4 recherche sur le mot-clé 'Support Vector Machine'

The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study / Alessandra RETICO in Molecular Autism, 7 (2016)  

Public ISBD [article]  inMolecular Autism > 7 (2016) . - 5p. Titre : The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study Type de document : texte imprimé Auteurs : Alessandra RETICO, Auteur ; Alessia GIULIANO, Auteur ; Raffaella TANCREDI, Auteur ; Angela COSENZA, Auteur ; Fabio APICELLA, Auteur ; Antonio NARZISI, Auteur ; Laura BIAGI, Auteur ; Michela TOSETTI, Auteur ; Filippo MURATORI, Auteur ; Sara CALDERONI, Auteur Article en page(s) : 5p. Langues : Anglais (eng) Mots-clés : Area Under Curve  Autism Spectrum Disorder/pathology  Cerebrospinal Fluid  Child  Child, Preschool  Female  Gray Matter/pathology  Humans  Infant  Intelligence  Magnetic Resonance Imaging  Male  Neuroimaging  Organ Size  Phenotype  Research Design  Severity of Illness Index  Sex Characteristics  Support Vector Machine  White Matter/pathology  Autism spectrum disorders  Gender differences  Structural MRI  Young children Index. décimale : PER Périodiques Résumé : BACKGROUND: Genetic, hormonal, and environmental factors contribute since infancy to sexual dimorphism in regional brain structures of subjects with typical development. However, the neuroanatomical differences between male and female children with autism spectrum disorders (ASD) are an intriguing and still poorly investigated issue. This study aims to evaluate whether the brain of young children with ASD exhibits sex-related structural differences and if a correlation exists between clinical ASD features and neuroanatomical underpinnings. METHODS: A total of 152 structural MRI scans were analysed. Specifically, 76 young children with ASD (38 males and 38 females; 2-7 years of age; mean = 53 months, standard deviation = 17 months) were evaluated employing a support vector machine (SVM)-based analysis of the grey matter (GM). Group comparisons consisted of 76 age-, gender- and non-verbal-intelligence quotient-matched children with typical development or idiopathic developmental delay without autism. RESULTS: For both genders combined, SVM showed a significantly increased GM volume in young children with ASD with respect to control subjects, predominantly in the bilateral superior frontal gyrus (Brodmann area -BA- 10), bilateral precuneus (BA 31), bilateral superior temporal gyrus (BA 20/22), whereas less GM in patients with ASD was found in right inferior temporal gyrus (BA 37). For the within gender comparisons (i.e., females with ASD vs. controls and males with ASD vs. controls), two overlapping regions in bilateral precuneus (BA 31) and left superior frontal gyrus (BA 9/10) were detected. Sex-by-group analyses revealed in males with ASD compared to matched controls two male-specific regions of increased GM volume (left middle occipital gyrus-BA 19-and right superior temporal gyrus-BA 22). Comparisons in females with and without ASD demonstrated increased GM volumes predominantly in the bilateral frontal regions. Additional regions of significantly increased GM volume in the right anterior cingulate cortex (BA 32) and right cerebellum were typical only of females with ASD. CONCLUSIONS: Despite the specific behavioural correlates of sex-dimorphism in ASD, brain morphology as yet remains unclear and requires future dedicated investigations. This study provides evidence of structural brain gender differences in young children with ASD that possibly contribute to the different phenotypic disease manifestations in males and females. En ligne : http://dx.doi.org/10.1186/s13229-015-0067-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=329 [article] The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study [texte imprimé] / Alessandra RETICO, Auteur ; Alessia GIULIANO, Auteur ; Raffaella TANCREDI, Auteur ; Angela COSENZA, Auteur ; Fabio APICELLA, Auteur ; Antonio NARZISI, Auteur ; Laura BIAGI, Auteur ; Michela TOSETTI, Auteur ; Filippo MURATORI, Auteur ; Sara CALDERONI, Auteur . - 5p. Langues : Anglais (eng) in Molecular Autism > 7 (2016) . - 5p. Mots-clés : Area Under Curve  Autism Spectrum Disorder/pathology  Cerebrospinal Fluid  Child  Child, Preschool  Female  Gray Matter/pathology  Humans  Infant  Intelligence  Magnetic Resonance Imaging  Male  Neuroimaging  Organ Size  Phenotype  Research Design  Severity of Illness Index  Sex Characteristics  Support Vector Machine  White Matter/pathology  Autism spectrum disorders  Gender differences  Structural MRI  Young children Index. décimale : PER Périodiques Résumé : BACKGROUND: Genetic, hormonal, and environmental factors contribute since infancy to sexual dimorphism in regional brain structures of subjects with typical development. However, the neuroanatomical differences between male and female children with autism spectrum disorders (ASD) are an intriguing and still poorly investigated issue. This study aims to evaluate whether the brain of young children with ASD exhibits sex-related structural differences and if a correlation exists between clinical ASD features and neuroanatomical underpinnings. METHODS: A total of 152 structural MRI scans were analysed. Specifically, 76 young children with ASD (38 males and 38 females; 2-7 years of age; mean = 53 months, standard deviation = 17 months) were evaluated employing a support vector machine (SVM)-based analysis of the grey matter (GM). Group comparisons consisted of 76 age-, gender- and non-verbal-intelligence quotient-matched children with typical development or idiopathic developmental delay without autism. RESULTS: For both genders combined, SVM showed a significantly increased GM volume in young children with ASD with respect to control subjects, predominantly in the bilateral superior frontal gyrus (Brodmann area -BA- 10), bilateral precuneus (BA 31), bilateral superior temporal gyrus (BA 20/22), whereas less GM in patients with ASD was found in right inferior temporal gyrus (BA 37). For the within gender comparisons (i.e., females with ASD vs. controls and males with ASD vs. controls), two overlapping regions in bilateral precuneus (BA 31) and left superior frontal gyrus (BA 9/10) were detected. Sex-by-group analyses revealed in males with ASD compared to matched controls two male-specific regions of increased GM volume (left middle occipital gyrus-BA 19-and right superior temporal gyrus-BA 22). Comparisons in females with and without ASD demonstrated increased GM volumes predominantly in the bilateral frontal regions. Additional regions of significantly increased GM volume in the right anterior cingulate cortex (BA 32) and right cerebellum were typical only of females with ASD. CONCLUSIONS: Despite the specific behavioural correlates of sex-dimorphism in ASD, brain morphology as yet remains unclear and requires future dedicated investigations. This study provides evidence of structural brain gender differences in young children with ASD that possibly contribute to the different phenotypic disease manifestations in males and females. En ligne : http://dx.doi.org/10.1186/s13229-015-0067-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=329

Machine Learning-Based Early Prediction Model for Autism Spectrum Disorder in Infants Using Acoustic Feature / Shengjian YIN in Autism Research, 19-3 (March 2026)  

Public ISBD [article]  inAutism Research > 19-3 (March 2026) . - e70179 Titre : Machine Learning-Based Early Prediction Model for Autism Spectrum Disorder in Infants Using Acoustic Feature Type de document : texte imprimé Auteurs : Shengjian YIN, Auteur ; Zhijia LI, Auteur ; Luyang GUAN, Auteur ; Zenghe YUE, Auteur ; Jincen WANG, Auteur ; Jinyi ZHU, Auteur ; Yazhu HAN, Auteur ; Qian LI, Auteur ; Lan LIN, Auteur ; Yaxin DAI, Auteur ; Haozhen CHEN, Auteur ; Yuheng CHEN, Auteur ; Yun LI, Auteur ; Xiaoyan KE, Auteur Article en page(s) : e70179 Langues : Anglais (eng) Mots-clés : acoustic features  autism spectrum disorder  machine learning  model  support vector machine Index. décimale : PER Périodiques Résumé : ABSTRACT This study aimed to create a machine learning-based predictive model for early detection of autism spectrum disorder (ASD) in infants using acoustic features. Conducted as a prospective cohort at Nanjing Medical University from 2019 to 2024, infants aged 9?18?months from an ASD sibling cohort participated. Behavioral and vocalization data were gathered during the Still-Face Paradigm, with ASD diagnoses confirmed at 36?months through ADOS and ADI-R assessments. Researchers extracted 4368 acoustic features from the recordings and applied LASSO regression for dimensionality reduction, identifying 39 key features. A support vector machine (SVM) classifier was then developed, tested with four kernel functions?linear, radial basis function, polynomial, and sigmoid?via tenfold cross-validation. The final sample included 88 infants, 28 of whom were diagnosed with ASD. The sigmoid kernel yielded the best results, achieving a 92.86% sensitivity, 93.33% specificity, and a 93.18% accuracy. Notably, spectral and energy-related features were significantly higher in ASD infants (p? En ligne : https://doi.org/10.1002/aur.70179 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=583 [article] Machine Learning-Based Early Prediction Model for Autism Spectrum Disorder in Infants Using Acoustic Feature [texte imprimé] / Shengjian YIN, Auteur ; Zhijia LI, Auteur ; Luyang GUAN, Auteur ; Zenghe YUE, Auteur ; Jincen WANG, Auteur ; Jinyi ZHU, Auteur ; Yazhu HAN, Auteur ; Qian LI, Auteur ; Lan LIN, Auteur ; Yaxin DAI, Auteur ; Haozhen CHEN, Auteur ; Yuheng CHEN, Auteur ; Yun LI, Auteur ; Xiaoyan KE, Auteur . - e70179. Langues : Anglais (eng) in Autism Research > 19-3 (March 2026) . - e70179 Mots-clés : acoustic features  autism spectrum disorder  machine learning  model  support vector machine Index. décimale : PER Périodiques Résumé : ABSTRACT This study aimed to create a machine learning-based predictive model for early detection of autism spectrum disorder (ASD) in infants using acoustic features. Conducted as a prospective cohort at Nanjing Medical University from 2019 to 2024, infants aged 9?18?months from an ASD sibling cohort participated. Behavioral and vocalization data were gathered during the Still-Face Paradigm, with ASD diagnoses confirmed at 36?months through ADOS and ADI-R assessments. Researchers extracted 4368 acoustic features from the recordings and applied LASSO regression for dimensionality reduction, identifying 39 key features. A support vector machine (SVM) classifier was then developed, tested with four kernel functions?linear, radial basis function, polynomial, and sigmoid?via tenfold cross-validation. The final sample included 88 infants, 28 of whom were diagnosed with ASD. The sigmoid kernel yielded the best results, achieving a 92.86% sensitivity, 93.33% specificity, and a 93.18% accuracy. Notably, spectral and energy-related features were significantly higher in ASD infants (p? En ligne : https://doi.org/10.1002/aur.70179 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=583

Abnormal gray matter volume and functional connectivity patterns in social cognition-related brain regions of young children with autism spectrum disorder / Chen BAI in Autism Research, 16-6 (June 2023)  

Public ISBD [article]  inAutism Research > 16-6 (June 2023) . - p.1124-1137 Titre : Abnormal gray matter volume and functional connectivity patterns in social cognition-related brain regions of young children with autism spectrum disorder Type de document : texte imprimé Auteurs : Chen BAI, Auteur ; Yunlei WANG, Auteur ; Yan ZHANG, Auteur ; Xianna WANG, Auteur ; Zhenbo CHEN, Auteur ; Weiyong YU, Auteur ; Haojie ZHANG, Auteur ; Xingzhu LI, Auteur ; Kaixuan ZHU, Auteur ; Yuxiang WANG, Auteur ; Tong ZHANG, Auteur Article en page(s) : p.1124-1137 Langues : Anglais (eng) Mots-clés : autism spectrum disorder  functional connectivity  gray matter volume  support vector machine Index. décimale : PER Périodiques Résumé : Abstract Autism spectrum disorder (ASD) is associated with abnormal brain imaging findings, but descriptions thereof are inconsistent. The aim of the present study was to investigate brain abnormalities in young children with ASD using a combination of structural and functional brain magnetic resonance imaging (MRI). Structural and resting-state functional MRI was performed in 67 children with ASD (aged 2 7 years) and 39 age-matched typically developing (TD) controls. Voxel-based morphometry was used to evaluate differences in brain structure between groups. Topologic parameters of the functional brain network were compared by graph theoretic analysis and network connectomes were compared with network-based statistics. A support vector machine (SVM) was used to discriminate between ASD and TD groups. Results demonstrated young children with ASD had increased gray matter volumes (GMVs) in the right medial superior frontal gyrus and left fusiform gyrus compared with the TD group. The ASD group had altered subnetwork connectivity in frontal and temporal lobes and other social cognition-related brain regions. Functional connectivity in the left superior temporal gyrus and left temporal pole of the middle temporal gyrus was positively correlated with adaptability and language developmental quotient (DQ) in children with ASD. The combination of the brain structural and functional features had 86.2% accuracy in discriminating between ASD and TD. The present study shows that young children with ASD have altered GMVs and functional networks in social cognition-related brain regions, which are potential neuroimaging biomarkers for ASD. En ligne : https://doi.org/10.1002/aur.2936 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=507 [article] Abnormal gray matter volume and functional connectivity patterns in social cognition-related brain regions of young children with autism spectrum disorder [texte imprimé] / Chen BAI, Auteur ; Yunlei WANG, Auteur ; Yan ZHANG, Auteur ; Xianna WANG, Auteur ; Zhenbo CHEN, Auteur ; Weiyong YU, Auteur ; Haojie ZHANG, Auteur ; Xingzhu LI, Auteur ; Kaixuan ZHU, Auteur ; Yuxiang WANG, Auteur ; Tong ZHANG, Auteur . - p.1124-1137. Langues : Anglais (eng) in Autism Research > 16-6 (June 2023) . - p.1124-1137 Mots-clés : autism spectrum disorder  functional connectivity  gray matter volume  support vector machine Index. décimale : PER Périodiques Résumé : Abstract Autism spectrum disorder (ASD) is associated with abnormal brain imaging findings, but descriptions thereof are inconsistent. The aim of the present study was to investigate brain abnormalities in young children with ASD using a combination of structural and functional brain magnetic resonance imaging (MRI). Structural and resting-state functional MRI was performed in 67 children with ASD (aged 2 7 years) and 39 age-matched typically developing (TD) controls. Voxel-based morphometry was used to evaluate differences in brain structure between groups. Topologic parameters of the functional brain network were compared by graph theoretic analysis and network connectomes were compared with network-based statistics. A support vector machine (SVM) was used to discriminate between ASD and TD groups. Results demonstrated young children with ASD had increased gray matter volumes (GMVs) in the right medial superior frontal gyrus and left fusiform gyrus compared with the TD group. The ASD group had altered subnetwork connectivity in frontal and temporal lobes and other social cognition-related brain regions. Functional connectivity in the left superior temporal gyrus and left temporal pole of the middle temporal gyrus was positively correlated with adaptability and language developmental quotient (DQ) in children with ASD. The combination of the brain structural and functional features had 86.2% accuracy in discriminating between ASD and TD. The present study shows that young children with ASD have altered GMVs and functional networks in social cognition-related brain regions, which are potential neuroimaging biomarkers for ASD. En ligne : https://doi.org/10.1002/aur.2936 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=507

Brain functional connectivity correlates of autism diagnosis and familial liability in 24-month-olds / John R. Jr PRUETT in Journal of Neurodevelopmental Disorders, 17 (2025)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 17 (2025) Titre : Brain functional connectivity correlates of autism diagnosis and familial liability in 24-month-olds Type de document : texte imprimé Auteurs : John R. Jr PRUETT, Auteur ; Alexandre A. TODOROV, Auteur ; Zoë W. HAWKS, Auteur ; Muhamed TALOVIĆ, Auteur ; Tomoyuki NISHINO, Auteur ; Steven E. PETERSEN, Auteur ; Savannah DAVIS, Auteur ; Lyn STAHL, Auteur ; Kelly N. BOTTERON, Auteur ; John N. CONSTANTINO, Auteur ; Stephen R. DAGER, Auteur ; Jed T. ELISON, Auteur ; Annette M. ESTES, Auteur ; Alan C. EVANS, Auteur ; Guido GERIG, Auteur ; Jessica B. GIRAULT, Auteur ; Heather HAZLETT, Auteur ; Leigh MACINTYRE, Auteur ; Natasha MARRUS, Auteur ; Robert C. MCKINSTRY, Auteur ; Juhi PANDEY, Auteur ; Robert T. SCHULTZ, Auteur ; William D. SHANNON, Auteur ; Mark D. SHEN, Auteur ; Abraham Z. SNYDER, Auteur ; Martin STYNER, Auteur ; Jason J. WOLFF, Auteur ; Lonnie ZWAIGENBAUM, Auteur ; Joseph PIVEN, Auteur ; THE IBIS NETWORK, Auteur Langues : Anglais (eng) Mots-clés : Humans  Male  Female  Magnetic Resonance Imaging  Autism Spectrum Disorder/physiopathology/diagnostic imaging  Child, Preschool  Brain/physiopathology/diagnostic imaging  Support Vector Machine  Connectome  Nerve Net/physiopathology/diagnostic imaging  Infant  Siblings  Default mode network  Familial  Functional connectivity  MRI  reviewed and approved by the internal review boards of Washington University  School of Medicine, IRB IDs 201103140 and 201301110, the University of  Washington, IRB IDs 12317 and STUDY00012991, The Children’s Hospital of  Philadelphia, IRB ID 07-005689, and the University of North Carolina at Chapel  Hill, IRB ID 05-2293. Informed consent was signed by all study participants.  Competing interests: Dr. Robert McKinstry serves on the advisory board of Nous  Imaging, Inc. and receives funding for meals and travel from Siemens Healthineers  and Philips Healthcare. Abraham Z. Snyder is a consultant for Sora Neuroscience,  LLC. All other authors report no financial relationships with commercial  interests. Index. décimale : PER Périodiques Résumé : BACKGROUND: fcMRI correlates of autism spectrum disorder (ASD) diagnosis and familial liability were studied in 24-month-olds at high (older affected sibling) and low familial likelihood for ASD. METHODS: fcMRI comparisons of high-familial-likelihood (HL) ASD-positive (HLP, N = 23) and ASD-negative (HLN, N = 91), and low-likelihood ASD-negative (LLN, N = 27) 24-month-olds from the Infant Brain Imaging Study (IBIS) Network were conducted, employing object oriented data analysis (OODA), support vector machine (SVM) classification, and network-level fcMRI enrichment analyses. RESULTS: OODA (alpha = 0.0167, 3 comparisons) revealed differences in HLP and LLN fcMRI matrices (p = 0.012), but none for HLP versus HLN (p = 0.047) nor HLN versus LLN (p = 0.225). SVM distinguished HLP from HLN (accuracy = 99%, PPV = 96%, NPV = 100%), based on connectivity involving many networks. SVM accurately classified (non-training) LLN subjects with 100% accuracy. Enrichment analyses identified a cross-group fcMRI difference in the posterior cingulate default mode network 1 (pcDMN1)- temporal default mode network (tDMN) pair (p = 0.0070). Functional connectivity for implicated connections in these networks was consistently lower in HLP and HLN than in LLN (p = 0.0461 and 0.0004). HLP did not differ from HLN (p = 0.2254). Secondary testing showed HL children with low ASD behaviors still differed from LLN (p = 0.0036). CONCLUSIONS: 24-month-old high-familial-likelihood infants show reduced intra-DMN connectivity, a potential neural finding related to familial liability, while widely distributed functional connections correlate with ASD diagnosis. En ligne : https://dx.doi.org/10.1186/s11689-025-09621-9 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=576 [article] Brain functional connectivity correlates of autism diagnosis and familial liability in 24-month-olds [texte imprimé] / John R. Jr PRUETT, Auteur ; Alexandre A. TODOROV, Auteur ; Zoë W. HAWKS, Auteur ; Muhamed TALOVIĆ, Auteur ; Tomoyuki NISHINO, Auteur ; Steven E. PETERSEN, Auteur ; Savannah DAVIS, Auteur ; Lyn STAHL, Auteur ; Kelly N. BOTTERON, Auteur ; John N. CONSTANTINO, Auteur ; Stephen R. DAGER, Auteur ; Jed T. ELISON, Auteur ; Annette M. ESTES, Auteur ; Alan C. EVANS, Auteur ; Guido GERIG, Auteur ; Jessica B. GIRAULT, Auteur ; Heather HAZLETT, Auteur ; Leigh MACINTYRE, Auteur ; Natasha MARRUS, Auteur ; Robert C. MCKINSTRY, Auteur ; Juhi PANDEY, Auteur ; Robert T. SCHULTZ, Auteur ; William D. SHANNON, Auteur ; Mark D. SHEN, Auteur ; Abraham Z. SNYDER, Auteur ; Martin STYNER, Auteur ; Jason J. WOLFF, Auteur ; Lonnie ZWAIGENBAUM, Auteur ; Joseph PIVEN, Auteur ; THE IBIS NETWORK, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 17 (2025) Mots-clés : Humans  Male  Female  Magnetic Resonance Imaging  Autism Spectrum Disorder/physiopathology/diagnostic imaging  Child, Preschool  Brain/physiopathology/diagnostic imaging  Support Vector Machine  Connectome  Nerve Net/physiopathology/diagnostic imaging  Infant  Siblings  Default mode network  Familial  Functional connectivity  MRI  reviewed and approved by the internal review boards of Washington University  School of Medicine, IRB IDs 201103140 and 201301110, the University of  Washington, IRB IDs 12317 and STUDY00012991, The Children’s Hospital of  Philadelphia, IRB ID 07-005689, and the University of North Carolina at Chapel  Hill, IRB ID 05-2293. Informed consent was signed by all study participants.  Competing interests: Dr. Robert McKinstry serves on the advisory board of Nous  Imaging, Inc. and receives funding for meals and travel from Siemens Healthineers  and Philips Healthcare. Abraham Z. Snyder is a consultant for Sora Neuroscience,  LLC. All other authors report no financial relationships with commercial  interests. Index. décimale : PER Périodiques Résumé : BACKGROUND: fcMRI correlates of autism spectrum disorder (ASD) diagnosis and familial liability were studied in 24-month-olds at high (older affected sibling) and low familial likelihood for ASD. METHODS: fcMRI comparisons of high-familial-likelihood (HL) ASD-positive (HLP, N = 23) and ASD-negative (HLN, N = 91), and low-likelihood ASD-negative (LLN, N = 27) 24-month-olds from the Infant Brain Imaging Study (IBIS) Network were conducted, employing object oriented data analysis (OODA), support vector machine (SVM) classification, and network-level fcMRI enrichment analyses. RESULTS: OODA (alpha = 0.0167, 3 comparisons) revealed differences in HLP and LLN fcMRI matrices (p = 0.012), but none for HLP versus HLN (p = 0.047) nor HLN versus LLN (p = 0.225). SVM distinguished HLP from HLN (accuracy = 99%, PPV = 96%, NPV = 100%), based on connectivity involving many networks. SVM accurately classified (non-training) LLN subjects with 100% accuracy. Enrichment analyses identified a cross-group fcMRI difference in the posterior cingulate default mode network 1 (pcDMN1)- temporal default mode network (tDMN) pair (p = 0.0070). Functional connectivity for implicated connections in these networks was consistently lower in HLP and HLN than in LLN (p = 0.0461 and 0.0004). HLP did not differ from HLN (p = 0.2254). Secondary testing showed HL children with low ASD behaviors still differed from LLN (p = 0.0036). CONCLUSIONS: 24-month-old high-familial-likelihood infants show reduced intra-DMN connectivity, a potential neural finding related to familial liability, while widely distributed functional connections correlate with ASD diagnosis. En ligne : https://dx.doi.org/10.1186/s11689-025-09621-9 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=576