26 results on '"D. Rosenberg"'
Search Results
2. Connectome-based models predict attentional control in aging adults.
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Stephanie Fountain-Zaragoza, Shaadee Samimy, Monica D. Rosenberg, and Ruchika Shaurya Prakash
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- 2019
- Full Text
- View/download PDF
3. Ten simple rules for predictive modeling of individual differences in neuroimaging.
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Dustin Scheinost, Stephanie Noble, Corey Horien, Abigail S. Greene, Evelyn M. R. Lake, Mehraveh Salehi, Siyuan Gao, Xilin Shen, David O'Connor, Daniel S. Barron, Sarah W. Yip, Monica D. Rosenberg, and R. Todd Constable
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- 2019
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- View/download PDF
4. Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors.
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Kwangsun Yoo, Monica D. Rosenberg, Stephanie Noble, Dustin Scheinost, R. Todd Constable, and Marvin M. Chun
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- 2019
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- View/download PDF
5. An open-access accelerated adult equivalent of the ABCD Study neuroimaging dataset (a-ABCD).
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Kristina M. Rapuano, May I. Conley, Anthony C. Juliano, Gregory M. Conan, Maria T. Maza, Kylie Woodman, Steven A. Martinez, Eric A. Earl, Anders Perrone, Eric Feczko, Damien A. Fair, Richard Watts, B. J. Casey, and Monica D. Rosenberg
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- 2022
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6. A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome.
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Kwangsun Yoo, Monica D. Rosenberg, Young Hye Kwon, Dustin Scheinost, R. Todd Constable, and Marvin M. Chun
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- 2022
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- View/download PDF
7. Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets.
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Kwangsun Yoo, Monica D. Rosenberg, Wei-Ting Hsu, Sheng Zhang 0005, Chiang-shan Ray Li, Dustin Scheinost, R. Todd Constable, and Marvin M. Chun
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- 2018
- Full Text
- View/download PDF
8. A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task.
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David C. Jangraw, Javier Gonzalez-Castillo, Daniel A. Handwerker, Merage Ghane, Monica D. Rosenberg, Puja Panwar, and Peter A. Bandettini
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- 2018
- Full Text
- View/download PDF
9. Connectome-based neurofeedback: A pilot study to improve sustained attention
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Dustin Scheinost, Tiffany W. Hsu, Emily W. Avery, Michelle Hampson, R. Todd Constable, Marvin M. Chun, and Monica D. Rosenberg
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Real-time fMRI ,Neurofeedback ,Connectome-based predictive modeling ,Functional connectivity ,Attention ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is a non-invasive, non-pharmacological therapeutic tool that may be useful for training behavior and alleviating clinical symptoms. Although previous work has used rt-fMRI to target brain activity in or functional connectivity between a small number of brain regions, there is growing evidence that symptoms and behavior emerge from interactions between a number of distinct brain areas. Here, we propose a new method for rt-fMRI, connectome-based neurofeedback, in which intermittent feedback is based on the strength of complex functional networks spanning hundreds of regions and thousands of functional connections. We first demonstrate the technical feasibility of calculating whole-brain functional connectivity in real-time and provide resources for implementing connectome-based neurofeedback. We next show that this approach can be used to provide accurate feedback about the strength of a previously defined connectome-based model of sustained attention, the saCPM, during task performance. Although, in our initial pilot sample, neurofeedback based on saCPM strength did not improve performance on out-of-scanner attention tasks, future work characterizing effects of network target, training duration, and amount of feedback on the efficacy of rt-fMRI can inform experimental or clinical trial designs.
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- 2020
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10. Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes.
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Emily S. Finn and Monica D. Rosenberg
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- 2021
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11. Functional connectivity patterns predict naturalistic viewing versus rest across development.
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Sara Sanchez-Alonso, Monica D. Rosenberg, and Richard N. Aslin
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- 2021
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12. Relationships between depressive symptoms and brain responses during emotional movie viewing emerge in adolescence.
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David C. Gruskin, Monica D. Rosenberg, and Avram J. Holmes
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- 2020
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13. Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention?
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Esther X. W. Wu, Gwenisha J. Liaw, Rui Zhe Goh, Tiffany T. Y. Chia, Alisia M. J. Chee, Takashi Obana, Monica D. Rosenberg, B. T. Thomas Yeo, and Christopher L. Asplund
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- 2020
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14. Predicting moment-to-moment attentional state.
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Monica D. Rosenberg, Emily S. Finn, R. Todd Constable, and Marvin M. Chun
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- 2015
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15. An open-access accelerated adult equivalent of the ABCD Study neuroimaging dataset (a-ABCD)
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Kristina M. Rapuano, May I. Conley, Anthony C. Juliano, Gregory M. Conan, Maria T. Maza, Kylie Woodman, Steven A. Martinez, Eric Earl, Anders Perrone, Eric Feczko, Damien A. Fair, Richard Watts, B.J. Casey, and Monica D. Rosenberg
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Adult ,Memory, Short-Term ,Neurology ,Adolescent ,Cognitive Neuroscience ,Brain ,Humans ,Reproducibility of Results ,Neuroimaging ,Magnetic Resonance Imaging - Abstract
As public access to longitudinal developmental datasets like the Adolescent Brain Cognitive Development Study
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- 2021
16. Ten simple rules for predictive modeling of individual differences in neuroimaging
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David H. O’Connor, Stephanie Noble, Corey Horien, Daniel S. Barron, Mehraveh Salehi, Siyuan Gao, Dustin Scheinost, Monica D. Rosenberg, R. Todd Constable, Evelyn M. R. Lake, Xilin Shen, Abigail S. Greene, and Sarah W. Yip
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Computer science ,Cognitive Neuroscience ,Models, Neurological ,Neuroimaging ,Machine learning ,computer.software_genre ,Article ,050105 experimental psychology ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Simple (abstract algebra) ,Connectome ,medicine ,Humans ,0501 psychology and cognitive sciences ,medicine.diagnostic_test ,Artificial neural network ,business.industry ,Functional connectivity ,05 social sciences ,Brain ,Cross-validation ,Classification ,Magnetic Resonance Imaging ,Neurology ,Artificial intelligence ,Functional magnetic resonance imaging ,business ,computer ,Advice (complexity) ,Neural networks ,030217 neurology & neurosurgery - Abstract
Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.
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- 2019
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17. Functional connectivity patterns predict naturalistic viewing versus rest across development
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Monica D. Rosenberg, Richard N. Aslin, and Sara Sanchez-Alonso
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Male ,Naturalistic imaging ,Adolescent ,Databases, Factual ,Rest ,Cognitive Neuroscience ,Motion Pictures ,Development ,050105 experimental psychology ,Motion (physics) ,lcsh:RC321-571 ,Functional networks ,Resting-state ,Young Adult ,03 medical and health sciences ,Functional connectivity ,0302 clinical medicine ,Humans ,0501 psychology and cognitive sciences ,Child ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,Movie-watching ,Rest (physics) ,Resting state fMRI ,Brain state ,05 social sciences ,Brain ,Cognition ,Task engagement ,Magnetic Resonance Imaging ,Variation (linguistics) ,Neurology ,Visual Perception ,Female ,Nerve Net ,Psychology ,Photic Stimulation ,030217 neurology & neurosurgery ,Forecasting ,Cognitive psychology - Abstract
Cognitive states, such as rest and task engagement, share an 'intrinsic' functional network organization that is subject to minimal variation over time and yields stable signatures within an individual. Importantly, there are also transient state-specific functional connectivity (FC) patterns that vary across neural states. Here, we examine functional brain organization differences that underlie distinct states in a cross-sectional developmental sample. We compare FC fMRI data acquired during naturalistic viewing (i.e., movie-watching) and resting-state paradigms in a large cohort of 157 children and young adults aged 6-20. Naturalistic paradigms are commonly implemented in pediatric research because they maintain the child's attention and contribute to reduced head motion. It remains unknown, however, to what extent the brain-wide functional network organization is comparable during movie-watching and rest across development. Here, we identify a widespread FC pattern that predicts whether individuals are watching a movie or resting. Specifically, we develop a model for prediction of multilevel neural effects (termed PrimeNet), which can with high reliability distinguish between movie-watching and rest irrespective of age and that generalizes across movies. In turn, we characterize FC patterns in the most predictive functional networks for movie-watching versus rest and show that these patterns can indeed vary as a function of development. Collectively, these effects highlight a 'core' FC pattern that is robustly associated with naturalistic viewing, which also exhibits change across age. These results, focused here on naturalistic viewing, provide a roadmap for quantifying state-specific functional neural organization across development, which may reveal key variation in neurodevelopmental trajectories associated with behavioral phenotypes.
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- 2021
18. Connectome-based neurofeedback: A pilot study to improve sustained attention
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Marvin M. Chun, Dustin Scheinost, Tiffany W. Hsu, Michelle Hampson, R. Todd Constable, Monica D. Rosenberg, and Emily W. Avery
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Adult ,Male ,Brain activity and meditation ,Computer science ,Cognitive Neuroscience ,Pilot Projects ,Machine learning ,computer.software_genre ,050105 experimental psychology ,Article ,Task (project management) ,lcsh:RC321-571 ,03 medical and health sciences ,Functional connectivity ,0302 clinical medicine ,medicine ,Connectome ,Humans ,0501 psychology and cognitive sciences ,Attention ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,medicine.diagnostic_test ,business.industry ,05 social sciences ,Brain ,Neurofeedback ,Magnetic Resonance Imaging ,Neurology ,Connectome-based predictive modeling ,Real-time fMRI ,Female ,Artificial intelligence ,business ,Functional magnetic resonance imaging ,computer ,030217 neurology & neurosurgery - Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is a non-invasive, non-pharmacological therapeutic tool that may be useful for training behavior and alleviating clinical symptoms. Although previous work has used rt-fMRI to target brain activity in or functional connectivity between a small number of brain regions, there is growing evidence that symptoms and behavior emerge from interactions between a number of distinct brain areas. Here, we propose a new method for rt-fMRI, connectome-based neurofeedback, in which intermittent feedback is based on the strength of complex functional networks spanning hundreds of regions and thousands of functional connections. We first demonstrate the technical feasibility of calculating whole-brain functional connectivity in real-time and provide resources for implementing connectome-based neurofeedback. We next show that this approach can be used to provide accurate feedback about the strength of a previously defined connectome-based model of sustained attention, the saCPM, during task performance. Although, in our initial pilot sample, neurofeedback based on saCPM strength did not improve performance on out-of-scanner attention tasks, future work characterizing effects of network target, training duration, and amount of feedback on the efficacy of rt-fMRI can inform experimental or clinical trial designs.
- Published
- 2020
19. Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention?
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Monica D. Rosenberg, Esther X.W. Wu, B.T. Thomas Yeo, Rui Zhe Goh, Tiffany T.Y. Chia, Alisia M.J. Chee, Gwenisha J. Liaw, Takashi Obana, and Christopher L. Asplund
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Adult ,Male ,Elementary cognitive task ,Cognitive Neuroscience ,Poison control ,Neuropsychological Tests ,Connection-based predictive modeling ,050105 experimental psychology ,Task (project management) ,lcsh:RC321-571 ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Salience (neuroscience) ,Connectome ,Humans ,Relevance (information retrieval) ,Attention ,0501 psychology and cognitive sciences ,Attentional blink ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,Default mode network ,Cerebral Cortex ,Recall ,Functional architecture ,Functional connectivity ,05 social sciences ,Cognition ,Magnetic Resonance Imaging ,Sustained attention ,Pattern Recognition, Visual ,Neurology ,Auditory Perception ,Female ,Selective attention ,Nerve Net ,Diffuse attention ,Psychology ,Biomarkers ,Psychomotor Performance ,030217 neurology & neurosurgery ,Cognitive psychology - Abstract
Attention is a critical cognitive function, allowing humans to select, enhance, and sustain focus on information of behavioral relevance. Attention contains dissociable neural and psychological components. Nevertheless, some brain networks support multiple attentional functions. Connectome-based Predictive Models (CPM), which associate individual differences in task performance with functional connectivity patterns, provide a compelling example. A sustained attention network model (saCPM) successfully predicted performance for selective attention, inhibitory control, and reading recall tasks. Here we constructed a visual attentional blink (VAB) model (vabCPM), comparing its performance predictions and network edges associated with successful and unsuccessful behavior to the saCPM’s. In the VAB, attention devoted to a target often causes a subsequent item to be missed. Although frequently attributed to attentional limitations, VAB deficits may attenuate when participants are distracted or deploy attention diffusely. Participants (n=73; 24 males) underwent fMRI while performing the VAB task and while resting. Outside the scanner, they completed other cognitive tasks over several days. A vabCPM constructed from these data successfully predicted VAB performance. Strikingly, the network edges that predicted better VAB performance (positive edges) predicted worse selective and sustained attention performance, and vice versa. Predictions from the saCPM mirrored these results, with the network’s negative edges predicting better VAB performance. Furthermore, the vabCPM’s positive edges significantly overlapped with the saCPM’s negative edges, and vice versa. We conclude that these partially overlapping networks each have general attentional functions. They may indicate an individual’s propensity to diffusely deploy attention, predicting better performance for some tasks and worse for others.Significance statementA longstanding question in psychology and neuroscience is whether we have general capacities or domain-specific ones. For such general capacities, what is the common function? Here we addressed these questions using the attentional blink (AB) task and neuroimaging. Individuals searched for two items in a stream of distracting items; the second item was often missed when it closely followed the first. How often the second item was missed varied across individuals, which was reflected in attention networks. Curiously, the networks’ pattern of function that was good for the AB was bad for other tasks, and vice versa. We propose that these networks may represent not a general attentional ability, but rather the tendency to attend in a less focused manner.
- Published
- 2020
20. A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task
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Peter A. Bandettini, Javier Gonzalez-Castillo, Puja Panwar, Monica D. Rosenberg, David C. Jangraw, Merage Ghane, and Daniel A. Handwerker
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Adult ,Male ,0301 basic medicine ,Cognitive Neuroscience ,media_common.quotation_subject ,Article ,Task (project management) ,Sustaining attention ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Supramarginal gyrus ,Reading (process) ,Generalization (learning) ,Connectome ,medicine ,Humans ,Attention deficit hyperactivity disorder ,Attention ,Set (psychology) ,Eye Movement Measurements ,media_common ,Recall ,Brain ,medicine.disease ,Magnetic Resonance Imaging ,030104 developmental biology ,Reading ,Neurology ,Mental Recall ,Female ,Nerve Net ,Comprehension ,Psychology ,Biomarkers ,030217 neurology & neurosurgery ,Cognitive psychology - Abstract
Sustaining attention to the task at hand is a crucial part of everyday life, from following a lecture at school to maintaining focus while driving. Lapses in sustained attention are frequent and often problematic, with conditions such as attention deficit hyperactivity disorder affecting millions of people worldwide. Recent work has had some success in finding signatures of sustained attention in whole-brain functional connectivity (FC) measures during basic tasks, but since FC can be dynamic and task-dependent, it remains unclear how fully these signatures would generalize to a more complex and naturalistic scenario. To this end, we used a previously defined whole-brain FC network - a marker of attention that was derived from a sustained attention task - to predict the ability of participants to recall material during a free-viewing reading task. Though the predictive network was trained on a different task and set of participants, the strength of FC in the sustained attention network predicted reading recall significantly better than permutation tests where behavior was scrambled to simulate chance performance. To test the generalization of the method used to derive the sustained attention network, we applied the same method to our reading task data to find a new FC network whose strength specifically predicts reading recall. Even though the sustained attention network provided significant prediction of recall, the reading network was more predictive of recall accuracy. The new reading network's spatial distribution indicates that reading recall is highest when temporal pole regions have higher FC with left occipital regions and lower FC with bilateral supramarginal gyrus. Right cerebellar to right frontal connectivity is also indicative of poor reading recall. We examine these and other differences between the two predictive FC networks, providing new insight into the task-dependent nature of FC-based performance metrics.
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- 2018
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21. Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets
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Dustin Scheinost, Chiang-Shan R. Li, R. Todd Constable, Kwangsun Yoo, Monica D. Rosenberg, Sheng Zhang, Wei-Ting Hsu, and Marvin M. Chun
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Adult ,Cognitive Neuroscience ,Datasets as Topic ,Machine learning ,computer.software_genre ,Article ,050105 experimental psychology ,Task (project management) ,Correlation ,Executive Function ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Linear regression ,Partial least squares regression ,Connectome ,Humans ,Attention ,0501 psychology and cognitive sciences ,Models, Statistical ,business.industry ,05 social sciences ,Linear model ,Brain ,Reproducibility of Results ,Regression analysis ,Magnetic Resonance Imaging ,Regression ,Neurology ,Attention Deficit Disorder with Hyperactivity ,Artificial intelligence ,Psychology ,business ,computer ,Psychomotor Performance ,030217 neurology & neurosurgery - Abstract
Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's
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- 2018
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22. Connectome-based models predict attentional control in aging adults
- Author
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Ruchika Shaurya Prakash, Shaadee Samimy, Monica D. Rosenberg, and Stephanie Fountain-Zaragoza
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Adult ,Male ,Aging ,Adolescent ,Cognitive Neuroscience ,Context (language use) ,050105 experimental psychology ,03 medical and health sciences ,Executive Function ,Young Adult ,0302 clinical medicine ,Connectome ,Humans ,0501 psychology and cognitive sciences ,Generalizability theory ,Attention ,Young adult ,Default mode network ,Aged ,Aged, 80 and over ,Functional connectivity ,05 social sciences ,Attentional control ,Brain ,Middle Aged ,Magnetic Resonance Imaging ,Neurology ,Stroop Test ,Female ,Nerve Net ,Psychology ,Neurocognitive ,030217 neurology & neurosurgery ,Cognitive psychology - Abstract
There are well-characterized age-related differences in behavioral and neural responses to tasks of attentional control. However, there is also increasing recognition of individual variability in the process of neurocognitive aging. Using connectome-based predictive modeling, a method for predicting individual-level behaviors from whole-brain functional connectivity, a sustained attention connectome-based prediction model (saCPM) has been derived in young adults. The saCPM consists of two large-scale functional networks: a high-attention network whose strength predicts better attention and a low-attention network whose strength predicts worse attention. Here we examined the generalizability of the saCPM for predicting inhibitory control in an aging sample. Forty-two healthy young adults (n = 21, ages 18–30) and older adults (n = 21, ages 60–80) performed a modified Stroop task, on which older adults exhibited poorer performance, indexed by higher reaction time cost between incongruent and congruent trials. The saCPM generalized to predict reaction time cost across age groups, but did not account for age-related differences in performance. Exploratory analyses were conducted to characterize the effects of age on functional connectivity and behavior. We identified subnetworks of the saCPM that exhibited age-related differences in strength. The strength of two low-attention subnetworks, consisting of frontoparietal, medial frontal, default mode, and motor nodes that were more strongly connected in older adults, mediated the effect of age group on performance. These results support the saCPM's ability to capture attention-related patterns reflected in each individual's functional connectivity signature across both task context and age. However, older and younger adults exhibit functional connectivity differences within components of the saCPM networks, and it is these connections that better account for age-related deficits in attentional control.
- Published
- 2018
23. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study
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Kara Bagot, Finnegan J. Calabro, Julie A. Dumas, Leo P. Sugrue, Christian J. Hopfer, Scott Peltier, Steven Grant, Beatriz Luna, James M. Bjork, Alexandra Potter, Darrick Sturgeon, Adolf Pfefferbaum, Devin Prouty, Florence J. Breslin, Michael C. Riedel, Perry F. Renshaw, Andrew P. Prescot, Aimee Goldstone, Thanh T. Trinh, Oscar Miranda-Dominguez, Hugh Garavan, Susan Y. Bookheimer, Roger Little, Luke W. Hyde, Hermine H. Maes, Michael P. Harms, Christopher J. Pung, Mary E. Soules, Laura Hilmer, David A. Lewis, Kevin M. Gray, Sean N. Hatton, John M. Hettema, Katia D. Howlett, Masha Y. Ivanova, Jonathan R. Polimeni, B. J. Casey, Antonio Noronha, M Deanna, Yi Li, John K. Hewitt, Jay N. Giedd, Deborah A. Yurgelun-Todd, Carolina Makowski, Michael E. Charness, Chandra Sripada, Anthony Steven Dick, Sandra A. Brown, Paul D. Shilling, Fiona C. Baker, Lindsay M. Squeglia, Anders M. Dale, Paul Florsheim, Terry L. Jernigan, Susan R.B. Weiss, Steve Heeringa, Damien A. Fair, Sarah W. Feldstein Ewing, John J. Foxe, Raul Gonzalez, Daniel W. Mruzek, Amanda Sheffield Morris, Joel L. Steinberg, Michael C. Neale, Adriana Galván, Andrew C. Heath, Matthew T. Sutherland, Kevin Patrick, Christine L. Larson, Gayathri J. Dowling, Andrey P. Anokhin, Krista M. Lisdahl, Susan F. Tapert, Kilian M. Pohl, Wesley K. Thompson, Martin P. Paulus, Joshua M. Kuperman, Dana L. Wolff-Hughes, Carlo Pierpaoli, Mirella Dapretto, Rebecca DelCarmen-Wiggins, Donald J. Hagler, Michael J. Mason, Marie T. Banich, Bernard F. Fuemmeler, Naomi P. Friedman, Robert A. Zucker, Linda B. Cottler, M. Daniela Cornejo, Mariana Sanchez, Eric Earl, Andrew S. Nencka, Edward G. Freedman, Christine C. Cloak, Claudiu Schirda, W. Kyle Simmons, Jody Tanabe, Thomas Ernst, Paul E.A. Glaser, Gloria Reeves, M. Alejandra Infante, Elizabeth R. Sowell, Bonnie J. Nagel, Richard Watts, Angela R. Laird, Meyer D. Glantz, Anders Perrone, Jazmin Diaz, Tufikameni Brima, Mary M. Heitzeg, Vani Pariyadath, Rahul S. Desikan, Joseph T. Sakai, Linda Chang, Sara Jo Nixon, Megan M. Herting, Rebekah S. Huber, William G. Iacono, Samuel W. Hawes, Marsha F. Lopez, Monica D. Rosenberg, Arielle R. Baskin-Sommers, Feng Xue, Kevin P. Conway, John A. Matochik, Pamela A. F. Madden, Joanna Jacobus, Duncan B. Clark, Elizabeth Hoffman, Will M. Aklin, Andre van der Kouwe, Ruben P. Alvarez, Kristina A. Uban, Chelsea S. Sicat, Nicholas Allgaier, Erin McGlade, Hauke Bartsch, Octavio Ruiz de Leon, David N. Kennedy, R. Todd Constable, Jerzy Bodurka, John E. Schulenberg, and Monica Luciana
- Subjects
Adolescent ,Cognitive Neuroscience ,Multimodal Imaging ,Article ,050105 experimental psychology ,03 medical and health sciences ,0302 clinical medicine ,Neuroimaging ,Image Processing, Computer-Assisted ,Cognitive development ,medicine ,Humans ,Brain segmentation ,0501 psychology and cognitive sciences ,Brain Mapping ,medicine.diagnostic_test ,05 social sciences ,Brain ,Signal Processing, Computer-Assisted ,Cognition ,Magnetic resonance imaging ,Adolescent Development ,Magnetic Resonance Imaging ,Mental health ,Diffusion Magnetic Resonance Imaging ,Neurology ,Psychology ,030217 neurology & neurosurgery ,Cognitive psychology ,Psychopathology ,Diffusion MRI - 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 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 is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. 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. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.
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- 2019
- Full Text
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24. Predicting moment-to-moment attentional state
- Author
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Emily S. Finn, Monica D. Rosenberg, R. Todd Constable, and Marvin M. Chun
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Adult ,Male ,Support Vector Machine ,Cognitive Neuroscience ,media_common.quotation_subject ,Task (project management) ,Developmental psychology ,Young Adult ,Perception ,Task-positive network ,Image Processing, Computer-Assisted ,Humans ,Attention ,Default mode network ,media_common ,Brain Mapping ,Working memory ,Brain ,Cognition ,Fusiform face area ,Magnetic Resonance Imaging ,Sensory Systems ,Support vector machine ,Ophthalmology ,Neurology ,Pattern Recognition, Visual ,Female ,Psychology ,Facial Recognition ,Coding (social sciences) ,Cognitive psychology - Abstract
Although fluctuations in sustained attention are ubiquitous, most psychological experiments treat them as noise, averaging performance over many trials. The current study uses multi-voxel pattern analysis (MVPA) to decode whether, on each trial of a cognitive task, participants are in an optimal or suboptimal attentional state. During fMRI, participants performed n-back tasks, composed of central face images overlaid on distractor scenes, with low, perceptual, and working memory load. Instructions were to respond to novel faces and withhold response to rare repeats. To index attentional state, reaction time variability was calculated at each correct response. Participants' 50% least variable trials were labeled optimal, or "in the zone," and their 50% most erratic trials were labeled suboptimal, or "out of the zone." Support vector machine classifiers trained on activity in the default mode network (DMN), dorsal attention network (DAN), and task-relevant fusiform face area (FFA) distinguished in-the-zone and out-of-the-zone trials in all tasks. Consistent with evidence that distractors are processed when central task load is low, parahippocampal place area (PPA) classifiers were only successful in the low load task. Classification in anatomical regions across the brain revealed widespread coding of attentional state. In contrast to these robust pattern analyses, univariate signal in DMN, DAN, FFA, and PPA did not distinguish states, suggesting a nuanced relationship to sustained attention. In sum, MVPA can be used to decode trial-by-trial attentional state throughout much of cortex, helping to characterize how attention network fluctuations correlate with performance variability.
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- 2014
25. Synthesizing pseudo-T2w images to recapture missing data in neonatal neuroimaging with applications in rs-fMRI
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Sydney Kaplan, Anders Perrone, Dimitrios Alexopoulos, Jeanette K. Kenley, Deanna M. Barch, Claudia Buss, Jed T. Elison, Alice M. Graham, Jeffrey J. Neil, Thomas G. O'Connor, Jerod M. Rasmussen, Monica D. Rosenberg, Cynthia E. Rogers, Aristeidis Sotiras, Damien A. Fair, and Christopher D. Smyser
- Subjects
Structural MRI ,Synthetic medical images ,Deep learning ,Multi-atlas fusion ,Neuroimaging ,Neonate ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
T1- and T2-weighted (T1w and T2w) images are essential for tissue classification and anatomical localization in Magnetic Resonance Imaging (MRI) analyses. However, these anatomical data can be challenging to acquire in non-sedated neonatal cohorts, which are prone to high amplitude movement and display lower tissue contrast than adults. As a result, one of these modalities may be missing or of such poor quality that they cannot be used for accurate image processing, resulting in subject loss. While recent literature attempts to overcome these issues in adult populations using synthetic imaging approaches, evaluation of the efficacy of these methods in pediatric populations and the impact of these techniques in conventional MR analyses has not been performed. In this work, we present two novel methods to generate pseudo-T2w images: the first is based in deep learning and expands upon previous models to 3D imaging without the requirement of paired data, the second is based in nonlinear multi-atlas registration providing a computationally lightweight alternative. We demonstrate the anatomical accuracy of pseudo-T2w images and their efficacy in existing MR processing pipelines in two independent neonatal cohorts. Critically, we show that implementing these pseudo-T2w methods in resting-state functional MRI analyses produces virtually identical functional connectivity results when compared to those resulting from T2w images, confirming their utility in infant MRI studies for salvaging otherwise lost subject data.
- Published
- 2022
- Full Text
- View/download PDF
26. Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness.
- Author
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Posse S, Fitzgerald D, Gao K, Habel U, Rosenberg D, Moore GJ, and Schneider F
- Subjects
- Adult, Brain Mapping, Brain Stem physiology, Cerebellum physiology, Dominance, Cerebral physiology, Echo-Planar Imaging, Facial Expression, Female, Hippocampus physiology, Humans, Imagination physiology, Male, Nerve Net physiology, Pattern Recognition, Visual physiology, Amygdala physiology, Cerebral Cortex physiology, Emotions physiology, Image Processing, Computer-Assisted, Limbic System physiology, Magnetic Resonance Imaging, Temporal Lobe physiology
- Abstract
Temporolimbic circuits play a crucial role in the regulation of human emotion. A highly sensitive single-shot multiecho functional magnetic resonance imaging (fMRI) technique with gradient compensation of local magnetic field inhomogeneities and real-time data analysis were used to measure increases in amygdala activation during single 60-s trials of self-induced sadness. Six healthy male and female subjects performed a validated mood induction paradigm with randomized presentation of sad or neutral faces in 10 trials per scan. Subjects reported the intensity of experienced sadness after each trial. Immediate feedback of amygdala activation was given to the subjects during the ongoing scan to reinforce mood induction. Correspondence between increased intensity of predominantly left sided amygdala activation and self-rating of sadness was found in 78% of 120 sad trials, in contrast to only 14% of neutral trials. Amygdala activation was reproducible during repeated scanning sessions and displayed the strongest correlation with self-rating among all regions. These results suggest that amygdala activation may be closely associated with self-induced sadness. This novel real-time fMRI technology is applicable to a wide range of neuroscience studies, particularly those of the limbic system, and to neuropsychiatric conditions, such as depression, in which pathology of the amygdala has been implicated.
- Published
- 2003
- Full Text
- View/download PDF
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