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M. Daniela Cornejo

Bio: M. Daniela Cornejo is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Cognitive development & Cognition. The author has an hindex of 7, co-authored 9 publications receiving 875 citations. Previous affiliations of M. Daniela Cornejo include University of Wisconsin-Madison & Pontifical Catholic University of Chile.

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Journal ArticleDOI
TL;DR: An overview of the imaging procedures of the ABCD study is provided, the basis for their selection and preliminary quality assurance and results that provide evidence for the feasibility and age-appropriateness of procedures and generalizability of findings to the existent literature are provided.

1,114 citations

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. Charness15, Michael E. Charness30, 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: For instance, this article found that negative self-focused thought (negative-SFT) is associated with depression and negative self focused thought (SFT was associated with increased resting-state functional connectivity (rsFC) for brain regions implicated in SFT.
Abstract: A central feature of major depression (MDD) is heightened negative self-focused thought (negative-SFT). Neuroscientific research has identified abnormalities in a network of brain regions in MDD, including brain areas associated with SFT such as medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC). To our knowledge no studies have investigated the behavioral and neural correlates of negative-SFT using a sentence completion task in a sample of individuals with varying depression histories and severities. We test the following hypotheses: (1) negative-SFT will be associated with depression; and (2) depression and negative-SFT will be related to resting-state functional connectivity (rsFC) for brain regions implicated in SFT. Seventy-nine women with varying depression histories and severities completed a sentence completion task and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Standard seed-based voxelwise rsFC was conducted for self-network regions of interest: dorsomedial PFC (dmPFC) and pregenual ACC (pgACC). We performed linear regression analyses to examine the relationships among depression, negative-SFT, and rsFC for the dmPFC and pgACC. Greater negative-SFT was associated with depression history and severity. Greater negative-SFT predicted increased rsFC between dmPFC and pgACC seeds and dorsolateral prefrontal (dlPFC) and parietal regions; depression group was also associated with increased pgACC-dlPFC connectivity. These findings are consistent with previous literature reporting elevated negative-SFT thought in MDD. Our rs-fMRI results provide novel support linking negative-SFT with increased rsFC between self-network and frontoparietal network regions across different levels of depression. Broadly, these findings highlight a dimension of social-affective functioning that may underlie MDD and other psychiatric conditions.

46 citations

Journal ArticleDOI
TL;DR: Analysis of data from over 11,500 9- to 10-year-olds enrolled in the Adolescent Brain Cognitive Development Study establishes relationships between working memory, other cognitive abilities, and frontoparietal brain activity during a working memory challenge, but not during other cognitive challenges.
Abstract: Working memory function changes across development and varies across individuals. The patterns of behavior and brain function that track individual differences in working memory during human development, however, are not well understood. Here, we establish associations between working memory, other cognitive abilities, and functional MRI (fMRI) activation in data from over 11,500 9- to 10-year-old children (both sexes) enrolled in the Adolescent Brain Cognitive Development (ABCD) Study, an ongoing longitudinal study in the United States. Behavioral analyses reveal robust relationships between working memory, short-term memory, language skills, and fluid intelligence. Analyses relating out-of-scanner working memory performance to memory-related fMRI activation in an emotional n-back task demonstrate that frontoparietal activity during a working memory challenge indexes working memory performance. This relationship is domain specific, such that fMRI activation related to emotion processing during the emotional n-back task, inhibitory control during a stop-signal task (SST), and reward processing during a monetary incentive delay (MID) task does not track memory abilities. Together, these results inform our understanding of individual differences in working memory in childhood and lay the groundwork for characterizing the ways in which they change across adolescence.SIGNIFICANCE STATEMENT Working memory is a foundational cognitive ability that changes over time and varies across individuals. Here, we analyze data from over 11,500 9- to 10-year-olds to establish relationships between working memory, other cognitive abilities, and frontoparietal brain activity during a working memory challenge, but not during other cognitive challenges. Our results lay the groundwork for assessing longitudinal changes in working memory and predicting later academic and other real-world outcomes.

44 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article , the authors used three of the largest neuroimaging datasets currently available, with a total sample size of around 50,000 individuals, to quantify brain-wide association studies effect sizes and reproducibility as a function of sample size.
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.

611 citations

Journal ArticleDOI
TL;DR: In this paper , the authors used three of the largest neuroimaging datasets currently available, with a total sample size of around 50,000 individuals, to quantify brain-wide association studies effect sizes and reproducibility as a function of sample size.
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.

520 citations

Journal Article
TL;DR: Clark, Beck, and Alford as mentioned in this paper provide a comprehensive review of the literature pertaining to the key hypotheses of the cognitive model of depression and provide a valuable source companion to the classic but outdated treatment manual originally published in 1979 by Dr. Beck and colleagues (Cognitive Therapy of Depression) and the excellent how-to book, Cognitive Therapy: Basics and Beyond, which was published in 1995.
Abstract: Although there are dozens of books on cognitive therapy of depression, a majority are edited volumes and relatively few are distinguished by the comprehensive mastery of the material and clarity of exposition apparent in this book by Clark, Beck, and Alford. This volume offers a relatively up-to-date (circa 1999) and scholarly review of the phenomenology of depressive disorders from the cognitive perspective, along with detailed evaluations of the literature pertaining to the key hypotheses of the cognitive model of depression. The book is well written, but it is not for the cognitive therapy neophyte. It is rather lengthy and detailed. Moreover, as might be expected, the discussions of criticisms of the cognitive model are somewhat partisan, and the authors consistently present the cognitive model as dynamic and organic (as opposed to static) in response to new and at times contradictory data. Nevertheless, it provides a valuable source companion to the classic but outdated treatment manual originally published in 1979 by Dr. Beck and colleagues (Cognitive Therapy of Depression) and the excellent how-to book by Dr. Judith Beck, Cognitive Therapy: Basics and Beyond, which was published in 1995. Subheads, periodic summaries, and statements of key points within each chapter focus the reader's attention and enhance comprehension; the authors are, after all, expert cognitive therapists. There is a minimum of redundancy across the 11 chapters, and although the copyediting is not infallible (e.g., influential early behaviorist Charles Ferster is referred to as “Fester” in both the text and the reference list), typographical errors are few. As a treatment researcher, I was disappointed that the authors did not devote at least one chapter to reviewing the comparative outcome research studies of CT. Outcomes data has been one of the key aspects of the scientific foundation of CT for nearly 25 years.1 This is a shortcoming, particularly in view of work linking early evidence of CT's superiority (over other therapies) to strong allegiance effects2 and the increasing number of studies in which CT has not performed so well under more “neutral”3,4 or even potentially “allegiance-disadvantaged”5–7 conditions. Ultimately, the most pragmatic benefit of an elegant, scientifically strong model of psychopathology is the ability to translate such knowledge into greater or more enduring benefits for our patients. In this regard, it is not yet clear that the elaborate suprastructure of schema theory actually adds such benefits relative to simpler behavioral5 or interpersonal6 models of intervention.

457 citations

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. Charness15, Michael E. Charness30, 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