{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T07:04:31Z","timestamp":1785135871475,"version":"3.55.0"},"reference-count":42,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Developmental Neurobiology"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In today's society, autism spectrum disorder (ASD) is a common neurological disorder that affects a person's behavior and communication. Hence, an early ASD prediction is essential for improving the lifecycle of ASD patients. Recently, many works have been deployed for diagnosing ASD using the electroencephalogram (EEG) and magnetic resonance imaging (MRI). But they are inefficient owing to the irrelevant noises and misclassification outcomes. Hence, a well\u2010ordered logistic regression with scaled function\u2010based deep learning neural network (LRSF\u2010DLNN)\u2010based ASD prediction using EEG and functional MRI (fMRI) is proposed. Initially, the inputs, like the EEG signal and fMRI image of the eye, are gathered and then subjected to the preprocessing phase. Here, for performing noise removal of EEG, the cosine\u2010based Butterworth filter (CBF) approach is established. Similarly, the fMRI undergoes slice time correction and a GF filter\u2010based smoothing process. Then, the preprocessed EEG is decomposed by utilizing weighted penalty factor\u2010centric variational mode decomposition (WPFVMD), and the process of alpha and theta band estimation is performed on the basis of single\u2010scale time dependent\u2013based event\u2010related spectral perturbation (SSTD\u2010ERSP). Likewise, the features are extracted from both the decomposed EEG and smoothed fMRI. Next, the extracted features are given for feature selection (FS) utilizing distance functional green anaconda optimization (DFGAO). Lastly, the optimal features are given to the LRSF\u2010DLNN classifier, which efficiently predicts ASD. Hence, the evaluation outcomes exposed that the proposed technique achieved better performance with a higher accuracy (98.8%) than other prevailing models.<\/jats:p>","DOI":"10.1002\/dneu.70047","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T05:21:11Z","timestamp":1781241671000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Effective LRSF\u2010DLNN\u2010Based Autism Spectrum Disorder Prediction Using EEG and fMRI"],"prefix":"10.1002","volume":"86","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0213-8305","authenticated-orcid":false,"given":"Sahunthala","family":"S.","sequence":"first","affiliation":[{"name":"Department of Information Technology, Easwari Engineering College Anna University  Chennai India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7413-2386","authenticated-orcid":false,"given":"Malathi","family":"S.","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Panimalar Engineering College Anna University  Chennai India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8994-3694","authenticated-orcid":false,"given":"TamilThendral","family":"M.","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Bannari Amman Institute of Technology Anna University  Chennai India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9361-7650","authenticated-orcid":false,"given":"Sudhahar","family":"T. N.","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Jeppiaar Institute of Technology Anna University  Chennai India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"crossref","unstructured":"Simoes M.2020.BCIAUT_P300 Multi\u2010Session and Multi\u2010Subject Benchmark Dataset on Autism for P300\u2010BCI.Kaggle.https:\/\/www.kaggle.com\/datasets\/disbeat\/bciaut\u2010p300.","DOI":"10.3389\/fnins.2020.568104"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2020.01.008"},{"key":"e_1_2_15_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11040530"},{"key":"e_1_2_15_5_1","doi-asserted-by":"crossref","unstructured":"Al\u2010Hiyali M. I. N.Yahya I.Faye Z.Khan andK.Alsaih.2021. \u201cClassification of BOLD FMRI Signals Using Wavelet Transform and Transfer Learning for Detection of Autism Spectrum Disorder.\u201d InProceedings IEEE EMBS Conference on Biomedical Engineering and Sciences.IEEE.https:\/\/doi.org\/10.1109\/IECBES48179.2021.9398803.","DOI":"10.1109\/IECBES48179.2021.9398803"},{"key":"e_1_2_15_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.06.006"},{"key":"e_1_2_15_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104376"},{"key":"e_1_2_15_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104548"},{"key":"e_1_2_15_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102099"},{"key":"e_1_2_15_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.spen.2020.100805"},{"key":"e_1_2_15_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103108"},{"key":"e_1_2_15_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11633\u2010020\u20101231\u20106"},{"key":"e_1_2_15_13_1","doi-asserted-by":"crossref","unstructured":"Haputhanthri D. G.Brihadiswaran S.Gunathilaka et\u00a0al.2019. \u201cAn EEG Based Channel Optimized Classification Approach for Autism Spectrum Disorder.\u201d InProceedings of the2019 Moratuwa Engineering Research Conference (MERCon).IEEE.https:\/\/doi.org\/10.1109\/MERCon.2019.8818814.","DOI":"10.1109\/MERCon.2019.8818814"},{"key":"e_1_2_15_14_1","doi-asserted-by":"crossref","unstructured":"Herath L. D.Meedeniya J.Marasingha andV.Weerasinghe.2022. \u201cOptimize Transfer Learning for Autism Spectrum Disorder Classification With Neuroimaging: A Comparative Study.\u201d InProceedings of the 2022 2nd International Conference on Advanced Research in Computing (ICARC).IEEE.https:\/\/doi.org\/10.1109\/ICARC54489.2022.9753949.","DOI":"10.1109\/ICARC54489.2022.9753949"},{"key":"e_1_2_15_15_1","doi-asserted-by":"crossref","unstructured":"Herath L. D.Meedeniya M. A. J. C.Marasingha andV.Weerasinghe.2021. \u201cAutism Spectrum Disorder Diagnosis Support Model Using Inception V3.\u201d InProceedings of the2021 International Research Conference on Smart Computing and Systems Engineering (SCSE).IEEE.https:\/\/doi.org\/10.1109\/SCSE53661.2021.9568314.","DOI":"10.1109\/SCSE53661.2021.9568314"},{"key":"e_1_2_15_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13623"},{"key":"e_1_2_15_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3007943"},{"key":"e_1_2_15_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2021.3080259"},{"key":"e_1_2_15_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnrt.2023.100068"},{"key":"e_1_2_15_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103722"},{"key":"e_1_2_15_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2025.110379"},{"key":"e_1_2_15_22_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12020518"},{"key":"e_1_2_15_23_1","doi-asserted-by":"crossref","unstructured":"Karampasi A. I.Kakkos S. T.Miloulis et\u00a0al.2020. \u201cA Machine Learning fMRI Approach in the Diagnosis of Autism.\u201d InProceedings of the IEEE International Conference on Big Data.IEEE.https:\/\/doi.org\/10.1109\/BigData50022.2020.9378453.","DOI":"10.1109\/BigData50022.2020.9378453"},{"key":"e_1_2_15_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2023.100774"},{"key":"e_1_2_15_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102658"},{"key":"e_1_2_15_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/ima.70038?urlappend=?utm_source=researchgate.net&utm_medium=article"},{"key":"e_1_2_15_27_1","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12340"},{"key":"e_1_2_15_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/life15101524"},{"key":"e_1_2_15_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2021.116184"},{"key":"e_1_2_15_30_1","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-7998-3069-6.ch016"},{"key":"e_1_2_15_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40747\u2010021\u201000408\u20108"},{"key":"e_1_2_15_32_1","doi-asserted-by":"publisher","DOI":"10.3844\/jcssp.2022.695.704"},{"key":"e_1_2_15_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10803\u2010021\u201005141\u20102"},{"key":"e_1_2_15_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.399"},{"key":"e_1_2_15_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.yebeh.2022.108619"},{"key":"e_1_2_15_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11571\u2010021\u201009683\u20100"},{"key":"e_1_2_15_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2023.100211"},{"key":"e_1_2_15_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijchp.2023.100409"},{"key":"e_1_2_15_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2023.100178"},{"key":"e_1_2_15_40_1","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging6060047"},{"key":"e_1_2_15_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102556"},{"key":"e_1_2_15_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.06.152"},{"key":"e_1_2_15_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2021.116195"}],"container-title":["Developmental Neurobiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/dneu.70047","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/dneu.70047","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/dneu.70047","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T06:51:47Z","timestamp":1785135107000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/dneu.70047"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,11]]},"references-count":42,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["10.1002\/dneu.70047"],"URL":"https:\/\/doi.org\/10.1002\/dneu.70047","archive":["Portico"],"relation":{},"ISSN":["1932-8451","1932-846X"],"issn-type":[{"value":"1932-8451","type":"print"},{"value":"1932-846X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,11]]},"assertion":[{"value":"2026-03-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-29","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70047"}}