Feedback
schliessen

Filtern

 

Bibliotheken

Siegel TU Braunschweig Universit�tsbibliothek Braunschweig
Sie scheinen nicht im Netzwerk der TU Braunschweig zu sein.
Als Student/in oder Mitarbeiter/in der TU-Braunschweig können Sie Ihren VPN Zugang nutzen, um Zugriff auf elektronischen Publikationen zu erhalten.

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease... Ausführliche Beschreibung

1. Verfasser: Schouten, Tijn M
Beteiligte Personen: Koini, Marisa | Verfasser/in
de Vos, Frank | Verfasser/in
Seiler, Stephan | Verfasser/in
van der Grond, Jeroen | Verfasser/in
Lechner, Anita | Verfasser/in
Hafkemeijer, Anne | Verfasser/in
Möller, Christiane | Verfasser/in
Schmidt, Reinhold | Verfasser/in
de Rooij, Mark | Verfasser/in
Rombouts, Serge A R B | Verfasser/in
Enthalten in: NeuroImage. Clinical Vol. 11 (2016), p. 46-51
Suche nach Artikeln/Bänden: Suche nach Artikeln/Bänden
Zeitschriftentitel: NeuroImage. Clinical
Volltextzugang:
Elektronische Verfügbarkeit wird geprüft...
Fernleihe: Bestellbarkeit über Fernleihe prüfen
Links: Volltext (dx.doi.org)
ISSN: 2213-1582
Schlagworte: Alzheimer's disease
Classification
DWI
Journal Article
MRI
Multimodal
Research Support, Non-U.S. Gov't
fMRI
Weitere Schlagworte: Aged
Aged, 80 and over
Alzheimer Disease
Brain
Cognitive Dysfunction
*Diffusion Tensor Imaging
Female
Gray Matter
Humans
*Magnetic Resonance Imaging
Male
Prospective Studies
White Matter
Sprache: Englisch
Anmerkungen: Date Completed 21.12.2016
Date Revised 21.09.2018
published: Electronic-eCollection
Cites: Front Hum Neurosci. 2013 May 29;7:235. - PMID 23755002
Citation Status MEDLINE
Copyright: From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Umfang: Online-Ressource
ID (z.B. DOI, URN): 10.1016/j.nicl.2016.01.002
PPN (Katalog-ID): NLM257798854
weitere Titelinformationen ...

Suche nach Artikeln/Bänden

Zugehörige Publikationen/Bände

  • Zugehörige Publikationen werden geladen...
mehr (+)
Internes Format
LEADER 04680nma a2200829 c 4500
001 NLM257798854
003 DE-601
005 20180924002657.0
007 cr uuu---uuuuu
008 180206s2016 000 0 eng d
024 7 |a 10.1016/j.nicl.2016.01.002  |2 doi 
028 5 2 |a pubmed18n1272.xml 
035 |a S2213-1582(16)30001-8 
035 |a (DE-599)NLM26909327 
040 |b ger  |c GBVCP 
041 0 |a eng 
100 1 |a Schouten, Tijn M  |e verfasserin  |4 aut 
245 1 0 |a Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease  |h Elektronische Ressource 
300 |a Online-Ressource 
500 |a Date Completed 21.12.2016 
500 |a Date Revised 21.09.2018 
500 |a published: Electronic-eCollection 
500 |a Cites: Front Hum Neurosci. 2013 May 29;7:235. - PMID 23755002 
500 |a Citation Status MEDLINE 
500 |a Copyright: From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine 
520 |a Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification 
611 2 7 |a Journal Article  |2 gnd 
611 2 7 |a Research Support, Non-U.S. Gov't  |2 gnd 
653 2 |a Aged  |6 D000368 
653 2 |a Aged, 80 and over  |6 D000369 
653 2 |a Alzheimer Disease  |6 D000544  |a *diagnosis  |6 Q000175  |a pathology  |6 Q000473 
653 2 |a Brain  |6 D001921  |a *pathology  |6 Q000473  |a physiopathology  |6 Q000503 
653 2 |a Cognitive Dysfunction  |6 D060825  |a classification  |6 Q000145  |a *diagnosis  |6 Q000175  |a pathology  |6 Q000473 
653 2 |a *Diffusion Tensor Imaging  |6 D056324  |a methods  |6 Q000379 
653 2 |a Female  |6 D005260 
653 2 |a Gray Matter  |6 D066128  |a pathology  |6 Q000473 
653 2 |a Humans  |6 D006801 
653 2 |a *Magnetic Resonance Imaging  |6 D008279  |a methods  |6 Q000379 
653 2 |a Male  |6 D008297 
653 2 |a Prospective Studies  |6 D011446 
653 2 |a White Matter  |6 D066127  |a pathology  |6 Q000473 
655 7 |a Alzheimer's disease  |2 gnd 
655 7 |a Classification  |2 gnd 
655 7 |a DWI  |2 gnd 
655 7 |a MRI  |2 gnd 
655 7 |a Multimodal  |2 gnd 
655 7 |a fMRI  |2 gnd 
689 0 0 |A f  |a Journal Article 
689 0 1 |A f  |a Research Support, Non-U.S. Gov't 
689 0 |5 DE-601 
689 1 0 |a Alzheimer's disease 
689 1 1 |a Classification 
689 1 2 |a DWI 
689 1 3 |a MRI 
689 1 4 |a Multimodal 
689 1 5 |a fMRI 
689 1 |5 DE-601 
700 1 |a Koini, Marisa  |e verfasserin  |4 aut 
700 1 |a de Vos, Frank  |e verfasserin  |4 aut 
700 1 |a Seiler, Stephan  |e verfasserin  |4 aut 
700 1 |a van der Grond, Jeroen  |e verfasserin  |4 aut 
700 1 |a Lechner, Anita  |e verfasserin  |4 aut 
700 1 |a Hafkemeijer, Anne  |e verfasserin  |4 aut 
700 1 |a Möller, Christiane  |e verfasserin  |4 aut 
700 1 |a Schmidt, Reinhold  |e verfasserin  |4 aut 
700 1 |a de Rooij, Mark  |e verfasserin  |4 aut 
700 1 |a Rombouts, Serge A R B  |e verfasserin  |4 aut 
773 0 8 |i in  |t NeuroImage. Clinical  |g Vol. 11 (2016), p. 46-51  |q 11<46-51  |w (DE-601)NLM227366700  |x 2213-1582 
856 4 1 |u http://dx.doi.org/10.1016/j.nicl.2016.01.002  |3 Volltext 
912 |a GBV_NLM 
951 |a AR 
952 |d 11  |j 2016  |h 46-51