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    More articles from FUNCTIONAL | American Journal of Neuroradiology  
    
















    




















    
  
  




    
    

  

      


    

      

  
  
  

    

  

    

  

                
    

      

  

    

  
      
  
  

    


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    EDITOR'S CHOICEPediatrics 
    
  
      
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      Characterizing White Matter Tract Organization in Polymicrogyria and Lissencephaly: A Multifiber Diffusion MRI Modeling and Tractography Study  
    	
    F. Arrigoni, D. Peruzzo, S. Mandelstam, G. Amorosino, D. Redaelli, R. Romaniello, R. Leventer, R. Borgatti, M. Seal and J.Y.-M. Yang
    
  
    	
    American Journal of Neuroradiology August 2020, 41 (8) 1495-1502; DOI: https://doi.org/10.3174/ajnr.A6646 
    
  
    	
    The authors retrospectively reviewed 50 patients (mean age, 8.3 years) with different polymicrogyria (n = 42) and lissencephaly (n = 8) subtypes. The fiber direction-encoded color maps and 6 different white matter tracts reconstructed from each patient were visually compared with corresponding images reconstructed from 7 age-matched, healthy control WM templates. The authors demonstrated a range of white matter tract structural abnormalities in patients with polymicrogyria and lissencephaly. The patterns of white matter tract involvement are related to polymicrogyria and lissencephaly subgroups, distribution, and, possibly, their underlying etiologies.
    
  
  

    

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      Characterizing the Subcortical Structures in Youth with Congenital Heart Disease  
    	
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    American Journal of Neuroradiology August 2020, 41 (8) 1503-1508; DOI: https://doi.org/10.3174/ajnr.A6667 
    
  
  
  

    

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      Lower Lactate Levels and Lower Intracellular pH in Patients with IDH-Mutant versus Wild-Type Gliomas  
    	
    K.J. Wenger, J.P. Steinbach, O. Bähr, U. Pilatus and E. Hattingen
    
  
    	
    American Journal of Neuroradiology August 2020, 41 (8) 1414-1422; DOI: https://doi.org/10.3174/ajnr.A6633 
    
  
  
  

    

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    EDITOR'S CHOICEAdult Brain 
    
  
      
    
  
      Black Dipole or White Dipole: Using Susceptibility Phase Imaging to Differentiate Cerebral Microbleeds from Intracranial Calcifications  
    	
    C.-L. Weng, Y. Jeng, Y.-T. Li, C.-J. Chen and D.Y.-T. Chen
    
  
    	
    American Journal of Neuroradiology August 2020, 41 (8) 1405-1413; DOI: https://doi.org/10.3174/ajnr.A6636 
    
  
    	
    The authors evaluated the diagnostic accuracy of differentiating cerebral microbleeds and calcifications from phase patterns in axial locations in 31 consecutive patients undergoing both CT and MR imaging for acute infarction and exhibiting dark spots in gradient-echo magnitude images. Six patients had additional quantitative susceptibility mapping images. To determine their susceptibility, 2 radiologists separately investigated the phase patterns in the border and central sections. Among 190 gradient-echo dark spots, 62 calcifications and 128 cerebral microbleeds were detected from CT. Interobserver reliability was higher for the border phase patterns than for the central phase patterns. The sensitivity and specificity of the border phase patterns in identifying calcifications were higher than those of the central phase patterns, particularly for lesions >2.5 mm in diameter and quantitative susceptibility mapping of dark spots. They conclude that the border phase patterns were more accurate than the central phase patterns in differentiating calcifications and cerebral microbleeds and were as accurate as quantitative susceptibility mapping.
    
  
  

    

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    Adult Brain 
    
  
      
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      Cerebral Microbleeds Are Associated with Loss of White Matter Integrity  
    	
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      In Vivo Evaluation of White Matter Abnormalities in Children with Duchenne Muscular Dystrophy Using DTI  
    	
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    EDITOR'S CHOICEPediatrics 
    
  
      
    Open Access
    
  
      Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging  
    	
    H. Zhou, R. Hu, O. Tang, C. Hu, L. Tang, K. Chang, Q. Shen, J. Wu, B. Zou, B. Xiao, J. Boxerman, W. Chen, R.Y. Huang, L. Yang, H.X. Bai and C. Zhu
    
  
    	
    American Journal of Neuroradiology July 2020, 41 (7) 1279-1285; DOI: https://doi.org/10.3174/ajnr.A6621 
    
  
    	
    This retrospective study included preoperative MR imaging of 288 pediatric patients with pediatric posterior fossa tumors, including medulloblastoma (n=111), ependymoma (n=70), and pilocytic astrocytoma (n=107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation. The authors conclude that automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.
    
  
  

    

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    Pediatrics 
    
  
      
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      Brain Cortical Structure and Executive Function in Children May Be Influenced by Parental Choices of Infant Diets  
    	
    T. Li, T.M. Badger, B.J. Bellando, S.T. Sorensen, X. Lou and X. Ou
    
  
    	
    American Journal of Neuroradiology July 2020, 41 (7) 1302-1308; DOI: https://doi.org/10.3174/ajnr.A6601 
    
  
  
  

    

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    Pediatrics 
    
  
      
    Open Access
    
  
      Changes of Neurotransmitters in Youth with Internet and Smartphone Addiction: A Comparison with Healthy Controls and Changes after Cognitive Behavioral Therapy  
    	
    H.S. Seo, E.-K. Jeong, S. Choi, Y. Kwon, H.-J. Park and I. Kim
    
  
    	
    American Journal of Neuroradiology July 2020, 41 (7) 1293-1301; DOI: https://doi.org/10.3174/ajnr.A6632 
    
  
  
  

    

  • 
  
  
      
    EDITOR'S CHOICEFunctional 
    
  
      
    Open Access
    
  
      Resting-State Brain Activity for Early Prediction Outcome in Postanoxic Patients in a Coma with Indeterminate Clinical Prognosis  
    	
    D. Pugin, J. Hofmeister, Y. Gasche, S. Vulliemoz, K.-O. Lövblad, D. Van De Ville and S. Haller
    
  
    	
    American Journal of Neuroradiology June 2020, 41 (6) 1022-1030; DOI: https://doi.org/10.3174/ajnr.A6572 
    
  
    	
    The authors used resting-state fMRI in a prospective study to compare whole-brain functional connectivity between patients with good and poor outcomes, implementing support vector machine learning. They automatically predicted coma outcome using resting-state fMRI and also compared the prediction based on resting-state fMRI with the outcome prediction based on DWI. Of 17 eligible patients who completed the study procedure (among 351 patients screened), 9 regained consciousness and 8 remained comatose. They found higher functional connectivity in patients recovering consciousness, with greater changes occurring within and between the occipitoparietal and temporofrontal regions. Coma outcome prognostication based on resting-state fMRI machine learning was very accurate, notably for identifying patients with good outcome. They conclude that resting-state fMRI might bridge the gap left in early prognostication of postanoxic patients in a coma by identifying those with both good and poor outcomes.
    
  
  

    

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