Madhura Ingalhalikar1, William A Parker2, Luke Bloy3, Timothy P L Roberts4, Ragini Verma5. 1. Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: Madhura.Ingalhalikar@uphs.upenn.edu. 2. Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: William.Parker@uphs.upenn.edu. 3. Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA. Electronic address: Luke.Bloy@uphs.upenn.edu. 4. Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA. Electronic address: robertstim@email.chop.edu. 5. Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: Ragini.Verma@uphs.upenn.edu.
Abstract
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by wide range of symptoms and severity including domains such as language impairment (LI). This study aims to create a quantifiable marker of ASD and a stratification marker for LI using multimodality imaging data that can handle missing data by including subjects that fail to complete all the aspects of a multimodality imaging study, obviating the need to remove subjects with incomplete data, as is done by conventional methods. METHODS: An ensemble of classifiers with several subsets of complete data is employed. The outputs from such subset classifiers are fused using a weighted aggregation giving an aggregate probabilistic score for each subject. Such fusion classifiers are created to obtain a marker for ASD and to stratify LI using three categories of features, two extracted from separate auditory tasks using magnetoencephalography (MEG) and the third extracted from diffusion tensor imaging (DTI). RESULTS: A clear distinction between ASD and neurotypical controls (5-fold accuracy of 83.3% and testing accuracy of 87%) and between ASD/+LI and ASD/-LI (5-fold accuracy of 70.1% and testing accuracy of 61.1%) was obtained. One of the MEG features, mismatch field (MMF) latency contributed the most to group discrimination, followed by DTI features from superior temporal white matter and superior longitudinal fasciculus as determined by feature ranking. COMPARISON WITH EXISTING METHODS: Higher classification accuracy was achieved in comparison with single modality classifiers. CONCLUSION: This methodology can be readily applied in large studies where high percentage of missing data is expected.
BACKGROUND:Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by wide range of symptoms and severity including domains such as language impairment (LI). This study aims to create a quantifiable marker of ASD and a stratification marker for LI using multimodality imaging data that can handle missing data by including subjects that fail to complete all the aspects of a multimodality imaging study, obviating the need to remove subjects with incomplete data, as is done by conventional methods. METHODS: An ensemble of classifiers with several subsets of complete data is employed. The outputs from such subset classifiers are fused using a weighted aggregation giving an aggregate probabilistic score for each subject. Such fusion classifiers are created to obtain a marker for ASD and to stratify LI using three categories of features, two extracted from separate auditory tasks using magnetoencephalography (MEG) and the third extracted from diffusion tensor imaging (DTI). RESULTS: A clear distinction between ASD and neurotypical controls (5-fold accuracy of 83.3% and testing accuracy of 87%) and between ASD/+LI and ASD/-LI (5-fold accuracy of 70.1% and testing accuracy of 61.1%) was obtained. One of the MEG features, mismatch field (MMF) latency contributed the most to group discrimination, followed by DTI features from superior temporal white matter and superior longitudinal fasciculus as determined by feature ranking. COMPARISON WITH EXISTING METHODS: Higher classification accuracy was achieved in comparison with single modality classifiers. CONCLUSION: This methodology can be readily applied in large studies where high percentage of missing data is expected.
Authors: Thomas W Frazier; Eric W Klingemier; Sumit Parikh; Leslie Speer; Mark S Strauss; Charis Eng; Antonio Y Hardan; Eric A Youngstrom Journal: J Am Acad Child Adolesc Psychiatry Date: 2018-09-13 Impact factor: 8.829
Authors: Mustafa S Cetin; Jon M Houck; Barnaly Rashid; Oktay Agacoglu; Julia M Stephen; Jing Sui; Jose Canive; Andy Mayer; Cheryl Aine; Juan R Bustillo; Vince D Calhoun Journal: Front Neurosci Date: 2016-10-19 Impact factor: 4.677
Authors: José María Mateos-Pérez; Mahsa Dadar; María Lacalle-Aurioles; Yasser Iturria-Medina; Yashar Zeighami; Alan C Evans Journal: Neuroimage Clin Date: 2018-08-10 Impact factor: 4.881