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Aimee Goldstone

Bio: Aimee Goldstone is an academic researcher from Oklahoma State University Center for Health Sciences. The author has contributed to research in topics: Polysomnography & Cognition. The author has an hindex of 15, co-authored 35 publications receiving 968 citations. Previous affiliations of Aimee Goldstone include SRI International & University of Birmingham.

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. Charness15, Michael E. Charness31, 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: Fitbit Charge 2™ shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection.
Abstract: We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2™), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults.In-l...

260 citations

Journal ArticleDOI
TL;DR: Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health; however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine.
Abstract: The accurate assessment of sleep is critical to better understand and evaluate its role in health and disease. The boom in wearable technology is part of the digital health revolution and is producing many novel, highly sophisticated and relatively inexpensive consumer devices collecting data from multiple sensors and claiming to extract information about users' behaviors, including sleep. These devices are now able to capture different biosignals for determining, for example, HR and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large data sets (big data), with the potential of offering an unprecedented window on users' health. Unfortunately, little guidance exists within and outside the scientific sleep community for their use, leading to confusion and controversy about their validity and application. The current state-of-the-art review aims to highlight use, validation and utility of consumer wearable sleep-trackers in clinical practice and research. Guidelines for a standardized assessment of device performance is deemed necessary, and several critical factors (proprietary algorithms, device malfunction, firmware updates) need to be considered before using these devices in clinical and sleep research protocols. Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health; however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine.

216 citations

Journal ArticleDOI
TL;DR: Current evidence indicates that insomnia in adolescence is an independent entity that warrants attention as a public health concern in its own right, raising the possibility that treating insomnia symptoms in early adolescence may reduce risk for these adverse outcomes.

202 citations


Cited by
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Journal ArticleDOI
28 Feb 2001-JAMA

1,258 citations

Journal ArticleDOI
TL;DR: This review aims to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that the authors see as most promising for the future developments of the field.

1,032 citations

01 Jan 1998
TL;DR: The lateral intraparietal area (LIP) as mentioned in this paper has been shown to have visual responses to stimuli appearing abruptly at particular retinal locations (their receptive fields) and the visual representation in LIP is sparse, with only the most salient or behaviourally relevant objects being strongly represented.
Abstract: When natural scenes are viewed, a multitude of objects that are stable in their environments are brought in and out of view by eye movements. The posterior parietal cortex is crucial for the analysis of space, visual attention and movement 1 . Neurons in one of its subdivisions, the lateral intraparietal area (LIP), have visual responses to stimuli appearing abruptly at particular retinal locations (their receptive fields)2. We have tested the responses of LIP neurons to stimuli that entered their receptive field by saccades. Neurons had little or no response to stimuli brought into their receptive field by saccades, unless the stimuli were behaviourally significant. We established behavioural significance in two ways: either by making a stable stimulus task-relevant, or by taking advantage of the attentional attraction of an abruptly appearing stimulus. Our results show that under ordinary circumstances the entire visual world is only weakly represented in LIP. The visual representation in LIP is sparse, with only the most salient or behaviourally relevant objects being strongly represented.

1,007 citations

Book ChapterDOI
08 Oct 2016
TL;DR: This paper forms an approach for learning a visual representation from the raw spatiotemporal signals in videos using a Convolutional Neural Network, and shows that this method captures information that is temporally varying, such as human pose.
Abstract: In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal order. With this simple task and no semantic labels, we learn a powerful visual representation using a Convolutional Neural Network (CNN). The representation contains complementary information to that learned from supervised image datasets like ImageNet. Qualitative results show that our method captures information that is temporally varying, such as human pose. When used as pre-training for action recognition, our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51. To demonstrate its sensitivity to human pose, we show results for pose estimation on the FLIC and MPII datasets that are competitive, or better than approaches using significantly more supervision. Our method can be combined with supervised representations to provide an additional boost in accuracy.

683 citations