Structural and diffusion weighted brain imaging biomarkers for ADHD and its symptomology in very young (4–7-year-old) children

The current study aimed to identify the key neurobiology of Attention-Deficit/Hyperactivity Disorder (ADHD), as it relates to ADHD diagnostic category and symptoms of hyperactive/impulsive behavior and inattention. To do so, we adapted a predictive modeling approach to identify the key structural and diffusion weighted brain imaging measures, and their relative standing with respect to teacher ratings of executive function – EF (measured by the Metacognition Index of the Behavior Rating Inventory of Executive Function– BRIEF), negativity and emotion regulation – ER, (measured by the Emotion Regulation Checklist, ERC), in a critical young age range (ages 4 to 7, mean age 5.52 years, 82.2% Hispanic/Latino), where initial contact with educators and clinicians typically take place. Teacher ratings of EF and ER were predictive of both ADHD diagnostic category and symptoms of hyperactive/impulsive behavior and inattention. Among the neural measures evaluated, the current study identified the critical importance of the largely understudied diffusion weighted imaging measures for the underlying neurobiology of ADHD and its associated symptomology. Specifically, our analyses implicated the inferior frontal gyrus, the pericallosal sulcus, and the caudate as critical predictors of ADHD diagnostic category and its associated symptomology, above and beyond teacher ratings of EF and ER. Collectively, the current set of findings have implications for theories of ADHD, the relative utility of neurobiological measures with respect to teacher ratings of EF and ER, and the developmental trajectory of its underlying neurobiology.

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Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation

10.1101/2020.08.19.257600 ◽

2020 ◽

Author(s):

Ilke Öztekin ◽

Mark A. Finlayson ◽

Paulo A. Graziano ◽

Anthony S. Dick

Keyword(s):

Machine Learning ◽

Executive Function ◽

Young Children ◽

Diagnostic Category ◽

Teacher Ratings ◽

Future Research ◽

Cognitive Measures ◽

Hyperactivity Disorder ◽

Incremental Value ◽

Extensive Evaluation

ABSTRACTGiven the negative trajectories of early behavior problems associated with Attention-Deficit/Hyperactivity Disorder (ADHD), early diagnosis of ADHD is considered critical to enable early intervention and treatment. To this end, the current investigation employed machine learning to evaluate the relative predictive value of parent/teacher ratings, as well as behavioral and neural measures of executive function in predicting ADHD diagnostic category in a sample consisting of 162 young children (53.7% ADHD, ages 4 to 7, mean age 5.55, 67.9% male, 82.6% Hispanic/Latino). Among all the target measures assessed in the study, teacher ratings of executive function were identified as by far the most important measure in predicting ADHD diagnostic category. While a more extensive evaluation of neural measures, such as diffusion-weighted imaging, may provide more information as they relate to the underlying cognitive deficits associated with ADHD, the current study indicates that commonly used structural imaging measures of cortical thickness, as well as widely used cognitive measures of executive function, have little incremental value in differentiating typically developing children from those diagnosed with ADHD. Future research evaluating the importance of such measures in predicting children’s functional impairment in academic and social areas would provide additional insight into their contributing role in ADHD.

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Longitudinal Structural and Diffusion Weighted Neuroimaging of Young Children Born Preterm

10.31234/osf.io/r87dx ◽

2021 ◽

Author(s):

Julia Anna Adrian ◽

Carolyn Sawyer ◽

Roger Bakeman ◽

Frank Haist ◽

Natacha Akshoomoff

Keyword(s):

Preterm Birth ◽

Brain Injury ◽

White Matter ◽

Young Children ◽

Brain Development ◽

Mean Diffusivity ◽

Full Term ◽

Developmental Trajectory ◽

Diffusion Weighted ◽

And Diffusion

Children born preterm are at risk for diffuse injury to subcortical gray and white matter. This study’s objective was to examine structural brain development of young children born preterm. Participants were 47 children born preterm (less than 33 weeks gestational age) and 28 children born full-term. None of the children born preterm had significant brain injury. Children received structural and diffusion weighted MRI scans at 5, 6, and 7 years of age. The effect of preterm birth on volume of subcortical gray matter, and volume, fractional anisotropy (FA) and mean diffusivity (MD) of white matter tracts was examined via general linear models. Volumes of thalamus, brain stem, cerebellar white matter, and several cerebral fiber tracts were smaller, and ventricles were larger in children born preterm compared to full-term controls. We found no significant effect of preterm birth on diffusivity measures. Despite developmental changes and growth, group differences were present and similarly strong at all three ages. Even in the absence of significant neonatal brain injury, preterm birth has a persistent impact on early brain development. The lack of a significant birth status by age interaction suggests a delayed developmental trajectory.

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Hyperacute Middle Cerebral Artery Territory Infarction: Comparison of Unenhanced CT, Spin-Echo T2-weighted,Fast FLAIR, and Diffusion-weighted MR Imaging

Journal of the Korean Radiological Society ◽

10.3348/jkrs.1999.41.1.1 ◽

1999 ◽

Vol 41 (1) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Dae Seob Choi ◽

Dong Gyu Na ◽

Hong Sik Byun ◽

Kwang Ho Lee ◽

Chin Sang Chung ◽

...

Keyword(s):

Mr Imaging ◽

Middle Cerebral Artery ◽

Cerebral Artery ◽

Spin Echo ◽

Middle Cerebral Artery Territory ◽

Unenhanced Ct ◽

Diffusion Weighted ◽

And Diffusion

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Brain Imaging Biomarkers of Pathological Brain Aging in Late-life Depression

Case Medical Research ◽

10.31525/ct1-nct03849417 ◽

2019 ◽

Author(s):

Keyword(s):

Brain Imaging ◽

Brain Aging ◽

Late Life ◽

Imaging Biomarkers ◽

Late Life Depression

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Neurobiological Mechanisms of Psychopathology and Treatment Action

10.1093/oxfordhb/9780190634841.013.54 ◽

2018 ◽

Author(s):

Theodore P. Beauchaine ◽

Aimee R. Zisner ◽

Elizabeth P. Hayden

Keyword(s):

Emotion Regulation ◽

Executive Function ◽

Treatment Response ◽

Trait Anxiety ◽

Environmental Influence ◽

Externalizing Disorders ◽

Psychosocial Treatment ◽

Childhood And Adolescence ◽

Trait Impulsivity ◽

Heterotypic Continuity

In recent years, it has become increasingly clear that common forms of psychopathology derive from complex interactions among neurobiological vulnerabilities and environmental adversities. These interactions can alter neurobehavioral development to yield progressively intractable forms of psychopathology across childhood and adolescence. This chapter focuses on neurobiological mechanisms of trait impulsivity, trait anxiety, stress reactivity, and emotion regulation/executive function. How these traits confer vulnerability to externalizing disorders, internalizing disorders, heterotypic comorbidity, and heterotypic continuity is described. Next, neurobiological mechanisms of treatment response are considered. Trait impulsivity and trait anxiety are highly heritable and derive initially from subcortical structures that mature early in life. In contrast, emotion regulation and executive function, which modulate trait impulsivity and trait anxiety, are more sensitive to environmental influence and derive from cortical structures that mature into young adulthood. Neurobiological mechanisms of psychosocial treatment response are represented largely in the cortex and its neuromodulatory connections with the subcortex.

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The combination of baseline magnetic resonance perfusion-weighted imaging-derived tissue volume with severely prolonged arterial-tissue delay and diffusion-weighted imaging lesion volume is predictive of MCA-M1 recanalization in patients treated with endovascular thrombectomy

Neuroradiology ◽

10.1007/s00234-013-1310-2 ◽

2013 ◽

Vol 56 (2) ◽

pp. 117-127 ◽

Cited By ~ 6

Author(s):

F. Nicoli ◽

◽

F. Scalzo ◽

J. L. Saver ◽

F. Pautot ◽

...

Keyword(s):

Magnetic Resonance ◽

Diffusion Weighted Imaging ◽

Lesion Volume ◽

Endovascular Thrombectomy ◽

Magnetic Resonance Perfusion ◽

Tissue Volume ◽

Perfusion Weighted Imaging ◽

Diffusion Weighted ◽

Arterial Tissue ◽

And Diffusion

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The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas

Neurodegenerative Diseases ◽

10.1159/000512545 ◽

2021 ◽

pp. 1-8

Author(s):

Haimei Cao ◽

Xiang Xiao ◽

Jun Hua ◽

Guanglong Huang ◽

Wenle He ◽

...

Keyword(s):

Diagnostic Accuracy ◽

Roc Curve ◽

Diffusion Weighted Imaging ◽

Low Grade ◽

High Grade ◽

Mr Images ◽

Vascular Space ◽

Diffusion Weighted ◽

High Grade Gliomas ◽

And Diffusion

Objectives: The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas. Methods: Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters. Results: Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10−3 mm2/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92. Conclusion: The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

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