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Furthermore, such brain markers need to meet the requirements of precision medicine and be accurate in aiding diagnosis at an individual rather than only a group level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to identify and model brain-wide differences in structural connectivity using diffusion tensor imaging (DTI) in young ASD and typically developing (TD) children.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A discovery cohort including 93 ASD and 26 TD children and two independent validation cohorts including 12 ASD and 9 TD children from three different cities in China were included. Brain-wide (294 regions) structural connectivity was measured using DTI (fractional anisotropy, FA) together with symptom severity and cognitive development. A connection matrix was constructed for each child for comparisons between ASD and TD groups. Pattern classification was performed on the discovery dataset and the resulting model was tested on the two independent validation datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Thirty-three structural connections showed increased FA in ASD compared to TD children and associated with both autistic symptom severity and impaired general cognitive development. The majority (29\/33) involved the frontal lobe and comprised five different networks with functional relevance to default mode, motor control, social recognition, language and reward. Overall, classification achieved very high accuracy of 96.77% in the discovery dataset, and 91.67% and 88.89% in the two independent validation datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Identified structural connectivity differences primarily involving the frontal cortex can very accurately distinguish novel individual ASD from TD children and may therefore represent a robust early brain biomarker which can address the requirements of precision medicine.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/psyrad\/kkad005","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T01:20:15Z","timestamp":1682040015000},"source":"Crossref","is-referenced-by-count":24,"title":["A brain structural connectivity biomarker for autism spectrum disorder diagnosis in early 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IDG\/McGovern Institute for Brain Research, Beijing Normal University , Beijing 100875 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongbo","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China , Chengdu 611731 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhong","family":"Chen","sequence":"additional","affiliation":[{"name":"The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China , Chengdu 611731 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan-Chao","family":"Meng","sequence":"additional","affiliation":[{"name":"Neuroscience Research Institute; Key Laboratory for 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