High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points

Almeida, Jorge R. C.

Antal, Andrea

Arnold, Eugene L.

Axelson, David

Bebko, Genna

Bertocci, Michele A.

Birmaher, Boris

Bonar, Lisa

Ciuffetelli, Gary

Demeter, Christine

Diwadkar, Vaibhav A.

Findling, Robert L.

Forbes, Erika E.

Fristad, Mary A.

Gill, Mary Kay

Hinze, Amanda K.

Holland, Scott K.

Horwitz, Sarah M.

Kowatch, Robert A.

Mourao-Miranda, Janaina

Oliveira, Leticia

Pereira, Mirtes

Perlman, Susan B.

Phillips, Mary Louise

Portugal, Liana C. L.

Rao, Anil

Rodriguez, Eric

Rosa, Maria João

Schirda, Claudiu

Sunshine, Jeffrey L.

Travis, Michael

Versace, Amelia

Youngstrom, Eric A.

PLoS ONE

English

PubMed

Introduction\ud
\ud
High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points.\ud
\ud
Methods\ud
\ud
A sample of fifty-seven youth (mean age: 14.5 years; 32 males) was selected from a multi-site study of youth with parent-reported behavioral and emotional dysregulation. Participants performed a block-design reward paradigm during functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Relevance Vector Regression (RVR) and two cross-validation strategies implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Medication was treated as a binary confounding variable. Decoded and actual clinical scores were compared using Pearson’s correlation coefficient (r) and mean squared error (MSE) to evaluate the models. Permutation test was applied to estimate significance levels.\ud
\ud
Results\ud
\ud
Relevance Vector Regression identified patterns of neural activity associated with symptoms of behavioral and emotional dysregulation at the initial study screen and close to the fMRI scanning session. The correlation and the mean squared error between actual and decoded symptoms were significant at the initial study screen and close to the fMRI scanning session. However, after controlling for potential medication effects, results remained significant only for decoding symptoms at the initial study screen. Neural regions with the highest contribution to the pattern regression model included cerebellum, sensory-motor and fronto-limbic areas.\ud
\ud
Conclusions\ud
\ud
The combination of pattern regression models and neuroimaging can help to determine the severity of behavioral and emotional dysregulation in youth at different time points

Schirda, Claudiu V

Diwadkar, Vaibhav

Phillips, Mary L.

Name not available

Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging?

Portugal, Liana C.L.

Carolina Digital Repository

<div>IntroductionHigh comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points.MethodsA sample of fifty-seven youth (mean age: 14.5 years; 32 males) was selected from a multi-site study of youth with parent-reported behavioral and emotional dysregulation. Participants performed a block-design reward paradigm during functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Relevance Vector Regression (RVR) and two cross-validation strategies implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Medication was treated as a binary confounding variable. Decoded and actual clinical scores were compared using Pearson’s correlation coefficient (r) and mean squared error (MSE) to evaluate the models. Permutation test was applied to estimate significance levels.ResultsRelevance Vector Regression identified patterns of neural activity associated with symptoms of behavioral and emotional dysregulation at the initial study screen and close to the fMRI scanning session. The correlation and the mean squared error between actual and decoded symptoms were significant at the initial study screen and close to the fMRI scanning session. However, after controlling for potential medication effects, results remained significant only for decoding symptoms at the initial study screen. Neural regions with the highest contribution to the pattern regression model included cerebellum, sensory-motor and fronto-limbic areas.ConclusionsThe combination of pattern regression models and neuroimaging can help to determine the severity of behavioral and emotional dysregulation in youth at different time points.</div

Liana C. L. Portugal (843648)

Maria João Rosa (5666395)

Anil Rao (843649)

Genna Bebko (843650)

Michele A. Bertocci (843651)

Amanda K. Hinze (843652)

Lisa Bonar (843653)

Jorge R. C. Almeida (843654)

Susan B. Perlman (262906)

Amelia Versace (843655)

Claudiu Schirda (843656)

Michael Travis (843657)

Mary Kay Gill (843658)

Christine Demeter (843659)

Vaibhav A. Diwadkar (843660)

Gary Ciuffetelli (843661)

Eric Rodriguez (843662)

Erika E. Forbes (328017)

Jeffrey L. Sunshine (843663)

Scott K. Holland (843664)

Robert A. Kowatch (843665)

Boris Birmaher (183189)

David Axelson (183195)

Sarah M. Horwitz (843666)

Eugene L. Arnold (843667)

Mary A. Fristad (843668)

Eric A. Youngstrom (843669)

Robert L. Findling (843670)

Mirtes Pereira (843671)

Leticia Oliveira (183167)

Mary L. Phillips (183203)

Janaina Mourao-Miranda (5654344)

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Can emotional and behavioral dysregulation in youth be decoded from functional neuroimaging?

IntroductionHigh comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points.MethodsA sample of fifty-seven youth (mean age: 14.5 years; 32 males) was selected from a multi-site study of youth with parent-reported behavioral and emotional dysregulation. Participants performed a block-design reward paradigm during functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Relevance Vector Regression (RVR) and two cross-validation strategies implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Medication was treated as a binary confounding variable. Decoded and actual clinical scores were compared using Pearson's correlation coefficient (r) and mean squared error (MSE) to evaluate the models. Permutation test was applied to estimate significance levels.ResultsRelevance Vector Regression identified patterns of neural activity associated with symptoms of behavioral and emotional dysregulation at the initial study screen and close to the fMRI scanning session. The correlation and the mean squared error between actual and decoded symptoms were significant at the initial study screen and close to the fMRI scanning session. However, after controlling for potential medication effects, results remained significant only for decoding symptoms at the initial study screen. Neural regions with the highest contribution to the pattern regression model included cerebellum, sensory-motor and fronto-limbic areas.ConclusionsThe combination of pattern regression models and neuroimaging can help to determine the severity of behavioral and emotional dysregulation in youth at different time points

Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging?

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