Predicting disease progression in behavioral variant frontotemporal dementia
Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive...
Ausführliche Beschreibung
Autor*in: |
Sarah Anderl‐Straub [verfasserIn] Ludwig Lausser [verfasserIn] Jolina Lombardi [verfasserIn] Ingo Uttner [verfasserIn] Klaus Fassbender [verfasserIn] Klaus Fliessbach [verfasserIn] Hans‐Jürgen Huppertz [verfasserIn] Holger Jahn [verfasserIn] Johannes Kornhuber [verfasserIn] Hellmuth Obrig [verfasserIn] Anja Schneider [verfasserIn] Elisa Semler [verfasserIn] Matthis Synofzik [verfasserIn] Adrian Danek [verfasserIn] Johannes Prudlo [verfasserIn] Jan Kassubek [verfasserIn] Bernhard Landwehrmeyer [verfasserIn] Martin Lauer [verfasserIn] Alexander E. Volk [verfasserIn] Jens Wiltfang [verfasserIn] Janine Diehl‐Schmid [verfasserIn] Albert C. Ludolph [verfasserIn] Matthias L. Schroeter [verfasserIn] Hans A. Kestler [verfasserIn] Markus Otto [verfasserIn] FTLD consortium [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2021 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring - Wiley, 2020, 13(2021), 1, Seite n/a-n/a |
---|---|
Übergeordnetes Werk: |
volume:13 ; year:2021 ; number:1 ; pages:n/a-n/a |
Links: |
---|
DOI / URN: |
10.1002/dad2.12262 |
---|
Katalog-ID: |
DOAJ004545222 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ004545222 | ||
003 | DE-627 | ||
005 | 20230311014053.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230225s2021 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1002/dad2.12262 |2 doi | |
035 | |a (DE-627)DOAJ004545222 | ||
035 | |a (DE-599)DOAJ87427b433a554980845284f0264f1543 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a RC346-429 | |
050 | 0 | |a RC952-954.6 | |
100 | 0 | |a Sarah Anderl‐Straub |e verfasserin |4 aut | |
245 | 1 | 0 | |a Predicting disease progression in behavioral variant frontotemporal dementia |
264 | 1 | |c 2021 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model. | ||
650 | 4 | |a behavioral variant frontotemporal dementia | |
650 | 4 | |a brain volume | |
650 | 4 | |a classification models | |
650 | 4 | |a disease progression | |
650 | 4 | |a frontotemporal dementia | |
650 | 4 | |a prognosis | |
653 | 0 | |a Neurology. Diseases of the nervous system | |
653 | 0 | |a Geriatrics | |
700 | 0 | |a Ludwig Lausser |e verfasserin |4 aut | |
700 | 0 | |a Jolina Lombardi |e verfasserin |4 aut | |
700 | 0 | |a Ingo Uttner |e verfasserin |4 aut | |
700 | 0 | |a Klaus Fassbender |e verfasserin |4 aut | |
700 | 0 | |a Klaus Fliessbach |e verfasserin |4 aut | |
700 | 0 | |a Hans‐Jürgen Huppertz |e verfasserin |4 aut | |
700 | 0 | |a Holger Jahn |e verfasserin |4 aut | |
700 | 0 | |a Johannes Kornhuber |e verfasserin |4 aut | |
700 | 0 | |a Hellmuth Obrig |e verfasserin |4 aut | |
700 | 0 | |a Anja Schneider |e verfasserin |4 aut | |
700 | 0 | |a Elisa Semler |e verfasserin |4 aut | |
700 | 0 | |a Matthis Synofzik |e verfasserin |4 aut | |
700 | 0 | |a Adrian Danek |e verfasserin |4 aut | |
700 | 0 | |a Johannes Prudlo |e verfasserin |4 aut | |
700 | 0 | |a Jan Kassubek |e verfasserin |4 aut | |
700 | 0 | |a Bernhard Landwehrmeyer |e verfasserin |4 aut | |
700 | 0 | |a Martin Lauer |e verfasserin |4 aut | |
700 | 0 | |a Alexander E. Volk |e verfasserin |4 aut | |
700 | 0 | |a Jens Wiltfang |e verfasserin |4 aut | |
700 | 0 | |a Janine Diehl‐Schmid |e verfasserin |4 aut | |
700 | 0 | |a Albert C. Ludolph |e verfasserin |4 aut | |
700 | 0 | |a Matthias L. Schroeter |e verfasserin |4 aut | |
700 | 0 | |a Hans A. Kestler |e verfasserin |4 aut | |
700 | 0 | |a Markus Otto |e verfasserin |4 aut | |
700 | 0 | |a FTLD consortium |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring |d Wiley, 2020 |g 13(2021), 1, Seite n/a-n/a |w (DE-627)834396874 |w (DE-600)2832898-X |x 23528729 |7 nnns |
773 | 1 | 8 | |g volume:13 |g year:2021 |g number:1 |g pages:n/a-n/a |
856 | 4 | 0 | |u https://doi.org/10.1002/dad2.12262 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/87427b433a554980845284f0264f1543 |z kostenfrei |
856 | 4 | 0 | |u https://doi.org/10.1002/dad2.12262 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2352-8729 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_171 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_224 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_636 | ||
912 | |a GBV_ILN_2004 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2006 | ||
912 | |a GBV_ILN_2007 | ||
912 | |a GBV_ILN_2010 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2026 | ||
912 | |a GBV_ILN_2027 | ||
912 | |a GBV_ILN_2034 | ||
912 | |a GBV_ILN_2037 | ||
912 | |a GBV_ILN_2038 | ||
912 | |a GBV_ILN_2044 | ||
912 | |a GBV_ILN_2048 | ||
912 | |a GBV_ILN_2049 | ||
912 | |a GBV_ILN_2050 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2056 | ||
912 | |a GBV_ILN_2057 | ||
912 | |a GBV_ILN_2059 | ||
912 | |a GBV_ILN_2061 | ||
912 | |a GBV_ILN_2064 | ||
912 | |a GBV_ILN_2068 | ||
912 | |a GBV_ILN_2088 | ||
912 | |a GBV_ILN_2106 | ||
912 | |a GBV_ILN_2108 | ||
912 | |a GBV_ILN_2110 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2118 | ||
912 | |a GBV_ILN_2122 | ||
912 | |a GBV_ILN_2143 | ||
912 | |a GBV_ILN_2144 | ||
912 | |a GBV_ILN_2147 | ||
912 | |a GBV_ILN_2148 | ||
912 | |a GBV_ILN_2152 | ||
912 | |a GBV_ILN_2153 | ||
912 | |a GBV_ILN_2232 | ||
912 | |a GBV_ILN_2336 | ||
912 | |a GBV_ILN_2470 | ||
912 | |a GBV_ILN_2507 | ||
912 | |a GBV_ILN_2522 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4035 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4046 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4242 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4251 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4326 | ||
912 | |a GBV_ILN_4333 | ||
912 | |a GBV_ILN_4334 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4336 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 13 |j 2021 |e 1 |h n/a-n/a |
author_variant |
s a sa l l ll j l jl i u iu k f kf k f kf h h hh h j hj j k jk h o ho a s as e s es m s ms a d ad j p jp j k jk b l bl m l ml a e v aev j w jw j d jd a c l acl m l s mls h a k hak m o mo f c fc |
---|---|
matchkey_str |
article:23528729:2021----::rdcigiesporsinneairlainfo |
hierarchy_sort_str |
2021 |
callnumber-subject-code |
RC |
publishDate |
2021 |
allfields |
10.1002/dad2.12262 doi (DE-627)DOAJ004545222 (DE-599)DOAJ87427b433a554980845284f0264f1543 DE-627 ger DE-627 rakwb eng RC346-429 RC952-954.6 Sarah Anderl‐Straub verfasserin aut Predicting disease progression in behavioral variant frontotemporal dementia 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model. behavioral variant frontotemporal dementia brain volume classification models disease progression frontotemporal dementia prognosis Neurology. Diseases of the nervous system Geriatrics Ludwig Lausser verfasserin aut Jolina Lombardi verfasserin aut Ingo Uttner verfasserin aut Klaus Fassbender verfasserin aut Klaus Fliessbach verfasserin aut Hans‐Jürgen Huppertz verfasserin aut Holger Jahn verfasserin aut Johannes Kornhuber verfasserin aut Hellmuth Obrig verfasserin aut Anja Schneider verfasserin aut Elisa Semler verfasserin aut Matthis Synofzik verfasserin aut Adrian Danek verfasserin aut Johannes Prudlo verfasserin aut Jan Kassubek verfasserin aut Bernhard Landwehrmeyer verfasserin aut Martin Lauer verfasserin aut Alexander E. Volk verfasserin aut Jens Wiltfang verfasserin aut Janine Diehl‐Schmid verfasserin aut Albert C. Ludolph verfasserin aut Matthias L. Schroeter verfasserin aut Hans A. Kestler verfasserin aut Markus Otto verfasserin aut FTLD consortium verfasserin aut In Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring Wiley, 2020 13(2021), 1, Seite n/a-n/a (DE-627)834396874 (DE-600)2832898-X 23528729 nnns volume:13 year:2021 number:1 pages:n/a-n/a https://doi.org/10.1002/dad2.12262 kostenfrei https://doaj.org/article/87427b433a554980845284f0264f1543 kostenfrei https://doi.org/10.1002/dad2.12262 kostenfrei https://doaj.org/toc/2352-8729 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 n/a-n/a |
spelling |
10.1002/dad2.12262 doi (DE-627)DOAJ004545222 (DE-599)DOAJ87427b433a554980845284f0264f1543 DE-627 ger DE-627 rakwb eng RC346-429 RC952-954.6 Sarah Anderl‐Straub verfasserin aut Predicting disease progression in behavioral variant frontotemporal dementia 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model. behavioral variant frontotemporal dementia brain volume classification models disease progression frontotemporal dementia prognosis Neurology. Diseases of the nervous system Geriatrics Ludwig Lausser verfasserin aut Jolina Lombardi verfasserin aut Ingo Uttner verfasserin aut Klaus Fassbender verfasserin aut Klaus Fliessbach verfasserin aut Hans‐Jürgen Huppertz verfasserin aut Holger Jahn verfasserin aut Johannes Kornhuber verfasserin aut Hellmuth Obrig verfasserin aut Anja Schneider verfasserin aut Elisa Semler verfasserin aut Matthis Synofzik verfasserin aut Adrian Danek verfasserin aut Johannes Prudlo verfasserin aut Jan Kassubek verfasserin aut Bernhard Landwehrmeyer verfasserin aut Martin Lauer verfasserin aut Alexander E. Volk verfasserin aut Jens Wiltfang verfasserin aut Janine Diehl‐Schmid verfasserin aut Albert C. Ludolph verfasserin aut Matthias L. Schroeter verfasserin aut Hans A. Kestler verfasserin aut Markus Otto verfasserin aut FTLD consortium verfasserin aut In Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring Wiley, 2020 13(2021), 1, Seite n/a-n/a (DE-627)834396874 (DE-600)2832898-X 23528729 nnns volume:13 year:2021 number:1 pages:n/a-n/a https://doi.org/10.1002/dad2.12262 kostenfrei https://doaj.org/article/87427b433a554980845284f0264f1543 kostenfrei https://doi.org/10.1002/dad2.12262 kostenfrei https://doaj.org/toc/2352-8729 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 n/a-n/a |
allfields_unstemmed |
10.1002/dad2.12262 doi (DE-627)DOAJ004545222 (DE-599)DOAJ87427b433a554980845284f0264f1543 DE-627 ger DE-627 rakwb eng RC346-429 RC952-954.6 Sarah Anderl‐Straub verfasserin aut Predicting disease progression in behavioral variant frontotemporal dementia 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model. behavioral variant frontotemporal dementia brain volume classification models disease progression frontotemporal dementia prognosis Neurology. Diseases of the nervous system Geriatrics Ludwig Lausser verfasserin aut Jolina Lombardi verfasserin aut Ingo Uttner verfasserin aut Klaus Fassbender verfasserin aut Klaus Fliessbach verfasserin aut Hans‐Jürgen Huppertz verfasserin aut Holger Jahn verfasserin aut Johannes Kornhuber verfasserin aut Hellmuth Obrig verfasserin aut Anja Schneider verfasserin aut Elisa Semler verfasserin aut Matthis Synofzik verfasserin aut Adrian Danek verfasserin aut Johannes Prudlo verfasserin aut Jan Kassubek verfasserin aut Bernhard Landwehrmeyer verfasserin aut Martin Lauer verfasserin aut Alexander E. Volk verfasserin aut Jens Wiltfang verfasserin aut Janine Diehl‐Schmid verfasserin aut Albert C. Ludolph verfasserin aut Matthias L. Schroeter verfasserin aut Hans A. Kestler verfasserin aut Markus Otto verfasserin aut FTLD consortium verfasserin aut In Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring Wiley, 2020 13(2021), 1, Seite n/a-n/a (DE-627)834396874 (DE-600)2832898-X 23528729 nnns volume:13 year:2021 number:1 pages:n/a-n/a https://doi.org/10.1002/dad2.12262 kostenfrei https://doaj.org/article/87427b433a554980845284f0264f1543 kostenfrei https://doi.org/10.1002/dad2.12262 kostenfrei https://doaj.org/toc/2352-8729 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 n/a-n/a |
allfieldsGer |
10.1002/dad2.12262 doi (DE-627)DOAJ004545222 (DE-599)DOAJ87427b433a554980845284f0264f1543 DE-627 ger DE-627 rakwb eng RC346-429 RC952-954.6 Sarah Anderl‐Straub verfasserin aut Predicting disease progression in behavioral variant frontotemporal dementia 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model. behavioral variant frontotemporal dementia brain volume classification models disease progression frontotemporal dementia prognosis Neurology. Diseases of the nervous system Geriatrics Ludwig Lausser verfasserin aut Jolina Lombardi verfasserin aut Ingo Uttner verfasserin aut Klaus Fassbender verfasserin aut Klaus Fliessbach verfasserin aut Hans‐Jürgen Huppertz verfasserin aut Holger Jahn verfasserin aut Johannes Kornhuber verfasserin aut Hellmuth Obrig verfasserin aut Anja Schneider verfasserin aut Elisa Semler verfasserin aut Matthis Synofzik verfasserin aut Adrian Danek verfasserin aut Johannes Prudlo verfasserin aut Jan Kassubek verfasserin aut Bernhard Landwehrmeyer verfasserin aut Martin Lauer verfasserin aut Alexander E. Volk verfasserin aut Jens Wiltfang verfasserin aut Janine Diehl‐Schmid verfasserin aut Albert C. Ludolph verfasserin aut Matthias L. Schroeter verfasserin aut Hans A. Kestler verfasserin aut Markus Otto verfasserin aut FTLD consortium verfasserin aut In Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring Wiley, 2020 13(2021), 1, Seite n/a-n/a (DE-627)834396874 (DE-600)2832898-X 23528729 nnns volume:13 year:2021 number:1 pages:n/a-n/a https://doi.org/10.1002/dad2.12262 kostenfrei https://doaj.org/article/87427b433a554980845284f0264f1543 kostenfrei https://doi.org/10.1002/dad2.12262 kostenfrei https://doaj.org/toc/2352-8729 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 n/a-n/a |
allfieldsSound |
10.1002/dad2.12262 doi (DE-627)DOAJ004545222 (DE-599)DOAJ87427b433a554980845284f0264f1543 DE-627 ger DE-627 rakwb eng RC346-429 RC952-954.6 Sarah Anderl‐Straub verfasserin aut Predicting disease progression in behavioral variant frontotemporal dementia 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model. behavioral variant frontotemporal dementia brain volume classification models disease progression frontotemporal dementia prognosis Neurology. Diseases of the nervous system Geriatrics Ludwig Lausser verfasserin aut Jolina Lombardi verfasserin aut Ingo Uttner verfasserin aut Klaus Fassbender verfasserin aut Klaus Fliessbach verfasserin aut Hans‐Jürgen Huppertz verfasserin aut Holger Jahn verfasserin aut Johannes Kornhuber verfasserin aut Hellmuth Obrig verfasserin aut Anja Schneider verfasserin aut Elisa Semler verfasserin aut Matthis Synofzik verfasserin aut Adrian Danek verfasserin aut Johannes Prudlo verfasserin aut Jan Kassubek verfasserin aut Bernhard Landwehrmeyer verfasserin aut Martin Lauer verfasserin aut Alexander E. Volk verfasserin aut Jens Wiltfang verfasserin aut Janine Diehl‐Schmid verfasserin aut Albert C. Ludolph verfasserin aut Matthias L. Schroeter verfasserin aut Hans A. Kestler verfasserin aut Markus Otto verfasserin aut FTLD consortium verfasserin aut In Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring Wiley, 2020 13(2021), 1, Seite n/a-n/a (DE-627)834396874 (DE-600)2832898-X 23528729 nnns volume:13 year:2021 number:1 pages:n/a-n/a https://doi.org/10.1002/dad2.12262 kostenfrei https://doaj.org/article/87427b433a554980845284f0264f1543 kostenfrei https://doi.org/10.1002/dad2.12262 kostenfrei https://doaj.org/toc/2352-8729 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 n/a-n/a |
language |
English |
source |
In Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring 13(2021), 1, Seite n/a-n/a volume:13 year:2021 number:1 pages:n/a-n/a |
sourceStr |
In Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring 13(2021), 1, Seite n/a-n/a volume:13 year:2021 number:1 pages:n/a-n/a |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
behavioral variant frontotemporal dementia brain volume classification models disease progression frontotemporal dementia prognosis Neurology. Diseases of the nervous system Geriatrics |
isfreeaccess_bool |
true |
container_title |
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring |
authorswithroles_txt_mv |
Sarah Anderl‐Straub @@aut@@ Ludwig Lausser @@aut@@ Jolina Lombardi @@aut@@ Ingo Uttner @@aut@@ Klaus Fassbender @@aut@@ Klaus Fliessbach @@aut@@ Hans‐Jürgen Huppertz @@aut@@ Holger Jahn @@aut@@ Johannes Kornhuber @@aut@@ Hellmuth Obrig @@aut@@ Anja Schneider @@aut@@ Elisa Semler @@aut@@ Matthis Synofzik @@aut@@ Adrian Danek @@aut@@ Johannes Prudlo @@aut@@ Jan Kassubek @@aut@@ Bernhard Landwehrmeyer @@aut@@ Martin Lauer @@aut@@ Alexander E. Volk @@aut@@ Jens Wiltfang @@aut@@ Janine Diehl‐Schmid @@aut@@ Albert C. Ludolph @@aut@@ Matthias L. Schroeter @@aut@@ Hans A. Kestler @@aut@@ Markus Otto @@aut@@ FTLD consortium @@aut@@ |
publishDateDaySort_date |
2021-01-01T00:00:00Z |
hierarchy_top_id |
834396874 |
id |
DOAJ004545222 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ004545222</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230311014053.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/dad2.12262</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ004545222</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ87427b433a554980845284f0264f1543</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC346-429</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC952-954.6</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Sarah Anderl‐Straub</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predicting disease progression in behavioral variant frontotemporal dementia</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">behavioral variant frontotemporal dementia</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">brain volume</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">classification models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">disease progression</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">frontotemporal dementia</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prognosis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurology. Diseases of the nervous system</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Geriatrics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ludwig Lausser</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jolina Lombardi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ingo Uttner</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Klaus Fassbender</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Klaus Fliessbach</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hans‐Jürgen Huppertz</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Holger Jahn</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Johannes Kornhuber</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hellmuth Obrig</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Anja Schneider</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Elisa Semler</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Matthis Synofzik</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Adrian Danek</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Johannes Prudlo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jan Kassubek</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bernhard Landwehrmeyer</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Martin Lauer</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Alexander E. Volk</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jens Wiltfang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Janine Diehl‐Schmid</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Albert C. Ludolph</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Matthias L. Schroeter</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hans A. Kestler</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Markus Otto</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">FTLD consortium</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring</subfield><subfield code="d">Wiley, 2020</subfield><subfield code="g">13(2021), 1, Seite n/a-n/a</subfield><subfield code="w">(DE-627)834396874</subfield><subfield code="w">(DE-600)2832898-X</subfield><subfield code="x">23528729</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:n/a-n/a</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1002/dad2.12262</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/87427b433a554980845284f0264f1543</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1002/dad2.12262</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2352-8729</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">13</subfield><subfield code="j">2021</subfield><subfield code="e">1</subfield><subfield code="h">n/a-n/a</subfield></datafield></record></collection>
|
callnumber-first |
R - Medicine |
author |
Sarah Anderl‐Straub |
spellingShingle |
Sarah Anderl‐Straub misc RC346-429 misc RC952-954.6 misc behavioral variant frontotemporal dementia misc brain volume misc classification models misc disease progression misc frontotemporal dementia misc prognosis misc Neurology. Diseases of the nervous system misc Geriatrics Predicting disease progression in behavioral variant frontotemporal dementia |
authorStr |
Sarah Anderl‐Straub |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)834396874 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
RC346-429 |
illustrated |
Not Illustrated |
issn |
23528729 |
topic_title |
RC346-429 RC952-954.6 Predicting disease progression in behavioral variant frontotemporal dementia behavioral variant frontotemporal dementia brain volume classification models disease progression frontotemporal dementia prognosis |
topic |
misc RC346-429 misc RC952-954.6 misc behavioral variant frontotemporal dementia misc brain volume misc classification models misc disease progression misc frontotemporal dementia misc prognosis misc Neurology. Diseases of the nervous system misc Geriatrics |
topic_unstemmed |
misc RC346-429 misc RC952-954.6 misc behavioral variant frontotemporal dementia misc brain volume misc classification models misc disease progression misc frontotemporal dementia misc prognosis misc Neurology. Diseases of the nervous system misc Geriatrics |
topic_browse |
misc RC346-429 misc RC952-954.6 misc behavioral variant frontotemporal dementia misc brain volume misc classification models misc disease progression misc frontotemporal dementia misc prognosis misc Neurology. Diseases of the nervous system misc Geriatrics |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring |
hierarchy_parent_id |
834396874 |
hierarchy_top_title |
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)834396874 (DE-600)2832898-X |
title |
Predicting disease progression in behavioral variant frontotemporal dementia |
ctrlnum |
(DE-627)DOAJ004545222 (DE-599)DOAJ87427b433a554980845284f0264f1543 |
title_full |
Predicting disease progression in behavioral variant frontotemporal dementia |
author_sort |
Sarah Anderl‐Straub |
journal |
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring |
journalStr |
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring |
callnumber-first-code |
R |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2021 |
contenttype_str_mv |
txt |
author_browse |
Sarah Anderl‐Straub Ludwig Lausser Jolina Lombardi Ingo Uttner Klaus Fassbender Klaus Fliessbach Hans‐Jürgen Huppertz Holger Jahn Johannes Kornhuber Hellmuth Obrig Anja Schneider Elisa Semler Matthis Synofzik Adrian Danek Johannes Prudlo Jan Kassubek Bernhard Landwehrmeyer Martin Lauer Alexander E. Volk Jens Wiltfang Janine Diehl‐Schmid Albert C. Ludolph Matthias L. Schroeter Hans A. Kestler Markus Otto FTLD consortium |
container_volume |
13 |
class |
RC346-429 RC952-954.6 |
format_se |
Elektronische Aufsätze |
author-letter |
Sarah Anderl‐Straub |
doi_str_mv |
10.1002/dad2.12262 |
author2-role |
verfasserin |
title_sort |
predicting disease progression in behavioral variant frontotemporal dementia |
callnumber |
RC346-429 |
title_auth |
Predicting disease progression in behavioral variant frontotemporal dementia |
abstract |
Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model. |
abstractGer |
Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model. |
abstract_unstemmed |
Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
1 |
title_short |
Predicting disease progression in behavioral variant frontotemporal dementia |
url |
https://doi.org/10.1002/dad2.12262 https://doaj.org/article/87427b433a554980845284f0264f1543 https://doaj.org/toc/2352-8729 |
remote_bool |
true |
author2 |
Ludwig Lausser Jolina Lombardi Ingo Uttner Klaus Fassbender Klaus Fliessbach Hans‐Jürgen Huppertz Holger Jahn Johannes Kornhuber Hellmuth Obrig Anja Schneider Elisa Semler Matthis Synofzik Adrian Danek Johannes Prudlo Jan Kassubek Bernhard Landwehrmeyer Martin Lauer Alexander E. Volk Jens Wiltfang Janine Diehl‐Schmid Albert C. Ludolph Matthias L. Schroeter Hans A. Kestler Markus Otto FTLD consortium |
author2Str |
Ludwig Lausser Jolina Lombardi Ingo Uttner Klaus Fassbender Klaus Fliessbach Hans‐Jürgen Huppertz Holger Jahn Johannes Kornhuber Hellmuth Obrig Anja Schneider Elisa Semler Matthis Synofzik Adrian Danek Johannes Prudlo Jan Kassubek Bernhard Landwehrmeyer Martin Lauer Alexander E. Volk Jens Wiltfang Janine Diehl‐Schmid Albert C. Ludolph Matthias L. Schroeter Hans A. Kestler Markus Otto FTLD consortium |
ppnlink |
834396874 |
callnumber-subject |
RC - Internal Medicine |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1002/dad2.12262 |
callnumber-a |
RC346-429 |
up_date |
2024-07-04T00:09:50.459Z |
_version_ |
1803605026239676416 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ004545222</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230311014053.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/dad2.12262</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ004545222</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ87427b433a554980845284f0264f1543</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC346-429</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC952-954.6</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Sarah Anderl‐Straub</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predicting disease progression in behavioral variant frontotemporal dementia</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">behavioral variant frontotemporal dementia</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">brain volume</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">classification models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">disease progression</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">frontotemporal dementia</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prognosis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurology. Diseases of the nervous system</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Geriatrics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ludwig Lausser</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jolina Lombardi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ingo Uttner</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Klaus Fassbender</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Klaus Fliessbach</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hans‐Jürgen Huppertz</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Holger Jahn</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Johannes Kornhuber</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hellmuth Obrig</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Anja Schneider</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Elisa Semler</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Matthis Synofzik</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Adrian Danek</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Johannes Prudlo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jan Kassubek</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bernhard Landwehrmeyer</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Martin Lauer</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Alexander E. Volk</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jens Wiltfang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Janine Diehl‐Schmid</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Albert C. Ludolph</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Matthias L. Schroeter</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hans A. Kestler</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Markus Otto</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">FTLD consortium</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring</subfield><subfield code="d">Wiley, 2020</subfield><subfield code="g">13(2021), 1, Seite n/a-n/a</subfield><subfield code="w">(DE-627)834396874</subfield><subfield code="w">(DE-600)2832898-X</subfield><subfield code="x">23528729</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:n/a-n/a</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1002/dad2.12262</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/87427b433a554980845284f0264f1543</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1002/dad2.12262</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2352-8729</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">13</subfield><subfield code="j">2021</subfield><subfield code="e">1</subfield><subfield code="h">n/a-n/a</subfield></datafield></record></collection>
|
score |
7.402128 |