{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T06:05:37Z","timestamp":1784786737003,"version":"3.55.0"},"reference-count":117,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Autism spectrum disorder (ASD) is currently diagnosed through behavioral observations and evaluations, but there is still a lack of objective and consistent biomarkers. Children and adolescents with ASD exhibit impairments in advanced social, emotional, and cognitive functions, such as a lack of empathy. In general, the concept of empathy encompasses several socio-emotional and cognitive components based on interacting brain circuits. Identification of disease-related biomarkers at the brain network level could provide a crucial avenue for advancing ASD imaging research and improving diagnostic accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study examined 80 individuals with ASD aged 6\u201316 years old and 50 matched control subjects, using resting-state functional magnetic resonance imaging and clinical psychological assessment datasets. Specifically, a set of functional brain networks was constructed using dictionary learning and sparse coding (DLSC) in a group-wise manner. Then, the localized common functional brain networks from both the ASD and matched control groups were automatically decomposed into a set of regions of interest (ROIs) for further functional connectivity analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using the derived functional connectivity matrix, we investigated three parameters, namely, correlation, partial correlation, and tangent embedding, to differentiate participants with ASD from control subjects. We achieved classification accuracies of 95%, 100%, and 100%, respectively, indicating that the proposed DLSC method could extract representative and characteristic brain ROI atlases for both ASD and control participants. Further analysis of functional connectivity results showed that ASD participants had multiple atypical connections, especially those connecting the left inferior temporal and left inferior parietal regions, which belonged to the temporoparietal junction (TPJ), and connections related to the right insula and anterior cingulum, which belonged to the salience network (SN). Together, our results suggest that individuals with ASD exhibited a lower empathy capability than control subjects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our results suggest that DLSC can effectively extract robust brain ROI atlases. Functional connectomes with high differentiation powers were mainly distributed within the brain networks of SN, social brain networks (SBNs), and the theory of mind (ToM) network (including the TPJ hub). Children and adolescents with ASD exhibited lower empathy capabilities than control subjects, which may be attributed to dysfunctions in the salience and social brain networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Trial Registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/www.chictr.org.cn<\/jats:ext-link>\n                      , identifier ChiCTR-ROC-17012877.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2026.1743755","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T05:37:58Z","timestamp":1784785078000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Group-wise sparse coding for the discovery of functional connectivity-based biomarkers in children and adolescents with autism spectrum 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