Characterizing the Clinical Features and Atrophy Patterns of MAPT-Related Frontotemporal Dementia With Disease Progression Modeling

Mutations in the gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of mutations have grouped all different mutations together and shown an association with focal atrophy of the temporal lobe. The variability in atrophy patterns between each p...

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Veröffentlicht in:Neurology 2021-08, Vol.97 (9), p.e941-e952
Hauptverfasser: Young, Alexandra L., Bocchetta, Martina, Russell, Lucy L., Convery, Rhian S., Peakman, Georgia, Todd, Emily, Cash, David M., Greaves, Caroline V., van Swieten, John, Jiskoot, Lize, Seelaar, Harro, Moreno, Fermin, Sanchez-Valle, Raquel, Borroni, Barbara, Laforce, Robert, Masellis, Mario, Tartaglia, Maria Carmela, Graff, Caroline, Galimberti, Daniela, Rowe, James B., Finger, Elizabeth, Synofzik, Matthis, Vandenberghe, Rik, de Mendonça, Alexandre, Tagliavini, Fabrizio, Santana, Isabel, Ducharme, Simon, Butler, Chris, Gerhard, Alex, Levin, Johannes, Danek, Adrian, Otto, Markus, Sorbi, Sandro, Williams, Steven C.R., Alexander, Daniel C., Rohrer, Jonathan D.
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container_end_page e952
container_issue 9
container_start_page e941
container_title Neurology
container_volume 97
creator Young, Alexandra L.
Bocchetta, Martina
Russell, Lucy L.
Convery, Rhian S.
Peakman, Georgia
Todd, Emily
Cash, David M.
Greaves, Caroline V.
van Swieten, John
Jiskoot, Lize
Seelaar, Harro
Moreno, Fermin
Sanchez-Valle, Raquel
Borroni, Barbara
Laforce, Robert
Masellis, Mario
Tartaglia, Maria Carmela
Graff, Caroline
Galimberti, Daniela
Rowe, James B.
Finger, Elizabeth
Synofzik, Matthis
Vandenberghe, Rik
de Mendonça, Alexandre
Tagliavini, Fabrizio
Santana, Isabel
Ducharme, Simon
Butler, Chris
Gerhard, Alex
Levin, Johannes
Danek, Adrian
Otto, Markus
Sorbi, Sandro
Williams, Steven C.R.
Alexander, Daniel C.
Rohrer, Jonathan D.
description Mutations in the gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of mutations have grouped all different mutations together and shown an association with focal atrophy of the temporal lobe. The variability in atrophy patterns between each particular mutation is less well-characterized. We aimed to investigate whether there were distinct groups of mutation carriers based on their neuroanatomical signature. We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning technique that identifies groups of individuals with distinct progression patterns, to characterize patterns of regional atrophy in associated FTD within the Genetic FTD Initiative (GENFI) cohort study. Eighty-two mutation carriers were analyzed, the majority of whom had P301L, IVS10+16, or R406W mutations, along with 48 healthy noncarriers. SuStaIn identified 2 groups of mutation carriers with distinct atrophy patterns: a temporal subtype, in which atrophy was most prominent in the hippocampus, amygdala, temporal cortex, and insula; and a frontotemporal subtype, in which atrophy was more localized to the lateral temporal lobe and anterior insula, as well as the orbitofrontal and ventromedial prefrontal cortex and anterior cingulate. There was one-to-one mapping between IVS10+16 and R406W mutations and the temporal subtype and near one-to-one mapping between P301L mutations and the frontotemporal subtype. There were differences in clinical symptoms and neuropsychological test scores between subtypes: the temporal subtype was associated with amnestic symptoms, whereas the frontotemporal subtype was associated with executive dysfunction. Our results demonstrate that different mutations give rise to distinct atrophy patterns and clinical phenotype, providing insights into the underlying disease biology and potential utility for patient stratification in therapeutic trials.
doi_str_mv 10.1212/WNL.0000000000012410
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Most previous studies investigating the neuroanatomical signature of mutations have grouped all different mutations together and shown an association with focal atrophy of the temporal lobe. The variability in atrophy patterns between each particular mutation is less well-characterized. We aimed to investigate whether there were distinct groups of mutation carriers based on their neuroanatomical signature. We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning technique that identifies groups of individuals with distinct progression patterns, to characterize patterns of regional atrophy in associated FTD within the Genetic FTD Initiative (GENFI) cohort study. Eighty-two mutation carriers were analyzed, the majority of whom had P301L, IVS10+16, or R406W mutations, along with 48 healthy noncarriers. SuStaIn identified 2 groups of mutation carriers with distinct atrophy patterns: a temporal subtype, in which atrophy was most prominent in the hippocampus, amygdala, temporal cortex, and insula; and a frontotemporal subtype, in which atrophy was more localized to the lateral temporal lobe and anterior insula, as well as the orbitofrontal and ventromedial prefrontal cortex and anterior cingulate. There was one-to-one mapping between IVS10+16 and R406W mutations and the temporal subtype and near one-to-one mapping between P301L mutations and the frontotemporal subtype. There were differences in clinical symptoms and neuropsychological test scores between subtypes: the temporal subtype was associated with amnestic symptoms, whereas the frontotemporal subtype was associated with executive dysfunction. Our results demonstrate that different mutations give rise to distinct atrophy patterns and clinical phenotype, providing insights into the underlying disease biology and potential utility for patient stratification in therapeutic trials.</description><identifier>ISSN: 0028-3878</identifier><identifier>ISSN: 1526-632X</identifier><identifier>EISSN: 1526-632X</identifier><identifier>DOI: 10.1212/WNL.0000000000012410</identifier><identifier>PMID: 34158384</identifier><language>eng</language><publisher>United States: Lippincott Williams &amp; Wilkins</publisher><subject>Adult ; Aged ; Atrophy - genetics ; Atrophy - pathology ; Brain - pathology ; Disease Progression ; Female ; Frontotemporal Dementia - genetics ; Frontotemporal Dementia - pathology ; Humans ; Image Interpretation, Computer-Assisted - methods ; Machine Learning ; Magnetic Resonance Imaging ; Male ; Medicin och hälsovetenskap ; Middle Aged ; Mutation ; Neuroimaging - methods ; tau Proteins - genetics</subject><ispartof>Neurology, 2021-08, Vol.97 (9), p.e941-e952</ispartof><rights>Lippincott Williams &amp; Wilkins</rights><rights>Copyright © 2021 The Author(s). 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Most previous studies investigating the neuroanatomical signature of mutations have grouped all different mutations together and shown an association with focal atrophy of the temporal lobe. The variability in atrophy patterns between each particular mutation is less well-characterized. We aimed to investigate whether there were distinct groups of mutation carriers based on their neuroanatomical signature. We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning technique that identifies groups of individuals with distinct progression patterns, to characterize patterns of regional atrophy in associated FTD within the Genetic FTD Initiative (GENFI) cohort study. Eighty-two mutation carriers were analyzed, the majority of whom had P301L, IVS10+16, or R406W mutations, along with 48 healthy noncarriers. 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Our results demonstrate that different mutations give rise to distinct atrophy patterns and clinical phenotype, providing insights into the underlying disease biology and potential utility for patient stratification in therapeutic trials.</description><subject>Adult</subject><subject>Aged</subject><subject>Atrophy - genetics</subject><subject>Atrophy - pathology</subject><subject>Brain - pathology</subject><subject>Disease Progression</subject><subject>Female</subject><subject>Frontotemporal Dementia - genetics</subject><subject>Frontotemporal Dementia - pathology</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Machine Learning</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>Medicin och hälsovetenskap</subject><subject>Middle Aged</subject><subject>Mutation</subject><subject>Neuroimaging - methods</subject><subject>tau Proteins - 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Modeling</atitle><jtitle>Neurology</jtitle><addtitle>Neurology</addtitle><date>2021-08-31</date><risdate>2021</risdate><volume>97</volume><issue>9</issue><spage>e941</spage><epage>e952</epage><pages>e941-e952</pages><issn>0028-3878</issn><issn>1526-632X</issn><eissn>1526-632X</eissn><abstract>Mutations in the gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of mutations have grouped all different mutations together and shown an association with focal atrophy of the temporal lobe. The variability in atrophy patterns between each particular mutation is less well-characterized. We aimed to investigate whether there were distinct groups of mutation carriers based on their neuroanatomical signature. We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning technique that identifies groups of individuals with distinct progression patterns, to characterize patterns of regional atrophy in associated FTD within the Genetic FTD Initiative (GENFI) cohort study. Eighty-two mutation carriers were analyzed, the majority of whom had P301L, IVS10+16, or R406W mutations, along with 48 healthy noncarriers. SuStaIn identified 2 groups of mutation carriers with distinct atrophy patterns: a temporal subtype, in which atrophy was most prominent in the hippocampus, amygdala, temporal cortex, and insula; and a frontotemporal subtype, in which atrophy was more localized to the lateral temporal lobe and anterior insula, as well as the orbitofrontal and ventromedial prefrontal cortex and anterior cingulate. There was one-to-one mapping between IVS10+16 and R406W mutations and the temporal subtype and near one-to-one mapping between P301L mutations and the frontotemporal subtype. There were differences in clinical symptoms and neuropsychological test scores between subtypes: the temporal subtype was associated with amnestic symptoms, whereas the frontotemporal subtype was associated with executive dysfunction. Our results demonstrate that different mutations give rise to distinct atrophy patterns and clinical phenotype, providing insights into the underlying disease biology and potential utility for patient stratification in therapeutic trials.</abstract><cop>United States</cop><pub>Lippincott Williams &amp; Wilkins</pub><pmid>34158384</pmid><doi>10.1212/WNL.0000000000012410</doi><orcidid>https://orcid.org/0000-0001-6237-2502</orcidid><orcidid>https://orcid.org/0000-0001-8857-5383</orcidid><orcidid>https://orcid.org/0000-0003-1551-5691</orcidid><orcidid>https://orcid.org/0000-0001-7833-616X</orcidid><orcidid>https://orcid.org/0000-0003-4273-4267</orcidid><orcidid>https://orcid.org/0000-0002-9284-5953</orcidid><orcidid>https://orcid.org/0000-0002-7772-781X</orcidid><orcidid>https://orcid.org/0000-0003-4299-1941</orcidid><orcidid>https://orcid.org/0000-0003-1989-7527</orcidid><orcidid>https://orcid.org/0000-0002-2031-490X</orcidid><oa>free_for_read</oa></addata></record>
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identifier ISSN: 0028-3878
ispartof Neurology, 2021-08, Vol.97 (9), p.e941-e952
issn 0028-3878
1526-632X
1526-632X
language eng
recordid cdi_swepub_primary_oai_swepub_ki_se_461089
source MEDLINE; SWEPUB Freely available online; Journals@Ovid Complete; Alma/SFX Local Collection
subjects Adult
Aged
Atrophy - genetics
Atrophy - pathology
Brain - pathology
Disease Progression
Female
Frontotemporal Dementia - genetics
Frontotemporal Dementia - pathology
Humans
Image Interpretation, Computer-Assisted - methods
Machine Learning
Magnetic Resonance Imaging
Male
Medicin och hälsovetenskap
Middle Aged
Mutation
Neuroimaging - methods
tau Proteins - genetics
title Characterizing the Clinical Features and Atrophy Patterns of MAPT-Related Frontotemporal Dementia With Disease Progression Modeling
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