Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms

• Published in: Frontiers in aging neuroscience Vol. 9; p. 290
• Main Authors: Höller, Yvonne, Bathke, Arne C., Uhl, Andreas, Strobl, Nicolas, Lang, Adelheid, Bergmann, Jürgen, Nardone, Raffaele, Rossini, Fabio, Zauner, Harald, Kirschner, Margarita, Jahanbekam, Amirhossein, Trinka, Eugen, Staffen, Wolfgang
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 07.09.2017; Frontiers Media S.A
• Subjects: Alzheimer's disease; Biomarkers; Brain research; Classification; Cognitive ability; Computed tomography; Dementia; Dementia disorders; depression with cognitive impairment; Differential diagnosis; EEG; EEG connectivity; Electroencephalography; Fourier transforms; Learning algorithms; Memory; mild cognitive impairment; Neural networks; Neurodegenerative diseases; Neuroimaging; Neurology; Neuroscience; Neurosciences; Older people; Patients; Perfusion; Physiology; Signal processing; Single photon emission computed tomography; SPECT; subjective cognitive complaints; Volumetric analysis; Austria; dementia; depression with cognitive impairment; mild cognitive impairment; EEG connectivity; subjective cognitive complaints; SPECT
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2017.00290

Single photon emission computed tomography (SPECT) and Electroencephalography (EEG) have become established tools in routine diagnostics of dementia. We aimed to increase the diagnostic power by combining quantitative markers from SPECT and EEG for differential diagnosis of disorders with amnestic symptoms. We hypothesize that the combination of SPECT with measures of interaction ( ) in the EEG yields higher diagnostic accuracy than the single modalities. We examined 39 patients with Alzheimer's dementia (AD), 69 patients with depressive cognitive impairment (DCI), 71 patients with amnestic mild cognitive impairment (aMCI), and 41 patients with amnestic subjective cognitive complaints (aSCC). We calculated 14 measures of interaction from a standard clinical EEG-recording and derived graph-theoretic network measures. From regional brain perfusion measured by 99mTc-hexamethyl-propylene-aminoxime (HMPAO)-SPECT in 46 regions, we calculated relative cerebral perfusion in these patients. Patient groups were classified pairwise with a linear support vector machine. Classification was conducted separately for each biomarker, and then again for each EEG- biomarker combined with SPECT. Combination of SPECT with EEG-biomarkers outperformed single use of SPECT or EEG when classifying aSCC vs. AD (90%), aMCI vs. AD (70%), and AD vs. DCI (100%), while a selection of EEG measures performed best when classifying aSCC vs. aMCI (82%) and aMCI vs. DCI (90%). Only the contrast between aSCC and DCI did not result in above-chance classification accuracy (60%). In general, accuracies were higher when measures of interaction (i.e., connectivity measures) were applied directly than when graph-theoretical measures were derived. We suggest that quantitative analysis of EEG and machine-learning techniques can support differentiating AD, aMCI, aSCC, and DCC, especially when being combined with imaging methods such as SPECT. Quantitative analysis of EEG connectivity could become an integral part for early differential diagnosis of cognitive impairment.