheimer1, Mirella Dapretto1, Adriana Galván1, Kara Bagot1, Jay N. Giedd1, M. Alejandra Infante1, Joanna Jacobus1, Kevin Patrick1, Paul D. Shilling1, Rahul S. Desikan1, Yi Li1, Leo P. Sugrue1, Marie T. Banich18, Naomi P. Friedman18, John K. Hewitt18, Christian J. Hopfer18, Joseph T. Sakai18, Jody Tanabe18, Linda B. Cottler19, Sara Jo Nixon19, Linda Chang20, Christine C. Cloak20, Thomas Ernst20, Gloria Reeves20, David N. Kennedy21, Steve Heeringa9, Scott Peltier9, John E. Schulenberg9, Chandra Sripada9, Robert A. Zucker9, William G. Iacono22, Monica Luciana22, Finnegan J. Calabro23, Duncan B. Clark23, David A. Lewis23, Beatriz Luna23, Claudiu Schirda23, Tufikameni Brima24, John J. Foxe24, Edward G. Freedman24, Daniel W. Mruzek24, Michael J. Mason25, Rebekah S. Huber26, Erin McGlade26, Andrew P. Prescot26, Perry F. Renshaw26, Deborah A. Yurgelun-Todd26, Nicholas Allgaier8, Julie A. Dumas8, Masha Y. Ivanova8, Alexandra Potter8, Paul Florsheim27, Christine L. Larson27, Krista M. Lisdahl27, Michael E. Charness28, Bernard F. Fuemmeler7, John M. Hettema7, Joel L. Steinberg7, Andrey P. Anokhin6, Paul E.A. Glaser6, Andrew C. Heath6, Pamela A. F. Madden6, Arielle R. Baskin-Sommers5, R. Todd Constable5, Steven Grant11, Gayathri J. Dowling11, Sandra A. Brown1, Terry L. Jernigan1, Anders M. Dale1 
04 Nov 2018-bioRxiv
TL;DR: The baseline neuroimaging processing and subject-level analysis methods used by the ABCD DAIC in the centralized processing and extraction of neuroanatomical and functional imaging phenotypes are described.
Abstract: The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The ABCD Study is a collaborative effort, including a Coordinating Center, 21 data acquisition sites across the United States, and a Data Analysis and Informatics Center (DAIC). The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data will provide a resource of unprecedented scale and depth for studying typical and atypical development. Here, we describe the baseline neuroimaging processing and subject-level analysis methods used by the ABCD DAIC in the centralized processing and extraction of neuroanatomical and functional imaging phenotypes. Neuroimaging processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI.

276 citations

Journal ArticleDOI
TL;DR: Results from a meta-analysis examining the effectiveness of text message interventions for tobacco and alcohol cessation within adolescent and young adult populations indicate that in general, text interventions have a positive effect on reducing substance use behaviors.
Abstract: Tobacco and alcohol use continues to be associated with negative health outcomes among adolescents and young adults. New technologies such as text messaging can increase access to substance use interventions and have now been established as an evidence-based, recommended approach towards substance use prevention. This review presents results from a meta-analysis examining the effectiveness of text message interventions for tobacco and alcohol cessation within adolescent and young adult populations. Results from 14 studies with effect sizes are ranging from −0.25 to 0.54. Combining the effect sizes across studies yielded a summary effect size of 0.25, indicating that in general, text interventions have a positive effect on reducing substance use behaviors. Results are discussed in the context of prevention opportunities and recommendations for future text messaging intervention research.

128 citations

Journal ArticleDOI
TL;DR: A research strategy and prevention methodology for substance using urban youth that incorporates individual, social, and geographical parameters to systematically understand the ecology of risk and protection for urban youth is demonstrated.
Abstract: This article demonstrates a research strategy and prevention methodology for substance using urban youth that incorporates individual, social, and geographical parameters to systematically understand the ecology of risk and protection for urban youth. The primary goal of this study was to describe and analyze substance using and nonusing urban adolescents' social networks; risky and protective settings where they socialize; and the relationship to health outcomes such as substance use, depression, and stress. Geographic Information Systems (GIS) derived spatial relationships and analyses between the specific locations where the teens are active, their subjective ratings of these locations, and objective environmental risk data. These social network and GIS data were merged to form a detailed description and analysis of the social ecology of urban adolescent substance use. A case study was constructed to illustrate the methodology of creating a three-dimentional ecological profile that helps explain these relationships and provides preventive applications. Linear distances were computed between the homes of the users and the risky and safe places that they identified. On average, the distance between users' homes and their identified safe places was three times the distance between their homes and their identified risky places. This study provides support for understanding urban adolescent substance use through the detailed and multiple dimensional analysis of teens' social ecologies.

108 citations

Journal ArticleDOI
TL;DR: Results indicate that urban greenspace is associated with lower stress when subjects are away from home, which it is speculated is due to the properties of stress reduction and attention restoration associated with exposure to natural areas, and to the primacy of other family dynamics mechanisms of stress within the home.

96 citations


Cited by
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TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

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Journal Article
TL;DR: In this article, the optimal characteristics of strength-specific programs include the use of concentric (CON), eccentric (ECC), and isometric muscle actions and the performance of bilateral and unilateral single and multiple-joint exercises.
Abstract: In order to stimulate further adaptation toward specific training goals, progressive resistance training (RT) protocols are necessary The optimal characteristics of strength-specific programs include the use of concentric (CON), eccentric (ECC), and isometric muscle actions and the performance of bilateral and unilateral single- and multiple-joint exercises In addition, it is recommended that strength programs sequence exercises to optimize the preservation of exercise intensity (large before small muscle group exercises, multiple-joint exercises before single-joint exercises, and higher-intensity before lower-intensity exercises) For novice (untrained individuals with no RT experience or who have not trained for several years) training, it is recommended that loads correspond to a repetition range of an 8-12 repetition maximum (RM) For intermediate (individuals with approximately 6 months of consistent RT experience) to advanced (individuals with years of RT experience) training, it is recommended that individuals use a wider loading range from 1 to 12 RM in a periodized fashion with eventual emphasis on heavy loading (1-6 RM) using 3- to 5-min rest periods between sets performed at a moderate contraction velocity (1-2 s CON; 1-2 s ECC) When training at a specific RM load, it is recommended that 2-10% increase in load be applied when the individual can perform the current workload for one to two repetitions over the desired number The recommendation for training frequency is 2-3 d·wk -1 for novice training, 3-4 d·wk -1 for intermediate training, and 4-5 d·wk -1 for advanced training Similar program designs are recommended for hypertrophy training with respect to exercise selection and frequency For loading, it is recommended that loads corresponding to 1-12 RM be used in periodized fashion with emphasis on the 6-12 RM zone using 1- to 2-min rest periods between sets at a moderate velocity Higher volume, multiple-set programs are recommended for maximizing hypertrophy Progression in power training entails two general loading strategies: 1) strength training and 2) use of light loads (0-60% of 1 RM for lower body exercises; 30-60% of 1 RM for upper body exercises) performed at a fast contraction velocity with 3-5 min of rest between sets for multiple sets per exercise (three to five sets) It is also recommended that emphasis be placed on multiple-joint exercises especially those involving the total body For local muscular endurance training, it is recommended that light to moderate loads (40-60% of 1 RM) be performed for high repetitions (>15) using short rest periods (<90 s) In the interpretation of this position stand as with prior ones, recommendations should be applied in context and should be contingent upon an individual's target goals, physical capacity, and training status

3,421 citations

Journal ArticleDOI
TL;DR: In order to stimulate further adaptation toward a specific training goal(s), progression in the type of resistance training protocol used is necessary and emphasis should be placed on multiple-joint exercises, especially those involving the total body.
Abstract: In order to stimulate further adaptation toward a specific training goal(s), progression in the type of resistance training protocol used is necessary. The optimal characteristics of strength-specific programs include the use of both concentric and eccentric muscle actions and the performance of both single- and multiple-joint exercises. It is also recommended that the strength program sequence exercises to optimize the quality of the exercise intensity (large before small muscle group exercises, multiple-joint exercises before single-joint exercises, and higher intensity before lower intensity exercises). For initial resistances, it is recommended that loads corresponding to 8-12 repetition maximum (RM) be used in novice training. For intermediate to advanced training, it is recommended that individuals use a wider loading range, from 1-12 RM in a periodized fashion, with eventual emphasis on heavy loading (1-6 RM) using at least 3-min rest periods between sets performed at a moderate contraction velocity (1-2 s concentric, 1-2 s eccentric). When training at a specific RM load, it is recommended that 2-10% increase in load be applied when the individual can perform the current workload for one to two repetitions over the desired number. The recommendation for training frequency is 2-3 d x wk(-1) for novice and intermediate training and 4-5 d x wk(-1) for advanced training. Similar program designs are recommended for hypertrophy training with respect to exercise selection and frequency. For loading, it is recommended that loads corresponding to 1-12 RM be used in periodized fashion, with emphasis on the 6-12 RM zone using 1- to 2-min rest periods between sets at a moderate velocity. Higher volume, multiple-set programs are recommended for maximizing hypertrophy. Progression in power training entails two general loading strategies: 1) strength training, and 2) use of light loads (30-60% of 1 RM) performed at a fast contraction velocity with 2-3 min of rest between sets for multiple sets per exercise. It is also recommended that emphasis be placed on multiple-joint exercises, especially those involving the total body. For local muscular endurance training, it is recommended that light to moderate loads (40-60% of 1 RM) be performed for high repetitions (> 15) using short rest periods (< 90 s). In the interpretation of this position stand, as with prior ones, the recommendations should be viewed in context of the individual's target goals, physical capacity, and training status.

2,845 citations