000145601 001__ 145601
000145601 005__ 20200925154556.0
000145601 037__ $$aDZNE-2020-00931
000145601 041__ $$aEnglish
000145601 1001_ $$0P:(DE-2719)2810283$$aDyrba, Martin$$b0$$eFirst author$$udzne
000145601 1112_ $$aMBIA 2012$$cNice$$d2012-10-01 - 2012-10-05$$wFrance
000145601 245__ $$aCombining DTI and MRI for the automated detection of Alzheimer's disease using a large European multicenter dataset
000145601 260__ $$c2012
000145601 3367_ $$033$$2EndNote$$aConference Paper
000145601 3367_ $$2DataCite$$aOther
000145601 3367_ $$2BibTeX$$aINPROCEEDINGS
000145601 3367_ $$2DRIVER$$aconferenceObject
000145601 3367_ $$2ORCID$$aLECTURE_SPEECH
000145601 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1597390948_15935$$xOther
000145601 520__ $$aDiffusion tensor imaging (DTI) allows assessing neuronal fiber tract integrity in vivo to support the diagnosis of Alzheimer’s disease (AD). It is an open research question to which extent combinations of different neuroimaging techniques increase the detection of AD. In this study we examined different methods to combine DTI data and structural T 1-weighted magnetic resonance imaging (MRI) data. Further, we applied machine learning techniques for automated detection of AD. We used a sample of 137 patients with clinically probable AD (MMSE 20.6 ±5.3) and 143 healthy elderly controls, scanned in nine different scanners, obtained from the recently created framework of the European DTI study on Dementia (EDSD). For diagnostic classification we used the DTI derived indices fractional anisotropy (FA) and mean diffusivity (MD) as well as grey matter density (GMD) and white matter density (WMD) maps from anatomical MRI. We performed voxel-based classification using a Support Vector Machine (SVM) classifier with tenfold cross validation. We compared the results from each single modality with those from different approaches to combine the modalities. For our sample, combining modalities did not increase the detection rates of AD. An accuracy of approximately 89% was reached for GMD data alone and for multimodal classification when GMD was included. This high accuracy remained stable across each of the approaches. As our sample consisted of mildly to moderately affected patients, cortical atrophy may be far progressed so that the decline in structural network connectivity derived from DTI may not add additional information relevant for the SVM classification. This may be different for predementia stages of AD. Further research will focus on multimodal detection of AD in predementia stages of AD, e.g. in amnestic mild cognitive impairment (aMCI), and on evaluating the classification performance when adding other modalities, e.g. functional MRI or FDG-PET.
000145601 536__ $$0G:(DE-HGF)POF3-344$$a344 - Clinical and Health Care Research (POF3-344)$$cPOF3-344$$fPOF III$$x0
000145601 7001_ $$0P:(DE-2719)2630400$$aWegrzyn, Martin$$b1$$udzne
000145601 7001_ $$0P:(DE-2719)2810394$$aKilimann, Ingo$$b2$$udzne
000145601 7001_ $$0P:(DE-2719)2000026$$aTeipel, Stefan$$b3$$eLast author$$udzne
000145601 8564_ $$uhttps://link.springer.com/chapter/10.1007/978-3-642-33530-3_2
000145601 909CO $$ooai:pub.dzne.de:145601$$pVDB
000145601 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2810283$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b0$$kDZNE
000145601 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2630400$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b1$$kDZNE
000145601 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2810394$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b2$$kDZNE
000145601 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2000026$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b3$$kDZNE
000145601 9131_ $$0G:(DE-HGF)POF3-344$$1G:(DE-HGF)POF3-340$$2G:(DE-HGF)POF3-300$$aDE-HGF$$bForschungsbereich Gesundheit$$lErkrankungen des Nervensystems$$vClinical and Health Care Research$$x0
000145601 9141_ $$y2012
000145601 9201_ $$0I:(DE-2719)1510100$$kClinical Dementia Research Rostock /Greifswald ; AG Teipel$$lClinical Dementia Research Rostock /Greifswald$$x0
000145601 980__ $$aconf
000145601 980__ $$aVDB
000145601 980__ $$aI:(DE-2719)1510100
000145601 980__ $$aUNRESTRICTED