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Novel imaging technologies for genetic diagnoses in the inborn errors of metabolism

Many inborn errors of metabolism and genetic disorders affect the brain. The brain biochemistry may differ from that in the periphery and is not accessible by simple blood and urine sampling. Therefore, neuroimaging has proven to be a valuable tool to not only evaluate the brain structure, but also...

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Autores principales: Gropman, Andrea L., Anderson, Afrouz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9075742/
https://www.ncbi.nlm.nih.gov/pubmed/35529470
http://dx.doi.org/10.20517/jtgg.2020.09
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author Gropman, Andrea L.
Anderson, Afrouz
author_facet Gropman, Andrea L.
Anderson, Afrouz
author_sort Gropman, Andrea L.
collection PubMed
description Many inborn errors of metabolism and genetic disorders affect the brain. The brain biochemistry may differ from that in the periphery and is not accessible by simple blood and urine sampling. Therefore, neuroimaging has proven to be a valuable tool to not only evaluate the brain structure, but also biochemistry, blood flow and function. Neuroimaging in patients with inborn errors of metabolism can include additional sequences in addition to T1 and T2-weighted imaging because in early stages, there may be no significant findings on the routine sequnces due to the lack of sensitivity or the evolution of abnormalities lags behind the ability of the imaging to detect it. In addition, findings on T1 and T2-weighted imaging of several inborn errors of metabolism may be non-specific and be seen in other non-genetic conditions. Therefore, additional neuroimaging modalities that have been employed including diffusion tensor imaging (DTI), magnetic resonance spectroscopy, functional MRI (fMRI), functional near infrared spectroscopy (fNIRS), or positron emission tomography (PET) imaging may further inform underlying changes in myelination, biochemistry, and functional connectivity. The use of Magnetic Resonance Spectroscopy in certain disorders may add a level of specificity depending upon the metabolite levels that are abnormal, as well as provide information about the process of brain injury (i.e., white matter, gray matter, energy deficiency, toxic buildup or depletion of key metabolites). It is even more challenging to understand how genetic or metabolic disorders contribute to short and/or long term changes in cognition which represent the downstream effects of IEMs. In order to image “cognition” or the downstream effects of a metabolic disorder on domains of brain function, more advanced techniques are required to analyze underlying fiber tracts or alternatively, methods such as fMRI enable generation of brain activation maps after both task based and resting state conditions. DTI can be used to look at changes in white matter tracks. Each imaging modality can explore an important aspect of the anatomy, physiology or biochemisty of the central nervous system. Their properties, pros and cons are discussed in this article. These imaging modalities will be discussed in the context of several inborn errors of metabolism including Galactosemia, Phenylketonruia, Maple syrup urine disease, Methylmalonic acidemia, Niemann-Pick Disease, type C1, Krabbe Disease, Ornithine transcarbamylase deficiency, Sjogren Larsson syndrome, Pelizeaus-Merzbacher disease, Pyruvate dehydrogenase deficiency, Nonketotic Hyperglycinemia and Fabry disease. Space constraints do not allow mention of all the disorders in which one of these modalities has been investigated, or where it would add value to diagnosis or disease progression.
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spelling pubmed-90757422022-05-06 Novel imaging technologies for genetic diagnoses in the inborn errors of metabolism Gropman, Andrea L. Anderson, Afrouz J Transl Genet Genom Article Many inborn errors of metabolism and genetic disorders affect the brain. The brain biochemistry may differ from that in the periphery and is not accessible by simple blood and urine sampling. Therefore, neuroimaging has proven to be a valuable tool to not only evaluate the brain structure, but also biochemistry, blood flow and function. Neuroimaging in patients with inborn errors of metabolism can include additional sequences in addition to T1 and T2-weighted imaging because in early stages, there may be no significant findings on the routine sequnces due to the lack of sensitivity or the evolution of abnormalities lags behind the ability of the imaging to detect it. In addition, findings on T1 and T2-weighted imaging of several inborn errors of metabolism may be non-specific and be seen in other non-genetic conditions. Therefore, additional neuroimaging modalities that have been employed including diffusion tensor imaging (DTI), magnetic resonance spectroscopy, functional MRI (fMRI), functional near infrared spectroscopy (fNIRS), or positron emission tomography (PET) imaging may further inform underlying changes in myelination, biochemistry, and functional connectivity. The use of Magnetic Resonance Spectroscopy in certain disorders may add a level of specificity depending upon the metabolite levels that are abnormal, as well as provide information about the process of brain injury (i.e., white matter, gray matter, energy deficiency, toxic buildup or depletion of key metabolites). It is even more challenging to understand how genetic or metabolic disorders contribute to short and/or long term changes in cognition which represent the downstream effects of IEMs. In order to image “cognition” or the downstream effects of a metabolic disorder on domains of brain function, more advanced techniques are required to analyze underlying fiber tracts or alternatively, methods such as fMRI enable generation of brain activation maps after both task based and resting state conditions. DTI can be used to look at changes in white matter tracks. Each imaging modality can explore an important aspect of the anatomy, physiology or biochemisty of the central nervous system. Their properties, pros and cons are discussed in this article. These imaging modalities will be discussed in the context of several inborn errors of metabolism including Galactosemia, Phenylketonruia, Maple syrup urine disease, Methylmalonic acidemia, Niemann-Pick Disease, type C1, Krabbe Disease, Ornithine transcarbamylase deficiency, Sjogren Larsson syndrome, Pelizeaus-Merzbacher disease, Pyruvate dehydrogenase deficiency, Nonketotic Hyperglycinemia and Fabry disease. Space constraints do not allow mention of all the disorders in which one of these modalities has been investigated, or where it would add value to diagnosis or disease progression. 2020 2020-11-13 /pmc/articles/PMC9075742/ /pubmed/35529470 http://dx.doi.org/10.20517/jtgg.2020.09 Text en https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Gropman, Andrea L.
Anderson, Afrouz
Novel imaging technologies for genetic diagnoses in the inborn errors of metabolism
title Novel imaging technologies for genetic diagnoses in the inborn errors of metabolism
title_full Novel imaging technologies for genetic diagnoses in the inborn errors of metabolism
title_fullStr Novel imaging technologies for genetic diagnoses in the inborn errors of metabolism
title_full_unstemmed Novel imaging technologies for genetic diagnoses in the inborn errors of metabolism
title_short Novel imaging technologies for genetic diagnoses in the inborn errors of metabolism
title_sort novel imaging technologies for genetic diagnoses in the inborn errors of metabolism
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9075742/
https://www.ncbi.nlm.nih.gov/pubmed/35529470
http://dx.doi.org/10.20517/jtgg.2020.09
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