Cargando…

Contributions to auditory system conduction velocity: insights with multi-modal neuroimaging and machine learning in children with ASD and XYY syndrome

INTRODUCTION: The M50 electrophysiological auditory evoked response time can be measured at the superior temporal gyrus with magnetoencephalography (MEG) and its latency is related to the conduction velocity of auditory input passing from ear to auditory cortex. In children with autism spectrum diso...

Descripción completa

Detalles Bibliográficos
Autores principales: Berman, Jeffrey I., Bloy, Luke, Blaskey, Lisa, Jackel, Carissa R., Miller, Judith S., Ross, Judith, Edgar, J. Christopher, Roberts, Timothy P. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219612/
https://www.ncbi.nlm.nih.gov/pubmed/37252131
http://dx.doi.org/10.3389/fpsyt.2023.1057221
_version_ 1785049050490339328
author Berman, Jeffrey I.
Bloy, Luke
Blaskey, Lisa
Jackel, Carissa R.
Miller, Judith S.
Ross, Judith
Edgar, J. Christopher
Roberts, Timothy P. L.
author_facet Berman, Jeffrey I.
Bloy, Luke
Blaskey, Lisa
Jackel, Carissa R.
Miller, Judith S.
Ross, Judith
Edgar, J. Christopher
Roberts, Timothy P. L.
author_sort Berman, Jeffrey I.
collection PubMed
description INTRODUCTION: The M50 electrophysiological auditory evoked response time can be measured at the superior temporal gyrus with magnetoencephalography (MEG) and its latency is related to the conduction velocity of auditory input passing from ear to auditory cortex. In children with autism spectrum disorder (ASD) and certain genetic disorders such as XYY syndrome, the auditory M50 latency has been observed to be elongated (slowed). METHODS: The goal of this study is to use neuroimaging (diffusion MR and GABA MRS) measures to predict auditory conduction velocity in typically developing (TD) children and children with autism ASD and XYY syndrome. RESULTS: Non-linear TD support vector regression modeling methods accounted for considerably more M50 latency variance than linear models, likely due to the non-linear dependence on neuroimaging factors such as GABA MRS. While SVR models accounted for ~80% of the M50 latency variance in TD and the genetically homogenous XYY syndrome, a similar approach only accounted for ~20% of the M50 latency variance in ASD, implicating the insufficiency of diffusion MR, GABA MRS, and age factors alone. Biologically based stratification of ASD was performed by assessing the conformance of the ASD population to the TD SVR model and identifying a sub-population of children with unexpectedly long M50 latency. DISCUSSION: Multimodal integration of neuroimaging data can help build a mechanistic understanding of brain connectivity. The unexplained M50 latency variance in ASD motivates future hypothesis generation and testing of other contributing biological factors.
format Online
Article
Text
id pubmed-10219612
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-102196122023-05-27 Contributions to auditory system conduction velocity: insights with multi-modal neuroimaging and machine learning in children with ASD and XYY syndrome Berman, Jeffrey I. Bloy, Luke Blaskey, Lisa Jackel, Carissa R. Miller, Judith S. Ross, Judith Edgar, J. Christopher Roberts, Timothy P. L. Front Psychiatry Psychiatry INTRODUCTION: The M50 electrophysiological auditory evoked response time can be measured at the superior temporal gyrus with magnetoencephalography (MEG) and its latency is related to the conduction velocity of auditory input passing from ear to auditory cortex. In children with autism spectrum disorder (ASD) and certain genetic disorders such as XYY syndrome, the auditory M50 latency has been observed to be elongated (slowed). METHODS: The goal of this study is to use neuroimaging (diffusion MR and GABA MRS) measures to predict auditory conduction velocity in typically developing (TD) children and children with autism ASD and XYY syndrome. RESULTS: Non-linear TD support vector regression modeling methods accounted for considerably more M50 latency variance than linear models, likely due to the non-linear dependence on neuroimaging factors such as GABA MRS. While SVR models accounted for ~80% of the M50 latency variance in TD and the genetically homogenous XYY syndrome, a similar approach only accounted for ~20% of the M50 latency variance in ASD, implicating the insufficiency of diffusion MR, GABA MRS, and age factors alone. Biologically based stratification of ASD was performed by assessing the conformance of the ASD population to the TD SVR model and identifying a sub-population of children with unexpectedly long M50 latency. DISCUSSION: Multimodal integration of neuroimaging data can help build a mechanistic understanding of brain connectivity. The unexplained M50 latency variance in ASD motivates future hypothesis generation and testing of other contributing biological factors. Frontiers Media S.A. 2023-05-11 /pmc/articles/PMC10219612/ /pubmed/37252131 http://dx.doi.org/10.3389/fpsyt.2023.1057221 Text en Copyright © 2023 Berman, Bloy, Blaskey, Jackel, Miller, Ross, Edgar and Roberts. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Berman, Jeffrey I.
Bloy, Luke
Blaskey, Lisa
Jackel, Carissa R.
Miller, Judith S.
Ross, Judith
Edgar, J. Christopher
Roberts, Timothy P. L.
Contributions to auditory system conduction velocity: insights with multi-modal neuroimaging and machine learning in children with ASD and XYY syndrome
title Contributions to auditory system conduction velocity: insights with multi-modal neuroimaging and machine learning in children with ASD and XYY syndrome
title_full Contributions to auditory system conduction velocity: insights with multi-modal neuroimaging and machine learning in children with ASD and XYY syndrome
title_fullStr Contributions to auditory system conduction velocity: insights with multi-modal neuroimaging and machine learning in children with ASD and XYY syndrome
title_full_unstemmed Contributions to auditory system conduction velocity: insights with multi-modal neuroimaging and machine learning in children with ASD and XYY syndrome
title_short Contributions to auditory system conduction velocity: insights with multi-modal neuroimaging and machine learning in children with ASD and XYY syndrome
title_sort contributions to auditory system conduction velocity: insights with multi-modal neuroimaging and machine learning in children with asd and xyy syndrome
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219612/
https://www.ncbi.nlm.nih.gov/pubmed/37252131
http://dx.doi.org/10.3389/fpsyt.2023.1057221
work_keys_str_mv AT bermanjeffreyi contributionstoauditorysystemconductionvelocityinsightswithmultimodalneuroimagingandmachinelearninginchildrenwithasdandxyysyndrome
AT bloyluke contributionstoauditorysystemconductionvelocityinsightswithmultimodalneuroimagingandmachinelearninginchildrenwithasdandxyysyndrome
AT blaskeylisa contributionstoauditorysystemconductionvelocityinsightswithmultimodalneuroimagingandmachinelearninginchildrenwithasdandxyysyndrome
AT jackelcarissar contributionstoauditorysystemconductionvelocityinsightswithmultimodalneuroimagingandmachinelearninginchildrenwithasdandxyysyndrome
AT millerjudiths contributionstoauditorysystemconductionvelocityinsightswithmultimodalneuroimagingandmachinelearninginchildrenwithasdandxyysyndrome
AT rossjudith contributionstoauditorysystemconductionvelocityinsightswithmultimodalneuroimagingandmachinelearninginchildrenwithasdandxyysyndrome
AT edgarjchristopher contributionstoauditorysystemconductionvelocityinsightswithmultimodalneuroimagingandmachinelearninginchildrenwithasdandxyysyndrome
AT robertstimothypl contributionstoauditorysystemconductionvelocityinsightswithmultimodalneuroimagingandmachinelearninginchildrenwithasdandxyysyndrome