The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification
ObjectiveAutism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-05-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.913377/full |
| _version_ | 1818544485426003968 |
|---|---|
| author | Liling Peng Xiao Liu Di Ma Xiaofeng Chen Xiaowen Xu Xin Gao |
| author_facet | Liling Peng Xiao Liu Di Ma Xiaofeng Chen Xiaowen Xu Xin Gao |
| author_sort | Liling Peng |
| collection | DOAJ |
| description | ObjectiveAutism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional brain network (FBN) is deemed important for extracting data on brain imaging biomarkers. Unfortunately, most existing studies have reported the utilization of the information from the connection to train the classifier; such an approach ignores the topological information and, in turn, limits its performance. Thus, effective utilization of the FBN provides insights for improving the diagnostic performance.MethodsWe propose the combination of the information derived from both FBN and its corresponding graph theory measurements to identify and distinguish ASD from normal controls (NCs). Specifically, a multi-kernel support vector machine (MK-SVM) was used to combine multiple types of information.ResultsThe experimental results illustrate that the combination of information from multiple connectome features (i.e., functional connections and graph measurements) can provide a superior identification performance with an area under the receiver operating characteristic curve (ROC) of 0.9191 and an accuracy of 82.60%. Furthermore, the graph theoretical analysis illustrates that the significant nodal graph measurements and consensus connections exists mostly in the salience network (SN), default mode network (DMN), attention network, frontoparietal network, and social network.ConclusionThis work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning. |
| first_indexed | 2024-12-11T22:49:06Z |
| format | Article |
| id | doaj.art-5758e09d3e8b4b31b6ab57709bae66a8 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-11T22:49:06Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-5758e09d3e8b4b31b6ab57709bae66a82022-12-22T00:47:31ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-05-011610.3389/fnins.2022.913377913377The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder IdentificationLiling Peng0Xiao Liu1Di Ma2Xiaofeng Chen3Xiaowen Xu4Xin Gao5Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, ChinaSchool of Business Administration, José Rizal University, Mandaluyong, PhilippinesCollege of Information Science and Technology, Nanjing Forestry University, Nanjing, ChinaCollege of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, ChinaDepartment of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, ChinaShanghai Universal Medical Imaging Diagnostic Center, Shanghai, ChinaObjectiveAutism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional brain network (FBN) is deemed important for extracting data on brain imaging biomarkers. Unfortunately, most existing studies have reported the utilization of the information from the connection to train the classifier; such an approach ignores the topological information and, in turn, limits its performance. Thus, effective utilization of the FBN provides insights for improving the diagnostic performance.MethodsWe propose the combination of the information derived from both FBN and its corresponding graph theory measurements to identify and distinguish ASD from normal controls (NCs). Specifically, a multi-kernel support vector machine (MK-SVM) was used to combine multiple types of information.ResultsThe experimental results illustrate that the combination of information from multiple connectome features (i.e., functional connections and graph measurements) can provide a superior identification performance with an area under the receiver operating characteristic curve (ROC) of 0.9191 and an accuracy of 82.60%. Furthermore, the graph theoretical analysis illustrates that the significant nodal graph measurements and consensus connections exists mostly in the salience network (SN), default mode network (DMN), attention network, frontoparietal network, and social network.ConclusionThis work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning.https://www.frontiersin.org/articles/10.3389/fnins.2022.913377/fullPearson’s correlationfunctional magnetic resonance imagingfunctional brain networkautism spectrum disorderMK-SVM |
| spellingShingle | Liling Peng Xiao Liu Di Ma Xiaofeng Chen Xiaowen Xu Xin Gao The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification Frontiers in Neuroscience Pearson’s correlation functional magnetic resonance imaging functional brain network autism spectrum disorder MK-SVM |
| title | The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification |
| title_full | The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification |
| title_fullStr | The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification |
| title_full_unstemmed | The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification |
| title_short | The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification |
| title_sort | altered pattern of the functional connectome related to pathological biomarkers in individuals for autism spectrum disorder identification |
| topic | Pearson’s correlation functional magnetic resonance imaging functional brain network autism spectrum disorder MK-SVM |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.913377/full |
| work_keys_str_mv | AT lilingpeng thealteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification AT xiaoliu thealteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification AT dima thealteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification AT xiaofengchen thealteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification AT xiaowenxu thealteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification AT xingao thealteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification AT lilingpeng alteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification AT xiaoliu alteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification AT dima alteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification AT xiaofengchen alteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification AT xiaowenxu alteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification AT xingao alteredpatternofthefunctionalconnectomerelatedtopathologicalbiomarkersinindividualsforautismspectrumdisorderidentification |