Cristina Cara, Giulia Zantonello, Marta Ghio, Marco Tettamanti
{"title":"成人阅读障碍背后的神经认知缺陷的多模式研究。","authors":"Cristina Cara, Giulia Zantonello, Marta Ghio, Marco Tettamanti","doi":"10.1093/cercor/bhaf193","DOIUrl":null,"url":null,"abstract":"<p><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.</p>","PeriodicalId":9715,"journal":{"name":"Cerebral cortex","volume":"35 10","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12499769/pdf/","citationCount":"0","resultStr":"{\"title\":\"Multimodal investigation of the neurocognitive deficits underlying dyslexia in adulthood.\",\"authors\":\"Cristina Cara, Giulia Zantonello, Marta Ghio, Marco Tettamanti\",\"doi\":\"10.1093/cercor/bhaf193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><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.</p>\",\"PeriodicalId\":9715,\"journal\":{\"name\":\"Cerebral cortex\",\"volume\":\"35 10\",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12499769/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cerebral cortex\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/cercor/bhaf193\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cerebral cortex","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/cercor/bhaf193","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Multimodal investigation of the neurocognitive deficits underlying dyslexia in adulthood.
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.
期刊介绍:
Cerebral Cortex publishes papers on the development, organization, plasticity, and function of the cerebral cortex, including the hippocampus. Studies with clear relevance to the cerebral cortex, such as the thalamocortical relationship or cortico-subcortical interactions, are also included.
The journal is multidisciplinary and covers the large variety of modern neurobiological and neuropsychological techniques, including anatomy, biochemistry, molecular neurobiology, electrophysiology, behavior, artificial intelligence, and theoretical modeling. In addition to research articles, special features such as brief reviews, book reviews, and commentaries are included.