Multivariate Spatial Covariance Analysis of ^99MTc-Exametazime SPECT Images in Dementia with Lewy Bodies and Alzheimer'S Disease: Utility in Differential Diagnosis

Abstract

We examined 99m Tc-exametazime brain blood flow single-photon emission computed tomography (SPECT) images using a spatial covariance analysis (SCA) approach to assess its diagnostic value in distinguishing dementia with Lewy bodies (DLB) from Alzheimer's disease (AD). Voxel SCA was simultaneously applied to a set of preprocessed images (AD, n ¼ 40; DLB, n ¼ 26), generating a series of eigenimages representing common intercorrelated voxels in AD and DLB. Linear regression derived a spatial covariance pattern (SCP) that discriminated DLB from AD. To investigate the diagnostic value of the model SCP, the SCP was validated by applying it to a second, independent, AD and DLB cohort (AD, n ¼ 34; DLB, n ¼ 29). Mean SCP expressions differed between AD and DLB (F 1,64 ¼ 36.2, Po0.001) with good diagnostic accuracy (receiver operating characteristic (ROC) curve area 0.87, sensitivity 81%, specificity 88%). Forward application of the model SCP to the independent cohort revealed similar differences between groups (F 1,61 ¼ 38.4, Po0.001), also with good diagnostic accuracy (ROC 0.86, sensitivity 80%, specificity 80%). Multivariate analysis of blood flow SPECT data appears to be robust and shows good diagnostic accuracy in two independent cohorts for distinguishing DLB from AD.

Regional covariance of muscarinic acetylcholine receptors in Alzheimer’s disease using (R, R) [123I]-QNB SPECT

“…The multivariate SCA approach offers practical advantages over traditional univariate methods in radioligand image analysis, since SCA is concerned only with inter-voxel correlations and does not rely upon any absolute quantification in uptake which requires either information of a distribution volume from arterial blood sampling or selection of a suitable reference tissue/region. It has also been demonstrated that the multivariate SCA technique is superior relative to univariate procedures in differentiating AD from controls [22] and AD from dementia with Lewy bodies [12]. We found a voxel SCP derived from 123 I-QNB SPECT scans that significantly differentiated AD from controls.…”

“…One form of voxel spatial covariance analysis (SCA) is the scaled subprofile model (SSM), an extension of principal component analysis (PCA), which generates a series of PCA eigenimages of brain uptake, representing significant sources of variance in the data that may indicate specific disease features. These procedures have previously been applied successfully to glucose metabolism PET and perfusion SPECT imaging data in AD [12,22,25,46] and Parkinson's disease (PD) [3,16,33]. Multivariate approaches thus may afford a unique perspective on receptor alterations as a result of neurodegenerative disease given the complex inter-dependency between cortical and subcortical regions in receptor levels, as a result of either direct neuropathological change or consequent up-/down-regulation of receptors.…”

Update on Dementia with Lewy Bodies

“…Occipital hypoperfusion on SPECT [49] or hypometabolism on PET [50] is more typical of DLB than AD, and represents a potential marker of DLB. Occipital hypometabolism and relative preservation of the posterior cingulate gyrus distinguished DLB from AD patients with a sensitivity of 77% and a specificity of 80% [51].…”

Covariant perfusion patterns provide clues to the origin of cognitive fluctuations and attentional dysfunction in Dementia with Lewy bodies

“…In addition, more recently we have shown that a multivariate approach can usefully discriminate between DLB and AD with high sensitivity and specificity (Colloby et al ., 2013). …”