3 recherche sur le mot-clé 'brain morphometry'

Annual Research Review: Progress in using brain morphometry as a clinical tool for diagnosing psychiatric disorders / Alexander HAUBOLD in Journal of Child Psychology and Psychiatry, 53-5 (May 2012)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 53-5 (May 2012) . - p.519-535 Titre : Annual Research Review: Progress in using brain morphometry as a clinical tool for diagnosing psychiatric disorders Type de document : texte imprimé Auteurs : Alexander HAUBOLD, Auteur ; Bradley S. PETERSON, Auteur ; Ravi BANSAL, Auteur Année de publication : 2012 Article en page(s) : p.519-535 Langues : Anglais (eng) Mots-clés : Machine learning  brain morphometry  automated diagnosis  cortical thickness  psychiatric disorders  cross validationsupport vector machines Index. décimale : PER Périodiques Résumé : Brain morphometry in recent decades has increased our understanding of the neural bases of psychiatric disorders by localizing anatomical disturbances to specific nuclei and subnuclei of the brain. At least some of these disturbances precede the overt expression of clinical symptoms and possibly are endophenotypes that could be used to diagnose an individual accurately as having a specific psychiatric disorder. More accurate diagnoses could significantly reduce the emotional and financial burden of disease by aiding clinicians in implementing appropriate treatments earlier and in tailoring treatment to the individual needs. Several methods, especially those based on machine learning, have been proposed that use anatomical brain measures and gold-standard diagnoses of participants to learn decision rules that classify a person automatically as having one disorder rather than another. We review the general principles and procedures for machine learning, particularly as applied to diagnostic classification, and then review the procedures that have thus far attempted to diagnose psychiatric illnesses automatically using anatomical measures of the brain. We discuss the strengths and limitations of extant procedures and note that the sensitivity and specificity of these procedures in their most successful implementations have approximated 90%. Although these methods have not yet been applied within clinical settings, they provide strong evidence that individual patients can be diagnosed accurately using the spatial pattern of disturbances across the brain. En ligne : http://dx.doi.org/10.1111/j.1469-7610.2012.02539.x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=154 [article] Annual Research Review: Progress in using brain morphometry as a clinical tool for diagnosing psychiatric disorders [texte imprimé] / Alexander HAUBOLD, Auteur ; Bradley S. PETERSON, Auteur ; Ravi BANSAL, Auteur . - 2012 . - p.519-535. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 53-5 (May 2012) . - p.519-535 Mots-clés : Machine learning  brain morphometry  automated diagnosis  cortical thickness  psychiatric disorders  cross validationsupport vector machines Index. décimale : PER Périodiques Résumé : Brain morphometry in recent decades has increased our understanding of the neural bases of psychiatric disorders by localizing anatomical disturbances to specific nuclei and subnuclei of the brain. At least some of these disturbances precede the overt expression of clinical symptoms and possibly are endophenotypes that could be used to diagnose an individual accurately as having a specific psychiatric disorder. More accurate diagnoses could significantly reduce the emotional and financial burden of disease by aiding clinicians in implementing appropriate treatments earlier and in tailoring treatment to the individual needs. Several methods, especially those based on machine learning, have been proposed that use anatomical brain measures and gold-standard diagnoses of participants to learn decision rules that classify a person automatically as having one disorder rather than another. We review the general principles and procedures for machine learning, particularly as applied to diagnostic classification, and then review the procedures that have thus far attempted to diagnose psychiatric illnesses automatically using anatomical measures of the brain. We discuss the strengths and limitations of extant procedures and note that the sensitivity and specificity of these procedures in their most successful implementations have approximated 90%. Although these methods have not yet been applied within clinical settings, they provide strong evidence that individual patients can be diagnosed accurately using the spatial pattern of disturbances across the brain. En ligne : http://dx.doi.org/10.1111/j.1469-7610.2012.02539.x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=154

Amygdala Volume Differences in Autism Spectrum Disorder Are Related to Anxiety / John D. HERRINGTON in Journal of Autism and Developmental Disorders, 47-12 (December 2017)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 47-12 (December 2017) . - p.3682-3691 Titre : Amygdala Volume Differences in Autism Spectrum Disorder Are Related to Anxiety Type de document : texte imprimé Auteurs : John D. HERRINGTON, Auteur ; Brenna B. MADDOX, Auteur ; Connor M. KERNS, Auteur ; Keiran M. RUMP, Auteur ; Julie A. WORLEY, Auteur ; Jennifer C. BUSH, Auteur ; Alana J. MCVEY, Auteur ; Robert T. SCHULTZ, Auteur ; Judith S. MILLER, Auteur Année de publication : 2017 Article en page(s) : p.3682-3691 Langues : Anglais (eng) Mots-clés : Amygdala  Anxiety disorders  Brain morphometry  Comorbidity Index. décimale : PER Périodiques Résumé : Recent studies suggest that longstanding findings of abnormal amygdala morphology in ASD may be related to symptoms of anxiety. To test this hypothesis, fifty-three children with ASD (mean age = 11.9) underwent structural MRI and were divided into subgroups to compare those with at least one anxiety disorder diagnosis (n = 29) to those without (n = 24) and to a typically developing control group (TDC; n = 37). Groups were matched on age and intellectual level. The ASD and anxiety group showed decreased right amygdala volume (controlled for total brain volume) relative to ASD without anxiety (p = .04) and TDCs (p = .068). Results suggest that youth with ASD and co-occurring anxiety have a distinct neurodevelopmental trajectory. En ligne : http://dx.doi.org/10.1007/s10803-017-3206-1 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=325 [article] Amygdala Volume Differences in Autism Spectrum Disorder Are Related to Anxiety [texte imprimé] / John D. HERRINGTON, Auteur ; Brenna B. MADDOX, Auteur ; Connor M. KERNS, Auteur ; Keiran M. RUMP, Auteur ; Julie A. WORLEY, Auteur ; Jennifer C. BUSH, Auteur ; Alana J. MCVEY, Auteur ; Robert T. SCHULTZ, Auteur ; Judith S. MILLER, Auteur . - 2017 . - p.3682-3691. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 47-12 (December 2017) . - p.3682-3691 Mots-clés : Amygdala  Anxiety disorders  Brain morphometry  Comorbidity Index. décimale : PER Périodiques Résumé : Recent studies suggest that longstanding findings of abnormal amygdala morphology in ASD may be related to symptoms of anxiety. To test this hypothesis, fifty-three children with ASD (mean age = 11.9) underwent structural MRI and were divided into subgroups to compare those with at least one anxiety disorder diagnosis (n = 29) to those without (n = 24) and to a typically developing control group (TDC; n = 37). Groups were matched on age and intellectual level. The ASD and anxiety group showed decreased right amygdala volume (controlled for total brain volume) relative to ASD without anxiety (p = .04) and TDCs (p = .068). Results suggest that youth with ASD and co-occurring anxiety have a distinct neurodevelopmental trajectory. En ligne : http://dx.doi.org/10.1007/s10803-017-3206-1 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=325