{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T02:19:43Z","timestamp":1789957183138,"version":"4.0.1"},"reference-count":119,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Dyslexia is a neurobiological disorder characterized by reading difficulties, yet its causes remain unclear. Neuroimaging and behavioral studies found anomalous responses in tasks requiring phonological processing, motion perception, and implicit learning, and showed gray and white matter abnormalities in dyslexics compared to controls, indicating that dyslexia is highly heterogeneous and promoting a multifactorial approach. To evaluate whether combining behavioral and multimodal MRI improves sensitivity in identifying dyslexia neurocognitive traits compared to monocomponential approaches, 19 dyslexic and 19 control subjects underwent cognitive assessments, multiple (phonological, visual motion, rhythmic) mismatch-response functional MRI tasks, structural diffusion-weighted imaging (DWI) and T1-weighted imaging. Between group differences in the neurocognitive measures were tested with univariate and multivariate approaches. Results showed that dyslexics performed worse than controls in phonological tasks and presented reduced cerebellar responses to mismatching rhythmic stimuli, as well as structural disorganization in white matter tracts and cortical regions. Most importantly, a machine learning model trained with features from all three MRI modalities discriminated between dyslexics and controls with greater accuracy than single-modality models. The individual classification scores in the multimodal machine learning model correlated with behavioral reading accuracy. These results characterize dyslexia as a composite condition with multiple distinctive cognitive and brain traits.<\/jats:p>","DOI":"10.1093\/cercor\/bhaf193","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T17:16:37Z","timestamp":1759770997000},"source":"Crossref","is-referenced-by-count":3,"title":["Multimodal investigation of the neurocognitive deficits underlying dyslexia in adulthood"],"prefix":"10.1093","volume":"35","author":[{"given":"Cristina","family":"Cara","sequence":"first","affiliation":[{"name":"CIMeC\u2014Center for Mind\/Brain Sciences, University of Trento , Corso Bettini 31, 38068 Rovereto (TN) ,","place":["Italy"]},{"name":"Institute of Experimental Psychology, Heinrich Heine University , Universit\u00e4tsstrasse 1, 40225 D\u00fcsseldorf ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Zantonello","sequence":"additional","affiliation":[{"name":"CIMeC\u2014Center for Mind\/Brain Sciences, University of Trento , Corso Bettini 31, 38068 Rovereto (TN) ,","place":["Italy"]},{"name":"Department of Educational Psychology, University of G\u00f6ttingen , Waldweg 26, 37073 G\u00f6ttingen ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Ghio","sequence":"additional","affiliation":[{"name":"Institute of Experimental Psychology, Heinrich Heine University , Universit\u00e4tsstrasse 1, 40225 D\u00fcsseldorf ,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3039-1500","authenticated-orcid":false,"given":"Marco","family":"Tettamanti","sequence":"additional","affiliation":[{"name":"Department of Psychology, University of Milano-Bicocca , Piazza dell\u2019Ateneo Nuovo 1, 20126 Milano 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